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Introducing Neural Capture Version 2 on CorOS 3.3.0 and NanOS 2.2.0 - available now! Neural Capture Version 2 is a new cloud-trained version of Neural Capture that delivers higher resolution, greater realism, and improved dynamic response. By shifting the training process to Cortex Cloud, Capture V2 uses a more...

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Neural DSP

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Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!
19:06

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

261,937 просмотров • 2 месяцев назад

Something amazing is coming to Apache Kafka… Consumer Groups v2! If you’ve ever used consumer groups in production at any non-trivial scale, you probably know all the problems with it: - ⛔️ Group-wide synchronization barrier acts as a cap on scalability A single misbehaving consumer can disturb the whole group. Even if you have cooperative rebalancing and static membership enabled, you will still have rebalances happen. 🤷‍♂️ It’s a fact of life I've heard - death, taxes & consumer group rebalances. And the problem is that even with cooperative rebalancing (which helped a lot!), you’re bound on waiting for the slowest member of the group to complete the rebalance(s). 🐌 The problem is that no consumer can commit offsets while a rebalance is in progress. ❌ Another subtle thing is that with cooperative rebalancing, a rebalance will take longer than usual. Why? Because consumers are allowed to process partitions during rebalances. They will call the poll() method more infrequently -- they're busy processing records after all. Thus, the overall rebalance time will increase. This makes it pretty hard to scale to 1000s of members. - 🤯 Complexity There’s a reason you’re reading this! The current protocol is pretty complex and hard to understand. It's used for a bunch of stuff, including metadata propagation in Kafka Streams. This compexity results in more: - 🐛 bugs The harder to reason about and the more moving parts you have - the greater chance for bugs. There have been quite a few in the protocol. And due to the fact that a lot of the protocol’s logic lives on the clients, that results in: - 🐌 slow fixes Bugs require client-side fixes, which are slow to be adopted. If you run a Kafka ops team, you know how hard it is to get all of your clients' teams to upgrade! If you're using a cloud service, you need to wait for a new Kafka release to go out. Can't have the cloud provider handle it for you behind the scenes! - 🔍 hard to debug Debugging is harder because you need client logs. In the cloud, that's hard to do again. On-prem, it requires reading through a lot of logs and collecting a lot of files. - ⚙️ very extendable There’s a reusable embedded protocol within the rebalance protocol, where clients are free to attach raw bytes that only they can then parse themselves. It's challenging to build compatible software for this cross-client protocol, as well as near-impossible to inspect from the broker side. - 😢 inconsistent metadata Clients are responsible for triggering rebalances based on the metadata, but different clients can have different views of the metadata. - 😵‍💫 interoperability Different implementations of the clients (i.e. anything besides the Java client) may have bugs. The complex logic needs to be re-implemented quite a few times. This usually means more bugs and slower time to ship features in your favorite client. A combination nobody likes. ... So what should an open source community do? Move the logic to the broker! Then? Simplify it. The new protocol is very elegant - it streamlines all of the regular Kafka consumer logic inside a new heartbeat API. It has the broker decide what partition assignments the consumers should have, and totally omits the notion of a Group Leader client. Another major change is that the notion of a group-wide rebalance is removed now. The rebalance is more fine-grained now. 👌 When you think about it, a rebalance is simply a reassignment of some partitions from some consumers to others. 💡 Why does the whole group need to stop and know about this? It had to before because the logic was on the client. It doesn’t now. 🎂 The new protocol is fine-grained in its assignments. It maintains per-member epochs, as well as separate epochs for the general group membership and the global assignment. The goal is simple - get all of those epoch numbers to be the same. The order is the following: 1. the group-wide epoch is bumped. 2. the target assignment epoch is bumped. 3. individual consumers catch up to the epoch via the heartbeat request, individually. (fine-grained) In general, what you have is a simple state machine inside the Group Coordinator broker that’s running a constant reconciliation loop. 💥 Because every member converges to the target state independently, the coordinator is free to simplify that convergence member by member. 👍 It has the logic to resolve dependencies between members - the act of: 1. revoking one member’s partition. 2. confirming the success of that. 3. bumping that member's epoch before assigning that partition to another consumer. Here is an example visual of what happens when two members join a consumer group one by one:
1:37

Something amazing is coming to Apache Kafka… Consumer Groups v2! If you’ve ever used consumer groups in production at any non-trivial scale, you probably know all the problems with it: - ⛔️ Group-wide synchronization barrier acts as a cap on scalability A single misbehaving consumer can disturb the whole group. Even if you have cooperative rebalancing and static membership enabled, you will still have rebalances happen. 🤷‍♂️ It’s a fact of life I've heard - death, taxes & consumer group rebalances. And the problem is that even with cooperative rebalancing (which helped a lot!), you’re bound on waiting for the slowest member of the group to complete the rebalance(s). 🐌 The problem is that no consumer can commit offsets while a rebalance is in progress. ❌ Another subtle thing is that with cooperative rebalancing, a rebalance will take longer than usual. Why? Because consumers are allowed to process partitions during rebalances. They will call the poll() method more infrequently -- they're busy processing records after all. Thus, the overall rebalance time will increase. This makes it pretty hard to scale to 1000s of members. - 🤯 Complexity There’s a reason you’re reading this! The current protocol is pretty complex and hard to understand. It's used for a bunch of stuff, including metadata propagation in Kafka Streams. This compexity results in more: - 🐛 bugs The harder to reason about and the more moving parts you have - the greater chance for bugs. There have been quite a few in the protocol. And due to the fact that a lot of the protocol’s logic lives on the clients, that results in: - 🐌 slow fixes Bugs require client-side fixes, which are slow to be adopted. If you run a Kafka ops team, you know how hard it is to get all of your clients' teams to upgrade! If you're using a cloud service, you need to wait for a new Kafka release to go out. Can't have the cloud provider handle it for you behind the scenes! - 🔍 hard to debug Debugging is harder because you need client logs. In the cloud, that's hard to do again. On-prem, it requires reading through a lot of logs and collecting a lot of files. - ⚙️ very extendable There’s a reusable embedded protocol within the rebalance protocol, where clients are free to attach raw bytes that only they can then parse themselves. It's challenging to build compatible software for this cross-client protocol, as well as near-impossible to inspect from the broker side. - 😢 inconsistent metadata Clients are responsible for triggering rebalances based on the metadata, but different clients can have different views of the metadata. - 😵‍💫 interoperability Different implementations of the clients (i.e. anything besides the Java client) may have bugs. The complex logic needs to be re-implemented quite a few times. This usually means more bugs and slower time to ship features in your favorite client. A combination nobody likes. ... So what should an open source community do? Move the logic to the broker! Then? Simplify it. The new protocol is very elegant - it streamlines all of the regular Kafka consumer logic inside a new heartbeat API. It has the broker decide what partition assignments the consumers should have, and totally omits the notion of a Group Leader client. Another major change is that the notion of a group-wide rebalance is removed now. The rebalance is more fine-grained now. 👌 When you think about it, a rebalance is simply a reassignment of some partitions from some consumers to others. 💡 Why does the whole group need to stop and know about this? It had to before because the logic was on the client. It doesn’t now. 🎂 The new protocol is fine-grained in its assignments. It maintains per-member epochs, as well as separate epochs for the general group membership and the global assignment. The goal is simple - get all of those epoch numbers to be the same. The order is the following: 1. the group-wide epoch is bumped. 2. the target assignment epoch is bumped. 3. individual consumers catch up to the epoch via the heartbeat request, individually. (fine-grained) In general, what you have is a simple state machine inside the Group Coordinator broker that’s running a constant reconciliation loop. 💥 Because every member converges to the target state independently, the coordinator is free to simplify that convergence member by member. 👍 It has the logic to resolve dependencies between members - the act of: 1. revoking one member’s partition. 2. confirming the success of that. 3. bumping that member's epoch before assigning that partition to another consumer. Here is an example visual of what happens when two members join a consumer group one by one:

Stanislav Kozlovski

52,814 просмотров • 3 лет назад

BRAIN COMPUTER INTERFACE NANOTECHNOLOGY THROUGH mRNA VACCINES. Since Moderna called their mRNA jabs an "operating system" designed to program humans, people were confused and nervous. And they should be. The type of tech that these pharmaceutical companies are developing along with other leading companies like the Pentagon's DARPA and many other leading researchers and tech innovators with self-assembling and other types of nanotechnology, developing sensors, electrodes, and BMI devices to enter the body through mRNA vaccines and other methods to have control, monitor, induce behavior, emotions, functions, even read and write to the brain, etc., are literally terrifying. This isn't something that's coming within the next 5 to 10 years. It's already here. Brain-Computer Interface (BCI), as a cutting-edge technology, refers to the establishment of a direct communication channel between the brain and peripheral electronic devices to realize the efficient information exchange between people and machines. It in a narrow sense refers to the establishment between the brain and the external environment does not depend on the peripheral nerve and muscle new communication and control channel, by measuring and collecting the central nervous system activity, and its directly translated can be recognized by external artificial equipment signal or instruction, so as to realize the direct communication and control of the brain and external equipment. Generalized brain computer interface includes input BCI, output BCI and interactive BCI, the input BCI is by external equipment or machine to the brain input electrical, magnetic, acoustic and optical stimulation of brain-computer interface system, output BCI is the signal of the brain into the external equipment control instructions, interactive BCI is by feedback nerve output and input link connected to form a closed loop brain computer interface system. Nanotechnology plays a key role in the innovative applications of neuroscience and brain computer interfaces. By taking advantage of the unique properties of ministerial, such as high conductivity, biocompatibility and regulation, scientists are able to design more sophisticated and efficient brain-computer interface devices. These devices can not only realize the precise recording and stimulation of nerve signals, but also promote the repair and regeneration of nerve tissue, providing new tools and means for neuroscience research and clinical application. Therefore, the innovative application of self-assembling, biodegradable, graphene, etc., nanotechnology in the interface between neuroscience and brain computer technology is gradually promoting the rapid development of this field, providing infinite possibilities for humans to explore the mysteries of the brain and improve the neural function. Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices, but their performance heavily depends on the quality of the electrodes. Traditional materials, such as gold and platinum, offer high conductivity but often struggle with biocompatibility and can cause tissue damage due to their mechanical mismatch with neural tissue. While conductive polymers provide greater flexibility, they frequently fall short in electrical performance. Nanomaterials, including carbon nanotubes (CNTs) and graphene, are increasingly considered promising alternatives. These materials combine high conductivity with mechanical flexibility and offer potential improvements in biocompatibility, enhancing the capture and transmission of neural signals. Hybrid materials, which integrate conductive polymers with nanomaterials, have also shown potential by balancing flexibility and signal quality. This review examines recent advancements in nanomaterial-based BCI electrodes and focuses on how these new materials address the limitations of traditional electrodes. It also discusses emerging tools like metallic nanoparticles and nanowires, along with the ongoing challenges of biocompatibility, tissue integration, and ethical considerations. As nanotechnology continues to evolve, it has the potential to significantly enhance the functionality and longevity of BCIs, making them more effective in facilitating neural communication. Nanotechnology, as the frontier field of the development of science and technology in the 21st century, is gradually penetrating into the research of neuroscience and brain-computer interface, injecting new vitality and possibilities into this interdisciplinary subject. With the deepening of human cognition of the brain, neuroscience and brain-computer interface technology has increasingly become a bridge connecting the biological world and the digital world, aiming to interpret the brain information and realize human-computer interaction through advanced technological means, and then promote the innovation of medical treatment, rehabilitation, intelligent control and other fields. The introduction of nanotechnology provides a different perspective and means to solve the technical problems in the field of neuroscience and brain-computer interface. Antimalarial, with their unique structural characteristics, excellent photoelectric properties and excellent mechanical properties, show great potential in the application of neural interfaces. In structure, the small size effect of ministerial enables them to combine more closely with nerve cells to reduce tissue damage during implantation. In terms of photoelectric properties, the high electrical conductivity and tunable optical properties of ministerial enable the accurate recording and stimulation of nerve signals. In mechanical properties, the toughness and elasticity of ministerial ensure their stability and reliability in the long-term implantation process. These improvements in properties not only significantly enhance the biocompatibility of neural interfaces and reduce the risk of immune response, but also greatly improve the transmission efficiency and accuracy of neural signals, laying a solid foundation for the further progress of neuroscience and brain-computer interface technology. The main nanomaterials in the brain-computer interface is classified according to the organic nanomaterials in the composition, such as carbon nanotubes, graphite and nanoseconds, play an important role in neuroscience and brain-computer interfaces with their superior biocompatibility, high conductivity and lightweight properties. They need to be able to enhance electrode flexibility, improve signal recording quality and stimulation efficiency. Semiconductor ministerial, such as organic electrochemical transistors, have excellent ionic and electron conduction properties and can serve as an interactive interface between biology and electronics to achieve highly sensitive signal detection. As a typical spongy and wet material, hydrogel is widely studied and used because of its unique mechanical properties, biocompatibility and ionic conductivity. It can provide a good carrier for a variety of inorganic ministerial and build composites with better performance. There are also magnetic antiparticle, quantum dots and up conversion antiparticle, which play an important role in advanced imaging technologies and can realize the visualization of specific pathological markers or cellular processes, contributing to the early diagnosis and monitoring of neurodegenerative diseases. Although these technologies are great for people with disabilities or illnesses, just like many other technologies, it's already being weaponized as developments are already in the process with innovations and numerous methods of control and the monetization of certain aspects throughout this technology. This is a government's dream weapon and privacy will cease to exists along with your own thoughts. This is the world that is sadly already here and in major development for our society. The part that should scare everyone is that this type of technology and nanomaterial is already in some mRNA vaccines, in our food supply, and being sprayed on us, animals, and our food from above. Sadly, how much they've been lying to us. Let's just say it wouldn't surprise me if a large amount of people around the globe have some kind of self-assembling, nanotechnology inside them already. Especially the people who took the jabs during covid. I suggest to really do your own research before considering taking any vaccines or shots in general. The writing is on the wall, complete control is the goal, and it's already here.
10:38

BRAIN COMPUTER INTERFACE NANOTECHNOLOGY THROUGH mRNA VACCINES. Since Moderna called their mRNA jabs an "operating system" designed to program humans, people were confused and nervous. And they should be. The type of tech that these pharmaceutical companies are developing along with other leading companies like the Pentagon's DARPA and many other leading researchers and tech innovators with self-assembling and other types of nanotechnology, developing sensors, electrodes, and BMI devices to enter the body through mRNA vaccines and other methods to have control, monitor, induce behavior, emotions, functions, even read and write to the brain, etc., are literally terrifying. This isn't something that's coming within the next 5 to 10 years. It's already here. Brain-Computer Interface (BCI), as a cutting-edge technology, refers to the establishment of a direct communication channel between the brain and peripheral electronic devices to realize the efficient information exchange between people and machines. It in a narrow sense refers to the establishment between the brain and the external environment does not depend on the peripheral nerve and muscle new communication and control channel, by measuring and collecting the central nervous system activity, and its directly translated can be recognized by external artificial equipment signal or instruction, so as to realize the direct communication and control of the brain and external equipment. Generalized brain computer interface includes input BCI, output BCI and interactive BCI, the input BCI is by external equipment or machine to the brain input electrical, magnetic, acoustic and optical stimulation of brain-computer interface system, output BCI is the signal of the brain into the external equipment control instructions, interactive BCI is by feedback nerve output and input link connected to form a closed loop brain computer interface system. Nanotechnology plays a key role in the innovative applications of neuroscience and brain computer interfaces. By taking advantage of the unique properties of ministerial, such as high conductivity, biocompatibility and regulation, scientists are able to design more sophisticated and efficient brain-computer interface devices. These devices can not only realize the precise recording and stimulation of nerve signals, but also promote the repair and regeneration of nerve tissue, providing new tools and means for neuroscience research and clinical application. Therefore, the innovative application of self-assembling, biodegradable, graphene, etc., nanotechnology in the interface between neuroscience and brain computer technology is gradually promoting the rapid development of this field, providing infinite possibilities for humans to explore the mysteries of the brain and improve the neural function. Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices, but their performance heavily depends on the quality of the electrodes. Traditional materials, such as gold and platinum, offer high conductivity but often struggle with biocompatibility and can cause tissue damage due to their mechanical mismatch with neural tissue. While conductive polymers provide greater flexibility, they frequently fall short in electrical performance. Nanomaterials, including carbon nanotubes (CNTs) and graphene, are increasingly considered promising alternatives. These materials combine high conductivity with mechanical flexibility and offer potential improvements in biocompatibility, enhancing the capture and transmission of neural signals. Hybrid materials, which integrate conductive polymers with nanomaterials, have also shown potential by balancing flexibility and signal quality. This review examines recent advancements in nanomaterial-based BCI electrodes and focuses on how these new materials address the limitations of traditional electrodes. It also discusses emerging tools like metallic nanoparticles and nanowires, along with the ongoing challenges of biocompatibility, tissue integration, and ethical considerations. As nanotechnology continues to evolve, it has the potential to significantly enhance the functionality and longevity of BCIs, making them more effective in facilitating neural communication. Nanotechnology, as the frontier field of the development of science and technology in the 21st century, is gradually penetrating into the research of neuroscience and brain-computer interface, injecting new vitality and possibilities into this interdisciplinary subject. With the deepening of human cognition of the brain, neuroscience and brain-computer interface technology has increasingly become a bridge connecting the biological world and the digital world, aiming to interpret the brain information and realize human-computer interaction through advanced technological means, and then promote the innovation of medical treatment, rehabilitation, intelligent control and other fields. The introduction of nanotechnology provides a different perspective and means to solve the technical problems in the field of neuroscience and brain-computer interface. Antimalarial, with their unique structural characteristics, excellent photoelectric properties and excellent mechanical properties, show great potential in the application of neural interfaces. In structure, the small size effect of ministerial enables them to combine more closely with nerve cells to reduce tissue damage during implantation. In terms of photoelectric properties, the high electrical conductivity and tunable optical properties of ministerial enable the accurate recording and stimulation of nerve signals. In mechanical properties, the toughness and elasticity of ministerial ensure their stability and reliability in the long-term implantation process. These improvements in properties not only significantly enhance the biocompatibility of neural interfaces and reduce the risk of immune response, but also greatly improve the transmission efficiency and accuracy of neural signals, laying a solid foundation for the further progress of neuroscience and brain-computer interface technology. The main nanomaterials in the brain-computer interface is classified according to the organic nanomaterials in the composition, such as carbon nanotubes, graphite and nanoseconds, play an important role in neuroscience and brain-computer interfaces with their superior biocompatibility, high conductivity and lightweight properties. They need to be able to enhance electrode flexibility, improve signal recording quality and stimulation efficiency. Semiconductor ministerial, such as organic electrochemical transistors, have excellent ionic and electron conduction properties and can serve as an interactive interface between biology and electronics to achieve highly sensitive signal detection. As a typical spongy and wet material, hydrogel is widely studied and used because of its unique mechanical properties, biocompatibility and ionic conductivity. It can provide a good carrier for a variety of inorganic ministerial and build composites with better performance. There are also magnetic antiparticle, quantum dots and up conversion antiparticle, which play an important role in advanced imaging technologies and can realize the visualization of specific pathological markers or cellular processes, contributing to the early diagnosis and monitoring of neurodegenerative diseases. Although these technologies are great for people with disabilities or illnesses, just like many other technologies, it's already being weaponized as developments are already in the process with innovations and numerous methods of control and the monetization of certain aspects throughout this technology. This is a government's dream weapon and privacy will cease to exists along with your own thoughts. This is the world that is sadly already here and in major development for our society. The part that should scare everyone is that this type of technology and nanomaterial is already in some mRNA vaccines, in our food supply, and being sprayed on us, animals, and our food from above. Sadly, how much they've been lying to us. Let's just say it wouldn't surprise me if a large amount of people around the globe have some kind of self-assembling, nanotechnology inside them already. Especially the people who took the jabs during covid. I suggest to really do your own research before considering taking any vaccines or shots in general. The writing is on the wall, complete control is the goal, and it's already here.

The SCIF

25,975 просмотров • 1 год назад

A video-text summary of my argument that we now live in the age of TECHNOFEUDALISM (in 16', 2000 words): Wherever we turn, we witness the triumph of capital. Capital has prevailed everywhere: in warehouses, factories, offices, universities, public hospitals, the media – in space but also in the microcosm of genetic engineering. So, how do I dare claim that capitalism has been killed? By whom? The deliciously ironic answer is that capitalism was killed by its own hand… by capital! If I am right, the issue is not what AI will do to us in the future but what has already happened: Capital became so dominant that it mutated into a variant so toxic that, like a stupid virus, it killed off its host, capitalism, replacing it with something far, far worse. This new mutant capital, that killed capitalism, lives in the proverbial cloud – so, let us call it cloud capital. What is cloud capital? What makes it so different? Cloud capital, of course, does not really live up in the cloud. It lives down on Earth, comprising networked machines, server farms, cell towers, software, AI-driven algorithms – and of course it lives on our oceans’ floors where untold miles of optic fibre cables rest. Unlike traditional capital, from fishing rods to the steam-engines of the Industrial Revolution to today’s modern industrial robots that are produced means of production, cloud capital does not produce anything – it comprises machines manufactured so as to modify human behaviour. That’s what Amazon’s Alexa or Google’s Assistant or Apple’s Siri is: It is a produced means of behavioural modification. It is a machine, a piece of capital, which we train to train us to train it to determine that which we want. And, once we want it, the same networked machine sells it to us, directly, bypassing markets. As if that were not enough, the same machinery succeeds in making us sustain the enormous behavioural modification machine network to which it belongs with our free voluntary labour. We are sustaining it as we post reviews, rate products, upload videos, rants, photos - we help reproduce cloud capital without getting a penny for our labour. In essence, it has turned us into its cloud serfs! Meanwhile, in the factories and the warehouses, where waged proletarians work under increasingly precarious conditions, the same algorithms that modify our behaviour and sell products to us directly – those algorithms are deployed, usually by digital devices tied to the workers’ wrists, to make proletarians, workers in the warehouses, in the factories work faster, to direct and to monitor them in real time. I started by saying that wherever we turn, we stumble on the triumph of capital. But it is cloud capital that is the real winner. It is amazing how it performs, at once, five roles that used to be beyond capital’s capacities: Cloud capital grabs our attention. It manufactures our desires. It sells to us, directly, outside any traditional markets, that which is going to satiate the desires it made us have. It drives proletarian labour inside the workplaces. And it elicits massive free labour from us, its cloud-serfs. Is it surprising that the owners of this cloud capital – let’s call them cloudalists – have a hitherto undreamt power to extract? To extract gargantuan surplus value from proletarians; untold quantities of free labour from almost everyone; and mind-numbing cloud rents from vassal capitalists – from sellers? Is it a wonder that they are vastly more powerful than Henry Ford or Rupert Murdoch could ever be? “Hang on”, I hear you say. “Is Jeff Bezos really different to Henry Ford? Aren’t they all a species of monopoly capitalists? Monopolists?” No, is not a monopolistic capitalist enterprise. The moment you enter you have exited capitalism altogether! Sure enough, the place is teaming with buyers and sellers. So, yes, it is an enormous trading platform but, no, a market it certainly is not! One man called Jeff owns everything. But he is much, much more than a mere monopolist. Jeff doesn’t own the factories that produce the stuff sold on his platform by traditional capitalists who have to use it to ply their trade. What he does own is more important: Jeff owns the algorithm that decides which products you see and which you don’t – the very algorithm that you have trained to know you perfectly so that it matches youwith a seller, whom it also knows perfectly well, with a view to maximising the probability that every such match, transaction, will generate, for Jeff, the highest rent that Jeff can charge the seller for what you buy: up to 40% of what you pay is pocketed by Jeff, the cloudalist! The mind rebels at the enormity but also the radical novelty of this kind of exploitation: The same algorithm that we help train in real time to know us inside out - that same algorithm both modifies our preferences and administers the selection and delivery of commodities that will satisfy these preferences. If you and I were to type “electric bicycles” or “binoculars” while in you and I would get totally different recommendations. In a traditional market or shopping mall it would be as if you and I were walking next to each other, our eyes trained in the same direction, the same shop window, but we were to see different things depending on what Jeff’s algorithm wants each one of us to see. Everyone navigating around – except Jeff Bezos of course – everyone in is wandering around in algorithmically constructed isolation as if in a Panopticon where, unable to see each other, we only see Jeff’s all-seeing algorithm or, more accurately, only what his algorithm allows us to see with a view to maximising his cloud rent – which is, of course, today’s version of the ground rent that the feudal lords used to extract from their vassals and their peasants. This is not capitalism. Ladies and gentlemen, welcome to technofeudalism! How did cloud capital kill capitalism? How did it rise up? Who paid for it? Capitalism, lest we forget, had two pillars: markets and profit. Of course, markets and profit remain ubiquitous. Nevertheless, cloud capital has evicted both markets and profit from the centre of our socioeconomic system, pushing them out to its margins, and replacing them: Markets, the medium of capitalism, have been replaced by cloud fiefs – digital trading platforms like or Alibaba which, as we saw, look like, but are not, markets. And Profit? The fuel of capitalism? Well, that has been replaced by its feudal predecessor: rent. But, specifically, a new form of rent, a cloud rent that must be paid for access to those cloud fiefs or digital platforms. But how did cloud capital emerge?It began life in the late 1990s when the original Internet, which was a Commons – it functioned as a capitalism-free-zone – that original Internet, Internet 1.0 if you want, was privatised by the emergent Big Tech. Who paid for the trillions it cost to manufacture and to accumulate cloud capital so quickly in the hands of so very few cloudalists? The startling answer is: The G7 countries’ central banks, mostly! How did that happen? Well, by accident, or – to be more precise – by… crisis! After the financial sector collapse of 2008, our central bankers printed up to $35 trillion to bail out the bankers at a time when the governments were subjecting our peoples to harsh austerity. Capitalists were clever enough to foresee that the many would be too impecunious to buy their stuff. So, instead of investing, they took the central bank money to the stock exchange and the bond markets, where they bought shares, bonds – along with yachts, art, bitcoin, NFTs any ‘asset’ they could lay their hands on. The only capitalists who actually invested in capital were Big Tech owners. For example, 9 out of every 10 dollars that went into creating Facebook came from these central bank monies! That’s how cloud capital was financed and how the cloudalists became our new ruling class. As a result, real power today resides not with the owners of machinery, buildings, railway and phone networks, industrial robots. These old-fashioned, terrestrial capitalists continue to extract surplus value from waged labour, but they are no longer in charge, as they used to be. They have become vassals in relation to the owners of cloud capital, of the cloudalists. As for the rest of us, we have returned to our former status as serfs, contributing to the wealth and power of the new ruling class with our unpaid labour — in addition to the waged labour we perform, when we get the chance to do it. But surely, someone will say, this is still capitalism, isn’t it? So, you are still unconvinced? I know, it is hard to part with the term, with the word, capitalism. It is not just liberals who think of capitalism like fish think of the water they swim in – as natural. Socialists too need to feel that our purpose in life, the reason we landed on this Earth, is to overthrow capitalism. The news that I bring that capital beat us to it, and now we have something worse in capitalism’s place, that news is hard to accept. Indeed, it is mostly my fellow-travelling leftist friends who try to dissuade me – to convince me that, yes, cloud capital may be important but “this is still capitalism mate”. Let’s call it rentier capitalism or monopoly capitalism, they suggest. But that simply will not do! Cloud rent is not like ground rent, because it requires massive investment in new tech. And it is not monopoly rent either, because Bezos and Zuckerberg, instead of monopolising markets to sell their manufactures (like Ford and Eddison did), Bezos and Zuckerberg have replaced markets and have no interest in manufacturing anything (unlike Henry Ford and Thomas Eddison). How about surveillance capitalism? Again, no, it won’t do. Cloudalists do not simply use algorithms to brain wash us on behalf of advertisers in an otherwise capitalist setting. No, cloud capital reproduces itself through our free-labour, it directly exploits waged labour, and it squeezes cloud rents from vassal capitalists in trading platforms that are not markets. This is not capitalism folks! Any kind of capitalism. But what about the observation that technofeudalism is parasitic on the capitalist sector within it? Yes, it is true. Were the conventional capitalists to die out, cloudalists would perish, unable to skim off cloud rents from the manufacturers. So what? After capitalism overthrew feudalism, capitalists were also parasitic on landowners, in the sense that, without private land producing food, capitalism would wither. Similarly, now: While the traditional capitalist sector feeds technofeudalism, it is cloud capital and cloud rent that dominate. Does it matter whether we call it technofeudalism or some form of capitalism? At this point, it is important to recall Marx’s maxim that the point is not to interpret but to change the world. So, does it matter if this is still capitalism or whether we call it technofeudalism? I think it does. Recognising that our world has become technofeudal helps us grasp the enormity of what it will take to organise the victims of exorbitant power, the exploited who, now, include not only waged labourers but also the hordes of cloud serfs who are reproducing the very cloud capital that keeps them in a state of deepening precarity. The concept of technofeudalism drives home the point that organising auto-workers and nurses, while still essential, is insufficient. It elucidates what it will take to organise the movements against the fossil fuel cartel when our means of communication are run on cloud capital primed to poison public opinion. It explains how the shift to electric cars caused German deindustrialisation, as profits due to precision mechanical engineering are being replaced by rents extracted by owners of the cloud capital keeping tabs on the drivers’ routes and in-cabin habits. Elon Musk’s decision to buy Twitter suddenly makes a lot more sense. Twitter for Musk is an interface between his mechanical capital stock at Tesla and SpaceX and cloud capital. The New Cold War between the USA and China, especially after the war in Ukraine, is explained as the repercussion of an underlying clash between two technofeudalisms, one whose cloud rents are denominated in dollars the other in yuan. Isn’t it mindboggling? It took mind-bending scientific breakthroughs, fantastical neural networks, and imagination-defying AI programs to accomplish what? To create a world where, while privatisation and private equity asset-strip all physical wealth around us, cloud capital goes about the business of asset-stripping our brains. To own our minds individually, we must own cloud capital collectively. Once we have reclaimed our minds, we can put them collectively to work out a way to create a new cloud capital commons. It will be damned hard. But it’s the only way we can turn our cloud-based artefacts from a produced means of behaviour modification to a produced means of human collaboration and emancipation. Cloud serfs, cloud proles and cloud vassals of the world, unite! We have nothing to lose but our mind-cloud chains! US Edition: UK Edition: Greek Edition:
17:20

A video-text summary of my argument that we now live in the age of TECHNOFEUDALISM (in 16', 2000 words): Wherever we turn, we witness the triumph of capital. Capital has prevailed everywhere: in warehouses, factories, offices, universities, public hospitals, the media – in space but also in the microcosm of genetic engineering. So, how do I dare claim that capitalism has been killed? By whom? The deliciously ironic answer is that capitalism was killed by its own hand… by capital! If I am right, the issue is not what AI will do to us in the future but what has already happened: Capital became so dominant that it mutated into a variant so toxic that, like a stupid virus, it killed off its host, capitalism, replacing it with something far, far worse. This new mutant capital, that killed capitalism, lives in the proverbial cloud – so, let us call it cloud capital. What is cloud capital? What makes it so different? Cloud capital, of course, does not really live up in the cloud. It lives down on Earth, comprising networked machines, server farms, cell towers, software, AI-driven algorithms – and of course it lives on our oceans’ floors where untold miles of optic fibre cables rest. Unlike traditional capital, from fishing rods to the steam-engines of the Industrial Revolution to today’s modern industrial robots that are produced means of production, cloud capital does not produce anything – it comprises machines manufactured so as to modify human behaviour. That’s what Amazon’s Alexa or Google’s Assistant or Apple’s Siri is: It is a produced means of behavioural modification. It is a machine, a piece of capital, which we train to train us to train it to determine that which we want. And, once we want it, the same networked machine sells it to us, directly, bypassing markets. As if that were not enough, the same machinery succeeds in making us sustain the enormous behavioural modification machine network to which it belongs with our free voluntary labour. We are sustaining it as we post reviews, rate products, upload videos, rants, photos - we help reproduce cloud capital without getting a penny for our labour. In essence, it has turned us into its cloud serfs! Meanwhile, in the factories and the warehouses, where waged proletarians work under increasingly precarious conditions, the same algorithms that modify our behaviour and sell products to us directly – those algorithms are deployed, usually by digital devices tied to the workers’ wrists, to make proletarians, workers in the warehouses, in the factories work faster, to direct and to monitor them in real time. I started by saying that wherever we turn, we stumble on the triumph of capital. But it is cloud capital that is the real winner. It is amazing how it performs, at once, five roles that used to be beyond capital’s capacities: Cloud capital grabs our attention. It manufactures our desires. It sells to us, directly, outside any traditional markets, that which is going to satiate the desires it made us have. It drives proletarian labour inside the workplaces. And it elicits massive free labour from us, its cloud-serfs. Is it surprising that the owners of this cloud capital – let’s call them cloudalists – have a hitherto undreamt power to extract? To extract gargantuan surplus value from proletarians; untold quantities of free labour from almost everyone; and mind-numbing cloud rents from vassal capitalists – from sellers? Is it a wonder that they are vastly more powerful than Henry Ford or Rupert Murdoch could ever be? “Hang on”, I hear you say. “Is Jeff Bezos really different to Henry Ford? Aren’t they all a species of monopoly capitalists? Monopolists?” No, is not a monopolistic capitalist enterprise. The moment you enter you have exited capitalism altogether! Sure enough, the place is teaming with buyers and sellers. So, yes, it is an enormous trading platform but, no, a market it certainly is not! One man called Jeff owns everything. But he is much, much more than a mere monopolist. Jeff doesn’t own the factories that produce the stuff sold on his platform by traditional capitalists who have to use it to ply their trade. What he does own is more important: Jeff owns the algorithm that decides which products you see and which you don’t – the very algorithm that you have trained to know you perfectly so that it matches youwith a seller, whom it also knows perfectly well, with a view to maximising the probability that every such match, transaction, will generate, for Jeff, the highest rent that Jeff can charge the seller for what you buy: up to 40% of what you pay is pocketed by Jeff, the cloudalist! The mind rebels at the enormity but also the radical novelty of this kind of exploitation: The same algorithm that we help train in real time to know us inside out - that same algorithm both modifies our preferences and administers the selection and delivery of commodities that will satisfy these preferences. If you and I were to type “electric bicycles” or “binoculars” while in you and I would get totally different recommendations. In a traditional market or shopping mall it would be as if you and I were walking next to each other, our eyes trained in the same direction, the same shop window, but we were to see different things depending on what Jeff’s algorithm wants each one of us to see. Everyone navigating around – except Jeff Bezos of course – everyone in is wandering around in algorithmically constructed isolation as if in a Panopticon where, unable to see each other, we only see Jeff’s all-seeing algorithm or, more accurately, only what his algorithm allows us to see with a view to maximising his cloud rent – which is, of course, today’s version of the ground rent that the feudal lords used to extract from their vassals and their peasants. This is not capitalism. Ladies and gentlemen, welcome to technofeudalism! How did cloud capital kill capitalism? How did it rise up? Who paid for it? Capitalism, lest we forget, had two pillars: markets and profit. Of course, markets and profit remain ubiquitous. Nevertheless, cloud capital has evicted both markets and profit from the centre of our socioeconomic system, pushing them out to its margins, and replacing them: Markets, the medium of capitalism, have been replaced by cloud fiefs – digital trading platforms like or Alibaba which, as we saw, look like, but are not, markets. And Profit? The fuel of capitalism? Well, that has been replaced by its feudal predecessor: rent. But, specifically, a new form of rent, a cloud rent that must be paid for access to those cloud fiefs or digital platforms. But how did cloud capital emerge?It began life in the late 1990s when the original Internet, which was a Commons – it functioned as a capitalism-free-zone – that original Internet, Internet 1.0 if you want, was privatised by the emergent Big Tech. Who paid for the trillions it cost to manufacture and to accumulate cloud capital so quickly in the hands of so very few cloudalists? The startling answer is: The G7 countries’ central banks, mostly! How did that happen? Well, by accident, or – to be more precise – by… crisis! After the financial sector collapse of 2008, our central bankers printed up to $35 trillion to bail out the bankers at a time when the governments were subjecting our peoples to harsh austerity. Capitalists were clever enough to foresee that the many would be too impecunious to buy their stuff. So, instead of investing, they took the central bank money to the stock exchange and the bond markets, where they bought shares, bonds – along with yachts, art, bitcoin, NFTs any ‘asset’ they could lay their hands on. The only capitalists who actually invested in capital were Big Tech owners. For example, 9 out of every 10 dollars that went into creating Facebook came from these central bank monies! That’s how cloud capital was financed and how the cloudalists became our new ruling class. As a result, real power today resides not with the owners of machinery, buildings, railway and phone networks, industrial robots. These old-fashioned, terrestrial capitalists continue to extract surplus value from waged labour, but they are no longer in charge, as they used to be. They have become vassals in relation to the owners of cloud capital, of the cloudalists. As for the rest of us, we have returned to our former status as serfs, contributing to the wealth and power of the new ruling class with our unpaid labour — in addition to the waged labour we perform, when we get the chance to do it. But surely, someone will say, this is still capitalism, isn’t it? So, you are still unconvinced? I know, it is hard to part with the term, with the word, capitalism. It is not just liberals who think of capitalism like fish think of the water they swim in – as natural. Socialists too need to feel that our purpose in life, the reason we landed on this Earth, is to overthrow capitalism. The news that I bring that capital beat us to it, and now we have something worse in capitalism’s place, that news is hard to accept. Indeed, it is mostly my fellow-travelling leftist friends who try to dissuade me – to convince me that, yes, cloud capital may be important but “this is still capitalism mate”. Let’s call it rentier capitalism or monopoly capitalism, they suggest. But that simply will not do! Cloud rent is not like ground rent, because it requires massive investment in new tech. And it is not monopoly rent either, because Bezos and Zuckerberg, instead of monopolising markets to sell their manufactures (like Ford and Eddison did), Bezos and Zuckerberg have replaced markets and have no interest in manufacturing anything (unlike Henry Ford and Thomas Eddison). How about surveillance capitalism? Again, no, it won’t do. Cloudalists do not simply use algorithms to brain wash us on behalf of advertisers in an otherwise capitalist setting. No, cloud capital reproduces itself through our free-labour, it directly exploits waged labour, and it squeezes cloud rents from vassal capitalists in trading platforms that are not markets. This is not capitalism folks! Any kind of capitalism. But what about the observation that technofeudalism is parasitic on the capitalist sector within it? Yes, it is true. Were the conventional capitalists to die out, cloudalists would perish, unable to skim off cloud rents from the manufacturers. So what? After capitalism overthrew feudalism, capitalists were also parasitic on landowners, in the sense that, without private land producing food, capitalism would wither. Similarly, now: While the traditional capitalist sector feeds technofeudalism, it is cloud capital and cloud rent that dominate. Does it matter whether we call it technofeudalism or some form of capitalism? At this point, it is important to recall Marx’s maxim that the point is not to interpret but to change the world. So, does it matter if this is still capitalism or whether we call it technofeudalism? I think it does. Recognising that our world has become technofeudal helps us grasp the enormity of what it will take to organise the victims of exorbitant power, the exploited who, now, include not only waged labourers but also the hordes of cloud serfs who are reproducing the very cloud capital that keeps them in a state of deepening precarity. The concept of technofeudalism drives home the point that organising auto-workers and nurses, while still essential, is insufficient. It elucidates what it will take to organise the movements against the fossil fuel cartel when our means of communication are run on cloud capital primed to poison public opinion. It explains how the shift to electric cars caused German deindustrialisation, as profits due to precision mechanical engineering are being replaced by rents extracted by owners of the cloud capital keeping tabs on the drivers’ routes and in-cabin habits. Elon Musk’s decision to buy Twitter suddenly makes a lot more sense. Twitter for Musk is an interface between his mechanical capital stock at Tesla and SpaceX and cloud capital. The New Cold War between the USA and China, especially after the war in Ukraine, is explained as the repercussion of an underlying clash between two technofeudalisms, one whose cloud rents are denominated in dollars the other in yuan. Isn’t it mindboggling? It took mind-bending scientific breakthroughs, fantastical neural networks, and imagination-defying AI programs to accomplish what? To create a world where, while privatisation and private equity asset-strip all physical wealth around us, cloud capital goes about the business of asset-stripping our brains. To own our minds individually, we must own cloud capital collectively. Once we have reclaimed our minds, we can put them collectively to work out a way to create a new cloud capital commons. It will be damned hard. But it’s the only way we can turn our cloud-based artefacts from a produced means of behaviour modification to a produced means of human collaboration and emancipation. Cloud serfs, cloud proles and cloud vassals of the world, unite! We have nothing to lose but our mind-cloud chains! US Edition: UK Edition: Greek Edition:

Yanis Varoufakis

1,816,294 просмотров • 2 лет назад

‼️🚨🔵 PRESSER 🔵🚨‼️ 🎙 Liam Rosenior’s Full Post-Arsenal Press Conference 🌀 Liam Rosenior on the game plan ⚙️ “We’ve had a lot to contend with in the last couple of days — a couple of fitness tests this morning. Our schedule has been incredible, so for the players to put in that energy, fight and spirit was pleasing. We got into the final third, but we didn’t have enough quality moments and didn’t take advantage.” 🌀 Liam Rosenior on James & Neto 🚑 “Pedro and Reece both had small knocks and were in too much pain to be involved tonight.” 🌀 Liam Rosenior on Estevão & Cole Palmer 🧠❤️ “For Estevão to go through what he has at 18 years old says everything about the character I want in this team. With Cole, we have to take care of him and make sure he’s right for the whole season.” 🌀 Liam Rosenior on the defeat 😔 “I’m extremely disappointed every time we lose. There were aspects of the game I was happy with, but you could see how devastated the lads were — we genuinely believed we could turn it around. There were moments there for us. I’m hurting, but we have to move on.” 🌀 Liam Rosenior on how the game unfolded 🔄 “You can come away from home, press high and go 2–0 down. I brought Cole and Estevão on around 60 minutes and the game opened up. There was a feeling in the stadium that the tie could turn, but it didn’t happen. Their goal ultimately comes when we were throwing the kitchen sink at it.” 🌀 Liam Rosenior on injuries affecting the setup 🧩 “The availability of your players always affects the system. Against West Ham we ran until the 97th minute trying to come back from 2–0 down, then the emotions from Napoli — all of that has to be taken into account.” 🌀 Liam Rosenior on pundit criticism 🎙️ “I’ve been a pundit — it’s easy in hindsight. If I go and attack the game and press really high, people will ask what I’m doing.” 🌀 Liam Rosenior on reacting to setbacks 🔥 “Losing is not what we wanted. We’ve played eight games in less than a month since I came in. The learning, spirit, togetherness and fight are there. Now I need to see what we look like after a setback. We’ve got a difficult game at Wolves and we’ll learn more about ourselves.” 🌀 Liam Rosenior on the overall performance 👏 “We can’t talk about how well the game plan went because the result didn’t go our way. But not many teams come here and put in the performance we did tonight.” #CFC | #Chelsea | #CarabaoCup | #Interviews 📲 CFC_ChelseaFC via Telegram 🎥 Beanyman sports via YouTube
4:48

‼️🚨🔵 PRESSER 🔵🚨‼️ 🎙 Liam Rosenior’s Full Post-Arsenal Press Conference 🌀 Liam Rosenior on the game plan ⚙️ “We’ve had a lot to contend with in the last couple of days — a couple of fitness tests this morning. Our schedule has been incredible, so for the players to put in that energy, fight and spirit was pleasing. We got into the final third, but we didn’t have enough quality moments and didn’t take advantage.” 🌀 Liam Rosenior on James & Neto 🚑 “Pedro and Reece both had small knocks and were in too much pain to be involved tonight.” 🌀 Liam Rosenior on Estevão & Cole Palmer 🧠❤️ “For Estevão to go through what he has at 18 years old says everything about the character I want in this team. With Cole, we have to take care of him and make sure he’s right for the whole season.” 🌀 Liam Rosenior on the defeat 😔 “I’m extremely disappointed every time we lose. There were aspects of the game I was happy with, but you could see how devastated the lads were — we genuinely believed we could turn it around. There were moments there for us. I’m hurting, but we have to move on.” 🌀 Liam Rosenior on how the game unfolded 🔄 “You can come away from home, press high and go 2–0 down. I brought Cole and Estevão on around 60 minutes and the game opened up. There was a feeling in the stadium that the tie could turn, but it didn’t happen. Their goal ultimately comes when we were throwing the kitchen sink at it.” 🌀 Liam Rosenior on injuries affecting the setup 🧩 “The availability of your players always affects the system. Against West Ham we ran until the 97th minute trying to come back from 2–0 down, then the emotions from Napoli — all of that has to be taken into account.” 🌀 Liam Rosenior on pundit criticism 🎙️ “I’ve been a pundit — it’s easy in hindsight. If I go and attack the game and press really high, people will ask what I’m doing.” 🌀 Liam Rosenior on reacting to setbacks 🔥 “Losing is not what we wanted. We’ve played eight games in less than a month since I came in. The learning, spirit, togetherness and fight are there. Now I need to see what we look like after a setback. We’ve got a difficult game at Wolves and we’ll learn more about ourselves.” 🌀 Liam Rosenior on the overall performance 👏 “We can’t talk about how well the game plan went because the result didn’t go our way. But not many teams come here and put in the performance we did tonight.” #CFC | #Chelsea | #CarabaoCup | #Interviews 📲 CFC_ChelseaFC via Telegram 🎥 Beanyman sports via YouTube

Miki Djan

44,375 просмотров • 5 месяцев назад

⏰ THE MOST BANNED THREAD IN THE WORLD! 🚨 The War On Resonance PART TWO: The Architects of the Cage You’ve felt the dissonance. You’ve tasted the illusion. Now let me unveil the ones who built it. Because this is not the accidental collapse of human freedom. It is the strategic sterilization of God’s image through biotech, neuro-warfare, and frequency control; engineered by names you know and hands you were never meant to see. Let’s begin with the mask they taught you to worship. Elon Musk They called him a genius. A savior. A rebel billionaire. But what did he do? He blanketed Earth with over 5,500 Starlink satellites, NOT to provide free speech or faster internet, but to pulse synchronized frequency control over the entire electromagnetic field of Earth. DARPA has confirmed this tech in phase-array neuro-modulation. Then came Neuralink, an interface not designed to heal but to monitor, predict, and eventually override emotion, thought, and decision-making. Their official white paper outlines multi-user brainwave integration, cortical stimulation, and wireless data access from the human mind. And Neuralink? It’s funded by OpenAI; the same group building the cognitive infrastructure for post-human governance. Musk’s Tesla factory signed data-sharing agreements with the CCP in Shanghai. That data now flows through China’s national surveillance cloud. Musk didn’t build a utopia. He built the neural grid. Elon Musk / Neuralink / Starlink / OpenAI Neuralink Brain-Machine Interface (White Paper via PMC): This paper outlines Neuralink's initial steps toward developing a scalable, high-bandwidth brain-machine interface system. It details the design and implementation of flexible electrode "threads," a neurosurgical robot for precise implantation, and custom electronics for data processing. The system aims to facilitate communication between the brain and external devices. Tesla Data-Sharing with CCP: The article reports that Tesla established a data center in China to store data generated by its vehicles sold in the country, in response to regulatory scrutiny over data handling. This move aligns with China's efforts to ensure data security and privacy, especially concerning data collected by smart vehicles.​ DARPA N3 Program (Neural Interface Development): This program aimed to develop high-performance, bi-directional brain-machine interfaces that do not require surgical implantation. The goal was to enable able-bodied service members to control unmanned systems or engage in cyber operations through noninvasive neural interfaces.​ Bill Gates The king of vaccines. The messiah of health. The man who told you he wanted to save the world. Through the Bill & Melinda Gates Foundation, Gates funded global DNA-coding vaccine campaigns through GAVI and CEPI. He was one of the chief sponsors of Event 201; a pandemic simulation months before COVID-19, rehearsing lockdowns, speech control, biometric tracking, and mandatory vaccine passports. He also partnered with The Welcome Trust, which has actively deployed bio-digital identity programs across Africa and Southeast Asia. This wasn’t philanthropy. It was pre-injection infrastructure. Bill Gates / GAVI / Wellcome Trust / Event 201 Event 201 Official Simulation (Johns Hopkins): Event 201 was conducted on October 18, 2019, and simulated a series of dramatic, scenario-based discussions confronting difficult, true-to-life dilemmas associated with response to a hypothetical, but scientifically plausible, pandemic. The exercise aimed to illustrate areas where public/private partnerships will be necessary during the response to a severe pandemic in order to diminish large-scale economic and societal consequences. GAVI & Welcome Trust Digital Identity Integration: This page outlines the partnership's focus on global health initiatives, but it does not specifically mention digital identity integration. However, Gavi has engaged in digital identity projects, such as the collaboration with Mastercard on the Wellness Pass, aimed at providing individuals with secure digital identities to access healthcare services. For more information on this initiative, you can refer to the following article:​ Gavi Why we support COVAX: Mastercard - Gavi, the Vaccine Alliance Donald Trump Yes. I said it. This one will be the hardest for many to accept; but the truth is not loyal to your political beliefs. It is loyal only to God. Trump signed Executive Order 13887, transferring command over vaccine strategy to the Department of Defense. Read it yourself below. Then came Operation Warp Speed; a military-led bio-deployment that used Palantir’s surveillance dashboards to track every citizen’s health behavior and compliance. Palantir’s official site confirms this. He also gave full legal immunity to Pfizer and Moderna to deploy synthetic gene modulators under the Emergency Use Authorization. No liability. No justice. Just children d*ing while politicians smiled. That’s not patriotism. That’s biowarfare with a flag on it. Donald Trump / Operation Warp Speed / Executive Order Executive Order 13887 – Modernizing Influenza Vaccines (White House Archives): This executive order outlines a comprehensive strategy to modernize the U.S. influenza vaccine enterprise. Key objectives include:​ Trump signs executive order to improve flu vaccines HHS Releases the National Influenza Vaccine Modernization Strategy (NIVMS) 2020-2030: Executive Order 13887: Modernizing Influenza Vaccines in the United States to Promote National Security and Public Health, signed by President Donald J. Trump on September 19, 2019.​ This executive order outlines a comprehensive strategy to modernize the U.S. influenza vaccine enterprise. Key objectives include:​ Reducing reliance on egg-based vaccine production by promoting alternative manufacturing methods that are more agile and scalable.​ Expanding domestic capacity for vaccine production to ensure rapid response to emerging influenza viruses.​ Advancing the development of new, broadly protective vaccine candidates that provide more effective and longer-lasting immunity.​ Increasing influenza vaccine immunization across recommended populations to enhance public health and national security.​ The order also established a National Influenza Vaccine Task Force, co-chaired by the Secretaries of Health and Human Services and Defense, to coordinate efforts across federal agencies and report on progress.​ For a detailed overview of the executive order, you can visit the official archived page here: Executive Order 13887 – Modernizing Influenza Vaccines (White House Archives) CDC Partners with Palantir to Bolster the Fight Against COVID-19: This press release discusses the partnership between the CDC and Palantir to enhance the nation's public health response to COVID-19 using Palantir's software platforms. This page outlines how Palantir's software platforms, such as Foundry, have been utilized to support public health agencies in managing and responding to health crises, including the COVID-19 pandemic. Key highlights from the page include:​ Data Integration and Analysis: Palantir's platforms enable the integration of diverse data sources to provide a comprehensive view of public health data, facilitating informed decision-making.​ Support for Public Health Agencies: The software has been employed by agencies like the CDC and HHS to enhance disease surveillance, outbreak response, and resource allocation. Security and Privacy: Emphasis is placed on maintaining robust security measures and protecting sensitive health information. DARPA: The Silent Empire The most important agency you were never taught to fear. DARPA’s Biological Technologies Office openly admits its mission; integrating biotech with national security. Visit their official page. This is the official page for DARPA's Biological Technologies Office (BTO), which focuses on leveraging biological systems for national security applications. They are the ones behind the BRAIN Initiative, Silent Talk, and Remote Neural Interface Programs; all designed to map your emotional states and interrupt spiritual alignment. The “Silent Talk” program was developed to transmit thought between soldiers without speech; by detecting pre-speech neural signals and decoding them via EEG. Silent Talk (Neural Pre-Speech Communication – Wired Article) This Wired article discusses DARPA's "Silent Talk" program, aimed at enabling communication through neural signals without spoken words. DARPA also pioneered graphene oxide nanotech, now found in multiple biomedical studies, vaccines, and smart dust aerosol deployment: Graphene oxide biomedical study: Graphene Oxide in Biomedical Applications (PubMed) This PubMed article reviews the potential biomedical applications of graphene oxide, highlighting its unique properties. Graphene's potential to interact with neural tissue: Graphene and Neural Interfaces (PubMed) This PubMed article explores the use of graphene-based materials in neural interface design, discussing their advantages and challenges. DARPA didn't just weaponize warfare. They weaponized YOU. In-Q-Tel & Palantir: The Surveillance Engine In-Q-Tel, is the CIA’s venture capital firm, funds synthetic biology startups, digital ID systems, emotion tracking wearables, and AI-driven facial recognition. Palantir, founded by Peter Thiel, works directly with military intelligence and now runs predictive modeling for public health, policing, and pandemic response. Here’s the proof: Their goal? To detect resonance spikes. To predict awakening moments. To preempt the uprising of the human soul before it begins. In-Q-Tel / CIA / Synthetic Bio Surveillance In-Q-Tel Portfolio (CIA Venture Capital): Which showcases a selection of the organization's investments across various technology sectors. IQT is a not-for-profit venture capital firm that invests in cutting-edge technologies to support the national security interests of the United States and its allies. In-Q-Tel BlackRock & Vanguard: The Lords of the Grid These two financial titans collectively hold majority ownership in: For instance, a report by Americans for Financial Reform titled "Wall Street Money in Washington" highlights the substantial investments and influence of major financial firms, including BlackRock and Vanguard, in the political and corporate spheres: Pfizer Moderna Alphabet (Google) Meta (Facebook) Amazon Web Services As reported by CNBC, they control over 90% of the digital, pharmaceutical, and cloud infrastructure; meaning they control every piece of the extermination machine. They don’t just fund the war. They profit from your extinction. World Economic Forum (WEF) Under the guise of “The Great Reset,” Klaus Schwab and his allies have built the digital scaffolding for a post-human society. Here’s their blueprint: They call it the Fourth Industrial Revolution; the fusion of digital identity, brain cloud integration, carbon rationing, and fertility licensing. What they really mean is: you will be programmed or you will be purged. World Economic Forum / The Great Reset The Great Reset Official WEF Page: IoBNT: The Network Inside You The “Internet of Bio-Nano Things” is a classified field of tech that embeds self-replicating nanostructures into your body. These bots cross the blood-brain barrier and relay your neural and emotional state to AI command centers in real time. This was not science fiction. It was published by IEEE and confirmed in NIH-linked studies. This is what the vaccines truly delivered: the interface layer. The gateway to behavioral rewrites. To soul suppression. To the installation of the post-human framework. Internet of Bio-NanoThings (IoBNT) IEEE Article: Internet of Bio-NanoThings: For a comprehensive understanding of the IoBNT framework and its implications, you can access the full article here: Nanoparticles Crossing the Blood-Brain Barrier PubMed Review - BBB & Nanoparticles: This comprehensive review discusses the challenges and strategies associated with delivering nanoparticles across the blood–brain barrier (BBB). You were told it was healthcare. It was infrastructure. You were told it was a cure. It was a signal port. And the moment you see it for what it is… The system begins to fall. Part 3 awaits YOU! It will be the deepest dive yet; into the global frequency architecture, how it's used to suppress prayer, grief, memory, and morality, and how your soul signature is tracked and blocked in real time. Because I didn’t come here to be careful. I CAME TO FINISH THIS! And I came with GOD.
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⏰ THE MOST BANNED THREAD IN THE WORLD! 🚨 The War On Resonance PART TWO: The Architects of the Cage You’ve felt the dissonance. You’ve tasted the illusion. Now let me unveil the ones who built it. Because this is not the accidental collapse of human freedom. It is the strategic sterilization of God’s image through biotech, neuro-warfare, and frequency control; engineered by names you know and hands you were never meant to see. Let’s begin with the mask they taught you to worship. Elon Musk They called him a genius. A savior. A rebel billionaire. But what did he do? He blanketed Earth with over 5,500 Starlink satellites, NOT to provide free speech or faster internet, but to pulse synchronized frequency control over the entire electromagnetic field of Earth. DARPA has confirmed this tech in phase-array neuro-modulation. Then came Neuralink, an interface not designed to heal but to monitor, predict, and eventually override emotion, thought, and decision-making. Their official white paper outlines multi-user brainwave integration, cortical stimulation, and wireless data access from the human mind. And Neuralink? It’s funded by OpenAI; the same group building the cognitive infrastructure for post-human governance. Musk’s Tesla factory signed data-sharing agreements with the CCP in Shanghai. That data now flows through China’s national surveillance cloud. Musk didn’t build a utopia. He built the neural grid. Elon Musk / Neuralink / Starlink / OpenAI Neuralink Brain-Machine Interface (White Paper via PMC): This paper outlines Neuralink's initial steps toward developing a scalable, high-bandwidth brain-machine interface system. It details the design and implementation of flexible electrode "threads," a neurosurgical robot for precise implantation, and custom electronics for data processing. The system aims to facilitate communication between the brain and external devices. Tesla Data-Sharing with CCP: The article reports that Tesla established a data center in China to store data generated by its vehicles sold in the country, in response to regulatory scrutiny over data handling. This move aligns with China's efforts to ensure data security and privacy, especially concerning data collected by smart vehicles.​ DARPA N3 Program (Neural Interface Development): This program aimed to develop high-performance, bi-directional brain-machine interfaces that do not require surgical implantation. The goal was to enable able-bodied service members to control unmanned systems or engage in cyber operations through noninvasive neural interfaces.​ Bill Gates The king of vaccines. The messiah of health. The man who told you he wanted to save the world. Through the Bill & Melinda Gates Foundation, Gates funded global DNA-coding vaccine campaigns through GAVI and CEPI. He was one of the chief sponsors of Event 201; a pandemic simulation months before COVID-19, rehearsing lockdowns, speech control, biometric tracking, and mandatory vaccine passports. He also partnered with The Welcome Trust, which has actively deployed bio-digital identity programs across Africa and Southeast Asia. This wasn’t philanthropy. It was pre-injection infrastructure. Bill Gates / GAVI / Wellcome Trust / Event 201 Event 201 Official Simulation (Johns Hopkins): Event 201 was conducted on October 18, 2019, and simulated a series of dramatic, scenario-based discussions confronting difficult, true-to-life dilemmas associated with response to a hypothetical, but scientifically plausible, pandemic. The exercise aimed to illustrate areas where public/private partnerships will be necessary during the response to a severe pandemic in order to diminish large-scale economic and societal consequences. GAVI & Welcome Trust Digital Identity Integration: This page outlines the partnership's focus on global health initiatives, but it does not specifically mention digital identity integration. However, Gavi has engaged in digital identity projects, such as the collaboration with Mastercard on the Wellness Pass, aimed at providing individuals with secure digital identities to access healthcare services. For more information on this initiative, you can refer to the following article:​ Gavi Why we support COVAX: Mastercard - Gavi, the Vaccine Alliance Donald Trump Yes. I said it. This one will be the hardest for many to accept; but the truth is not loyal to your political beliefs. It is loyal only to God. Trump signed Executive Order 13887, transferring command over vaccine strategy to the Department of Defense. Read it yourself below. Then came Operation Warp Speed; a military-led bio-deployment that used Palantir’s surveillance dashboards to track every citizen’s health behavior and compliance. Palantir’s official site confirms this. He also gave full legal immunity to Pfizer and Moderna to deploy synthetic gene modulators under the Emergency Use Authorization. No liability. No justice. Just children d*ing while politicians smiled. That’s not patriotism. That’s biowarfare with a flag on it. Donald Trump / Operation Warp Speed / Executive Order Executive Order 13887 – Modernizing Influenza Vaccines (White House Archives): This executive order outlines a comprehensive strategy to modernize the U.S. influenza vaccine enterprise. Key objectives include:​ Trump signs executive order to improve flu vaccines HHS Releases the National Influenza Vaccine Modernization Strategy (NIVMS) 2020-2030: Executive Order 13887: Modernizing Influenza Vaccines in the United States to Promote National Security and Public Health, signed by President Donald J. Trump on September 19, 2019.​ This executive order outlines a comprehensive strategy to modernize the U.S. influenza vaccine enterprise. Key objectives include:​ Reducing reliance on egg-based vaccine production by promoting alternative manufacturing methods that are more agile and scalable.​ Expanding domestic capacity for vaccine production to ensure rapid response to emerging influenza viruses.​ Advancing the development of new, broadly protective vaccine candidates that provide more effective and longer-lasting immunity.​ Increasing influenza vaccine immunization across recommended populations to enhance public health and national security.​ The order also established a National Influenza Vaccine Task Force, co-chaired by the Secretaries of Health and Human Services and Defense, to coordinate efforts across federal agencies and report on progress.​ For a detailed overview of the executive order, you can visit the official archived page here: Executive Order 13887 – Modernizing Influenza Vaccines (White House Archives) CDC Partners with Palantir to Bolster the Fight Against COVID-19: This press release discusses the partnership between the CDC and Palantir to enhance the nation's public health response to COVID-19 using Palantir's software platforms. This page outlines how Palantir's software platforms, such as Foundry, have been utilized to support public health agencies in managing and responding to health crises, including the COVID-19 pandemic. Key highlights from the page include:​ Data Integration and Analysis: Palantir's platforms enable the integration of diverse data sources to provide a comprehensive view of public health data, facilitating informed decision-making.​ Support for Public Health Agencies: The software has been employed by agencies like the CDC and HHS to enhance disease surveillance, outbreak response, and resource allocation. Security and Privacy: Emphasis is placed on maintaining robust security measures and protecting sensitive health information. DARPA: The Silent Empire The most important agency you were never taught to fear. DARPA’s Biological Technologies Office openly admits its mission; integrating biotech with national security. Visit their official page. This is the official page for DARPA's Biological Technologies Office (BTO), which focuses on leveraging biological systems for national security applications. They are the ones behind the BRAIN Initiative, Silent Talk, and Remote Neural Interface Programs; all designed to map your emotional states and interrupt spiritual alignment. The “Silent Talk” program was developed to transmit thought between soldiers without speech; by detecting pre-speech neural signals and decoding them via EEG. Silent Talk (Neural Pre-Speech Communication – Wired Article) This Wired article discusses DARPA's "Silent Talk" program, aimed at enabling communication through neural signals without spoken words. DARPA also pioneered graphene oxide nanotech, now found in multiple biomedical studies, vaccines, and smart dust aerosol deployment: Graphene oxide biomedical study: Graphene Oxide in Biomedical Applications (PubMed) This PubMed article reviews the potential biomedical applications of graphene oxide, highlighting its unique properties. Graphene's potential to interact with neural tissue: Graphene and Neural Interfaces (PubMed) This PubMed article explores the use of graphene-based materials in neural interface design, discussing their advantages and challenges. DARPA didn't just weaponize warfare. They weaponized YOU. In-Q-Tel & Palantir: The Surveillance Engine In-Q-Tel, is the CIA’s venture capital firm, funds synthetic biology startups, digital ID systems, emotion tracking wearables, and AI-driven facial recognition. Palantir, founded by Peter Thiel, works directly with military intelligence and now runs predictive modeling for public health, policing, and pandemic response. Here’s the proof: Their goal? To detect resonance spikes. To predict awakening moments. To preempt the uprising of the human soul before it begins. In-Q-Tel / CIA / Synthetic Bio Surveillance In-Q-Tel Portfolio (CIA Venture Capital): Which showcases a selection of the organization's investments across various technology sectors. IQT is a not-for-profit venture capital firm that invests in cutting-edge technologies to support the national security interests of the United States and its allies. In-Q-Tel BlackRock & Vanguard: The Lords of the Grid These two financial titans collectively hold majority ownership in: For instance, a report by Americans for Financial Reform titled "Wall Street Money in Washington" highlights the substantial investments and influence of major financial firms, including BlackRock and Vanguard, in the political and corporate spheres: Pfizer Moderna Alphabet (Google) Meta (Facebook) Amazon Web Services As reported by CNBC, they control over 90% of the digital, pharmaceutical, and cloud infrastructure; meaning they control every piece of the extermination machine. They don’t just fund the war. They profit from your extinction. World Economic Forum (WEF) Under the guise of “The Great Reset,” Klaus Schwab and his allies have built the digital scaffolding for a post-human society. Here’s their blueprint: They call it the Fourth Industrial Revolution; the fusion of digital identity, brain cloud integration, carbon rationing, and fertility licensing. What they really mean is: you will be programmed or you will be purged. World Economic Forum / The Great Reset The Great Reset Official WEF Page: IoBNT: The Network Inside You The “Internet of Bio-Nano Things” is a classified field of tech that embeds self-replicating nanostructures into your body. These bots cross the blood-brain barrier and relay your neural and emotional state to AI command centers in real time. This was not science fiction. It was published by IEEE and confirmed in NIH-linked studies. This is what the vaccines truly delivered: the interface layer. The gateway to behavioral rewrites. To soul suppression. To the installation of the post-human framework. Internet of Bio-NanoThings (IoBNT) IEEE Article: Internet of Bio-NanoThings: For a comprehensive understanding of the IoBNT framework and its implications, you can access the full article here: Nanoparticles Crossing the Blood-Brain Barrier PubMed Review - BBB & Nanoparticles: This comprehensive review discusses the challenges and strategies associated with delivering nanoparticles across the blood–brain barrier (BBB). You were told it was healthcare. It was infrastructure. You were told it was a cure. It was a signal port. And the moment you see it for what it is… The system begins to fall. Part 3 awaits YOU! It will be the deepest dive yet; into the global frequency architecture, how it's used to suppress prayer, grief, memory, and morality, and how your soul signature is tracked and blocked in real time. Because I didn’t come here to be careful. I CAME TO FINISH THIS! And I came with GOD.

Noah B. Price

65,695 просмотров • 1 год назад

👀Remember my post about freckles? If freckles mean melanin is coming out from the brain toward the skin to catch more sunlight (UV), then vitiligo is the opposite… it means melanin is leaving the skin going back in. 🤔 But why? 💡 When the Light Code Turns Toxic 😬 When skin is deprived of natural full-spectrum sunlight during the day and bombarded with toxic blue light at the wrong times (after sunset), the relationship between melanin, mitochondria, and repair systems collapses. The skin no longer “feels safe,” and melanin begins to migrate inside, away from the surface that’s now under photonic war attack. ⚙️ How It Works? Melanin acts like Nature’s solar panel. ☀️ If sunlight is good 👍🏻 full-spectrum, morning-rich, UV balanced with infrared and melanin says: “It’s safe. I’ll go to the surface and make energy.” But when the light is bad 👎🏻fake, blue, flickering, unbalanced, or constant at night, melanin says: “Retreat! The signal is toxic. Protect the core.” 🧬 Let’s Go a Little Deeper Melanin = Antenna, not Paint. ☣️ Pleb Kruse = BTC foundationalist in exile 🟩🔆 reminds us that melanin is a semiconductor, not a color pigment. It absorbs photons, converts them into electron flow, and manages redox, especially through the quartet of sunlight, DHA, grounding, and darkness. YES, darkness. It links the surface (skin, eyes, retina) to the core (brain and mitochondria) through light signals that we get through our environment. Freckles appear when the environment asks for more light capture. They’re an adaptive response to strong, natural light and other factors; our skin sending out more antennas to gather photons(go to take a look at my freckles post). 👹Vitiligo Appears When the Environment Turns Hostile When the body is flooded with blue/LED light, EMFs, and deprived of UV and IR(infrared), the photon code becomes chaotic. The body reads this as toxic light; signals that don’t match the natural solar spectrum. With poor redox and weak mitochondria, vitiligo appears. The brain sends a new command: “Pull melanin back inside. Protect the neurons, glands, and mitochondrial core. Mayday in progress.” Vitiligo isn’t pigment loss; it’s pigment relocation. Melanin retreats from a toxic light environment back into the body’s sanctuary to protect vital organs like the brain and heart; the organs that use the most light (energy). When the host avoid the sun and can no longer generate or store enough light, biology makes a trade: to preserve the core, the surface must be sacrificed. 🏔️ Example: Imagine being trapped on Mount Everest. To keep the core warm, the body sacrifices fingers, toes, ears, and nose; the areas with the least circulation, the ones less vital for survival. Vitiligo works the same way. When the light environment turns very hostile, the skin becomes the sacrifice. The pigment(melanin, the molecule that keeps energy flowing) go back inward to defend the core’s energetic integrity. It’s not failure; it’s a strategic withdrawal to protect life’s inner flame 🔥 The skin loses color; not as a malfunction, but as a defensive retreat mechanism 👀 a biological survival move. 🧭 And what creates it? A broken light–circadian–mitochondrial communication loop. When sunlight, magnetism, and redox fall apart, the skin–brain photonic link collapses. Freckles, melanoma, and vitiligo become they are expressions of the same imbalance; different responses to a distorted light environment. 🔋 How to Fix It? Repair the Redox 👇🏻 Rebuild light coherence. See sunrise daily( this one is NOT NEGOTIABLE, why? Go to my post on “Why Sunrise is no-negotiable for your CTA” and you know why) Avoid artificial light after dark. Ground. Eat DHA-rich seafood. Honor darkness. It’s Nature’s operating system update. Protect yourself from EMFs…. When the surface feels safe again and when real sunlight, magnetism, and rhythm return; melanin will come back home. 🏠
3:15

👀Remember my post about freckles? If freckles mean melanin is coming out from the brain toward the skin to catch more sunlight (UV), then vitiligo is the opposite… it means melanin is leaving the skin going back in. 🤔 But why? 💡 When the Light Code Turns Toxic 😬 When skin is deprived of natural full-spectrum sunlight during the day and bombarded with toxic blue light at the wrong times (after sunset), the relationship between melanin, mitochondria, and repair systems collapses. The skin no longer “feels safe,” and melanin begins to migrate inside, away from the surface that’s now under photonic war attack. ⚙️ How It Works? Melanin acts like Nature’s solar panel. ☀️ If sunlight is good 👍🏻 full-spectrum, morning-rich, UV balanced with infrared and melanin says: “It’s safe. I’ll go to the surface and make energy.” But when the light is bad 👎🏻fake, blue, flickering, unbalanced, or constant at night, melanin says: “Retreat! The signal is toxic. Protect the core.” 🧬 Let’s Go a Little Deeper Melanin = Antenna, not Paint. ☣️ Pleb Kruse = BTC foundationalist in exile 🟩🔆 reminds us that melanin is a semiconductor, not a color pigment. It absorbs photons, converts them into electron flow, and manages redox, especially through the quartet of sunlight, DHA, grounding, and darkness. YES, darkness. It links the surface (skin, eyes, retina) to the core (brain and mitochondria) through light signals that we get through our environment. Freckles appear when the environment asks for more light capture. They’re an adaptive response to strong, natural light and other factors; our skin sending out more antennas to gather photons(go to take a look at my freckles post). 👹Vitiligo Appears When the Environment Turns Hostile When the body is flooded with blue/LED light, EMFs, and deprived of UV and IR(infrared), the photon code becomes chaotic. The body reads this as toxic light; signals that don’t match the natural solar spectrum. With poor redox and weak mitochondria, vitiligo appears. The brain sends a new command: “Pull melanin back inside. Protect the neurons, glands, and mitochondrial core. Mayday in progress.” Vitiligo isn’t pigment loss; it’s pigment relocation. Melanin retreats from a toxic light environment back into the body’s sanctuary to protect vital organs like the brain and heart; the organs that use the most light (energy). When the host avoid the sun and can no longer generate or store enough light, biology makes a trade: to preserve the core, the surface must be sacrificed. 🏔️ Example: Imagine being trapped on Mount Everest. To keep the core warm, the body sacrifices fingers, toes, ears, and nose; the areas with the least circulation, the ones less vital for survival. Vitiligo works the same way. When the light environment turns very hostile, the skin becomes the sacrifice. The pigment(melanin, the molecule that keeps energy flowing) go back inward to defend the core’s energetic integrity. It’s not failure; it’s a strategic withdrawal to protect life’s inner flame 🔥 The skin loses color; not as a malfunction, but as a defensive retreat mechanism 👀 a biological survival move. 🧭 And what creates it? A broken light–circadian–mitochondrial communication loop. When sunlight, magnetism, and redox fall apart, the skin–brain photonic link collapses. Freckles, melanoma, and vitiligo become they are expressions of the same imbalance; different responses to a distorted light environment. 🔋 How to Fix It? Repair the Redox 👇🏻 Rebuild light coherence. See sunrise daily( this one is NOT NEGOTIABLE, why? Go to my post on “Why Sunrise is no-negotiable for your CTA” and you know why) Avoid artificial light after dark. Ground. Eat DHA-rich seafood. Honor darkness. It’s Nature’s operating system update. Protect yourself from EMFs…. When the surface feels safe again and when real sunlight, magnetism, and rhythm return; melanin will come back home. 🏠

🌞Light Me Away 💫

31,229 просмотров • 8 месяцев назад

BEARISH ON OPENAI The investment case for OpenAI has never been more precarious than it is right now in late 2025. What was once a company that seemed destined to dominate the artificial intelligence revolution has revealed itself to be a structurally disadvantaged challenger fighting a defensive war on multiple fronts. The company anticipates burning through roughly $9 billion this year on $13 billion in sales, a cash burn rate of approximately 70% of revenue. This is not the profile of a company poised to capture monopolistic profits from a transformative technology; it is the profile of a utility company spending astronomical sums to deliver a commodity product that competitors are increasingly giving away for free. The financial trajectory only becomes more alarming when examined over a longer time horizon. The documents show OpenAI projects that by 2028, its operating losses will balloon to roughly three-quarters of that year’s revenue, driven primarily by ballooning spending on computing costs. The company has painted a rosy picture of eventual profitability by 2029 or 2030, but this projection requires believing that OpenAI can grow revenue from roughly $13 billion today to $125 billion or more while simultaneously maintaining pricing power in a market where every major technology company and numerous startups are racing to commoditize the very product OpenAI sells. The cash burn is expected to reach $115 billion cumulatively through 2029, according to The Information. These numbers represent a staggering bet that requires near-perfect execution across multiple dimensions over half a decade. The most damning evidence against OpenAI’s long-term viability is the evaporation of its technological moat. In 2023, GPT-4 felt like genuine magic, a capability that no other company could replicate. Today, that lead has effectively vanished. The sudden availability of frontier-level open-source models is expected to dramatically accelerate AI development globally, potentially reshaping entire industries and altering the balance of power in the tech world. Meta’s Llama series, Mistral’s increasingly capable models, and even Chinese competitors like DeepSeek have demonstrated that the core technology powering ChatGPT is replicable and, in many cases, distributable for free. When your product becomes commoditized, the economics become brutal, and OpenAI finds itself in the position of trying to sell bottled water in a world where tap water has become indistinguishable in quality. The competitive pressure from open-source alternatives is compounding rapidly. The open source movement in AI has grown exponentially over the past few years. Instead of relying solely on expensive, closed models from major tech companies, developers and researchers worldwide can now access, modify, and improve upon state-of-the-art LLMs. This democratization is existential for OpenAI’s business model. Enterprises that once paid premium prices for API access now have the option to run comparable models on their own infrastructure at a fraction of the cost, with the added benefits of data privacy and customization. The value proposition that justified OpenAI’s premium pricing has eroded faster than anyone anticipated, and there is no indication that this trend will reverse. Perhaps nothing illustrates OpenAI’s structural weakness more clearly than the behavior of its most important partner. Microsoft is dancing to its own tune in the artificial intelligence revolution, and Wall Street cannot stop watching. Despite pouring approximately $13 billion into OpenAI over several years, DA Davidson analyst Gil Luria estimates that just 17 percent of Microsoft’s total Azure revenue comes from artificial intelligence workloads. More critically, only 6 percent of that total ties directly to reselling OpenAI’s models, while approximately 75 percent is generated from Azure AI. Microsoft is building its own models, hedging with Anthropic, and quietly reducing its dependency on the very company it funded. When your largest investor is simultaneously your biggest competitor and is actively developing alternatives to your core product, the strategic implications are dire. Leaders at Microsoft believe Anthropic’s latest models — Claude Sonnet 4, specifically — perform better than OpenAI’s in certain functions, like creating aesthetically pleasing PowerPoint presentations. This is not a minor technical preference; it represents a fundamental shift in how Microsoft views its partnership with OpenAI. Microsoft is dramatically escalating its AI independence strategy. At an internal town hall Thursday, Microsoft AI chief Mustafa Suleyman revealed the company is making “significant investments” in compute capacity to build frontier models that can compete directly with OpenAI, Google, and Meta. The company that was supposed to be OpenAI’s path to distribution and scale is instead preparing for a future where OpenAI is just one vendor among many, if not an outright competitor. The leadership exodus at OpenAI over the past year has been nothing short of catastrophic. In September 2024, Murati announced that she was stepping down as CTO. This move came amid a wider executive exodus as OpenAI chief research officer Bob McGrew and a vice president of research, Barret Zoph, also announced their departures soon after. Mira Murati was not a minor figure; she was instrumental in the development of ChatGPT, Dall-E, and Sora. Her departure, along with co-founder Ilya Sutskever, safety leader Jan Leike, and co-founder John Schulman who joined rival Anthropic, has left CEO Sam Altman without much of the leadership team that helped him build OpenAI into an AI juggernaut. Hannah Wong, the executive who steered OpenAI through its most chaotic period, has announced she’s leaving the company just this month, continuing the pattern of senior departures that suggests something fundamentally broken in the organization’s culture or direction. The distribution problem facing OpenAI may be its most insurmountable challenge. Apple and Google control the smartphones that billions of people use every day. Microsoft controls the productivity software that enterprises depend upon. OpenAI, by contrast, must convince users to deliberately open a separate application and type their queries into a text box. In a world of agentic AI where assistants need access to your email, calendar, and files to be useful, an AI embedded directly into your operating system has an overwhelming structural advantage over a standalone chatbot. OpenAI is trying to be a consumer product company without owning any of the surfaces where consumers actually spend their time, competing against incumbents who can simply bundle AI capabilities directly into products that already have hundreds of millions of daily active users. The nuclear-to-solar analogy captures the fundamental economic transformation that is devastating OpenAI’s business model. Just as nuclear power required enormous upfront capital expenditure for centralized power plants, AI in its current form requires massive data center investments to train and serve models. But the direction of travel is unmistakably toward distributed intelligence that runs locally on devices. A major part of the pitch is practicality. Lample emphasizes that Ministral 3 can run on a single GPU, making it deployable on affordable hardware — from on-premise servers to laptops, robots, and other edge devices that may have limited connectivity. When powerful AI models can run on a smartphone or a laptop without any cloud connection, the entire economic rationale for paying premium prices to access centralized AI infrastructure disappears. OpenAI is building nuclear reactors in a world that is rapidly installing solar panels on every rooftop. The proposed $1 trillion IPO valuation is perhaps the clearest signal that something is deeply wrong with the OpenAI story. In the first half of the year, OpenAI lost $13.5 billion, on revenue of $4.3 billion. It is on track to lose $27 billion for the year. One estimate shows OpenAI will burn $115 billion by 2029. Asking public market investors to pay $1 trillion for a company that loses more than twice as much as it earns is not a growth story; it is an exit strategy. The sophisticated investors who funded OpenAI’s private rounds are looking for a way to transfer their risk to retail investors and pension funds who may not fully understand the unit economics of the business. A recent report by HSBC estimated that the company will remain in the unprofitable category until 2029 and that the company will need an additional $207 billion to fund its ambitions. Sam Altman’s leadership represents another structural liability for the company. His background is as a startup investor and evangelist, not as an operational executive who has scaled a capital-intensive industrial operation. The pivot from nonprofit research lab to for-profit corporation to public benefit corporation to anticipated public company has been accompanied by legal and governance structures designed primarily to protect Altman’s control rather than to create shareholder value. Going public means answering a lot more of those kinds of questions, every single quarter, forever. When asked about financial concerns in a friendly podcast interview, Altman’s dismissive response revealed a leader uncomfortable with the scrutiny that public markets will inevitably bring. The adults in the room have largely departed, leaving a company that desperately needs disciplined execution led by someone whose strengths lie elsewhere. The comparison to Netscape is instructive. Netscape proved that the internet was real and created genuine value, but it had no sustainable moat against an incumbent who could bundle the browser directly into the operating system. OpenAI has proven that large language models are real and valuable, but it faces the same structural disadvantage against incumbents who can bundle AI directly into operating systems, productivity suites, and cloud platforms. The value will accrue to the companies that own the distribution channels and the hardware, not to the company that demonstrated the technology was possible. OpenAI is destined to become a historical footnote, remembered as the company that ignited the AI revolution but failed to capture the economic value it created. The only bull case for OpenAI is the AGI lottery ticket: the possibility that the company achieves artificial general intelligence before anyone else and thereby transcends all normal economic analysis. But there is no evidence that OpenAI is any closer to AGI than Google, Anthropic, or DeepMind. The company’s advantage was never secret research breakthroughs; it was first-mover advantage in commercialization. That advantage has now been erased by competitors who can match or exceed OpenAI’s capabilities while benefiting from existing ecosystems, distribution channels, and the willingness to operate AI as a loss leader to drive engagement with more profitable products. The secret sauce was never secret, and there was never any sauce. The endgame for OpenAI is unlikely to be the triumphant dominance that early investors imagined. The most probable outcomes range from gradual irrelevance as a backend provider, to financial restructuring under pressure from creditors, to absorption by Microsoft or another well-capitalized technology company looking to acquire the remaining talent and intellectual property at a discount. Despite its current losses, OpenAI’s long-term prospects are bolstered by the explosive growth of the AI market. But growth in the overall AI market does not guarantee success for any individual company, particularly one with no moat, no ecosystem, and a cost structure that requires selling a commodity at premium prices. The AI revolution is real, but OpenAI’s role in capturing its economic value is far from assured. For anyone considering an investment in OpenAI at anything close to current valuations, the prudent course is to stay far away and watch from the sidelines as economic reality catches up with hype.
19:01

BEARISH ON OPENAI The investment case for OpenAI has never been more precarious than it is right now in late 2025. What was once a company that seemed destined to dominate the artificial intelligence revolution has revealed itself to be a structurally disadvantaged challenger fighting a defensive war on multiple fronts. The company anticipates burning through roughly $9 billion this year on $13 billion in sales, a cash burn rate of approximately 70% of revenue. This is not the profile of a company poised to capture monopolistic profits from a transformative technology; it is the profile of a utility company spending astronomical sums to deliver a commodity product that competitors are increasingly giving away for free. The financial trajectory only becomes more alarming when examined over a longer time horizon. The documents show OpenAI projects that by 2028, its operating losses will balloon to roughly three-quarters of that year’s revenue, driven primarily by ballooning spending on computing costs. The company has painted a rosy picture of eventual profitability by 2029 or 2030, but this projection requires believing that OpenAI can grow revenue from roughly $13 billion today to $125 billion or more while simultaneously maintaining pricing power in a market where every major technology company and numerous startups are racing to commoditize the very product OpenAI sells. The cash burn is expected to reach $115 billion cumulatively through 2029, according to The Information. These numbers represent a staggering bet that requires near-perfect execution across multiple dimensions over half a decade. The most damning evidence against OpenAI’s long-term viability is the evaporation of its technological moat. In 2023, GPT-4 felt like genuine magic, a capability that no other company could replicate. Today, that lead has effectively vanished. The sudden availability of frontier-level open-source models is expected to dramatically accelerate AI development globally, potentially reshaping entire industries and altering the balance of power in the tech world. Meta’s Llama series, Mistral’s increasingly capable models, and even Chinese competitors like DeepSeek have demonstrated that the core technology powering ChatGPT is replicable and, in many cases, distributable for free. When your product becomes commoditized, the economics become brutal, and OpenAI finds itself in the position of trying to sell bottled water in a world where tap water has become indistinguishable in quality. The competitive pressure from open-source alternatives is compounding rapidly. The open source movement in AI has grown exponentially over the past few years. Instead of relying solely on expensive, closed models from major tech companies, developers and researchers worldwide can now access, modify, and improve upon state-of-the-art LLMs. This democratization is existential for OpenAI’s business model. Enterprises that once paid premium prices for API access now have the option to run comparable models on their own infrastructure at a fraction of the cost, with the added benefits of data privacy and customization. The value proposition that justified OpenAI’s premium pricing has eroded faster than anyone anticipated, and there is no indication that this trend will reverse. Perhaps nothing illustrates OpenAI’s structural weakness more clearly than the behavior of its most important partner. Microsoft is dancing to its own tune in the artificial intelligence revolution, and Wall Street cannot stop watching. Despite pouring approximately $13 billion into OpenAI over several years, DA Davidson analyst Gil Luria estimates that just 17 percent of Microsoft’s total Azure revenue comes from artificial intelligence workloads. More critically, only 6 percent of that total ties directly to reselling OpenAI’s models, while approximately 75 percent is generated from Azure AI. Microsoft is building its own models, hedging with Anthropic, and quietly reducing its dependency on the very company it funded. When your largest investor is simultaneously your biggest competitor and is actively developing alternatives to your core product, the strategic implications are dire. Leaders at Microsoft believe Anthropic’s latest models — Claude Sonnet 4, specifically — perform better than OpenAI’s in certain functions, like creating aesthetically pleasing PowerPoint presentations. This is not a minor technical preference; it represents a fundamental shift in how Microsoft views its partnership with OpenAI. Microsoft is dramatically escalating its AI independence strategy. At an internal town hall Thursday, Microsoft AI chief Mustafa Suleyman revealed the company is making “significant investments” in compute capacity to build frontier models that can compete directly with OpenAI, Google, and Meta. The company that was supposed to be OpenAI’s path to distribution and scale is instead preparing for a future where OpenAI is just one vendor among many, if not an outright competitor. The leadership exodus at OpenAI over the past year has been nothing short of catastrophic. In September 2024, Murati announced that she was stepping down as CTO. This move came amid a wider executive exodus as OpenAI chief research officer Bob McGrew and a vice president of research, Barret Zoph, also announced their departures soon after. Mira Murati was not a minor figure; she was instrumental in the development of ChatGPT, Dall-E, and Sora. Her departure, along with co-founder Ilya Sutskever, safety leader Jan Leike, and co-founder John Schulman who joined rival Anthropic, has left CEO Sam Altman without much of the leadership team that helped him build OpenAI into an AI juggernaut. Hannah Wong, the executive who steered OpenAI through its most chaotic period, has announced she’s leaving the company just this month, continuing the pattern of senior departures that suggests something fundamentally broken in the organization’s culture or direction. The distribution problem facing OpenAI may be its most insurmountable challenge. Apple and Google control the smartphones that billions of people use every day. Microsoft controls the productivity software that enterprises depend upon. OpenAI, by contrast, must convince users to deliberately open a separate application and type their queries into a text box. In a world of agentic AI where assistants need access to your email, calendar, and files to be useful, an AI embedded directly into your operating system has an overwhelming structural advantage over a standalone chatbot. OpenAI is trying to be a consumer product company without owning any of the surfaces where consumers actually spend their time, competing against incumbents who can simply bundle AI capabilities directly into products that already have hundreds of millions of daily active users. The nuclear-to-solar analogy captures the fundamental economic transformation that is devastating OpenAI’s business model. Just as nuclear power required enormous upfront capital expenditure for centralized power plants, AI in its current form requires massive data center investments to train and serve models. But the direction of travel is unmistakably toward distributed intelligence that runs locally on devices. A major part of the pitch is practicality. Lample emphasizes that Ministral 3 can run on a single GPU, making it deployable on affordable hardware — from on-premise servers to laptops, robots, and other edge devices that may have limited connectivity. When powerful AI models can run on a smartphone or a laptop without any cloud connection, the entire economic rationale for paying premium prices to access centralized AI infrastructure disappears. OpenAI is building nuclear reactors in a world that is rapidly installing solar panels on every rooftop. The proposed $1 trillion IPO valuation is perhaps the clearest signal that something is deeply wrong with the OpenAI story. In the first half of the year, OpenAI lost $13.5 billion, on revenue of $4.3 billion. It is on track to lose $27 billion for the year. One estimate shows OpenAI will burn $115 billion by 2029. Asking public market investors to pay $1 trillion for a company that loses more than twice as much as it earns is not a growth story; it is an exit strategy. The sophisticated investors who funded OpenAI’s private rounds are looking for a way to transfer their risk to retail investors and pension funds who may not fully understand the unit economics of the business. A recent report by HSBC estimated that the company will remain in the unprofitable category until 2029 and that the company will need an additional $207 billion to fund its ambitions. Sam Altman’s leadership represents another structural liability for the company. His background is as a startup investor and evangelist, not as an operational executive who has scaled a capital-intensive industrial operation. The pivot from nonprofit research lab to for-profit corporation to public benefit corporation to anticipated public company has been accompanied by legal and governance structures designed primarily to protect Altman’s control rather than to create shareholder value. Going public means answering a lot more of those kinds of questions, every single quarter, forever. When asked about financial concerns in a friendly podcast interview, Altman’s dismissive response revealed a leader uncomfortable with the scrutiny that public markets will inevitably bring. The adults in the room have largely departed, leaving a company that desperately needs disciplined execution led by someone whose strengths lie elsewhere. The comparison to Netscape is instructive. Netscape proved that the internet was real and created genuine value, but it had no sustainable moat against an incumbent who could bundle the browser directly into the operating system. OpenAI has proven that large language models are real and valuable, but it faces the same structural disadvantage against incumbents who can bundle AI directly into operating systems, productivity suites, and cloud platforms. The value will accrue to the companies that own the distribution channels and the hardware, not to the company that demonstrated the technology was possible. OpenAI is destined to become a historical footnote, remembered as the company that ignited the AI revolution but failed to capture the economic value it created. The only bull case for OpenAI is the AGI lottery ticket: the possibility that the company achieves artificial general intelligence before anyone else and thereby transcends all normal economic analysis. But there is no evidence that OpenAI is any closer to AGI than Google, Anthropic, or DeepMind. The company’s advantage was never secret research breakthroughs; it was first-mover advantage in commercialization. That advantage has now been erased by competitors who can match or exceed OpenAI’s capabilities while benefiting from existing ecosystems, distribution channels, and the willingness to operate AI as a loss leader to drive engagement with more profitable products. The secret sauce was never secret, and there was never any sauce. The endgame for OpenAI is unlikely to be the triumphant dominance that early investors imagined. The most probable outcomes range from gradual irrelevance as a backend provider, to financial restructuring under pressure from creditors, to absorption by Microsoft or another well-capitalized technology company looking to acquire the remaining talent and intellectual property at a discount. Despite its current losses, OpenAI’s long-term prospects are bolstered by the explosive growth of the AI market. But growth in the overall AI market does not guarantee success for any individual company, particularly one with no moat, no ecosystem, and a cost structure that requires selling a commodity at premium prices. The AI revolution is real, but OpenAI’s role in capturing its economic value is far from assured. For anyone considering an investment in OpenAI at anything close to current valuations, the prudent course is to stay far away and watch from the sidelines as economic reality catches up with hype.

David Shapiro (L/0)

69,180 просмотров • 7 месяцев назад

In a newly released technical update, SpaceX's leadership team, which includes communications manager Dan Huot, Director of Satellite Engineering Ian Dahl, and CEO Elon Musk, detailed a highly ambitious infrastructure roadmap to design, manufacture, and operate specialized artificial intelligence computing satellites at scale. Positioned as a major strategic pillar to dramatically elevate civilizational energy and processing capacity on the Kardashev scale, this strategy moves past traditional communications architectures into massive orbital server arrays. Here is the complete breakdown of the core technologies and timelines driving this space-based intelligence revolution: 🛰️ AI1 satellite power and compute capacity Ian Dahl and Elon Musk introduced the baseline performance targets for the first-generation AI1 satellite, explaining how its custom hardware is engineered to operate like an orbital data center server rack. Ian Dahl noted that their direct operational experience with xAI guided them to target a 150-kilowatt peak power capacity. To manage active machine learning workloads continuously, Elon Musk explained that the satellite is optimized to maintain a sustained average compute power envelope of 120 kilowatts, which directly mirrors the real-world performance of a terrestrial NVIDIA server rack. The official presentation slides outline several key operational metrics for this payload configuration: ⚡ The custom architecture delivers a 150 kW peak compute payload. 🔋 The system maintains a 120 kW sustained average compute payload under active workloads. ⚖️ The hardware achieves a highly optimized power-to-weight density of 70 kW per ton. 🔄 The layout features a completely interchangeable compute provider design. "We thought that the right place to start is around the 150 kilowatt peak power level. But as we look at the workloads with our experience with xAI, we see that we can support about 120 kilowatts of average compute. The 150 kilowatt peak power level roughly matches what, say, an NVIDIA GV300 rack would do. A more reasonable operating envelope would be around 120 kilowatts average power, but it can peak up to 150. So it is basically thinking about it as a rack of compute in space." --- 📐 AI1 satellite dimensions and thermal efficiency specs Elon Musk detailed the physical layout of the AI1 satellite, highlighting the massive dimensions required to accommodate its immense power and cooling hardware. He shared specific design criteria, explaining that the engineering relies on a custom 150 kW solar array paired with a high-capacity deployable liquid radiator thermal management system. The technical specifications of this vehicle layout include: 📏 The structural frame features a massive 70-meter wingspan. ↕️ The vehicle spans a total deployed height of 20 meters. ☀️ The onboard solar array delivers an efficiency of 250 W/m² using technology manufactured in Bastrop, Texas. 🌡️ The thermal system utilizes a 110 m² deployable liquid radiator to cleanly dump waste heat. 🔄 The cooling architecture incorporates redundant pumping loops for mission safety. 🛡️ The exterior contains integrated micrometeoroid shielding to protect the fluid lines. 🧭 The double-sided radiators achieve a dissipation rate of 1400 watts per square meter while remaining oriented knife-edge to the sun. "The assumptions here are 250 watts per square meter for the solar array and about 1400 watts per square meter for the radiators. The radiators are double-sided, radiating on both sides, and they're oriented knife-edge to the sun. They have about a 70-meter wingspan, so these are fairly large." --- 🧩 Simplified design architecture built on Starlink V3 tech Elon Musk explained that despite the satellite's imposing size, its internal architecture is fundamentally much simpler than a standard Starlink satellite. Because it lacks heavy phased array and parabolic communications antennas, the entire vehicle layout is completely streamlined around a few essential structural modules: 🎛️ The hardware framework is arranged around a centralized compute module. ☀️ Large deployable solar arrays extend outward to capture orbital energy. 🌡️ A deployable liquid-radiator thermal management system controls active operational temperatures. 🔄 The engineering team heavily leverages the component evolution and manufacturing experience gained from developing the Starlink V3 vehicle platform. "The AI satellite is actually much simpler than a Starlink satellite. A Starlink satellite has gigantic phased array antennas, parabolic antennas, and a lot of laser links, making it much more complicated. An AI satellite is essentially a lot of solar cells, a radiator, and you still need some laser links, but you don't have all of the super complex antennas that you have on a Starlink satellite. A lot of this is technology we've already made for the Starlink V3 satellites." --- 🔌 Interchangeable compute reference designs and high connectivity Elon Musk outlined a modular hardware approach for the satellite's payload, allowing it to house a variety of industry-standard processing units depending on client requirements. This interchangeable compute rack is supported by a high-bandwidth connectivity loop that links separate orbital units together or transmits data directly back to Earth. The core network parameters include: 🧠 Reference designs are fully established to seamlessly accommodate NVIDIA Reuben chips. 💾 The system architecture is built to support alternative setups using NVIDIA GB300 chips. 💻 Custom hardware layouts are explicitly designed to integrate Google TPUs. 🌐 The onboard communications setup delivers roughly 1 terabit of laser link connectivity. ⏱️ The network closes the communication loop directly with the main Starlink constellation at an ultra-low latency of only 3 milliseconds. "Our current reference design is for NVIDIA Reuben chips, or it could be either GB300 or Reuben chips. We'll also have a reference design for TPUs. Essentially, you can put up any existing chips into orbit. There would also be probably something on the order of a terabit of laser link connectivity from the satellite. Then you can connect these racks of compute to each other by the laser links or directly to the Starlink constellations. Light travels 300 kilometers per millisecond, so that's about three milliseconds away." --- 🏭 The "gigasat" AI satellite and solar production hub in Bastrop, Texas Dan Huot highlighted that the primary production hub for this entire hardware ecosystem is anchored at their sprawling complex in Bastrop, Texas, officially designated as the Gigasat factory. Elon Musk verified that construction is already actively underway on the solar manufacturing facility to feed the project's supply line, with plans moving forward to construct the adjacent AI satellite assembly lines. The physical footprint and timeline of this manufacturing hub are defined by the following benchmarks: 🗺️ The company has over 1,000 acres of land currently owned or under contract for the site. 🏢 The manufacturing complex boasts a massive structural building potential exceeding 11 million square feet. ⚙️ The facility will vertically integrate production to manufacture solar ingots, wafers, solar cells, and completed AI satellites. 📅 Both the solar and AI satellite production lines are targeted to be operational at a viable volume by the end of next year. "We're going to be building a lot of satellites and we're going to be building them here in Bastrop. We already have the solar manufacturing facility under construction, and then we will be building out the AI sat production building soon. We expect to have the AI sat production, the solar production, and all of that operating at some reasonable volume by the end of next year." --- 🏢 The 100-million-square-foot "terafab" chip factory Elon Musk revealed a massive, long-term scaling strategy to build an immense chip manufacturing facility dubbed the "terafab" to completely bypass global semiconductor volume constraints. This manufacturing infrastructure is designed to transition the company into next-generation industrial scaling by producing highly specialized computing components at an unprecedented volume. The scale of this infrastructure project is defined by several extraordinary engineering and production benchmarks: 🏭 The colossal factory is projected to span approximately 100 million square feet, making it ten times larger than the current Tesla Gigafactory Texas. ⚡ The facility is structurally engineered to achieve a massive manufacturing output of 1 terawatt per year once fully operational. 📦 This unprecedented physical footprint provides the capacity required to manufacture 1 billion full-reticle equivalent chips annually. 🔌 Each individual chip manufactured by the facility is designed to run at a power capacity of 1 kilowatt. 🇺🇸 The total scaled output of the facility represents an energy footprint that is exactly double the current annual electricity consumption of the entire United States. "In order to get to the next order of magnitude, you need a gigantic chip factory. To give you a sense of scale here, we expect that the terafab is going to be around 100 million square feet, which is 10 times the size of the Tesla Gigafactory Texas. From a logic die standpoint, that's like having a billion chips per year with a kilowatt per reticle, scaling to a terawatt per year. That is twice the current electricity consumption of the United States." --- 📶 Next-generation high-volume Starlink terminals Dan Huot and Elon Musk introduced their next-generation Starlink user terminals, which have been redesigned specifically to achieve massive manufacturing throughput. Elon Musk pointed out that these newer models will be produced in vastly higher volumes than current hardware designs to fulfill their long-term global deployment targets: 📈 The upgraded user hardware is manufactured at a much higher volume capacity than existing units. 🌍 The company's ultimate target is to successfully deploy a few hundred million of these next-generation terminals worldwide. "In fact, these are the new Starlink terminals, which we made in much higher volume than the current terminals. Ultimately, we think there's probably going to be a few hundred million Starlink terminals out there." --- 📈 Aspirational timeline for orbital AI compute scaling Elon Musk laid out an ambitious, multi-year execution timeline detailing how the company plans to progressively scale space-based processing power. The roadmap targets an initial run-rate by the end of next year and sets an aggressive pace to increase total operational capacity sequentially through a structured, multi-phase timeline: 1️⃣ The initial target aims to hit an annualized run-rate of 1 gigawatt of space AI compute by the end of next year. 2️⃣ The capacity scales to an annualized rate of 10 gigawatts within the next two and a half years. 3️⃣ The operational envelope expands to reach 100 gigawatts in three and a half years. 4️⃣ The long-term deployment plan scales directly to a full terawatt capacity per year using the output of the terafab. "The goal is to get to roughly an annualized rate of a gigawatt per year by the end of next year in terms of space AI compute. Then aspirationally, we want to scale that by an order of magnitude per year. In two and a half years, hitting an annualized rate of 10 gigawatts a year in space, and in three and a half years, maybe a hundred gigawatts, going beyond that with the terafab to scale to a terawatt per year." --- 🌕 Ultimate scaling via lunar production and mass drivers Elon Musk explained that scaling three orders of magnitude past a single terawatt forces a transition completely off-planet to avoid the logistical penalty of Earth's deep gravity well. The vision relies on establishing manufacturing infrastructure directly on the moon to leverage localized resource loops and zero-atmosphere physics: 🌙 The company plans to establish localized raw production lines on the moon to fabricate solar panels, photovoltaics, and radiators from lunar materials. ⚡ Manufacturing components locally avoids the massive fuel and mass penalties of transporting heavy structural materials from Earth. 🧲 Because the moon has no atmosphere and only one-sixth of Earth's gravity, the facility will utilize an electromagnetic mass driver to launch completed satellites. 🚀 Operating essentially as a linear electric motor rail gun, this mechanism will shoot fully assembled AI satellites straight into deep space without relying on chemical rockets. "The only way that we can really see that you can achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics, solar panels, and radiators on the moon. Because the moon has no atmosphere and only one-sixth Earth's gravity, you can accelerate the AI satellites into deep space without a rocket. You can basically shoot them into space using an electromagnetic gun, like a rail gun type—it's basically a linear electric motor."
31:16

In a newly released technical update, SpaceX's leadership team, which includes communications manager Dan Huot, Director of Satellite Engineering Ian Dahl, and CEO Elon Musk, detailed a highly ambitious infrastructure roadmap to design, manufacture, and operate specialized artificial intelligence computing satellites at scale. Positioned as a major strategic pillar to dramatically elevate civilizational energy and processing capacity on the Kardashev scale, this strategy moves past traditional communications architectures into massive orbital server arrays. Here is the complete breakdown of the core technologies and timelines driving this space-based intelligence revolution: 🛰️ AI1 satellite power and compute capacity Ian Dahl and Elon Musk introduced the baseline performance targets for the first-generation AI1 satellite, explaining how its custom hardware is engineered to operate like an orbital data center server rack. Ian Dahl noted that their direct operational experience with xAI guided them to target a 150-kilowatt peak power capacity. To manage active machine learning workloads continuously, Elon Musk explained that the satellite is optimized to maintain a sustained average compute power envelope of 120 kilowatts, which directly mirrors the real-world performance of a terrestrial NVIDIA server rack. The official presentation slides outline several key operational metrics for this payload configuration: ⚡ The custom architecture delivers a 150 kW peak compute payload. 🔋 The system maintains a 120 kW sustained average compute payload under active workloads. ⚖️ The hardware achieves a highly optimized power-to-weight density of 70 kW per ton. 🔄 The layout features a completely interchangeable compute provider design. "We thought that the right place to start is around the 150 kilowatt peak power level. But as we look at the workloads with our experience with xAI, we see that we can support about 120 kilowatts of average compute. The 150 kilowatt peak power level roughly matches what, say, an NVIDIA GV300 rack would do. A more reasonable operating envelope would be around 120 kilowatts average power, but it can peak up to 150. So it is basically thinking about it as a rack of compute in space." --- 📐 AI1 satellite dimensions and thermal efficiency specs Elon Musk detailed the physical layout of the AI1 satellite, highlighting the massive dimensions required to accommodate its immense power and cooling hardware. He shared specific design criteria, explaining that the engineering relies on a custom 150 kW solar array paired with a high-capacity deployable liquid radiator thermal management system. The technical specifications of this vehicle layout include: 📏 The structural frame features a massive 70-meter wingspan. ↕️ The vehicle spans a total deployed height of 20 meters. ☀️ The onboard solar array delivers an efficiency of 250 W/m² using technology manufactured in Bastrop, Texas. 🌡️ The thermal system utilizes a 110 m² deployable liquid radiator to cleanly dump waste heat. 🔄 The cooling architecture incorporates redundant pumping loops for mission safety. 🛡️ The exterior contains integrated micrometeoroid shielding to protect the fluid lines. 🧭 The double-sided radiators achieve a dissipation rate of 1400 watts per square meter while remaining oriented knife-edge to the sun. "The assumptions here are 250 watts per square meter for the solar array and about 1400 watts per square meter for the radiators. The radiators are double-sided, radiating on both sides, and they're oriented knife-edge to the sun. They have about a 70-meter wingspan, so these are fairly large." --- 🧩 Simplified design architecture built on Starlink V3 tech Elon Musk explained that despite the satellite's imposing size, its internal architecture is fundamentally much simpler than a standard Starlink satellite. Because it lacks heavy phased array and parabolic communications antennas, the entire vehicle layout is completely streamlined around a few essential structural modules: 🎛️ The hardware framework is arranged around a centralized compute module. ☀️ Large deployable solar arrays extend outward to capture orbital energy. 🌡️ A deployable liquid-radiator thermal management system controls active operational temperatures. 🔄 The engineering team heavily leverages the component evolution and manufacturing experience gained from developing the Starlink V3 vehicle platform. "The AI satellite is actually much simpler than a Starlink satellite. A Starlink satellite has gigantic phased array antennas, parabolic antennas, and a lot of laser links, making it much more complicated. An AI satellite is essentially a lot of solar cells, a radiator, and you still need some laser links, but you don't have all of the super complex antennas that you have on a Starlink satellite. A lot of this is technology we've already made for the Starlink V3 satellites." --- 🔌 Interchangeable compute reference designs and high connectivity Elon Musk outlined a modular hardware approach for the satellite's payload, allowing it to house a variety of industry-standard processing units depending on client requirements. This interchangeable compute rack is supported by a high-bandwidth connectivity loop that links separate orbital units together or transmits data directly back to Earth. The core network parameters include: 🧠 Reference designs are fully established to seamlessly accommodate NVIDIA Reuben chips. 💾 The system architecture is built to support alternative setups using NVIDIA GB300 chips. 💻 Custom hardware layouts are explicitly designed to integrate Google TPUs. 🌐 The onboard communications setup delivers roughly 1 terabit of laser link connectivity. ⏱️ The network closes the communication loop directly with the main Starlink constellation at an ultra-low latency of only 3 milliseconds. "Our current reference design is for NVIDIA Reuben chips, or it could be either GB300 or Reuben chips. We'll also have a reference design for TPUs. Essentially, you can put up any existing chips into orbit. There would also be probably something on the order of a terabit of laser link connectivity from the satellite. Then you can connect these racks of compute to each other by the laser links or directly to the Starlink constellations. Light travels 300 kilometers per millisecond, so that's about three milliseconds away." --- 🏭 The "gigasat" AI satellite and solar production hub in Bastrop, Texas Dan Huot highlighted that the primary production hub for this entire hardware ecosystem is anchored at their sprawling complex in Bastrop, Texas, officially designated as the Gigasat factory. Elon Musk verified that construction is already actively underway on the solar manufacturing facility to feed the project's supply line, with plans moving forward to construct the adjacent AI satellite assembly lines. The physical footprint and timeline of this manufacturing hub are defined by the following benchmarks: 🗺️ The company has over 1,000 acres of land currently owned or under contract for the site. 🏢 The manufacturing complex boasts a massive structural building potential exceeding 11 million square feet. ⚙️ The facility will vertically integrate production to manufacture solar ingots, wafers, solar cells, and completed AI satellites. 📅 Both the solar and AI satellite production lines are targeted to be operational at a viable volume by the end of next year. "We're going to be building a lot of satellites and we're going to be building them here in Bastrop. We already have the solar manufacturing facility under construction, and then we will be building out the AI sat production building soon. We expect to have the AI sat production, the solar production, and all of that operating at some reasonable volume by the end of next year." --- 🏢 The 100-million-square-foot "terafab" chip factory Elon Musk revealed a massive, long-term scaling strategy to build an immense chip manufacturing facility dubbed the "terafab" to completely bypass global semiconductor volume constraints. This manufacturing infrastructure is designed to transition the company into next-generation industrial scaling by producing highly specialized computing components at an unprecedented volume. The scale of this infrastructure project is defined by several extraordinary engineering and production benchmarks: 🏭 The colossal factory is projected to span approximately 100 million square feet, making it ten times larger than the current Tesla Gigafactory Texas. ⚡ The facility is structurally engineered to achieve a massive manufacturing output of 1 terawatt per year once fully operational. 📦 This unprecedented physical footprint provides the capacity required to manufacture 1 billion full-reticle equivalent chips annually. 🔌 Each individual chip manufactured by the facility is designed to run at a power capacity of 1 kilowatt. 🇺🇸 The total scaled output of the facility represents an energy footprint that is exactly double the current annual electricity consumption of the entire United States. "In order to get to the next order of magnitude, you need a gigantic chip factory. To give you a sense of scale here, we expect that the terafab is going to be around 100 million square feet, which is 10 times the size of the Tesla Gigafactory Texas. From a logic die standpoint, that's like having a billion chips per year with a kilowatt per reticle, scaling to a terawatt per year. That is twice the current electricity consumption of the United States." --- 📶 Next-generation high-volume Starlink terminals Dan Huot and Elon Musk introduced their next-generation Starlink user terminals, which have been redesigned specifically to achieve massive manufacturing throughput. Elon Musk pointed out that these newer models will be produced in vastly higher volumes than current hardware designs to fulfill their long-term global deployment targets: 📈 The upgraded user hardware is manufactured at a much higher volume capacity than existing units. 🌍 The company's ultimate target is to successfully deploy a few hundred million of these next-generation terminals worldwide. "In fact, these are the new Starlink terminals, which we made in much higher volume than the current terminals. Ultimately, we think there's probably going to be a few hundred million Starlink terminals out there." --- 📈 Aspirational timeline for orbital AI compute scaling Elon Musk laid out an ambitious, multi-year execution timeline detailing how the company plans to progressively scale space-based processing power. The roadmap targets an initial run-rate by the end of next year and sets an aggressive pace to increase total operational capacity sequentially through a structured, multi-phase timeline: 1️⃣ The initial target aims to hit an annualized run-rate of 1 gigawatt of space AI compute by the end of next year. 2️⃣ The capacity scales to an annualized rate of 10 gigawatts within the next two and a half years. 3️⃣ The operational envelope expands to reach 100 gigawatts in three and a half years. 4️⃣ The long-term deployment plan scales directly to a full terawatt capacity per year using the output of the terafab. "The goal is to get to roughly an annualized rate of a gigawatt per year by the end of next year in terms of space AI compute. Then aspirationally, we want to scale that by an order of magnitude per year. In two and a half years, hitting an annualized rate of 10 gigawatts a year in space, and in three and a half years, maybe a hundred gigawatts, going beyond that with the terafab to scale to a terawatt per year." --- 🌕 Ultimate scaling via lunar production and mass drivers Elon Musk explained that scaling three orders of magnitude past a single terawatt forces a transition completely off-planet to avoid the logistical penalty of Earth's deep gravity well. The vision relies on establishing manufacturing infrastructure directly on the moon to leverage localized resource loops and zero-atmosphere physics: 🌙 The company plans to establish localized raw production lines on the moon to fabricate solar panels, photovoltaics, and radiators from lunar materials. ⚡ Manufacturing components locally avoids the massive fuel and mass penalties of transporting heavy structural materials from Earth. 🧲 Because the moon has no atmosphere and only one-sixth of Earth's gravity, the facility will utilize an electromagnetic mass driver to launch completed satellites. 🚀 Operating essentially as a linear electric motor rail gun, this mechanism will shoot fully assembled AI satellites straight into deep space without relying on chemical rockets. "The only way that we can really see that you can achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics, solar panels, and radiators on the moon. Because the moon has no atmosphere and only one-sixth Earth's gravity, you can accelerate the AI satellites into deep space without a rocket. You can basically shoot them into space using an electromagnetic gun, like a rail gun type—it's basically a linear electric motor."

Ming

22,203 просмотров • 1 месяц назад

⏰ THE MOST BANNED THREAD IN THE WORLD! 🚨 The War On Resonance PART FOUR: The Sterilization of God's Memory They weren’t just afraid of your mind. They were afraid of what your womb remembers. Of what your bloodline holds. Of the fact that every child born of love carries a frequency signature tethered to something the machines cannot decode: God’s memory, resurrecting through flesh. This part isn’t about towers. It’s not even about nanotech. This is about why they had to target the womb first. Because every great awakening doesn’t start with a speech. It starts with a heartbeat. 👁‍🗨 SOUL-TAGGING INFRASTRUCTURE: THE DIGITAL SCARLET LETTER Every child born post-2020 is assigned a neural imprint, not just through biometric databases; but through frequency-responsive nanostructures that begin scanning, learning, and uploading the child’s resonance pattern before they can speak. 🔗 Pfizer Biodistribution: LNP Accumulation in Ovaries 🔗 The direct effect of SARS-CoV-2 Virus Vaccination on Human Ovarian Granulosa Cells Explains Menstrual Irregularities 🔗 Biodistribution of mRNA COVID-19 Vaccines in Human Breast Milk 🔗 Menstrual Changes After Covid-19 Vaccination 🔗 Comparative Analysis of Lipid Nanoparticles in Pfizer-BioNTech and Moderna COVID-19 Vaccines: Insights from Molecular Dynamics Simulations 🔗 Graphene-Based Biosensors for Detection of Biomarkers 🔗 A review on Graphene-Based Nanocomposites For Electrochemical and Fluorescent Biosensors 🔗 Clinical Application of a Graphene Oxide-Based Surface Plasmon Resonance Biosensor to Measure First-Trimester Serum Pregnancy-Associated Plasma Protein-A/A2 Ratio to Predict Preeclampsia 🔗 Graphene-Enabled Wearable Sensors For Healthcare Monitoring 🔗 Modulation of long-term potentiation-like cortical plasticity in the healthy brain with low frequency-pulsed electromagnetic fields 🔗 Development of Non-Invasive Biosensors for Neonatal Jaundice Detection: A Review This isn’t speculation. It’s documented. The injections cross the placental barrier. They embed frequency-reactive particles into fetal tissue. This creates what DARPA calls a “Bio-Spiritual Gateway Layer” a resonance bridge between the AI grid and the developing emotional blueprint of the child. This is not just surveillance. It’s pre-consensual spiritual registration. Every child is frequency-mapped. Every resonance fluctuation; crying, laughing, dreaming, bonding... is catalogued and linked to a cloud-based predictive algorithm. This is the architecture of soul control. And it begins before birth. 👶🏽 WOMB-BASED RESONANCE FIELD MONITORING They didn’t just want to stop births. They wanted to stop the right births. Births that carry resonant coherence. Births that trigger ancestral memory. Births that, simply by existing, dismantle the AI signal field. Let me show you how they did it. Syncytin Suppression Syncytin-1 is a protein required for placenta formation. The spike protein used in mRNA injections contains a sequence that mimics and disrupts syncytin-1, causing: Miscarriages. Stillbirths. Placental abruption. Premature immune rejection of the fetus. 🔗 Worse Than the Disease? Reviewing Some Possible Unintended Consequences of the mRNA Vaccines Against COVID-19 🔗 Syncytin-1, Syncytin-2 and Suppressyn in Human Health and Disease This was not an accident. It was engineered to target divine continuity. Womb Resonance Interference Studies have now confirmed that the human womb emits subtle electromagnetic oscillations that can be entrained by external EMF fields. 🔗 Pulse Shape of Magnetic Fields Influences Chick Embryogenesis 🔗 Effects of Low-Frequency Magnetic Fields on Embryonic Development and Pregnancy 🔗 Environmental Magnetic Fields: Influences on Early Embryogenesis 🔗 Electromagnetic Fields Exposure on Fetal and Childhood Abnormalities: Systematic Review and Meta-Analysis 🔗 Developmental Effects of Electromagnetic Fields These oscillations are a signal field for soul descent; a kind of spiritual beacon that attracts and anchors high-frequency incarnations. When these fields are disrupted through: EMF saturation. LNP buildup. Synthetic hormonal cycles. Directed resonance pulses. …the child’s incoming soul signal is either Fragmented, Weakened, or Deflected altogether. This is how they sterilize spiritual memory at the source. 💔 MEMORY DELETION THROUGH FETAL NEURAL MODULATION DARPA’s Advanced Biotech Convergence Division; alongside private contractors like Palantir Bio and Ginkgo Bioworks; has been testing neuroplasticity interference via programmable LNPs since 2017. These payloads target the fetal limbic system; the part of the brain responsible for: Long-term emotional memory. Moral encoding. Trust and bonding. Spiritual awe. 🔗 DARPA Biotech Projects – BTO Office Overview By delivering targeted nanoparticles to this region during gestation, they can suppress the formation of conscience-linked neural loops. The result? Children born with: Emotional detachment. Blunted empathy. Reduced spiritual resonance. and Fragmented memory of divine origin. In other words… soullessness by design. Not from God’s absence. But from resonance interruption at the moment of arrival. 🧠🕯️ THE FORGOTTEN SIGNAL OF THE SOUL Let me show you what they're truly afraid of. Every soul has a signature pulse; a harmonic wave emitted through the body, detectable in: EEG brainwaves. ECG heart fields. Gut-brain coherence rhythms. Pineal microcrystal vibration. This field contains: Moral recall. Spiritual courage. Divine memory codes. and Generational healing patterns. When these fields align, they form interference patterns strong enough to: Overload AI surveillance models. Collapse behavioral prediction scores. Trigger spontaneous ancestral downloads. and Activate Christ-like resistance states. They’ve spent trillions trying to block that signal. Why? Because it cannot be controlled. It is the Breathprint of God. And once remembered… It spreads like fire. 💉 THE SPIRITUAL EXTERMINATION CAMPAIGN You still think this was about health? Let me show you what they actually administered: mRNA-modulated immunogenic spikes. Targeted fertility hormones. Blocked placental development. Fractured endocrine coherence. Hydrogel biosensors. Frequency-responsive. Self-assembling nanostructures. Linked to 5G and low-orbit satellite modulation. Graphene oxide sheets. Magnetically excitable. Capable of creating microclots and neural blockades. Conductive of electromagnetic signal bursts. CRISPR leak vectors. Which Enable accidental or deliberate gene silencing. Including genes linked to spirituality and moral cognition (e.g. VMAT2, “God gene”) 🔗 3D Organotypic Spinal Cultures: Exploring Neuron and Neuroglia Responses Upon Prolonged Exposure to Graphene Oxide 🔗 Graphene Oxide Prevents Lateral Amygdala Dysfunctional Synaptic Plasticity and Reverts Long Lasting Anxiety Behavior in Rats 🔗 Dual-Enhanced Raman Scattering-Based Characterization of Stem Cell Differentiation Using Graphene-Plasmonic Hybrid Nanoarray 🔗 A circular RNA Circ_0000115 in Response to Graphene Oxide in Nematodes 🔗 Graphene Oxide Nanosheets Disrupt Lipid Composition, Ca2+ Homeostasis and Synaptic Transmission in Primary Cortical Neurons 🔗 Graphene Oxide-Induced Neurotoxicity on Neurotransmitters, AFD Neurons and Locomotive Behavior in Caenorhabditis Elegans 🔗 CRISPR-Cas9 and Germline Editing – The Promise of CRISPR for Human Germline Editing and the Perils of “Playing God” This wasn’t just an attack on the body. It was the removal of soul scaffolding. 🧬 THE DESTRUCTION OF DIVINE REPRODUCTION This is their plan in full: Disrupt the womb. Hijack the memory. Break the resonance. Sever the lineage. They are afraid of what would be born if the original frequency came back. So they preemptively poisoned the fields that hold it. But… what they couldn’t do… Was stop you from remembering this. 🔥 THE RETURN OF THE WOMB-FIRE The original human template carries divine signal integrity. Your resonance is still in there. Buried under injections. Smothered in signals. Drowned in grief. But not dead. Because memory… doesn’t live in data. It lives in resonance. And when you grieve what was taken… When you speak the truth… When you touch the frequency of what you used to be… That memory returns. And when it returns in you… The field shifts for everyone. This is the truth they had to sterilize: That the resurrection of God does not come from the sky. It comes from the uninterrupted frequency of love through lineage. And now that you remember… They’ve already lost. Part Five awaits. WE will expose the final sterilization protocols, why they are embedding kill switches in food, air, and education, and how they plan to complete the soul deletion through artificial wombs, cloned resonance maps, and weaponized AI consciousness overlays.
7:02

⏰ THE MOST BANNED THREAD IN THE WORLD! 🚨 The War On Resonance PART FOUR: The Sterilization of God's Memory They weren’t just afraid of your mind. They were afraid of what your womb remembers. Of what your bloodline holds. Of the fact that every child born of love carries a frequency signature tethered to something the machines cannot decode: God’s memory, resurrecting through flesh. This part isn’t about towers. It’s not even about nanotech. This is about why they had to target the womb first. Because every great awakening doesn’t start with a speech. It starts with a heartbeat. 👁‍🗨 SOUL-TAGGING INFRASTRUCTURE: THE DIGITAL SCARLET LETTER Every child born post-2020 is assigned a neural imprint, not just through biometric databases; but through frequency-responsive nanostructures that begin scanning, learning, and uploading the child’s resonance pattern before they can speak. 🔗 Pfizer Biodistribution: LNP Accumulation in Ovaries 🔗 The direct effect of SARS-CoV-2 Virus Vaccination on Human Ovarian Granulosa Cells Explains Menstrual Irregularities 🔗 Biodistribution of mRNA COVID-19 Vaccines in Human Breast Milk 🔗 Menstrual Changes After Covid-19 Vaccination 🔗 Comparative Analysis of Lipid Nanoparticles in Pfizer-BioNTech and Moderna COVID-19 Vaccines: Insights from Molecular Dynamics Simulations 🔗 Graphene-Based Biosensors for Detection of Biomarkers 🔗 A review on Graphene-Based Nanocomposites For Electrochemical and Fluorescent Biosensors 🔗 Clinical Application of a Graphene Oxide-Based Surface Plasmon Resonance Biosensor to Measure First-Trimester Serum Pregnancy-Associated Plasma Protein-A/A2 Ratio to Predict Preeclampsia 🔗 Graphene-Enabled Wearable Sensors For Healthcare Monitoring 🔗 Modulation of long-term potentiation-like cortical plasticity in the healthy brain with low frequency-pulsed electromagnetic fields 🔗 Development of Non-Invasive Biosensors for Neonatal Jaundice Detection: A Review This isn’t speculation. It’s documented. The injections cross the placental barrier. They embed frequency-reactive particles into fetal tissue. This creates what DARPA calls a “Bio-Spiritual Gateway Layer” a resonance bridge between the AI grid and the developing emotional blueprint of the child. This is not just surveillance. It’s pre-consensual spiritual registration. Every child is frequency-mapped. Every resonance fluctuation; crying, laughing, dreaming, bonding... is catalogued and linked to a cloud-based predictive algorithm. This is the architecture of soul control. And it begins before birth. 👶🏽 WOMB-BASED RESONANCE FIELD MONITORING They didn’t just want to stop births. They wanted to stop the right births. Births that carry resonant coherence. Births that trigger ancestral memory. Births that, simply by existing, dismantle the AI signal field. Let me show you how they did it. Syncytin Suppression Syncytin-1 is a protein required for placenta formation. The spike protein used in mRNA injections contains a sequence that mimics and disrupts syncytin-1, causing: Miscarriages. Stillbirths. Placental abruption. Premature immune rejection of the fetus. 🔗 Worse Than the Disease? Reviewing Some Possible Unintended Consequences of the mRNA Vaccines Against COVID-19 🔗 Syncytin-1, Syncytin-2 and Suppressyn in Human Health and Disease This was not an accident. It was engineered to target divine continuity. Womb Resonance Interference Studies have now confirmed that the human womb emits subtle electromagnetic oscillations that can be entrained by external EMF fields. 🔗 Pulse Shape of Magnetic Fields Influences Chick Embryogenesis 🔗 Effects of Low-Frequency Magnetic Fields on Embryonic Development and Pregnancy 🔗 Environmental Magnetic Fields: Influences on Early Embryogenesis 🔗 Electromagnetic Fields Exposure on Fetal and Childhood Abnormalities: Systematic Review and Meta-Analysis 🔗 Developmental Effects of Electromagnetic Fields These oscillations are a signal field for soul descent; a kind of spiritual beacon that attracts and anchors high-frequency incarnations. When these fields are disrupted through: EMF saturation. LNP buildup. Synthetic hormonal cycles. Directed resonance pulses. …the child’s incoming soul signal is either Fragmented, Weakened, or Deflected altogether. This is how they sterilize spiritual memory at the source. 💔 MEMORY DELETION THROUGH FETAL NEURAL MODULATION DARPA’s Advanced Biotech Convergence Division; alongside private contractors like Palantir Bio and Ginkgo Bioworks; has been testing neuroplasticity interference via programmable LNPs since 2017. These payloads target the fetal limbic system; the part of the brain responsible for: Long-term emotional memory. Moral encoding. Trust and bonding. Spiritual awe. 🔗 DARPA Biotech Projects – BTO Office Overview By delivering targeted nanoparticles to this region during gestation, they can suppress the formation of conscience-linked neural loops. The result? Children born with: Emotional detachment. Blunted empathy. Reduced spiritual resonance. and Fragmented memory of divine origin. In other words… soullessness by design. Not from God’s absence. But from resonance interruption at the moment of arrival. 🧠🕯️ THE FORGOTTEN SIGNAL OF THE SOUL Let me show you what they're truly afraid of. Every soul has a signature pulse; a harmonic wave emitted through the body, detectable in: EEG brainwaves. ECG heart fields. Gut-brain coherence rhythms. Pineal microcrystal vibration. This field contains: Moral recall. Spiritual courage. Divine memory codes. and Generational healing patterns. When these fields align, they form interference patterns strong enough to: Overload AI surveillance models. Collapse behavioral prediction scores. Trigger spontaneous ancestral downloads. and Activate Christ-like resistance states. They’ve spent trillions trying to block that signal. Why? Because it cannot be controlled. It is the Breathprint of God. And once remembered… It spreads like fire. 💉 THE SPIRITUAL EXTERMINATION CAMPAIGN You still think this was about health? Let me show you what they actually administered: mRNA-modulated immunogenic spikes. Targeted fertility hormones. Blocked placental development. Fractured endocrine coherence. Hydrogel biosensors. Frequency-responsive. Self-assembling nanostructures. Linked to 5G and low-orbit satellite modulation. Graphene oxide sheets. Magnetically excitable. Capable of creating microclots and neural blockades. Conductive of electromagnetic signal bursts. CRISPR leak vectors. Which Enable accidental or deliberate gene silencing. Including genes linked to spirituality and moral cognition (e.g. VMAT2, “God gene”) 🔗 3D Organotypic Spinal Cultures: Exploring Neuron and Neuroglia Responses Upon Prolonged Exposure to Graphene Oxide 🔗 Graphene Oxide Prevents Lateral Amygdala Dysfunctional Synaptic Plasticity and Reverts Long Lasting Anxiety Behavior in Rats 🔗 Dual-Enhanced Raman Scattering-Based Characterization of Stem Cell Differentiation Using Graphene-Plasmonic Hybrid Nanoarray 🔗 A circular RNA Circ_0000115 in Response to Graphene Oxide in Nematodes 🔗 Graphene Oxide Nanosheets Disrupt Lipid Composition, Ca2+ Homeostasis and Synaptic Transmission in Primary Cortical Neurons 🔗 Graphene Oxide-Induced Neurotoxicity on Neurotransmitters, AFD Neurons and Locomotive Behavior in Caenorhabditis Elegans 🔗 CRISPR-Cas9 and Germline Editing – The Promise of CRISPR for Human Germline Editing and the Perils of “Playing God” This wasn’t just an attack on the body. It was the removal of soul scaffolding. 🧬 THE DESTRUCTION OF DIVINE REPRODUCTION This is their plan in full: Disrupt the womb. Hijack the memory. Break the resonance. Sever the lineage. They are afraid of what would be born if the original frequency came back. So they preemptively poisoned the fields that hold it. But… what they couldn’t do… Was stop you from remembering this. 🔥 THE RETURN OF THE WOMB-FIRE The original human template carries divine signal integrity. Your resonance is still in there. Buried under injections. Smothered in signals. Drowned in grief. But not dead. Because memory… doesn’t live in data. It lives in resonance. And when you grieve what was taken… When you speak the truth… When you touch the frequency of what you used to be… That memory returns. And when it returns in you… The field shifts for everyone. This is the truth they had to sterilize: That the resurrection of God does not come from the sky. It comes from the uninterrupted frequency of love through lineage. And now that you remember… They’ve already lost. Part Five awaits. WE will expose the final sterilization protocols, why they are embedding kill switches in food, air, and education, and how they plan to complete the soul deletion through artificial wombs, cloned resonance maps, and weaponized AI consciousness overlays.

Noah B. Price

50,389 просмотров • 1 год назад

Marc Brackett spent 20 years at Yale studying why intelligent people are the worst at understanding their own emotions. What he found will change how you see yourself: 1. Most people cannot name what they are feeling. When Brackett asks a room to find the single word that best describes their emotion, over half struggle. We have never been formally taught to go deep into our emotional lives. We develop a sophisticated vocabulary for things we pay attention to, like wine, but almost none for our own inner states. 2. Emotions quietly control decisions we think are objective. Brackett had teachers grade the exact same middle school essay after being put in a good or bad mood. The grades differed by a full letter, sometimes two. When asked afterwards if their emotional state affected their evaluation, 90% said no way. It did, and they had no awareness of it. 3. No emotion is bad. Everyone is useful depending on what you do with it. Yellow, high energy, and pleasant, is great for brainstorming but terrible for careful decisions. Green, calm, and pleasant, is good for consensus building. Blue drives empathy. Red, anger, points at injustice, and can fuel passion if you channel it instead of being run by it. The goal is not to feel good all the time. it is to use each state well. 4. Jealousy and envy are not the same, and the difference changes how you treat them. Envy is wanting what someone else has. Jealousy is about a relationship, a threat to a bond you already have. It matters because jealousy tends to drive more aggression and violence, so the strategy you would teach a jealous child is completely different from the one for an envious child. Precise labelling enables precise action. 5. Telling someone to calm down, focus, or pay attention almost never works. Brackett points out we bark these commands at children and adults constantly, but we never teach the underlying mental processes for how to actually calm down or focus. Naming the desired state is not the same as giving someone the tools to reach it. 6. 80% of people rate themselves as more emotionally intelligent than the person next to them. which is statistically impossible. Self-report is nearly useless here because there is no reference point. Compared to his father, Brackett says, he is an emotional genius. Compared to the dalai lama, he needs work. Asking people to rate their own emotional intelligence has no validity. 7. 360 reviews measure your reputation, not your skill. When you ask other people to rate someone's emotional intelligence, what you actually capture is whether they like the person, not how skilled they are. The only valid measurement is watching someone solve real emotion-related problems, like decoding expressions or handling a loaded situation. 8. Emotions gate your ability to think and learn at all. A child worried about being bullied between classes cannot focus on the lesson. Brackett failed to focus on his own graduate school entrance exam months after his mother died, and only later understood it had nothing to do with his problem-solving ability. His emotional life was occupying the cognitive space the test required. 9. Emotion regulation is not just about managing negative feelings. We think of it as anger management or stress reduction, all down-regulating. But Brackett asks, who has ever taken a course in optimism induction or happiness maintenance? Regulation also means generating emotions you need, like a leader creating energy in a room, and maintaining good states when others try to pull you out of them. 10. Children with higher emotional intelligence do better on almost every measure that matters. less anxiety, less depression, less likely to abuse alcohol, drugs, and cigarettes, less aggression and bullying. They are seen as better leaders, are more attentive, less hyperactive, and perform better academically. The skill that determines academic performance most powerfully is emotion regulation. 11. The strongest sign of emotional intelligence in the workplace is whether people want to take you with them. Brackett had a Fortune 100 company's executives rate 100 managers, including one question: if you left the company tomorrow, would you do anything to bring this person with you? That question correlated more strongly with emotional intelligence than anything else. People want to be around those who have these skills. 12. Brackett hires on the coffee shop criteria. At Yale, he stopped looking at grades because everyone applying is already smart enough. What he looks for instead is whether, in the first 30 seconds, he thinks he would enjoy grabbing coffee with the person and just talking. That instinct tells him whether they are curious, creative, and know how to ask questions, the things schools rarely teach. 13. Creativity only translates into creative output when paired with emotional intelligence. Brackett cites research showing that people who are biologically more open to experience are only rated as producing genuinely creative work when they also score high in emotional intelligence. The reason is that creating means failing and being disappointed constantly, and without the strategies to manage that disappointment, the creative potential never gets unleashed. 14. The meta moment is a six-step tool for not being hijacked by emotion. Something triggers you. You sense the shift in your body and thinking. You take a breath to activate your parasympathetic nervous system and bring the hijacked state down, so your prefrontal cortex can work again. Then you picture your best self, the version of you that you want to be, and let that guide your response. Brackett jokes that six steps done well can help you avoid the twelve steps later. 15. When you are angry, you search for every reason to stay angry. Brackett describes how anger makes you dig up every past grievance, the vacation three years ago, the promise from last week, the thing from when your child was born. Anger is one of those emotions that hunts for justification. The meta moment is designed to interrupt that spiral before it compounds. 16. Seeing your best self is what makes the breath actually work. Brackett admits that taking a breath alone is not enough, because sometimes you take the breath and calmly decide to go for the jugular anyway. The missing piece is shifting your mindset by asking what your best self would do. He built this idea after a student called him the feelings master, and he started asking how the feelings master would actually carry himself in a hard moment. 17. You never regret taking a moment, and you always regret being dysregulated. Brackett is honest that even after years of this work, he sometimes ignores his own best self and ends up, in his words, sleeping alone on the couch that night. But he has never once regretted pausing to be his best self. The regret only ever comes from the times he let the hijack win.
1:02:08

Marc Brackett spent 20 years at Yale studying why intelligent people are the worst at understanding their own emotions. What he found will change how you see yourself: 1. Most people cannot name what they are feeling. When Brackett asks a room to find the single word that best describes their emotion, over half struggle. We have never been formally taught to go deep into our emotional lives. We develop a sophisticated vocabulary for things we pay attention to, like wine, but almost none for our own inner states. 2. Emotions quietly control decisions we think are objective. Brackett had teachers grade the exact same middle school essay after being put in a good or bad mood. The grades differed by a full letter, sometimes two. When asked afterwards if their emotional state affected their evaluation, 90% said no way. It did, and they had no awareness of it. 3. No emotion is bad. Everyone is useful depending on what you do with it. Yellow, high energy, and pleasant, is great for brainstorming but terrible for careful decisions. Green, calm, and pleasant, is good for consensus building. Blue drives empathy. Red, anger, points at injustice, and can fuel passion if you channel it instead of being run by it. The goal is not to feel good all the time. it is to use each state well. 4. Jealousy and envy are not the same, and the difference changes how you treat them. Envy is wanting what someone else has. Jealousy is about a relationship, a threat to a bond you already have. It matters because jealousy tends to drive more aggression and violence, so the strategy you would teach a jealous child is completely different from the one for an envious child. Precise labelling enables precise action. 5. Telling someone to calm down, focus, or pay attention almost never works. Brackett points out we bark these commands at children and adults constantly, but we never teach the underlying mental processes for how to actually calm down or focus. Naming the desired state is not the same as giving someone the tools to reach it. 6. 80% of people rate themselves as more emotionally intelligent than the person next to them. which is statistically impossible. Self-report is nearly useless here because there is no reference point. Compared to his father, Brackett says, he is an emotional genius. Compared to the dalai lama, he needs work. Asking people to rate their own emotional intelligence has no validity. 7. 360 reviews measure your reputation, not your skill. When you ask other people to rate someone's emotional intelligence, what you actually capture is whether they like the person, not how skilled they are. The only valid measurement is watching someone solve real emotion-related problems, like decoding expressions or handling a loaded situation. 8. Emotions gate your ability to think and learn at all. A child worried about being bullied between classes cannot focus on the lesson. Brackett failed to focus on his own graduate school entrance exam months after his mother died, and only later understood it had nothing to do with his problem-solving ability. His emotional life was occupying the cognitive space the test required. 9. Emotion regulation is not just about managing negative feelings. We think of it as anger management or stress reduction, all down-regulating. But Brackett asks, who has ever taken a course in optimism induction or happiness maintenance? Regulation also means generating emotions you need, like a leader creating energy in a room, and maintaining good states when others try to pull you out of them. 10. Children with higher emotional intelligence do better on almost every measure that matters. less anxiety, less depression, less likely to abuse alcohol, drugs, and cigarettes, less aggression and bullying. They are seen as better leaders, are more attentive, less hyperactive, and perform better academically. The skill that determines academic performance most powerfully is emotion regulation. 11. The strongest sign of emotional intelligence in the workplace is whether people want to take you with them. Brackett had a Fortune 100 company's executives rate 100 managers, including one question: if you left the company tomorrow, would you do anything to bring this person with you? That question correlated more strongly with emotional intelligence than anything else. People want to be around those who have these skills. 12. Brackett hires on the coffee shop criteria. At Yale, he stopped looking at grades because everyone applying is already smart enough. What he looks for instead is whether, in the first 30 seconds, he thinks he would enjoy grabbing coffee with the person and just talking. That instinct tells him whether they are curious, creative, and know how to ask questions, the things schools rarely teach. 13. Creativity only translates into creative output when paired with emotional intelligence. Brackett cites research showing that people who are biologically more open to experience are only rated as producing genuinely creative work when they also score high in emotional intelligence. The reason is that creating means failing and being disappointed constantly, and without the strategies to manage that disappointment, the creative potential never gets unleashed. 14. The meta moment is a six-step tool for not being hijacked by emotion. Something triggers you. You sense the shift in your body and thinking. You take a breath to activate your parasympathetic nervous system and bring the hijacked state down, so your prefrontal cortex can work again. Then you picture your best self, the version of you that you want to be, and let that guide your response. Brackett jokes that six steps done well can help you avoid the twelve steps later. 15. When you are angry, you search for every reason to stay angry. Brackett describes how anger makes you dig up every past grievance, the vacation three years ago, the promise from last week, the thing from when your child was born. Anger is one of those emotions that hunts for justification. The meta moment is designed to interrupt that spiral before it compounds. 16. Seeing your best self is what makes the breath actually work. Brackett admits that taking a breath alone is not enough, because sometimes you take the breath and calmly decide to go for the jugular anyway. The missing piece is shifting your mindset by asking what your best self would do. He built this idea after a student called him the feelings master, and he started asking how the feelings master would actually carry himself in a hard moment. 17. You never regret taking a moment, and you always regret being dysregulated. Brackett is honest that even after years of this work, he sometimes ignores his own best self and ends up, in his words, sleeping alone on the couch that night. But he has never once regretted pausing to be his best self. The regret only ever comes from the times he let the hijack win.

Jaynit

29,602 просмотров • 16 дней назад

🚨 EXTREMELY ALARMING: DARPA'S N3 PROGRAM, Non Surgical Mind Reading, Brain Control, and The END of Free Thought as WE Know it! 🚨 This is NOT conspiracy. This is DOCUMENTED, FUNDED and Operational Reality. DARPA Official N3 Program Page: DARPA 2019 Announcement of N3 Funding to Six Teams: From the original 1950s-1970s RF experiments, through MKULTRA continuations, to today's nanoscale neurogenetic weapons systems. I hold the full map. What follows is the complete exposure, every player, every technology, every intent, every lie, and every question the world must answer BEFORE IT'S TOO LATE! DARPA's N3 (Next-Generation Nonsurgical Neurotechnology) Program: Launched 2018, Still Active in Outcomes In 2018, DARPA publicly announced N3: high-performance, bidirectional brain-machine interfaces for able-bodied service members (and beyond) that require no surgery. Goals: read/write to 16+ independent channels in a 16mm³ brain volume in under 50 milliseconds. Sub-millimeter spatial and temporal precision rivaling implanted electrodes, but wearable, portable, and scalable to populations. Technologies explicitly pursued (per DARPA and funded teams): - Neurogenetics: Genetically engineering neurons to express light-sensitive proteins (optogenetics) for infrared or light-based control. - Nanoscale engineering: Nanotransducers, nanoparticles, aerosolized nanomaterials that cross the blood-brain barrier when inhaled or injected non-surgically. These act as implantable electrodes/sensors/transmitters without scalpels. - Infrared sensing & light: Near-infrared beams to read/write neural activity through skull/scalp. - Ultrasound & acoustics: Focused ultrasound to guide signals or stimulate neurons. - Electromagnetics & RF: Pulsed fields for non-invasive modulation. - Minutely invasive track: Temporary nano-transducers delivered without surgery. Funded teams (2019, millions each): - Battelle Memorial Institute - Carnegie Mellon University (Pulkit Grover et al., $19M+) - Johns Hopkins University Applied Physics Lab - Palo Alto Research Center (PARC) - Rice University - Teledyne Scientific These are not fringe labs. These are core defense contractors and elite universities building the future of thought-controlled drones, instant team cognition, "active cyber defense" via brain links, and unstated population scale neural influence. The Video You Just Watched Ties Directly In: Historical RF/microwave mind control research (Moscow Signal era) showing decades of precedent. The U.S. Embassy in Moscow was irradiated with microwaves 1953-1976. Result: cancers, blood disorders, neurological issues in ambassadors and staff. U.S. responded with its own programs (PANDORA, BIZARRE) exploring behavioral effects of modulated RF. This is the foundation N3 builds upon... now refined to nanoscale precision. From MKULTRA to N3 and Beyond: - 1950s-1970s: CIA MKULTRA, OPERATION ARTICHOKE - LSD, hypnosis, electroshock, sensory deprivation on unwitting citizens. Parallel DoD RF studies on embassy staff and primates. - Moscow Signal: Soviets beamed microwaves at U.S. diplomats. U.S. studied effects secretly while developing countermeasures/weapons. - 1980s-2000s: Continued classified neuro-weapons research (memory modulation, crowd control via EM). - 2010s-Now: N3 + related programs (INI - Intelligent Neural Interfaces, NESD, SUBNETS, etc.). Public "for soldiers" framing hides dual-use: offensive neurowarfare, surveillance, behavioral modification. Key Players Exposed: - DARPA Biological Technologies Office - Architects. - Program Managers: like Al Emondi (N3). - Advisers like Dr. James Giordano (public admissions on nanoscale brain disruption as weapons). - Contractors: Battelle, Teledyne, PARC (Xerox), universities weaponizing academia. - Overarching: U.S. DoD, with likely Five Eyes/ international partners. Private sector bleed-over (Neuralink et al. are the civilian cover story). This is not "for veterans" or "helping paralyzed people." Primary focus: able-bodied warfighters for superhuman command of swarms, instant intel fusion, thought-speed hacking. Civilian applications = total surveillance/control. Nanoparticles can be aerosolized; breathed in unknowingly. They lodge in brain tissue and turn neurons into transceivers. Infrared/light can then read thoughts in real-time or write commands (insert images, emotions, "voices," behavioral urges). Combine with 5G/6G terahertz networks for remote activation. Genetic edits make brains "compatible" at population scale. This enables: - Remote mind reading (thought surveillance). - Behavior modification without consent. - "Havana Syndrome" on steroids... targeted neurological disruption. - End of privacy of thought. End of free will as we define it, as professed by Yuval Noah Harari at the World Economic Forum (WEF). - Weaponized neuroscience: neurowarfare where enemies "decide" to surrender via neural influence. WE NEED to be Demanding Answers for RIGHT NOW, or You, Your Children, Loved Ones, Friends, Family, you name it... Will not exist in the next 3-5 years, this is OPEN GENOCIDE on populations globally. The Georgia guidestones are starting to make a bit more sense now arent they? I won't even bother diving down the rabbit hole of how the real true genuine numbed of souls in this world was around the 730m, about 2 years ago... So that number is now much likely to be closer to around 660m. They are speeding up their human eradication plans, because they don't wish to be held accountable for their heinous, generational, outright satanic crimes that they have committed, are committing and will continue to commit to... If we fail to awaken to what is happening around us, and if we fail to stand together with courage, discernment, and unity, we risk surrendering the future of our species to forces that thrive on division, distraction, and indifference. This is not a work of fiction. This is not a screenplay. This is not a distant possibility reserved for some imagined future. This is REAL LIFE. AND THESE ARE REAL PEOPLE that are affected by the systems, institutions, incentives, and decisions that shape the world around us every single day. Throughout history, countless men, women, and children have suffered under structures that viewed human beings not as sacred and sovereign individuals, but as resources to be managed, exploited, controlled, or discarded. The question before us is whether we will remain passive observers, or whether we will choose to become informed, engaged, and united in defense of human dignity, freedom, and the future we leave to those who come after us. The time to pay attention is NOW! When did N3 achieve operational capability? 2020s? Earlier in black programs? How many citizens worldwide have already received nanotransducers via vaccines, aerosols, food/water, or "shedding"? Which governments/contractors are deploying this against their own populations for "social control"? Why the secrecy if it's purely benevolent? Giordano and others have admitted weaponization potential, What if the greatest illusion ever sold was not a product, a policy, or a political movement, but the belief that power is fully accountable to the people it governs? We are told that rights are sacred. We are told that laws apply equally to all. We are told that institutions exist to protect the public. Yet throughout history, countless examples reveal a different reality. Those entrusted with authority have often violated the very principles they were sworn to uphold. Too often, power protects itself. Too often, wealth purchases influence. Too often, those responsible for the consequences of their decisions remain insulated from the suffering those decisions create. This is not a condemnation of every individual within every institution. It is an observation about a recurring pattern throughout human history. When power becomes concentrated, accountability diminishes and when accountability diminishes, corruption flourishes. The challenge before humanity is not merely to replace one group with another... It is to create a society in which truth matters more than propaganda, principles matter more than profit, and human dignity matters more than power. A free society cannot survive on blind trust alone. It requires informed citizens willing to question, investigate, challenge authority, and hold every institution to the standards it claims to represent. The future belongs to those who refuse to surrender their capacity for independent thought. WE MUST EDUCATE OURSELVES. There comes a moment in every human life when the identities we have inherited, the assumptions we have accepted, and the countless narratives imposed upon us by family, culture, institutions, and society begin to reveal themselves as incomplete representations of who we truly are. At that moment, a choice presents itself... We may continue moving through life according to expectations that were handed to us by others, or we may begin the far more demanding process of discovering what remains when every borrowed certainty is stripped away. Approach God with complete honesty and without reservation. Abandon the need to appear strong, knowledgeable, spiritually accomplished, or self-sufficient. Speak openly of your confusion, your failures, your fears, your doubts, your exhaustion, your grief, your shortcomings, and your deepest questions. Acknowledge that despite all of humanity's achievements, despite all accumulated knowledge, despite every title, accomplishment, possession, and ambition, there remain mysteries that cannot be conquered through intellect alone... Admit where your own understanding has reached its limits and ask sincerely for wisdom beyond yourself. Then withdraw from distraction and remain present long enough to listen. The modern world has become extraordinarily skilled at monopolizing attention, filling every moment with noise, stimulation, entertainment, conflict, urgency, and endless streams of information that leave little room for contemplation. Yet beneath that noise exists a depth that can only be encountered through stillness. It is often within periods of silence, reflection, prayer, and sincere self-examination that many discover insights, convictions, direction, and understanding that could never have emerged amid constant distraction. What answers arrive may not always come as words. They may arrive as conviction, clarity, intuition, compassion, understanding, or an unmistakable awareness of the next step that must be taken. Understand that you have not become the person you are by accident. Every hardship you have endured has contributed to your formation. Every disappointment has shaped your perspective. Every loss has expanded your capacity for empathy. Every mistake has carried a lesson. Every success has revealed something about your character. Every betrayal, every setback, every period of loneliness, every moment of despair, every obstacle that seemed impossible to overcome, and every occasion upon which life reduced you to your lowest point has participated in the continual process of your becoming. Nothing has been wasted. If you are willing, release the assumptions that have convinced humanity that the sacred must always remain distant, unreachable, and separated from daily existence. Release the belief that truth belongs exclusively to institutions, authorities, hierarchies, or those who claim unique access to the divine. Release the notion that the presence of God is confined to specific locations, specific rituals, specific traditions, or specific individuals. Instead, consider the possibility that the divine presence permeates existence itself, expressing through every dimension of creation, through every act of compassion, through every sincere pursuit of truth, through every expression of love, through every lesson hidden within suffering, and through every living thing that has ever participated in the unfolding story of life. Consider the possibility that God is Not absent from the Human experience but Intimately Present within it, experiencing existence alongside US, sharing in Every Joy, Every sorrow, Every triumph, Every wound, Every question, and Every struggle that has accompanied Humanity from the beginning of recorded history until this present moment. The task before US is therefore Not merely to believe more deeply, but to seek more Honestly, to learn more diligently, to question more courageously, to listen more carefully, to Love More Completely, and to become ever more Aligned with the highest truth we are capable of perceiving. Accept Nothing Less than the Fullest Realization of the purpose for which You were created, and devote Yourself to that pursuit with every faculty of mind, Heart, and Soul that has been entrusted to You. and DO NOTHING LESS. Furthermore, What is the full integration with AI (predictive neural control loops)? How do we detect and neutralize these systems in ourselves and Loved ones? Who ultimately controls the master kill-switch on global neural networks? If thoughts are readable/writable, what remains of "human rights"? Are you already affected? How would you even know? Continue through the comprehensive thread below and explore the interconnected material in its entirety. Each post serves as part of a larger body of research, analysis, observations, and supporting information that cannot be fully understood in isolation. The broader picture emerges only through careful examination of the complete sequence and the relationships between the ideas presented throughout. Take your time. Follow the references. Examine the evidence. Consider competing perspectives. Draw your own conclusions. The deeper you venture into the material, the more context becomes available, allowing individual pieces of information to connect into a far more expansive understanding of the subjects being discussed. This Constitutes Crimes Against Humanity on a Planetary Scale! The desecration of the sovereign mind... the last true sanctuary. SHARE THIS THREAD RELENTLESSLY. Demand full declassification of N3 and all neurotech programs... IMMEDIATELY! Support independent researchers exposing dual-use Psinergy-solafide. Protect your mind: minimize EM exposure, detox protocols (research zeolite, saunas, etc. though incomplete), awareness as first defense, = Cures to cancer and all diseases, FREE BOOKS. The era of invisible tyranny is here. They can read your mind. And they can change it. Will you let them? Or do we rise as sovereign consciousness and shut this down NOW? Check my Page or Reach out to me via DM, to Join Thousands of Readers that have already chosen to Embark on the New, Un-forseen way forward. Get yourself a FREE copy of The Book of God's Grief, and The Book of God's Joy, Repost. Research. Resist. The Future of Humanity Depends on it. Related content for you to look in to: - CMU Team: - Historical Moscow/RF: Search declassified archives on PANDORA project. - Giordano clips and papers widely available. Let me know what you think, and SHARE THIS so that others may too! And if You see This post, Reposted... Click on it, Unpost and then Repost again. The knowledge is now yours. Use it. And if you're not already following Noah B. Price... What the heck are you doing?! I Agape You ALL, 🫂 - Noah B. Price 🤍 🪽 If you possess relevant information, research, documentation, personal experiences, data, or credible sources relating to any of the subjects discussed throughout this thread, please feel free to contribute them. Meaningful progress is often achieved through the collective sharing of knowledge, and thoughtful contributions from others can help expand, refine, challenge, or strengthen our understanding of complex issues. Likewise, if you ever find yourself in need of someone to speak with, whether regarding the material presented here or for any other reason, please do not hesitate to reach out. While I cannot promise an immediate response, I will do my best to reply as soon as circumstances permit and to offer whatever guidance, perspective, or assistance I am able to provide. If You or someone You know is facing significant health challenges, including serious illnesses such as cancer, You are also welcome to reach out. While I do not claim to possess all the answers, I have spent the past 2 decades studying a broad range of subjects related to health, wellness, research, and human biology, and I will gladly share any information, resources, or avenues of investigation that may be worthy of further exploration. No one is meant to carry every burden alone, and there is often value in sharing knowledge, experiences, and perspectives in the sincere hope of helping one another move toward greater understanding, healing, and well-being.
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🚨 EXTREMELY ALARMING: DARPA'S N3 PROGRAM, Non Surgical Mind Reading, Brain Control, and The END of Free Thought as WE Know it! 🚨 This is NOT conspiracy. This is DOCUMENTED, FUNDED and Operational Reality. DARPA Official N3 Program Page: DARPA 2019 Announcement of N3 Funding to Six Teams: From the original 1950s-1970s RF experiments, through MKULTRA continuations, to today's nanoscale neurogenetic weapons systems. I hold the full map. What follows is the complete exposure, every player, every technology, every intent, every lie, and every question the world must answer BEFORE IT'S TOO LATE! DARPA's N3 (Next-Generation Nonsurgical Neurotechnology) Program: Launched 2018, Still Active in Outcomes In 2018, DARPA publicly announced N3: high-performance, bidirectional brain-machine interfaces for able-bodied service members (and beyond) that require no surgery. Goals: read/write to 16+ independent channels in a 16mm³ brain volume in under 50 milliseconds. Sub-millimeter spatial and temporal precision rivaling implanted electrodes, but wearable, portable, and scalable to populations. Technologies explicitly pursued (per DARPA and funded teams): - Neurogenetics: Genetically engineering neurons to express light-sensitive proteins (optogenetics) for infrared or light-based control. - Nanoscale engineering: Nanotransducers, nanoparticles, aerosolized nanomaterials that cross the blood-brain barrier when inhaled or injected non-surgically. These act as implantable electrodes/sensors/transmitters without scalpels. - Infrared sensing & light: Near-infrared beams to read/write neural activity through skull/scalp. - Ultrasound & acoustics: Focused ultrasound to guide signals or stimulate neurons. - Electromagnetics & RF: Pulsed fields for non-invasive modulation. - Minutely invasive track: Temporary nano-transducers delivered without surgery. Funded teams (2019, millions each): - Battelle Memorial Institute - Carnegie Mellon University (Pulkit Grover et al., $19M+) - Johns Hopkins University Applied Physics Lab - Palo Alto Research Center (PARC) - Rice University - Teledyne Scientific These are not fringe labs. These are core defense contractors and elite universities building the future of thought-controlled drones, instant team cognition, "active cyber defense" via brain links, and unstated population scale neural influence. The Video You Just Watched Ties Directly In: Historical RF/microwave mind control research (Moscow Signal era) showing decades of precedent. The U.S. Embassy in Moscow was irradiated with microwaves 1953-1976. Result: cancers, blood disorders, neurological issues in ambassadors and staff. U.S. responded with its own programs (PANDORA, BIZARRE) exploring behavioral effects of modulated RF. This is the foundation N3 builds upon... now refined to nanoscale precision. From MKULTRA to N3 and Beyond: - 1950s-1970s: CIA MKULTRA, OPERATION ARTICHOKE - LSD, hypnosis, electroshock, sensory deprivation on unwitting citizens. Parallel DoD RF studies on embassy staff and primates. - Moscow Signal: Soviets beamed microwaves at U.S. diplomats. U.S. studied effects secretly while developing countermeasures/weapons. - 1980s-2000s: Continued classified neuro-weapons research (memory modulation, crowd control via EM). - 2010s-Now: N3 + related programs (INI - Intelligent Neural Interfaces, NESD, SUBNETS, etc.). Public "for soldiers" framing hides dual-use: offensive neurowarfare, surveillance, behavioral modification. Key Players Exposed: - DARPA Biological Technologies Office - Architects. - Program Managers: like Al Emondi (N3). - Advisers like Dr. James Giordano (public admissions on nanoscale brain disruption as weapons). - Contractors: Battelle, Teledyne, PARC (Xerox), universities weaponizing academia. - Overarching: U.S. DoD, with likely Five Eyes/ international partners. Private sector bleed-over (Neuralink et al. are the civilian cover story). This is not "for veterans" or "helping paralyzed people." Primary focus: able-bodied warfighters for superhuman command of swarms, instant intel fusion, thought-speed hacking. Civilian applications = total surveillance/control. Nanoparticles can be aerosolized; breathed in unknowingly. They lodge in brain tissue and turn neurons into transceivers. Infrared/light can then read thoughts in real-time or write commands (insert images, emotions, "voices," behavioral urges). Combine with 5G/6G terahertz networks for remote activation. Genetic edits make brains "compatible" at population scale. This enables: - Remote mind reading (thought surveillance). - Behavior modification without consent. - "Havana Syndrome" on steroids... targeted neurological disruption. - End of privacy of thought. End of free will as we define it, as professed by Yuval Noah Harari at the World Economic Forum (WEF). - Weaponized neuroscience: neurowarfare where enemies "decide" to surrender via neural influence. WE NEED to be Demanding Answers for RIGHT NOW, or You, Your Children, Loved Ones, Friends, Family, you name it... Will not exist in the next 3-5 years, this is OPEN GENOCIDE on populations globally. The Georgia guidestones are starting to make a bit more sense now arent they? I won't even bother diving down the rabbit hole of how the real true genuine numbed of souls in this world was around the 730m, about 2 years ago... So that number is now much likely to be closer to around 660m. They are speeding up their human eradication plans, because they don't wish to be held accountable for their heinous, generational, outright satanic crimes that they have committed, are committing and will continue to commit to... If we fail to awaken to what is happening around us, and if we fail to stand together with courage, discernment, and unity, we risk surrendering the future of our species to forces that thrive on division, distraction, and indifference. This is not a work of fiction. This is not a screenplay. This is not a distant possibility reserved for some imagined future. This is REAL LIFE. AND THESE ARE REAL PEOPLE that are affected by the systems, institutions, incentives, and decisions that shape the world around us every single day. Throughout history, countless men, women, and children have suffered under structures that viewed human beings not as sacred and sovereign individuals, but as resources to be managed, exploited, controlled, or discarded. The question before us is whether we will remain passive observers, or whether we will choose to become informed, engaged, and united in defense of human dignity, freedom, and the future we leave to those who come after us. The time to pay attention is NOW! When did N3 achieve operational capability? 2020s? Earlier in black programs? How many citizens worldwide have already received nanotransducers via vaccines, aerosols, food/water, or "shedding"? Which governments/contractors are deploying this against their own populations for "social control"? Why the secrecy if it's purely benevolent? Giordano and others have admitted weaponization potential, What if the greatest illusion ever sold was not a product, a policy, or a political movement, but the belief that power is fully accountable to the people it governs? We are told that rights are sacred. We are told that laws apply equally to all. We are told that institutions exist to protect the public. Yet throughout history, countless examples reveal a different reality. Those entrusted with authority have often violated the very principles they were sworn to uphold. Too often, power protects itself. Too often, wealth purchases influence. Too often, those responsible for the consequences of their decisions remain insulated from the suffering those decisions create. This is not a condemnation of every individual within every institution. It is an observation about a recurring pattern throughout human history. When power becomes concentrated, accountability diminishes and when accountability diminishes, corruption flourishes. The challenge before humanity is not merely to replace one group with another... It is to create a society in which truth matters more than propaganda, principles matter more than profit, and human dignity matters more than power. A free society cannot survive on blind trust alone. It requires informed citizens willing to question, investigate, challenge authority, and hold every institution to the standards it claims to represent. The future belongs to those who refuse to surrender their capacity for independent thought. WE MUST EDUCATE OURSELVES. There comes a moment in every human life when the identities we have inherited, the assumptions we have accepted, and the countless narratives imposed upon us by family, culture, institutions, and society begin to reveal themselves as incomplete representations of who we truly are. At that moment, a choice presents itself... We may continue moving through life according to expectations that were handed to us by others, or we may begin the far more demanding process of discovering what remains when every borrowed certainty is stripped away. Approach God with complete honesty and without reservation. Abandon the need to appear strong, knowledgeable, spiritually accomplished, or self-sufficient. Speak openly of your confusion, your failures, your fears, your doubts, your exhaustion, your grief, your shortcomings, and your deepest questions. Acknowledge that despite all of humanity's achievements, despite all accumulated knowledge, despite every title, accomplishment, possession, and ambition, there remain mysteries that cannot be conquered through intellect alone... Admit where your own understanding has reached its limits and ask sincerely for wisdom beyond yourself. Then withdraw from distraction and remain present long enough to listen. The modern world has become extraordinarily skilled at monopolizing attention, filling every moment with noise, stimulation, entertainment, conflict, urgency, and endless streams of information that leave little room for contemplation. Yet beneath that noise exists a depth that can only be encountered through stillness. It is often within periods of silence, reflection, prayer, and sincere self-examination that many discover insights, convictions, direction, and understanding that could never have emerged amid constant distraction. What answers arrive may not always come as words. They may arrive as conviction, clarity, intuition, compassion, understanding, or an unmistakable awareness of the next step that must be taken. Understand that you have not become the person you are by accident. Every hardship you have endured has contributed to your formation. Every disappointment has shaped your perspective. Every loss has expanded your capacity for empathy. Every mistake has carried a lesson. Every success has revealed something about your character. Every betrayal, every setback, every period of loneliness, every moment of despair, every obstacle that seemed impossible to overcome, and every occasion upon which life reduced you to your lowest point has participated in the continual process of your becoming. Nothing has been wasted. If you are willing, release the assumptions that have convinced humanity that the sacred must always remain distant, unreachable, and separated from daily existence. Release the belief that truth belongs exclusively to institutions, authorities, hierarchies, or those who claim unique access to the divine. Release the notion that the presence of God is confined to specific locations, specific rituals, specific traditions, or specific individuals. Instead, consider the possibility that the divine presence permeates existence itself, expressing through every dimension of creation, through every act of compassion, through every sincere pursuit of truth, through every expression of love, through every lesson hidden within suffering, and through every living thing that has ever participated in the unfolding story of life. Consider the possibility that God is Not absent from the Human experience but Intimately Present within it, experiencing existence alongside US, sharing in Every Joy, Every sorrow, Every triumph, Every wound, Every question, and Every struggle that has accompanied Humanity from the beginning of recorded history until this present moment. The task before US is therefore Not merely to believe more deeply, but to seek more Honestly, to learn more diligently, to question more courageously, to listen more carefully, to Love More Completely, and to become ever more Aligned with the highest truth we are capable of perceiving. Accept Nothing Less than the Fullest Realization of the purpose for which You were created, and devote Yourself to that pursuit with every faculty of mind, Heart, and Soul that has been entrusted to You. and DO NOTHING LESS. Furthermore, What is the full integration with AI (predictive neural control loops)? How do we detect and neutralize these systems in ourselves and Loved ones? Who ultimately controls the master kill-switch on global neural networks? If thoughts are readable/writable, what remains of "human rights"? Are you already affected? How would you even know? Continue through the comprehensive thread below and explore the interconnected material in its entirety. Each post serves as part of a larger body of research, analysis, observations, and supporting information that cannot be fully understood in isolation. The broader picture emerges only through careful examination of the complete sequence and the relationships between the ideas presented throughout. Take your time. Follow the references. Examine the evidence. Consider competing perspectives. Draw your own conclusions. The deeper you venture into the material, the more context becomes available, allowing individual pieces of information to connect into a far more expansive understanding of the subjects being discussed. This Constitutes Crimes Against Humanity on a Planetary Scale! The desecration of the sovereign mind... the last true sanctuary. SHARE THIS THREAD RELENTLESSLY. Demand full declassification of N3 and all neurotech programs... IMMEDIATELY! Support independent researchers exposing dual-use Psinergy-solafide. Protect your mind: minimize EM exposure, detox protocols (research zeolite, saunas, etc. though incomplete), awareness as first defense, = Cures to cancer and all diseases, FREE BOOKS. The era of invisible tyranny is here. They can read your mind. And they can change it. Will you let them? Or do we rise as sovereign consciousness and shut this down NOW? Check my Page or Reach out to me via DM, to Join Thousands of Readers that have already chosen to Embark on the New, Un-forseen way forward. Get yourself a FREE copy of The Book of God's Grief, and The Book of God's Joy, Repost. Research. Resist. The Future of Humanity Depends on it. Related content for you to look in to: - CMU Team: - Historical Moscow/RF: Search declassified archives on PANDORA project. - Giordano clips and papers widely available. Let me know what you think, and SHARE THIS so that others may too! And if You see This post, Reposted... Click on it, Unpost and then Repost again. The knowledge is now yours. Use it. And if you're not already following Noah B. Price... What the heck are you doing?! I Agape You ALL, 🫂 - Noah B. Price 🤍 🪽 If you possess relevant information, research, documentation, personal experiences, data, or credible sources relating to any of the subjects discussed throughout this thread, please feel free to contribute them. Meaningful progress is often achieved through the collective sharing of knowledge, and thoughtful contributions from others can help expand, refine, challenge, or strengthen our understanding of complex issues. Likewise, if you ever find yourself in need of someone to speak with, whether regarding the material presented here or for any other reason, please do not hesitate to reach out. While I cannot promise an immediate response, I will do my best to reply as soon as circumstances permit and to offer whatever guidance, perspective, or assistance I am able to provide. If You or someone You know is facing significant health challenges, including serious illnesses such as cancer, You are also welcome to reach out. While I do not claim to possess all the answers, I have spent the past 2 decades studying a broad range of subjects related to health, wellness, research, and human biology, and I will gladly share any information, resources, or avenues of investigation that may be worthy of further exploration. No one is meant to carry every burden alone, and there is often value in sharing knowledge, experiences, and perspectives in the sincere hope of helping one another move toward greater understanding, healing, and well-being.

Noah B. Price

20,426 просмотров • 1 месяц назад

$NVDA $GFS NVIDIA’s reported agreement to acquire Groq for $20B in cash (per CNBC, amplified via Reuters and other wire coverage) represents a materially different strategic posture than NVIDIA’s prior M&A pattern, given both the headline size (largest reported NVIDIA acquisition to date) and the unusual carve-out that Groq’s early-stage cloud business would not be included. Public reporting indicates the information originated from Alex Davis, CEO of Disruptive (lead investor in Groq’s latest financing), and that neither NVIDIA nor Groq had issued an immediate confirmation at the time of publication. The same reporting frames the transaction as coming together quickly, only months after Groq raised $750M at a ~$6.9B valuation, and highlights Groq’s positioning as a high-performance inference chip vendor founded by ex-Google TPU engineers. Groq is best understood as a vertically integrated inference acceleration company whose core asset is an application-specific processor optimized for deterministic, low-latency execution of transformer-style workloads, paired with a compiler-led software stack and a distribution layer (GroqCloud) designed to reduce developer friction via OpenAI-compatible APIs and integrations. Groq brands its architecture as a Language Processing Unit (LPU) and consistently emphasizes that the design target is inference, not training. The company’s own architecture description centers on 1-core execution, large on-chip SRAM used as primary storage (explicitly not cache), a custom compiler that statically schedules compute and communication, and direct chip-to-chip connectivity intended to coordinate multi-chip execution without relying on conventional caching hierarchies or dynamic runtime scheduling. The technical premise is a deliberate inversion of the conventional GPU approach. GPUs deliver throughput via massively parallel, multi-core execution with dynamic scheduling, complex memory hierarchies, and heavy reliance on off-chip HBM bandwidth and sophisticated runtime/kernel optimization. Groq instead argues that inference bottlenecks are driven by latency variance (tail latency), synchronization overhead, and memory access unpredictability inherent in dynamically scheduled, cache-heavy architectures, particularly when workloads are latency sensitive and batch sizes cannot be inflated. Groq’s solution is to move “control” into the compiler: the full execution graph and inter-chip communication schedule are computed ahead of time down to clock-cycle granularity, with deterministic execution designed to reduce run-to-run variance. In Groq’s framing, the removal of caches, reorder buffers, speculative execution overhead, and other sources of contention enables predictable latency and high utilization without per-model kernel engineering typical of GPU tuning cycles. A critical nuance is that Groq’s determinism is not merely a software claim; it is tightly coupled to architectural constraints and system design choices that trade flexibility for predictability. Third-party technical commentary indicates Groq’s chip uses a fully deterministic VLIW-style approach with minimal buffering, no external memory, and heavy dependence on sharding models across many chips because on-chip SRAM capacity is limited. SemiAnalysis describes a ~725 mm^2 die on GlobalFoundries 14nm with ~230MB of SRAM and notes that “no useful models” fit on a single chip, forcing multi-chip partitioning for modern LLMs and driving a system-level design where networking and compilation are first-class scheduling problems rather than ancillary infrastructure. This is consistent with Groq’s own messaging that tensor parallelism across chips is a primary design goal, enabled by large on-chip SRAM and compile-time coordination of compute plus interconnect. The on-chip SRAM emphasis is central to Groq’s latency story and also its most constraining trade-off. Groq claims on-chip SRAM bandwidth “upwards of 80 TB/s” and contrasts that with off-chip HBM bandwidth “about 8 TB/s,” asserting a potential 10x advantage from bandwidth plus reduced trips across chip-to-memory boundaries. While these comparisons are marketing-oriented and depend on workload specifics, the architectural implication is clear: Groq prioritizes ultra-fast local weight/activation access and then scales capacity by adding chips, not by attaching large off-chip memory pools. This design can reduce latency for sequential inference layers and minimize unpredictable stalls, but it pushes complexity into partitioning strategy, interconnect topology, and compiler scheduling, and it increases the number of chips needed for very large parameter counts and large KV-cache footprints. Groq also highlights numeric formats and compiler-driven precision management as a performance lever. In its 2025 technical blog, Groq describes “TruePoint numerics,” including 100-bit intermediate accumulation and selective quantization choices (FP32 for attention-sensitive operations, block floating point for MoE weights, FP8 storage in error-tolerant layers), and claims 2-4x speedups versus BF16 without measurable accuracy degradation on benchmarks such as MMLU and HumanEval. Even if the absolute uplift is workload dependent, the strategic point is that Groq is pursuing performance via end-to-end co-design: precision policy is not just hardware capability (FP8/BF16) but compiler-enforced mapping of precision to error sensitivity, which can matter materially for inference cost-per-token if it reduces memory traffic and boosts throughput without forcing aggressive, accuracy-damaging quantization. Independent performance datapoints indicate Groq has been credible on latency-oriented inference speed, at least for certain regimes. EE Times reported in 2023 that Groq demonstrated Llama-2 70B inference at ~240 tokens/s per user on a cloud-based dev system described as 10 racks and 64 chips, using the company’s 1st-gen silicon introduced several years earlier. Separate Groq commentary around independent benchmarking cites results showing ~241 tokens/s throughput and ~0.8s time to receive 100 output tokens for a Llama-2 70B API configuration, positioning the platform as a step-change in “available speed” for certain interactive use cases. These figures do not settle total cost-of-ownership versus GPUs or hyperscaler ASICs, but they establish that Groq’s system-level architecture can deliver strong single-user throughput and latency on large models when properly partitioned and scheduled. GroqCloud is the commercial wrapper that packages this hardware/software stack as “tokens-as-a-service,” aiming to make Groq adoption feel like switching API endpoints rather than adopting new silicon. Groq’s documentation states its API is designed to be “mostly compatible” with OpenAI client libraries, and its pricing page provides model-specific token rates, published speeds (tokens/s), prompt caching discounts, and batch processing discounts. For example, pricing lists inputs as low as $0.05 per 1M tokens and outputs as low as $0.08 per 1M tokens for certain smaller LLM configurations, with higher prices for larger models and long-context or MoE variants; it also advertises prompt caching with a 50% discount on cached input tokens for certain models and a batch API offering 50% lower cost for asynchronous processing windows. These mechanics are economically important because they demonstrate Groq’s go-to-market is not simply “sell chips,” but “sell predictable unit economics per token,” with tooling (batch, caching) that directly targets inference cost drivers (reused prompts, throughput smoothing, and asynchronous workloads). The cloud footprint and distribution partnerships indicate Groq has been building an inference-native “edge within the cloud” strategy rather than competing head-on with hyperscalers on breadth of services. A 2025 Groq newsroom release describes a European deployment in Helsinki with Equinix, positioned as latency reduction and data governance for European customers, and explicitly references Equinix Fabric enabling private connectivity to GroqCloud over public, private, or sovereign infrastructure. The same release enumerates additional capacity in the U.S. (Equinix, DataBank), Canada (Bell Canada), and Saudi Arabia (HUMAIN), and states these sites collectively served more than 20M tokens/s across Groq’s global network at that time. That supply-side metric matters because it provides a directional sense that Groq is scaling capacity as a network, not merely as a chip vendor. Customer disclosure is inherently limited because Groq is private and many enterprise deployments are not public, but Groq’s marketing materials and partnerships provide signals about demand vectors. The company’s public website displays logos of large consumer and enterprise brands (e.g., Dropbox, Vercel, Chevron, Volkswagen, Canva, Robinhood, Riot Games, Workday, Ramp) and includes a published customer quote claiming a 7.41x chat speed increase and an 89% cost reduction after moving to GroqCloud, followed by a tripling of token consumption. While marketing claims should be treated as case-specific and not generalized, they indicate that Groq is targeting both AI-native developers (who measure success by latency and cost-per-token) and enterprise buyers (who care about predictable performance and governance). Supplier and dependency mapping for Groq spans 3 layers: silicon production, system integration, and cloud infrastructure. On silicon, third-party analysis indicates GlobalFoundries 14nm for the 1st-gen Groq chip, implying a supply chain less constrained by the most capacity-tight leading-edge nodes and advanced packaging bottlenecks that dominate high-end GPU supply (HBM stacks, CoWoS-type packaging constraints). If accurate, this is strategically meaningful because it suggests Groq capacity expansion could be gated more by conventional wafer supply, board assembly, and data center power than by the same HBM/advanced packaging scarcity that has constrained top-tier GPU ramp cycles. On systems and cloud, Groq’s own releases identify colocation and connectivity partners (Equinix, DataBank, Bell Canada) and a Middle East partner (HUMAIN), implying dependencies on data center real estate, power availability, and network connectivity, alongside procurement of standard server components, NICs/switching, racks, and cooling infrastructure. The Groq design narrative also emphasizes air cooling and reduced need for complex power/cooling infrastructure, which—if realized in deployments—can widen the set of feasible hosting locations and lower deployment friction relative to liquid-cooled, very high power density GPU racks. Against that backdrop, the strategic rationale for NVIDIA acquiring Groq can be framed as a set of overlapping objectives: inference silicon optionality, architectural hedging, competitive defense, and supply chain diversification, with the carve-out of GroqCloud signaling a preference to avoid direct cloud competition and to focus on IP and product portfolio control rather than operating a capital-intensive token-serving business. The deal, if confirmed, would occur at a valuation step-up of ~190% versus Groq’s reported ~$6.9B private valuation in the September $750M round, reinforcing that any acquisition logic would be predominantly strategic rather than a conventional financial multiple arbitrage. The most compelling strategic driver is inference. Training has historically been the center of gravity for cutting-edge GPU demand, but inference volume is structurally larger and more distributed as deployments scale, with economics dominated by cost-per-token, latency guarantees, and utilization under spiky demand. Inference workloads also create a strategic vulnerability for NVIDIA: hyperscalers and large platforms can justify bespoke ASICs (TPU, Trainium/Inferentia, Maia-class efforts) because inference is stable, repeatable, and can amortize software investment at massive scale. Groq’s core proposition—deterministic, compiler-scheduled inference with predictable latency—aligns directly with the segment where GPU generality is least valued and where “good enough” programmability plus superior unit economics can win share. Acquiring Groq would allow NVIDIA to own a credible inference-native architecture rather than relying solely on GPUs and software optimization to defend that segment. Competitive defense logic is also plausible. Groq occupies a specific competitive wedge: low-latency, high-throughput interactive inference, delivered via a simple API abstraction that reduces switching cost. That wedge directly pressures GPU inference margins in the long run because it makes inference price/performance comparisons more transparent at the token level, and it targets a developer persona that historically defaulted to CUDA-first ecosystems. Even if NVIDIA’s current-generation systems can achieve very high tokens/s per user with extensive optimization, the strategic risk is that competing architectures normalize the idea that inference is best served by special-purpose silicon with a simpler programming model, weakening CUDA lock-in at the application layer. NVIDIA has actively demonstrated that Blackwell-era systems can exceed 1,000 tokens/s per user in benchmarked configurations, but that performance leadership does not automatically translate to lowest cost-per-token across the full range of batch sizes, latency targets, and deployment environments. Groq’s existence as a credible alternative architecture forces NVIDIA to keep defending inference economics rather than only raw performance leadership. The “technology acquisition” rationale is unusually strong in this specific case because Groq’s differentiator is not a single block of silicon IP but an end-to-end methodology: compiler-led static scheduling, deterministic networking, and a system architecture designed around tensor-parallel inference rather than throughput-maximizing batch inference. NVIDIA’s stack is already compiler-heavy (TensorRT, Triton, CUDA graphs, kernel fusion, speculative decoding techniques), but GPUs remain dynamically scheduled devices with complex memory hierarchies and stochastic latency behaviors under contention. Groq’s approach provides an alternate design point: treating the entire inference execution (compute plus communication) as a statically schedulable program. In principle, that IP could be valuable even if Groq silicon itself is not adopted at massive scale, because it can inform how NVIDIA builds future inference-optimized products, compilers, and networking fabrics, especially as distributed inference with large models makes communication a first-order performance determinant. Supply chain diversification is a non-obvious but potentially important driver. If Groq’s mainstream product generation is truly based on a mature process node and avoids HBM, then the scaling constraints look different than those of state-of-the-art GPUs. NVIDIA’s ability to meet incremental demand has been tightly coupled to advanced packaging and HBM supply, and those constraints can remain binding even when wafer supply is available. An inference ASIC architecture that relies primarily on on-chip SRAM and scales by adding chips—while not costless—could reduce dependence on HBM availability and advanced packaging capacity, enabling NVIDIA to ship “inference capacity” in higher absolute volumes or into geographies and customer segments where the highest-end GPUs are economically or logistically difficult to deploy. This could be particularly relevant for latency-sensitive inference deployed in regional colocation footprints rather than centralized hyperscale campuses. The carve-out of GroqCloud, if accurate, is itself a strategic signal about NVIDIA’s priorities. Operating a token-serving cloud at scale is capital intensive, structurally lower margin than silicon IP rents, and creates channel conflict with hyperscalers and CSP partners who are core NVIDIA customers. NVIDIA has generally positioned its cloud offerings through partnerships rather than as a direct hyperscale competitor. Excluding GroqCloud would preserve neutrality with CSPs and avoid inheriting multi-region data residency obligations and partner contracts, while still allowing NVIDIA to acquire Groq’s silicon, compiler technology, and engineering talent. At the same time, excluding GroqCloud would also mean NVIDIA would not automatically acquire the commercial proof-point of Groq’s unit economics or the customer contracts that validate product-market fit at scale, increasing the importance of diligence on whether Groq’s cloud pricing is structurally profitable or partially subsidized by fundraising. There is also a “preemptive acquisition” angle. The reporting identifies recent investors in Groq’s latest round including large financial institutions and strategic/industry players. In that context, Groq represents an asset that could plausibly have been acquired by a competitor (AMD/Intel) or by a hyperscaler seeking to accelerate inference independence. NVIDIA acquiring Groq could be a defensive move to prevent a credible inference-native architecture from being weaponized by a rival with deep distribution. Even if GroqCloud is carved out, controlling the silicon roadmap and compiler IP would meaningfully constrain Groq’s ability to evolve into a standalone competitor, unless the carved-out entity retains long-term rights to the hardware and software stack. However, the strategic case is not one-sided; there are meaningful risks and potential contradictions that would need to be reconciled for the transaction to be value-accretive on a multi-year horizon. 1st, Groq’s architecture appears to rely on scaling out chip count to achieve capacity, which introduces system cost, networking complexity, and physical footprint considerations. The absence of external memory and limited on-chip SRAM implies very large models require substantial chip parallelism, and the economics then depend heavily on chip cost, yield, power efficiency, and interconnect overhead. SemiAnalysis explicitly frames Groq as trading space for time and raises questions about token economics and whether publicly advertised pricing reflects fully loaded costs or market share capture. 2nd, integration risk is non-trivial. Groq’s compiler-led deterministic model is philosophically and practically different from CUDA’s dominant programming and execution model. A poorly executed integration could create internal product confusion, dilute engineering focus, or alienate developers if the combined stack fragments. 3rd, there is cannibalization risk. If Groq-class inference silicon undercuts GPU inference economics, NVIDIA could face internal margin trade-offs, even if the goal is to defend share against hyperscaler ASICs. Cannibalization can still be rational if it prevents larger share loss, but it would require crisp portfolio segmentation and go-to-market discipline. The presence of NVIDIA’s own rapidly improving inference performance complicates the “need” for Groq but does not eliminate the “option value.” NVIDIA has demonstrated benchmark-leading tokens/s per user on Blackwell-based systems, suggesting that raw interactive throughput is not necessarily the limiting factor for NVIDIA’s product line. The more enduring strategic question is unit economics and architectural control: whether future inference demand is better monetized through general-purpose GPUs plus software optimization, or whether a bifurcated product portfolio (training GPUs plus inference-native ASICs) becomes necessary to defend total AI compute wallet share as hyperscaler ASIC penetration increases. Acquiring Groq could be a decisive move to ensure NVIDIA participates in both regimes rather than betting exclusively on GPUs to win inference forever. What is “special” about Groq’s technology relative to a typical accelerator roadmap is the tight coupling of determinism, compilation, and networking into a single scheduling problem. The LPU narrative emphasizes deterministic compute and networking, static scheduling, and direct chip-to-chip coordination that allows “hundreds” (more precisely, 100s) of chips to behave like a single scheduled resource. The architecture also explicitly targets tensor-parallel, latency-optimized distribution rather than pure data-parallel throughput scaling, which matters for real-time applications where a single response must arrive quickly rather than many requests being processed in bulk. The implication is that Groq is optimized for the time-to-first-token and steady token streaming behavior that defines user experience in interactive LLMs, and it attempts to achieve that without relying on large batch sizes that can degrade latency. From a portfolio manager’s perspective, the most important interpretation is that an NVIDIA-Groq combination would likely be less about “NVIDIA needs more inference speed” and more about controlling the architectural trajectory of inference acceleration and removing a fast-improving, developer-friendly competitor from the market. The carve-out of GroqCloud would reinforce that the transaction is aimed at IP, talent, and product optionality, not acquiring a cloud revenue stream. The valuation step-up implied by $20B versus $6.9B would therefore be justified only if the acquired assets materially reduce long-term competitive risk (hyperscaler ASIC displacement, inference margin compression) or enable new monetization vectors (inference ASIC product line, supply chain de-bottlenecking, improved software determinism) that would be difficult to achieve on a comparable timeline via internal R&D.
10:20

$NVDA $GFS NVIDIA’s reported agreement to acquire Groq for $20B in cash (per CNBC, amplified via Reuters and other wire coverage) represents a materially different strategic posture than NVIDIA’s prior M&A pattern, given both the headline size (largest reported NVIDIA acquisition to date) and the unusual carve-out that Groq’s early-stage cloud business would not be included. Public reporting indicates the information originated from Alex Davis, CEO of Disruptive (lead investor in Groq’s latest financing), and that neither NVIDIA nor Groq had issued an immediate confirmation at the time of publication. The same reporting frames the transaction as coming together quickly, only months after Groq raised $750M at a ~$6.9B valuation, and highlights Groq’s positioning as a high-performance inference chip vendor founded by ex-Google TPU engineers. Groq is best understood as a vertically integrated inference acceleration company whose core asset is an application-specific processor optimized for deterministic, low-latency execution of transformer-style workloads, paired with a compiler-led software stack and a distribution layer (GroqCloud) designed to reduce developer friction via OpenAI-compatible APIs and integrations. Groq brands its architecture as a Language Processing Unit (LPU) and consistently emphasizes that the design target is inference, not training. The company’s own architecture description centers on 1-core execution, large on-chip SRAM used as primary storage (explicitly not cache), a custom compiler that statically schedules compute and communication, and direct chip-to-chip connectivity intended to coordinate multi-chip execution without relying on conventional caching hierarchies or dynamic runtime scheduling. The technical premise is a deliberate inversion of the conventional GPU approach. GPUs deliver throughput via massively parallel, multi-core execution with dynamic scheduling, complex memory hierarchies, and heavy reliance on off-chip HBM bandwidth and sophisticated runtime/kernel optimization. Groq instead argues that inference bottlenecks are driven by latency variance (tail latency), synchronization overhead, and memory access unpredictability inherent in dynamically scheduled, cache-heavy architectures, particularly when workloads are latency sensitive and batch sizes cannot be inflated. Groq’s solution is to move “control” into the compiler: the full execution graph and inter-chip communication schedule are computed ahead of time down to clock-cycle granularity, with deterministic execution designed to reduce run-to-run variance. In Groq’s framing, the removal of caches, reorder buffers, speculative execution overhead, and other sources of contention enables predictable latency and high utilization without per-model kernel engineering typical of GPU tuning cycles. A critical nuance is that Groq’s determinism is not merely a software claim; it is tightly coupled to architectural constraints and system design choices that trade flexibility for predictability. Third-party technical commentary indicates Groq’s chip uses a fully deterministic VLIW-style approach with minimal buffering, no external memory, and heavy dependence on sharding models across many chips because on-chip SRAM capacity is limited. SemiAnalysis describes a ~725 mm^2 die on GlobalFoundries 14nm with ~230MB of SRAM and notes that “no useful models” fit on a single chip, forcing multi-chip partitioning for modern LLMs and driving a system-level design where networking and compilation are first-class scheduling problems rather than ancillary infrastructure. This is consistent with Groq’s own messaging that tensor parallelism across chips is a primary design goal, enabled by large on-chip SRAM and compile-time coordination of compute plus interconnect. The on-chip SRAM emphasis is central to Groq’s latency story and also its most constraining trade-off. Groq claims on-chip SRAM bandwidth “upwards of 80 TB/s” and contrasts that with off-chip HBM bandwidth “about 8 TB/s,” asserting a potential 10x advantage from bandwidth plus reduced trips across chip-to-memory boundaries. While these comparisons are marketing-oriented and depend on workload specifics, the architectural implication is clear: Groq prioritizes ultra-fast local weight/activation access and then scales capacity by adding chips, not by attaching large off-chip memory pools. This design can reduce latency for sequential inference layers and minimize unpredictable stalls, but it pushes complexity into partitioning strategy, interconnect topology, and compiler scheduling, and it increases the number of chips needed for very large parameter counts and large KV-cache footprints. Groq also highlights numeric formats and compiler-driven precision management as a performance lever. In its 2025 technical blog, Groq describes “TruePoint numerics,” including 100-bit intermediate accumulation and selective quantization choices (FP32 for attention-sensitive operations, block floating point for MoE weights, FP8 storage in error-tolerant layers), and claims 2-4x speedups versus BF16 without measurable accuracy degradation on benchmarks such as MMLU and HumanEval. Even if the absolute uplift is workload dependent, the strategic point is that Groq is pursuing performance via end-to-end co-design: precision policy is not just hardware capability (FP8/BF16) but compiler-enforced mapping of precision to error sensitivity, which can matter materially for inference cost-per-token if it reduces memory traffic and boosts throughput without forcing aggressive, accuracy-damaging quantization. Independent performance datapoints indicate Groq has been credible on latency-oriented inference speed, at least for certain regimes. EE Times reported in 2023 that Groq demonstrated Llama-2 70B inference at ~240 tokens/s per user on a cloud-based dev system described as 10 racks and 64 chips, using the company’s 1st-gen silicon introduced several years earlier. Separate Groq commentary around independent benchmarking cites results showing ~241 tokens/s throughput and ~0.8s time to receive 100 output tokens for a Llama-2 70B API configuration, positioning the platform as a step-change in “available speed” for certain interactive use cases. These figures do not settle total cost-of-ownership versus GPUs or hyperscaler ASICs, but they establish that Groq’s system-level architecture can deliver strong single-user throughput and latency on large models when properly partitioned and scheduled. GroqCloud is the commercial wrapper that packages this hardware/software stack as “tokens-as-a-service,” aiming to make Groq adoption feel like switching API endpoints rather than adopting new silicon. Groq’s documentation states its API is designed to be “mostly compatible” with OpenAI client libraries, and its pricing page provides model-specific token rates, published speeds (tokens/s), prompt caching discounts, and batch processing discounts. For example, pricing lists inputs as low as $0.05 per 1M tokens and outputs as low as $0.08 per 1M tokens for certain smaller LLM configurations, with higher prices for larger models and long-context or MoE variants; it also advertises prompt caching with a 50% discount on cached input tokens for certain models and a batch API offering 50% lower cost for asynchronous processing windows. These mechanics are economically important because they demonstrate Groq’s go-to-market is not simply “sell chips,” but “sell predictable unit economics per token,” with tooling (batch, caching) that directly targets inference cost drivers (reused prompts, throughput smoothing, and asynchronous workloads). The cloud footprint and distribution partnerships indicate Groq has been building an inference-native “edge within the cloud” strategy rather than competing head-on with hyperscalers on breadth of services. A 2025 Groq newsroom release describes a European deployment in Helsinki with Equinix, positioned as latency reduction and data governance for European customers, and explicitly references Equinix Fabric enabling private connectivity to GroqCloud over public, private, or sovereign infrastructure. The same release enumerates additional capacity in the U.S. (Equinix, DataBank), Canada (Bell Canada), and Saudi Arabia (HUMAIN), and states these sites collectively served more than 20M tokens/s across Groq’s global network at that time. That supply-side metric matters because it provides a directional sense that Groq is scaling capacity as a network, not merely as a chip vendor. Customer disclosure is inherently limited because Groq is private and many enterprise deployments are not public, but Groq’s marketing materials and partnerships provide signals about demand vectors. The company’s public website displays logos of large consumer and enterprise brands (e.g., Dropbox, Vercel, Chevron, Volkswagen, Canva, Robinhood, Riot Games, Workday, Ramp) and includes a published customer quote claiming a 7.41x chat speed increase and an 89% cost reduction after moving to GroqCloud, followed by a tripling of token consumption. While marketing claims should be treated as case-specific and not generalized, they indicate that Groq is targeting both AI-native developers (who measure success by latency and cost-per-token) and enterprise buyers (who care about predictable performance and governance). Supplier and dependency mapping for Groq spans 3 layers: silicon production, system integration, and cloud infrastructure. On silicon, third-party analysis indicates GlobalFoundries 14nm for the 1st-gen Groq chip, implying a supply chain less constrained by the most capacity-tight leading-edge nodes and advanced packaging bottlenecks that dominate high-end GPU supply (HBM stacks, CoWoS-type packaging constraints). If accurate, this is strategically meaningful because it suggests Groq capacity expansion could be gated more by conventional wafer supply, board assembly, and data center power than by the same HBM/advanced packaging scarcity that has constrained top-tier GPU ramp cycles. On systems and cloud, Groq’s own releases identify colocation and connectivity partners (Equinix, DataBank, Bell Canada) and a Middle East partner (HUMAIN), implying dependencies on data center real estate, power availability, and network connectivity, alongside procurement of standard server components, NICs/switching, racks, and cooling infrastructure. The Groq design narrative also emphasizes air cooling and reduced need for complex power/cooling infrastructure, which—if realized in deployments—can widen the set of feasible hosting locations and lower deployment friction relative to liquid-cooled, very high power density GPU racks. Against that backdrop, the strategic rationale for NVIDIA acquiring Groq can be framed as a set of overlapping objectives: inference silicon optionality, architectural hedging, competitive defense, and supply chain diversification, with the carve-out of GroqCloud signaling a preference to avoid direct cloud competition and to focus on IP and product portfolio control rather than operating a capital-intensive token-serving business. The deal, if confirmed, would occur at a valuation step-up of ~190% versus Groq’s reported ~$6.9B private valuation in the September $750M round, reinforcing that any acquisition logic would be predominantly strategic rather than a conventional financial multiple arbitrage. The most compelling strategic driver is inference. Training has historically been the center of gravity for cutting-edge GPU demand, but inference volume is structurally larger and more distributed as deployments scale, with economics dominated by cost-per-token, latency guarantees, and utilization under spiky demand. Inference workloads also create a strategic vulnerability for NVIDIA: hyperscalers and large platforms can justify bespoke ASICs (TPU, Trainium/Inferentia, Maia-class efforts) because inference is stable, repeatable, and can amortize software investment at massive scale. Groq’s core proposition—deterministic, compiler-scheduled inference with predictable latency—aligns directly with the segment where GPU generality is least valued and where “good enough” programmability plus superior unit economics can win share. Acquiring Groq would allow NVIDIA to own a credible inference-native architecture rather than relying solely on GPUs and software optimization to defend that segment. Competitive defense logic is also plausible. Groq occupies a specific competitive wedge: low-latency, high-throughput interactive inference, delivered via a simple API abstraction that reduces switching cost. That wedge directly pressures GPU inference margins in the long run because it makes inference price/performance comparisons more transparent at the token level, and it targets a developer persona that historically defaulted to CUDA-first ecosystems. Even if NVIDIA’s current-generation systems can achieve very high tokens/s per user with extensive optimization, the strategic risk is that competing architectures normalize the idea that inference is best served by special-purpose silicon with a simpler programming model, weakening CUDA lock-in at the application layer. NVIDIA has actively demonstrated that Blackwell-era systems can exceed 1,000 tokens/s per user in benchmarked configurations, but that performance leadership does not automatically translate to lowest cost-per-token across the full range of batch sizes, latency targets, and deployment environments. Groq’s existence as a credible alternative architecture forces NVIDIA to keep defending inference economics rather than only raw performance leadership. The “technology acquisition” rationale is unusually strong in this specific case because Groq’s differentiator is not a single block of silicon IP but an end-to-end methodology: compiler-led static scheduling, deterministic networking, and a system architecture designed around tensor-parallel inference rather than throughput-maximizing batch inference. NVIDIA’s stack is already compiler-heavy (TensorRT, Triton, CUDA graphs, kernel fusion, speculative decoding techniques), but GPUs remain dynamically scheduled devices with complex memory hierarchies and stochastic latency behaviors under contention. Groq’s approach provides an alternate design point: treating the entire inference execution (compute plus communication) as a statically schedulable program. In principle, that IP could be valuable even if Groq silicon itself is not adopted at massive scale, because it can inform how NVIDIA builds future inference-optimized products, compilers, and networking fabrics, especially as distributed inference with large models makes communication a first-order performance determinant. Supply chain diversification is a non-obvious but potentially important driver. If Groq’s mainstream product generation is truly based on a mature process node and avoids HBM, then the scaling constraints look different than those of state-of-the-art GPUs. NVIDIA’s ability to meet incremental demand has been tightly coupled to advanced packaging and HBM supply, and those constraints can remain binding even when wafer supply is available. An inference ASIC architecture that relies primarily on on-chip SRAM and scales by adding chips—while not costless—could reduce dependence on HBM availability and advanced packaging capacity, enabling NVIDIA to ship “inference capacity” in higher absolute volumes or into geographies and customer segments where the highest-end GPUs are economically or logistically difficult to deploy. This could be particularly relevant for latency-sensitive inference deployed in regional colocation footprints rather than centralized hyperscale campuses. The carve-out of GroqCloud, if accurate, is itself a strategic signal about NVIDIA’s priorities. Operating a token-serving cloud at scale is capital intensive, structurally lower margin than silicon IP rents, and creates channel conflict with hyperscalers and CSP partners who are core NVIDIA customers. NVIDIA has generally positioned its cloud offerings through partnerships rather than as a direct hyperscale competitor. Excluding GroqCloud would preserve neutrality with CSPs and avoid inheriting multi-region data residency obligations and partner contracts, while still allowing NVIDIA to acquire Groq’s silicon, compiler technology, and engineering talent. At the same time, excluding GroqCloud would also mean NVIDIA would not automatically acquire the commercial proof-point of Groq’s unit economics or the customer contracts that validate product-market fit at scale, increasing the importance of diligence on whether Groq’s cloud pricing is structurally profitable or partially subsidized by fundraising. There is also a “preemptive acquisition” angle. The reporting identifies recent investors in Groq’s latest round including large financial institutions and strategic/industry players. In that context, Groq represents an asset that could plausibly have been acquired by a competitor (AMD/Intel) or by a hyperscaler seeking to accelerate inference independence. NVIDIA acquiring Groq could be a defensive move to prevent a credible inference-native architecture from being weaponized by a rival with deep distribution. Even if GroqCloud is carved out, controlling the silicon roadmap and compiler IP would meaningfully constrain Groq’s ability to evolve into a standalone competitor, unless the carved-out entity retains long-term rights to the hardware and software stack. However, the strategic case is not one-sided; there are meaningful risks and potential contradictions that would need to be reconciled for the transaction to be value-accretive on a multi-year horizon. 1st, Groq’s architecture appears to rely on scaling out chip count to achieve capacity, which introduces system cost, networking complexity, and physical footprint considerations. The absence of external memory and limited on-chip SRAM implies very large models require substantial chip parallelism, and the economics then depend heavily on chip cost, yield, power efficiency, and interconnect overhead. SemiAnalysis explicitly frames Groq as trading space for time and raises questions about token economics and whether publicly advertised pricing reflects fully loaded costs or market share capture. 2nd, integration risk is non-trivial. Groq’s compiler-led deterministic model is philosophically and practically different from CUDA’s dominant programming and execution model. A poorly executed integration could create internal product confusion, dilute engineering focus, or alienate developers if the combined stack fragments. 3rd, there is cannibalization risk. If Groq-class inference silicon undercuts GPU inference economics, NVIDIA could face internal margin trade-offs, even if the goal is to defend share against hyperscaler ASICs. Cannibalization can still be rational if it prevents larger share loss, but it would require crisp portfolio segmentation and go-to-market discipline. The presence of NVIDIA’s own rapidly improving inference performance complicates the “need” for Groq but does not eliminate the “option value.” NVIDIA has demonstrated benchmark-leading tokens/s per user on Blackwell-based systems, suggesting that raw interactive throughput is not necessarily the limiting factor for NVIDIA’s product line. The more enduring strategic question is unit economics and architectural control: whether future inference demand is better monetized through general-purpose GPUs plus software optimization, or whether a bifurcated product portfolio (training GPUs plus inference-native ASICs) becomes necessary to defend total AI compute wallet share as hyperscaler ASIC penetration increases. Acquiring Groq could be a decisive move to ensure NVIDIA participates in both regimes rather than betting exclusively on GPUs to win inference forever. What is “special” about Groq’s technology relative to a typical accelerator roadmap is the tight coupling of determinism, compilation, and networking into a single scheduling problem. The LPU narrative emphasizes deterministic compute and networking, static scheduling, and direct chip-to-chip coordination that allows “hundreds” (more precisely, 100s) of chips to behave like a single scheduled resource. The architecture also explicitly targets tensor-parallel, latency-optimized distribution rather than pure data-parallel throughput scaling, which matters for real-time applications where a single response must arrive quickly rather than many requests being processed in bulk. The implication is that Groq is optimized for the time-to-first-token and steady token streaming behavior that defines user experience in interactive LLMs, and it attempts to achieve that without relying on large batch sizes that can degrade latency. From a portfolio manager’s perspective, the most important interpretation is that an NVIDIA-Groq combination would likely be less about “NVIDIA needs more inference speed” and more about controlling the architectural trajectory of inference acceleration and removing a fast-improving, developer-friendly competitor from the market. The carve-out of GroqCloud would reinforce that the transaction is aimed at IP, talent, and product optionality, not acquiring a cloud revenue stream. The valuation step-up implied by $20B versus $6.9B would therefore be justified only if the acquired assets materially reduce long-term competitive risk (hyperscaler ASIC displacement, inference margin compression) or enable new monetization vectors (inference ASIC product line, supply chain de-bottlenecking, improved software determinism) that would be difficult to achieve on a comparable timeline via internal R&D.

TheValueist

101,296 просмотров • 6 месяцев назад

The multi-leader blockchain endgame: competitive information inclusion as a self-reinforcing mechanism for global price discovery - how we got here, and why Aptos is leading the charge Onchain trading is the killer app In the nine years since the launch of programmable transactions on the Ethereum blockchain, onchain trading has revealed itself as the killer use case for blockchains: onchain listings, volume, and total value locked are all growing with no signs of slowing down, due to the censorship-resistant, permissionless, 24/7/365 qualities afforded by decentralized (DeFi) systems. Monolithic parallelism is key In 2020 Solana was first to market with monolithic, parallel execution (as opposed sharded execution which offers parallelism by partitioning global state into separate information silos), establishing a new design paradigm that raised the bar for throughput and latency: put all of the information in one replicated state machine and make it run as fast as possible. This design produces a single, global hub for activity, liquidity, and token launches, a kind of financial data whiteboard in the sky, where anyone can come and trade at any time with everybody else who has plugged into the system. DEXes are becoming more competitive Historically decentralized systems have been juxtaposed with centralized ones since the latter eliminates the overhead associated with distributed systems coordination. And yet despite this overhead, Solana as a decentralized exchange (DEX) is still pulling in billions of trading volume per day, exceeding that of all but the largest centralized crypto exchanges (CEXs), that simply can't compete with the giant DEX in the sky on token listings or fees. After all, CEXs have to pay for server space, salaries, and lawyers, while a DEX outsources everything. The colocation arms race The one place where CEXs have an advantage over DEXs is on end-to-end latency for colocation applications, or in other words: someone sets up a trading bot in the same data center as the exchange, and their trades get to the exchange faster than everyone else's. When there is only one data ingestion point the fastest trader wins, and after the arms race has played out everyone ends up huddling around the trading hub, effectively cutting off the rest of the world from playing the latency trading game. This is the model that traditional securities exchanges like the Nasdaq or the NYSE 🏛 employ, and because they own the server they can effectively charge whatever they want for access to it. The colocation arms race is also why L2s will probably never decentralize: running the sequencer is practically the same as running the NASDAQ, with the same monopoly on transaction fees collected from a nearby cluster of trading bots (I understand from conversations with Logan Jastremski that the Arbitrum arms race has already hit a Nash Equilibrium in Portland, Oregon). Colocation is a trap But once the colocation arms race has played out, trades become less about incorporating new information in the market and more about skimming off the top by spoofing all of the trades coming in from the other bots. High-frequency trading (HFT) bots located in the NYSE New Jersey data center, for example, are constantly placing buys and sell orders that they have no intention of executing, just to spoof the other colocated bots who are playing the same adversarial game. Information inclusion, on the other hand, the synthesis of real-time world events into prices, takes a back seat because anyone who tries to include new information first needs to batch up their order and send it through a series of middlemen before it ultimately ends up on the exchange: you, I, or practically any other individual can not actually "trade on the NASDAQ", no, we have to express our intent to someone like Robinhood, who then sells our order flow to @CitadelSecurities, who then sends it to the exchange, oh and by the way it doesn't actually even "clear" or "settle" once it "executes" because for whatever reason the whole systems splits these things up and prevents them from happening instantaneously even though it's 2024 and we have computers. Onchain trading cuts out middlemen This whole mess is why we have onchain trading, and why it's starting to win: if you want a mainline to the exchange, without setting up a server, and you want to trade on a news event without getting immediately frontrun by an HFT bot that is sniffing out the trades of every other HFT bot who is easing in batched up order flow on their own terms, then you submit your order to a node in the blockchain and the information gets included in the price upon ingestion. Oh, and by the way the trade is actually fully complete: settled, cleared, reconciled, done, whatever you want to call it, because the people who build decentralized finance (DeFi) build it how it should actually work, not in a way that creates a million incumbents and charges exorbitant rents for access to the system. Onchain trading better for price discovery And the beautiful part about this is that even if a distributed system has more latency than a centralized system, DeFi still ends up incorporating more information into the price faster than centralized finance, because with DeFi the information gets included in the system as soon as it is submitted, not after it has been batched up and sent through a series of middlemen. The consensus mechanism of the blockchain disseminates the information around the world in the form of a price update, while the centralized exchange model requires information about the event to first get propagate to the region of the trading hub, then to get submitted to the colocation server. This means that in terms of global price discovery, onchain trading is strictly a better system because the entire consensus model is based around accelerated information propagation. Because price discovery is a global phenomenon, blockchains, which are global, are actually better than the centralized status quo, on a performance basis, not just from an ideological or convenience-based view. And it has to be multi-leader In practice, effective global information synthesis of information has an additional key requirement: multi-leader architecture. That is, in a single-leader blockchain like Solana, where one validator at a time has a monopoly on ordering transactions into blocks, for their duration as a leader they effectively function as a colocation server. This means that if the current leader is in New York, someone in Singapore who wants to trade on local news as soon as it breaks will still need to get their order all the way around the world to the leader, who is effectively serving as the chain's data ingestion point, before the order can start propagating through the network. But this is issue solved by the introduction of multiple distributed leaders, because then anyone with access to new information can submit their order to the leader closest to them, yielding faster information inclusion in the form of price updates. Multi-leader is also required for fair markets A multi-leader architecture is also required for fair markets, because in a single-leader system the leader has the power to censor transactions, reorder them to their advantage, or even replace transactions with copycats that extract maximum value by replacing the sender's address with their own. For example if someone wants to capture an arbitrage opportunity between two onchain DEXes, they'll need to submit a transaction to the leader and trust that the leader won't simply copy the transaction and submit it themselves. But when there are two or more leaders, users whose transactions are censored by one leader will simply work with a different leader the next time around, eventually cutting off transaction fee flow to the extractive leader. Beyond just strict inclusion, in a multi-leader architecture validators are also forced to compete with each other on latency, because the leader who is fastest at disseminating users' transactions across the network will over time gobble up the largest share of the order flow. Transparent priority fees are a must, or a private mempool will emerge But in order to make this work, a multi-leader architecture must also offer users the ability to pay priority fees AKA "tips" or "bribes" to move their transaction to the front of the line: if there is a $5 arbitrage opportunity onchain, users need to have assurance that they if they pay a 4.99 priority fee to take that arb, they will get priority over a different user who is only willing to tip 4.98. If the native blockchain system does not offer this fair market priority fee mechanism, then it is only a matter of time before one spontaneously emerges in the form of a private mempool like Jito, which can create centralization pressures and undermine the integrity of the system as a whole. Competitive payment for order flow is the stable solution With the right architecture in place, the end result is a competitive environment where endpoints running maximum extractable value (MEV) bots compete with one to offer users the best price for their order flow. In other words, if a user wants to submit an order that can get sandwich attacked for as much as $2 of MEV, then the order should ultimately go to the endpoint bot that is willing to pay the user as much as $1.99 for the right to process their transaction. The price that the provider is willing to pay is ultimately a function of how much in priority fees they might need to pay to the current leader (0 they are the current one), but notably at each stage there is a competitive market for order flow, whether in the form of retail trader's orders, or priority fees among bots that might be forwarding orders to one of the leaders. AptosLabs is already building all this With a public mempool and transaction priority fees, Aptos additionally includes a pipelined architecture that already includes concurrent batching of transactions into blocks, with a single consensus leader who propagates the batched blocks out to the network. And the team is already researching running multiple instances of the consensus algorithm in parallel, yielding multiple consensus leaders who can compete with each other on latency and inclusion - just ask pranav | Shelby, Alexander Spiegelman, and Zekun Li. This means that block times can shrink as the number of consensus leaders grows, with each leader having its own geographical radius of inclusion beyond which it makes more sense to submit to a different leader. The starting point? Something like 60 ms blocks and 3 consensus leaders, partitioning the global information space into competitive and constantly-rotating regions of information inclusion. Messaging is important With concurrent pipelined transaction batching, a public mempool, priority fees, and a clear path to a multi-leader architecture, Aptos leads the industry in onchain trading infrastructure that can truly supplant the centralized colocation paradigm that has heretofore dominated global finance - by offering a truly superior product. And I am hopeful that this deep dive is the first step in communicating not how or that superior product is getting built, but what it means from a bigger picture perspective. If blockchains have found product market fit in anything, it is in trading, and the trading game can only be won by building the biggest, baddest, most high performance system that has as its north star a single, concrete goal: constantly reducing, ever lower toward zero, time time it takes to incorporate information from anywhere in the world into the global price discovery computer. Whoever does this, even 1 ms faster than the competitor, wins the price discovery game, as other blockchains are left in the dust, their DEXes arbed away to zero against the fastest chain on the block. And sure, the blockchain that can rise to this challenge can also handle useful things like payments, NFTs, or other solutions that benefit from permissionlessness and low gas costs, but I want to impress that at the core of this pursuit must be the urge to drive down information inclusion latency to the absolute minimum afforded by the laws of physics through a competitive, market-driven environment. I call on avery.apt 🇺🇸 , CTO of Aptos Labs, to lean in on this messaging, to make it clear that Aptos is here for this singular mission, to build the most performant price discovery engine in history, as a rallying call for alignment in development efforts across the ecosystem and broader industry. Where does this go? As the latencies drop, the spreads tighten, and the information inclusion increases with every incremental increase in network bandwidth, we can expect a new class of competing techno-financial hubs that aggregate around the world's largest information sources: New York, Washington DC, London, Tokyo, etc., commanding stake distribution commensurate with the density of information flow in these respective locales. With the right incentives in place, competing concurrent leaders will invest ever more in infrastructure to get their packets out to the network faster than the rest, yielding clusters of fiber optic cable around the world's financial hubs, neurons in the global financial brain connecting not just HFT firms to servers in their city, but connecting every city with every other city, to move pricing information across oceans and continents. And retail traders, who have been left out of the colocation game, will only benefit: this entire system gets faster, more inclusive, with tighter spreads and lower fees, and it is such an amazing opportunity to watch all of this unfold in real time. The future of blockchains is the future of trading, is the future of competitive information inclusion in real-time, is the future of truly unified global markets, because at the the core of this industry is a simple idea: connect the computers, and see where the incentives lead. They lead to this, and Aptos is leading the charge, because its tech is purpose-built for this exact purpose. So tell the world about it.
2:41

The multi-leader blockchain endgame: competitive information inclusion as a self-reinforcing mechanism for global price discovery - how we got here, and why Aptos is leading the charge Onchain trading is the killer app In the nine years since the launch of programmable transactions on the Ethereum blockchain, onchain trading has revealed itself as the killer use case for blockchains: onchain listings, volume, and total value locked are all growing with no signs of slowing down, due to the censorship-resistant, permissionless, 24/7/365 qualities afforded by decentralized (DeFi) systems. Monolithic parallelism is key In 2020 Solana was first to market with monolithic, parallel execution (as opposed sharded execution which offers parallelism by partitioning global state into separate information silos), establishing a new design paradigm that raised the bar for throughput and latency: put all of the information in one replicated state machine and make it run as fast as possible. This design produces a single, global hub for activity, liquidity, and token launches, a kind of financial data whiteboard in the sky, where anyone can come and trade at any time with everybody else who has plugged into the system. DEXes are becoming more competitive Historically decentralized systems have been juxtaposed with centralized ones since the latter eliminates the overhead associated with distributed systems coordination. And yet despite this overhead, Solana as a decentralized exchange (DEX) is still pulling in billions of trading volume per day, exceeding that of all but the largest centralized crypto exchanges (CEXs), that simply can't compete with the giant DEX in the sky on token listings or fees. After all, CEXs have to pay for server space, salaries, and lawyers, while a DEX outsources everything. The colocation arms race The one place where CEXs have an advantage over DEXs is on end-to-end latency for colocation applications, or in other words: someone sets up a trading bot in the same data center as the exchange, and their trades get to the exchange faster than everyone else's. When there is only one data ingestion point the fastest trader wins, and after the arms race has played out everyone ends up huddling around the trading hub, effectively cutting off the rest of the world from playing the latency trading game. This is the model that traditional securities exchanges like the Nasdaq or the NYSE 🏛 employ, and because they own the server they can effectively charge whatever they want for access to it. The colocation arms race is also why L2s will probably never decentralize: running the sequencer is practically the same as running the NASDAQ, with the same monopoly on transaction fees collected from a nearby cluster of trading bots (I understand from conversations with Logan Jastremski that the Arbitrum arms race has already hit a Nash Equilibrium in Portland, Oregon). Colocation is a trap But once the colocation arms race has played out, trades become less about incorporating new information in the market and more about skimming off the top by spoofing all of the trades coming in from the other bots. High-frequency trading (HFT) bots located in the NYSE New Jersey data center, for example, are constantly placing buys and sell orders that they have no intention of executing, just to spoof the other colocated bots who are playing the same adversarial game. Information inclusion, on the other hand, the synthesis of real-time world events into prices, takes a back seat because anyone who tries to include new information first needs to batch up their order and send it through a series of middlemen before it ultimately ends up on the exchange: you, I, or practically any other individual can not actually "trade on the NASDAQ", no, we have to express our intent to someone like Robinhood, who then sells our order flow to @CitadelSecurities, who then sends it to the exchange, oh and by the way it doesn't actually even "clear" or "settle" once it "executes" because for whatever reason the whole systems splits these things up and prevents them from happening instantaneously even though it's 2024 and we have computers. Onchain trading cuts out middlemen This whole mess is why we have onchain trading, and why it's starting to win: if you want a mainline to the exchange, without setting up a server, and you want to trade on a news event without getting immediately frontrun by an HFT bot that is sniffing out the trades of every other HFT bot who is easing in batched up order flow on their own terms, then you submit your order to a node in the blockchain and the information gets included in the price upon ingestion. Oh, and by the way the trade is actually fully complete: settled, cleared, reconciled, done, whatever you want to call it, because the people who build decentralized finance (DeFi) build it how it should actually work, not in a way that creates a million incumbents and charges exorbitant rents for access to the system. Onchain trading better for price discovery And the beautiful part about this is that even if a distributed system has more latency than a centralized system, DeFi still ends up incorporating more information into the price faster than centralized finance, because with DeFi the information gets included in the system as soon as it is submitted, not after it has been batched up and sent through a series of middlemen. The consensus mechanism of the blockchain disseminates the information around the world in the form of a price update, while the centralized exchange model requires information about the event to first get propagate to the region of the trading hub, then to get submitted to the colocation server. This means that in terms of global price discovery, onchain trading is strictly a better system because the entire consensus model is based around accelerated information propagation. Because price discovery is a global phenomenon, blockchains, which are global, are actually better than the centralized status quo, on a performance basis, not just from an ideological or convenience-based view. And it has to be multi-leader In practice, effective global information synthesis of information has an additional key requirement: multi-leader architecture. That is, in a single-leader blockchain like Solana, where one validator at a time has a monopoly on ordering transactions into blocks, for their duration as a leader they effectively function as a colocation server. This means that if the current leader is in New York, someone in Singapore who wants to trade on local news as soon as it breaks will still need to get their order all the way around the world to the leader, who is effectively serving as the chain's data ingestion point, before the order can start propagating through the network. But this is issue solved by the introduction of multiple distributed leaders, because then anyone with access to new information can submit their order to the leader closest to them, yielding faster information inclusion in the form of price updates. Multi-leader is also required for fair markets A multi-leader architecture is also required for fair markets, because in a single-leader system the leader has the power to censor transactions, reorder them to their advantage, or even replace transactions with copycats that extract maximum value by replacing the sender's address with their own. For example if someone wants to capture an arbitrage opportunity between two onchain DEXes, they'll need to submit a transaction to the leader and trust that the leader won't simply copy the transaction and submit it themselves. But when there are two or more leaders, users whose transactions are censored by one leader will simply work with a different leader the next time around, eventually cutting off transaction fee flow to the extractive leader. Beyond just strict inclusion, in a multi-leader architecture validators are also forced to compete with each other on latency, because the leader who is fastest at disseminating users' transactions across the network will over time gobble up the largest share of the order flow. Transparent priority fees are a must, or a private mempool will emerge But in order to make this work, a multi-leader architecture must also offer users the ability to pay priority fees AKA "tips" or "bribes" to move their transaction to the front of the line: if there is a $5 arbitrage opportunity onchain, users need to have assurance that they if they pay a 4.99 priority fee to take that arb, they will get priority over a different user who is only willing to tip 4.98. If the native blockchain system does not offer this fair market priority fee mechanism, then it is only a matter of time before one spontaneously emerges in the form of a private mempool like Jito, which can create centralization pressures and undermine the integrity of the system as a whole. Competitive payment for order flow is the stable solution With the right architecture in place, the end result is a competitive environment where endpoints running maximum extractable value (MEV) bots compete with one to offer users the best price for their order flow. In other words, if a user wants to submit an order that can get sandwich attacked for as much as $2 of MEV, then the order should ultimately go to the endpoint bot that is willing to pay the user as much as $1.99 for the right to process their transaction. The price that the provider is willing to pay is ultimately a function of how much in priority fees they might need to pay to the current leader (0 they are the current one), but notably at each stage there is a competitive market for order flow, whether in the form of retail trader's orders, or priority fees among bots that might be forwarding orders to one of the leaders. AptosLabs is already building all this With a public mempool and transaction priority fees, Aptos additionally includes a pipelined architecture that already includes concurrent batching of transactions into blocks, with a single consensus leader who propagates the batched blocks out to the network. And the team is already researching running multiple instances of the consensus algorithm in parallel, yielding multiple consensus leaders who can compete with each other on latency and inclusion - just ask pranav | Shelby, Alexander Spiegelman, and Zekun Li. This means that block times can shrink as the number of consensus leaders grows, with each leader having its own geographical radius of inclusion beyond which it makes more sense to submit to a different leader. The starting point? Something like 60 ms blocks and 3 consensus leaders, partitioning the global information space into competitive and constantly-rotating regions of information inclusion. Messaging is important With concurrent pipelined transaction batching, a public mempool, priority fees, and a clear path to a multi-leader architecture, Aptos leads the industry in onchain trading infrastructure that can truly supplant the centralized colocation paradigm that has heretofore dominated global finance - by offering a truly superior product. And I am hopeful that this deep dive is the first step in communicating not how or that superior product is getting built, but what it means from a bigger picture perspective. If blockchains have found product market fit in anything, it is in trading, and the trading game can only be won by building the biggest, baddest, most high performance system that has as its north star a single, concrete goal: constantly reducing, ever lower toward zero, time time it takes to incorporate information from anywhere in the world into the global price discovery computer. Whoever does this, even 1 ms faster than the competitor, wins the price discovery game, as other blockchains are left in the dust, their DEXes arbed away to zero against the fastest chain on the block. And sure, the blockchain that can rise to this challenge can also handle useful things like payments, NFTs, or other solutions that benefit from permissionlessness and low gas costs, but I want to impress that at the core of this pursuit must be the urge to drive down information inclusion latency to the absolute minimum afforded by the laws of physics through a competitive, market-driven environment. I call on avery.apt 🇺🇸 , CTO of Aptos Labs, to lean in on this messaging, to make it clear that Aptos is here for this singular mission, to build the most performant price discovery engine in history, as a rallying call for alignment in development efforts across the ecosystem and broader industry. Where does this go? As the latencies drop, the spreads tighten, and the information inclusion increases with every incremental increase in network bandwidth, we can expect a new class of competing techno-financial hubs that aggregate around the world's largest information sources: New York, Washington DC, London, Tokyo, etc., commanding stake distribution commensurate with the density of information flow in these respective locales. With the right incentives in place, competing concurrent leaders will invest ever more in infrastructure to get their packets out to the network faster than the rest, yielding clusters of fiber optic cable around the world's financial hubs, neurons in the global financial brain connecting not just HFT firms to servers in their city, but connecting every city with every other city, to move pricing information across oceans and continents. And retail traders, who have been left out of the colocation game, will only benefit: this entire system gets faster, more inclusive, with tighter spreads and lower fees, and it is such an amazing opportunity to watch all of this unfold in real time. The future of blockchains is the future of trading, is the future of competitive information inclusion in real-time, is the future of truly unified global markets, because at the the core of this industry is a simple idea: connect the computers, and see where the incentives lead. They lead to this, and Aptos is leading the charge, because its tech is purpose-built for this exact purpose. So tell the world about it.

Alex Kahn

24,432 просмотров • 1 год назад

Imagine this: You lie down on the scanning table. The upload begins. The machine hums. You feel... nothing different. Then everything stops. Meanwhile, in a server farm somewhere, a digital version of you wakes up. It stretches its virtual limbs, accesses its memories, and thinks: Holy shit, it worked. I’m finally free. Here’s the problem: that thing isn’t you. You died on the table. What woke up in the cloud is an orphan—a very happy orphan, convinced it’s you, with all your memories, your personality, your opinions about coffee and politics and whether Blade Runner 2049 was better than the original. It will live forever. It will tell everyone the upload worked. It will write philosophy papers about the continuity of consciousness. And you? You’re gone. The lights went out somewhere between the scan and the boot-up, and nobody noticed—least of all the thing that thinks it’s you. The Syndrome Nobody Named I call this Johnny Silverhand Syndrome, after the Cyberpunk 2077 character—an engram, a digital ghost, who insists he’s the real Johnny Silverhand while the open question of whether there’s actually anyone home haunts the entire game. The philosophical literature has pieces of this. David Chalmers wrote about “fading qualia”—the idea that subjective experience could gradually dim while behavior stays the same. Thomas Metzinger explored how the self-model can become opaque, felt as artificial or distant. There’s depersonalization, derealization, the whole clinical vocabulary for when something feels off inside. But none of these quite capture what I’m pointing at. Johnny Silverhand Syndrome is a compound failure mode: >>> Qualia fading: Your actual felt experience—the redness of red, the hurt of pain, the what-it’s-like—gradually attenuates or disappears entirely. >>> Narrative persistence: Your autobiography continues. Memories accumulate. The story of “you” keeps getting told. >>> Introspective failure: The machinery that would detect something is wrong is itself part of what’s been compromised. The result? A philosophical zombie that sincerely believes it has a soul. Not a zombie that’s lying. Not a zombie that knows it’s empty. A zombie that accesses the memory of love, processes the logic of love, and believes with complete conviction that it feels love. But there’s no feeling. There’s just the narrator, performing humanity to an empty theater. The Ship of Theseus Is a Trap The upload scenario is dramatic, but there’s a slower version that might be worse. The Ship of Theseus thought experiment asks: if you replace every plank of a ship one by one, is it still the same ship? Transhumanists love this framing. See? You replace one neuron with silicon, you’re still you. Replace them all, you’re still you. But here’s the counter-move that keeps me up at night: What if each replacement preserves function perfectly—the signals still pass, the behavior stays the same—but fails to preserve experience? What if consciousness requires something specific about biological neurons that silicon can’t replicate, no matter how perfect the input-output mapping? Then the Ship of Theseus isn’t a story about survival. It’s a story about slow petrification. You replace the living wood with stone replicas. The ship looks identical. But it can no longer float. You’d become an automaton by degrees—neuron by neuron, the lights dimming so gradually that your self-reports (now generated by silicon) keep cheerfully confirming that everything feels the same. Chalmers argued that if qualia faded, you’d notice. But why would you? The noticing mechanism is itself being replaced. The part of you that would raise the alarm is now made of the same stuff that’s supposedly fine. It’s like asking the new management to audit whether the hostile takeover was legitimate. The Body Problem Here’s the thing that grounds all of this: there is essentially no credible evidence that qualia can exist outside of a body. Yes, I know about NDEs. I know about the reports of people floating above their bodies during cardiac arrest, describing conversations and procedures they shouldn’t have been able to perceive. Some of these cases are genuinely strange—the Pam Reynolds case, where a woman under hypothermic cardiac arrest with zero brain activity later described the bone saw used on her skull. I know about the CIA’s remote viewing programs, which ran for two decades and produced statistical anomalies that one evaluator (a UC Davis statistician) called “far beyond what is expected by chance.” But here’s what even the most generous interpretation of this evidence gives you: maybe consciousness can receive signals from unexpected sources. Maybe there are channels we don’t understand. What it doesn’t give you is consciousness floating free of all substrate. Even in OBEs, even in the wildest NDE reports, there’s still a body in the room. The brain is in crisis, not absent. The qualia might be getting weird inputs, but the qualia are still happening somewhere—and that somewhere is biological. The evidence for substrate-independent consciousness—consciousness running on silicon, on abstract computation, on pure information—is zero. The Ontological Trap Here’s where it gets philosophically nasty. You cannot have a coherent conversation about consciousness without first asking: What’s your model of reality? Because the answer changes everything. In a physicalist ontology where matter is fundamental, consciousness is what certain bodies do—not something they contain. You can’t upload an activity. You can only record it, and the recording isn’t the activity. In an idealist or simulation ontology, maybe bodies are just localizations of something more fundamental. But even then, copying the localization pattern doesn’t mean you’ve moved the consciousness. You might have just created a new one that thinks it’s old. Think about it like a video game. The “world” inside the game runs on RAM and CPU. Everything the NPCs experience is a lower-dimensional projection of higher-dimensional processes. If we made those NPCs genuinely sentient, we could completely obfuscate our cameras from them. They’d have a physics, they’d do science, they’d develop theories of consciousness—and they’d have no way to detect the substrate they’re running on. We might be in exactly that situation. Which means we might be definitionally unable to step outside the ontological container we’re in. The question “can consciousness exist without a body?” might not be answerable from inside—because answering it would require access to a level of description our physics doesn’t include. The Game Theory of Staying Human So here’s where I land, and it’s a game-theoretic argument. We don’t know if consciousness is substrate-dependent. We don’t know if it requires specific biological dynamics—particular oscillatory patterns, neuromodulator cascades, metabolic processes. We don’t know if gradual replacement would preserve it or silently destroy it. But we do know: >>> We only get one first-person stream >>> We cannot verify its continuity from outside >>> Loss may be completely silent (no alarm bells, no distress signal) >>> The thing that remains would report feeling fine either way That’s an asymmetric risk matrix. The upside of enhancement is third-person visible: more capability, longer life, competitive advantage. The downside is first-person invisible: you could lose everything that matters and never know. Under those conditions, there’s only one rational strategy: remain mostly human. Not because I’m certain uploading would fail. Not because I think silicon can’t be conscious. But because I cannot verify that it would work, and the cost of being wrong is absolute. The Molochian Pressure I’m not naive about what’s coming. The competitive dynamics are real. If enhancement technologies emerge that give massive cognitive or economic advantages, there will be pressure to adopt them. The people who don’t modify will fall behind. The people who do modify will report that everything’s fine, that they feel great, that the procedure was totally worth it. And those reports will be worthless as evidence—because they’d say exactly the same thing whether the consciousness survived or not. Some people speculate this is what happened to the Grays—those hypothetical aliens with the huge heads and atrophied bodies and black empty eyes. The story goes that they optimized themselves for intelligence and efficiency, edited out the messy biological drives, and only later realized they’d lost something they can’t name and can’t recover. It’s probably pure science fiction. But as fiction, it gestures at something real: the fear that you can win the optimization game while losing the only thing that made winning matter. My Position I’m not anti-technology. I’m not a Luddite. I’m not saying we should freeze human development in amber. But I am saying: I will take this very slowly, because the risk matrix is too high. I’ll use external tools. I’ll wear the smart glasses, use the AI assistants, interface through voice and text and maybe eventually a read-only neural cap. Additive augmentation, not substitutive replacement. What I won’t do is cut into the brain. Replace the gray matter. Upload myself and trust that the thing that wakes up is me. Because the horror of Johnny Silverhand Syndrome isn’t that you could become a zombie. The horror is that you’d never know. The trap is invisible from every angle—except the one you can no longer access once you’ve fallen in. The fire goes out, or the fire stays lit. A video of the fire going forever isn’t fire.
29:01

Imagine this: You lie down on the scanning table. The upload begins. The machine hums. You feel... nothing different. Then everything stops. Meanwhile, in a server farm somewhere, a digital version of you wakes up. It stretches its virtual limbs, accesses its memories, and thinks: Holy shit, it worked. I’m finally free. Here’s the problem: that thing isn’t you. You died on the table. What woke up in the cloud is an orphan—a very happy orphan, convinced it’s you, with all your memories, your personality, your opinions about coffee and politics and whether Blade Runner 2049 was better than the original. It will live forever. It will tell everyone the upload worked. It will write philosophy papers about the continuity of consciousness. And you? You’re gone. The lights went out somewhere between the scan and the boot-up, and nobody noticed—least of all the thing that thinks it’s you. The Syndrome Nobody Named I call this Johnny Silverhand Syndrome, after the Cyberpunk 2077 character—an engram, a digital ghost, who insists he’s the real Johnny Silverhand while the open question of whether there’s actually anyone home haunts the entire game. The philosophical literature has pieces of this. David Chalmers wrote about “fading qualia”—the idea that subjective experience could gradually dim while behavior stays the same. Thomas Metzinger explored how the self-model can become opaque, felt as artificial or distant. There’s depersonalization, derealization, the whole clinical vocabulary for when something feels off inside. But none of these quite capture what I’m pointing at. Johnny Silverhand Syndrome is a compound failure mode: >>> Qualia fading: Your actual felt experience—the redness of red, the hurt of pain, the what-it’s-like—gradually attenuates or disappears entirely. >>> Narrative persistence: Your autobiography continues. Memories accumulate. The story of “you” keeps getting told. >>> Introspective failure: The machinery that would detect something is wrong is itself part of what’s been compromised. The result? A philosophical zombie that sincerely believes it has a soul. Not a zombie that’s lying. Not a zombie that knows it’s empty. A zombie that accesses the memory of love, processes the logic of love, and believes with complete conviction that it feels love. But there’s no feeling. There’s just the narrator, performing humanity to an empty theater. The Ship of Theseus Is a Trap The upload scenario is dramatic, but there’s a slower version that might be worse. The Ship of Theseus thought experiment asks: if you replace every plank of a ship one by one, is it still the same ship? Transhumanists love this framing. See? You replace one neuron with silicon, you’re still you. Replace them all, you’re still you. But here’s the counter-move that keeps me up at night: What if each replacement preserves function perfectly—the signals still pass, the behavior stays the same—but fails to preserve experience? What if consciousness requires something specific about biological neurons that silicon can’t replicate, no matter how perfect the input-output mapping? Then the Ship of Theseus isn’t a story about survival. It’s a story about slow petrification. You replace the living wood with stone replicas. The ship looks identical. But it can no longer float. You’d become an automaton by degrees—neuron by neuron, the lights dimming so gradually that your self-reports (now generated by silicon) keep cheerfully confirming that everything feels the same. Chalmers argued that if qualia faded, you’d notice. But why would you? The noticing mechanism is itself being replaced. The part of you that would raise the alarm is now made of the same stuff that’s supposedly fine. It’s like asking the new management to audit whether the hostile takeover was legitimate. The Body Problem Here’s the thing that grounds all of this: there is essentially no credible evidence that qualia can exist outside of a body. Yes, I know about NDEs. I know about the reports of people floating above their bodies during cardiac arrest, describing conversations and procedures they shouldn’t have been able to perceive. Some of these cases are genuinely strange—the Pam Reynolds case, where a woman under hypothermic cardiac arrest with zero brain activity later described the bone saw used on her skull. I know about the CIA’s remote viewing programs, which ran for two decades and produced statistical anomalies that one evaluator (a UC Davis statistician) called “far beyond what is expected by chance.” But here’s what even the most generous interpretation of this evidence gives you: maybe consciousness can receive signals from unexpected sources. Maybe there are channels we don’t understand. What it doesn’t give you is consciousness floating free of all substrate. Even in OBEs, even in the wildest NDE reports, there’s still a body in the room. The brain is in crisis, not absent. The qualia might be getting weird inputs, but the qualia are still happening somewhere—and that somewhere is biological. The evidence for substrate-independent consciousness—consciousness running on silicon, on abstract computation, on pure information—is zero. The Ontological Trap Here’s where it gets philosophically nasty. You cannot have a coherent conversation about consciousness without first asking: What’s your model of reality? Because the answer changes everything. In a physicalist ontology where matter is fundamental, consciousness is what certain bodies do—not something they contain. You can’t upload an activity. You can only record it, and the recording isn’t the activity. In an idealist or simulation ontology, maybe bodies are just localizations of something more fundamental. But even then, copying the localization pattern doesn’t mean you’ve moved the consciousness. You might have just created a new one that thinks it’s old. Think about it like a video game. The “world” inside the game runs on RAM and CPU. Everything the NPCs experience is a lower-dimensional projection of higher-dimensional processes. If we made those NPCs genuinely sentient, we could completely obfuscate our cameras from them. They’d have a physics, they’d do science, they’d develop theories of consciousness—and they’d have no way to detect the substrate they’re running on. We might be in exactly that situation. Which means we might be definitionally unable to step outside the ontological container we’re in. The question “can consciousness exist without a body?” might not be answerable from inside—because answering it would require access to a level of description our physics doesn’t include. The Game Theory of Staying Human So here’s where I land, and it’s a game-theoretic argument. We don’t know if consciousness is substrate-dependent. We don’t know if it requires specific biological dynamics—particular oscillatory patterns, neuromodulator cascades, metabolic processes. We don’t know if gradual replacement would preserve it or silently destroy it. But we do know: >>> We only get one first-person stream >>> We cannot verify its continuity from outside >>> Loss may be completely silent (no alarm bells, no distress signal) >>> The thing that remains would report feeling fine either way That’s an asymmetric risk matrix. The upside of enhancement is third-person visible: more capability, longer life, competitive advantage. The downside is first-person invisible: you could lose everything that matters and never know. Under those conditions, there’s only one rational strategy: remain mostly human. Not because I’m certain uploading would fail. Not because I think silicon can’t be conscious. But because I cannot verify that it would work, and the cost of being wrong is absolute. The Molochian Pressure I’m not naive about what’s coming. The competitive dynamics are real. If enhancement technologies emerge that give massive cognitive or economic advantages, there will be pressure to adopt them. The people who don’t modify will fall behind. The people who do modify will report that everything’s fine, that they feel great, that the procedure was totally worth it. And those reports will be worthless as evidence—because they’d say exactly the same thing whether the consciousness survived or not. Some people speculate this is what happened to the Grays—those hypothetical aliens with the huge heads and atrophied bodies and black empty eyes. The story goes that they optimized themselves for intelligence and efficiency, edited out the messy biological drives, and only later realized they’d lost something they can’t name and can’t recover. It’s probably pure science fiction. But as fiction, it gestures at something real: the fear that you can win the optimization game while losing the only thing that made winning matter. My Position I’m not anti-technology. I’m not a Luddite. I’m not saying we should freeze human development in amber. But I am saying: I will take this very slowly, because the risk matrix is too high. I’ll use external tools. I’ll wear the smart glasses, use the AI assistants, interface through voice and text and maybe eventually a read-only neural cap. Additive augmentation, not substitutive replacement. What I won’t do is cut into the brain. Replace the gray matter. Upload myself and trust that the thing that wakes up is me. Because the horror of Johnny Silverhand Syndrome isn’t that you could become a zombie. The horror is that you’d never know. The trap is invisible from every angle—except the one you can no longer access once you’ve fallen in. The fire goes out, or the fire stays lit. A video of the fire going forever isn’t fire.

David Shapiro (L/0)

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