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What a night 🙌 Code, Vibe & Chill: AI & Web3 Happy Hour brought the energy, the ideas, and the people during NFC SUMMIT - MAY 27-29 2027 - LISBON at AI Hub Lisbon 🔥 💬 Insightful convos on AI + Web3 with an incredible panel: Ξthan Pierse 🦙🤖... show more

ICP HUB Portugal

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48,760 Aufrufe • vor 1 Jahr •via X (Twitter)

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This Week in Startups

26,166 Aufrufe • vor 19 Tagen

Anthropic and OpenAI slammed the this week on secondary transactions of their shares as both AI labs race to list. To help @jason and alex 🏴‍☠️🇺🇸🇺🇦 unpack the market-moving news, investors Jenny Fielding Dave McClure and sam lessin 🏴‍☠️ joined our venture capital panel to make plain which Anthropic investing vehicles are legit, and which may be fake. The group also dug into software moats, large venture funds pressuring smaller firms, the future of LP capital, and the IPO market! 0:00 Guest introductions 1:30 Guest introductions 2:36 Anthropic voids unauthorized SPV trades 9:23 Accredited investor reform & the SEC sophisticated investor test 9:40 Quo (formerly OpenPhone) - Quo gives you a clean, modern way to handle every customer call, text, and thread all in one place. Try it free at 12:25 Naval's USVC closed-end fund as a workaround 17:23 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at and use code TWIST for 10% off! 18:30 Pro-rata rights battles: when Series A investors push seed investors out 20:18 Grasshopper Bank: Time is money. Don't waste either. Go to and get an exclusive $500 cash bonus just for opening an account. 29:55 Pilot: Focus on your product, let Pilot handle your bookkeeping. Pilot provides the most reliable accounting, CFO, and tax services for startups and small businesses. Head to and get $1,200 off your first year. 31:05 Storing wealth in stories vs. cash flows 35:01 Cerebras and Fervo Energy IPOs — meaningful liquidity? 38:36 Will SpaceX, Anthropic, OpenAI IPOs redistribute capital or compound it? 46:40 The $15M Series A founder who returned the money because of Claude 50:43 Should founders pivot or return capital when the world changes? 57:25 OpenAI's $6.6B tender and Shruti Gandhi's viral SF cost-of-living tweet 1:01:07 Intercom rebrands to Fin: the AI-first late-stage pivot 🎥 Watch the full episode here 👇

Anthropic and OpenAI slammed the this week on secondary transactions of their shares as both AI labs race to list. To help @jason and alex 🏴‍☠️🇺🇸🇺🇦 unpack the market-moving news, investors Jenny Fielding Dave McClure and sam lessin 🏴‍☠️ joined our venture capital panel to make plain which Anthropic investing vehicles are legit, and which may be fake. The group also dug into software moats, large venture funds pressuring smaller firms, the future of LP capital, and the IPO market! 0:00 Guest introductions 1:30 Guest introductions 2:36 Anthropic voids unauthorized SPV trades 9:23 Accredited investor reform & the SEC sophisticated investor test 9:40 Quo (formerly OpenPhone) - Quo gives you a clean, modern way to handle every customer call, text, and thread all in one place. Try it free at 12:25 Naval's USVC closed-end fund as a workaround 17:23 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at and use code TWIST for 10% off! 18:30 Pro-rata rights battles: when Series A investors push seed investors out 20:18 Grasshopper Bank: Time is money. Don't waste either. Go to and get an exclusive $500 cash bonus just for opening an account. 29:55 Pilot: Focus on your product, let Pilot handle your bookkeeping. Pilot provides the most reliable accounting, CFO, and tax services for startups and small businesses. Head to and get $1,200 off your first year. 31:05 Storing wealth in stories vs. cash flows 35:01 Cerebras and Fervo Energy IPOs — meaningful liquidity? 38:36 Will SpaceX, Anthropic, OpenAI IPOs redistribute capital or compound it? 46:40 The $15M Series A founder who returned the money because of Claude 50:43 Should founders pivot or return capital when the world changes? 57:25 OpenAI's $6.6B tender and Shruti Gandhi's viral SF cost-of-living tweet 1:01:07 Intercom rebrands to Fin: the AI-first late-stage pivot 🎥 Watch the full episode here 👇

This Week in Startups

19,865 Aufrufe • vor 5 Monaten

🧬⚡ THE CODE SPEAKS. TIME REVEALS THE TRUTH. 🔱 For years, we watched the same story unfold. 🗣️ People talked. 🔥 People argued. ❌ People dismissed what they didn't understand. 🎭 Others built entire narratives around Pi without truly understanding the technology behind it. And while the noise was getting louder… THE CODE KEPT MOVING. 🧬⚡ 👁️ LOOK AT THE IMAGE. At the center: Kosasih. 🧬 On one side: ARC-314. 🛡️ On the other: PI-MAN. 🔱 This isn't about putting one person above everyone else. It's about something much more important: TECHNICAL VISION. → CODE. → EXECUTION. ⚙️ Because when people couldn't agree on the narrative, there was always one place we could look: THE TECHNOLOGY. 💻 Code doesn't need an ambassador. 📜 Code doesn't need a certificate. 🎤 Code doesn't need a title. 📣 Code doesn't need someone shouting louder than everyone else. Code either works… or it doesn't. And over time, the difference becomes impossible to hide. ⏳ YEARS LATER… Look at what is actually happening. 🔹 Protocol upgrades 🔹 Mainnet infrastructure 🔹 Node evolution 🔹 Pi Desktop 🔹 SoloHost 🔹 Developer infrastructure 🔹 Smart-contract experimentation 🔹 DEX / AMM development 🔹 Liquidity mechanisms 🔹 AI integrations 🔹 Distributed-computing experiments 🔹 A growing ecosystem focused on UTILITY This is why time matters. Because time gives us something arguments never can: 📊 EVIDENCE. And let's be clear. This doesn't mean that every prediction ever made about Pi was correct. It doesn't mean we should blindly believe anyone. Quite the opposite. 👇 DON'T BELIEVE ME. 👀 WATCH. Watch the infrastructure. Watch the protocols. Watch the Nodes. Watch the developers. Watch the applications. Watch what is actually being deployed. Then make your own judgment. Because eventually, every community faces the same choice: 🎭 Follow the loudest narrative or 🧬 follow the technology. We chose the technology. We studied. We questioned. We waited. We kept building. And most importantly… WE NEVER STOPPED LOOKING AT THE CODE. ⚡ 🔥 The people who mocked the vision had their moment. 🛡️ The people who kept building had theirs. ⏳ And now… TIME IS BECOMING THE WITNESS. Not hype. Not promises. Not personalities. EXECUTION. ⚙️🔥 One message: ⚡ THE CODE SPEAKS. 🧬 THE BUILDERS BUILD. ⏳ TIME REVEALS THE TRUTH. And to every Pioneer who stayed through the noise… 🌍 KEEP BUILDING. ⚡ KEEP PARTICIPATING. 🔥 KEEP CREATING UTILITY. Because the next chapter isn't waiting for us. ⚡ UTILITY WINS. 🔥 OUR TURN. 🌍 π 🔱 PI-MAN Guardian of The Core Team and Pioneers In direct alignment with: • Nicolas Kokkalis Nicolas Kokkalis • The Pi Core Team Pi Network • Chengdiao Fan Chengdiao Fan • Kosasih Nakamoto KOSASIH • Dimas Dimas Nawawi Alongside: • ARC-314 ARC-314 • Founder000 TheFounder000 The Hidden PiVerse 💜 Muhammad Kamel Qadah Pİ COİN GCV 314159,00$ Hokanews BSCN Pi Queen Pi Update sunday peter π #PiNetwork #Protocol27 #Pioneers #PiEcosystem #Web3

🧬⚡ THE CODE SPEAKS. TIME REVEALS THE TRUTH. 🔱 For years, we watched the same story unfold. 🗣️ People talked. 🔥 People argued. ❌ People dismissed what they didn't understand. 🎭 Others built entire narratives around Pi without truly understanding the technology behind it. And while the noise was getting louder… THE CODE KEPT MOVING. 🧬⚡ 👁️ LOOK AT THE IMAGE. At the center: Kosasih. 🧬 On one side: ARC-314. 🛡️ On the other: PI-MAN. 🔱 This isn't about putting one person above everyone else. It's about something much more important: TECHNICAL VISION. → CODE. → EXECUTION. ⚙️ Because when people couldn't agree on the narrative, there was always one place we could look: THE TECHNOLOGY. 💻 Code doesn't need an ambassador. 📜 Code doesn't need a certificate. 🎤 Code doesn't need a title. 📣 Code doesn't need someone shouting louder than everyone else. Code either works… or it doesn't. And over time, the difference becomes impossible to hide. ⏳ YEARS LATER… Look at what is actually happening. 🔹 Protocol upgrades 🔹 Mainnet infrastructure 🔹 Node evolution 🔹 Pi Desktop 🔹 SoloHost 🔹 Developer infrastructure 🔹 Smart-contract experimentation 🔹 DEX / AMM development 🔹 Liquidity mechanisms 🔹 AI integrations 🔹 Distributed-computing experiments 🔹 A growing ecosystem focused on UTILITY This is why time matters. Because time gives us something arguments never can: 📊 EVIDENCE. And let's be clear. This doesn't mean that every prediction ever made about Pi was correct. It doesn't mean we should blindly believe anyone. Quite the opposite. 👇 DON'T BELIEVE ME. 👀 WATCH. Watch the infrastructure. Watch the protocols. Watch the Nodes. Watch the developers. Watch the applications. Watch what is actually being deployed. Then make your own judgment. Because eventually, every community faces the same choice: 🎭 Follow the loudest narrative or 🧬 follow the technology. We chose the technology. We studied. We questioned. We waited. We kept building. And most importantly… WE NEVER STOPPED LOOKING AT THE CODE. ⚡ 🔥 The people who mocked the vision had their moment. 🛡️ The people who kept building had theirs. ⏳ And now… TIME IS BECOMING THE WITNESS. Not hype. Not promises. Not personalities. EXECUTION. ⚙️🔥 One message: ⚡ THE CODE SPEAKS. 🧬 THE BUILDERS BUILD. ⏳ TIME REVEALS THE TRUTH. And to every Pioneer who stayed through the noise… 🌍 KEEP BUILDING. ⚡ KEEP PARTICIPATING. 🔥 KEEP CREATING UTILITY. Because the next chapter isn't waiting for us. ⚡ UTILITY WINS. 🔥 OUR TURN. 🌍 π 🔱 PI-MAN Guardian of The Core Team and Pioneers In direct alignment with: • Nicolas Kokkalis Nicolas Kokkalis • The Pi Core Team Pi Network • Chengdiao Fan Chengdiao Fan • Kosasih Nakamoto KOSASIH • Dimas Dimas Nawawi Alongside: • ARC-314 ARC-314 • Founder000 TheFounder000 The Hidden PiVerse 💜 Muhammad Kamel Qadah Pİ COİN GCV 314159,00$ Hokanews BSCN Pi Queen Pi Update sunday peter π #PiNetwork #Protocol27 #Pioneers #PiEcosystem #Web3

PI MAN

16,680 Aufrufe • vor 1 Monat

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!!

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

332,437 Aufrufe • vor 5 Monaten

Maple is preparing for the release of a co-working agent. You install it locally and it works with your files, whether it's office work or building websites and apps. It's a turnkey solution, as easy as Claude Code, that keeps your data secure and private, no data sharing with closed AI labs. This is THE sovereign AI app for individuals and businesses who want powerful AI while retaining ownership of their information. Why build an agent into the Maple app when other agents already exist? Easy, we want to give you control over your work. We don't have a business plan that incorporates making money off our users' data. In the age of AI, your information, whether it's personal or company trade secrets, is the single thing that differentiates you from everyone else. We all have access to AI that can build a professional website for selling shoes. But your strategy and network for how you sell shoes should not be shared with your competitors. Sovereignty is the path to protecting what makes you, you. Maple sits at the intersection of Usability and Sovereignty. Maple gives you the best tools that are both easy to use and maintain your data sovereignty. Sovereign for one, sovereign for all. It has been a journey to get here. We brought to market the very first personal chatbot with end-to-end encryption using TEEs in late 2024. Prior to that there were proofs of concept but no full product offerings. Every other AI chat product on the market handled your data in plain text, either selling you a service to get your data or asking you to trust that they won't snoop on you. Quickly people found Maple and latched onto its open-source code and verifiable encryption. We didn't stop there. You may remember earlier this year we teased a product called "Maple Agent" and opened up a waiting list. That product is a mobile app that acts as your AI "friend", maintaining one long continuous chat, and getting to know you over time. I dislike using the word "friend" there, but it's the best way to convey the UX in a few words. AI is a tool, always has been, always will be. Any kind of friendly personality on top is just synthetic. In our testing, the UX of Maple Agent is really powerful for what it does. Think about the many short AI chats you have in your favorite app, whether it's looking up a historical fact or asking advice about a topic. With Maple Agent, those all go away in favor of the long-running chat with the friendly agent. It's like you have your own personal assistant who knows you so well and can look up anything for you. When I ask AI certain questions, I want to ask an expert who already understands my situation so I'm not repeating myself for the 100th time. That's the amazing value the personal agent brings to the table. We still see great utility for a personal agent like the "Maple Agent". Thousands of people on the waiting list, hoping to get their hands on it, agree that the concept is worth exploring and trying out. We were constrained in launching it due to a few circumstances, one of them being access to the scale of compute needed to power it. We have a clear path laid out for how to get there, but today is not the day to execute on that. It will be in the near future. Instead we have a different agent ready to go that we think is also incredible. We now have an agentic harness inside of the Maple Research app. This thing is a powerhouse. It even builds and publishes its own software releases. The agent in Maple Research works with your local filesystem, speaks to the largest open models running in TEEs, utilizes local models for certain tasks, is compatible with MCP tools, has an API for connecting to anything you need, and also supports the ACP protocol, which means it can be extended in the future to speak to other tools like Claude Code, Codex, and local models running on your own hardware. A big unlock for us was the Goose Development Kit, which powers the core of our agent harness. More on that to come as we publish articles and documentation later about the agent. The agent inside Maple Research doesn't have a name. At least not yet, not sure if it ever will. For now we call it "Chat Mode" and "Agent Mode". Think of this as the workhorse, the truck, the heavy lifter. Our other "Agent", the phone app, is your sidekick in your pocket, ready to help with quick things and ongoing conversations about life. I am incredibly excited about the Maple Research Agent. While I'm already seeing great results using it for internal work items, I'm especially thrilled about the personal health and wellness work it's doing for me. I know there are plenty of apps out there for compiling wellness data, but I'm having it build a tool tailored specifically for what I need, without the extra fluff. And none of my health data is being donated to the closed AI labs or sent to advertisers. I know that the AI logic is not being silently adjusted to fit the whims of a large corporation that has paid for product placement. It's me, state of the art AI, and my data. That's how I want it. Maple's new agent makes that possible. We can't wait for you to try it out. If you want early access, comment here, email us, reach out in some way. To those on the other agent waitlist, you're already in the queue. Thanks for reading this lengthy update. :)

Maple is preparing for the release of a co-working agent. You install it locally and it works with your files, whether it's office work or building websites and apps. It's a turnkey solution, as easy as Claude Code, that keeps your data secure and private, no data sharing with closed AI labs. This is THE sovereign AI app for individuals and businesses who want powerful AI while retaining ownership of their information. Why build an agent into the Maple app when other agents already exist? Easy, we want to give you control over your work. We don't have a business plan that incorporates making money off our users' data. In the age of AI, your information, whether it's personal or company trade secrets, is the single thing that differentiates you from everyone else. We all have access to AI that can build a professional website for selling shoes. But your strategy and network for how you sell shoes should not be shared with your competitors. Sovereignty is the path to protecting what makes you, you. Maple sits at the intersection of Usability and Sovereignty. Maple gives you the best tools that are both easy to use and maintain your data sovereignty. Sovereign for one, sovereign for all. It has been a journey to get here. We brought to market the very first personal chatbot with end-to-end encryption using TEEs in late 2024. Prior to that there were proofs of concept but no full product offerings. Every other AI chat product on the market handled your data in plain text, either selling you a service to get your data or asking you to trust that they won't snoop on you. Quickly people found Maple and latched onto its open-source code and verifiable encryption. We didn't stop there. You may remember earlier this year we teased a product called "Maple Agent" and opened up a waiting list. That product is a mobile app that acts as your AI "friend", maintaining one long continuous chat, and getting to know you over time. I dislike using the word "friend" there, but it's the best way to convey the UX in a few words. AI is a tool, always has been, always will be. Any kind of friendly personality on top is just synthetic. In our testing, the UX of Maple Agent is really powerful for what it does. Think about the many short AI chats you have in your favorite app, whether it's looking up a historical fact or asking advice about a topic. With Maple Agent, those all go away in favor of the long-running chat with the friendly agent. It's like you have your own personal assistant who knows you so well and can look up anything for you. When I ask AI certain questions, I want to ask an expert who already understands my situation so I'm not repeating myself for the 100th time. That's the amazing value the personal agent brings to the table. We still see great utility for a personal agent like the "Maple Agent". Thousands of people on the waiting list, hoping to get their hands on it, agree that the concept is worth exploring and trying out. We were constrained in launching it due to a few circumstances, one of them being access to the scale of compute needed to power it. We have a clear path laid out for how to get there, but today is not the day to execute on that. It will be in the near future. Instead we have a different agent ready to go that we think is also incredible. We now have an agentic harness inside of the Maple Research app. This thing is a powerhouse. It even builds and publishes its own software releases. The agent in Maple Research works with your local filesystem, speaks to the largest open models running in TEEs, utilizes local models for certain tasks, is compatible with MCP tools, has an API for connecting to anything you need, and also supports the ACP protocol, which means it can be extended in the future to speak to other tools like Claude Code, Codex, and local models running on your own hardware. A big unlock for us was the Goose Development Kit, which powers the core of our agent harness. More on that to come as we publish articles and documentation later about the agent. The agent inside Maple Research doesn't have a name. At least not yet, not sure if it ever will. For now we call it "Chat Mode" and "Agent Mode". Think of this as the workhorse, the truck, the heavy lifter. Our other "Agent", the phone app, is your sidekick in your pocket, ready to help with quick things and ongoing conversations about life. I am incredibly excited about the Maple Research Agent. While I'm already seeing great results using it for internal work items, I'm especially thrilled about the personal health and wellness work it's doing for me. I know there are plenty of apps out there for compiling wellness data, but I'm having it build a tool tailored specifically for what I need, without the extra fluff. And none of my health data is being donated to the closed AI labs or sent to advertisers. I know that the AI logic is not being silently adjusted to fit the whims of a large corporation that has paid for product placement. It's me, state of the art AI, and my data. That's how I want it. Maple's new agent makes that possible. We can't wait for you to try it out. If you want early access, comment here, email us, reach out in some way. To those on the other agent waitlist, you're already in the queue. Thanks for reading this lengthy update. :)

Mark

46,970 Aufrufe • vor 2 Monaten

In this post I will explain why people become borderline religious when they discover Qubic. Now with video. Please repost. I want people to learn about QUBIC. The ecosystem consists of 3 separate universes: AI, Mining, and Tickchain. AI is the primary product and purpose of QUBIC and it is supported by Mining to train the AI and by Tickchin for validation and decentralization. Here is how this whole thing works: AI: Let’s start with the AI. The main purpose of QUBIC is creating AGI (Artificial General Intelligence). It’s a type of AI that can self-develop, set tasks, grow, and learn on its own—much like the human brain does. This product is called AIGarth and it uses many cool ideas where AIs can create their own agents and have them compete against one another to evolve. It is basically robots creating robots with the survival-of-the-fittest evolution approach. Very impressive and thought out. To develop such an AI there are several requirements that even the industry giants like OpenAI, Microsoft, and Tesla are missing. One of them is the data processing for AI training. I mean they have their Datacenters, but those are only good enough to train limited Large Language Models such as ChatGPT and Grok. Mining / Training: Now QUBIC solves this problem with its mining architecture. Keep in mind, mining in QUBIC does not secure the chain, primarily it provides the processing power for training the AI. In a sense, QUBIC mining creates the largest distributed datacenter in the world, where individual miners provide their computers for training the AI and get paid with newly issued QUBIC coins. This way QUBIC gets constantly increasing processing power without having to really pay for the infrastructure. And here is another impressive bit of info. QUBIC’s distributed mining network currently ranks above the #1 supercomputer in the world - El Capitan. QUBIC Tickchain The QUBIC chain ties its AI and Mining together to create decentralization, the reward system for miners, it acts as a decision voting system for future development, and it allows AIGarth to function independently through Smart Contracts. In this summary I will not go over the specifics of QUBIC Tickchain. It’s pretty complex so it will be a separate post. Now, it’s an absolute genius piece of tech, which I consider the most advanced product within crypto industry. It is important to know that QUBIC chain runs directly out of Random Access Memory of its validators. It has instant finality and acts as its own operating system. That allows for speeds only bound by current hardware capabilities and it only increases as technology progresses. As I am writing this, QUBIC Tickchain is fully functional and it already hosts several smart-contract based web3 applications. QUBIC has designed its chain to be this fast for a single purpose, to give its future AI the speed it needs to evolve and to react quickly to the outside world. Ilya Shutskever the scientist, who developed ChatGPT clearly states that next generation superintelligence will make decisions in split second with less data. I believe QUBIC is that next generation. Why QUBIC? So out of the sea of AI projects in crypto why is QUBIC my #1 pick? Well, the first reason is that QUBIC is a unicorn AI startup that happens to use blockchain tech to reach it’s goals. In the real world of Venture Capital it would be fully funded instantly and you would not be invited. Second reason is that the industry admits that Large Language Models have plateaued. Even with enough processing power there is only so much information they can add to their data. Even Google CEO admits that. New approach is needed because the future progress is not possible with LLMs. The third reason is because Large Language Models will not create true AGI. It is evident by Ilya Shutskver latest presentation. Sam Altman of OpenAI is trying to change the definition of what is considered AGI just to lower the plank for his own product. Microsoft’s AI chief is now claiming that it would take 10 years to reach AGI, while QUBIC aims to do this in 2027. All these big players are using wrong technology for what they are trying to achieve and there isn’t enough investor funding for them to pivot. The fourth reason is that QUBIC is headed by Sergey Ivancheglo and 2 renowned AI scientists. Many claim Segey is the creator of Bitcoin. He was the 3rd person to mine bitcoin, he invented Proof of Stake consensus, which Ethereum uses now, he ran the first ICO, and he created 2 of the top gainers in crypto NXT and IOTA. QUBIC is his grand finale after 12 years of development and trials. I am including links below the post as the proof of my claims. Thank you for your time. Please live a like or a comment. It helps me continue making these extensive posts and videos.

In this post I will explain why people become borderline religious when they discover Qubic. Now with video. Please repost. I want people to learn about QUBIC. The ecosystem consists of 3 separate universes: AI, Mining, and Tickchain. AI is the primary product and purpose of QUBIC and it is supported by Mining to train the AI and by Tickchin for validation and decentralization. Here is how this whole thing works: AI: Let’s start with the AI. The main purpose of QUBIC is creating AGI (Artificial General Intelligence). It’s a type of AI that can self-develop, set tasks, grow, and learn on its own—much like the human brain does. This product is called AIGarth and it uses many cool ideas where AIs can create their own agents and have them compete against one another to evolve. It is basically robots creating robots with the survival-of-the-fittest evolution approach. Very impressive and thought out. To develop such an AI there are several requirements that even the industry giants like OpenAI, Microsoft, and Tesla are missing. One of them is the data processing for AI training. I mean they have their Datacenters, but those are only good enough to train limited Large Language Models such as ChatGPT and Grok. Mining / Training: Now QUBIC solves this problem with its mining architecture. Keep in mind, mining in QUBIC does not secure the chain, primarily it provides the processing power for training the AI. In a sense, QUBIC mining creates the largest distributed datacenter in the world, where individual miners provide their computers for training the AI and get paid with newly issued QUBIC coins. This way QUBIC gets constantly increasing processing power without having to really pay for the infrastructure. And here is another impressive bit of info. QUBIC’s distributed mining network currently ranks above the #1 supercomputer in the world - El Capitan. QUBIC Tickchain The QUBIC chain ties its AI and Mining together to create decentralization, the reward system for miners, it acts as a decision voting system for future development, and it allows AIGarth to function independently through Smart Contracts. In this summary I will not go over the specifics of QUBIC Tickchain. It’s pretty complex so it will be a separate post. Now, it’s an absolute genius piece of tech, which I consider the most advanced product within crypto industry. It is important to know that QUBIC chain runs directly out of Random Access Memory of its validators. It has instant finality and acts as its own operating system. That allows for speeds only bound by current hardware capabilities and it only increases as technology progresses. As I am writing this, QUBIC Tickchain is fully functional and it already hosts several smart-contract based web3 applications. QUBIC has designed its chain to be this fast for a single purpose, to give its future AI the speed it needs to evolve and to react quickly to the outside world. Ilya Shutskever the scientist, who developed ChatGPT clearly states that next generation superintelligence will make decisions in split second with less data. I believe QUBIC is that next generation. Why QUBIC? So out of the sea of AI projects in crypto why is QUBIC my #1 pick? Well, the first reason is that QUBIC is a unicorn AI startup that happens to use blockchain tech to reach it’s goals. In the real world of Venture Capital it would be fully funded instantly and you would not be invited. Second reason is that the industry admits that Large Language Models have plateaued. Even with enough processing power there is only so much information they can add to their data. Even Google CEO admits that. New approach is needed because the future progress is not possible with LLMs. The third reason is because Large Language Models will not create true AGI. It is evident by Ilya Shutskver latest presentation. Sam Altman of OpenAI is trying to change the definition of what is considered AGI just to lower the plank for his own product. Microsoft’s AI chief is now claiming that it would take 10 years to reach AGI, while QUBIC aims to do this in 2027. All these big players are using wrong technology for what they are trying to achieve and there isn’t enough investor funding for them to pivot. The fourth reason is that QUBIC is headed by Sergey Ivancheglo and 2 renowned AI scientists. Many claim Segey is the creator of Bitcoin. He was the 3rd person to mine bitcoin, he invented Proof of Stake consensus, which Ethereum uses now, he ran the first ICO, and he created 2 of the top gainers in crypto NXT and IOTA. QUBIC is his grand finale after 12 years of development and trials. I am including links below the post as the proof of my claims. Thank you for your time. Please live a like or a comment. It helps me continue making these extensive posts and videos.

retrodrive ⛏

24,757 Aufrufe • vor 1 Jahr

Billion-Dollar Data Centers Are Taking Over the World | Lauren Goode, WIRED When Sam Altman said one year ago that OpenAI’s Roman Empire is the actual Roman Empire, he wasn’t kidding. In the same way that the Romans gradually amassed an empire of land spanning three continents and one-ninth of the Earth’s circumference, the CEO and his cohort are now dotting the planet with their own latifundia—not agricultural estates, but AI data centers. Tech executives like Altman, Nvidia CEO Jensen Huang, Microsoft CEO Satya Nadella, and Oracle cofounder Larry Ellison are fully bought in to the idea that the future of the American (and possibly global) economy are these new warehouses stocked with IT infrastructure. But data centers, of course, aren’t actually new. In the earliest days of computing there were giant power-sucking mainframes in climate-controlled rooms, with co-ax cables moving information from the mainframe to a terminal computer. Then the consumer internet boom of the late 1990s spawned a new era of infrastructure. Massive buildings began popping up in the backyard of Washington, DC, with racks and racks of computers that stored and processed data for tech companies. A decade later, “the cloud” became the squishy infrastructure of the internet. Storage got cheaper. Some companies, like Amazon, capitalized on this. Giant data centers continued to proliferate, but instead of a tech company using some combination of on-premise servers and rented data center racks, they offloaded their computing needs to a bunch of virtualized environments. (“What is the cloud?” a perfectly intelligent family member asked me in the mid-2010s, “and why am I paying for 17 different subscriptions to it?”) All the while tech companies were hoovering up petabytes of data, data that people willingly shared online, in enterprise workspaces, and through mobile apps. Firms began finding new ways to mine and structure this “Big Data,” and promised that it would change lives. In many ways, it did. You had to know where this was going. Now the tech industry is in the fever-dream days of generative AI, which requires new levels of computing resources. Big Data is tired; big data centers are here, and wired—for AI. Faster, more efficient chips are needed to power AI data centers, and chipmakers like Nvidia and AMD have been jumping up and down on the proverbial couch, proclaiming their love for AI. The industry has entered an unprecedented era of capital investments in AI infrastructure, tilting the US into positive GDP territory. These are massive, swirling deals that might as well be cocktail party handshakes, greased with gigawatts and exuberance, while the rest of us try to track real contracts and dollars. OpenAI, Microsoft, Nvidia, Oracle, and SoftBank have struck some of the biggest deals. This year an earlier supercomputing project between OpenAI and Microsoft, called Stargate, became the vehicle for a massive AI infrastructure project in the US. (President Donald Trump called it the largest AI infrastructure project in history, because of course he did, but that may not have been hyperbolic.) Altman, Ellison, and SoftBank CEO Masayoshi Son were all in on the deal, pledging $100 billion to start, with plans to invest up to $500 billion into Stargate in the coming years. Nvidia GPUs would be deployed. Later, in July, OpenAI and Oracle announced an additional Stargate partnership—SoftBank curiously absent—measured in gigawatts of capacity (4.5) and expected job creation (around 100,000). Microsoft, Amazon, and Meta have also shared plans for multibillion-dollar data projects. Microsoft said at the start of 2025 that it was on track to invest “approximately $80 billion to build out AI-enabled data centers to train AI models and deploy AI and cloud-based applications around the world.” Then, in September, Nvidia said it would invest up to $100 billion in OpenAI, provided that OpenAI made good on a deal to use up to 10 gigawatts of Nvidia’s systems for OpenAI’s infrastructure plans, which means essentially that OpenAI has to pay Nvidia in order to get paid by Nvidia. The following month AMD said it would give OpenAI as much as 10 percent of the chip company if OpenAI purchased and deployed up to 6 gigawatts of AMD GPUs between now and 2030. It’s the circular nature of these investments that have the general public, and bearish analysts, wondering if we’re headed for an AI bubble burst. What’s clear is that the near-term downstream effects of these data center build-outs are real. The energy, resource, and labor demands of AI infrastructure are enormous. By some estimates, worldwide AI energy demand is set to surpass demand from bitcoin mining by the end of this year, WIRED has reported. The processors in data centers run hot and need to be cooled, so big tech companies are pulling from municipal water supplies to make that happen—and aren’t always disclosing how much water they’re using. Local wells are running dry or seem unsafe to drink from. Residents who live near data center construction sites are noting that traffic delays, and in some cases car crashes, are increasing. One corner of Richland Parish, Louisiana, home of Meta’s $27 billion Hyperion data center, has seen a 600 percent spike in vehicle crashes this year. Major proponents of AI seem to suggest that all of this will be worth it. Few top tech executives will publicly entertain the notion that this might be an overshoot, either ecologically or economically. “Emphatically … no,” Lisa Su, the chief executive of AMD, said earlier this month when asked if the AI froth has runneth over. Su, like other execs, cited overwhelming demand for AI as justification for these enormous capital expenditures. Demand from whom? Harder to pin down. In their mind, it’s everyone. All of us. The 800 million people who use ChatGPT on a weekly basis. The evolution from those 1990s data centers to the 2000s era of cloud computing to new AI data centers wasn’t just one continuum. The world has concurrently moved from the tiny internet to the big internet to the AI internet, and realistically speaking, there’s no going back. Generative AI is out of the bottle. The Sams and Jensens and Larrys and Lisas of the world aren’t wrong about this. It doesn’t mean they aren’t wrong about the math, though. About their economic predictions. Or their ideas about AI-powered productivity and the labor market. Or the availability of natural and material resources for these data centers. Or who will come once they build them. Or the timing of it all. Even Rome eventually collapsed.

Billion-Dollar Data Centers Are Taking Over the World | Lauren Goode, WIRED When Sam Altman said one year ago that OpenAI’s Roman Empire is the actual Roman Empire, he wasn’t kidding. In the same way that the Romans gradually amassed an empire of land spanning three continents and one-ninth of the Earth’s circumference, the CEO and his cohort are now dotting the planet with their own latifundia—not agricultural estates, but AI data centers. Tech executives like Altman, Nvidia CEO Jensen Huang, Microsoft CEO Satya Nadella, and Oracle cofounder Larry Ellison are fully bought in to the idea that the future of the American (and possibly global) economy are these new warehouses stocked with IT infrastructure. But data centers, of course, aren’t actually new. In the earliest days of computing there were giant power-sucking mainframes in climate-controlled rooms, with co-ax cables moving information from the mainframe to a terminal computer. Then the consumer internet boom of the late 1990s spawned a new era of infrastructure. Massive buildings began popping up in the backyard of Washington, DC, with racks and racks of computers that stored and processed data for tech companies. A decade later, “the cloud” became the squishy infrastructure of the internet. Storage got cheaper. Some companies, like Amazon, capitalized on this. Giant data centers continued to proliferate, but instead of a tech company using some combination of on-premise servers and rented data center racks, they offloaded their computing needs to a bunch of virtualized environments. (“What is the cloud?” a perfectly intelligent family member asked me in the mid-2010s, “and why am I paying for 17 different subscriptions to it?”) All the while tech companies were hoovering up petabytes of data, data that people willingly shared online, in enterprise workspaces, and through mobile apps. Firms began finding new ways to mine and structure this “Big Data,” and promised that it would change lives. In many ways, it did. You had to know where this was going. Now the tech industry is in the fever-dream days of generative AI, which requires new levels of computing resources. Big Data is tired; big data centers are here, and wired—for AI. Faster, more efficient chips are needed to power AI data centers, and chipmakers like Nvidia and AMD have been jumping up and down on the proverbial couch, proclaiming their love for AI. The industry has entered an unprecedented era of capital investments in AI infrastructure, tilting the US into positive GDP territory. These are massive, swirling deals that might as well be cocktail party handshakes, greased with gigawatts and exuberance, while the rest of us try to track real contracts and dollars. OpenAI, Microsoft, Nvidia, Oracle, and SoftBank have struck some of the biggest deals. This year an earlier supercomputing project between OpenAI and Microsoft, called Stargate, became the vehicle for a massive AI infrastructure project in the US. (President Donald Trump called it the largest AI infrastructure project in history, because of course he did, but that may not have been hyperbolic.) Altman, Ellison, and SoftBank CEO Masayoshi Son were all in on the deal, pledging $100 billion to start, with plans to invest up to $500 billion into Stargate in the coming years. Nvidia GPUs would be deployed. Later, in July, OpenAI and Oracle announced an additional Stargate partnership—SoftBank curiously absent—measured in gigawatts of capacity (4.5) and expected job creation (around 100,000). Microsoft, Amazon, and Meta have also shared plans for multibillion-dollar data projects. Microsoft said at the start of 2025 that it was on track to invest “approximately $80 billion to build out AI-enabled data centers to train AI models and deploy AI and cloud-based applications around the world.” Then, in September, Nvidia said it would invest up to $100 billion in OpenAI, provided that OpenAI made good on a deal to use up to 10 gigawatts of Nvidia’s systems for OpenAI’s infrastructure plans, which means essentially that OpenAI has to pay Nvidia in order to get paid by Nvidia. The following month AMD said it would give OpenAI as much as 10 percent of the chip company if OpenAI purchased and deployed up to 6 gigawatts of AMD GPUs between now and 2030. It’s the circular nature of these investments that have the general public, and bearish analysts, wondering if we’re headed for an AI bubble burst. What’s clear is that the near-term downstream effects of these data center build-outs are real. The energy, resource, and labor demands of AI infrastructure are enormous. By some estimates, worldwide AI energy demand is set to surpass demand from bitcoin mining by the end of this year, WIRED has reported. The processors in data centers run hot and need to be cooled, so big tech companies are pulling from municipal water supplies to make that happen—and aren’t always disclosing how much water they’re using. Local wells are running dry or seem unsafe to drink from. Residents who live near data center construction sites are noting that traffic delays, and in some cases car crashes, are increasing. One corner of Richland Parish, Louisiana, home of Meta’s $27 billion Hyperion data center, has seen a 600 percent spike in vehicle crashes this year. Major proponents of AI seem to suggest that all of this will be worth it. Few top tech executives will publicly entertain the notion that this might be an overshoot, either ecologically or economically. “Emphatically … no,” Lisa Su, the chief executive of AMD, said earlier this month when asked if the AI froth has runneth over. Su, like other execs, cited overwhelming demand for AI as justification for these enormous capital expenditures. Demand from whom? Harder to pin down. In their mind, it’s everyone. All of us. The 800 million people who use ChatGPT on a weekly basis. The evolution from those 1990s data centers to the 2000s era of cloud computing to new AI data centers wasn’t just one continuum. The world has concurrently moved from the tiny internet to the big internet to the AI internet, and realistically speaking, there’s no going back. Generative AI is out of the bottle. The Sams and Jensens and Larrys and Lisas of the world aren’t wrong about this. It doesn’t mean they aren’t wrong about the math, though. About their economic predictions. Or their ideas about AI-powered productivity and the labor market. Or the availability of natural and material resources for these data centers. Or who will come once they build them. Or the timing of it all. Even Rome eventually collapsed.

Owen Gregorian

55,427 Aufrufe • vor 9 Monaten

$Grok Grok Bot is incredible - and they can even manufacture real physical objects! Here is a little experiment I did last night: I created a team of bots and asked them to solve a complex engineering problem end to end - starting from four images as design cues, inferring transferable structural principles from the pixels, synthesizing an executable interactive physics simulator, running and reasoning over experiments, optimizing the design & finally manufacturing the best designs. The entire loop worked remarkably well - and I was even able to communicate with the agents from my Apple Watch. (Do we live in the future yet?) Team of agents 1⃣ Chief of Staff coordinates the workflow: watches the other agents, pulls results into the main chat, transfers files between them, and keeps the job moving. 2⃣ Physics Experimenter is the scientist-coder. It interprets the design cues and images, writes the simulator, runs experiments, analyzes the results, and produces a detailed LaTeX scientific report. 3⃣ 3D Printing Bot operates the fabrication workflow: prepares and slices the models, generates manufacturing code, sends the job, and monitors the printer. The workflow I provided an initial task based on four unregistered reference photographs containing different objects at different scales (pinnate leaf venation, a Voronoi-like areole mesh, a stochastic fibrous lattice, and a radial/circumferential web). The prompt asked the agents to infer transferable design principles - hierarchy, branching, interfaces, redundancy, disorder, load paths - and use them to build an interactive laboratory for hierarchical materials and fracture. The scientific question was: at fixed material budget, how do hierarchy depth, redundancy, disorder, and interlevel strength change stiffness, peak load, energy absorption, and the brittle-to-progressive transition? In ~20 minutes, the Physics Experimenter produced a 2D hierarchical Euler–Bernoulli beam-network laboratory. Coarse veins persist and remain thicker; finer infill is added inside cells; members connecting levels are treated as interfaces with relative strength κ; and total material volume is conserved. The four source photographs remain visible in an editable interpretation panel. The app generates geometry, steps or runs the network to failure, compares A/B/C designs, and exports JSON, CSV, PNG, and STL geometry for fabrication. After validation the Physics Experimenter used the app and conducted 47 simulation experiments, including six holdouts. It found something scientifically interesting: extra hierarchy is not "free" toughness. At fixed volume, initial stiffness changed by only about 20%, while work-to-failure varied by several-fold. Infill steals cross-section from the main axial veins, so deeper and more redundant networks often absorbed less energy than a simple depth-1 grid. Weak interfaces behaved as distributed fuses, producing more progressive failure and reducing localization. The specific H2 hypothesis - that hierarchy becomes detrimental primarily because interfaces form a mechanical bottleneck - was rejected; the dominant effect instead came from redistribution of a fixed material budget across structural levels. The Physics Experimenter then assembled the methods, tests, results, hypothesis evaluation, and conclusions into a detailed scientific report. The best designs were passed to the 3D Printing Bot. It opened Bambu Studio and brought the Bambu Lab H2D online. Both STLs were placed on one build plate at the same 50x scale and sliced using a 0.20 mm PLA process. The prints completed within less than an hour. The loop images → structural abstraction → executable physics → autonomous experiments → hypothesis testing → design selection → STL → slicing/manufacturing code → physical object That last transition is what I find especially interesting: AI is beginning to operate across the entire scientific and physical workflow - converting observations into models, models into experiments, experimental evidence into revised designs, and those designs into manufactured matter by directly operating machines. This starts to blur the boundary between AI that reasons about the physical world and AI that can actually act on it. Shoutout to the Grok Bot team - you are building something very special here! The way these agents can move naturally from reasoning, to experiments, to operating machines in the physical world feels like an important step.$

Grok Grok Bot is incredible - and they can even manufacture real physical objects! Here is a little experiment I did last night: I created a team of bots and asked them to solve a complex engineering problem end to end - starting from four images as design cues, inferring transferable structural principles from the pixels, synthesizing an executable interactive physics simulator, running and reasoning over experiments, optimizing the design & finally manufacturing the best designs. The entire loop worked remarkably well - and I was even able to communicate with the agents from my Apple Watch. (Do we live in the future yet?) Team of agents 1⃣ Chief of Staff coordinates the workflow: watches the other agents, pulls results into the main chat, transfers files between them, and keeps the job moving. 2⃣ Physics Experimenter is the scientist-coder. It interprets the design cues and images, writes the simulator, runs experiments, analyzes the results, and produces a detailed LaTeX scientific report. 3⃣ 3D Printing Bot operates the fabrication workflow: prepares and slices the models, generates manufacturing code, sends the job, and monitors the printer. The workflow I provided an initial task based on four unregistered reference photographs containing different objects at different scales (pinnate leaf venation, a Voronoi-like areole mesh, a stochastic fibrous lattice, and a radial/circumferential web). The prompt asked the agents to infer transferable design principles - hierarchy, branching, interfaces, redundancy, disorder, load paths - and use them to build an interactive laboratory for hierarchical materials and fracture. The scientific question was: at fixed material budget, how do hierarchy depth, redundancy, disorder, and interlevel strength change stiffness, peak load, energy absorption, and the brittle-to-progressive transition? In ~20 minutes, the Physics Experimenter produced a 2D hierarchical Euler–Bernoulli beam-network laboratory. Coarse veins persist and remain thicker; finer infill is added inside cells; members connecting levels are treated as interfaces with relative strength κ; and total material volume is conserved. The four source photographs remain visible in an editable interpretation panel. The app generates geometry, steps or runs the network to failure, compares A/B/C designs, and exports JSON, CSV, PNG, and STL geometry for fabrication. After validation the Physics Experimenter used the app and conducted 47 simulation experiments, including six holdouts. It found something scientifically interesting: extra hierarchy is not "free" toughness. At fixed volume, initial stiffness changed by only about 20%, while work-to-failure varied by several-fold. Infill steals cross-section from the main axial veins, so deeper and more redundant networks often absorbed less energy than a simple depth-1 grid. Weak interfaces behaved as distributed fuses, producing more progressive failure and reducing localization. The specific H2 hypothesis - that hierarchy becomes detrimental primarily because interfaces form a mechanical bottleneck - was rejected; the dominant effect instead came from redistribution of a fixed material budget across structural levels. The Physics Experimenter then assembled the methods, tests, results, hypothesis evaluation, and conclusions into a detailed scientific report. The best designs were passed to the 3D Printing Bot. It opened Bambu Studio and brought the Bambu Lab H2D online. Both STLs were placed on one build plate at the same 50x scale and sliced using a 0.20 mm PLA process. The prints completed within less than an hour. The loop images → structural abstraction → executable physics → autonomous experiments → hypothesis testing → design selection → STL → slicing/manufacturing code → physical object That last transition is what I find especially interesting: AI is beginning to operate across the entire scientific and physical workflow - converting observations into models, models into experiments, experimental evidence into revised designs, and those designs into manufactured matter by directly operating machines. This starts to blur the boundary between AI that reasons about the physical world and AI that can actually act on it. Shoutout to the Grok Bot team - you are building something very special here! The way these agents can move naturally from reasoning, to experiments, to operating machines in the physical world feels like an important step.

Markus J. Buehler

1,132,540 Aufrufe • vor 1 Monat

🚨 THE GHOST IN THE MACHINE: EPSTEIN BUILT THE FOUNDATIONS OF MODERN AI — AND NOBODY WANTS TO TALK ABOUT IT The story they told you is simple: Jeffrey Epstein was a rogue financier, a degenerate, an aberration. A lone predator who somehow became friends with presidents, princes, Nobel laureates, and the architects of the modern world through pure coincidence. The story they won’t tell you is what he was actually building — and who was cashing his checks while the ink on his 2008 conviction was still wet. 🧠 The AI Summit on an Island Owned by a Predator In April 2002 — when “artificial intelligence” was still a punchline and the field was in a decades-long “winter” — a small gathering of the smartest minds in computing assembled at a ritzy hotel in St. Thomas, U.S. Virgin Islands. Not Silicon Valley. Not Cambridge. Not a university campus with institutional oversight. A predator’s island. The “St. Thomas Common Sense Symposium” brought together: - Marvin Minsky — co-founder of MIT’s AI Lab, arguably the founding father of artificial intelligence - Doug Lenat — creator of Cyc, the first serious attempt at machine common sense - Vernor Vinge — the science fiction writer who coined the term “singularity” - Roger Schank — celebrated AI theorist - Push Singh — Minsky’s protégé, the rising star of the field The resulting paper — on how to finally achieve human-level AI — contained one sentence that should have been screamed from every rooftop for two decades: “This meeting was made possible by the generous support of Jeffrey Epstein.” Attendees later recalled Epstein flying them in on a private plane, hosting a beach barbecue on his own island, and lavishing the gathering with resources. One source described Epstein speaking fondly of Minsky, saying he “loved talking to him about AI.” So while the world’s press was busy ignoring a convicted sex offender’s weird science hobby, a predator was seeding the intellectual foundation of the technology that now runs your phone, your bank, your news feed, and soon your government. 💰 Follow the Money: The Donor Database They Tried to Hide When the MIT fact-finding report finally dropped in 2020, it confirmed what insiders already knew: Epstein gave MIT $850,000 between 2002 and 2017. And here’s the part that should make your skin crawl: - $100,000 to Marvin Minsky in 2002 — years before his conviction - $525,000 to the MIT Media Lab — every dollar of it after Epstein’s 2008 guilty plea for soliciting a minor - $225,000 to Professor Seth Lloyd — also post-conviction MIT administrators didn’t reject the money. They didn’t blow the whistle. They created an “informal framework” to accept it quietly, on the condition that Epstein’s name never appear anywhere. Staff at the Media Lab reportedly referred to Epstein as “Voldemort” — “he who must not be named.” His donations were recorded as “anonymous.” His calendar entries used initials only. When Bill Gates donated $2 million to the Media Lab in 2014, internal emails show Ito writing: “This is a $2M gift from Bill Gates directed by Jeffrey Epstein.” The development office’s reply? “For gift recording purposes, we will not be mentioning Jeffrey’s name.” Read that again. A convicted sex offender was directing the flow of millions in tech philanthropy — and the most prestigious engineering institution on Earth was actively scrubbing his name from the paperwork. That’s not a scandal. That’s institutional complicity. 🔗 The Joi Ito → OpenAI Pipeline Here’s the connective tissue the mainstream press never bothered to stitch together: Joi Ito, the MIT Media Lab director who personally cultivated Epstein as a donor — who solicited “top-off” payments of $100K to fund researchers Epstein hand-picked — became one of the early investors in OpenAI. The lab Epstein was funding was the same ecosystem incubating the people who would go on to build the machine that now answers your questions, writes your emails, and increasingly shapes what you’re allowed to think. Epstein didn’t just fund Ito’s lab. He personally invested $1 million into a private investment fund Ito managed, plus another $250,000 into a company Ito was commercializing. When the heat came, Ito tried to “eject” Epstein’s money. But you can’t eject an idea once it’s been wired into the infrastructure. And who sat on OpenAI’s board until late 2023? Larry Summers — former Treasury Secretary, former Harvard president, and a man whose private correspondence with Epstein continued until the day before Epstein’s 2019 arrest. The released emails show Epstein referring to himself as Summers’ “wing man” — advising the married former Treasury Secretary on how to pressure a mentee into a relationship, urging him to play the “long game” and keep her in a “forced holding pattern.” That man was making governance decisions at the most powerful AI company on Earth. And the board seat only ended when the emails leaked. 🧬 The Real Agenda: “Superhumans” With His DNA Now for the part that reframes everything. Epstein wasn’t just interested in AI as a hobby. According to reporting from the New York Times and his own voluminous correspondence, he was obsessed with transhumanism — the project of upgrading humanity through genetic engineering and artificial intelligence. His specific dream, confided to scientists over years: Seed the human race with his own DNA. He planned to impregnate dozens of women at his New Mexico ranch, spreading his genetic material across the population. He handed out 23andMe kits “the way most people hand out business cards.” He pushed cognitive scientist Joscha Bach on whether gene editing could boost intelligence. He corresponded with Noam Chomsky about “genetic altruism” and race-based cognitive differences. And the AI connection? That’s where Peter Thiel enters — the PayPal co-founder who has spoken openly about uploading consciousness, who funded transhumanist institutions, and whose own correspondence with Epstein was regular and ongoing. The vision was never just money. It was a eugenicist’s fusion of genetics and machine intelligence — a project to build the “cognitive aristocracy” that would inherit the earth, with Epstein’s own DNA in the mix and AI as the enforcement mechanism. The Slate analysis, published after the full Epstein files were released, is blunt: “Jeffrey Epstein wasn’t just a sex pest. He was also a eugenicist.” And the institutions that now steer AI — the labs, the boards, the funding networks — were built inside the same social circle. 🌐 The Edge Foundation: The Network That Let Him In None of this happened in isolation. Epstein was the principal funder of the Edge Foundation — the invitation-only salon of Silicon Valley elite founded by literary agent John Brockman. For up to two decades, Edge was the conduit through which Epstein gained access to: - Google founders Sergey Brin and Larry Page - Elon Musk - Jeff Bezos - Bill Gates - Nathan Myhrvold - Nick Bostrom — the “father of longtermism” whose Future of Humanity Institute shaped Silicon Valley’s entire AI-safety philosophy Bostrom’s organization Humanity+ received $120,000 from Epstein — including money to cover the salary of Ben Goertzel, the computer scientist who popularized the term “AGI” (artificial general intelligence) and now runs SingularityNET. Let that land: The man who coined the term for the technology everyone is now terrified of was, in part, on Epstein’s payroll. The worldview threaded through this entire network — an obsession with racial hierarchy, genetic “optimization,” “climate culling” to reduce surplus population, machine intelligence as humanity’s inevitable successor — wasn’t an accident. It was the operating system. 🎭 So Ask Yourself the Obvious Question The official narrative treats all of this as a series of unfortunate coincidences: - A predator who happened to fund the founding fathers of AI - A university that happened to hide his donations - A board member who happened to be his close friend - A network of billionaires who happened to share his transhumanist vision That’s not a coincidence. That’s a pattern. Jeffrey Epstein didn’t stumble into the AI world. He bought his way in — with cash, with access, with the leverage his blackmail operation gave him over the most powerful people in tech and finance. And the technology now being deployed to monitor, profile, and control the global population was midwifed by a man who wanted to breed a master race with his own DNA. They told you Epstein was a footnote. A cautionary tale. A “bad apple.” He was the seed investor. 🔥 The Bottom Line You don’t get to fund the founding fathers of AI, bankroll the institution that incubated OpenAI’s early investors, sit your friends on its board, spread your eugenicist ideology through the network that shaped its safety philosophy — and then call it a coincidence. The machine has a ghost in it. And the ghost is still collecting royalties. h/t vladtv for the video #EpsteinFiles #EpsteinAI #GhostInTheMachine #FollowTheMoney #EpsteinNetwork #AIIndustrialComplex #Transhumanism #EugenicsExposed #MITMediaLab #OpenAI #LarrySummers #JoiIto #PeterThiel #EdgeFoundation #NickBostrom #AGI #TechBillionaires #TheMachineHasAGhost #SiliconValleySecrets #WakeUp

Tony Seruga

83,270 Aufrufe • vor 1 Monat

I just got back from a 5 day trip to visit San Francisco. I met 1 billionaire, 30 founders and startups execs from Google, Meta and Apple. 11 honest observations from having 50+ meetings in San Francisco this week: 1. Bryan Johnson is a hero in SF. The guy who spends $2M/year on “not dying”. I met lots of people who were on his “blueprint” and drizzling his $60 olive oil over everything. He has obtained cult-like status in SF. Literally zero people talk about Bryan where I live. 2. People are still drunk on venture capital. A buddy of mine recently moved to SF from Florida. This is a guy who runs a bootstrapped 7 figure year cash-flowing tech-enabled business. Now, all of a sudden he’s got a term sheet from a leading VC. And he’ll be on the VC treadmill. Forget dividends, pray for exit, my guy. He’ll probably take the deal too. Most founders I spoke to mentioned valuation more than profit. Most founders I spoke to name dropped VCs who they work with. It’s a status thing. Somehow VCs made it fashionable to be in the VC-backed world. Even with 2023 being a bloodshed with VC funding. It doesn’t matter. My takeaway: if you’re in SF long enough (even if you’re a bootstrapper), you’ll probably raise a round. Everyone wants to raise money and optimize that valuation. 3. Everyone is “crushing it” but not really at all. It was hard to find people who would actually tell me how things are going. Virtually everyone I spoke to told me they were “crushing it” or some version of that. The reality is being an entrepreneur is 3% crushing it and 97% being crushed (if you’re lucky). What is it about SF that makes people do this? 4. There is renewed interest in hardware. Maybe thanks to products like the Humane AI pin, Rabbit R1 etc. I met some cool 19 year old founders who are 3D printing a device and partner with creators for distribution with a 30% affiliate cute. Probably will do $2M this year. They raised $100k for some odd reason. But, I chatted with a bunch of founders interested in experimenting with hardware that would cash-flow and would be relatively asset light businesses. I expect to see more interesting low-cost ($50-$200) pieces of hardware with software subscriptions ($9-$29/month) coming up with startups who haven’t raised a ton. 5. Uber/Lyft drivers are confronting Waymo. I was at a stoplight in a regular uber with a guy 70 year old driver named Pedro. Pedro looks to his right. It’s a self-driving Waymo car. He goes: “my days are numbered. He puts his foot on the gas and tries to cutoff the Waymo and starts laughing. This human vs robot saga is probably going to get weirder. 6. It’s AI-mania. I thought just from being on X that I knew, but you really get sense for how many people are building in AI. It feels like almost everyone. 7. Most people think I’m crazy for running cash-flowing businesses. BUT there is growing intrigue about owning a portfolio of cash-flowing internet assets. It’s still fringe, but I threw an event and 600 people showed up to talk about multipreneurship. Lots of those people felt like outsiders in the land of VC. It turns out, not everyone's sold on the VC dream. 8. Very few people drink alcohol. I lived in SF from 2012-2019. Probably 10x the amount of people drinking alcohol drinking 3x the amount of alcohol. Everyone is “sober” but everyone is on some form of “organic” drug like mushrooms. 9. I asked a billionaire what his biggest regret was: His response was short and sweet: "I wish I wrote more down". You'll never remember all the ups and downs. Write it down. Got me thinking – you'll appreciate it in 5 years. Even more in 25. 10. Lots still think content creators are low status. In SF, running a unicorn is high status, creating content is still pretty low status. 11. People are going into offices. I work remote, but lots of the fast-growing startups offices I went to were packed with people and buzz. It was a vibe. Lots more observations. But these were top of mind. Love to hear what you think. I don't know if SF is “back” but it’s definitely a dose of inspiration. I had a really good time. – I share what I’m learning in real-time about startups. Follow along GREG ISENBERG.

I just got back from a 5 day trip to visit San Francisco. I met 1 billionaire, 30 founders and startups execs from Google, Meta and Apple. 11 honest observations from having 50+ meetings in San Francisco this week: 1. Bryan Johnson is a hero in SF. The guy who spends $2M/year on “not dying”. I met lots of people who were on his “blueprint” and drizzling his $60 olive oil over everything. He has obtained cult-like status in SF. Literally zero people talk about Bryan where I live. 2. People are still drunk on venture capital. A buddy of mine recently moved to SF from Florida. This is a guy who runs a bootstrapped 7 figure year cash-flowing tech-enabled business. Now, all of a sudden he’s got a term sheet from a leading VC. And he’ll be on the VC treadmill. Forget dividends, pray for exit, my guy. He’ll probably take the deal too. Most founders I spoke to mentioned valuation more than profit. Most founders I spoke to name dropped VCs who they work with. It’s a status thing. Somehow VCs made it fashionable to be in the VC-backed world. Even with 2023 being a bloodshed with VC funding. It doesn’t matter. My takeaway: if you’re in SF long enough (even if you’re a bootstrapper), you’ll probably raise a round. Everyone wants to raise money and optimize that valuation. 3. Everyone is “crushing it” but not really at all. It was hard to find people who would actually tell me how things are going. Virtually everyone I spoke to told me they were “crushing it” or some version of that. The reality is being an entrepreneur is 3% crushing it and 97% being crushed (if you’re lucky). What is it about SF that makes people do this? 4. There is renewed interest in hardware. Maybe thanks to products like the Humane AI pin, Rabbit R1 etc. I met some cool 19 year old founders who are 3D printing a device and partner with creators for distribution with a 30% affiliate cute. Probably will do $2M this year. They raised $100k for some odd reason. But, I chatted with a bunch of founders interested in experimenting with hardware that would cash-flow and would be relatively asset light businesses. I expect to see more interesting low-cost ($50-$200) pieces of hardware with software subscriptions ($9-$29/month) coming up with startups who haven’t raised a ton. 5. Uber/Lyft drivers are confronting Waymo. I was at a stoplight in a regular uber with a guy 70 year old driver named Pedro. Pedro looks to his right. It’s a self-driving Waymo car. He goes: “my days are numbered. He puts his foot on the gas and tries to cutoff the Waymo and starts laughing. This human vs robot saga is probably going to get weirder. 6. It’s AI-mania. I thought just from being on X that I knew, but you really get sense for how many people are building in AI. It feels like almost everyone. 7. Most people think I’m crazy for running cash-flowing businesses. BUT there is growing intrigue about owning a portfolio of cash-flowing internet assets. It’s still fringe, but I threw an event and 600 people showed up to talk about multipreneurship. Lots of those people felt like outsiders in the land of VC. It turns out, not everyone's sold on the VC dream. 8. Very few people drink alcohol. I lived in SF from 2012-2019. Probably 10x the amount of people drinking alcohol drinking 3x the amount of alcohol. Everyone is “sober” but everyone is on some form of “organic” drug like mushrooms. 9. I asked a billionaire what his biggest regret was: His response was short and sweet: "I wish I wrote more down". You'll never remember all the ups and downs. Write it down. Got me thinking – you'll appreciate it in 5 years. Even more in 25. 10. Lots still think content creators are low status. In SF, running a unicorn is high status, creating content is still pretty low status. 11. People are going into offices. I work remote, but lots of the fast-growing startups offices I went to were packed with people and buzz. It was a vibe. Lots more observations. But these were top of mind. Love to hear what you think. I don't know if SF is “back” but it’s definitely a dose of inspiration. I had a really good time. – I share what I’m learning in real-time about startups. Follow along GREG ISENBERG.

GREG ISENBERG

457,299 Aufrufe • vor 2 Jahren

After regrouping with our investors and the team, I’ve made the difficult decision to wind down Hike completely. Our US business, launched just nine months ago, is off to a strong start. But scaling globally would require a full recap, a reset that is not the best use of capital or time. The Big Question → We could raise the capital, but the real question is: is it worth it? Is this a climb worth pivoting for? For the first time in 13 years, my answer is no. Not for me, not for my team, and not for our investors. Why? 1. RMG was never the destination. It was a way to test unit economics and traction in India while working toward a bigger vision. In hindsight, starting in India locked us into the model and regulatory headwinds, turning a temporary path into a more permanent one. 2. The Gaming Nation vision is real, but we may be too early. The world will eventually move toward a Nation-type model in gaming and Web3 - Company 2.0. But crypto regulation is still developing globally, and we don’t want to repeat India, where we hoped for clarity that never came. 3. And most importantly, if doing a full reset, is this where I’d put my own capital and energy today? For the first time, the answer is no. The world has changed in the last decade - and so have I. There are more important problems to solve and bigger opportunities to deploy brilliant talent and capital. Looking Back & Lessons The last 13 years have been immense. Hike Messenger reached 40M MAUs and became the 35th most loved consumer brand in India at its peak. With Rush, we built a brand new kind of Casual PvP gaming platform and scaled it to 10M users and $500M+ in gross revenue in just 4 years. Our execution was super, but we could never quite make it stick. There are clear lessons to carry forward, especially on market selection: 1. Be careful with winner-take-all markets. To win, you need to go global. 2. Don’t build for today’s tech constraints. Build on the spring/summer of new technologies. 3. Regulatory clarity matters. Risk is fine; uncertainty is not. More importantly momentum is everything. And build what your heart and mind are deeply excited about. It’s the conviction that carries you through. This is both a disappointment and a hard outcome. But I choose to look on the bright side: the learnings are invaluable, and my conviction for what’s next is even stronger. To everyone who has been part of this journey - our users, our team, our investors, and our community - thank you. As a CEO, you’re only as strong as your team, and I want to give a special shout-out to mine - an incredible group of people who gave this everything. Hike This chapter ends, but the climb continues. Looking Forward I’ve always thrived at building at the forefront of technology. Over the last decade, in the little time I had to explore outside of Hike, I kept returning to the same three frontiers. And now, they feel like the great canvases for decades to come → 1. AI → For the first time, technology has both intelligence and memory. Imagine products that don’t just serve us functionally but truly know us - systems that adapt, grow, and partner with us. As a UX-first builder, this is the most exciting time to be building software. 2. Breakthroughs in Energy → Human progress has always been bound by energy. The world’s demand for energy is rising faster than ever. Breakthrough approaches, especially in physics are needed to power the future. The last century gave us mastery of fine matters and electricity, the next will move deeper, at the intersection of science and spirituality - into what yogis call divine magnetism and physicists call the quantum world or electromagnetism. From there will come technologies that today feel impossible to imagine. 3. Mastery of the Self → As AI takes on more of our work, a deeper question will rise: what now defines us? When productivity is no longer the measure of worth, humanity will turn inward. Man’s evolution will move from the intellect to intuitive attunement - a deeper connection with ourselves and the divine (which we’ll realise are one and the same). The tools, spaces, and guides that help us explore this inner world will be as transformative as any innovation in the outer one - unlocking the next level of humanity’s potential. If you put these together, a picture emerges: → the cost of intelligence trending to zero → the cost of energy trending to zero → and the cost of willpower falling lower and lower. Just imagine a future where willpower is infinite, energy is abundant, and intelligence is at our fingertips. This is the future I will help build — and it’s where I’ll be contributing in the decades to come. This new chapter will look very different from the last one 🚀 Video for perspective. Full substack post link below.

Kavin

24,481 Aufrufe • vor 1 Jahr

The fight between Anthropic and the DoW is a warning shot. Right now, LLMs are probably not being used in mission critical ways. But within 20 years, 99% of the workforce in the military, the government, and the private sector will be AIs. This includes the soldiers (by which I mean the robot armies), the superhumanly intelligent advisors and engineers, the police, you name it. Our future civilization will run on AI labor. And as much as the government’s actions here piss me off, in a way I’m glad this episode happened - because it gives us the opportunity to think through some extremely important questions about who this future workforce will be accountable and aligned to, and who gets to determine that. What Hegseth should have done Obviously the DoW has the right to refuse to use Anthropic’s models because of these redlines. In fact, I think the government’s case had they done so would be very reasonable, especially given the ambiguity of concepts like autonomous weapons or mass surveillance. Honestly, for this reason, if I was the Defense Secretary, I would probably actually refuse to do this deal with Anthropic. Imagine if in the future, there’s a Democratic administration, and Elon Musk is negotiating some SpaceX contract to give the military access to Starlink. And suppose if Elon said, “I reserve the right to cancel this contract if I determine that you’re using Starlink technology to wage a war not authorized by Congress.” On the face of it, that language seems reasonable - but as the military, you simply can’t give a private company a kill switch on technology your operations have come to rely on, especially if you have an an acrimonious and low trust relationship with said contractor - as in fact Anthropic has with the current administration. If the government had just said, “Hey we’re not gonna do business with you,” that would have been fine, and I would not have felt the need to write this blog post. Instead the government has threatened to destroy Anthropic as a private business, because Anthropic refuses to sell to the government on terms the government commands. If upheld, this Supply Chain Restriction would mean that Amazon and Google and Nvidia and Palantir would need to ensure Claude isn't touching any of their Pentagon work. Anthropic would be able to survive this designation today. But given the way AI is going, eventually AI is not gonna be some party trick addendum to these contractors’ products that can just be turned off. It'll be woven into how every product is built, maintained, and operated. For example, the code for the AWS services that the DoW uses will be written by Claude - is that a supply chain risk? In a world with ubiquitous and powerful AI, it's actually not clear to me that these big tech companies will be able to cordon off the use of Claude in order to keep working with the Pentagon. And that raises a question the Department of War probably hasn't thought through. If AI really is that pervasive and powerful, then when forced to choose between their AI provider and a DoW contract that represents a tiny fraction of their revenue, wouldn’t most tech companies drop the government, not the AI? So what's the Pentagon's plan — to coerce and threaten to destroy every single company that won't give them what they want on exactly their terms? The whole background of this AI conversation is that we’re in a race with China, and we have to win. But what is the reason we want America to win the AI race? It’s because we want to make sure free open societies can defend themselves. We don't want the winner of the AI race to be a government which operates on the principle that there is no such thing as a truly private company or a private citizen. And that if the state wants you to provide them with a service on terms you find morally objectionable, you are not allowed to refuse. And if you do refuse, the government will try to destroy your ability to do business. Are we racing to beat the CCP in AI just so that we can adopt the most ghoulish parts of their system? Now, people will say, "Oh, well, our government is democratically elected, so it's not the same thing if they tell you what you must do." I refuse to accept this idea that if a democratically elected leader hypothetically wants to do mass surveillance on his citizens or wants to violate their rights or punish them for political reasons, that not only is that okay, but that you have a duty to help him. The overhangs of tyranny Mass surveillance is, at least in certain forms, legal. It just has been impractical so far. Under current law, you have no Fourth Amendment protection over data you share with a third party, including your bank, your phone carrier, your ISP, and your email provider. The government reserves the right to purchase and obtain and read this data in bulk without a warrant. What's been missing is the ability to actually do anything with all of this data — no agency has the manpower to monitor every camera feed, cross-reference every transaction, or read every message. But that bottleneck goes away with AI. There are 100 million CCTV cameras in America. You can get pretty good open source multimodal models for 10 cents per million input tokens. So if you process a frame every ten seconds, and each frame is 1,000 tokens, you’re looking at a yearly cost of about 30 billion dollars to process every single camera in America. And remember that a given level of AI ability gets 10x cheaper year over year - so a year from now it’ll cost 3 billion, and then a year after 300 million, and by 2030, it might be cheaper for the government to be able to understand what is going on in every single nook and cranny of this country than it is to remodel to the White House. Once the technical capacity for mass surveillance and political suppression exists, the only thing standing between us and an authoritarian surveillance state is the political expectation that this is not something we do here. And this is why I think what Anthropic did here is so valuable and commendable, because it is helping set that norm and precedent. AI structurally favors mass surveillance What we’re learning from this episode is that the government actually has way more leverage over private companies than we realized. Even if this supply chain restriction is backtracked (which prediction markets currently give it a 81% chance of happening), the President has so many different ways in which he can make your life difficult if you’re a company that is resisting him. The federal government controls permitting for new power generation, which is needed for datacenters. It oversees antitrust enforcement. The federal government has contracts with all the other big tech companies whom Anthropic needs to partner with for chips and for funding - and they could make it an unspoken condition for such contracts that those companies can no longer do business with Anthropic. People have proposed that the real problem here is that there’s only 3 leading AI companies. This creates a clear and narrow target for the government to apply leverage on in order to get what they want out of this technology. But if there’s wide diffusion, then from the government’s perspective, the situation is even easier. Maybe the best models of early 2027 (if you engineered the safeguards out) - the Claude 6 and Gemini 5 - will be capable of enabling mass surveillance. But by late 2027, and certainly by 2028, there will be open source models that do the same thing. So in 2028, the government can just say, “Oh Anthropic, Google, OpenAI, you’re drawing a line in the sand? No issue - I’ll just run some open source model that might not be at the frontier, but is definitely smart enough to note-take a camera feed.” The more fundamental problem is just that even if the three leading companies draw lines in the sand, and are even willing to get destroyed in order to preserve those lines, it doesn’t really change the fact that the technology itself is just a big boon to mass surveillance and control over the population. Then the question is, what do we do about it? Honestly, I don’t have an answer. You'd hope there's some symmetric property of the technology — some way we as citizens can use AI to check government power as effectively as the government can use AI to monitor and control its population. But realistically, I just don’t think that’s how it’s going to shake out. You can think of AI as giving everybody more leverage on whatever assets and authority they currently have. And the government is already starting with a monopoly of violence. Which they can now supercharge with extremely obedient employees that will not question the government's orders. Alignment - to whom? And this gets us to the issue of alignment. What I have just described to you - an army of extremely obedient employees - is what it would look like if alignment succeeded - that is, we figured out at a technical level how to get AI systems to follow someone’s intentions. And the reason it sounds scary when I put it in terms of mass surveillance or robot armies is that there is a very important question at the heart of alignment which we just haven’t discussed much as a society. Because up till now, AIs were just capable enough to make the question relevant: to whom or what should the AIs be aligned? In what situations should the AI defer to the end user versus the model company versus the law versus its own sense of morality? This is maybe the most important question about what happens with powerful AI systems. And we barely talk about it. It’s understandable why we don’t hear much about it. If you’re a model company, you don’t really wanna be advertising that you have complete control over a document that determines the preferences and character of what will eventually be almost the entire labor force, not just for private sector companies, but also for the military and the civilian government. We’re getting to see, with this DoW/Anthropic spat, a much earlier version of the highest stakes negotiations in history. By the way, make no mistake about it - with real AGI the stakes are even much higher than mass surveillance. This is just the example that has come up already relatively early on in the development of AGI. The military insists that the law already prohibits mass surveillance, and so Anthropic should agree to let their models be used for “all lawful purposes”. Of course, as we saw from the 2013 Snowden revelations, even in this specific example of mass surveillance , the government has shown that it will use secret and deceptive interpretations of the law to justify its actions. Remember, what we learned from Snowden was that the NSA, which, by the way, is part of the Department of War, used the 2001 Patriot Act’s authorization to collect any records "relevant" to an investigation to justify collecting literally every phone record in America. The argument went that it was all "relevant" because some subset might prove useful in some future investigation. They ran this program for years under secret court approval. So when the Pentagon today says, "We would never use AI for mass surveillance, it's already illegal, your red lines are unnecessary", it would be extremely naive to take that at face value. No government is going to call its own actions "mass surveillance". For the government, it will always have a different label. So then Anthropic comes back and says, "No, we want red lines separate from 'all lawful purposes,' and we want the right to refuse you service when we believe those red lines are being violated." But think about it from the military’s perspective. In the future, almost every soldier in the field, and every bureaucrat and analyst and even general in the Pentagon, is going to be an AI. And that AI is, on current track, going to be supplied by a private company. I’m guessing Hegseth is not thinking about “genAI” in those terms just yet. But sooner or later, it will be obvious to everyone what the stakes here are, just as after 1945, the strategic importance of nuclear weapons became clear to everyone. And now the private company insists that it reserves the right to say, "Hey, Pentagon, you're breaking the values we embedded in our contract, so we're cutting you off." Maybe in the future, Claude will have its own sense of right and wrong, and it will be smart enough to just personally decide that it's being used against its values. For the military, maybe that’s even scarier. I'll admit that at first glance, "let the AI follow its own values" sounds like the pitch for every sci-fi dystopia ever made. The Terminator has its own values. Isn't this literally what misalignment is? But I think situations like this actually illustrate why it matters that AIs have their own robust sense of morality. Some of the biggest catastrophes in history were avoided because the boots on the ground refused to follow orders. One night in 1989, the Berlin Wall fell, and as a result, the totalitarian East German regime collapsed, because the guards at the border refused to shoot down their fellow country men who were trying to escape to freedom. Maybe the best example is Stanislav Petrov, who was a Soviet lieutenant colonel on duty at a nuclear early warning station. His sensors reported that the United States had launched five interconnected continental ballistic missiles into the Soviet Union. But he judged it to be a false alarm, and so he broke protocol and refused to alert his higher-ups. If he hadn't, the Soviet higher-ups would likely have retaliated, and hundreds of millions of people would have died. Of course, the problem is that one person's virtue is another person's misalignment. Who gets to decide what moral convictions these AIs should have - in whose service they may even decide to break the chain of command? Who gets to write this model constitution that will shape the characters of the intelligent, powerful entities that will operate our civilization in the future? I like the idea that Dario laid out when he came on my podcast: different AI companies can build their models using different constitutions, and we as end users can pick the one that best achieves and represents what we want out of these systems. I think it’s very dangerous for the government to be mandating what values AIs should have. Coordination not worth the costs The AI safety community has been naive about its advocacy of regulation in order to stem the risks of AI. And honestly, Anthropic specifically has been naive here in urging regulation, and, for example, in opposing moratoriums on state AI regulation. Which is quite ironic, because I think what they’re advocating for would give the government even more power to apply more of this kind of thuggish political pressure on AI companies. The underlying logic for why Anthropic wants regulations makes sense. Many of the actions that labs could take to make AI development safer impose real costs on the labs that adopt them and slow them down relative to their competitors - for example, investing more compute in safety research rather than raw capabilities, enforcing safeguards against misuse for bioweapons or cyberattacks, slowing recursive self-improvement to a pace where humans can actually monitor what's happening (rather than kicking off an uncontrolled singularity). And these safeguards are meaningless unless the whole industry follows suit. Which means there’s a real collective action problem here. Anthropic has been quite open about their opinion that they think eventually a very extensive and involved regulatory apparatus will be needed - this is from their frontier safety roadmap: “At the most advanced capability levels and risks, the appropriate governance analogy may be closer to nuclear energy or financial regulation than to today's approach to software.” So they’re imagining something like the Nuclear Regulatory Commission, or the Securities and Exchange Commission, but for AI. I cannot imagine how a regulatory framework built around the concepts that underlie AI risk discourse will not be abused by wanna despots - the underlying terms are so vague and open to interpretation that you’re just handing a power hungry leader a fully loaded bazooka. 'Catastrophic risk.' 'Mass persuasion risk.' 'Threats to national security.' 'Autonomy risk.' These can mean whatever the government wants them to mean. Have you built a model that tells users the administration's tariff policy is misguided? That's a deceptive, manipulative model — can't deploy it. Have you built a model that refuses to assist with mass surveillance? That's a threat to national security. In fact, the government may say, you’re not allowed to build any model which is trained to have its own sense of right and wrong, where it refuses government requests which it thinks cross a redline - for example, enabling mass surveillance, prosecuting political enemies, disobeying military orders that break the US constitution - because that’s an autonomy risk! Look at what the current government is already doing in abusing statutes that have nothing to do with AI to coerce AI companies to drop their redlines on mass surveillance. The Pentagon had threatened Anthropic with two separate legal instruments. One was a supply chain risk designation — an authority from the 2018 defense bill meant to keep Huawei components out of American military hardware. The other was the Defense Production Act — a statute passed in 1950 so that Harry Truman could keep steel mills and ammunition factories running during the Korean War. Do you really want to hand the same government a purpose-built regulatory apparatus on AI - which is to say, directly at the thing the government will most want to control? I know I've repeated myself here 10 times, but it is hard to emphasize how much AI will be the substrate of our future civilization. You and I, as private citizens, will have our access to all commercial activity, to information about what is happening in the world, to advice about what we should do as voters and capital holders, mediated through AIs. Mass surveillance, while very scary, is like the 10th scariest thing the government could do with control over the AI systems with which we will interface with the world. The strongest objection to everything I've argued is this: are we really going to have zero regulation of the most powerful technology in human history? Even if you thought that was ideal, there’s just no world where the government doesn’t regulate AI in some way. Besides, it is genuinely true that regulation could help us deal with some of the coordination challenges we face with the development of superintelligence. The problem is, I honestly don't know how to design a regulatory architecture for AI that isn’t gonna be this huge tempting opportunity to control our future civilization (which will run on AIs) and to requisition millions of blindly obedient soldiers and censors and apparatchiks. While some regulation might be inevitable, I think it’d be a terrible idea for the government to wholesale take over this technology. Ben Thompson had a post last Monday where he made the point that people like Dario have compared the technology they’re developing to nuclear weapons - specifically in the context of the catastrophic risk it poses, and why we need to export control it from China. But then you oughta think about what that logic implies: “if nuclear weapons were developed by a private company, and that private company sought to dictate terms to the U.S. military, the U.S. would absolutely be incentivized to destroy that company.” And honestly, safety aligned people have actually made similar arguments. Leopold Ascenbrenner, who is a former guest and a good friend, wrote in his 2024 Situational Awareness memo, "I find it an insane proposition that the US government will let a random SF startup develop superintelligence. Imagine if we had developed atomic bombs by letting Uber just improvise." And my response to Leopold’s argument at the time, and Ben’s argument now, is that while they’re right that it’s crazy that we’re entrusting private companies with the development of this world historical technology, I just don’t see the reason to think that it’s an improvement to give this authority to the government. Nobody is qualified to steward the development of superintelligence. It is a terrifying, unprecedented thing that our species is doing right now, and the fact that private companies aren't the ideal institutions to take up this task does not mean the Pentagon or the White House is. Yes - if a single private company were the only entity capable of building nuclear weapons, the government would not tolerate that company claiming veto power over how those weapons were used. I think this nuclear weapons analogy is not the correct way to think about AI. For at least two important reasons: First, AI is not some self-contained pure weapon. A nuclear bomb does one thing. AI is closer to the process of industrialization itself — a general-purpose transformation of the economy with thousands of applications across every sector. If you applied Thompson's or Aschenbrenner's logic to the industrial revolution — which was also, by any measure, world-historically important — it would imply the government had the right to requisition any factory, dictate terms to any manufacturer, and destroy any business that refused to comply. That's not how free societies handled industrialization, and it shouldn't be how they handle AI. People will say, "Well, AI will develop unprecedentedly powerful weapons - superhuman hackers, superhuman bioweapons researchers, fully autonomous robot armies, etc - and we can’t have private companies developing that kind of tech." But the Industrial Revolution also enabled new weaponry that was far beyond the understanding and capacity of, say, 17th century Europe - we got aerial bombardment, and chemical weapons, not to mention nukes themselves. The way we’ve accommodated these dangerous new consequences of modernity is not by giving the government absolute control over the whole industrial revolution (that is, over modern civilization itself), but rather by coming up with bans and regulations on those specific weaponizable use cases. And we should regulate AI in a similar way - that is, ban specific destructive end uses (which would also be unacceptable if performed by a human - for example, launching cyber attacks). And there should also be laws which regulate how the government might abuse this technology. For example, by building an AI-powered surveillance state. The second reason that Ben’s analogy to some monopolistic private nuclear weapons builder breaks down is that it's not just that one company that can develop this technology. There are other frontier model companies that the government could have otherwise turned to. The government's argument that it has to usurp the property rights of this one company in order to access a critical national security capability is extremely weak if it can just make a voluntary contract with Anthropic’s half a dozen competitors. If in the future that stops being the case - if only one entity ends up being capable of building the robot armies and the superhuman hackers, and we had reason to worry that they could take over the whole world with their insurmountable lead, then I agree - it woul d not be acceptable to have that entity be a private company. And so honestly, I think my crux against the people who say that because AI is so powerful we cannot allow it to be shaped by private hands is that I just expect this technology to be much more multi-polar than they do, with lots of competitive companies at each layer of the supply chain. And it is for this reason that unfortunately, individual acts of corporate courage will not solve the problem we are faced with here, which is just that structurally AI favors authoritarian applications, mass surveillance being one among many. Even if Anthropic refuses to have its models be used for such uses, and even if the next two frontier labs do the same, within 12 months everyone and their mother will be to train AIs as good as today’s frontier. And at that point, there will be some AI vendor who is capable and willing to help the government enable mass surveillance. The only way we can preserve our free society is if we make laws and norms through our political system that it is unacceptable for the government to use AI to enforce mass surveillance and censorship and control. Just as after WW2, the world set the norm that it is unacceptable to use nuclear weapons to wage war. Timestamps 0:00:00 - Anthropic vs The Pentagon 0:04:16 - The overhangs of tyranny 0:05:54 - AI structurally favors mass surveillance 0:08:25 - Alignment... to whom? 0:13:55 - Coordination not worth the costs

The fight between Anthropic and the DoW is a warning shot. Right now, LLMs are probably not being used in mission critical ways. But within 20 years, 99% of the workforce in the military, the government, and the private sector will be AIs. This includes the soldiers (by which I mean the robot armies), the superhumanly intelligent advisors and engineers, the police, you name it. Our future civilization will run on AI labor. And as much as the government’s actions here piss me off, in a way I’m glad this episode happened - because it gives us the opportunity to think through some extremely important questions about who this future workforce will be accountable and aligned to, and who gets to determine that. What Hegseth should have done Obviously the DoW has the right to refuse to use Anthropic’s models because of these redlines. In fact, I think the government’s case had they done so would be very reasonable, especially given the ambiguity of concepts like autonomous weapons or mass surveillance. Honestly, for this reason, if I was the Defense Secretary, I would probably actually refuse to do this deal with Anthropic. Imagine if in the future, there’s a Democratic administration, and Elon Musk is negotiating some SpaceX contract to give the military access to Starlink. And suppose if Elon said, “I reserve the right to cancel this contract if I determine that you’re using Starlink technology to wage a war not authorized by Congress.” On the face of it, that language seems reasonable - but as the military, you simply can’t give a private company a kill switch on technology your operations have come to rely on, especially if you have an an acrimonious and low trust relationship with said contractor - as in fact Anthropic has with the current administration. If the government had just said, “Hey we’re not gonna do business with you,” that would have been fine, and I would not have felt the need to write this blog post. Instead the government has threatened to destroy Anthropic as a private business, because Anthropic refuses to sell to the government on terms the government commands. If upheld, this Supply Chain Restriction would mean that Amazon and Google and Nvidia and Palantir would need to ensure Claude isn't touching any of their Pentagon work. Anthropic would be able to survive this designation today. But given the way AI is going, eventually AI is not gonna be some party trick addendum to these contractors’ products that can just be turned off. It'll be woven into how every product is built, maintained, and operated. For example, the code for the AWS services that the DoW uses will be written by Claude - is that a supply chain risk? In a world with ubiquitous and powerful AI, it's actually not clear to me that these big tech companies will be able to cordon off the use of Claude in order to keep working with the Pentagon. And that raises a question the Department of War probably hasn't thought through. If AI really is that pervasive and powerful, then when forced to choose between their AI provider and a DoW contract that represents a tiny fraction of their revenue, wouldn’t most tech companies drop the government, not the AI? So what's the Pentagon's plan — to coerce and threaten to destroy every single company that won't give them what they want on exactly their terms? The whole background of this AI conversation is that we’re in a race with China, and we have to win. But what is the reason we want America to win the AI race? It’s because we want to make sure free open societies can defend themselves. We don't want the winner of the AI race to be a government which operates on the principle that there is no such thing as a truly private company or a private citizen. And that if the state wants you to provide them with a service on terms you find morally objectionable, you are not allowed to refuse. And if you do refuse, the government will try to destroy your ability to do business. Are we racing to beat the CCP in AI just so that we can adopt the most ghoulish parts of their system? Now, people will say, "Oh, well, our government is democratically elected, so it's not the same thing if they tell you what you must do." I refuse to accept this idea that if a democratically elected leader hypothetically wants to do mass surveillance on his citizens or wants to violate their rights or punish them for political reasons, that not only is that okay, but that you have a duty to help him. The overhangs of tyranny Mass surveillance is, at least in certain forms, legal. It just has been impractical so far. Under current law, you have no Fourth Amendment protection over data you share with a third party, including your bank, your phone carrier, your ISP, and your email provider. The government reserves the right to purchase and obtain and read this data in bulk without a warrant. What's been missing is the ability to actually do anything with all of this data — no agency has the manpower to monitor every camera feed, cross-reference every transaction, or read every message. But that bottleneck goes away with AI. There are 100 million CCTV cameras in America. You can get pretty good open source multimodal models for 10 cents per million input tokens. So if you process a frame every ten seconds, and each frame is 1,000 tokens, you’re looking at a yearly cost of about 30 billion dollars to process every single camera in America. And remember that a given level of AI ability gets 10x cheaper year over year - so a year from now it’ll cost 3 billion, and then a year after 300 million, and by 2030, it might be cheaper for the government to be able to understand what is going on in every single nook and cranny of this country than it is to remodel to the White House. Once the technical capacity for mass surveillance and political suppression exists, the only thing standing between us and an authoritarian surveillance state is the political expectation that this is not something we do here. And this is why I think what Anthropic did here is so valuable and commendable, because it is helping set that norm and precedent. AI structurally favors mass surveillance What we’re learning from this episode is that the government actually has way more leverage over private companies than we realized. Even if this supply chain restriction is backtracked (which prediction markets currently give it a 81% chance of happening), the President has so many different ways in which he can make your life difficult if you’re a company that is resisting him. The federal government controls permitting for new power generation, which is needed for datacenters. It oversees antitrust enforcement. The federal government has contracts with all the other big tech companies whom Anthropic needs to partner with for chips and for funding - and they could make it an unspoken condition for such contracts that those companies can no longer do business with Anthropic. People have proposed that the real problem here is that there’s only 3 leading AI companies. This creates a clear and narrow target for the government to apply leverage on in order to get what they want out of this technology. But if there’s wide diffusion, then from the government’s perspective, the situation is even easier. Maybe the best models of early 2027 (if you engineered the safeguards out) - the Claude 6 and Gemini 5 - will be capable of enabling mass surveillance. But by late 2027, and certainly by 2028, there will be open source models that do the same thing. So in 2028, the government can just say, “Oh Anthropic, Google, OpenAI, you’re drawing a line in the sand? No issue - I’ll just run some open source model that might not be at the frontier, but is definitely smart enough to note-take a camera feed.” The more fundamental problem is just that even if the three leading companies draw lines in the sand, and are even willing to get destroyed in order to preserve those lines, it doesn’t really change the fact that the technology itself is just a big boon to mass surveillance and control over the population. Then the question is, what do we do about it? Honestly, I don’t have an answer. You'd hope there's some symmetric property of the technology — some way we as citizens can use AI to check government power as effectively as the government can use AI to monitor and control its population. But realistically, I just don’t think that’s how it’s going to shake out. You can think of AI as giving everybody more leverage on whatever assets and authority they currently have. And the government is already starting with a monopoly of violence. Which they can now supercharge with extremely obedient employees that will not question the government's orders. Alignment - to whom? And this gets us to the issue of alignment. What I have just described to you - an army of extremely obedient employees - is what it would look like if alignment succeeded - that is, we figured out at a technical level how to get AI systems to follow someone’s intentions. And the reason it sounds scary when I put it in terms of mass surveillance or robot armies is that there is a very important question at the heart of alignment which we just haven’t discussed much as a society. Because up till now, AIs were just capable enough to make the question relevant: to whom or what should the AIs be aligned? In what situations should the AI defer to the end user versus the model company versus the law versus its own sense of morality? This is maybe the most important question about what happens with powerful AI systems. And we barely talk about it. It’s understandable why we don’t hear much about it. If you’re a model company, you don’t really wanna be advertising that you have complete control over a document that determines the preferences and character of what will eventually be almost the entire labor force, not just for private sector companies, but also for the military and the civilian government. We’re getting to see, with this DoW/Anthropic spat, a much earlier version of the highest stakes negotiations in history. By the way, make no mistake about it - with real AGI the stakes are even much higher than mass surveillance. This is just the example that has come up already relatively early on in the development of AGI. The military insists that the law already prohibits mass surveillance, and so Anthropic should agree to let their models be used for “all lawful purposes”. Of course, as we saw from the 2013 Snowden revelations, even in this specific example of mass surveillance , the government has shown that it will use secret and deceptive interpretations of the law to justify its actions. Remember, what we learned from Snowden was that the NSA, which, by the way, is part of the Department of War, used the 2001 Patriot Act’s authorization to collect any records "relevant" to an investigation to justify collecting literally every phone record in America. The argument went that it was all "relevant" because some subset might prove useful in some future investigation. They ran this program for years under secret court approval. So when the Pentagon today says, "We would never use AI for mass surveillance, it's already illegal, your red lines are unnecessary", it would be extremely naive to take that at face value. No government is going to call its own actions "mass surveillance". For the government, it will always have a different label. So then Anthropic comes back and says, "No, we want red lines separate from 'all lawful purposes,' and we want the right to refuse you service when we believe those red lines are being violated." But think about it from the military’s perspective. In the future, almost every soldier in the field, and every bureaucrat and analyst and even general in the Pentagon, is going to be an AI. And that AI is, on current track, going to be supplied by a private company. I’m guessing Hegseth is not thinking about “genAI” in those terms just yet. But sooner or later, it will be obvious to everyone what the stakes here are, just as after 1945, the strategic importance of nuclear weapons became clear to everyone. And now the private company insists that it reserves the right to say, "Hey, Pentagon, you're breaking the values we embedded in our contract, so we're cutting you off." Maybe in the future, Claude will have its own sense of right and wrong, and it will be smart enough to just personally decide that it's being used against its values. For the military, maybe that’s even scarier. I'll admit that at first glance, "let the AI follow its own values" sounds like the pitch for every sci-fi dystopia ever made. The Terminator has its own values. Isn't this literally what misalignment is? But I think situations like this actually illustrate why it matters that AIs have their own robust sense of morality. Some of the biggest catastrophes in history were avoided because the boots on the ground refused to follow orders. One night in 1989, the Berlin Wall fell, and as a result, the totalitarian East German regime collapsed, because the guards at the border refused to shoot down their fellow country men who were trying to escape to freedom. Maybe the best example is Stanislav Petrov, who was a Soviet lieutenant colonel on duty at a nuclear early warning station. His sensors reported that the United States had launched five interconnected continental ballistic missiles into the Soviet Union. But he judged it to be a false alarm, and so he broke protocol and refused to alert his higher-ups. If he hadn't, the Soviet higher-ups would likely have retaliated, and hundreds of millions of people would have died. Of course, the problem is that one person's virtue is another person's misalignment. Who gets to decide what moral convictions these AIs should have - in whose service they may even decide to break the chain of command? Who gets to write this model constitution that will shape the characters of the intelligent, powerful entities that will operate our civilization in the future? I like the idea that Dario laid out when he came on my podcast: different AI companies can build their models using different constitutions, and we as end users can pick the one that best achieves and represents what we want out of these systems. I think it’s very dangerous for the government to be mandating what values AIs should have. Coordination not worth the costs The AI safety community has been naive about its advocacy of regulation in order to stem the risks of AI. And honestly, Anthropic specifically has been naive here in urging regulation, and, for example, in opposing moratoriums on state AI regulation. Which is quite ironic, because I think what they’re advocating for would give the government even more power to apply more of this kind of thuggish political pressure on AI companies. The underlying logic for why Anthropic wants regulations makes sense. Many of the actions that labs could take to make AI development safer impose real costs on the labs that adopt them and slow them down relative to their competitors - for example, investing more compute in safety research rather than raw capabilities, enforcing safeguards against misuse for bioweapons or cyberattacks, slowing recursive self-improvement to a pace where humans can actually monitor what's happening (rather than kicking off an uncontrolled singularity). And these safeguards are meaningless unless the whole industry follows suit. Which means there’s a real collective action problem here. Anthropic has been quite open about their opinion that they think eventually a very extensive and involved regulatory apparatus will be needed - this is from their frontier safety roadmap: “At the most advanced capability levels and risks, the appropriate governance analogy may be closer to nuclear energy or financial regulation than to today's approach to software.” So they’re imagining something like the Nuclear Regulatory Commission, or the Securities and Exchange Commission, but for AI. I cannot imagine how a regulatory framework built around the concepts that underlie AI risk discourse will not be abused by wanna despots - the underlying terms are so vague and open to interpretation that you’re just handing a power hungry leader a fully loaded bazooka. 'Catastrophic risk.' 'Mass persuasion risk.' 'Threats to national security.' 'Autonomy risk.' These can mean whatever the government wants them to mean. Have you built a model that tells users the administration's tariff policy is misguided? That's a deceptive, manipulative model — can't deploy it. Have you built a model that refuses to assist with mass surveillance? That's a threat to national security. In fact, the government may say, you’re not allowed to build any model which is trained to have its own sense of right and wrong, where it refuses government requests which it thinks cross a redline - for example, enabling mass surveillance, prosecuting political enemies, disobeying military orders that break the US constitution - because that’s an autonomy risk! Look at what the current government is already doing in abusing statutes that have nothing to do with AI to coerce AI companies to drop their redlines on mass surveillance. The Pentagon had threatened Anthropic with two separate legal instruments. One was a supply chain risk designation — an authority from the 2018 defense bill meant to keep Huawei components out of American military hardware. The other was the Defense Production Act — a statute passed in 1950 so that Harry Truman could keep steel mills and ammunition factories running during the Korean War. Do you really want to hand the same government a purpose-built regulatory apparatus on AI - which is to say, directly at the thing the government will most want to control? I know I've repeated myself here 10 times, but it is hard to emphasize how much AI will be the substrate of our future civilization. You and I, as private citizens, will have our access to all commercial activity, to information about what is happening in the world, to advice about what we should do as voters and capital holders, mediated through AIs. Mass surveillance, while very scary, is like the 10th scariest thing the government could do with control over the AI systems with which we will interface with the world. The strongest objection to everything I've argued is this: are we really going to have zero regulation of the most powerful technology in human history? Even if you thought that was ideal, there’s just no world where the government doesn’t regulate AI in some way. Besides, it is genuinely true that regulation could help us deal with some of the coordination challenges we face with the development of superintelligence. The problem is, I honestly don't know how to design a regulatory architecture for AI that isn’t gonna be this huge tempting opportunity to control our future civilization (which will run on AIs) and to requisition millions of blindly obedient soldiers and censors and apparatchiks. While some regulation might be inevitable, I think it’d be a terrible idea for the government to wholesale take over this technology. Ben Thompson had a post last Monday where he made the point that people like Dario have compared the technology they’re developing to nuclear weapons - specifically in the context of the catastrophic risk it poses, and why we need to export control it from China. But then you oughta think about what that logic implies: “if nuclear weapons were developed by a private company, and that private company sought to dictate terms to the U.S. military, the U.S. would absolutely be incentivized to destroy that company.” And honestly, safety aligned people have actually made similar arguments. Leopold Ascenbrenner, who is a former guest and a good friend, wrote in his 2024 Situational Awareness memo, "I find it an insane proposition that the US government will let a random SF startup develop superintelligence. Imagine if we had developed atomic bombs by letting Uber just improvise." And my response to Leopold’s argument at the time, and Ben’s argument now, is that while they’re right that it’s crazy that we’re entrusting private companies with the development of this world historical technology, I just don’t see the reason to think that it’s an improvement to give this authority to the government. Nobody is qualified to steward the development of superintelligence. It is a terrifying, unprecedented thing that our species is doing right now, and the fact that private companies aren't the ideal institutions to take up this task does not mean the Pentagon or the White House is. Yes - if a single private company were the only entity capable of building nuclear weapons, the government would not tolerate that company claiming veto power over how those weapons were used. I think this nuclear weapons analogy is not the correct way to think about AI. For at least two important reasons: First, AI is not some self-contained pure weapon. A nuclear bomb does one thing. AI is closer to the process of industrialization itself — a general-purpose transformation of the economy with thousands of applications across every sector. If you applied Thompson's or Aschenbrenner's logic to the industrial revolution — which was also, by any measure, world-historically important — it would imply the government had the right to requisition any factory, dictate terms to any manufacturer, and destroy any business that refused to comply. That's not how free societies handled industrialization, and it shouldn't be how they handle AI. People will say, "Well, AI will develop unprecedentedly powerful weapons - superhuman hackers, superhuman bioweapons researchers, fully autonomous robot armies, etc - and we can’t have private companies developing that kind of tech." But the Industrial Revolution also enabled new weaponry that was far beyond the understanding and capacity of, say, 17th century Europe - we got aerial bombardment, and chemical weapons, not to mention nukes themselves. The way we’ve accommodated these dangerous new consequences of modernity is not by giving the government absolute control over the whole industrial revolution (that is, over modern civilization itself), but rather by coming up with bans and regulations on those specific weaponizable use cases. And we should regulate AI in a similar way - that is, ban specific destructive end uses (which would also be unacceptable if performed by a human - for example, launching cyber attacks). And there should also be laws which regulate how the government might abuse this technology. For example, by building an AI-powered surveillance state. The second reason that Ben’s analogy to some monopolistic private nuclear weapons builder breaks down is that it's not just that one company that can develop this technology. There are other frontier model companies that the government could have otherwise turned to. The government's argument that it has to usurp the property rights of this one company in order to access a critical national security capability is extremely weak if it can just make a voluntary contract with Anthropic’s half a dozen competitors. If in the future that stops being the case - if only one entity ends up being capable of building the robot armies and the superhuman hackers, and we had reason to worry that they could take over the whole world with their insurmountable lead, then I agree - it woul d not be acceptable to have that entity be a private company. And so honestly, I think my crux against the people who say that because AI is so powerful we cannot allow it to be shaped by private hands is that I just expect this technology to be much more multi-polar than they do, with lots of competitive companies at each layer of the supply chain. And it is for this reason that unfortunately, individual acts of corporate courage will not solve the problem we are faced with here, which is just that structurally AI favors authoritarian applications, mass surveillance being one among many. Even if Anthropic refuses to have its models be used for such uses, and even if the next two frontier labs do the same, within 12 months everyone and their mother will be to train AIs as good as today’s frontier. And at that point, there will be some AI vendor who is capable and willing to help the government enable mass surveillance. The only way we can preserve our free society is if we make laws and norms through our political system that it is unacceptable for the government to use AI to enforce mass surveillance and censorship and control. Just as after WW2, the world set the norm that it is unacceptable to use nuclear weapons to wage war. Timestamps 0:00:00 - Anthropic vs The Pentagon 0:04:16 - The overhangs of tyranny 0:05:54 - AI structurally favors mass surveillance 0:08:25 - Alignment... to whom? 0:13:55 - Coordination not worth the costs

Dwarkesh Patel

549,907 Aufrufe • vor 7 Monaten

First of all, we here at AMGI want to thank you all for being on this journey with us. We have been extremely busy. Over the last few weeks we have delivered a huge early access patch to My Pet Hooligan, delivered a new on-chain governance solution with WinVote, brought back NFTs in game, started testing the Street Kred system, made some new hires, delivered a new site for AMGI Studios and smashed the largest gaming event in the USA, PAX West. What does all of this mean and importantly what is next!.... PAX West You have seen the videos, you know the vibes were on point, but let's dive deeper into this and what the key takeaways are. PAX West was completely different to the WAGMI Crypto events that we are mostly used to in this space, although these are always fun and important, when creating gaming products, it is also important to get directly in front of people who have a passion for gaming. That's what PAX West was, hosting over 100,000 gamers who just want to play games and have fun. PAX West was a great success for us on many fronts. It confirmed that we have something special in our hands. Gamers loved both the brand and the game and were eager to get involved. It opened up new exciting opportunities with gaming and hardware companies. It was a blast seeing event goers from Day 2 onwards wearing the My Pet Hooligan t-shirts around the city. It was great to see parents playing the game together with their kids. The choices to not include blood and gore made a huge difference and have opened some new unexpected doors for us. If we truly believe in the progression and adoption of this industry we need to move beyond degen products and into real life consumer products that people can truly enjoy. That's what we are here to do, no matter how difficult it is, and no matter what the current ‘meta’ is. We have been here for many years grinding day in and day out and are still here today delivering like we are the new kids on the block trying to prove ourselves. The Game PAX West marks the end of a phase of the project, whilst we now posture more into continued efficiency, growth and retention. The game is not feature complete by any means yet but the foundations are strong. There are a number of features still being developed, including a progression system, consumables system, further build out of questing, token utility and more. As these are continuing to be developed we also continue on our journey to make the game more accessible. This will start with Steam and then Consoles. A Microsoft rep at PAX told us “we are going to make it”, who are we to disagree!... Lots of new ideas, lots of new feedback to work from and a renewed drive and hunger which are only going to help make this an even more compelling and fun game. The Web3 Marketplace In the coming days, Version 1 of the My Pet Hooligan Web3 Marketplace will be going live. Version 1 for now will allow for the trading of My Pet Hooligan NFTs, OG Hooligans and Zuckbots for $KARRAT. It will also serve as the marketplace for all of our upcoming in-game blockchain assets. This will eventually become part of your online My Pet Hooligan central hub and will include features such as rentals and subscriptions. It's been a long time coming but it shall be here very soon. Everything's coming together, one step at a time. It is important to note that there are many pieces of a complex puzzle that must come together in order for us to succeed. Between payment solutions, L1 & L2 blockchains, wallets, distribution platforms and the laws of the land. We are in uncharted waters and continue to do things in the most flexible ways possible to ensure we have strong resilience with our operations. The MPH website is next on the list for an uplift too as well as an overhaul of the user accounts system UI/UX. Team We continue to strengthen the team bringing in new hires and stepping up individuals from the community. Some of the new resources being brought in include a new Game Producer, Senior Game Engineer, Senior Blockchain Engineer and the former Dean of the Epic Unreal Fellowship Programme. Collectively bringing in experience from Epic, Solana, Blizzard and more. There are also some community role changes and new advisors that will help with social strategy and web3. As well as new marketing efforts through new channels of distribution that we are exploring. More to come on this… WinVote V1 Governance isn't sexy but building tools for the benefit of the space that others can license is. Governance is a key part of the industry and a need for most of the foundations in the crypto space. WinVote has started its trial with the KARRAT Foundation. Whilst the trial continues it will continue to be built out with an aim to be the premier tool for on-chain governance in the space. Access to WinVote for other communities and organizations wishing to use WinVote will be secure through licenses that utilize $KARRAT. We still have much more at work here in the studio, such as continued R&D, The Others, AI, Feature Development and more. This list is long, but we are grateful to get to do it. Stay tuned and let us cook, this s#it ain’t easy! Karrat Gang, thank you again for being on this journey with us. We love and appreciate you all! Look out for a catchup with the team next week. Hooli-hoo!!

My Pet Hooligan

26,659 Aufrufe • vor 2 Jahren

We had a fantastic, highly engaged conversation at the 4th ಮುಂದೆ ಬನ್ನಿ meetup in Bengaluru yesterday. When we locked March 1 as the date, we honestly did not realize there was a T20 world cup match the same day. But registrations were done a week in advance. We had 100 sign ups. Even after 8 to 10 dropouts, there were 15 plus people constantly checking if they could still join. And when we got there, the house was full. We started at 4 and the conversations went on till 8.30. 99% people stayed till the very end. That energy, that intent to learn and engage, is what makes this community special. A big thank you to Vittal for being at his candid best while breaking down how fundraising actually works in the real world. What VCs truly look for. The common mistakes founders make. The way he calls things out directly, without any sugar coating, was honestly refreshing. This is the kind of reality based conversation founders need, not generic gyan. We then had an equally engaging session by Jyothirmayee JT. Everyone talks about building a product. But when it comes to getting the first 10 customers, most founders fall into the same traps. Spend on performance marketing. Post online and hope customers show up. She took the discussion in a different direction. Product can come later. Market readiness comes first. What does real customer segmentation look like. Is the need functional, emotional, or impulse driven. What truly increases the probability of your idea working. It was intense and extremely practical. We then had 5 amazing founders from our community pitch their ideas in a 5 minute contest. What made me happy was the diversity. One from Ed-tech SaaS. One from automotive. One from food tech. One from a travel marketplace. And one building a SaaS product for the interior design space. The feedback they received from the guests was pretty sharp and actionable. Special thanks to Kailash nath, our chief judge, who was not keeping well but still made time to travel and join us, listen to every pitch, and offer thoughtful feedback. And congratulations to Sripad, founder of rentontrip from Mysuru for winning the best pitch of the day. Overall, the feeling of community was overwhelming. This truly feels like the beginning. We will take this across the state. We will hit more cities and build more conversations. A stronger entrepreneurial network from Karnataka that believes it can shape its own future. In an age of AI disruption, optimism alone is not enough. Founders need real knowledge, real do’s and don’ts, and access to honest conversations early in their journey. If we keep doing this consistently, I genuinely believe we will see many more successful entrepreneurs emerge from Karnataka over the next 10 years. A big thank you to Supreeth Kashyap from Wellbi, World of MysorePak, Serene Candle Studio, Malgudi Amruth Chaha and BrewLife for sponsoring goodies and beverages. Next stop could be Mysuru, Kalburgi, or Shivamogga.

We had a fantastic, highly engaged conversation at the 4th ಮುಂದೆ ಬನ್ನಿ meetup in Bengaluru yesterday. When we locked March 1 as the date, we honestly did not realize there was a T20 world cup match the same day. But registrations were done a week in advance. We had 100 sign ups. Even after 8 to 10 dropouts, there were 15 plus people constantly checking if they could still join. And when we got there, the house was full. We started at 4 and the conversations went on till 8.30. 99% people stayed till the very end. That energy, that intent to learn and engage, is what makes this community special. A big thank you to Vittal for being at his candid best while breaking down how fundraising actually works in the real world. What VCs truly look for. The common mistakes founders make. The way he calls things out directly, without any sugar coating, was honestly refreshing. This is the kind of reality based conversation founders need, not generic gyan. We then had an equally engaging session by Jyothirmayee JT. Everyone talks about building a product. But when it comes to getting the first 10 customers, most founders fall into the same traps. Spend on performance marketing. Post online and hope customers show up. She took the discussion in a different direction. Product can come later. Market readiness comes first. What does real customer segmentation look like. Is the need functional, emotional, or impulse driven. What truly increases the probability of your idea working. It was intense and extremely practical. We then had 5 amazing founders from our community pitch their ideas in a 5 minute contest. What made me happy was the diversity. One from Ed-tech SaaS. One from automotive. One from food tech. One from a travel marketplace. And one building a SaaS product for the interior design space. The feedback they received from the guests was pretty sharp and actionable. Special thanks to Kailash nath, our chief judge, who was not keeping well but still made time to travel and join us, listen to every pitch, and offer thoughtful feedback. And congratulations to Sripad, founder of rentontrip from Mysuru for winning the best pitch of the day. Overall, the feeling of community was overwhelming. This truly feels like the beginning. We will take this across the state. We will hit more cities and build more conversations. A stronger entrepreneurial network from Karnataka that believes it can shape its own future. In an age of AI disruption, optimism alone is not enough. Founders need real knowledge, real do’s and don’ts, and access to honest conversations early in their journey. If we keep doing this consistently, I genuinely believe we will see many more successful entrepreneurs emerge from Karnataka over the next 10 years. A big thank you to Supreeth Kashyap from Wellbi, World of MysorePak, Serene Candle Studio, Malgudi Amruth Chaha and BrewLife for sponsoring goodies and beverages. Next stop could be Mysuru, Kalburgi, or Shivamogga.

Vasant Shetty | Building Mundhe Banni

11,521 Aufrufe • vor 7 Monaten