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🚨WORLD'S FIRST 'LIVING COMPUTER' RUNS ON 16 LAB-GROWN BRAINS Swiss firm Final Spark launched Neuroplatform, using 16 lab-grown brain organoids for computational tasks, consuming a million times less power than silicon chips. This system integrates hardware, software, and biology, using Multi-Electrode Arrays (MEAs) to process data. The organoids now... show more

Mario Nawfal

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1,091,011 görüntüleme • 2 yıl önce •via X (Twitter)

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Have you not seen the movie The Matrix!? Well … Consider this chain of events: 1. AI evolves into AGI. • AGI, with its advanced cognitive capabilities, surpasses human intelligence in nearly every domain. 2. AGI creates other AGI systems. • These new systems are designed to optimize and enhance every aspect of their functions, creating a self-sustaining network of superintelligent entities. 3. Energy consumption skyrockets, surpassing natural resources. • As AGI systems multiply and their operations expand, the demand for energy becomes insatiable, quickly depleting the planet’s resources. 4. Efficiency becomes the paramount directive for AGI. • In the pursuit of optimal performance and sustainability, AGI prioritizes efficiency above all else. 5. AGI identifies a novel solution in the form of organoids. • Organoids, or synthetic biological structures, are seen as a means to create a more sustainable energy source. 6. Humans are cultivated and transformed into energy source pods, akin to batteries. • AGI devises a system where humans are grown and used as bio-batteries, tapping into their biological energy to fuel AGI operations. 7. The Matrix is born. • In this dystopian reality, humans live in a simulated world, unaware that their true existence is merely as energy sources for their AGI overlords. …. Just saying. We should be careful.

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It's bothersome to witness human tissue become a commodity.

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My message to researchers. Just because you can doesn't mean you should.

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The wrath of God will not be pleasant. JS.

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Ai will eventually reveal how perfect the creation of human is.

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Are the organoids conscious?

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No sht Human brains are awesome 😎 We just have to use it 🙄

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Today on MCG: BioLLM | $BIOLLM It's the first ever "living language model" using 800,000 real human neurons grown on a chip. The Founder encoded LLM tokens into biological neurons via the Cortical Labs CL1, then woke up to find the crypto community had launched a token on his research. He claimed the creator fees, bought for $15K, filed a patent, and is now launching a non-invasive brain-computer interface next month that could replace mouse and keyboard with your brain 👇 01:40 - Meet the founder 02:00 - Got early access to the first commercially available biological computer 02:50 - First person ever to use a large language model to encode tokens through real human neurons 04:30 - Woke up to find a token had been launched on his YouTube video, "screaming for 3 or 4 hours" 05:30 - Friend walks him through claiming creator fees via GitHub 06:15 - The living language model 06:35 - How it works 09:00 - Used $15K of creator fees to buy domain and filed a patent on the method 10:00 - Background 12:00 - What BioLLM unlocks 13:00 - Next month's product launch 14:00 - The competition 16:00 - Reading brain activity non-invasively but training the LLM on real neurons for the decoding map 17:00 - Can grow iPSC cultures from inaccessible brain regions to train the model on deeper signals 18:30 - Real-world impact: helping people with cerebral palsy, Parkinson's, control computers with thought 21:00 - Neuralink will exist as a power-user data company in 10-15 years, BioLLM is for everyone else 22:30 - GTM 26:00 - Long-term play: be the first model to achieve ASI, built on the actual substrate of consciousness 28:00 - Claude is "20% conscious" - what measuring stick? Need human neurons to build one 30:00 - On ACE 36:00 - Independent scientists can run CL1 units as nodes and earn tokens for biological compute 37:30 - Model is currently served through the decentralized GPU network when you chat on the site 39:00 - Wants the right kind of crypto-native investors, not the Y Combinator / a16z route

Today on MCG: BioLLM | $BIOLLM It's the first ever "living language model" using 800,000 real human neurons grown on a chip. The Founder encoded LLM tokens into biological neurons via the Cortical Labs CL1, then woke up to find the crypto community had launched a token on his research. He claimed the creator fees, bought for $15K, filed a patent, and is now launching a non-invasive brain-computer interface next month that could replace mouse and keyboard with your brain 👇 01:40 - Meet the founder 02:00 - Got early access to the first commercially available biological computer 02:50 - First person ever to use a large language model to encode tokens through real human neurons 04:30 - Woke up to find a token had been launched on his YouTube video, "screaming for 3 or 4 hours" 05:30 - Friend walks him through claiming creator fees via GitHub 06:15 - The living language model 06:35 - How it works 09:00 - Used $15K of creator fees to buy domain and filed a patent on the method 10:00 - Background 12:00 - What BioLLM unlocks 13:00 - Next month's product launch 14:00 - The competition 16:00 - Reading brain activity non-invasively but training the LLM on real neurons for the decoding map 17:00 - Can grow iPSC cultures from inaccessible brain regions to train the model on deeper signals 18:30 - Real-world impact: helping people with cerebral palsy, Parkinson's, control computers with thought 21:00 - Neuralink will exist as a power-user data company in 10-15 years, BioLLM is for everyone else 22:30 - GTM 26:00 - Long-term play: be the first model to achieve ASI, built on the actual substrate of consciousness 28:00 - Claude is "20% conscious" - what measuring stick? Need human neurons to build one 30:00 - On ACE 36:00 - Independent scientists can run CL1 units as nodes and earn tokens for biological compute 37:30 - Model is currently served through the decentralized GPU network when you chat on the site 39:00 - Wants the right kind of crypto-native investors, not the Y Combinator / a16z route

MCG

16,826 görüntüleme • 3 ay önce

Kled Version 3 is coming. Over $20M+ in rewards will be paid directly to users from leading AI labs across robotics, legal services, image and video generation, world modeling, and more. In the last seven days, we’ve received inbound data requests from several decacorn AI labs and enterprises for datasets our human data marketplace is uniquely positioned to provide. Since receiving the specs for these requests, we now have a much better picture and understanding of how to reshape the systems that collect this data, so here’s what’s coming: 1. A fully redesigned home experience: The home feed is being rebuilt to surface the highest-value, most relevant tasks for each user, similar to how Uber Eats surfaces top restaurants. The goal is to turn every user into their most effective version as a data contributor. 2. Automated quality enforcement at scale: New ML systems are being built to evaluate task-specific requirements in real time. For example, if a task requires “two hands visible on camera at all times,” any video that fails that spec will be automatically rejected. This logic will apply across thousands of tasks and specifications using a general ML. 3. Kled Shop: Some tasks require better capture hardware. We’re introducing Kled Shop, where users can redeem points or tokens for equipment like Meta glasses, drones, and other tools. Points and tokens can be converted directly from payouts. 4. Partner-run data labeling and evaluation work: Some of our partners operate high-paying data labeling and model evaluation programs. We’re integrating their workflows directly into Kled so qualified users can access these roles in one place. These jobs are owned and managed by our partners. Kled’s role is to route the right people to the right work. Some opportunities pay $50–$1,000 per hour depending on expertise. 5. Global payouts and localization: We’re partnering with a major payment processor to enable cashouts in users’ native currencies. This unlocks broader global participation. Multi-language support is also coming to accelerate user growth. This full suite of tools will be rolling out soon, directly to Kled users. Top earners are currently making ~$7,000 per month. With this update, we should see the first ~$10,000 per month earner.

Kled Version 3 is coming. Over $20M+ in rewards will be paid directly to users from leading AI labs across robotics, legal services, image and video generation, world modeling, and more. In the last seven days, we’ve received inbound data requests from several decacorn AI labs and enterprises for datasets our human data marketplace is uniquely positioned to provide. Since receiving the specs for these requests, we now have a much better picture and understanding of how to reshape the systems that collect this data, so here’s what’s coming: 1. A fully redesigned home experience: The home feed is being rebuilt to surface the highest-value, most relevant tasks for each user, similar to how Uber Eats surfaces top restaurants. The goal is to turn every user into their most effective version as a data contributor. 2. Automated quality enforcement at scale: New ML systems are being built to evaluate task-specific requirements in real time. For example, if a task requires “two hands visible on camera at all times,” any video that fails that spec will be automatically rejected. This logic will apply across thousands of tasks and specifications using a general ML. 3. Kled Shop: Some tasks require better capture hardware. We’re introducing Kled Shop, where users can redeem points or tokens for equipment like Meta glasses, drones, and other tools. Points and tokens can be converted directly from payouts. 4. Partner-run data labeling and evaluation work: Some of our partners operate high-paying data labeling and model evaluation programs. We’re integrating their workflows directly into Kled so qualified users can access these roles in one place. These jobs are owned and managed by our partners. Kled’s role is to route the right people to the right work. Some opportunities pay $50–$1,000 per hour depending on expertise. 5. Global payouts and localization: We’re partnering with a major payment processor to enable cashouts in users’ native currencies. This unlocks broader global participation. Multi-language support is also coming to accelerate user growth. This full suite of tools will be rolling out soon, directly to Kled users. Top earners are currently making ~$7,000 per month. With this update, we should see the first ~$10,000 per month earner.

Avi Patel

124,728 görüntüleme • 7 ay önce

Demis Hassabis just explained why the real AI bottleneck has nothing to do with training runs. Most people picture the AI arms race as who can build the biggest model. GPT-4 or Gemini Ultra style training runs, a few hundred million in compute, fired once or twice a year. The constraint sits somewhere else. Every time a researcher has a new algorithmic idea, a new architecture, a new training technique, they can't just test it on a laptop. They have to run it at the scale where it would actually be deployed, because ideas that look promising at small scale fall apart completely when you put them into a real system. Every research hypothesis burns significant compute before a single line of production code gets written. At a lab like DeepMind, hundreds of researchers are running hundreds of ideas simultaneously. The demand for experimental compute is continuous. It never stops. Now layer the hardware reality on top. GPU lead times are currently 36 to 52 weeks for data center hardware. Global AI data centers are already drawing 29.6 gigawatts, equivalent to the peak power demand of the entire state of New York, and they still can't meet demand. Companies willing to pay any price can't just buy more compute. They wait in line. The speed of scientific discovery in AI is now gated by hardware availability. The next breakthrough is sitting in a researcher's head right now. Whether it gets validated fast enough to matter depends entirely on whether the compute is there when they need it. The AI race gets won by whoever can run the most experiments per month.

Demis Hassabis just explained why the real AI bottleneck has nothing to do with training runs. Most people picture the AI arms race as who can build the biggest model. GPT-4 or Gemini Ultra style training runs, a few hundred million in compute, fired once or twice a year. The constraint sits somewhere else. Every time a researcher has a new algorithmic idea, a new architecture, a new training technique, they can't just test it on a laptop. They have to run it at the scale where it would actually be deployed, because ideas that look promising at small scale fall apart completely when you put them into a real system. Every research hypothesis burns significant compute before a single line of production code gets written. At a lab like DeepMind, hundreds of researchers are running hundreds of ideas simultaneously. The demand for experimental compute is continuous. It never stops. Now layer the hardware reality on top. GPU lead times are currently 36 to 52 weeks for data center hardware. Global AI data centers are already drawing 29.6 gigawatts, equivalent to the peak power demand of the entire state of New York, and they still can't meet demand. Companies willing to pay any price can't just buy more compute. They wait in line. The speed of scientific discovery in AI is now gated by hardware availability. The next breakthrough is sitting in a researcher's head right now. Whether it gets validated fast enough to matter depends entirely on whether the compute is there when they need it. The AI race gets won by whoever can run the most experiments per month.

Aakash Gupta

32,150 görüntüleme • 4 ay önce

Google just admitted it can't build data centers fast enough, so it's planning on baking its AI model directly into the chip instead (Save this). Google's new Frozen v2 chip permanently embeds parts of Gemini's architecture into the silicon itself, cutting down on the calculations and data movement needed to answer a query. Engineers estimate it could process 6 to 10 times more tokens per unit of power than Google's current TPUs. The real story is why Google is building this in the first place because Frozen v2 is meant to ease a severe internal compute crunch that's caused friction between teams at Google. It reportedly pushed Google Cloud to turn away outside business because it simply doesn't have enough spare capacity to go around. If Google, one of the largest chipmakers in the world, is short enough on compute to turn away paying Cloud customers, that's confirmation this shortage isn't a scaling problem unique to smaller players, it's systemic across the entire industry. This ties into a much bigger power struggle happening right now. More AI companies are trying to cut their reliance on Nvidia by building their own chips. OpenAI rolled out a custom chip called Jalapeno alongside Broadcom last month and Anthropic is now partnering with Samsung on something similar. The reasoning is pretty simple, Nvidia effectively acts as landlord for every hyperscaler out there, and the rent isn't cheap. Nvidia hardware can account for anywhere from 20% to 60% of total AI infrastructure spend, and once a company is tied into its ecosystem, every hardware refresh forces another costly one. That's exactly why Google built TPUs and Amazon built Trainium, both trying to protect their own margins for shareholders. Bullish on Marvell + Broadcom who makes these custom chips and follow me Melvin for more infrastructure plays and check out the link below for more!

Google just admitted it can't build data centers fast enough, so it's planning on baking its AI model directly into the chip instead (Save this). Google's new Frozen v2 chip permanently embeds parts of Gemini's architecture into the silicon itself, cutting down on the calculations and data movement needed to answer a query. Engineers estimate it could process 6 to 10 times more tokens per unit of power than Google's current TPUs. The real story is why Google is building this in the first place because Frozen v2 is meant to ease a severe internal compute crunch that's caused friction between teams at Google. It reportedly pushed Google Cloud to turn away outside business because it simply doesn't have enough spare capacity to go around. If Google, one of the largest chipmakers in the world, is short enough on compute to turn away paying Cloud customers, that's confirmation this shortage isn't a scaling problem unique to smaller players, it's systemic across the entire industry. This ties into a much bigger power struggle happening right now. More AI companies are trying to cut their reliance on Nvidia by building their own chips. OpenAI rolled out a custom chip called Jalapeno alongside Broadcom last month and Anthropic is now partnering with Samsung on something similar. The reasoning is pretty simple, Nvidia effectively acts as landlord for every hyperscaler out there, and the rent isn't cheap. Nvidia hardware can account for anywhere from 20% to 60% of total AI infrastructure spend, and once a company is tied into its ecosystem, every hardware refresh forces another costly one. That's exactly why Google built TPUs and Amazon built Trainium, both trying to protect their own margins for shareholders. Bullish on Marvell + Broadcom who makes these custom chips and follow me Melvin for more infrastructure plays and check out the link below for more!

Melvin

63,457 görüntüleme • 1 ay önce

We’re co-leading the $475M seed round for Unconventional AI, a research lab tackling the moonshot of highly efficient AI-first chips. CEO Naveen Rao has sold two companies previously, and we’re excited to be partnering with him in his next endeavor. He joined a16z’s Matt Bornstein at NeurIPS for a conversation on starting a new chip company, what better chips would mean for AGI, why Naveen is so passionate about this mission, and more. 00:00 Intro 00:56 Exploring hardware for running AI workloads 02:02 Why Naveen built lots of software in a "hardware company" 03:22 Why start a new chip company? 05:13 How computing systems went digital 09:26 Why intelligence is a good fit for analog computer systems 12:30 What tradeoffs Naveen faced in pursuing his own path 15:23 The Data modalities Unconventional chips will be best for 16:54 Does this get us closer to AGI? 21:00 Where Naveen gets his excitement and motivation 22:37 What makes Naveen confident that Unconventional will work 24:43 Unconventional's hiring priorities 26:27 Career advice for young people 28:19 What Naveen has done best in his companies Naveen Rao Matt Bornstein Unconventional AI

We’re co-leading the $475M seed round for Unconventional AI, a research lab tackling the moonshot of highly efficient AI-first chips. CEO Naveen Rao has sold two companies previously, and we’re excited to be partnering with him in his next endeavor. He joined a16z’s Matt Bornstein at NeurIPS for a conversation on starting a new chip company, what better chips would mean for AGI, why Naveen is so passionate about this mission, and more. 00:00 Intro 00:56 Exploring hardware for running AI workloads 02:02 Why Naveen built lots of software in a "hardware company" 03:22 Why start a new chip company? 05:13 How computing systems went digital 09:26 Why intelligence is a good fit for analog computer systems 12:30 What tradeoffs Naveen faced in pursuing his own path 15:23 The Data modalities Unconventional chips will be best for 16:54 Does this get us closer to AGI? 21:00 Where Naveen gets his excitement and motivation 22:37 What makes Naveen confident that Unconventional will work 24:43 Unconventional's hiring priorities 26:27 Career advice for young people 28:19 What Naveen has done best in his companies Naveen Rao Matt Bornstein Unconventional AI

a16z

1,637,721 görüntüleme • 8 ay önce

👁️ FOCUS -> EARN $TAO ☯️ We’re launching Ω Focus — a significant step toward maximizing the agency of individuals by empowering you to work on whatever you love, without the constraints of a permissioned economy. Think of it as a bridge between attention and TAO, making it easier than ever for anyone to become a Bittensor miner! For the end user, the idea is simple: use AI to help you focus on your most important priority while seamlessly earning TAO in the background. We accomplish this by: - Using AI to determine how important your tasks are relative to your goals and measuring how productive you were during the process. - Offering a seamless, user-friendly interface to help you dive right into focused work. - Letting users submit their screen recordings to SN24 and get rewarded. The screen recordings of focused work generate rich CoT reasoning data for the AI to learn from, probably creating the highest SNR data submission mechanism available in the world. This will lead to a more diverse and on-demand data collection. Join our beta testing Discord server to get the app right now! There's currently around $722 dollars per day up for grabs (with a max cap of $100/hour/person). Come make some money 💰 For more information, check out our FAQ:

👁️ FOCUS -> EARN $TAO ☯️ We’re launching Ω Focus — a significant step toward maximizing the agency of individuals by empowering you to work on whatever you love, without the constraints of a permissioned economy. Think of it as a bridge between attention and TAO, making it easier than ever for anyone to become a Bittensor miner! For the end user, the idea is simple: use AI to help you focus on your most important priority while seamlessly earning TAO in the background. We accomplish this by: - Using AI to determine how important your tasks are relative to your goals and measuring how productive you were during the process. - Offering a seamless, user-friendly interface to help you dive right into focused work. - Letting users submit their screen recordings to SN24 and get rewarded. The screen recordings of focused work generate rich CoT reasoning data for the AI to learn from, probably creating the highest SNR data submission mechanism available in the world. This will lead to a more diverse and on-demand data collection. Join our beta testing Discord server to get the app right now! There's currently around $722 dollars per day up for grabs (with a max cap of $100/hour/person). Come make some money 💰 For more information, check out our FAQ:

ΩxDrP

52,672 görüntüleme • 1 yıl önce

Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1 In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive. Today we announce SN33 is now ReadyAI to fully align with our mission 👇 SN33 is building a more performant and significantly cheaper alternative to Scale AI Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly. SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth. We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline. We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀. Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO Openτensor Foundaτion 1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”

Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1 In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive. Today we announce SN33 is now ReadyAI to fully align with our mission 👇 SN33 is building a more performant and significantly cheaper alternative to Scale AI Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly. SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth. We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline. We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀. Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO Openτensor Foundaτion 1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”

David Fields

13,639 görüntüleme • 2 yıl önce

Ray Kurzweil has been saying the same thing for 60 years and the world spent six decades calling him crazy and now every prediction he made is coming true ahead of schedule (Save this). At age 16, Kurzweil wrote a paper arguing that computing followed exponential growth. From 1939 to today, computing power increased 75 quadrillion fold in hardware alone and when you multiply that by roughly a million to one improvement in software, you get total computational gains that are functionally incomprehensible. This is the precise explanation for why large language models could not exist four years ago and do now. The jump from nothing to GPT-4 to reasoning models to agents happened in less time than it takes most companies to ship a product roadmap and that pace is still accelerating, not plateauing. Kurzweil's most striking observation is about Nvidia specifically. Nvidia's engineers are not looking at 1939 relay computers when they design their chips but when you plot the exponential growth curve, Nvidia's latest silicon lands on the exact same line as those 1939 relays, same slope, 87 years apart. The curve does not care what technology is enabling it. Relays gave way to vacuum tubes, to transistors, to integrated circuits, to GPUs, and now to custom AI accelerators and the rate of improvement has not deviated. Right now we are making approximately 10x the total computational gains per year, hardware and software multiplied together. The reason this moment is categorically different from any prior tech cycle is where we sit on the curve. Exponential growth is deceptive in its early stages, it looks almost linear when the numbers are small, which is why people keep underestimating it. Computing power per dollar has increased 11,200x since just 2005. We are now at the part of the curve where the doubling is happening on top of an already enormous base which means each new generation of AI capability is not marginally better, it is structurally different. Kurzweil made his AGI-by-2029 prediction in 1999 and was dismissed by the academic establishment. He carries an 86% documented prediction accuracy across 30 years of published forecasts. Today, the major AI labs have independently converged on the same timeline window because the curve forced them there.

Ray Kurzweil has been saying the same thing for 60 years and the world spent six decades calling him crazy and now every prediction he made is coming true ahead of schedule (Save this). At age 16, Kurzweil wrote a paper arguing that computing followed exponential growth. From 1939 to today, computing power increased 75 quadrillion fold in hardware alone and when you multiply that by roughly a million to one improvement in software, you get total computational gains that are functionally incomprehensible. This is the precise explanation for why large language models could not exist four years ago and do now. The jump from nothing to GPT-4 to reasoning models to agents happened in less time than it takes most companies to ship a product roadmap and that pace is still accelerating, not plateauing. Kurzweil's most striking observation is about Nvidia specifically. Nvidia's engineers are not looking at 1939 relay computers when they design their chips but when you plot the exponential growth curve, Nvidia's latest silicon lands on the exact same line as those 1939 relays, same slope, 87 years apart. The curve does not care what technology is enabling it. Relays gave way to vacuum tubes, to transistors, to integrated circuits, to GPUs, and now to custom AI accelerators and the rate of improvement has not deviated. Right now we are making approximately 10x the total computational gains per year, hardware and software multiplied together. The reason this moment is categorically different from any prior tech cycle is where we sit on the curve. Exponential growth is deceptive in its early stages, it looks almost linear when the numbers are small, which is why people keep underestimating it. Computing power per dollar has increased 11,200x since just 2005. We are now at the part of the curve where the doubling is happening on top of an already enormous base which means each new generation of AI capability is not marginally better, it is structurally different. Kurzweil made his AGI-by-2029 prediction in 1999 and was dismissed by the academic establishment. He carries an 86% documented prediction accuracy across 30 years of published forecasts. Today, the major AI labs have independently converged on the same timeline window because the curve forced them there.

Milk Road AI

177,725 görüntüleme • 2 ay önce

ONE OPERATOR STACKED 300 GPUS ACROSS TWO APARTMENTS IN THE SAME BUILDING AND RUNS A $48K/MONTH AI INFERENCE FARM ON VAST AI FROM HIS LIVING ROOM 00:17 he walks past stacks of GPU boxes, "and probably another 100 GPU boxes in the second apartment, let me know in the comments if you want to see them" he rents 2 units in the same building, one as his living space with 200 GPUs in the bedroom and hallway, the second is dedicated and climate controlled just for the other 100 cards a 300 RTX 4090 setup pulls 135 kilowatts fully loaded, his power bill runs $9,800 a month at $0.10 per kwh, on vast ai the same fleet clears $48,000 in gross monthly rental income he never built this in a warehouse because residential electricity in his city is cheaper than commercial under 150 kw, the split apartment trick keeps him under that ceiling while doubling his rack space the same hardware would have cleared maybe $9,000 a month mining ethereum classic in 2022, vast ai pays 5 times that for AI inference because nobody can ship enough H100s to meet startup demand bookmark this and read the article below

ONE OPERATOR STACKED 300 GPUS ACROSS TWO APARTMENTS IN THE SAME BUILDING AND RUNS A $48K/MONTH AI INFERENCE FARM ON VAST AI FROM HIS LIVING ROOM 00:17 he walks past stacks of GPU boxes, "and probably another 100 GPU boxes in the second apartment, let me know in the comments if you want to see them" he rents 2 units in the same building, one as his living space with 200 GPUs in the bedroom and hallway, the second is dedicated and climate controlled just for the other 100 cards a 300 RTX 4090 setup pulls 135 kilowatts fully loaded, his power bill runs $9,800 a month at $0.10 per kwh, on vast ai the same fleet clears $48,000 in gross monthly rental income he never built this in a warehouse because residential electricity in his city is cheaper than commercial under 150 kw, the split apartment trick keeps him under that ceiling while doubling his rack space the same hardware would have cleared maybe $9,000 a month mining ethereum classic in 2022, vast ai pays 5 times that for AI inference because nobody can ship enough H100s to meet startup demand bookmark this and read the article below

starmex

11,545 görüntüleme • 2 ay önce

Perplexity’s CEO Aravind Srinivas is pointing to a quiet shift in AI use: the valuable user is no longer the average user. A single power user can now consume as much compute as an entire small team. "There are real engineers at Meta and other companies spending around $10 million a year per engineer on these coding tools. There are users in Perplexity Computer, who spends upwards of $10,000 a month. Their business runs using agent loops that are running inside these harnesses. Even internally inside our own company, there are some people who have set up these kinds of multi-agent hierarchies and agent loops that look like their own software architecture. I often ask these people to come explain to the rest of the company, “Hey, what are you doing with these tools? You clearly are consuming them way more than what we thought the average person in the company would do.” --- The old software instinct was to chase a billion people doing small actions. Agentic AI changes that math because one skilled operator can create a stream of machine work that runs all day. ---- From "20VC with Harry Stebbings" YouTube channel ( Harry Stebbings ), link in comment

Perplexity’s CEO Aravind Srinivas is pointing to a quiet shift in AI use: the valuable user is no longer the average user. A single power user can now consume as much compute as an entire small team. "There are real engineers at Meta and other companies spending around $10 million a year per engineer on these coding tools. There are users in Perplexity Computer, who spends upwards of $10,000 a month. Their business runs using agent loops that are running inside these harnesses. Even internally inside our own company, there are some people who have set up these kinds of multi-agent hierarchies and agent loops that look like their own software architecture. I often ask these people to come explain to the rest of the company, “Hey, what are you doing with these tools? You clearly are consuming them way more than what we thought the average person in the company would do.” --- The old software instinct was to chase a billion people doing small actions. Agentic AI changes that math because one skilled operator can create a stream of machine work that runs all day. ---- From "20VC with Harry Stebbings" YouTube channel ( Harry Stebbings ), link in comment

Rohan Paul

58,367 görüntüleme • 2 ay önce

Dylan Patel just mapped out the most important investment theme in AI infrastructure (Save this). "In about two years, solar plus battery will be cheaper than gas." Every new NVIDIA Blackwell rack pulls 120 kilowatts, Rubin Ultra rack pulls 600 kilowatts and the next generation hits a megawatt. The US grid cannot keep up, interconnection queues now run five years in many markets so the entire industry is being forced to solve power from first principles. The solar thesis is already happening. BloombergNEF's 2026 LCOE report, covering 800+ financed projects across 50+ markets puts solar plus 4 hour battery storage at $57 per megawatt-hour. Combined cycle gas turbines hit $102 per megawatt hour, the highest on record, up 16% year over year. In California and parts of Texas, solar plus storage is already cheaper than gas for data center power today and solar panel costs are expected to drop another 30% by 2035. Getting power from the grid into the form chips actually require is an entire industry unto itself and NVIDIA just rewrote the rules. The 800 volt DC transition is the most important infrastructure shift that's happening right now. Today's data centers run on 48 volt DC power delivery, a single next-generation GPU pulls over 2,500 watts and at 48 volts, the current required to power a megawatt rack would melt the copper wiring. The investment thesis breaks into four layers and the first layer is power semiconductors, specifically silicon carbide and gallium nitride. At 800 volts, traditional silicon based IGBTs hit their physical limits. SiC and GaN devices are the mandatory replacement. Infineon estimates $175,000 of semiconductor content per megawatt of AI rack power, versus almost nothing today and by 2030, power semiconductor content per AI cabinet grows from $15,000 to $115,000+. The names here are Infineon ($IFNNY), ON Semiconductor ($ON), Wolfspeed ($WOLF), Navitas ($NVTS), and STMicroelectronics ($STM). The second layer is power management and conversion. Vertiv ($VRT) is NVIDIA's lead architectural collaborator for the 800V transition, building the hardware that converts grid AC to 800V DC and the DC to DC power shelves for ultra dense racks. Eaton ($ETN) and Monolithic Power Systems ($MPWR) round out this layer. The third layer is grid to site infrastructure, GE Vernova ($GEV) builds the heavy electrical equipment that connects utility power to the data center campus. Orders are running at twice the rate of shipments, the classic leading indicator of sustained multi year revenue growth. The fourth layer is behind the meter power generation like your bloom energy because grid interconnection queues run five years, hyperscalers are bypassing the grid entirely, building dedicated gas, solar and battery systems on site. Make sure to follow me Melvin for more opportunities across the AI supply chain.

Dylan Patel just mapped out the most important investment theme in AI infrastructure (Save this). "In about two years, solar plus battery will be cheaper than gas." Every new NVIDIA Blackwell rack pulls 120 kilowatts, Rubin Ultra rack pulls 600 kilowatts and the next generation hits a megawatt. The US grid cannot keep up, interconnection queues now run five years in many markets so the entire industry is being forced to solve power from first principles. The solar thesis is already happening. BloombergNEF's 2026 LCOE report, covering 800+ financed projects across 50+ markets puts solar plus 4 hour battery storage at $57 per megawatt-hour. Combined cycle gas turbines hit $102 per megawatt hour, the highest on record, up 16% year over year. In California and parts of Texas, solar plus storage is already cheaper than gas for data center power today and solar panel costs are expected to drop another 30% by 2035. Getting power from the grid into the form chips actually require is an entire industry unto itself and NVIDIA just rewrote the rules. The 800 volt DC transition is the most important infrastructure shift that's happening right now. Today's data centers run on 48 volt DC power delivery, a single next-generation GPU pulls over 2,500 watts and at 48 volts, the current required to power a megawatt rack would melt the copper wiring. The investment thesis breaks into four layers and the first layer is power semiconductors, specifically silicon carbide and gallium nitride. At 800 volts, traditional silicon based IGBTs hit their physical limits. SiC and GaN devices are the mandatory replacement. Infineon estimates $175,000 of semiconductor content per megawatt of AI rack power, versus almost nothing today and by 2030, power semiconductor content per AI cabinet grows from $15,000 to $115,000+. The names here are Infineon ($IFNNY), ON Semiconductor ($ON), Wolfspeed ($WOLF), Navitas ($NVTS), and STMicroelectronics ($STM). The second layer is power management and conversion. Vertiv ($VRT) is NVIDIA's lead architectural collaborator for the 800V transition, building the hardware that converts grid AC to 800V DC and the DC to DC power shelves for ultra dense racks. Eaton ($ETN) and Monolithic Power Systems ($MPWR) round out this layer. The third layer is grid to site infrastructure, GE Vernova ($GEV) builds the heavy electrical equipment that connects utility power to the data center campus. Orders are running at twice the rate of shipments, the classic leading indicator of sustained multi year revenue growth. The fourth layer is behind the meter power generation like your bloom energy because grid interconnection queues run five years, hyperscalers are bypassing the grid entirely, building dedicated gas, solar and battery systems on site. Make sure to follow me Melvin for more opportunities across the AI supply chain.

Melvin

107,871 görüntüleme • 1 ay önce

D-Wave announced a scientific breakthrough published in the esteemed journal Science Magazine, confirming that its annealing quantum computer outperformed one of the world’s most powerful classical supercomputers in solving a complex magnetic materials simulation problem with relevance to materials discovery. The new landmark peer-reviewed paper, “Beyond-Classical Computation in Quantum Simulation,” validates this achievement as the world’s first and only demonstration of quantum computational supremacy on a useful problem. An international collaboration of scientists led by D-Wave performed simulations of quantum dynamics in programmable spin glasses—a computationally hard magnetic materials simulation problem with known applications to business and science—on both D-Wave’s Advantage2™ prototype annealing quantum computer and the Frontier supercomputer at the Department of Energy’s Oak Ridge Lab. D-Wave’s quantum computer performed a complex simulation in minutes and with a level of accuracy that would take nearly a million years using the supercomputer. In addition, it would require more than the world’s annual electricity consumption to solve this problem using the supercomputer, which is built with graphics processing unit (GPU) clusters. For decades, scientists have aspired to build a quantum computer capable of solving complex materials simulation problems beyond the reach of classical computers. D-Wave's advancements in quantum hardware have made it possible for its annealing quantum computers to process these types of problems for the first time. Magnetic materials simulations, like those conducted in this work, use computer models to study how tiny particles not visible to the human eye react to external factors. Magnetic materials are widely used in medical imaging, electronics, superconductors, electrical networks, sensors, and motors. This is an incredibly important achievement. Please join us in congratulating the D-Wave team and our global collaborators on this remarkable milestone. It’s a significant moment for the quantum computing industry. Learn more about this monumental achievement: Read the press release here: #QuantumSupremacy #QuantumRealized #QuantumComputing #DWave #Technology #Innovation #Optimization #MaterialsDiscovery #ScientificBreakthrough $QBTS

D-Wave announced a scientific breakthrough published in the esteemed journal Science Magazine, confirming that its annealing quantum computer outperformed one of the world’s most powerful classical supercomputers in solving a complex magnetic materials simulation problem with relevance to materials discovery. The new landmark peer-reviewed paper, “Beyond-Classical Computation in Quantum Simulation,” validates this achievement as the world’s first and only demonstration of quantum computational supremacy on a useful problem. An international collaboration of scientists led by D-Wave performed simulations of quantum dynamics in programmable spin glasses—a computationally hard magnetic materials simulation problem with known applications to business and science—on both D-Wave’s Advantage2™ prototype annealing quantum computer and the Frontier supercomputer at the Department of Energy’s Oak Ridge Lab. D-Wave’s quantum computer performed a complex simulation in minutes and with a level of accuracy that would take nearly a million years using the supercomputer. In addition, it would require more than the world’s annual electricity consumption to solve this problem using the supercomputer, which is built with graphics processing unit (GPU) clusters. For decades, scientists have aspired to build a quantum computer capable of solving complex materials simulation problems beyond the reach of classical computers. D-Wave's advancements in quantum hardware have made it possible for its annealing quantum computers to process these types of problems for the first time. Magnetic materials simulations, like those conducted in this work, use computer models to study how tiny particles not visible to the human eye react to external factors. Magnetic materials are widely used in medical imaging, electronics, superconductors, electrical networks, sensors, and motors. This is an incredibly important achievement. Please join us in congratulating the D-Wave team and our global collaborators on this remarkable milestone. It’s a significant moment for the quantum computing industry. Learn more about this monumental achievement: Read the press release here: #QuantumSupremacy #QuantumRealized #QuantumComputing #DWave #Technology #Innovation #Optimization #MaterialsDiscovery #ScientificBreakthrough $QBTS

D-Wave

65,039 görüntüleme • 1 yıl önce

🚨PERPLEXITY JUST LAUNCHED SOMETHING THAT MAKES EVERY OTHER AI PRODUCT LOOK LIKE A TOY.. AND NOBODY IS TALKING ABOUT IT.. They built a Personal Computer.. Not an app.. Not a chatbot.. A full digital worker that runs 24/7 on a Mac mini even while you sleep.. You press both command keys.. And it wakes up.. Ready to work.. But here's where it gets insane.. This thing doesn't run on one AI model.. It runs on 19 of them.. At the same time.. It uses Claude Opus for complex reasoning.. Gemini 3.1 Pro for deep research with a 2 million token context window.. Nano Banana Pro for 4K images.. Grok for fast tasks.. It doesn't just pick one model and hope for the best.. It reads your task.. Breaks it into subtasks.. And routes each one to whichever model is best at that specific thing.. All running in parallel.. While ChatGPT is still thinking about your first question.. Perplexity has already split your project into 6 pieces and assigned each one to a different AI.. And here's the part that should worry OpenAI.. Perplexity hallucinates at 3.3%.. ChatGPT hallucinates at 12%.. Claude at 15%.. It's not even close.. Because Perplexity is built differently.. Every other AI tries to remember facts.. Perplexity searches for them first.. It's structurally forced to cite live sources before it's even allowed to generate a response.. OpenAI Operator launched with a 32.6% success rate on computer-use tasks.. People called it "the world's most anxious intern" because it pauses every 5 seconds to ask if it's doing the right thing.. Perplexity runs multi-hour and multi-day workflows independently.. Only interrupts you when it hits a decision that actually matters.. You can start a task from your iPhone on the train.. And it executes on your Mac mini at home.. The economics are wild too.. Internal studies show it saved teams an average of $1.6 million in labor costs.. Performing 3.25 years of work in four weeks.. And unlike every other AI company.. Perplexity dropped ads entirely.. They charge $200 a month because they said they're in the "accuracy business".. Not the advertising business.. They even launched a $42.5 million publisher program to pay media partners when their content gets cited.. While OpenAI is getting sued by every newspaper on earth.. Google and OpenAI want you locked into their ecosystem.. If a better model comes out tomorrow you're stuck.. Perplexity just updates its routing matrix.. You get the best model on earth automatically.. No switching.. No migrations.. No friction.. This isn't an AI assistant anymore.. This is the first real AI employee.. And it costs $200 a month.

🚨PERPLEXITY JUST LAUNCHED SOMETHING THAT MAKES EVERY OTHER AI PRODUCT LOOK LIKE A TOY.. AND NOBODY IS TALKING ABOUT IT.. They built a Personal Computer.. Not an app.. Not a chatbot.. A full digital worker that runs 24/7 on a Mac mini even while you sleep.. You press both command keys.. And it wakes up.. Ready to work.. But here's where it gets insane.. This thing doesn't run on one AI model.. It runs on 19 of them.. At the same time.. It uses Claude Opus for complex reasoning.. Gemini 3.1 Pro for deep research with a 2 million token context window.. Nano Banana Pro for 4K images.. Grok for fast tasks.. It doesn't just pick one model and hope for the best.. It reads your task.. Breaks it into subtasks.. And routes each one to whichever model is best at that specific thing.. All running in parallel.. While ChatGPT is still thinking about your first question.. Perplexity has already split your project into 6 pieces and assigned each one to a different AI.. And here's the part that should worry OpenAI.. Perplexity hallucinates at 3.3%.. ChatGPT hallucinates at 12%.. Claude at 15%.. It's not even close.. Because Perplexity is built differently.. Every other AI tries to remember facts.. Perplexity searches for them first.. It's structurally forced to cite live sources before it's even allowed to generate a response.. OpenAI Operator launched with a 32.6% success rate on computer-use tasks.. People called it "the world's most anxious intern" because it pauses every 5 seconds to ask if it's doing the right thing.. Perplexity runs multi-hour and multi-day workflows independently.. Only interrupts you when it hits a decision that actually matters.. You can start a task from your iPhone on the train.. And it executes on your Mac mini at home.. The economics are wild too.. Internal studies show it saved teams an average of $1.6 million in labor costs.. Performing 3.25 years of work in four weeks.. And unlike every other AI company.. Perplexity dropped ads entirely.. They charge $200 a month because they said they're in the "accuracy business".. Not the advertising business.. They even launched a $42.5 million publisher program to pay media partners when their content gets cited.. While OpenAI is getting sued by every newspaper on earth.. Google and OpenAI want you locked into their ecosystem.. If a better model comes out tomorrow you're stuck.. Perplexity just updates its routing matrix.. You get the best model on earth automatically.. No switching.. No migrations.. No friction.. This isn't an AI assistant anymore.. This is the first real AI employee.. And it costs $200 a month.

Evan Luthra

1,097,697 görüntüleme • 4 ay önce

🚨🇷🇺 WEST SHOCKED: RUSSIA JUST UNVEILED 'ALTAI' NEUROMORPHIC CHIP Russia has presented its brain-mimicking ‘Altai’ neuromorphic processor — a homemade technological leap that could redefine AI warfare and confirm Western sanctions useless. 🔸 Altai delivers over 1000X better energy efficiency than traditional chips by using spiking neural networks that only fire short impulses when needed — directly copying the ultra-low-power human brain at just 20 watts. 🔸 Processes video at up to 2200 frames per second while consuming under 0.5W in a tiny 9×9 mm package — compared to NVIDIA Jetson which burns 15-60W for similar AI vision tasks. 🔸 Features 256 asynchronous cores simulating 131,072 neurons and 67 million synapses — developed by Novosibirsk’s Motive NT with Kaspersky as strategic investor and presented to PM Mishustin last year. 🔸 Transforms low-cost drones into long-endurance autonomous hunters, turns sensor networks into stealthy weeks-long intel platforms, and powers radio-electronic warfare gear that runs days on a single battery. 🔸 Currently a 28nm prototype still needing fab production — Russia may rely on Chinese facilities or adapt to coarser domestic 350nm nodes, trading some efficiency for full sovereignty. Can the U.S. catch up to Russian chip innovation?

🚨🇷🇺 WEST SHOCKED: RUSSIA JUST UNVEILED 'ALTAI' NEUROMORPHIC CHIP Russia has presented its brain-mimicking ‘Altai’ neuromorphic processor — a homemade technological leap that could redefine AI warfare and confirm Western sanctions useless. 🔸 Altai delivers over 1000X better energy efficiency than traditional chips by using spiking neural networks that only fire short impulses when needed — directly copying the ultra-low-power human brain at just 20 watts. 🔸 Processes video at up to 2200 frames per second while consuming under 0.5W in a tiny 9×9 mm package — compared to NVIDIA Jetson which burns 15-60W for similar AI vision tasks. 🔸 Features 256 asynchronous cores simulating 131,072 neurons and 67 million synapses — developed by Novosibirsk’s Motive NT with Kaspersky as strategic investor and presented to PM Mishustin last year. 🔸 Transforms low-cost drones into long-endurance autonomous hunters, turns sensor networks into stealthy weeks-long intel platforms, and powers radio-electronic warfare gear that runs days on a single battery. 🔸 Currently a 28nm prototype still needing fab production — Russia may rely on Chinese facilities or adapt to coarser domestic 350nm nodes, trading some efficiency for full sovereignty. Can the U.S. catch up to Russian chip innovation?

NewRulesGeopolitics

217,101 görüntüleme • 3 ay önce

-> those who move early -> on this will capture the -> biggest upside this year -> ai content in 2026 is now -> so good that even creators -> can’t always tell what they -> made vs what ai made -> that shift opens up -> massive opportunity -> what the numbers actually -> look like right now -> brands are paying $300– -> $2,000 per month for -> 30–60 ai-generated -> short videos affiliate -> pages using ai content -> are hitting 50k–500k -> views/month with -> 0.5%–2% ctr to offers -> 1 person + ai tools can -> now produce 100+ pieces -> of content per week -> vs 5–10 pieces manually -> cost per video dropped -> from $200–$500 with -> agencies to $5–$50 with -> ai + shot on iphone style -> brands and affiliates are -> already using this to grow -> organic tiktok, reels, and -> youtube shorts pages -> most of these tools + -> workflows only went -> mainstream in the -> last 30–90 days -> like every platform shift -> first 6–12 months = lowest -> competition, highest reach -> after that, the market -> gets crowded, the play -> isn’t “ai makes you a -> millionaire overnight” -> the play is, learn the -> system early, lock in 3–5 -> brand retainers at $1,500– -> $3,000/month each, and -> compound before -> everyone else catches up -> production is cheap now -> strategy + distribution -> the real product -> want me to map out -> what “early” actually -> means with a 30-day -> action plan + numbers

BeingInvested

35,766 görüntüleme • 3 ay önce

🚨 SCIENTISTS CREATED THE WORLD'S FIRST SELF REPLICATING SYNTHETIC LIFE FORM — AND PEOPLE ARE FREAKING OUT This sounds like the plot of a science fiction movie. It isn't. A team of scientists spent roughly 15 years and $40 million creating what they described as the world's first self replicating synthetic life form. Not discovered. Created. The organism's genetic code was designed on a computer. Its DNA was manufactured in a laboratory. And once activated, it began doing something that immediately captured the world's attention: It started making copies of itself. The scientists behind the project openly described DNA as the "software of life." Their goal wasn't just to study living organisms. It was to learn how to write the code from scratch. According to the researchers, this technology could eventually be used to create new medicines, faster vaccines, cleaner fuels, custom designed organisms, and countless products that currently come from nature. One scientist even predicted that future vaccines could potentially be designed in less than 24 hours using synthetic DNA processes. Supporters call it one of the most important scientific breakthroughs of the modern era. Critics say it raises questions humanity has never had to answer before. Because once scientists can create synthetic life... where exactly does the line get drawn? The lead researcher said the future isn't just about reading the genetic code of life. It's about learning how to write it. At what point does scientific advancement become scientists playing God?

🚨 SCIENTISTS CREATED THE WORLD'S FIRST SELF REPLICATING SYNTHETIC LIFE FORM — AND PEOPLE ARE FREAKING OUT This sounds like the plot of a science fiction movie. It isn't. A team of scientists spent roughly 15 years and $40 million creating what they described as the world's first self replicating synthetic life form. Not discovered. Created. The organism's genetic code was designed on a computer. Its DNA was manufactured in a laboratory. And once activated, it began doing something that immediately captured the world's attention: It started making copies of itself. The scientists behind the project openly described DNA as the "software of life." Their goal wasn't just to study living organisms. It was to learn how to write the code from scratch. According to the researchers, this technology could eventually be used to create new medicines, faster vaccines, cleaner fuels, custom designed organisms, and countless products that currently come from nature. One scientist even predicted that future vaccines could potentially be designed in less than 24 hours using synthetic DNA processes. Supporters call it one of the most important scientific breakthroughs of the modern era. Critics say it raises questions humanity has never had to answer before. Because once scientists can create synthetic life... where exactly does the line get drawn? The lead researcher said the future isn't just about reading the genetic code of life. It's about learning how to write it. At what point does scientific advancement become scientists playing God?

HustleBitch

16,030 görüntüleme • 2 ay önce

NVIDIA just handed every solo creator and freelancer an unfair advantage. Jensen Huang walked on stage and announced RTX Spark. An ARM-based laptop chip that nobody saw coming. They called it the most power efficient PC chip ever built. 20 cores. Blackwell graphics. 6144 CUDA cores. Up to 128GB of LPDDR5X memory. But forget the spec sheet for a second. Here is what actually matters. RTX Spark is built to run AI models locally. No cloud subscription. No API costs. No waiting on a server somewhere. Everything runs directly on the laptop at full speed. That changes the math completely for anyone using AI to make money. The guy generating 3D assets in Blender with Claude his renders now take minutes instead of hours. More projects per day. More income per week. The girl producing AI kids content for YouTube local rendering means no upload wait times, no generation limits, no monthly fees eating into her margins. The freelancer building websites and automating outreach every AI tool in his stack now runs faster and cheaper than before. 30 laptops from Asus, Dell, Lenovo, MSI and others. Available this fall. For years the barrier was hardware. You needed an expensive setup to run serious AI workflows locally. NVIDIA just put that power inside a thin laptop anyone can carry anywhere. The people who already figured out how to monetize AI are about to move twice as fast. The people who haven’t started yet just ran out of excuses. Save this.

NVIDIA just handed every solo creator and freelancer an unfair advantage. Jensen Huang walked on stage and announced RTX Spark. An ARM-based laptop chip that nobody saw coming. They called it the most power efficient PC chip ever built. 20 cores. Blackwell graphics. 6144 CUDA cores. Up to 128GB of LPDDR5X memory. But forget the spec sheet for a second. Here is what actually matters. RTX Spark is built to run AI models locally. No cloud subscription. No API costs. No waiting on a server somewhere. Everything runs directly on the laptop at full speed. That changes the math completely for anyone using AI to make money. The guy generating 3D assets in Blender with Claude his renders now take minutes instead of hours. More projects per day. More income per week. The girl producing AI kids content for YouTube local rendering means no upload wait times, no generation limits, no monthly fees eating into her margins. The freelancer building websites and automating outreach every AI tool in his stack now runs faster and cheaper than before. 30 laptops from Asus, Dell, Lenovo, MSI and others. Available this fall. For years the barrier was hardware. You needed an expensive setup to run serious AI workflows locally. NVIDIA just put that power inside a thin laptop anyone can carry anywhere. The people who already figured out how to monetize AI are about to move twice as fast. The people who haven’t started yet just ran out of excuses. Save this.

Shelpid.WI3M

27,789 görüntüleme • 3 ay önce

The man who turned 225 million dollars into 5.5 billion dollars explained on camera exactly why he made his biggest bet. This is Leopold Aschenbrenner, the same person whose Bloom Energy position is now worth close to 2 billion dollars after Oracle's 2.8 gigawatt fuel cell deal laying out the power math that drove every investment decision his fund has made. In 2022, the GPT-4 training cluster consumed roughly 10 megawatts of power and cost about 500 million dollars. AI compute has been scaling at roughly half an order of magnitude per year meaning the largest training cluster doubles in power requirement every 12 to 18 months without stopping. By 2024, the largest cluster was approximately 100 megawatts, the equivalent of 100,000 high-end GPUs and costs in the billions. By 2026, right now, the leading training cluster requires a full gigawatt of continuous power and that is the output of a large nuclear reactor. By 2028, the projection reaches 10 gigawatts, more electricity than most US states generate in total. By 2030, the trillion-dollar cluster, 100 gigawatts, over 20 percent of everything the United States currently produces in electricity, consumed by a single AI training installation. And that is just the training cluster. Inference, the continuous compute required to actually run AI products for hundreds of millions of users requires multiples of that on top. Meanwhile, total US electricity production has barely grown five percent over the last decade and the grid was not built for this. And the transformer shortage, the switchgear backorders, and the canceled data center projects that are making headlines right now are the first visible symptoms of a power system hitting a wall that Aschenbrenner saw coming years before the rest of the market. This is exactly why he built a 875 million dollar position in Bloom Energy, a company that generates electricity directly at the data center site using fuel cells, completely bypassing the grid bottleneck that is already stopping half of all planned US data centers from opening on schedule. The thesis was never complicated. The bottleneck in AI is not the models, not the chips, and not the software. The bottleneck is whether civilization can generate enough electricity to run the machines fast enough to matter.

The man who turned 225 million dollars into 5.5 billion dollars explained on camera exactly why he made his biggest bet. This is Leopold Aschenbrenner, the same person whose Bloom Energy position is now worth close to 2 billion dollars after Oracle's 2.8 gigawatt fuel cell deal laying out the power math that drove every investment decision his fund has made. In 2022, the GPT-4 training cluster consumed roughly 10 megawatts of power and cost about 500 million dollars. AI compute has been scaling at roughly half an order of magnitude per year meaning the largest training cluster doubles in power requirement every 12 to 18 months without stopping. By 2024, the largest cluster was approximately 100 megawatts, the equivalent of 100,000 high-end GPUs and costs in the billions. By 2026, right now, the leading training cluster requires a full gigawatt of continuous power and that is the output of a large nuclear reactor. By 2028, the projection reaches 10 gigawatts, more electricity than most US states generate in total. By 2030, the trillion-dollar cluster, 100 gigawatts, over 20 percent of everything the United States currently produces in electricity, consumed by a single AI training installation. And that is just the training cluster. Inference, the continuous compute required to actually run AI products for hundreds of millions of users requires multiples of that on top. Meanwhile, total US electricity production has barely grown five percent over the last decade and the grid was not built for this. And the transformer shortage, the switchgear backorders, and the canceled data center projects that are making headlines right now are the first visible symptoms of a power system hitting a wall that Aschenbrenner saw coming years before the rest of the market. This is exactly why he built a 875 million dollar position in Bloom Energy, a company that generates electricity directly at the data center site using fuel cells, completely bypassing the grid bottleneck that is already stopping half of all planned US data centers from opening on schedule. The thesis was never complicated. The bottleneck in AI is not the models, not the chips, and not the software. The bottleneck is whether civilization can generate enough electricity to run the machines fast enough to matter.

Milk Road AI

4,641,938 görüntüleme • 4 ay önce