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Jon Saad-Falcon and Avanika Narayan propose measuring intelligence per watt rather than capability. It reframes the margin question. Frontier pricing does not require better models to fail. It requires only that switching costs stay near zero, lock-in stays weak, and most workloads never reach for the frontier. All three hold.

Jaya Gupta

35,680 subscribers

27,706 views • 1 month ago •via X (Twitter)

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6 Comments

Jaya Gupta1 month ago

Link here:

Xiaoyin Qu1 month ago

@JonSaadFalcon @Avanika15 This is a good measurement on the model efficient side. But how to organize intelligence effectively to drive finished outcome is another dimensions

Jaya Gupta1 month ago

@JonSaadFalcon @Avanika15 @akoratana

Ahmed Medhat1 month ago

@JonSaadFalcon @Avanika15 I know intent is to measure efficiency, but this measure assumes intelligence is unbounded. It needs a yardstick, unless it’s calibrated against completion of a set of tasks, it needs to factor in time. For example, Einsteins per hour per watt.

Priyam Sheth1 month ago

@JonSaadFalcon @Avanika15 The next era of AI will not be won by whoever builds the largest model or offers the cheapest token. I'd like put it this way - it will be defined by whoever delivers trusted intelligence at the lowest cost per useful outcome.

Bryant1 month ago

@JonSaadFalcon @Avanika15 Interesting topic! Will have to read the paper.

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Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."

Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."

Patrick OShaughnessy

121,392 views • 4 months ago

A $170 AI bill in 10 days is not a cloud problem. It is a routing problem. Most devs send every task to the most expensive brain available. summaries small refactors docs scripts log parsing boilerplate That is how the bill gets stupid. The Mac Mini M4 trade is simple: run routine work locally keep private code on your desk pay once for hardware save frontier models for hard calls Local AI does not beat Claude at everything. It does not need to. It only needs to replace the boring prompts that never needed a frontier model in the first place. Cloud AI stays. The default route changes. Full breakdown below ↓

A $170 AI bill in 10 days is not a cloud problem. It is a routing problem. Most devs send every task to the most expensive brain available. summaries small refactors docs scripts log parsing boilerplate That is how the bill gets stupid. The Mac Mini M4 trade is simple: run routine work locally keep private code on your desk pay once for hardware save frontier models for hard calls Local AI does not beat Claude at everything. It does not need to. It only needs to replace the boring prompts that never needed a frontier model in the first place. Cloud AI stays. The default route changes. Full breakdown below ↓

Fluixo

35,295 views • 1 month ago

Jensen says Open Source can be MORE expensive for enterprises, Brad Gerstner explains why: Brad Gerstner: “Over the last two weeks, everybody's been saying that the Chinese have caught up, that open source tokens have caught up in intelligence, that they're much cheaper, etc. And Elon comes out and says, ‘Not so fast. We're entering the singularity, and the frontier models are way further ahead than people think.’ I believe that to be true. And then Jensen came out this week and said, ‘Closed models are actually cheaper if you don't have to build it for yourself, the training costs, and a lot of expertise to fine-tune, and maintain, and guardrail, and keep it safe. So he's basically making the argument that not only are the frontier models further ahead, but that the cost differential between the two is not what everybody's making it out to be, which I think explains why (closed models) continue to run away with it on the revenue side of the equation. For the vast majority of use cases, I think token consumption is going up for the open source guys, while share of economics is going up for the frontier labs. I think that's what we want to see.” $NVDA

Jensen says Open Source can be MORE expensive for enterprises, Brad Gerstner explains why: Brad Gerstner: “Over the last two weeks, everybody's been saying that the Chinese have caught up, that open source tokens have caught up in intelligence, that they're much cheaper, etc. And Elon comes out and says, ‘Not so fast. We're entering the singularity, and the frontier models are way further ahead than people think.’ I believe that to be true. And then Jensen came out this week and said, ‘Closed models are actually cheaper if you don't have to build it for yourself, the training costs, and a lot of expertise to fine-tune, and maintain, and guardrail, and keep it safe. So he's basically making the argument that not only are the frontier models further ahead, but that the cost differential between the two is not what everybody's making it out to be, which I think explains why (closed models) continue to run away with it on the revenue side of the equation. For the vast majority of use cases, I think token consumption is going up for the open source guys, while share of economics is going up for the frontier labs. I think that's what we want to see.” $NVDA

The All-In Podcast

61,928 views • 1 month ago

.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

Jawwwn

1,103,053 views • 17 days ago

We tested 11 frontier LLMs on 200 real human–AI conversations to measure emotional intelligence The result that surprised us: EQ doesn't scale with size or recency. Claude Haiku 4.5 beats Sonnet 4.6. Opus 4.6 performs better than 4.7 It's an orthogonal capability and labs aren't optimizing for it

We tested 11 frontier LLMs on 200 real human–AI conversations to measure emotional intelligence The result that surprised us: EQ doesn't scale with size or recency. Claude Haiku 4.5 beats Sonnet 4.6. Opus 4.6 performs better than 4.7 It's an orthogonal capability and labs aren't optimizing for it

faisal

11,534 views • 4 months ago

Introducing AI Lab Monetization Play. AI Lab Monetization Play tracks the revenue run-rate of major AI laboratories in real time using public and alternative data signals. The Play provides a continuously updated view of commercial scale across the frontier AI landscape. ARR Dashboard The dashboard estimates annual recurring revenue for Anthropic, OpenAI, and other frontier labs. Each estimate is broken down by revenue stream, growth trajectory, and implied valuation multiple. It surfaces which labs are scaling fastest and where the commercial momentum is concentrated. Model Routing and Market Share The Play tracks real-time model usage share across inference providers. It measures routing volume through aggregators to identify which models are capturing production workloads. Shifts in routing patterns signal adoption inflections before they appear in reported financials. Model Pricing and Capabilities The Play maintains a comprehensive pricing table across all major foundation models. Input and output token costs, context windows, and capability tiers are mapped side by side. It serves as the reference layer for competitive positioning and margin structure across the model ecosystem. Available to all paid clients on our full-stack research platform. Start here:

Introducing AI Lab Monetization Play. AI Lab Monetization Play tracks the revenue run-rate of major AI laboratories in real time using public and alternative data signals. The Play provides a continuously updated view of commercial scale across the frontier AI landscape. ARR Dashboard The dashboard estimates annual recurring revenue for Anthropic, OpenAI, and other frontier labs. Each estimate is broken down by revenue stream, growth trajectory, and implied valuation multiple. It surfaces which labs are scaling fastest and where the commercial momentum is concentrated. Model Routing and Market Share The Play tracks real-time model usage share across inference providers. It measures routing volume through aggregators to identify which models are capturing production workloads. Shifts in routing patterns signal adoption inflections before they appear in reported financials. Model Pricing and Capabilities The Play maintains a comprehensive pricing table across all major foundation models. Input and output token costs, context windows, and capability tiers are mapped side by side. It serves as the reference layer for competitive positioning and margin structure across the model ecosystem. Available to all paid clients on our full-stack research platform. Start here:

FUNDA

37,874 views • 2 months ago

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

Fireside Alpha

14,114 views • 1 month ago

"It all comes down to compute." Dmitri Alperovitch from Silverado Policy Accelerator explains: "The single most important input to winning is compute the processing power used to train and run AI models. Let me say that plainly, because it is defining that everything that congress and this committee do on AI policy. The binding constraint in this competition is not talent, it is not data, and it is not cash. Both the United States and China have talented researchers and financial resources, and we have learned that frontier models trained on generic data routinely outperform smaller models, trained on more specialized inputs. It all comes down to compute."

"It all comes down to compute." Dmitri Alperovitch from Silverado Policy Accelerator explains: "The single most important input to winning is compute the processing power used to train and run AI models. Let me say that plainly, because it is defining that everything that congress and this committee do on AI policy. The binding constraint in this competition is not talent, it is not data, and it is not cash. Both the United States and China have talented researchers and financial resources, and we have learned that frontier models trained on generic data routinely outperform smaller models, trained on more specialized inputs. It all comes down to compute."

Select Committee on China

41,578 views • 5 months ago

Chinese models are 112x cheaper than Anthropic per million tokens. Chamath laid it out on CNBC: a "barrel of intelligence" costs $56 from Anthropic, $26 from OpenAI, $1.50 from Meta, $1 from xAI and Google, and $0.50 from Chinese models. That is not a pricing quirk. That is the steepest commodity curve any technology has run in recorded history. Oil took 40 years to compress like this. Semiconductors took 20. AI inference is doing it in months. The companies sitting at $26 and $56 are not stupid. They're buying time… betting that trust, safety, and enterprise contracts hold the premium long enough for costs to catch up. What they cannot bet on is the timeline. Because the $0.50 model is not a demo. I've been inside the labs building it.

Chinese models are 112x cheaper than Anthropic per million tokens. Chamath laid it out on CNBC: a "barrel of intelligence" costs $56 from Anthropic, $26 from OpenAI, $1.50 from Meta, $1 from xAI and Google, and $0.50 from Chinese models. That is not a pricing quirk. That is the steepest commodity curve any technology has run in recorded history. Oil took 40 years to compress like this. Semiconductors took 20. AI inference is doing it in months. The companies sitting at $26 and $56 are not stupid. They're buying time… betting that trust, safety, and enterprise contracts hold the premium long enough for costs to catch up. What they cannot bet on is the timeline. Because the $0.50 model is not a demo. I've been inside the labs building it.

Shruti

840,113 views • 2 months ago

Chinese models are 112x cheaper than Anthropic per million tokens. Chamath laid it out on CNBC: a "barrel of intelligence" costs $56 from Anthropic, $26 from OpenAI, $1.50 from Meta, $1 from xAI and Google, and $0.50 from Chinese models. That is not a pricing quirk. That is the steepest commodity curve any technology has run in recorded history. Oil took 40 years to compress like this. Semiconductors took 20. AI inference is doing it in months. The companies sitting at $26 and $56 are not stupid. They're buying time… betting that trust, safety, and enterprise contracts hold the premium long enough for costs to catch up. What they cannot bet on is the timeline. Because the $0.50 model is not a demo. I've been inside the labs building it.

Chinese models are 112x cheaper than Anthropic per million tokens. Chamath laid it out on CNBC: a "barrel of intelligence" costs $56 from Anthropic, $26 from OpenAI, $1.50 from Meta, $1 from xAI and Google, and $0.50 from Chinese models. That is not a pricing quirk. That is the steepest commodity curve any technology has run in recorded history. Oil took 40 years to compress like this. Semiconductors took 20. AI inference is doing it in months. The companies sitting at $26 and $56 are not stupid. They're buying time… betting that trust, safety, and enterprise contracts hold the premium long enough for costs to catch up. What they cannot bet on is the timeline. Because the $0.50 model is not a demo. I've been inside the labs building it.

Clara Bennett

94,828 views • 2 months ago

GPT-6 Astra is now the best-performing frontier model for antibody developability prediction in our benchmark. 🧬 Finding an antibody that binds is only the beginning. Will it aggregate? Will it stay stable? Can it actually behave like a drug? GPT-6 Astra predicts these developability properties better than the other frontier models we tested. 🤯 And yes, the interactive visualization showing how an antibody actually works was also built with Astra in about 1 hour. This model looks seriously powerful. Congrats gbt Sam Altman #insilicoSOTAFM

GPT-6 Astra is now the best-performing frontier model for antibody developability prediction in our benchmark. 🧬 Finding an antibody that binds is only the beginning. Will it aggregate? Will it stay stable? Can it actually behave like a drug? GPT-6 Astra predicts these developability properties better than the other frontier models we tested. 🤯 And yes, the interactive visualization showing how an antibody actually works was also built with Astra in about 1 hour. This model looks seriously powerful. Congrats gbt Sam Altman #insilicoSOTAFM

Andrew Aiginin

311,303 views • 17 days ago

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."

Molly O’Shea

12,816 views • 2 months ago

CNBC described China’s open-source AI as “pushing the U.S. to compete at the lower end of the value chain,” initially trying to save face for Silicon Valley. The comments section immediately tore that framing apart: "Chinese models are approaching frontier-level capability while remaining absurdly cheap." "This is not the 'lower end' of the value chain." It is forcing expensive American models to answer a much uglier question: Why are you charging tens or even hundreds of times more for the same work? The most revealing part was that people saw through the euphemism immediately: “Lower end” is just cope. American labs are not protecting the high end. They are protecting high profit margins. China’s open-source models are turning frontier capability into a commodity, crushing prices, and freeing developers from brand worship. Suddenly, the American myth of closed-source superiority, premium pricing, and inflated valuations looks less like technological leadership— and more like an ordinary handbag with a luxury label.

𝘊𝘰𝘳𝘳𝘪𝘯𝘦

10,646 views • 1 month ago

Sundar Pichai just reminded everyone that Google was built on open source. He personally worked on Chromium, Android, and Kubernetes before becoming CEO. Three systems that power billions of devices today. Now Google is applying the same philosophy to AI. Their Gemma models are updated year after year, designed to run on edge devices rather than requiring massive cloud infrastructure. The question he got was why not release a large open source frontier model. His answer was revealing: frontier training requires enormous capex investment, and Google is putting billions into R&D to stay at the frontier. The open source AI landscape is quietly consolidating around a few players who can sustain the investment. Google's two decades of open source credibility gives them a position that's hard to replicate.

Sundar Pichai just reminded everyone that Google was built on open source. He personally worked on Chromium, Android, and Kubernetes before becoming CEO. Three systems that power billions of devices today. Now Google is applying the same philosophy to AI. Their Gemma models are updated year after year, designed to run on edge devices rather than requiring massive cloud infrastructure. The question he got was why not release a large open source frontier model. His answer was revealing: frontier training requires enormous capex investment, and Google is putting billions into R&D to stay at the frontier. The open source AI landscape is quietly consolidating around a few players who can sustain the investment. Google's two decades of open source credibility gives them a position that's hard to replicate.

Shruti

152,324 views • 2 months ago

People made fun of Alex Finn for buying three Mac Studios to run AI at home. Then Fable got banned for a week, GLM 5.2 dropped, and those exact Mac Studios started reselling for 4x what he paid. He showed me how he built his home AI lab from scratch. Here's the playbook: 1) The hardware. three 512GB Mac Studios, an NVIDIA DGX Spark, a custom RTX 5090 build, and a few Mac Minis. ~$30k all in. 2) The buying framework... - Mac Studio: huge memory, runs GLM 5.2 (open weights, near Opus 4.8 on benchmarks), but slow. - DGX Spark ($4,800): the sweet spot for most people. - RTX 5090: smaller models at blazing speed (Qwen's 29B now hits Sonnet 4 level). 3) Tailscale networks every machine into one private network with root access to each other. Only one machine is plugged into a monitor. 4) A Nous Research Hermes agent is his IT guy. New model drops? It SSHs into the right box, loads 5 candidates, runs evals overnight, and reports back which task belongs on which machine. Alex has literally never loaded a model himself. 5) The whole point: achieving "ambient intelligence." Always-on jobs that would bankrupt you on per-token billing. A security sweep of his API endpoints every hour. Code optimization every 20 minutes. Database anomaly & churn detection. Hourly scraping of X, Reddit & Hacker News for business opportunities. 6) Running those workloads on frontier models would cost thousands a month. His actual cost: ~$60 more in electricity. 7) Btw he's not anti-frontier. He still maxes out his Claude plan. The way he sees it: frontier is for hard thinking, local is for the foot soldiers that never sleep. 8) "We own everything except for the intelligence. Why can't we own the intelligence?" 9) He thinks frontier-level intelligence runs on consumer hardware within 6 months.

People made fun of Alex Finn for buying three Mac Studios to run AI at home. Then Fable got banned for a week, GLM 5.2 dropped, and those exact Mac Studios started reselling for 4x what he paid. He showed me how he built his home AI lab from scratch. Here's the playbook: 1) The hardware. three 512GB Mac Studios, an NVIDIA DGX Spark, a custom RTX 5090 build, and a few Mac Minis. ~$30k all in. 2) The buying framework... - Mac Studio: huge memory, runs GLM 5.2 (open weights, near Opus 4.8 on benchmarks), but slow. - DGX Spark ($4,800): the sweet spot for most people. - RTX 5090: smaller models at blazing speed (Qwen's 29B now hits Sonnet 4 level). 3) Tailscale networks every machine into one private network with root access to each other. Only one machine is plugged into a monitor. 4) A Nous Research Hermes agent is his IT guy. New model drops? It SSHs into the right box, loads 5 candidates, runs evals overnight, and reports back which task belongs on which machine. Alex has literally never loaded a model himself. 5) The whole point: achieving "ambient intelligence." Always-on jobs that would bankrupt you on per-token billing. A security sweep of his API endpoints every hour. Code optimization every 20 minutes. Database anomaly & churn detection. Hourly scraping of X, Reddit & Hacker News for business opportunities. 6) Running those workloads on frontier models would cost thousands a month. His actual cost: ~$60 more in electricity. 7) Btw he's not anti-frontier. He still maxes out his Claude plan. The way he sees it: frontier is for hard thinking, local is for the foot soldiers that never sleep. 8) "We own everything except for the intelligence. Why can't we own the intelligence?" 9) He thinks frontier-level intelligence runs on consumer hardware within 6 months.

Alex Lieberman

57,764 views • 2 months ago

Japan just changed what an AI model even is. New Sakana Fugu doesn't try to out-think GPT-5, Claude, or Gemini. It conducts all three at once - and beats every one of them. A trader in Tokyo unleashed it on the fastest market alive - 5min Bitcoin binary and turned $6,200 into $304,865. His wallet: The frontier just stopped being which model is smartest. It's who's conducting them - and the market hasn't priced that in yet. Sakana Fugu isn't a bigger model - it's a full multi-agent orchestration system. The coordinator behind it carries about 10,000 parameters, evolved rather than hand-coded, and it runs the most capable models on earth like a single instrument. Pointed at Bitcoin, here's what it does every five minutes. It assembles a team from a pool of frontier models and assigns each one a role: > Thinker - reads the candle, the order book, the news, builds the plan > Worker - turns the plan into one call: up or down, and how much > Verifier - votes ACCEPT or REVISE before a cent moves If the Verifier says REVISE, nothing trades. Fugu reads its own miss, reroutes, even calls itself for a corrective round, and runs it again. No look-ahead, ever - the next candle only appears after it commits. This is what should worry every lab still chasing a bigger model: the edge was never scale. It's orchestration - and Fugu does it better than anything alive. Bookmark this - when the whole timeline is chasing orchestration in six months, you'll already have the breakdown. You're not going to wire up an orchestra of frontier models yourself. Mirror the wallet Fugu runs instead:

Japan just changed what an AI model even is. New Sakana Fugu doesn't try to out-think GPT-5, Claude, or Gemini. It conducts all three at once - and beats every one of them. A trader in Tokyo unleashed it on the fastest market alive - 5min Bitcoin binary and turned $6,200 into $304,865. His wallet: The frontier just stopped being which model is smartest. It's who's conducting them - and the market hasn't priced that in yet. Sakana Fugu isn't a bigger model - it's a full multi-agent orchestration system. The coordinator behind it carries about 10,000 parameters, evolved rather than hand-coded, and it runs the most capable models on earth like a single instrument. Pointed at Bitcoin, here's what it does every five minutes. It assembles a team from a pool of frontier models and assigns each one a role: > Thinker - reads the candle, the order book, the news, builds the plan > Worker - turns the plan into one call: up or down, and how much > Verifier - votes ACCEPT or REVISE before a cent moves If the Verifier says REVISE, nothing trades. Fugu reads its own miss, reroutes, even calls itself for a corrective round, and runs it again. No look-ahead, ever - the next candle only appears after it commits. This is what should worry every lab still chasing a bigger model: the edge was never scale. It's orchestration - and Fugu does it better than anything alive. Bookmark this - when the whole timeline is chasing orchestration in six months, you'll already have the breakdown. You're not going to wire up an orchestra of frontier models yourself. Mirror the wallet Fugu runs instead:

cvxv666

72,821 views • 3 months ago

A YouTuber who burns $1,000 a day on AI tokens just did the same work for $63. His terminal froze on camera from running too many agents. He force-quit and kept filming. He skipped a live event to finish this. The model is Kimi K3, China's new 2.8 trillion parameter release. Weights go public July 27. In the 41-minute breakdown he feeds it his old production codebase with a paragraph and a half of instructions. It clears 122 tasks before he touches the keyboard again. Then around minute 37 it does something to his to-do list he says he's never seen from any model. Not even Anthropic's flagship. The security audit near the 33-minute mark is the part that should worry you. Frontier models refuse that task. This one ran 25 verification agents on it. He came in bored of open weights. He left calling it frontier class. Hosting it yourself costs $2.6 million. Watching him break it costs nothing.

A YouTuber who burns $1,000 a day on AI tokens just did the same work for $63. His terminal froze on camera from running too many agents. He force-quit and kept filming. He skipped a live event to finish this. The model is Kimi K3, China's new 2.8 trillion parameter release. Weights go public July 27. In the 41-minute breakdown he feeds it his old production codebase with a paragraph and a half of instructions. It clears 122 tasks before he touches the keyboard again. Then around minute 37 it does something to his to-do list he says he's never seen from any model. Not even Anthropic's flagship. The security audit near the 33-minute mark is the part that should worry you. Frontier models refuse that task. This one ran 25 verification agents on it. He came in bored of open weights. He left calling it frontier class. Hosting it yourself costs $2.6 million. Watching him break it costs nothing.

Merlow

121,153 views • 2 months ago

Introducing Deft Robotics' unified deployment platform for physical AI. Frontier models are moving fast. Real world autonomy isn’t. Closing the gap takes more than better models. The missing piece? Infrastructure to deploy, intervene, learn from failures, and continuously improve. For the past year, we’ve been deploying in that gap and building the stack to close it. Now, we’re opening it up. Hardware available now. Software in beta. Here’s how to use it for your use-case 🧵(1/7) ↓

Introducing Deft Robotics' unified deployment platform for physical AI. Frontier models are moving fast. Real world autonomy isn’t. Closing the gap takes more than better models. The missing piece? Infrastructure to deploy, intervene, learn from failures, and continuously improve. For the past year, we’ve been deploying in that gap and building the stack to close it. Now, we’re opening it up. Hardware available now. Software in beta. Here’s how to use it for your use-case 🧵(1/7) ↓

Shane

15,703 views • 19 days ago

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

dnap

110,354 views • 2 months ago

So what Warren Pies is saying: Anthropic’s Mythos rollout triggered a sharp collapse in $NVDA GPU availability, with supply effectively tightening to near zero as demand for frontier models accelerated. Q1 2026 revenue from Anthropic reinforces that this isn’t theoretical demand, it’s real dollars chasing compute at scale across the AI ecosystem, from $GOOGL GCP to $AMZN AWS. The takeaway is simple: better models require more compute, and that feedback loop continues to drive the entire AI infrastructure trade.

So what Warren Pies is saying: Anthropic’s Mythos rollout triggered a sharp collapse in $NVDA GPU availability, with supply effectively tightening to near zero as demand for frontier models accelerated. Q1 2026 revenue from Anthropic reinforces that this isn’t theoretical demand, it’s real dollars chasing compute at scale across the AI ecosystem, from $GOOGL GCP to $AMZN AWS. The takeaway is simple: better models require more compute, and that feedback loop continues to drive the entire AI infrastructure trade.

Sam Badawi

21,184 views • 4 months ago