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Why is OpenAI cutting codex pricing right now? Gavin Baker Gavin Baker of Atreides Management thinks every major player knows the answer - and is acting on it privately. Baker told John Coogan on TBPN: OpenAI's codex price cuts are not a margin decision. They're a strategic move to...

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Bottleneck trade: buy whatever AI can't get enough of. That trade is ending. Gavin Baker Gavin Baker of Atreides Management told Jordi Hays on TBPN: the tell was Ajinomoto - maker of ABF substrate, critical for semiconductor packaging - declining to raise prices at peak demand. Constrained suppliers who don't extract rent are signaling the squeeze is over. The next game is harder: finding companies with enduring franchise value once supply normalizes. That's a different analysis than "find the constraint and buy the input." Baker's framework for the post-bottleneck era, and where SpaceX sits in it: Source: TBPN -
0:58

Bottleneck trade: buy whatever AI can't get enough of. That trade is ending. Gavin Baker Gavin Baker of Atreides Management told Jordi Hays on TBPN: the tell was Ajinomoto - maker of ABF substrate, critical for semiconductor packaging - declining to raise prices at peak demand. Constrained suppliers who don't extract rent are signaling the squeeze is over. The next game is harder: finding companies with enduring franchise value once supply normalizes. That's a different analysis than "find the constraint and buy the input." Baker's framework for the post-bottleneck era, and where SpaceX sits in it: Source: TBPN -

Podcast Alpha

16,343 views • 2 months ago

If you hold $NET, this one changes an input. Gavin Baker Gavin Baker of Atreides Management told Jordi Hays on TBPN: CDNs like Cloudflare and Akamai deliver under 1% of global tokens today. Possibly under 10 basis points. The bulk of token generation happens inside hyperscaler and neocloud data centers. CDNs handle the final delivery hop - real, but small. Cloudflare is trading at all-time highs on AI narrative. Baker's framework says the stock is pricing in infrastructure upside that isn't flowing through token throughput yet. Token factory = yes. Token deliverer = maybe. The gap between those two labels is a valuation gap. Who passes Baker's token path test and who doesn't: Source: TBPN -
1:12

If you hold $NET, this one changes an input. Gavin Baker Gavin Baker of Atreides Management told Jordi Hays on TBPN: CDNs like Cloudflare and Akamai deliver under 1% of global tokens today. Possibly under 10 basis points. The bulk of token generation happens inside hyperscaler and neocloud data centers. CDNs handle the final delivery hop - real, but small. Cloudflare is trading at all-time highs on AI narrative. Baker's framework says the stock is pricing in infrastructure upside that isn't flowing through token throughput yet. Token factory = yes. Token deliverer = maybe. The gap between those two labels is a valuation gap. Who passes Baker's token path test and who doesn't: Source: TBPN -

Podcast Alpha

44,033 views • 2 months ago

xAI charges $50B per gigawatt. Neoclouds charge $17-25B. Gavin Baker Gavin Baker of Atreides Management told John Coogan and Jordi Hays on TBPN that Mars is not the SpaceX bull case. Altimeter's research shows xAI monetizes compute at $50B per gigawatt on the Google deal - 2-3x what any neocloud competitor charges. Baker stayed long through the IPO. His 12-month model doesn't track rocket launches. It tracks gigawatt additions. xAI also ordered roughly 20% of NVIDIA's Ruben production. Ruben is easier to deploy than Blackwell. Each new gigawatt at that pricing premium is a significant revenue event. Model gigawatts, not launch cadence. That's the trade. Full breakdown of Baker's near-term SpaceX case: Source: TBPN -
1:14

xAI charges $50B per gigawatt. Neoclouds charge $17-25B. Gavin Baker Gavin Baker of Atreides Management told John Coogan and Jordi Hays on TBPN that Mars is not the SpaceX bull case. Altimeter's research shows xAI monetizes compute at $50B per gigawatt on the Google deal - 2-3x what any neocloud competitor charges. Baker stayed long through the IPO. His 12-month model doesn't track rocket launches. It tracks gigawatt additions. xAI also ordered roughly 20% of NVIDIA's Ruben production. Ruben is easier to deploy than Blackwell. Each new gigawatt at that pricing premium is a significant revenue event. Model gigawatts, not launch cadence. That's the trade. Full breakdown of Baker's near-term SpaceX case: Source: TBPN -

Podcast Alpha

54,609 views • 2 months ago

AI Bull vs Bear #1: 60-70% gross margins on AI inference. Gavin Baker Gavin Baker of Atreides ran the math on BG2 Pod: inference revenue exits 2026 above $200B, and he calls $300B a low estimate for 2027. Ed Zitron's Ed Zitron bear case says costs scale with revenue, so the curve never bends. Baker's number says the unit economics already work at the inference layer. The catch: that is gross margin, not the whole P&L. It does not answer OpenAI's $21B operating loss. Where the bull margin math meets Zitron's receipts:
5:16

AI Bull vs Bear #1: 60-70% gross margins on AI inference. Gavin Baker Gavin Baker of Atreides ran the math on BG2 Pod: inference revenue exits 2026 above $200B, and he calls $300B a low estimate for 2027. Ed Zitron's Ed Zitron bear case says costs scale with revenue, so the curve never bends. Baker's number says the unit economics already work at the inference layer. The catch: that is gross margin, not the whole P&L. It does not answer OpenAI's $21B operating loss. Where the bull margin math meets Zitron's receipts:

Podcast Alpha

15,261 views • 2 months ago

Gavin Baker Gavin Baker: the Anthropic S1 is going to break a lot of people's brains. The entire AI bear case rests on one assumption, that tokens are sold below cost. What the filing settles, and who has to cover: Baker says that assumption is wrong. Open-source tokens are profitable. Anthropic is profitable and generating cash. OpenAI is close. Most tokens in the chain make money today. The S1 is the first audited document that settles it either way. Jim Chanos James Chanos has spent the year on the other side, calling the AI buildout a circular financing story. Plain version for a position holder: a lot of macro and value money is bearish on AI because it believes tokens are subsidized. If the filing shows real margins, that money covers into a re-rating and $NVDA, $TSM, $MU and the hyperscalers all move on it. If the filing shows subsidized tokens, every valuation downstream of it is sitting on sand. One document, one binary, a date on the calendar. David Sacks David Sacks and Jason Calacanis @jason worked him over on it for a full segment. Source: All-In Podcast -
2:40

Gavin Baker Gavin Baker: the Anthropic S1 is going to break a lot of people's brains. The entire AI bear case rests on one assumption, that tokens are sold below cost. What the filing settles, and who has to cover: Baker says that assumption is wrong. Open-source tokens are profitable. Anthropic is profitable and generating cash. OpenAI is close. Most tokens in the chain make money today. The S1 is the first audited document that settles it either way. Jim Chanos James Chanos has spent the year on the other side, calling the AI buildout a circular financing story. Plain version for a position holder: a lot of macro and value money is bearish on AI because it believes tokens are subsidized. If the filing shows real margins, that money covers into a re-rating and $NVDA, $TSM, $MU and the hyperscalers all move on it. If the filing shows subsidized tokens, every valuation downstream of it is sitting on sand. One document, one binary, a date on the calendar. David Sacks David Sacks and Jason Calacanis @jason worked him over on it for a full segment. Source: All-In Podcast -

Podcast Alpha

29,375 views • 8 days ago

Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)
2:00

Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)

Ihtesham Ali

68,231 views • 2 months ago

Gavin Baker Gavin Baker: HBM DRAM will be 30-40% of all hyperscaler capex in 2027. Baker manages Atreides - his 2025 Micron call is now 14x. This is his next call. HBM requires stacking 12-16 DRAM dies in a single package. Only three companies can do it: Micron, SK Hynix, Samsung. No fourth supplier arrives in 2027. Hundreds of billions annually to three firms. Micron's new supply chain agreements lock in floor pricing above prior cycle gross margin peaks. If you still hold $MU at a commodity discount to $ASML and $LRCX, Baker says that discount is no longer earned. Baker's full case at The All-In Podcast: Source: All-In Podcast -
5:47

Gavin Baker Gavin Baker: HBM DRAM will be 30-40% of all hyperscaler capex in 2027. Baker manages Atreides - his 2025 Micron call is now 14x. This is his next call. HBM requires stacking 12-16 DRAM dies in a single package. Only three companies can do it: Micron, SK Hynix, Samsung. No fourth supplier arrives in 2027. Hundreds of billions annually to three firms. Micron's new supply chain agreements lock in floor pricing above prior cycle gross margin peaks. If you still hold $MU at a commodity discount to $ASML and $LRCX, Baker says that discount is no longer earned. Baker's full case at The All-In Podcast: Source: All-In Podcast -

Podcast Alpha

503,026 views • 1 month ago

$SNDK is capping its own margins, and the market is reading it as the top. It is the most bullish thing in the print. (Save this) Gross margin ran from 51% to 84.6% over 3 quarters, and now Q1 guidance sits between 83-85%. On the surface, that looks like the classic warning sign. Every memory cycle ended with strong pricing pulling in CapEx until oversupply crushed margins. My view is that this plateau has a completely different cause. Gavin Baker explains it in the video below. Memory companies are trading short-term upside for long-term agreements, where customers prepay and both a floor and a ceiling are set on prices. Basically, SanDisk gave up the top of the price spike in exchange for guaranteed demand years out. Less/no cyclicality! My view is that a business with contracted revenue, a protected floor and volumes climbing deserves to trade like infrastructure, not like a commodity producer. = higher multiples for memory!
1:37

$SNDK is capping its own margins, and the market is reading it as the top. It is the most bullish thing in the print. (Save this) Gross margin ran from 51% to 84.6% over 3 quarters, and now Q1 guidance sits between 83-85%. On the surface, that looks like the classic warning sign. Every memory cycle ended with strong pricing pulling in CapEx until oversupply crushed margins. My view is that this plateau has a completely different cause. Gavin Baker explains it in the video below. Memory companies are trading short-term upside for long-term agreements, where customers prepay and both a floor and a ceiling are set on prices. Basically, SanDisk gave up the top of the price spike in exchange for guaranteed demand years out. Less/no cyclicality! My view is that a business with contracted revenue, a protected floor and volumes climbing deserves to trade like infrastructure, not like a commodity producer. = higher multiples for memory!

Vincent

56,810 views • 18 days ago

Gavin Baker, CIO of the $6.5 billion fund Atreides Management, revealed at Sohn 2026 how he makes money on tech and AI the man who previously managed $17 billion at Fidelity and posted one of the best returns in the industry in the interview he explains why the AI cycle is only just beginning, which chip is the most underrated right now, and why the shortage of power and chips is actually a plus for investors bookmark it if you follow tech and AI, real insights here from a man who manages billions ↓
25:18

Gavin Baker, CIO of the $6.5 billion fund Atreides Management, revealed at Sohn 2026 how he makes money on tech and AI the man who previously managed $17 billion at Fidelity and posted one of the best returns in the industry in the interview he explains why the AI cycle is only just beginning, which chip is the most underrated right now, and why the shortage of power and chips is actually a plus for investors bookmark it if you follow tech and AI, real insights here from a man who manages billions ↓

Damir Akaza

728,763 views • 1 month ago

$NVDA sits at its lowest forward P/E in a decade. Gavin Baker says that multiple is missing two moats. The Street prices it like AMD and custom silicon are closing the gap. Baker's case: Nvidia rolled out a financing structure that pairs equity stakes in AI buyers with a GPU-price-floor revenue share, indirect vendor credit since Nvidia is contractually barred from buying its own chips. It bridges buyers through their cash-flow-negative years and could grow into a large, royalty-like cloud business on its own. David Sacks made the parallel case on All-In, that July's selloff was leverage unwinding, not a break in AI fundamentals, the same read that keeps a name like Nvidia's actual moat intact even while the multiple compresses. The financing edge does not show up on a P/E screen. That is exactly why Baker says the market has not priced it.
7:00

$NVDA sits at its lowest forward P/E in a decade. Gavin Baker says that multiple is missing two moats. The Street prices it like AMD and custom silicon are closing the gap. Baker's case: Nvidia rolled out a financing structure that pairs equity stakes in AI buyers with a GPU-price-floor revenue share, indirect vendor credit since Nvidia is contractually barred from buying its own chips. It bridges buyers through their cash-flow-negative years and could grow into a large, royalty-like cloud business on its own. David Sacks made the parallel case on All-In, that July's selloff was leverage unwinding, not a break in AI fundamentals, the same read that keeps a name like Nvidia's actual moat intact even while the multiple compresses. The financing edge does not show up on a P/E screen. That is exactly why Baker says the market has not priced it.

Podcast Alpha

34,734 views • 18 days ago

🚨Gavin Baker: Apple's ineptitude in AI is shocking, "it almost feels like they're trying to lose." @jason: “Apple has bought back, let this sink in, $700B worth of shares over the past decade.” Gavin Baker: “Maybe they should have spent $200B less and used that for data centers to be a real competitor in AI." "And I just continue to be baffled, you know, they’re comfortable being late to markets, the iPhone was not the first smartphone.” “But I’m shocked at their inaction and ineptitude in AI and how bad the products are from a user perspective. They’re terrible." Jason: "Siri is worse. How is that possible?” Gavin: “Incompetence is indistinguishable from malice, but it almost feels like they’re trying to lose."
0:50

🚨Gavin Baker: Apple's ineptitude in AI is shocking, "it almost feels like they're trying to lose." @jason: “Apple has bought back, let this sink in, $700B worth of shares over the past decade.” Gavin Baker: “Maybe they should have spent $200B less and used that for data centers to be a real competitor in AI." "And I just continue to be baffled, you know, they’re comfortable being late to markets, the iPhone was not the first smartphone.” “But I’m shocked at their inaction and ineptitude in AI and how bad the products are from a user perspective. They’re terrible." Jason: "Siri is worse. How is that possible?” Gavin: “Incompetence is indistinguishable from malice, but it almost feels like they’re trying to lose."

The All-In Podcast

224,567 views • 1 year ago

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today
0:50

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

Sprytix

153,690 views • 1 month ago

Atreides' Gavin Baker says that once Starship is reusable, the economics of orbital compute crush the economics of terrestrial compute. He lays out the math: "Once $SPCX can reuse Starship, the math for orbital compute becomes pretty compelling." "It's $60B to bring on a GW terrestrially. $25B is power and cooling — you don't need that in space. And so the right comp for that $35B in IT equipment — GPU, CPU, switches, memory, storage — is that $25B [in power and cooling] vs. the cost of launch." "And once Starship is reusable, I think the cost to launch is $5B. So that means you can put a GW into space for [$40B]. The GW on earth is $60B."
0:53

Atreides' Gavin Baker says that once Starship is reusable, the economics of orbital compute crush the economics of terrestrial compute. He lays out the math: "Once $SPCX can reuse Starship, the math for orbital compute becomes pretty compelling." "It's $60B to bring on a GW terrestrially. $25B is power and cooling — you don't need that in space. And so the right comp for that $35B in IT equipment — GPU, CPU, switches, memory, storage — is that $25B [in power and cooling] vs. the cost of launch." "And once Starship is reusable, I think the cost to launch is $5B. So that means you can put a GW into space for [$40B]. The GW on earth is $60B."

TBPN

102,778 views • 2 months ago

Elon Musk: We're in the hard takeoff right now. Every successive AI model is built by the one before it. Peter Diamandis: Do you see Grok doing recursive self-improvement at this point? And how? What's the timeline for AGI and ASI? Give us a sense of that. Elon Musk: Yeah, I think we've been in recursive improvement for a while here. If you mean by, like, recursive self-improvement without a human in the loop, is that what you mean? Peter Diamandis: I do. Elon Musk: On the AI software side, I mean, humans are gradually getting less and less in the loop on the recursive self-improvement. So every successive model is built by the one before it. So that is happening to a large degree, but it's not yet fully automated. It may be there end of this year, but not later than next year. Peter Diamandis: Do you see a hard takeoff at that point? Elon Musk: We're in the hard takeoff. Peter Diamandis: Okay. Elon Musk: Right now, yes. I mean, at this point I go to sleep, there's some massive AI breakthrough, and when I wake up, there's another one. Yes. Yeah, it's hard to keep track, honestly. So, yeah, it's a bit of a head spinner. Moonshots with Peter Diamandis (Abundance Summit 2026), March 18, 2026.
1:24

Elon Musk: We're in the hard takeoff right now. Every successive AI model is built by the one before it. Peter Diamandis: Do you see Grok doing recursive self-improvement at this point? And how? What's the timeline for AGI and ASI? Give us a sense of that. Elon Musk: Yeah, I think we've been in recursive improvement for a while here. If you mean by, like, recursive self-improvement without a human in the loop, is that what you mean? Peter Diamandis: I do. Elon Musk: On the AI software side, I mean, humans are gradually getting less and less in the loop on the recursive self-improvement. So every successive model is built by the one before it. So that is happening to a large degree, but it's not yet fully automated. It may be there end of this year, but not later than next year. Peter Diamandis: Do you see a hard takeoff at that point? Elon Musk: We're in the hard takeoff. Peter Diamandis: Okay. Elon Musk: Right now, yes. I mean, at this point I go to sleep, there's some massive AI breakthrough, and when I wake up, there's another one. Yes. Yeah, it's hard to keep track, honestly. So, yeah, it's a bit of a head spinner. Moonshots with Peter Diamandis (Abundance Summit 2026), March 18, 2026.

ELON CLIPS

19,692 views • 1 month ago

Sam Altman reveals building Codex was a "crazy kamikaze mission" to beat Claude Code and that most of the best coders he knows now use Codex. "We were way behind Claude Code, and it seemed like a kind of crazy kamikaze mission to try to beat them with a coding app." "The consensus is that this kind of thing never works, and you just move on to the next one. It's a fool's errand to try to win when someone else already has momentum in a particular product category." "But we decided we thought it was really important. We asked a team to do it, and they performed a legitimate, unbelievable, very rare in the history of business thing." "Now it is the product that most of the best coders I know use." "That felt like an impossible thing, but if we hadn't asked the team, hey, we have a really important but extremely hard mission for you, it just wouldn't have happened."
0:58

Sam Altman reveals building Codex was a "crazy kamikaze mission" to beat Claude Code and that most of the best coders he knows now use Codex. "We were way behind Claude Code, and it seemed like a kind of crazy kamikaze mission to try to beat them with a coding app." "The consensus is that this kind of thing never works, and you just move on to the next one. It's a fool's errand to try to win when someone else already has momentum in a particular product category." "But we decided we thought it was really important. We asked a team to do it, and they performed a legitimate, unbelievable, very rare in the history of business thing." "Now it is the product that most of the best coders I know use." "That felt like an impossible thing, but if we hadn't asked the team, hey, we have a really important but extremely hard mission for you, it just wouldn't have happened."

Fireside Alpha

1,261,432 views • 1 month ago

Zuckerberg says Meta's Llama race changes in the next 12-18 months: most of the code will be written by AI - not autocomplete, but agents that run tests and improve the model loop. "We're not trying to build a general developer tool. We are trying to build a coding agent and an AI research agent that basically advances Llama research specifically." "Sometime in the next 12 to 18 months, we'll reach the point where most of the code that's going towards these efforts is written by AI." "And I don't mean autocomplete." "I'm talking more like you give it a goal, it can run tests, it can improve things, it can find issues." "It writes higher quality code than the average very good person on the team already." The hidden bottleneck is not chat UX. It is closing the loop between AI research, code, tests, and self-improvement inside the lab.
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Zuckerberg says Meta's Llama race changes in the next 12-18 months: most of the code will be written by AI - not autocomplete, but agents that run tests and improve the model loop. "We're not trying to build a general developer tool. We are trying to build a coding agent and an AI research agent that basically advances Llama research specifically." "Sometime in the next 12 to 18 months, we'll reach the point where most of the code that's going towards these efforts is written by AI." "And I don't mean autocomplete." "I'm talking more like you give it a goal, it can run tests, it can improve things, it can find issues." "It writes higher quality code than the average very good person on the team already." The hidden bottleneck is not chat UX. It is closing the loop between AI research, code, tests, and self-improvement inside the lab.

Karl Mehta

36,347 views • 2 months ago

Gavin Baker: "Anthropic is roughly four times more capital efficient than OpenAI - they're growing really fast and they're not burning that much cash" this is him explaining why he thinks token efficiency decides who wins, why xAI has the lowest cost per token of anyone, and what a Tesla board member says is actually the most advanced AI in the world right now "an Anthropic model can give you a comparable quality answer to a Google model using half as many tokens - and each token is literally cost" "xAI has the lowest cost per token because they build data centers faster and at a lower cost than everyone, and they run GPUs at a higher utilization" Tesla board member Antonio Gracias: "Tesla actually is today the most advanced AI in the world - it does the most complex thing a human does, drive a car" bookmark & watch the full conversation ↓
15:52

Gavin Baker: "Anthropic is roughly four times more capital efficient than OpenAI - they're growing really fast and they're not burning that much cash" this is him explaining why he thinks token efficiency decides who wins, why xAI has the lowest cost per token of anyone, and what a Tesla board member says is actually the most advanced AI in the world right now "an Anthropic model can give you a comparable quality answer to a Google model using half as many tokens - and each token is literally cost" "xAI has the lowest cost per token because they build data centers faster and at a lower cost than everyone, and they run GPUs at a higher utilization" Tesla board member Antonio Gracias: "Tesla actually is today the most advanced AI in the world - it does the most complex thing a human does, drive a car" bookmark & watch the full conversation ↓

Сarm1ne

128,114 views • 26 days ago

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast
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China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

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