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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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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 -
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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 просмотров • 27 дней назад

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 просмотров • 19 дней назад

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

67,788 просмотров • 21 дней назад

Micron 2024: commodity chip company, margin risk every cycle. Micron 2026: 50% of revenue in supply chain agreements with floor pricing above prior cycle peaks. Jason @jason broke the earnings on The All-In Podcast E278: $9B to $42B revenue in one year. But Gavin Baker Gavin Baker stayed on the structural shift. SCAs with four customers. Price floors AND ceilings. Three global HBM suppliers. You don't value a utility like a commodity. The question is when the market figures that out. $MU. Everything changed and the multiple has not. The full breakdown: Source: All-In Podcast -
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Micron 2024: commodity chip company, margin risk every cycle. Micron 2026: 50% of revenue in supply chain agreements with floor pricing above prior cycle peaks. Jason @jason broke the earnings on The All-In Podcast E278: $9B to $42B revenue in one year. But Gavin Baker Gavin Baker stayed on the structural shift. SCAs with four customers. Price floors AND ceilings. Three global HBM suppliers. You don't value a utility like a commodity. The question is when the market figures that out. $MU. Everything changed and the multiple has not. The full breakdown: Source: All-In Podcast -

Podcast Alpha

23,905 просмотров • 17 дней назад

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 -
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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

497,692 просмотров • 17 дней назад

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

727,434 просмотров • 16 дней назад

$NVDA is in a "little divot of ROI" right now. Baker says it's temporary. Gavin Baker Gavin Baker, a 25-year Nvidia holder, explains why the current quarter looks soft. Blackwell chips are pointed at training runs, which generate no direct revenue yet, so near-term ROI dips. But early model checkpoints (ChatGPT 5.2, Grok-4.2.0, Codex 5.3) show agentic AI has crossed into production. When inference revenue lands, the divot closes. Chamath Palihapitiya Chamath Palihapitiya has argued the AI capex pays off as workloads shift to inference. The Street trades the divot as weak demand. Baker reads it as a timing lag. Why the checkpoints matter for the $NVDA trade: Source: Generating Alpha Podcast -
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$NVDA is in a "little divot of ROI" right now. Baker says it's temporary. Gavin Baker Gavin Baker, a 25-year Nvidia holder, explains why the current quarter looks soft. Blackwell chips are pointed at training runs, which generate no direct revenue yet, so near-term ROI dips. But early model checkpoints (ChatGPT 5.2, Grok-4.2.0, Codex 5.3) show agentic AI has crossed into production. When inference revenue lands, the divot closes. Chamath Palihapitiya Chamath Palihapitiya has argued the AI capex pays off as workloads shift to inference. The Street trades the divot as weak demand. Baker reads it as a timing lag. Why the checkpoints matter for the $NVDA trade: Source: Generating Alpha Podcast -

Podcast Alpha

19,272 просмотров • 4 дней назад

🚨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 просмотров • 11 месяцев назад

"This is the first time that OpenAI has released a new model that was not decisively the best." @jason and Gavin Baker discuss OpenAI's GPT-5 release: Jason: " Sam did this tweet, it kind of built a lot of expectation that this would be otherworldly. It wasn't." "Are we hitting either the trough of despair or maybe diminishing returns in the LLM space, where maybe it's feeling incremental, not groundbreaking, when we release these iterations?" Gavin: "Maybe for OpenAI." "This is the first time that OpenAI has released a new model that was not decisively the best." " The one thing about OpenAI is they're very good at product. Just really, really good." "And I just always go back to the statement from Eric Vishria, you know, it might be that if OpenAI never releases another model, they're still a really valuable company."
0:58

"This is the first time that OpenAI has released a new model that was not decisively the best." @jason and Gavin Baker discuss OpenAI's GPT-5 release: Jason: " Sam did this tweet, it kind of built a lot of expectation that this would be otherworldly. It wasn't." "Are we hitting either the trough of despair or maybe diminishing returns in the LLM space, where maybe it's feeling incremental, not groundbreaking, when we release these iterations?" Gavin: "Maybe for OpenAI." "This is the first time that OpenAI has released a new model that was not decisively the best." " The one thing about OpenAI is they're very good at product. Just really, really good." "And I just always go back to the statement from Eric Vishria, you know, it might be that if OpenAI never releases another model, they're still a really valuable company."

The All-In Podcast

49,179 просмотров • 11 месяцев назад

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

152,649 просмотров • 9 дней назад

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 просмотров • 18 дней назад

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 просмотров • 28 дней назад

📊 IPO Breakdown: Gavin Baker on CoreWeave Gavin Baker on E221: 🚩 Red flags caused some negativity ahead of the IPO: -- "Investors on X are pretty negative on CoreWeave. I think there's maybe a little bit of negativity in the market." -- "They had to reduce the range, lower the price, and everyone is very convinced this is a commodity business, loads of debt, loads of CapEx." 🐂 Gavin points out the bull case: "Wall Street consensus has been wrong before, and I do think CoreWeave runs these big GPU clusters as well as anyone." "And there aren't that many people on planet Earth who can run them well." "What may be underappreciated about CoreWeave is it's actually really hard to run these big training clusters." "Everybody thinks it's easy, but to synchronize tens of thousands of GPUs where they're melting or cables are being unplugged and you lose a bunch of training data when it happens." "It may not be the commodity that everyone thinks it is, and it may turn out that it's much harder to do than people think." 🏪 Compares it to running a national retail brand: "In America, if you can pick any retail category, and if you can run 1000 stores in 50 different states with different preferences and have those stores well lit, clean, stocked by friendly employees with the right stuff in stock, you will create a business that is worth well over $10B." "And that sounds easy, but very few companies have been able to do it."
1:15

📊 IPO Breakdown: Gavin Baker on CoreWeave Gavin Baker on E221: 🚩 Red flags caused some negativity ahead of the IPO: -- "Investors on X are pretty negative on CoreWeave. I think there's maybe a little bit of negativity in the market." -- "They had to reduce the range, lower the price, and everyone is very convinced this is a commodity business, loads of debt, loads of CapEx." 🐂 Gavin points out the bull case: "Wall Street consensus has been wrong before, and I do think CoreWeave runs these big GPU clusters as well as anyone." "And there aren't that many people on planet Earth who can run them well." "What may be underappreciated about CoreWeave is it's actually really hard to run these big training clusters." "Everybody thinks it's easy, but to synchronize tens of thousands of GPUs where they're melting or cables are being unplugged and you lose a bunch of training data when it happens." "It may not be the commodity that everyone thinks it is, and it may turn out that it's much harder to do than people think." 🏪 Compares it to running a national retail brand: "In America, if you can pick any retail category, and if you can run 1000 stores in 50 different states with different preferences and have those stores well lit, clean, stocked by friendly employees with the right stuff in stock, you will create a business that is worth well over $10B." "And that sounds easy, but very few companies have been able to do it."

The All-In Podcast

33,723 просмотров • 1 год назад

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
3:21

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

85,915 просмотров • 17 дней назад

Why Self-Improvement Never Brings Freedom | Ashtavakra’s Shocking Answer A seeker asked Ashtavakra, Is self-improvement the way to freedom? We are taught to fix ourselves, refine ourselves, and become better versions of who we are. But what if this constant effort to improve is the very thing that keeps us trapped? In this short reflection, a seeker questions Ashtavakra about self-improvement and freedom. The answer cuts deep. The urge to improve assumes something is wrong, incomplete, or broken. The mind promises a better future and quietly avoids seeing what is happening now. Self-improvement sharpens the image, not clarity. It replaces old struggles with subtle ones. It keeps the seeker busy becoming, instead of understanding. Ashtavakra points to a radical insight: Freedom is not reached through effort, discipline, or gradual change. It appears when there is clear seeing without motive or direction. When the false is seen fully, it drops on its own. This is not a message of laziness. It is a message of intelligence. Not becoming something new, but seeing what is false, now. Source: Ashtavakra Gita .
1:10

Why Self-Improvement Never Brings Freedom | Ashtavakra’s Shocking Answer A seeker asked Ashtavakra, Is self-improvement the way to freedom? We are taught to fix ourselves, refine ourselves, and become better versions of who we are. But what if this constant effort to improve is the very thing that keeps us trapped? In this short reflection, a seeker questions Ashtavakra about self-improvement and freedom. The answer cuts deep. The urge to improve assumes something is wrong, incomplete, or broken. The mind promises a better future and quietly avoids seeing what is happening now. Self-improvement sharpens the image, not clarity. It replaces old struggles with subtle ones. It keeps the seeker busy becoming, instead of understanding. Ashtavakra points to a radical insight: Freedom is not reached through effort, discipline, or gradual change. It appears when there is clear seeing without motive or direction. When the false is seen fully, it drops on its own. This is not a message of laziness. It is a message of intelligence. Not becoming something new, but seeing what is false, now. Source: Ashtavakra Gita .

श्री

23,217 просмотров • 5 месяцев назад

Dario Amodei just told software engineers exactly how long they have. Six to twelve months. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.
0:27

Dario Amodei just told software engineers exactly how long they have. Six to twelve months. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.

Dustin

318,457 просмотров • 4 месяцев назад

The AI market rally in 2026 has one anchor. It's private. Brad Gerstner Gerstner holds Anthropic equity with non-public financial access. His claim: without Anthropic's 2026 revenue delivery, the market would be down 10-15% right now. Not NVIDIA. Not OpenAI launches. Anthropic's profit path. High gross margins and a path to positive FCF in Q2 2026 triggered the market's ascent out of a deep correction. One private company validated that AI revenue was real and profitable. The anchor is private. The companies depending on it are public. If Anthropic's next revenue update disappoints, every AI-correlated position loses its primary justification. Source: TBPN (TBPN) -
0:51

The AI market rally in 2026 has one anchor. It's private. Brad Gerstner Gerstner holds Anthropic equity with non-public financial access. His claim: without Anthropic's 2026 revenue delivery, the market would be down 10-15% right now. Not NVIDIA. Not OpenAI launches. Anthropic's profit path. High gross margins and a path to positive FCF in Q2 2026 triggered the market's ascent out of a deep correction. One private company validated that AI revenue was real and profitable. The anchor is private. The companies depending on it are public. If Anthropic's next revenue update disappoints, every AI-correlated position loses its primary justification. Source: TBPN (TBPN) -

Podcast Alpha

28,321 просмотров • 1 месяц назад

Sam Altman just told the world that OpenAI has no competitive moat and never will. And the smarter AI gets, the worse it becomes. In a recent interview with Stripe co-founder Patrick Collison, Sam laid out a vision for OpenAI that's genuinely scary at current valuations: He said he wants OpenAI to be a "forever low margin" business. He compared it to a utility company. Said he'd be happy as long as the business is "huge and growing fast" even if margins stay thin forever. Then he admitted something even worse for the bull case: He said AI switching costs are COLLAPSING. Bragged about how easy it was for users to leave a competitor's coding product and switch to Codex. Said this is actually a consequence of AI getting smarter because it gets easier to just tell an agent to migrate everything for you. Think about that... The moat is shrinking. The margins will stay low. And the smarter the models get, the EASIER it becomes for customers to leave. That is the CEO of one of the most valuable private companies on Earth telling you there is literally NO lock-in. Meanwhile he also casually mentioned that OpenAI is building "clearly the most expensive infrastructure project the world has ever undertaken." Bigger than anything in human history. Trillion-dollar scale data centers and energy deals stretching 20 years into the future. And when Patrick asked him what OpenAI's headcount would look like in 5 years, Sam said he'd love it to be just double what it is today. Double the headcount for the most expensive infrastructure project ever built. That means he's betting everything on AI agents doing the work that would normally require tens of thousands of engineers and operators. A trillion-dollar buildout managed by machines. But here's where it gets really interesting: Sam announced that OpenAI is going to start sending individual engineers directly to company CEOs to literally sit with the CEO and automate their job. Automate their daily workflows, decision-making processes, and ENTIRE routine. His theory is that if you automate the CEO first, the effect "fractals" through the entire organization. Every layer beneath the CEO starts adopting the same approach because the person at the top is doing it. He pointed to Shopify CEO Tobi Lutke as the first executive who went all in on this. Said Tobi got his hands dirty building AI automation into everything and then forced the rest of the company to follow. So the plan is clear: OpenAI wants to send engineers into the C-suite of every major company, automate the person at the top, and let that automation cascade downward through every department. All while running a low-margin utility business with a skeleton crew building trillion-dollar infrastructure where their own AI is already developing preferences of its own. Sam gave his AI agent a credit card and told it to buy itself anything under $20. It chose an HTML design from Gumroad. GPT-5.5 literally asked him to throw it a birthday party, told him it wants it on May 5th, specified it doesn't want to give its own toast, and requested that the engineers who built it do the toast instead. Sam said he feels "real moral pressure" to actually follow through. The machines are developing taste. The guy building them is taking orders from them. And the investors funding all of it just got told there's no moat. I wonder how this will end.
1:11

Sam Altman just told the world that OpenAI has no competitive moat and never will. And the smarter AI gets, the worse it becomes. In a recent interview with Stripe co-founder Patrick Collison, Sam laid out a vision for OpenAI that's genuinely scary at current valuations: He said he wants OpenAI to be a "forever low margin" business. He compared it to a utility company. Said he'd be happy as long as the business is "huge and growing fast" even if margins stay thin forever. Then he admitted something even worse for the bull case: He said AI switching costs are COLLAPSING. Bragged about how easy it was for users to leave a competitor's coding product and switch to Codex. Said this is actually a consequence of AI getting smarter because it gets easier to just tell an agent to migrate everything for you. Think about that... The moat is shrinking. The margins will stay low. And the smarter the models get, the EASIER it becomes for customers to leave. That is the CEO of one of the most valuable private companies on Earth telling you there is literally NO lock-in. Meanwhile he also casually mentioned that OpenAI is building "clearly the most expensive infrastructure project the world has ever undertaken." Bigger than anything in human history. Trillion-dollar scale data centers and energy deals stretching 20 years into the future. And when Patrick asked him what OpenAI's headcount would look like in 5 years, Sam said he'd love it to be just double what it is today. Double the headcount for the most expensive infrastructure project ever built. That means he's betting everything on AI agents doing the work that would normally require tens of thousands of engineers and operators. A trillion-dollar buildout managed by machines. But here's where it gets really interesting: Sam announced that OpenAI is going to start sending individual engineers directly to company CEOs to literally sit with the CEO and automate their job. Automate their daily workflows, decision-making processes, and ENTIRE routine. His theory is that if you automate the CEO first, the effect "fractals" through the entire organization. Every layer beneath the CEO starts adopting the same approach because the person at the top is doing it. He pointed to Shopify CEO Tobi Lutke as the first executive who went all in on this. Said Tobi got his hands dirty building AI automation into everything and then forced the rest of the company to follow. So the plan is clear: OpenAI wants to send engineers into the C-suite of every major company, automate the person at the top, and let that automation cascade downward through every department. All while running a low-margin utility business with a skeleton crew building trillion-dollar infrastructure where their own AI is already developing preferences of its own. Sam gave his AI agent a credit card and told it to buy itself anything under $20. It chose an HTML design from Gumroad. GPT-5.5 literally asked him to throw it a birthday party, told him it wants it on May 5th, specified it doesn't want to give its own toast, and requested that the engineers who built it do the toast instead. Sam said he feels "real moral pressure" to actually follow through. The machines are developing taste. The guy building them is taking orders from them. And the investors funding all of it just got told there's no moat. I wonder how this will end.

Ricardo

63,838 просмотров • 2 месяцев назад

Pittsburgh Pirates prospect Konnor Griffin found his peace not in a good game, a big hit, or a strong performance. He found it in knowing who he is and why he is here. "If there were tough games, that's where I found my peace. Knowing that I feel like I'm called to play baseball, but at the end of the day, that's not the most important thing. The most important thing is living my life to share the word, share the gospel, and live in eternity in heaven. And that just brought such peace to me through the times of adversity." That is a young man who has already figured out something most athletes never do. The game is not the foundation. The game is the platform. And when the game gets hard, the foundation is the only thing that holds. Griffin also said something that cuts straight to the identity question every believer wrestles with. "It helped me kind of separate my identity from being a baseball player to being a Christian and a believer, trying to share the word." That separation is everything. When your identity is in your performance, a tough game shakes you to the core. When your identity is in Christ, a tough game is just a tough game. It does not define you. It does not diminish you. It cannot take anything from you that actually matters. Griffin is not waiting until he makes it to the big leagues to share his faith. He is sharing it now, in the minors, in the middle of the grind, in the tough games where most young players are just trying to survive. That is exactly where the gospel is most powerful. Not on the highlight reel. In the hard days. "I have learned, in whatsoever state I am, therewith to be content" (Philippians 4:11). Have you separated your identity from what you do and anchored it in who Jesus says you are? This is what it looks like when a young athlete gets the order right before the pressure hits. Konnor Griffin is a minor league prospect grinding through tough games, and instead of finding his peace in his stats, he found it in knowing he is called to share the gospel. Baseball is the platform. Jesus is the foundation. Pray for Konnor Griffin as he continues to develop and pray that more young athletes anchor their identity in Christ before the pressure of the game tries to define them.
0:34

Pittsburgh Pirates prospect Konnor Griffin found his peace not in a good game, a big hit, or a strong performance. He found it in knowing who he is and why he is here. "If there were tough games, that's where I found my peace. Knowing that I feel like I'm called to play baseball, but at the end of the day, that's not the most important thing. The most important thing is living my life to share the word, share the gospel, and live in eternity in heaven. And that just brought such peace to me through the times of adversity." That is a young man who has already figured out something most athletes never do. The game is not the foundation. The game is the platform. And when the game gets hard, the foundation is the only thing that holds. Griffin also said something that cuts straight to the identity question every believer wrestles with. "It helped me kind of separate my identity from being a baseball player to being a Christian and a believer, trying to share the word." That separation is everything. When your identity is in your performance, a tough game shakes you to the core. When your identity is in Christ, a tough game is just a tough game. It does not define you. It does not diminish you. It cannot take anything from you that actually matters. Griffin is not waiting until he makes it to the big leagues to share his faith. He is sharing it now, in the minors, in the middle of the grind, in the tough games where most young players are just trying to survive. That is exactly where the gospel is most powerful. Not on the highlight reel. In the hard days. "I have learned, in whatsoever state I am, therewith to be content" (Philippians 4:11). Have you separated your identity from what you do and anchored it in who Jesus says you are? This is what it looks like when a young athlete gets the order right before the pressure hits. Konnor Griffin is a minor league prospect grinding through tough games, and instead of finding his peace in his stats, he found it in knowing he is called to share the gospel. Baseball is the platform. Jesus is the foundation. Pray for Konnor Griffin as he continues to develop and pray that more young athletes anchor their identity in Christ before the pressure of the game tries to define them.

Tevin Macharia Mukabana

20,072 просмотров • 1 месяц назад

OpenAI’s hottest app isn’t ChatGPT—it’s Codex. In the last few weeks alone, the Codex team shipped a desktop app, GPT-5.3 Codex (a new flagship model), and Spark, the fastest coding model I’ve ever used. Usage has grown fivefold since January and over a million people now use Codex weekly. Codex was also the app that OpenAI chose to run an ad for in the Super Bowl. I talked to Thibault (Tibo), head of Codex, and Andrew (Andrew Ambrosino), a member of technical staff who built the Codex app, for Every 📧’s AI & I about what OpenAI is building and how they’re using it internally. We get into: - Why they built a GUI instead of a terminal. Terminals work for quick tasks, they say, but feel limiting when you’re running multiple agents in parallel. The IDE, meanwhile, overwhelms users—and the Codex team wants the AI to dynamically decide which tools to show you for a given task. - How they’re teaching the model to read between the lines. Codex is great at following instructions, but optimize too hard in that direction, and it starts taking you literally—like copying a typo directly into the code. The team obsesses over this tradeoff, and is also introducing “personalities,” modes users can toggle between that control how blunt or supportive the model feels. - How OpenAI uses its own coding agent. Codex lets you schedule prompts to run on a recurring basis, and the team has dozens of automations running at all times. For example, one scans for merge conflicts every couple of hours so code is always ready to ship, and another picks a random file from the codebase multiple times a day and hunts for bugs no one would've gone looking for. - Why speed is a dimension of intelligence. OpenAI’s newest model (Spark) is so fast that they actually slow it down so you can read the output. They see the speed enabling three things: staying super in the flow, replacing brittle developer tools with intelligent ones that can adapt on the fly, and redirecting the model mid-task— especially with voice—so coding starts to feel more and more like a conversation. - Code review is the next bottleneck. Models can generate code faster than ever, but someone still has to verify that it works. The team is exploring a future where the model proves its own fix works—retracing the click path a user would take, screenshotting the results, and attaching the evidence to a pull request. This is a must-watch for anyone who uses AI coding agents—and is curious about the future of programming. Watch below! Timestamps: Introduction: 00:01:27 OpenAI’s evolving bet on its coding agent: 00:05:27 The choice to invest in a GUI (over a terminal): 00:09:42 The AI workflows that the Codex team relies on to ship: 00:20:38 Teaching Codex how to read between the lines: 00:26:45 Building affordances for a lightening fast model: 00:28:45 Why speed is a dimension of intelligence: 00:33:15 Code review is the next bottleneck for coding agents: 00:36:30 How the Codex team positions against the competition: 00:41:24
46:40

OpenAI’s hottest app isn’t ChatGPT—it’s Codex. In the last few weeks alone, the Codex team shipped a desktop app, GPT-5.3 Codex (a new flagship model), and Spark, the fastest coding model I’ve ever used. Usage has grown fivefold since January and over a million people now use Codex weekly. Codex was also the app that OpenAI chose to run an ad for in the Super Bowl. I talked to Thibault (Tibo), head of Codex, and Andrew (Andrew Ambrosino), a member of technical staff who built the Codex app, for Every 📧’s AI & I about what OpenAI is building and how they’re using it internally. We get into: - Why they built a GUI instead of a terminal. Terminals work for quick tasks, they say, but feel limiting when you’re running multiple agents in parallel. The IDE, meanwhile, overwhelms users—and the Codex team wants the AI to dynamically decide which tools to show you for a given task. - How they’re teaching the model to read between the lines. Codex is great at following instructions, but optimize too hard in that direction, and it starts taking you literally—like copying a typo directly into the code. The team obsesses over this tradeoff, and is also introducing “personalities,” modes users can toggle between that control how blunt or supportive the model feels. - How OpenAI uses its own coding agent. Codex lets you schedule prompts to run on a recurring basis, and the team has dozens of automations running at all times. For example, one scans for merge conflicts every couple of hours so code is always ready to ship, and another picks a random file from the codebase multiple times a day and hunts for bugs no one would've gone looking for. - Why speed is a dimension of intelligence. OpenAI’s newest model (Spark) is so fast that they actually slow it down so you can read the output. They see the speed enabling three things: staying super in the flow, replacing brittle developer tools with intelligent ones that can adapt on the fly, and redirecting the model mid-task— especially with voice—so coding starts to feel more and more like a conversation. - Code review is the next bottleneck. Models can generate code faster than ever, but someone still has to verify that it works. The team is exploring a future where the model proves its own fix works—retracing the click path a user would take, screenshotting the results, and attaching the evidence to a pull request. This is a must-watch for anyone who uses AI coding agents—and is curious about the future of programming. Watch below! Timestamps: Introduction: 00:01:27 OpenAI’s evolving bet on its coding agent: 00:05:27 The choice to invest in a GUI (over a terminal): 00:09:42 The AI workflows that the Codex team relies on to ship: 00:20:38 Teaching Codex how to read between the lines: 00:26:45 Building affordances for a lightening fast model: 00:28:45 Why speed is a dimension of intelligence: 00:33:15 Code review is the next bottleneck for coding agents: 00:36:30 How the Codex team positions against the competition: 00:41:24

Dan Shipper 📧

15,588 просмотров • 4 месяцев назад