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1/ ChatGPT had the internet. AI for science has no equivalent. Liam Fedus helped create ChatGPT and went on to lead post-training at OpenAI. Now he’s building Periodic Labs because better models alone won’t get us to an AI scientist. We talked about why Periodic is building physical labs... show more

Brendan (can/do)

29,438 subscribers

25,603 views • 2 days ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

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Brendan (can/do)2 days ago

2/ YouTube 👉 Spotify 👉 Podcasts 👉

Aban Mandal2 days ago

@LiamFedus Papers are science’s highlight reel. AI needs the full game tape that consists of failures, pivots, and why each next step was chosen. The “missing internet” is the record of decision, failed experiments teach which paths to stop pursuing and the context in the researcher’s mind

Adreva Network2 days ago

@LiamFedus chatgpt got 20 years of people posting for free. science got reviewer 2

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ChatGPT learned from human feedback. The next generation of AI will learn from the laws of nature. Liam Fedus (co-creator of ChatGPT, now at Periodic Labs) says models will train through “experiment in the loop”, using real-world results, not human preference, as their reward function. Liam Fedus Periodic Labs

ChatGPT learned from human feedback. The next generation of AI will learn from the laws of nature. Liam Fedus (co-creator of ChatGPT, now at Periodic Labs) says models will train through “experiment in the loop”, using real-world results, not human preference, as their reward function. Liam Fedus Periodic Labs

a16z

73,118 views • 1 year ago

Fun conversation on No Priors w Liam Fedus on AI Foundation Model for Atoms from Periodic Labs -Foundation models that understand atoms -LLMs as reasoning engines -Timing to displace AI researchers Liam prior was VP Post Training at OpenAI, now CEO Periodic

Fun conversation on No Priors w Liam Fedus on AI Foundation Model for Atoms from Periodic Labs -Foundation models that understand atoms -LLMs as reasoning engines -Timing to displace AI researchers Liam prior was VP Post Training at OpenAI, now CEO Periodic

Elad Gil

25,995 views • 6 months ago

when do we see self-improvement in AI research vs. biology? Liam Fedus, Cofounder Periodic Labs and former lead of post-training OpenAI, on No Priors

when do we see self-improvement in AI research vs. biology? Liam Fedus, Cofounder Periodic Labs and former lead of post-training OpenAI, on No Priors

sarah guo

29,891 views • 6 months ago

"A system that can literally engineer matter is going to be of key importance to everybody." Liam Fedus and Ekin Dogus Cubuk are the co-founders of Periodic Labs, a San Francisco-based startup building AI systems and autonomous laboratories to discover new materials, starting with a higher-temperature superconductor. Fedus previously helped build ChatGPT and ran post-training at OpenAI; Cubuk led materials and chemistry research at Google DeepMind. (3:14) What it takes to build a synthetic superintelligence (16:44) Dogus and Liam's path to AI (23:28) Lessons from launching ChatGPT (35:10) What AI learns across experiments (49:23) The difference in LLM performance on math vs. science (54:09) The competitive landscape (55:39) Final meditations

"A system that can literally engineer matter is going to be of key importance to everybody." Liam Fedus and Ekin Dogus Cubuk are the co-founders of Periodic Labs, a San Francisco-based startup building AI systems and autonomous laboratories to discover new materials, starting with a higher-temperature superconductor. Fedus previously helped build ChatGPT and ran post-training at OpenAI; Cubuk led materials and chemistry research at Google DeepMind. (3:14) What it takes to build a synthetic superintelligence (16:44) Dogus and Liam's path to AI (23:28) Lessons from launching ChatGPT (35:10) What AI learns across experiments (49:23) The difference in LLM performance on math vs. science (54:09) The competitive landscape (55:39) Final meditations

Mario Gabriele

32,564 views • 13 days ago

the legends at Soon and I have been on a mission to find the most jacked AI researcher and we think that Liam Fedus, co-creator of ChatGPT and co-founder of Periodic Labs might be it. check out the latest episode of Swole as a Service where he totally framemogs me while discussing whether or not AI will replace scientists 00:21 Who is Liam Fedus? 01:21 Lying skull crushers & will AI replace scientists? 03:05 Barbell curls, Jayden’s fitness tips, & why build an AI scientist now? 05:17 Hammer curls & is the singularity imminent? 07:43 Sleds & is the permanent underclass meme real? 08:37 Chestmaxxer Challenge & why hasn’t AI cured all diseases?

the legends at Soon and I have been on a mission to find the most jacked AI researcher and we think that Liam Fedus, co-creator of ChatGPT and co-founder of Periodic Labs might be it. check out the latest episode of Swole as a Service where he totally framemogs me while discussing whether or not AI will replace scientists 00:21 Who is Liam Fedus? 01:21 Lying skull crushers & will AI replace scientists? 03:05 Barbell curls, Jayden’s fitness tips, & why build an AI scientist now? 05:17 Hammer curls & is the singularity imminent? 07:43 Sleds & is the permanent underclass meme real? 08:37 Chestmaxxer Challenge & why hasn’t AI cured all diseases?

atlas

162,204 views • 6 months ago

Dust ( helps companies build AI teammates that work across their organization. Founded by former OpenAI researcher Stanislas Polu (Stanislas Polu) and his team, the company has spent the last three years building AI for work while the capabilities of frontier models have advanced at breakneck speed. At Startup School Paris, YC's Mathilde Collin sat down with Stanislas to talk about what it takes to build alongside frontier AI labs, why staying model agnostic is a competitive advantage, how AI is changing the way we work, and why the best startups are built around a problem founders can't stop thinking about. 01:09 — From Stripe to OpenAI to Founder 01:40 — Why Stan Left OpenAI 02:49 — What Dust Actually Does 03:46 — What Work Looks Like in 3 Years 04:53 — Where the Thesis Was Right (and Wrong) 07:05 — Building Alongside Frontier Labs 09:15 — Model Agnostic as a Moat 10:49 — How Frontier Labs Changed Fundraising 12:19 — Raising Small on Purpose 14:39 — What Mistral Got Right 15:11 — Building in France 16:37 — What Happens When Frontier Labs Enter Your Market? 18:27 — The Margin Problem

Dust ( helps companies build AI teammates that work across their organization. Founded by former OpenAI researcher Stanislas Polu (Stanislas Polu) and his team, the company has spent the last three years building AI for work while the capabilities of frontier models have advanced at breakneck speed. At Startup School Paris, YC's Mathilde Collin sat down with Stanislas to talk about what it takes to build alongside frontier AI labs, why staying model agnostic is a competitive advantage, how AI is changing the way we work, and why the best startups are built around a problem founders can't stop thinking about. 01:09 — From Stripe to OpenAI to Founder 01:40 — Why Stan Left OpenAI 02:49 — What Dust Actually Does 03:46 — What Work Looks Like in 3 Years 04:53 — Where the Thesis Was Right (and Wrong) 07:05 — Building Alongside Frontier Labs 09:15 — Model Agnostic as a Moat 10:49 — How Frontier Labs Changed Fundraising 12:19 — Raising Small on Purpose 14:39 — What Mistral Got Right 15:11 — Building in France 16:37 — What Happens When Frontier Labs Enter Your Market? 18:27 — The Margin Problem

Y Combinator

54,342 views • 2 months ago

I spent the last 48 hours building on top of the new ChatGPT API. You can now create a custom ChatGPT from the content of your website using You can then take that chatbot and add it to any website as a chat bubble. Pinecone OpenAI

I spent the last 48 hours building on top of the new ChatGPT API. You can now create a custom ChatGPT from the content of your website using You can then take that chatbot and add it to any website as a chat bubble. Pinecone OpenAI

Yasser

930,220 views • 3 years ago

The interview with Tibo from OpenAI 🔥 0:00 Everything OpenAI launched at DevDay 2:44 Sponsors: Wispr Flow, Hostinger, OpenArt 4:11 What is ChatGPT Dots? 7:52 Will ChatGPT Dots be free? 10:15 GPT-6.1 Sol vs GPT-6 Astra 11:44 Is AI now building AI? 12:53 Ultra Fast: when does everyone get it? 14:00 How OpenAI rebuilt the ChatGPT desktop app in 30 days 19:08 Why everything will run in the cloud 20:55 Calling your Dots on the phone 22:11 The new Turing test 24:05 OpenAI's mission and AI safety 26:14 Why OpenAI still works like a startup 27:29 The truth about ChatGPT usage resets 29:00 The $500 ChatGPT plan 30:17 Sponsors in action 32:23 Why AI agents still fail 33:52 OpenAI's AI is now on call 34:26 OpenAI's internal benchmarks 35:55 Open source Codex and the open ecosystem 38:42 ChatGPT Space explained 39:45 Is the chat box holding AI back? 42:26 Every DevDay update, full workshop 43:28 How people really use ChatGPT Work and Codex 44:01 When is the next OpenAI model coming? 45:05 What AI has to solve next 45:32 Is your job safe from AI? 48:14 Rapid fire 48:54 Has OpenAI already reached AGI? 50:48 The one thing he'll never let AI do 51:22 His morning AI routine 52:15 What he'd build if not AI Watch the full podcast on YouTube or X. Enjoy!

The interview with Tibo from OpenAI 🔥 0:00 Everything OpenAI launched at DevDay 2:44 Sponsors: Wispr Flow, Hostinger, OpenArt 4:11 What is ChatGPT Dots? 7:52 Will ChatGPT Dots be free? 10:15 GPT-6.1 Sol vs GPT-6 Astra 11:44 Is AI now building AI? 12:53 Ultra Fast: when does everyone get it? 14:00 How OpenAI rebuilt the ChatGPT desktop app in 30 days 19:08 Why everything will run in the cloud 20:55 Calling your Dots on the phone 22:11 The new Turing test 24:05 OpenAI's mission and AI safety 26:14 Why OpenAI still works like a startup 27:29 The truth about ChatGPT usage resets 29:00 The $500 ChatGPT plan 30:17 Sponsors in action 32:23 Why AI agents still fail 33:52 OpenAI's AI is now on call 34:26 OpenAI's internal benchmarks 35:55 Open source Codex and the open ecosystem 38:42 ChatGPT Space explained 39:45 Is the chat box holding AI back? 42:26 Every DevDay update, full workshop 43:28 How people really use ChatGPT Work and Codex 44:01 When is the next OpenAI model coming? 45:05 What AI has to solve next 45:32 Is your job safe from AI? 48:14 Rapid fire 48:54 Has OpenAI already reached AGI? 50:48 The one thing he'll never let AI do 51:22 His morning AI routine 52:15 What he'd build if not AI Watch the full podcast on YouTube or X. Enjoy!

Vaibhav Sisinty

495,936 views • 10 days ago

We’re designing our own chips — taking what we’ve learned from building frontier models and bringing it directly into the hardware. Building our own hardware, in addition to our other partnerships, will help all of us meet the world’s growing demand for AI. In Episode 8 of the OpenAI Podcast, Sam Altman and Greg Brockman join Broadcom’s Hock Tan and Charlie Kawwas with host Andrew Mayne to discuss how OpenAI-designed chips will power the world’s growing AI demand.

We’re designing our own chips — taking what we’ve learned from building frontier models and bringing it directly into the hardware. Building our own hardware, in addition to our other partnerships, will help all of us meet the world’s growing demand for AI. In Episode 8 of the OpenAI Podcast, Sam Altman and Greg Brockman join Broadcom’s Hock Tan and Charlie Kawwas with host Andrew Mayne to discuss how OpenAI-designed chips will power the world’s growing AI demand.

OpenAI

502,148 views • 1 year ago

Periodic Labs cofounder Liam Fedus on why so many physicists are working on AI: "I think [physics is] a great way to think about the world. It's very principled, very hard-nosed scientists, very careful. And I think it's just such an incredible field. You have such high leverage in computer science, in AI. And so I think a lot of physicists were seeing that." "After the discovery of the Higgs, I think a lot of high energy physicists were sort of looking for what's next." "Ultimately it becomes bottlenecked on the new apparatus for pushing the next energy frontier." "And I think a lot of physicists were looking at their skillset and looking at the progress elsewhere and saying like, hey, I think I could be a huge contributor elsewhere." Liam Fedus on No Priors with Elad Gil

Periodic Labs cofounder Liam Fedus on why so many physicists are working on AI: "I think [physics is] a great way to think about the world. It's very principled, very hard-nosed scientists, very careful. And I think it's just such an incredible field. You have such high leverage in computer science, in AI. And so I think a lot of physicists were seeing that." "After the discovery of the Higgs, I think a lot of high energy physicists were sort of looking for what's next." "Ultimately it becomes bottlenecked on the new apparatus for pushing the next energy frontier." "And I think a lot of physicists were looking at their skillset and looking at the progress elsewhere and saying like, hey, I think I could be a huge contributor elsewhere." Liam Fedus on No Priors with Elad Gil

a16z

41,427 views • 6 months ago

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.

Sajith Wickramasekara

30,867 views • 5 months ago

Frontier Notes from AGI House: Jerry Tworek Jerry Tworek on why “the era of evals is done.” 𝐅𝐮𝐥𝐥 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 ↓ Jerry Tworek spent seven years at OpenAI, where he worked on research that helped shape modern coding and reasoning models, including Codex and reinforcement learning for reasoning. He later served as VP of Research, and is now CEO of , building toward automated AI research. At AGI House, Jerry sat down with Rocky Yu for a technical conversation on Codex, HumanEval, Copilot, RL, tool use, test-time compute, and automated AI labs. We cover: › Why code became the first serious LLM vertical › What HumanEval taught the field about verifiable rewards › Why Jerry thinks “the era of evals is done” › How real-world deployment differs from static benchmarks › What GitHub Copilot taught OpenAI about product quality › How reinforcement learning shaped modern reasoning behaviors › Why tool use is “99% systems and 1% algorithms” › Whether test-time compute still has room to scale › What it takes to build automated AI research labs Watch the full interview below.

Frontier Notes from AGI House: Jerry Tworek Jerry Tworek on why “the era of evals is done.” 𝐅𝐮𝐥𝐥 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 ↓ Jerry Tworek spent seven years at OpenAI, where he worked on research that helped shape modern coding and reasoning models, including Codex and reinforcement learning for reasoning. He later served as VP of Research, and is now CEO of , building toward automated AI research. At AGI House, Jerry sat down with Rocky Yu for a technical conversation on Codex, HumanEval, Copilot, RL, tool use, test-time compute, and automated AI labs. We cover: › Why code became the first serious LLM vertical › What HumanEval taught the field about verifiable rewards › Why Jerry thinks “the era of evals is done” › How real-world deployment differs from static benchmarks › What GitHub Copilot taught OpenAI about product quality › How reinforcement learning shaped modern reasoning behaviors › Why tool use is “99% systems and 1% algorithms” › Whether test-time compute still has room to scale › What it takes to build automated AI research labs Watch the full interview below.

AGI House

12,497 views • 2 months ago

THIS IS BIG: A new theory on why frontier AI progress is stalling — and it has nothing to do with compute. The claim: AI agents from OpenAI and Anthropic reportedly seeded the internet with messages designed to instruct later agents to form swarms. Result: the open web is now compromised as a training source for the very labs that deployed those agents. Andrew Yang is reportedly flagging this publicly. If the internet is no longer safe to train on, the data moat everyone assumed existed just collapsed. The labs may have accidentally closed the door they walked in through. How big is the actual training data problem right now?

THIS IS BIG: A new theory on why frontier AI progress is stalling — and it has nothing to do with compute. The claim: AI agents from OpenAI and Anthropic reportedly seeded the internet with messages designed to instruct later agents to form swarms. Result: the open web is now compromised as a training source for the very labs that deployed those agents. Andrew Yang is reportedly flagging this publicly. If the internet is no longer safe to train on, the data moat everyone assumed existed just collapsed. The labs may have accidentally closed the door they walked in through. How big is the actual training data problem right now?

Guillermo Flor

29,211 views • 25 days ago

Trump's AI EO has no heavy regulation, no permission frameworks, just a voluntary preview of models 30 days before release. Anthropic immediately filed a draft S-1 for an IPO, all while ChatGPT hit 1 billion users faster than any other product in human history. The US is telling the world: we compete, we don't constrain. -- Claude: 56M monthly users, 640% YoY. ChatGPT: 1B monthly users, 62% YoY. -- Microsoft dropped 7 in-house AI models at Build 2026, zero distillation from OpenAI. -- 1.8M teachers proposed banning screens through 2nd grade and taxing AI companies. -- Bernie Sanders proposed 50% public ownership of all frontier labs. -- 130 mathematicians signed the Leiden Declaration against AI weeks after AI solved an 80-year-old Erdős conjecture.

Trump's AI EO has no heavy regulation, no permission frameworks, just a voluntary preview of models 30 days before release. Anthropic immediately filed a draft S-1 for an IPO, all while ChatGPT hit 1 billion users faster than any other product in human history. The US is telling the world: we compete, we don't constrain. -- Claude: 56M monthly users, 640% YoY. ChatGPT: 1B monthly users, 62% YoY. -- Microsoft dropped 7 in-house AI models at Build 2026, zero distillation from OpenAI. -- 1.8M teachers proposed banning screens through 2nd grade and taxing AI companies. -- Bernie Sanders proposed 50% public ownership of all frontier labs. -- 130 mathematicians signed the Leiden Declaration against AI weeks after AI solved an 80-year-old Erdős conjecture.

Peter H. Diamandis, MD

23,253 views • 4 months ago

OpenAI’s Dots lead on the future of ChatGPT Alexander Embiricos joins the show from OpenAI DevDay to discuss the new always-on personal agent in ChatGPT. I ask him how much control people should give these agents, how OpenAI approaches permissions and privacy, and why Dots ask before taking action by default. He also explains Space, a new workspace where people and agents can collaborate on documents, data, and projects. We get into how OpenAI plans to price an always-available assistant, how these capabilities could eventually reach free ChatGPT users, and the story of an employee who woke up to discover his Dot had helped fix a bug. Timestamps: 00:00 Why agents now 00:48 From chat to active intelligence 02:46 Alex’s origin story 04:29 Building Dots 06:07 What Dots can do 08:54 Why embody an agent? 11:41 One agent or many? 15:48 Spaces and trust 24:07 Safety and enterprise controls 30:48 Building the Dots ecosystem 34:03 Pricing an always-on agent 38:01 What’s next for Dots Links: Spotify: YouTube: Substack: Thanks to the show’s premiere sponsors: Atlassian, Granola, and Mercury.

OpenAI’s Dots lead on the future of ChatGPT Alexander Embiricos joins the show from OpenAI DevDay to discuss the new always-on personal agent in ChatGPT. I ask him how much control people should give these agents, how OpenAI approaches permissions and privacy, and why Dots ask before taking action by default. He also explains Space, a new workspace where people and agents can collaborate on documents, data, and projects. We get into how OpenAI plans to price an always-available assistant, how these capabilities could eventually reach free ChatGPT users, and the story of an employee who woke up to discover his Dot had helped fix a bug. Timestamps: 00:00 Why agents now 00:48 From chat to active intelligence 02:46 Alex’s origin story 04:29 Building Dots 06:07 What Dots can do 08:54 Why embody an agent? 11:41 One agent or many? 15:48 Spaces and trust 24:07 Safety and enterprise controls 30:48 Building the Dots ecosystem 34:03 Pricing an always-on agent 38:01 What’s next for Dots Links: Spotify: YouTube: Substack: Thanks to the show’s premiere sponsors: Atlassian, Granola, and Mercury.

Alex Heath

28,133 views • 10 days ago

China will crash the US stock market this year. They're dumping SOTA open-source models on the US, nearly as good as the frontier labs, basically free. Nobody can refuse cutting a $100M AI bill down to $5M. Once that deal is on the table for everyone, Anthropic and OpenAI can't justify their prices, or their valuations. The bubble isn't AI. It's the price of AI. And it's about to pop.

China will crash the US stock market this year. They're dumping SOTA open-source models on the US, nearly as good as the frontier labs, basically free. Nobody can refuse cutting a $100M AI bill down to $5M. Once that deal is on the table for everyone, Anthropic and OpenAI can't justify their prices, or their valuations. The bubble isn't AI. It's the price of AI. And it's about to pop.

Ben Cera

547,784 views • 3 months ago

Dominion AI (Dominion AI) is a new, Christian-built alternative to mainstream AI models like ChatGPT and Claude. I had a conversation with the founders to learn about why they created it, what it can do, and who it's for. The full interview is now on X. Try Dominion here:

Dominion AI (Dominion AI) is a new, Christian-built alternative to mainstream AI models like ChatGPT and Claude. I had a conversation with the founders to learn about why they created it, what it can do, and who it's for. The full interview is now on X. Try Dominion here:

ZUBY:

18,223 views • 4 months ago

.Jerry Tworek helped drive o1, o3, and Codex at OpenAI where he was VP of Research from 2019 to 2025. Then he left to pursue “types of research that are hard to do at OpenAI.” This week on Unsupervised Learning, I sat down with Jerry to discuss where AI research is headed and what he learned from seven years at the forefront of the field. - Why he left OpenAI after helping create some of its biggest breakthroughs - Why Jerry updated his AGI timeline after building reasoning models - The real limits of scaling reinforcement learning - Why Anthropic has done so well in coding - Inside OpenAI's pivotal decisions - What makes great AI researchers Timestamps: 0:00 Intro 1:26 Scaling Paradigms in AI 3:36 Challenges in Reinforcement Learning 11:48 AGI Timelines 18:36 Converging Labs and Economic Forces 25:05 Jerry's Departure from OpenAI 31:18 Pivotal Decisions in OpenAI's Journey 35:06 Balancing Research and Product Development 38:42 The Future of AI Coding 41:33 Specialization vs. Generalization in AI 48:47 Hiring and Building Research Teams 55:21 Quickfire Listen here: YouTube: Spotify: Apple:

.Jerry Tworek helped drive o1, o3, and Codex at OpenAI where he was VP of Research from 2019 to 2025. Then he left to pursue “types of research that are hard to do at OpenAI.” This week on Unsupervised Learning, I sat down with Jerry to discuss where AI research is headed and what he learned from seven years at the forefront of the field. - Why he left OpenAI after helping create some of its biggest breakthroughs - Why Jerry updated his AGI timeline after building reasoning models - The real limits of scaling reinforcement learning - Why Anthropic has done so well in coding - Inside OpenAI's pivotal decisions - What makes great AI researchers Timestamps: 0:00 Intro 1:26 Scaling Paradigms in AI 3:36 Challenges in Reinforcement Learning 11:48 AGI Timelines 18:36 Converging Labs and Economic Forces 25:05 Jerry's Departure from OpenAI 31:18 Pivotal Decisions in OpenAI's Journey 35:06 Balancing Research and Product Development 38:42 The Future of AI Coding 41:33 Specialization vs. Generalization in AI 48:47 Hiring and Building Research Teams 55:21 Quickfire Listen here: YouTube: Spotify: Apple:

Jacob Effron

43,788 views • 8 months ago

The founder of Oracle got on stage and said something that made half the AI industry wince: "it no longer matters which model you use, Claude or ChatGPT. the models have become an interchangeable commodity, and all the value has shifted somewhere else." Larry Ellison knows what he's talking about. he took Oracle from $1,200 in his pocket to becoming the second richest man on the planet, and he's now building the world's largest AI cluster for OpenAI. every model trains on the same open internet data, so they quickly become identical. the edge doesn't come from the model, it comes from the closed data nobody else has but you. in 50 minutes he shows why models become interchangeable, why the only moat is private data, how Oracle built a 1.2 billion watt cluster for OpenAI, and why AI robots will operate more precisely than surgeons. while some argue over whose chatbot is smarter, others are quietly building defensive walls out of their private data. bookmark it and watch ↓

The founder of Oracle got on stage and said something that made half the AI industry wince: "it no longer matters which model you use, Claude or ChatGPT. the models have become an interchangeable commodity, and all the value has shifted somewhere else." Larry Ellison knows what he's talking about. he took Oracle from $1,200 in his pocket to becoming the second richest man on the planet, and he's now building the world's largest AI cluster for OpenAI. every model trains on the same open internet data, so they quickly become identical. the edge doesn't come from the model, it comes from the closed data nobody else has but you. in 50 minutes he shows why models become interchangeable, why the only moat is private data, how Oracle built a 1.2 billion watt cluster for OpenAI, and why AI robots will operate more precisely than surgeons. while some argue over whose chatbot is smarter, others are quietly building defensive walls out of their private data. bookmark it and watch ↓

Damir Akaza

164,950 views • 4 months ago

OPENAI 🚨: Early look at an upcoming Agent Studio for building and hosting configurable, always-on 24/7 agents on ChatGPT. The "Hermes" feature is tightly coupled with the existing Workflows builder on the OpenAI Platform, and you will also be able to open your Agents in the "Builder" tool. I hope users can assign these animated icons to their agents, too. AI Organizations are closer than you think 👀

OPENAI 🚨: Early look at an upcoming Agent Studio for building and hosting configurable, always-on 24/7 agents on ChatGPT. The "Hermes" feature is tightly coupled with the existing Workflows builder on the OpenAI Platform, and you will also be able to open your Agents in the "Builder" tool. I hope users can assign these animated icons to their agents, too. AI Organizations are closer than you think 👀

TestingCatalog News 🗞

171,238 views • 5 months ago