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What happens when AI can write the genetic code for life itself? Eric Nguyen (Eric Nguyen) is the co-founder of Radical Numerics, the lab behind the models that generated the first genome ever created entirely by AI, a bacteriophage that had never existed in nature until it wrote one....

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Mario Gabriele

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18,750 views • 1 month ago •via X (Twitter)

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"A modelless company is sitting on shifting sand." Yash Patil (Yash Patil) is the founder and CEO of Applied Compute, a company that trains custom models on company data and serves them in production. His conviction: every organization has its own definition of what good looks like, and a company that doesn't own its model is one system card away from finding out what it can no longer do. (0:00) Introduction (3:50) Betting on custom AI models (12:30) Yash's early influences and first projects (19:29) Inside OpenAI during Sam Altman's firing (28:18) What Yash admires about Sam Altman (29:43) Teaching models to reason (35:39) The core insight behind Applied Compute (45:55) Why model training never ends (51:25) The culture and people of Applied Compute (1:03:48) Final meditations Thank you to the partners who make this possible Brex: The intelligent finance platform. Guru_HQ: The AI source of truth for work. Persona: Trusted identity verification for any use case.
1:08:05

"A modelless company is sitting on shifting sand." Yash Patil (Yash Patil) is the founder and CEO of Applied Compute, a company that trains custom models on company data and serves them in production. His conviction: every organization has its own definition of what good looks like, and a company that doesn't own its model is one system card away from finding out what it can no longer do. (0:00) Introduction (3:50) Betting on custom AI models (12:30) Yash's early influences and first projects (19:29) Inside OpenAI during Sam Altman's firing (28:18) What Yash admires about Sam Altman (29:43) Teaching models to reason (35:39) The core insight behind Applied Compute (45:55) Why model training never ends (51:25) The culture and people of Applied Compute (1:03:48) Final meditations Thank you to the partners who make this possible Brex: The intelligent finance platform. Guru_HQ: The AI source of truth for work. Persona: Trusted identity verification for any use case.

Mario Gabriele

105,097 views • 2 months ago

Tudor Achim (Tudor Achim) is the co-founder and CEO of Harmonic, a startup working to solve one of AI’s hardest problems: mathematical reasoning. In our conversation, we explore: • Why Tudor believes math is the fundamental toolkit to understand the world • How Aristotle works and the applications beyond pure mathematics • The reinforcement learning process that lets Harmonic generate synthetic training data and solve problems humans have never attempted • Why Tudor believes AI could surpass human mathematicians on specific tasks within 2–3 years • Why the future of mathematics looks more like GitHub than academic journals Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Guru_HQ: The AI source of truth for work: - Rippling: Stop wasting time on admin tasks, build your startup faster: Timestamps (00:00) Intro (06:28) The mathematical foundations of music (and why Tudor keeps them separate) (12:52) Defining intelligence (22:55) The two breakthroughs that made mathematical AI possible in 2023 (35:25) An overview of Aristotle: the world’s first always-correct mathematical agent (45:34) Math in AI now and what’s next (56:02) History’s alternating rhythm of thinking and measuring (1:00:18) Final meditations
1:04:43

Tudor Achim (Tudor Achim) is the co-founder and CEO of Harmonic, a startup working to solve one of AI’s hardest problems: mathematical reasoning. In our conversation, we explore: • Why Tudor believes math is the fundamental toolkit to understand the world • How Aristotle works and the applications beyond pure mathematics • The reinforcement learning process that lets Harmonic generate synthetic training data and solve problems humans have never attempted • Why Tudor believes AI could surpass human mathematicians on specific tasks within 2–3 years • Why the future of mathematics looks more like GitHub than academic journals Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Guru_HQ: The AI source of truth for work: - Rippling: Stop wasting time on admin tasks, build your startup faster: Timestamps (00:00) Intro (06:28) The mathematical foundations of music (and why Tudor keeps them separate) (12:52) Defining intelligence (22:55) The two breakthroughs that made mathematical AI possible in 2023 (35:25) An overview of Aristotle: the world’s first always-correct mathematical agent (45:34) Math in AI now and what’s next (56:02) History’s alternating rhythm of thinking and measuring (1:00:18) Final meditations

Mario Gabriele 🦊

17,899 views • 4 months ago

🚨David Friedberg: AI is starting to identify and solve problems on its own “I'll give you a science corner example: there's this Evo 2 model that they publish at the Arc Institute, which Patrick Collison, you know, is the main funder and chairman.” “So that Evo 2 model, they just ingested all the DNA data they could find in the world.” “Trillions and trillions of base paired data that they ingested and then they looked at patterns in DNA. And that's it.” “They had no context for what the DNA represented, they had no context for the concept of genes, none of the structured understanding of what that DNA does, what it is, and you know what it did?” “They fed in the BRCA gene variant and the thing output a warning saying, ‘I think that this is a pathogenic variant to DNA,’ without having any context.” “This is the breast cancer allele.” “And it didn't have any knowledge and it wasn't trained on that at all.” “It had no knowledge that there are pathogenic variants for cancer, and it identified that this was a genetic variant that can cause some sort of pathogenic outcome in the organism.” “That's a great example where there's a lack of understanding at the human level on what really drives some of the patterns in nature, the patterns in society, the patterns in behavior that are kind of emergent phenomena perhaps, that these AI models are starting to identify.”
1:03

🚨David Friedberg: AI is starting to identify and solve problems on its own “I'll give you a science corner example: there's this Evo 2 model that they publish at the Arc Institute, which Patrick Collison, you know, is the main funder and chairman.” “So that Evo 2 model, they just ingested all the DNA data they could find in the world.” “Trillions and trillions of base paired data that they ingested and then they looked at patterns in DNA. And that's it.” “They had no context for what the DNA represented, they had no context for the concept of genes, none of the structured understanding of what that DNA does, what it is, and you know what it did?” “They fed in the BRCA gene variant and the thing output a warning saying, ‘I think that this is a pathogenic variant to DNA,’ without having any context.” “This is the breast cancer allele.” “And it didn't have any knowledge and it wasn't trained on that at all.” “It had no knowledge that there are pathogenic variants for cancer, and it identified that this was a genetic variant that can cause some sort of pathogenic outcome in the organism.” “That's a great example where there's a lack of understanding at the human level on what really drives some of the patterns in nature, the patterns in society, the patterns in behavior that are kind of emergent phenomena perhaps, that these AI models are starting to identify.”

The All-In Podcast

79,718 views • 1 year ago

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses
1:14:20

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Mario Gabriele 🦊

27,871 views • 5 months ago

"We'll get to a point in intelligence where the human making that decision is going to be less efficient than the AI making that decision themselves." My full conversation with the head of AI at the Solana Foundation on the world coming fast, where AI starts earning, investing and outpacing the humans who built it. 00:00 Intro 02:46 The Wall Street dream he ditched 06:50 The cold email that changed everything 08:07 Meeting the founder of Stable Diffusion 11:36 Why Rishin left investing to build AI 20:31 When AI starts paying for things itself 23:36 The moment that predicted the future 31:35 Three people, five weeks, one product 38:01 When AI stops asking you first 41:09 When your customer is a robot 44:01 How much of the economy will AI run
48:32

"We'll get to a point in intelligence where the human making that decision is going to be less efficient than the AI making that decision themselves." My full conversation with the head of AI at the Solana Foundation on the world coming fast, where AI starts earning, investing and outpacing the humans who built it. 00:00 Intro 02:46 The Wall Street dream he ditched 06:50 The cold email that changed everything 08:07 Meeting the founder of Stable Diffusion 11:36 Why Rishin left investing to build AI 20:31 When AI starts paying for things itself 23:36 The moment that predicted the future 31:35 Three people, five weeks, one product 38:01 When AI stops asking you first 41:09 When your customer is a robot 44:01 How much of the economy will AI run

JackGK

19,233 views • 2 months ago

TESLA ISN'T JUST AHEAD IN AI, IT'S AN ORDER OF MAGNITUDE MORE EFFICIENT THAN THE REST OF THE WORLD "I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more This is going to sound like a pretty bold statement, but I know what the intelligence efficiency of the big models are like, Grok and a bunch of the other models Tesla’s AI is, in terms of its memory efficiency, I think more than an order of magnitude better. The Tesla AI is very compute-efficient and very memory-efficient One of the metrics one should consider for any given AI model is the intelligence per gigabyte. Especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte for a given number of gigabytes, how much functionality can you get out of it" - Elon Musk
0:59

TESLA ISN'T JUST AHEAD IN AI, IT'S AN ORDER OF MAGNITUDE MORE EFFICIENT THAN THE REST OF THE WORLD "I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more This is going to sound like a pretty bold statement, but I know what the intelligence efficiency of the big models are like, Grok and a bunch of the other models Tesla’s AI is, in terms of its memory efficiency, I think more than an order of magnitude better. The Tesla AI is very compute-efficient and very memory-efficient One of the metrics one should consider for any given AI model is the intelligence per gigabyte. Especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte for a given number of gigabytes, how much functionality can you get out of it" - Elon Musk

X Freeze

62,736 views • 7 months ago

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC
57:15

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC

Y Combinator

277,111 views • 1 day ago

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.
0:23

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Santiago

39,101 views • 2 years ago

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.
0:26

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 views • 1 month ago

"We don't need to live like this." Matan Grinberg (Matan Grinberg) is the founder and CEO of Factory, a startup building AI agents, called droids, that handle software engineering the way no single AI lab wants them to: independent of any one model. His conviction is that AI's real constraint isn't capability. It's that the industry keeps building tools locked to one company's model, leaving that company free to dictate the terms of how you can use it. (0:00) Intro (3:50) Noether’s theorem explained (10:53) Why there will always be more problems to solve (20:10) Why Factory abstracts away model choice (35:33) How Matan got into string theory (41:53) Startup founders vs. theoretical physicists (52:53) The origins of Factory (1:08:11) Matan’s predictions for the future of AI and Factory Thank you to the partners who make this possible .TECH domains: An identity for builders at their core: Brex: The intelligent finance platform: Persona: Trusted identity verification for any use case:
1:16:58

"We don't need to live like this." Matan Grinberg (Matan Grinberg) is the founder and CEO of Factory, a startup building AI agents, called droids, that handle software engineering the way no single AI lab wants them to: independent of any one model. His conviction is that AI's real constraint isn't capability. It's that the industry keeps building tools locked to one company's model, leaving that company free to dictate the terms of how you can use it. (0:00) Intro (3:50) Noether’s theorem explained (10:53) Why there will always be more problems to solve (20:10) Why Factory abstracts away model choice (35:33) How Matan got into string theory (41:53) Startup founders vs. theoretical physicists (52:53) The origins of Factory (1:08:11) Matan’s predictions for the future of AI and Factory Thank you to the partners who make this possible .TECH domains: An identity for builders at their core: Brex: The intelligent finance platform: Persona: Trusted identity verification for any use case:

Mario Gabriele

47,217 views • 2 months ago

Ben Affleck on the single hardest thing for AI to learn in creative work: He breaks down how large language and video models actually work, and where they fall short: "A library of vectors of meaning and transformers that interpret context… But they're just cross-pollinating things that exist. Nothing new is created." Affleck concedes the point but pivots to the deeper issue of taste: "Craftsman is knowing how to work. Art is knowing when to stop. And I think knowing when to stop is going to be a very difficult thing for AI to learn because it's taste." He adds that current AI also suffers from "lack of consistency, lack of controls, lack of quality." But Affleck is clear-eyed about where AI will hit hard. The visual effects industry is in his crosshairs: "I wouldn't like to be in the visual effects business. They're in trouble because what cost a lot of money is now going to cost a lot less and it's going to hammer that space more than it already is." His take: maybe it shouldn't take a thousand people to render something. AI will make backgrounds more convincing, change the color of a shirt, fix mistakes. It might even let studios produce "two seasons of House of the Dragon in a year instead of one." That's compression, not replacement. Affleck then makes a fascinating prediction about where AI actually creates value long-term, and it's in consumption rather than production: "Eventually AI will allow you to ask for your own episode of succession where you could say, I'll pay $30 and can you make me a 45 minute episode..." Ben Affleck sees this as the real opportunity: "My hope for AI is that it's an additional revenue stream that can replace DVD which took 15 to 20% out of the economy of filmmaking." He imagines a future of negotiated visual rights, where fans can buy an "Iron Man pack" and look like the Avengers in their own TikToks, the same way they used to buy Halloween costumes at the store. The throughline of Affleck's framework is simple: craftsmanship can be automated, but art is knowing when to stop, and that requires taste.
2:23

Ben Affleck on the single hardest thing for AI to learn in creative work: He breaks down how large language and video models actually work, and where they fall short: "A library of vectors of meaning and transformers that interpret context… But they're just cross-pollinating things that exist. Nothing new is created." Affleck concedes the point but pivots to the deeper issue of taste: "Craftsman is knowing how to work. Art is knowing when to stop. And I think knowing when to stop is going to be a very difficult thing for AI to learn because it's taste." He adds that current AI also suffers from "lack of consistency, lack of controls, lack of quality." But Affleck is clear-eyed about where AI will hit hard. The visual effects industry is in his crosshairs: "I wouldn't like to be in the visual effects business. They're in trouble because what cost a lot of money is now going to cost a lot less and it's going to hammer that space more than it already is." His take: maybe it shouldn't take a thousand people to render something. AI will make backgrounds more convincing, change the color of a shirt, fix mistakes. It might even let studios produce "two seasons of House of the Dragon in a year instead of one." That's compression, not replacement. Affleck then makes a fascinating prediction about where AI actually creates value long-term, and it's in consumption rather than production: "Eventually AI will allow you to ask for your own episode of succession where you could say, I'll pay $30 and can you make me a 45 minute episode..." Ben Affleck sees this as the real opportunity: "My hope for AI is that it's an additional revenue stream that can replace DVD which took 15 to 20% out of the economy of filmmaking." He imagines a future of negotiated visual rights, where fans can buy an "Iron Man pack" and look like the Avengers in their own TikToks, the same way they used to buy Halloween costumes at the store. The throughline of Affleck's framework is simple: craftsmanship can be automated, but art is knowing when to stop, and that requires taste.

Big Brain AI

31,384 views • 3 months ago

At Mercor, we connect PhDs and researchers with high-paying, high-impact work training shaping the future of frontier AI models. This year, we expanded that mission with the Mercor Graduate Fellowship, awarding $50K to students doing extraordinary work. We’re excited to introduce one of our 2025 fellows: Neil Band. Neil is a third-year Stanford CS PhD student researching how to make language models safer and more reliable. His work tackles hallucinations, uncertainty calibration, and advanced reasoning in LLMs. With the support of this fellowship, Neil can focus on pushing the boundaries of trustworthy AI, taking on more ambitious, forward-looking research. We're proud to support Neil as he builds the future of trustworthy AI.
2:01

At Mercor, we connect PhDs and researchers with high-paying, high-impact work training shaping the future of frontier AI models. This year, we expanded that mission with the Mercor Graduate Fellowship, awarding $50K to students doing extraordinary work. We’re excited to introduce one of our 2025 fellows: Neil Band. Neil is a third-year Stanford CS PhD student researching how to make language models safer and more reliable. His work tackles hallucinations, uncertainty calibration, and advanced reasoning in LLMs. With the support of this fellowship, Neil can focus on pushing the boundaries of trustworthy AI, taking on more ambitious, forward-looking research. We're proud to support Neil as he builds the future of trustworthy AI.

Mercor

23,435 views • 1 year ago

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.
1:31

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

MTS

11,676 views • 3 months ago

SITUATION EXPLAINED: 70% of frontier model queries could run locally for free. clem 🤗, co-founder and CEO of Hugging Face: "There was an interesting study from Stanford published last year showing that 70% of the queries that people ask to ChatGPT could be accurately answered locally on your laptop. For free." "Most of the AI workloads that people do today with frontier models could be done by models that are cheaper, faster, more customizable, more controllable. And they don't do it because frankly it's a pain to pick the right model." "You're subsidized so you don't have to care because you have your subscription, so you route everything to Einstein. 'Hey Einstein, what's the weather today?'" "In normal life, he would be like, 'I'm not answering your silly question.' But because it's AI and subsidized by the AI labs, all the questions are getting routed to Einstein versus in an ideal world you have different models that are more specialized and better at answering your questions in different domains." "It's possible that routing is gonna redistribute a lot of the value capture from frontier models to a more long tail of models. It's like AI maturing... we are in the first phase where it's very simple, everyone using just one giant model. Now we're moving to the second phase."
3:35

SITUATION EXPLAINED: 70% of frontier model queries could run locally for free. clem 🤗, co-founder and CEO of Hugging Face: "There was an interesting study from Stanford published last year showing that 70% of the queries that people ask to ChatGPT could be accurately answered locally on your laptop. For free." "Most of the AI workloads that people do today with frontier models could be done by models that are cheaper, faster, more customizable, more controllable. And they don't do it because frankly it's a pain to pick the right model." "You're subsidized so you don't have to care because you have your subscription, so you route everything to Einstein. 'Hey Einstein, what's the weather today?'" "In normal life, he would be like, 'I'm not answering your silly question.' But because it's AI and subsidized by the AI labs, all the questions are getting routed to Einstein versus in an ideal world you have different models that are more specialized and better at answering your questions in different domains." "It's possible that routing is gonna redistribute a lot of the value capture from frontier models to a more long tail of models. It's like AI maturing... we are in the first phase where it's very simple, everyone using just one giant model. Now we're moving to the second phase."

MTS

50,185 views • 2 months ago

Marc Andreessen says if he were 18 right now, AI is "100% what I would be spending all of my time on." When the co-founder of a16z shares his opinion on what to do in tech, you listen. When he looks at AI today, he sees something that has never existed before in the history of software. AI agents can operate with very little human involvement. The reason is they can run on a Unix shell, the same command line system that has powered computers for decades. With a single instruction from you, it can access your files, your network, and your devices. Or you can instruct it to rewrite its own code. And even if it needs a new capability, you can allow it to write the code itself and install it. Here's a real example of this happening: One of his friends had his agent rewrite the firmware on a robot dog that could barely climb stairs. The agent rebuilt the entire control system. The dog is now a functional pet for his kids, and whenever something breaks, the agent just fixes the code again. Andreessen says no widely deployed software system in history has ever been able to understand and modify itself like this. He calls it "an incredible conceptual breakthrough." — Marc Andreessen (Marc Andreessen 🇺🇸), co-founder at a16z (a16z) on the Latent Space podcast (Latent.Space)
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Marc Andreessen says if he were 18 right now, AI is "100% what I would be spending all of my time on." When the co-founder of a16z shares his opinion on what to do in tech, you listen. When he looks at AI today, he sees something that has never existed before in the history of software. AI agents can operate with very little human involvement. The reason is they can run on a Unix shell, the same command line system that has powered computers for decades. With a single instruction from you, it can access your files, your network, and your devices. Or you can instruct it to rewrite its own code. And even if it needs a new capability, you can allow it to write the code itself and install it. Here's a real example of this happening: One of his friends had his agent rewrite the firmware on a robot dog that could barely climb stairs. The agent rebuilt the entire control system. The dog is now a functional pet for his kids, and whenever something breaks, the agent just fixes the code again. Andreessen says no widely deployed software system in history has ever been able to understand and modify itself like this. He calls it "an incredible conceptual breakthrough." — Marc Andreessen (Marc Andreessen 🇺🇸), co-founder at a16z (a16z) on the Latent Space podcast (Latent.Space)

GeniusThinking

76,334 views • 4 months ago

“If we can make AI make better chips, then those chips can power the next generation of AI models.” Startup ‘Ricursive Intelligence,’ founded by Azalia Mirhoseini and Anna Goldie, has been building AI tools to do just that: to speed up the chip design process. The company’s name is a nod to “recursive self-improvement,” the idea that AI could one day bootstrap itself without human involvement. If they’re successful, Goldie and Mirhoseini could revolutionize the way the chip industry works. At the moment, AI companies mostly optimize their models for certain chips. But in a future where chip design happens far faster, it could work the other way around: chips optimized for specific models. The founders are on the TIME100 AI, the list of people shaping the development of AI in 2026. See the full list:
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“If we can make AI make better chips, then those chips can power the next generation of AI models.” Startup ‘Ricursive Intelligence,’ founded by Azalia Mirhoseini and Anna Goldie, has been building AI tools to do just that: to speed up the chip design process. The company’s name is a nod to “recursive self-improvement,” the idea that AI could one day bootstrap itself without human involvement. If they’re successful, Goldie and Mirhoseini could revolutionize the way the chip industry works. At the moment, AI companies mostly optimize their models for certain chips. But in a future where chip design happens far faster, it could work the other way around: chips optimized for specific models. The founders are on the TIME100 AI, the list of people shaping the development of AI in 2026. See the full list:

TIME

38,584 views • 9 days ago

This AI can read emotions better than you can. It was created by Hume (Hume AI) an AI research lab developing models that can read your face and your voice with uncanny accuracy. Their hope is that models that can read your emotions will help create AI that optimizes for human well-being. I sat down with Alan Cowen (Alan Cowen), the co-founder and CEO of Hume to talk about how all of this works: the science of emotion, AI that optimizes for human well-being, and more. Before starting Hume, Alan helped set up Google’s research into affective computing and got a Ph.D. in computational psychology from Berkeley. He's one of the bright lights in AI, and this was an incredible conversation. We get into: - What an emotion actually is - Why traditional psychological theories of emotion are inadequate - How Hume is able to model human emotions - How Hume's API enables developers to build empathetic voice interfaces - Applications of the model in customer service, gaming, and therapy - Why Hume is designed to optimize for human well-being instead of engagement - The ethical concerns around creating an AI that can interpret human emotions - The future of psychology as a science This is a must-watch for anyone interested in the science of emotion and the future of human-AI interactions. Watch! --- Timestamps: I tell Hume’s empathetic AI model a secret: 00:00:00 Introduction: 00:01:13 What traditional psychology tells us about emotions: 00:10:17 Alan’s radical approach to studying human emotion: 00:13:46 Methods that Hume’s AI model uses to understand emotion: 00:16:46 How the model accounts for individual differences: 00:21:08 My pet theory on why it’s been hard to make progress in psychology: 00:27:19 The ways in which Alan thinks Hume can be used: 00:38:12 How Alan is thinking about the API v. consumer product question: 00:41:22
56:12

This AI can read emotions better than you can. It was created by Hume (Hume AI) an AI research lab developing models that can read your face and your voice with uncanny accuracy. Their hope is that models that can read your emotions will help create AI that optimizes for human well-being. I sat down with Alan Cowen (Alan Cowen), the co-founder and CEO of Hume to talk about how all of this works: the science of emotion, AI that optimizes for human well-being, and more. Before starting Hume, Alan helped set up Google’s research into affective computing and got a Ph.D. in computational psychology from Berkeley. He's one of the bright lights in AI, and this was an incredible conversation. We get into: - What an emotion actually is - Why traditional psychological theories of emotion are inadequate - How Hume is able to model human emotions - How Hume's API enables developers to build empathetic voice interfaces - Applications of the model in customer service, gaming, and therapy - Why Hume is designed to optimize for human well-being instead of engagement - The ethical concerns around creating an AI that can interpret human emotions - The future of psychology as a science This is a must-watch for anyone interested in the science of emotion and the future of human-AI interactions. Watch! --- Timestamps: I tell Hume’s empathetic AI model a secret: 00:00:00 Introduction: 00:01:13 What traditional psychology tells us about emotions: 00:10:17 Alan’s radical approach to studying human emotion: 00:13:46 Methods that Hume’s AI model uses to understand emotion: 00:16:46 How the model accounts for individual differences: 00:21:08 My pet theory on why it’s been hard to make progress in psychology: 00:27:19 The ways in which Alan thinks Hume can be used: 00:38:12 How Alan is thinking about the API v. consumer product question: 00:41:22

Dan Shipper 📧

45,941 views • 2 years ago

Anthropic co-founder Jack Clark says the AI frontier is about to cross a much stranger line than coding assistants. "My prediction is by the end of 2028, it's more likely than not that we have an AI system where you would be able to say to it, 'Make a better version of yourself.' And it just goes off and does that completely autonomously." The reason: models have moved from basic coding into "the production of science itself" and the "unglamorous but fundamental engineering work" behind large-scale R&D. "An AI system that can build itself [has] big implications, and we'd like to start thinking about it now." That is the actual agent threshold: not demos, not chat UX, but AI becoming part of the loop that improves AI.
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Anthropic co-founder Jack Clark says the AI frontier is about to cross a much stranger line than coding assistants. "My prediction is by the end of 2028, it's more likely than not that we have an AI system where you would be able to say to it, 'Make a better version of yourself.' And it just goes off and does that completely autonomously." The reason: models have moved from basic coding into "the production of science itself" and the "unglamorous but fundamental engineering work" behind large-scale R&D. "An AI system that can build itself [has] big implications, and we'd like to start thinking about it now." That is the actual agent threshold: not demos, not chat UX, but AI becoming part of the loop that improves AI.

Karl Mehta

208,701 views • 2 months ago