Unsupervised Learning with Jacob Effron

Ari Morcos and Rob Toews return for their spiciest conversation yet. Fresh from NeurIPS, they debate whether models are truly plateauing or if we're just myopically focused on LLMs while breakthroughs happen in other modalities. They reveal why infinite capital at labs may actually constrain innovation, explain the narrow "Goldilocks zone" where RL actually works, and argue why U.S. chip restrictions may have backfired catastrophically—accelerating China's path to self-sufficiency by a decade. The conversation covers OpenAI's code red moment and structural vulnerabilities, the mystique surrounding SSI and Ilya's "two words," and why the real bottleneck in AI research is compute, not ideas. The episode closes with bold 2026 predictions: Rob forecasts Sam Altman won't be OpenAI's CEO by year-end, while Ari gives 50%+ odds a Chinese open-source model will be the world's best at least once next year.

Edwin Chen is the founder and CEO of Surge AI, the data infrastructure company behind nearly every major frontier model. Surge works with OpenAI, Anthropic, Meta, and Google, providing the high-quality data and evaluation infrastructure that powers their models. Edwin reveals why optimizing for popular benchmarks like LMArena is "basically optimizing for clickbait," how one frontier lab's models regressed for 6-12 months without anyone knowing, and why the industry's approach to measurement is fundamentally broken. Jacob and Edwin discuss what actually makes elite AI evaluators, why "there's never going to be a one size fits all solution" for AI models, and how frontier labs are taking surprisingly divergent paths to AGI.

This episode features Olivier Godement, Head of Product for Business Products at OpenAI, discussing the current state and future of AI adoption in enterprises, with a particular focus on the recent releases of GPT 5.1 and Codex. The conversation explores how these models are achieving meaningful automation in specific domains like coding, customer support, and life sciences: where companies like Amgen are using AI to accelerate drug development timelines from months to weeks through automated regulatory documentation. Olivier reveals that while complete job automation remains challenging and requires substantial scaffolding, harnesses, and evaluation frameworks, certain use cases like coding are reaching a tipping point where engineers would "riot" if AI tools were taken away. The discussion covers the importance of cost reduction in unlocking new use cases, the emerging significance of reinforcement fine-tuning (RFT) for frontier customers, and OpenAI's philosophy of providing not just models but reference architectures and harnesses to maximize developer success.

This week on Unsupervised Learning, Jacob Effron is joined by Jordan Schneider, host of China Talk, who challenges widespread assumptions about US-China AI competition. China's AI development is driven by private capital and market competition—not central government planning—with companies like DeepSeek, Alibaba, and ByteDance operating more like Silicon Valley startups than state projects. The critical bottleneck is compute: the West maintains a 10-15x advantage in advanced chips, and US export controls implemented one month before ChatGPT created a structural edge favoring America for years. Chinese companies aggressively open-source models from strategic necessity—they couldn't establish a quality gap justifying paid access like OpenAI. Jordan explains why the "Goldilocks strategy" of controlled chip dependency fails, why expert consensus opposes selling advanced semiconductors to China despite Nvidia's lobbying, and how Taiwan's invasion risk is driven more by domestic politics than AGI scenarios. China's real advantage may emerge in robotics manufacturing at scale, where they're already deploying while the US debates strategy.

This episode features Dianne Na Penn, a senior product leader at Anthropic, discussing the launch of Claude Opus 4.5 and the evolution of frontier AI models. The conversation explores how Anthropic approaches model development—balancing ambitious capability roadmaps with user feedback, making strategic bets on areas like agentic coding and computer use while deliberately avoiding others like image generation. Dianne shares insights on the shifting nature of AI evaluation (moving beyond saturated benchmarks like SWE-bench toward more open-ended measures), the evolution of scaffolding from "training wheels" to intelligence amplifiers, and why she believes we're closer to transformative long-running AI than most people think. She also discusses Anthropic's distinctive culture of authenticity, the under appreciated benefits of model alignment for producing independent-thinking AI, and why the real bottleneck to AI agents isn't model capability anymore but product innovation.

This episode features the core team behind Sora, OpenAI's groundbreaking video generation platform that became the #1 app in the App Store. Bill Peebles (research lead), Rohan Sahai (product lead), and Thomas Dimson (engineering/product lead with Instagram background) discuss the unexpected viral success of Sora's launch, the product journey that led to the breakthrough "cameo" feature (putting yourself in AI-generated videos), and their philosophy of building a creator-first social network that prioritizes human creativity over passive consumption. They reveal the technical milestones in video generation, their small team size (under 50 people total at launch), navigation of content moderation challenges, early monetization strategy, and their ambitious vision for video models as world simulators that could eventually contribute to scientific breakthroughs by 2028. The conversation captures both the tactical product decisions and strategic philosophy that made Sora a cultural phenomenon.

This episode features Rob Toews from Radical Ventures and Ari Morcos, Head of Research at Datology AI, reacting to Andrej Karpathy's recent statement that AGI is at least a decade away and that current AI capabilities are "slop." The discussion explores whether we're in an AI bubble, with both guests pushing back on overly bearish narratives while acknowledging legitimate concerns about hype and excessive CapEx spending. They debate the sustainability of AI scaling, examining whether continued progress will come from massive compute increases or from efficiency gains through better data quality, architectural innovations, and post-training techniques like reinforcement learning. The conversation also tackles which companies truly need frontier models versus those that can succeed with slightly-behind-the-curve alternatives, the surprisingly static landscape of AI application categories (coding, healthcare, and legal remain dominant), and emerging opportunities from brain-computer interfaces to more efficient scaling methods.

This episode features a deep dive into the current state of AI model progress with Ari Morcos (CEO of Datalogy AI and former DeepMind/Meta researcher) and Rob Toews (partner at Radical Ventures). The conversation tackles whether model progress is genuinely slowing down or simply shifting into new paradigms, exploring the role of reinforcement learning in scaling capabilities beyond traditional pre-training. They examine the talent wars reshaping AI labs, Google's resurgence with Gemini, the sustainability of massive valuations for companies like OpenAI and Anthropic, and the infrastructure ecosystem supporting this rapid evolution. The discussion weaves together technical insights on data quality, synthetic data generation, and RL environments with strategic perspectives on acquisitions, regulatory challenges, and the future intersection of AI with physical robotics and brain-computer interfaces.

This week on Unsupervised Learning, Jacob sits down with Nicole Brichtova and Oliver Wang, the Google researchers behind "Nano Banana" - the breakthrough AI image model that achieved unprecedented character consistency and took over social media. The conversation covers how their model fits into creative workflows, why we're still in the early innings of image AI development despite impressive current capabilities, and how image and video generation are converging toward unified models. They also share honest perspectives on current limitations, safety approaches, and why the expectation of going from prompt to production-ready content is fundamentally overhyped.

Tri Dao, Chief Scientist at Together AI and Princeton professor who created Flash Attention and Mamba, discusses how inference optimization has driven costs down 100x since ChatGPT's launch through memory optimization, sparsity advances, and hardware-software co-design. He predicts the AI hardware landscape will shift from Nvidia's current 90% dominance to a more diversified ecosystem within 2-3 years, as specialized chips emerge for distinct workload categories: low-latency agentic systems, high-throughput batch processing, and interactive chatbots. Dao shares his surprise at AI models becoming genuinely useful for expert-level work, making him 1.5x more productive at GPU kernel optimization through tools like Claude Code and O1. The conversation explores whether current transformer architectures can reach expert-level AI performance or if approaches like mixture of experts and state space models are necessary to achieve AGI at reasonable costs. Looking ahead, Dao sees another 10x cost reduction coming from continued hardware specialization, improved kernels, and architectural advances like ultra-sparse models, while emphasizing that the biggest challenge remains generating expert-level training data for domains lacking extensive internet coverage.

In this episode, Jacob sits down with Peter Deng, General Partner at Felicis and former Product Leader at OpenAI, Facebook, and Uber. Peter shares his insider perspective on building ChatGPT Enterprise in just seven weeks and leading voice mode development at OpenAI. The conversation covers everything from why traditional SaaS pricing models are broken for AI products to how evals became the new product specs, the "AI under your fingernails" test for founding teams, and why current agents are massively overhyped. They also explore how consumer AI will fragment across multiple winners rather than consolidate into a single super app, the coming integration between ChatGPT and apps like Uber, and why voice AI will unlock entirely new categories of applications. Plus, insights on the changing dynamics between foundation models and startups, and what it really takes to build defensible AI companies. It's a comprehensive look at AI product strategy from someone who's been at the center of the industry's biggest breakthroughs.

In this episode, Jacob sits down with Joshua Meier, co-founder of Chai Discovery and former Chief AI Officer at Absci, to explore the breakthrough moment happening in AI drug discovery. They discuss how the field has evolved through three distinct waves, with the current generation of companies finally achieving success rates that seemed impossible just years ago. The conversation covers everything from moving drug discovery out of the lab and into computers, to why AI models think differently than human chemists, to the strategic decisions around open sourcing foundational models while keeping design capabilities proprietary. It's an in-depth look at how AI is fundamentally changing pharmaceutical innovation and what it means for the future of medicine.

In this episode, Simon Eskildsen, co-founder and CEO of TurboPuffer, lays out a compelling vision for how AI-native infrastructure needs to evolve in an era where every application wants to connect massive amounts of context to large language models. He breaks down why traditional databases and even large context windows fall short—especially at scale—and why object-storage-native search is the inevitable next step. Drawing on his experience from Shopify and Readwise, Simon introduces the SCRAP framework to explain the limits of context stuffing and makes a clear case for why cost, recall, performance, and access control drive the need for smarter retrieval systems. From practical lessons in building highly reliable infra to hard technical problems in vector indexing, this conversation distills the future of AI infra into first principles—with clarity and depth.

In this episode, Jacob sits down with Karol Hausman (Co-Founder) and Danny Driess (Research Scientist) from Physical Intelligence, two of the minds behind some of the most exciting advances in robotics. They unpack the last decade of progress in AI robotics, from early skepticism to the breakthroughs powering today’s generalist robot models. The conversation covers everything from folding laundry with robots to building scalable data pipelines, the limits of simulation, and what it’ll take to bring robot assistants into everyday homes. It's a wide-ranging and thoughtful look at where robotics is headed, as well as how fast we might get there.

Ion Stoica helped define the modern data stack. Now he’s coming for AI evaluation. From co-founding Databricks and Anyscale to launching LMArena, Ion has shaped the infrastructure underlying some of the biggest shifts in computing. In this conversation, he unpacks what most people get wrong about model evaluation, the infrastructure challenges ahead for agents and heterogeneous compute, and why he believes the U.S. is structurally disadvantaged in open-source AI compared to China.

Brendan Foody is the co-founder and CEO of Mercor, a company building the infrastructure for AI-native labor markets. Mercor’s platform is already used by top AI labs to label data, evaluate human and AI candidates, and make performance-driven hiring decisions. They’re operating at the intersection of recruiting, evals, and foundation model development—helping companies shift from intuition to measurable prediction. Brendan and his team recently raised $100M and are working with some of the most advanced players in the AI ecosystem today.

Jacob and Logan sit down with Max Junestrand, founder and CEO of Legora - a rapidly growing legal AI platform (and Redpoint portfolio company). After announcing their Series B last week, Max joined the show to discuss why law is uniquely suited for AI, what it takes to scale an enterprise-ready product across global markets, and a few crazy moments from Legora’s journey so far. They dig into product strategy, lessons on evolving alongside foundational models, and how AI is reshaping the future of law firms. Whether you're building in AI or just curious how it’s being applied in complex industries, this one’s packed with practical insights.

Sholto Douglas, a Member of Technical Staff at Anthropic, joined Unsupervised Learning to break down why coding is the clearest early signal of model progress, how AI agents are already accelerating research, and what it’ll take to unlock real-world breakthroughs in fields like biology and robotics.

The recent AI 2027 report sparked widespread discussion with its stark warnings about the near-term risks of unaligned AI. Authors @Daniel Kokotajlo (former OpenAI researcher now focused full-time on alignment through his nonprofit, @AI Futures, and one of TIME’s 100 most influential people in AI) and @Thomas Larsen joined the show to unpack their findings. We talk through the key takeaways from the report, its policy implications, and what they believe it will take to build safer, more aligned models.

In this episode, I sit down with Michelle Pokrass, who leads a research team at OpenAI within post-training focused on improving models for power users: developers using OpenAI models in the API and power users in ChatGPT. We unpack how OpenAI prioritized instruction-following and long context, why evals have a 3-month shelf life, what separates successful AI startups, and how the best teams are fine-tuning to push past the current frontier. If you’ve ever wondered how OpenAI really decides what to build, and how it affects what you should build, this one’s for you.

When Khan Academy launched Khanmigo, Salman Khan thought they might reach 100k users by 2025. Today, they’re at 1.4 million. 🎧 Sal joined us on Unsupervised Learning to talk about AI's role in education— from the vantage point of someone deploying it at scale. As founder of Khan Academy, he’s overseen the rollout of AI tools to over a million teachers and students, giving him a front-row seat to what’s actually working in classrooms.

Aidan joined this week’s Unsupervised Learning for a wide-ranging conversation on model architectures, enterprise adoption, and what’s breaking in the foundation model stack. If you’re building or investing in AI infrastructure, Aidan is worth listening to. He co-authored the original Transformer paper, leads one of the most advanced model labs outside of the hyperscalers, and is now building for real-world enterprise deployment with Cohere’s agent platform, North. Cohere serves thousands of customers across sectors like finance, telco, and healthcare — and they’ve made a name for themselves by staying model-agnostic, privacy-forward, and deeply international (with major bets in Japan and Korea)

At Redpoint’s annual meeting with investors, Redpoint partners Scott Raney, Alex Bard, Patrick Chase and Jacob Effron shared unfiltered thoughts on some of the most topical questions in AI today, where value will accrue, which industries are best positioned for defensibility at the application layer, and more.

To unpack some of the most topical questions in AI, I’m joined by two fellow AI podcasters: Swyx and Alessio Fanelli, co-hosts of the Latent Space podcast. We’ve been wanting to do a cross-over episode for a while and finally made it happen. Swyx brings deep experience from his time at AWS, Temporal, and Airbyte, and is now focused on AI agents and dev tools. Alessio is an investor at Decibel, where he’s been backing early technical teams pushing the boundaries of infrastructure and applied AI. Together they run Latent Space, a technical newsletter and podcast by and for AI engineers.

Two weeks ago, OpenAI released its set of tools to help developers build agentic systems. Today on Unsupervised Learning, Nikunj Handa (Product Lead) and Steve Coffey (Eng Lead) answer some of the biggest questions around how developers should be thinking about building in the agentic paradigm in 2025.

On the latest episode of Unsupervised Learning, Jacob is joined by two of the most influential minds in AI today. 🔹 Noam Shazeer, co-inventor of the Transformer 🔹 Jack Rae, Research Director at DeepMind and one of the leads behind Gemini’s Flash Thinking We got to ask them all of the top-of-mind questions in AI today about where we are, where we’re headed and what it means for businesses and the world.

On today’s Unsupervised Learning, Mike Schroepfer (ex-CTO of Meta and founder of Gigascale Capital) reveals why energy is a key bottleneck holding AI progress back. Mike discusses how we can scale energy production to democratize AI globally and explores AI’s role in climate change. He also reflects on a decade as Meta’s CTO and how AI coding is transforming the CTO role. Finally, he offers predictions on the future of AI developer tools, VR, and open-source models.

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. The company operates a 24/7 public ride-hail service and provides over 150,000 trips each week across San Francisco, Los Angeles, Phoenix, and Austin, making mobility more accessible, sustainable, and safer for everyone. In this week’s episode of Unsupervised Learning, we dive deep into the frontier where AI meets hardware — and there’s no better guide than Vincent Vanhoucke, Distinguished Engineer at Waymo and former Head of Robotics at DeepMind.

David is an OG in AI who has been at the forefront of many of the major breakthroughs of the past decade. His resume: VP of Engineering at OpenAI, a key contributor to Google Brain, co-founder of Adept, and now leading Amazon’s SF AGI Lab. In this episode we focused on how far test-time compute gets us, the real implications of DeepSeek, what agents milestones he’s looking for and more.

Arvind Narayanan is one of the leading voices in AI when it comes to cutting through the hype. As a Princeton professor and co-author of AI Snake Oil, he’s one of the most thoughtful voices cautioning against both unfounded fears and overblown promises in AI. In this episode, Arvind dissects the future of AI in education, its parallels to past tech revolutions, and how our jobs are already shifting toward managing these powerful tools.

In this episode of Unsupervised Learning, we sit down with Dylan Patel, Chief Analyst at SemiAnalysis, to break down what these sweeping changes really mean. From how they consolidate power among Big Tech to China's narrowing options for AI dominance, we unpacked the impact of this regulatory shift.

In our new world of AI, few minds shine as brightly as Bob McGrew's. Until November Bob was the Chief Research Officer at OpenAI, and before that led Palantir’s engineering and product management for the first decade of its existence. He’s seen it all and we were fortunate to get his insights and vision for the future in one of my favorite episodes of Unsupervised Learning to date.

Lin Qiao, the co-founder of Fireworks.ai, sits down for a deep dive into the future of AI. Lin ran the PyTorch team at Meta, which developed some of the most fundamental open-source AI software in use today. She’s got a riveting perspective on the AI landscape that is a must-listen.

Noam Brown, renowned AI researcher and key figure at OpenAI, joins us for a deep dive into the o1 release. Recorded just one day before o1’s full public debut, this episode explores the groundbreaking advancements and challenges behind this innovative test-time compute model. We discuss the technical breakthroughs that set o1 apart, its unique capabilities compared to previous models, and how it disrupts traditional paradigms in AI development. Noam also shares insights into OpenAI’s approach to innovation, the economic realities of scaling AI, and what the future holds for the field.

For this episode of Unsupervised Learning we spoke with Russ d'Sa, co-founder of LiveKit, a company at the forefront of voice AI technology. Russ thinks of LiveKit as a “nervous system,” powering the sensory interfaces humans use to interact with AI – including the Advanced Voice feature in ChatGPT as well as applications like Character.ai, Spotify and many more. Russ talked about when voice makes sense as an interface, the exciting new UX paradigms on the horizon, the intersection of voice and robotics and Anthropic's Computer Use API.

Jonathan Frankle is the Chief AI Scientist at Databricks ($43B), which he joined through the acquisition of MosaicML in July 2023. Databricks has over 12,000 customers on the cutting edge of AI; Jonathan works to anticipate their needs and offer solutions even as the tech is rapidly evolving.

I sat down with DeepL cofounder Jarek Kutylowski. DeepL is a comprehensive Language AI platform that enables organizations to communicate effectively across languages, cultures, and markets. Jarek shared a treasure trove of insights on the past, present, and future of AI translation.

Joshua Xu is co-founder and CEO of HeyGen -- the fast-growing AI video creation and translation platform. You can upload a video, or create a new one from a script using an AI avatar as your star, and HeyGen will translate it into 175 languages. HeyGen now serves over 40,000 customers and is generating $35+ million in revenue.

Percy Liang is a Stanford professor and co-founder of Together AI, driving some of the most critical advances in AI research. Percy is also a trained classical pianist, which clearly influences the way he thinks about technology. We explored the evolution of AI from simple token prediction to autonomous agents capable of long-term problem-solving, the problem of interpretability, and the future of AI safety in complex, real-world systems.

Douwe’s contributions to AI are truly a part of its bedrock foundations. He wrote the first paper on retrieval-augmented generation (RAG) and has raised over $100 million to help enterprises build contextual language models that fit their use cases. Before Contextual he was the head of research at Hugging Face, worked on the Facebook AI research team (i.e. Llama) and remains a professor at Stanford. Douwe was incredibly open about his take on AI’s recent history and where he thinks it’s going.

If you don’t know Grammarly, it’s a personalized AI assistant for writing that has over 30 Million Daily Active Users. Grammarly has been building AI productivity tooling long before the most recent GenAI wave and has raised over $400M, with a current valuation of $13B. Rahul believes AI will enable everyone to focus on more meaningful, creative interactions by automating the "drudgery" of daily tasks. It was interesting to hear him talk about how he thinks about competition and his longer-term perspective on how AI will be adopted by the enterprise.

Sarah Buchner is the Founder and CEO of Trunk Tools (https://trunktools.com/). She is a one-of-a-kind founder, having spent her young life as a carpenter in Austria and then working her way up the ranks of the construction industry. She’s also earned several graduate degrees including an MS in Civil Eng, a PHD in Data Science and an MBA from Stanford. Trunk Tools is an AI Tool for the $13T construction industry aimed at enhancing project management and addressing the skilled Labor shortage. We wanted to have Sarah on to share her experience in building a vertical AI tool - specifically: where AI will have the biggest impact on construction and behind the scenes of how they’ve built tools like TrunkText and TrunkScheduler to massively reduce the amount of rework in construction.

Snowflake sits in a unique position in the AI landscape: They enable fast, secure, and scalable proprietary data access for thousands of customers, many of whom are building AI tools. They also maintain their own suite of AI products, increasing the utility of their platform and empowering customers who may not have the resources to build their own. That’s why it was so fascinating to speak with Baris Gultekin, Snowflake’s Head of AI, on our latest Unsupervised Learning. Baris has helped Snowflake launch several key products, including Cortex, AI Data Cloud, and even Snowflake’s own LLM, called Arctic. Baris was a founder himself — he joined Snowflake through an acquisition of his blockchain API startup, nxyz. He has a unique window into the future of AI via his role building key infrastructure. It was really fun to dive deep into this side of the AI ecosystem with Baris.

We’re excited to bring you Connor Zwick, CEO and cofounder of Speak, on the podcast this week! Speak helps people learning a language have conversations with an AI speaking partner, which is critical to gaining fluency. It’s backed by OpenAI and most recently raised at a $500M valuation. Since launching in its inaugural market of South Korea in 2019, Speak has grown to over 10 million users and now has customers in more than 40 countries.

It was very special to have Github CEO Thomas Dhomke on the pod for many reasons, not the least of which is that my partner Erica Brescia was COO at Gitlab just before joining Redpoint! Thomas has been at Github for almost 6 years, and has been CEO for almost 3 of those years. He has a pulse on what engineers around the world are looking for from the world’s leading developer platform, and incredible vision and empathy in making Github delivers it.

Cris is the co-founder and CEO of Runway, which builds breathtakingly real video AI tools, including the incredible Gen-3 Alpha foundation model. Cris sits right at the intersection of technology and creativity, and in 2023 was named to TIME’s 100 Most Influential People in AI. Runway is reported to be in talks to raise capital at a $4 Billion valuation.

According to Ramp’s (ramp.com) quarterly spend report, Fireflies was the 4th highest AI platform by spend. With over 300,000 customers worldwide and 16M users, Fireflies.ai is operating at some of the largest scale amongst AI companies today. This week on Unsupervised Learning we had Krish Ramineni, Co-founder & CEO of Fireflies on to talk about how he sees AI changing the way we work and conduct meetings, and share his biggest learnings around AI in building Fireflies.

Scott Belsky is the founder of Behance, a creative platform sold to Adobe in 2012. He’s since gone on to take on the role of Chief Product Officer at Adobe leading design for all products across Creative Cloud, Document Cloud and the Digital Experience business. This week on Unsupervised Learning Scott shares his thoughts on the future of creative tools with AI, a future where hyper-personalization wins and what humans will do when content is commoditized.

Last week, Suno announced $125M in funding, marking a significant milestone in their journey to reshape the music creation landscape. On this week's episode of Unsupervised Learning, we caught up with Suno's founder, Mikey Shulman, to dive into their approach to multiplayer music collaboration, how they got Suno to be so fast and the future of digital concerts (and workshopped new intro music for the pod 👀).