Adam Brown is back!
General relativity is said to be the most beautiful idea the human mind has ever produced. Most of us will never get to fully appreciate its elegance by taking the 20-lecture graduate course Adam taught on it at Stanford. But in this episode, Adam distills the key idea at its heart so clearly and compellingly that even I could keep up lol.
At the core of general relativity, Einstein is trying to figure out the principle behind a particular coincidence: that the mass that resists acceleration and the mass that gravity pulls on just happen to be exactly the same. Adam then leads us through the path of insight which Einstein called his “happiest thought.”
Then Adam lectures on black holes. First, by showing how even under special relativity you could create a perpetual motion machine if black holes weren’t truly black. And then, by explaining why the observations of an infalling observer and a distant bystander to the black hole would be so radically different
Adam leads Blueshift, the team at Google DeepMind cracking science and reasoning, which gave us the opportunity to discuss at the very end how close we are to AIs that could rediscover general relativity from scratch. Stay till the close for some philosophy of science.
Watch on YouTube; read the transcript.
Sponsors
* Jane Street has traders from all sorts of different backgrounds. For example, I recently got to speak with Jed Thompson, a trader who started his career in particle physics. Jed told me how the habits he built as a physicist (like never running a calculation without first having a good guess at the answer) helped him build good trading intuition. So no matter what field you’re working in right now, your experience may be more applicable than you think. Check out open positions at janestreet.com/dwarkesh
* Crusoe gave me early access to their new serverless fine-tuning product, so I decided to try fine-tuning a Dwarkesh-style question generator. Crusoe made this really easy: I just turned my interview transcripts into training data and then kicked off a run – I never had to touch infra or tweak hyperparameters. After training was done, I ran a blind eval with my team: they preferred the fine-tuned model’s proposed questions over my own suggestions about 30% of the time. Serverless fine-tuning goes live next week. Learn more at crusoe.ai/dwarkesh
* Cursor’s iOS app lets me kick off real work no matter where I am. For example, recently I was at dinner with friends when I had an idea about how to investigate the past few years of progress in sample efficiency. I pulled out the Cursor app, dumped my thoughts into a voice note, and 15 minutes later, Cursor had cloned the relevant repo, done the necessary analysis, and written up its findings. And now I’m expanding that work into a full write-up. Without the Cursor app, the idea would’ve floated away. Check out the app now at cursor.com/dwarkesh
Timestamps
(00:00:00) – The coincidence that led Einstein to general relativity
(00:16:42) – Gravity is a consequence of curved spacetime, not a force
(00:31:46) – Why black holes prevent unlimited energy extraction
(00:47:12) – Black holes are the ultimate power plants
(01:13:50) – What falling into a black hole would actually feel like
(01:18:51) – The three ways we know black holes are real
(01:24:21) – The first time we saw gravity bend light
(01:29:33) – How far can AI get without experimental evidence?
Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
The entire startup ecosystem is racing to build agent harnesses. Logan Kilpatrick, who leads Google AI Studio and the Gemini API, argues that scramble has a roughly 12-month shelf life. Models will absorb the scaffolding and run it natively, so the edge moves elsewhere. Google's own bet runs in parallel: a single agent harness, born from the Windsurf team and now called Antigravity, has become the connective tissue across search, the Gemini app, Cloud, and AI Studio — the role Gemini-the-model used to play. Logan makes the case that coding already feels like narrow superintelligence, and that "jagged" vertical superintelligence (in math, finance, and science) will arrive well before AGI. He argues Google's real goal is maximizing outcomes for users, not eyeball time. He unpacks Omni, the single model built to replace multiple separate systems Google once trained for text, audio, music, image, and video. His throughline: AI is an accelerant for human ambition, not a substitute for it.
Hosted by Sonya Huang, Sequoia Capital
Logan Kilpatrick and Tulsee Doshi of Google DeepMind join for a first-ever in-person episode recorded just days before Google I/O, covering headline launches like Gemini 3.5 Flash, the Omni video generation model, and the new Gemini Spark agentic product. The conversation digs into Google's strategic decision to lead with cost-adjusted efficiency over raw capability, how DeepMind now ships a full agent harness rather than bare models, and technical questions around context window limits and knowledge cutoffs. They also explore how the team thinks about model psychology, AI welfare, and recursive self-improvement.
Sponsors:
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Claude:
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Demis Hassabis, co-founder and CEO of Google DeepMind and 2024 Nobel laureate in chemistry for AlphaFold, joins Sequoia partner Konstantine Buhler at AI Ascent 2026 for a wide-ranging conversation about the path to AGI and what comes after. He explains why he believes AGI is achievable by 2030, why drug discovery could collapse from ten years to days, and why we should think of information, not matter or energy, as the most fundamental substance in the universe. Also: what Einstein would tell us about the limits of today's models, and why the next year or two will be critical for humanity.
Live from The Royal Institution of Great Britain, it's TechStuff! Oz sat down with two visionaries at an event hosted by Quilt.AI. First, he spoke with Ali Eslami, a Distinguished Research Scientist at Google DeepMind, who built the prototype for what is now AI Search. Ali talked about how working on AI can feel like surfing, and what went into connecting Gemini to Google Search to create what he called "neural Google."
After that, Oz chats with Saad Mohseni about his work with MOBY Group. Saad guides Oz through his twenty-year effort to bring top-tier news and entertainment to Afghanistan and beyond — from a reality TV singing competition that changed the country, to using WhatsApp and AI to provide education to girls banned from school.
Additional Reading:
Radio Free Afghanistan – HarperCollins
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Demis Hassabis is the Co-Founder & CEO of Google DeepMind - working on AGI, responsible for AI breakthroughs such as AlphaGo, the first program to beat the world champion at the game of Go; and AlphaFold, which cracked the 50-year grand challenge of protein structure prediction and was recognised with the 2024 Nobel Prize in Chemistry. Demis is revolutionising drug discovery at Isomorphic Labs. Ultimately, trying to understand the fundamental nature of reality.
AGENDA:
00:04:00 — What Actually Counts as AGI; and Where Are We Today?
00:05:00 — What Are the Biggest Bottlenecks Holding AI Back Today?
00:06:00 — Have We Hit the Limits of Scaling Laws?
00:07:00 — Where Is AI Ahead of Expectations; and What's Still Missing?
00:07:30 — Why Can't AI Systems Learn Continuously Like Humans?
00:08:30 — How Did DeepMind Go from Behind to Leading the Pack?
00:11:00 — Are We Heading Toward Model Commoditization; or Winner-Takes-All?
00:12:00 — What Does the Future of Open Source Really Look Like?
00:13:00 — What Does a Post LLM World Look Like?
00:14:45 — Can AI Really Fix Drug Discovery—and Cut the 10-Year Timeline?
00:17:00 — What Does "Good" AI Regulation Actually Look Like?
00:18:00 — Who Should Be the Ultimate Arbiter of Truth in an AI World?
00:19:30 — If Demis Had One Shot to Fix AI Safety, What Would He Do?
00:21:00 — Is This Time Different for Jobs; or Will History Repeat Itself?
00:22:00 — Is AGI Bigger Than the Industrial Revolution; and Faster?
00:23:00 — Are We Underestimating AI Despite All the Hype?
00:23:30 — Does AI Lead to Massive Inequality; or Universal Prosperity?
00:24:30 — How Do We Solve the Energy Crisis Created by AI?
00:26:00 — Why Stay in the UK Instead of Moving to Silicon Valley?
00:28:00 — Will Europe Ever Build a Trillion-Dollar Tech Giant?
00:29:30 — Meeting Elon Musk for the First Time?
00:31:00 — What Big Questions About AI Is No One Talking About?
00:31:30 — What Does Demis Want His Legacy to Be?
Retrieval-augmented generation, or RAG, has become a foundational approach to building production AI systems. However, deploying RAG in practice can be complex and costly. Developers typically have to manage vector databases, chunking strategies, embedding models, and indexing infrastructure. Designing effective RAG systems is also a moving target, as techniques and best practices evolve in step with rapidly advancing language models.
Google DeepMind recently released the File Search Tool, a fully managed RAG system built directly into the Gemini API. File Search abstracts away the retrieval pipeline, allowing developers to upload documents, code, and other text data, automatically generate embeddings, and query their knowledge base. We wanted to understand how the DeepMind team designed a general-purpose RAG system that maintains high retrieval quality.
Animesh Chatterji is a Software Engineer at Google DeepMind and Ivan Solovyev is a Product Manager at DeepMind, and they worked on File Search Tool. They joined the podcast with Sean Falconer to discuss the evolution of RAG, why simplicity and pricing transparency matter, how embedding models have improved retrieval quality, the tradeoffs between configurability and ease of use, and what’s next for multimodal retrieval across text, images, and beyond.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post DeepMind’s RAG System with Animesh Chatterji and Ivan Solovyev appeared first on Software Engineering Daily.
Lila Ibrahim is the COO of Google DeepMind. James Manyika is the senior Vice President for Research, Technology, and Society at Google. The two join Big Technology Podcast to discuss how Google's AI effort operates and runs experiments. In this conversation, we discuss the fundamental operating structure of DeepMind, how Google proper has become more experimental with the revival of Labs and other programs, and how the company is thinking about AI and education. We also cover weather and flood prediction at global scale, and training AI in space. Hit play for a deep inside look at the mechanics behind Google’s AI research machine and the big ideas it’s betting on next.
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From shipping Gemini Deep Think and IMO Gold to launching the Reasoning and AGI team in Singapore, Yi Tay has spent the last 18 months living through the full arc of Google DeepMind’s pivot from architecture research to RL-driven reasoning—watching his team go from a dozen researchers to 300+, training models that solve International Math Olympiad problems in a live competition, and building the infrastructure to scale deep thinking across every domain, and driving Gemini to the top of the leaderboards across every category. Yi Returns to dig into the inside story of the IMO effort and more!
We discuss:
* Yi’s path: Brain → Reka → Google DeepMind → Reasoning and AGI team Singapore, leading model training for Gemini Deep Think and IMO Gold
* The IMO Gold story: four co-captains (Yi in Singapore, Jonathan in London, Jordan in Mountain View, and Tong leading the overall effort), training the checkpoint in ~1 week, live competition in Australia with professors punching in problems as they came out, and the tension of not knowing if they’d hit Gold until the human scores came in (because the Gold threshold is a percentile, not a fixed number)
* Why they threw away AlphaProof: “If one model can’t do it, can we get to AGI?” The decision to abandon symbolic systems and bet on end-to-end Gemini with RL was bold and non-consensus
* On-policy vs. off-policy RL: off-policy is imitation learning (copying someone else’s trajectory), on-policy is the model generating its own outputs, getting rewarded, and training on its own experience—”humans learn by making mistakes, not by copying”
* Why self-consistency and parallel thinking are fundamental: sampling multiple times, majority voting, LM judges, and internal verification are all forms of self-consistency that unlock reasoning beyond single-shot inference
* The data efficiency frontier: humans learn from 8 orders of magnitude less data than models, so where’s the bug? Is it the architecture, the learning algorithm, backprop, off-policyness, or something else?
* Three schools of thought on world models: (1) Genie/spatial intelligence (video-based world models), (2) Yann LeCun’s JEPA + FAIR’s code world models (modeling internal execution state), (3) the amorphous “resolution of possible worlds” paradigm (curve-fitting to find the world model that best explains the data)
* Why AI coding crossed the threshold: Yi now runs a job, gets a bug, pastes it into Gemini, and relaunches without even reading the fix—”the model is better than me at this”
* The Pokémon benchmark: can models complete Pokédex by searching the web, synthesizing guides, and applying knowledge in a visual game state? “Efficient search of novel idea space is interesting, but we’re not even at the point where models can consistently apply knowledge they look up”
* DSI and generative retrieval: re-imagining search as predicting document identifiers with semantic tokens, now deployed at YouTube (symmetric IDs for RecSys) and Spotify
* Why RecSys and IR feel like a different universe: “modeling dynamics are strange, like gravity is different—you hit the shuttlecock and hear glass shatter, cause and effect are too far apart”
* The closed lab advantage is increasing: the gap between frontier labs and open source is growing because ideas compound over time, and researchers keep finding new tricks that play well with everything built before
* Why ideas still matter: “the last five years weren’t just blind scaling—transformers, pre-training, RL, self-consistency, all had to play well together to get us here”
* Gemini Singapore: hiring for RL and reasoning researchers, looking for track record in RL or exceptional achievement in coding competitions, and building a small, talent-dense team close to the frontier
—
Yi Tay
* Google DeepMind: https://deepmind.google
* X: https://x.com/YiTayML
Full Video Episode
Timestamps
00:00:00 Introduction: Returning to Google DeepMind and the Singapore AGI Team00:04:52 The Philosophy of On-Policy RL: Learning from Your Own Mistakes00:12:00 IMO Gold Medal: The Journey from AlphaProof to End-to-End Gemini00:21:33 Training IMO Cat: Four Captains Across Three Time Zones00:26:19 Pokemon and Long-Horizon Reasoning: Beyond Academic Benchmarks00:36:29 AI Coding Assistants: From Lazy to Actually Useful00:32:59 Reasoning, Chain of Thought, and Latent Thinking00:44:46 Is Attention All You Need? Architecture, Learning, and the Local Minima00:55:04 Data Efficiency and World Models: The Next Frontier01:08:12 DSI and Generative Retrieval: Reimagining Search with Semantic IDs01:17:59 Building GDM Singapore: Geography, Talent, and the Symposium01:24:18 Hiring Philosophy: High Stats, Research Taste, and Student Budgets01:28:49 Health, HRV, and Research Performance: The 23kg Journey
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe
Demis Hassabis is the CEO of Google DeepMind. Hassabis joins Big Technology Podcast to discuss where AI progress really stands today, where the next breakthroughs might come from, and whether we’ve hit AGI already. Tune in for a deep discussion covering the latest in AI research, from continual learning to world models. We also dig into product, discussing Google’s big bet on AI glasses, its advertising plans, and AI coding. We also cover what AI means for knowledge work and scientific discovery. Hit play for a wide-ranging, high-signal conversation about where AI is headed next from one of the leaders driving it forward.
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Demis Hassabis is the CEO of Google DeepMind. He joined Big Technology Podcast in early 2025 discuss the cutting edge of AI and where the research is heading. In this conversation, we cover the path to artificial general intelligence, how long it will take to get there, how to build world models, whether AIs can be creative, and how AIs are trying to deceive researchers. Stay tuned for the second half where we discuss Google's plan for smart glasses and Hassabis's vision for a virtual cell. Hit play for a fascinating discussion with an AI pioneer that will both break news and leave you deeply informed about the state of AI and its promising future.
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Emmett Shear and Séb Krier debate whether today’s AI alignment paradigm—focused on control and instruction-following—is fundamentally flawed. PSA for AI builders: Interested in alignment, governance, or AI safety? Learn more about the MATS Summer 2026 Fellowship and submit your name to be notified when applications open: https://matsprogram.org/s26-tcr. They explore what changes if advanced AIs are better understood as beings with their own values, and why current control methods could drift toward something like slavery. The conversation dives into “organic alignment,” multi-agent simulations, evolving cooperation, and the possibility of AI moral standing as systems gain memory and continual learning.
Sponsors:
MATS:
MATS is a fully funded 12-week research program pairing rising talent with top mentors in AI alignment, interpretability, security, and governance. Apply for the next cohort at https://matsprogram.org/s26-tcr
Tasklet:
Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai
Agents of Scale:
Agents of Scale is a podcast from Zapier CEO Wade Foster, featuring conversations with C-suite leaders who are leading AI transformation. Subscribe to the show wherever you get your podcasts
Shopify:
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CHAPTERS:
(00:00) About the Episode
(03:44) Defining organic AI alignment
(14:48) Technical vs value alignment (Part 1)
(19:55) Sponsors: MATS | Tasklet
(22:56) Technical vs value alignment (Part 2) (Part 1)
(31:34) Sponsors: Agents of Scale | Shopify
(34:21) Technical vs value alignment (Part 2) (Part 2)
(34:22) Labs, tools, and beings
(43:22) AI personhood and consciousness
(56:53) Safe futures and Softmax
(01:04:17) Chatbots, mirrors, simulations
(01:10:14) Doom, futures, and OpenAI
(01:17:25) Outro
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Youtube: https://youtube.com/@CognitiveRevolutionPodcast
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We often think of Large Language Models (LLMs) as all-knowing, but as the team reveals, they still struggle with the logic of a second-grader. Why can’t ChatGPT reliably add large numbers? Why does it "hallucinate" the laws of physics? The answer lies in the architecture. This episode explores how *Category Theory* —an ultra-abstract branch of mathematics—could provide the "Periodic Table" for neural networks, turning the "alchemy" of modern AI into a rigorous science.
In this deep-dive exploration, *Andrew Dudzik*, *Petar Velichkovich*, *Taco Cohen*, *Bruno Gavranović*, and *Paul Lessard* join host *Tim Scarfe* to discuss the fundamental limitations of today’s AI and the radical mathematical framework that might fix them.
TRANSCRIPT:
https://app.rescript.info/public/share/LMreunA-BUpgP-2AkuEvxA7BAFuA-VJNAp2Ut4MkMWk
---
Key Insights in This Episode:
* *The "Addition" Problem:* *Andrew Dudzik* explains why LLMs don't actually "know" math—they just recognize patterns. When you change a single digit in a long string of numbers, the pattern breaks because the model lacks the internal "machinery" to perform a simple carry operation.
* *Beyond Alchemy:* deep learning is currently in its "alchemy" phase—we have powerful results, but we lack a unifying theory. Category Theory is proposed as the framework to move AI from trial-and-error to principled engineering. [00:13:49]
* *Algebra with Colors:* To make Category Theory accessible, the guests use brilliant analogies—like thinking of matrices as *magnets with colors* that only snap together when the types match. This "partial compositionality" is the secret to building more complex internal reasoning. [00:09:17]
* *Synthetic vs. Analytic Math:* *Paul Lessard* breaks down the philosophical shift needed in AI research: moving from "Analytic" math (what things are made of) to "Synthetic" math [00:23:41]
---
Why This Matters for AGI
If we want AI to solve the world's hardest scientific problems, it can't just be a "stochastic parrot." It needs to internalize the rules of logic and computation. By imbuing neural networks with categorical priors, researchers are attempting to build a future where AI doesn't just predict the next word—it understands the underlying structure of the universe.
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TIMESTAMPS:
00:00:00 The Failure of LLM Addition & Physics
00:01:26 Tool Use vs Intrinsic Model Quality
00:03:07 Efficiency Gains via Internalization
00:04:28 Geometric Deep Learning & Equivariance
00:07:05 Limitations of Group Theory
00:09:17 Category Theory: Algebra with Colors
00:11:25 The Systematic Guide of Lego-like Math
00:13:49 The Alchemy Analogy & Unifying Theory
00:15:33 Information Destruction & Reasoning
00:18:00 Pathfinding & Monoids in Computation
00:20:15 System 2 Reasoning & Error Awareness
00:23:31 Analytic vs Synthetic Mathematics
00:25:52 Morphisms & Weight Tying Basics
00:26:48 2-Categories & Weight Sharing Theory
00:28:55 Higher Categories & Emergence
00:31:41 Compositionality & Recursive Folds
00:34:05 Syntax vs Semantics in Network Design
00:36:14 Homomorphisms & Multi-Sorted Syntax
00:39:30 The Carrying Problem & Hopf Fibrations
Petar Veličković (GDM)
https://petar-v.com/
Paul Lessard
https://www.linkedin.com/in/paul-roy-lessard/
Bruno Gavranović
https://www.brunogavranovic.com/
Andrew Dudzik (GDM)
https://www.linkedin.com/in/andrew-dudzik-222789142/
---
REFERENCES:
Model:
[00:01:05] Veo
https://deepmind.google/models/veo/
[00:01:10] Genie
https://deepmind.google/blog/genie-3-a-new-frontier-for-world-models/
Paper:
[00:04:30] Geometric Deep Learning Blueprint
https://arxiv.org/abs/2104.13478
https://www.youtube.com/watch?v=bIZB1hIJ4u8
[00:16:45] AlphaGeometry
https://arxiv.org/abs/2401.08312
[00:16:55] AlphaCode
https://arxiv.org/abs/2203.07814
[00:17:05] FunSearch
https://www.nature.com/articles/s41586-023-06924-6
[00:37:00] Attention Is All You Need
https://arxiv.org/abs/1706.03762
[00:43:00] Categorical Deep Learning
https://arxiv.org/abs/2402.15332
This year-end live show features nine rapid-fire conversations to make sense of AI’s 2025 and what might define 2026. PSA for AI builders: Interested in alignment, governance, or AI safety? Learn more about the MATS Summer 2026 Fellowship and submit your name to be notified when applications open: https://matsprogram.org/s26-tcr. Zvi Moshowitz maps the OpenAI–Anthropic–Google race, the denialism gap, and why his PDoom is still ~60–70%. Greg (ARC-AGI Prize), Eugenia Kuyda, Ali Behrouz, Logan Kirkpatrick, and Jungwon Hwang cover sample-efficient benchmarks and ARC-AGI 3, companions and human-flourishing metrics, continual-learning memory, Gemini 3 Flash for developers, and AI for scientific decisions.
Sponsors:
Gemini 3 in Google AI Studio:
Gemini 3 in Google AI Studio lets you build fully functional apps from a simple description—no coding required. Start vibe coding your idea today at https://ai.studio/build
MATS:
MATS is a fully funded 12-week research program pairing rising talent with top mentors in AI alignment, interpretability, security, and governance. Apply for the next cohort at https://matsprogram.org/s26-tcr
Framer:
Framer is the all-in-one tool to design, iterate, and publish stunning websites with powerful AI features. Start creating for free and use code COGNITIVE to get one free month of Framer Pro at https://framer.com/design
Shopify:
Shopify powers millions of businesses worldwide, handling 10% of U.S. e-commerce. With hundreds of templates, AI tools for product descriptions, and seamless marketing campaign creation, it's like having a design studio and marketing team in one. Start your $1/month trial today at https://shopify.com/cognitive
Tasklet:
Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai
CHAPTERS:
(00:00) Sponsor: Gemini 3 in Google AI Studio
(00:31) Live show experiment
(02:26) Zvi: discourse and denial
(13:28) Continual learning and doom
(22:05) ArcAGI: what's missing (Part 1)
(22:09) Sponsors: MATS | Framer
(25:28) ArcAGI: what's missing (Part 2)
(31:58) Scaffolds and tiny models
(38:58) ArcAGI 3 game worlds
(45:13) AI companions landscape (Part 1)
(45:21) Sponsors: Shopify | Tasklet
(48:29) AI companions landscape (Part 2)
(58:14) Wabi apps and caution
(01:08:16) Nested learning, layered memory
(01:23:14) Gemini 3 Flash launch
(01:34:09) RAG, agents, dev advice
(01:42:20) Elicit speeds evidence synthesis
(01:57:57) Outro
PRODUCED BY:
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Google’s much anticipated new large language model Gemini 3 begins rolling out today. We’ll tell you what we learned from an early product briefing and bring you our conversation with Google executives Demis Hassabis and Josh Woodward, just ahead of the launch.
Guests:
Demis Hassabis, chief executive and co-founder of Google DeepMind
Josh Woodward, vice president of Google Labs and Google Gemini
Additional Reading:
The Man Who ‘A.G.I.-Pilled’ Google
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When Google launched Nano Banana, it instantly became a global phenomenon, introducing an image model that finally made it possible for people to see themselves in AI-generated worlds. In this episode, Nicole Brichtova and Hansa Srinivasan, the product and engineering leads behind Nano Banana, share the story behind the model’s creation and what it means for the future of visual AI.
Nicole and Hansa discuss how they achieved breakthrough character consistency, why human evaluation remains critical for models that aim to feel right, and how “fun” became a gateway to utility. They explain the craft behind Gemini’s multimodal design, the obsession with data quality that powered Nano Banana’s realism, and how user creativity continues to push the technology in unexpected directions—from personal storytelling to education and professional design. The conversation explores what comes next in visual AI, why accessibility and imagination must evolve together, and how the tools we build can help people capture not just reality but possibility.
Hosted by: Stephanie Zhan and Pat Grady, Sequoia Capital
Google DeepMind’s new image model Nano Banana took the internet by storm.
In this episode, we sit down with Principal Scientist Oliver Wang and Group Product Manager Nicole Brichtova to discuss how Nano Banana was created, why it’s so viral, and the future of image and video editing.
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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Today, we’re joined by Oliver Wang, principal scientist at Google DeepMind and tech lead for Gemini 2.5 Flash Image—better known by its code name, “Nano Banana.” We dive into the development and capabilities of this newly released frontier vision-language model, beginning with the broader shift from specialized image generators to general-purpose multimodal agents that can use both visual and textual data for a variety of tasks. Oliver explains how Nano Banana can generate and iteratively edit images while maintaining consistency, and how its integration with Gemini’s world knowledge expands creative and practical use cases. We discuss the tension between aesthetics and accuracy, the relative maturity of image models compared to text-based LLMs, and scaling as a driver of progress. Oliver also shares surprising emergent behaviors, the challenges of evaluating vision-language models, and the risks of training on AI-generated data. Finally, we look ahead to interactive world models and VLMs that may one day “think” and “reason” in images.
The complete show notes for this episode can be found at https://twimlai.com/go/748.
(0:00) Introducing Sir Demis Hassabis, reflecting on his Nobel Prize win
(2:39) What is Google DeepMind? How does it interact with Google and Alphabet?
(4:01) Genie 3 world model
(9:21) State of robotics models, form factors, and more
(14:42) AI science breakthroughs, measuring AGI
(20:49) Nano-Banana and the future of creative tools, democratization of creativity
(24:44) Isomorphic Labs, probabilistic vs deterministic, scaling compute, a golden age of science
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Today, we're joined by Jack Parker-Holder and Shlomi Fruchter, researchers at Google DeepMind, to discuss the recent release of Genie 3, a model capable of generating “playable” virtual worlds. We dig into the evolution of the Genie project and review the current model’s scaled-up capabilities, including creating real-time, interactive, and high-resolution environments. Jack and Shlomi share their perspectives on what defines a world model, the model's architecture, and key technical challenges and breakthroughs, including Genie 3’s visual memory and ability to handle “promptable world events.” Jack, Shlomi, and Sam share their favorite Genie 3 demos, and discuss its potential as a dynamic training environment for embodied AI agents. Finally, we will explore future directions for Genie research.
The complete show notes for this episode can be found at https://twimlai.com/go/743.
Genie 3 can generate fully interactive, persistent worlds from just text, in real time.
In this episode, Google DeepMind’s Jack Parker-Holder (Research Scientist) and Shlomi Fruchter (Research Director) join Anjney Midha, Marco Mascorro, and Justine Moore of a16z, with host Erik Torenberg, to discuss how they built it, the breakthrough “special memory” feature, and the future of AI-powered gaming, robotics, and world models.
They share:
How Genie 3 generates interactive environments in real time
Why its “special memory” feature is such a breakthrough
The evolution of generative models and emergent behaviors
Instruction following, text adherence, and model comparisons
Potential applications in gaming, robotics, simulation, and more
What’s next: Genie 4, Genie 5, and the future of world models
This conversation offers a first-hand look at one of the most advanced world models ever created.
Timecodes:
0:00 Introduction & The Magic of Genie 3
0:41 Real-Time World Generation Breakthroughs
1:22 The Team’s Journey: From Genie 1 to Genie 3
5:03 Interactive Applications & Use Cases
8:03 Special Memory and World Consistency
12:29 Emergent Behaviors and Model Surprises
18:37 Instruction Following and Text Adherence
19:53 Comparing Genie 3 and Other Models
21:25 The Future of World Models & Modality Convergence
27:35 Downstream Applications and Open Questions
31:42 Robotics, Simulation, and Real-World Impact
39:33 Closing Thoughts & Philosophical Reflections
Resources:
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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This episode features Shlomi Fuchter and Jack Parker Holder from Google DeepMind, who are unveiling a new AI called Genie 3. The host, Tim Scarfe, describes it as the most mind-blowing technology he has ever seen. We were invited to their offices to conduct the interview (not sponsored).Imagine you could create a video game world just by describing it. That's what Genie 3 does. It's an AI "world model" that learns how the real world works by watching massive amounts of video. Unlike a normal video game engine (like Unreal or the one for Doom) that needs to be programmed manually, Genie generates a realistic, interactive, 3D world from a simple text prompt.**SPONSOR MESSAGES***Prolific: Quality data. From real people. For faster breakthroughs.https://prolific.com/mlst?utm_campaign=98404559-MLST&utm_source=youtube&utm_medium=podcast&utm_content=script-gen***Here’s a breakdown of what makes it so revolutionary:From Text to a Virtual World: You can type "a drone flying by a beautiful lake" or "a ski slope," and Genie 3 creates that world for you in about three seconds. You can then navigate and interact with it in real-time.It's Consistent: The worlds it creates have a reliable memory. If you look away from an object and then look back, it will still be there, just as it was. The guests explain that this consistency isn't explicitly programmed in; it's a surprising, "emergent" capability of the powerful AI model.A Huge Leap Forward: The previous version, Genie 2, was a major step, but it wasn't fast enough for real-time interaction and was much lower resolution. Genie 3 is 720p, interactive, and photorealistic, running smoothly for several minutes at a time.The Killer App - Training Robots: Beyond entertainment, the team sees Genie 3 as a game-changer for training AI. Instead of training a self-driving car or a robot in the real world (which is slow and dangerous), you can create infinite simulations. You can even prompt rare events to happen, like a deer running across the road, to teach an AI how to handle unexpected situations safely.The Future of Entertainment: this could lead to a "YouTube version 2" or a new form of VR, where users can create and explore endless, interconnected worlds together, like the experience machine from philosophy.While the technology is still a research prototype and not yet available to the public, it represents a monumental step towards creating true artificial worlds from the ground up.Jack Parker Holder [Research Scientist at Google DeepMind in the Open-Endedness Team]https://jparkerholder.github.io/Shlomi Fruchter [Research Director, Google DeepMind]https://shlomifruchter.github.io/TOC:[00:00:00] - Introduction: "The Most Mind-Blowing Technology I've Ever Seen"[00:02:30] - The Evolution from Genie 1 to Genie 2[00:04:30] - Enter Genie 3: Photorealistic, Interactive Worlds from Text[00:07:00] - Promptable World Events & Training Self-Driving Cars[00:14:21] - Guest Introductions: Shlomi Fuchter & Jack Parker Holder[00:15:08] - Core Concepts: What is a "World Model"?[00:19:30] - The Challenge of Consistency in a Generated World[00:21:15] - Context: The Neural Network Doom Simulation[00:25:25] - How Do You Measure the Quality of a World Model?[00:28:09] - The Vision: Using Genie to Train Advanced Robots[00:32:21] - Open-Endedness: Human Skill and Prompting Creativity[00:38:15] - The Future: Is This the Next YouTube or VR?[00:42:18] - The Next Step: Multi-Agent Simulations[00:52:51] - Limitations: Thinking, Computation, and the Sim-to-Real Gap[00:58:07] - Conclusion & The Future of Game EnginesREFS:World Models [David Ha, Jürgen Schmidhuber]https://arxiv.org/abs/1803.10122POEThttps://arxiv.org/abs/1901.01753[Akarsh Kumar, Jeff Clune, Joel Lehman, Kenneth O. Stanley]The Fractured Entangled Representation Hypothesishttps://arxiv.org/pdf/2505.11581TRANSCRIPT:https://app.rescript.info/public/share/Zk5tZXk6mb06yYOFh6nSja7Lg6_qZkgkuXQ-kl5AJqM
Demis Hassabis is the CEO of Google DeepMind and Nobel Prize winner for his groundbreaking work in protein structure prediction using AI.
Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep475-sc
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Transcript:
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OUTLINE:
(00:00) – Introduction
(00:29) – Sponsors, Comments, and Reflections
(08:40) – Learnable patterns in nature
(12:22) – Computation and P vs NP
(21:00) – Veo 3 and understanding reality
(25:24) – Video games
(37:26) – AlphaEvolve
(43:27) – AI research
(47:51) – Simulating a biological organism
(52:34) – Origin of life
(58:49) – Path to AGI
(1:09:35) – Scaling laws
(1:12:51) – Compute
(1:15:38) – Future of energy
(1:19:34) – Human nature
(1:24:28) – Google and the race to AGI
(1:42:27) – Competition and AI talent
(1:49:01) – Future of programming
(1:55:27) – John von Neumann
(2:04:41) – p(doom)
(2:09:24) – Humanity
(2:12:30) – Consciousness and quantum computation
(2:18:40) – David Foster Wallace
(2:25:54) – Education and research
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Pushmeet Kohli leads AI for Science at DeepMind, where his team has created AlphaEvolve, an AI system that discovers entirely new algorithms and proves mathematical results that have eluded researchers for decades. From improving 50-year-old matrix multiplication algorithms to generating interpretable code for complex problems like data center scheduling, AlphaEvolve represents a new paradigm where LLMs coupled with evolutionary search can outperform human experts. Pushmeet explains the technical architecture behind these breakthroughs and shares insights from collaborations with mathematicians like Terence Tao, while discussing how AI is accelerating scientific discovery across domains from chip design to materials science.
Hosted by Sonya Huang and Pat Grady, Sequoia Capital
Much of the scientific process involves searching. But rather than continue to rely on the luck of discovery, Google DeepMind has engineered a more efficient AI agent that mines complex spaces to facilitate scientific breakthroughs. Sarah Guo speaks with Pushmeet Kohli, VP of Science and Strategic Initiatives, and research scientist Matej Balog at Google DeepMind about AlphaEvolve, an autonomous coding agent they developed that finds new algorithms through evolutionary search. Pushmeet and Matej talk about how AlphaEvolve tackles the problem of matrix multiplication efficiency, scaling and iteration in problem solving, and whether or not this means we are at self-improving AI. Together, they also explore the implications AlphaEvolve has to other sciences beyond mathematics and computer science.
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Chapters:
00:00 Pushmeet Kohli and Matej Balog Introduction
0:48 Origin of AlphaEvolve
02:31 AlphaEvolve’s Progression from AlphaGo and AlphaTensor
08:02 The Open Problem of Matrix Multiplication Efficiency
11:18 How AlphaEvolve Evolves Code
14:43 Scaling and Predicting Iterations
16:52 Implications for Coding Agents
19:42 Overcoming Limits of Automated Evaluators
25:21 Are We At Self-Improving AI?
28:10 Effects on Scientific Discovery and Mathematics
31:50 Role of Human Scientists with AlphaEvolve
38:30 Making AlphaEvolve Broadly Accessible
40:18 Applying AlphaEvolve Within Google
41:39 Conclusion
Logan Kilpatrick from Google DeepMind returns for his fifth appearance to discuss Google’s transformation from "sleeping giant" to AI powerhouse, sharing insights from his year at the company as AI usage grew 50 times to 500 trillion tokens per month. He examines Google’s strengths, including superior compute infrastructure, frontier models like Gemini 2.5 Pro, viral products like NotebookLM, and the deepest AI research talent in the industry. The conversation covers whether leading AI companies will become more similar or different as easy opportunities disappear, why startups still have unique chances, and the potential impact of Google’s ultra-fast diffusion language models. Logan also shares practical advice for joining early access programs and getting noticed by industry insiders, including his personal email and an open invitation to reach out.
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Gemini 2.5 is DeepMind’s most advanced model yet, with strong reasoning and coding capabilities.
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Check out our previous episode with Logan, we discussed his unique path from coding to AI to product, the challenges of non-determinism in AI models, and surprising lessons from working at the Apple Store.
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As part of our AI Engineer World’s Fair preview, we’re releasing a special cross podcast recorded with Sam Charrington of TWiML AI at last week’s Google I/O!
TUESDAY: Shrestha and Kwindla’s workshop: https://www.ai.engineer/schedule#milliseconds-to-magic-real-time-workflows-using-the-gemini-live-api-and-pipecat
TUESDAY: Kwindla’s workshop: https://www.ai.engineer/schedule#building-voice-agents-with-gemini-and-pipecat
WEDNESDAY: Shrestha and Kwindla’s talk: https://www.ai.engineer/schedule#milliseconds-to-magic-real-time-workflows-using-the-gemini-live-api-and-pipecat
WEDNESDAY: Kwindla’s keynote: https://www.ai.engineer/schedule#-voice-keynote-your-realtime-ai-is-ngmi
THURSDAY: Logan’s keynote: https://www.ai.engineer/schedule#a-year-of-gemini-progress-what-comes-next
Catch all the speakers at AIE (both workshops and talks):
Logan Kilpatrick: https://www.latent.space/p/chatgpt-gpt4-hype-and-building-llm
Shrestha Basu Mallick: https://www.linkedin.com/in/shresthabm/
Kwindla Hultman Kramer: https://www.linkedin.com/in/kwkramer
Full Video Episode
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Today, I’m excited to share a special crossover edition of the podcast recorded live from Google I/O 2025! In this episode, I join Shawn Wang aka Swyx from the Latent Space Podcast, to interview Logan Kilpatrick and Shrestha Basu Mallick, PMs at Google DeepMind working on AI Studio and the Gemini API, along with Kwindla Kramer, CEO of Daily and creator of the Pipecat open source project. We cover all the highlights from the event, including enhancements to the Gemini models like thinking budgets and thought summaries, native audio output for expressive voice AI, and the new URL Context tool for research agents. The discussion also digs into the Gemini Live API, covering its architecture, the challenges of building real-time voice applications (such as latency and voice activity detection), and new features like proactive audio and asynchronous function calling. Finally, don’t miss our guests’ wish lists for next year’s I/O!
The complete show notes for this episode can be found at https://twimlai.com/go/733.
This week, we take a field trip to Google and report back about everything the company announced at its biggest show of the year, Google I/O. Then, we sit down with Google DeepMind’s chief executive and co-founder, Demis Hassabis, to discuss what his A.I. lab is building, the future of education, and what life could look like in 2030.
Guest:
Demis Hassabis, co-founder and chief executive of Google DeepMind
Additional Reading:
At Google I/O, everything is changing and normal and scary and chill
Google Unveils A.I. Chatbot, Signaling a New Era for Search
Google DeepMind C.E.O. Demis Hassabis on the Path From Chatbots to A.G.I.
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