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Satya Nadella — How Microsoft is preparing for AGI
As part of this interview, Satya Nadella gave Dylan Patel (founder of SemiAnalysis) and me an exclusive first-look at their brand-new Fairwater 2 datacenter.
Microsoft is building multiple Fairwaters, each of which has hundreds of thousands of GB200s & GB300s. Between all these interconnected buildings, they’ll have over 2 GW of total capacity. Just to give a frame of reference, even a single one of these Fairwater buildings is more powerful than any other AI datacenter that currently exists.
Satya then answered a bunch of questions about how Microsoft is preparing for AGI across all layers of the stack.
Watch on YouTube; read the transcript.
Sponsors
* Labelbox produces high-quality data at massive scale, powering any capability you want your model to have. Whether you’re building a voice agent, a coding assistant, or a robotics model, Labelbox gets you the exact data you need, fast. Reach out at labelbox.com/dwarkesh
* CodeRabbit automatically reviews and summarizes PRs so you can understand changes and catch bugs in half the time. This is helpful whether you’re coding solo, collaborating with agents, or leading a full team. To learn how CodeRabbit integrates directly into your workflow, go to coderabbit.ai
To sponsor a future episode, visit dwarkesh.com/advertise.
Timestamps
(00:00:00) - Fairwater 2
(00:03:20) - Business models for AGI
(00:12:48) - Copilot
(00:20:02) - Whose margins will expand most?
(00:36:17) - MAI
(00:47:47) - The hyperscale business
(01:02:44) - In-house chip & OpenAI partnership
(01:09:35) - The CAPEX explosion
(01:15:07) - Will the world trust US companies to lead AI?
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An audio version of my blog post, Thoughts on AI progress (Dec 2025)
Read the essay here.
Timestamps
00:00:00 What are we scaling?
00:03:11 The value of human labor
00:05:04 Economic diffusion lag is cope00:06:34 Goal-post shifting is justified
00:08:23 RL scaling
00:09:18 Broadly deployed intelligence explosion
Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
Andrej Karpathy — AGI is still a decade away
The Andrej Karpathy episode.
During this interview, Andrej explains why reinforcement learning is terrible (but everything else is much worse), why AGI will just blend into the previous ~2.5 centuries of 2% GDP growth, why self driving took so long to crack, and what he sees as the future of education.
It was a pleasure chatting with him.
Watch on YouTube; read the transcript.
Sponsors
* Labelbox helps you get data that is more detailed, more accurate, and higher signal than you could get by default, no matter your domain or training paradigm. Reach out today at labelbox.com/dwarkesh
* Mercury helps you run your business better. It’s the banking platform we use for the podcast — we love that we can see our accounts, cash flows, AR, and AP all in one place. Apply online in minutes at mercury.com
* Google’s Veo 3.1 update is a notable improvement to an already great model. Veo 3.1’s generations are more coherent and the audio is even higher-quality. If you have a Google AI Pro or Ultra plan, you can try it in Gemini today by visiting https://gemini.google
Timestamps
(00:00:00) – AGI is still a decade away
(00:29:45) – LLM cognitive deficits
(00:40:05) – RL is terrible
(00:49:38) – How do humans learn?
(01:06:25) – AGI will blend into 2% GDP growth
(01:17:36) – ASI
(01:32:50) – Evolution of intelligence & culture
(01:42:55) - Why self driving took so long
(01:56:20) - Future of education
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Nick Lane – Life as we know it is chemically inevitable
Nick Lane has some pretty wild ideas about the evolution of life.
He thinks early life was continuous with the spontaneous chemistry of undersea hydrothermal vents.
Nick’s story may be wrong, but I find it remarkable that with just that starting point, you can explain so much about why life is the way that it is — the things you’re supposed to just take as givens in biology class:
* Why are there two sexes? Why sex at all?
* Why are bacteria so simple despite being around for 4 billion years? Why is there so much shared structure between all eukaryotic cells despite the enormous morphological variety between animals, plants, fungi, and protists?
* Why did the endosymbiosis event that led to eukaryotes happen only once, and in the particular way that it did?
* Why is all life powered by proton gradients? Why does all life on Earth share not only the Krebs Cycle, but even the intermediate molecules like Acetyl-CoA?
His theory implies that early life is almost chemically inevitable (potentially blooming on hundreds of millions of planets in the Milky Way alone), and that the real bottleneck is the complex eukaryotic cell.
Watch on YouTube; listen on Apple Podcasts or Spotify.
Sponsors
* Gemini in Sheets lets you turn messy text into structured data. We used it to classify all our episodes by type and topic, no manual tagging required. If you’re a Google Workspace user, you can get started today at docs.google.com/spreadsheets/
* Labelbox has a massive network of domain experts (called Alignerrs) who help train AI models in a way that ensures they understand the world deeply, not superficially. These Alignerrs are true experts — one even tutored me in chemistry as I prepped for this episode. Learn more at labelbox.com/dwarkesh
* Lighthouse helps frontier technology companies like Cursor and Physical Intelligence navigate the U.S. immigration system and hire top talent from around the world. Lighthouse handles everything, maximizing the probability of visa approval while minimizing the work you have to do. Learn more at lighthousehq.com/employers
To sponsor a future episode, visit dwarkesh.com/advertise.
Timestamps
(00:00:00) – The singularity that unlocked complex life
(00:08:26) – Early life continuous with Earth's geochemistry
(00:23:36) – Eukaryotes are the great filter for intelligent life
(00:42:16) – Mitochondria are the reason we have sex
(01:08:12) – Are bioelectric fields linked to consciousness?
Ref: 868329
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#2454 - Robert Malone, MD
Dario Amodei — "We are near the end of the exponential"
Dario Amodei thinks we are just a few years away from AGI — or as he puts it, from having “a country of geniuses in a data center”. In this episode, we discuss what to make of the scaling hypothesis in the current RL regime, why task-specific RL might lead to generalization, and how AI will diffuse throughout the economy. We also dive into Anthropic’s revenue projections, compute commitments, path to profitability, and more.
Watch on YouTube; read the transcript.
Sponsors
* Labelbox can get you the RL tasks and environments you need. Their massive network of subject-matter experts ensures realism across domains, and their in-house tooling lets them continuously tweak task difficulty to optimize learning. Reach out at labelbox.com/dwarkesh.
* Jane Street sent me another puzzle… this time, they’ve trained backdoors into 3 different language models — they want you to find the triggers. Jane Street isn’t even sure this is possible, but they’ve set aside $50,000 for the best attempts and write-ups. They’re accepting submissions until April 1st at janestreet.com/dwarkesh.
* Mercury’s personal accounts make it easy to share finances with a partner, a roommate… or OpenClaw. Last week, I wanted to try OpenClaw for myself, so I used Mercury to spin up a virtual debit card with a small spend limit, and then I let my agent loose. No matter your use case, apply at mercury.com/personal-banking.
Timestamps
(00:00:00) - What exactly are we scaling?
(00:12:36) - Is diffusion cope?
(00:29:42) - Is continual learning necessary?
(00:46:20) - If AGI is imminent, why not buy more compute?
(00:58:49) - How will AI labs actually make profit?
(01:31:19) - Will regulations destroy the boons of AGI?
(01:47:41) - Why can’t China and America both have a country of geniuses in a datacenter?
Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
Another Year in the Garbage Can: Friday, February 13th, 2026
