Zhengdong Wang is an AI researcher who writes annual letters from the frontier.
I discovered Zhengdong by way of his 2025 letter in which he describes his “compute theory of everything”, and feeling the wave of AI crash over him after working in the field for five years. It blew me away.
I talked to him about what it will take for the rest of the world to believe, how AGI is a moving target and we might better conceive of progress as simply: “the model does the eval,” and where all of this might leave us on the other side.
Full episode: transcript & links, Spotify, Apple, YouTube, X/Twitter. If you enjoy it, please share it with a friend.
Dialectic 53: Zhengdong Wang - Feeling the Wave
23 Lessons from Zhengdong
Follow questions whose endings you can’t see. Zhengdong enjoyed software internships, but after two or three months he could already see where it was going. Research felt like a better long-term game because of its expansive aperture: “[I] really, really want to find the answer.”
Don’t leave the weird thing unexplained. One of his favorite researcher archetypes notices an anomaly and keeps asking why: “you just keep going and asking, why is this weird?” Chase enough of them and you see patterns you couldn’t have planned to find. It’s the spirit of Laura Deming’s “The Rage of Research,” though he translates the rage gently: “just really wanting to know something.”
Knowing what to skip is learned, not stated. Zhengdong overcorrects every project: move fast and you skip things that mattered; examine everything and the field moves without you. There’s no rule for the balance between covering ground and going deep — “finding this balance is just something that you need to get a lot of reps in to improve at.”
Zoomed out, research is just choosing the eval. AI models are becoming “this super laser that once you focus it on something, it will be solved.” He thinks research resolves to creating a new evaluation, with the rest just optimizing numbers. Even recursive self-improvement is “just some kind of meta-search” that procrastinates the real question: what makes a good eval?
Know the conversation, think for yourself. Entry into a field runs through its discourse: “you just need to be part of the conversation, whatever that conversation is,” even when that means Twitter instead of journals. But knowing it is in service of independence: close enough to not miss the obvious, removed enough to have ideas that aren’t in everyone’s stream.
Automating the tasks doesn’t automate the job. Humans have struggled to assess job performance since the pyramids (“How many rocks did you place?”). Every time a tool swallows the specifiable part, the job reforms around what’s left: judgment, purpose, even how much joy you bring your colleagues. Automate the tasks and the work migrates to what the spreadsheet can’t capture.
We’ll build AGI before we can define it. From his 2023 letter: “I more and more see the world where we build a machine that people agree is AGI before we write some words that people agree defines AGI.” Defining it may even be “possibly hopeless”: humans have written books about distinctions for thousands of years. The machines will keep marching past goalposts and we’ll keep moving them.
Genius may just be very large search. AlphaGo’s Move 37 looked like genius intuition. Zhengdong’s suspicion: “Without having exhaustively done that search, it looks like genius to you. But if you had looked at all the millions of moves, then it’s sort of obvious.” Brute-forced creativity: first Go, then knowledge work, eventually invention.
Surpassing humans is not the same as understanding reality. AI may reveal that language and much of human intelligence are more statistically predictable & lower dimensional than we imagined. It’s possible AI speed-runs us and still hardly knows anything in the grand scheme.
Pick a test you know cold, then watch AI break it. It took Zhengdong five years of setting tests for himself—problems he knew well enough that models “can’t cheat on this in ways that I would know about”—and watching AI confound his predictions anyway. Felt conviction comes from evidence across time.
Let the future clarify the present. The only timeline change that mattered to him: “I used to think [AGI] wasn’t going to happen in my lifetime and now I think it is.” Whether that lands like a terminal diagnosis or like winning the lottery, the effect is the same: the petty things fall away, and “it just accelerates you asking the question of what do you value?”
Myth-making is part of technical progress. AI has powerful stories for attracting potential researchers: cure cancer, understand reality, join a world-historical project. But if researchers treat public storytelling as something beneath “real” work, they shouldn’t be surprised when society adopts other (doomer) stories they dislike.
Competition is a safety mechanism. He worries more about malicious humans misusing AI than machines escaping control. The second-order—and larger—risk is a loss of individual liberty. His solve: “if you want to prevent really serious power concentration, there just needs to be a lot of competition.” It’s his deepest value surfacing as policy: “maybe pluralism is just the most highly prized value to me.”
If you don’t like who’s deciding, get involved. His answer to those who resent that a few labs are steering the future: “If you don’t like that big tech is deciding all these things, then just get involved.” One popular position is the incoherent one: calling it a scam while begging it to stop disrupting your field.
The universe is not running out of quests. Even if AI makes today’s work seem trivial, it won’t leave us with nothing to do, “we’ll just do everything faster. We’ll do more things, get even more niches, write even more fiction, and create even more universes for ourselves.” Better tools don’t exhaust the frontier; they expand it faster than we can reach it.
It matters because it’s hard. A machine may write something readers enjoy more. Writers and readers can still decide, together, that the human version is the one that counts. “The value comes from the fact that I spent a part of my short and precious life to write this thing for you, even if by some eval, it’s worse.”
Your friends want your answer, not the best answer. Your friends want to know your favorite flavor of ice cream, “not the LLM’s objective, optimal best flavor of ice cream.” The personal stays sacred because nobody else, human or synthetic, ever gets to have been you.
Are you having fun? After hearing more theories of research taste than he can count, Zhengdong’s only contribution is a simpler question.
Take the break. Humans need unfilled time to think; packing every day end-to-end can replace thought with noise. On an exponential, today is the least powerful these tools will ever be, which makes it the best moment to step away. Practically, “you could vacation for a year and come back, and your $20 a month subscription to AI will just be 10 times more effective.”
Produce so they let you consume. Zhengdong borrows Tyler Cowen’s line. The most rarefied experiences are priced in production: the room, the people, the napkin with the presidential seal on Air Force One. Be a part of making something great and you get to be there.
You can’t plan ahead, so plan very far ahead. The near future is too volatile to forecast, so aim at a horizon where the brittle details fall off and only what won’t change survives. “Who am I going to be 12 years ahead?”
Give yourself a vessel to fill. Zhengdong’s annual letter is the only deadline he gives himself every year. It makes him more observant, nudges him toward more interesting experiences, and leaves him catching himself thinking “this will be good for the bit.”
Be both the hedgehog and the fox. Isaiah Berlin’s hedgehog knows one big thing; the fox knows many. Zhengdong’s most repeated answer in this conversation is a refusal to choose. Depth or breadth, fast or careful, explore or exploit: “everyone should really be both at the same time. Always.”
Description
Zhengdong Wang (Website, X, LinkedIn) is an AI researcher based in London.
He writes annual letters (inspired by Dan Wang), mainly about AI progress, and his 2025 letter blew me away and inspired me to meet him. In it, he describes his ‘compute theory of everything,’ and makes the case that it would be stranger if AI progress slowed down than if it continued. Put a different way, reading his letter helped me get closer to truly feeling the wave of AI progress.
It took Zhengdong a long time to become “AGI-pilled,” despite years of feeling like he was late to AI and working as a research engineer for the last five. I talked to him about what it will take for the rest of us to see what he sees, and feel what he feels. We also discuss why AGI may be a hazier target than simply seeing AI progress as “the model does the eval,” and why recursive self-improvement may be both happening and less fantastical than it may seem. ZD makes the case that based on the rate of progress and scaling, skeptics will simply be proven right or wrong soon. We talk a bit through what narrative and political challenges the AI industry faces ahead of this transition.
Then there are the implications of all of this—namely how AI progress accelerates the question of what we each value, how we will spend our time, and where we will find or create meaning. ZD takes a stab at some of those impossible questions and shares some other favorite miscellanea that is reflective of the range of his letters.
I hope this conversation helps you feel the wave a bit yourself, and that you remember that, regardless of how much change lies ahead, we still get to make our meaning. We get to choose.
Full transcript and all links: dialectic.fm/zhengdong
Dialectic is presented by Notion. Notion is an AI-powered connected workspace where teams think together and create their best work. You can learn more at notion.com/dialectic.
Timestamps
(0:00) Opening Highlights
(1:16) Intro to Zhengdong & Thanks to Notion
(4:33) Start: Why Research: “I Just Want to Know”
(12:25) The Model Does the Eval: AGI as a Moving Target, RSI, and Laser Beams
(33:17) The Compute Theory of Everything and Feeling the Wave
(53:17) The Myths AI Needs, Competition vs. Power Concentration, and Whether Progress Is Inevitable
(1:13:42) “Who Could Possibly Compete?”: Post-AGI Meaning, Work, and Questing
(1:29:28) Are You Having Fun? Vacations, Mattering, and How to Live
(1:47:13) Annual Letters, Economist Obituaries, London, and Burke
(2:06:33) Closing & Thanks to Notion
Links & transcript: https://dialectic.fm/zhengdong
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