Misha Laskin
Co-founder & CEO, Reflection AI · 1 appearance on the record.
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founderexecutivescientist@mishalaskin ↗LinkedIn ↗reflection.ai ↗
Misha Laskin is an artificial intelligence researcher and former Google DeepMind scientist who worked on reinforcement learning. He is the co-founder and CEO of Reflection AI, an AI laboratory developing autonomous engineering agents.
28statements → 9claims → 2claims resolved → 3.54/5average certainty → 2.82/5average debate potential → ≈4.5/5argument clarity, estimated →
1 supported 0 partly supported 1 contradicted 7 not checkable as stated how the 9 claims stand · each chip opens the sources
7 predictions · 2 assertions · 5 opinions · 13 insights · 1 what if · every statement was checked. The predictions and assertions are the 9 claims: statements the public record can support or contradict. 2 are resolved, and 7 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.
The record, in short
What the tape says about how Misha argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.
Their most notable supported claim
Expressed certainty vs assessment result
weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet
How they sound: not measured why? →
We measure speaking style by listening to the audio itself, and a fair number needs at least 2,000 words from one person on tape we have measured. There is too little of Misha Laskin on measured tape to publish a rate. This says nothing about how they speak.
Everything Misha Laskin said on No Priors that made the record, most notable first. Filter by type, assessment or year in the ledger →
Laskin: Enterprise AI coding tool productivity impact is negligible or negative
“Within enterprises, when you know, they're adopting coding tools and you see the impact that this is having on their actual productivity. And I think it's much lower than people expect. So it's in fact, it's sometimes negative, sometimes negligible.”
Laskin: Teaching AI agents to take action is mostly solved
“To me, it seems like really, 20% of the problem is teaching these agents how to act, and it's more or less solved.”
Laskin: New frontier labs can succeed without cloud provider ownership
“Our thought was that this was the time where you can actually start a you know, a generational frontier lab that does not need to be coupled to a, you know, to a big cloud provider because if you do it right, you'll actually be able to generate you know, suffi…”
Laskin: Machine learning generalization is just bringing test distribution into training
“There's no such thing as generalization. There's just bringing the test distribution into train.”
Laskin: Scaling RL on LLMs is the final paradigm before ASI
“The next paradigm, and effectively the final paradigm that we need to have in place before a, you know, what people used to call AGI, or now I think the goalposts have shifted to ASI, is reached, is just figuring out how to scale reinforcement learning on top …”
Laskin: Humanity's Last Exam Benchmark Barely Matters to End Users
“Now, that's great, but I think the downside of that is that does humanity's last exam actually matter in any meaningful way for an end user? And I would argue that some weak correlation, but the answer is most likely no.”
Laskin: AI Apps Without Custom Model Training Are Fundamentally Limited
“The important part, I think, is to be able to tweak every part of the system from, you know, the product features to the agent design to the model training in order to build the best overall system. And if you are capped in which parts you can change, like if …”
Laskin: RL requires far fewer FLOPs than pre-training for frontier models
“We're in this brief period in history right now where the RL flops are still manageable. Like you can really have a best in class product if you're focused. And yes, you'll need to put, you know, you still need a decent amount of GPUs, but from a flops perspec…”
Laskin: Accurately verifying arbitrary outcomes is ASI-complete
“The reward problem in itself is at the time I called, I thought it was AGI complete. Now I'd say it's ASI complete, but by the time you have a neural network that can accurately verify any outcome, that is probably a super intelligence.”
Laskin: Definitive superintelligence in meaningful categories will arrive in a couple years
“I think that where I think we'll be in a couple of years from now is that there'll be kind of definitive super intelligence in, Some meaningful categories of work.”
Laskin: AI coding startups face existential risk without in-house frontier models
“And then from the startup side, I think it actually puts companies that Are in these kind of critical path categories like search and coding in a pretty existential place if they can't build their own frontier models. Not all frontier labs will be able to vert…”
Laskin: Reinforcement learning is the only scalable path for synthetic data
“When we're generating synthetic data there is the only scalable path is really reinforcement learning.”
Laskin: Code reasoning models will generalize across other enterprise work
“The reason code is special is if you believe that the way a language model will interact with almost any piece of software is through function calls and therefore code, then if you build very capable reasoners coding reasoners that, you know, are sort of purpo…”
Laskin: Deploying superintelligence and reaching 10% GDP growth is multi-decade
“Actually going in and deploying it and building it for, you know, specific categories of work. There are going to be a lot of product and kind of research innovation specific to those categories that will probably make this a multi-decade thing. So I don't thi…”
Laskin: Enterprise AI coding deployment is within dozens of months, not decades
“I think coding is this era as well. This one I think will take longer than people thought as well, because again, enterprise is organizational problems. There's much different than The benchmarks that we have today, but I think it will be one of the faster one…”
Laskin: Narrow-domain superintelligence has already been achieved by AlphaGo
“To some extent super intelligence in that sense has already been achieved. So right, AlphaGo was a super intelligent system, and there were other systems during that time that were built that were super intelligent in narrow domains.”
Laskin: Building ASI requires co-designing product and research together
“As long as you pick a category that I would say is kind of big enough to be ASI complete I think, and this is kind of our approach at Reflection, is it makes a lot more sense to be focused and co-design those two things together, the product of the research.”
Laskin: Dota and AlphaStar would have achieved superintelligence with more compute
“Dota V and AlphaStar were near super intelligent systems, and if OpenAI and DeepMind had sunk more compute into them, they would have definitely become super intelligent.”
Laskin: Frontier labs cannot easily buy end-user distribution via acquisitions
“I don't think it's guaranteed that a big lab can, you know, buy their way to the end user because the fundamental problems of your, you know, research team being far away from your product team will still be true. And the company having, you know, a hundred di…”
Laskin: Engineers spend 80% of their time comprehending complex systems
“When you look at what an engineer does in an organization, 80% of their time they're spending trying to comprehend complex systems and collaborating with teammates.”
Laskin: Evaluation Is the Most Important Differentiator for Frontier AI Labs
“This is I think the least spoken about part of what frontier labs do, but Possibly the most important, which is figuring out how they evaluate, like what makes Claude magically feel better at code than you know, another model out there. They did something righ…”
Laskin: Most Contributors on OpenAI's o1 Paper Worked on Evals
“When you look at the model card for, let's say, the O-one paper that came out, I think, last year. If you look at the distribution of what most people worked on, on that paper, it was evals.”
Laskin: Single-file coding questions do not require deep research agents
“If you're looking at a file, and there's like a specific thing in that file, and you're just trying to get a quick answer to it, you don't really need the hammer of like a deep research like experience. You don't need to wait, you know, like tens of seconds or…”
Laskin: Team-Wide AI Memory Governance Will Mirror Pull Request Workflows
“Where if you want to change the agents, the team wide memory, then it probably is going to look something like a pull request where the person who really understands that system Approves or, you know, edits it or something like this. I don't think it's going t…”
Appearances (1)
| Episode | Date | Speaking time |
|---|---|---|
| No Priors Ep. 123 | With ReflectionAI Co-Founder and CEO Misha Laskin | Jul 17, 2025 | 46m |