Misha Laskin
Co-founder & CEO, Reflection AI. On 3 shows, 3 appearances. The Shows tab opens the full record on each.
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.
3shows
3appearances
68statements
3resolved
1supported
1contradicted
33%fully supported
Everything Misha Laskin said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Laskin: The core ingredients to build AGI and ASI are now known
“But that was only meaningful when the ingredients for how to build artificial general intelligence, or ASI were not known. I think now they're known, and so.”
Laskin: AI work awarded Physics Nobel Prize has had little impact on physics
“What's interesting is that the Physics Nobel Prize was given to something that has not really had that much impact in physics, but it is but I still buy it because it's kind of there's a physics smell to the breakthroughs that led to you know, these systems li…”
Laskin: Superintelligence will emerge from multiple specialized labs, not one company
“I do think there'll be a general super intelligence, but I think that it won't be one lab that has built it, but it'll be kind of the plurality, like the collection of all intelligences will be a general super intelligence.”
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: AI models will interact with enterprise software primarily via APIs
“And so the way these language models are going to interact with any piece of software, not just Software engineering software, like Salesforce and other CRMs and creative tools and so forth. The majority of those interactions are going to be through function c…”
Laskin: Advanced AI without comprehension are 'L9 engineers with amnesia'
“What we're going to get to if we don't solve the comprehension piece is basically L-nine engineers with amnesia.”
Laskin: Solving organizational code context yields all capabilities for superintelligence
“Like if you really solve this oracle for organizations just for coding, you've basically built all the capabilities you need to have super intelligence.”
Laskin: Reinforcement learning makes LLM capabilities jagged, not broadly general
“When you train large language models with reinforcement learning, they become jagged in the sense that they become good at what you wanted them to be good at. And there are some generalization capabilities, but they're much weaker than people think.”
Laskin: Principal-level AI engineers are a couple of years away
“And that the combination of this you know, L-Nine with Amnesia and the L-Nine's context core will together, you know, that will become the principal level engineer, the AI engineer. And so I actually think that that's not too far away. That's I would say in, y…”
Laskin: Reflection AI regularly beats OpenAI, Anthropic, and DeepMind for talent
“We win over candidates over OpenAI and Anthropic Meta, DeepMind regularly.”
Laskin: Reflection AI will ship research requiring 100k GPU equivalence in 2025
“Later this year we'll be shipping things that I don't think anyone ever thought a startup could do. Like, I think that we're going to be shipping some things on the research side that I think everyone thinks you need to be a giant lab with a 100,000 GPUs to do…”
Laskin: Focused AI startups can operate with 10x less capital than frontier labs
“You can't operate at a hundred X less capital than a frontier lab, but you can operate at, say, 10 X, like an order of magnitude less capital when you're really focused.”
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…”
One line per show, most statements first. The link opens Misha's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 NO PRIORSLEDGER | Co-founder & CEO, Reflection AI | 1 | 28 | 50% 1/2 | full record on No Priors → |
 MADLEDGER | Co-founder & CEO, Reflection AI | 1 | 24 | 0% 0/1 | full record on the MAD Podcast → |
 LATENT SPACELEDGER | Co-founder & CEO, Reflection AI | 1 | 16 | — | full record on Latent Space → |