Laskin: AI models will beat humans at competitive coding within a year
Misha Laskin · No Priors Ep. 123 | With ReflectionAI Co-Founder and CEO Misha Laskin · Jul 17, 2025 · at 43:00
Reflection AI co-founder Misha Laskin contrasts narrow benchmark success with practical enterprise productivity improvements.
0:00 / 0:08exact quote · 8.3s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“Code forces and other competitive coding environments. The models are almost best in the world, and within the year will probably be just the best in the world.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Misha Laskin
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 …”