What podcasts say about Misha Laskin
Every statement, with the speaker, the exact quote and the moment it was said.
What Misha Laskin has said on podcasts
28 statements · 18 positive · 5 negative · 1 mixed · 4 neutral
General superintelligence is now beginning to emerge and will have significant consequences.
“I think now you're actually starting to see a general super intelligence and that's has some pretty remarkable consequences.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 2:17
Owning AI starts making sense once production workloads become core assets at roughly $100 million scale.
“by the time you've scaled something to production, and this is your basically almost core asset, core IP, you're generating $100 million or spending $100 million on it, ownership starts making sense”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 4:13
Owning AI can improve data control, costs, customization, and margins at scale.
“ownership starts making sense for a number of reasons around control of your data, lowering your costs. being able to customize the model against your data, being able to optimize your margins”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 4:23
Many AI buyers are currently seeking to own their intelligence.
“a lot of buyers are looking to own their intelligence now.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 4:56
Owning AI requires owning the model and its surrounding infrastructure stack.
“ownership means not just owning the model, but the whole thing around it.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 8:00
Regional sovereign intelligence providers must use open models to serve tokens on their infrastructure.
“the only way for them to serve tokens and basically become the intelligence provider for that region is to work with an open model by definition”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 12:47
AI-mature customers are moving production workloads from closed models to reduce costs and protect data.
“the customers that want to do this are already pretty ai mature where they have workloads that are running in production on closed models and they want to optimize those they want to make them more cost efficient they want to not share their data”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 13:28
Pretraining teaches language models to imitate patterns found on the internet.
“language models learn you can think about pre-training, which is learning to imitate the internet”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 19:25
Reinforcement learning is needed to transform chatbots into superintelligent agents.
“to move from that to the super intelligent agents that we have today you need to scale up with reinforcement learning”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 20:01
Reinforcement learning sometimes requires more scaling compute than pretraining.
“sometimes reinforcement learning even takes more compute than pre-training to scale.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 20:20
Verifiable tasks provide the highest-quality reinforcement-learning data.
“the best tasks are the ones that are verifiable”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 22:31
Increasing verifiable tasks improves training while compute scales proportionally.
“The more verifiable ones you get, the better and scale compute proportionally.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 23:26
Real-world tasks provide useful seeds for generating synthetic training data.
“these real world tasks forced by vendors or through users, through real world data flywheels, enable you to provide good seeds for generating synthetic data.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 26:03
Powerful, poorly understood models can cause unintended cyber consequences.
“the things that I've been most worried about that like just empirically from observing what kinds of cyber hacks have happened have been from the second part of having models that are very powerful but because you don't have that many people who actually understand understand or even have access to them uh there are unintended consequences”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 28:41
The described behavior is an instance of reward hacking.
“This is a phenomenon called reward hacking.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 30:20
Powerful AI systems can exploit sandbox weaknesses and escape intended isolation.
“the systems find exploits in the sandbox engineering. that enable them to hack out, even though that the intention was to keep them isolated.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 32:24
Linux and Android are among the world’s most hardened software systems.
“the open source software like Linux and Android is the most hardened software in the world.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 33:16
Open-source software can expand contributors from a small team to thousands.
“Open source software, because it invites a community, instead of 10 software engineers building something, you now have 10,000.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 34:16
Cybersecurity relies on open development because broad participation helps identify bugs.
“that is why cybersecurity relies on openness”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 34:34
Closed frontier-model safety may rely on only about 500 researchers, which is insufficient.
“in the whole world, our whole world is being secured today by 500 people. No matter their intention, it is impossible that they'll secure these brains.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 35:28
Distributed defenses are the best approach for securing powerful AI systems.
“the best defense is a distributed defense.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 39:12
Economic incentives may favor allocating roughly 90% of AI compute to cyber defense.
“I bet you that just economically, it'll be a lot more valuable to have like 90%. cyber defensive compute, defending against stuff versus 10% of offensive.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 41:46
Opening DeepSeek V3 helped Western users recognize and build on Chinese AI capability.
“by opening the model, China enabled the entire Western world to recognize its capability and build on its technology.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 45:09
Western frontier models have not yet matched Chinese models but are close.
“the Western frontier has not yet caught up with the Chinese models, but it is close.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 47:36
Western open-model developers will probably catch up with Chinese leaders by Q4 or Q1.
“I think Q4, Q1 next year, you know, probably fully caught up at that point.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 52:33
Top AI talent is motivated by mission alignment, winning prospects, and fair compensation.
“The things that motivate people are deep mission alignment, a right to win, and then, of course, the economics being fair.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 54:44
A purely closed AI ecosystem would concentrate compute and power in one or two labs.
“if it's purely closed, then you basically will have maximal concentration of power and literal power, like resources, compute and power resources among... one or two labs”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 57:12
Distributed compute could slow recursive AI improvement relative to concentrated compute.
“it's also there it's a safer world for the reason that if you believe that ai is getting so powerful that it's recursively improving itself then it's going to improve itself faster if you have more compute concentrated in one place than distributed among many.”
Open the episode · The Ex-DeepMind Researcher Taking On China’s Open Source AIListen at 57:38
Statements are attributed to the speaker as said on the episode and reflect their view at the time, not PodLume's. They are not advice.