Zico Kolter
Professor and Department Head, Carnegie Mellon University. On 3 shows, 3 appearances. The Shows tab opens the full record on each.
academicscientistfounderengineer@zicokolter ↗LinkedIn ↗zicokolter.com ↗Wikipedia ↗
Kolter is an artificial intelligence researcher known for his work on machine learning safety, LLM security, and adversarial robustness, and is a co-founder of the AI security company Gray Swan AI. He serves on the Board of Directors of OpenAI, where he chairs the Safety and Security Committee with oversight over model deployment.
3shows
3appearances
96statements
9resolved
9supported
0contradicted
100%fully supported
Everything Zico Kolter said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Kolter: Neural network architectures matter less than commonly believed
“I actually think architectures don't matter as much as everyone else thinks they do.”
AI capabilities would have been achieved even without inventing transformers
“I think if we hadn't invented the transformer, we would have gotten there with whatever LSTM you know, state space model, whatever, anything else people were developing, we would have gotten there.”
Kolter: LLMs are demonstrably intelligent despite being next-word predictors
“Oftentimes I know people say, oh, well, AI is, it's just predicting words. That's all it's doing. Therefore it can't be intelligent. It can't be. And I think that's just. Demonstrably wrong. What I think is amazing, though, is the scientific fact that when you…”
Kolter: AI model performance is not plateauing in coding tasks
“The domain I use models for most probably is coding, and also doing things like transcribing lectures and stuff like this. On those tasks, I am absolutely not seeing plateauing gains. The latest models, they are notably better than the previous iteration, and …”
Kolter: AI models contain an unpatchable buffer overflow vulnerability
“This is sort of like these models have a buffer overflow in all of them that we know about, and most importantly, that we don't know how to patch and fix. We don't know how to fix this yet with models”
Kolter: AI safety should focus on practical risks, not sci-fi doom
“The first is that I think the vast majority of AI safety should not be about these topics. The vast majority should be about quite practical concerns we have on making systems safer, like the kind that I've talked with you about so far.”
Kolter: AI model architectures and Transformers no longer matter
“I, for a lot of my career, was thinking that model architectures really mattered, and by having clever, complex architectures and sub-modules inside architectures, you would have, that was the route to sort of better AI systems. For the most part, I don't beli…”
Kolter: Scaling model size does not automatically improve AI safety or red teaming
“Traditionally this has been an area where both in terms of safety models don't get better by just being bigger, unlike most other areas where models do get better by being bigger. Safety has not been like that traditionally. You know, you have to train them ex…”
Kolter: Gray Swan's Shade system outperforms human red teamers at breaking models
“However, one thing that we are finding, and this is actually, I think we're kind of crossing this point too. Is that in a lot of the latest experiments, we can do much better than people, than human red teamers now at breaking these models. When I say we, I me…”
Kolter: AI is an alien intelligence with completely distinct failure modes from humans
“It is clearly a different form of intelligence than people. It's some alien intelligence that is vastly different, and that difference is actually often brought out to a large degree by things like adversarial attacks and red teaming, because there are certain…”
Kolter: Full experimental observability has not produced fundamental understanding of AI
“It's like we could kind of run experiments on the brain, observe every neuron in it, reset its state to prior states, and run counterfactuals, none of which we can do with humans, and yet we still understand neither very well. Even with that, all that ability,…”
Kolter: Adversarial Red Teaming Is Essential for True Capability Elicitation
“One of the most effective ways of doing capability elicitation is actually through some amount of what you would call red teaming, right? So if a model refuses a task because it thinks it's being evaluated, but it knows how to complete that task, getting it to…”
Kolter: Security and science will explode as AI agents automate tedious verification
“So I think this is really sort of an underappreciated point that we're reaching this point, this sort of phase where a lot of security, a lot of science has this potential to kind of explode. Not because we're going to get better at it, but because agents can …”
Kolter: AI models do not get safer automatically by scaling up
“You can't just sort of trust models to get safer by getting bigger. You have to put in the work to actually make them safer. And this is, I think what a lot of AI companies are investing in. This is why we in fact do have models that are improving on these dim…”
Kolter: Robotics AI is not yet ready for pure compute scaling
“Certain fields. I think things like robotics is still one. I don't think we're quite at the, let's just scale it up level with robotics yet. Some companies might argue we are. I don't think we are. I think we're still in the let's explore methods to find the r…”
Kolter: Adversarial prompts optimized on open-source LLMs break commercial models
“Once we had done that, we found that when you had these weird terms that you sort of flipped around to optimize one, to optimize the response for one model, you could just take those same exact strings you would optimize, paste them into a commercial model, an…”
Kolter: AI agent benefits outweigh security risks if deployed with proper guardrails
“Yes, I think so, actually. I think if you run with proper guardrails, you know, we release guardrails for coding agents, for example. If you're on proper guardrails with proper sandboxing, and right now, yes, you probably also take some care to be a little bit…”
Kolter no longer writes code manually, relying entirely on AI agents
“I don't write code anymore. I do all my work now, and I do lots of, you know, I still do some research, right? It's entirely telling Codex what to do.”
The vast majority of modern AI intelligence comes from self-training
“I don't think people have properly internalized the fact that the vast majority of intelligence comes from self training effectively.”
Zico Kolter: Current AI trajectory will yield capable systems without breakthroughs
“I think the current trajectory we're on is going to get us, even if there were no more breakthroughs, I think, you know, with the minor additions that we are doing right now, we will get to incredibly capable systems, even if we were to freeze things right now…”
Text model scaling is one of humanity's top scientific discoveries
“The discovery That when you train big enough models on lots of text, and then turn, and then a little bit of additional sort of, you know, fine-tuning text, and then turn them loose to generate, that this generates long-form coherent thought. That was probably…”
Kolter: Word prediction intelligence is the top scientific discovery in decades
“You can train word predictors, and they produce intelligent, coherent, long-form responses. This is one of the most notable, if not the most notable, scientific discovery of the past 1020 years. Maybe much longer than that, right?”
Kolter: AI progress faces compute bottlenecks rather than a data shortage crisis
“We are nowhere close to hitting the limits of available data in these models. Arguably, we're unable to process it because we don't have enough compute and things like this, but we're nowhere close to data limits in other senses.”
Kolter: Multimodal AI data is abundant but constrained by compute bottlenecks
“There are massive amounts of data available, And I think we have not yet figured out how to properly leverage those due to either limitations of compute. I mean, you have to process all that data and it does take, we don't have current models to do this very w…”
One line per show, most statements first. The link opens Zico's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 20VCLEDGER | Professor and Department Head, Carnegie Mellon University | 1 | 45 | 100% 6/6 | full record on 20VC → |
 MADLEDGER | Professor and Department Head, Carnegie Mellon University | 1 | 35 | 100% 2/2 | full record on the MAD Podcast → |
 LATENT SPACELEDGER | Professor and Department Head, Carnegie Mellon University | 1 | 16 | 100% 1/1 | full record on Latent Space → |