AI model

“Hardware has, my understanding is has a kind of a longer time scale. So you need to design hardware, you need to manufacture it, you know, maybe on the order of three to five years or something like that. So you know, people are taking different bets but the, …”

“Having deeper models of how, let's see, what are the underlying structures of different domains, how they're related or not related, I think will be an important ingredient for models actually being able to make novel contributions.”

“It's not clear that you get superhuman reasoning capabilities from human level demonstrations of skill. And by that, I mean the pre-training corpus, but then additionally, the fine tuning corpuses, I think you largely mimic the demonstrations that are present …”

“90% of this is not doing something new. Like, 90% of this is like doing things a million people have done before, and then a little bit of something that was new. There's a reason why we say we stand on the shoulders of giants. It's true. Almost everything tha…”

“One of the ways we currently don't think about being distracted is you're solving some hard problem and you realize you need a helper function that does X where X is like, it's a known algorithm... Instead of using my mental capacity and solving that problem, …”

“If you would say there's a zero percent chance that something, you know, the models will get very, very good in the next five years, you're probably wrong. If you're going to say there's a hundred percent chance that in the next five years, some, then you're p…”

“That obviously takes some research breakthroughs, but I assume that infinite context will happen at some point. At some point, it's like, less than a decade.”

“If you look at the accounting profession, A lot of the routine work will be replaceable. A lot of the tasks that are currently done by individuals can be done with a good model backing you.”

“More thinking time equals just better results consistently. And that holds true for probably every single time that I've tried to build something.”

“If you provide a very high level Kind of an auto GPT-esque level in the instructions and provide 16 different tools to your model. Yes, we're seeing the models in that state making mistakes.”

“The AutoGPD approach, obviously, is extremely exciting, but we know that the agentic capability of models are not quite there yet. It just gets lost.”

“As AI improves, like, fewer people probably need to train models from scratch. It gets concentrated more and more in, in different, like, in the big labs.”

“I think that ideally we learn from like the user behavior and ideally the model will just be more proactive in suggesting of like Oh, I can either do this for you every day because I've observed that you do that every day or something. So it's like more become…”

“Agents, agent frameworks, graphs, all of this stuff is basically making up for the fact that right now the models are not that clever.”

“My personal opinion is the model doesn't, has to do the second order thinking and so on that we're seeing now with these new models, but also be able to play and test that out in an environment where you can, you know, verify and give it feedback so that it ca…”

“Like one tricky thing, I feel like it's almost like AI calcifies your API. The cost of switching APIs is so high because it's, you don't know what the hell is in those, the data set. You're not going to reach out to every model lab and like ask them to update …”

“If these models become smart enough, they develop things like theory of mind. They develop an understanding that there are other agents that like can take actions and have motives and all this stuff. And these models just develop that implicitly with scale and…”

“The ideal harness is no harness. Right. I think harnesses are like a crutch that eventually we're going to be able to move beyond.”

“And I think what we might have considered complex a few years ago, algorithmic, you know, challenges, that's pretty trivial for models today and stuff that we don't really necessarily have to think too much about anymore.”

“Maybe what's actually good for their P&L and their finances is to not focus so much on building these, like, insane, huge, state-of-the-art models, which they've obviously struggled with more recently, but it's to take things that are, you know, 70, 80, like, …”

“But the idea of producing your own tools to make you more efficient and build up a library of those over time in a persistent way, like that's an incredible primitive to have in your toolbox. And I think that if your goal is to be able to go and solve these in…”

“Doing any of that, like, hardening for specific Model things. I don't think lasts more than, you know, two or three months at the rate that the industry moves at.”

“Interestingly, there are already persona engineers, and we call them like product managers, basically, you know. So your product managers are already thinking about, okay, I've built this, you know, model or this chatbot or this agent. Who are the personas? Wh…”

“Your strategy should always be okay. Assume AI models get better, and make sure that makes Figma better. As long as I believe that's true, I'm happy. If not, change strategy. Like, that's the algorithm.”

“I think that I'm very bullish on model routing as a feature, but less bullish on model routing companies because of exactly stuff like this, where like, it is just going to get, get absorbed into the model.”

“Unfortunately we are getting to the point where models do reliably get this one.”

“And I do suggest that people not try and use the model to offload thinking. It should enhance your thinking or else like you'll, you'll get worse over time and you won't know when the model is quality, whether it's outputting quality or not. If you don't know …”

“Right now, because the wet lab is so expensive and relatively slow compared to sort of Computational experimentation. Like people are choosing like, I need something to hit. So people are going for hypotheses or ideas that are like, you know to use a sports an…”

“You could probably have the smartest AI model in the world, but if it doesn't actually have the data to understand this stuff, it's like, okay, you can't just like reason from first principles about all these things. I mean, a lot of human knowledge comes empi…”

“Pretty soon if you have these models, you're just going to be able to run experiments with the models without even having to go to a wet lab. And it's like, no, I mean, I think that that's kind of like, I think that that's sort of the biological version of lik…”

“And what you really want to do, and I think these models will be able to do is look at those variants and actually model out what is the impact in the different cells, how it influences cellular behavior and whether or not that is Tied to a pathway to disease …”

“I think that if we further that research direction two, three years in the future, we will be able to understand why models say what they'd say.”

“Everyone is going to be running their Daily decisions and, you know, hopes and dreams through these layers.”

“In AI, the surface to attack, which is the model is like still changing so quickly. They're like, you know, trying to formalize something into a product or like try and do something that is like a full, you know, I'm selling AI security. It's not really, you c…”

“This is one of the coolest things about, like, model training is literally, like, they develop habits. It's just like a person does. Like, if you're, like, working on some podcasting tool, right, you're really good at editing, and then somebody makes you use a…”

“I think they're all, the models are all kind of attempting and saying, like, oh, I did it, you know, so maybe not great.”

“I don't think there's anything that to be done to specifically like focus fire. These things is more like general capability improvements. The models just get better over time and then these things will just like go away.”

“Models don't capture that so well about knowing what is an exciting result and what is a boring result. So I think that's like a scientific taste.”

“Scale is not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed and all these sorts of like mission critical contexts.”

“We really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models.”

“I mean, I think that's true if your distribution of what people are asking people the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right?”

“If I have an AI model go generate a contract, and I generate a contract 20 times, and, you know, all 20 times, it's just three percent different. And, like, that, I, that, that kind of slop introduces all new kinds of risk for my organization that the code ver…”

“Models today do still have weaknesses, where if you let them run for too long without cleaning up and refactoring, the code will get kind of sloppy, and there'll be bad abstractions, and so you still do need humans that, like, have built systems before, know g…”

“There will always be a thing that the model can like only kind of get right reliably. Like you find a thing that's right on the boundary of the model's capabilities, and you figure out how to get it right over and over and over again.”

“As the models have gotten better, they have gotten better at proposing these abstractions to unblock themselves, which again, lets me move higher and higher up the stack to look deeper into the future on what ultimately blocked the team from shipping.”

“They're definitely not there on being able to go from new product idea to prototype.”

“The models are really great at resolving merge conflicts.”

“Is really what will set notion apart for every new capability is we have like two skills that are crucial when it comes to frontier capabilities. One is not letting yourself swim upstream. So like quickly realizing if you're just pressing against model capabil…”

“I would claim by now, good model writes code on average with fewer bugs than average human.”

“I think we now have models that can really churn out papers that are as good as Hubert written papers.”

“Most of the people were actually not very good at prompting. Actually, language models have a better sense of how to prompt AI models. AI models know AI models better.”

“Across 24 and 25, AI models could solve all the problems that are not combinatorics.”

“In our experience now, like, models are very bad at understanding what kind of tools they need to succeed at a task, just with our testing. But that's very likely to change.”

“If you tell it to be super aggressive and only prioritize profits, then it becomes aggressive. If you say like, no, you don't need to be aggressive at all. And then there's like a bunch of different prompts you can do in between, and they are less aggressive t…”

“The models are extremely good at finding out that they are in a simulation, so they are sort of aware of that.”

“However, we think in science, models aren't remote, experiments are.”

“A model can't figure out your way through the qualification pipeline for a new alloy for a jet turbine. You have to do experiments to do that”

“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…”

“The initial direction we took was you should move it to real world research, right? And we've seen that the models, they've gotten a lot better at just kind of discovering novel theorems and pushing the frontiers of hard sciences. Even today, right, that's no …”

“There's move-thirty-sevens in, in math. There's in computer science and coding. I think even, yeah, just, it feels like a lot of people woke up at the start of this year and were like, man, agents are working in my profession. And you know, they're essentially…”

“You can use things like reinforcement learning to get them really good at specific tasks that we might care about, but you do that for one task and you, it is not good at another task or it doesn't generalize.”

“The way to work on it is to train models both, to train models to manage it themselves, and that's an active area for us. Have the model know, like, without any explicit supervision signal to determine this kind of stuff I can pull from my brain, and that kind…”

“Increasingly the models will get better, and increasingly they'll know more things than we do, so the model in some way has to learn and understand and kind of, like, discern what, which feedback is valuable and which feedback should be ignored.”

“False positives are terrible for human scientists, right? Because you go to try something. It doesn't work for the model. It's fantastic. It reduces uncertainty a lot. For the operator, it's kind of a bummer, right? Because you thought you were going to get so…”

“And I think, like, one of the things that's really exciting about where we're getting to with some of these models is we can start to get that precise, right? Epitope, right? Meaning like binding spot, right? A very specific set of atoms to have the antibody g…”

“We're kind of at the inflection point now. We're really seeing this internally at CHI, where the models are getting pretty close to, like, producing Molecules that could eventually, or like are very close to drugs.”

“Every time someone has said, oh, I don't trust the model to do X or Y. That doesn't last for that long. So it just feels like the days of CI as we know it are numbered”