The Wisdom Wall

“And I think that the question of what's the interface you want, what is the product that you want, is what we spend a lot of time thinking about. And the answer is you want almost no interface. You want no product. Right. You want this to be like, what's the interface between you and me, right? Just being able to talk…”

“every time we've kind of run into a like, oh, this isn't quite scaling the way we expect, it's we have a problem. We have a bug that our math wasn't quite right, that, oh, our implementation Isn't isn't isn't quite matching the math, whatever the thing is.”

“You want almost no interface. You want no product, right? You want this to be like, what's the interface between you and me, right? Just being able to talk to a persistent entity of some form that's able to go and accomplish goals for you.”

“the place we're going is one where you as A person doing work that you are the overseer. You are the, the CEO of almost this autonomous corporation, or, you know, of this, this fleet of agents perhaps is more, is, is the way to say it, and that they are operating according to your goals.”

“Now, it is also the case that it's not as simple as you can take the output to these models and distill and you have exactly the model of the same capability, It's just smaller and can run fast. If that were the case, we would just do that”

“And we talked about it a little bit in the case of the underlying models, but the thing that's really changed over the past couple of years has been that it's no longer just about the model. It's about the harness. It's about how does the model get context? How is it connected to the world? What What actions can it…”

“every single step of the model production pipeline multiplies. And so you want to improve all of them. And the thing that we see is we prove the pre-training. It makes all the other steps much easier. And it makes sense because it's a model is able to learn faster. It's a model that is because it already is like more…”

“Well, because the, there's multiple reasons, um, but one is that even as the balance of how much inference versus training changes, that you cannot get massive scale training Through any other way besides this concentration of compute on one problem.”

“you can think of compute not as a cost center, but as a revenue center. Think of it a little bit like hiring salespeople. How many salespeople do you want to hire? As long as you can sell your product, as long as you have a scalable way to sell that product, then the more salespeople you have, the more revenue you will…”

“If you're Trying to build a website and your agent messes it up and your user is affected. It's not really the agent's fault. It's your fault. And so you need to care. And I think that for people to use these tools, right, you need to realize that human agency, human accountability, that's a core part of the system,…”

“we used to really just focus on the raw pre-training capability, but not think as much about the inference ability. And that's been a big change over the past 24 months to realize that it's a balance between You can have this model that has all those great properties in the base, but then you really need it to be able…”

“And that's something we view as earned, right? It's not something that we can we can grant, but by providing lots of tools and control and oversight and supervision to the operator, to the person who this AI is operating on behalf of, like, we think that that is going to be such an important thing. So That's a key…”

“it's not going to be like that. It's going to be more like an interface, like no more than your phone is you, right? It's an interface to you. It's a way that I can sort of, you know, call you up whenever I need you, whenever I want to ask you a question. And there's different ways of accessing, right? There's like…”

“intelligence is not a unidimensional thing, right? That that if you really zoom in, being good at different domains is something where even if you're you have a lot of raw intelligence, getting good, if you've never practiced, like you've never actually done a Pitch or something like you're not going to be good at it…”

“And that we're moving out of testing on benchmarks and sort of these almost cerebral demonstrations of capability to it actually being the case that for us to develop it further, we need to see it in the real world and get feedback from how people are using it in knowledge work in various applications.”