Bret Taylor
speaker
184 appearances
1 recordings
1 series
first heard Oct 2024
last heard Oct 2024
Bret Taylor’s voice in public audio — every appearance, attributed to the second.
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I'm optimistic, you know, in the progress of these models towards something that resembles artificial general intelligence. And I'm excited about it.
What is the purpose of building AGI? It should be to benefit humanity. And so what does it mean to benefit humanity? You know, the OpenAI mission is to ensure that artificial general intelligence benefits all of humanity. That can mean a lot of things.
I think it means a lot different things to different people, which is why OpenAI has been sort of a honeypot for controversy, you know, in a lot of ways, because it's very important in this space. And that mission can be reflected through the lens of your own values to mean a lot of different things. But One, it can mean access.
So when you think about how do people access this amazing new technology, one could argue that ChatGPT has perhaps been the biggest breakthrough in providing universal access to AI. I'm not sure the idea of building this conversational agent that everyone can just use by visiting a URL was a, that was not a thing people conceived of probably before that.
And it's why, at least my understanding why it has a sort of a goofy name as it was a research preview that turned out to be the most important product of the past decade, you know, and, um, And I think that one of the things I think about is, wow, what an important mechanism to deliver the value of AI and AGI to the world. And I think it's very aligned with that high level mission.
And I think that to your point on the things you would do building consumer products that are different than AGI, Yes, and that's sort of the complexity that all of these research labs or mission-driven companies are dealing with.
But to imply that sort of building a widely used consumer experience is somehow contrary to delivering value of AGI, I don't buy because how else are you going to deliver it? And there could be different answers, by the way. But you really want to ensure that once these technologies exist, that it's broadly available to everyone in the world, obviously, and responsible in a safe way.
And so I think it's really great that a lot of these research labs have found a form factor that resonates with so many people.
I think these issues are complex, to be honest with you, Harry. I mean, I don't think it's, you know, you could describe it, you know, as a enterprise team. You could also say you're trying to take the value of these models and ensure that they benefit humanity. Do you want every product that benefits humanity to be built exclusively by OpenAI?
And so enabling developers to build on top of it is a meaningful part of distributing the value to the world. So I don't want to minimize the complexity of all of these decisions, but I also think that as you think about delivering the the value of these models in a way that maximizes their benefit.
It doesn't seem that far off, you know, and it's, and I'm, and it's also, you know, what a lot of other research labs are doing for, I think, similar reasons with similar missions. So I think it's, I'm excited about the impact that it's having.
You know, I, I ended up so many of the entrepreneurs I know who are working in AI do it in large part because of how inspired they were by using these models as consumers using the APIs. And I think it's having a super positive impact on the world right now.
Certainly some knowledge is. I also think that there is a right now a lot of these companies are pursuing a mission that's bigger than any one organization. And so, you know, a lot of these the folks working on AGI are. are in or come from academia where the ethos is to publish, which has obviously shifted, you know, a bit over the past few years. So it's a very complex question right now.
I think the breakthrough ideas sort of like, I don't know the story actually, but, you know, the Wright brothers invented the plane. Apparently there was another group of, you know, I actually don't know who it was, like came close as well. And they were the ones who hit it. I think there's also this dynamic where these ideas are sort of in the air, you know, between different researchers as well.
So when I made the comment earlier about skeptical of companies doing pre-training, it was really based on the premise that most companies should be applying AI to build solutions and most companies should have relatively modest training costs. And most of their costs should be correlated with inference, which should be correlated with revenue and usage of your product.
And I think that that's essentially because if you end up pre-training a very large model, you end up with such upfront capital requirements. You have to have a really valuable business model on the other side of that to justify that investment. So first...
I think companies should really focus on how to find product market fit prior to taking on meaningful training costs that are fine-tuning, might be fine, certainly sort of pre-training models. On the inference side, I actually think the costs of AI are huge. going down really, really rapidly. I've seen a lot of people tracking sort of the cost of the GPT models over time.
And what's remarkable about the cost going down is the quality is also going up. So it reminds me, you joked about when you were born, but what Well, around the time you were born, every time I got a new computer in my house, it was twice as cheap and twice as good. So I think on the inference side, I think that margins will probably improve for a lot of these use cases.
There's a lot of interesting technology trends like distillation, taking a large parameter count model and making a smaller parameter count model from it that has similar levels of quality. And essentially, what that means is you're sort of transferring some of the well, you trained a very large model, the you can run inference on something that's much smaller, cheaper and faster.
And then there's obviously a bunch of improvements on the hardware side as well. And I'm incredibly optimistic that just the cost of running AI will could probably track something like Moore's law like Moore's law. I don't think it's a law. I just think it's a trend. And I think that's a really exciting thing for all companies.
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