Because AI models keep improving every few months, it's better to ship products early even when they only work about 70% of the time, rather than waiting for perfection.
Weil explains OpenAI's philosophy of iterative deployment: ship products that are only partially good now, because the underlying models will rapidly close the gap within months. ✦ AI generated
Kevin Weil · BG2 Pod · 2025-06-21 · original ↗
starts at this moment · 7:33
“So, talk to us about the things you're actively doing to take somebody from a monthly to a weekly, from a weekly to a daily.”
If you build a product that can do amazing things, even if it's only 70%, you know, right at that thing, in two or three months, the next model's going to come along and it's going to be 95% good at that thing, and all of a sudden, you know, you've got something great.
verbatim transcript · starts at 7:33
7:33can do, then we suddenly have this product that can do things that no one's ever seen a product do before. And even if it's not perfect right when it starts because we tend to we we believe in iterative deployment. We release early we release often like better to better to make lots of small mistakes and you know we kind of collectively as society like understand how how what AI is good
7:56at what it's bad at how we work through it. we'd rather do that. And and if you if you build a product that can do amazing things, even if it's only 70%, you know, right at at at that thing, in two or three months, the next model's going to come along and it's going to be 95% good at that thing, and all of a sudden, you know, you've
8:15you've got something great, but but people we've we've sort of brought everybody along with us and and learn from them at the same time. Like learn from the way there's the way that they're using it. Yeah. So, there's a I think the the the thing that we've really tried to get right and we're not perfect at this, but I think we're a lot better than we used to be, is the the
8:33really tight loop between research and product. Yes. Because when we're when we have that loop, right, and we're we're solving products, the problems that people have, feedback goes back to research, the research team goes, "Oh, you oh, it can't do that very well. Oh, we can fix that." Right. And then the product gets better and we have that. Like, that's when magic happens. You said something prophetic on stage today
8:54and simple, but I it really caught me. You said um you said today is the worst product from you. The model the model that you use today is the worst AI model that you'll ever use for the rest of your life. Yeah. Which is really it's a simple thing and it's really kind of obviously true when you think about it. But it just changes the way you think
9:16about building products because I think it if you think about it the right way, it makes you much more open to building products that only kind of work. You know, whether you're us building chatpt and other products or whether you're an enterprise, you know, building some internal tool or because if the model only kind if the model can kind of do it, then it's going to be great at it in a few months. What
- ·OpenAI ships products that are only partially good now
- ·Models improve fast — gap closes within months
- ·Better to launch early than wait for perfection
- ·Underlying models rapidly improve on their own
- ·A 70%-good feature becomes 95%-good soon after
- ·Early shipping turns partial capability into something great