Ryan J. Salva: evaluation

Transcript context

…In the experience of scaling this thing, what would you say has been the biggest challenge either technologically or even operationally just kind of scaling it to a real product that people are paying for? There's a few dimensions of that. One is a problem that's very much of our time in the world, namely that supply chains have been disrupted dramatically over the course of the last few years. It turns out that Copilot for both training and operating the models requires some very rare and unique GPUs that there's not a lot of global supply of. Part of it is just like, can we get enough hardware in order to run these things? We've actually earmarked quite a bit of capacity, and we are greedy, greedy, greedy for more capacity globally. As soon as we can produce those chips and get them in data centers, we do it. That's been one kind of unique challenge. I would also say here that operationally, another challenge has been, how do we create a model that the community really feels like ownership over, right? The dialogue that's had to happen as we brought an AI tool to market, especially one that is trained on public code, has required a lot of dialogue between us and our community. Every good product manager should be spending as much of their time as possible with their customers, with their potential customers. Copilot, in particular, has been a more complicated kind of rollout because we as an industry, as a society are still figuring out how to make sense of it. The amount of give and take between developers and us as a product team has really required us to scale up more of the product team than it has the engineering team. Interesting. And why is that?…

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