Satya Nadella: prediction

Transcript context

…Speaking of model companies, you say not only will you have the infrastructure, you’ll have the model itself. Right now, Microsoft AI’s most recent model that was released two months ago is 36 in Chatbot Arena. You obviously have the IP rights to OpenAI. To the extent you agree with that, it seems to be behind. Why is that the case, especially given the fact that you theoretically have the right to fork OpenAI’s monorepo or distill their models, especially if it’s a big part of your strategy that you need to have a leading model company? First of all, we are absolutely going to use the OpenAI models to the maximum across all of our products. That’s the core thing that we’re going to continue to do all the way for the next seven years, and not just use it but then add value to it. That’s where the analyst and this Excel agent, these are all things that we will do where we’ll do RL fine-tuning. We’ll do some mid-training runs on top of a GPT family where we have unique data assets and build capability. With the MAI model, the way that I think we’re going to think about it is that the good news here with the new agreement is we can be very, very clear that we’re going to build a world-class superintelligence team and go after it with a high ambition. But at the same time, we’re also going to use this time to be smart about how to use both these things. That means we will, on one end, be very product-focused, and on the other end, be very research-focused. Because we have access to the GPT family, the last thing I want to do is use my flops in a way that is just duplicative and doesn’t add much value. I want to be able to take the flops that we use to generate a GPT family and maximize its value, while my MAI flops are being used for… Let’s take the image model that we launched, which I think is at number nine in the image arena. We’re using it both for cost optimization, it’s on Copilot, it’s in Bing, and we’re going to use that. We have an audio model in Copilot. It’s got personality and what have you. We optimized it for our product. So we will do those. Even on the LMArena, we started on the text one and it debuted at like 13. By the way, it was done only on around 15,000 H100s. It was a very small model. So it was, again, to prove out the core capability, the instruction following, and everything else. We wanted to make sure we could match what was state of the art. That shows us, given scaling laws, what we are capable of doing if we gave more flops to it. The next thing we will do is an omni-model where we will take the work we have done in audio, what we have done in image, and what we have done in text. That will be the next pit stop on the MAI side. So when I think about the MAI roadmap, we are going to build a first-class superintelligence team. We are going to continue to drop, and do it in the open, some of these models. They will either be used in our products, because they’re going to be latency-friendly, cost-friendly, or what have you, or they’ll have some special capability. And we will do real research in order to be ready for the next five, six, seven, eight breakthroughs that are all needed on this march towards superintelligence—while exploiting the advantage we have of having the GPT family that we can work on top of as well. Say we roll forward seven years, you no longer have access to OpenAI models. What does Microsoft do to make sure they are leading, or have a leading AI lab? Today, OpenAI has developed many of the breakthroughs, whether it be scaling or reasoning. Or Google’s developed all the breakthroughs like transformers. But it is also a big talent game. You’ve seen Meta spend north of $20 billion on talent. You’ve seen Anthropic poach the entire Blueshift reasoning team from Google last year. You’ve seen Meta poach a large reasoning and post-training team from Google more recently. These sorts of talent wars are very capital intensive. Arguably, if you’re spending $100 billion on infrastructure, you should also spend X amount of money on the people using the infrastructure so that they’re more efficiently making these new breakthroughs. What confidence can one get that Microsoft will have a team that’s world-class that can make these breakthroughs? Once you decide to turn on the money faucet—you’re being a bit capital efficient right now, which is smart it seems, to not waste money doing duplicative work—but once you decide you need to, how can one say, “Oh yeah, now you can shoot up to the top five model?”…

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