Microsoft CTO Kevin Scott discusses AI art, Nvidia, and competing with Google with Decoder.
Microsoft’s Kevin Scott sat down with us at Code to talk about Bing’s competition with Google, the race to acquire and develop high-end GPUs, and how art can survive in the age of AI.I co-hosted the Code Conference last week, and today’s episode is one of my favorite conversations from the show: Microsoft CTO and EVP of AI Kevin Scott. If you, you know that he and I love talking about technology together.
The context of this question is, as we sit here on the West Coast having this conversation, on the East Coast, Google is in the middle of an antitrust trial about how it might’ve unfairly created a monopoly in search. And a huge theme in that trial is, “Well, hey, Microsoft exists. If they wanted to compete, they could. We’re just so good at this that they can’t.
Yeah. So I think what you want from a search engine and what you’re going to want from an agent is a little more complicated than just asking a question and getting an answer. A whole bunch of the time, what you want is you’re trying to accomplish a task, and asking questions are part of the task, but sometimes, it’s just the beginning. Sometimes, it’s in the middle.
So, I think that’s one of the opportunities that we can have right now in the conversation about how these AI agents are going to show up in the world.
So, there’s nothing about an AI being 100 percent of that interaction that seems interesting to me. I don’t know why I would want to be consuming a bunch of AI-generated content versus things that you are producing.I think you are almost certainly going to want to use some of these AI tools to help produce content.
We’ve arrived now at the nature of art, so I’m going to make a hard shift to GPUs. This is what I mean about Kevin — we can go everywhere with Kevin. I just want to make sure we hit it all.Why do people make art? The AI moment has provided us the opportunity to ask that question in a serious way. Because the internet has basically been like, “To make money.” And I think there’s a divergence there, as our distribution channels get flooded.
And the other thing, too, is the folks at OpenAI, with some help from folks at Microsoft, have been working furiously on optimizing the big models, as well. So it’s not an either-or. You want both, and you want both to be getting cheaper and faster and more performance and higher quality over time.I’m looking at Copilot in Office 365. It’s $30 a seat. That’s an insane price. I think some people are going to think it’s very valuable, but that’s not a massive market for an AI pricing scheme.
When I think about compute — these big models, running tools for customers — obviously, the story there is Nvidia chips, right? It’s access to H100s. It’s building capacity there. They’ve got 80 percent of the overall market share. How much do they represent for you?What’s your relationship with Nvidia like? Is that a good working relationship?
Is that because of the processing power in the chip, or is it because of the CUDA platform? Because what I’ve heard from folks, what I heard from Lisa yesterday, is that actually, what we need to do is optimize one level higher. We need to optimize at the level of PyTorch or training or inference. And CUDA is not the thing, and that’s what Nvidia’s perceived mode is.
Well, let me deploy my finest press training and say that if you are an API customer right now — like you’re using the Azure OpenAI API or using OpenAI’s instance of the API — you don’t have to think about what the underlying hardware looks like. It’s an API. It is presented to you to be the simplest possible way to go build an AI application on top of that API.
So, I think the open-source stuff is super interesting, and I think it’s going to help everybody. We’ve open-sourced this super good model calledthat’s trending on Hugging Face as of last week. A bunch of open-source innovations we’re excited about. But I think the big models will continue to make really amazing progress for years to come.
Text is definitely harder. There are some things that are research-y that folks are working on, where you can, in the generation of the text, subtly add a statistical fingerprint to how you’re generating the text. But it’s much harder than visual content, where it’s easy to just hide the watermark in the noise in the pixels and not have it really alter the experience you have as a user viewing the image or the video. So it’s a tougher problem, for sure.
Kevin Scott: Yeah, we are thinking a lot about that. And I think there’s some interesting stuff here on the research front that shows that those expert contributions that you can make to the model’s training data, particularly in this step called reinforcement learning from human feedback, can really substantially improve the quality of the model in that domain of expertise. We’ve been thinking in particular a lot about the medical applications.
NP: Music copyright is like... Just find me later, and we’ll talk about it. It’s one of my favorite things. Go ahead.
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