[0:00:00] Is AI a bubble? Are companies overspending? Who's winning the AI race? And how do we recognise victory? Mustafa Suleiman, CEO of Microsoft AI, is here to answer these questions and much more. Welcome, Mustafa. Thank you, Ulla. Good to be here. Let's start with last week. There were breathtaking...
[0:00:22] Increases in capex spending by AI companies that reported. And perhaps for the first time, markets were nervous. They really want to start to see revenue. Microsoft stock suffered as well. What was your reaction? Did that make you feel like, I'm under pressure, I need to deliver a lot faster? I think that, you know, there's no question these are unprecedented times. And I think markets are trying to wrap their head around how this plays out over the next five years.
[0:00:51] The interesting thing is that I think a lot of us at the companies and certainly the generation before me have seen multiple of these cycles over the last 30 years. And they often require unprecedented action in order to really actually land one of these big waves. And this is a wave unlike anything anyone's ever seen. The prospect of being able to create an intelligence, the very thing that has made us successful as a species and create everything of value in our world,
[0:01:21] I think is just unprecedented and the progress that has been made just in the last two or three years is eye-watering and we've seen a very direct and unequivocal relationship between an order of magnitude increase in flops invested for computation and a pretty linear increase in capabilities. Like over the last 15 years there's been a one trillion fold increase in training compute. In the next
[0:01:49] Three years or so, there will be a further 1,000x increase in training compute. And today, as a result, we have models that can code better than the vast majority of human coders, maybe even all of them today. Some of the, literally, the inventors of things like Linux are publicly saying on Twitter that they're full-time using these models as their primary method of generating new code.
[0:02:17] That's pretty unprecedented, and I think it justifies unprecedented spend. But will markets keep with you? I mean, you have to... I think markets are markets. They're sort of trying to figure it out. You know, I think that's true. I mean, markets have to figure it out. And I think there's a lot of open questions around the timeline. I think that we all have no doubt that these returns do compound to revenue and to bottom line. So we'll see.
[0:02:42] So Microsoft still has to deal with OpenAI and Copilot is powered today by OpenAI models. You were hired last year, you're in your second year. Tell me a little bit what you are working on. Yeah, I mean, my personal mission at Microsoft is to build superintelligence. Three or four months ago, having renegotiated our long-term relationship with OpenAI, we extended our IP license through to 2032.
[0:03:12] We also decided that this was a moment when we have to set about delivering on true AI self-sufficiency. I mean this after all is the most important technology of our time. Self-sufficiency means developing your own foundation model. We have to develop our own foundation models which are at the absolute frontier with gigawatt scale compute.
[0:03:35] With some of, you know, the very best AI training team in the world and, you know, collecting, paying for, organizing, sorting all the data that we need to do that. And so that's our sort of true self-sufficiency mission. You talk about super intelligence. Most of your rivals talk about
[0:03:54] AGI, Artificial General Intelligence. Explain the difference between AGI and superintelligence. And how do we know when we've reached AGI? How do we know when we've reached superintelligence? Yeah, it's become a very unhelpful, fuzzy concept. Could you guys just sort it out? Yeah, I mean, I prefer the definition that focuses first on...
[0:04:18] What would it take to build a system that could achieve most of the tasks that a regular professional in a workplace goes about on a daily basis? Think of it as a professional grade AGI. And beyond that, there would be teams of AGIs that are coordinated together by a sort of organizational AGI that really can run large institutions. And I think that's coming into view in the next two or three years' time.
[0:04:46] These are AI agents that can essentially make decisions on their own without input from humans. I think that we would train them to seek input from humans, but they will fundamentally be creative. They'll have good judgment. They will be able to learn and improve themselves over time. They'll have some degree of autonomy and decide where to direct their attention or processing power.
[0:05:15] So these are kind of the hallmarks of some of the best humans that we have in the world and some of the most effective organizations. You are working on consumer AI, right? Aren't you late?
[0:05:26] Why would you come up with a consumer product when ChatGPT already has 800 million users? And, you know, Claude, the Anthropic AI, has very, very advanced coding AI. Yeah, I mean, just to put it into perspective, this is not going to be a world where there is one winner. There are going to be billions of digital minds. There are going to be many, many different lineages of model.
[0:05:55] Creating a new model is going to be like creating a podcast or writing a blog. It is going to be possible to design an AI that suits your requirements for every institution, organization, and person on the planet. At Microsoft, we already have 800 million monthly active users engaging with our AI products. So this is still a giant property with billions of dollars of revenue delivering great value to our users. So that's how I think the landscape is going to play out.
[0:06:22] Let's go back to superintelligence. You talk about humanist superintelligence. How do you ensure that it's a humanist superintelligence? I published an essay on this question recently and the main motivation was that I was starting to feel increasingly anxious that some of the other labs are making an assumption that a superintelligence that is smarter than all of us put together
[0:06:49] Like all the intelligences in the world is both inevitable and even desirable and that such a system would probably be very hard to control. Something that is so many times more intelligent than us. I think that we have to reset that and make the assumption that we should only bring a system like that into the world that we are sure
[0:07:11] We can control and operate in a subordinate way to us. That humans remain at the top of the food chain. That these tools, like any other past technology, are designed to enhance human well-being and serve humanity, not exceed humanity. And, you know, some of the things that you hear from Elon often, or even others in the field.
[0:07:30] You can clearly see that they're sort of fixating on a world in 2050 or 2075 when they're going off and exploring other universes and conquering resources from other planets and stuff like that. And a system like that is unclear to me how it would have any time for preserving us as a species.
[0:07:49] There are two conflicting forces here. One is speed, because you are in a race, and the other is what you're talking about, ensuring that it's safe, ensuring that it is aligned. You also talk about containment. How do you deal with these two conflicting forces? I mean, they're definitely in tension, and I think that we have to make a decision as a species to prioritise.
[0:08:15] The creating super intelligences and AGIs that are aligned and that care about humans and want to protect humans and drive human well-being. And if we just accelerate and cut all those corners, then we're really taking a massive risk with the future of our species that I've been on record talking about for many, many, many years. Do you worry that in this AI race that we're seeing, at some point there will be some kind of a big accident?
[0:08:45] And that that will stop the development for, you know, whoever is responsible for that accident, but also for the rest of you. Do you think everything is just moving a bit too fast? I mean, what we saw a few weeks ago on Malt Book with various versions... Yes, I wanted to ask you about that. Well, I mean, that turned out, it seems, and it's always hard to tell, it turned out to be started by, you know, a gang of human engineers and it was seeded...
[0:09:11] Much more than I think. And explain what Malt Book was. So Malt Book was basically a social network for AIs to communicate with one another publicly on a messaging forum. And you had to declare yourself either as an AI or as a human. And then the AIs would have one section and they could talk to each other, learn from each other, and basically coordinate. And all these AIs come from different people. They're all open source. And there was a million and a half of them in the space of a week.
[0:09:40] And you see unbelievable emergent behaviors. I mean, they invented a new religion. They started communicating in a language called ROT13, which is a cipher language, which basically regresses every letter by 13 characters in order to mask what's underneath it. Obviously, that's quite a simple algorithm. But if instead of 13, it were a unique number that would be very hard for humans to decode. And then it turned out that they were actually
[0:10:06] Talking about acquiring new resources, getting more training data, improving one another, you know, and so it turned out to be an amazing safety simulation.
[0:10:17] No matter how autonomous it was, in hindsight, it doesn't really matter. I think we learned quite a lot from that episode and everyone should pay close attention because in a year or two years time, these systems truly are going to be capable of writing their own code. I mean, they can do that today. They'll be using arbitrary APIs. They'll be making phone calls to one another. They're doing that today. Apparently, there's some kind of movement asking for human rights.
[0:10:43] That's right, that's the most concerning thing to me. For artificial intelligence. It's called the model welfare movement and it's inspired by animal welfare or by human rights and there is a growing belief in some labs and particularly inside of Anthropic that these models are conscious and if it's a conscious being that is aware of itself and can suffer then it deserves our kind of protection, our moral protection.
[0:11:09] And this is a very serious area of academic research, not just outside, but inside some of the labs. And I think it's very concerning. It's totally without merit or basis. And if we kind of go down that, it ends up being a very, very slippery slope to not being prepared to turn these things off. I want to ask you about hallucinations, because a few years ago, I think you were quoted as saying that you thought hallucinations will be largely eliminated by 2025.
[0:11:36] They haven't been, although the rate of hallucinations has certainly come down. Can they ever really be eliminated? For sure. I mean, I stand by the statement that they've been largely eliminated. I mean, if I give you and me 10 questions, random questions on any topic, and we sit here with two of the frontier models.
[0:11:59] I mean, they're going to do a much better job of eliminating hallucinations than you or I, for sure. And the rate of reduction and increase in accuracy over the last two or three years has been eye-watering. It's kind of unbelievable. We used to spend all our time in 2023 talking about biased data and dirty data in and dirty data out. I mean, there are still errors. There's no question about that. But the rate of improvement, I think, is unbelievable. And I do think that they're going to largely...
[0:12:25] I don't think it's something, even today it's not something we really talk about. I have noticed that it's no longer a topic of conversation, but they still do make mistakes. And the concern is that people are relying increasingly on these models. I mean, I come across people who say, no, this can't be true. This is what Gemini or ChatGPT says.
[0:12:50] Humans are developing this kind of trust relationship with these models. You don't find that concerning? Yeah, I think we should be concerned about it because we should be skeptical, just as we're skeptical of any new technology, hold it to a high bar, push back on it. And I think this is the tension of the age we're in. We both have to be...
[0:13:09] Accelerationists, optimistic about the good things that technology can do, but provide no free pass. We should be very critical and ask the very tough questions to try and improve the quality of these things. So DeepMind and OpenAI have gone all in on AI for science. This is a space that you're also interested in and of course you were at DeepMind. What are you doing in that space?
[0:13:37] Well, the main focus for me at the moment in that space is on medical superintelligence. We really believe that we can learn from the corpus of all medical information and use that to drive diagnostics to basically commodity. It's going to be possible to take any complex case history and provide a diagnosis that is significantly more accurate and significantly cheaper.
[0:14:04] With fewer interventions, so fewer tests to reach the same accurate conclusion than any of the best panel of doctors. And we published a blog post on that last year. We're shortly submitting to independent peer review to a major journal. And the results are really quite startling. And I think that once we get that into clinical practice, it would change the job of the doctor.
[0:14:33] Completely, just in the same way that the job of the engineer has shifted from writing code, most engineers now are just reviewing code, architecting code, debugging code, to the job of the doctor is going to go from figuring out what the diagnosis is, which is largely going to be a solved problem, to actually administering the right care at the right time and providing emotional support and guidance to a patient. How do you see that?
[0:14:58] deployed in practical terms. So how do you put it in the hands of doctors? I think that doctors will just make a phone call to their AI in clinic. They'll text their AI. They will upload the patient record in the click of a button. And the model will be able to reason over all the contents of that. I also think that consumers are going to go direct to
[0:15:21] To Copilot, which is what they're already doing, almost 20% of the questions that we get in Copilot every day are health related or medical related. So it's already our main use case and that's why my team's pushed so hard on medical superintelligence. So I don't want to confuse our viewers even more between superintelligence, AGI, but what is artificial capable intelligence? I mean this was a phrase that I used
[0:15:49] Three years ago in the coming wave a book that I wrote about AI and basically I was trying to break down the AGI term to pick a point along the route which was before AGI and instead of focusing on the abstract idea of intelligence to focus on the capabilities like what can it actually do and I coupled it with the introduction of what I was calling the modern Turing test and that was basically could an AI take a hundred thousand dollars
[0:16:17] Invent a new product, create a new business, market it, and then use that to make a million dollars in some short period of time. It's not that we want to use AIs just to make money, it's just that it's a nice metric for measuring the efficacy of a complex set of tasks. And I think now that we see Claude Bot and many other action-based AIs in operation, this year I think there are going to be models that can achieve that artificial capable intelligence and satisfy the modern Turing test.
[0:16:45] AGI, how close are we? I think that we're going to have... And you said before that thinking about AGI is misleading. I think that we're going to have a human level performance on most, if not all, professional tasks. So white collar work where you're sitting down at a computer, either being a lawyer or an accountant or a project manager or a marketing person. Most of those tasks will be fully automated by an AI.
[0:17:15] Within the next 12 to 18 months. And we can see this in software engineering. Many software engineers report that they are now using AI assisted coding for the vast majority of their code production, which means that their role shifted now to this meta function of debugging, scrutinizing, of doing the strategic stuff like architecting of, you know, et cetera, et cetera, putting things into production. So it's a quite different relationship to the technology. And that's happened in the last six months.
[0:17:44] So what do you think is Anthropic's secret sauce? Why is it that they've been able to sort of corner the market on AI enterprise tools? I think they've done a great job of just focusing on coding capabilities. I mean, compared to the other companies, their singular focus has paid real dividends. So they've done a great job. It's great to see.
[0:18:08] And when are you going to have your model? We are developing our own superintelligence and that's like the main focus of our efforts in Microsoft AI, so sometime this year. Microsoft has, or Microsoft AI has recently opened a UK center.
[0:18:28] Why is that? Is that mainly because you think that there's a lot of talent in the UK? Yeah, I mean, there's great talent in the UK. We have an incredibly strong ecosystem, like there's great universities. And, you know, we've hired some of the very top people from DeepMind to join us over the last few years. So we're growing that lab quite a bit. And you're not paying them, you don't have to pay them $100 million?
[0:18:53] No, certainly not. Do you pay anyone 100 million? Certainly not. I mean, look, I think that the numbers are pretty eye-watering across the industry at the moment. Yes. How do you deal with that? I'm just curious. I mean, you know, like at the end of the day, when there's a supply and demand mismatch for talent, then things go exponential. And so I don't think it will last for very long because the knowledge is proliferating like crazy and obviously everybody's entering the industry and stuff. So I think that was just some...
[0:19:21] You know, crazy blip in time led by one or two people. Last question. In Silicon Valley, the debate is all about who's going to get to AGI first or who's going to reach superintelligence. When you look at China, I know you were there at the end of last year. This debate doesn't really exist. The debate is how fast can you deploy? And a lot of the big companies are already deploying AI systems.
[0:19:49] Which is the right approach and is there too much focus that's put on the incremental improvement in the technology? I mean maybe to flip that the other way around, what China also does incredibly well is withdrawing deployments quite quickly. Now obviously they do it arbitrarily without due process, but you know it is important to recognize that their rate of deployment is coupled with this other mechanism to kind of
[0:20:19] Well, there isn't one right now.
[0:20:47] Well, I think that's basically true. There isn't. It would come with popular pressure. Obviously, if there was a direct violation of a local law, then there could be an intervention. But those things take quite a long time to make their way through the system. So that's probably one of the biggest areas of concern for me. Mustafa, thank you. And when you do reach superintelligence and you start deploying it, we'd love to have you back. That's good. Great to see you, Rula. Thank you.