I think that, well, first of all, we've definitely flopped on a bunch of things. We don't need to get into all of them right now. But we've had a long list of failures at the same time. So, part of it is just trying. I think that because of the kind of academic roots, maybe we were more inclined to try hard things. And I think coming into this last decade or so, especially the hard things have become more and more valuable. I guess if you look at AI, which is obviously a huge trend, the amount of computing that has to go into that, the amount of deep math that has to go into that, those are all technically deep and challenging problems. And I guess it's just a twist of fate that that turns out to be important at this stage in the world. I mean, there was a while where you could do pets.com, you remember, you can put anything on .com. It wasn't really that technically deep. You need marginal understanding of the web and you can do whatever .com. Fortunately, we were doing search, which did require some deeper technical skills. But the technical sophistication level has only gone up. In fact, now the people we hire are much more qualified than I was at the time. I was kind of a mathy computer science major because during college I did both math and computer science, which was somewhat unusual in my class. But nowadays, as we hire people out of Stanford and all the other top programs, these people are pretty sharp mathematically and computer science wise, and a bunch of them are physicists because physicists have to do the hard math and a lot of the stuff they do is very computationally limited, so they need to have some degree of computation skills. So I just think somehow it has happened to be the case that some of the deep hard tech has become increasingly important, and I think we just kind of lucked out on having set the bit early on in that direction.
That's an interesting observation that the technical problems have come to the fore again as a competitive advantage for companies. So let's talk about AI for a minute. Everyone's thinking about it. You're back at Google working on it. You guys are at the forefront in a whole bunch of ways, and it's incredibly competitive. The amount of capital going into AI infrastructure is hundreds of billions of dollars, even at the level of individual companies, it's really extraordinary. How are you seeing the landscape right now for what's going on in AI?
Okay, let me think how to answer that without just pounding my own chest. Yes, it's a huge amount of investment for sure. I guess I would say in some ways we for sure messed up and underinvested and didn't take it as seriously as we should have, say, eight years ago when we published the transformer paper. We actually didn't take it all that seriously and didn't necessarily invest in scaling the compute, and also we were too scared to bring it to people because chatbots say dumb things. And OpenAI ran with it, which good for them, it was a super smart insight, and there's also our people like Ilya who went there to do that. But I do think we still benefited from that long history. We had a lot of the research and development of neural networks going back to Google Brain. That was also kind of lucky. It wasn't luck that we hired Jeff Dean. I mean, we were lucky to get him, but we were in this mindset that deep technical things mattered, and so we hired him. We hired a lot of people from DEC honestly because they had the top research labs at the time. He was passionate about neural networks, and it stemmed from his college experiment. He was like, whatever, curing third world disease and figuring out neural networks when he was 16. He's done crazy things. He was passionate about it, he built up a whole effort. In my division at the time in Google X, we had him, but I was like, okay, Jeff, you do whatever you want. He's like, oh, we can tell cats from dogs. I'm like, oh, okay, cool. But you also trust your technical people. Soon enough, they were developing all these algorithms, these neural nets that were doing some of our search, and then Noam came up with a transformer. We were able to do more and more. So we had the underpinnings, we had the R&D, we did underinvest for a number of years and didn't take it as seriously as we should have, but we also at the time had developed the chips for it, like the TPUs go back 12 years or something. Initially we were using GPUs, we were probably among the earliest to use GPUs, and then we used FPGAs, and then we tried to develop our own chips, which have now evolved through a bazillion generations. So I guess it was the trust in going after the deep tech, getting the more computation out, developing the algorithms, and in parallel we were big investors in compute for a long time. So we've had the data centers for a long time on a scale that I don't think many have. Amazon AWS also has very sizable data centers, but very few have that scale of data center, have their own semiconductors, have the algorithmic learning algorithms and so forth to be able to perform at the forefront of modern AI.
How are you thinking about the technology? It keeps getting better every year. There are a lot of different visions for what artificial intelligence is going to look like. Are AIs really going to be able to do everything humans can do, at least in front of a computer, and maybe more broadly? What will that world look like? Do you have a view on where the technology is going?
It is absolutely amazing, the rate of innovation, and it's hugely competitive now, as all of you see, between the top US companies and the top Chinese companies. If you skip the news and AI for a month, you're way behind. So where is it going to go? I think we just don't know. Is there a ceiling to intelligence? In addition to the question you raised, can it do anything a person can do, there's the question of what things can it do that a person cannot do. That's sort of a superintelligence question. I think that's just not known. How smart can a thing be? We've had however many hundreds of thousands of years of human evolution and whatever millions of primate, but that's a pretty slow process compared to what's going on with AI.
Do you think we're ready for the speed at which the technology is advancing?
Are we ready for the speed the technology is advancing? So far, I think people are getting great use out of technology. Even though there are doom and gloom forecasts here and there, everybody's pretty well empowered. The AIs, truth be told, are periodically dumb enough that you're always supervising them anyway. But occasionally they're brilliant and give you a great idea. Especially as a non-expert, if I want to figure out how to create a new AI chip, I could talk to our expert designers, but as a base case, I can whip out my phone and talk to an AI about it. It'll probably give me an 80 to 90% decent overview and understand it, or whatever my health questions or whatnot. I do think it makes individuals very empowered because generally you don't have experts in XYZ all around you all the time. I think that empowerment can create a lot of potential, whether it's career, enterprise, health, or living well. I don't think I have all the answers. I just think it has a huge potential to improve individual capability.
Yeah, that's certainly the positive vision, that it could be an incredible augment of human capability. It's great that you're thinking about it that way. Let me ask a question that is always asked in the entrepreneurial thought leaders class, but is particularly salient with the discussion of AI because one of the things I think every student at Stanford and probably every college age student in the country is thinking about is how this technology will affect their careers and job opportunities and what they might go on to do. I'm curious if you have any advice for the students about what they ought to be studying or thinking about as they look out at the job market in the future.
I think it's super hard to predict exactly what will happen. From the advent of the web to cell phones and so forth, those have transformed our society profoundly, have transformed the kinds of jobs and careers and studies people do for sure. AI will 100% change that. But I think it's very hard right now in a rapidly shifting landscape to say exactly what, and also the AI we have today is very different from the AI we had five years ago or the AI we are going to have in five years. So I don't know, I think it's tough to really forecast. I would for sure use AI to your benefit. There are just so many things you can do. Just myself as an individual, whether it's choosing a gift for my friends or family, brainstorming new ideas for products, or for art, I just turn to AI all the time now. It doesn't do it for me because I'll ask, give me five ideas, and probably three of them are going to be junk in some way that I'll be able to tell, but two will have some grain of brilliance, or possibly put it in perspective for me so I can refine and think through my ideas.
Let me jump in with a really concrete question. We have about 250 students out there. A lot of them are undergraduates. A great number of them have not selected their major yet because we give them a lot of flexibility here at Stanford. A few years ago we could predict that a large number would choose computer science as their major. Are you recommending they continue to pick computer science as their major? They're listening closely.
I chose computer science because I had a passion for it. It was a no-brainer for me. I guess you could say I was also lucky because it was such a transformative field. I wouldn't not choose computer science just because AI can be decent at coding nowadays. AI is pretty decent at a lot of things. Coding just happens to have a lot of market value, which is why a lot of people pursue it. Furthermore, better coding makes for better AI, so a lot of the companies like our own that work on it care a lot about it. We use it a lot for our own coding and even for our algorithmic ideas. But that's because it's such an important thing. So I wouldn't switch to comparative literature because you think AI is good at coding. The AI is probably even better at comparative literature, to be perfectly honest. I don't mean to disrespect comparative literature majors, but when the AI writes code, sometimes it doesn't work. It'll make a mistake that's pretty significant. Getting a sentence wrong in your essay about comparative literature isn't going to have that consequence. So it's honestly easier for AI to do some of the creative things.
I think it's a very interesting observation about the technology because one inclination is to say AI is going to be really good at solving technical problems, but it won't necessarily do the things we associate with humans, like being empathetic in a conversation. If you ask one of these AI engines to simulate a conversation, it's actually pretty good at giving you the structure for a complicated conversation. So I like that you're pointing to that uncertainty. One more question that I want to open up to the audience so that we give people a chance to ask questions. This is the 100th anniversary of the School of Engineering. If you were Jennifer and had to launch the school's second century, what would you be thinking about for the second century of the School of Engineering?
Wow, okay. That's a big responsibility, like planning the dean job will come.
It is a big responsibility.
I guess I would just rethink what it means to have a university. I know that sounds kind of annoying. That's the kind of thing Larry would say and I would be really annoyed with him. But we have this geographically concentrated thing with buildings and fancy lecture halls. But realistically, information spreads very quickly. Many universities have gone online, including Stanford, but MIT with open courseware early on, and all these startups like Coursera, Udacity, you name it. Teaching is getting spread, and anybody can go online and learn. You can talk to an AI or take one of these classes and watch YouTube videos. So what does it mean to have a university? Are you trying to maximize impact? In that case, limiting it geographically is not going to be so effective. To be fair, the Bay Area is a special place, but I don't know that for the coming century the idea of a School of Engineering and university is going to mean the same thing as it used to. People move around, work remotely, collaborate across. It's a little at odds because we're trying to get people into the office, and I think they do work better in person together, but that's at a certain scale. If you have a hundred people together, they don't have to be at the same place as these other hundred people. Increasingly, I see individuals who create new things regardless of degree. We've hired a lot of academic stars, but we've also hired tons of people who don't have a bachelor's degree or anything like that, and they just figure things out on their own in some weird corner. I think it's a really hard question. I don't feel like I'm going to magically deliver the new recipe, but I just don't think this format is likely to be the one for the next hundred years.
You took that in a deeper direction than I was.
No, it was great. Actually, it was a bit deeper.
Sounded more presidential than dean-like. I think he's talking to you. I agree it applies to the whole university. You actually surfaced the most fundamental questions about the university. Part of the university is about the creation and transmission of knowledge. That's the fundamental mission. Those can be done in different ways as technology advances. And then there's a question about the model of having a density of talent all in one place bumping into each other, which of course led to you creating Google and many great things. Will there be substitutes for that kind of ecosystem on a university campus? How fundamental is that and will it continue to be? I appreciate that you surfaced such a deep question in this session. All right, I want to make sure we give some questions to other folks in the audience. Jennifer, I'm going to turn to you to take some questions from the folks out here.
Yes. The students in the entrepreneurial thought leaders class submitted questions in advance, and a number of those were selected. With the time we have left, we're going to have a few questions from our students. I think the first one is over here.
Dean Widow, President Levan, and Sergey Brin, thank you for your time. My name is Rashad Barv from Kansas City, studying MS&E and IR. My first question goes out to Sergey. It touches on what we were discussing. Google largely grew out of the academic work you authored on PageRank. With industry now driving so much of today's innovation, do you still feel that the academia to industry pipeline is crucial? If so, how might you strengthen it?
Wow, it's a great question. Is the academia to industry pipeline crucial? I'm going to give you an I don't know on that. When I was a grad student, the time from a new idea to it being commercially valuable was many decades. If that compresses, then that no longer makes as much sense. In academia, you have freedom to think about it for a while, apply for grants, and you can spend a couple decades thinking about it, and then it percolates, and eventually maybe there's some big company or your startup pursues it. The question is, does that make sense if that timeline shrinks a lot? I think there are certain things that for sure make sense. Even with AI, I periodically keep up with Stanford research and other universities, and occasionally we hire those folks and collaborate with them. But I don't know that they needed to have that period of time. They were trying some new attention thing, spent a couple years experimenting, and then took it to industry. Obviously, industry is also doing all those things, so probably not a huge argument for that. Radical new architectures and things, maybe, but the time industry will scale it will be much faster. Quantum computing comes to mind. It was first brainstormed by Feynman in the 80s, postulating the idea of quantum computing, and now there are a bunch of companies doing it. There are also university labs trying new ways to do it. That's kind of on the fence. I would say if you have some completely new idea, like you're not doing superconducting qubits or trapped ions, but you have some new way, maybe you need to let it marinate in university for some number of years. Those things are kind of hard. It could make sense, but at some point if you decide it's really compelling, you're probably going to take it commercial. I want to give you a clear-cut answer, but the top companies now invest in much more fundamental research, and with AI starting to pay off, those investments are paying off. So it would shift the proportion of endeavors you would do, but I do think there are still some things that take a decade of more pure research that companies might be reluctant to pursue because that's too long a time to market.
All right, next question I think is over here.
Hi everyone, my name is Arnov and I'm a freshman studying computer science and math. My question is for Sergey Brin. As AI accelerates at this unprecedented rate, what mindset should young aspiring entrepreneurs like myself adopt to avoid repeating earlier mistakes?
Oh, what mindset should you adopt to avoid repeating earlier mistakes? When you have your cool new wearable device idea, really fully bake it before you have a cool stunt involving skydiving on airships. That's one to both of you. No, I actually like what we were doing back in the day with Google Glass as an example of prior mistakes. I tried to commercialize it too quickly before we could make it as cost-effectively and as polished as needed from a consumer standpoint. I jumped the gun. I thought, oh, I'm the next Steve Jobs, I can make this thing. That's probably one. If I encapsulate, everybody thinks they're the next Steve Jobs. I've definitely made that mistake, but he was a pretty unique guy. So I would say, make sure you've baked your idea long enough and developed it to a far enough point before you get on a treadmill where expectations increase, expenses increase, and you have to deliver by a certain time. You might not be able to do everything you need in that amount of time. You get a snowball of expectations and you don't give yourself all the time you need. That's the mistake I would have tried to avoid.
All right, I think we're going to go over to this side.
Hi, thank you for the talk. My name is Ian Pragataki. I'm an undergraduate freshman at Stanford University. This question is for Sergey Brin and Jennifer. We see a lot of AI companies improving large language models via scaling data and scaling compute. My question is, once we do run out of data and once we do run out of compute, what do you think will be the next direction? Would it be newer architecture, an alternative to transformers, or a better learning method, something better than supervised learning or RL that we use to train these large language models, or is it a completely different direction that you have thought of? Thank you.
From my point of view, all the things you listed have already been bigger factors than scaling compute and scaling data. I think people notice the scaling because you're building data centers and buying chips, and there were publications from OpenAI and Anthropic about different kinds of scaling laws. So that attracts a lot of attention. But if you carefully line things up, you'll see that algorithmic progress has outpaced even the scaling over the last decade. A while ago, when I was in grad school, I saw a plot for the n-body problem. There was huge Moore's law increase in compute since people started worrying about that in the 50s, but the algorithms to do the n-body problem far outpaced that compute scale-up. So I think companies like ours are never going to turn down being at the frontier of compute, but that's just the dessert after the main course and veggies of actually having done your algorithmic work.
I'll jump in and say that in terms of running out of compute or running out of data, we're very familiar with that here already. It's difficult for a university to have the type of compute that companies have. We don't even come close. But that does lead us to do quite a bit of innovative work in what happens when you have less compute and how to make more of less. So we do a lot of that work here already.
Next question, I think also on this side.
Hi everyone, my name is Andy Seavozi. I'm a second year graduate student in chemical engineering. My question is to all the speakers. What emerging technology do you think is seriously underestimated in terms of its long-term impact? Thank you.
Okay. What emerging technology is being seriously underestimated? Wow. I obviously can't say AI because it's hard to argue, but it could be underestimated.
It could be underestimated, but probably not emergent at this point. We couldn't use that one.
