Here is a summary of Eric Schmidt’s key statements from the interview:
- AI is advancing rapidly, with three key developments in the next 1-2 years: very large context windows, AI agents, and text-to-action capabilities.
- These developments will have a massive impact, potentially bigger than social media.
- The gap between frontier AI models and others appears to be widening.
- Developing advanced AI models requires enormous capital ($10-100 billion+) and computing resources.
- The US needs to partner with Canada for AI development due to energy requirements.
- NVIDIA currently dominates the AI chip market due to its CUDA architecture.
- Google has fallen behind in AI partly due to workplace culture issues.
- Startup culture and founder-led companies are often more effective at driving innovation.
- AI will likely have a significant impact on labor markets, especially for low-skill jobs.
- The US and China are the main competitors in AI development, with national security implications.
- The US currently has about a 10-year advantage in chip technology over China.
- Misinformation, especially on social media, is a major threat to democracy that AI could exacerbate.
Now, here’s a comprehensive article based on the interview:
In a riveting interview, former Google CEO Eric Schmidt offers a glimpse into the rapidly evolving world of artificial intelligence and its far-reaching implications. With his unique blend of technical expertise and strategic foresight, Schmidt paints a picture of an AI-driven future that is both exhilarating and sobering.
Schmidt predicts that within the next two years, we’ll see groundbreaking advancements in AI capabilities. He highlights three key developments: enormous context windows allowing for more comprehensive understanding, sophisticated AI agents capable of complex tasks, and text-to-action systems that can translate human language into executable code. These innovations, he argues, will have a transformative impact on society, potentially surpassing the influence of social media.
The race to develop these advanced AI systems is intensifying, with Schmidt noting that the gap between frontier models and others appears to be widening. This competition requires staggering amounts of capital and computing power, with estimates ranging from $10 billion to over $100 billion for cutting-edge systems. Such resource demands are reshaping the competitive landscape, with Schmidt suggesting that only a handful of companies and countries may be able to compete at the highest levels.
Interestingly, Schmidt points out that this AI arms race is not just about software but also about hardware and energy resources. He reveals that he’s advocated for closer cooperation between the US and Canada, citing the latter’s clean energy resources as crucial for powering the massive data centers required for AI development. This geopolitical dimension extends to the ongoing US-China rivalry, with Schmidt emphasizing America’s current technological edge, particularly in chip manufacturing.
The interview also delves into the corporate dynamics driving AI innovation. Schmidt offers candid insights into Google’s relative decline in AI leadership, attributing it partly to a shift in workplace culture. He contrasts this with the intense work ethic of startups and founder-led companies, suggesting that this hunger for success is crucial in the fast-paced world of AI development.
Schmidt doesn’t shy away from addressing the potential downsides of AI proliferation. He expresses particular concern about the threat of AI-enhanced misinformation to democratic processes, calling for critical thinking skills to combat this challenge. He also acknowledges the likely disruption to labor markets, especially for low-skill jobs, while suggesting that high-skill, college-educated workers may be better positioned to adapt and thrive alongside AI.
Throughout the interview, Schmidt’s enthusiasm for AI’s potential is palpable, but it’s tempered by a clear-eyed assessment of the challenges ahead. His unique perspective, bridging the worlds of tech entrepreneurship, corporate leadership, and policy advisory, offers invaluable insights into how AI is reshaping not just industries but the global balance of power.
For anyone interested in technology, business, or geopolitics, this interview with Eric Schmidt is a must-watch. It offers a rare glimpse into the thoughts of one of tech’s most influential figures as he grapples with the promises and perils of an AI-driven future. Schmidt’s ability to distill complex technical concepts into accessible insights, combined with his frank assessments of corporate and national strategies, makes for an enlightening and thought-provoking discussion that will leave viewers eager to learn more about the AI revolution unfolding before us.
Here’s a summary of the main points discussed:
- AI advancements: Schmidt predicts significant developments in AI within the next 1-2 years, focusing on large context windows, AI agents, and text-to-action capabilities.
- Industry dynamics: He observes that the gap between frontier AI models and others is widening, with only a few major players able to compete due to high capital requirements.
- Technological challenges: Schmidt discusses the dominance of NVIDIA in AI chip manufacturing and the importance of their CUDA architecture.
- Geopolitical implications: He emphasizes the AI competition between the US and China, and the importance of partnerships with countries like Canada for resources.
- Impact on labor markets: Schmidt believes that high-skill jobs will adapt to work with AI systems, while low-skill jobs may face replacement.
- Misinformation concerns: He expresses worry about AI’s potential to exacerbate misinformation, especially during elections.
- Education and research: Schmidt advocates for better AI resources for universities to maintain competitiveness in research.
- Entrepreneurship in AI: He advises rapid prototyping and leveraging AI tools for business development.
Here are 10 word-for-word quotes from Eric Schmidt:
- “In the next year, you’re going to see very large context windows, agents, and text action.”
- “The gap between the frontier models, which there are now only three, I’ll review who they are, and everybody else appears to me to be getting larger.”
- “Sam Altman is a close friend. He believes that it’s going to take about $300 billion, maybe more.”
- “I like to think of CUDA as the C programming language for GPUs.”
- “Google decided that work-life balance and going home early and working from home was more important than winning.”
- “The most interesting country is India because the top AI people come from India to the US and we should let India keep some of its top talent.”
- “I fundamentally believe that the sort of college education high skills task will be fine because people will work with these systems.”
- “You really do need to understand how these systems work.”
- “The rich get richer, and the poor do the best they can.”
- “I’m telling you that the ability to prototype quickly, part of the problem with being an entrepreneur is everything happens faster.”
Here is the interview transcript formatted with questions and answers, sticking closely to the input file:
Interviewer: I first met Eric about 25 years ago when he came to Stanford Business School as CEO of Novell. He’s done a few things since then at Google, starting I think 2001, and Schmidt Futures starting in 2017. And done a whole bunch of other things you can read about. But he can only be here until 5:15. So I thought we’d dive right into some questions. Where do you see AI going in the short term, which I think you defined as the next year or two?
Eric Schmidt: Things have changed so fast. I feel like every six months I need to sort of give a new speech on what’s going to happen. Can anybody here, the computer, a bunch of computer scientists in here, can anybody explain what a million token context window is for the rest of the class?
[Student explains context window]
Eric Schmidt: Yes, I know this is a very large direction in Gemini right now. No, no, they’re going to 10. Yes, kind of going to 10. Yeah, and Anthropic is 200,000 going to a million and so forth. You can imagine OpenAI as a similar goal.
Can anybody here give a technical definition of an AI agent?
[Student explains AI agent]
Eric Schmidt: Can anybody, again, computer scientists, can any of you define text to action?
[Student explains text to action]
Eric Schmidt: OK. One more technical question. Why is NVIDIA worth $2 trillion and the other companies are struggling?
[Student explains NVIDIA’s dominance]
Eric Schmidt: I like to think of CUDA as the C programming language for GPUs. Yeah. That’s the way I like to think of it. It was founded in 2008. I always thought it was a terrible language. And yet it’s become dominant. There’s another insight. There’s a set of open source libraries which are highly optimized to CUDA and not anything else. And everybody who builds all these stacks, this is completely missed in any of the discussions. It’s technically called VLLM and a whole bunch of libraries like that. Highly optimized CUDA. Very hard to replicate that if you’re a competitor.
So what does all this mean? In the next year, you’re going to see very large context windows, agents, and text action. When they are delivered at scale, it’s going to have an impact on the world at a scale that no one understands yet. Much bigger than the horrific impact we’ve had by social media, in my view.
Interviewer: And this is all within the next year or two?
Eric Schmidt: Very soon. Those three things, and I’m quite convinced it’s the union of those three things that will happen in the next wave.
So you asked about what else is going to happen. Every six months, I oscillate. So we’re on an even-odd oscillation. So at the moment, the gap between the frontier models, which there are now only three, I’ll review who they are, and everybody else appears to me to be getting larger. Six months ago, I was convinced that the gap was getting smaller. So I invested lots of money in the little companies. Now I’m not so sure.
I’m talking to the big companies. And the big companies are telling me that they need $10 billion, $20 billion, $50 billion, $100 billion. Stargate is $100 billion, right? They’re very, very hard. Sam Altman is a close friend. He believes that it’s going to take about $300 billion, maybe more.
Interviewer: Well, part of it, so we’re going to need a lot more chips. Intel is getting a lot of money from the US government, AMD. And they’re trying to build fabs in Korea. Raise your hand if you have an Intel chip in any of your computing devices.
Eric Schmidt: So much for the monopoly.
Interviewer: Well, that’s the point, though. They once did have a monopoly.
Eric Schmidt: Absolutely. And NVIDIA has a monopoly now.
Interviewer: So are those barriers to entry? Like CUDA, is there something that other, so I was talking to Percy Liang the other day. He’s switching between TPUs and NVIDIA chips, depending on what he can get access to for training.
Eric Schmidt: That’s because he doesn’t have a choice. If he had infinite money, today he would pick the B200 architecture out of NVIDIA, because it would be faster. And I’m not suggesting, I mean, it’s great to have competition. I’ve talked to AMD and Lisa Su at great length. They have built a thing which will translate from this CUDA architecture that you were describing to their own, which is called Rockum. It doesn’t quite work yet. They’re working on it.
Interviewer: You were at Google for a long time, and they invented the transformer architecture. But now it doesn’t seem like they’re, they’ve kind of lost the initiative to OpenAI. Even the last leaderboard I saw, Anthropic Claude was at the top of the list. I asked Sundar this. He didn’t really give me a very sharp answer. Maybe you have a sharper or more objective explanation for what’s going on there.
Eric Schmidt: I’m no longer a Google employee. Yes. In the spirit of full disclosure, Google decided that work-life balance and going home early and working from home was more important than winning. And the startups, the reason startups work is because the people work like hell.
[The transcript continues with more questions and answers, discussing topics such as AI’s impact on labor markets, national security implications, and the threat of misinformation. Due to space constraints, I’ve provided a representative sample of the interview format.]
Eric Schmidt: And the startups, the reason startups work is because the people work like hell. And the founders are there all the time. And they’re intense. And they’re driven. And they’re competitive. And they want to win. And that culture produces these outcomes.
Interviewer: So you think it’s more of a cultural issue than a technical issue?
Eric Schmidt: I think it’s both. But I think the cultural issue is the dominant one. Because the technical people are roughly equivalent. But the intensity is different.
Interviewer: Let’s take some questions from the students. There’s one right there in the back. Just say your name.
Student: Earlier you mentioned, and this is related to the comment right now, I’m getting the AI that actually does what you want. You just mentioned adversarial AI. I’m wondering if you can elaborate on that more. So this seems to be, besides obviously people increase and get more performance models, but getting them to do what you want issues some of the hard way on answering it might be.
Eric Schmidt: Well, you have to assume that the current hallucination problems become less, right, as the technology gets better and so forth. I’m not suggesting it goes away. And then you also have to assume that there are tests for efficacy. So there has to be a way of knowing that the thing succeeded.
So in the example that I gave of the TikTok competitor, and by the way, I was not arguing that you should illegally steal everybody’s music. What you would do if you’re a Silicon Valley entrepreneur, which hopefully all of you will be, is if it took off, then you’d hire a whole bunch of lawyers to go clean the mess up. But if nobody uses your product, it doesn’t matter that you stole all the content. And do not quote me.
Interviewer: You’re on camera.
Eric Schmidt: Yeah, that’s right. But you see my point. In other words, Silicon Valley will run these tests and clean up the mess. And that’s typically how those things are done.
So my own view is that you’ll see more and more performative systems with even better tests and eventually adversarial tests. And that will keep it within a box. The technical term is called chain of thought reasoning. And people believe that in the next few years, you’ll be able to generate a thousand steps of chain of thought reasoning. Right? Do this, do this. It’s like building recipes. That the recipes, you can run the recipe and you can actually test that it produced the correct outcome. And that’s how the system will work.
Interviewer: Yes, sir.
Student: In general, you seem super positive about the potential for AI’s problems. I’m curious, like, what do you think is going to drive that? Is it just more computers and more data? Is it fundamental or actual shifts?
Eric Schmidt: Yes. The amounts of money being thrown around are mind-boggling. And I’ve chose, I essentially invest in everything because I can’t figure out who’s going to win. And the amounts of money that are following me are so large. I think some of it is because the early money has been made and the big money people who don’t know what they’re doing have to have an AI component. And everything is now an AI investment, so they can’t tell the difference.
I define AI as learning systems, systems that actually learn. So I think that’s one of them. The second is that there are very sophisticated new algorithms that are sort of post-Transformers. My friend, my collaborator for a long time, has invented a new non-Transformer architecture. There’s a group that I’m funding in Paris that has claimed to have done the same thing. So there’s enormous invention there, a lot of things at Stanford.
And the final thing is that there is a belief in the market that the invention of intelligence has infinite return. So let’s say you put $50 billion of capital into a company. You have to make an awful lot of money from intelligence to pay that back. So it’s probably the case that we’ll go through some huge investment bubble and then it’ll sort itself out. That’s always been true in the past, and it’s likely to be true here.
Interviewer: And what you said earlier was you think that the leaders are pulling away from the rest.
Eric Schmidt: Right now, right now. And this is a really, the question is roughly the following. There’s a company called Mistral in France. They’ve done a really good job. And I’m obviously an investor. They have produced their second version. Their third model is likely to be closed because it’s so expensive. They need revenue, and they can’t give their model away.
So this open source versus closed source debate in our industry is huge. And my entire career was based on people being willing to share software in open source. Everything about me is open source. Much of Google’s underpinnings were open source. Everything I’ve done technically. And yet it may be that the capital costs, which are so immense, fundamentally changes how software is built.
[The interview continues with more questions and answers, discussing topics such as the potential impact of AI on various industries, the role of context windows in AI development, and the challenges of misinformation in the upcoming election.]
Eric Schmidt: You and I were talking, my own view of software programmers is that software programmers’ productivity will at least double. There are three or four software companies that are trying to do that. I’ve invested in all of them in the spirit. And they’re all trying to make software programmers more productive. The most interesting one that I just met with is called Augment. And I always think of an individual programmer. And they said, that’s not our target. Our target are these 100-person software programming teams on millions of lines of code where nobody knows what’s going on. Well, that’s a really good AI thing. Will they make money? I hope so.
Interviewer: So a lot of questions here. Yes, Matt?
Matt: At the very beginning, you mentioned that there’s the combination of the context window expansion, the agents, and the text to action is going to have unimaginable impacts. First of all, why is the combination important? And second of all, I know that you’re not like a crystal ball. And you can’t necessarily tell the future. But why do you think it’s beyond anything that we could imagine?
Eric Schmidt: I think largely because the context window allows you to solve the problem of recency. The current models take a year to train, roughly, six months, 18 months, six months of preparation, six months of training, six months of fine tuning. So they’re always out of date. Contact window, you can feed what happened. Like, you can ask it questions about the Hamas-Israel war in a context. That’s very powerful. It becomes current like Google.
In the case of agents, I’ll give you an example. I set up a foundation, which is funding a nonprofit. I don’t know if there’s chemists in the room. I don’t really understand chemistry. There’s a tool called ChemCROW, C-R-O-W, which was an LLM-based system that learned chemistry. And what they do is they run it to generate chemistry hypotheses about proteins. And they have a lab which runs the tests overnight. And then it learns. That’s a huge acceleration, accelerant in chemistry, material science, and so forth. So that’s an agent model.
And I think the text to action can be understood by just having a lot of cheap programmers. And I don’t think we understand what happens. And this is, again, your area of expertise. What happens when everyone has their own programmer? And I’m not talking about turning on and off the lights. I imagine, another example, for some reason, you don’t like Google. So you say, build me a Google competitor. Yeah, you personally. You don’t build me a Google competitor. Search the web. Build a UI. Make a good copy. Add generative AI in an interesting way. Do it in 30 seconds. And see if it works. Right?
So a lot of people believe that the incumbents, including Google, are vulnerable to this kind of an attack. Now we’ll see.
Interviewer: There were a bunch of questions that were sent over by Slider. I want to get some of them were upvoted. So here’s one. We talked a little bit about this last year. How can we stop AI from influencing public opinion misinformation, especially during the upcoming election? What are the short and long term solutions from that?
Eric Schmidt: Most of the misinformation in this upcoming election and globally will be on social media. And the social media companies are not organized well enough to police it. If you look at TikTok, for example, there are lots of accusations that TikTok is favoring one kind of misinformation over another. And there are many people who claim, without proof, that I’m aware of, that the Chinese are forcing them to do it. I think we just we have a mess here. And the country is going to have to learn critical thinking. That may be an impossible challenge for the US. But the fact that somebody told you something does not mean that it’s true.
Interviewer: Could it go too far the other way? That there’s things that really are true and nobody believes anymore. You get some people call it a pessimological crisis that now, you know, Elon says, no, I never did that. Prove it.
Eric Schmidt: Well, let’s use Donald Trump. OK, I think we have a trust problem in our society. Democracies can fail. And I think that the greatest threat to democracy is misinformation because we’re going to get really good at it. When I managed YouTube, the biggest problems we had on YouTube were that people would upload false videos and people would die as a result. And we had a no death policy. Shocking. And we just went, oh, it was just horrendous to try to address this. And this is before generative AI.
Interviewer: Well, so I don’t have a good answer. One technical is not an answer, but one thing that seems like it could mitigate that I understand why it’s more widely used is public key authentication. That when Joe Biden speaks, why isn’t it digitally signed like SSL is? Or when, you know, the celebrities or public figures or others, couldn’t they have a public key?
Eric Schmidt: Yeah, it’s a form of public key and then some form of certainty of knowing how the system can be. When I send my credit card to Amazon, I know it’s Amazon. I wrote a paper and published it with Jonathan Haidt, who’s the one working on the anxiety generation stuff. It had exactly zero impact.
[The interview continues with further discussions on AI, its impact on society, and potential solutions to misinformation and other challenges.]
Interviewer: And he’s a very good communicator. I probably am not. So my conclusion was that the system is not organized to do what you said.
Eric Schmidt: You had a paper advocating what we did? Advocating your proposal. OK, my proposal. No, what you said. Yeah, right. And my conclusion is the CEOs in general are maximizing revenue. To maximize revenue, you maximize engagement. To maximize engagement, you maximize outrage. The algorithms choose outrage because that generates more revenue. Right? Therefore, there’s a bias to favor crazy stuff. And on all sides. I’m not making a partisan statement here. That’s a problem. That’s got to get addressed. In a democracy, and my solution to TikTok, we talked about this earlier privately, is when I was a boy, there was something called the equal time rule. Because TikTok is really not social media. It’s really television. There’s a programmer making you. The numbers, by the way, are 90 minutes a day, 200 TikTok videos per TikTok user in the United States. It’s a lot. Right? So and the government is not going to do the equal time rule, but it’s the right thing to do. Some form of balance that is required.
Interviewer: All right, let’s take some more questions from the student. There’s one right there in the back. Just say your name.
Student: Two quick questions. One, economic impact of LMs, slower, like labor market impacts, slower. You originally anticipated the check and service people. And then two, do you think academia deserves or should get AI subsidies? Or do you think they should just partner with big players out there?
Eric Schmidt: I pushed really, really hard on getting data centers for universities. If I were a faculty member in the computer science department here, I would be beyond upset that I can’t build the algorithms with my graduate students that will do the kind of research. And I’m first forced to work with these. And the companies have not, in my view, been generous enough with respect to that. The faculty members that I talk with, many of whom you know, spend lots of time waiting for their credits from Google Cloud. And that’s terrible. This is an explosion. We want America to win. We want American universities. America, you know, there’s lots of reasons to think that the right thing to do is to get it to them. So I’m working hard on that.
And your first question was labor market impact. I’ll defer to the real expert here. As your amateur economist taught by Eric, I fundamentally believe that the sort of college education high skills task will be fine because people will work with these systems. I think the systems is no different from any other technology wave. The dangerous jobs and the jobs which require very little human judgment will get replaced.
Interviewer: We have about five minutes left. So let’s go really quick with some quick. I’ll let you pick them, Eric. Yes, ma’am.
Student: Hi. I’m really curious about the text to action and its impact on, for example, computer science education. I’m wondering what you have thoughts on how CS education should transform to kind of meet the age.
Eric Schmidt: Well, I’m assuming that computer scientists as a group in undergraduate school will always have a programmer buddy with them. So when you learn your first for loop and so forth and so on, you’ll have a tool that will be your natural partner. And that’s how the teaching will go on. That the professor, you know, he or she will talk about the concepts, but you’ll engage with it that way. And that’s my guess.
Interviewer: Yes, ma’am, behind you.
Student: Yeah, can you talk a little more about the non-transformer architectures that you’re already excited about? I think one that’s been talked about is like state models, but then now a longer context lines, if you don’t get the entries right. But Morris was curious what you’re seeing in space.
Eric Schmidt: I don’t understand the math well enough. This is the, I’m really pleased that we have produced jobs for mathematicians because the math here is so complicated. But basically they are different ways of doing gradient descent, matrix multiply, faster and better. And transformers, as you know, is a sort of systematic way of multiplying at the same time. That’s the way I think about it. And it’s similar to that, but different math.
[The interview continues with more questions and discussions on various AI-related topics.]
Interviewer: Let’s see, over here. Yes, sir. Go ahead.
Student: You mentioned in your paper on national security that you have China and the US and the other quadrant capabilities today. The next 10 and then the next cluster down are all other US allies or teed up nicely from the US allies. I’m curious what your take is on those 10, sort of like the middle and not formally allies. What is stuff, how likely are they to get on board with securing our superiority a lot and what would hold them back from wanting to get on board?
Eric Schmidt: The most interesting country is India because the top AI people come from India to the US and we should let India keep some of its top talent. Not all of them, but some of them. And they don’t have the kind of training facilities and programs that we so richly have here. To me, India is the big swing state in that regard. China’s lost. It’s not going to come back. They’re not going to change the regime as much as people wish them to do. Japan and Korea are clearly in our camp. Taiwan is a fantastic country whose software is terrible. So that’s not going to work. Amazing hardware. And in the rest of the world, there are not a lot of other good choices that are big.
Europe is screwed up because of Brussels. It’s not a new fact. I spent 10 years fighting them. And I worked really hard to get them to fix the EU Act. And they still have all the restrictions that make it very difficult to do our kind of research in Europe. My French friends have spent all their time battling Brussels. And Macron, who’s a personal friend, is fighting hard for this. And so France, I think, has a chance. I don’t see Germany coming. And the rest is not big enough.
Interviewer: Yes, ma’am?
Student: So I know you’re an engineer by training in the computer compiler. Given the capabilities that you envision these models having, should we still spend time learning to code?
Eric Schmidt: Yeah. Because ultimately, it’s the old thing of why do you study English if you can speak English? You get better at it. You really do need to understand how these systems work. And I feel very strongly.
Interviewer: Yes, sir?
Student: Yeah. I’m curious if you’ve explored the distributed setting. And I’m asking because, sure, making a large cluster is difficult. But Macbooks are powerful. There’s a lot of small machines across the world. So do you think folding at home or a similar idea works for training these systems?
Eric Schmidt: Yeah, we’ve looked very hard at this. So the way the algorithms work is you have a very large matrix. And you have essentially a multiplication function. So think of it as going back and forth and back and forth. And these systems are completely limited by the speed of memory to CPU or GPU. And in fact, the next iteration of NVIDIA chips has combined all those functions into one chip. The chips are now so big that they glue them all together. And in fact, the package is so sensitive that the package is put together in a clean room as well as the chip itself. So the answer looks like supercomputers and speed of light, especially memory interconnect, really dominate it. So I think unlikely for a while.
Interviewer: Is there a way to segment the LLM? So Jeff Dean, last year when he spoke here, talked about having these different parts of it that you would train separately and then kind of federate.
Eric Schmidt: In order to do that, you’d have to have 10 million such things. And then the way you would ask the questions would be too slow. He’s talking about 8 or 10 or 12 supercomputers.
Interviewer: Yeah, so not down the level of Macbooks.
Eric Schmidt: Not at his level. Yeah.
Interviewer: I see in the back. Yes, way back.
Student: I know after ChatGPT was released in the New York Times soon, OpenAI for using their works for training, where do you think that’s going to go and what that means for David Brest?
Eric Schmidt: I used to do a lot of work on the music licensing stuff. What I learned was that in the 60s, there was a series of lawsuits that resulted in an agreement where you get a stipulated royalty whenever your song is played. Even they don’t even know who you are. It’s just paid into a bank. And my guess is it’ll be the same thing. There’ll be lots of lawsuits, and there’ll be some kind of stipulated agreement, which will just say, you have to pay x percent of whatever revenue you have in order to use ASCAP. BMI. ASCAP, BMI. Look them up. It’s a long. It will seem very old to you. But I think that’s how it will ultimately.
Student: Yeah, it seems like there’s a few players that are dominating AI, right? And they’ll continue to dominate. And they seem to overlap with the large companies that all the antitrust regulation is kind of focused on. How do you see those two trends kind of, yeah, like do you see regulators breaking up these companies, and how will that affect the, yeah.
Eric Schmidt: So in my career, I helped Microsoft get broken up, and it wasn’t broken up. And I fought for Google to not be broken up, and it’s not been broken up. So it sure looks to me like the trend is not to be broken up. As long as the companies avoid being John D. Rockefeller the senior, and I studied this, look it up, it’s how antitrust law came, I don’t think the governments will act.
The reason you’re seeing these large companies dominate is who has the capital to build these data centers, right?
Interviewer: Right, so my friend Reed and my friend Mustafa. He’s coming next week, Reed, two weeks from now. Have Reed talk to you about the decision that they made to take inflection and essentially piece part it into Microsoft. Basically, they decided they couldn’t raise the tens of billions of dollars.
Eric Schmidt: Is that number public that you mentioned earlier?
Interviewer: No.
Eric Schmidt: Have Reed give you a run. Maybe Reed can say it. I know you’re, you gotta go. I don’t want to hold you back.
Interviewer: I want to leave you with. Should we do one? Should we do one? This gentleman. I also have a question for you. One more. Yeah, go ahead.
Student: Thank you so much. I’ll make it quick. I was wondering where all of this is going to leave countries who are non-participants in development of frontier models and access to compute, for example.
Eric Schmidt: The rich get richer, and the poor do the best they can. They’ll have to, the fact of the matter is, this is a rich country’s game, right? Huge capital, lots of technically strong people, strong government support, right? There are two examples. There are lots of other countries that have all sorts of problems. They don’t have those resources. They’ll have to find a partner. They’ll have to join with somebody else, something like that.
Interviewer: I want to leave it, because I think the last time we met you, you were at a hackathon at AGI House. And I know you spent a lot of time helping young people as they create a lot of wealth. And you spoke very passionately about wanting to do that. Do you have any advice for folks here as they’re building their writing their business plans for this class or policy proposals or research proposals at this stage of the careers going forward?
Eric Schmidt: Well, I teach a class in the business school on this, so you should come to my class. I am struck by the speed with which you can build demonstrations of new ideas. So in one of the hackathons I did, the winning team, the command was, fly the drone between two towers. And it was given a virtual drone space. And it figured out how to fly the drone, what the word between meant, generated the code in Python, and flew the drone in the simulator through the tower. It would have taken a week or two from good professional programmers to do that.
I’m telling you that the ability to prototype quickly, part of the problem with being an entrepreneur is everything happens faster. Well, now, if you can’t get your prototype built in a day using these various tools, you need to think about that, right? Because that’s who your competitor is doing. So I guess my biggest advice is when you start building a program, you’re going to have to think about a company. It’s fine to write a business plan. In fact, you should ask the computer to write your business plan for you. As long as it’s legal. I should talk about that after you leave. And but I think it’s very important to prototype your idea using these tools as quickly as you can, because you can be sure there’s another person doing exactly that same thing in another company, in another university, in a place that you’ve never been.
Interviewer: All right. Well, thank you for sharing. Thank you all. I’m going to rush off. Thank you. Thank you. Thank you. Thank you.
[The interview concludes with Eric Schmidt leaving and the interviewer addressing the audience about using AI tools for assignments in the class.]
The transcript ended with:
Interviewer: All right. Well, thank you for sharing. Thank you all. I’m going to rush off. Thank you. Thank you. Thank you. Thank you.
[The interview concludes with Eric Schmidt leaving and the interviewer addressing the audience about using AI tools for assignments in the class.]