# Eric Schmidt WARNS: "You Have No Idea What's Coming In 2027"

*AI Upload* · https://www.youtube.com/watch?v=5qiGIqS-hEY

## Summary

This video presents an interview with former Google CEO Eric Schmidt at an AI conference, framed by a narrator who adds commentary. Schmidt discusses the current state of AI, the anticipated arrival of recursive self-improvement within 2 to 3 years, the impact on programmers and education, and the risks around jobs, teenagers and agent orchestration. He also reflects on Google's creation of the Transformer, TPU and protein folding breakthroughs, and warns that China is positioned to win the low-cost robotics race the way it won electric vehicles.

## Key points

- Eric Schmidt estimates we are only 10 or 15% into the impacts of AI, with hardware like robots taking longer than software.
- Recursive self-improvement, where AI improves itself, is not happening yet, but the San Francisco consensus believes it could arrive within 2 to 3 years.
- Schmidt says reasoning systems are already perfect partners for humans, for good and bad, and the trend is not stoppable by any government, individual or corporation.
- Top programmers will become more valuable, not less, because these systems still need human control.
- Schmidt proposes universities teach a prompt engineering course to all freshmen starting in September.
- He warns it is not okay for 13-year-olds to be committing suicide because of an LLM and that this needs to be addressed right now.
- Schmidt argues China, a competitor not an enemy, will win low-cost robotics because of its electric vehicle industry expertise and vertical integration.
- The TPU decisions made 10 years ago set Google up well, and Nvidia managed to build real supercomputers in a way Intel never could.

## Notes

### The current moment in AI
- Eric Schmidt believes we are only 10 or 15% into the impacts of AI, with hardware like robots taking longer than software.
- Recursive self-improvement, where systems improve themselves, is not happening yet, but reasoning systems already act as perfect partners for humans, for good and bad.
- Even if development stopped today, which Schmidt says is not stoppable by any government, individual or corporation, these reasoning agents would still have advanced humanity.

### The San Francisco consensus
- Schmidt describes a "San Francisco consensus": this is the year of agents, with scaling of agents and reasoning growing enormously.
- Everyone is out of hardware and electricity; he calls it the biggest boom he has seen across three or four cycles in his career.
- Once recursive self-improvement arrives, intelligence could learn faster than humans because we are biologically limited.
- His example: a tech company with a thousand AI researchers could run perhaps a million AI research agents, limited only by electricity, producing a superintelligence moment. The belief in San Francisco is this occurs within 2 to 3 years.
- The channel narrator pushes back, arguing recursive self-improvement is not exponential because technology shows diminishing improvements as it matures.

### Claude Opus 4.5 and programming
- Everyone in the Bay Area doing software says the split went from 80/20 to 20/80 with Claude Opus 4.5, because the underlying LLM produces better quality tokens and thinks longer.
- Schmidt, a former programmer, says there is nothing he could do that it cannot do, citing a C compiler written in Rust.
- Top programmers will become more valuable, not less, because these systems need human control.

### Education and risks
- Schmidt proposes universities stop everything and design a prompt engineering course for all freshmen starting in September, covering every discipline including liberal arts.
- Key risks he names: eventual jobs impact (already visible in software and customer service), maintaining moral values while racing China, teenagers being harmed by LLMs, and unpredictable effects from orchestrating agents from non-compatible vendors.

### Google's legacy and chips
- The Transformer, TPU and protein folding all happened at Google under his tenure; protein folding does in an hour what took a PhD student 4 years, roughly 300 million times more efficient.
- TPU version two's algorithm changes made it the perfect inference engine, positioning Google well; Nvidia managed to build real supercomputers that Intel never could.

### China and robotics
- China is a competitor, not an enemy, with money, intelligence, a strong work ethic and dominance of key industries.
- Schmidt argues China will win low-cost robotics because the electric vehicle industry produces the same motors and systems; letting China dominate EVs was an error.
- The narrator adds China built the Tesla Shanghai Gigafactory in 168 days and has dozens of humanoid robot makers producing thousands of robots annually, while Boston Dynamics has worked on Atlas for almost 15 years.

## Transcript

[00:00] I keep asking my friends, when does the asymptote arrive and when does the curve slow down? We've not seen it yes yet. There will be one, right? It is actually true that there is a limit to our craziness. >> [laughter] >> We have not found it yet. The next thing that's really interesting and terrifying also is recursive self-improvement, but we don't have it yet. What we do have Guys, Eric Schmidt just gave a banger interview during an AI conference last week. Eric talked about the upcoming AI and its dangers for humanity. And he said if we don't control AI now, it could become an existential threat to humanity in the coming years. Guys, I must say here that Eric is one guy who's been warning us about AGI for a very

[00:44] long time now. In a recent interview, he literally said that uncontrolled AI has the potential to become a bigger problem for us comparatively to nuclear bombs. So, I mean, he's been the CEO of Google and when he says AI is a bigger threat than nukes, it should be enough to open everyone's eyes. Let's watch the interview and I'll give my opinion as we go. So, let's start with a a question that uh I'd love to hear you expand on, which is we're living through a historic moment right now. Right? Um could you define the moment we're in and give us sort of a a state of the union of what's going on in AI? We're 10 or 15% into the impacts of this. And you can see it. You can feel it. And some of it will happen, some of it

[01:36] will take longer, right? So, for example, hardware takes longer than software. So, the robots take longer than digital systems uh on traditional hardware, things like that. Um we've not um the next thing that's really interesting and terrifying also is recursive self-improvement. >> Mhm. It's not happening yet. And so, it's easy to convince yourself that you're going to have human agents uh sorry, computer agents that are human-like completely within a year or two. We don't have the science for that yet. People are working on it. I can describe how I think it'll play out. But we don't have it yet. What we do have is reasoning systems that are perfect partners for human beings for good and bad. Right? And that has a lot of

[02:22] implications. So if we stopped today, which we're not, and it's not stoppable or controllable by any government or any single individual or corporation, we would still have advanced humanity because of these reasoning agents. How fast do you imagine this is going to accelerate? Um the there's a thing which I call the San Francisco consensus. And the reason I call it is cuz everyone in San Francisco believes this. Uh everyone I know anyway, which is that it it it's easy to understand. Uh this is the year of agents, which we can discuss why agents will take over everything this year. Mhm. During this year, the scaling of the use of agents and reasoning will sort of grow at this enormous rate. Everybody's out of

[03:06] hardware, everyone's out of electricity. It's a real boom, right? It's like the biggest boom I've seen, and I've been through three or four of these in my career. Um the in this thinking, once you have recursive self-improvement, where the system can begin to improve itself, you have intelligence learning on its own. And it will in this argument, it will learn faster than we can cuz we're biologically limited. And the way this is expressed in San Francisco, and I'll give a simple example, you have a tech company with a thousand fantastic AI researchers. Um so one day they turn on AI research, that is an AI research agent. Well, how many AI research agents do you have? >> Mhm. Well, as many as you're limited by

[03:53] electricity, right? You don't have to feed them, they don't need housing, there's no more housing in San Francisco, you know, all that kind of stuff, right? You don't have those problems. You don't have an HR department for them, if you will. Uh and you don't have to pay them, you just have to feed them electricity. So, how many could you have? Well, maybe a maybe a million of these agents. Now, in AI, the way you determine you've made progress is you have clear metrics that the reasoning or testing or whatever the evaluation framework is better, right? So, that's what happens. So, in that scenario, the slope goes like this because you're already at this slope, then you add more people, then you get the agents and you go like this,

[04:36] and this is essentially um a superintelligence moment. The belief in San Francisco is this occurs within 2 to 3 years. The evidence in favor goes something like this. Um Claude code came out a couple months ago, the the latest one, Opus, whatever it is. 4.5, yes, thank you. And everyone I know in the Bay Area that's doing software says it was 80/20, now it's 20/80. The best analysis I can come up with is it's not the Claude code part, it's that the that the underlying LLM can produce more reasoning over time, better quality tokens over time. It's a deeper thinker, right? And all the labs now are competing for that. This is not just the size of the context window, it's actually the reasoning skill and the

[05:29] length of which it can think. It can just think longer and produce more stuff, right? I'm going to pause here for a bit, guys. I've been hearing tech people on podcasts saying that the recursive self-improvement is exponential. That's wrong. Recursive self-improvement is not exponential. Just because a system can improve itself doesn't mean that it does so by a constant factor each time. In fact, technology generally tends to show diminishing improvements as it matures. Yes, a better model can make improvements faster, but this ignores the current state of things that is being improved upon. And as it improves, it gets harder and harder to improve, which can cancel out the fact that the models are better and better at

[06:10] improving. >> I watched this stuff when I was I moved about the Bay Area when I was 21. And I was a programmer in high school way back when. And I was pretty good programmer. And I watch what it does and I go, my god, I'm over, you know. There's there's not a thing that I could do that it cannot do. So, when they wrote a C compiler in Rust, I got it's over, you know, like declare success. So, I think part of this part of this is because the people who are building it are also seeing the diminution of their own skill. They're being forced to go from programmers, which is what I'm very proud to have been, to being the the director of a programming system. Right? And the most likely scenario, by the

[06:58] way, there's a lot of implications for this. One is that it's always been true, speaking as your local arrogant programmer, that the very top programmers were worth 10 times more than the ones right below. There's something special about the mathematical reasoning skills of programmers. Those people will become more valuable, not less valuable, because these systems need to be controlled by humans at the moment. Those people will be capable of grasping the parallelization and activities of this. But I do have a proposal for universities, those of you who are associated with universities, you should stop everything else you're doing in the university right now and design a course for freshmen, men and

[07:36] women, uh uh who starting in September, which is a prompt engineering class. Why Why Why university? Why not high school? God, you're so aggressive, Peter. >> [laughter] >> All right, let's start with universities. You can improve my idea. I I thought 18-year-olds would be would be young enough. Maybe you think it's younger. Here's the most important thing. Spend Spend Spend a quarter or a semester, the first thing they learn in university is how to use these tools. Universities are completely opposed to my idea, as usual. >> Yeah. Because it violates every one of their tenets. But if you think about the student, and I mean every student, liberal arts, you know, math, whatever, they're going This platform will be the

[08:21] expression platform for their art, their music, their writing, and so forth. Why wouldn't you teach them immediately? Peter, improve my proposal. No, I I just I just feel like we're that AI is going to impact every student in high school today. Yeah. And that they're they're living an unnatural life by not engaging with it. And when they hit universities, for those that still exist, um Well, plus your kids are that age, and so they're literally right now >> Yeah. doing exactly what you're describing, so. People here who have teenagers, you know what I'm talking about, cuz they're all in it already. So So I think you're That's an improvement to my argument. There's a problem of age restriction.

[09:04] Uh you really have to think about vulnerable teenagers with this technology. Um I did [clears throat] some some analysis of where the real problems are with this stuff. Um A simple summary is that at some point there will be jobs impact from this stuff. We're seeing it in software, and we're seeing it in certain customer service industries, not across the board. At some point that will happen. That's an issue. Another one is how do we as a country maintain our moral values while we're also racing against China? Another one is the impact on um um uh young people. It is not okay for 13-year-olds to be committing suicide because of an LLM. It's just not okay. It should It's It's It needs to be addressed. It

[09:50] needs to be addressed right now. For sure. Uh and there's all sorts of other issues. The other one I came up with was in agent orchestration. Agents can be combined. I I've always been worried that when you put the agents together, especially if they're from non uh non-compatible vendors, you get unpredictable effects. >> Mhm. Yeah. So So these are problems to be solved. So we herald the future and we solve the problems that it brought. So we're we're going to talk about China and government and jobs. But before we do that, um I want to say I'm I'm in this savor-the-moment's kind of mode right now because I feel like the world to a year from today will be nothing like the world today. And all

[10:28] everything we're doing right now, I've enjoyed so much for so long. And I just want to savor the moment. But reminisce for 1 minute about the fact that while you were running Google, the Transformer got invented there, the TPU got invented there, Demis Hassabis solved protein folding, which is now universally used. You know, it does the work in I think it's does the work in an hour that used to take a PhD student 4 years 4 years. Yeah. It's like 300 million times more efficient. Um all of that and all the diaspora from that, all the people working in the field in San Francisco as you mentioned, they all were your people. Yeah. And so you were there at the creation of everything we're experiencing right now.

[11:10] Is there anything anything like that profoundly strikes you about that moment? I mean, did you even realize at the time that I think when you're making history, you typically don't know it. Um I give a lot of credit to Larry and Sergey. Because they were ahead of me. I'm I'm an operating CEO. And they pushed pushed and pushed for excellence. And I'll give you an example. In the early years of Google, Larry would uh my favorite interaction was one day I said we need to hire some people doing Java. And Larry and Sergey said, "This is the stupidest idea we have ever heard." And and I could never tell with them were they were being serious or not or they were just joking with me. But their argument was that real programmers were

[11:51] programming one level lower. Today Google has many thousands of everything. But they but they were so precise and so driven to excellence >> in technology. Yeah. >> Right? That I could not fool them. I couldn't market around them. I needed to have the technical expert. And they say, "Oh, that's boring. Don't do that. That's another one of your ideas." >> [laughter] >> Right? We want a new idea. Wow. And I give them a lot of credit for it. >> But what what about the TPU in particular? Like tell that to me cuz I I didn't even hear about it until much later. And it takes you years to to build design and build your own internal chips. And now it's it's about to explode. I don't know how much is public

[12:29] or not, but it's it's just >> the TPU version one was essentially a matrix multiplier of a particular kind. When they went to version two, they changed the algorithm in a complicated way. And it's particularly good for inference. Whether it's brilliance or just luck, those decisions made 10 years ago set up the TPU as the perfect inference engine. And for everybody's benefit, inference is what the reasoning steps that I'm describing on. So Google is particularly well positioned. As you know, Nvidia purchased Croc um for the reason of getting that in Yeah, trying to catch up to what you thought of 10 years ago. >> interesting about Nvidia, if you look at them, I was looking at the Ruben architecture.

[13:09] They managed to do Nvidia managed to do what Intel could never do. Intel could never get control of the complete server architecture. And they tried. Nvidia has managed to build real supercomputers that you can really buy with enough time and money and so forth. And it will really be delivered to your to your And and it just do the whole thing. These are major industrial achievements and that's why both companies will do incredibly well. Okay, in the following part Eric talks about robotics and how he fears that China might defeat the United States in the robotics race. Guys, one thing is pretty clear at this point. China is advancing much more rapidly on the hardware level compared to the United States. I mean,

[13:49] if you look at the electric vehicle production and advancement, they've outpaced the US with a huge margin. Guys, China is fundamentally a socialist country. Their long-term goal is to reduce labor for the benefit of the people. Now, if that actually materializes, which is of course a real question in itself, but at their core they believe that the productivity of the working class is a shared national good. So, I mean, it's really important for the United States to understand the dynamics of our competition with China. I I read an a Time op-ed piece you wrote last night. Um China can dominate the physical AI future or uh what did you Can you summarize that for us? It was an important conversation.

[14:30] >> in the geopolitical context, um they and I've said this many times, I'll say it again. The American competitor, not enemy, but competitor is China. I I think it's a By the way, I think it's an important distinction for you to make, so thank you. >> Not Not enemy, competitor. Um and how do understand them as a competitor? They have lots of money. They're very, very smart. Their work ethic is equal or stronger than ours and they dominate key industries. Right? I- I- With respect to robotics, we somehow decided it was okay for them to dominate the electric vehicle industry. This was an error. T- To be very clear, it's an error. Why do you not understand it's an error? Because we don't allow their cars in.

[15:17] And spend some time outside of this country in Chinese cars, trust me. They are real competitors, they've done a great job. As I understand it, China is capable of vertical integration and build these these gigafactories at a scale that we can't for all sorts of reasons. That's got to get addressed. Mhm. So, if you want to compete, and I want to compete and win with China, and the and the and I compete not enemy, I want us to have the same kind of system. So, in robotics, it turns out robotics, um you can understand robots as essentially actuators, these little stepper motors, book click click click click click, and a brain. Ignoring the appearance and the Google, you know, eyes and all that kind of stuff.

[15:58] It turns out that the industry of the electric vehicle produces the same kind of motors and the same kind of systems. They have an expertise that we don't. My own view is that at least for very low cost, China is going to win that. And that was what I was trying to say in that piece. Mhm. And I worry about that. Now, today these are not particularly useful, you know, they're they're fun toys, they're a replacement for the dog if you get mad at your dog. Sorry, I love I love dogs, but you get the idea. Um so so we we we need to address this. But at the moment, it sure looks to me like the robotic hardware of China is the winner at the low end. I'm not talking about the high end, I'm not talking about the expensive stuff,

[16:42] I'm not talking about industrial robots. And if you're confused, watch the Unitree um robot dance with the humans. Yeah. It came out about a month ago. >> Unitree here in the Tech Hub, and the co-founder will be on stage with us later today. Pay pay attention to them. They're they're very very impressive. Uh and I spent some time with them last time I was in China. Uh and they're one of many. And the way China works is that they have brutal competition. Um brutal brutal it's like unbelievable. I was talking to my friend, we teach at Stanford, and And said, you know, in China, we don't have the board dinner. We have a 2-hour meeting, we get back to work. And there's no preamble. We're not, you know, we don't say hello, how

[17:22] are you, how's the family, that kind of stuff. We're boom, boom. Right? Uh, it's just cultural. Yeah. >> And the workaholic the the the work ethic, the precision, and the scale that is possible in China is a real competitor. Um, I don't want to lose the robotic revolution, in my view, the way we lost the electric vehicle revolution, at least on >> the low end. Interesting. Cuz I had the Chinese model very much has a a well-built out supply chain with many vendors in the loop. But we you know, we've been on a worldwide tour of all of the humanoid robotics companies. And I just like coincidence, I guess. But when you look at the Gigafactory and look at Elon's vertical integration, and also Brett Adcock at Figure, same thing, it's

[18:03] all vertically integrated. Why? Well, cuz there's no vendor. >> Yeah. I have no choice. I have >> it appears that to get to again, we're in this is the abundance group. This is the abundance club. The way you get to abundance is you drive prices down and you get vertically integrated. And Elon in our country pioneered that, to his credit. >> Yeah. Right? You know, the the old joke about Google was we would build anything including the buildings. Well, Elon is actually doing that. Yeah. >> Right? You know, he's And why? Not because he's insane, but because he actually That's how you drive cost down. >> Yeah. Guys, the Chinese are very good at scaling at incredible rates. For example, they built the Tesla Shanghai

[18:43] Gigafactory in only 168 days. People point to Boston Dynamics as evidence that the US is winning, but they're missing the speed and scale. Boston Dynamics is basically one of two advanced humanoid robot companies based in the US. And they've been working on Atlas for almost 15 years. China has dozens and dozens of manufacturers with advanced humanoid robot solutions that have sprung up in only two to three years. Today, they're producing thousands of robots annually and deploying them to their megafactories. At this rate, the US is going to lose the race just like we did with drones and electric vehicles. All right, guys, that's it for today's video. I'll see you guys next week with another one.