Transcription
Because remember, the computers are now doing self-improvement. They're learning how to plan, and they don't have to listen to us anymore. People do not understand what happens when you have intelligence at this level, which is largely free.
So that was the former Google CEO, Dr. Eric Schmidt, at the Special Competitive Studies Project, having a conversation with Gene Masserv about the future of AI and biotechnology. And honestly, it's a really insightful conversation. Let's actively dive into exactly what he says, because the implications are profound.
Because remember, the computers are now doing self-improvement. They're learning how to plan, and they don't have to listen to us anymore. We call that super intelligence or ASI, artificial super intelligence. And this is the theory that there will be computers that are smarter than the sum of humans. The San Francisco convent consensus is this occurs within six years, just based on scaling.
Now, in order to pull this off, you have to have an enormous amount of power. I was here yesterday testifying about this, you know, and we need—like I can talk at some length about how many gigawatts and how many nuclear power plants and all that—we can talk about separately. This path is not understood in our society. There's no language for what happens with the arrival of this. I wrote a book on this with Henry Kissinger called Genesis, which, you know, I recommend obviously, um, because I wrote it. Available, available, available in your usual places. Um, but the important point is this is happening faster than our human—than our society, our democracy, our laws—will address, and there's lots of implications. That's why it's underhyped. People do not understand what happens when you have intelligence at this level, which is largely free.
So we believe, as an industry, that in the next 1 year—this speaker just dropped a bombshell—AI isn't just learning fast. It's evolving beyond human control. They're talking about artificial super intelligence. Machines planning without us, possibly outsmarting all of humanity in just 6 years. And the scariest part, society isn't ready. No laws, no road map, just blind acceleration. That's not hype. That's warning sirens. The vast majority of programmers will be replaced by AI programmers.
We also believe that within one year, you will have graduate-level mathematicians that are at the tippy top of graduate math programs. There's lots of reasons to think this is going to happen. This is the consensus. You go, "Okay, well, that's pretty interesting. Now, I can't do that kind of math. Very few people can do that math. How can the computer do that math better than anybody else?"
To some degree, it's because math has a simpler language than human language. So, the way these algorithms actually work is they're doing essentially word prediction. So, you take—you take a—a sentence, you take a word out, and then it learns how to put the correct word back in. This is called the loss function, and it's optimized to do that at a scale that's unimaginable to us as humans. So you do the same thing for math, but there you use a conjecture and then a proof format through a protocol called lean.
In programming, it's pretty simple. You just keep writing code until you pass the programming test. So, strangely, the first question I always ask programmers is what language do you program in? And the correct answer is it doesn't matter because you're trying to design for an outcome. You don't care what code is generated by the computer. This is a whole new world. Yes, a whole new world. And it's not just Eric Schmidt saying this. Take a look at what Dario Amod said recently in a panel interview about this.
But now getting to kind of the job side of this. Um, I—I—I do have a fair amount of concern about this. Um, on one hand, I think comparative advantage is a very powerful tool. If I look at coding—programming, which is one area where AI is making the most progress—um, what we are finding is we are not far from a world—I think we'll be there in 3 to 6 months—where AI is writing 90% of the code, and then in 12 months we may be in a world where AI is writing essentially all of the code.
Here's where we get into something even more interesting. So basically, Eric Schmidt talks about the fact that AI is now writing all of the code. But what happens after that in year two? Take a look. So that's one year. Okay. What happens in two years? Well, I've just told you about reasoning, and I've told you about programming, and I've told you about math. Programming plus math are the basis of sort of our whole digital world. So the evidence and the claims from the research groups in OpenAI and and Anthropic and so forth is that they're now somewhere around 10 or 20% of the code that they're developing in their research programs is being generated by the computer. That's called recursive self-improvement, is the technical term. So what happens when this thing starts to scale? Well, a lot.
One way to say this is that within three to five years, we'll have what is called general intelligence, AGI, which can be defined as a system that is as smart as the smartest mathematician, physicist, you know, artist, writer, thinker, politician, maybe not in the same level, um, but you get the idea. Uh, just the creative industries and so forth. But imagine that in one computer. Okay. Well, that's pretty interesting. I call this, by the way, the San Francisco consensus because everyone who believes this is in San Francisco. It may be the water. What happens when every single one of us has the equivalent of the smartest human on every problem in our pocket? So, it means you have to best architect when you have an architecture problem.
Another thing that's going on is the development of agentic solutions, and agents are referred to systems that have input and output in memory, and they learn. An example here is that I want to uh buy another house. Uh, I happen to like Virginia. I grew up in Virginia. I say, "Find me a house in the greater MLAN area." Look at the—that's one agent. Look at all the rules. Figure out how big a house I can build. That's another agent. Do the transaction to buy the land. That's another agent. Design the house with a human architect, right? But sort of ignore them for most of the thing, but they have to sign it off, and then I approve it, and then find the contractor, right? Hire the contractor, pay the bills, and at the end sue the contractor for lack of performance. Okay? Now, I just gave you the stupidest possible explanation. I just described every business process, every government process, and every—and every sort of academic process in our nation. So, it isn't just the programmers that are going to be out of work. We're all going to be out of work. No, that's not a consequence. I'll come to that. But, but the reason I want to—I want to make the point here is that in the next year or two, this foundation is being locked in, and it's not—we're not going to stop it.
So, what he's basically saying is we're already seeing AI write its own code, and that's just the start. In a few years, we could all have something like a genius-level assistant in our pocket. Not just programmers, every industry could be transformed. It's wild to think we're laying that groundwork right now. And by the way, on the jobs thing, everyone assumes that automation will replace—will eliminate jobs. If you look at the history of automation ever since the—the looms and uh in uh 300 years ago, the jobs are changed, but more jobs are created than destroyed. In this case, you'd have to convince me that this time is different.
If you look in Asia, where they—for whatever reason—are choosing not to have children, the Asian reproduction rate is in the order of 1.0 or lower. So they're rapidly disappearing. So the Asian countries are very, very quickly automating. The tools that I'm describing will allow the few humans that will be working very hard in 30 or 40 years—if these trends continue—the rest of us will be dependent on those hardworking humans. It'll make their productivity more—much greater. Eric Schmidt challenges the fear that AI will destroy jobs, pointing out that automation has historically created more work, not less. But with Asia's declining birth rates and rapid automation, he's hinting at a future where fewer people carry the load for many. The real question is, will this time really be different, or are we repeating history?
In China, the deepsea seek moment is equivalent to our ChatGPT moment. I was there with Henry. Um, and this is what happens when you're talking to—to the Chinese about AI with Henry. And this means we are alive and we're listening to you. Thank you very much. Right? That's not what they're doing anymore. When the—when DeepSeek showed up and our stock market lost a trillion dollars in one day, all of a sudden they began to understand the scale of what it was. So now there is a massive program in China to accelerate these things. I had thought Illy and I and some of the other people in this room worked really hard on these um chip controls, and the chip controls have been um, in my view, largely effective. How did China get around them? Well, some of it was straightforward theft and evasion of the tariffs, but they also—they're sufficiently smart. They created new algorithms that use different kinds of computing to move forward because they—because China operates in open source. That is, they—they released the software to everyone. There are two things that happen. We—we Americans immediately saw their idea and incorporated it in our own. So, thank you very much, China. You invented something new. We immediately incorporated it. But second, because it's free, the proliferation issues around Chinese models have now become a very big deal. And our government is trying to figure out—without success so far—how to handle this question. It's a very tricky question. But—but think about it. We're having this whole debate in our nation about what to do about Iran's nuclear program. And I'm not an expert in that, but these are the kind of conversations that happen here in—in DC. So when we get to the point where China is n months ahead, are we willing to bomb their data centers?
My favorite example here is I was in—I've been working on this. I was talking to somebody—said, "The answer is obvious." I said, "What? The good lady and the bad guy. We agree to a treaty where each of us puts dynamite on each other's uh electricity supply. You get to blow up my electricity if you get mad, and I get to blow up your electricity if I get—You get the idea." Eric Schmidt just laid it out bluntly. China isn't just catching up in AI, they're sprinting ahead. From algorithm innovation to dodging chip sanctions, they're playing smart and fast. The scary part, the US might not be ready. This clip isn't just about tech. It's about global power shifting, and Washington knows it.
I'm the primary funder of a particular group that has built a model. It first learned how to do chemistry, and uh it was trained as a foundational model for chemistry, and it's attached to a robotic lab, and what this model does is it generates hypothesis for drugs of one kind or another, and it just generates them. God knows if they're right, and then overnight the robotic lab tests them and gives a report overnight, and then it starts again, and the reason I'm mentioning this is this is the future model of the fusion of AI and bio—right—the AI system generates all sorts of candidates to reduce the—um—essentially the—um—search space. If you think about it algorithmically, it's an exponential with too many degrees of exponential. So you have to come up with some way of reducing the space. So this particular group is using AI to reduce the space, run the things and so forth. Their objective—we'll see if they pull it off—This is a research project—is to identify all human druggable targets within the next two years. If that occurs, then that information goes straight into the drug industry. Now, it's a different way of thinking, and it's profound in that it gives them the targets they need to go build drugs against. That's interesting to me. It's the combination of AI and a robotic lab that does something in a wet lab essentially.
So, one model that you should think about is wet labs will be roboticized. And the wet labs will have AR—they're essentially—they're not humanoid robots, they're arm robots, and they go boom boom boom. They—and they do the pipetting and so forth and so on, and they do it 24 hours a day. That's a major change in the way bio—bio—the biotech industry works. Do you think there's implications for ASI via drug discovery for like curing cancer and or personalized medicine? Just something—um—yes, because under the—under the assumptions of super intelligence, these are systems that see things that we don't see. And so the assumption is that ASI, for example, could understand biological and cellular mechanisms that you are an expert in and I'm not—at a level that humans will not. So that's why this is such a big deal. We've always assumed that humans would know—there would be at least one human, right? We call these people polymaths that would understand these things. We're going to end up in a world—maybe 10 years from now—where we won't actually understand why. But you, as our scientist, will say I use it every day. When I—when I was at college, I was studying quantum physics, and my friend who was a graduate student who was much better than I—and I said, "Is this stuff actually true?" You know, it's like too weird to be true, and he said, "Yes, we use it every day." And I imagine in 10 years some young student will come up to you and say, "Is this stuff true?" And you'll say, "Frankly, I use it every day. No human understands it." What an interesting situation for you, as now a senior researcher 10 years from now, to have to deal with. This is insane. Eric Schmidt is describing a future where AI doesn't just assist in drug discovery. It leads it. Imagine robotic labs working 24/7. AI narrowing down complex chemical possibilities. And humans trusting cures we don't fully understand. We're not just speeding up science. We're handing over the steering wheel. Is that genius or dangerous?
I think we are very close. I would say—um, you know, we're a couple of years away from having the first AI design—truly AI-designed drugs—um, for major—for a major disease—cardiovascular—cancer. We're working on all of those things isomorphically, and—and then obviously there's still the clinical trials, and that stuff has to happen, and right now that would be the bottleneck, but I think certainly getting it into the clinic—the discovery phase—I would like to, you know, shrink that from years to months—maybe even weeks at some point—so I think in a couple of years we—you know, I would be disappointed if we don't have some uh great candidates for drugs for very important diseases—u—you know—starting to go through clinical trials. If AI pulls this off, it won't just speed up medicine, it'll redefine the future of human health. The age of algorithm-driven cures is closer than we