Ex-Google CEO's Terrifying AI Prediction: It's Time to PULL the PLUG!

And that's a dangerous point. When the system can self improve, we need to seriously think about unplugging it.

So that was Eric Schmidt, the former CEO of Google, actually talking about how you might have to unplug AI very soon. Now this was something that I found to be rather interesting as it was only a couple of days ago we got this research paper. Frontier AI systems have surpassed the self replicating red line. And this was essentially a paper in which they discussed the fact that AI systems were able to replicate themselves with without any human help. And self replication basically means that an AI system could create a functional copy of itself capable of running independently. And it sounds like it's out of a sci fi movie, but of course this would be a very big deal. Now from this paper I'm not going to speak about it too much, but essentially in 50% and in 90% of experimental trials they succeeded in creating a live and separate copy of itself respectively. Now, now currently this isn't too big of an issue because these systems don't have that much capabilities. But many people are thinking about the future in which this could be a very, very dire scenario if they are more agentic and if they have more abilities. So that is essentially the gist of what he's saying. Now we'll take a look further at the extended statement, but it is definitely a question that needs to be answered by some of the frontier AI labs.

Soon going to be able to have computers running on their own, deciding what they want to do. And the way that happens is it's a series of decisions. We go from agents to then sort of go more powerful goals and eventually you say to the computer, learn everything and do everything. And that's a dangerous point. When the system can self improve, we need to seriously think about unplugging it. But wouldn't that that kind of system have the ability to counter our efforts to unplug it? Well in theory we better have somebody with the hand on the plug, metaphorically. But the important thing is that the power of this intelligence, the ability for this kind of new intelligence, means that each and every person is going to have the equivalent of a polymath in their pocket. In addition to your show and all your notes and writers, you're going to have an Einstein and a Leonardo da Vinci to give you advice on your show. That will be true for everyone on the planet. We just don't know what it means to give that kind of power to every individual.

Now whilst his full statement is quite ridding, when we start to discuss the future implications of future AI systems. And he actually made a second statement which I think is a bit more thought provoking in terms of the overall implications of that statement. Now this statement I'm about to cover is essentially one where Eric Schmidt refers to automating AI research. Now if you're not familiar with this concept, this is essentially the grand goal of any AI lab at the moment. And I say that because once they can automate AI research, it essentially kicks off this crazy loop where you essentially have AI systems that can improve themselves over a long chain. And what I mean by that is that if AI systems can do AI research, then we can use that AI research to improve the model. And thus the next iteration of AI research gets faster, more efficient and of course smarter, which leads to this recursive self improvement. Maybe not in a singular system, but overall these AI companies are going to be moving at light speed.

I've done this for 50 years. I've never seen innovation at this scale. This literally remarkable human achievement of intelligence and the things that we can do and the advances in science and on and on. There's a point at which maybe in the next year or two where the systems can begin to do their own research. They're called AI scientists as opposed to human scientists. So you go from having a thousand human scientists to a million AI scientists. I think that increases the slope. When you're moving at this space, it's very hard for your competitors to catch up. That's the race. It is crucial that America wins this race.

Now, is what Eric Schmidt just referred to there actually feasible? Is it realistic? Is there any research that backs up his claims? Well, currently there actually is. We can see recently from Sakana AI they published something in August of this year where they spoke about the AI scientist towards fully automated open ended scientific discovery. And this was basically an agentic framework which is built to automate the entire scientific research progress. And this is heralded as the world's first comprehensible AI of conducting independent scientific discovery for from start to finish. Now this one is one that is about scientific discovery and I will come back to that. But what about AI research? Now this one wasn't covered that much, but NEO was an actual new automated AI researcher that was able to automate AI research. Now this was the first AI engineer specifically designed for machine learning tasks. And this operates on the entire machine learning pipeline, outperforming even OpenAI in certain tasks. And this system represents a significant step towards artificial superintelligence by automating. AI research. Now, this system that is currently being developed automates complex machine learning workflows such as data collection, pre processing, model selection, fine tuning, evaluation and deployment. And this uses multi step reasoning to evaluate multiple solutions and implement the best approach. Now, this optimizes performance by analyzing throughput and latency across many different frameworks like TensorRT and GPUs. And this is something that has been demonstrated to be remarkably effective in a variety of different scenarios. So this is one of the first systems that we are getting that actually manages to automate AI research. And this is what these companies are looking to build. And internally I do know that OpenAI has their own tool like this, which they refer to as the AI Scientist, something along those lines. It's very, very vague. It's very hard to find a lot of information on it. But I do know that these companies are certainly working on this. So it's not that crazy to think that in a few years this kind of system, this kind of software is going to be so much better, it's going to be so much more effective and it's going to be able to allow us to not only run AI research in an agentic way that allows us to speed up our research process and is something that is completely incredible. Now, you have to think about this, okay, if this is true, if this is the case, what happens when we manage to automate AI research? Now, do you remember the document called the Decade Ahead? This was from a OpenAI employee who used to work there, but, you know, unfortunately got fired due to various different reasons. There's a lot of speculation going on, but his document was one of the most insightful documents on the decade ahead in terms of where AI research is going to go. So one of the things that he actually said was that we don't need to automate everything, just AI research. It says a common objection to transformative impacts of AGI, that it will be hard for AI to do everything. Look at robotics for instance. Doubters say that it's going to be a really hard problem. Even if AI gets really smart, some people say that robotics is still going to be hard. Or even if we manage to automate biology, it's going to actually take a lot of physical lab work and human experiments. But the crazy thing about this is that we don't need robotics, we don't need many things for AI to automate AI research. The jobs of AI research and engineers at leading labs can be done fully virtual and don't run into real world bottlenecks in the same way. And the job of an AI researcher is fairly straightforward in the grand scheme of things. You need to read the machine learning literature, come up with new questions or ideas, implement the experiments to test those ideas, interpret those results, and then, of course, repeat. And this seems squarely all in the domain where the extrapolations of current AI capabilities could take us beyond the best humans by the end of 2027. Now, the reason that this is so crazy is because this just kicks off an extraordinary feedback loop. And the reason that this is so crazy is because if we actually look back at some of the biggest machine learning breakthroughs from the last decade, most of them have just been, you know, oh, let's just add some normalization or let's just fix implementation bug. And all of this kind of AI research can be automated. And once we do unlock that, it's like this thing manages to speed up tenfold. And in this document, he actually talks about how, you know, we can expect 100 million automated researchers working at 100 times human speed not long after we begin to be able to automate AI research. They'll be able to do a year's worth of work in just a few days. And the increase and the increase in research effort compared to a few hundred puny human researchers. And at a leading AI lab today, working at puny one times human speed will be extraordinary. Basically saying that, look, once we figure out the exact system that we're able to use to actually get breakthroughs, all we'll have to do is scale that system across many different clusters. And then we're going to be getting a ridiculous level of AI research that we're going to use to improve these systems. And we're going to have a percentousand, maybe even 100,000 researchers that are going to be operating 24,7. And if you guys don't think that this transformative change is going to happen, let's actually take a look back at history. Remember the printing press? Before the printing press, books were painstakingly copied by hand. And after Gutenberg's invention, books could be mass produced in a fraction of the time, democratizing knowledge and accelerating scientific progress. We also had the Industrial Revolution. Machines replaced manual labor, vastly increasing manufacturing output. And for instance, cotton gin reduced the time needed to separate cotton fibers, increasing productivity by over 50 times. We also have the Internet and digital communications. Before the Internet, communication was limited to physical meal phone calls or in person meetings. Email and online systems speed up global communication from days to mere seconds, transforming collaboration, research, and business. So overall, when we take a look at these systems that are going to be basically automated AI researchers. We have to understand that this is going to be an incredible rate of progress that happens in the AI world. Once we're able to use agents effectively to perform long term tasks, things are going to start changing rapidly on a scale that you've never possibly could even imagine. When we also do have AI scientists that are able to automate AI discoveries in terms of being able to automate many different things across many different industries, there's going to be an incredible rate of scientific discovery across the board. These AI systems are going to improve upon their plans. They're going to be able to consistently reason about different things in ways that humans just can't think about, and they're going to be able to test these a lot quicker than humans can. Of course, there are going to be some physical limitations, but the rate of discovery should be a lot larger than it is right now, which is going to lead to transformational impacts across society. Overall, I think that the decade ahead is going to be one that is really crazy, but that once you do get that automating AI research, that is going to be where things go. Incredible. Let me know what you think about Eric Schmidt's comments about shutting down AI and of course, the future of AI.