Situational Awareness: The Decade Ahead

You can see the future first in San Francisco. Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there's a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.

The AGI race has begun. We are building machines that can think and reason. By 2025 and 2026, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be unleashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.

Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the willful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change. Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them.

A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride. Let me tell you what we see.

We have machines now that we can basically talk to like humans. It’s a remarkable testament to the human capacity to adjust that this seems normal, that we’ve become inured to the pace of progress. But it’s worth stepping back and looking at the progress of just the last few years.

GPT-2, circa 2019, was like a preschooler: “Wow, it can string together a few plausible sentences.” A very cherry-picked example of a semi-coherent story about unicorns in the Andes was incredibly impressive at the time. And yet GPT-2 could barely count to 5 without getting tripped up; when summarizing an article, it just barely outperformed selecting three random sentences from the article.

GPT-3, circa 2020, was like an elementary schooler: “Wow, with just some few-shot examples it can do some simple useful tasks.” It started being cohesive over even multiple paragraphs much more consistently, and could correct grammar and do some very basic arithmetic. For the first time, it was also commercially useful in a few narrow ways: for example, GPT-3 could generate simple copy for SEO and marketing.

GPT-4, circa 2023, was like a smart high schooler: “Wow, it can write pretty sophisticated code and iteratively debug, it can write intelligently and sophisticatedly about complicated subjects, it can reason through difficult high-school competition math, it’s beating the vast majority of high schoolers on whatever tests we can give it, etc.” From code to math to Fermi estimates, it can think and reason.

The pace of deep learning progress in the last decade has simply been extraordinary. A mere decade ago it was revolutionary for a deep learning system to identify simple images. Today, we keep trying to come up with novel, ever harder tests, and yet each new benchmark is quickly cracked. It used to take decades to crack widely-used benchmarks; now it feels like mere months. We’re literally running out of benchmarks. If there’s one lesson we’ve learned from the past decade of AI, it’s that you should never bet against deep learning.

How did this happen? The magic of deep learning is that it just works—and the trendlines have been astonishingly consistent, despite naysayers at every turn. With each order of magnitude of effective compute, models predictably, reliably get better. I make the following claim: it is strikingly plausible that by 2027, models will be able to do the work of an AI researcher or engineer. That doesn’t require believing in science fiction; it just requires believing in straight lines on a graph.

The upshot is pretty simple. GPT-2 to GPT-4—from models that were impressive for sometimes managing to string together a few coherent sentences, to models that ace high-school exams—was not a one-time gain. We are racing through the orders of magnitude extremely rapidly. Moreover, and critically, that doesn’t just mean a better chatbot; picking the many obvious low-hanging fruit on “unhobbling” gains should take us from chatbots to agents, from a tool to something that looks more like drop-in remote worker replacements.

Another jump like that very well could take us to AGI, to models as smart as PhDs or experts that can work beside us as coworkers. Perhaps most importantly, if these AI systems could automate AI research itself, that would set in motion intense feedback loops. Even now, barely anyone is pricing all this in. But situational awareness on AI isn’t actually that hard, once you step back and look at the trends. If you keep being surprised by AI capabilities, just start counting the orders of magnitude.