Situational Awareness: The Decade Ahead to AGI by Leopold Aschenbrenner

Leopold Aschenbrenner wrote a long essay called "Situational Awareness" in 2024. His main idea is simple. AI is getting better very fast. If you draw that speed on a chart and follow the line, AI reaches human-level smarts (people call this AGI) around 2027. And soon after that, something much bigger happens. I read it back then. Now it is 2026, so I want to look again and say what he got right and what he got wrong.

What he gets right

The best part of the essay is that he uses numbers, not feelings. He says AI is growing for three reasons. First, we give it more computer power. Second, the training methods get smarter each year, so we get more from the same power. Third, we add tools and memory around the model, so it can plan and do real tasks, not just chat. All three grow at the same time. When three things grow together, the total grows very fast.

I like this because it is clear. If you want to say he is wrong, you have to point to one of these three and explain why it will stop. Most people who say "AI is just hype" never get that clear. He does. And the third point, adding tools around the model, turned out to be very true. A lot of the progress since 2024 came from that, not from bigger models.

What he gets wrong

Here is my main problem. His first step is careful and honest: AI will soon work like a smart remote worker. But then he takes a big jump. He says this AI worker will make even better AI by itself, faster and faster, until it becomes super smart. He acts like this second step is as solid as the first. It is not.

More smart workers do not mean instant progress. Research also needs real experiments, real machines, real time, and real electricity. You cannot think your way past these limits. The essay even shows this itself. It says the biggest problem soon is not chips but power, because these AI data centers need huge amounts of electricity, like a small city. But if power is the wall, then AI has to wait in line too. You cannot build power plants overnight. So the same limit that makes his warning real is the limit that slows down his scary "AI explosion" story.

The last part of the essay turns into politics. He says the US government must take control of AI labs, keep the secrets safe like a weapon, and treat it like a race against China. Some of this is fair. AI labs really did have weak security. But "the government should run all of this" is his opinion, not a fact. He writes it like it must happen. That is the part I trust the least.

How it looks in 2026

The score is mixed, and that is already better than most guesses. He was right that AI keeps getting better, that it moved from chatbot to real worker, and that power became the big problem. People who bet AI would hit a wall look wrong. Good for him.

But the exact "AGI by 2027" claim looks shaky. As we got close, people started calling every new model "almost AGI," which is an easy way to never be wrong. The big AI explosion has not come. The real-world limits, like power, hit harder and sooner than he said. So the careful half of the book is holding up. The scary half is still just a story.

What this means for you

You do not need to prepare for one exact doom day. Just learn one habit from this book: do not plan around what AI cannot do today, because that changes fast. People who built their work around "AI will never do X" got hurt when AI started doing X.

So the safe skill is not typing clever prompts. It is being the person who takes a smart but clueless AI and gives it what it needs: your company knowledge, your rules, and your judgment about what it should and should not touch. A model can be smart and still useless until someone teaches it the job, the same way a new human needs a few weeks to learn a team. Be that person. Keep the AI behind a simple switch you control, so you can swap it easily. And keep your own data and secrets safe, because if even part of this book is right, they matter.

Should you read it?

Yes. It is short and better than its reputation. The number part alone will make you think about AI in a clearer way, even if you do not agree with the ending. Just read it as two things in one. The first half is good, careful analysis, and it mostly held up. The second half is an exciting movie about robots and governments, and it borrows trust from the first half without earning its own. The skill is knowing which half you are reading, and not letting the good math make you believe the big story.

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