This is a genuinely insane world. What’s even more remarkable is how quickly we’ve adapted to it. OpenAI president Greg Brockman pointed out that GPT-4 finished training four years ago today. It wasn’t released until March 14, 2023. OpenAI spent seven months evaluating it, red-teaming it, and tuning it for safety. So when GPT-4 blew everyone away, the model itself was already more than six months old. By the time the public got access to GPT-4, the leading AI labs were already at least half a year beyond what the rest of us could see. The model felt like the future to us. Inside the lab, it was already yesterday’s work. Chinese models are now closing the gap fast on public benchmarks. Kimi, Qwen, and DeepSeek are competing near the top, and the gap between them and the leading US models looks much smaller than it did a few years ago. But public benchmarks only compare models that have already shipped. They don’t tell us how far ahead the labs are behind closed doors. If there is still a six-to-seven-month gap between training and release, the race we’re watching may look very different from the race unfolding inside the labs. And six months means far more now than it did in 2022. There is more compute. The algorithms are better. Synthetic data, reinforcement learning, inference-time compute, and AI-assisted research are all speeding up the development cycle. Sam Altman has also said he isn’t particularly concerned about distillation, which suggests that OpenAI is confident in what it has coming next. Of course, no one outside these labs knows how large the gap really is. But GPT-4 showed that the gap can be real. That also changes how we should think about unreleased research systems like Astra. The ten hard problems Astra revealed on August 1 were solved by a research system, not a polished consumer product. The system hadn’t even reached the public frontier yet. If public releases still trail internal capabilities by several months, the results we’re seeing in papers and demos may already be well behind the real state of the art. GPT-4 finished training only four years ago. At the time, having a conversation with it felt unreal. Today, it feels painfully slow and limited. It’s now something we use as a baseline when comparing the cost and performance of newer models. In those four years, AI has learned to write code, use tools, browse the web, and carry out tasks that take hours. Now it is starting to make headway on math and science problems that have resisted human efforts for decades. AI is no longer something only researchers talk about. It comes up at work, at school, in boardrooms, in politics, and around the dinner table. All of that happened in four years. The speed of human adaptation may be almost as remarkable as the speed of AI progress. A capability feels mind-blowing when it first appears. A few months later, it feels normal. A few months after that, it feels slow and outdated. The danger may be that we’ve become numb to the pace. In a linear world, you can look at the last four years and make a reasonable guess about the next four. That doesn’t work when progress itself is accelerating. If we use the last four years as our yardstick, we will keep underestimating what comes next. It took only four years for GPT-4 to go from science fiction to legacy software. We shouldn’t picture the future moving at today’s speed. We should expect the pace itself to keep picking up. And exponential change is hard to grasp from the sidelines. Sometimes you have to jump in before you understand how quickly the ground is shifting. How long will it take before today’s frontier models feel just as outdated? We’re already living through changes that would have sounded too far-fetched for science fiction not long ago. That may be why even people at the heart of Big Tech and frontier AI labs are leaving secure positions to build what they believe comes next. Even Jeff Dean, after 27 years at Google, has decided to take on something new. People at that level are walking away from comfortable, established careers to bet on the next chapter. Maybe this is a moment to spend less time bracing for change and more time thinking ahead, taking risks, and building. Every major technological shift has given people plenty of reasons to be pessimistic. But the people who moved the world forward were usually the ones who saw possibility beyond the immediate risks and got to work. Pessimism has always sounded smart. Over the long run, optimism has had the better track record. If we’re living through a future stranger than fiction, I’d rather help shape it than watch from the sidelines.