The 801st Lifetime

What Alvin Toffler saw coming, what he got wrong, and what it feels like to work from inside the acceleration.

A few weeks ago I moved all my work over to the newest AI model. I learned where it was sharp and where it would state something false without hesitation, rebuilt the instructions I feed it around those habits, and got comfortable. Yesterday the company that makes it shipped a better one. Six weeks had passed since the version I was still learning. I had not finished learning it.

The models are Anthropic’s, the family called Claude, and lately the release schedule has been doing something that gives me a small case of vertigo. Version 4.5 arrived in late November. Then 4.6 in early February. Then 4.7 in the middle of April. Then 4.8 this week. The spaces between them keep narrowing, from roughly seventy days down to forty-two, and a larger model sitting above the whole line, one that has been running quietly inside a few dozen companies for a couple of months, is said to be weeks away from reaching the rest of us. Every release is better at the work I hand it. Every release makes the version I just figured out a little obsolete.

There is a name for the feeling, and it turned fifty-six this year.

In 1970 a writer named Alvin Toffler published a book called Future Shock. He defined the title phrase plainly, as the distress people feel when they are subjected to too much change in too short a time. The danger he was pointing at was never a particular invention. It was the rate. Toffler argued that the speed of change had crossed a threshold where human beings could no longer process it, and that the result would be a population that felt permanently disoriented, off balance, unable to find footing because the ground kept moving under them.

He had a device for making the speed legible. Picture the last fifty thousand years of human existence divided into lifetimes of about sixty-two years each. You get roughly eight hundred of them. Only in the last six did large numbers of people ever see a printed word. Almost everything in the room you are sitting in was invented in the most recent one, the eight hundredth. Toffler wrote that in 1970. Someone made the point recently that we are now living at the start of the eight hundred and first.

Toffler is also the man who took the phrase information overload and put it into ordinary speech. He predicted that people drowning in input would start making worse decisions, would retreat into rigid routines, would cling harder to old certainties, and would grow suspicious of the experts who kept assuring them the future was under control. Read that sentence again and notice how little of it needs updating.

Toffler got the diagnosis right. He got the outcome wrong, and the way he got it wrong is the part worth sitting with. He thought the overload would break us. He used the phrase adaptational breakdown and meant it close to literally, a society of people made sick by the pace, unable to cope. That did not happen, or it has not happened yet in the way he imagined. The rate of change kept climbing and most people are, by the ordinary measures, fine. They adapted. They always do.

What happened instead was quieter and in some ways harder to live with. The pace did not break everyone evenly. It sorted them. Some people and some companies turned out to be very good at absorbing change quickly, throwing out what they learned last month and relearning, and those people pulled ahead at a speed that would have looked like cheating a decade ago. Everyone else kept running and stayed in roughly the same place. The shock did not produce collapse. It produced a widening gap between the fast and the rest, and the gap is the thing that destabilizes a life, because you can feel yourself falling behind even while you are working harder than you ever have.

We have started calling the present moment the singularity, a word that used to belong to physics and science fiction and now turns up on earnings calls. The original idea was a point where machine intelligence starts improving itself faster than we can follow, after which prediction stops working. Sam Altman, who runs OpenAI, wrote last summer that we are already past the event horizon, that the takeoff has started, and that so far it has felt less strange than people feared. Ray Kurzweil, who has been making this forecast since the late 1990s (really the 80s) and has been right more often than he has any business being, still puts human-level machine intelligence around 2029 and the full merger of people and machines around 2045.

Then there are the people who think this is mostly a story we are telling ourselves. Yann LeCun, one of the researchers who built the foundations of modern AI, calls the timeline overhyped and argues that the systems everyone is marveling at do not reason in any deep way. Gary Marcus, a cognitive scientist who has spent years as the loyal opposition, likes to point out that these models can recite the rules of chess flawlessly and then, a few moves later, slide a piece across the board in a way the rules forbid, because they never built a real model of the game. They match patterns. Sometimes the pattern breaks.

Both camps are serious, and both are partly right, which is the only honest place to stand. The singularity as a felt experience is here. Anyone trying to stay current with what these tools can do has been living inside Toffler’s too much change in too short a time for a year or more. The singularity as a technical event, machines bootstrapping themselves to something past us, remains a forecast. Smart people are betting careers and billions on it, smart people are betting against it, and the predictions that were supposed to land by now have a habit of sliding a year into the future every time the calendar catches up to them.

The next step in the story, the one I think is still genuinely far off, is wiring the machine straight into the person. Brain-computer interfaces. The progress is real and worth respecting. Neuralink has put its implant into a couple dozen people who could not move, and they are steering cursors, typing, playing chess, in one case operating a robotic arm well enough to feed themselves. A company called Synchron threads its device in through a blood vessel in the neck so it can skip open-brain surgery entirely, trading resolution for a path that might actually reach a lot of patients. For someone living with paralysis, this is close to a miracle and it is arriving now.

The dream people reach for when they hear about it, plugging a healthy brain into the network and thinking faster, sits on a different timeline. Nobody has done it. The technologists who talk about it openly put it in the 2030s and 2040s, and they tie it to hardware that does not exist yet. So when I say the next move is augmenting our own minds directly, I mean it the way you mean a city on Mars. Plausible, underway in its earliest form, and further off than the excitement around it suggests.

I want to bring this down to the work I do, which is putting small amounts of money into very young companies and trying to guess which ones will matter. The acceleration is not abstract there. It is the whole texture of the job now.

A few years ago a software company with ten or twenty people and ten or twenty million dollars in annual revenue was a serious accomplishment that took years to build. Garry Tan, who runs the startup program Y Combinator, said last year that he is now watching teams that size reach those numbers in well under two years, and that his most recent groups of companies have been growing about ten percent a week across the entire batch, something he had never seen in his career. A coding tool called Cursor reached half a billion dollars in annual revenue with fewer than fifty employees. One of the better-known image companies runs on a team you could seat around a dinner table. Dario Amodei, who runs Anthropic, was asked when we would see the first company worth a billion dollars run by a single person, and he answered 2026 (one could argue this already happened with OpenClaw).

So here is the comfortable version of the thesis, the one that feels good if you are on the right side of it. The founders and the investors who win from here are the ones who metabolize change the fastest. They adopt the new tool the week it ships, rebuild around it, and move before the advantage evaporates. Speed of adaptation decides everything.

I believe a version of that. I also think it becomes a trap the moment you stop there, because the same speed that mints the winners is quietly dissolving the instruments we used to tell winners apart. For my whole VC career, reading a young company meant leaning on a few reliable signals. How many people did it take to build this. How much money are they burning. How fast is revenue growing and how steady is it. Those numbers meant something because they were expensive to fake and slow to move. Now a tiny team with the right tools can produce numbers that used to require a real organization, which means a chart that would have made me reach for a checkbook in 2018 tells me almost nothing on its own today. Some of that revenue is durable. Some of it is people trying the shiny new thing for a month and leaving. On a spreadsheet the two look identical.

And the failure mode of pure speed is moving fast in the wrong direction while certain you are right. I have watched founders use these tools to manufacture a year of apparent traction in a few months, and in more than one case the corner they cut to get there became the thing that sank them. A regulator’s letter. A customer who turned out not to be real. A number that did not survive contact with diligence. Adapting quickly to the new pace is necessary. It is not enough. The thing it cannot replace is judgment, which moves at its own stubborn speed and refuses to compress.

Which brings me to a scene from this week that I keep turning over. I have spent years building a model that reads thousands of past investments and tries to predict which new companies will succeed. A few days ago I pointed it at two hundred startups from a single Y Combinator batch and let it score all of them in one pass, work that would once have eaten a whole venture team’s week. Then I sat down and started going through the two hundred by hand, one at a time, checking what the machine concluded, hunting for the place where its tidy pattern slid a piece across the board in a way the rules forbid.

That is the whole thing, right there. The machine gives me reach I never had. I give it the one thing it does not have, which is the willingness to be slow on purpose about the decisions that matter. At the end of his alarming book, Toffler landed somewhere I did not expect. He told readers to build what he called stability zones, places and habits that hold still so the rest of life can move fast around them. I used to read that as nostalgia. I think now it was the most practical advice in the book. You survive too much change in too short a time by choosing, deliberately, the few places where you hold still.

We are at the start of the eight hundred and first lifetime. It is moving faster than the eight hundred before it put together. The people who do well will not be the ones who feel calm about that, because nobody sane feels calm about it. They will be the ones who learn to move fast on the tools and slow on the judgment, and who can tell, in the moment, which is which.