One Useful Thing

I generally think I have done a good job anticipating the direction and pace of AI over the few years I have been writing this Substack, but I think I recently got something fairly large wrong. In the last year I have been posting about how I suspected that humans would have to approach working with agents as a manager, deciding how to delegate work to agents and specifying how those agents should be organized.

We are still on an exponential curve of AI development. I try to put out a Substack post every couple weeks or so, yet, as the pace speeds up, that sometimes feels too slow. In the weeks since my last post, we had the apparent cracking of one of the most famous problems in math by an AI (accompanied by controversy) and widespread discussions about the risks posed by AI and what to do about it (also accompanied by controversy).

Agency is the initiative to act. Increasingly, it is going to determine what happens next with AI, and whether that is good or bad for us. But whose agency? Human agency, the willingness to push, experiment and act without waiting for instructions, seems increasingly important to getting value out of AI, and I have a longer post on that coming soon. But this post is about the agency of AI, and how the choices we make about how to use it (or constrain it) will shape all of our futures.

Every few months, I write a guide for people who want to use AI to do stuff. This time, a lot has changed, in part because what it means to “use AI to do stuff” encompasses so much more “stuff” than it used to. Until recently, using AI meant talking to a model through a chatbot in a constant back-and-forth conversation.

If you feel like things are accelerating in AI, you are probably right. Better AI models from the leading American AI labs have been releasing more quickly than ever (though government interventions have stopped access to two of the most powerful models, Claude Fable and GPT-5.6). But it isn't just release timing. The evidence points to accelerating capability gains as well (though the frontier stays jagged, and AIs remain weak in many places).

I had early access to the first Mythos-class AI model being released to the public, Claude 5 Fable. Much of the discussion of Mythos has centered on its impact on software security, but I tested it on everything except that (the guardrails around Fable essentially prevent it from being used for cybersecurity at all). My conclusion is that it represents a very real leap over every model I have used before, and, maybe more important, suggests our relationship with AI is changing in drastic ways.

It has been two years since Co-Intelligence, my book about AI, was published, and it was successful beyond what I could have hoped (it was a New York Times bestseller and has been translated into 25+ languages, with the biggest markets being the Netherlands and Korea). I don’t think the book is out-of-date, exactly, but it was written about a world of chatbots and earlier AI models.

If you go to your favorite social media site, you will find it full of posts that start to look suspiciously similar to each other: Many of the comments to these posts are also generated by AI. So are an increasing number of academic papers and New York Times opinion articles, and, apparently, award-winning short stories.

I had early access to GPT-5.51, and I think it is a big deal. It is a big deal because it indicates that we are not done with the rapid improvement in AI. It is also a big deal because it is just plain good. And it is a big deal because even with all of this, the frontier of AI ability remains jagged.

AIs are already far more capable than most people realize. A large part of this so-called capability overhang comes not from the limits of AI (though, of course, they still have many limits), but from how people interact with it. The vast majority of people access AI through chatbots, and usually the free versions with less capable models. A chatbot is fine for a quick question, but it is a bad way to get real work done.