The start of the Super Human era
Fully autonomous agents aren't the future. Humans who think with models are. And they are already pulling ahead of everyone who's still waiting for models to mature.
What is a Super Human?
A Super Human is not someone with better tools. It's someone whose bottleneck has shifted.
Information access has been the biggest bottleneck to figuring things out throughout human history.
Models do in seconds what used to take weeks of reading.
So the bottleneck shifts to judgment. Reasoning, decision-making, the ideas you can't quite explain. The more context you have, the better the decisions you make. Models take care of the context while you develop the right judgment.
In practice this shift looks small in the moment and huge in aggregate. Looking something up takes minutes instead of an afternoon. Drafting takes one pass instead of three. The hard part of the day stops being finding the answer and becomes deciding which of three answers is the right one to act on.
Judgment isn't built from past information alone, or from what's in front of you right now. It comes from past experimentation, from things you noticed without trying to. Models don't have that.
Demis Hassabis made this point about Go: top players often choose moves they can't fully explain. They just feel right. DeepMind did teach AlphaGo that intuition, but only Go's. Yours is still yours to build.
So we run a loop. Explore with models, build judgment from what comes back, fail in public, sharpen, repeat.
Even when agents catch up
The autonomous alternative is arriving faster than the people waiting for it expected.
METR's Time Horizon 1.1, published January 2026, measured Claude Opus 4.5 completing about five hours of work autonomously at 50% success. The doubling time accelerated from seven months to under three. The benchmark is starting to saturate; METR is racing to build tasks the latest models can't beat.
Ethan Mollick, who used to call this era "co-intelligence," now calls it something else: "This is an era of managing AIs, rather than working with them." You give an agent hours of work and review the finished product.
That's the same bottleneck shift, accelerated. A Super Human is someone whose bottleneck has already shifted to judgment. The agent era doesn't change that. It makes it the only thing that matters.
The smart voices in the field have been saying the same thing for a year. Anthropic recommends starting with the simplest non-agentic solution; agent autonomy "increases costs and compounds errors." Karpathy calls for an autonomy slider, not a slider stuck at 100%. Simon Willison spent late 2025 designing agentic loops carefully, not turning them loose. The people who get the most out of these systems are the ones who know when to grab the wheel.
That judgment gets built in the loop. The people who started running it a year ago will be the ones managing agents well next year. Everyone else will be reviewing output they can't grade.
Stop waiting
Models are already good enough, and the people using them are already pulling ahead.
You can see it already. I built tusk over eight weeks. The idea kept outrunning the code, until the complexity itself was the signal. I made the call: scrap the task-management CLI I'd started with, rewrite it as an agent-first brain. The information for that call was already in front of me. What I needed was the judgment to trust it.
Better judgment improves how you use these models, which accelerates your access to information, which sharpens judgment again. That loop compounds. While others wait.