AI 2026: First-Half Review, Second-Half Forecast

Information is cheap. What is expensive is the ability to process it.

This column sets out to do something plain: take a handful of things that actually happened in AI Agent land during the first half of 2026, explain them properly, and then reason forward into what the second half most likely holds.

I don’t chase hype. Hype depreciates the day it lands, and chasing it only leaves you busier and emptier. What I chase are signals — the things that, once you see them clearly, change what you do next. These five essays are the signals left standing after repeated filtering against a large number of frontier system releases, primary sources, papers, and benchmarks:

One — the ceiling on information automation. Point an AI at a field and tell it to track everything: how far does it actually get, and where does it jam?

Two — the shift in interaction. Agents are moving from “you prompt it” to “it prompts you.” What that step actually costs.

Three — the collapse in cost. Once open models get roughly an order of magnitude cheaper, how large a fleet can one person really keep running?

Four — the engineering of trust. What it takes to hand work to an agent nobody is watching: evals, guardrails, and a human in the loop.

Five — the map of opportunity. The closer: where the red ocean has filled in, and what blue ocean is left.

The five interlock, but each stands on its own. Read 1 → 5 and you’ll get the full path: from observation, to forecast, to what you should actually bet on.