This is a reaction to one viral post, written the week it landed. I've kept it as a dated perspective. The capability claims below are Matt Shumer's, summarized as he made them. My own view since then runs on firsthand work rather than urgency, and it lives in the later essays, starting with What Over How. I've since rewritten the five actions at the end to match that view: aim at one real task, measure it, and decide from what you see.
Matt Shumer, CEO of HyperWrite AI, six-year AI startup founder and investor, published a piece in February 2026 about what was happening with artificial intelligence at that moment. It was one of the clearest reads I'd seen on it, and if you run a business, it's still worth twenty minutes.
The short version, and what I'd do with it.
Shumer's argument: as of February 2026, the latest models from OpenAI and Anthropic had moved past the helpful-assistant stage and were completing complex, multi-day work that used to require senior professionals. He quotes software engineers describing finished products built largely by AI, and a managing partner at a major law firm who says the models already rival his associates. He also reports that the labs are using the models to help build the next versions, and that people inside the industry find the pace hard to absorb. Those are his sources and his framing, not mine.
Why it matters for you: Shumer's core message is that the biggest advantage available is being early. Most people are judging AI on an experience from a year or two ago that no longer describes the tools. The gap between what the models can do and what most professionals think they can do is where the opportunity sits, and it narrows as your industry catches up.
Five things worth doing
Reading the article is step one. Each of these starts with work you already do.
Aim the experiments at real work.
The article says "spend one hour a day experimenting with AI." That hour pays off when it's aimed at a task you already do, not at whatever demo is trending. If a working session with someone who knows the tools would shorten the search, use one. Start with what matters, then pick the tool.
Run one bounded trial.
Pick a recurring task: proposals, intake, reporting, client follow-up. Write down how long it takes now and what a good result looks like. Try an AI workflow on it for two weeks, inspect every output before it goes anywhere, and compare against your notes. If it holds up, keep it and pick the next task. If it doesn't, you've spent two weeks and learned something specific instead of arguing in the abstract.
Practice on the team's own tasks.
Casual use builds familiarity. The article's point is that the people getting ahead use AI in their actual work. Practice on this week's real tasks sticks better than general tutorials, and it leaves behind prompts and checklists the team will use again.
Make AI know your business.
Out-of-the-box AI is impressive. AI set up with your voice, your processes, your client history, and your standards is in a different category. To be precise about the mechanism: this is configuration, meaning instructions, examples, and access to your documents, not training a model. Give the system approved reference material, examples, and clear instructions. Test whether that improves the same task before expanding its access or responsibility.
Start small, and start now.
The article puts it this way: "the person who walks into a meeting and says 'I used AI to do this in an hour instead of three days' is the most valuable person in the room." That edge shrinks as others do the same. One small trial this month is worth more than a large plan next year.
You don't need the whole map before you start.
Start with one recurring task.
We help businesses choose one recurring task, run a bounded trial, and decide with evidence instead of headlines. Plain answers about what's slowing you down.
Pick the first task