The loudest claim about AI right now is that it makes us more productive. It writes our emails. It summarizes our meetings. It drafts our memos. It does in seconds what used to take an hour. Productivity, productivity, productivity.
This claim is true. And almost entirely beside the point.
The interesting thing about a productivity revolution is what it doesn't do. It doesn't change what you are doing. It just changes how fast you do it. It is, by definition, a multiplier on a direction you have already chosen.
And the trouble is that most operators I work with haven't chosen their direction. They have inherited it. From the last quarter, from the last hire, from the last competitor. From the advice of someone who was solving a different problem at a different time. From the gravity of what they are already doing.
If you apply a productivity multiplier to an unexamined direction, you get more of what you already have, faster. If what you have is wrong — and statistically, in any ambitious business, some of it always is — then AI is helping you accelerate into a wall.
The shift the moment is actually asking for
The real shift AI is forcing is not at the level of speed. It is at the level of decision-making. For the first time in business history, the cost of producing a good first draft of anything — a strategy, a campaign, a system design, a piece of writing — has collapsed to nearly zero.
That changes the bottleneck. The bottleneck used to be capacity: who has the bandwidth to make the next thing. The bottleneck is now discernment: which is the next thing worth making at all.
This is a quiet shift. It does not announce itself. It does not show up in a tools list. It shows up six months later, when the team that asked the better questions has built a more durable business than the team that produced more deliverables.
The teams that win the next decade will not be the ones with the most models. They will be the ones that did the hardest thinking about which decisions are actually worth making.
Three places to look for direction
When I sit down with a founder, an operator, an educator, or a creator, we almost never start with AI. We start with three questions about direction.
1. What is this business actually for?
Not the deck answer. Not the website answer. The honest answer. The one you would give a smart friend over dinner who had nothing to gain from telling you nicely. Most businesses cannot survive this question, and the way they fail it is not by giving the wrong answer — it is by not having an answer at all.
2. What does the market understand you to be?
This is the positioning question. And the gap between “what we are” and “what the market thinks we are” is almost always the most expensive thing on the balance sheet — even though it shows up nowhere. AI cannot close this gap for you. It can only amplify whichever side of it you are operating from.
3. What is the smallest set of decisions that determines the next 12 months?
In almost every business, the answer is two or three decisions. Maybe five. The other thousand things you spend your weeks on are consequences of those few. AI is great at helping you act on a decision. It is useless at helping you make one — unless you have first done the work of narrowing what the decisions actually are.
Slower, deeper, on purpose
The work I do is built on a quiet bet: that the people who win the AI era will be the ones who use it to think more carefully, not the ones who use it to ship more carelessly.
That is a contrarian bet right now. The dominant story is faster. More tools, more output, more dashboards, more decks. The trade for that story is direction — and the people taking the trade are not noticing the cost.
The market for clarity, for positioning that holds up, for systems that compound — that market is being quietly underbuilt in the middle of the loudest AI cycle in history. That is the gap AInov is built into.
If you are using AI to do the wrong things faster, the best gift I can give you is a conversation about whether they are the right things to be doing at all.
What this looks like in practice
The work isn't mystical. It looks like a small number of long, slow conversations. It looks like questions that don't have an answer yet. It looks like a piece of writing that takes a month to get right because the thinking takes a month to get right. It looks like a strategy that ten people on your team can repeat from memory because it is actually clear, not because they have memorized a deck.
And then — only then — does AI become useful in the way the market thinks it already is. Not as a productivity multiplier on an unexamined direction. As a precision instrument inside a direction that has been worked out.
That is the new intelligence. Not the kind that lives in the model. The kind that lives in the operator who has earned the right to use it well.
