You Have to Work With Them

Here is something I have learned from decades of working with people.

Most professionals, and I include myself in this, are extraordinarily good at presenting competence. We have been trained since school to perform well. We know how to speak the right language, follow the right patterns, mirror the people we respect. We can simulate understanding convincingly enough that in a short interaction, you would believe we know what we are doing.

Real insight is different. And it is rare.

You know it when you encounter it because it surprises you. I have had thirty-minute conversations with someone and walked away thinking they were capable but unremarkable. Then, in a meeting six weeks later, they say one sentence that reframes a problem I had been carrying for months. From that moment, my entire assessment of that person changes. Suddenly they are someone I need to work with, not just work alongside.

But here is the uncomfortable truth. If that meeting had never happened, I would never have known.

I have been thinking about this a lot lately in the context of AI agents.

The majority of people I speak to who have tried working with AI models reach a verdict very quickly. An hour, maybe a day. The verdict is usually: not there yet. Impressive party trick. Useful for some things. But not the transformational capability everyone is claiming.

I understand that instinct. I have felt it myself.

But I think it is the same mistake as writing off the quiet person in the office.

Capability is rarely a property of the individual alone. It is a property of what happens when the right person meets the right context.

Think of a footballer who looks ordinary in one team and extraordinary in another. He did not get better. The people around him learned how to play with him, and he learned how to play with them. The runs started getting made. The passes started arriving. The space he created started getting used. The capability was there the whole time. It needed a relationship to surface.

The quiet person in the meeting may be the same. Not necessarily holding back genius, but holding capability that has never been given the pass it could run onto.

Sometimes "not there yet" means the capability genuinely is not there. But often it means we have not built the team in which it could appear.

Daniel Kahneman spent his career documenting how our minds take shortcuts. System 1 thinking, the fast and intuitive kind, is extraordinarily good at helping us navigate a world that mostly behaves the way it has always behaved. It is also extraordinarily good at helping us dismiss things that feel unfamiliar, uncomfortable, or insufficiently proven.

"Not there yet" is a System 1 verdict. It requires no effort. It is immediately available. And it is enormously convenient if you would prefer not to change how you work.

Often, though, it is the verdict of someone who has not yet found their groove with the tool.

I had my own version of this.

For months I was going back and forth between ChatGPT, Claude, Gemini, and Manus. I would get useful work out of each of them, then hit a wall, then switch, then hit another wall. A colleague kept telling me to try Replit. I kept resisting. I was not a coder. I did not want to "vibe code", a term I did not fully understand at the time but had already decided was not for me.

Eventually I got stuck on something none of the chat models could solve. Out of frustration I tried the tools I had been avoiding. Replit. Cursor. A couple of others.

Replit responded to me. Not in a generic sense. In a specific one. It worked with me in a way I had not experienced with the others. The back-and-forth had a rhythm. The corrections landed where they needed to. The scaffolding it built around what I was trying to do matched the way I was thinking.

I have built thirty or forty applications with it since. I have not looked back.

The part that matters is this. I know people who feel exactly the same way about Cursor. I know people who feel exactly the same way about Lovable. The tools are not interchangeable, and neither are the people using them. Everyone is finding their own combination. Everyone is building their own little football team.

That is not a flaw in the technology. It is the nature of what these tools actually are. They are not appliances that produce identical output regardless of who uses them. They are collaborators. And collaborators have to be matched.

Every model has a different flavour. Not just in obvious ways like speed, cost, or the domains where it performs best, but in subtler ones. How it reasons. How it pushes back. What it does when the problem is genuinely ambiguous. The scaffolding that builders are constructing on top of these models is making them even more distinct.

You will not know what you are working with until you have worked with them. Not read about them. Not watched someone else use them. Worked with them. On your actual problems. With your actual context. Over enough interactions that the surface performance gives way to something more.

That is when the groove appears.

Some models will not fit you. Some tools will not work for the way your mind works. That is fine. It is the same with people, and it is the same with teams. But you will not know which until you have given the relationship enough time to develop.

What I am offering is a perspective.

The same mental process that causes us to misjudge quiet colleagues, the fast verdict, the insufficient data, the unbuilt team, is the process many of us are applying to AI right now. The people who are pulling away from the rest did not reach that verdict. They kept working with the tools until the rhythm appeared. Until they found which model played well with how they think. Until they had built the team.

That understanding is not available for purchase. It is only available through time and genuine engagement.

The question worth sitting with is this. What would change about your assessment of the colleague in that meeting, if you had never been in the room when he spoke?

And who is sitting in your office right now, and which tool is sitting on your desk right now, waiting for you to put in enough time to find out what they can actually do?