Even If It Stopped Today

If AI never got any better than it is this afternoon, we would still spend the next ten years learning how to use it.

Every conversation about AI I sit in ends up in the future tense.

The optimists talk about what the next model will do: agents that run whole departments, scientific breakthroughs, general intelligence by the end of the decade. The sceptics talk about what it won’t: the bubble that will burst, the plateau that is surely coming, the returns that never arrive. They disagree about almost everything, except the tense. Both are arguing about tomorrow.

That shared habit has a cost. It lets everyone in the room off the hook for today. If the real thing is still coming, there is no hurry to use the current thing well. Wait for the next model. Wait for the hype to settle. Wait for the market to sort out the winners. Meanwhile, the tools on our desks are already doing things that would have sounded like science fiction five years ago, and most organisations are judging them on promises instead of performance.

Try a different test. Imagine that every AI lab stopped today. No new models, no new releases, no further progress. Just what we have now, for the next ten years. Would that be a disappointment, or a revolution?

I think it would still be a revolution, which means most of the debate is about the wrong question.

The future-tense trap

Hype and doom are mirror images. Hype says: what you see now is only the beginning, so invest in the vision. Doom says: what you see now is overrated, so wait for the correction. Both push the decision into the future, and in large organisations, a decision pushed into the future rarely gets taken.

The futurist Roy Amara put it neatly: we tend to overestimate the effect of a technology in the short run and underestimate it in the long run. The AI debate manages both at once. We overestimate what next year’s model will do, and underestimate what this year’s model already can.

There is also a quieter version of the trap. When AI is always judged against what it is supposed to become, everything it does today looks provisional. A prototype is “just a demo”. A good analysis is “promising”. A working tool is “a pilot”. Nothing counts yet, because the real thing is always one release away. That is how capability that already exists becomes invisible.

The freeze test

Let’s take the test seriously. What can these tools do, today, if nothing ever improves?

I can only answer from my own work. I use AI every day, for three things in particular.

Prototyping. An idea that used to need a team, a budget and a few weeks now becomes something you can click on in an afternoon. The code doesn’t have to be perfect for the conversation to change: people stop debating a slide and start reacting to a thing.

Making hard ideas graspable. This is the use I find most underrated. A model can take a dense concept, such as a regulatory regime, a data architecture or a statistical argument, and turn it into an analogy, a diagram or an interactive explainer that someone gets in two minutes. For a consultant, that is half the job. For an organisation, it is the difference between a strategy that a few people understand and one that everybody can act on.

Writing. As I say on this site, I write with AI, openly. It tests my arguments, checks my sources and cuts what doesn’t earn its place. The thinking is mine. The speed and the rigour are better than I could manage alone.

None of this depends on the next model. And the evidence was already there with the last one. In 2023, researchers from Harvard, Wharton, MIT and Warwick gave 758 Boston Consulting Group consultants access to GPT-4, a model that now looks old. On tasks within its reach, they completed 12% more work, 25% faster, at 40% higher quality. The models on our desks today are far more capable than the one in that study. Freeze them here, and that is the floor.

Open-weight models make the thought experiment even more concrete. Mistral, from Paris, publishes models that anyone can download and run on their own servers. Even if every lab closed its doors tomorrow, those models would keep working, indefinitely, on hardware you own.

The capability overhang

If the capability is already here, why doesn’t it show?

According to Eurostat, only one in five EU businesses with ten or more employees used any form of AI in 2025. Denmark leads at 42%; most countries are far below. And among those who do use it, the measured effect is modest. A large Danish study by Anders Humlum and Emilie Vestergaard followed 25,000 workers across 7,000 workplaces and found that AI chatbots saved them, on average, about 3% of their working time.

At first glance that supports the sceptics. Read closely, the figures describe the gap between what the models can already do and what organisations let them do. OpenAI has started calling this the “capability overhang”: the distance between what the technology can do and how it is really used.

We have been here before. Electric motors entered factories in the 1880s, yet productivity barely moved for decades. The economic historian Paul David showed why: factories first simply swapped the steam engine for an electric one and kept everything else the same. The gains came only in the 1920s, when a new generation of managers redesigned the factory floor around the new power source.

AI is in its steam-engine-swap phase. Most organisations have dropped a chatbot into their existing processes, rationed it, stripped it of the features that made it useful, and then measured the result. In “Embrace the Mess” I wrote about how the instinct to establish order first keeps pilots from scaling. It is the same story: the bottleneck sits in the organisation around the model.

Judge it on its merits

This doesn’t mean we should stop thinking about the future. Progress may well continue at its current pace; it may slow down; it may surprise us. Serious people should prepare for all three. But a strategy built on the next model is a strategy that never starts. The question for any leader is simpler, and more useful: what can this technology do for us now, and what is stopping us from getting it?

As a linguist, I notice how much of the debate is carried by grammar. “AI will transform”, “AI could replace”, “AI might never”. Modal verbs, all of them. Try describing it in the present tense instead, and the conversation changes. AI drafts. AI explains. AI prototypes. AI checks. Those are claims you can test on Monday morning.

But what about…

“It still makes mistakes.” It does. So do people, which is why we built reviews, four-eyes principles and audits. The fair comparison sets AI plus a good process against the same process without it.

“Why invest now if next year’s model will be better?” Because the skills, processes and data your people build with today’s tools carry over to tomorrow’s. The organisations that will get the most out of the next model are the ones that learned on this one. Waiting only postpones the learning curve.

“The productivity numbers don’t show it.” Not yet, and the dynamo explains why. Productivity statistics measure organisations. Flat numbers usually mean the work hasn’t been redesigned yet.

“It’s a bubble.” Perhaps the market is. Valuations can crash while the technology stays. The internet survived the dot-com bust; the companies that had learned to use it came out ahead.

The present tense

Here is what I’d ask of any leadership team. For one quarter, ban the future tense from your AI discussions. No roadmaps for models that don’t exist, no fear of a correction that hasn’t come. Ask instead what today’s tools can do for your work, and measure that, honestly, against how you work now. Then remove the things that stop your people from using them.

If AI never got any better than it is this afternoon, we would still spend the next ten years learning how to use it.