And the next scriptorium is yours

The previous article ended on a promise, or a threat, depending on your mood. After the developers, the next workshop to get its printing press might be yours. The “might” was a courtesy. Let’s drop it.

This is no longer an IT matter

The numbers no longer talk about code. The International Monetary Fund estimates that about 40% of jobs worldwide are exposed to AI, and nearly 60% in advanced economies like ours. Goldman Sachs puts the share of work exposed to automation at the equivalent of three hundred million full-time jobs. And the World Economic Forum, in its benchmark report, projects that 41% of employers plan to cut headcount where AI automates tasks, and that 40% of the skills in demand will change within five years.

Exposed does not mean eliminated, and I’ll come back to that in a moment, because that’s where the lying is heaviest. But note the direction. The wave doesn’t stop at the IT department. It’s rising toward marketing, finance, legal, analytics, and yes, human resources.

And since this is my trade, let me add a practitioner’s note, because it applies to every one of your functions. Not every use of AI is equal, and the dividing line fits in one word: data. Who owns it, and who does it serve. Take recruiting. You’re being sold automated sourcing built on data trapped inside LinkedIn. But that data isn’t yours, LinkedIn’s internal AI pursues its own goals, not yours, and the supposedly magical solutions that claim to access it only survive thanks to data harvested through means whose compliance could raise questions. The whole point of my training program, peopleattractiontheory.com, is to dismantle that mirage. Conversely, when I take the data I truly own, structured in my own recruiting software, Jarvi, and I can now query it with precision thanks to the MCPs we saw in chapter three, then it’s a tremendous lever. The same difference as between a crutch and a lever: borrowing someone else’s data makes you dependent, mastering your own makes you powerful.

The optimistic objection, and why it misses the point

At this stage, a voice always rises, and it’s a respectable one. The voice of those who remind us that AI will also create jobs, that new professions will be born, as with every technical revolution.

The most illustrious person saying this is Yann LeCun, one of the fathers of modern AI. Let me be blunt: I admire him. Scientifically, without him and the lab he built in Paris, there would probably be less chance of a European champion like Mistral existing, since two of its three founders came out of it. When he says tomorrow’s work will be about augmentation, that everyone will become the conductor of their own team of AIs, and that entirely new professions will appear, I believe he’s right.

I even have a confession to make. I was lucky enough to spend some time close enough to that world to find myself taking back-to-back calls in the room next to his. I crossed paths with him. And I, who am not exactly known for holding my tongue, didn’t dare speak to him. Pure fanboy effect. I mention it because it shows where I’m speaking from: not from a pedestal, but as a guy sincerely in awe of what he has accomplished.

And that’s precisely why I can contradict him without the slightest animosity. On the destination, I’m with him. Not on the path.

The real problem is the tempo

The World Economic Forum report actually proves him right on the raw numbers. One hundred seventy million jobs created by 2030, against ninety-two million destroyed. A net positive balance of seventy-eight million. On paper, everything is fine.

Except that a net balance masks a brutal asymmetry of speed. Deciding not to replace a departure, because three well-equipped people are now enough, takes one meeting. Deciding to cut positions takes one meeting. But creating professions at scale, defining them, writing the job descriptions, training people, hiring them, getting them settled in — that takes years.

The balance may well be positive in 2030. It says nothing about what the people for whom the cuts arrive years ahead of the creation will go through between now and then.

The example is right before our eyes, and it’s ironic. While we’re being promised new professions, Meta cut thousands of jobs to fund its AI infrastructure — we saw how in the previous chapter. The new professions, we’re still waiting for. The cut jobs, they’re already gone. That’s the tempo. And the tempo is the real subject for human resources. Not the 2030 horizon, but the three years leading up to it.

Besides, the executives themselves don’t agree. Anthropic’s CEO warns that AI could wipe out half of entry-level white-collar jobs within one to four years. Nvidia’s CEO argues, on the contrary, that it will create more highly skilled jobs. When the people building the technology diverge that much, beware. Nobody holds the truth, and that’s exactly why you must act without waiting for them to settle it.

The honesty I promised

Exposed does not mean doomed. The figures measure automatable tasks, not actual layoffs, which are far lower. A task is not a job, and a job is not a person.

But don’t reassure yourself too quickly. Between the intact job and the eliminated job, there’s a third category the statistics don’t count: the job emptied of its substance. Kept, but transformed. The one from which the meaningful tasks have been removed, one by one, until only a shell remains. That’s often where it starts, well before the layoff.

So what do we do, without panicking

Faced with this, there are three wrong answers and one right one.

The first wrong answer is to close your eyes. We’ve covered that — the curve won’t wait for you.

The second is to run off and find a training course. We’ve covered that too. By the time it’s written and approved, it’s already obsolete. You don’t learn this in a classroom, you learn it with your hands in the machine.

The third is to cut fast to save money. It’s the most tempting, and the most dangerous. In the final chapter we’ll see how a company that was nonetheless fanatical about AI got burned doing it, then had to rehire.

The right answer fits in an old word brought back into fashion. The hackathon. But not the developers’ kind. A hackathon for the company’s other functions. The principle is simple and powerful. You take a real problem you don’t know how to solve, you give yourselves a limited time, and you search. Together. No process, no infamous “that’s how we’ve always done it.”

This isn’t a whim of mine. Deloitte observes that the vast majority of companies remain stuck at the experimentation stage, unable to move their AI ideas into production, and presents the internal hackathon as one of the fastest ways to close that gap. Canva shuts its offices for an entire week to do this, and gets features that actually ship in its products. The IT services firm TCS gathered more than two hundred eighty thousand employees from fifty-eight countries in a single AI hackathon. And, closer to my point, a Belgian government agency organized a hackathon where teams worked locally, on secured machines, with open models and their own data, to demonstrate that sovereign AI can improve internal processes without ever letting information leak.

That’s what needs to be done. Set the plasticity of your teams’ brains back in motion. Get them out of the corridor they’ve been walking down for ten years. Give them a sandbox, a real problem, and the right to fail several times before succeeding. That’s how you learn now. No other way.

One question remains

To lead all this, you need someone who knows. Someone who walks into the company, observes the processes, and shows where AI can fit in without breaking everything. That someone exists, they carry a title still little known, and they’re the subject of the next chapter.