Salary Plus Tokens

Ask yourself a simple question, you who recruit, who evaluate, who set compensation. What is an employee worth? Until now, the answer fit on one line: their salary. Tomorrow, it will fit in a sum: their salary, plus the tokens they consume.

That sentence sounds abstract. It has already become company policy.

The new payroll line already exists

In March 2026, Nvidia’s boss, Jensen Huang, proposed giving his engineers an allowance of AI tokens worth nearly half their base salary. And he pitched it as a recruiting argument. Why be tempted by a signing bonus elsewhere, he said in essence, when here you get power.

Read carefully what that says. One of the most powerful executives on the planet now considers that an engineer’s value is no longer just what they can do, but what they can get the machine to do. The salary pays for the head. The tokens pay for the leverage. And the company is willing to fund both, because it knows the combination is what produces.

The asymmetry that changes everything

Here’s why this equation is so brutal. The two terms don’t cost anything alike.

A mid-level developer costs between seven and thirteen thousand francs a month, salary and payroll charges included. Running AI agents at full throttle to multiply that developer’s output costs, depending on usage, from a few hundred to one or two thousand francs a month. The models themselves are dropping in price by fifty to seventy percent a year. In other words, for a fraction of a salary, you multiply what that salary produces. And the gap widens every year in favor of the tokens.

Some have understood this so well they’ve turned it into a competition. In Silicon Valley they call it tokenmaxxing, and star engineers burn through the equivalent of more than eighty thousand francs in tokens a month. One of them puts it bluntly: I probably spend more on AI than my own salary. In his eyes, that’s not waste. It’s an investment in his own leverage.

What it looks like at my place, without a Silicon Valley budget

I don’t have a hundred thousand dollars of tokens a month. Far from it. And I’m going to be completely transparent, because that’s the whole spirit of this guide. Here is my actual monthly bill. A Gemini Pro account attached to my Google Workspace, enough to run AI Studio and NotebookLM, eighteen francs. A ChatGPT Plus I’ve had since the beginning and barely use right now, mostly for images, twenty francs. And a Claude Max account, the five-times plan, one hundred francs, with which I do ninety percent of my projects. Total, about one hundred and forty francs a month.

One hundred and forty francs. Put that figure next to a single salary, and the asymmetry hits you in the face. This isn’t a thought experiment, it’s my bank statement.

That’s the equation, seen from a small operation. My time, plus one hundred and forty francs of power, against the payroll of an entire team. And the result tips the way nobody expected.

And it doesn’t stop at developers

People often assume this story only concerns technical jobs. Let me tell you another one.

A few months ago, to prepare a talk to HR people about what AI can really do, I built a complete marketing strategy for a new market segment. Product visuals, a campaign draft, a slogan, social media ads. All of it in forty-five minutes, with AIs, on the wifi of a TGV doing three hundred kilometers an hour.

I’m not a marketer. And I’m not claiming for a second that a team of marketers wouldn’t have done better than me. They would have, no question. But here’s the real question, the one that matters to a company. While a team without AI polishes one concept in a week, I, alone with my tokens, could have tested fifty. The quality of one finished concept versus the speed of exploring fifty leads. For leadership, that trade-off is settled fast.

Extrapolate. One salary plus tokens moves faster, in the exploration phase, than five or even ten salaries without AI. Yesterday that was only true for developers. It’s becoming true for marketing, communications, analysis, legal, and the rest.

The link that snaps, and nobody wants to name it

Of course, exploration is not production. In a real company, there are bottlenecks slowing all of this down. But look at where those bottlenecks often come from. They don’t come from the technology. They come from the chain of command, the layers of sign-off, the management.

And what’s happening right now, at this very moment? Those layers are being removed.

The phenomenon has a name, the Great Flattening. According to Gartner, one organization in five will use AI to flatten its structure by 2026, cutting more than half of its middle management positions. And the most brutal example is brand new. In May 2026, Meta notified eight thousand layoffs, about a tenth of its workforce, and canceled six thousand open positions — nearly fourteen thousand jobs hit in one week. The trigger: AI capable of executing and coordinating what those managers used to coordinate.

But the most chilling part isn’t the number. The very day the notifications went out, a recording leaked. In it, Zuckerberg explains, during an internal meeting, that his AI models learn by watching really smart people do their jobs. Follow the sequence carefully: the company tracked the activity on its employees’ computers, with no way to opt out, to train its AIs on how they work, then laid people off. Learn from the best, then let them go. In the same recording, he admits it was not in the company’s strategic interest to explain frankly to the teams how the program worked. The employees understood it just fine: more than a thousand signed an internal petition against the program.

Take in the irony, because it’s cruel. For decades, we promoted the best, the most senior, the most brilliant, into management positions. It was the royal road, the only recognized way to climb the hierarchy. We spent years pushing talent toward the middle of the pyramid. And it’s precisely that middle that’s now being removed.

Let’s be honest about the downside, because it’s heavy. Cutting out the middle is not painless. Thirty-seven percent of employees already say they feel lost without managers, and nearly half of executives doubt they can manage what’s left. You’re also removing the drive belt, the mentorship, the next generation. Flattening without thinking means creating a void where culture and judgment used to flow.

But the direction of the movement leaves no doubt. The pyramid is being squeezed through the middle.

The line HR will have to learn to read

If you work in human resources, here’s what’s landing on your desk, whether you want it or not. Soon, comparing two candidates for the same position will no longer be done at equal salary. It will be done at equal leverage. The real question will no longer be how much they cost, but how much they produce per euro of tokens spent.

And that’s a reading grid no current job description contains. Nobody, in your competency frameworks, has a box for that.

The nuance, because people will lie to you about it

You’ll be sold the equation as a magic formula. Buy tokens, get an engineer ten times more productive. That’s false, and chapter 2 of this guide explained why. Tokens multiply no one on their own. Plugged into someone who hasn’t changed the way they work, they burn money without producing anything. It’s been measured: entire teams have watched their AI bill become the second-largest expense after salaries, with no measurable productivity gain.

So the right equation isn’t salary plus tokens equals ten times more. It’s salary, plus tokens, plus the skill to use them, equals ten times more. And that skill is the only one of the three you can’t buy by the million tokens.

It’s also the only one still in your hands.