Does AI replace jobs or expand work?

But let me just show you a couple of the ones to provide some balance. So US white collar jobs as a percent of total employment in the US. Very steady line. You see a blip there around COVID because so many blue collar jobs were lost. That created the percentage of white collar to bounce up. But post COVID, it's been on a very stable trajectory. If it were true that we were seeing massive AI job loss, you would see the percent of white collar jobs dropping in the economy. That has not happened. Okay? And every time there is some sort of job loss story, you glom onto it being AI related like you do with Amazon. And then Andy Jassy comes out and refutes that. Then you're like, oh, no, no, I'm talking about something in the future.

And it was an actual study of over 21,000 firms in the US, and they looked at their payroll data combined with their spending on AI and what they saw is that firms that spent the most on AI actually grew the fastest and they tended to grow their headcount roughly 10% in the two years following the adoption of AI. And entry-level headcount rose even faster, it grew at 12%. So, all this stuff about how entry-level headcount is going to get wiped out, not true. The more firms adopted AI, the more hiring they did, at least that was a correlation. Obviously, they can't prove causation, but that was the correlation. And then companies that were not high-intensity adopters of AI, they either didn't adopt or they were low-intensity adopters, they just saw flatness in their headcount.

So for example, I'll give you at LinkedIn, we used to have product managers, we had designers, we had front-end engineers, and then we had back-end engineers and so on. So what we did is we took those first four roles and combined them. In fact, increased scope and said, they're all full-stack builders. So I like that because that's a structural change that allows for us to increase the change both the work and the workflow between these functions.

I would assume the velocity because you don't have four people communicating and that throughput of ideas such as one person and vibe coding.

Computer vision has been integrated into all of the radiology technologies and radiology platforms in the world 100 percent. The surprising outcome is the number of radiologists actually went up and the demand for radiologists is skyrocketed. The reason for that is because everybody's job has a purpose and its task. The task that you do is studying the scans, but your purpose is to help the doctors, help the patient diagnose disease. What's surprising is because the scans are now being done so quickly, they could do more scans, improving health care. Yes. But doing more scans more quickly allows patients to be on boarded a lot more quick, treated a lot more quickly. As it turns out, because hospitals enjoy making money too, they're doing more scans, they're treating more customers and more patients, the revenues go up, and guess what?

He said, we're putting recording software on every single person in the company's computers to study and train our model. And people were like, oh, and previous people said, I built during the AI hackathons they had months ago, I built all this AI tools to make my job more efficient. And then Zuckerberg laid me off. So the now, the perception people have now, and it's quite correct, I think, is the most you can hope for here is you keep this job for some amount of time and train your way out of it. And hopefully there's some more work for you, but they're studying you. And Zuckerberg just said it plainly there, Hey, we're going to study everybody here. And that's going to lead to more replacements.

And it was an actual study of over 21,000 firms in the US, and they looked at their payroll data combined with their spending on AI and what they saw is that firms that spent the most on AI actually grew the fastest and they tended to grow their headcount roughly 10% in the two years following the adoption of AI. And entry-level headcount rose even faster, it grew at 12%. So, all this stuff about how entry-level headcount is going to get wiped out, not true. The more firms adopted AI, the more hiring they did, at least that was a correlation. Obviously, they can't prove causation, but that was the correlation. And then companies that were not high-intensity adopters of AI, they either didn't adopt or they were low-intensity adopters, they just saw flatness in their headcount.

So for example, I'll give you at LinkedIn, we used to have product managers, we had designers, we had front-end engineers, and then we had back-end engineers and so on. So what we did is we took those first four roles and combined them. In fact, increased scope and said, they're all full-stack builders. So I like that because that's a structural change that allows for us to increase the change both the work and the workflow between these functions.

I would assume the velocity because you don't have four people communicating and that throughput of ideas such as one person and vibe coding.

So AI is a lot harder to implement than anybody expected. For sure. You can do an agent pretty straightforward. You can prompt away, we cheated on tests, we cheated at work, did projects, improved our productivity 100x, right? All easy-peasy. We just assumed at the enterprise it would be just as easy. It's hard and it's terrifying and not terrifying for employees because Dario and everybody is saying 50% of white-collar people are going to lose their jobs. Here we are two years later, they said within two years and employment is still growing, people are hiring, we need more AI literate people, right? So CEOs have no clue what is going on, none whatsoever, and that's not going to change, right? And so trying to explain to them that you need to add a harness to all the, there's no chance, right?