> It requires an accounting perspective on the inflows and outflows of an ongoing process and not just a small slice of data.
Correct, but we don't have that information so we have to rely on a best guess. We're not dealing with code here where everything is known exactly.
> Yet, the main question for actually measuring the (firm and investor expected) impact of AI is whether that rate of headcount growth has changed without the stock price going down.
Stock price is not necessarily a proxy for headcount either. Some companies have much lower headcounts per stock price (for example, Nvidia).
> That is, is it now proposed by management and accepted by investors that the firm will henceforth require less human labor in order to deliver on the present expected value of future free cash flow.
You are just talking without thinking now are you? It makes no sense. Human labor and free cash flow are probably correlated, but one isn't a proxy for the other. First you relate stock price to headcount and then you say "that is" and then you suddenly relate cash flow to headcount. Cash flow and stock price are correlated per industry, but it's also a very loose one.
> You cannot infer anything about this question from the data shown.
False. I only need to infer a tiny bit in order to falsify your extremely strong claim.
> For this, you would need to look at net hires year after year and prove that a growing percent thereof are H1-Bs.
Yes, this is something concrete that I can agree on. But how would you prove that given that we don't have access to that data?
Apologies if my presentation is inadequate, but I’m not suggesting that stock price is a proxy for headcount. I’m simply arguing that you have to look at the two together in the context of a particular firm — for exactly the reason you note, ie NVIDIA has a lower head count per share etc. It’s the ratio that matters and is the best proxy we have for inferring the cause of a shift in hiring. Lower growth of headcount and stock price going down suggests the cause of the former is reduced expectation of return, ie particular industry or general economic slowdown; lower growth of headcount and stock price remaining constant or rising would be more suggestive of (not causal proof of course) of an AI-related effect.
If we don’t have the data we shouldn’t argue that the data we do have says what it doesn’t. I actually agree with you and the author that trying to figure out what’s going on in labor markets right now is incredibly important — but for that very reason we have to be careful not to accept too easy conclusions. There’s a lot of information we do have about firm expenditure on labor within public filings — I’ll actually try to pull some figures this weekend, can let you know when I do if you’re interested. I think the interesting question on the AI side is whether it allows more firms to present a ratio like NVIDIA, ie reducing expenditure on labor that is considered a “cost center” in a particular industry.
>> lower growth of headcount and stock price remaining constant or rising would be more suggestive of (not causal proof of course) of an AI-related effect.
Since stock prices are basically future expectations, wouldnt it probably be because investors expect the company to be more efficient cashflow wise? (Earn more per employee)
Not necessarily because they already are more cashflow efficient?
> Yes, this is something concrete that I can agree on. But how would you prove that given that we don't have access to that data?
Without that data you can't really draw any conclusion can you? For all we know companies are hiring a higher percentage of American workers now. All we know from the data shown is that companies are hiring some new H1-B holders and retaining a lot of existing H1-B holders.
Actually, we do have some of that data. You can just look at the H-1B employer datahub, filter by Google (or whichever other employer you want to scrutinize), and then look at the crosstab.
Looking at Google specifically, I observe two things:
1. The number of new approvals has gone down drastically since 2019 (2019: 2706, 2020: 1680, 2021: 1445, 2022: 1573, 2023: 1263, 2024: 1065, 2025: 1250). (These numbers were derived by summing up all the rows for a given year, but it's close enough to just look at the biggest number that represents HQ.)
Compared to the overall change in total employees as reported in the earnings calls (which was accelerating up through 2022 but then stagnated around 2025), we don't actually see much anything noteworthy.
2. Most approvals are either renewals ("Continuation Approval"), external hires who are just transferring their existing H-1B visas ("Change of Employer Approval"), and internal transfers ("Amended Approval").
> Without that data you can't really draw any conclusion can you?
We're not dealing with software here. It's not black and white. We can try to make best guesses and then try to support it by evidence or reject it by evidence. A theory that is presented is that H1-B appear to replace US workers, but you are encouraged to provide another theory by using evidence.
> All we know from the data shown is that companies are hiring some new H1-B holders and retaining a lot of existing H1-B holders.
And why do you think that's not suitable evidence for the given theory? Another possible point of data that seems to point in the same direction could be the US Employment-Population Ratio, which, apart from some ups and downs, seems to have been steadily going down since 2000 [1]. More specifically, California's unemployment rate seems to go up since 2021 [2]. But these two data sources are way more broad than just big tech of course.
Of course it’s not black and white: it’s the very fact that there are so many possible explanations of the current changes that makes pretending data supports a given explanation more than another when it doesn’t so misleading. There’s no doubt that offshoring and H1-B hiring is an important trend — as it has been since the 1990s, NAFTA etc. We don’t need this article to tell us that. The difficult question is whether AI is actually introducing a new dynamic into the situation. The article suggests it isn’t. All I’m arguing is that, whether or not the conclusion is ultimately true, the data presented does not shed light on this important question.
Correct, but we don't have that information so we have to rely on a best guess. We're not dealing with code here where everything is known exactly.
> Yet, the main question for actually measuring the (firm and investor expected) impact of AI is whether that rate of headcount growth has changed without the stock price going down.
Stock price is not necessarily a proxy for headcount either. Some companies have much lower headcounts per stock price (for example, Nvidia).
> That is, is it now proposed by management and accepted by investors that the firm will henceforth require less human labor in order to deliver on the present expected value of future free cash flow.
You are just talking without thinking now are you? It makes no sense. Human labor and free cash flow are probably correlated, but one isn't a proxy for the other. First you relate stock price to headcount and then you say "that is" and then you suddenly relate cash flow to headcount. Cash flow and stock price are correlated per industry, but it's also a very loose one.
> You cannot infer anything about this question from the data shown.
False. I only need to infer a tiny bit in order to falsify your extremely strong claim.
> For this, you would need to look at net hires year after year and prove that a growing percent thereof are H1-Bs.
Yes, this is something concrete that I can agree on. But how would you prove that given that we don't have access to that data?