Back in 2000, the rule of thumb at Microsoft was that each employee needed to average roughly $600K in top-line revenue. Inflation adjusted, that is about $1.1M to $1.2M today. Microsoft was a high-margin software monopoly at peak, so it is not a universal benchmark, but it gives a sense of what disciplined operating leverage looked like even at a company printing money.

Over the last decade, and especially during the COVID-era zero-rate and QE environment, many companies responded to dysfunction by hiring around it instead of fixing it. Cheap capital reduced the pressure to make hard operating decisions. Necessity is the mother of invention, but cheap money suppressed that necessity for a long time.

Then two things changed at roughly the same time. Rates went from zero to five, and Section 174 of the tax code stopped letting companies expense software developer salaries in the year incurred. The R&D amortization rule from TCJA kicked in for the 2022 tax year, forcing five-year amortization domestically and fifteen years for work done offshore. At the exact moment capital got expensive, a major software-company cost center became less friendly from a cash-tax and after-tax economics perspective.

Now AI has added a new pressure. Companies are adopting AI quickly, but we are still early. Much of what is happening inside enterprises is still R&D, experimentation, platform buildout, workflow redesign, and internal tooling. That work is not free. It comes with token costs, infrastructure commitments, GPU capacity, vendor contracts, and a lot of expensive trial and error.

Jensen Huang has made the point, in characteristically aggressive form, that if he pays someone $500K, he expects them to use a meaningful amount of compute to become more productive. Whether or not you take the specific numbers literally, and you probably should not since Nvidia sells the machines that consume those tokens, the economic point matters. AI spend has to come from somewhere.

That is the part many layoff narratives miss. Companies are not simply replacing workers with AI. They are also reallocating budget toward AI. Token budgets, model access, inference costs, internal AI platforms, data infrastructure, and R&D commitments are becoming real line items. To fund them, companies are looking at the headcount they accumulated under different interest-rate assumptions, different tax assumptions, and a different view of software demand.

There is also a demand-side story. COVID pulled years of enterprise software adoption into eighteen months, and a lot of what gets reported as growth now is ARR rotating through M&A rather than new logos landing. In parts of the market, revenue is moving around as much as it is expanding.

That is the real backdrop for the wave of layoffs. AI is the story being told on earnings calls. The reality is accumulated management debt finally meeting a cost of capital that punishes it. Layers of process. Unclear ownership. Duplicated work. Headcount that grew faster than execution improved. And now, on top of that, companies need to make room for a new class of AI-related spend.

The pressure also lands hard on old farts like me. We are expensive. And to be honest, some of us (not all) do not want to change how we work or keep up with how the technology is evolving. That makes us easy targets when finance needs to hit a cost number. AI gives the story a forward-looking sheen, but the underlying move is simpler: reduce expensive headcount, flatten layers, correct years of operational laziness, and redirect budget toward the new thing everyone believes they must fund.

AI is real. The layoff narrative around it usually is not. When you read a layoff announcement blaming AI, you are mostly reading a press release about cost of capital, tax policy, demand pull-forward, AI infrastructure spend, and an org chart that finally got too expensive to defend.

Read the 10-Qs, not the blog posts.