The Hiring Freeze Is the AI Story. Not the Layoffs.

From May 1st through May 19th, thirteen working days, I’ve spent $1,677.05 on Claude. That’s about $129 a working day. Running a normal 40-to-45-hour week. Annualized, that’s somewhere around $31,000 a year.

For reference, Steve Yegge — a longtime engineer at Sourcegraph, ex-Google and ex-Amazon, who’s been writing publicly about agentic coding for two years — recommends companies budget exactly that: $80 to $100 per developer per day on AI tokens. Tomasz Tunguz, the VC who tracks SaaS economics for a living, calls it “the fourth component of engineering compensation.” I’m not an outlier. I’m just early.

Now scale me up. Take a 5,000-person company. Tech-heavy, mixed knowledge workers, fully loaded payroll of around $650 million — that’s roughly $130,000 a person, which is reasonable for a mid-cap tech-ish org. Now imagine everyone is using AI at my pace.

That’s about $168 million a year in tokens. Twenty-six percent of payroll. In CFO terms: roughly 1,290 jobs worth of cost, just in compute.

You read that and you think, holy shit, that’s a lot. And it is.

Until you look at the output side.

I’ve been tracking my own performance for a while, and the productivity gain doesn’t collapse into one clean number — it stratifies by what you’re measuring. Task-level execution, the actual work I ship, is running about 20x my pre-agent baseline. Breadth of capability, the range of things I can credibly take on in a week, is around 16x. Project oversight at my level — I run AD-level work — is closer to 6x, because management leverage has different ceilings than execution. Sustained overall output and velocity, the headline number, lands around 15x. And on a single best-fit project, where everything aligned, I hit 120x.

Pick a multiplier and run the math.

If 5,000 people produce only 1.67x — that’s the measured jump in merged pull requests per engineer per day at Anthropic after they rolled out Claude Code, and it’s a measured number, not a self-report — that’s 3,350 extra full-time-equivalents of work for 1,290 jobs of token spend. About 2.6 to one. Even the floor pays back more than double.

At a defensible middle of 3x: 10,000 extra FTEs for 1,290 jobs. About eight to one.

At my sustained 15x: 70,000 extra FTEs for 1,290 jobs. Fifty-four to one.

At the 120x peak — which won’t sustain across a whole org, but tells you where the ceiling is — 461 to one.

There is no plausible number on this curve where a CFO can rationally cut the AI budget.

The one wrinkle worth naming, because it changes the calculus for some companies: if you build and serve your own AI products, you don’t just buy the tokens. You buy the compute that produces them. For a hyperscaler, the choice isn’t “tokens versus headcount” — it’s “data centers versus headcount.” That’s part of why a company can post record revenue and still trim staff: the capex bill has to come from somewhere. If you’re the rest of us — buying inference, not building it — your math is simpler. But know which game your company is in before you read your own numbers.

If any of this had been possible in 2025, we’d have seen it then. We didn’t. So what changed?

A few things happened at once, somewhere between December 2025 and February 2026.

The models hit the inflection point on the hockey-stick capability curve everyone had been predicting. Sonnet 4 and Opus 4 weren’t just incrementally better than their predecessors — they crossed a reliability threshold where you could leave them running for a few hours on something multi-step and ambiguous, and trust the output enough to ship it. Claude 3.5 was a smart assistant. Claude 4 was a competent colleague.

The harnesses matured. Claude Code, a curiosity in February 2025, hit $1 billion in annualized revenue by November and $2.5 billion by February — the fastest product ramp Anthropic has had. Cursor’s agent mode became genuinely usable. Sub-agents, MCP, hooks, parallel runs — the boring infrastructure of agentic work all got good in the same six-month window.

The patterns spread. Boris Cherny, who runs Claude Code at Anthropic, started publicly describing a workflow where he runs five parallel Claude instances on five git checkouts, with another five to ten browser sessions going at once. “A few thousand agents overnight,” he says. He ships 20 to 30 pull requests a day. Three years ago that was a parody of how engineering works. Now it’s the template, and dev influencers have spent six months building tools — Straude, ccusage, agent farms — to help everyone else copy the pattern.

The result, for individual operators, is a step change that doesn’t show up in the older studies — because the older studies were run before it happened. Hold onto that timing. It matters later, when we get to the research that says none of this is real.

Open any newspaper this year and you’ll read that AI is taking jobs. Then you’ll read that AI isn’t taking jobs. Then you’ll read a Nobel economist saying it’s actually a 1 percent bump to GDP over a decade. The takes cancel each other out, and most of them are missing what’s actually in the data.

Here’s what the data says.

Challenger Gray, the firm that tracks announced layoffs, reported 1.2 million job cuts in 2025. AI was cited as the cause for only 54,836 of them. The biggest single driver wasn’t AI — it was DOGE, with nearly 294,000. Tech led the private sector at 154,000. So if you’re looking for the white-collar bloodbath in the layoff numbers, it isn’t there. AI is a small minority of the cuts.

But layoffs are the wrong place to look. Look at hiring.

2025 was the lowest year of hiring since 2010. The BLS revised down employment for the year ending March 2025 by 991,000 jobs — nearly a million people who never got hired. Indeed Hiring Lab reports software developer postings sitting 36 percent below their February 2020 baseline. Stanford’s Canaries in the Coal Mine paper, published in August, found that 22-to-25-year-olds in highly AI-exposed jobs have seen a 13 percent relative drop in employment since late 2022. Entry-level software engineering and entry-level customer service are each down about 20 percent. Older workers in the same roles grew.

The single most important sentence in macroeconomic policy right now was published in the November 2025 Federal Reserve Beige Book. Almost no one outside Fed watchers has read it. Here it is:

More Districts reported contacts limiting headcounts using hiring freezes, replacement-only hiring, and attrition than through layoffs… A few firms noted that AI replaced entry-level positions or made existing workers productive enough to curb new hiring.

The Philadelphia Fed put it even more cleanly: AI is letting employers “skip a recruiting class of entry-level workers.”

That’s the story. Not a bloodbath. Not nothing. A long, quiet, structural freeze.

The “AI is cutting jobs” narrative is wrong because the cuts are mostly other things. The “AI isn’t cutting jobs” narrative is wrong because it’s only counting cuts. What’s actually happening is a generation of unfilled requisitions, evaporated entry-level cohorts, and headcount lines that quietly never get added back when somebody leaves.

A layoff is loud. An empty job slot is silent. We’re watching the silent thing.

The funny part is, the people running these companies aren’t being subtle about it. They’re saying it on stage, in memos, on earnings calls. We just keep missing it because we’re scanning for “we cut 8,000 people” instead of “we will not hire.”

Tobi Lütke, CEO of Shopify, sent a memo to his whole company in April 2025 that read, almost word for word: before you ask for headcount, prove that AI cannot do the job. He said he’d seen employees “getting 100X the work done.” That’s not a phrase you smuggle into a memo lightly. He went on the record.

Marc Benioff, on a February 2025 earnings call: Salesforce wasn’t hiring any new software engineers that year. He cited a 30-percent-plus engineering productivity lift from Agentforce, and reportedly committed around $300 million in Anthropic spend for 2026 to back the bet.

Mike Krieger — Anthropic’s CPO, the guy who co-founded Instagram — told Lenny Rachitsky that 90 to 95 percent of Claude Code is now written by Claude Code. Satya Nadella said in May 2025 that AI was writing up to 30 percent of the code on some Microsoft projects; Microsoft cut about 6,000 people that same month. And this spring, Meta cut 8,000 and quietly canceled another 6,000 open reqs— even as its most recent quarter brought in a record $56 billion. They called it “flattening middle management.”

IBM is the case worth studying. Its AskHR agent automated 94 percent of routine HR tasks and displaced hundreds of HR jobs. But the CEO also noted — and this is the part the bloodbath story leaves out — that the savings got redeployed into engineering, sales, and marketing. Total headcount grew.

The pattern is loud once you start listening for it: not “we fired everyone,” but “we stopped hiring, and the people we kept are doing more.”

This would be an easier piece to write if I left out the parts that complicate it. I’m leaving them in, because if you walk away thinking “AI is a 15x miracle, fire your team,” you’ll deserve what happens next.

One thing to hold first: the step change I just described happened between December 2025 and February 2026. Almost every published study skeptical of AI’s productivity gains was collected before that window.

The single most-cited counter-evidence is the METR study from July 2025. A randomized controlled trial: 16 experienced open-source developers, working on their own codebases, with Cursor Pro and the best Claude models available. The result — they were 19 percent slower with AI than without it. And those same developers believed they’d been 20 percent faster. A 39-point gap between perception and reality. Critics of AI productivity claims wave this paper around constantly. It’s the silver bullet.

Here’s the wrinkle. The study was published in July 2025, but the data was collected earlier — spring 2025 at the latest. The developers were using whatever was best at the time: Claude 3.5 and 3.7 Sonnet, an early version of Cursor’s agent mode, no mature sub-agent patterns, no MCP ecosystem. They were running the pre-unlock tools. A year in this technology is the difference between a learner driver and an F1 pit crew. I’d bet a 2026 re-run — same developers, same repos, but Claude Code and Sonnet 4 with sub-agents and parallel runs — would invert the result. METR has said publicly it’s revisiting the study. We don’t have the new numbers yet, but the burden of proof has quietly shifted.

The other two big skeptical findings tell a quality-and-organization story, and both drew on pre-agentic data. GitClear’s analysis of 211 million lines of production code through 2024 found copy-pasted lines rising from 8.3 to 12.3 percent and duplicate code blocks up eight-fold — consistent with the worry that AI favors volume over the structural rework that keeps a codebase healthy. A Faros AI analysis of over 10,000 developers found AI users completing 21 percent more tasks and merging 98 percent more PRs, while organizational delivery metrics stayed flat. Both are real signals worth watching, neither is a verdict, and both measured a world that has already moved on. The 2025 DORA report's line is still worth pinning to the wall: “AI doesn’t fix a team; it amplifies what’s already there.”

And then there’s Klarna. In 2024 the CEO publicly claimed they’d replaced 700 customer-service agents with AI. By 2025 he was quietly reversing course — rehiring humans, admitting customer satisfaction had collapsed on anything emotional or multi-step. The most famous AI-replacement story in business has already boomeranged. The system they deployed was a 2024-era model, not good enough for the job they handed it. Whether a 2026 redo would land differently is the obvious question, and Klarna isn’t running it.

So what do I do with my 15x?

I keep it. And I tell you the truth: I’m a self-selected, agent-native operator whose work happens to map well to what these tools are now good at. I’m in the tail of the distribution. I’m also a self-interested narrator — I want this technology to be transformative because my career is built on it being transformative. Discount accordingly.

But the math demolishes anyway. Take Anthropic’s measured number — 67 percent more merged PRs per engineer per day, not a self-report — and the cost-output ratio in the 5,000-person scenario is still better than two and a half to one. You don’t need to believe my 15x, or Lütke’s 100X, or DORA’s 21 percent. At every published figure, in every study, the AI spend pays back many times over.

Here’s the model I keep coming back to.

A company has two main inputs: capital and people. AI doesn’t change capital much for most companies — tokens are cheap relative to almost everything else. AI massively changes what a person can produce. The output of any given headcount line has just been multiplied. Not 15x for everyone, but materially, measurably more.

That means every company faces a question it didn’t face two years ago: with each person producing more, do you ship more, or do you keep producing the same amount with fewer people?

The answer depends on what economists call demand elasticity. If your market can absorb more — if you can ship more software, sell more services, expand into adjacent products — the rational move is to keep everyone and ride the productivity wave. Stripe. Shopify. Anthropic. These companies are growing their effective output faster than their headcount because there’s more market out there to take.

If your market is saturated, the rational move is to cut. You don’t need 700 customer service agents if 200 of them can handle the volume. So you fire 500. Klarna’s original move was correct in theory; it just turned out the AI wasn’t yet good enough to do the job. The shape of the argument was right.

Most companies are in between. They have some expandable market, but not enough to absorb their current headcount at the new productivity ceiling. So they freeze. They stop posting new reqs. They let attrition do the work. They take credit when revenue per employee goes up. They don’t say “we replaced you with AI” — they say “we’re being disciplined about hiring this year.”

That’s a layoff with a soft edge. It’s still a layoff, in aggregate, across the economy. It just doesn’t show up in Challenger Gray’s monthly report, because there’s nothing to announce.

The Beige Book describes exactly this. The BLS quietly revised down a million jobs that never materialized. The Stanford paper found the freeze landing hardest on the people just entering the workforce. The macro picture is a million unfilled chairs, not a million empty desks.

If you’re running a company with somewhere to grow: this is the biggest leverage opportunity of any tooling decision in twenty years. Find out who your tokenmaxxers are, point IT at the budget instead of at them, and measure people on outcomes per quarter, not hours in a chair. The economics aren’t subtle, and they hold at even the most skeptical multiplier. If your team isn’t spending Yegge’s $80-to-$100 a day per developer, your competitors will — and they’ll ship more.

If you’re running a mature business with limited expansion: the cost-savings math is real, but Klarna is the warning. Take the productivity gains. Don’t take them all the way to “fire the humans.” Customer satisfaction collapses, quality drops, you spend the next year crawling back. Use the savings to fund the next product line instead.

If you’re an employee: the work itself is changing faster than the headlines suggest. The skill appreciating most isn’t writing code or copy or shipping designs — those are getting commoditized. The skill appreciating is directing the agents that do that work. Being the person who runs the system, not the person whose work becomes the system. That’s where the multiplier lives.

And if you’re an entry-level worker in a knowledge job: the recruiting class you didn’t get hired into is not bad luck. It’s structural, it’s still in early innings, and the path forward is to learn to operate the tools that are otherwise eating your job. The cohort that figures this out will compound. The cohort that doesn’t will be the lasting damage from this transition.

The IT email wasn’t an alarm. It was a leading indicator. Somewhere in your company, the first person is already tokenmaxxing. Maybe they got flagged this week, like I did. Maybe they’re about to file an expense report that’s going to surprise someone in finance.

Pay attention to that person. They’re showing you the future of the next budget cycle, the next reorg, the next earnings call where you have to explain why revenue per employee is up and headcount is flat.

The hiring freeze is the AI story. The layoffs are the headline. Don’t confuse them.