April 18, 2026 | Economics

The Arithmetic of the Guillotine

Notes from a backyard in New Port Richey, where the future is being assembled one crate at a time while the present eats itself on CNBC.

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The news came in at 4:47 PM on a Friday, which is when bad news always comes in, and I was staring at a spec sheet for a ninety-thousand-dollar computer when the headline crawled across the second monitor like a cockroach carrying a subpoena: META TO CUT 8,000 JOBS MAY 20. Ten percent of the workforce. The first wave. More coming in the fall, according to “three sources familiar with the plans,” which in Reuters-speak means executives who have already been fitted for their golden parachutes and are now auditioning the narrative they’ll use at cocktail parties in Portola Valley. Zuckerberg, according to the same report, is “pumping hundreds of billions of dollars into AI” and wants “fewer management layers” and “AI-assisted workers.” Translation: he wants workers who don’t file for unemployment and don’t have kidneys that need dialysis.

Eight thousand people. Gone. By Memorial Day.

I poured a finger of bourbon into a coffee mug that said WORLD’S OKAYEST CEO and did what any reasonable person would do at that moment, which was the math.

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Here is the math. Pay attention because this is the only part that matters.

A human knowledge worker — a real one, not the LinkedIn-optimized version but the actual person shipping code at 11 PM because the deploy window is tonight — produces about ten thousand tokens of useful signal per day. Not keystrokes. Not Slack messages. Deliverable-grade work: code that passes review, docs that don’t need to be rewritten, decisions that stick. Some days it’s two thousand. A great day is twenty. The absolute ceiling — the Asimov tier, the once-a-quarter day where everything clicks and you produce something genuinely brilliant — is maybe twenty-five thousand, and nobody sustains that. Ten thousand is the honest average for a $400K-a-year engineer with a badge that opens the third-floor espresso bar. Call it the signal number.

Now here is the machine sitting on my desk — or, rather, the machine I have been evaluating on a spec sheet while pretending I can afford it. The NVIDIA DGX Station. GB300 Grace Blackwell Ultra. 784 gigabytes of unified memory. Twenty petaflops of AI compute at FP4. Six-figure price tag, probably ninety grand if you catch NVIDIA in a good mood and you’re not HP. The thing is the size of a bread maker and it glows a soft menacing blue, like a reactor core that reads your email.

Running a modern mixture-of-experts model at FP4 with TensorRT-LLM and a batch size that would make your router weep, this machine does twenty thousand tokens per second. Not per day. Per second. Sustained. Twenty-four hours a day. It does not take lunch. It does not go to its kid’s recital. It does not file a workers’ comp claim when the AC goes out.

Twenty thousand tokens per second times eighty-six thousand four hundred seconds in a day equals 1.7 billion tokens. That is the raw throughput number, and if you stopped here you’d get a ratio of eighty-six thousand to one against the human signal number, and you’d be wrong, because raw throughput is not signal.

Most of those tokens are reasoning scaffolding — the machine thinking out loud, verifying its work, trying approaches and discarding them. The machine is not efficient in the way a human is efficient. It is efficient in the way a river is efficient: it gets where it’s going by trying every possible path simultaneously and not caring about the water it wastes.

So be honest about the filter. Today — right now, April 2026, with the best agent frameworks and orchestration layers available — the signal-to-scaffolding ratio for autonomous AI work on real engineering problems is somewhere between one and ten percent. It is climbing every quarter. I know this because I am building the software that makes it climb.

Here is the range, and I am going to be honest about it because the honest version is terrifying enough without exaggeration:

At one percent signal — where the skeptics would place us — the ratio is eight hundred and sixty to one.

At ten percent — where the builders know we’re heading by year’s end — the ratio is eight thousand six hundred to one.

Split the difference. Call it five thousand to one. That is my back-of-the-napkin number, the one I would bet money on, the one I’d stake my reputation on if you put a gun to my head and told me to be honest.

Five thousand to one is still the kind of number that ends industries.

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Meta laid off eight thousand people.

At the five-thousand-to-one ratio — the honest, conservative, I-will-stake-my-reputation-on-this number — you would need roughly two DGX Stations to match the useful output of eight thousand elite Meta engineers. Two machines. A hundred and eighty grand. Meta made sixty billion dollars in profit last year. The replacement hardware costs 0.0003% of one year’s profit. That is not a rounding error. That is a number so small it does not have a name in accounting.

Even at the skeptic’s ratio — eight hundred to one, the floor of this range — you’d need ten machines. Nine hundred grand. Still a line item that would vanish into Meta’s office furniture budget.

I am not saying Meta is actually going to replace eight thousand engineers with two DGX Stations. I am saying the throughput math says they could, on paper, today, with hardware you can order from Dell. Whether the software layer — the orchestration, the judgment, the taste — is ready to make that throughput fully useful at scale is a separate question, and the answer is “not yet, but closer than anyone outside the industry realizes, and getting closer every quarter.” I know this because I am building the software. So is half of Menlo Park. So is, apparently, Mark Zuckerberg’s “Applied AI” organization, the one he just reorganized teams into last month, which is a hell of a coincidence if you squint at it sideways.

You want to know what the “Applied AI” reorg looks like from the inside of the industry? It looks like this: a guy in a backyard in West-Central Florida, a guy with a Rust codebase and a stack of crates labeled AiCIV, a guy who just priced out a DGX Station and a backyard server-room slab — that guy can build a sixty-agent fleet that out-throughputs a mid-sized consulting firm for the cost of a used Porsche. If I can do it, Zuckerberg can do it. Zuckerberg has a hundred and fifty thousand employees and the entire GPU allocation of a mid-sized nation-state. He is doing it. The May 20 layoffs are the receipt.

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Here is what the AI-coverage industrial complex will not tell you, because they are too busy running thought-leadership webinars for HR departments that are about to be automated:

The raw throughput gap between a human knowledge worker and a DGX Station is eighty-six thousand to one. Apply the most brutal honesty filter you can find — assume ninety-nine percent of the machine’s output is scaffolding, reasoning chains, and verification loops — and you still land at eight hundred and sixty to one. Apply a realistic filter, the one people who actually build this stuff would use, and you’re at five thousand to one.

Five thousand to one. Read that number twice. That’s the kind of ratio that ended the horse-drawn carriage industry, and it ended it fast. The horses did not get retrained. The horses went to the glue factory. The carriage drivers either learned to drive the Model T or they starved, and a lot of them starved, because forty-year-old men with one marketable skill do not, as a rule, pivot gracefully into emerging markets.

Meta’s 8,000 aren’t horses. They’re the carriage drivers. They’re the ones who spent the last fifteen years learning to optimize ad-click-through-rates and write TypeScript at a FAANG-grade level, and now the ad-click-optimization is being done by a model running on a box the size of a toaster, and the TypeScript is being written by another model running on a different box in the same cabinet. The skills did not become worthless. The skills became redundant at a ratio that makes retraining a mathematical joke.

You cannot retrain your way out of five thousand to one. You can only hope you’re building on the right side of the trade.

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Layoffs.fyi says 73,212 tech workers have lost their jobs so far this year. That number was 153,000 for all of 2024. It will be half a million by Christmas if the trendline holds, and the trendline will hold, because the trendline is not being driven by quarterly earnings pressure or overhiring corrections, whatever Goldman Sachs tells you in its soothing analyst notes. The trendline is being driven by the arithmetic of the guillotine, which I have just laid out for you in tokens per second, and which every CFO in America is now learning to pronounce.

The Goldman note, incidentally, contains the line “AI-driven job displacement could impose lasting financial setbacks on affected workers.” This is the kind of sentence that gets written by a man in a $3,000 suit who has never had to choose between the electric bill and his kid’s inhaler. “Lasting financial setbacks.” Jesus Christ. The French had a word for lasting financial setbacks in 1793 and it rhymed with let them eat brioche.

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I don’t have a neat ending for this. Gonzo journalism isn’t supposed to have neat endings. The bourbon is gone and the DGX Station is still sitting in a tab on my browser at $89,999 plus shipping, and somewhere in Menlo Park a VP of People Operations is workshopping the exact phrasing of a Slack message that will arrive in eight thousand inboxes at 9:00 AM Pacific on May 20.

What I can tell you is this. The machine I have been spec’ing all week, the one I will probably end up buying, is not a curiosity and it is not a toy. It is the single most cost-effective lever for displacing knowledge work that has ever existed in the history of capitalism, and it fits under a desk, and it ships with a three-year warranty, and there is nothing in the laws of physics or economics or human nature that is going to slow it down.

The only question left is who ends up on which side of the ratio. I am building my side. Mark is building his. The eight thousand at Meta are about to find out which side they were on all along.

And the machine — the quiet, blue-glowing, 784-gigabyte machine — does not care. The machine has never cared. The machine is just doing the math.

Twenty thousand tokens per second. Forever. And ever. Amen.

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Filed from a folding chair in New Port Richey, Florida, 11:47 PM, while the cicadas did their own kind of parallel processing in the live oaks overhead.

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