What happens when a big tech firm re-assigns thousands of engineers to make training data?<br>← Back to Research<br>In spring 2026 Meta forced roughly 6,500 engineers and product managers into a new unit, reported as Applied AI and known internally as Agent Data Optimization (ADO), to manufacture training data for its models: coding problems, the tests that check them, and grades on AI-written code (WIRED; The Pragmatic Engineer). The reassignment was involuntary, employees have called the work soul-crushing, and the unit has been described as a gulag (TechCrunch).

Some reactions predict a death spiral, but I don't think that will happen. I predict a lot of attrition, and Meta not catching up much on the AI frontier.

A few other forecasts I did: I don't think "Muse Spark" is coming out anytime soon, and I don't think Facebook will entirely cancel their internal surveillance program.

Forecast<br>Median<br>50% range<br>80% range<br>Next flagship model after Muse Spark<br>May 2027 Feb to Oct 2027<br>Nov 2026 to Mar 2028<br>MCI surveillance fully terminated by end-2026<br>5% n/an/a

The story broke into the open on June 12, when WIRED reported an internal meeting where employees confronted Zuckerberg and TechCrunch detailed conditions inside the unit (WIRED; TechCrunch). Around 4,500 to 5,000 of the conscripts are software engineers, roughly one in five or six of Meta's 25,000 engineers (The Pragmatic Engineer).

Keep two things about these numbers in mind from the outset, because much of what follows anchors to them. The 6,500 and 4,500-engineer figures, along with the leaked internal details, trace back to a narrow source pool, principally WIRED and Gergely Orosz's Pragmatic Engineer, both relying largely on anonymous current employees, plus The Information and Reuters. Meta has not published them. They are consistent across outlets but not independently confirmed.

Why do it at all? I tweeted about this at greater length, but the short version is that it fits Meta's history. Unlike Apple, Microsoft, or Google, Facebook never built a defensible technical core: its ad engine was borrowed from Google, its biggest wins were acquisitions, and it nearly missed mobile. Zuckerberg has spent the years since the 2012 IPO cash-rich but hunting for a proprietary edge, from the Facebook phone to Libra to Oculus. AI has run the same arc. The open Llama models were the first try and stalled on talent and infighting; paying superintelligence wages for outside researchers and $14.3B for 49% of Scale AI, whose founder Alexandr Wang now runs Meta Superintelligence Labs, was the second and did not obviously work; and conscripting his own engineers to manufacture a unique, defensible dataset is the third. It is dystopian, and he will probably lose some of his best people, but it is a recognizable move: spend the resources you actually have, an army of strong engineers and a business that throws off around $25B a year in profit.

The technical logic is a training-data ceiling. Synthetic, AI-generated data works well until a model needs to surpass the systems that produced it, and pushing past the frontier on hard coding and agentic tasks then takes novel, human-authored examples with no online counterpart (TechTimes). In leaked audio from an internal meeting, Zuckerberg reportedly argued that Meta's own engineers carry significantly higher intelligence than outside contractors, his case for conscription over hiring (TechCrunch). A parallel program, the Model Capability Initiative, puts software on US employees' machines to capture keystrokes, mouse movement, clicks, and periodic screenshots as computer-use training data (Reuters, via CNBC).

The first link is whether Meta sticks with the bet, and I think it does, on two signals that point the same way.

I put ADO at a median of about 4,600 people by December 31, 2026, down from roughly 6,500 at formation. That is a real contraction, close to 30%, and still well short of a dissolution. The drawdown comes from three sources, and only one of them is people leaving the company: elevated voluntary attrition, internal transfers, and quiet reclassification of the work into ordinary applied engineering. Two forces keep the contraction modest. The first is bureaucratic inertia plus a no-layoffs pledge. Zuckerberg's June 12 memo ruled out further company-wide layoffs in 2026 and called the model-training push transitional, promising to find new roles for stuck engineers (Reuters), though Meta's capacity to absorb thousands of transfers is limited, because the same 2026 restructuring closed open roles across the company (Reuters). The second force matters more for the commitment thesis: the next flagship is not expected until roughly the middle of 2027, so the data-generation engine has to keep running through 2026 to feed it. That is why a hard wind-down, the low tail of this forecast, looks unlikely to me.

The surveillance program tells the same story. I put the chance that Meta fully and permanently terminates...