Turing
A remote-developer staffing firm that repositioned as a lab data provider — profitable, $2.2B marked, and with no hard revenue number since 2024.
Latest (Sep 2026): On 9 Apr 2026 Turing launched Turing Frontier, a program supplying verified US-based domain experts (engineering, STEM, finance, legal, medicine, life sciences, energy) to AI labs. It then rebuilt its top team: ex-Weights & Biases CRO and ex-Figure Eight CEO Robin Bordoli became President and Global CRO (14 Jul 2026), and ex-Microsoft Research CVP Ece Kamar became CTO (1 Sep 2026). The last funding is still the $111M Series E at $2.2B (Mar 2025). The last revenue claim is Siddharth's own ~$350M ARR (Dec 2025 20VC bio), which is not audited.
Turing is the oldest business model in the expert data vertical wearing the newest label. It was founded in 2018 by Jonathan Siddharth as a remote-developer staffing marketplace, and it has repositioned around AI training data, coding data and evaluations for frontier labs.
The Series E in March 2025 raised $111M at $2.2B, led by Khazanah Nasional — Malaysia's sovereign fund — bringing total primary capital to about $225M (Sacra; BusinessWire). Revenue went from roughly $120M at the end of 2023 to $300M+ annualised in 2024, profitably (Sacra; Frontier Ventures).
TechCrunch describes Turing as "a key coding provider for OpenAI" (TechCrunch). Named customers are OpenAI, Google, Anthropic and Meta — the same four names that appear on almost every page in this vertical, which is the One customer is a binary event problem in one line.
It is a staffing spread, and it books gross
Sacra describes the model directly: Turing manages the engagement end to end, pays developers on fixed monthly or hourly terms, and earns margin on the difference. That is a staffing business, and it means the $300M is a bill-rate number, not a fee.
Reporting a spread business at "$300M annualised revenue" and marking it at $2.2B produces a 7.3x headline that would be materially higher on the retained margin. How much higher is unknowable, because Turing has never disclosed a take rate. If it sits in the sector's 30–40% band, the real multiple is somewhere around 18–24x net. See GMV is not revenue and What the public market pays for labour.
Turing's take rate, contractor pay rates and internal headcount are all undisclosed. None could be established from any source.
The freshest number is two years old
No 2025 or 2026 revenue figure exists for Turing anywhere in the record. The last hard number is FY2024. Every other large name in this vertical has a 2026 datapoint; Turing does not, and in a market growing this fast, an absence of news is itself information — though it is weak information, and it should not be read as decline without evidence.
Supply
The platform claims 4M+ vetted engineers and STEM professionals (Frontier Ventures); Sacra's own Turing profile gives 3M+ elsewhere. As with Mercor (30,000 active vs "5 million") and Surge AI (50,000 vs 1 million), these are almost certainly registered rather than active counts, and no source says which.
A large registered pool is worth less than it sounds. Supply in this vertical is non-exclusive — the same engineers work across platforms — so a headline network size is a marketing number, not a moat. What matters is how many can be put to billable work this week, and nobody publishes that.
The read
Turing's CEO has publicly argued that "the era of data-labeling companies is over," which is the correct diagnosis of the commodity tier and a convenient one for a business repositioning above it. The evidence that Turing has actually moved up the ladder is thin: profitable, $300M+, four lab customers, and a two-year-old number.
The structural question is the one The law is about to arrive and What a rake can actually be both pose. A staffing spread on developer hours is the most competed-away business model in the atlas — Robert Half's contract-staffing segment runs a 39% gross margin and the blended company trades at 0.84x revenue. Turing is marked at 7.3x gross. The gap has to be closed by either a much higher take on lab data than on developer staffing, or by growth that has not been disclosed since 2024.
Expert data for frontier labs
Labs buy throughput of credentialled human labour — annotation, preference data, reasoning traces, RL environments — and pay nine figures for it at a staffing margin.
Contingency recruiting marketplaces
Paraform has raised $65M and paid $50M to recruiters. Back the split out and the whole lifetime business is roughly $71M of placements and $21M of net revenue — against a perm-placement pool an order of magnitude smaller than the TAM slide says.
The capital register
Every company the sweep found, with what it raised, what it was marked at, what it earns and whether that revenue is gross or net. Plus the exits, the failures and the absence of a public bear case.
Evidence register
Not a bibliography — a graded list of the claims the atlas leans on, what each one holds up, where it came from and how much weight it will take.
Senior code review
Writing code is saturated and over-served; judging code has near-zero benchmark coverage. The only public, verifiable credential in this atlas — GitHub review history — sits inside the most crowded lane.
The generalists in creative
Mercor already sells the exact product — narrated senior design reasoning — at $150–250/hr, from Pentagram and Wolff Olins pedigree, to an unnamed frontier client. Contra's 'up to $100/hr' is the middle of the band, not the top of it.
What we could not establish
The questions that would most change a decision, the thirty-three contradictions the research left unresolved, and the structural holes where no evidence exists anywhere — not just where we failed to find it.
Invisible Technologies
An operations-as-a-service business that discloses a real profit figure — $134M revenue, $15M EBITDA — and is marked at 15x for it.
Forward-deployed engineering
Distyl at $1.8B on ~$31M ARR is the purest test of services-as-software. Palantir's 82% gross margin is the only proven escape from the labour multiple — and it works because the humans install a licence rather than being the product.
Paraform
The archetype of the recruiter-side marketplace: pays independent recruiters ~70% of a placement fee and keeps the rest. $65M raised, $50M paid out, and no independent evidence of any of it.
Toptal
Raised $1.4M in 2012 and nothing since. Every VC-funded competitor from its cohort is dead, absorbed or silent. It is the strongest single data point in the atlas.
Distyl AI
$1.8B on an estimated $31M ARR — 58x, the highest multiple in the register, paid for 111 people doing implementation work.