Appen
The only pure-play with audited numbers, and a 97% drawdown from peak — the base rate for what a concentrated data vendor is worth when one hyperscaler leaves.
Latest (Sep 2026): FY2025 results (25 Feb 2026): operating revenue of US$230.8M, underlying EBITDA of US$12.2M (up 251%), Q4 gross margin of 45%, and Appen China up 75% to US$102.9M. Appen also gave FY2026 revenue guidance of US$270-300M. Vanessa Liu became Non-Executive Chair on 1 Jan 2026, replacing Richard Freudenstein, and a new General Counsel (Jaime Frasca) started in Jan 2026. At the 22 May 2026 AGM, shareholders approved CEO Ryan Kolln's incentive grants. Market cap was ~A$330M (28 Aug 2026), against a ~US$4.3B peak in 2020.
Appen is the base rate. It is the only pure-play in the expert data vertical that has run a full cycle in public with audited numbers, and everything the private cohort is currently being marked on has already been tested here.
The cycle
| Year | Revenue (US$) | Net income (US$) |
|---|---|---|
| FY2021 | $447.3M | $28.5M |
| FY2022 | $388.3M | –$239.1M |
| FY2023 | $273.8M | –$118.1M |
| FY2024 | $235.2M | –$20.0M |
| FY2025 | $232.7M | –$21.8M |
Source: stockanalysis.com. A 48% revenue decline over four years, with the equity value falling further: peak market capitalisation surpassed the equivalent of US$4.3 billion in August 2020, against A$330M on 28 August 2026 — a drawdown The Verge computes at 97%. (The atlas's own two figures do not quite reproduce that. A$330M against the ~US$4.3B peak converted to Australian dollars gives about 92%; converting the current cap to US dollars instead and comparing to US$4.3B gives about 95%. The 97% is carried here as reported by The Verge, not as computed by this page — the gap is a currency-basis artefact of exactly the sort item 7 of the atlas's contradiction list warns about, and it does not change the finding, which is that essentially all of the equity value went.)
Appen's collapse was not caused by bad execution on labelling. It was caused by buyer concentration plus a change in training technique. Both conditions are present, in more extreme form, across the current private cohort. See One customer is a binary event.
The Google contract
At peak, 80% of revenue came from five clients — Microsoft, Apple, Meta, Google and Amazon. On 22 January 2024, Alphabet terminated a contract worth roughly US$83M, about a third of remaining revenue, as it cut thousands of search quality raters (NBC). Appen shares fell 40–41% in a single day. North American offices closed the following month and executives left through May 2024.
One buyer decision, one third of the revenue, one day. That is the mechanism the entire vertical is exposed to, and it is why Mercor's ~91% revenue share from foundation-model companies is the most important number on its page.
FY2025 — stabilisation, and what it cost
The most recent audited year is more interesting than the headline suggests (Appen FY2025 Annual Report):
- Operating revenue US$230.8M, underlying EBITDA US$12.2M — up 250% from $3.5M.
- Gross margin 40.3%. Higher than Mercor's leaked 33%.
- Appen Global fell 21.1% to $127.9M. Appen China grew 74.8% to $102.9M. The business is now nearly half Chinese.
- Generative-AI revenue rose to 33% of total, from 22%.
- Top five customers = 74.3% of revenue, up from 67.3%. Concentration is increasing, not falling.
- Headcount 1,185; crowd of 1M+ contributors across 200+ countries and 500+ languages.
- Crowd NPS fell from 33 to 22.
That last line deserves more attention than it gets. Worker satisfaction deteriorated even as the business stabilised — the same pattern visible in falling rates at Outlier, Mercor's Musen-to-Nova cut and Handshake's Project HH. The supply pools are shared across platforms, so a deteriorating crowd is a real cost of goods, not a PR problem.
What the market pays
~0.9x revenue — A$330M of market cap against A$361.9M of TTM revenue, both in AUD — on a 40.3% gross margin business that is growing again. Innodata, the other public comparable, trades at ~6.2x on a ~40% gross margin (49% adjusted, Q2 2026) and 40%+ growth. Both sit far below the private marks: Mercor at roughly 29–38x net, Invisible Technologies at ~15x, Handshake AI at ~7.8x net.
Appen at 0.9x and Innodata at 6.2x are not opinions. They are the market's price for this business when it can see inside it. Everything above 6x in the private cohort is a bet that those companies are structurally different from the public ones — and the difference has to come from growth rate and gross margin, because the labour network is demonstrably not what gets paid for. iMerit, a decade-old annotation company with an expert network, sold to EXL for up to $310M in the same quarter Mercor was talking at $20B.
See What the public market pays for labour for the full restatement.
Two revenue figures circulate for FY2025 — US$230.8M in the annual report's operating-revenue line and US$232.7M in the market-data record — and a TTM figure of A$361.9M (+11.5%) is quoted on a different currency basis. The differences are small but they are not reconciled, and the AUD and USD series should never be mixed.
The A$60M placement attributed to 2024 is unverified. So is any claim about Appen's current customer names beyond the audited concentration disclosure.
Building the supply side
Seed supply first, but only as much as the first contract consumes — and answer the utilisation question before anything else, because it is what kills these companies.
Contra Labs
Not a startup — a business line of a six-year-old freelance marketplace, launched five months after a $740K cheque. The template everyone wants to copy, and the parts of it that do not survive inspection.
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.
What the model actually is
Buyers with money but not capability, sellers who are fragmented, and a middleman who organises them and keeps a big cut. Four conditions have to hold at once — and calling the result a marketplace is how it gets mis-priced by a factor of ten.
GMV is not revenue
The same company is worth 13x or 38x depending on which number you divide by. Nearly every headline in this sector quotes the one that flatters — here is how to tell in ten seconds.
How to evaluate one of these
Six scores, one to five, higher always better for the operator — what each is trying to capture, where each misleads, and the three disqualifiers that override any total.
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.
What the public market pays for labour
Below ~40% gross margin the revenue multiple is capped near 1.6x, permanently. Accenture proves the ceiling; Fiverr proves that clearing it is necessary and not sufficient.
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.
How much money is actually in the buyer pool
There are two buyers, not one: about 10–20 labs signing six-to-nine-figure contracts, and several thousand startups buying at roughly $19K. Quoting the $510B headline as a TAM confuses them.
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.
When to stop
Five numbers and one calendar. Write them down before you need them, because every one of them will arrive attached to a reason it does not count this quarter.
How this was built
Eight research passes, about 54,000 words of notes, a writing pass and a verification pass — built with a search budget that ran out partway through, which shaped what is here and what is missing.
Expert networks
$200 to the expert, $800–900 to the client, held for four decades. The oldest version of this model has the most durable rake in the atlas — and may not sell to AI labs at all.
One customer is a binary event
Above 15% of revenue a customer is a coin-flip, above 25% you are a division of that customer — and when your buyers compete with each other, neutrality is the product you are actually selling.
Marketplace, staffing firm, BPO or agency
Almost every company in this atlas is sold as a marketplace and operated as an agency. Two questions separate them, and the answers set the gross margin, the multiple and whether there is a moat at all.
Mechanize
$9.1M of seed capital, ~35 people, no disclosed revenue, and reported Google talks at $1.5B+ — the licence-and-hire template for selling environments instead of hours.
Prolific
The only company in the vertical that publishes its take rate — 42.8% on top of participant pay — and the best public anchor for what this spread actually is.
What better models do to each layer
Three different things get called 'AI will eat this': models doing the crowd's work, models doing the middleman's work, and models creating the budget in the first place. They point in opposite directions and every vertical in the atlas sits in a different one.
Gray Swan AI
15,000 red-teamers paid in prize money; revenue from the software their attacks train. The widest inferred spread in the atlas, on the thinnest evidence.
Distyl AI
$1.8B on an estimated $31M ARR — 58x, the highest multiple in the register, paid for 111 people doing implementation work.
GLG
The largest expert network, a 70–80% take held for decades, and a market share that halved anyway. Its 2021 S-1 is the highest-value document in the atlas — quoted in one research file and recorded as unopened in another.