Build the Environments,
Not the Workforce

What the market looks like right now

Demand is enormous and highly concentrated, and it has split cleanly in two: the bottom is collapsing in price while the top is starved of supply.

Synthetic data is a complement rather than a replacement: cited optimal mixes sit around 60–70% human to 30–40% synthetic, and pure-synthetic training risks model collapse. Machines now handle volume; humans set policy, audit edges, and build the environments the machines train inside. That is where the money moved.

The bench

Six categories measured on the axes that decide whether two engineers can actually win: how badly buyers want it, whether a small team can hold a position, how much capital it eats, how fast it pays, and whether it can be delivered from India without a jurisdictional blocker.

Each gauge is five segments; more filled is better. Scores are judgements drawn from the evidence in the dossiers below, not published metrics.

The dossiers

What actually ships, who buys it, what it costs, who already owns the position, and what would stop you delivering it from India.

1. RL environments with verifiers Recommended

What ships

An action space plus surrounding state (file systems, simulated apps, environment variables), usually delivered as a Docker container, plus tasks (a prompt and a grader). Graders are unit tests, state inspection, programmatic checks, or LLM-judges against rubrics. A commercial example: a repo snapshot, task spec, and a verifier scoring each attempt on a continuous 0–1 reward, shipped as "the container and the reward entrypoint," plus reward-labeled agent trajectories. Coding environments usually bundle one task each; a computer-use replica (an Excel, Bloomberg or Airbnb clone) can carry hundreds.

Who buys

Frontier labs (Anthropic; OpenAI and xAI named via job postings), neolabs such as Cursor, and product partnerships (Benchling with Anthropic for biology; OpenAI with Shopify and Stripe for shopping). Environment vendors also subcontract build capacity overseas.

Price anchors

Contracts often six to seven figures per quarter; deal sizes of $300k–$500k cited by one neolab; website replicas around $20k; a complex product replica such as Slack around $300k; per-task $200–$2,000, rarely to $20k for hard software engineering. Exclusive deals cost 4–5× non-exclusive. (Epoch AI survey of 18 insiders, Jan 2026; SemiAnalysis)

Size signal

The Information reported in September 2025 that Anthropic discussed spending "at the level of $1 billion per year on training environments alone." For scale, OpenAI's R&D consumed roughly $19B of about $34B total 2025 spending, and Greg Brockman testified it would spend around $50B on compute in 2026, triple 2025.

Who's there

Specialists: Mechanize (SF, ~$9.1M raised, working with Anthropic, $500k engineer salaries), Halluminate (YC S25, narrowed to financial-services environments, ~$1.3M ARR reported 2025), Prime Intellect (open Environments Hub, 500+ community environments, Verifiers library), HUD, and others catalogued on rl-list.com. The large data vendors (Mercor, Surge, Turing, Handshake) are moving in with distribution, but it is an open question whether labeling operations translate into sound verifier engineering. The field is fragmented and early.

Barriers

Capital: negligible. Skill: high. Reward-hacking resistance takes many iterations; difficulty must be calibrated to roughly a 2–3% minimum pass rate with a smooth gradient and compositional skills. Every interviewee named the same hard problem: scaling task volume without quality collapse. That is a management problem a software-minded team can partly convert into a tooling problem, which is exactly the opening.

Automation risk

Low, and inverted: environments are themselves the machinery that generates synthetic data, so the synthetic shift increases demand here. Real risks are labs in-housing (already "substantially more in-housing") and a flood of vibe-coded clones at the low end: "a large amount of useless bad environments out there."

India delivery

Clear. Environments are non-personal software artifacts, outside the DPDP Act's personal-data scope. Commercial frontier work is open to India-based delivery, and environment vendors already hire overseas developers to replicate site UIs. A US wrapper additionally opens anything government-adjacent.

2. Computer-use & long-horizon agentic trajectories Strong second

A natural bolt-on to environments rather than a separate business: the same engineering that builds an environment also produces the recorded trajectories labs want. Build synthetic capture rather than instrumenting real users, and keep real-user personal data out of scope entirely, because that is what keeps the category clean under the DPDP Act and simple to sell across borders. Full dossier in the interactive assessment.

3. Expert data generation Big pool, weak position

What ships

Credentialed experts author original problems, worked solutions, grading rubrics, evaluations and reasoning traces: production, not labeling. Domains in demand: medicine, law, senior software engineering, advanced mathematics, quantitative finance, accounting, PhD sciences.

The model

Mercor, Surge, Handshake, Micro1, Turing and AfterQuery place vetted experts onto lab projects. Mercor runs about a 35% take rate; The Information, citing internal documents, put gross revenue at roughly $614M in H1 2026 and a $2B annualized gross run-rate by June (up from about $760M at end-2025 and $500M in September 2025), with 91% of revenue from foundation-model companies such as OpenAI and Anthropic. Sacra estimates contractors keep 60–70% of top line, implying H1 net revenue of roughly $180M–$250M. It distributes about $1.5M per day to 30,000+ contractors across 45+ countries, India its largest talent source.

Rates paid

Mercor average around $85/hr (advertised $81–$114). Tiers: $12–$25/hr entry generalist; $25–$53/hr specialized RLHF, coding and translation; $75–$200+/hr credentialed experts. Medical MDs $100–$210/hr with a top decile near $250/hr; offensive-security $200–$250/hr; law, finance and quant in between. (Contractor-reported, 2026)

Scale of rivals

Sacra estimates Handshake reached $1.1B annualized gross revenue in April 2026, up 349% year over year from about $245M, with roughly $450M net after contractor payouts; The Information put its AI-training gross ARR near $1B, up from $550M in January 2026.

Why it's a weak fit

The moat is the credentialed-expert network and the vetting engine (Mercor's APEX AI interview), not technology, and not replicable by two engineers. You would be reselling the same India expert labour the incumbents already recruit directly, with no differentiation.

India delivery

Open but undifferentiated. Only worth pursuing as a jurisdiction-specific vertical where you hold real advantage (Indian law, ICAI accounting, Indian medical boards) rather than head-on.

4. Failure-mode data & red-teaming Skip as primary

Automating fast, and the premium tier (the defence- and government-adjacent work that pays $200–$250 an expert hour) is gated to US persons. That combination closes the high end to an India-based team while the low end erodes. Skip it as a primary business; revisit only as an add-on to environment work. Full dossier in the interactive assessment.

5. Indic & multilingual data Home turf, low ceiling

Genuine home-turf advantage, but the buyer is a thin domestic budget reached through slow government tenders (IndiaAI Mission, CPP portal empanelment, BharatGen, Sarvam). Defensibility is real; the ceiling and the sales cycle are the problem. A reasonable second line once delivery credibility exists, not a first revenue source. Full dossier in the interactive assessment.

6. Physical AI & robotics data Avoid

Real demand, wrong profile. Teleoperation episodes price at $8–$40 each, and earning that requires rigs, physical space, operators, and geographic anchoring: capital-heavy, ops-heavy and slow, the exact inverse of a two-person software team's advantages. Avoid. Full dossier in the interactive assessment.

Price anchors across the market

The single most important structural fact for an India-based team: categories priced per artifact preserve the cost arbitrage, categories priced per expert hour have already competed it away.

Why margin has to come from software

The one hard net-margin figure available in this market is sobering, and it should shape the whole business model.

Invisible Technologies runs roughly 11% EBITDA on about $134M of revenue (Sacra). Human-in-the-loop is a thin-margin business unless automation is layered on top. Note too that headline "ARR" figures across this sector are usually gross marketplace volume before contractor payouts. Turing's economics are explicitly described as a staffing spread, earning the difference between what clients pay and what talent receives.

Your arbitrage survives only where pricing is per artifact. An India engineering team building environments sold at $20k apiece into US per-environment pricing captures the gap. The same team reselling expert hours does not.

Three ways in, ranked

Structure the company as a US wrapper over India engineering, the Turing pattern (Palo Alto incorporation, India-heavy delivery). India-domiciled entities do not appear to win direct frontier RL contracts at scale today, and the wrapper also opens government-adjacent work later. iMerit made the same move: India-founded, now headquartered in Los Gatos, climbing from annotation into frontier expert-data tiers.

What you must clear before serious work

Certifications

• SOC 2 Type II: the baseline security assurance for enterprise and lab procurement. No AI-specific controls, but a standard gate.
• ISO 27001: information security management, usually required in parallel. Sequence this first: holding it cuts the ISO 42001 timeline to about 5–9 months versus 9–14 greenfield.
• ISO 42001: AI management system, increasingly a line item on 2026 vendor questionnaires. Audit fees roughly $25k–$75k for a 51–200-person firm on the accredited path; SMB audit-only $7.5k–$25k. Timeline 3–6 months if mature, 9–14 if starting cold.

Data residency limits

• Regulated and government-adjacent data can require US-based workforces, dedicated no-retention environments, and US-persons-only access (FedRAMP, CMMC, DFARS 252.239-7010, and GSA's 2026 draft AI-safeguarding clause).
• ITAR deemed-export rules make offshore access to controlled technical data a licensing question, not a staffing choice.
• These close defence-adjacent and classified work to an India-based team, but not commercial frontier RL and coding environments.

India: DPDP Act

• Rules notified 13 November 2025. A permissive blacklist model under Section 16(1) and Rule 15: data may transfer anywhere except government-restricted countries. No standard-contractual-clause or adequacy regime.
• Government retains discretion to restrict specific corridors or categories, and Significant Data Fiduciary designation could trigger localisation.
• Crucially, DPDP governs personal data. Synthetic RL environments, code, and non-personal agentic traces largely fall outside its scope, another argument for the recommended category.

Worker classification

• The US is in a litigation wave: class actions against Scale AI (including a PAGA suit alleging roughly $15/hr effective pay against California minimum wage, and a traumatic-content claim), Surge AI (Clarkson, San Francisco Superior Court, May 2025: misclassification, unpaid training, sub-minimum effective wage) and Mercor (Texas, May 2026: ERISA and Internal Revenue Code claims). The pattern is operating like an employer while classifying workers as contractors.
• India's four Labour Codes, in force 21 November 2025, formally recognise gig, platform and aggregator workers, mandate appointment letters for all hires, and fund social security through aggregator contributions of 1–2% of turnover, while preserving non-employee status. Karnataka (2025) and Rajasthan have dedicated gig-worker acts.
• Employing a small India engineering team, rather than gig-contracting it, sidesteps this risk entirely.

Twelve months, three stages

What would change the plan

• Labs accelerate in-housing environment construction. Pivot to applied-AI companies and neolabs that cannot build their own.
• Commodity vibe-coded environments crater per-unit prices. Move up-market into verifier robustness and reward-hacking QA as a service.
• US wrapper plus certification proves too slow or costly for two people. Fall back to a specialised expert-data vertical using India's domain supply (Indian law, medicine, accounting) and accept lower margins.
• Cross-border training-data flows come under export scrutiny. Watch this: Forbes reported in August 2026 a roughly $500M US-to-China training-data trade, with AfterQuery earning about $50M from Chinese labs. Single-source, but it signals a regime risk for any cross-border data vendor.

How much to trust these numbers

Compiled September 2026 from Epoch AI, SemiAnalysis, Sacra, The Information, Time, Contrary Research, Pebblous, The Business Research Company, Counterpoint, SalaryExpert, Precise BPO, Shaip, AI4Bharat, and company and government sources including MeitY and the IndiaAI Mission.

Prepared as a market-entry assessment. Not investment advice. Figures are dated and, in several cases, estimates or single-source; see the reliability notes above.