AI Training Data Companies: The Leading Private Players in 2026

Every leading AI model depends on data, and increasingly on data created by skilled people. AI labs pay experts to write difficult problems, solve them step by step, judge model answers and build tasks that teach models to code, reason and act as agents. That spending has created one of the fastest-growing markets in technology, with several private companies valued in the billions. This guide ranks the AI training data companies in our database by sourced valuation, explains how the work is done and paid for, and covers the risks facing a market that could change quickly as AI itself improves.

Every company in the table links to a full profile with its sourced valuation, funding history and investors. We include private companies whose main business is creating, labelling or evaluating data for AI models. Companies without a disclosed valuation appear at the end of the table.

The ranking

From labelling images to expert reasoning

The AI data industry began with relatively simple tasks: drawing boxes around cars in images for self-driving systems, transcribing audio or tagging text. Large numbers of workers, often in lower-cost countries, did this work through crowdsourcing platforms. Generative AI changed the requirements. To make chatbots helpful and safe, labs needed people to rank responses and write better ones, a process known as reinforcement learning from human feedback. As models improved, the work became more specialised. Labs now seek mathematicians, physicists, doctors, lawyers and software engineers to create problems that challenge the best models and to check their reasoning. Expert time is expensive, which is why spending in this market has grown so fast.

The leaders

Scale AI is the most valuable company in our ranking, valued at US$29 billion after Meta invested for a 49% stake in 2025. That deal changed the market: some AI labs reportedly reduced their work with Scale, concerned about sharing sensitive information with a company partly owned by a competitor, which created openings for independent providers. Surge AI, founded by Edwin Chen, grew without venture capital and reported more than US$1 billion in 2024 revenue, though it hasn't disclosed a valuation from a priced round. Mercor, which runs a marketplace connecting AI labs with experts, reached a US$10 billion valuation.

Talent networks turned data providers

Several companies entered AI data from adjacent businesses. Turing began as a platform for vetting and hiring remote software engineers, then used that network to supply coding data to AI labs; it raised at a US$2.2 billion valuation in March 2025 and reported annualised revenue of about US$300 million. Handshake, the student career network last valued at US$3.5 billion in 2022, launched Handshake AI in 2025 to recruit students, graduates and PhD holders as experts. These companies' advantage is access to large pools of vetted talent, which is the hardest part of the business to build.

Platforms and services

Invisible Technologies combines a software platform with an expert marketplace, and has expanded into helping enterprises deploy AI; Bloomberg reported a valuation of more than US$2 billion in September 2025. Snorkel AI, a Stanford spin-out valued at US$3.5 billion after a September 2026 round, started with software that labels data programmatically and has added expert data services and evaluation tools. The mix of software and services varies across companies, which affects their margins and how investors value them.

How the work is done

A typical AI data project starts when a lab defines what it needs, for example thousands of challenging chemistry problems with step-by-step solutions, or evaluations of how well an agent completes software tasks. The data company recruits suitable experts, tests their knowledge, trains them on guidelines and assigns tasks through its platform. Work is reviewed by other experts and automated checks, and samples are audited by the client. Experts are usually independent contractors paid by the hour or task, with specialised work commanding high rates. The data company charges the lab a premium over what it pays experts, covering recruitment, quality control, project management and technology.

Why coding and agents matter

Coding has become one of the most valuable areas of AI data, because code can often be checked automatically by running tests, making it ideal for training models to reason reliably. AI coding tools have also become one of the largest commercial uses of AI, so labs compete fiercely to improve coding performance. Agentic tasks, where models take actions in software environments, have created demand for new kinds of data: realistic environments, tasks and evaluations that test whether agents complete multi-step work correctly. Companies that can build these environments at scale are in high demand.

Evaluation as a growing market

Beyond training data, companies and labs need to measure how well models perform on specific tasks. General benchmarks quickly become outdated or contaminated, so custom evaluations built by experts are increasingly valuable. Enterprises deploying AI also need evaluations for their own use cases, such as checking whether an AI assistant gives correct answers about company policies. Snorkel AI, Scale AI and others offer evaluation products, and this area may become more important as AI adoption spreads beyond labs.

Business models and margins

AI data companies typically earn revenue from contracts with labs and enterprises, priced by project, task or expert hour. Revenue can grow very quickly when a lab expands a project, but it can also fall quickly if priorities change. Gross margins depend on how much of the price goes to experts and how efficiently the platform manages work. Companies with more software and automation, or those that charge for proprietary tools, can earn higher margins than pure services businesses. Investors pay close attention to customer concentration, because a few large AI labs account for much of the market's spending.

Labour and ethics

The AI data industry has faced scrutiny over how workers are treated. Earlier generations of data labelling relied on low-paid workers, sometimes exposed to disturbing content. Expert data work generally pays much more, but contractors may still face unpredictable workloads, sudden project cancellations and limited benefits. Some companies have been criticised or sued over classification of workers and payment practices. Responsible companies are transparent about pay, provide support and treat experts as valued contributors, which also helps them attract the best talent.

Risks for the industry

The biggest long-term risk is that AI models become capable of generating their own training data and evaluations, reducing the need for human experts. Labs already use synthetic data extensively, though human expertise remains important for frontier capabilities and verification. Other risks include concentration among a few customers, intense competition, pricing pressure as more providers enter, and the possibility that labs build their own expert networks. On the other hand, as AI expands into more specialised fields, from medicine to law to engineering, demand for domain experts could keep growing.

How to evaluate an AI data company

Key questions include how many large customers account for revenue, whether the company has proprietary technology or relies mainly on labour, how it recruits and retains experts, and how it ensures quality and confidentiality. Gross margin trends and net revenue retention reveal whether customers expand their work over time. For investors, understanding whether reported revenue figures are gross or net of expert payments is also important, because they can differ substantially.

What to watch

Watch revenue disclosures, new funding rounds at higher or lower valuations, shifts in AI lab spending, the role of synthetic data, and consolidation as larger players acquire smaller ones. Our guides to the most valuable AI startups, AI agent companies and AI coding companies cover the customers and applications that drive demand for AI data.

Key takeaways

Scale AI leads the private AI data market by valuation, with Mercor, Handshake, Turing, Invisible and Snorkel among other significant players and Surge AI a large bootstrapped competitor. The market has shifted from simple labelling to expert reasoning, coding and evaluation work. Growth has been extraordinary, but customer concentration, synthetic data and labour practices are risks to watch.