Professionals are becoming key players in training artificial intelligence and opening up new income opportunities

Human expertise as a resource for AI models

The artificial intelligence sector has started paying experts to use their knowledge to train and evaluate automated systems. Instead of creating algorithms from scratch, these professionals review generated responses, compare alternatives, develop complex scenarios, and identify flaws that a machine would not be able to detect on its own.

Fields that rely on technical expertise, such as law, medicine, engineering, programming, finance, and science, offer the most opportunities of this kind.

How the work is done

An AI model doesn’t just learn from large volumes of text. During and after training, companies need people who can assess the quality of the answers produced by the systems.

A lawyer might receive two answers to a legal question and determine which one is more accurate. A programmer might review code generated by AI and point out errors. A finance professional might check whether an investment analysis makes sense.

These evaluations generate signals that developers use to adjust the models’ behavior.

OpenAI runs an initiative that brings together experts with at least five years of experience in knowledge-based activities. These professionals can participate in AI system evaluation projects and share information about different areas of expertise.

Types of activities in the market

• Answer evaluation: Compare results and determine which one best meets the established criteria.
• Data creation: Develop questions, answers, and examples for use in training.
• Capability testing: Create problems that test what a model can do.
• Red teaming: Try to find flaws, inappropriate behaviors, or vulnerabilities.
• Specialized evaluation: Analyze answers that require specific professional knowledge.

The goal is to transform human knowledge into training and evaluation data that can be used to develop models.

An example of this is a position opened by OpenAI itself for an investment banking specialist, who is responsible for converting professional practices into product requirements, evaluations, and training signals for AI systems.

Remuneration and income prospects

The Financial Times reported in 2025 that AI companies were expanding their hiring of specialists in fields such as biology, finance, and physics to produce more sophisticated data for reasoning models.

Specialized platforms also advertise opportunities. Aced, for example, advertises payment rates that can exceed $100 per hour for highly specialized projects. The rates are provided by the platform and do not guarantee a fixed income.

Payment varies depending on specialty, experience, project, location, and selection criteria. This does not mean that any professional will get rich by training AI.

Professional knowledge gains new value

Knowing how to use a chatbot is a skill, but judging the quality of an answer, identifying inconsistencies, and explaining how a task should be performed require mastery of the subject.

Therefore, the experience accumulated in a profession can be useful even for those who have never worked directly with technology.

AI companies also incorporate these functions. Anthropic maintains teams dedicated to human data, model evaluations, and specialists from various fields within its research and development structure.

How the market works in practice

A professional interested in this type of work usually needs to prove their education and experience in the field in which they intend to work.

Projects can be temporary, paid by the hour, or linked to specific tasks. Some platforms act as intermediaries between specialists and companies that develop AI systems.

It is important to verify who is offering the work, the selection criteria, the method of calculating remuneration, and what data will be shared with the company.

There is also a difference between highly specialized tasks and simpler annotation activities. Not every job related to AI training requires advanced technical knowledge, and the rates can vary significantly.

In the current scenario, “teaching an AI” is not limited to chatting with a chatbot. In many cases, it means applying professional knowledge to measure, correct, and improve the performance of systems.

The result is the creation of a new layer of work around artificial intelligence models: while engineers build the systems, specialists help define what a good answer, decision, or execution should represent.