A $75-an-Hour Project That Disappeared After Two Weeks

On October 13, a contributor in Reddit’s r/mercorai_workers community described a frustrating experience with Mercor. They reported being recruited for a project paying $75 per hour, receiving another $550 for onboarding, and completing two weeks of work with positive feedback.

Then the project ended. According to the contributor, the reason was a change in scope.

They subsequently joined another project, completed training and assessments, and watched that opportunity disappear before substantial work began.

Their frustration was understandable. Why would a company spend money recruiting qualified professionals, pay for their training, and then terminate projects while continuing to advertise similar roles?

The discussion expanded into a broader debate about whether AI training companies were genuinely hiring workers or using applications, assessments, and recorded interviews to collect valuable information.

Some participants suspected deliberate data harvesting. Others suggested poor project management, changing client requirements, or recruiting systems designed to maintain an enormous pool of available contractors.

The First Thing to Understand: Human Expertise Is the Product

Mercor provides an unusually direct explanation of its business. Its research division develops expert-generated datasets, evaluations, benchmarks, and complex tasks that AI developers can use to improve their models.

That work can include realistic business scenarios, advanced coding tasks, professional reports, reference answers, and grading rubrics. A marketing professional might evaluate a go-to-market plan, while a financial analyst reviews a model’s interpretation of financial statements.

These completed tasks are commercially valuable because they provide examples and evaluations of work that artificial intelligence systems need to learn to perform.

So when a contributor earns $75 or $100 per hour for creating high-quality evaluations, the company is not necessarily purchasing that time for an ongoing business role. It is purchasing a contribution to a specific dataset or evaluation project.

One completed assignment could be incorporated into a larger commercial product that generates substantially more value than the contractor was paid.

This isn’t inherently unethical. Businesses routinely pay specialists to produce work that becomes part of a larger commercial product.

The problem is what happens when the recruitment experience creates expectations that do not match that underlying arrangement.

Why Are These Companies Still Advertising Jobs That Don’t Seem to Exist?

This is one of the strongest criticisms from the Reddit discussion, and Mercor’s own listings provide a useful explanation.

Some listings are not conventional vacancies. They are standing talent-network applications designed to collect qualified candidates for future client projects.

An official Mercor management consulting listing explains that it remains open even when there is no immediately available project. Qualified applicants may be contacted later if a suitable client assignment becomes available.

That is a rational business strategy for a staffing platform whose clients may suddenly need hundreds of specialized evaluators.

It is less helpful for applicants who interpret the advertised pay rate and detailed description as evidence of a position ready to be filled.

It is reasonable for an AI data company to maintain a roster of qualified professionals.

It is equally reasonable for applicants to expect that the company will clearly identify whether it is actively filling a position or merely preparing for potential future demand.

The industry should be more transparent about the distinction.

Is There Evidence That the Paid AI Training Work Is Fake?

Not convincing evidence from this discussion.

The original poster reported receiving substantial onboarding compensation and completing two weeks of paid work. Other contributors have separately described earning real money through AI training projects.

The companies also publicly describe the specialized datasets, expert evaluations, and other products that their workers help create.

These facts support the existence of genuine commercial work.

They do not prove that every applicant receives work, that all assignments are managed well, or that every contributor receives the level of earnings advertised in recruitment materials.

That distinction is central to evaluating AI training as a side hustle.

The most important missing statistics are not the highest hourly rates. They are the percentage of applicants receiving paid projects, the median number of paid hours available, average time between assignments, and how often projects end unexpectedly.

Those numbers would tell prospective contributors far more about the opportunity than another impressive job advertisement.

The Problem Looks More Like Poor Transparency Than Fake Employment

I think the Reddit discussion raises the right questions, but the evidence does not support the strongest accusation.

Mercor and similar companies have real customers, documented AI development products, and genuine paid assignments. The collection of high-quality professional data is central to their business, not a hidden alternative to providing work.

There is also nothing inherently suspicious about a client ending a project after a few weeks. That can happen when the required dataset is complete, the evaluation changes, or the project is no longer commercially useful.

The more defensible criticism is how these opportunities are presented to applicants.

When a company continuously advertises high-paying roles, conducts extensive interviews, evaluates professional expertise, and maintains standing candidate pools, workers deserve to know whether an active paid opportunity actually exists.

Applicants also deserve clear information about what happens to their interview recordings, assessments, professional histories, and submitted work.

I don’t think the evidence establishes a coordinated scheme to collect interviews under the pretense of employment.

I do think the business model creates a serious mismatch between recruiting expectations and the reality of temporary, uncertain client projects.

And that is a problem worth discussing even when the companies involved are operating legitimately.

We’re Training the Systems That May Eventually Do Parts of Our Jobs

For years, people have worried that artificial intelligence would replace their jobs. Now many of those same professionals are being recruited to help AI systems learn to perform portions of the work they do.

Marketing experts evaluate strategy. Financial professionals review models. Lawyers assess legal reasoning. Developers correct code. Writers help distinguish useful answers from weak ones.

Their expertise becomes part of the material that helps AI systems improve.

That is not necessarily exploitation. Professionals can choose to sell their knowledge, and AI training companies can legitimately compensate them for doing so.

But it raises questions about transparency, ownership, bargaining power, and the long-term value of professional expertise.

The important question is not simply whether AI training platforms collect data. We already know that human-generated data is central to their business.

The more important question is whether workers understand how that data is used, what rights they retain, how likely they are to receive paid assignments, and how quickly those assignments can disappear.

Until the industry provides clearer answers, skepticism from experienced contributors should be expected.