How this 34 year old earns 200 an hour training AI

As artificial intelligence rapidly reshapes the job market and fuels AI market growth, a new kind of knowledge work has emerged: people who are paid to “teach” AI systems how to think more like humans. One 34‑year‑old entrepreneur, featured by CNBC, has turned this into a flexible, high‑paying career, earning about $200 an hour training AI models.

His story offers a window into how everyday professionals can participate in the AI economy without needing to write a single line of code—and why this kind of work is becoming more important as companies race to build better, safer AI tools.

From traditional work to building a business around AI

The entrepreneur profiled by CNBC didn’t start as a machine learning engineer or data scientist. Instead, like many people navigating today’s economic outlook, he experimented with side hustles, freelance projects and remote work opportunities before discovering AI training as a viable business model.

He now runs his own operation, contracting with platforms and companies that develop large language models and other AI tools. These organizations need people who can:

• Review AI-generated responses for accuracy and clarity
• Rank or compare different model outputs
• Provide detailed feedback on tone, structure and usefulness
• Create high‑quality prompts and example conversations
• Flag harmful, biased or unsafe content

Instead of building algorithms, he focuses on what humans still do best: judgment, nuance and context. By positioning himself as a specialist in this work—and understanding how AI products are built and refined—he has been able to charge premium rates and structure his time like a consultant rather than a gig worker.

What it means to “train” AI models

Modern AI systems, especially large language models, rely on human feedback and high‑quality data to improve. This process often includes:

• Reinforcement learning from human feedback (RLHF) – People rate AI responses so the model can learn what “good” and “bad” answers look like.
• Annotation and labeling – Human workers tag text, images or audio with relevant information that algorithms can use to identify patterns.
• Prompt engineering and evaluation – Experts design prompts and evaluate outputs to ensure models respond correctly to real‑world use cases.
• Safety and policy testing – Reviewers test how the AI behaves under edge cases, such as sensitive topics or harmful requests.

The entrepreneur’s $200‑an‑hour work sits at the higher‑skill end of this spectrum. Instead of performing simple labeling tasks, he helps refine how models respond in complex, professional contexts—where precision, tone and reliability matter.

Why AI training work is in demand

As businesses across industries—from finance and healthcare to marketing and education—experiment with AI tools, the pressure to improve quality and reduce errors is growing. At the same time, concerns about bias, misinformation and safety are at the center of ongoing public debates about AI regulation and the broader economic outlook.

Companies are responding by investing more in:

• Human review of AI outputs
• Specialized domain knowledge (e.g., legal, medical, financial)
• Responsible AI practices and governance
• Continuous model monitoring after deployment

This creates opportunities for professionals who understand both their own field and how AI is used within it. The CNBC profile underscores that you don’t need to be a software engineer to participate in AI market growth; you need to understand quality, context and how humans actually use these tools.

How he gets clients and justifies $200 an hour

Instead of signing up for low‑pay microtask sites, the entrepreneur treats his work like a consulting business. According to the CNBC report, he:

• Works with specialized platforms that connect experts to AI developers
• Focuses on higher‑value projects that require judgment, writing and analysis
• Builds long‑term relationships with organizations that need ongoing evaluation and refinement
• Demonstrates how his feedback directly improves product performance and user satisfaction

By clearly communicating the value of better AI performance—fewer mistakes, better user experience, stronger brand reputation—he is able to set rates that reflect the impact of his work, not just the hours spent.

Skills that matter for high‑pay AI training work

While the details of his background are specific to him, the broader skill set highlighted in the article is widely applicable. People interested in this space can focus on:

• Strong writing and communication – Explaining why an AI answer is weak, confusing or misleading in clear language.
• Critical thinking – Spotting subtle errors, missing context or biased assumptions.
• Domain expertise – Understanding a particular industry well enough to judge whether an answer is realistic or compliant.
• Attention to detail – Carefully following guidelines and documenting feedback so models can be improved systematically.
• Comfort with technology – Navigating dashboards, annotation tools and prompt interfaces efficiently.

These are the same types of skills that have long been valued in consulting, editorial work and research—now redirected toward AI systems instead of only human audiences.

Where this fits in the future of work

The CNBC story reflects a broader shift: in a labor market shaped by automation, some of the most resilient roles are those that guide, audit and improve the technology itself. While questions about inflation trends, wage growth and job displacement continue to dominate economic headlines, AI training roles highlight a parallel development—new categories of work that didn’t exist a few years ago.

For knowledge workers, freelancers and entrepreneurs, this kind of AI‑adjacent work can offer:

• Flexible, remote‑friendly income streams
• Exposure to cutting‑edge tools and products
• Experience that may translate into roles in AI strategy, product management or policy

The 34‑year‑old entrepreneur’s $200‑an‑hour rate is not a universal benchmark, but his path illustrates a broader principle: by combining human expertise with an understanding of how AI systems learn, individuals can carve out high‑value roles in a changing economy.

Reference Sources

CNBC – 34-year-old entrepreneur earns $200 an hour training AI models