Teaching the Machines: Experts, AI Training Roles and the Future of Work (C1 - C2 English reading)

 This is an advanced reading text about the growing number of expert roles in teaching AI and what it might mean for the future of human expertise.

Read the article, reflect on the questions below, and explore key expressions with explanations in English.

Teaching the Machines: Experts, AI Training Roles and the Future of Work

In the past few years a new category of work has proliferated: specialists with domain-expertise (in science, language, regulation, education, analytics) are being asked to review, label, correct and explain outputs of artificial intelligence (AI) systems. These roles might be described as “AI training”, “data annotation”, “model reviewer”, or “domain feedback specialist”. On the face of it, this trend presents an interesting paradox: those who might feel they hold expert, irreplaceable knowledge are now contributing that knowledge to a system which could, in time, replicate or mimic them.

The nature of the new roles

These emerging roles have several characteristic features:

They demand domain knowledge and human judgment (for example, correcting model responses, assessing logic, bias, or domain-specific accuracy).

They often involve tasks that are repetitive or process-oriented (e.g., labeling data, grading model outputs) though guided by expertise.

They sit at the intersection of human work and machine learning: humans help shape the algorithm, but the ultimate output may be machine-driven.

Potential benefits (the positive side)

Amplification of expertise. By working with AI systems, experts may leverage their knowledge more efficiently. Research suggests that AI can augment human skills (especially high-skill work) rather than simply replace them. 

New job creation and higher productivity. Some firms report higher employment growth and productivity where AI is adopted broadly. 

A shift in tasks towards higher value. When routine tasks are automated, experts may spend more of their time on strategic, creative, and judgement-based tasks. For example, roles may evolve from “doing” to “designing” or “supervising”.

Learning and adaptability. These roles can serve as a bridge: experts learn how AI models work and how their domain knowledge interacts with algorithmic systems. This may position them well in a changing workplace.

Key risks and challenges

Substitution risk. While the data is still emerging, some studies indicate that for lower-skill or more routine occupations, AI automation is already reducing demand. For expert roles tied to reviewing and annotation, there is a possibility that once the model is sufficiently trained, fewer human reviewers will be needed, or the cost per review will fall.

Skill devaluation or commodification. When expert judgement becomes a set of tasks (label, rate, correct), there is a risk that the unique value of that expertise is reduced to mechanical processes. Workers might feel their higher-order thinking is under-utilised.

Workplace conditions and fairness. The gig or platform-based nature of many AI-labelling/annotation tasks means issues of worker rights, pay fairness, psychological load (e.g., reviewing harmful content) may arise. 

Unclear long-term career trajectory. Experts may accept these roles for flexibility or income, but the question remains: do these roles lead to sustainable careers? Or are they stepping stones into roles with diminishing human value?

Uneven benefits and increased inequality. Some research suggests that AI increases demand and wage premiums for complementary skills (digital literacy, judgement, creative thinking), while automating substitutable tasks, possibly widening wage and job-quality gaps. 

What this means for experts in “AI training” roles

For someone with strong domain expertise considering or engaged in one of these roles, there are several implications:

View the role as strategic rather than purely transactional: ask how it fits into your longer-term career path.

Focus on developing the complementary skills that models cannot easily replicate: judgement, critical thinking, ethics, context, domain-specific creativity.

Negotiate or choose roles where you are designing, guiding, or supervising AI tasks rather than merely executing annotation.

Be aware of the pace of automation: what looks like expert work today may become standardised in the model tomorrow.

Advocate for fair work conditions, transparency in value delivered, and recognition of higher-order expertise rather than reducing to “for-hire reviewer” status.

Balanced outlook: neither dystopia nor utopia

It would be misleading to claim that AI-training roles are unambiguously good or bad. The truth lies in the balance. On one hand, these roles offer entry into the AI economy, leverage for domain specialists, and opportunity to gain new skills. On the other hand, they carry risks of substitution, commodification, and career stagnation. The ultimate outcome depends significantly on the choice architecture of how organisations structure work, how workers approach their roles, and how society regulates evolving labor markets.

Looking ahead

The key questions for the near future include:

How will the nature of expert labour evolve as AI becomes more capable of higher-order tasks?

What professional trajectories will emerge for those currently in AI-training roles?

How will value be allocated between human expertise, machine automation, and new hybrid roles?

What regulatory, educational, and institutional frameworks are needed to ensure that expertise remains valued, work remains meaningful, and transitions do not produce wide-scale displacement or inequality?

In sum, as we teach machines, we must also ask: what are we preserving as uniquely human in work? And how do we ensure that expertise remains more than a bridge to someone else’s machine?

Written by AI, edited by a human - ...for now. Food for thought 😉

Make sure to check out the useful vocab and discussion questions below the references 😁

References:

1. Wang, K.H. “AI-induced job impact: Complementary or substitution?” (2025) ScienceDirect. 

2. Muehlemann, S. “Artificial intelligence adoption and workplace training.” (2025) ScienceDirect. 

3. “New MIT Sloan research suggests that AI is more likely to complement, not replace, human workers.” (2025) MIT Sloan. 

4. “AI and Labour Markets: What We Know and Don’t Know.” (2025) Stanford. 

5. Rony, M.K.K. “I Wonder if my Years of Training and Expertise Will be …” (2024) PMC. 

6. Economic Development Research Partners. “Artificial Intelligence Impact on Labour Markets.” (2025). 

7. Marguerit, D. “Augmenting or Automating Labor? The Effect of AI Development on New Work, Employment, and Wages.” (2025) arXiv. 

8. Mäkelä, E. & Stephany, F. “Complement or substitute? How AI increases the demand for human skills.” (2024) arXiv. 

9. “AI labour displacement and the limits of worker re

training.” Brookings. (2025) 

10. “The hidden health dangers of data labeling in AI.” (2024) 4SO. 

Useful Vocabulary and Expressions

1. domain expertise – deep, specialized knowledge in one specific professional area (for example: medicine, chemistry, law).

2. data annotation – the process of labeling or tagging data (like text, images, or audio) so that AI systems can learn from it.

3. model reviewer / AI trainer – a person who checks, corrects, and improves what an AI system produces.

4. intersection of human work and machine learning – the point where people and technology collaborate or overlap in doing tasks.

5. amplification of expertise – using technology to make human knowledge more powerful or productive.

6. complementary skills – abilities that work well with AI, such as creativity or critical thinking, rather than the ones AI can replace.

7. substitution risk – the possibility that a job or task could be replaced by machines or software.

8. skill devaluation – when valuable human knowledge or work becomes less respected or less well paid because of automation.

9. workplace conditions – the environment and circumstances in which people work (for example, pay, hours, stress).

10. career trajectory – the direction or path that someone’s career usually follows over time.

11. wage and job-quality gaps – differences between groups of workers in how much they earn or how stable their jobs are.

12. strategic vs. transactional work – strategic means thoughtful, creative, and long-term; transactional means short, routine, or repetitive.

13. commodification of expertise – turning unique human skills into something that can be bought and sold cheaply, like a product.

14. hybrid roles – jobs that mix human and AI work together.

15. choice architecture – the way options or systems are structured to influence people’s decisions (used here metaphorically for job design).

16. meaningful work – work that feels important, creative, or personally satisfying — not just functional.

17. automation – using technology or machines to perform tasks without human help.

18. displacement – when workers lose their jobs because of new technology or economic change.

19. institutional frameworks – the rules, systems, and organizations that shape how societies and economies work.

20. as we teach machines, we must also ask... – a reflective expression meaning we should stop and think about the consequences of what we are doing.

Discussion Questions

1. Would you like to work in a role where you “teach” artificial intelligence? Why or why not?

(Do you think it would be interesting, or too repetitive?)

2. Do you believe AI will replace experts — or will it simply change what experts do?

(Think about your own profession or area of interest.)

3. What kind of human skills will always be important, even when AI becomes more advanced?

4. How can AI amplify human expertise instead of replacing it?

(Can you think of an example where technology makes someone’s job easier or smarter?)

5. What are the advantages and disadvantages of using human experts to train AI systems?

6. Do you think people who train AI should be paid more — or less — than traditional experts? Why?

7. In your opinion, what makes work “meaningful”? Could AI ever do meaningful work?

8. How can governments or companies protect workers from the negative effects of automation?