"Each time one person contributes data to improve an AI model, that model becomes better — not just at doing that individual’s job, but at doing the job of everyone in that role, anywhere.

In that sense, workers can end up competing against one another by supplying data too cheaply to companies or intermediaries that recruit people to train AI to do their jobs. When this happens, individuals may unintentionally undermine the bargaining power of others in their occupation.

If the goal is to ensure that workers share in the gains from AI, co-ordination may be necessary. Without it, individuals may strengthen the very systems that weaken their collective bargaining power. Instead, co-operation can benefit workers and companies alike. If fear about career risk leads people to hold back knowledge from AI systems, productivity may suffer. Smart companies will know that finding ways to recognise workers for their talent will ensure that they continue to supply it.

What, then, is to be done? As workers, people should think about how to use AI to expand their skills: whether by building complementary capabilities or by finding ways to scale their expertise through AI systems. As citizens, they should press for policies that give workers clearer rights over the data generated by their work and compensation for it."

https://www.ft.com/content/23c70905-147d-4213-8c30-c43e3bde7fec

#AI #AITraining #GenerativeAI #Automation #Productivity