GenAI and Its Impact on Talent Pools

Is it only just Productivity?

It has been more than a year since ChatGPT stole the minds of many. (Yes, you saw that right, I use “stole”. That will be the topic for another article.)

Many have espoused that GenAI will increase the productivity of many. While I do not dispute this I feel there are possibilities of undercurrents that we may have overlooked. I want to discuss these possible undercurrents because I feel these undercurrents can catch many off guard if we do not pay attention to them.

Pulling up “Average”

If you are familiar with Generative AI, you will know that GenAI models are the “average” of the data it collects. A note that the average here does not mean 50% here. It can be any probability.

Generative AI now sets a benchmark within any talent pool and any tasks that Generative AI is used to perform. That benchmark will split any talent pool into three groups.

Group 1: Talent that performs above average for ALL tasks in the job.

Group 2: Talent that performs above average for SOME tasks in the job.

Group 3: Talent that performs below average for ALL tasks in the job.

Group 1: Above Average for ALL tasks

This group will benefit a lot from GenAI because now GenAI can help move these talents to the average level as the starting point easily for most of the tasks. These are the group of talents that has their productivity increased.

Group 3: Below Average for ALL tasks

This group is likely to be eliminated by GenAI, as their current career is in peril since they cannot even outperform GenAI. This group will need help transitioning to another job function where they can perform better than GenAI. Is this possible and how long will that transition be will be some of the next questions to tackle for this group. Long story short, this group will need to transition and pick up new skills for sure, followed by being more proficient than GenAI. This group is still pretty straightforward.

Group 2: Above Average for SOME tasks

This group will suffer the most. They have to determine if they want to continue with their current career or switch to another job function. This decision by itself can be quite challenging because the talent can suffer from analysis paralysis, to determine if to continue or leave, and loss aversion may work against them. The time lag from deciding to leave or continue might be a killer blow to these folks actually and maybe the group that may need more help from policymakers.

Conclusion

While GenAI will improve productivity, it will only improve the productivity of Group 1 talents (above average on all tasks). Talents who perform below average for all tasks will be forced out of their current career because of GenAI. The talents that perform above average for SOME of the tasks will be struggling a lot more and have a good chance of falling behind in society.

How shall we help this group? To be honest, at the time of writing, I do not have a solution yet. Do you have one? Care to share in the comments for me to think about?

Other thoughts? Please share them with me too! Will love to hear them!

By the way, I am a guest contributor at AI in Asia. Over there I have other content that is in a longer form than my newsletter. Have a look!

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