Lots of attention has been given to the concerns of job losses that can be caused by AI, and I’ve even written of my own concerns about AI’s impact on the job market. However, it is also necessary to explore the upside of AI on jobs and the economy. Here, I’ll provide several examples of how generative AI specifically can lead to new jobs, rather than just taking them, while providing other benefits too. The key is that it isn’t a zero-sum game. We should be able to split up a bigger pie that will have more net benefit than losses. Let’s explore why.
Example 1: The Modeling Industry
Generative AI would appear to pose a major threat to human models. However, where some opportunities shrink, others see growth. In particular, there are many cases where it simply isn’t affordable to hire a model and photographer. Even on Amazon, typically at least 50% of the clothing items shown have no human model included, but simply a picture of the item. This is driven by the need to keep costs down since models and photographers are expensive.
However, with generative AI, we’ll soon be able to see all clothing on a virtual model. Better yet, we will be able to select a model that matches our body type, skin tone, and other physical features. This will allow us to choose clothing with more confidence and will provide us with an improved customer experience. The customers will win. But wait, there’s more!
As we’ve already discussed, models weren’t going to be hired for many of these product photos anyway and so often no models lost work for the customers to win. At the same time, jobs are created for people who build and maintain the generative AI platforms that will allow us to view the clothing on the virtual models. The builders of the platforms will have new jobs and they win. To the extent that the virtual models help sell more clothing, the manufacturers will generate additional sales which will enable them to expand their businesses and hire a range of employees to support it. The manufacturers and their employees win. Of course, sales channels like Amazon also win as they benefit from all the above.
Example 2: The Music Industry
There has also been a lot of handwringing over how generative AI might destroy musicians. However, as with modeling, it isn’t a zero-sum game. Assuming that the licensing and IP concerns artists have about their content being used to train models is addressed (I talked about the lines around fair use of IP here), there are also paths to substantive upside for musicians.
First, to the extent that generative AI starts to create popular songs that nudge aside some human musicians, the humans can then incorporate the style of the AI-generated hits in the same way the AI-generated hits incorporated the styles from its human-generated training data. In other words, human musicians can evolve their craft alongside generative AI. This could help them become better, more creative musicians.
Next, successful musicians stand to receive new royalty streams. Singer Grimes, for example, has encouraged people to mimic her in AI-generated music as long as she gets 50% of the royalties generated. While she can create a limited flow of new music herself, she can have an army of fans creating music on her behalf with generative AI. Those fans will be working a royalty generating job that wasn’t there before while she’ll get a cut of the royalties of each. Both the musician and the AI-creating partner win.
Of course, fans will also win by having an even wider selection of their favorite types of music and artists available to stream. Streaming platforms will also win since they’ll have more music to offer subscribers.
Example 3: Call Centers
We all hate getting caught in a long menu of number pressing to get through to a business. When we do get through, agents on the other end often aren’t very good at assisting us either. Companies are rolling out generative AI tools that provide virtual call center reps able to handle the majority of common interactions. For example, most people have one of a limited number of questions in mind when calling a restaurant.
In the case of a restaurant, it will win by being able to handle more customer calls with speed and accuracy. It can also redirect the time of the greeters from answering repetitive phone calls to focusing on the customers in the restaurant. The customers win by having better service and the greeters win by having fewer mundane phone interactions and more in-person interactions. In the case of larger call centers, call center employees can be deployed on higher value work. Or, if jobs are cut in the call center, that money can be redirected to provide more resources and jobs to other parts of the business.
Can Generative AI Create Jobs?
Naturally, knowing that there are multiple parties benefitting from a new AI capability isn’t of much comfort to the person who has lost their job because of it. However, while unfortunate for some, it is possible that AI can create new jobs, new revenue, and improved service levels that will net out to be of higher value all around than today’s status quo. We’ll be able expand the pie and share it differently. While many people won’t get a piece of the pie in the same way they did before, there will still be opportunities for them to get a piece.
This isn’t to say that things will always work out well for everyone and it isn’t to minimize the real concerns about the potential negative impacts of AI, many of which I share. The goal of this blog is to encourage us to also look for the “glass half full” view of what’s to come and to focus on guiding toward the positive outcomes that are possible while minimizing the negative. If we succumb to a feeling of helplessness and don’t even try to avoid the negative, we’ll certainly end up down that path.
Originally posted in the Analytics Matters newsletter on LinkedIn
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