Why does AI seem so focused on our jobs?

I’ve written before that adopting AI often means automating jobs, but this, in my view, is not our only choice . Having said that, I think it’s worth stepping back to ask, “Why does this keep happening”? Invention after invention seems hell-bent on replacing us. For example, perhaps you saw this product announcement for the world’s first, fully autonomous, AI software engineer.?

What’s with that??

The answer is, perhaps, unsurprising. Inventors find it easier to copy what humans do. Instead of trying to invent a machine that does entirely new-to-the-world work, which creates abundance and value for us all, they reverse engineer work that they already know is valuable–popular jobs.?

Just take a look at how the company above describes their mission: “We’re building AI teammates…By solving reasoning, we can unlock new possibilities in a wide range of disciplines—code is just the beginning.”

For an engineer, it’s relatively easy to look at an existing task or team to ask: “Could I make a machine that does some/most/all of this”? The harder alternative might be to ask, “What new or better result could this person, team, or company achieve if combined with machine strengths”?

The latter requires finding new, valuable outputs, not just cheaper inputs (ie work activities). And, as you might imagine, finding new value is quite a bit more ambiguous.?

As per my usual approach, let’s look at this in a geeky way. In their book about building a better healthcare system (2006), Michael Porter and Elizabeth Teisberg proposed that value is equal to a satisfactory outcome (or quality) over the costs (or time) to produce it.

When I streamline an existing task, I do so already knowing the outcome. I reduce direct and indirect cost, and then ensure the outcome’s quality and desirability using past performance. To increase the value result, I just need to prove I can get the result faster or cheaper.?

On the other hand, to improve the outcome or its quality I need to discover a deeper and more meaningful measure of value. This could be an unfulfilled need or aspiration currently beyond our grasp. For example, in healthcare, it might be improving lifespan or curing cancer. In HR, it might be helping people utilize their potential or self-develop.

Why wouldn’t we want to work on both halves of the equation? Because, when you have such a large and diverse range of valuable autonomous agents (ie people and their jobs) to copy, why would take the risk to build technology to do something new? It’s easier to focus on the denominator.?

But perhaps we should consider this. How will we get to a better world if not by inventing it? Also, financially, we know inventing new categories is often superior to simply streamlining existing ones.?

After spending a few years (via some work at the LSE ) looking into the propositions of thousands of AI companies, I found this. About a third of new AI products want to outright copy a job in whole or part. The rest are doing some form of reinvention of how an outcome will be achieved. They might do so by helping a person or through a new capability.?

And then, when we take the extra step of looking at what happens in a company when AI is adopted, as this 2021 study did, it seems that the companies who use AI don’t replace jobs or retire them. Instead, they hire more. The individuals that suffer work for firms that don’t use or delay their adoption of new technology.?

Technology isn’t good or bad. What matters is how WE choose to use it.?

This leads me to where I ultimately stand. AI could be both grand and terrible for human work. How far it shifts to either depends on what we choose to invent, and how we decide to buy. So, whether you are a maker or a user, you have a role to play in shaping AI's future.?

Late last year, I spent a few minutes asking HR leaders deliberately shape AI’s role in their workforce. Since then, many have taken up the torch to do just that! It's been heartening to see. I hope you’ll do the same.

References

Porter, M. E., & Teisberg, E. O. (2006). Redefining Health Care: Creating Value-Based Competition on Results. Harvard Business Press.

Dixon, J., Hong, B., & Wu, L. (2021). The Robot Revolution: Managerial and Employment Consequences for Firms. Management Science, 67(9), 5586-5605.

Yoon, E., & Deeken, L. (2013). Why it Pays to be a Category Creator. Harvard Business Review, 91(3), 21-23.

We Are All Agents

AI is rapidly shifting how we organize and work. But we can’t just react to technology if we want a human-centered future. We Are All Agents in shaping change. Let’s help technology and talent work better together.

We’ll share new ways of thinking about and working with machines discovered in our own research and from the work of our guests. Our mission is to help talent, leadership, HR, and developers build more human-centered organizations. How we create, assign, and organize work, is all on the table for change. Let’s use this moment to make our work even more valuable for organizations and people alike.?

If you share this mission and would like to collaborate, or have a question you need help with, sign up for office hours . To learn more about us, here’s our website . We look forward to learning about your work as well. And until then, be brave.