Warning to employees: “You will no longer be judged by how much you know, but by how well you can allocate and manage resources to get the job done”

A fundamental change is coming to the labor market, believes Dan Shipper, co-founder and CEO of Every.to – a platform specialized in technology, productivity and AI. What until now only occupied the minds of managers, i.e. about 1% of employees, is becoming a requirement for more people. Employees are moving into an economy where they “need to know things like how to evaluate talent, manage without micromanaging, and estimate how long a project will take.”

The author starts the analysis from how ChatGPT changed his perception of the world. The ability to synthesize was one of his skills, but since the advent of the chatbot created by OpenAI, he prefers to use this AI means for syntheses, including regarding the emails and articles he writes.

If the way he uses ChatGPT expands in the future, it could have a significant impact on the economy, Shipper believes, as today’s economy is one that is based on knowledge.

What the employee knows and his ability to apply that knowledge in any given circumstance is what now creates economic value. This was primarily driven by the advent of computers and the Internet. This way of working began with the 1970s and has extended to the present day.

If computers can do these things as well as humans, if not faster, then employees will transform from creators to managers. More precisely, the transition will be made from the realization of the work to the allocation of resources: the decision on what work to be executed, if the product is good enough, and human intervention should be only when the final product is not satisfactorily realized.

“It means a transition from a knowledge economy to an allocation economy. You will not be judged by how much you know, but instead by how well you can allocate and manage resources to get the job done,” explains the author.

There are already people who engage in this type of work every day: managers. But there are only about a million managers in the U.S., or about 1.2 percent of the workforce. They need to know things like how to evaluate talent, manage without micromanaging, and estimate how long a project will take. The individual contributors, the people in the rest of the economy, who do the actual work do not need this skill today, but they will have to acquire it in the economy of the future.

Even junior employees will have to use artificial intelligence, which will force them to adopt the role of manager – model manager. Instead of managing humans, they will assign work to AI models and make sure the work is done well, says Dan Shipper.

What qualities will managers need to have?

Managers in the economy of the future must have a coherent vision, know what they want and how to talk about it, assess talent and know when to get into the details.

The better the vision is articulated, the more likely the AI ​​model will execute it properly. As the requests become more specific and concise, the work will improve.

Now the best managers know what they want and how to talk about it. The worst managers are the ones who say, “That’s not right,” but when asked, “Why?” I can’t explain the problem. Managers of the future will face the same problem. The better defined their wants, the better language models will be able to create something coherent for them.

Every manager knows that recruiting is everything. If employees do the work, the quality of the results will be a direct reflection of their skills and abilities. Tomorrow’s managers will have to learn the same things. They will need to know which AI models to use and for which tasks.

Good managers know when to get into the details, know what questions to ask, when to step in, and when to let things play out. They understand that just because something isn’t done the way they would do it, doesn’t mean it wasn’t done right. These qualities will have to be replicated in the economy of the future.

Is this passage good for mankind?

Shipper states that a transition from a knowledge-based economy to an allocation economy will not happen overnight. And to answer the question, he points out that when it comes to changes, how big the dose is determines whether they are poisonous. The economy is big and complex and I think we will have time to adjust to these changes. And the slow shift from human to machine thinking is nothing new. Generative AI models are part of a long-term process.

In a 2013 book, Average Is Over, economist Tyler Cowen wrote about a stratification of the economy driven by intelligent machines. He argued that there is a small elite group of highly skilled workers who are able to work with computers and who will reap great rewards, and the rest of the economy may be left behind.

“If you and your skills are a complement to the computer, your salary and job market prospects are likely to be positive. If your skills don’t complement the computer, you may want to address this mismatch. More and more people are beginning to fall on one side of the divide or the other,” wrote the economist.

At the time, he wasn’t writing about generative AI models, but about the iPhone and the Internet. But generative AI models extend the same trend. The better prepared people are to use AI models in their daily lives, the greater advantage they will have in the economy, and those who know how to manage intelligence will be rewarded.

Shipper concludes that it will be up to our society as a whole to make sure that with the incredible new tools at our disposal, we bring the rest of the economy on track. (Photo: Dreamstime.com)