I asked AI to write an article using a half dozen inputs. Then I wrote one using the same inputs. Both are below.
Economists have spent decades arguing that technology changes jobs more than it eliminates them. Automation replaces some tasks, creates others, and the workforce adjusts over time. That view is now shifting. Recent reporting shows a growing recognition that artificial intelligence is not just another incremental tool. It is affecting large portions of white-collar work in ways that are starting to resemble what happened to manufacturing.
In manufacturing, productivity gains reduced the number of workers needed to produce the same output. Fifty workers who produced a certain number of cars in 1970 can now produce far more. Demand did not increase at the same rate, so fewer workers were needed. Efficiency did not destroy the industry, but it reduced the number of people required to sustain it.
A similar dynamic is beginning to emerge in knowledge work. AI allows a single worker to complete tasks that previously required more time, more people, or both. Drafting, summarizing, analyzing, and communicating can all be done faster. On the surface, this looks like a clear benefit. Workers save time. Companies increase output. Stress decreases. Productivity rises.
But this is only part of the story.
Another recent analysis suggests that the vast majority of people are using AI in a very specific way: to reduce effort. They use it to write emails, draft documents, complete assignments, and move through their workday more quickly. A much smaller group uses AI differently. Instead of replacing effort, they use it to challenge their thinking, test assumptions, and improve their work.
These two approaches lead to very different outcomes.
When AI is used primarily to complete tasks, the worker becomes more efficient. But they also become more interchangeable. If the value of the job is defined by AI-assisted output, then multiple workers can produce similar results. Over time, this makes it easier for organizations to reduce headcount or replace individuals with others who can generate comparable work using the same tools.
In contrast, workers who use AI to improve their reasoning and judgment increase their value in a different way. They are not just producing output more quickly. They are producing better decisions, identifying errors, and adapting to new problems. Their work becomes less standardized and more difficult to replace.
This creates a divide within the same workforce.
On one side are workers who use AI to reduce friction. They complete tasks faster, lower their immediate stress, and meet expectations efficiently. In the short term, this improves their experience of work. In the longer term, however, it can flatten their skill set. If they rely on AI to generate answers without developing their own thinking, they risk becoming dependent on the tool in a way that limits their growth.
On the other side are workers who use AI to increase friction where it matters. They still benefit from speed, but they also use the technology to examine their own reasoning. They ask different questions. They compare outputs. They look for errors. Over time, this strengthens their ability to operate without the tool and to use it more effectively when needed.
The distinction is not about access to AI. Both groups have it. The difference is how it is used.
This is where the comparison to manufacturing becomes more precise. Efficiency gains reduce the number of people needed to perform a task. But in knowledge work, efficiency also changes the nature of the task itself. Work that can be standardized and accelerated becomes easier to consolidate. Work that depends on judgment, interpretation, and adaptation remains more resistant.
As AI continues to improve, these dynamics are likely to intensify. Tools will become more capable. Outputs will become more polished. The baseline level of performance will rise. At the same time, the gap between those who rely on AI for answers and those who use it to refine their thinking will widen.
Some workers will adapt. Others will not. Many will fall somewhere in between.
This is not simply a story about technology replacing jobs. It is also a story about how individuals respond to that technology. The same tool can lead to different outcomes depending on how it is used.
In the short term, using AI to complete tasks more quickly is appealing. It saves time. It reduces effort. It makes work more manageable. But over time, the habits that form around that use can shape what a worker is able to do without the tool.
Those habits matter.
AI will continue to change how work is done. It will increase productivity. It will alter expectations. It will reshape roles. The question is not whether these changes will occur, but how individuals position themselves within them.
I went to a speed dating event in Northern New Jersey in February with a friend. I instantly surveyed the landscape and recognized that I wasn’t interested in anyone, so I used the twelve women I talked to as a focus group to find out how they used AI.
A businesswoman who worked at Revlon told me that she used it to draft all of her emails at work, write reports and help make decisions. I asked her if she was concerned at all about losing skills or her boss finding out. She told me that she is more productive, less stressed and finally has time “to live my life again.” I didn’t have the time or desire to tell her that if I were her boss, I would find a high school graduate who could use AI as well as her and pay them one-third her salary. I expect she’ll find that out herself in the next few years.
A middle-school teacher from Montclair told me that she used it to write her lesson plans. “If it could grade papers and tests, I’d have it do that too,” she happily told me. I asked her if she felt less like a teacher because she wasn’t designing her course. She did not like that question at all. “I’m able to spend more time actually teaching the kids, so it makes me a better teacher,” she said, a bit defensively. Her job is probably safe for a while, because of a teacher shortage and the low expectations of the field. That safety removes the need to develop and improve. Hence the offloading of tasks that are key parts of teaching. I suspect other teachers are doing this also, which means the level of instruction will slowly degrade over time. If true, this is dire for education.
A scientist from a German bio-tech company stated that she used AI to create, analyze and synthesize “just a massive amount” of excel spreadsheets. She said she uses three different AI systems and then cross checks their work to ensure there are no errors. “I am so much more efficient. The agents can create the data sets so much faster than any human worker. Taking away the grunt work allows me to utilize my other skills.” She was the only woman that night who I felt confident would still be employed in ten years.
All three of the women use AI to take care of tasks that they view as dull, tedious and time consuming. What I saw that night lines up almost exactly with what recent reporting is starting to show. A recent Business Insider article stated that at least 95% of workers use AI to think less, while the remaining 5% use it to think more. A New York Times article this morning quoted several economists who have finally come to the conclusion that AI probably will be very disruptive to the workforce, particularly if it continues to rapidly evolve and improve. The workers who are most in jeopardy of either having their salaries reduced or losing their jobs altogether are those who use it to think less. The businesswoman and the teacher are clear examples of professionals who have offloaded work to AI and are thinking less. The scientist uses AI as a collaborative partner; she uses it to challenge her assumptions and discuss the data before going to her team with it.
The professionals who are using AI to offload work, increase productivity and reduce stress are currently enjoying wonderful benefits from AI. But they are unknowingly removing the processes which once made them valuable. By using AI to increasingly complete their tasks, they are becoming more easily replaceable by another person who can do AI-assisted work. A professional with a deep knowledge base who uses AI to challenge their assumptions, test their models, edit their writing and expand upon their ideas is the rare worker who is likely to be inoculated from becoming redundant.
All three use AI. They all use it to complete some of their work. The businesswoman and the teacher are using it to think less. They are getting faster, but not better. The businesswoman will probably be the first to lose her job. The teacher’s job is safe, for now, but I believe that the quality of her work is already suffering. The scientist was the only one of the twelve women I met that night that uses AI to improve her thinking. The other 91% are actively participating in their own career extinction.
It took AI less than ten seconds to write a competent, passable article about how workers are using AI to complete tasks without increasing their knowledge or skills. Mine took about an hour.
AI explains the situation. It’s a boring, monotonous piece. It’s interchangeable. Any AI system could have written it.
Mine makes an argument. It uses real examples. It passes judgement. It’s entertaining and informative and could have only been written by me.
Some people would argue that both approaches work. The difference is obvious.
These are the choices workers are making every day.
