Back in 2013, I wrote a post called “Will Smart Machines Take Your Job?” in which I skeptically reviewed predictions about how smart machines and software would impact employment. Four years later, I attended a seminar on AI and the Future of Work, where participants concluded that technology is always both creating and destroying jobs, and that AI was unlikely to cause a major reduction in the number of jobs. In 2026, that still seems right to me.
We have all seen the stories about how the current generation of AI is making us more productive. We’ve also heard from numerous tech leaders who say AI will dramatically improve productivity. However, there is no real evidence of that.
Consider this month’s statistics. In the second quarter, US labor productivity rose 1.4% compared with the year before. In the previous quarter, the numbers were even worse, at 0.3%. Neither matches the longterm average of 2.1%, and both are well behind what we saw in the early 2000s when the internet and enterprise resource planning software were booming.
(Note that productivity is defined as gross domestic product divided by hours worked, and the numbers tend to be a bit volatile, a phenomenon that showed up more than usual during the pandemic.)
(Credit: US Bureau of Labor Statistics)
Now we’ve all seen anecdotes from individuals or firms saying this or that task has gotten 50% more productive—but they’re generally not talking about a full business process, and likely not the whole organization. When we look at the overall economy or indeed any large subset of businesses, this productivity boost just does not show up. In other words: Don’t believe the hype.
AI Boosters Have Unrealistic Expectations
This is not what the leaders of the big AI companies were telling us to expect. In 2021, OpenAI CEO Sam Altman predicted that “Achieving 50% GDP growth sounds like it would take a long time… But once AI starts to arrive, growth will be extremely rapid.” In his late 2024 essay “Machines of Loving Grace,” Anthropic CEO Dario Amodei suggested that “a 10–20% sustained annual GDP growth rate may be possible.”
But more recently, these leaders have taken a step back. “I think I was wrong about a few things,” Altman recently said, “but one in terms of the speed: The economy just has so much inertia. People keep doing the same things, buying from the same company, wanting to use their tools the same way… Even with this incredible technology, society and the economy will adapt more slowly.”
The biggest issue is that while individual parts of a job might be getting easier or faster, other bottlenecks often get in the way.
I can’t say I’m surprised. I’ve been writing about productivity for decades, and it’s clear that technology’s impact on the overall economy always takes longer than people think. The biggest issue is that while individual parts of a job might be getting easier or faster, other bottlenecks often get in the way.
Take the area where almost everyone agrees AI has made the biggest change: software development. There’s no question that tools like Codex, Cursor, Claude Code, and GitHub Copilot enable faster code generation, and the newest tools even allow non-programmers to write code.
But in professional settings, that code still needs to be reviewed, tested, integrated, and deployed. (And even that doesn’t matter if you’re not solving the right problems.) All those things still take time—so while some companies have been able to cut junior programmers, I’m hearing from my friends in the space about a need for more senior folks to review code. (In the long run, there’s the question of how junior programmers will become seniors, but we’re not there yet.)
Technology Is Easy, Change Is Hard
The biggest issue is that a new technology almost never impacts productivity—until organizations change their processes to fully utilize it. This can take a long time. Electrification took almost 40 years. After the first PCs showed up in the mid 1970s, it was 20 years before we saw a spike in productivity, leading to economist Robert Solow’s famous quip: “You can see the computer age everywhere but in the productivity statistics.”
Years ago, just about every big organization had a “typing pool.” Now no one does. But it took a surprisingly long time for the number of professional typists and word processors to decline. In the 1970s, people thought ATMs would replace bank tellers, but the number of tellers employed actually rose until about 2010 because the cost of opening a branch declined. (The advent of mobile banking since then has resulted in fewer teller jobs.)
A decade ago, AI pioneer Geoffrey Hinton said, “People should stop training radiologists now,” because AI could outperform humans. Now, though, we need radiologists more than ever, and it’s become clear that combining a human and an AI tool is the best approach. Today, even Hinton agrees with that assessment.
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That doesn’t mean I don’t worry about the impact of AI on jobs. There is evidencethat it may be leading to slower wage growth. I worry that when the current boom in data center construction slows, it could very well lead to more job losses. In the longer run, I stand by the theory that AI will cause some jobs to disappear while others will emerge, just as happens with every new technology.
I’m sure there are some companies where AI really is resulting in the loss of jobs, but for a lot of firms, it seems that AI is just a convenient excuse for having hired too many people coming out of the pandemic, or for dealing with a challenging business environment.
I still think productivity won’t really change unless organizations alter how they work. That doesn’t mean just using AI as a chatbot or to automate a few individual tasks—it means the harder work of examining all the significant processes that make up a business and redesigning them to make the best use of the available tools.
That will likely mean a future in which many business processes will be designed to use software agents (with appropriate controls), either working with humans or under human supervision. Such a future may mean fewer repetitive jobs and more creative ones, including some we can’t even envision yet.
But there will be winners and losers, and we need to ensure that the transition is as painless as possible, and that we keep the essential human qualities of our businesses and other organizations. And that’s not up to the AI providers—it’s up to all of us.
About Our Expert
Michael J. Miller
Former Editor in Chief
Experience
Michael J. Miller is chief information officer at Ziff Brothers Investments, a private investment firm. From 1991 to 2005, Miller was editor-in-chief of PC Magazine,responsible for the editorial direction, quality, and presentation of the world’s largest computer publication. No investment advice is offered in this column. All duties are disclaimed. Miller works separately for a private investment firm which may at any time invest in companies whose products are discussed, and no disclosure of securities transactions will be made.
Until late 2006, Miller was the Chief Content Officer for Ziff Davis Media, responsible for overseeing the editorial positions of Ziff Davis’s magazines, websites, and events. As Editorial Director for Ziff Davis Publishing since 1997, Miller took an active role in helping to identify new editorial needs in the marketplace and in shaping the editorial positioning of every Ziff Davis title. Under Miller’s supervision,PC Magazinegrew to have the largest readership of any technology publication in the world. PC Magazineevolved from its successful PCMagNet service on CompuServe to become one of the earliest and most successful web sites.
As an accomplished journalist, well versed in product testing and evaluating and writing about software issues, and as an experienced public speaker, Miller has become a leading commentator on the computer industry. He has participated as a speaker and panelist in industry conferences, has appeared on numerous business television and radio programs discussing technology issues, and is frequently quoted in major newspapers. His areas of special expertise include the Internet and its applications, desktop productivity tools, and the use of PCs in business applications. Prior to joining PC Magazine, Miller was editor-in-chief of InfoWorld, which he joined as executive editor in 1985. At InfoWorld, he was responsible for development of the magazine’s comparative reviews and oversaw the establishment of the InfoWorld Test Center. Previously, he was the west coast bureau chief for Popular Computing, and senior editor for Building Design & Construction. Miller earned a BS in computer science from Rensselaer Polytechnic Institute in Troy, New York and an MS in journalism from the Medill School of Journalism at Northwestern University in Evanston, Illinois. He has received several awards for his writing and editing, including being named to Medill’s Alumni Hall of Achievement
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