Seemmingly contradictory processes are at work. We may have reached peak AI, at least in terms of growth of use of the seemingly dominant type, large language models, which are conflated with “generative AI” (sticklers are encouraged to clear up fine points in comments). Several key growth measures are stalling out or even falling. Yet a wave of igh profile employers like Amazon, UPS, and Walmart announcing of big headcount cuts, citing AI implementation as the driver of the firings, says AI uptake is gaining steam. What gives?
And more important, even if AI is not performing all that well in terms of accuracy and reliabilty, that may not matter much as long as it is (barely) good enough to corporate executives to screw down furhter on workers, here in comparatively well-paid white collar roles, to the benefit of their bonuses and shareholders. While an AI bubble crash (and accompanying reports of AI not living up to its promise) may put this trend on pause or even in reverse, if this trend continues at its current pace and intensity, it will further damage the career and income prospects of college educated workers, most of all recent graduates, so as to be counter-revolutionary. It will upend the heretofore tacitly accepted responsiblity of governments and the managerial classes to provide for an adequate level of employment and social safety nets in a system that requires most of the population to sell its labor as a condition of survival. It will also reverse the virtous circle created by Henry Ford1 of giving workers steady enough jobs at high enough pay levels that they could afford to buy the new products made possible by widespread electrification and the mass uptake of automobiles….including in due course suburban homes. And it will further erode class mobilty, aka The American Dream.
The further thinning of the already increasingly precarious middle class will also greatly weaken social stability, particularly at a time when social safety nets are being gutted. Go long bodyguard services.
While we don’t have definitive answers on what is happening in Corporate America, particularly given the diversity of the perps, at least one motivation is to take advantage of the AI mania, still very much alive in the markets, and goose the stock by wrapping what is largely good old fashioned work force squeezing/process re-engineering in the AI mantle (although CEOs are going to some lengths to depict AI implementations as pervasive). We may get a better grip on what is happening internally in the coming months. On the one hand, a well-publicized MIT study found that 95% of corporate pilots of AI did not pan out. On the other hand, there are more and more breathless stories of big employers and professional services firms reducing hiring, particularly of newbies, and even, slashing white collar staff levels.
So is the way to square this circle that AI actually does not perform well, save as a weapon in class warfare?
The Corporate AI Firing Frenzy
Before unpacking the implications in more detail, let’s first look at the apparent magnitude of this trend. We’ve reported before that company leaders, in a sharp departure from histofical practice, are touting running their companies with fewer warm bodies. This pursuit of corporate anexoria is long-standing (and extends to capex); we described it in 2005 in a Conference Review Board article, The Incredible Shrinking Corporation. The Wall Street Journal reported on this new enthusism in June and affirmed by Chief Executive Council:
In today’s rapidly evolving business landscape, it’s no longer rare to hear leaders celebrate layoffs as a badge of honor. CEOs are openly touting staff reductions; not as last-resort damage control, but as strategic wins. Companies are embracing workforce slimming as a deliberate tactic to prop up profitability and signal AI-readiness to investors.
As reported by Wall Street Journal by Chip Cutter on June 27th, 2025.
CEOs Are Shrinking Their Workforces—and They Couldn’t Be Prouder
Big companies are getting smaller—and their CEOs want everyone to know it.
The careful, coded corporate language executives once used in describing staff cuts is giving way to blunt boasts about ever-shrinking workforces. Gone are the days when trimming head count signaled retrenchment or trouble. Bosses are showing off to Wall Street that they are embracing artificial intelligence and serious about becoming lean.
After all, it is no easy feat to cut head count for 20 consecutive quarters, an accomplishment Wells Fargo’s chief executive officer touted this month. The bank is using attrition “as our friend,” Charlie Scharf said on the bank’s quarterly earnings call as he told investors that its head count had fallen every quarter over the past five years—by a total of 23% over the period.
Loomis, the Swedish cash-handling company, said it is managing to grow while reducing the number of employees, while Union Pacific, the rail operator, said its labor productivity had reached a record quarterly high as its staff size shrank by 3%. Last week, Verizon’s CEO told investors that the company had been “very, very good” on head count.
Translation? “It’s going down all the time,” Verizon’s Hans Vestberg said.
The shift reflects a cooling labor market, in which bosses are gaining an ever-stronger upper hand, and a new mindset on how best to run a company. Pointing to startups that command millions of dollars in revenue with only a handful of employees, many executives see large workforces as an impediment, not an asset, according to management specialists. Some are taking their cues from companies such as Amazon.com, which recently told staff that AI would likely lead to a smaller workforce.
A new Wall Street Journal article recaps some of the recent headcount cuts, with Amazon layoff of 14,000 (initially mis-reported by Reuters as 30,000) driving a new set of press sightings:
The nation’s largest employers have a new message for office workers: help not wanted.
Amazon.com said this week that it would cut 14,000 corporate jobs, with plans to eliminate as much as 10% of its white-collar workforce eventually. United Parcel Service UPS 1.14%increase; green up pointing triangle said Tuesday that it had reduced its management workforce by about 14,000 positions over the past 22 months, days after the retailer Target said it would cut 1,800 corporate roles.
Earlier in October, white-collar workers from companies including Rivian Automotive, Molson Coors TAP -2.43%decrease; red down pointing triangle, Booz Allen Hamilton and General Motors received pink slips—or learned that they would come soon. Added up, tens of thousands of newly laid off white-collar workers in America are entering a stagnant job market with seemingly no place for them…
A leaner new normal for employment in the U.S. is emerging. Large employers are retrenching, making deep cuts to white-collar positions and leaving fewer opportunities for experienced and new workers who had counted on well-paying office work to support families and fund retirements. Nearly two million people in the U.S. have been without a job for 27 weeks or more, according to recent federal data.
Behind the wave of white-collar layoffs, in part, is the embrace by companies of artificial intelligence, which executives hope can handle more of the work that well-compensated white-collar workers have been doing. Investors have pushed the C-suite to work more efficiently with fewer employees. Factors driving slower hiring include political uncertainty and higher costs.
Altogether, these factors are remaking what office work looks like in the U.S., leaving the managers that remain with more workers to supervise and less time to meet with them, while saddling the employees fortunate enough to have jobs with heavier workloads.
The cascade of restructurings has created a precarious feeling for managers and staff alike. It is also tightening the options for those who are looking for employment. Around 20% of Americans surveyed by WSJ-NORC this year said they were very or extremely confident that they could find a good job if they wanted to, lower than in past years.
Mind you, this is not the first time we have seen this movie, but this time, the production promises to be on an unprecedented scale. For instance, tarting in the 1980s, big companies started greatly thinning their corporate centers. The leveraged buyout wave precipitated this shift. Raiders targeted over-diversified conglomerates, which tended to be over-manned at their head offices, breaking them up and reducing need for big bureaucracies to ride herd on the operations. Many companies greatly trimmed the size of these activites to make themselves less attractive as prey. The rise of personal computers and improved management information systems facilitated this shift. It also became common simply to fire managers and require the survivors to do what had formerly been one and a half or even two jobs. Similarly, the outsourcing of tech jobs to foreign body shops and H1-B holders may not have reduced headcounts overall (Robert Cringley claims total manning increased) but did make it well-nigh impossible to land an entry level job in software at large companies, as over two decades of frustrated new graduates seeking advice at Slashdot attests.
For another summary (with a preview of broader implications, which we’ll discuss later), see this Breaking Points segment:
Signs of Weakening AI Demand Due
Given all the wonderous things AI is supposedly able to do, a slackening of demand would seem to reflect performance shortfalls. Admittedly these data points are indicative as opposed to dispositive:
Hartley, Jolevski, Melo, and Moore have been tracking GenAI use for Americans at work since December 2024.
They find that GenAI use fell to 36.7% of survey respondents in September 2025 from 45.6% in June.
I wonder if OpenAI, Google or Anthropic are seeing a similar decline… https://t.co/hjnss5u6vj
— Erik Brynjolfsson (@erikbryn) October 22, 2025
A must-read post by Gary Marcus contains more contra-hype signtings. For instance:
3. This also fits with this graph from Apollo Global Management’s Chief Economist Torsten Slok, based on Census Data, from early September:
4. TechCrunch just reported that “ChatGPT’s mobile app is seeing slowing download growth and daily use” based on data from Apptopia
Even with these signs of more and more dogs rejecting the AI dogfood, the C Suite is still heavily populated with true believers. As we indicated above, AI might not have to work well to suit their purposes. In most types of goods and services, crapification has become the norm. Customer service and product qualty too often stink. So the top brass may feel, particularly in a world of too many big incumbents (as in few alternative providers), that they can degrade their offerings further at no/minimal risk.
What Happens as the Gutting of the Middle Class Accelerates?
Cynics might point out that office workers are finally being hosit on their own petard. They participate in, or at least did not object to, the outsourcing and offshoring of US jobs. We have pointed out that in many, arguably most, the business case was weak, even before factoring in the increase in risk due to greater complexity and dependence on outside contractors. But it made perfect sense if you viewed outsourcing as mainly intended to increase the pay and authority of executives and middle managers at the expense of hourly/lower level workers.
One of the big rationalizations of the AI boom, from an economic perspective, is that the implementation of new technology increases prosperity on a broad basis. Aside from ignoring externalities like pollution and the fact that these big changes produce winners and losers, and the winners seldom ameliorate the damage done to the losers, the idea that incomes rise isn’t even always true. The Industrial Revolution in England ushered in at least a geneartion, and according to some studies, two generations of a decline in living standards. Victorian workhouses were one indicator of how bad conditions became for those in the lower orders who failed to find employment.
In Europe, living standards have already been falling due to the sanctions-induced energy shock producing de-industrialization, as in job losses and pay cuts, in tandem with higher consumer power costs and often increased food expenses due to climate change reducing agricultural output. And that’s before getting to reductions in social safety nets due to falling tax revenues (see Russia sanctions blowback), turbo-charged by commitments to militarize even as the US trade wars are adding to economic damage.
In the US, while power costs are not dire (although the AI is increasing electrical bills and more is in the offing), food, housing, and medical expenses are high and genearlly rising. And tariff costs are kicking in as many producers have exhaused inventories stockpiled before Liberation Day. A new Financial Times story confirms that the young, traditionally the most employable, are hardest hit:
Income growth in the US has slowed to a near-decade low, with young workers especially hard hit, in a sign American consumers are struggling with a weakening labour market and persistent inflation.
Real income growth for workers aged between 25 and 54 this year dropped to its slowest pace — excluding periods of pandemic volatility — since the 2010s when the 2008 financial crisis led to high levels of unemployment, according to new research by the JPMorgan Chase Institute.
The slowdown is the latest sign of an increasingly distressed US labour market, and comes as Federal Reserve officials are expected to cut rates on Wednesday.
George Eckerd, co-author of the report, told the Financial Times: “We’re looking at a level of year-on-year growth that’s actually similar to [the 2010s] when the labour market was a lot weaker and the unemployment rate was higher.”
“Inflation is eating into otherwise decent levels of pay gains,” he added.
Keep in mind that the measure of inflation impacts the real income computation. Most experts consider inflation to be understated; measurement have been repeatedly jiggered to produce lower results so as to reduce the disbursements for cost-of-living adjustments, as well as for political purposes. So a higher inflation figure would lower the level of real income, and probably push the claimed growth into contraction, consistent with the lived experience of most Americans.
There are big implications if the US middle class suffers a big leg down in its size and health. For starters, many, particularly the young, took on student debt to go to college and get a “good” job. But employers are now playing musical chairs, except they suddenly removed all the seats save one. What happens when even more fall behind on payments and are hit with penalty interest rates?
And even putting aside the pernicuos effect of student debt, young adults with low and erratic incomes means more living with parents, less household formation (econ speak for getting married and having kids), less home buying.
The much-lowered value of a college degree will blow back to universities in terms of less competition for admission, save at the very top schools, and likely lower enrollment overall. If more parents are telling their kids to become plunbers or painting contractors or HVAC techs, that will also somewhat unwind the Elizabeth Warren “two income trap” phenomenon of parents bidding up housing in school districts seen as being advantaged in college admissions terms.
In the modern era, in societies that do not have religions that justify yawming class/income differences, a combination of rising income levels, the perception of elite comptence, which includes at least some concern for the welfare of the lower orders, and class mobility have been the bulwark of legitimacy. All these pillars are crumbing in the US.
In the Breaking Points segment above, Saagar Enjeti suggested that the AI revolution, if it lives up to its hype, could foment an actual revolution by producing two conditons considered to be key to the Russian revolution: declining living standards and elite overproduction.
Based on the flaccidness of the No Kings protests, which had significant middle/upper middle class representation, I don’t see it. The impediements include atomization and weak social ties, the perception that protests with muscle (as that entail a real risk of police thuggery in response) are the province of losers like union workers (and there is also perilous little knowledge what early labor protestors endured to secure basic workplace protections). More practically, in our high survellance/recordkeeping state, organizing and participating in demonstrations, or worse, being arrested, is sure to be a job prospect killer. So many of the newly-downtrodden quite rationally may conclude that they can’t afford to take a personal stand.
As for elite overproduction being a factor that will amplify protest, given the lack of competence of our elite incumbents, yours truly is not optimistic that those who put themselves on education tracks for those roles are any more able to manage their way out of a paper bag….or organize a revolt.
Again, to use the analogy of the early Industrial Revolution, the enclosure movement, which shifted formerly self-sufficient farmers to factories and cities, did produce William Blake’s “dark Satanic mills” and the eventual 1848 revolts across Europe. However, depending on which historian you believe, that upheaval was either ineffective or produced only some concessions from the ruling classes in terms of improved social protections. But it did not upend the existing order.
That is not to say some sort of dislocation of the existing order is not baked in. But I don’t see it as being something as Manichean or potentially decisive as a revolution. If not a full-bore Jackpot, it’s likely to have an anachic flavor of a big increase in dysfunction, disorder, and desperation.
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1 This move was not altruistic, as some like to pretend. Ford found it hard to find and keep assembly line workers. The jobs still required training yet were tedious and restrictive. Ford found he had to pay his men well in order to have his factories operate on a high uptime basis. But his and other carmakers hiring (on similar employment terms) was on such a scale as to create a massive pool of blue collar elite that could afford to buy cars.
To be fair, the corporate hiring frenzy during the pandemic was too much. They could be cutting jobs and just putting AI into their announcements so Wallstreet loves them more.
Also, if you look at FRED data, median household income has been flat since 2020. The big gains were in the last Trump admin and the end of the Obama administration. So at the median its been no gain for about five years.
In IT I think this is most of the story, and may even be most of it. The factors seem to be:
– reverse to the mean after the pandemic hiring.
– responding to a general slowdown in the economy.
– the collapse of the VC ‘app’ economy.
AI is in the mix, but IT is a hype driven industry. There’s always some stupidity complicating hiring.
There are actually two AI technologies in play, and while they’re related, they have important differences. One is LLMs/generative models (same thing essentially), the other is Machine Learning. While Machine Learning has been massively over hyped, it is still very useful and has many practical purposes. China is investing heavily in this, and its strengths are in control engineering such as industrial processes, logistics and complex engineering. It has limitations (self-driving cars are probably impossible), is not intelligent, requires a ton of training, but used appropriately is very powerful.
LLMs are really just smart text complete. They’re very good at spotting patterns, and then creating facsimiles of those patterns (forgeries essentially). But they’re limited by their training data (which by necessity must be incomplete), complexity (which means above a certain point they require huge amounts of energy and time, for diminishing returns – and there’s not much to be done about that) and the accuracy of those training them (underpaid graduates in the Philippines and Eastern Europe – best I can tell).
This makes LLMs good for repetitive office jobs, where accuracy isn’t critical, the outputs don’t vary significantly and where the cost of the work justifies a couple of nuclear power stations. That stuff exists (marketing, a lot of low level translation stuff), but its a comparatively small market.
For programming, even if this tech works (and it doesn’t really), the cost of the tools for a single developer are going to be more than that developer’s salary, for output that’s essentially similar to a mediocre intern and you still need that developer to supervise. The economics just don’t add up once the VC subsidies disappear. This makes Uber look sane.
Following up on this, a couple of additional points
> Even with these signs of more and more dogs rejecting the AI dogfood, the C Suite is still heavily populated with true believers.
I think it’s always important to remember that many members of the C Suite are not terribly bright and have only a limited understanding of what is happening on the shop floor. They also tend to be terrible with technology – even CIOs, at least in my experience, do not actually know much about IT. They will go to conferences aimed at CIOs, listen to podcasts and read the media selling stuff to them. And then cravenly try to do what everyone else is doing, so even if they fail (and the majority of IT projects fail), they can point to ‘industry trends’. When it comes to technology, the corporate class are sheep. Nobody wants to be the odd guy out.
Senior execs, even at places at Amazon, tend to be bad at identifying which jobs matter. For years corporate America has been dreaming of eliminating their IT department, and there have been many failed attempts. The original outsourcing boom of the 90s (which mostly failed) was an attempt to do this. Developers are expensive, tend to fail more than they succeed (the IT failure rate should be a scandal) and to the MBA class, in ways that accountants or sales people, are not.
When it comes to AI, I think its sweet spot is ‘bullshit jobs’. As someone who’s been marketing adjacent for much of their career, I’ve always been struck by how this is essentially a facts free zone. Does marketing work? Nobody knows. How would you measure that. Even if AI made marketing worse, would it matter? And I’m sure there are other jobs with lots of unnecessary work that could be automated easily enough, even if the results were garbage (a lot of corporate translation work has always been very low quality, to give an example).
Where it will do a lot of damage is in areas where those making the decisions (the c-suite) do not actually understand the work that is being done. This is already happening in IT (the next Y2K will be cleaning up the slop being generated by AI tools), and I’m sure there are other areas such as accounting where all kinds of errors are creeping in without anyone realizing. In addition, as people get used to AI (and I’ve observed this directly), they stop checking the work. I forsee a lot of corporate disasters over the next few years caused by AI slop due to cost cutting.
I also forsee some interesting conversations in the next few years as companies are forced to pay the true cost for AI tools…
re: the C-suite, I agree 100%. Upper management at my company talks about AI so much that sometimes it seems like they are glitching. But you can tell that above a certain level they really don’t know what they are talking about. But my company is owned by one of the biggest private equity companies who in turn are heavily invested in AI companies, data centers, and the power generating to run them. So senior execs at my company are in turn just being pummeled at every turn by our PE overlords with the AI directive. No one gets fired for buying AI is the new “no one gets fired for buying IBM” (for now).
there is still a strong belief in what remains of the american middle and upper-middle class that trumpismo (including AI, crypto, ICE, the whole sordid package) is simply a bizarre aberration and that “soon” things will snap back to the neoliberal status quo of our youth. nobody’s gonna risk everything on mass action if they think the sun will be back out in ’28, and even if they broadly sympathize with the cause they’d rather other folks take the risk. it should also go without saying that even if power was found lying in the street, nobody on the soi-disant american left would pick it up.
For LLMs, I’m going with yes. This is class warfare and the ego-mad fever dream of what Ed Zitron calls business idiots.
It won’t just be a gutting of middle class jobs, the executives will be going as well. Especially since the executive role is symbolic anyway, perhaps the most non-essential of anyone in the org – more so given there are very few real leaders anymore who drive success with charisma and contribution. Their role is merely to be the public or legal face of the org, the signature, the talking head, but since when was a CEO legally liable or responsible for anything? It become an obsolete legal construct. And the BBC has with their AI generated news anchor shown what corporations can do with the C-suite, have they not?
I had a prof once who, by way of introduction to programming languages, used the peanut butter and jam sandwich as an example. There is a repeated pattern to the making of the sandwich, and programming is all about capturing to a logic language that repeated pattern, which can then be interfaced with the real world in some way. This was not said, nor intended by the prof, but think about this – any work which is repetitive in any way can be replaced with an algorithm. Therefore, even without AI or LLM, most modern roles being highly repetitive in nature could be replaced with an algorithm.
A little more low-tech anecdote—-did rounds at the nearby tech/industrial park, full of the US equivalent of Mittelstand.
Only 2 firms (say 16%) had receptionists. everyone else had locked doors and you were met at the door by your party.
…which makes utilitarian sense, but still culturally jarring from being used to an environment in which every self-respecting firm had their own receptionist that greeted you when you exited the elevator. lmao.
There has been a generational change in what constitutes an “essential” employee, and even the weak-sauce LLM-driven AI is going to accelerate that change.
After listening to a interview with Sam Corcos the other day, I’m getting the feeling that the Tech Bros. have decided that their ‘Move fast and break things.’ method is ready for deployment, (Outside of their ‘lane’ so to speak.)
I think that’s a dangerous notion.
In the wider world, there are all sorts of dangerous pitfalls in breaking things you don’t understand how to fix, or things you don’t have a replacement for.
Sam Corcos, Trump’s Chief Technology Officer for Treasury, left me with the distinct impression that the ultimate ( If unstated ) intent of the DOGE program,
besides making Peter Theil the anti-christ, is to take control of the government’s procurement system as a whole, in the same manner that they took over USAID’s .
After chasing the current population of hogs away from that trough, they intend to put ‘procurement’ under much tighter control, and in my opinion, that means a simple re-direct of all that money into a different set of pockets.
The trouble as I see it, is a lot of their plan seems to include ‘Hope,‘ the hope that they can clean up the mess that not knowing enough about the stuff they are about to break is going to cause.
Hope is not a tactic…. and all that.
Which brings me to the massive recent lay-offs by big tech firms;
What the he*l were all those tens of thousands of people doing before they got fired?
I think the Tech Bros. are ‘Hoping‘ to demonstrate the immense productivity gains possible with AI.
This demonstration could be likened to a monster raise in the betting on a poker hand, IOW, what if the big bet, is a bluff. ‘Demonstrate‘ to the investor class, and all your potential customers that AI is already generating gigantic increases in productivity in your businesses, (look at the layoffs) it will do it for you too.
Did the Tech Bros. really replace tens of thousands of important employees with AI?
It’s hard for me to think they had tens of thousands of un-important people on their payroll?
It’s also hard for me to think that whatever all those people were doing, is now being done really well by AI.
I’m not necessarily well informed about AI, but I think it is 99% hype. What they call AI is not ‘intelligence’ but is merely super-computing.
Stoking fear about AI taking over jobs serves to make workers (even if their jobs are actually perfectly safe from AI) more compliant and more exploitable.
I’ve seen videos of how AI may take over the world and cause human extinction. There was a click bait story about how AI tried to murder a human when it believed a human was trying to deactivate it.
The concept of an all powerful AI being is ludicrous. AI is a machine, and machines require electric components and electricity to operate. The idea that somehow AI is going to defend itself, repair itself and ensure a constant power supply to itself to prevent being shut down is fantasy.
AI is never going to be invincible to having it’s very life shut off by a human cutting it’s circuits or otherwise immobilizing it as there are 1001 ways to kill a machine, even with bare hands.
So no, I’m not buying into the fear and hype.
Someone on Bloomberg suggested that AI not tight money is cause for new structural UE.
In my under grad we used slide rules. The calculator and PC did not replace many engineers, but slide rule engineers have a better feel for the math under the solution.
To give positive ROI for the trillions or so in AI bubble building, much of it bleeding edge rapidly evolving HW and SW they will have to replace a huge sum of salary.
Some of these headlines concern the sales pitch.
Long way to go to see an exorbitantly trained LLM compete with a human.
AI absolutely will decimate white collar jobs. And AI absolutely isn’t ready to do that today. Making an analogy to relational databases, a concept generated by IBM in the 1970s, it took a decade from the creation of the idea to a working model by Oracle. It then took another decade for the world to adopt them and create valuable, usable products. LLMs are a result of the idea of the “transformer” (the T in ChatGPT) invented by a small team at Google in 2019. Transformers are what made LLMs actually seem to produce valuable information suddenly. And they really do for a lot of use cases. Over time, companies will move from LLMs to much more specialized models for more specific use cases, and an array of products will get created based on those specialized models and needs. Like relational databases, this will take a couple of decades. Once created though, the decimation process will proceed fairly quickly. I expect to hear a lot of RIF announcements every year for a while. Then I expect a massive cascade, resulting in some form of political chaos.