The Slowdown: Why?
Causes and contributions to the current US economic malaise
Audience: Job seekers, management, owners, investors and decision-makers
Contents
Executive Summary
Introduction
· Blowback from the bad job market: future skill shortages
· Nine big factors and a few smaller ones
AGI Investments and Unprofitability
IRS Section 174 R&D Amortization (now “fixed”)
Political Factors (tariffs, trade wars)
Macroeconomic Uncertainty and Inflation
AI and Automation Impact on Workforce
Supply Chain Disruptions
Talent Shortages and Hiring Freezes
Regulatory and Cybersecurity Risks
Market Valuation Bubbles and Investor Caution
Conclusions
End Notes
· LinkedIn
· Substack
Executive Summary
The U.S. economy in 2025 is experiencing a noticeable slowdown, despite a relatively healthy 4.2–4.4% official unemployment rate. White-collar job seekers, particularly newcomers and older workers, face unprecedented challenges, with many sending out over 1,000 applications and enduring prolonged silence from employers. Amazingly, help for them is right at their computers and cell phones, did they but know. We have also shared the conventional pathways (necessary but not sufficient) to getting hired in an earlier article, which covers common actions expected of job seekers.
This article examines nine key factors contributing to this malaise, accounting for roughly 95% of the slowdown: overinvestment in unprofitable artificial general intelligence (AGI, 20%), tariffs and trade wars (15%), macroeconomic uncertainty and inflation (15%), AI and automation impacts (15%), supply chain disruptions (10%), IRS Section 174 R&D amortization effects (10%), talent shortages and hiring freezes (5%), regulatory and cybersecurity risks (5%), and market valuation bubbles with investor caution (5%). These overlapping factors create a complex web of challenges, including ethical and legal risks, job market frustration, reduced corporate investment, and disrupted supply chains. None of them are fixable by individuals.
The exodus of disillusioned job seekers from white-collar industries, driven by layoffs, ghost jobs, and unresponsive hiring processes, foreshadows future skill shortages, exacerbated by declining birth rates and distrust in corporate employment. While no solutions are proposed here, the article underscores the urgency for good-willed industry and policymakers to recognize these dynamics, and soon, to mitigate long-term economic damage.
We strongly believe the behavior of companies, particularly management, with respect to employees and job seekers is setting up near future, extreme skill shortages, as frustrated job seekers leave the market for greener pastures, or even mere survival. Today’s managers are sowing the seeds now for the mother of all persistent employees’ markets not too long from now. The costs will be enormous, and won’t be mitigated to any significant degree by AI.
Over-leveraged companies, poorly managed companies, and companies with toxic or otherwise predatory management will be especially vulnerable as job candidates and employees increasingly take proactive and / or defensive measures, and wise up. Once diligent company research, amplified by readily available AIs, becomes common place among job seekers, toxic managers and companies are likely doomed. Moreover, it is becoming increasingly obvious to savvy AI users that artificial intelligence will very soon be more likely to replace management than individual contributors.
Introduction
In this brief study, we discuss the collection of causes for the current growth slowdown of the US economy. As of this writing, the economy is still relatively healthy, with only about 4.2 - 4.4 % unemployment; however, newcomers to and older workers in the white collar labor market are experiencing difficulties in getting hired, to the point that some candidates for jobs have spent over a year looking for work and have sent out more than 1000 applications. They overwhelmingly report little to no responses to their applications, and have experienced significant frustration and stress as a result.
Economics is a complicated topic; the various factors we discuss here are far from mutually independent, and often have considerable overlap. Our purpose with this article is to show, as best as can be found, what is happening to slow economic growth. We explicitly avoid proposing solutions for the issues described here, reserving such topics for future articles where each contributing factor can be studied at some length and reasonable solutions proposed and examined for practicality.
Blowback from the bad job market: future skill shortages
We can point out that the extreme difficulties job seekers are experiencing, now have already, and will continue to motivate, more and more of this cohort to seek alternatives to white collar employment, whether by taking jobs in unrelated sectors of the economy, starting their own businesses, or resorting to other means to pay their bills and stay alive. This does not augur well for the future of industry; when people leave a job market, disillusioned by long, fruitless job searches, few of them return. They develop “a bad taste in their mouths” for white-collar employment, just as we did decades ago after being laid off from the semiconductor industry.
Those layoffs, and equally frustrating attempts at later securing employment, were bad enough so we never came back. We (rightly so, it turned out) assumed the industry was far too competitive, far too dog-eat-dog, too volatile to trust, and retreaded to other alternatives. That process took years, and was essentially irreversible. We can remember hearing radio advertisements years later, practically begging people to apply for jobs in the semiconductor industry. No, thanks!
Similar things are happening now. People, by the hundreds of thousands worldwide, are gradually becoming fed up with industry practices, with ghosted job applications, layoffs, perceived incompetent management, huge barriers to entry, and a host of other devils. Once jaded, these job market candidates find alternatives to working for companies, creating persistent, profound and permanent future skills shortages.
The management which orchestrates these fiascos will live to regret the over-hiring, the layoffs, the hyper-competitiveness and mean-spirited, callous behaviors they perpetrated. Already, in nearly every country on earth, birth rates have slowed to well less than replacement levels, a trend which began decades ago. People who must live with constant employment uncertainty are understandably skittish about starting families, and delay marriage or having children.
There won’t be many new programmers, engineers, administrators, etc. in a few years. Today’s management, intoxicated by the current employer’s market, are setting themselves up for a very long employee’s market in the not too distant future. Future recruitment success will absolutely depend on an employer’s reputation, in particular, how they treat their employees. That is bad news for today’s first-line and middle managers; by the time they make it to senior management levels, there many not be enough industry left to employ them.
Nine big factors and a few smaller ones
In examining the causes of the slowing economy, we consider nine factors: overinvestment in artificial general intelligence (AGI); the lingering effects of the very foolish IRS section 174 R&D amortization, now rescinded; the tariffs and trade wars kicked off by the tariffs; macroeconomic uncertainty, inflation; AI and automation; supply chain disruptions; hiring freezes and talent shortages; regulatory and security risks; market bubbles and the resulting investor caution; and a list of lesser factors which contribute less than the 5% threshold, but nevertheless further slow the economy.
Some of these factors we have already considered; in particular, the question of narrow AI versus AGI. We’ve written about (and been upset by) the Section 174 tax provisions. The debacle reinforced our attitude that Congress should be recycled, almost entirely. Even though apparently fixed now, the damage has already been done. We have also repeatedly commented on AI’s role in automation (minimal due to reliability issues with AI) and the likelihood of AI replacing large numbers of human workers (low, low, low).
AGI Investments and Unprofitability
Estimated economic impact: 20% of total
Issues: Large, slow-to-monetize investments, profit pressure
As far as we have been able to determine, none of the largest AI companies are even in the same zip code as profitability, as far as income from their AI offerings are concerned. The monetary amounts of investment in AI has been staggering. We often wonder how investors can expect to make any profits at all, and see early signs that investors are pulling back from their previous faith in AGI.
We’ve seen training costs for a single AGI model as high as $190M; this number may have been exceeded since we last looked. This particular cost is equivalent to the annual costs of between 950 and 1900 employees; we have doubts that the AI provides value anywhere close to that many people.
As an example, we briefly considered paying for what we perceived as the best of breed AI, Anthropic’s Claude, and even began the process. Fortunately for us, the process failed; frustrated, we gave up trying. We’re very glad we did, for as it turns out, we discovered that we rarely exceed the token usage that triggers hours of delays before we can resume. Moreover, we came to understand that none of the current crop of AGIs can be relied upon to produce quality answers; nearly 100% fact-checking of the results turns out to be necessary for responsible use.
That need for rigorous quality control obviates much of the benefit of using AI in the first place. We cannot predict, in advance, where the things might hallucinate, where we have to be very careful, nor the degree of hallucinations we might encounter. It’s a crapshoot, and, currently, not worth paying for.
We are fairly certain almost all other long-term users of AI are in the same boat, to the extent that using the current crop of AIs to routinely solve commercially important problems is simply out of the question. This renders them to questionable value status, and begs the question of how, exactly, can the companies producing these AIs ever expect to make a profit.
While we have private notions of how AI might be fixed, we have little faith that the AGI sector of the industry will be profitable any time soon. The biggest expense in creating an AI lies in training it with vast amounts of data; gathering, formatting and checking that data is expensive; once the training data has been accumulated and curated, the actual computational training must be done, almost always requiring high levels of computing power (“compute”, now a commodity) and a lot of time.
Even then, users of the AI cannot know in advance where the training is dense enough to produce quality answers and where it is not. The general nature of AGI, “ask me anything”, covers a very large amount of intellectual territory and requires huge amounts of training data. Users can reasonably expect that there will be plenty of subjects where the training data in a given AI is sparse or even non-existent, increasing the chances of hallucinatory output.
By contrast, narrow AIs, such as used for medical devices, cover only a relatively small collection of subject areas. Dense training over such a small subset of knowledge is not only possible, but is now routine. The reliability of such systems is very high due to the narrow topic range. As a result of this high reliability, users of such systems can employ them with confidence, and the vendors can be profitable. Many already are profitable.
Investor money allocated to AGI cannot be used for other purposes, such as development of profitable narrow AIs or ordinary automation. In our opinion, the monies so far invested in AGI, not expected to be profitable anytime soon, has been misallocated, an unusual case where investors put too much money in very long-term ventures. These ventures might well fail before the companies solve the AGI problem, leading to a crash in the AGI industry, which will certainly hurt adjacent suppliers as well as investors.
Even before failures happen, as can be seen now, interest in AGI wanes. People are used to it now, use it frequently, and don’t give it a second thought. AGI has taken the place of search engines, and more. It is thought of as a free commodity, much like Google search used to be, and its stunning momentum has cooled considerably. The large investments, already made and perhaps misallocated, starved other, more prosaic and pedestrian developments, in part leading to the current slowdown.
IRS Section 174 R&D Amortization (now “fixed”)
Estimated economic impact: 10% of total
Issues: Cash flow, innovation drag (mitigated after July 2025)
One of the worst things Congress ever did to the American people was the introduction of income taxation in 1913, ostensibly to fund World War I, and never rescinded. Since then, the system was gradually expanded to include nearly all earning Americans, not just the wealthy, and has grown extremely complex, to the extent that almost all people with significant economic activity, and nearly all businesses, require the services of a certified public accountant. The costs are significant, the intrusion into privacy far-reaching, and the consequences very damaging to average people.
One particularly damaging provision of the tax code, enacted roughly in 2017, was the Section 174 clause mandating amortization of research and development costs over five years, instead of expensing them the year the expenses were incurred. This provision obviously placed serious cost restrictions on research and development, and was rescinded as of 2025. However, companies have been hamstrung by this provision now for eight years, a severe dampener on R&D, and hence on subsequent business. It will take some time for the ill effects of this poorly-conceived tax code to wear off.
Political Factors (tariffs, trade wars)
Estimated economic impact: 15% of total
Issues: Cost, supply chain, market access, uncertainty
We’re guessing that the Trump administration’s throwing around high tariff numbers to our trading partners is a way to get them to the negotiating table and work out some perceived trade imbalances. Results have so far been mixed, and we’re unsure whether suppliers of critical materials and parts were properly backed up (replaced) before tariffs were imposed. That’s a very big job, probably too big for the administration, and so plugging up shortages would be left to the consumers (buyers) of the materials and parts.
The upshot has been marketplace chaos, significantly more than usual, supply chain upheaval and slowdowns in manufacturing. Given the very manipulative mercantilist behavior of China over the last decade or so, some degree of tariffs might be justified; from a geopolitical point of view, putting China’s gigantic export market at risk makes some sense, dampening China’s lust for the invasion of Taiwan. They are well aware the west can hurt them, badly, without firing a shot.
However, the administration has not appeared to have thought through the trade matters very thoroughly, presenting to the world a dizzying barrage of constantly changing tariff policies. This has motivated even friendly trading partners to question the wisdom of trading with the US and to seek alternatives. Uncertainty is bad for business; we’d like to see the US tighten up its act and present the rest of the world a much more coherent, proportional, predictable and enforceable trade policy. We suspect Trump is far too mercurial, transactional and spontaneous (superficial) about trade policy. It does no good to win a battle while losing the war.
In the meantime, the new administration has managed to collect some tariff monies, but trade has understandably gone elsewhere, disrupting numerous supply chains and introducing a strong element of uncertainty and chaos into the US market. We can’t help but believe that the tariffs and trade tensions have done far more damage than good; the US manufacturing build-out is in progress, but far from complete, so there aren’t yet domestic alternatives for many of the products produced abroad.
Macroeconomic Uncertainty and Inflation
Estimated economic impact: 15% of total
Issues: Cautious spending, hiring freezes, debt service
Corporate debt and cost-cutting are widespread, leading to hiring freezes, layoffs, and reduced investment in new projects. As implied earlier, if the US government is going to intervene in economic affairs, their first priority must be righting the ship, staying the course, ensuring predictability and stability, so that business can procure needed materials and goods and plan ahead. Chaos might yield leverage on the foreign policy front, but it does so at the cost of US business and jobs.
It is true that there are trade imbalances with our trading partners, particularly China, which seems to have gone out of its way to implement predatory trade policies. They have replaced a staggering fraction of American manufacturing with cheap import goods, effectively frozen American firms from selling in the Chinese market, lied, cheated and stolen billions worth of intellectual property. However, both American industries and the US government actively encouraged this behavior for much of the last three decades.
Now, the US needs to rebuild its industrial plant, and this is already under way. To do that right, business needs to know that if they start building a factory now, with high upfront costs and long time horizons, they can depend on making their money back in the next five to ten years. Economic chaos slows this buildout considerably, sometimes stopping it entirely. The Chinese well know that they depend very intensely on export business with the US, that such business is an existential issue for them. Coherence and predictability in US trade policy needs to take the driver’s seat; the chaos must be damped very rapidly.
Until the dust settles, we see early damage to the US economy, contributing significantly to the current economic cooling. While there are signs the administration is softening its stance on many tariff issues, the process isn’t yet complete and significant market chaos remains.
AI and Automation Impact on Workforce
Estimated economic impact: 15% of total
Issues: Job displacement, restructuring, skills mismatch
We view claims by several companies that X% of their code is now written by AI as propaganda intended to sell AI products and services. Our own, now substantial experience with AIs as coding assistants, and other purposes, has been mixed. We like some of the products we have tried; the sellers made the mistake of asking for money too quickly, before we could thoroughly evaluate the impact and efficacy of the tools, and so we abandoned them.
The freely available AIs (ChatGPT, Gemini, Grok, Claude, Llama, Perplexity and others) tend to be very good at writing prototype software, and even test cases for software. However, even with expanded context windows (think short-term memory), AI written code quickly reveals many shortcomings, sometimes hilariously so. As we have written many times, there has to be an adult in the room.
We look upon AI as an assistant to human work, not a replacement. Adoption of AI is slow, despite C-suite salivation at the prospect of using fewer employees to get the same or greater productivity. The good news for employees is that AI is extremely unlikely to replace large numbers of human workers, and demands sophistication from those using it.
The bad news is that many CEOs think they can do such replacements now, and replace eight-hour, five days a week humans, their salaries and their output, with relatively inexpensive AI machines that can work 24/7/365. It’s a nice dream, but is won’t happen any time soon. AI just isn’t that smart yet, and is now hitting a practical limit to further progress.
More important by far: humans using AI to write code, for example, need to know what the hell they are doing. They need to be able to judge the quality of the code, test it thoroughly, catch mistakes as they happen, see the big picture AI cannot. Replacing humans with AI too fast will mean a collapse in the market for new coding talent, happening now. Those people need income, and cannot wait, so they will seek employment elsewhere. The resulting brain drain will leave industry without humans who can steer the machines, forcing industry to place ever larger degrees of trust on unreliable software, a situation which cannot end well.
However, it will take senior leadership some time to come to terms with exactly where and how AI can comfortably fit into their organizations. Initial enthusiasm will be replaced with disillusionment, and finally, enter the slope of enlightenment and better productivity. In the meantime, it behooves leadership to avoid cutting off their noses to spite their faces.
We believe that there has been minor productivity benefits to the rise of AI products, but the process is still in its early stages and far from mature. The chaos generated is substantial, on the other hand, and more than a few layoffs have been conveniently blamed on replacement of humans by artificial intelligence. Anyone with significant experience using AI can easily see that this is a chimera; the real reasons for the layoffs are the same as layoffs over the last forty or so years: slowing business, over-hiring and boosting shareholder value. Blaming layoffs on AI is nonsense, and contributes to confusing market noise.
Supply Chain Disruptions
Estimated economic impact: 10% of total
Issues: Component shortages, higher costs, delays
Tariffs, export controls, and geopolitical tensions (especially with China and Taiwan) have created bottlenecks and raised costs for critical components, such as semiconductors and rare earth minerals. Companies are forced to diversify suppliers, build new domestic capacity, or absorb higher costs, all of which impact margins and hiring. We can only touch on this huge topic with a few notable examples, but supply chain issues are pervasive and limit industry in manifold ways.
Over-dependence on cheap imports: As mentioned above, industries in the US became too dependent on relatively inexpensive Chinese imports. Perhaps much more importantly, the entire world depends critically on the output of a single Taiwanese semiconductor maker, Taiwan Semiconductor Manufacturing Company Limited (TSMC). TSMC makes the most advanced semiconductor chips in the world.
Fortunately for all, TSMC now has manufacturing facilities in the continental US, not quite as advanced, but certainly progressing rapidly. This eases both concerns over China’s often stated intent to invade Taiwan and annex it, and the supply of large amounts of high-end chips.
Material shortages: Rare earth minerals, vital in the manufacture of many critical products, are another matter entirely. Additionally, shortages of nickel, titanium, and aluminum, driven by the Russia-Ukraine conflict and export bans (e.g., Australia’s ban on alumina exports to Russia), have impacted manufacturing. These materials are vital for batteries, automobiles, and aerospace.
Port Congestion: Despite improvements since the peak of the 2021-2022 crisis, U.S. ports like Los Angeles and Long Beach have faced significant backlogs, with a 25% drop in container volumes reported in May 2025. Congestion at East Coast ports has also risen as cargo is diverted from West Coast hubs.
Trucking and Labor Shortages: The U.S. trucking industry faces a persistent driver shortage, exacerbated by high turnover and low compensation. In 2021, the industry reported 490,000 job openings, impacting last-mile delivery.
Talent Shortages and Hiring Freezes
Estimated economic impact: 5% of total
Issues: Skills gap, competitive hiring, longer searches
Job applicants in recent years have become used to perhaps receiving an automated reply almost immediately after applying for a job, very occasionally a polite rejection letter soon after. Mostly, they hear crickets: nothing at all. This appalling lack of consideration for job seekers is now industry standard, even in advanced economies such as the US and Switzerland.
It is not unusual for job seekers to find 100:1 ratios for applications versus interviews, and 500:1 ratios for applications to job offers. Numbers like this mean practical unemployment periods of six months or more, typically disastrous for job seekers, even with unemployment insurance. We often compare white collar employment with jobs like garbagemen; the pay might be lower for the latter, but the job security is most definitely better. Who is better off in the long run?
Gradually, often after improving the LinkedIn profile as much as possible, learning to carefully match resumes to job descriptions, always following up applications with phone calls to the company, and applying for hundreds, perhaps thousands of jobs, even highly experienced and qualified job hunters give up, deciding not to waste further time on clearly futile job hunts. Some start their own businesses, others re-tread for new opportunities in other industries (notably health care, government, and hospitality) where it is easier to find paying employment.
Once committed to the change, these former white-collar job hunters typically do not return to their old industries. If the sector they left recovers and demand for skills returns, there are fewer and often far less competent applicants left, creating talent shortages. With long-term demographic trends pointing ever downward, the talent shortages persist, depressing the industries while simultaneously increasing labor costs.
The message to employers should be clear. Employer’s markets don’t last; extended unemployment thins the herd of available candidates, usually most competent first, and leads to future employee’s markets. In the contemporary case, we believe it very likely that there will be an extended employee’s market in the near future, and one that is significantly less capable than today’s job seekers. Extreme caution, now, on the part of employers is advisable, for tomorrow’s job market will resemble a game of musical chairs, with employers (companies) as the players.
Regulatory and Cybersecurity Risks
Estimated economic impact: 5% of total
Issues: Compliance costs, risk management
Abuses by industries are the most common sources of later government regulation, often thumb-fingered, political rather than necessary, and unfortunately, typically quite permanent. We have noticed in all western democracies that making new laws, particularly regulatory laws, is far easier and more frequent than repealing old laws. Thus regulations, when they happen, tend to linger, often far past their initial usefulness. The only certain and safe minimization strategy for industry with respect to regulations is to carefully avoid abuses in the first place.
Regulation, like taxation, imposes both monetary and time costs on industry, slowing development and innovation, inhibiting hiring and diminishing profitability. We believe that industry does not consider the past history of taxation and regulation, does not consciously modify their choices to stave it off, and so continues to blunder, giving the Karens of the political world ample fodder for new regulatory laws and new government oversight powers. Over time, short-sighted decisions by industry leaders creates regulatory regimes run by increasingly corrupt politicians, altering industry priorities from production to graft, capping growth and inhibiting employment.
Software security issues, flaws in software allowing third-party attacks, almost always result from aggressive development schedules, incomplete testing and premature releases to customers. We have noticed over the decades that companies have shifted from internal, pre-release, final testing to releasing what used to be known as beta quality software, allowing their customers to take on risks from using the software, in the hopes that the customers will report bugs to the makers. In fact, this has indeed happened, and with many of the major software companies using their own customers as integration testers, issuing regular updates as security bugs are found and documented.
Both regulation and security issues slow progress, complicate innovation and introduce risk, resulting in slower economic growth. While the effects of both may be small, they are difficult to fix and so persistent brakes on economic growth.
Market Valuation Bubbles and Investor Caution
Estimated economic impact: 5% of total
Issues: Bubble risk, cautious funding, volatility
We see definite signs that the current AI bubble is strained; the continued unprofitability of AGI projects is one such sign. Another, subtler sign is consolidation in the software industry.
We long ago became used to the notion that, if we perceived a need for software to do a particular set of tasks, it was extremely likely that someone, somewhere was already producing such software, and finding it was simply a matter of searching for it. This method was so successful, for so long, that we took it for granted, and almost never thought in terms of writing new software, unless directed to do so by a paying customer.
Inevitably, this led to a profusion of different software applications, each competing for attention in a crowded marketplace. Under such conditions, it was inevitable that large companies would take to buying up smaller, perhaps less successful products by smaller companies. For the most part, this process is complete - large software houses dominate the industry and produce and maintain the majority of products used.
The same can be said for AI. Although relatively new, at less than three years since the initial release of ChatGPT, we see, for example, far less chatter in our LinkedIn feed about AGI than we did in 2023. The zeitgeist has moved on; AGI is now a commodity, and we can expect individual companies lacking sufficient profits to fail or be bought by larger companies.
This same dampening of the hype curve also means restless investors, already skittish about throwing good money after bad. We can see in the near future a rather drastic slowing of investor interest in AGI; already there are early signs that this is happening, despite desperate searches on the part of AGI companies for practical applications of their core products (see AI agents, for real-world examples).
Conclusions
The current U.S. economic slowdown, while not a full recession, reflects a confluence of interconnected factors that demand attention from industry leaders and policymakers. Overinvestment in unprofitable AGI has diverted resources from practical innovation, while tariffs and trade wars introduce market chaos, disrupting supply chains and raising costs.
Macroeconomic uncertainty, fueled by inflation and inconsistent trade policies, stifles corporate investment and hiring, compounding the impact of supply chain bottlenecks and lingering effects of misguided tax policies like IRS Section 174. The overhyped promise of AI and automation has led to premature layoffs, falsely attributed to technological replacement, while talent shortages loom as disillusioned job seekers abandon white-collar fields for alternatives like entrepreneurship or stable sectors such as healthcare.
Regulatory burdens and cybersecurity risks further slow innovation, and investor caution signals a cooling AI bubble, limiting funding for growth. These dynamics, combined with demographic declines, set the stage for a prolonged employee’s market, where companies with poor reputations for layoffs, ghost jobs and toxic management will struggle to attract talent.
Industry must prioritize stability, transparency, and employee trust to avoid a future crippled by skill shortages and diminished innovation. Future articles will explore practical strategies to address these challenges, but for now, acknowledging their scope and impact is critical to navigating the evolving economic landscape.
Management: you have now been warned. Due to the high costs of ammunition, future warning shots will not happen.
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End Notes
Thank you for reading this article!
More information about Overlogix can be found at Welcome to Overlogix!
We currently publish on both LinkedIn (general interest articles, summaries, TL;DR’s: easier and faster to read) and Substack (in-depth articles, how-to’s, technical studies and new approaches to business).
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