Who's Left to Buy Your Product?
BLUF: Enterprise leaders are making AI efficiency decisions optimized for the wrong time horizon. The demand consequences are economy-wide, they’re already starting, and they belong in your model now, regardless of what you sell or who you sell it to.
You’ve seen the Block announcement. Jack Dorsey cut nearly half his workforce and framed it as an AI efficiency play. Then every board and CEO watched the stock jump 24%.
Now the question is sitting on the desk of every enterprise CEO: how much of this applies to us?
Over the last eighteen months I’ve been in that room across enterprise clients and PE advisory work, all around the world. The pattern I keep seeing is consistent enough that I want to name it directly, because I don’t see it being named at all anywhere else.
The leaders making these decisions aren’t wrong that AI creates genuine efficiency opportunity. They are, in many cases, running an incomplete model. They’re solving for the cost variable while leaving the demand consequence out of the forecast entirely. The incentive structure they operate inside makes that omission not just understandable but, in the short term, rational.
That’s the barrel of the gun the global economy is staring into. And before I explain the demand consequence, I want to address an error I see in almost every version of this conversation: the assumption that demand risk only applies to companies that sell directly to consumers or to knowledge workers specifically.
It doesn’t. The ripple is economy-wide, and world-wide. If your current model doesn’t account for that, it isn’t a complete model.
The efficiency gain is undeniable. The question is whether you’re modeling the full cost of how you capture it — and on whose watch the bill lands.
The Pattern I See
Let me be specific, because vague warnings about systemic risk are easy to dismiss.
In PE-backed enterprises, the pressure comes from the fact that the cheap pandemic-era debt they leveraged to gain liquidity in their acquisitions is now expensive. Variable interest is compressing margins, and the board needs EBITDA improvement to support an exit thesis. AI-driven workforce reduction is a clean story. It improves the multiple, it’s defensible to the next buyer, and the timeline to exit means the operational consequences of moving too fast land after the transaction closes. The incentive to optimize for the near-term number is almost perfectly constructed.
In public enterprises, the mechanism is different but the outcome is similar. Quarterly earnings pressure, institutional investor expectations, and compensation structures tied to near-term performance create a planning horizon that systematically discounts consequences arriving beyond the current cycle. When Block cut four thousand people and the stock jumped 24%, every public company CEO with a board to answer to ran the same mental calculation. The market just told them what it rewards.
In both cases, what gets left out of the model is the same: a serious analysis of what happens to the broader economy, and ultimately to demand across every sector, when you systematically eliminate the wages of people who spend almost all of what they earn.
That omission isn’t stupidity, but a systemic byproduct of how decisions get evaluated and rewarded. But it’s still an omission, and the consequences will impact us all.
The Economy Runs on Middle-Class Spending.
Here is the foundational fact that most AI workforce conversations skip entirely: consumer spending accounts for approximately two-thirds of U.S. GDP. Not corporate investment. Not government spending. Consumer spending.
Source: U.S. Bureau of Economic Analysis; BLS Consumer Expenditure Surveys, 2024
The professional middle class, the knowledge workers earning roughly $75,000 to $150,000, represents the core engine of that spending. Not the wealthiest households, whose consumption is increasingly driven by asset appreciation rather than wages, and who spend a smaller share of each incremental dollar. Not lower-income households, who are already stretched. The middle. The people buying cars on five-year loans, renovating kitchens, taking the family vacation, replacing electronics, furnishing homes, and spending steadily on Amazon.
When those wages disappear, the spending doesn’t get redirected. It contracts. And that contraction doesn’t stay contained to the categories those workers were buying from directly.
Consider the chain from a single layoff decision: A financial analyst at a mid-size firm loses her job to AI automation. She stops the kitchen renovation mid-project. The contractor who had two months of work loses the job. The contractor’s spending at the local hardware supplier drops. The supplier’s transaction volume, processed through a payment platform, declines. The building materials distributor sees order volume soften. The trucking company that moves those materials runs fewer loads. The fuel supplier sells less diesel. The regional bank that lends to all of these businesses sees loan demand weaken.
Your SaaS payment platform never sold anything to a financial analyst. But it just felt her layoff.
There is no sector structurally insulated from a contraction in the spending of people who earn $75,000 to $150,000.
This is not a hypothetical chain. It is how economic multipliers work, and it is why the Federal Reserve, the Congressional Budget Office, and every serious macroeconomic model treats middle-income wage contraction as a systemic risk.
What the Data Shows
If you look at the numbers, the canary has already died in the coal mine. But you need to know where to look.
The displacement is concentrating in exactly the income segment that drives the spending the broader economy depends on. In January 2025, the U.S. Bureau of Labor Statistics reported the lowest rate of job openings in professional services since 2013, representing a 20% year-over-year drop. Analysis by Vanguard found that hiring for positions paying over $96,000 annually had reached a decade low. White-collar job postings fell 12.7% between Q1 2024 and Q1 2025, while white-collar wage growth stalled even as blue-collar wages continued to rise. This is a structural shift in the professional labor market, not a cyclical one.
Sources: U.S. Bureau of Labor Statistics, January 2025; Vanguard Research, 2025; Revelio Labs White-Collar Labor Market Analysis, 2025
The consumer data is catching up. McKinsey’s ConsumerWise research found that 75% of U.S. consumers reported trading down in at least one spending category through 2025, with sentiment declining considerably from the start of the year. Cardboard box shipments — a reliable proxy for goods movement and middle-market consumer activity — have fallen to multi-year lows, with the decline concentrated among lower- and middle-income households.
Sources: McKinsey ConsumerWise, December 2025; Cascade Partners U.S. Consumer Economy Analysis, October 2025
There is a nuance here worth naming. Recent Moody’s Analytics data shows that the top 10% of earners now account for nearly half of all U.S. consumer spending, and that spending has been sustained by asset appreciation rather than wages. Goldman Sachs Research found no significant statistical correlation between AI exposure and broad labor market measures as of mid-2025, and projects that AI-related displacement effects tend to be temporary at the aggregate level.
Sources: Moody’s Analytics/Wall Street Journal, February 2025; Goldman Sachs Research, August 2025
Here is why those findings only reframe the problem.
An economy increasingly dependent on top-earner spending sustained by asset prices is an economy with a fragile consumption base. Mark Zandi at Moody’s said it directly: relying on stock-market-driven spending from the top 10% makes the economy more vulnerable, not less. The professional middle class — the segment now in the AI displacement crosshairs — is precisely the stabilizing layer between subsistence spending at the bottom and asset-driven spending at the top. Remove it, and you destabilize the entire economic engine.
For enterprise leaders, the diagnostic question is this: what percentage of your revenue — directly or through one or two steps in the value chain — is downstream of consumer discretionary spending driven by professional middle-class wages? For most Fortune 1000 companies, that answer is closer to ‘most of it’ than any current model reflects.
Reskilling Isn’t the Solution
There’s a standard response to this concern: workers will reskill. New jobs will emerge. Technology transitions always create as many opportunities as they eliminate.
Partially true and wildly overstated as a complete answer.
The WEF’s Future of Jobs Report 2025 — surveying over 1,000 employers representing 14 million workers — projects 92 million jobs displaced by 2030 and 170 million created, a net gain. But the WEF itself noted that these are not direct exchanges happening in the same locations with the same people. The challenge is the gap — geographic, temporal, and skill-based — between where jobs vanish and where new ones appear.
Source: World Economic Forum, Future of Jobs Report 2025
That gap is where the spending contraction lives. The net employment number over five years does not prevent a demand trough in years two and three. And years two and three are on your planning horizon.
The workers most exposed right now are mid-career knowledge professionals: analysts, coordinators, compliance roles, financial planning support, junior legal and consulting functions. These are not people who absorb a multi-year retraining program without meaningful financial disruption. The AI engineering roles being created require mathematical foundations that six-week bootcamps don’t produce. The trades pipeline isn’t designed for this volume or this demographic.
More fundamentally: every previous automation wave pushed displaced workers toward cognitive roles. This wave is targeting the cognitive layer directly. The refuge that absorbed previous displacement is being automated. The historical analogy being used to reassure people doesn’t hold in the way it’s being applied.
For enterprise leaders, the practical implication is this: whether or not displaced workers eventually find new roles, the demand consequence of the transition period belongs in your model. Reskilling optimism doesn’t eliminate the timeline mismatch. It just means you didn’t account for it.
What the Full Analysis Looks Like
The organizations I’ve seen navigate this well — the ones that captured genuine AI efficiency gains without creating the downstream problems I’m describing — ran a four-part analysis that most enterprise AI workforce discussions skip.
First: demand exposure modeling. Not just cost reduction projections, but an honest mapping of your revenue base’s downstream exposure to middle-class wage contraction. This isn’t limited to your direct customers — it follows the value chain: who buys from your customers, and what drives their purchasing power? That exposure doesn’t show up in standard scenario planning, but it needs to.
Second: workforce transition sequencing. The speed and order of headcount reduction matters as much as the final number. Where is human judgment genuinely irreplaceable in your value chain? What institutional knowledge leaves with the people you’re cutting? Which transitions can be managed through attrition and redeployment, and which can’t? Organizations that answer these questions before executing perform meaningfully better in the medium term than the ones that optimize for the speed of the announcement.
Third: the trust variable. Your remaining workforce is watching. Your customers are watching. Your partners are watching. The reputational and cultural consequences of how you navigate AI workforce decisions will outlast the efficiency gain in both directions. I’ve seen organizations cut to a better multiple and lose the talent and customer relationships that justified the multiple in the first place.
Fourth: competitive positioning for the recovery. The demand softening I’m describing is not permanent. Economies adapt and new equilibria form. The organizations positioned to capture the recovery are the ones that managed their human capital intelligently through the transition, not the ones that cut fastest and rebuilt from scratch. What does your organization need to look like on the other side of this, and are your current decisions moving you toward that or away from it?
This is a four-variable problem being solved as a one-variable problem in most boardrooms right now. That gap is where the real strategic risk lives.
The Silence is Deafening.
Most enterprise AI workforce discussions I’m aware of are structured only around a single question: “where can AI replace headcount?” This is not the wrong question, but only part of what needs to be considered.
The missing conversation is around the second-order consequence of these decisions on the broader demand environment, organizational capability, customer relationships, and competitive positioning. And are we prepared to manage those consequences, or are we going to discover them reactively in three to five years?
That conversation requires integrating workforce strategy, demand modeling, value chain analysis, and competitive scenario planning in a way that most enterprise planning cycles don’t naturally produce. It also requires someone willing to say the uncomfortable thing in the room: that the efficiency number your board is excited about is achievable, and the demand consequence might also be real, and both of those things can be true simultaneously.
In PE-backed environments especially, that conversation is hard to have because the exit thesis depends on a clean story. A clean story and a complete analysis are not always the same thing. The ones I’ve seen go sideways are usually the ones where that tension got resolved in favor of the story.
The Takeaway.
I’m not arguing against AI adoption. I’m arguing for a longer planning horizon and a more complete model.
The enterprise leaders who look back on this period with satisfaction won’t be the ones who cut fastest. They’ll be the ones who ran the full analysis, mapped the demand exposure across their value chain, sequenced the workforce transition thoughtfully, preserved what was valuable in their organizations, and came out positioned to capture the next growth cycle rather than be forced to rebuild from zero.
That’s harder work than a cost reduction initiative. It requires holding the tension between near-term pressure that is absolutely real and medium-term consequences that are equally real but less immediately visible. And it’s genuinely difficult to run that analysis from inside the same incentive structure that’s creating the problem.
If you’re working through AI workforce decisions right now and you want a thinking partner who will run the full model — not just the efficiency math, and not a pitch for a predetermined answer — I’m interested in that conversation.
Find me on LinkedIn. Tell me what you’re looking at. That’s where the useful conversation starts.