Markets Are Not Suicidal
In 1936, The New York Times enthusiastically proclaimed that a rocket would never be able to leave Earth’s atmosphere.
Today, I keep a framed collection of similar “certainties” in my office. A graveyard of bold, confident predictions that were also, invariably, wrong. Some forecast permanent stagnation while others promised unstoppable dominance or inevitable collapse, yet time has a way of humbling even the most decorated experts.
Beneath those clippings sits a quote often attributed to Sir John Templeton: “The four most dangerous words in investing are, ‘This time it’s different.’” I look at that wall often, not because I believe risk is an illusion, but because it reminds me that certainty usually is, and that the future is notoriously difficult to predict.
Only months ago, markets were celebrating an AI supercycle as valuations expanded and legacy technology firms were rewarded for their strategic positioning in artificial intelligence.
Productivity gains were assumed, and growth projections were stretched further into the future than logic might usually allow. Now, however, the tone has flipped.
Each day, it feels as though a new artificial intelligence breakthrough makes another profession appear obsolete. And most recently, a published thought piece from Citrini Research, titled “The 2028 Global Intelligence Crisis,” envisioned a scenario involving massive white-collar displacement, with unemployment climbing toward 20 or even 30%. Markets reacted sharply to this narrative; legacy tech names sold off, and entire industries began being discounted as if obsolescence were already a foregone conclusion.
While the swing has been abrupt, we must remember that markets move at the speed of narrative, while economies transform at the speed of infrastructure.
The magnitude of what is being implied deserves scrutiny. Roughly 70% of U.S. economic activity is tied to consumer spending. If unemployment truly approached 30%, the consequences would not be sector-specific; rather, that level of joblessness would rival the worst years of the Great Depression, when unemployment peaked near 25%.
Consumption would contract sharply, tax revenues would fall, credit markets would tighten, and political pressure would intensify almost immediately. At that point, we would no longer be debating artificial intelligence—we would be debating systemic stability.
Public and private companies share a basic objective: survival. Because survival implies profitability, and profitability requires a demand base with income, a firm that systematically destroys its own customers is not maximizing value; it is undermining its own future.
This is not about executive virtue, but rather the hard-coded structure of capitalism. Boards are not assembled to oversee their own decline, and executives are not compensated to hollow out the foundations of the businesses they serve.
Artificial intelligence did not arrive overnight; it has been modeled, piloted, and integrated into strategy for years. If AI adoption were truly on track to eliminate a quarter of the consumer income base, the signs would be broad and unmistakable—reflected in sustained earnings compression, severe credit stress, and collapsing capital expenditure. That is not what aggregate profitability data reflect yet. What we are seeing is a repricing, a rotation of capital that markets often confuse with ruin.
None of this denies disruption.
Slower hiring in certain white-collar fields is plausible, some displacement is likely, and wage compression in specific sectors could emerge. In a country already experiencing populist strain, those pressures would matter. But the debate suffers from an intellectual asymmetry where we hear constant discussion of destruction, but very little about creation.
History does not show technological revolutions that only subtract. Electrification displaced certain trades while enabling entirely new industries, just as computing reduced clerical labor only to create the software, semiconductor, and internet-based models that define the modern age. Economists describe this as creative destruction, where productivity growth lowers input costs and expands the economic frontier.
However, expansion often leaves a wake. While the frontier grows, the territory left behind can wither, and we have seen this before.
In the 20th century, we marveled at the efficiency of manufacturing automation but failed to plan for the human cost of the transition. The result was not just a shift in labor, but the hollowing out of entire communities, a displacement that lasted generations and fueled deep social fractures. If we acknowledge that AI is a systemic shift, we must also acknowledge that the “Transition Gap”—the period between the destruction of old roles and the formation of new ones- is where societies break.
Creative destruction is an economic necessity, but it should not be a social death sentence. To avoid the mistakes of the past, our focus must shift from protecting specific jobs to protecting the workers themselves through a structural evolution in portable benefits, continuous education, and localized resilience. The market may not be suicidal, but it can be indifferent. It is our responsibility to ensure the leap into an AI-driven future doesn’t leave half the population behind.
If we navigate this transition with intent, the same forces of disruption provide the tools for recovery. If intelligence becomes cheaper, the cost of problem-solving falls, the cost of experimentation falls, and the cost of launching new ventures falls. It is incomplete to assume the only outcome is subtraction.
After 2008, many believed municipal bonds would collapse, yet they did not. Others were certain China would quickly overtake the United States’ economic position, but that has not unfolded as predicted. History is filled with confident forecasts that underestimated human adaptation. This does not mean AI carries no risk, but systems built on profit and participation do not quietly choose extinction.
If unemployment ever approached the levels now being feared, the reaction would be immediate and structural. Capital would reallocate, policy would intervene, and new industries would form. The system would mutate long before it surrendered.
The greater danger may not be artificial intelligence itself, but allowing volatility to harden into fatalism. We should prepare, and we should think, using fear in moderation to sharpen our planning.
Disruption is inevitable.
Certainty about collapse is not.
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