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AI Won't Just Kill Jobs. It Will Kill Companies.

Everyone's worried AI will take their job. The bigger risk? It might take their employer. Here's why the companies that fail to deploy AI strategically won't survive—and why the US government is betting everything on the winners.

The common assumption is that AI will make specific jobs or industries obsolete, but that might be the wrong framing. I think it’s more likely that AI will make specific companies obsolete.

Why Access Doesn’t Equal Advantage

AI is now available to every business. But there’s a massive gap between having access to a technology and knowing how to deploy it effectively. With prior waves of technology (e.g., ERP systems, analytics, cloud computing, mobile) laggards could stumble their way into the future. They fell behind, but they survived.

AI might be different for two reasons.

First, technology adoption has never been the real challenge. Companies with multi-million-dollar ERP systems still process transactions manually. Much of the financial system still runs on COBOL. I recently received a paper check in the mail from a Fortune 500 company that claims to be “AI-powered” on its website. There’s a consistent pattern where organizations adopt a new technology but lack the creativity or discipline to deploy it in ways that create breakthrough improvements.

The Value Capture Problem

Second, the value capture model for AI is fundamentally different. Most AI value creation right now is bottom-up—individuals using LLM capabilities to work faster and better. This creates two problems for organizations. The gains are difficult to measure and drive back to the P&L. And until roles and compensation are redesigned, employees have a strong disincentive to report efficiency gains. No one automates themselves out of a job.

This is a management challenge, not a technology challenge. The technology exists and improves monthly. But capturing AI value will require a serious rethink of several management systems that haven’t evolved meaningfully in decades—organizational designs, roles, incentives, workflows, and performance metrics. Most companies won’t do this work and will be overtaken by the few that do.

The Consolidation Scenario

The result could be significant industry consolidation. Companies that remake themselves for the AI era will compound their advantages. The winners will pull further ahead, and the laggards might never be able to catch up this time.

There’s a counterargument worth considering: AI tools are inexpensive relative to their value, which could allow smaller players to compete with incumbents in ways that weren’t previously possible. Instead of consolidation, we might see industries fragment into thousands of micro-competitors serving niche markets with highly customized offerings. That outcome is possible in certain sectors (e.g., more likely in apparel, less likely in aerospace).

But my base case is consolidation. The companies that treat AI as a new foundation to rebuild upon (not a feature to market or button to add to their application) will be the ones still standing in five years, perhaps stronger than ever.

The most obvious front-runners are the Big Tech firms. They don’t just understand AI better than companies in other industries; they build and own the models. Several have already made significant restructuring moves, laying off thousands of employees. Some of this reflects bloat accumulated during the era of cheap capital and high profits. But much of it, I believe, stems from successfully deploying AI for development tasks and operational work. They’re already realizing gains while other industries are still debating, planning, or regrouping after early failures.

If this plays out, further consolidation and increased influence from tech giants raises serious questions. There are already concerns about censorship, privacy, surveillance, and anti-competitive behavior. More concentration of power in these firms doesn’t improve that picture.

Why Policymakers Are All-In on AI

Yet policymakers don’t seem to share these concerns. Several tech giants have been allowed to operate with monopolistic advantages, largely without meaningful intervention from regulators or the Justice Department. Recent policy decisions—from favorable treatment on data centers and chip manufacturing to streamlined regulatory pathways—signal a mostly “hands-off” posture toward AI and Big Tech.

This stands in contrast to the broader regulatory environment. Federal laws and regulations have grown substantially over time, with some estimates suggesting the regulatory burden has become so extensive that the average American unknowingly violates multiple federal statutes in the course of ordinary life. One might reasonably ask: why has the government adopted a largely permissive regulatory posture toward this industry while maintaining or expanding regulation elsewhere?

I believe it comes down to fiscal necessity. The US faces a precarious financial situation. At this writing, the national debt stands at more than $40 trillion and continues growing at an unsustainable pace, adding approximately $1 trillion every 150 days. For context, it took the US roughly 220 years to accumulate $10 trillion in debt by 2008. Recent legislative actions, including substantial increases to the debt ceiling, signal that significant spending cuts are not forthcoming despite the recent campaign rhetoric about fiscal discipline and government efficiency.

There are only a handful of levers for policymakers to address the debt crisis: reducing spending, increasing taxes, expanding the money supply, or growing the economy. The first two are political non-starters. No one wants their programs defunded, and raising taxes is deeply unpopular.

The third option—money creation—has been the path of least resistance. The money supply has grown steadily since the US “temporarily” paused the convertibility of dollars to gold in 1971, effectively abandoning the gold standard. And money creation has accelerated dramatically in recent years, with approximately 40% of all US dollars in existence having been created in just the last six years. Money creation delivers short-term relief but carries long-term consequences. Like a tolerance effect, each round of monetary expansion produces diminishing returns while requiring larger doses to achieve the same stimulatory impact. The inevitable result is inflation, which quietly erodes purchasing power in ways most Americans don’t connect to monetary policy. Because there are no immediate political costs and no hard constraints on money creation, this cycle is likely to continue. The unresolved question is whether it represents a temporary stopgap or the beginning of a spiral toward hyperinflation and the end of the dollar’s status as the world’s reserve currency.

That leaves economic growth as the only sustainable path forward. Growth expands the tax base and reduces the national debt as a percentage of GDP without the destructive side effects of unchecked monetary expansion.

Economic growth is driven by two factors: population (number of workers) and productivity (value each worker produces). With plummeting birthrates in the US limiting population growth, productivity gains become essential. I believe AI represents the government’s Hail Mary—a big bet that it will deliver the massive productivity leap needed to grow out of the debt crisis. The tailwinds for Big Tech aren’t as much ideological as they are existential. Whether this bet pays off remains to be seen.

This policy environment—streamlined pathways, favorable treatment, minimal regulatory intervention—overwhelmingly benefits incumbents. The Big Tech firms building and controlling frontier models stand to capture most of the value and compound their advantages across infrastructure access, network effects, training data, and overall scale. This means smaller competitors will face mounting disadvantages, and unlike previous technology waves, there may be no opportunity to catch up.

The good news is that AI access is universal. The bad news is that the ability for company reinvention isn’t. And in five years, that gap will decide who’s still in business.

 

Joe Sagrilla is an independent management consultant and business advisor, top business school faculty, Board member, writer, and speaker. His specialties include business strategy, transformation, technology, process improvement, and organizational performance. He currently lives in Austin, TX.

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