What History Might Tell Us About Transformative Technologies, Huge Financial Investments, and How The AI Moment Might Play Out

(With some serious help from Grok and Claude.)

We're witnessing something remarkable: hundreds of billions of dollars pouring into artificial intelligence development, with projections suggesting $600 billion in AI-related capital expenditure by 2026. The technology feels genuinely transformative—one of those rare moments when you can sense the trajectory of human history shifting beneath your feet.

But there's a dissonance here worth examining. Transformative technology and profitable investment don't always coincide. In fact, history suggests they often diverge dramatically.

The Automobile Parallel

Consider the internal combustion engine and the automobile industry it enabled. Few would dispute its transformative impact: it restructured cities, created suburbs, enabled modern logistics, and fundamentally altered how humans relate to space and time. It was, without question, one of the most consequential technologies of the 20th century.

Yet over 2,000 automobile manufacturers emerged in the United States alone. By the 1930s, the vast majority had failed. Warren Buffett noted the irony: accurately predicting the automobile boom should have led to riches, but instead resulted in "corporate carnage." Even the survivors—Ford, General Motors, Chrysler—weren't spectacular long-term investments relative to the capital poured into the sector.

The pattern wasn't a single dramatic crash like the dot-com bubble. Instead, it unfolded as waves of entry, overcapacity, price competition, and consolidation spanning decades. Investors who bet on the auto industry's importance were right about its impact but often wrong about their returns.

Three Forces in Tension

Three distinct forces are currently shaping the AI investment landscape, and their interaction will likely determine outcomes:

1. The Reproduction Cost Curve

Three years ago, generating a million tokens (roughly processing a short novel's worth of text) cost around $60. Today it often costs less than a cent—a 99.9% reduction. Open-source models now rival proprietary ones in many applications. What cost hundreds of millions to develop can often be replicated for a fraction of that investment.

This commoditization dynamic punished automobile manufacturers who couldn't match Ford's assembly line efficiencies. The question for AI: if base capabilities become cheap and widely accessible, where do the trillion-dollar valuations go?

2. The Efficiency Revolution

The current AI paradigm relies on brute force: massive datasets, enormous compute resources, petabytes of training data. But neuromorphic computing and brain-inspired architectures are beginning to challenge this assumption. New approaches are achieving comparable results with 97% less energy and 90% less memory.

The analogy to human learning is instructive, if imperfect. A human who has read 100 books can demonstrate remarkable intelligence. Current AI systems process vastly more data to achieve their capabilities. If we can crack more efficient training methods—learning architectures that extract more intelligence from less input—the compute-intensive moats being built today might evaporate.

3. The Integration Advantage

But here's where the automobile parallel breaks down: Ford was building a new market from scratch. The modern tech giants are embedding AI into infrastructure they already control.

Microsoft has your operating system, your productivity suite, and your enterprise relationships. Google has your search, email, and cloud infrastructure. These aren't just first-mover advantages—they're compounding network effects and switching costs that didn't exist in physical manufacturing.

The value might not accrue to those who build the best models, but to those who can embed AI capabilities into existing workflows, relationships, and data ecosystems in ways that are genuinely hard to replicate.

The FOMO Multiplier

All of these dynamics are amplified by a powerful psychological force: the fear of missing out on a genuinely transformative technology.

This isn't irrational on its face. AI does appear to be one of those rare inflection points where being wrong—missing the shift—could mean irrelevance. But this legitimate concern creates its own distortions. When every major institution believes they must invest heavily or risk extinction, capital allocation becomes less about careful assessment of returns and more about defensive positioning.

History shows this pattern repeatedly. The 1840s Railway Mania in Britain wasn't driven by people who didn't understand railways were important—they understood it perfectly. That understanding drove overinvestment. Rational fear of missing a transformation led to irrational capital allocation as investors rushed in, valuations detached from fundamentals, and eventual losses mounted even as the technology succeeded.

The dot-com era followed the same arc: the internet was transformative, exactly as boosters claimed. But that didn't prevent spectacular losses for those who paid peak prices in 1999 or backed the wrong horses in the race.

The current AI investment surge shows similar characteristics: every earnings call emphasizes AI capabilities, every venture pitch includes AI components, every major tech company is racing to demonstrate AI leadership. The fear of being left behind is palpable—and expensive.

This FOMO dynamic doesn't make the technology less important. It makes prediction harder, because it decouples investment from careful calculation and creates self-reinforcing momentum that can persist longer than fundamentals would justify—until it doesn't.

Possible Scenarios

This creates space for several distinct outcomes:

Scenario 1: Classic Boom-Bust Consolidation Following the automobile pattern, most AI startups fail despite creating genuine value. A few giants survive but face margin pressure from open-source alternatives. Investors as a class lose money even as society transforms.

Scenario 2: Bifurcated Markets Model development commoditizes (supporting reproduction cost arguments), but value capture happens at the integration layer. Pure "AI companies" struggle, but those embedding AI into existing platforms profit handsomely. We're left with capable, cheap AI everywhere but concentrated returns.

Scenario 3: Infrastructure Play Like oil companies and road builders profiting from automobiles more than car manufacturers did, the real money flows to adjacent sectors: chip manufacturers, power generation, data center construction, or entirely new industries we're not yet focused on.

Scenario 4: Efficiency Breakthrough Brain-inspired computing or other architectural innovations dramatically reduce costs and democratize capabilities faster than expected. The current leaders' massive investments become stranded assets. A new generation of efficient, accessible AI emerges, but sustained market dominance proves elusive.

What We're Watching For

None of these scenarios are mutually exclusive, and elements of each could materialize simultaneously in different market segments.

The key variables to watch:

• How quickly reproduction costs continue falling
• Whether efficiency breakthroughs materialize that overturn scaling law assumptions
• How effectively incumbent tech platforms leverage integration advantages
• Where regulatory and safety considerations concentrate or disperse power
• Which adjacent industries prove unexpectedly crucial

The historical pattern suggests caution about assuming investment returns will match societal impact. The automobile transformed everything—but rewarded relatively few investors. The question isn't whether AI matters. It's whether the current investment surge represents rational capital allocation or another iteration of a very old pattern: revolutionary technology, transformative impact, and disappointing returns for most who bet early and big.