AI's Real Advantage Lies in Scarcity and Workflow Integration

The real story behind AI’s spring 2024 surge isn't the technology--it's the hidden reordering of value, attention, and power that no one saw coming. Beneath the hype of new models and coding agents lies a deeper shift: software development has become the first domain where generative AI has achieved true product-market fit, triggering a chain reaction that’s inflating demand, distorting pricing, and exposing a critical blind spot in how companies assess risk. The non-obvious implication? The AI arms race isn’t being won by those with the best models, but by those who understand that infrastructure scarcity today creates pricing power tomorrow--and that most industries are still mistaking tools for transformation. This post is for leaders, investors, and builders who need to see beyond the demo to the downstream consequences: where value will actually accrue, which jobs will evolve rather than disappear, and why the most dangerous assumption is believing you already know what AI is for. The advantage lies in recognizing that we’re not in the adoption phase--we’re in the confusion phase, and clarity is the only moat.

Why the Obvious Fix--More Compute--Makes the Problem Worse

Everyone sees the headline: AI demand is exploding. But few trace the full consequence chain. The immediate response--pour more money into GPUs, expand data centers, sign bigger contracts with Nvidia--is logical. Obvious. And dangerously incomplete.

Because when you treat compute as the solution, you miss that compute is now the bottleneck and the pricing lever. As Benedict Evans notes, the tech industry is in a moment of “radical supply-demand imbalance.” Demand for AI-powered software development has surged not because of theoretical potential, but because agentic coding tools have crossed into real-world utility. Developers are writing more code, faster, and that code needs to run. The result? A feedback loop: better tools → more usage → higher compute demand → capacity constraints → pricing power for infrastructure providers.

This creates a hidden dynamic: scarcity as strategy. When Sundar Pichai and Mark Zuckerberg say “the risk of underinvesting is significantly greater than the risk of overinvesting,” they’re not just being cautious. They’re acknowledging that missing this wave means losing control of the agenda. The real cost isn’t the capital spent--it’s becoming dependent on others who did invest and now hold leverage.

"The risk of underinvesting is significantly greater than the risk of overinvesting."

-- Sundar Pichai (via Benedict Evans)

And here’s the kicker: this isn’t about ROI in the traditional sense. The companies spending billions aren’t asking, “Will this generate profit?” They’re asking, “Can we afford to be excluded?” That shifts the entire calculus. Overinvestment isn’t waste--it’s insurance against irrelevance.

But this also distorts the market. When OpenAI reports $30 billion in annualized revenue and Anthropic rumors hit $45--50 billion, it’s easy to assume a bubble. Yet much of that spend isn’t vanity--it’s real money flowing from enterprises using AI to accelerate software development. And that demand isn’t evenly distributed. It’s hyper-concentrated in the tools that actually work today, which amplifies the imbalance.

The system responds by inflating prices, which rewards early infrastructure bets--but only temporarily. Because as Evans warns, history shows that expensive, sophisticated infrastructure often becomes low-margin commodity. Think cloud, semiconductors, telecom. TSMC doesn’t take a cut of every Uber ride. Intel didn’t get a percentage of Microsoft’s success. The pattern repeats: the enablers build the rails, but the value flows to those who build on top.

So the immediate benefit of scarcity--pricing power--creates a downstream risk: when supply catches up, margins collapse. The winners won’t be those who sold the shovels during the gold rush, but those who understood that the real opportunity was in what gets built once digging becomes cheap.

The 18-Month Payoff Nobody Wants to Wait For: When Free Tasks Unlock New Businesses

Most analysis stops at efficiency: “AI will automate tasks, saving time and money.” That’s first-order thinking. The deeper shift is what happens when a task becomes free.

Evans frames this with a simple but devastating question: “If the task becomes free, what does that unlock?” This isn’t about doing the same work faster. It’s about enabling work that was previously impossible--not because of skill, but because of cost.

Take accounting. In 1960, preparing financial statements took weeks. Today, it takes minutes. But accountants didn’t disappear. The job changed. They stopped doing repetitive calculations and started doing strategic analysis, forecasting, compliance--work that only became viable once the grunt work was automated.

"Clearly accountants today are not doing the work they were doing in 1960 because the stuff they did in 1960 now takes them 30 seconds."

-- Benedict Evans

The same is happening with AI. The immediate use case is automating coding, transcription, research, and drafting. But the lasting advantage comes from asking: What new work becomes possible when these tasks are frictionless?

For example: primary customer research. A project that once took a month--designing questions, conducting interviews, transcribing, analyzing--can now be done in days. The automation isn’t just faster; it changes the scope of what’s feasible. You can run more iterations, test more hypotheses, include more voices. The bottleneck shifts from execution to insight.

And that’s where the real value emerges: not in the tool, but in the judgment applied to its output. When everyone can generate a first draft, the differentiator becomes the editor, the strategist, the person who knows which questions to ask.

This also explains why adoption metrics are misleading. Yes, 40--50% of people are using AI tools monthly. But only 10--15% are daily users. Why? Because for most, the tools don’t map to their core work. Evans’ analogy is perfect: “Imagine you’re a lawyer seeing a spreadsheet for the first time. That’s very clever... but that’s not what I do all day.”

The implication? The real adoption curve isn’t about usage frequency--it’s about integration into workflow. And that takes time. It requires rethinking roles, redesigning processes, and, often, discomfort.

Which brings us to the second-order consequence: industries where the business model was built on scarcity will be disrupted. Newspapers didn’t die because journalists were replaced by AI. They died because the advertising monopoly that funded them collapsed when digital distribution removed the cost of printing and shipping.

So the question isn’t, “Will AI replace lawyers?” It’s, “What part of the legal business was actually funded by high-margin, repetitive work that AI can now do for free?” And when that revenue stream vanishes, what remains?

How the System Routes Around Your Solution: The Danger of Average

There’s a quiet crisis emerging in content, research, and decision-making: the rise of the average answer.

Generative AI excels at producing plausible, coherent, middle-of-the-road output. That’s useful--until it’s not. Because when everyone uses the same models, the same prompts, the same tools, the output converges. Insights flatten. Differentiation disappears.

Evans captures this perfectly: “Is that what you wanted? Did you just want what anyone would probably tell you?” The danger isn’t that AI is wrong. It’s that it’s correct but shallow. It gives you the average response--the thing most people would say--without depth, without context, without insight.

"It feels like the new version of the copy paste of the press release... run through some kind of ChatGPT version."

-- Benedict Evans

This creates a paradox: the more AI is used, the harder it becomes to find original thinking. The signal-to-noise ratio drops. And the value shifts to those who can curate, challenge, or transcend the average.

Consider Formula 1 journalism. New writers, using AI to summarize race data, produce articles that sound informed but lack nuance. They repeat the surface-level narrative because they haven’t spent years in the paddock, haven’t built relationships with engineers, don’t understand team dynamics. The result? Homogenized content. And the audience notices.

The same is happening in consulting, legal, and investment banking. Junior analysts used to spend months learning how to draft decks and contracts. Now, AI does it in seconds. But the value was never in the drafting--it was in the understanding gained through the process. When that learning loop is bypassed, judgment suffers.

So the system adapts: organizations that once valued speed and volume now seek differentiation. LVMH doesn’t compete on logistics like Amazon. It competes on curation, taste, scarcity. In an AI-saturated world, the same logic applies. The winners won’t be the fastest producers--they’ll be the best editors, the most discerning curators, the ones who know when to reject the AI output.

Key Action Items

• Over the next quarter: Audit your AI spending not by cost, but by dependency risk. Are you building on tools controlled by a few providers? Begin exploring alternatives or internal capabilities to reduce lock-in.

• Within 6 months: Identify one repetitive, high-volume task in your organization that AI can make nearly free. Don’t just automate it--design a new workflow around what becomes possible when it’s removed.

• Over 12--18 months: Shift investment from AI consumption to AI integration. Train teams not to prompt models, but to evaluate, refine, and build upon their output. This is where lasting advantage forms.

• Start now: Incentivize original thinking, not output volume. Reward employees who challenge AI-generated drafts, not those who accept them. Make “this looks like ChatGPT” a criticism, not a compliment.

• Immediately: Stop asking, “Which jobs will AI replace?” and start asking, “Which parts of our revenue depend on tasks that are about to become free?” This is where disruption hides.

• Over the next year: Build partnerships with domain-specific AI providers. General models produce average results. The edge goes to those who fine-tune for your business, your data, your customers.

• Long-term (18+ months): Position your organization as a curator, not just a producer. In a world of infinite content, the ability to say “this matters” is the ultimate scarce resource.