The $100B Illusion - Why Data, Not Hype, Will Drive AI ROI
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It's tempting to believe we've entered the golden age of artificial intelligence. Headlines tout a $100 billion generative AI market by 2026. CEOs mention 'AI' on nearly every earnings call. Consultants pitch productivity revolutions via PowerPoint.
But beneath the surface, a less convenient reality is setting in: for most companies, GenAI is still an expensive experiment—not a source of revenue.
Stephen Klein, CEO, Curiouser.AI, asks, 'Are we using GenAI to solve real problems, or just optimizing slide decks?' Klien is bullish on GenAI in the long run but states 'the near-term business model isn't intelligence. It's fear and influence and a false sense of trust.'
As Klein's remarks suggest, the promise of AI is being eclipsed by performative adoption. His take cuts through the hype with equal parts technical fluency and commercial realism.
Klein isn't alone. As outlined in a recent article from this column—AI Beyond Platforms - How Data Will Unlock New Value In 2025,—one of the most overlooked truths of the current AI cycle is simple: the platform isn't the value. The data is.
The $100 Billion Mirage
Microsoft's claim of a $3.70 return for every $1 spent on GenAI, cited in a white paper it commissioned, lacked any external validation. No Fortune 500 case studies were included.
Meanwhile, AI darlings like OpenAI and Anthropic are running massive deficits. CNBC reports that OpenAI lost $5 billion in 2024, with ChatGPT alone costing an estimated $700,000 per day to operate. Anthropic, according to The Information, burned through $2.7 billion in 2024.
These companies aren't profitable. They're surviving on subsidies—from investors, partners, and strategic alliances.
Consultants and Cohorts: Selling the AI Dream
Klein paints a vivid picture of today's AI marketplace: GPT-powered webinars, $2,000 cohort programs, and 100-slide decks promising to "future-proof" organizations. Klein says, 'This isn't innovation. It's monetized anxiety.' Companies aren't buying transformation—they're buying the appearance of AI readiness.
Take Accenture, which reportedly earned $900 million in GenAI revenue last year, with $3 billion in bookings. But most of that came from consulting—not from clients deploying AI at scale. According to Accenture CEO, Julie Sweet, most of Accenture's clients are in 'experimental mode' with generative AI. Their focus, she said, is on cloud, data, and application modernization.
Where the Money Actually Is: Human-Centered Data
So, where's the real return? It starts with data—specifically, high-quality, forward-looking, zero-party data.
Unlike scraped or synthetic alternatives, real people voluntarily provide zero-party data. It includes behavioral intent, psychographics, motivations, and—critically—future spending plans. These inputs are foundational for models that aim to predict, not just summarize. Here are two examples:
3 Quarter ahead forecasts for CVS Revenues
Ereteam, based on data from Prosper Insights & Analytics
XTech-MarchCPI Forecast
Exponential
The takeaway? Many failed GenAI initiatives didn't collapse because of weak models. They failed because they were built on bad data.
As Klein argues, AI should augment human intelligence—not just mimic or automate language.
The Overlooked Reality: A Data Gap, Not a Tech Gap
Klein's critique and the findings in AI Beyond Platforms converge on a core truth: what's holding back AI isn't processing power or better algorithms. It's irrelevant, outdated, or low-signal data.
The excitement around GenAI is real. But so is the gap between experimentation and enterprise-scale impact. Demos dazzle. ROI disappoints.
Unless organizations fuel their models with reliable, representative, and behaviorally rich data, they'll fall into the trap.
From Illusion to Impact: The Path Forward
There is real money to be made with AI—but it won't come from bigger models or louder marketing. It will come from solving real problems with clean, context-rich, human-anchored data.
As the GenAI boom rolls on, the biggest winners won't be those chasing the next model release. They'll be the ones starting with the right data.
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