We’re drowning in AI optimism. Goldman Sachs says it could lift global GDP by 7%. McKinsey calls it “the next productivity frontier.” ARK Invest, Morgan Stanley, and JPMorgan all agree: AI is transformative.
And yet—only 5% of companies are actually capturing measurable value from AI, according to BCG.
Why this chasm between promise and performance?
The answer lies not in compute power or data volume—but in how we understand intelligence itself.
Back in 1977, David Marr and Tomaso Poggio proposed a revolutionary framework for analysing intelligent systems across three levels:
- Computational: What is the system trying to achieve?
- Algorithmic: How does it process information to achieve that goal?
- Implementational: What physical substrate enables it?
Fast forward to 2025, and MIT’s Poggio reaffirms this hierarchy in discussions at EmTech Rome: Most organizations obsess over Level 3 (tools, infrastructure) while ignoring Level 1 (purpose, intent).
This misalignment is catastrophic.
As McKinsey’s latest research shows, the top AI performers aren’t just deploying models—they’re rewiring entire workflows around clear computational objectives. They’ve moved beyond automation to value creation: unlocking “superagency” in the workplace, personalizing at scale, and driving double the revenue growth of their peers.
Meanwhile, the rest remain trapped in what BCG calls “the adoption-impact gap”: usage is up, but results aren’t.
Compounding this is the Donroe Doctrine (Axios, January 6, 2026)—a stark warning that AI is no longer just a tech trend, but a strategic asset akin to oil or rare earth minerals. Nations and corporations are now in a race not just to use AI, but to control its trajectory.
So where do you stand?
If your AI strategy starts with “Let’s buy an LLM” or “We need a chatbot,” you’re already behind. But if it begins with:
“What human or business outcome are we fundamentally redefining?”
then you’re operating at the computational level—where real transformation begins.
The future won’t belong to those with the most GPUs, but to those with the clearest why.
And right now, only 5% are asking it correctly.
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