Imagine hiring every all-star on the market, paying top dollar, and then finishing sixth in your division. That’s not a hypothetical. It’s what happened to Sinan Aral’s beloved Liverpool F.C. last season, and it’s also, he argues, an almost perfect metaphor for how most organizations are deploying AI right now.
Aral is a professor at MIT’s Sloan School of Management and one of the leading researchers on human-AI collaboration. His lab has spent the last several years running large-scale, real-world experiments on what actually happens when humans and AI work together… and the results should give every leader pause.
“In about 85% of the studies we’ve seen,” he told me, “while adding AI to human beings improves human beings alone, most of the time it’s better to just let the AI do it alone.” That data point is what Aral calls the rational fork in the road: if AI alone outperforms human-AI teams, the logical managerial move is to replace employees with automation.
But that, he insists, is exactly where the logic goes wrong.
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When good enough becomes a trap
In one landmark study, Aral’s team randomized roughly 2,000 teams (some human-AI and some human-human) to create marketing ads for a real organization. The human-AI teams produced 50% more ads per worker, with higher-quality text. By conventional productivity metrics, that would be a clear win. But the ads also looked strikingly similar to one another. “Ad copy starts sounding the same. Ad images start looking the same,” Aral explained. He calls this “diversity collapse”, the slow homogenization of output that occurs when AI, trained on the same publicly available internet, starts flattening the edges that make creative work distinctive.
The more a team delegated to AI, the more productive they became- and the more vulnerable they were to this collapse. Short-term gains masked long-term creative erosion. Diversity collapse is a thinking problem.
The skills we’re quietly losing
Aral’s most recent paper, which he calls the “AI Augmentation Trap,” reveals something even more unsettling. Cognitive offloading to AI (the act of outsourcing tasks you could do yourself) erodes the very skills you’re handing off. Workers who lean heavily on AI for writing lose writing fluency. Junior employees de-skill faster than experienced ones, who have the professional reserves to retain their capabilities. “It leaves the worker worse off than if AI had never been adopted” in the long run, Aral said. The short-term productivity boost is real. So is the long-run trap.
This maps directly onto what I’ve been writing about in my own work: productivity, as we’ve inherited it from the First Industrial Revolution, is an either/or model that values speed, efficiency, and measurable output. It misses what happens during dormancy- the marination, the synthesis, the slow cultivation of judgment that makes truly original thinking possible. Aral’s research gives that perspective empirical teeth.
What leaders should do instead
The fix, Aral argues, isn’t to avoid AI because that’s not a real option. “This is possibly the most disruptive technology ever developed in human history,” he said, and burying your head in the sand is not a strategy. The fix is to get intentional about human-AI collaboration design.
His prescriptions are practical: measure human skill levels independently of AI output; build in structured, unassisted practice so workers regularly perform tasks without AI assistance; extend performance evaluation windows so managers aren’t seduced by short-term productivity spikes at the expense of long-run capability; and design workflows where workers review, evaluate, and reshape AI outputs rather than simply accepting them. Keep human judgment in the loop, not as a formality, but as a discipline. And I’d go one step further- incentivize that human judgement during review periods.
A second line of Aral’s research offers another lever: personality pairing. When his team matched approximately 1,300 participants with AI personalized to complementary Big Five personality traits (not mirroring, but complementing) both productivity and creative output improved, and diversity collapse was reduced. Just as with human teaming, who you pair together matters. The best partners aren’t identical, they’re complementary. This appears to be true even when one of those partners is an algorithm.
The counterintuitive imperative
Here’s what Aral’s data ultimately points to, and what I think every leader needs to hear: the organizations that will win in the Imagination Era are not those that replace the most humans with AI, but those that become genuinely excellent at human-AI collaboration. That’s a skill. It requires investment, design, and a willingness to resist the seduction of the easy productivity win.
Creativity has always required what I call the rigor of ambiguity: the courage to sit with uncertainty rather than reaching for the fastest, most frictionless answer. AI offers a very compelling shortcut. The leaders who understand that the shortcut is also a risk, and who build organizations capable of holding both the power of AI and the irreplaceable texture of human thought, will be the ones who are still competitive a decade from now.
Liverpool, Aral notes, is figuring out how to make their expensive roster fit together. So should we.
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