As companies rush to adopt AI to boost productivity and cut costs, they may be setting themselves up for a new problem: losing what makes them different.
Mehdi Paryavi, CEO of the International Data Center Authority, said widespread reliance on the same AI tools risks flattening competitive advantage across industries, because firms increasingly rely on identical systems to think, write, and decide for them.
Paryavi said that as AI tools become cheaper, more powerful, and more widely deployed, companies risk outsourcing the very thinking that once differentiated them.
While AI can boost efficiency in the short term, he said, relying on shared models and standardized systems could leave businesses competing on cost and speed alone — eroding originality, strategic depth, and long-term advantage.
“If you and your competitor are all using the same service, you have no edge over each other,” Paryavi told Business Insider.
“Their AI and your AI against each other — I don’t know who’s going to win.”
When everyone uses the same brain
As generative AI becomes embedded across workplaces, Paryavi warned that the biggest risk isn’t automation — it’s uniformity.
When companies rely on the same large language models trained on the same data, decision-making, writing, and problem-solving can start to converge, shrinking the space for creative divergence.
That concern echoes warnings from researchers and academics who say AI can produce polished output at scale, but also flips human thinking by delivering fluent answers before understanding, creating an illusion of expertise that weakens judgment and depth.
When everyone relies on the same models trained on the same data, Paryavi said, creative divergence shrinks.
“The beauty of our world is that we have different choices because we think differently,” he said. “That’s where innovation comes from.”
Efficiency today, dependence tomorrow
It’s not just a question of companies all thinking the same — Paryavi warned that treating AI as a shortcut to efficiency can quietly hollow out human judgment, expertise, and control, leaving businesses faster in the short term but more fragile over time.
Over time, Paryavi said, that shift can erode internal expertise and decision-making capacity.
“What they don’t think about is that initially it might sound more efficient and more productive and cheaper,” he said. “But this is going to be very expensive down the line.”
One risk, Paryavi said, is dependency. As firms replace employees with AI subscriptions, they become increasingly reliant on external vendors to function effectively.
Paryavi compared the AI boom to the early 2000s rush to cloud computing, when many companies initially adopted third-party infrastructure but later repatriated workloads in-house as costs, complexity, and vendor lock-in became concerns — a trend commonly referred to in tech as cloud repatriation.
The same dynamic could play out with AI, Paryavi said — except with even higher stakes. As companies downsize human teams, they also lose institutional knowledge and the ability to operate without automation, he said.
“You’ve killed all your chances of ever becoming independent as an organization,” he said. “You’ve fired your manpower. You’ve made them no good.”
AI, he said, is not inherently harmful. In fields such as medicine, scientific research, and disaster prediction, it can significantly accelerate progress.
But without clear guardrails, companies risk trading long-term resilience for short-term speed.
“It’s a very powerful tool,” Paryavi said, comparing AI to an atomic bomb. “If that [an atomic bomb] can eliminate an entire population physically, this [AI] can eliminate humanity cognitively.”
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