MUMBAI: Every prompt may solve a problem, but it could also leave a breadcrumb behind. That is the warning from Microsoft Chairman and CEO Satya Nadella, who says the next big challenge in enterprise AI may not be what companies gain from artificial intelligence, but what they unknowingly give away.
In a post on X, Nadella introduced what he calls the “Reverse Information Paradox”, arguing that businesses risk exposing the institutional knowledge that gives them a competitive edge every time they interact with AI systems.
The idea builds on economist Kenneth Arrow’s Information Paradox, which suggests that buyers can only judge the value of information after obtaining it. Nadella argues that AI flips the equation. Instead of sellers risking the loss of valuable knowledge, enterprises using AI may inadvertently reveal it through everyday interactions with the technology.
According to Nadella, companies effectively pay for AI twice. The first cost is straightforward—the money spent on AI models and services. The second is less visible but potentially far more valuable: the proprietary knowledge organisations feed into these systems to improve their performance.
“The more context, expertise and organisational data you provide, the smarter the AI becomes,” Nadella suggests. But that same process can also expose the unique know-how that differentiates one business from another.
He argues that the concern extends well beyond privacy or cybersecurity. Modern AI systems also learn from what he describes as “intelligence exhaust” employee prompts, workflow patterns, corrections to inaccurate outputs, evaluation methods and internal processes. Collectively, these interactions create a detailed picture of how an organisation thinks, makes decisions and solves problems.
Over time, Nadella warns, every refinement contributes to a growing repository of institutional knowledge that is difficult to quantify but central to a company’s competitive advantage. Unlike publicly available information, this expertise reflects years of operational experience, strategic priorities and organisational judgement.
That, he says, creates an imbalance in today’s AI ecosystem. As model providers continuously improve their systems through enterprise interactions, infrastructure owners could accumulate increasing economic value from customer knowledge, while businesses have little visibility into what is being learned in return.
Nadella also highlighted what he sees as a contradiction in the AI landscape. Many providers rely on fair-use principles and publicly available data to train their models, yet simultaneously restrict model distillation and retain the ability to learn from customer interactions. He argues this raises broader questions around ownership, control and value creation in the AI economy.
To address the issue, Nadella believes enterprises need greater control over their AI-powered organisational memory. Companies, he argues, should be able to benefit from AI-driven productivity without surrendering the knowledge that makes them unique.
His comments add a new dimension to the AI debate, shifting the conversation beyond automation and efficiency towards an increasingly important question: in the race to make AI smarter, who ultimately owns the intelligence it learns from?
