- Box CEO Aaron Levie says AI will commoditize expertise, forcing companies to compete on context.
- As AI agents spread, Levie says proprietary data and institutional knowledge will define winners.
- Tech leaders say "context engineering," not prompts, is becoming the most valuable AI skill.
AI is rapidly turning expert knowledge into a commodity — and that shift will force companies to rethink what actually gives them a competitive edge, according to Box CEO Aaron Levie.
In a LinkedIn post this week, the cofounder and CEO of cloud-storage giant Box said that AI models are becoming capable of performing high-level knowledge work across nearly every profession, from law and medicine to strategy and scientific research.
As those tools evolve into autonomous AI agents, he said, expert intelligence will no longer be scarce.
"The question that we will have to wrestle with is, in a world where everyone has access to the same expert intelligence, how does a company differentiate?" Levie wrote.
His answer: context.
Levie said the true advantage in an AI-driven economy won't come from having smarter models, but from giving those models access to the right proprietary information — the internal data, customer histories, workflows, decision-making patterns, and institutional knowledge that companies have built over time.
"Certainly it will be about how teams and employees use AI agents effectively," he wrote, "but the ultimate force-multiplier will be the context that the agents get."
Context over prompts
The idea is gaining traction across Silicon Valley.
Andrej Karpathy, who was on the founding team of OpenAI, and Shopify CEO Tobi Lütke have said that "context engineering" — not clever prompts — is what makes AI useful at scale, while Google Cloud CTO Will Grannis and GitHub CEO Thomas Dohmke have said the real skill shift is toward designing systems, data, and workflows that give AI the right context to operate.
But getting the right context into AI systems is far from simple, Box CEO Levie told Business Insider last August.
He said that feeding agents too much information can cause what he calls "context rot," where models become confused and focus on the wrong details.
Making sure AI agents receive precise, accurate, and task-specific context — without overwhelming them — is now one of the central challenges in building effective agent systems, he added.
The stakes are high. Companies that can capture, organize, and operationalize their internal knowledge will see major gains in productivity and output, Levie said in his LinkedIn post.
"Those that don't will find it harder and harder to serve customers competitively," he added.
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