AI Commoditizes Expertise: Box CEO's Context Strategy

Box CEO Aaron Levie is warning that the rise of artificial intelligence is rapidly turning expert knowledge into a commodity. In a recent statement, Levie explained that as AI models evolve into autonomous agents capable of high-level work in fields like law and medicine, the competitive advantage for companies will shift. The new battleground is not the AI models themselves, but the context provided to them.

Levie argues that companies must focus on managing and deploying their proprietary data and institutional knowledge. Tech leaders across Silicon Valley are echoing this sentiment, identifying 'context engineering' as the emerging critical skill. The ability to feed AI precise, task-specific information without overwhelming it is becoming the key to productivity and market differentiation.

The rapid advancement of artificial intelligence is fundamentally altering the value of human expertise. According to Aaron Levie, CEO of cloud-storage giant Box, AI models are becoming increasingly capable of performing high-level knowledge work. This capability spans nearly every profession, including law, medicine, strategy, and scientific research.

As these tools evolve into autonomous AI agents, Levie suggests that expert intelligence will no longer be scarce. This shift forces a critical question for business leaders. Levie posed the inquiry: "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?"

The answer, according to Levie, lies in moving beyond the quality of the models. When access to expert intelligence becomes universal, the competitive edge must come from a different source. Companies can no longer rely solely on the intelligence of the tools, but must look inward for differentiation.

In an AI-driven economy, the true advantage comes from context. Levie argues that winning companies will not be those with the smartest models, but those that give their models access to the right proprietary information. This information includes internal data, customer histories, specific workflows, decision-making patterns, and accumulated institutional knowledge.

Levie emphasized this point in a recent statement: "Certainly it will be about how teams and employees use AI agents effectively, but the ultimate force-multiplier will be the context that the agents get."

This perspective is gaining significant traction across Silicon Valley. Other technology leaders are reinforcing the importance of data management over model selection. The consensus is shifting toward designing systems that provide AI with the correct operational parameters.

Key figures supporting this shift include:

• Andrej Karpathy, founding team member of OpenAI
• Tobi Lütke, CEO of Shopify
• Will Grannis, CTO of Google Cloud
• Thomas Dohmke, CEO of GitHub

These leaders suggest that context engineering is becoming the most valuable skill, rather than crafting clever prompts.

While the concept is powerful, implementing effective context engineering is not without difficulties. Aaron Levie noted that getting the right context into AI systems is far from simple. One major risk is overwhelming the AI with too much data.

Levie identified a phenomenon he calls "context rot," which occurs when agents are fed excessive information. This causes models to become confused and focus on irrelevant details rather than the task at hand. Avoiding this requires a delicate balance.

The central challenge for developers and businesses today is ensuring that AI agents receive precise, accurate, and task-specific context. This must be done without overwhelming the system. Successfully navigating this challenge is essential for building effective agent systems that drive actual value.

The implications of this shift are high for the corporate world. Aaron Levie warns that the gap between companies that manage their knowledge well and those that do not will widen. The ability to capture, organize, and operationalize internal knowledge is now a survival requirement.

Companies that succeed in this endeavor will see major gains in productivity and output. They will be able to leverage their unique data assets to drive superior results from AI tools.

Conversely, Levie warns, "Those that don't will find it harder and harder to serve customers competitively." As AI democratizes access to raw expertise, the unique context of a business becomes its final moat.