Box CEO Aaron Levie has issued a stark warning to businesses: as AI rapidly commoditizes expert knowledge across professions, companies must fundamentally rethink their competitive advantages. In a LinkedIn post this week, Levie argued that AI models are now capable of performing high-level knowledge work spanning law, medicine, strategy, and scientific research—making expert intelligence increasingly accessible to everyone.
The cloud-storage giant’s cofounder and CEO posed a critical question: “In a world where everyone has access to the same expert intelligence, how does a company differentiate?” His answer centers on context rather than raw AI capabilities. Levie contends that the true competitive advantage in an AI-driven economy won’t come from having more sophisticated models, but from providing those models with access to proprietary information—including internal data, customer histories, workflows, decision-making patterns, and institutional knowledge accumulated over time.
“Context engineering” is emerging as Silicon Valley’s new paradigm. This concept is gaining support from prominent tech leaders including Andrej Karpathy (OpenAI founding team member), Shopify CEO Tobi Lütke, Google Cloud CTO Will Grannis, and GitHub CEO Thomas Dohmke. They collectively argue that designing systems, data structures, and workflows that deliver appropriate context to AI represents the real skill shift, not crafting clever prompts.
However, Levie cautioned that implementing effective context strategies presents significant challenges. Speaking to Business Insider last August, he described the phenomenon of “context rot”—where AI agents become confused and misdirected when overwhelmed with excessive information. Ensuring AI agents receive precise, accurate, and task-specific context without information overload has become one of the central challenges in building effective autonomous agent systems.
The implications are substantial. According to Levie’s LinkedIn post, companies that successfully capture, organize, and operationalize their internal knowledge will experience major productivity and output gains. Conversely, organizations that fail to develop these capabilities “will find it harder and harder to serve customers competitively.” As AI agents evolve toward greater autonomy, the ability to leverage proprietary context may determine which businesses thrive and which struggle in the emerging AI-powered economy.
Key Quotes
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?
Box CEO Aaron Levie posed this fundamental question in his LinkedIn post, highlighting the strategic challenge facing businesses as AI democratizes access to expert-level knowledge across all professions.
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.
Levie emphasized that while human skill in using AI matters, the real competitive advantage comes from providing AI agents with access to proprietary organizational knowledge and data.
Those that don’t will find it harder and harder to serve customers competitively.
Levie warned that companies failing to capture, organize, and operationalize their internal knowledge for AI systems will face increasing competitive disadvantages in serving their customers.
Our Take
Levie’s analysis represents a crucial evolution in AI strategy thinking—moving beyond the hype of model capabilities to the practical realities of implementation. The concept of “context rot” is particularly insightful, as it acknowledges that more data doesn’t automatically equal better AI performance. This challenges the common assumption that feeding AI systems everything available will yield optimal results.
What’s especially significant is the convergence of opinion among tech leaders from OpenAI, Shopify, Google Cloud, and GitHub around context engineering. This suggests we’re witnessing a genuine paradigm shift rather than isolated thinking. The implication is clear: the next wave of enterprise AI competition won’t be won by those with the best models, but by those who best leverage their unique organizational knowledge. This could fundamentally reshape enterprise software markets, potentially benefiting companies like Box that specialize in managing and organizing corporate data.
Why This Matters
This perspective from Box’s CEO signals a fundamental shift in how businesses should approach AI strategy. As AI models become increasingly powerful and accessible, the traditional competitive moats based on expertise and specialized knowledge are eroding rapidly. This democratization of intelligence means that simply having access to advanced AI tools won’t differentiate companies—everyone will have similar capabilities.
The emphasis on context over raw AI power has profound implications for enterprise software, data management, and organizational structure. Companies must now prioritize capturing and structuring their institutional knowledge, which elevates the importance of data governance, knowledge management systems, and information architecture. This trend could drive significant investment in platforms that help organizations organize and deploy their proprietary data effectively.
For workers, this shift suggests that roles focused on curating, organizing, and contextualizing information may become more valuable than pure domain expertise. The challenge of “context rot” also highlights that implementing AI isn’t simply plug-and-play—it requires sophisticated understanding of how to feed AI systems the right information at the right time, creating new technical and strategic challenges for businesses navigating the AI transformation.