For AI, Context Is King

The Context Revolution

18 months ago, state-of-the-art (SOTA) AI models were limited to processing about 4,000 tokens (roughly the length of this blog post). Today, advanced models can handle up to 1 million tokens, an extraordinary 250x improvement in under two years.

This exponential leap in capability isn't just a technical milestone. It represents a fundamental shift in how AI systems operate, understand information, and ultimately deliver value. Box CEO Aaron Levie highlighted this transformation in a recent post, noting that there are "essentially no examples in history of a technology improving at a rate of 250X in under 2 years."

Why Context Matters

Consider what happens when an AI system has access to greater context:

1. Deeper Understanding: With wider context windows, AI can grasp the nuances of complex documents, conversations, and problems. It can see connections between disparate pieces of information that narrower models would miss.

2. More Relevant Responses: Greater context allows AI to "store" memory about past interactions, making future answers more relevant to your specific situation. The AI becomes personalized to your needs rather than generic.

3. Enhanced Problem-Solving: Complex problems often require holding multiple variables in mind simultaneously. Expanded context windows enable AI to tackle increasingly sophisticated challenges across domains.

4. Institutional Knowledge: Context transforms content from mere storage to actionable intelligence. As Cheryl McKinnon, a Forrester analyst, notes, this shift represents "a turning point where now it's not just about storing files and folders, but can we put that stuff to work? Can we think about content, not just from the storage point of view, but across the context of a whole business activity?"

The Business Implications

The race to capture and leverage context is reshaping the competitive landscape in AI. Those who understand this shift are positioning themselves to capture disproportionate value.

Who Wins in a Context-First World?

1. Context Aggregators: Companies that can gather, organize, and make sense of vast amounts of information will have a significant advantage. This includes cloud storage providers, content management systems, and knowledge management platforms that are evolving beyond simple storage to become intelligent content orchestrators.

2. Industry-Specific Context Providers: Alan Pelz-Sharpe, founder and principal analyst at Deep Analysis, points out that "The ECM sector as a whole now has the biggest window of opportunity they have had in 20 years" precisely because these companies already ensure unstructured data is "accurate, relevant, secure and timely".

3. Integration Specialists: Companies that can connect disparate systems and data sources to provide a unified context layer will unlock tremendous value. The ability to pull information from various silos into a coherent whole is becoming a critical capability.

The Value Capture Equation

The economic implications are clear: whoever controls the context controls the value chain. When AI systems can access comprehensive context about a business problem, customer relationship, or industry trend, they deliver exponentially more value than systems working with limited information.

We're already seeing this dynamic play out in practical applications. Box CEO Aaron Levie recently noted that OpenAI's latest models successfully completed complex financial modeling tasks requiring math, logic, and understanding subtle business context, something that was impossible for AI just a year ago.

The Path Forward

For businesses looking to thrive in this new paradigm, several strategic imperatives emerge:

1. Audit Your Context Assets: What unique contextual information does your organization possess? Customer data, institutional knowledge, industry expertise, and proprietary datasets all represent potential context advantages.

2. Invest in Context Infrastructure: Building the systems to organize, access, and leverage your contextual information will be as important as the AI models themselves. This includes both technical infrastructure and organizational knowledge management practices.

3. Develop Context-Aware Workflows: Redesign business processes to capitalize on expanded context capabilities. Tasks that previously required human judgment due to their complexity and nuance may now be within AI's grasp when sufficient context is provided.

4. Form Strategic Partnerships: No single organization possesses all relevant context. Strategic partnerships that combine complementary context assets can create powerful synergies.

Conclusion

As AI capabilities continue to advance at unprecedented rates, the ability to provide rich, relevant context will increasingly determine who creates and captures value. The winners in this new era won't necessarily be those with the most advanced models or algorithms, they'll be those who can feed those systems the most comprehensive, high-quality context.

In a world where AI models become increasingly commoditized and accessible, context truly is king; and those who rule the context kingdom will reap the rewards.