The Era of Context

Peter Drucker, one of the most important thinkers on management and business (and coiner of the term knowledge worker), in the 90s said, "knowledge has become the key economic resource and the dominant-and perhaps even the only-source of competitive advantage." He couldn’t have predicted the AI era more accurately. As AI progress continues unabated, AI models will get more and more capable of augmenting or automating a variety of knowledge worker tasks. Just as we've seen in coding, these models will morph into AI agents that are expert lawyers, healthcare professionals, business strategists, scientific researchers, or other roles in almost every domain of work. But these models, by default, don't know anything about your particular team or organization. In an instant, they might be asked to review a legal contract for Ford, and then in the next second be writing code for a new piece of software at Goldman Sachs. They are fully general-purpose superintelligence systems that can take on any task for anyone that’s asking. As a consequence, that means that your company is getting the same expert lawyer as another company, the same engineer, and so on. The question that we will have to wrestle with is, in a world where everyone has access to the same intelligence, how does a company differentiate? 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. Context about the right products to build, the ways to serve customers, the markets to go after, the specific details of client interactions, the tribal knowledge in an organization, and endless amounts of other proprietary information to your company that's developed over time, licensed, or acquired. Context engineering has blown up over the past year to tackle just this, and it's not an easy problem. Just imagine taking an expert lawyer or engineer that by default knows absolutely nothing about your organization, and you only have a single document’s worth of space to describes their entire job, every system they have to leverage, all the data that they are supposed to work with for that particular task, what their objectives are, and so on. Getting the right information to them becomes a critically important requirement to drive their productivity. Now, this problem has already plagued companies well before agents existed. Companies have always been a collection of various forms of context that they try and extract the most value out of: their processes, intellectual property, unique ideas and roadmap, ways they makes decisions, information about their customers, and so on. And companies, as they scale, have only been able to utilize a small portion of this knowledge (digitized or not). Lew Platt, the CEO of HP in the 90s, once said, “if HP knew what HP knows, we would be three times more productive.” AI Agents finally make this vision possible. We can, for the first time, begin to tap into this wealth of knowledge and information sitting inside of our organizations. Mike Cannon-Brookes of Atlassian once framed this as a sort of a Metcalfe's law of data. Like the original theory on network effects, the more data you have, the more powerful the overall system becomes. This means that in the 21st century, one of the most critical forms of competitive advantage will be a company's ability to capture, manage, and build processes around the right context. The real estate firm that has better insights on market pricing and rent dynamics will get more clients, the pharmaceutical company that can develop and design new drugs using reams of data will generate more revenue, the marketing agency that can generate better campaigns will differentiate more. Now, getting the right data (or context) to agents is a hard problem at scale. As Jaya Gupta and Ashu Garg at Foundation Capital pointed out in their provocative essay on context graphs, many of the critical decision traces for AI agents to operate with either aren't in any existing software today. And this is just one example of the kind of context that agents will need to operate on. They’ll need customer data, enterprise documents, conversation history, project timelines, research data, marketing assets, financial records, codebases, HR information, and much more. Designing our systems to get agents access to that data, and ensuring that all of our agents can interoperate on that data is going to be incredibly important. In some cases existing systems of record will become the logical houses for that data and the corresponding agents; and in other cases -especially where there is no such software today- we will need all new tools, just as Jaya and Ashu laid out. Even then, managing the myriad conflicts that emerge in data access controls and permissions so agents don’t leak data, governance and compliance challenges around what things an agent can execute on, and connecting our disparate enterprise systems to make this