## The chorus

Start with Ali Ghodsi. The Databricks CEO opened his Data + AI Summit keynote this June with a blunt diagnosis: “AI doesn’t have an intelligence problem. It has a context problem.” He had the numbers to back it. PwC found that 56% of CEOs report zero financial benefit from AI. The share of companies scrapping AI projects before production jumped from 17% to 42% in a single year. Over the same period, models got dramatically better and API costs collapsed. Intelligence improved. Results got worse. The missing variable is not in the model.

Satya Nadella said a version of the same thing from the Davos stage in January, and regular readers, if I have any,

know I have written about it before: a firm that cannot embed its own knowledge in an asset it controls has no sovereignty, and is leaking enterprise value to a model company on every call. He predicted it would become the most talked about topic in AI this year. Six months in, he is winning that bet.

Pat Grady opened Sequoia’s AI Ascent with a warning to every founder in the room: “The things that you build might be irrelevant tomorrow.” The models move faster than any product built on top of them. Amazon’s top AI executive, Peter DeSantis, put the economic version to The Wall Street Journal in four words: “AI has a cost problem.” And Prukalpa Sankar, co-CEO of Atlan, compressed the whole thesis into the sharpest line of the year: while intelligence converges, context compounds.

Different companies, different incentives, same conclusion. Intelligence is commoditizing. Context is not.

## The tell

Now listen to what the model providers themselves are saying, because this is where it gets interesting.

Sam Altman has spent 2026 telling anyone with a podcast that memory is OpenAI’s real moat. Not model capability. Memory. His stated ambition is an AI with what he calls infinite, perfect memory: every document, every email, every decision you have ever consulted it on. He describes personalization as “extremely addictive” and says users who invest their history into ChatGPT will find it very hard to leave. We are, in his words, in the GPT-2 era of memory.

Read that carefully. The CEO of the largest model company in the world is telling you, in public, that his moat is built from your context. Your questions, your documents, your decision history, accumulated on his infrastructure, creating switching costs that keep you paying him. He is not hiding it. It is the strategy.

For a consumer, that might be a fair trade. For an enterprise, it is the sovereignty problem stated as a product roadmap. Every query your agents send to a frontier model is a small deposit into someone else’s moat.

## Rented context, owned context

Here is the structural choice underneath all of this. Every time one of your AI agents fires a query at a frontier LLM, one of two things happens.

**Rented context.** The query goes out. The model answers from its general training. The exchange is discarded, or worse, it accumulates on the provider’s side of the ledger. Your agent got an answer. You paid for it. You own nothing from the transaction, and you will pay full price to ask a semantically identical question tomorrow.

**Owned context.** The query is intercepted before it leaves your perimeter. The validated answer is stored in a customer-owned, compounding knowledge graph, alongside the semantic fingerprint of the question. The exchange becomes part of a growing corpus of proprietary intelligence about how your specific domain works: your terminology, your edge cases, your approval patterns, your exceptions. The intelligence stays inside. It compounds permanently.

Most enterprises are operating entirely in the first mode. The data says so: roughly 95% of enterprise AI usage still runs on frontier models, per Glean’s CEO, and Datadog’s telemetry shows 69% of enterprise LLM input tokens are system prompts and repeated context. That is the Rediscovery Tax at industrial scale. Firms are renting their own institutional knowledge back from an external model, one API call at a time.

## Why this compounds

The strategic implication takes a moment to land. An enterprise operating in owned-context mode is accumulating something the rented-context enterprise never will. A knowledge graph that grows with production usage is an AI system that gets smarter, faster, and cheaper over time. Not because the underlying model improved. Because your proprietary layer did.

Domain questions start resolving locally in milliseconds instead of round-tripping to a frontier API. The share of agent traffic answered from owned intelligence climbs as the graph matures. Token spend drops compoundingly, because more of the work resolves against knowledge the firm already validated and already owns.

And here is the question a software executive asked me recently that I have been thinking about since: if the model providers cut prices again, does the owned-context advantage shrink? It does not. It grows. Cheaper frontier inference makes the genuinely novel queries more affordable, which enables more agent deployment, which generates more validated answers, which deepens the graph. The moat compounds regardless of what happens to frontier pricing. Lower model prices are a tailwind for the enterprises that own their context and a treadmill for the ones that rent it.

This is Context as the New Moat. It is the difference between an AI system that costs the same per query forever and an AI system that becomes a durable competitive asset. And it is the one moat the model providers cannot absorb, because replicating it would mean commoditizing their own inference revenue.

## Two questions for your vendors

If you are evaluating AI infrastructure right now, two questions cut through most of the noise.

First: is this a capability the model provider is likely to build natively? If yes, understand what you actually own when they do. Features get absorbed. Infrastructure between you and the provider does not.

Second: does my usage of this platform accumulate proprietary intelligence I could not recreate elsewhere? If you could swap the vendor tomorrow and lose nothing but integration work, you are renting context, not building a moat. And if the intelligence accumulates on the vendor’s side rather than yours, you are building someone else’s.

The CEOs have already told you where value accrues next. The only open question is whose balance sheet your context compounds on.

## One thing to read

Jaya Gupta’s “Context graphs: AI’s trillion-dollar opportunity” from Foundation Capital. The venture-side articulation of the same thesis: the next platforms will be built on persistent records of enterprise decisions, not on better models. Systems of record store what happened. Context graphs store why. Worth your time.

## About Excipio

Excipio is a private memory layer for enterprise AI, built in C++, sitting between your agents and the frontier models. We intercept every agent query, serve validated answers from your customer-owned, compounding knowledge graph in under 10ms, and route only genuinely novel questions to the cheapest capable model. Roughly 42% blended token cost reduction. Zero bytes leave the perimeter on a cache hit. Zero changes to your agent code.

If you are running AI agents at scale and want your context compounding on your balance sheet instead of someone else’s, I would like to talk.

**tony@excipio.ai** · **excipio.ai**

*The Enterprise Token Economy publishes on Substack. Forward to anyone building or deploying AI agents at scale.*