Build a Business That Gets Stronger Every Quarter

Jerry Liu killed his own product to bet on context. It worked. He built one of the most installed pieces of AI infrastructure of the last three years — the indexing and retrieval layer a generation of RAG apps were stitched together with — and then he pivoted hard. His public argument: the AI framework era is over. The scaffolding layer is collapsing into the model itself. Once agent loops get good enough, the only durable moat left is context.

Here is why that matters for your business. Every competitor you have can call the same frontier models. The same GPT, the same Claude, the same Gemini. Identical APIs, identical reasoning capabilities, identical cost curves. No business wins on model access alone anymore. And most business owners are still playing the wrong game.

What Most Owners Get Wrong

Most business owners are chasing the best model. The businesses that pull ahead are building a Context Layer around whatever model they use.

Context is not a document you wrote. It lives in the history of why a specific client workflow takes five extra steps. It is why your team uses a non-standard approval path for a certain vertical. It is the decade of resolved tickets, edge-case decisions, and client-specific style guides that exist inside your business and nowhere else.

Jerry calls it “anything surrounding the model” — the set of services, data, and instructions the model accesses to actually do things. The frontier labs are all converging on the same capabilities. They are tuned for coding, reasoning, and general intelligence. They are not tuned for your client's naming conventions, your compliance path, or the specific way your best operator handles a cancellation call.

Consider two businesses. Both use the same frontier model. Business A opens a chat window and pastes a generic prompt. The output is competent. It also sounds identical to what every other business using the same model would produce. Business B has an agent system that pulls from a decade of client-specific resolutions, approved style guides, and the full history of edge cases the team has resolved. Before generating a single line, the system queries context that no competitor can access. The gap between those two outputs is the moat.

Map Context to Moat

Three layers. Each one compounds.

Unique data creates high switching costs. A client who has had every preference, every exception, every specific workflow encoded into your system over five years faces real friction to leave. Their next provider starts from zero.

Complex handoffs create network effects. When your system encodes the judgment of your best people — the ops lead who knows the three things that go wrong with West Coast logistics during fire season, the account manager who understands which client escalation path actually gets resolved — it compounds. Every resolved exception becomes a permanent asset the model can use without re-learning.

Real-world presence creates a trust moat. Physical infrastructure, local relationships, in-person delivery. These live outside any training set. No model update erases them.

And here is the part that changes the timeline. Context is not a grand upfront project. It is a daily capture habit. Every resolved ticket logged with the resolution. Every client preference saved as structured data. Every edge case documented not in a wiki no one reads, but in a memory layer your agent systems can query. You build it incrementally. It compounds.

Start Building Your Context Layer This Week

Five steps. None of them require a massive project.

1. Audit one workflow for hidden context. Pick a single process your team runs repeatedly. Walk through it and flag every step a new hire would not know on day one — the non-standard approval, the client-specific quirk, the informal workaround everyone just knows. That is your context inventory.

2. Capture the exceptions. Most teams document the happy path. The Context Layer lives in the exceptions. For one week, log every edge case your team resolves into a structured format your agent systems can query. Date, client, scenario, resolution. Do not write a manual. Build a queryable record.

3. Encode one client's history. Take a single long-term client and pull their resolution history, preferences, and style decisions into structured memory. Make it queryable. The test: can your system answer “what is this client's preferred format for quarterly reports” without a human checking?

4. Build the first handoff. Wire one of your agent systems to pull from that memory before producing output. The goal is not full automation. The goal is output that reflects the specific client, not a generic template. Even a human-in-the-loop workflow with context-aware drafting is a step change over starting from blank each time.

5. Measure the gap. After one month, compare the output your context-aware system produces against a raw model prompt for the same task. The gap — in accuracy, in client specificity, in time saved — is the size of your moat. Track it. It should widen every quarter.

The businesses that get stronger over time are not the ones with the best AI tools. They are the ones that own what others cannot copy. The models will keep improving. The agent harnesses will standardize. The only separator that compounds every quarter is the context layer you build around them.

Systematize one thing this week that a public AI model could not produce without your specific internal knowledge. That one thing, multiplied over years, becomes the business no competitor can displace.