There is a version of the AI augmentation story that has become the default: everyone gets access to the same tools, and the people who use them best win. The playing field flattens. Expertise becomes less of a moat.

I ran a version of this experiment, unintentionally, over the past year. The conclusion is different.

Past the ceiling

I started where everyone starts: new chats, general prompts. Then structured prompts, system instructions. Then Claude projects — the kind of discipline that already puts you ahead of most users. Then CoWork, Claude’s AI-native workspace for handling documents and complex files. Each step was genuinely good. Each step had a ceiling.

My technical background would not let me stop there. Not by ambition — by dissatisfaction. The tools plateaued too early. The context window filled and compressed. I could feel the ceiling. So I kept going, past what I would recommend to anyone without a technical baseline: a proprietary stack, a universal brain, a memory framework I designed and told the system how to maintain. Not because I wanted to overkill. Because overkill was the only way to get what I needed.

The result, twenty-four months in: a system that could cross-check a financial report against eighteen months of portfolio context in the time it takes to write an email. One instruction. One output. No iteration.

Then I started working alongside people who were using the same tools.

Three types of AI users

The gap was visible immediately. Not in the tools — everyone had access to ChatGPT, Claude, the usual suspects. Not in intelligence or work ethic. In one thing: context.

Context, in this piece, means two things at once: what you know, and what the system has access to. The gap I am describing is the distance between the two.

There are three types of AI users, and they live on different planets.

The first opens a new chat each time. General prompt. The output is a function of what they asked, which is a function of what they already knew. The tool is as blind as the user. This is where most knowledge workers are today.

The second is the power user of native tools — Claude projects, AI-integrated spreadsheets and documents, co-working features pushed actively by Anthropic and OpenAI over the past year. A growing population, actively cultivated. Better output. But they remain prisoners of the vendor roadmap: they use what is put at their disposal, nothing more. And the context window still saturates — send a thirty-page document, run three or four iterations, and the system starts compressing. What looked like memory is really just a long conversation with a ceiling.

The third is model-agnostic. They use the raw model and build the context themselves, independent of any product team’s priorities. Obsidian, custom memory frameworks, proprietary stacks. Same drive as the level-two user — just pushed past the point where off-the-shelf tools satisfied them.

Fifteen to twenty flags

As a partner at a VC firm, when my analysts — especially those coming from a pure business background rather than a technical one — received a data room, they were operating at level one or two. New chat. General prompt. When I worked a data room, my agents already knew the sector thesis, the portfolio comparables, the specific flags we look for at this stage, the terms we had seen and rejected. The question I asked was simple. The answer was specific, cross-referenced, and dense.

The difference in output was not subtle. On financial documents, my agents routinely surface 15 to 20 flags per review — not errors in isolation, but contradictions between the document and prior reporting, inconsistencies between what the team said last quarter and what they are presenting now. The same documents, reviewed without that accumulated context, produced a fraction of that.

The gradient

The impact is not uniform. The context gap is most visible in domains where things repeat, recur, and must remain coherent: finance, compliance, accounting. Cross-check this quarter’s numbers against last year’s. Flag what changed. Check the document against the term sheet. The agent’s value compounds with every prior document it has processed.

In deal analysis, the gap is real but smaller. The rules are more subjective, the thesis evolves, judgment is harder to encode. Context helps — knowing what we have passed on and why, what patterns tend to break down post-investment in this geography — but it does not replace judgment the way it replaces manual cross-checking.

Understanding this gradient matters. The technology does not work the same way everywhere. Deploying it as if it does is how you get disappointed teams and dismissed tools.

The opposite of what vendors sell

I looked at the vendor market for a solution. I wanted to avoid building everything myself.

What I found was a consistent mismatch. Most AI coworker tools are built on active context construction: they ask you to point at documents, feed a knowledge base, stop what you are doing and write things down. Some connect to your CRM, assuming your data is already structured and clean. Others position themselves as replacements for headcount — a thesis I do not share. These are tools. They augment people; they do not replace them.

The model that actually worked for me is the opposite of what vendors sell. My context grew passively — captured through every interaction, every document reviewed, every correction made. I did not stop to document. I worked, and the system learned from the work.

This works because I am the practitioner with authority in the loop. Every decision I feed into the system carries weight. The context it accumulates is not neutral — it reflects fifteen years of pattern recognition, a specific investment thesis, rules about what we look for and what we pass on. Someone feeding the same system without that grounding would accumulate a different kind of context: shallow rather than authoritative, potentially reinforcing errors rather than correcting them.

What makes context robust is auditability. Not all inputs carry the same weight. An authoritative judgment overrides a shallow one. A signed document overrides an email. An external auditor’s finding overrides an internal estimate. Building a reliable context means encoding not just information, but its source — and the hierarchy of those sources. Without that, the system amplifies noise with the same confidence it amplifies signal.

And here is the gap the vendor market has not addressed: there is no tool today that makes this kind of context shareable, diffusable, and auditable across a team. The level-three user can build it for themselves. Building it for others is still a custom job.

The model that transfers is not “give everyone the tool and let them build their own context.” It is: the expert builds the context, the system holds it, the team uses it. The output becomes a function of the system, not the user.

This model also assumes the expert controls the system. In corporate environments, data sovereignty, compliance, and confidentiality introduce constraints that can change that assumption entirely. I have been operating without them, for now — and I remain aware that the model may look different under them.

What would make me wrong

I have not fully deployed this yet. It is the bet I am making.

What would make me wrong in twelve months? Two things.

First, if the team does not adopt it — not because the tool fails but because the behavior change does not take. Augmentation requires a feedback loop: the agent surfaces something, the human acts on it, the system updates. If that loop does not close — through lack of rigor, lack of trust in the output, insufficient adoption — the context decays and the system becomes noise.

Second, if the context stops accumulating. What I have today is a function of twenty-four months of continuous input. A system that stops learning — because it hits a limit, because the maintenance breaks, because the inputs slow down — will slowly become a static prompt. The gap it closes will reopen.

The variable that matters in AI-augmented teams is not the tool. It is not even the person. It is the context — how much of it exists, how well it is structured, how it was built, and who controls it.

For knowledge workers who are not developers, context does not build itself. Someone has to design the system that captures it. And that person does not have to be technical — but they have to be experienced enough that their judgment is worth encoding, and honest enough to admit that the tool amplifies whoever feeds it.

That is not the story most AI vendors are telling. But it is the one I am watching play out.