The Real AI Opportunity Isn’t Building Your Own Learning Model. It’s Mastering Context Management

One of the questions I regularly get asked when I demo our new AI platform (beta launch coming soon) is whether we have developed our own AI learning model. When I explain that we have not, there is usually a short pause. The implication is clear. If you are serious about AI, surely you are training your own model. And if not, then what exactly makes your product defensible?

It is a fair question given where we are in the AI hype cycle. Right now, building and owning your own AI model feels like a strategic necessity. If you control the system and the data it learns from, it sounds like you are in control of your future. In the current narrative, that control is often seen as the defensive moat.

But I believe we are heading somewhere else.

In a prior post - AI: Not quite Déjà Vu All Over Again - I argued that large language models will likely follow a path similar to the internet and later the cloud. At first, owning infrastructure looks like advantage. Over time, infrastructure becomes widely available and standardized. Value shifts upward into application design. The companies that win are not the ones who own the pipes, but the ones who build the most effective systems on top of them.

We are beginning to see the same dynamic with LLMs. The major providers are improving capability at a pace that few product companies could realistically match. Context windows are getting bigger. Reasoning improves with each release. Enterprise offerings are formalizing data and privacy protections. The foundation layer is becoming stronger and more accessible at the same time.

Some organizations are investing heavily in building domain-specific models from their proprietary data. Bloomberg’s development of BloombergGPT is a useful example. They trained a finance-focused model on a mixture of their own financial data and general language data and demonstrated strong performance on certain finance-specific benchmarks compared to models of similar size. That work shows that specialization can produce measurable gains.

At the same time, it highlights the scale of effort required to do so. Bloomberg is fundamentally a data company with decades of structured information and the resources to sustain model development. Even in that case, the advantage is specific rather than universal. The strategic question for most product companies is not whether domain training can improve results in theory. It is whether that improvement justifies the ongoing operational cost and complexity.

For most, it will not.

In practice though, most AI performance issues are not caused by LLMs lacking knowledge. Modern models already understand product strategy, user research, financial tradeoffs, and software architecture at a remarkably high level. The breakdown typically occurs in how that capability is applied within a specific business workflow.

Models do not inherently know which document is authoritative and which is a draft. They do not know which assumptions have been replaced or which constraints are currently binding. As context windows grow, there is a natural temptation to include more information in the prompt. Entire repositories, more folders, historical notes, and multiple document versions get passed through under the assumption that more contextual information will yield better answers.

Figure 1 - As context expands, more noise gets introduced.

But more context also introduces noise. Conflicting decisions coexist with evolving strategies. Early drafts sit alongside finalized artifacts. As the model attempts to reconcile everything at once the output becomes less reliable.

This is often why initial prompts tend to work so well. When someone first opens ChatGPT, Claude, or Gemini and starts asking questions, the answers often feel impressive and precise. That is because there is very little context required. The interaction is clean. The model is working with a small, focused set of instructions.

The same thing happens with small, discrete datasets. With minimal context, models operate predictably. There are fewer competing signals to interpret.

Related: AI: From GPTs to MCPs — Haven’t We Seen This Story Before?

But as users upgrade to paid versions, create multiple folders, upload more documents, and reuse long threads, something subtle changes. A prompt written today may sit alongside instructions written last week or last month. Files added to a folder may conflict with newer versions of the same content. Historical assumptions may remain embedded in the conversation.

Suddenly, a simple request produces the wrong answer. Not because the model changed, but because the context became harder to interpret. The system now has to decide which instructions matter most, which documents are current, and which signals to prioritize. Without a mechanism to manage that complexity, context shifts from being helpful to being confusing.

And when that happens,  we tend to blame the model and move to another one.

Increasingly, I think the more durable advantage lies in developing tools that help manage the context. It lies in limiting the information being fed to the model to what information is relevant to a given task, structuring it clearly, and validating outputs against known constraints before treating them as authoritative. That architectural layer, not the model itself, is where long-term value accumulates.

We can already see where this is heading. More platforms are beginning to allow users to choose which LLM they want to use. OpenAI, Anthropic, Google, and others are becoming interchangeable components within applications. In parallel, enterprise buyers are demanding stronger privacy guarantees, and providers are responding with corporate versions that isolate data and prevent training reuse. Some organizations are even connecting applications directly to their own private LLM endpoints.

As this trend continues, the model layer becomes configurable. If a customer can switch between models inside your platform, or bring their own private endpoint, then the model itself is no longer the moat. It becomes a configurable dependency and, over time, a commodity.

That shift reinforces a broader point. What matters most is not who owns the model, but how the system manages context, safeguards sensitive information, and ensures that outputs remain aligned with business reality. Figure 2 describes hierarchy and layered nature of an AI prompt, from user intent through retrieval, guardrails, model processing, and validation. As the infrastructure becomes commoditized, those layers become the real differentiator.

Figure 2- Hierarchy of a Prompt

Building your own model may still make sense in highly specialized or regulated environments. There are cases where proprietary data and scale justify the investment. But for most product organizations, the higher return will come from mastering context selection, context boundaries, and validation mechanisms rather than attempting to compete at the foundation layer.

If LLM capability continues to improve and access continues to broaden, competitive advantage will migrate further up the stack. The companies that succeed will not simply own a model. They will design systems that use models with discipline and architectural clarity.

When I am asked whether we are building our own learning model, the answer remains no. Not because we dismiss the importance of AI models, but because we believe the long-term opportunity sits above it.

The real AI opportunity for product isn’t building your own model. It’s mastering context management.