The next AI battle is not about the smartest model, but about context
Why context is becoming more important than the model
The reflex with AI is still: which model is the smartest? That makes sense, because benchmarks are easy to compare. But in day-to-day work, that is increasingly not the real question. A good model without context just waits for an explanation. You have to paste the email, explain the customer, point to the right version of the document, say what was decided yesterday, and specify what can and cannot be shared. Only then can the model do something useful.
The video calls this the shift from intelligence to context. Frontier intelligence may become available more slowly or be rolled out more selectively. At the same time, open models are getting cheaper and better. As a result, the advantage shifts to the layer around them: who has organized their work, permissions, data, and processes so that any good model can immediately contribute?
For companies, that is a far more practical discussion than waiting for the next model name. The value is not only in a chatbot that gives nice answers. The value is in an AI system that knows which customer is involved, what the current status is, which actions are permitted, and where the boundary lies. That is exactly where many organizations are still getting stuck. They have tools, but no working context layer.
Apple, Claude, and Codex all show the same movement
The examples in the video may seem unrelated at first glance. Apple is trying to make Siri useful by getting closer to messages, calendar, photos, notes, and app status. Anthropic is putting Claude into Slack, where teams can give the model access to selected channels, tools, and codebases. Codex works from files, repositories, and local work context.
Underneath, it is the same product movement. AI should not only talk smarter, but get closer to the work. For Apple, that is personal context. For Claude, that is team context. For Codex, that is file and project context. In all three cases, the question becomes: what may AI see, what may AI remember, what may AI do, and how do we control that?
That makes the context layer strategic. Not because every company suddenly needs to build its own AI lab, but because standalone prompt sessions are becoming too slow. If an employee needs ten minutes every time to explain the situation, the advantage disappears. If the same context is neatly available within clear boundaries, AI becomes a much more natural part of the workflow.
The bottleneck for companies is not inspiration but governance
Many companies are past the inspiration phase. They have tried ChatGPT, they have a few internal prompts, someone has built an automation, and there are probably already people putting customer emails or documents into an AI tool. The problem is that this often happens outside the process.
That creates risk. Not because AI is inherently dangerous, but because it is unclear which data goes where, which source is authoritative, who checks the output, and when an action is actually allowed to be executed. The video highlights this very sharply in the context of Slack: business context is shared, political, outdated, half-finished, and scattered across multiple places. You cannot blindly trust that.
The solution is not to block AI. The solution is a better work structure: permissions, logging, source references, clear scope, human approval where needed, and automation where it is safe. That sounds less exciting than "the latest model," but in practice this determines whether AI becomes useful or remains just a standalone playground.
What this means for marketing and sales
For marketing and sales, this is especially relevant. Customer context is almost always scattered. A lead comes in through a form, may have opened an email earlier, is linked to a campaign, has had a quote conversation, and is also in a CRM. If AI only sees the isolated question, it misses half the picture.
A good context layer makes follow-up much sharper. Not because AI then sells more aggressively, but because it better understands what is relevant. An existing customer asks something different from a cold lead. Someone who reads an article about AI automation three times needs different follow-up than someone who only visits a general homepage. A real estate agent, dealer, or B2B service provider also has different language, concerns, and decision moments.
That is why this is a strong hook for Funnel Adviseur. We do not sell "an AI tool." We build routes in which data, content, CRM, follow-up, and automation work together. The AI layer then becomes not a standalone chatbot, but a practical assistant in the process: reading, summarizing, signaling, preparing, and only acting when the context is right.
The practical choice: build a context layer instead of model-hopping
In the coming months, new model names will keep appearing. Some are better at code, others cheaper, faster, or stronger with long context. That remains important. But for most companies, model-hopping is not a strategy.
The better question is: if we switch models tomorrow, will our system still work? Is our customer context clearly documented? Do we have clear permissions? Do we know which sources are authoritative? Can we see after the fact why an automation made a particular suggestion? Is there a human check on actions that have impact?
Whoever gets that right becomes less dependent on the hype cycle. Then you can use GPT, Claude, Gemini, open models, or specialized tools wherever they fit best. The context stays yours. The model becomes replaceable.
That is the real lesson from this video. The AI battle is not only about intelligence. It is about the layer that applies intelligence to real work. Companies that build that layer now will get more out of every model that comes after.
