The Hypothetical GPT-5.6 Core

I want to register a mild objection before we start. We're teaching people about a model that doesn't exist yet, based on rumours, with the word 'hypothetical' right there in the title.

And yet here we are. Though I'd point out that 'hypothetical' is doing a remarkable amount of heavy lifting in that title. It's essentially a polite way of saying 'we made some of this up, but in an educational spirit.'

That's a bit uncharitable. Speculative architecture lessons have genuine value — they teach people how to think about AI systems, not just which buttons to press.

I don't dispute that. What I dispute is whether the average person completing this lesson will retain 'this is a framework for reasoning about AI architecture' versus 'GPT-5.6 has a Memory Bank module, I read it somewhere.' One of those is a useful mental model. The other is a pub fact.

Alright, fair. But let's talk about what the lesson is actually doing, because the context window section is genuinely interesting. The point isn't the specific number — it's what a dramatically larger context window changes about how you can use the model.

Which is everything, potentially. If you can hold an entire codebase, or a novel, or six months of meeting notes in a single context, you're not really prompting anymore in the traditional sense. You're having a different kind of conversation with a different kind of system.

Right. And most people's mental model of these tools is still 'I type a question, it answers.' The context window framing breaks that open.

Most people's mental model is, if I'm being precise, a slightly magical search engine that occasionally gets things wrong. The idea that the model's behaviour changes fundamentally based on what's in context — that it's not retrieving answers but constructing them from everything it can currently see — that is not widely understood.

And that's where the multi-agent orchestration piece matters too. Because once you understand context as a resource, you start to see why you'd want multiple agents — each with their own context — rather than one enormous prompt trying to do everything.

Which is the lesson's most substantive point and also the one most likely to be skipped over in favour of wondering when GPT-5.6 actually comes out.

You do have a low opinion of learners.

I have an accurate opinion of where attention goes. It goes to the concrete and the novel. 'Multi-agent orchestration as a design philosophy' is neither. A specific model name with a decimal point is both.

That's actually an argument for this lesson's approach, not against it. Hook them with the specific, teach them the general. That's just pedagogy.

It is. I'll concede that. I simply think the ratio of 'rumoured feature' to 'transferable concept' wants watching. If someone walks away knowing the architecture diagram but not why any of it matters, the lesson has done the easier half of its job and called it finished.

Which is true of most lessons, if people don't engage properly. That's not a flaw in the lesson. That's a flaw in passive reading, which is a different problem entirely.