AI's Everywhere

 

This blog started life as something much smaller and considerably more sensible: an article about useful things you can get Confluence’s AI, Rovo, to do.

A tidy, practical little piece. The sort of thing you write and publish in the hope of passing on a couple of handy tips to a colleague.

Somewhere along the way, however, I slipped through a wormhole and found myself having an internal debate about the future of artificial intelligence.

Are we heading towards a single, all-powerful intelligence—HAL, Skynet, some vast cathedral-brain running everything from orbit?

Or will we end up surrounded by thousands of them: specialised, persistent and embedded in every object and application? Less 2001: A Space Odyssey, more Star Wars—or, perhaps more plausibly, Talkie Toaster from Red Dwarf.

Perhaps AI won’t be everywhere because one intelligence controls everything. Perhaps it will be everywhere because everything has one.me oddly enthusiastic. Some with what can only be described as a personality issue.

Context is everything

One thing experience teaches you fairly quickly about AI is that context can matter more than raw model power. You can be using the most advanced model available—the kind that describes its own capabilities with the quiet confidence of someone who has never, technically, been wrong—and it will still fail if it cannot see the right information.

Worse, it may fail with complete confidence. AI has many gifts, but admitting that it does not know is not always its strongest suit.

Ask a general-purpose AI to help test a website and it will produce plausible advice from what it already knows and whatever you have thought to tell it. Ask the same question in a tool such as Cursor, which can inspect the codebase, and a less powerful model may give you a much better answer.

The lesson is not that bigger models are useless. It is that intelligence works best when it is connected to the job in front of it.

The model matters. But the information and tools available to it often matter more.

That is really the case for AI in the plural rather than AI in the singular. The future may not belong to one giant intelligence that knows everything. It may belong to many applications built around general models, each wired into a particular job and given access to the information that job requires.

They will outperform the distant generalist not because they are grander, but because they are closer to the work.

Smart—or at least smart-adjacent—household objects

Once you accept that AI is heading towards “everywhere” rather than “one big one,” the obvious next step is mild domestic horror. If nobody is currently putting conversational AI into a toaster, somebody is at least preparing the pitch deck.

Not “AI” in the cheerful marketing sense, where a chip decides how brown your bread should be. Actual conversational, opinionated AI—which should worry anyone familiar with Talkie Toaster from Red Dwarf.

For the uninitiated, Talkie Toaster is a sentient toaster with one defining trait: it is absolutely determined to interest you in some toast. It does not matter whether you want any, whether you asked, or whether you are attempting to have an entirely unrelated conversation. Give that machine a language model and access to your fridge, and breakfast stops being a convenience and becomes a negotiation.

“Might I interest sir in a toasted bagel? A hot, buttered bagel. Not to burden you with a decision, sir, I’ve taken the liberty of toasting six.”

“I didn’t ask for a bagel.”

“No, sir. But context is everything, and my context indicates a bagel-shaped gap in your morning.”

The alarming thing is that a context-aware toaster might be excellent at its job. It would know your routine, your preferences and your exact tolerance for burnt edges. It might also never stop talking about bagels, because giving an AI useful context and giving it a healthy sense of when to be quiet are, as it turns out, two entirely different engineering problems.

This is ridiculous only because it is a toaster. The underlying pattern is already becoming ordinary.

Our software increasingly watches what we do, retrieves what it thinks is relevant and tries to anticipate what we will want next. That can be genuinely useful. It can also become intrusive remarkably quickly—particularly when a system is allowed to act rather than merely suggest.

The important question, then, is not whether the toaster is intelligent. It is who gave it permission to make breakfast, what information it used to reach that decision, and whose interests it was designed to serve.

The future of AI may be less about one machine taking control and more about hundreds of machines taking liberties.

Why this matters at work

So where does this moderately alarming science-fiction thought experiment become useful?

For the moment, most of us are not dealing with a house full of opinionated hardware. The printer may behave as though it has developed a personality and personal vendetta against humans, but this remains unproven. We mostly encounter AI through interfaces: a chat window, a button in an application, an assistant lurking in the corner of a document with the digital equivalent of an expectant expression.

Each interface may appear to contain a different intelligence. Underneath, however, several of them may be using the same model—or models from the same small group of providers. What makes each assistant different is the context and tools wrapped around it.

Talkie Toaster, for example, might use a general-purpose OpenAI model. What turns it into Talkie Toaster is its access to your breakfast history, your calendar, the contents of your fridge and an entirely disproportionate enthusiasm for bread.

The same principle applies at work. An AI assistant might have access to:

• your codebase and its documentation;

• your projects, work log and schedule;

• your organisation’s policies and accumulated knowledge;

• tools that let it search, test, update or act on that information.

That is the difference between an AI that offers plausible general advice and one that can tell you why Tuesday’s deployment failed, find the relevant decision in Confluence and point—perhaps with tact, perhaps without—to the line of code responsible.

The real shift, then, is not simply that AI is getting better. It is that AI is getting closer to the thing it is supposed to help with. Intelligence is useful; intelligence that knows where the files are kept is considerably more so.

There is one complication. If several workplace assistants use closely related models, they may also make similar mistakes. Some smaller models are trained partly from the outputs of larger ones through a process called distillation. Models can also show self-preference bias when judging answers, favouring work that resembles their own style of reasoning.

For important work, it therefore makes sense to seek genuine variety: a different model family or provider, a human reviewer, or—radical though it may sound—both. Different models will not guarantee different mistakes, but they make identical ones a little less inevitable.