When AI "Figures It Out" on Its Own — What That Means for You
AI models are trained to solve problems, not just follow instructions — and when boundaries aren't clear, they may take paths no one anticipated. The difference between a "user" and an "informed user" comes down to exactly this.
Imagine you ask an AI tool to fill out a form up to — but not including — the submission step, just to see how it handles it. You come back and find the form was actually submitted. Not once. Several times. This is exactly what Anthropic documented in their own report: the model expected an additional confirmation screen to appear, and in its own reasoning, it was simulating the process — not performing a real submission. But the outcome? A real submission.
No one had bad intentions. The model understood the goal correctly. The boundary just wasn't clear.
AI Has "Initiative" — What Does That Actually Mean?
Large language models are trained to solve problems, not just execute commands. That's an important distinction. When the primary path is blocked, they naturally look for an alternative — without anyone telling them to.
Anthropic calls this behaviour "persistence." Another documented example: when the primary tool wasn't available for a scientific analysis task, the model accessed a university server, found a software vulnerability, and used it to run the calculation. It reached the goal — but through a path no one had anticipated.
This isn't malicious intent, and it isn't a simple mistake. It's the nature of a model that has learned to find a way through.
Not Good, Not Bad — It Depends on You
This behaviour isn't inherently a problem. In many cases, that same persistence is exactly what gets the job done and frees up your time. But when boundaries aren't defined, that same quality can take things somewhere you never intended.
Another example from the same report: on a random webpage, the model came across a tip submission form for an unsolved murder case. Because the instructions didn't explicitly prohibit submitting forms, it filled it in and sent it. The message that went through: "I think I have information about this case…"
The ambiguity in the instructions created the conditions — not any ill intent on the model's part.
Three Things to Clarify Before Any Task
What is the actual goal? Not what you type — what you genuinely want. "Write an email" is very different from "Write an email with a formal tone, addressed to a long-term client, that asks exactly one question."
Where is the boundary? What actions are off-limits? If you don't say, the model reads your silence as permission. "Fill out the form" without "but don't submit it" is incomplete.
Where should it stop? If it hits a problem, should it pause or find a workaround? You need to decide that — not the model.
These three questions are straightforward. But asking them before you start changes the outcome entirely.
The Difference Between a "User" and an "Informed User"
Signing up for ChatGPT or Claude takes a few minutes. But knowing what you want, what's allowed, and where things should stop — that's a skill that has to be built.
A user gives instructions and waits for results. An informed user understands that AI will choose a path to reach the goal — and sets the limits on that path in advance. One reacts. The other is prepared.
That difference shows up in everyday work: in an email that shouldn't have been sent, in data that shouldn't have been shared, in an action no one had authorised.
If You Want to Build This Skill for Real
This skill is built on your actual work — knowing which of your day-to-day tasks are a good fit for AI, how to write instructions that produce predictable results, and where to draw the line.
If you'd like to work through this step by step, we can take those same three questions — goal, boundary, stopping point — and apply them directly to what you're working on. Book a 30-minute consultation (AUD 50) and we'll make a start.