AI Creep Is Real
The problem isn't how much AI you're using. It's that you can't stop.
A year ago, I had one ChatGPT window open and I was giving it my full attention. I remember hearing about people who ran multiple custom bots simultaneously — rotating through them, orchestrating conversations between agents — and thinking they were out of their minds.
Eight months later I had three Claude Projects open at once. Two or three different tasks, sometimes with the projects talking to each other, each playing a different role. There are moments when this actually works. A narrow field of vision with multiple agents in dialogue can be an expansive experience — you get angles you wouldn’t have reached alone.
And then AI Creep sets in.
Three projects becomes six. The narrow field widens into something unmanageable. I’m flipping between tabs like whack-a-mole. And at some point I feel it — that brain fry. I don’t know where I am or what I’ve been doing. I couldn’t tell you what I’d worked on because I hadn’t really been working. I’d been reacting.
Researchers at BCG have been studying this. Their term — brain fry — is more precise than it sounds. It describes a specific kind of cognitive overload that emerges not from working hard but from managing too many AI threads at once. And buried in their findings is a number that caught my attention: adverse productivity effects begin after three AI agents running simultaneously.
Three. I had six.
The research identifies three patterns that will sound familiar if you use AI regularly.
The first is task expansion. AI fills knowledge gaps, so people start stepping into work that isn’t theirs. Product managers write code. Researchers absorb engineering tasks. Job descriptions quietly double without anyone noticing — including the person whose job it is.
The second is blurred boundaries. Because AI removes the friction of starting — no blank page, no cold start — work seeps into every gap in the day. Prompting feels like chatting, so we do it at lunch, before bed, in meetings while half-listening to things we should be fully listening to. Workers often don’t realize their downtime has disappeared until well after it’s gone.
The third is cognitive overload — managing several threads at once, running agents in parallel, reviving old tasks because something can “handle them in the background.” It feels like momentum. The researchers use “AI brain fry” to describe it.
Workers reported feeling more productive but not less busy. In many cases, busier than before. You’d think being more capable would mean you work less, but it doesn’t. You work the same amount or more, just faster, with more things happening at once.
The BCG findings point at something obvious once you see it: the problem isn’t the tool. It’s the absence of any structure around the tool. No pauses. No norms. No way to step back and ask what’s actually happening.
When I read that I realized I’d already built it.
A few weeks ago I published a piece called “This Is How You Get Good at AI.” What I perhaps should have written was “This Is How You Protect Yourself From Losing Sleep.”
The core of it was the AI Journal — a free tool for reflecting on AI use. Three questions before you start a session, three questions after. A place to save what worked. I built it originally to coincide with my research, to act as a place where I metacognitively logged AI’s impact on my creativity, writing, and practice. Over time, I found it was more than just a diary that I could publish later — it was helping me to actually see what was “good” and “bad” when it came to using AI.
But the BCG research reframed the whole thing. Those two minutes are the structural pause the researchers are calling for. The pre-session questions force you to name what you’re actually trying to do before AI’s infinite possibilities pull you in six directions. The post-session questions force you to stop and assess before you reflexively open another tab.
It’s a speed bump. A deliberate one. Last I checked — speed bumps lead to decreased accidents in high traffic areas, do they not?
The individual version works. I’ve been using it and it’s changed how I approach sessions. When I use it with fidelity, I am more focused going in, more honest coming out, and — crucially — the engagement is contained. I plan to publish more thoughts and build more products that reflect the value of this process in the future, so stay tuned.
But the bigger question keeps nagging at me: what does this look like at the organizational level?
BCG found that when teams embed AI as a collective capability rather than an individual differentiator, cognitive burden diminishes significantly. But most organizations have no structure for that. No shared norms. No way to see whether the rollout they’re proud of is genuinely helping or quietly grinding people down. BCG found that employees who feel their organization values work-life balance had 28% lower mental fatigue scores. That gap doesn’t close by accident. It closes when someone actually builds the infrastructure for it.
So I built that too. It’s called AI Pulse — a team-level version of the AI Journal where shared reflections give managers and L&D leads a real picture of how AI adoption is actually going across their people. It’s live now.
If you’re a manager, a team lead, an L&D director — I’d like to know what you’re seeing. Not to sell you anything. To find out whether what I built is actually useful.
And if you’ve felt the creep yourself — the blurred boundaries, the context-switching, the growing sense that AI made you more capable but somehow also more scattered — the individual AI Journal is free.
I’ve been building other things related to all of this. More soon. https://frictionlabs.io
I don't know how this is supposed to be relatable or whose life you're describing but it isn't mine. I use AI like once a week. The rest of the time I'm just living my life and doing regular things like a normal not insane person.