What happens when everyone can prototype?
Everyone can prototype now. That doesn't make design irrelevant — it just proves how much of the job was never the prototype.
Everyone can prototype now. That doesn't make design irrelevant — it just proves how much of the job was never the prototype.
Tools like Figma AI, v0, Bolt, and Lovable have made it genuinely easy for anyone — a PM, a founder, an engineer — to generate a high-fidelity, clickable prototype in minutes. No design background required. That used to be the bottleneck: turning a requirement into something people could actually see and react to. Now it isn't.
So the debate that's kicked off is predictable. If anyone can prototype, what's a designer even for? Is this the death of design?
I think that's the wrong debate.
Prototyping was always the output, not the job
The actual job is what happens before and after the prototype: what are we assuming, what decision does this support, what breaks when a real user touches it, what edge case did nobody model. That's thinking work — discovery, validation, iteration. AI hasn't touched any of it. What it's sped up is the tactical craft: turning a decision into pixels. That was never the hard part.
The problem is that a high-fidelity AI prototype looks finished.
So teams treat it as finished. It reads as further along than a wireframe ever did, and that anchors everyone on a solution before it's been interrogated. The thinking gets skipped, not replaced.
Nobody's claiming that work right now. Not UX. Not PM. It's falling through the gap — and it compounds: five PMs using five different AI tools produce five different interaction dialects, with no one job responsible for making them cohere into one product.
Everyone can prototype now.
That's fine, genuinely. What I care about is whether the hard thinking — the framing, the validation, the edge cases — got done before the prototype became the answer.
AI that works has a job. One job.
Two 2025 studies on AI adoption, same technology, opposite outcomes — the difference is whether the AI actually removes work, or just moves it around.
I've been reading about AI adoption recently, and two 2025 studies stuck with me. Same technology, opposite outcomes.
The first looked at AI clinical scribes — tools that listen to a doctor's appointment and write up the notes afterward. Burnout among doctors using them dropped from 55% to 33%, and each appointment took 2.8 minutes less to document. The AI had one job: write the note. Nothing else.
The second looked at developers using general-purpose AI copilots. It found the opposite. 67% of developers spent more time on their work, not less — debugging code the AI had gotten wrong. 68% spent extra time fixing security issues the AI had introduced. The tool that was supposed to save time was, for most people using it, adding to their workload.
Same technology. Opposite results. The difference wasn't the model. It was what the AI was actually asked to do.
Scoped AI removes work. Broad AI adds it.
When AI is given one clear task — write the note, flag the anomaly, draft the reply — it takes that task off someone's plate. It's done. Nothing left to check.
When AI is broad — "here's a copilot, figure out how to use it" — it doesn't remove a task. It adds a new one: reading the output, checking whether it's right, fixing it when it isn't, deciding when to trust it and when not to. That's not a shortcut. It's a second job layered on top of the first.
This is why "AI-powered" isn't automatically a selling point. If the AI can't say exactly what it's replacing, it's probably not replacing anything — it's just adding a step.
The question worth asking
If your users aren't adopting your AI feature, this is the question I'd start with: what exact task does this replace? Not what it can help with — what does someone stop doing because this feature exists?
If you can't answer that clearly, your users can't either. And a feature nobody can name the job of is a feature nobody trusts enough to use.
Are you 100 percent confident?
A prompt that turned AI from a fast-answer machine into something closer to a thinking partner.
Most AI conversations optimise for speed: ask, generate, move on.
I tried this prompt recently and noticed the interaction changed quite a bit:
"Are you 100% confident in this strategy? If not, find all possible loopholes, suggest proper fixes, and run this loop until you are factually 100% confident." - source: The Neuron
The interesting part wasn't the output quality. It was that the AI stopped feeling like a fast answer machine and started acting more like a thinking partner — useful for getting it to sense-check all the ideas conversational agents throw at you.
The problem with helpful AI
Some notes from building a couple of AI tools that really forced this question — when AI removes effort, is it removing the thinking too?
Some notes from building a couple of AI tools that really forced this question — when AI removes effort, is it removing the thinking too?
AI reduces effort
Most AI product design today is built around a simple idea: reduce effort. Read less. Click less. Analyse less. Decide less.
For a lot of work, that's exactly right. If AI can write the SQL query, categorise support tickets, summarise a document, or automate a workflow, great. The value is the answer. Nobody benefits from doing those tasks manually.
But over the last few months, building AI products and agents, I've become interested in a different question: what happens when the thing being removed is the thinking itself?
Not all work is answer-finding work. Some work is judgment work. Product strategy is judgment work. Positioning is judgment work. Deciding what matters is judgment work. Understanding why you're stuck is judgment work.
In judgment work, the process of thinking isn't just a means to an end — it's where much of the value is created. And that's where I kept running into the same design tension: the more opinionated the AI became, the better the product felt. The less certain I became that it was helping people think.
The temptation to over-help
One of the tools I built is Untangle. It's for people who feel mentally overloaded but have no interest in journaling, mindfulness, or reflection exercises. The user records a voice note. The system structures what they've said and helps them find clarity — that's the goal, anyway.
What I discovered during development was that there's a very easy way to make the experience feel more magical: make the AI more interpretive. Instead of surfacing themes, explain them. Instead of identifying tensions, resolve them. Instead of helping users understand their thinking, tell them what's going on.
The outputs immediately felt more insightful. More useful. More satisfying. But they also became more authoritative. The AI was no longer helping users process their thoughts — it was starting to provide an interpretation of those thoughts. Was I building a thinking tool, or a very convincing explanation machine?
The same problem, a different layer
I ran into the same tension building a POV agent. The goal is simple: take a topic, map the conversation around it. What are the dominant narratives? What assumptions are people making? What's the contrarian view? What's getting attention, and what's being ignored?
The easy version of this product stops at "here's the best take." Many AI products do. But that wasn't what I wanted — the purpose wasn't to generate a position, it was to help someone develop their own. So the agent stops short. It maps the terrain, surfaces tensions, exposes gaps and blind spots — then hands the thinking back to the user. Less satisfying. The model could generate a conclusion, often a convincing one. But a convincing conclusion isn't the same thing as understanding.
Certainty and understanding are not the same thing
AI-generated conclusions can feel remarkably similar to understanding. That's what makes them powerful. It's also what makes them risky.
When a model explains your situation, it creates a feeling of clarity. When it gives you a position, it creates a feeling of certainty. Sometimes that certainty is deserved. Sometimes it isn't — and certainty and understanding are different things. You can understand something deeply and still be uncertain. You can also feel certain without understanding it at all.
The more I build with AI, the more I think product teams need to pay attention to that distinction.
A different question for AI products
Enterprise software has spent decades helping people access information. AI is helping software generate answers. The next challenge is figuring out when answers are actually the right output. For deterministic work, they usually are. For judgment work, I'm not so sure.
The question I find myself asking now isn't "can the model do this for the user?" It's "what understanding disappears if it does?"
The most delightful AI experience isn't always the most useful one. Sometimes the best thing an AI can do is help someone think a little more clearly for themselves.