Read about AI opinion online and you get two camps: those who believe it will fundamentally reshape how software gets built, and those who see it as useful tooling but doubt the transformative claims being made around it. Neither side has a monopoly on insight, and both can point to real evidence (or lack thereof).

I’ve watched this split play out everywhere over the last six months: in conversations with other engineering leaders, in how candidates talk about AI during interviews, and within my own teams. The hostility has been personal too. Over the past year I’ve written several very pro-AI articles, and some of the private messages and emails I’ve received in response have been borderline insulting(!), dismissing me as naive, a shill, or someone who’s drunk too much of the Kool-Aid. It’s been my first time blocking people on Substack.

But the hostility made me think more carefully about what’s actually going on, because the perceived professional risk doesn’t sit where most people think it does. It’s not about whether you’re optimistic or sceptical about AI, it’s about how you hold that view: whether you’ve arrived at it through hands-on experience, or whether it’s become part of your identity, something you defend rather than inquire into.

This article is about finding the right mixture of optimism and scepticism, and learning to hold a flexible, informed opinion that benefits both you and how you’re perceived by others. We’ll look at why there are two very different kinds of sceptic, why ungrounded enthusiasm is just as risky as ungrounded dismissal, and what it looks like to hold your view well, whatever that view happens to be.

If you’d like to dig deeper, here are some related articles from the archive:

• LLMs: an operator’s view was my first attempt to lay out an AI-positive but grounded position: what’s genuinely useful, what’s not, and where to focus your attention.
• Use it or lose it covered the opposite risk: what happens when you lean too far into AI and start outsourcing the thinking that makes you valuable.
• Invert, always invert explores the habit of seriously considering the opposing view, which is central to what we’ll cover here.

Let’s dig in.

The two kinds of sceptic

If scepticism isn’t the problem, what is? The distinction that matters is between scepticism as a conclusion and scepticism as an identity.

You might have used the tools extensively, kept on top of the latest research, and landed somewhere genuinely unconvinced. That’s totally fine. Or you might have absorbed your position through proximity, adopting the talking points of people around you without firsthand exposure, and stopped there. That’s not so fine.

You can often hear the difference: the first person says something like “I’ve been using AI for six months and I don’t think it’s made me measurably faster on the kind of work I do, here’s why,” while the second says “it’s just autocomplete” or “stochastic parrots” or some other meme and leaves it there. Both are sceptical views, but only one has done the real work, and the other is repeating another’s position, not deriving one.

I’ve seen this firsthand, in both directions. Some of the engineers and leaders I’ve spoken with continually dismissed AI as gimmicky, only to watch them completely change their position once they actually got hands-on with tools like Claude Code or Cursor as the models improved. Their scepticism had been inherited, not earned, and the experience exposed that.

But I’ve also seen people use the tools extensively and remain genuinely unconvinced, particularly those working in significantly large codebases or rarer languages where the models still aren’t as good. Their scepticism is specific, grounded, and hard to argue with. It’s the difference between “I looked and I’m not impressed” and “I haven’t looked but I already know.”

In some developer communities, dismissing AI has become a way of signalling belonging, and the position reinforces itself through repetition rather than evidence. Anyone who breaks ranks by actually engaging with the tools risks exclusion from their tribe, or a loss of identity (or both).

There’s a name for this pattern: the crab bucket describes a group where no individual is allowed to escape, because the others keep pulling them down. The problem isn’t that the crabs are wrong about the bucket, it’s that none of them have climbed out to check.

To be clear, climbing out doesn’t mean changing your mind. There are well-documented examples of developers who went from sceptic to enthusiast after engaging deeply and having breakthroughs with the tools, but the goal of engagement isn’t to make you an enthusiast. It’s to give you an informed position, whatever that position turns out to be.

Some of the crabs that climb out will come back down again, and that’s fine. Maybe the bucket is good enough. The view from outside the bucket might confirm everything they already thought, but at least they’d know.

The enthusiasm mirror

If ungrounded scepticism is one failure mode, ungrounded enthusiasm is the other, and it’s arguably done more damage while feeding the very scepticism it dismisses. Sceptics who disengage mostly hurt themselves: they miss opportunities; they look out of touch. But enthusiasts with budget authority can hurt entire organisations, because when they overcommit and underdeliver, they hand sceptics exactly the evidence they were looking for.

Think back over the last few years and the examples aren’t hard to find: Klarna announced AI would do the work of 700 support agents, only to quietly start rehiring months later when quality collapsed; CEOs from Microsoft to Google continually stated the percentage of code written by AI without any clear methodology for measuring whether it was actually any good; and startups promised autonomous AI agents that quietly turned into vaporware.

Each bold claim that didn’t land gave sceptics another reason to disengage, and you can’t entirely blame them given their position.

I’ve heard versions of this closer to home too. Friends and acquaintances have told me about receiving AI usage mandates from leadership at their own companies with zero guidance, no clear examples of leaders using the tools hands-on themselves, and access to only a limited set of enterprise tools chosen by people who clearly hadn’t done any real evaluation or had a single technical bone in their body. This breeds exactly the kind of cynicism this article is about.

But, if we look closely, the pattern is the same on both sides: a position adopted without sufficient evidence, defended as identity, and insulated from feedback. The enthusiast who dismisses every failure as “early days” is doing the same thing as the sceptic who dismisses every success as “cherry-picked.” Neither is thinking clearly, and both are optimising for being right over being accurate. And, if you think about it, AI just happens to be the catalyst for seeing this behaviour, and it’s a big human bug that we need to fix if we want to be clear and rational thinkers.

So if how you hold your position matters more than what the position is, what does holding it well actually look like? Whether you lean sceptical or optimistic, there are a handful of practices that separate a considered view from a reactive one.

How to hold your view well about AI

The first practice is the most obvious, and the one most often skipped: actually use the tools for yourself. Not a five-minute demo, not other people’s opinions on a Hacker News thread (did you read the article, or just the comments?), or a thirty-second YouTube clip of someone building an app. Sit down with the best available model, bring it a real problem from your actual work, and spend enough time to form a genuine impression for yourself.

If you come away unimpressed, that’s a legitimate data point. If you come away surprised, that’s a legitimate data point too. Either way, you’ve earned your opinion in a way that reading about it never provides, because the only truth comes from firsthand exposure.

I went through this journey myself. Years ago, I started with Copilot in Visual Studio Code and ChatGPT as a web-based prompt, then moved to daily usage of Cursor at Shopify, but to me they felt somewhat bounded and only applicable to individual contributors spending all of their time coding. The real shift came when Claude Code let me work entirely in the terminal and interact with the file system directly: suddenly the use cases, especially for leadership and general organisation and productivity, multiplied, and I ended up building an entire daily driver around it.

That progression took months, not minutes. Forming a real opinion takes sustained engagement over a long period of time.

The second is to separate capabilities from claims. “AI can generate working code from a natural language prompt” is an observable fact: you can verify it immediately. “AI will replace most software engineers within five years” is a prediction, and a speculative one at that.

Much of the AI discourse collapses these two categories, treating a genuine capability as proof of a sweeping conclusion. Arguments on both sides conflate these categories in surprisingly lazy ways, and it’s worth training yourself to spot when it’s happening.

The third is to make your scepticism specific. “AI is overhyped” is a slogan. “I’m sceptical that current LLMs can reliably handle complex, multi-step reasoning in large legacy codebases, and here’s what I’ve seen when I’ve tried” is a position worth engaging with. Specificity forces you to think about what exactly you believe, and it gives other people something concrete to respond to rather than a vibe to agree or disagree with.

The fourth, and perhaps the most revealing, is to ask yourself a simple question: what would change your mind? If you can answer that clearly, you’re holding a view, which is good. If you can’t, or if your honest answer is “nothing,” you’re holding an identity, which is bad. This is true whether you’re sceptical or enthusiastic, and it’s a useful check to run on yourself every few months as the tools and the evidence evolve.

Honestly, it’s hard for me to imagine changing my broadly enthusiastic position right now. I use AI every day at work and at home, and I have enough practical examples and workflows to talk about for hours. But that’s precisely why the question matters. If my answer is “nothing would change my mind,” I’ve crossed from having a view into having an identity, and I should worry about that as much as anyone.

The fifth is to engage seriously with the strongest version of the opposing view. If you’re sceptical, don’t just dismiss the most breathless hype: find someone thoughtful who’s genuinely optimistic and try to understand why. If you’re enthusiastic, seek out the most articulate critics, not the ones shouting at each other on social media, but the ones who’ve used the tools extensively and still have reservations alongside their praise.

This takes effort, because the most reasoned opinions rarely get the most attention: you’ll have to look past the hot takes to find them. But you’ll either sharpen your own position or discover a blind spot, and both of those are wins.

For me, the strongest opposing argument is the one I worry about most: that outsourcing cognitive work to AI gradually erodes the very thinking that makes you good at your job. I wrote about this in Use it or lose it, and it’s a concern I haven’t resolved. That’s what good scepticism feels like from the inside: not a slogan, but an open question you keep coming back to in order to refine your thinking and engagement with the matter at hand.

What this means for your team

Everything above applies to individuals, but if you’re managing a team, you have an additional responsibility: creating an environment where people can get the most out of these tools. That means resisting the temptation to mandate enthusiasm or punish scepticism, because both of those shortcuts produce compliance rather than genuine engagement. What you want is a team that’s actively experimenting, sharing what’s working and what isn’t, and building on each other’s discoveries.

I try to do this at Nordhealth by constantly finding new ways to use AI and then sharing what I discover with my team.

Take one recent-ish example. I took a recording of a video of someone using our app, had Claude Code split it into screenshots, and then ran UX analysis on each frame to generate a list of improvements, essentially creating an AI-powered friction log. It took minutes, and the output was genuinely useful and found numerous issues I wouldn’t have spotted myself.

Sharing experiments like that, including the ones that produce nothing interesting, signals to your team that this is about curiosity and outcomes, not about picking a side.

Creating an environment where people can be curious means framing conversations around outcomes rather than positions. “Did this tool help you ship faster, and if not, why not?” is a conversation that goes somewhere. “Do you believe in AI?” is not.

It also means modelling the behaviour yourself: share your own experiments openly, including the ones that didn’t work. If your team sees you being honest about what works and what doesn’t, they’ll follow your lead.

It’s also worth acknowledging openly that these tools don’t help equally in every context. A team working on a greenfield web application and a team maintaining a decades-old enterprise system with sparse documentation are going to have very different experiences, and both of those experiences are valid. The goal is a team that can figure out where AI helps them and say so honestly when it doesn’t, not one that agrees with you about it in the abstract.

Your turn

If you’re still sceptical about AI, ask yourself honestly: have you actually used the tools for real work, or have you formed your view from a distance? If it’s the latter, that’s the gap to close. Set aside an afternoon, pick a meaningful task, and see what happens. If it’s been a year since you genuinely tried, late-2025’s models were a huge step change for me.

If you’re enthusiastic, ask yourself the inverse: what would make you change your mind? If you can’t answer that, your enthusiasm might be doing the same work as the scepticism you’re dismissing.

Either way, find someone smart who disagrees with you and have a real conversation. Not a debate, not a performance: a genuine attempt to understand how they got to where they are. You might be surprised by what you learn.

Wrapping up

The people who thrive through this era won’t be the ones who called it right early. They’ll be the ones who thought clearly throughout: who engaged with the evidence as it changed, updated their views when the facts warranted it, and maintained intellectual honesty even when it was easier to pick a side and dig in.

Zealots are loud, but they’re brittle: the moment the evidence shifts, they break or double down. The people who hold their views lightly enough to update them are the ones you actually want to work with, and work for.

Buddhism has a name for this: the middle way, a path between extremes that favours clear seeing over fixed positions. You don’t have to be a Buddhist to recognise the wisdom in it.

That’s true for AI. It’s true for everything.

Until next time.