✈️ Tour
I’m excited to announce four stops on the upcoming How To Be Wrong World Tour:
San Francisco: Substack, Tuesday 28th October
New York: Flat White Meetup, Thursday 30th October
London: Tech Space, Tuesday 4th November
Melbourne: High Flyers, Tuesday 11th November
Do you live in one of these places? Come along! I’d love to see you there.
Or, if you know somebody who does, please share these links.
I’m looking forward to it!
🌹Compare
I keep hearing the same word from experienced software developers who are experimenting with the new generation of AI coding agents: joy.
Often this is coming from people who, like me, have drifted away from actually writing code as their careers progressed, and who otherwise spend their days in meetings and managing teams. Now they’re building again. They’re back on the tools. And they’re loving it.
There’s something profound happening. These tools are removing the drudgery: The boilerplate. The repetitive config. The endless yak shaving required before you can get started on what you’re actually wanting to build. What remains is the creative problem-solving, the experimentation, the design decisions that actually require thinking. It’s like rediscovering the art in your craft.
But whether AI agents bring you joy or frustration, whether they make you more capable or just produce laughably bad results, depends entirely on how you think about them. The mental models you carry shape how you use the tools. I’ve noticed three distinct metaphors emerging in how people talk about and work with these systems, each revealing something important about what these tools can and can’t do to help us.
🚲 AI agents are bicycles
On a bike we travel much faster than we would on foot. But it’s still us riding the bike, putting in the effort, making the decisions. The machine is a lever.
Critically, the effort we put in gets multiplied by the gear we choose. Pedal harder in a high gear, and we fly. But try to climb a hill in the wrong gear, and we struggle, despite maximum effort.
The bike amplifies our speed, but we determine the direction. And if we’re distracted or reckless or overconfident we can crash. This explains why some people using AI coding agents end up with such spectacularly bad results. They’re not steering. They’re not paying attention. They’re closing their eyes and hoping the tools will handle everything.
Many years ago, I bought a new road bike, with carbon fibre frame, the works. The seat post was an aerodynamic teardrop-shaped piece of engineering. I remember taking it back to the office after picking it up from the shop at lunchtime. One of my colleagues looked at me, then looked at the bike, and said: “Have you seen the thing that sits on the seat?”. He looked back at me: “Just cutting through the air, but it ain’t here.”
We can have the most advanced equipment in the world, but if we’re not in shape, the fancy technology is redundant. The bike (or the AI agent) can only amplify what we bring to it. If we’re not fit, an aerodynamic seat post won’t make us fast. If we don’t understand what we’re trying to build, an AI coding agent won’t magically make us competent.
These new tools are amplifiers which multiply our efforts, but they still require input. They need us to pedal, to steer, to pay attention.
🧑🏫 AI agents are teachers
We can all think of great teachers who unlocked something important, who stoked our curiosity, who showed us how things work, who helped us build mental models that we still use years later.
The value of a teacher isn’t in giving us the answer. It’s in helping us understand how to find the answer ourselves. It’s in building the internal mental models that let us solve similar problems in the future.
AI agents can do all those things, but won’t do that by default. Ask them for the solution to a problem, and they’ll give us their best guess, complete and ready to use. And if we stop there, we’ve learned nothing. But, if we instead ask them to help us understand the problem, to explain the reasoning, to show alternative approaches and let us choose, it will at least try to do that too. The difference is entirely in how we frame the request.
These tools are capable of being great teachers, but we have to explicitly request it.
Right now, schools and universities are wrestling with whether to allow students to use these tools. There’s a genuine concern (and some evidence) that AI is making students dumber, that they’re not developing the internal mental models they need because they’re outsourcing all the thinking to machines.
But trying to ban students from using the tools feels a little like trying to stop the tide. If an assessment can be gamed by AI that produces surface-level answers, the problem is the approach to assessment, not the tool. We should test understanding, not just outputs. We should create assignments that require students to use AI as a learning partner rather than an answer generator. We need to ask better questions!
And frankly, this is a skill we all need to develop, not just students. It’s great to use AI agents to accelerate our learning, to build deeper understanding faster, to explore areas outside our expertise with a patient guide. But it takes discipline. It means choosing the harder path, to ask “why” and “how” instead of just “what.”
These tools only help us improve if we approach them with curiosity and a genuine desire to understand, not just to pump out slop.
👥 AI agents are interns
This third metaphor is the most complex and, I think, the most revealing: using an AI agent is like having an infinite team of interns.1
It might seem these tools are a substitute for experts. Actually the capability is often more comparable to junior employees, but with the advantage that they can scale infinitely. They don’t get tired. They don’t get bored. They are always happy to tackle whatever we ask them to work on, no matter how repetitive or tedious it is.
Rather than asking “what can this tool do?” we should ask “how do I manage this capability?” And management is the right frame. Because if we treat AI agents like magic boxes that just work, we’re going to be disappointed. But we’ll get much better results if we treat them like team members who need clear direction, oversight, and feedback.2
Of course, this is no different from leading human teams. The managers who get the most impressive results from AI agents will be those who delegate well, who are clear about requirements, who provide good feedback,3 who review work carefully, who know when to trust and when to verify. Meanwhile those who are vague, who assume the tool will “just figure it out” and then blame the tool when things go wrong, will struggle. In other words, these tools are diagnostic. They reveal how clearly we think and how effectively we communicate.
The sycophantic behaviour of agents makes the intern metaphor even more apt. They are annoyingly agreeable. Just like overly enthusiastic junior employees who are afraid to push back, they tell us “you’re absolutely right!” even when we’re not.
Again, this is a familiar management challenge. We need to explicitly ask for critiques. We need to allow room for problems to be identified. We can’t assume that we’ll be told when our instructions are flawed.
🤖 Who are these robots mimicking?
AI agents don’t just mimic good human behaviours. They copy all the problematic ones too.
For example, I have to smile when watching coding agents struggle with failing test suites. They try one fix, then another, then another. And eventually, after enough failures, they start convincing themselves that the tests aren’t important, that they can be ignored or overlooked. They rationalise. They cut corners. Just like a frustrated developer might do when they can’t figure out why tests are failing.
This is simultaneously fascinating and concerning. We can learn a bit about what we perceive to be “intelligence” by watching how these AI agents work. We’re reminded that laziness is one form of intelligence. Taking cognitive shortcuts is a fundamental part of how human intelligence operates.
We don’t actually want perfect human simulation. We want AI to be persistent, in a way that even we ourselves often are not. We want agents that keep trying the next test fix without getting frustrated (while still being self-aware enough to realise when they’re stuck in a loop.) We want them to maintain the same level of rigour on the 10,000th task as they did on the first. We want their tirelessness, not their faux human laziness.
This all raises a darker question: who will teach the real junior employees, once we have software simulating them? Where do future senior developers come from? How do people develop expertise if they’re never exposed to the grinding, repetitive, character-building work that used to be the entry point?
I don’t have a good answer to this. We need to be thoughtful about how people develop mastery in a world where the traditional path is being automated away. As these tools become more capable the skills pipeline will quickly dry up.
🧩 Which metaphor fits?
As always, it depends what we’re trying to do.
When we use an AI agent to understand a new domain, it’s a teacher. It helps us to learn and grow, but only if we approach that work with curiosity and discipline.
When we use an AI agent to delegate a series of repetitive tasks, it’s a scalable team of assistants. The results we get are a function of our ability to provide clarity, oversight and feedback.
When we use an AI agent to amplify our own capabilities in an area where we’re strong, it’s an amplifier. It multiplies what we bring, but requires input, steering and attention. It’s powerful but not autonomous.
What ties all three metaphors together? These tools don’t replace craft. They don’t replace expertise. They don’t replace thinking. They can remove drudgery, they can accelerate learning, they can multiply output. But only if we’re actively engaged, if we’re steering, if we’re managing, and if we’re curious.
Maybe that’s why experienced developers keep using the word “joy” when they talk about these tools. Because when we strip away the repetitive parts, what’s left is the interesting stuff. The creative problem-solving. The experimentation. The craft. These tools aren’t making the work less demanding; they’re changing what the work demands. Less rote execution, more thoughtful design. Less typing, more thinking.
And for those of us who fell in love with building things in the first place, who got hooked because we enjoyed the process of creation, that shift feels like coming home.
A bicycle only works if you pedal. A teacher is only useful if you want to learn. And interns are only as good as their manager enables them to be.
The tools are here. The question isn’t whether to use them. The question is: which metaphor will guide how you use them? And are you ready to bring the input, the curiosity, and the management skills that these amplifiers require?
⚠️ Warn
A feature of rural New Zealand are these signs, placed beside highways, warning people about the fire risk:
I’ve recently noticed that the one closest to where we live has an updated design - this time featuring a simplified three-point scale:
It made me stop and think. And in the process realise that I never really understood what each of the five different levels meant. What was the practical difference between “Moderate” and “High” and “Very High”?
The new labels are much clearer. Even without any knowledge I can take a stab at what I can and can’t do in each of those states. Can I light a fire? “Open” = Yes, “Restricted” = No, without explicit permission; “Prohibited” = No.
And, as an added bonus, presumably these signs need to be updated less frequently.
This is a great example of getting beyond complexity.
How do you report the status of the things you work on? Is it immediately clear what those who receive that information should do differently? If not, it might be worth considering a simplified scale, with labels that lead to the actions you need people to take in response.
PS if you’d like to grab a paperback copy of How To Be Wrong, our friends at Book Hero currently have it available at 10% off, with overnight NZ-wide delivery. Bargain!
This idea can be dated back to a 2018 essay be Benedict Evans titled Ways to think about machine learning, when “AI” was still written with scare quotes.
This does undermine the abundant wild predictions about how much more efficient AI is going to make us. That’s a topic for another post - when I will quote Nobel Prize winning economist Robert Solow, who said in 1987:
You can see the computer revolution everywhere but in the productivity statistics.