AI isn't replacing CTOs. It's exposing why good ones matter.
I've done this job for more than ten years. The tools have changed enormously. The actual work has barely moved, and if anything it matters more now than it did.
Every few weeks I read another post explaining that my job is finished. The logic is always tidy. AI can write code, building software is getting faster and cheaper, so before long nobody will need a CTO.
I understand why it lands. If you have only ever seen the role from the outside, a CTO looks like the most senior person who writes code. Take the coding away and there is apparently nothing left.
That was never the job. Not in any team I have run. The work was never about producing as much software as possible. It was about making sure the technology actually served the business, solved the right problems, and could still be understood and maintained a year later, once the people who built it had moved on to something else.
AI has changed how a lot of that gets done. It has not changed the need for someone to do it.
The cost of building has genuinely dropped
Pretending otherwise would be blinkered.
Work that used to take my teams weeks now takes days, sometimes hours. I have watched an engineer stand up a working prototype over a lunch break that would have been a fortnight of effort when I started out. People move through unfamiliar code far more confidently than they used to, and a lot of the tedious middle of the job has quietly gone away.
That is a real change and I am glad of it.
But every time one constraint falls away, another becomes more obvious. Code got cheaper. Judgement did not. Someone still has to decide what is worth building, why it matters, how it sits with everything already running, and whether the business will still be glad it exists in two years. AI will help you build the wrong thing beautifully and at speed.
Code was never the interesting part of the decision
The decisions I lost sleep over were almost never programming problems.
Do we build this or buy it? Do we automate this process, or leave a person in it on purpose? Does this feature solve something a customer actually feels, or does it just look good on a roadmap? Can the team I have realistically support this once it is live? What breaks when we have ten times the traffic, and what new risk have we just quietly signed up for?
Those are technology questions, but they are product, commercial, operational and people questions at the same time. AI is genuinely useful here. It lays out options, argues back, surfaces things I had not thought of. What it does not do is understand my business well enough to make the call. And it is never the one in the room, or on the phone, when that call turns out to be wrong.
Speed makes architecture matter more, not less
This is the part that actually worries me.
AI makes it easy to produce a lot of software very quickly, and technical debt now builds up faster than anything I saw in my first ten years. A system can look completely healthy while quietly filling with duplicated logic, inconsistent patterns, fragile dependencies and small security holes. Teams can generate far more code than they can read, review or keep in their heads.
The danger has never been that AI produces rubbish. It is that it produces enough plausible, working-looking code to make everyone feel confident. I have inherited systems like that, built by people long gone, and confidence is exactly the wrong feeling to walk in with when you finally open them up.
Software does not live as a neat pile of separate features. It becomes one connected thing. It touches customer data, payments, reporting, support, fulfilment, other platforms you had half forgotten were wired in at all. A decision made casually in one corner turns up as a problem somewhere else months later. The faster everyone builds, the more you need someone whose actual job is to hold the shape of the whole thing.
Building the wrong thing is still the expensive mistake
AI lowers the cost of the first version. It does nothing about the cost of choosing badly.
A feature still has to be adopted. A platform still has to be maintained. An integration still has to be watched. A product still has to find a market. In all my time doing this, I have almost never seen a business fail because it could not write enough code. They fail because they solved the wrong problem, misread the customer, drowned in their own complexity, or never joined the technology to anything commercial.
Speed does not save you from any of that. Now and then it makes it worse, because a team can get much further down the wrong road before anyone stops to ask where the road was going.
My job was never to slow the team down. It was to make the speed count for something.
What the role actually turns into
Some of what I used to do is genuinely going away, and good riddance to parts of it. I spend far less time being the final word on routine implementation. The team leans on AI to dig through the codebase, write tests, chase down incidents and float approaches, and most of the time that is better than waiting on me.
What it frees up is the work I always wished I had more room for. Understanding how the technology actually supports the way the business makes money. Designing things that can change later without a rewrite. Being clear about where automation is safe and where I want a human hand on it. Being honest about who owns which data and which platform. Getting product, engineering and operations to behave like one system rather than three that resent each other. Making risk visible before it becomes a 2am phone call.
The job stops being about knowing every answer. It becomes about making sure the organisation is asking the right questions in the first place.
It will expose some of us
I am not going to pretend every CTO role is safe, because that would be dishonest.
If your whole value is that you are the one person who understands the codebase, that advantage is shrinking whether you like it or not. The same is true if you have quietly become a gatekeeper, turn every conversation into a technical argument, or have drifted a long way from customers and commercial reality. I have worked alongside people like that. AI is going to make that kind of narrowness very hard to hide.
That is not the same as the role disappearing. The best CTOs I have known always worked across the lines. Enough engineering to challenge a decision properly, enough product sense to see what actually matters, enough commercial understanding to weigh a trade-off honestly, and enough leadership to take people with them rather than drag them along. Those things get more valuable as the raw technology gets easier for everyone else to reach.
Someone still has to own the outcome
AI can propose an architecture. It can draft the migration plan. It can review the pull request, read the logs, point at the risk.
What it cannot do is carry it. It does not sit in the leadership meeting when priorities are pulling in opposite directions. It does not explain to a customer why something broke. It does not rebuild trust with a team after a bad month, or decide how much risk the company can genuinely stomach. A lot of this job is knowledge. A great deal more of it is standing behind the decision afterwards, and that does not move to a model because the model got better.
The next version of the job
If I had to guess where this goes, it is not "put AI into everything". It is building businesses that AI can actually work inside, safely and usefully.
That means data you can trust, workflows that are clear, permissions that are defined, decisions you can audit, and honest agreement about the points where a person has to step in. Agents are going to become real participants in how businesses run. But they only work when the business underneath them is put together clearly enough to let them. That is not really an AI problem. It is an operating model, architecture and leadership problem, which is to say it is the job I have always had, wearing slightly different clothes.
Where I have landed
The companies that get the most out of this will not be the ones generating the most code. They will be the ones making the clearest decisions about what to build, what to hand to a machine, and where people still matter most.
AI can write the code. It never has to answer for it.
After ten years, that is the part of the role I have made my peace with. It is also the part I cannot see going anywhere.