CTO

A chief technology officer (CTO) decides where a company's technology is going and makes sure it serves the business. They rarely ship features themselves, and that's the point: their job is choosing what engineers build, what they leave alone, and which bets are worth the money.

What a CTO is responsible for

The title means different things in a five-person startup and in a company of thousands, but the core stays the same. A CTO owns the technical direction: which problems the company solves with technology, how, and in what order.

In practice that splits into a few jobs:

• Direction. Choosing the platforms, languages and architectures the company commits to, and when to change course.
• Priorities. Turning business goals into technical work, and saying no to work that doesn't serve them.
• Risk. Security, reliability and the long-term cost of today's shortcuts, better known as technical debt.
• Investment. Spending the engineering budget on people, tools and infrastructure where it returns the most.
• Translation. Explaining technical trade-offs to the rest of the leadership team, and business constraints to engineers.

None of those is writing code, which is why the role can look suspicious from the inside.

Why the CTO is always in meetings

When the founder who wrote the first version of a product becomes its CTO, their calendar changes fast. Tickets and pull requests give way to planning sessions, budget reviews and slides. To the team it can look as if the CTO has stopped working, especially when words like 'platform', 'strategy' and 'AI' start turning up in every conversation.

The shift is real work, just less visible. Once a team is big enough to split into frontend, backend, database and DevOps engineers, the expensive mistakes stop being bugs in a function and start being decisions: building the wrong feature, adopting a framework nobody can maintain, or spending a year on a rewrite the business didn't need. A CTO spends their time where those decisions are made.

The buzzword complaint is fair, though. 'Platform' and 'strategy' only mean something when they turn into concrete choices the team can act on. A strategy nobody can explain in a sentence isn't doing its job.

Deciding what to build and what to ignore

Most of a CTO's influence comes from choosing between good options. There is always more worth doing than people to do it, so much of the job is ranking.

A useful way to think about each choice is as a bet: a cost paid now for a return expected later, with some chance of being wrong. Moving to a new database, adopting a cloud provider's managed services or letting a team try a new language are all bets. A smart bet is one where:

• the payoff clearly supports a business goal;
• the cost, including learning and maintaining it, is understood;
• there is a way to tell early whether it's working, and a way back if it isn't.

A worked example

Say a food-delivery startup's app is slowing down as orders grow. The engineers propose three fixes: rewrite the backend in a faster language, add a cache in front of the slowest database queries, or split the monolith into microservices.

A CTO weighs them against the business, not against each other's elegance. The company needs a faster checkout this quarter, the team is twelve people, and most of the slowness comes from a handful of queries. The cache is cheap, targets the actual problem and can be removed if it fails. The rewrite and the microservices might pay off one day, but with a small team either would stall feature work for months.

So the decision is to build the cache now, measure checkout times, and park the other two. Writing the decision down, with the reasons and what would make you revisit it, stops the same debate coming back every few weeks. Many teams keep these as short architecture decision records:

Decision: Cache the five slowest order queries
Status:   Accepted
Why:      Checkout takes 4s; these queries are
          most of it
Not now:  Backend rewrite, microservices
Revisit:  If orders triple, or checkout > 2s

Adopting AI without losing track of the cost

AI coding tools are the bet most CTOs are weighing right now, and they show why the role matters. It is easy for 'Can we use AI for that?' to become the answer to everything: writing code, writing tests, even renaming a button. Each use looks cheap on its own.

The cost shows up in aggregate. AI tools are usually billed by usage, often in tokens, the chunks of text a model reads and writes. An engineer running several coding agents at once, or an agent going round in circles on a trivial fix, can burn through a lot of tokens for very little. Multiply that across a team and the invoice grows faster than anyone expected, with nobody able to say what it paid for.

So the CTO's question isn't 'Are we using AI?' but 'What is it producing?'. Answering it means connecting two sets of data that usually live apart:

• Spend, from the AI providers' bills and usage data.
• Work, from the tracker where the team plans it, such as Jira epics, stories and bugs.

Once spend is tied to work items, the question has an answer. Here is what that view might look like for one month, with made-up figures:

The table doesn't say AI is good or bad. It shows where the money went. Three hundred pounds on a typo is a reason to look at how the tools are being used on small fixes, not a reason to ban them, and the checkout epic may well be excellent value. Without the link between spend and work, both look the same: a line on an invoice.

This is the problem tools such as Tempo's Workforce Intelligence, the one the video features, are built for: it connects to Jira, tracks AI coding spend and shows it next to the epics, stories and bugs it went into. Whatever the tool, the principle belongs to the CTO: judge AI like any other investment, by what it delivers.

How the CTO differs from nearby roles

Titles vary between companies, so treat these as common patterns rather than rules.

• A VP of Engineering usually runs the engineering organisation day to day: hiring, team structure, delivery and process. In companies with both, the CTO leans towards technical direction and the VP of Engineering towards how the teams carry it out.
• A tech lead or principal engineer makes technical decisions for one team or system, and usually still writes code. A CTO makes them for the whole company.
• A CIO (chief information officer), where there is one, often looks after the company's internal systems, while the CTO looks after the technology in the product.

In a small startup, one person may do all of these at once, often while still coding.

Common mistakes

• Chasing trends. Adopting a technology because it's popular, not because it solves a problem the business has.
• Staying the best coder on the team. A CTO who keeps taking the hardest tickets leaves the direction to chance.
• Strategy nobody can use. Slides full of 'platform' and 'AI' that don't say what to build next or what to stop.
• Measuring activity, not outcomes. Counting commits, pull requests or AI usage instead of what shipped and what it changed.
• Bets with no way back. Committing to a big change without a checkpoint to decide whether it's working.

Key takeaways

• A CTO decides where the company's technology is going: what to build, what to ignore and which bets to make.
• Most of the work is decisions, not code, because at scale the costly mistakes are in the decisions.
• Treat each technical choice as a bet with a cost, a payoff and an early way to check it.
• AI tools are an investment like any other: tie their spend to the work it produced before judging them.
• The job is keeping technology and business goals pointed the same way.