Idea

When software becomes abundant, what becomes scarce?

If AI makes producing software dramatically easier and cheaper, the important organisational constraint may move away from writing the software and towards deciding what should exist, why, and how it becomes useful.

Current thinking

For most of the history of digital organisations, engineering capacity has been scarce. Software takes time to design, build, test, integrate, secure and operate, so organisations have built structures around deciding where that scarce capacity should go.

AI changes at least part of that equation.

It is becoming possible for an individual or a much smaller team to produce working software at a speed that would previously have required considerably more engineering effort. That does not make engineering unimportant. Architecture, security, reliability, integration and deep technical judgement remain essential, particularly in consequential production systems.

But it does raise a more interesting organisational question. If producing software becomes dramatically easier, does the constraint simply move somewhere else?

When software becomes abundant, what becomes scarce?

Some likely answers are problem understanding, product judgement, organisational attention, access to users, trusted data and context, permissions, integration capacity, operational ownership and the ability to know whether something has actually improved the service.

The scarce thing may increasingly be clarity about what deserves to be built rather than the ability to build something.

This changes the AI coding conversation. The immediate story is developer productivity: an engineer can produce more code, faster. The larger story may be what happens to organisations when writing the software stops being the scarce bit.

That has consequences for product management and technology leadership. If implementation becomes cheaper, organisations may be able to test more propositions before committing significant resources to them. Small teams may be able to explore problems that would previously have struggled to compete for engineering capacity. The boundary between an idea and a working experiment becomes much thinner.

But abundance creates its own problems. More software means more things that can require ownership, security, integration, support and eventual decommissioning. Making something easy to build does not automatically make it worth operating.

The interesting constraint therefore moves upstream and downstream at the same time: upstream towards understanding the problem, defining the outcome and deciding what should exist; downstream towards integration, adoption, ownership and knowing whether it worked.

Perhaps the bigger change isn’t agentic software development at all. It’s what happens to organisations when writing the software stops being the scarce bit.

This is why software abundance connects directly to clarity. As execution becomes cheaper, ambiguity becomes relatively more expensive. It also connects to apprenticeship: if experienced people can orchestrate dramatically more execution, organisations still need a way of developing the people who will eventually have the judgement to direct and assure that work.

Software becoming abundant does not remove scarcity. It changes where scarcity lives.

Writing on this idea

Note

When Writing Software Stops Being the Scarce Bit

7 June 2026 · Technology & Leadership, Building Through AI

AI is making software dramatically easier to produce. The more interesting organisational question may be what becomes scarce when engineering capacity is no longer the constraint it once was.
Note

Centralise the Reusable. Distribute the Problem Solving.

10 June 2026 · Technology & Leadership, Building Through AI

A specialist AI capability can be valuable, but its success may ultimately be measured by how much useful capability becomes ordinary across the rest of the organisation.
Note

As Execution Gets Cheaper, Clarity Gets More Valuable

24 June 2026 · Technology & Leadership, Building Through AI

AI is making implementation and rework dramatically cheaper. It is not making ambiguity cheap.
Note

What If the Most Efficient Team Isn't the Best Team?

26 June 2026 · Technology & Leadership, AI & Work

AI may let smaller teams produce the same or greater output. But output is not the only thing teams produce.
Note

Building Is Getting Easier. Owning Still Matters.

27 June 2026 · Technology & Leadership, Services & Change

AI is lowering the cost of building and experimenting with software. That makes the boundary between an experiment and an owned service more important, not less.
Note

The Boundaries Around What We Can Do Have Moved

17 August 2026 · Technology & Leadership, Services & Change

The most interesting strategic question about AI may not be how much AI an organisation should use. It may be what has become possible now that some old constraints are moving.
Note

Turning Things Off Is Part of Building

31 August 2026 · Technology & Leadership, Services & Change

Technology organisations celebrate launches because new capability is visible. Decommissioning creates a quieter kind of value: less risk, less complexity and more freedom to change what comes next.
Essay

If AI Does the Doing, How Do We Learn the Judgement?

22 September 2026 · Technology & Leadership, AI & Work

AI can increasingly produce work that once required years of accumulated skill. That creates an enormous productivity opportunity, but it also raises a harder question about how people acquire the judgement to know whether the work is good.

Connected ideas

If AI does more of the doing, how do inexperienced people become experienced?

Some work that looks routine or low-value also develops judgement. If AI removes that work, organisations need to replace the learning rather than simply remove the task.
What does expertise become when AI can increasingly perform the execution?

As AI takes on more execution, expertise does not simply disappear. More of its value moves into understanding the problem, setting direction, decomposing work, challenging outputs, recognising failure and exercising judgement about what should happen next.
As execution becomes cheaper, does clarity become more valuable?

AI can reduce the cost of implementation and rework without reducing the cost of ambiguity. As machines become better at execution, understanding the problem, defining intent and deciding what good looks like become relatively more important.
What changes when it becomes cheaper to find out whether an idea works than to prove in advance that it will?

AI can reduce the cost of experimentation, rework and changing direction. That challenges organisational processes designed for a world in which change was expensive and certainty had to be purchased before committing.
If building becomes cheap, when does an experiment become something the organisation is prepared to own?

AI makes it easier to build and test software, which should lower the barriers to responsible experimentation. But permission to experiment is not the same as permission to operate: services still need ownership, support, security, resilience and accountability.
What if the most efficient team isn't the best team?

AI may allow smaller teams to produce the same or greater output, but teams also create learning, challenge, resilience, belonging and organisational memory. Those benefits are harder to measure than productivity and easier to lose.
When AI becomes infrastructure, how much of its cost and resilience is determined by architecture rather than procurement?

At enterprise scale, AI consumption is shaped by system design: model calls, context size, routing, retries, agent loops, evaluations and fallbacks all become cost and resilience decisions.
If models become interchangeable, where does durable organisational AI capability accumulate?

The durable capability around AI may sit less in the model itself and more in the context, permissions, tools, workflows, evaluations, controls and operating knowledge that allow models to work safely and repeatedly inside a real organisation.
If AI changes what an organisation can feasibly do, should the strategy focus on AI or on the possibilities that have opened up?

The strategic significance of AI is not simply that organisations can now use AI. It is that activities which were previously too expensive, slow, complex or impractical may become feasible. Strategy should examine those changed constraints rather than turn use of the technology itself into the objective.
Why are organisations better at celebrating what they launch than what they successfully remove?

Decommissioning rarely produces a visible new feature, but it can reduce cyber exposure, failure modes, support burden, architectural complexity and technical debt while making future change easier. Technology leadership needs to recognise removal as value creation, not merely cleanup.