AI is changing how we build software. Faster code, smaller teams, and fewer operational constraints are making execution cheaper and more scalable than ever.
While implementation accelerates, the real scarcity is rising: the ability to define the right problems, design coherent systems, and make difficult tradeoffs. In early-stage scale-ups, the companies that win won’t be the ones with the largest engineering teams. They will be the ones who place decision-making, not coding, at the centre of leverage.
Observation
AI is changing the economics of software. Either engineering headcount reduces materially, or headcount becomes cheaper because small teams can shepherd AI output at far higher velocity. Execution scales without proportional growth in people.
When execution scales, the real constraint moves. It is no longer typing code. It is clarity. AI will generate production-ready output for whatever we define. If our problem framing is weak, we will scale the wrong solution faster. If our architecture is incoherent, we will compound that incoherence.
In early-stage scale-ups, the CTO and CPO roles often split because engineering becomes operationally heavy. Delivery management, hiring, coordination, and risk consume cognitive bandwidth. The separation is often a response to organisational weight, not a belief that product and technology should be isolated.
If AI reduces operational load, the logic for that early split weakens.
Hypothesis
In AI-augmented scale-ups, the advantage shifts from execution capacity to decision quality. The scarcest capability is integration.
The highest leverage role is the integrator. The person who can hold product ambition, system architecture, and commercial tradeoffs in the same frame. The person who can decide what we will not build and absorb the consequences.
This role does not belong to engineers by default. In an AI-dominant world, architectural fluency is learnable without years of typing code. Product leaders can gain system understanding through deliberate study. What differentiates the integrator is not its technical origin. It is systems thinking and the courage to make uncomfortable tradeoffs.
This is not inevitable. It is a strategic advantage for 0-100 person scale-ups that choose to optimise around decision leverage instead of delivery throughput.
What Could That Look Like
Teams flatten and seniorise. Instead of scaling pods around implementation, organisations design smaller, high-trust units. A product architect shapes domain boundaries and system direction. One or two AI-augmented builders shepherd the implementation. Product leadership focuses on portfolio bets and capital allocation, not coordination overhead.
At the executive level, CPTO-style leadership remains viable longer because operational load no longer dominates. The critical skill is tradeoff authority. Choosing focus over expansion. Durability over speed. Simplicity over optionality.
In this model, leverage does not come from coding. It comes from designing systems intentionally and making the decisions others hesitate to make.
Reflection
This is my lens, shaped by years of systems thinking, capability design, and delivering against complexity. Execution is getting faster. AI will accelerate it further. The rare skill will be seeing the patterns, weighing the tradeoffs, and having the courage to act. That is where leverage compounds.