In the 1890s, factories installed electric motors into mills designed for steam. For 30 years there was almost no productivity gain. The upside only came when factories were redesigned around electricity: different floor plan, different roles, different flow entirely. The factories that electrified first didn't win. The ones that redesigned the floor did.
The same thing is happening now with AI. Every company has Copilot, most have ChatGPT, and some have Gemini or Claude. The redesign hasn't happened yet.
The assumption that has broken
The old operating model had a hidden assumption baked into every layer of it: building was the scarce resource. It was expensive.
Every role, every ceremony, every checkpoint existed because committing to build something was a costly decision. The PM qualified the idea. The designer validated the concept. The engineer estimated the effort. QA tested before release. Each was a gate before the next expensive step.
AI breaks that assumption. Building is now cheap. You can prototype overnight. You can generate requirements in an hour. You can ship in a week what used to take a quarter.
One thing has changed: the speed of the build. The checkpoint model is still intact. The sprint cadence is unchanged. The roles are the same. The measurement framework is the same.
What actually changes
Marty Cagan frames product risk as four questions: can we build it? Can people use it? Will they want it? Does it make business sense? For 20 years, the first one dominated everything. Feasibility was the constraint. The entire operating model was built around it.
across 121,000 developers at 450 companies — DX Survey
AI is dismantling feasibility first. Building is cheap. The old assumption, baked into every role and every ceremony, is no longer true.
Usability is following. AI can prototype, test, and iterate interfaces faster than any design sprint. Agents review code. Agents test flows. The designer's role is shifting from making screens to deciding what the screens need to accomplish.
That leaves two risks standing: value and viability. Will people actually want this? Does it make business sense? These cannot be automated. They require understanding customers, context, and commercial reality in ways that take time and human relationships to build.
I was at a senior product leadership roundtable recently. Founders, VPs and CPOs. One observation stayed with me: we've spent 20 years being slow enough that saying no happened naturally. You had to prioritise because you couldn't build everything. That constraint is gone. The risk now is a product with a thousand features, each used by a handful of people, a strategy you can no longer describe, and a sales team that won't be able to articulate what they're selling. Speed was never really the problem. Making it faster just exposes that the harder question was never properly answered.
The dark factory
Nate B Jones frames the destination as a dark factory: a fully automated manufacturing plant where the lights are off because no humans work there. Applied to software: a codebase that writes, reviews, deploys, and iterates itself. Humans present only for the two questions AI cannot answer.
It sounds distant but isn't. In January 2026, engineers at Anthropic and OpenAI said AI was writing 100% of their code. Anthropic's CEO confirmed 90% of Claude is now written by Claude itself. The dark factory isn't a thought experiment. It's already running at the companies building the tools.
Most organisations won't get all the way there. That's not the point. The direction matters more than the destination. Like agile, you're always moving toward it, never fully arriving. The question isn't "are we a dark factory?" It's "are we getting darker?" How many human touches does your process require? That number should be falling. The operating model redesign is the path.
The ripple effect across functions
This is not an engineering problem. When building becomes free, every function's relationship with product changes.
Commercial / Revenue
When building is fast, commercial gets what it asks for. Short term, that looks like winning. The problem compounds quietly. A product that says yes to everything becomes a product where features accumulate and the strategy dilutes. It surfaces later in churn, or in a diligence conversation that cannot tell a coherent story about what the product is for.
Marketing / CMO
When you can build personalised experiences faster than campaigns, the product becomes the marketing and the growth loop inverts. Marketing that sits downstream of product, waiting for features to package, is structurally in the wrong position.
Engineering / CTO
Engineering stops being the constraint and becomes the platform. The brief shifts: not "can we build it?" but "have we built the right foundation for agents to build it well?" Cost does not disappear — it shifts from headcount to compute. Architecture is now also a cost optimisation question.
Operations / COO
Sprints, stand-ups, ceremonies — these were designed for people. Agents do not need stand-ups. The ceremony structure built around human coordination becomes overhead without a purpose. What replaces it is visibility infrastructure: dashboards, status systems, decision logs. Knowing which processes to dissolve and which to keep is the hardest operational call of the next five years.
Product / CPO
Every other function gets faster, cheaper, or smaller. This one gets harder — and more important. Not just for the product function. For every function in the building.
Product leadership has always been an enabling role. Rally the organisation around the right problems. Create the conditions for teams to do their best work. Hold strategy when execution pressure pulls everyone off course. AI doesn't change that brief. It takes it to a level of orchestration and empowerment the role has never operated at before.
Done well, the CPO is the function that enables the entire executive team — and their organisations — to have greater impact through AI. Not just the product team. Every function.
Return to the four product risks: can we build it, can people use it, will they want it, does it make business sense? Feasibility has nearly disappeared. Usability is compressing. What remains — and what nothing automates — are value and viability. Will people actually want this? Does the business support it? These are the two risks that grow as everything else shrinks.
They are also the two that devalue fastest when product leadership is absent. A business that ships fast without answering them produces high velocity and declining relevance. The speed makes it harder to see.
This is not something product solves alone. Value requires proximity to clients, sales, and operations. Viability requires understanding finance, legal, and commercial reality. The product builder's job is to hold the question across all of them.
Design shrinks. Engineering shrinks. The product builder grows. Not a role — a capability. The ability to frame the right problem, think creatively about how to solve it, and drive a vision across the organisation. It can sit in an engineer, a designer, or a product manager. What matters is the disposition: someone willing to hold value and viability, bring the right context from across the business, and make the call.
The industry is beginning to recognise this and job descriptions are starting to change. The harder shift is accountability. Most companies have given product more tools and kept the same reporting line — but not the genuine authority to make the decisions that matter. Clear authority matched to accountability. A genuine mandate, not just a title.
We are installing AI. The harder challenge — the one most organisations have not yet addressed — is redesigning the factory around it.
