product strategy

Tenfold growth is not an optimisation problem. The interviewer wants to see you find new users the product does not serve today, and protect reliability and margins while doing it.

A principal-level design and strategy question. Every default assumption (literacy, a good phone, fast data, English, a credit card) fails. The answer has to pick a job worth solving and an economic model that survives.

Standard prioritisation frameworks miss the thing that makes AI ideas risky: nobody knows yet whether the model can do it reliably. The strong answer prices that uncertainty in.

A portfolio question for a company that already uses machine learning everywhere. Strong answers find where generative AI changes what users can do, not just how recommendations are computed, and weigh it against costs to artists and licensing.

A judgment call with no default answer. Principal-level candidates segment the users, price the value, check the strategic role, and make a decision with a dated review rather than a vague 'improve it'.

A principal-level question about recovering from failure in public. Apologies are necessary and not sufficient; users come back when the product visibly changes and the company keeps its promises.

The strategy question every AI application company faces. 'Better prompts' is not an answer. The interviewer wants a ladder of defensibility and a plan to climb it.

An incumbent's dilemma. Bolting a chatbot onto the old product rarely works; copying the startup ignores your strengths. The interviewer wants a response that uses the incumbent's assets.

AI features commoditise fast because everyone can access similar models. The interviewer wants a move from feature advantage to workflow, data and outcome advantages, with a plan for the feature itself.

A principal-level strategy question for a company whose discovery layer could be disrupted by AI assistants. The answer must cover defence, offence, operations and platform risk, with sequencing and measures.

Interrogate an impressive capability claim, choose a buyer and price the cost of being wrong before proposing a launch.

A portfolio process question. Prototypes make AI ideas cheap to test; the challenge is turning a stream of demos into consistent, evidence-based funding decisions.