Journal · Deep tech
Writing on deep tech commercialisation.
What it takes to turn a working technology into a paying customer: pilot design, moats, pricing before advantage, and the commercial hires a research team is missing.
9 pieces · most recent Sept 2025
The modern AI pricing dilemma
How to price a generative AI product when inference costs vary with use and the underlying models keep getting cheaper every few months.
Why trust is the essential moat for AI startups
When model performance becomes a utility, defensibility shifts to reliability, transparency and fairness. How to build and measure that.
Your first 100 users: a go-to-market playbook for vertical AI startups
Why SaaS growth playbooks fit vertical AI poorly, and what to do instead: one critical workflow, deep integration, and an enabled internal champion.
How to acquire your minimum viable dataset efficiently
Eight capital-efficient plays for acquiring the smallest useful dataset, and how each one evolves into a compounding data flywheel.
Five core moats for sustainable AI ventures
Data, workflow, network effects, distribution and brand: the five barriers that make an AI company defensible once model access is a commodity.
A 90-day plan for your first business executive in a scientific team
A three-phase plan for a senior commercial hire joining a research-led company: immersion, then mapping and translation, then co-creating the roadmap.
Hiring your first quantum commercial team: translators, not quota-crushers
Why a conventional enterprise sales hire struggles in a quantum company, what to look for instead, where to find those people and how to compensate them.
A quantum founder's guide to justifying enterprise value before advantage
Two ways to make an early quantum partnership legible to a finance team: frame the fee as an option premium, and cost it against building the team in-house.
Patent or trade secret? A founder's decision framework
Three questions to help a deep tech founder decide whether a core innovation belongs in a patent application or stays a trade secret.
This is the thinking behind the advisory work.
The deep tech practice is where this writing gets applied to one company: the pilot you are unsure about, the buyer who has no budget line, the plan that has to hold between technology milestones.
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