// BLOG
Notes on turning technology into a company.
Here is the deal. Most AI companies do not have a technology problem. They have a money problem nobody named yet. This is what I have learned turning deep tech into revenue, capital, and a story the market can repeat: AI commercialization, the Chief AI Officer role, pricing that survives diligence, and go-to-market for companies the market cannot read yet.
Start here · Guides
Chief AI Officer: the complete guide What the role is, what it owns, when a company needs one, fractional versus full-time, and how it differs from a CTO or CMO. The hub for the whole series. AI Commercialization: the complete guide Turning a working model into a business: pricing that survives diligence, unit economics that hold at scale, go-to-market that converts, and a story the market can repeat.All posts
The AI Attribution Gap: Why Your Best Traffic Hides in Direct AI is quietly your best-converting channel, and your analytics files it next to bookmark traffic. Why GA4's new AI channel won't fix it, and what does. The hub for a four-part series. The 2027 CMO: What Marketing Leaders Must Prepare For The marketing budget stopped growing in 2022. The job did not. Six preparations for 2027, the hub for a six-week series through planning season. AI Gross Margins: Why the 90% SaaS Benchmark Is Gone The 90 percent software margin was a consequence of near-zero marginal cost. AI hands you a bill with every request. What the verified 2026 data actually says. Metering Cost Per Customer: The AI Unit-Economics Playbook Almost nobody can say what any single customer costs to serve. The instrumentation playbook: what to tag, how to roll it up, and the scar-tissue cost of skipping it. The First 90 Days of a Chief AI Officer The seat is won or lost in the first quarter. Build the gate before the strategy, close the authority gap, and ship one measurable win before the honeymoon ends. Why Chief AI Officer Hires Fail The company buys a title instead of building a mandate. The Chief Data Officer already ran this experiment, and the tenure data says how it ends. Three failure modes, with the 2026 data. What Investors Look For in an AI Startup at Seed and Series A Real revenue, gross margin, retention, and capital efficiency, not demos. Verified 2026 benchmarks on what investors actually look for at seed and Series A. Who Should Own AI: CAIO vs CTO vs CIO The CIO owns the AI that runs the company, the CTO owns the AI in the product, and a Chief AI Officer owns whether AI turns into revenue. IBM found two-thirds of CIOs and CTOs are accountable for AI they do not control. The Commercialization Gap: Why Most Enterprise AI Never Makes Money A widely cited 2025 MIT report put the enterprise AI failure rate near 95 percent. The cause is the commercialization gap, not the technology. A named framework for closing it. Fractional Chief AI Officer: What It Costs and When to Hire One Senior AI-commercialization judgment part-time, for a fraction of a full executive salary. When fractional beats full-time, what it costs, and the need-signal checklist. Chief AI Officer vs Chief Data Officer: Who Owns What The CAIO owns whether AI becomes revenue and margin. The CDO owns whether the data is trustworthy and governed. Commercial versus infrastructural, side by side. Usage-Based vs Seat-Based Pricing for AI Products Seat-based pricing decouples revenue from the cost of compute, so margin erodes as usage grows. Why most AI products should price on usage or a hybrid. How to Price an AI Product Before Series A You heard the stories about raising on no revenue. They are real, and they are not about you. The AI Series A bar roughly doubled, to about $3.5M median ARR. Price for margin before the raise, because that is exactly what investors now read you for. What a Chief AI Officer Actually Owns Everybody is hiring a Chief AI Officer. Almost nobody can say what the job is. Here is the real scope: the path from AI capability to revenue, the unit economics, the build-buy-kill calls, the trust layer, and the part that never stops. Why Your AI Startup Isn't Making Money Yet The product works. The model is good. And the money still is not showing up. That is a commercialization gap, not a technology gap. Here is what to fix before the next raise, not after. Category creation is a proof system, not a positioning exercise. Naming a category is free, which is exactly why a name alone is worth nothing. The market believes proof, not slogans. Build the receipts first, name the category second. AI pricing has to respect compute reality. Seat-based pricing and AI do not mix. Every inference call costs you real money, the cost moves, and your best customers run up the bill the hardest. Price for that or watch your margin bleed at scale. The market does not buy complexity. It buys a story it can repeat. Complexity does not travel. If a buyer cannot repeat what you do in one sentence, your pipeline stalls no matter how good the tech is. Here is how to fix the story before the product pays for it. AI demos are easy. AI businesses are hard. A demo proves the model works. A business proves someone pays, keeps paying, and the margin survives once compute meets the real bill. Those are not the same thing, and the gap is where startups die.