// 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
What Enters SOC 2 Scope Anthropic announced customer-controlled zero retention on September 2. OpenAI's needs an Enterprise Agreement, per endpoint. Neither one answers the actual audit question: carve-out or inclusive, and can you prove it. The 2027 Marketing Stack Martech hit peak in 2026, up just 0.79 percent. The Gartner utilization figure everyone quotes has not been remeasured since 2023. The stack is not shrinking by vendor count. It is shrinking by what anyone can still prove. AI Governance for Commercial Teams IBM found organizations with a written AI policy fell from 37 to 32 percent this year while breaches climbed. 92 percent of AI-incident companies lacked access controls. The policy was never the control. Four surfaces are. The AI Budget Trap Gartner has CMOs putting 15.3 percent of budget into AI while only 30 percent are ready to scale it. The number nobody quotes: the ready teams allocate 21.3 percent. The trap is the order, not the amount. The Last CMO 31 percent of the S&P 500 has no enterprise CMO and tenure sits at 4.1 years. The job is not dying, it is being unbundled, and the CTO or CIO leads AI at 44 percent of companies against the CMO's 32 percent. Proving AI Pipeline to Your Board Reddit's ChatGPT citation share collapsed 86 percent in a week this month and even the trackers are not sure why. A citation screenshot is not board proof. What actually has to survive to closed-won. Personalization After the Cookie Reversal Cookies survived. Google's own six-year replacement for them died anyway, from low adoption. The real pressure now comes from 20 state privacy laws and a four-week-old EU AI Act rule, not cookies. Why Claude and Perplexity Out-Convert ChatGPT By revenue per visitor, Perplexity and Claude beat ChatGPT. By conversion rate, ChatGPT wins outright. Both studies are real, and only ChatGPT's rank moves between them. Your Search Traffic Is Not Coming Back Gartner said search volume would drop 25 percent by 2026. Google just posted record query volume instead. Both that and your falling clicks are true, and the word hiding in Google's own rebuttal explains why. Why GA4's AI Channel Still Undercounts Google's May 13 AI Assistant channel is real and works as documented. Three structural holes, verified against Google's own pages, still make it a floor, not a fix. Marketing to Machines: When AI Agents Become the Customer Gartner says 90 percent of B2B buying is agent-intermediated by 2028. No company has published one completed transaction. What the receipts show, and why llms.txt is not the fix marketers think it is. Dark AI Traffic: Finding the 70 Percent The viral stat behind the AI-traffic-in-Direct story does not survive a source check. What the mechanism actually does, what the best panel data shows instead, and the honest way to report a number nobody has measured cleanly. What to Ask When Hiring a Chief AI Officer Most interviews for this seat test AI fluency. The job is turning AI into money. The four questions that separate operators from narrators, and the one to answer yourself first. 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.