Tag
AI Product Strategy
27 articles tagged with AI Product Strategy.
94% Capable. 33% Deployed. The Gap That Explains Everything.
Anthropic research reveals a 61-point gap between AI capability and actual deployment. That gap explains why the workforce apocalypse has not arrived yet.
Your AI Budget Line Is Wrong. Tokens Are the New Headcount.
Engineering teams spend more on AI tokens than junior salaries. The cost structure of building software has inverted and most finance teams missed it.
Two Production SaaS Platforms, One Builder: Solo Vertical SaaS
50+ AI features, 6 LLMs, native mobile, Stripe billing. Two vertical SaaS platforms built solo to prove the one-person product company model transfers.
Fred Brooks Is Dead. Rewrite the Damn Thing.
The 'never rewrite' doctrine was based on rewrite cost. AI has collapsed that cost to days. Pre-launch rewrites are now a product strategy, not a failure.
The AI Pricing Stack: Usage, Outcome, and Hybrid Models
Per-seat pricing is dying but the replacement is not simple. A practical framework for AI pricing that covers usage-based, outcome-based, and hybrid models.
AI Makes Overbuilding the Default. Discipline Is the Antidote.
Going from zero to end in hours sounds like progress. It's also how you ship a product nobody can navigate. The real skill is knowing when to stop.
AI Governance Without Bureaucracy: A Framework That Ships
Most AI governance is either theatre or a bottleneck. A risk-tiered framework built from shipping AI features to AFSL-regulated Tier 1 banks in production.
AI Didn't Kill Coding. It Killed Typing.
AI coding is the sixth abstraction layer in 80 years. Every previous layer was dismissed as not real programming by the practitioners of the one below.
How to Measure an AI Product (When Traditional Metrics Lie)
DAU, time-in-app, and NPS were built for a world where humans do the work. AI products need different metrics. A framework for what to measure and why.
The Bitter Lesson Kills Your Orchestration Layer
Scaffolding gives you 10-20% gains that the next model wipes out. The bitter lesson for product builders: give the model tools and a goal, not a workflow.
Enterprise AI Adoption Playbook: Why Seats Go Unused
AI features that work in demos fail in deployment because adoption is a product problem, not a training problem. A playbook from rolling out AI to Tier 1 banks.
The Winning Agent Had No Better Model. It Got Shit Done.
Weekend build to 145K GitHub stars to acquisition in weeks. The pattern: agents that execute locally instead of chatting in a browser window win on adoption.
Build for the Model That Doesn't Exist Yet
Your AI product market fit depends on a model that has not shipped yet. Build your product architecture for the capability curve, not today's snapshot.
Voice Agent Playbook: AI Phone Calls in Production
I built AI voice receptionists that handle real phone calls for real businesses. Latency, conversation flow, graceful handoff. Here's what actually matters.
The Cartel Problem: Why AI Stalls at the Industry Gate
AI's biggest obstacles are not technical. They are structural: professional guilds, regulatory capture, procurement inertia, and incumbents profiting from it.
Your RAG Pipeline Is a Product Decision, Not an Engineering One
Chunking, retrieval, and grounding are not engineering details. They are product decisions that determine whether your AI feature helps or hallucinates.
Latent Demand Is Your AI Roadmap
The best AI products aren't imagined. They're discovered by watching how people misuse your existing ones. A framework for finding what to build next.
Product Discovery Is Dead: Why Prototyping Replaced It
The 6-week discovery sprint is a relic. When you can build a working prototype in a weekend, the fastest path to insight is shipping, not researching.
The AI Usage Gap Is a Product Architecture Problem
Most AI tools are deployed but unused. The friction isn't capability. AI lives in a separate tab instead of where work happens. Build inline, not destination.
Your Killer Feature Will Be Cloned by Friday. Build a Moat.
4 engineers, 10 days, a new product line. AI coding agents collapsed build economics. If code is a commodity, your moat is data, integrations, and trust.
Your Job Isn't Going Anywhere. Your Tasks Are.
AI does not replace jobs. It replaces tasks. That distinction changes everything about how you plan your career, your hiring strategy, and your org chart.
The Cannibalisation Paradox: Why Per-Seat Pricing Dies in the Agentic Era
If your AI roadmap succeeds, customers need fewer seats and you earn less revenue. The fix: price around the units of work completed, not user logins.
The 2,500% Audit Tax: The Math That Will Kill Your Multi-Agent P&L
A manager model checking every worker output increases unit cost by 2,500%. The fix: a spot-check architecture that can save 75% of your token margin.
The Hard Hat Era: Your 2026 AI Strategy Is an Org Chart
The shift is from prompt engineering to designing multi-agent hierarchies: AI managers overseeing AI workers that operate invisibly in the background.
Your AI Copilot Is a Margin Trap. Build for Replacement.
AI is not a feature, it is a new compute paradigm. Bolting GenAI onto legacy platforms destroys unit economics. If the AI is optional, it's a gimmick.
Text Is a Terrible Business Interface. Generative UI Wins.
Google's A2UI signals the end of the chatbot text wall. Agents that render native UI components instead of paragraphs change what product teams build.
Google's Real Estate Listings: Why Aggregators Should Worry
Google is testing native property listings in search. AI broke the constraint that stopped them in 2011. The aggregator model dies when the user is an agent.