AI Industry Strategy
Vendor moves, compute economics, what each lab is betting on. Less product, more strategy — Anthropic compute shortages, OpenAI vs Anthropic vs Google bets, business-of-AI takes.
You don't have a skills gap. You have a description gap.
Most orgs think AI adoption is blocked by too few people who can code. The real bottleneck is turning what your experts already understand into working software.
Banning Shadow IT Just Drives It Underground
Prohibition doesn't stop employees solving problems with software—it just makes the solving invisible. The winning move is to make building sanctioned and observable, not banned.
From problem to tool should take days, not quarters
The variable that decides who wins with AI is how fast an org turns a noticed problem into working software. Most measure that gap in quarters; winners in days.
Every SaaS Tool Is Built for a Company That Isn't Yours
Generic SaaS is designed for the average company, so it fits no one exactly. The orgs that win stop bending their work to fit the tool and build the fit themselves.
IT Doesn't Disappear When Everyone Can Build—It Moves Up the Stack
When domain experts build their own tools, IT doesn't shrink—it moves up the stack: from ticket-queue construction crew to the platform and review function.
The 80% You Don't Use: The Per-Seat SaaS Trap
Per-seat SaaS taxes every hire and bills you for features you'll never touch. The build-vs-buy math just quietly flipped for the long tail of internal tools.
Shadow IT Was Never a Building Problem. It's a Visibility Problem.
Leaders fight shadow IT by trying to stop the building. The building will happen regardless—what actually hurts is that it's invisible. The real fix is visibility, not prohibition.
Your Org Runs on 60 Tools and Can't Answer One Question Across Them
Leadership can't get a single answer that spans systems: who touched this customer, where this data lives, what depends on what. That unanswerable question is the real cost of fragmentation.
Citizen Development Failed Three Times. Here's What's Different Now.
Spreadsheets, low-code, and RPA all tried to let non-engineers build software—and all stalled on the same flaw. The fourth attempt removes it.
The Real Cost of SaaS Sprawl Isn't the Bill. It's the Blindness
Most leaders fight SaaS sprawl by cutting subscriptions. The expensive part is that 60 disconnected tools make your own organization impossible to see clearly.
The Winning Org in the AI Era: Everyone Builds, Nothing Hidden
The companies that win with AI won't be the ones with the biggest budget. They'll be the ones that let everyone build software while leadership keeps full visibility across it all.
Your Best Software Engineers Don't Work in Engineering
The people who understand your operations best aren't in the engineering org—they're in finance, ops, and support. The companies that let them build their own tools pull ahead.
Where the AI App Builder Category Is Headed in 2027
Seven predictions for the AI app builder in 2027 — why every tool ships a backend, the spec becomes the differentiator, and apps start composing each other.
The Death of the $50K Internal Tool Build (and What Survives It)
AI app builders now compile production-grade internal tools for the cost of dinner. Here's what that does to the custom-software dev-shop economy.
The Compiler Comparison: Is the LLM Actually a Compiler?
An LLM is non-deterministic where gcc is not — but reproducibility is a workflow property, not an engine one. Here is why that distinction matters.
What Lovable's Backend Push Reveals About the Spec-Layer Race
As Lovable, Bolt, and v0 all ship backends, feature parity stops being a differentiator. The real race is over who owns the spec layer.
The 'Build It For Me' Shift: Why No-Code Gave Way to AI App Builders
No-code asks you to assemble the app by hand. AI app builders generate it from a description. Here is what that shift in interaction model actually changes.
The One Layer of Your AI-Built App You Actually Own
"Open source AI app builder" hides four different things. Here's a taxonomy of what's open across Remy, Bolt, Lovable, and Replit — and what you keep.
The Unit Economics of $30 Full-Stack Apps (Yes, Really)
AI-compiled apps cost $30-40 in inference to build. Here's the cost breakdown—and what it means for traditional dev-shop pricing.
The Vertical Internal-Tool Market Is About to Restructure
As AI compiles custom internal apps for the cost of a lunch, the build-vs-buy line moves — and enterprise software spend reorganizes around owned apps.
Microsoft Build 2026: MAI Models, Scout Agent, and RTX Spark Explained
Microsoft Build 2026 introduced seven new AI models, the Scout autopilot agent, and RTX Spark chip. Here's what matters for AI builders.
The AI App Builder Category in Q3 2026: Where It Actually Stands
The AI app builder landscape is fracturing. Lovable, Bolt, Replit, v0, Remy, Cursor — who serves which workload, and where is the category headed?
The Cloud Was Built for Human Developers. Agents Need Something Else.
Per-seat pricing, dashboards, slow provisioning, human-team permissions: the modern cloud assumes people at every step. AI agents need a different shape.
What Your AI-Built App Actually Costs Once People Use It
Auth, media, email, error tracking, monitoring, abuse protection—the dozen vendors an AI-built app needs once it's used, and why a native stack wins.