Insights
Perspectives
Thoughts on AI implementation, Solution-Outcome Fit, and making technology investments land.
The Second Agent Problem: Microsoft Is Commoditising Models and Selling the Substrate
Microsoft's AI moat is a context substrate, not a model. The IQ layer is a bet that whoever owns enterprise context owns the marginal cost of every agent.
Every AI Giant Just Built an Army of Engineers. None of Them Are Coming to Your Business.
In one quarter the AI giants committed billions to embed engineers inside large enterprises. The independent small and mid-sized business — the long tail — was left out by design.
High Fashion, Fast Fashion: The Two-Speed Future of Enterprise AI
A 9-billion-parameter open-source model now outperforms one 13 times its size — and runs on a laptop. Enterprise AI is splitting into two tiers: frontier models for complex reasoning, and efficient models for everything else. Most companies never made a deliberate choice between tiers. The ones that do will spend less and deploy smarter.
Your AI Vendor Says You're Deployed. Your KPIs Say Otherwise.
56% of CEOs haven't seen revenue or cost benefits from AI in the last 12 months. Two-thirds report productivity gains. Both things are true — and the gap between them is where enterprise AI investments go to die.
The SaaSpocalypse Is Real. The Panic Is Not Useful.
$285 billion in software stocks evaporated. Matt Shumer's essay hit 80 million views. The SaaSpocalypse narrative says AI is destroying everything. The reality is more nuanced — and the companies that treat this as reinvention rather than destruction will be the ones still standing.
VCs Won't Fund Without Product-Market Fit. Why Are You Funding AI Without Solution-Outcome Fit?
Most enterprise AI initiatives fail not because the technology is wrong, but because nobody validated whether the solution fits the business outcome. Introducing Solution-Outcome Fit — the discipline that separates AI investments that land from those that burn.