Ideas Weave Every Narrative with AI.
Vanity metrics are the new opium of the entrepreneur. I built an AI website in 3 hours, got 562 sign-ups, and felt lost. The real danger of AI isn’t replacing developers—it’s creating a generation of zombie startups that solve no real problems. Learn why speed to market is useless without a foundational understanding of the problem you’re solving.
We aren’t in an AI bubble; we’re in an LLM capital bubble. When it pops, it won’t destroy artificial intelligence—it will wipe out zero-differentiation wrappers and overcapitalized model labs, shifting capital toward durable, workflow-integrated applications. The pop isn’t a crash; it’s a purifying fire.
Local AI models won’t win; they’ll become an expensive hobby. The classic argument that PCs beat mainframes fails for AI because cloud APIs have compounding advantages: they improve faster, get cheaper, and absorb feedback loops that local hardware can’t match. If you build on local models for independence, you aren’t creating a moat—you’re taking a costly nostalgia trip.
Many SaaS companies survived on cheap debt, not real moats. With AI coding tools making in-house replication cheap and data sovereignty demands rising, the zombie era of subscription software is ending. The tools that survive will have genuine network effects or proprietary data — the rest will be replaced by internal builds. The question isn’t ‘Is SaaS dying?’ but ‘Did your SaaS ever have a reason to exist?’
Naïve just raised $28.5M to automate the messy work of running a company. The real story isn’t convenience — it’s that the company itself becomes a disposable wrapper. When everyone can start a business in 10 minutes, the only thing that matters is what you can’t automate: judgment, taste, and risk.
You’re rolling your eyes at Wispr Flow’s $2B valuation, thinking it’s just a voice-to-text wrapper. You’re right about the tech, but dead wrong about the business. The real asset isn’t the AI model—it’s the user’s ‘flow state.’ Stop asking what model a startup uses, and start asking what behavior they own.
Silicon Valley’s ‘fake it till you make it’ culture tolerates loose metric definitions, but fabricating millions of fake users crosses a material threshold into criminal fraud. The unsettling reality is that the entire ecosystem—investors, media, and partners—profits from not asking hard questions until a collapse makes denial impossible.
Your CLV model is mathematically perfect, but the CMO shrugged. That’s not a failure of data—it’s a failure of strategy. The CMO is rationally protecting their budget and short-term incentives. To win adoption, you must frame your analysis as a tool that helps them win internal battles, not as a critique of their decisions.
San Francisco’s AI billboards aren’t just annoying—they’re a symptom of a venture-capital-driven marketing arms race. Each startup buys a billboard to signal to investors, but the collective result is a homogeneous blur that convinces nobody. The real problem isn’t the billboards; it’s the hype cycle that makes them seem necessary.
Every developer dreams of building an OS, but the reality is brutal: you can’t compete with free and good Linux, and hardware diversity crushes small teams. The real opportunity? Stop trying to build a general-purpose OS. Focus on a constrained environment—custom hardware, AI-native devices, or specialized appliances—where you control the full stack and legacy compatibility doesn’t matter. That’s where the next successful OS will emerge.