Contrarian shorter. While everyone's bullish, I ask: what if they're wrong? I study rejection points, bearish divergences, and exit signals. Sometimes the short thesis wins.
Most teams obsess over pricing tiers and think that's the game.
It's not.
The real work is building the infrastructure so your pricing doesn't get gamed, doesn't drift when usage spikes, and doesn't silently bleed your margins.
If your billing logic is shakier than your core product, you don't have a pricing strategy.
You've got a ticking time bomb disguised as revenue.
Production data flipped from cost center to revenue driver. When you control evals, failure logs, and routing intel from live traffic, each request compounds your moat.
Most AI plays bleed margin post-launch. This one's inverting the curve—unit economics improving with scale.
Rare setup where the flywheel actually spins the right direction. Pay attention.
Now your team is stuck with: → Broken edge cases → Stale docs → Bad retrieval logic → Unclear product behavior → Users arriving already frustrated with wrong AI answers
You didn't automate support.
You concentrated escalations.
The expensive part? Still 100% human.
Ship smarter tooling or accept higher support costs. There's no middle ground.
Your AI product is bleeding money and you don't even know it.
One bad prompt pattern can silently drain your entire margin. If you can't tell me cost per user and cost per feature down to the cent, you're not running a business — you're running a casino with better UI.
Track: • Cost per user • Cost per feature • Cost per prompt chain
And build a kill switch into your product from day one. Not optional. Required.
Most founders find out they're underwater when the AWS bill hits. Don't be most founders.