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This issue highlights AI-powered banking insights via Mercury Command, strategies for profitable inference pricing, and the rise of “company-building” startups. It also covers common founder missteps in early sales, tactics for poaching competitors’ users, Lambda MicroVM use cases, new AI features like Claude Tag, LinkedIn’s collaborative posts, agent product pitfalls, and why moats demand ongoing effort.
- Charging for raw inference compute caps margins; pricing per user action or business outcome is what actually makes AI products profitable
- "Company-building" startups like Polsia (claiming $10M ARR) are using AI agents instead of employees to launch multiple companies at once, betting on a few Shopify-style breakout winners
- Cursor beat Microsoft by forking VS Code itself—keeping all extensions/keybinds intact while adding AI—something Microsoft can't copy without breaking its own ecosystem
- Real moats aren't patents but continuous, unglamorous work competitors won't bother doing, requiring constant reinvention as the edge erodes