This issue explains how SaaS moats shift from data storage to workflow orchestration in an agent-driven world, why comprehensive eval frameworks become key AI IP, and how to pinpoint the smallest viable unit of sellable software. It also highlights pitfalls like fake traction, work whiplash, agentic web readiness, private session recall tools, AI’s impact on cybersecurity incumbents, and startup risk misconceptions.

• SaaS moats are shifting from data storage to agent orchestration—routing tasks, approvals, and logging—as the real lock-in point
• Eval frameworks (measuring tone, accuracy, tool use) are becoming the core defensible IP for AI products, not just quality checks
• There's a "minimum viable unit of saleable software": below a certain cost/time threshold teams hack scripts with LLMs, above it they buy packaged tools
• Traction metrics that impress in pitch meetings often fail due diligence if usage isn't sticky or revenue doesn't scale