Most SaaS Companies Got AI Wrong. Linear Waited.
Karri Saarinen, CEO of Linear, explains why the company waited years before integrating AI features—rejecting the rush to add chatbots that most SaaS competitors pursued. Rather than chasing token usage, Linear built agent-native workflows that position the product as the control center for AI-powered product development, creating a uniquely defensible business model where Linear owns the sticky interface and organizational context without bearing token costs.
Key takeaways
- • Most SaaS companies failed at AI by adding chatbots reflexively; Linear's advantage came from understanding which workflows actually needed AI before building features, then creating a platform that lets multiple agents integrate rather than owning the AI layer itself.
- • Linear's business model improves under AI adoption because it stays in the "decision-making upstream" position—collecting customer requests, bugs, and feature proposals—while agents execute the work, meaning Linear captures margin without paying LLM token costs.
- • The wrong metrics for AI productivity are token usage, PR count, and percentage of agent-generated code; the right ones are bug count, product quality, user love, and whether the output actually generates value.
- • Separate the problem-finding phase from the execution phase: spend time understanding the right problem and approach before committing resources, then use AI tools to accelerate execution once you've committed.
- • Linear is building a coding agent tightly integrated into the issue workflow so that bugs and feature requests automatically get spawned as agent tasks, with shared visibility for teams to collaborate on fixes in real-time.
- • Product development will shift toward "self-driving" workflows where agents follow explicit project guidance and rules (like a feature's memory and strategy), but humans must still do the thinking work—writing documents, setting strategy, and making judgment calls that no dataset can replace.
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