Jacob Lauritzen: Token Maxing Is the New Lines of Code
Jacob Lauritzen, CTO of Legora, discusses how AI coding agents are transforming legal tech and software development. He details Legora's engineering practices, including running multiple concurrent agents and building custom orchestration tools to manage shared resources (e.g., Postgres, Redis). The discussion emphasizes that while AI can automate menial tasks and handle verifiable processes (like math or coding tests), human judgment remains critical for tasks lacking objective truth (e.g., litigation strategy). Furthermore, the conversation critiques 'token maxing' as a poor metric for AI adoption, advocating instead for measuring high output and the efficiency of the overall system.
Key takeaways
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Scaling Agentic Workflows
5:34
Legora runs full agentic loops that don't involve human developers, such as a bug report in Slack triggering a cloud agent to reproduce the bug, fix it, write a regression test, and open a PR. This process is then reviewed by a human engineer.
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The Pitfalls of Token Maxing
16:50
Measuring AI adoption by tokens burned is ineffective. The focus should shift to setting high expectations for output and measuring the speed and quality of results, which is considered the 'new lines of code.'
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Decision Models vs. LLMs
23:33
Decision models (like Jev) are highly valuable because they provide an answer with a confidence score, potentially replacing large amounts of decision-making and workflow that do not require full autoregressive LLM capabilities.
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The Role of Human Judgment
33:00
Human judgment is irreplaceable when a problem lacks an objective truth (e.g., determining the best litigation strategy among multiple options).