a16z General Partner David Haber: Long-cycle intelligent agents can charge based on results

David Haber, a general partner at a16z, stated that long-cycle intelligent agents can now progress towards goals across dozens of steps and various interaction methods over weeks to months. He noted that AI previously handled short tasks with clear boundaries, where customer service was implemented earlier due to verifiable results and edge cases manageable by humans; now, intelligent agents can guide insurance clients from the initial call to payout, mortgage clients from pre-approval to closing, and business leads from demonstration to signing, gaining returns based on revenue growth rather than just cost savings.

He used the example of plaintiff lawyers preparing cases, stating that the process resembles a relay race, where value can easily be lost at handoff points. Potential clients often contact multiple law firms simultaneously, and slow responses or evaluations can lead to loss; after signing, evidence collection is limited by manpower, and firms may not follow up with witnesses who did not answer the fifth call, making it difficult to review low-value cases item by item. Missing bills or unexplained treatment gaps can also lower settlement amounts. Long-cycle intelligent agents can assess in real-time around the clock, utilizing sub-agents to advance evidence collection and client updates via phone, email, or text, while maintaining complete records from call logs to medical records.

Haber proposed three commercialization paths: selling platforms to existing teams, taking over outsourced services, or vertically integrating as a full-stack service provider, such as purchasing debt portfolios and completing collections. He mentioned that most states in the U.S. prohibit non-lawyers from sharing attorney fees, so the plaintiff legal scenario needs to align pricing with settlement amounts but cannot directly share profits.