VC Keith Rabois highlights a core conflict: law firms billing by the hour are disincentivized from adopting AI that makes associates more efficient, as it reduces revenue. This explains why corporate legal departments are faster adopters—their goal is to cut costs.
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While AI expands software's capabilities, vendors may not capture the value. Companies could use AI to build solutions in-house more cheaply. Furthermore, traditional "per-seat" pricing models are undermined when AI reduces the number of employees required, potentially shrinking revenue even as the software delivers more value.
The primary economic incentive driving AI development is not replacing software, but automating the vastly larger human labor market. This includes high-skill jobs like accountants, lawyers, and auditors, representing a multi-trillion dollar opportunity that dwarfs the SaaS industry and dictates where investment will flow.
AI tools are taking over foundational research and drafting, tasks traditionally done by junior associates. This automation disrupts the legal profession's apprenticeship model, raising questions about how future senior lawyers will gain essential hands-on experience and skills.
As agencies adopt AI to increase efficiency, clients will rightfully question traditional pricing models based on billable hours. This creates an "arbitrage" problem, forcing agencies to redefine and justify their value based on strategic insight and outcomes, not just the labor involved.
C-suites are more motivated to adopt AI for revenue-generating "front office" activities (like investment analysis) than for cost-saving "back office" automation. The direct, tangible impact on making more money overcomes the organizational inertia that often stalls efficiency-focused technology deployments.
Contrary to its reputation for slow tech adoption, the legal industry is rapidly embracing advanced AI agents. The sheer volume of work and potential for efficiency gains are driving swift innovation, with firms even hiring lawyers specifically to help with AI product development.
Historically, labor costs dwarfed software spending. As AI automates tasks, software budgets will balloon, turning into a primary corporate expense. This forces CFOs to scrutinize software ROI with the same rigor they once applied only to their workforce.
The most significant hurdle for businesses adopting revenue-driving AI is often internal resistance from senior leaders. Their fear, lack of understanding, or refusal to experiment can hold the entire organization back from crucial innovation.
An employee using AI to do 8 hours of work in 4 benefits personally by gaining free time. The company (the principal) sees no productivity gain unless that employee produces more. This misalignment reveals the core challenge of translating individual AI efficiency into corporate-level growth.
Venture capitalist Keith Rabois observes a new behavior: founders are using ChatGPT for initial legal research and then presenting those findings to challenge or verify the advice given by their expensive law firms, shifting the client-provider power dynamic.