The traditional "cost-plus" model pays contractors a percentage of their total costs. This creates a perverse incentive to increase project expenses and duration, as a higher cost base results in a larger absolute profit for the company.

Publicly traded Contract Research Organizations (CROs) are disincentivized from making deep investments in AI. Since their revenue is based on a cost-plus model (billable hours), AI-driven efficiencies would force them to charge less. This creates a challenging dynamic where investing in innovation directly hurts their top-line revenue.

Legacy defense contractors on "cost-plus" models are incentivized to increase costs to boost profits. This is the opposite of the startup model, which must innovate to deliver superior products faster and cheaper to gain market share, injecting much-needed competition into the sector.

AI tools drastically reduce the time needed to complete complex tasks, breaking the traditional billable-hour model for consultants and agencies. The focus must shift to value-based pricing, where compensation is tied to the problem solved or the output created, not the hours worked.

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.

While AI and modern tools are making software development significantly cheaper, government contracting models have not adapted. Agencies remain locked into expensive, outdated procurement processes, paying more for software even as its actual cost plummets.

A prompt takes seconds, but the expertise to write it effectively takes a career. This '30 seconds and 30 years' paradox breaks traditional time-based billing. Agencies must shift to value- or deliverable-based pricing that properly accounts for the senior human capital guiding the AI tools.

The standard "cost-plus" model guarantees contractors a profit margin on top of their expenses. This creates a perverse incentive to maximize costs and timelines, as 10% of a $3 billion project is far more lucrative than 10% of a $150 million one.

A primary reason for failed government digital transformations is that software vendors' main skill is securing contracts, not delivering quality products. An ex-Airbnb team had to fire a vendor and rebuild a system from scratch, highlighting how the incentive structure in government procurement leads to poor outcomes.