Instead of pure SaaS, Terra Security uses an "AI-enabled service" model. This hybrid approach allows them to tackle complex problems that fully autonomous AI can't yet solve, while still benefiting from software scalability and replacing existing, large budget items for manual services.
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Industries with historically low software adoption (like trial law or dentistry) are now viable markets. Instead of selling a tool, AI startups are selling an outcome—the automation of a specific labor role. This shifts the value proposition from a software expense to a direct labor cost replacement.
AI enables "software does labor" business models in industries previously deemed too small for specialized software, like dental offices or trial law. By replacing or augmenting specific labor tasks, startups can justify high-value contracts in markets that historically wouldn't pay for traditional SaaS tools.
Customers are hesitant to trust a black-box AI with critical operations. The winning business model is to sell a complete outcome or service, using AI internally for a massive efficiency advantage while keeping humans in the loop for quality and trust.
Founders are stuck in a SaaS mindset, selling tools to existing service providers. The bigger opportunity is to build new, AI-first service companies (e.g., accounting, legal) that use AI to deliver a superior end-to-end solution directly to customers.
Instead of selling AI co-pilots, legal tech startup Crosby operates as a full-stack law firm using AI internally. This model allows them to continuously re-orchestrate workflows between human lawyers and AI as models improve. This captures the entire value of automation rather than just the limited margin from selling a software tool to other firms.
To penetrate tech-resistant markets like personal injury law, the winning model is not selling AI software but offering an AI-powered service. Finch acts as an outsourced, AI-augmented paralegal team, an easier value proposition for firms to adopt than training existing staff on new, complex tools.
Unlike SaaS which sells to limited software budgets (e.g., 1% of revenue), vertical AI agents automate core business functions. This allows them to tap into much larger operational and labor budgets. Companies can capture 4-10% of a customer's total spend by replacing expensive human-led tasks like customer support.
Businesses previously considered non-venture scale due to service-based models and low margins, like Managed Service Providers (MSPs), are becoming investable. By building with an AI-first core, these companies can achieve the high margins and scalability required for venture returns, blurring the line between service and product.
In a world where AI makes software cheap or free, the primary value shifts to specialized human expertise. Companies can monetize by using their software as a low-cost distribution channel to sell high-margin, high-ticket services that customers cannot easily replicate, like specialized security analysis.
Traditionally, service businesses lack scalability for VC. But AI startups are adopting a 'manual first, automate later' approach. They deliver high-touch services to gain traction, while simultaneously building AI to automate 90%+ of the work, eventually achieving software-like margins and growth.
