Avoid trendy, saturated markets. Instead, focus on stable, 'boring' industries that are slow to innovate and still rely on manual processes. These markets are ripe for disruption, have less competition, and typically offer higher margins for AI solutions.
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Instead of selling software to traditional industries, a more defensible approach is to build vertically integrated companies. This involves acquiring or starting a business in a non-sexy industry (e.g., a law firm, hospital) and rebuilding its entire operational stack with AI at its core, something a pure software vendor cannot do.
Most companies are not Vanguard tech firms. Rather than pursuing speculative, high-failure-rate AI projects, small and medium-sized businesses will see a faster and more reliable ROI by using existing AI tools to automate tedious, routine internal processes.
Unsexy markets like plumbing or law have less competition, higher profit margins, and customers who are more receptive to expertise. This creates an environment for faster growth, akin to driving on an empty road.
VCs have traditionally ignored the massive $16T services sector due to its low margins. AI automation can fundamentally change this by eliminating repetitive tasks, allowing these companies to achieve margin profiles similar to software businesses, thus making the sector newly viable for venture investment.
While AI can improve existing software categories, the most significant opportunity lies in creating new applications that automate tasks previously performed by humans. This 'software eating labor' market is substantially larger than the traditional SaaS market, representing a massive greenfield opportunity for startups.
The ideal industry for an AI roll-up is not one that can be fully automated. If automation exceeds 70-80%, a pure software solution from an incumbent like Microsoft will likely win. The strategy thrives where a human services component remains essential but can be significantly augmented by AI.
Don't overlook seemingly "boring" industries like cybersecurity or compliance. These sectors often have massive, non-negotiable budgets and fewer competitors than glamorous, consumer-facing markets. Solving complex, high-stakes problems for large companies is a direct path to significant revenue.
YC Partner Harsh Taggar suggests a durable competitive moat for startups exists in niche, B2B verticals like auditing or insurance. The top engineering talent at large labs like OpenAI or Anthropic are unlikely to be passionate about building these specific applications, leaving the market open for focused startups.
In businesses with tight 5-8% margins, like retail, AI-driven efficiencies in areas like customer support aren't just incremental. They become extraordinarily powerful levers for profitability and scaling, fundamentally altering the cost structure of the business.
Investing in startups directly adjacent to OpenAI is risky, as they will inevitably build those features. A smarter strategy is backing "second-order effect" companies applying AI to niche, unsexy industries that are outside the core focus of top AI researchers.
