The rapid growth of AI products isn't due to a sudden market desire for AI technology itself. Rather, AI enables superior solutions for long-standing customer problems that were previously addressed with inadequate options. The demand existed long before the AI-powered supply arrived to meet it.

Shure's founders pivoted back to their original EOR concept, which failed years prior due to a lack of automation infrastructure. The recent maturity of AI agents and stablecoin rails made the initial vision feasible, showing that timing and technological readiness are critical for an idea's success.

Past tech solutions for fragmented industries like logistics often failed because they required universal adoption of a new platform. AI can succeed by meeting users in their existing, messy channels—email, texts, calls. It automates work within current workflows rather than forcing a difficult behavioral change, lowering adoption barriers.

To capitalize on a new technology wave (e.g., AI agents), you must be an active participant at the frontier. The best ideas come from building a solution to a problem you and other pioneers are facing while tinkering. This tool, built for the vanguard, is what the mainstream market will need in 6-12 months.

An AI appointment setter is an easy business to launch because its value proposition is simple. You're not selling a new concept, but rather a more efficient, cost-effective replacement for an existing, expensive full-time employee, making the ROI immediately clear to potential clients.

AI voice isn't just about cost savings. The technology has improved so much that it often provides a better customer experience (NPS) than human agents. This dual benefit of high ROI and improved experience means customers are eagerly adopting these solutions, creating a powerful market pull for founders.

Don't wait for perfect infrastructure like APIs or Model Context Protocol (MCP). Winning AI companies, particularly in voice, are building "interim" solutions that work today to solve a deeply broken user experience. The strategic challenge is then navigating from this interim approach to a more durable, long-term model.

Previously, building 'just a feature' was a flawed strategy. Now, an AI feature that replaces a human role (e.g., a receptionist) can command a high enough price to be a viable company wedge, even before it becomes a full product.

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.