Startup Ideas You Can Now Build With AI

Harj compares Mercor with Triplebyte, the recruiting startup he ran. Triplebyte spent years building a labeled dataset and relied on human interviewers, while LLMs let Mercor evaluate candidates from day one and expand beyond software engineers. He suggests looking for marketplaces where AI can take over a human evaluation role or simplify the number of sides involved.

The speakers describe personalized tutoring and grading as work AI can make more accessible. Examples include RevisionDojo’s tailored exam prep and Adexia’s assignment grading agent. They note that when an AI learning product approaches the value of a human tutor, parents may pay more than they would for a low-completion self-study app.

The speakers revisit full-stack startups that combine software with a service, such as Triplebyte’s recruiting operation and Atrium’s law firm. Earlier versions struggled with low gross margins, staffing needs, and operational complexity. They argue that agents could now handle much of the service work, making some full-stack companies operate more like software businesses.

AI agents still need deployment tools, evaluations, and other infrastructure. The speakers recall that many MLOps startups struggled before useful models had enough demand, while Replicate gained traction with image diffusion models and Ollama with Llama. Their examples suggest that infrastructure ideas can be early for years, then become valuable when the underlying technology catches up.

The speakers question whether customer discovery and selling before building should remain the default advice for founders. They propose exploring new AI capabilities directly, using prompts, data, evaluations, ingenuity, and taste to discover useful products. They also observe that many established companies have yet to change their roadmaps or start internal AI projects.