Better AI Models, Better Startups

Harge says stronger models can improve a startup simply by switching to a newer model, while added capabilities such as coding and structured JSON output make it easier to build useful products. He argues that model progress can let applications handle tasks that were difficult to solve with prompting alone.

Gemini 1.5’s million-token context window raises the question of whether it could make retrieval-augmented generation obsolete. The panel says retrieval still matters for privacy, precise recall, permissions, and audit logs, especially in enterprise settings. Gary compares retrieval layers to caches and databases, which remain useful even as memory gets faster and larger.

The panel compares AI startups’ concerns about OpenAI to earlier worries that Google would copy startups. They suggest avoiding products that look like obvious extensions of a general-purpose assistant, while pursuing specialized workflows, vertical data, and less flashy tasks. Examples include construction permit workflows, fintech compliance, and B2B software that requires detailed customer feedback and sales.

The speakers describe AI applications in identity checks, bank compliance, and payments as ways to automate labor intensive work. Better models can support premium features and upsells as products do more for customers. One speaker says YC companies in the prior batch grew from $6 million in annual revenue to more than $30 million over three or four months.

For consumer AI, the panel points to products that incumbents may avoid because of legal or public relations risk, including AI companions and tools for making movies with familiar characters. They note that Character.AI has strong user retention and that some people spend hours a day with such products. The discussion closes with excitement about expressive speech, live translation, lower model costs, and possible uses in robotics.