The future of AI will involve diverse models that allow businesses and creators to customize their AI agents for specific tasks and interactions.

The development of AI models should include layers of specialization for industries and specific companies to meet unique needs.

If a founder is working on something too general and not solving a specific user need, they risk being outcompeted by existing models.

The rules of AI businesses are being rewritten; for instance, OpenAI's investment in training models like GPT-4 was speculative but necessary for innovation.

A surprising number of AI-native products fail to expose the necessary application primitives to the models, limiting their effectiveness.

The demand for fine-tuning open-source models is driven by the need for cost-effective alternatives to expensive models like OpenAI's ChatGPT, but companies must also provide additional value beyond just lower costs to retain customers.

Different AI models have varying trade-offs, costs, and capabilities, leading enterprises to choose the right tool for specific tasks.

We realized that to scale our models, we needed to transition from pure research to a business model that could sustain the investment required for large AI models.

As AI becomes more integrated into various applications, we need to optimize for specific internal use cases, which may lead us to use other models if they better serve those needs.