As intelligence becomes a commodity, startups need to consider how much software is necessary on top of their value proposition to remain competitive.

Enterprises are now more focused on the value AI can deliver rather than the specific models or tools being used, seeking stable and reliable solutions.

As AI models improve, the expectation is that they will converge in quality and pricing, making it essential for startups to build software that leverages this intelligence effectively.

The challenge in AI lies in balancing supply and demand; while we invest in building models, we must also focus on how to effectively utilize these models for economic and mission advantages.

The commoditization of AI models is inevitable, but the enduring value will come from a small number of providers who can deliver highly personalized and context-aware models.

The demand for AI solutions is already present, so startups just need to deliver products that work effectively, rather than focusing heavily on sales.

The rapid advancements in AI technology are leading to a divergence in strategies among companies, with some focusing on self-improving AI while others prioritize consumer-oriented products.

There's a specialization in AI models, where reasoning models that require more compute time are becoming popular for tasks like math and coding, while for consumer products, latency and intelligence per cost are prioritized.

Ultimately, success in AI relies on the ability to effectively stitch together existing models into products that users genuinely love, rather than reinventing the wheel.