The AI co-pilot concept may be premature; companies need to find a genuine product-market fit rather than just iterating on a user interface that doesn't meet real needs.

Many startups are focusing on selling tools for AI applications, but the reality is that many potential customers are still figuring out how to effectively use AI, leading to a disconnect between product offerings and actual market needs.

Fine-tuning Copilot with proprietary data will give companies a competitive edge over generic AI solutions. The sustainable advantage lies in obtaining new samples to apply reasoning models with your data, enabling reinforcement learning in real-world applications.

We were inspired by the success of GitHub Copilot, which demonstrated that useful AI products could be created, motivating us to pursue our own ambitious ideas in AI.

Many AI demos look impressive, but the actual products often fail to deliver and aren't widely used in real-world applications.

In the AI era, many teams excel in technology but struggle with achieving product-market fit, often overlooking what users truly want.

AI as a general-purpose technology presents numerous opportunities, and our focus should be on building concrete use cases to harness its potential effectively.

In AI, if you can do X, users will want to do Y, which creates a challenge because you can't predict how users will interact with your product.

There's a distinction between using AI effectively to enhance products and merely claiming to use AI to attract investment without real benefits to customers.