When developing internal AI tools, adopt a 'fail fast' mantra. Many use cases fail not because the idea is bad, but because the underlying models aren't yet capable. It's critical to regularly revisit these failed projects, as rapid advancements in AI can quickly make a previously unfeasible idea viable.

The exponential improvement of AI models means product development must target future capabilities. Leaders should anticipate that problems taking months to solve will soon be trivial, and build roadmaps that assume a 6-12 month leap in technology.

To create a breakthrough AI product, design its capabilities around the projected power of models six months out. This means accepting poor initial performance, but ensures you'll be perfectly positioned when more capable models are released.

Users frequently write off an AI's ability to perform a task after a single failure. However, with models improving dramatically every few months, what was impossible yesterday may be trivial today. This "capability blindness" prevents users from unlocking new value.

In a rapidly evolving field like AI, waiting for mature tools is a mistake. The correct strategy is to invest now, assuming that capabilities that are almost working today will be fully functional tomorrow due to exponential, compounding progress.

AI models improve in significant step-changes monthly, making a user's prior experience an unreliable guide. Users must adopt a "beginner mindset" and continually re-test tasks that the AI previously failed at to fully leverage its evolving capabilities.

An AI tool's inability to perform a task a month ago doesn't mean it can't today. The guest notes Copilot went from producing useless spreadsheet templates to fully functional models in months. Users should periodically re-test tools on previously failed tasks to leverage rapid, often unannounced, improvements.

When developing AI-powered tools, don't be constrained by current model limitations. Given the exponential improvement curve, design your product for the capabilities you anticipate models will have in six months. This ensures your product is perfectly timed to shine when the underlying tech catches up.

In the rapidly advancing field of AI, building products around current model limitations is a losing strategy. The most successful AI startups anticipate the trajectory of model improvements, creating experiences that seem 80% complete today but become magical once future models unlock their full potential.