Token burn is often celebrated as a sign of progress in AI, but it’s not a reliable KPI. Burning more tokens doesn’t always mean you’re getting more done or creating more value. In fact, it can lead to wasted resources and unproductive outputs. This episode explores why token usage should be carefully considered and not treated as a badge of honor. Just like in Formula One, where speed and efficiency matter, in AI, we should focus on meaningful outcomes, not just high token consumption. The host shares examples of how inefficient token use can lead to poor results and compares it to outdated metrics like lines of code. The key takeaway is to think critically about what you’re measuring and why. Finally, the episode wraps up with practical advice on choosing the right model for the task and avoiding the trap of AI hype and meaningless metrics.
