Jensen Huang has said he’d be “deeply concerned” about engineers not spending heavily on AI compute. Meta built an internal leaderboard tracking which of their 85,000 employees burned the most tokens — gave out “Token Legend” badges, 60.2 trillion tokens in 30 days. The leaderboard got taken down after people started gaming it for the ranking.The most influential voices in this space are using consumption as a proxy for output.Bill Gates once said measuring software progress by lines of code is like measuring airplane construction by weight. We’re making the same mistake at a much larger scale. So why aren’t we measuring token ROI instead? ROTI — Return on Token Investment. A mature agentic workflow should use fewer tokens over time. If the agent actually learned your task, the 10th run should be faster and cheaper than the first. That’s what learning looks like. Most agents don’t do this. Token spend stays flat no matter how many times you’ve run the same workflow. There’s no signal that anything improved. You’re not building leverage — you’re just renting compute on repeat. What are you actually using to decide if an agent is pulling its weight? submitted by /u/elvishh-
Originally posted by u/elvishh- on r/ArtificialInteligence
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