AI Pricing: Why Cost Optimization Is The Wrong Battle

InnovationAI Pricing: Why Cost Optimization Is The Wrong BattleByScott Breitenother,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)May 08, 2026, 08:45am EDTScott Breitenother, Co-founder and CEO of Kilo Code. getty​While the conversation around AI costs swings between two extremes (are we spending too much, or not enough?), leaders are forgetting the only metric that matters: ROI. Venture capitalist Chamath Palihapitiya vented frustration over his software company’s AI costs, which had tripled since November, trending toward $10M annually. At the other end of the spectrum, "tokenmaxxers" are competing on the number of agents they have running, and some companies are celebrating their big spenders.AI cost optimizers want to know: Which pricing model is more predictable, and which tools or models are burning the most tokens? Is consumption-based billing finally going to sink the AI coding tool market? These are questions worth asking, but neither penny-pinching nor throwing money at AI will help you answer the most important one: What are you actually getting back from your AI investment?The pricing debate is a distraction.Seat-based and consumption-based pricing models each have their merits. Fixed subscriptions offer predictability (for now) as they shift cost risk onto providers—and most are absorbing losses on their heaviest users, having bet that inference costs will fall fast enough to catch up. Most platforms are likely losing money on that wager, because the engineers who use AI most intensively keep upgrading to newer, more expensive frontier models rather than staying on the cheaper ones the pricing assumed. if (!window.cnxel) { window.cnxel = {}; window.cnxel.cmd = []; var iframe = document.createElement('iframe'); iframe.style.display = 'none'; iframe.onload = function() { var iframeDoc = iframe.cont...