--- title: 'Too cheap to meter': AI token costs are hitting a... description: Three developers independently used the phrase 'too cheap to meter' this week to describe AI token costs, echoing a 1954 nuclear energy prediction. The core... canonical: https://daily.dev/posts/too-cheap-to-meter-ai-token-costs-are-hitting-a-psychological-floor-pynclmqi0 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: 'Too cheap to meter': AI token costs are hitting a psychological floor | daily.dev og:description: Three developers independently used the phrase 'too cheap to meter' this week to describe AI token costs, echoing a 1954 nuclear energy prediction. The core... og:url: https://daily.dev/posts/too-cheap-to-meter-ai-token-costs-are-hitting-a-psychological-floor-pynclmqi0 og:image: https://api.daily.dev/og/posts/pyNCLMQI0.png og:image:alt: 'Too cheap to meter': AI token costs are hitting a psychological floor og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # 'Too cheap to meter': AI token costs are hitting a psychological floor **[Trends](https://daily.dev/sources/trends)** · 3 min read · 1 upvotes · 0 comments ## Summary Three developers independently used the phrase 'too cheap to meter' this week to describe AI token costs, echoing a 1954 nuclear energy prediction. The core argument is that inference costs have dropped so dramatically that for many use cases the economic constraint has effectively disappeared — shifting the real limit to developer imagination rather than budget. A key sub-claim is that developers are still mentally anchored to 2023-era pricing and throttling their ambitions unnecessarily. The post acknowledges the counterargument: costs that feel negligible at prototype scale can look very different at millions of requests, and the nuclear analogy itself never panned out. ## Content Remember when "intelligence too cheap to meter" sounded like VC cope? It's starting to look literal. GPT-5.6's price cut by 80%, down to $0.20 per million input tokens, and the reaction across AI Twitter has been somewhere between giddy and slightly manic. Thdxr kicked off the round of takes: "we're brushing up against 'too cheap to meter,' this will feel unlimited for a good percentage of people." Yacine backed it up flatly: "it's actually true. it's basically infinite." Omarsar0 is pushing a related but different point, that people are sleeping on token efficiency: "try different harnesses, be more ambitious with these models." The numbers behind the vibes: rohanpaul_ai's chart ranking blended cost per 1M tokens across 26 commercial LLMs shows just how far the floor has dropped, and in a separate post he claims that after GPT-5.6's 10x price drop, token consumption jumped by more than 10x. Classic Jevons paradox, more supply at lower cost drives more total usage, not less revenue. Sequoia's Sonya Huang is fully in the Jevons camp too, and she's not being subtle about it: "Jevons Paradox is a freaking wonderful thing." Her read from the portfolio: gross margins are rising for companies using AI even as usage explodes, and the model companies aren't getting squeezed on price because their cohorts keep improving. "I actually think this is an 'everybody wins' situation," she says, the kind of line that either ages perfectly or becomes a museum piece. Sam Altman is playing the same tune from the supply side. His pitch: OpenAI doesn't need fat margins if usage is big enough. "We can enjoy a modest margin on trillions of dollars of revenue and still afford to train giant models." He's also framing intelligence as an eventual commodity, betting that whoever runs the cheapest, biggest compute fleet wins even if capabilities get copied via distillation. And his token-growth anecdote is doing a lot of work: from one OpenAI employee burning 100,000 tokens a month six years ago ("ludicrous" at the time) to an average user hitting that same number today, with his projection being the average person hits 500 billion tokens a month within another six and a half years. The skeptical framing worth holding onto: rohanpaul_ai notes only 17.8% of the world's working-age population used generative AI at all in Q1 2026. If Jevons holds, the interesting demand shock hasn't even started, it's whatever happens when the other 82% shows up. Everyone quoted here is financially or professionally invested in that story being true, which doesn't make it wrong, just worth remembering while you read the enthusiasm. ## Questions this post answers ### Have AI token costs dropped enough that I don't need to optimize for token usage anymore? For a meaningful chunk of use cases, token costs have fallen to a point where they feel negligible — especially at prototype or low-volume scale. The argument is that developers are still mentally anchored to pricing from roughly 18 months ago and throttling their ambitions unnecessarily. The caveat is real: costs that feel negligible at prototype scale can look very different when running millions of requests. _Developers rethinking their token budgets are tracking inference pricing shifts on daily.dev._ ## Community take How the wider developer community reacted, aggregated from 1 discussion across x (as of 2026-08-13). **TL;DR:** There isn't enough discussion content provided to gauge a meaningful community reaction to this topic. **Sentiment:** 30% positive · 40% mixed · 30% skeptical **By community** - x (mixed): No substantive replies were available to characterize a clear take. **Source threads** - [x](https://x.com/rohanpaul_ai/status/2087838201105002872) · 0 points · 0 comments ## Similar posts on daily.dev - [Google's Gemini 3.5 Flash costs 3x the model it replaced, and the era of cheap AI is ending](https://daily.dev/posts/google-s-gemini-3-5-flash-costs-3x-the-model-it-replaced-and-the-era-of-cheap-ai-is-ending-ochkeykzb) · XDA Developers · 2 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#ai-inference](https://daily.dev/tags/ai-inference) [View this post on daily.dev](https://daily.dev/posts/too-cheap-to-meter-ai-token-costs-are-hitting-a-psychological-floor-pynclmqi0) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"'Too cheap to meter': AI token costs are hitting a psychological floor","url":"https://daily.dev/posts/too-cheap-to-meter-ai-token-costs-are-hitting-a-psychological-floor-pynclmqi0","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/too-cheap-to-meter-ai-token-costs-are-hitting-a-psychological-floor-pynclmqi0"},"datePublished":"2026-08-07T14:05:52.027Z","dateModified":"2026-08-13T23:46:32.873Z","description":"Three developers independently used the phrase 'too cheap to meter' this week to describe AI token costs, echoing a 1954 nuclear energy prediction. 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