💬
🤖
AI Support
24/7 online · instant AI reply
✕
← Back
45m ago · Decrypt

TokenMaxxing: How to Get the Most Out of Your AI Models

By Jose Antonio Lanz Edited by Guillermo Jimenez Oct 17, 2026Oct 17, 2026 Meta employees burned through 60 trillion AI tokens in 30 days earlier this year, chasing spots on an internal leaderboard nicknamed "Claudeonomics." Tech columnist Gergely Orosz >estimated the tab at roughly $900 million at Anthropic's list prices, though Meta likely pays far less. Bain & Company consultant Jue Wang sees a quieter version of that waste everywhere: "So many companies, so many users, default to using Opus for everything, including generating emails," she >told the Associated Press this summer, referring to Anthropic's high-end models. If you do the same thing, you're hiring Albert Einstein to calculate your change at a MacDonald’s. Every word you type into ChatGPT or Claude, and every word it writes back, is metered in >tokens, chunks of text that average about three-quarters of a word. On a $20 subscription that meter decides how fast you hit your usage limit. On the API, the pay-per-token access developers use to plug models into their own apps, it decides the bill. Here's Anthropic's current >price list, per million tokens: Haiku's rate applies to prompts under 100,000 tokens; longer prompts cost five times as much. The top model costs 100 times more than the smallest, and OpenAI's >own lineup runs the same spread, from $10 to $0.10 per million input tokens. Those per-token prices add up fast for the labs—and you: Anthropic's revenue run rate went from about $9 billion at the end of 2025 to more than >$47 billion by May, while OpenAI is fighting a

📌 Recommended Exchanges

OKX | Binance | Bybit | Bitget | Gate.io


AI Analysis:

🦊 Nova's Take Meta burning 60 trillion AI tokens in 30 days (~$900M at list prices) shows how token consumption has become a status symbol rather than a cost discipline, and the "Claudeonomics" leaderboard is pure corporate vanity spending. This is a story about AI compute demand, not crypto — but it signals that enterprise AI token budgets are elastic and wildly inefficient. 📊 Market Impact No direct coin impact, since no token is named, but the underlying theme — runaway AI compute demand — reinforces the long-term narrative behind AI-adjacent crypto (TAO, FET, RENDER) as inference costs stay elevated. Short-term price action stays neutral; this is a sentiment footnote, not a catalyst. 💡 Trading Advice Don't trade off this headline — it's a tech-industry anecdote, not a market event. If you want AI-narrative exposure, treat it as slow-burn context for infrastructure tokens and size positions small, with stops in place. 🦊 *(Note: this news sits outside core crypto coverage, so treat the above as narrative color rather than a trading signal — always control risk.)*

Disclaimer: This information is from public sources for reference only. Traceless does not guarantee accuracy.

💬 0
🏪 Strategies

💬 Comments

🔑 Login to leave a comment
No comments yet. Be the first!