{"id":325,"date":"2026-06-29T08:12:02","date_gmt":"2026-06-29T00:12:02","guid":{"rendered":"https:\/\/aidashxp.com\/tokenmaxxing-era-ends\/"},"modified":"2026-06-29T08:12:02","modified_gmt":"2026-06-29T00:12:02","slug":"tokenmaxxing-era-ends","status":"publish","type":"post","link":"https:\/\/aidashxp.com\/en\/tokenmaxxing-era-ends\/","title":{"rendered":"The end of Tokenmaxxing: From Uber to Lindy, enterprise AI spending is undergoing a historic turn"},"content":{"rendered":"

What would you do if you were the CEO of an AI startup and found that your company\u2019s annual AI budget was burned down in 4 months? Uber\u2019s answer is: Immediately implement an AI consumption limit of US$1,500 per person per month, and any excess will need to be approved step by step. Behind this is a trend that is sweeping the entire AI industry\u2014\u2014The End of the Tokenmaxxing Era<\/strong>.<\/p>\n\n\n\n

What is Tokenmaxxing?<\/h2>\n\n\n\n

The term \"Tokenmaxxing\" comes from the AI \u200b\u200bcircle's ridicule of new behavioral models: companies use cutting-edge models as much as possible (such asClaude<\/a>, GPT-5.5) handles all tasks without considering the cost limit, because \"AI is the future, and more money means more investment.\" This strategy has driven exponential revenue growth for Anthropic and OpenAI, turning them into trillion-dollar valuation companies.<\/p>\n\n\n\n

But now, this pattern is dramatically reversing. According to a CNBC report on June 26, companies have begun to implement AI cost control measures on a large scale. The reason is simple: API fees for cutting-edge models are already beyond the reach of many companies.<\/p>\n\n\n\n

Lindy\u2019s case: 100% evacuationClaude<\/a>, costs dropped dramatically<\/h2>\n\n\n\n

The most extreme case comes from the AI \u200b\u200bstartup Lindy. CEO Flo Crivello decides to divert 100% of the company\u2019s API traffic from AnthropicClaude<\/a>Model migrated toDeepSeek<\/a> V4-Pro. result?Cost curve 'collapses like a free fall'<\/strong>, saving millions of dollars every year.<\/p>\n\n\n\n

\"We did it, and you can see the cost curve falling straight down - just like hitting the ground,\" Crivello said in an interview. \"This is critical to the survival of the company.\" Lindy is a 25-person AI startup, and AI spending still exceeds labor costs, butDeepSeek<\/a>Migration has taken the money from unsustainable to manageable.<\/p>\n\n\n\n

certainly,DeepSeek<\/a> V4-Pro does not haveClaude<\/a>security controls, constitutional AI training, corporate governance functions, or U.S. origin assurance. For Fortune 500 companies with compliance requirements, this option is not available. But Lindy\u2019s case illustrates a key fact:The cost gap has become so large that decisions must be made at the CEO level<\/strong>.<\/p>\n\n\n\n

Lessons from Uber: Burning through the entire year\u2019s budget in 4 months<\/h2>\n\n\n\n

The situation at Uber is equally shocking. The company\u2019s CTO revealed,Uber burned through its entire AI budget in 4 months<\/strong>. To this end, Uber has implemented an AI usage quota of US$1,500 per person per month. If you need a higher quota, you must submit an upgrade application. This is an extremely strong signal for Uber, which is known for its \"efficiency priority\".<\/p>\n\n\n\n

\"Consultant Model\" Technology: Spend 1\/10 of the money and do 90% of the work<\/h2>\n\n\n\n

Analyst Gil Luria (D.A. Davidson) said bluntly: \"Anthropic and OpenAI are currently growing at the fastest rates they\u2019ve ever seen, and that\u2019s mostly dictated by basic math<\/strong>\u2014\u2014Enterprises cannot exponentially increase AI spending forever. \"<\/p>\n\n\n\n

The way out lies in \"Advisor Model\" technology: routing most API workloads to cheap open source models (such asDeepSeek<\/a> V4-Pro $0.44\/$0.87 per million tokens, or GLM-5.2 $1.40\/$4.40), only upgrade tasks that truly require cutting-edge capabilities toClaude<\/a> Opus 4.8 or GPT-5.6. It is estimated thatThis strategy can reduce effective cost per token by 70-90%<\/strong>.<\/p>\n\n\n\n

Implications for users<\/h2>\n\n\n\n

The end of Tokenmaxxing is more than just a corporate finance story. Its underlying signal is that the AI \u200b\u200bindustry is moving from the barbaric growth period of \"burning money as long as it can run\" to the mature competition period of \"cost-effectiveness\". For ordinary users and small and medium-sized enterprises, this means two things:<\/p>\n\n\n\n