More tokens = more ROI, right? 🤔Maybe. But probably not. Maybe one of the weirdest AI trends that has oddly stuck in 2026 is tokenmaxxing -- the practice of individuals and companies racing to use as many AI tokens as possible and equating it with business progress. Reality check: token efficiency is the real rage. So, how do you measure token efficiency and how can your company avoid the cost pitfalls of tokenmaxxing? Join us as we break it down.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [email protected] with Jordan on LinkedInTopics Covered in This Episode:AI Token Maxing: Rise and FallDefining AI Tokens and TokenizationFour Main Types of AI Token UsageAI Agentic Loops and Token ConsumptionCorporate Token Leaderboards and Meta ExampleRisks of Unmonitored Token Burn in EnterprisesToken Subsidies and AI Pricing TrendsMeasuring Token Efficiency versus Token VolumeBenchmarking Models: Cost per Intelligence OutputShifting from Model Selection to Harness EfficiencyBest Practices for Enterprise Token OptimizationMonitoring AI Agents for Token and Cost ControlTimestamps:00:00 Rethinking AI token usage05:46 Token usage misconceptions in companies09:15 Using token incentives10:48 Tech companies adding usage limits13:21 Understanding model token usage17:16 Agentic models and tool use22:21 Experimenting with token efficiency25:18 Measuring AI's economic impact29:11 Comparing AI intelligence and cost30:36 Cost concerns with Anthropics' AI models35:20 Importance of token efficiency38:03 Takeaway from Microsoft CTO chatKeywords: token maxing, token efficiency, AI token usage, AI tokens, token consumption, large language models, agentic loops, AI spend, token cost, model subsidies, subsidized AI plans, enterprise AI strategy, context window, prompt engineering, API usage limits, output tokens, input tokens, reasoning tokens, tool use tokens, scheduling agents, agentic AI, model harness, Claude Opus, OpenAI GPT-5.5, Gemini 3.1 Pro, Anthropic models, artificial analysis intelligence score, DeepSuite benchmark, cost per intelligence, modular AI architecture, API overages, context window size, scheduled agents, human-in-the-loop, expert-driven loop, output monitoring, benchmarking AI models, economic value from AI, efficiency metrics, measuring ROI, AI model performance, cost per output, chain of thought, AI tool integration, AI cost management, long-running agents, dynamic data integration.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist.

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