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 newsletter More on this Episode: Episode Page Today'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 lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics 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 Control Timestamps: 00:00 Rethinking AI token usage 05:46 Token usage misconceptions in companies 09:15 Using token incentives 10:48 Tech companies adding usage limits 13:21 Understanding model token usage 17:16 Agentic models and tool use 22:21 Experimenting with token efficiency 25:18 Measuring AI's economic impact 29:11 Comparing AI intelligence and cost 30:36 Cost concerns with Anthropics' AI models 35:20 Importance of token efficiency 38:03 Takeaway from Microsoft CTO chat Keywords: 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)
