Ep 872: AI Cost Control 101: Why Your Chatbot Bill Is Becoming a Board-Level Problem (Start Here Series Vol 31)

AI’s all-you-can-eat era is ending. 🍲 For years, one subscription felt like unlimited access to frontier models. But that business model for the AI labs apparently breaks when agents can now run for days, use tools, retry work and burn through tokens. And with Anthropic's powerful Fable 5 model exiting subscription tiers today and moving to API only pricing, it's as imperative of a time as ever to figure out your AI spend strategy.  Frontier AI is becoming a metered utility. On today's show, we teach you how to deal with it.  AI Cost Control 101: Why Your Chatbot Bill Is Becoming a Board-Level Problem -- An Everyday AI Chat with Jordan Wilson 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: End of Unlimited AI Subscription Plans Anthropic Fable Five Subscription Removal Copilot and Grok Switching to Pay-Per-Use Enterprise AI Cost Control Challenges Token Consumption in Agentic AI Models Board-Level AI Spending Concerns Strategies for AI Spend Optimization Fine-Tuning and Multi-Model Routing Solutions Seven-Step AI Cost Reduction Playbook Timestamps: 00:00 Rising AI costs and usage 05:18 AI service cost challenges 10:18 Cost of AI and OpenAI's Future 14:18 Chatbot costs becoming a big issue 15:10 Automating work with desktop agents 19:26 Hidden costs of automation loops 24:13 The future of model mixtures 25:16 Microsoft Foundry's fine-tuning service 31:20 Fine tuning AI models 32:13 Closing thoughts on AI future Keywords:  AI cost control, chatbot bill, AI spend, token efficiency, metered AI, agentic models, AI subscription plans, Fable Five, Anthropic, API pricing, OpenAI, GPT-5.6, Copilot cowork, GitHub Copilot, Google Gemini, AI credits, usage limits, credit-based system, Grok, NeoCloud, board-level AI concerns, token maxing, spending limits, enterprise AI, SMB advantage, API token pricing, token-based billing, model routing, open source AI models, GLM 5.2, Kimmy 2.7, caching, difficulty-based routing, fine-tuning models, Microsoft Foundry, fine-tuning as a service, Thinking Machines Lab, tuned specialists, mixture of models, AI routers, perplexity, Merge, spend routers, AI budgeting, overage alerts, default model selection, AI model compaction, automation, human-in-the-loop AI, context length limits, token burn rate, Jovan’s paradox, AI tool escalation Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)