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

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Topics Covered in This Episode:

1. End of Unlimited AI Subscription Plans
2. Anthropic Fable Five Subscription Removal
3. Copilot and Grok Switching to Pay-Per-Use
4. Enterprise AI Cost Control Challenges
5. Token Consumption in Agentic AI Models
6. Board-Level AI Spending Concerns
7. Strategies for AI Spend Optimization
8. Fine-Tuning and Multi-Model Routing Solutions
9. 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

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