AI Is About to Become Too Expensive for Small Businesses

About this episode

AI used to feel like a flat-fee superpower. Now it is starting to behave like electricity: metered, variable, and tied to exactly how you use it. We dig into what that shift means for the people actually trying to run AI in the real world, from small businesses building their first workflows to sales, ops, and dev teams pushing tools like Microsoft Copilot, ChatGPT, Claude, and agents into daily work.

Josh unpacks the move from per-seat pricing to usage-based billing, where cost is driven by tokens, context retrieval, tool calls, and how long a model “thinks” behind the scenes. That change forces a new set of business questions: which workflows burn the most tokens, when premium reasoning is worth it, and how to stop paying for AI habits that feel productive but do not produce ROI. We also talk about the uncomfortable incentives that come with token-based revenue models and why customers need clearer visibility into what they are being billed for.

Then we zoom out to the market dynamics: if the best models become scarce at scale, enterprises with huge contracts may secure better pricing and reserved capacity while smaller teams get caps or slower access. Finally, we make it practical with an AI FinOps mindset: map workflows, set internal model tiers, put guardrails around agents, train teams to prompt efficiently, and tie spend to measurable outcomes.

If you are trying to budget for AI, prove business value, or keep your AI bill from quietly exploding, this one will give you a clear framework. Subscribe, share this with a teammate who owns the budget, and leave a review with your biggest question about AI costs.

Josh's LinkedIn