AI agent pricing is confusing because token costs keep falling while your agent bill keeps climbing, and this video explains why.
We walk through what actually happened to model prices since 2022, why the cheap floor fell through, and why that still tells you almost nothing about what an agent will cost you. The thing you actually run is a loop that keeps reading context, running code, watching it fail, and trying again, and that loop is the line item nobody warns you about. By the end you will know why the same task can cost a few cents or a few dollars depending only on how long the loop runs.
? What you'll learn:
1️⃣ Why OpenAI model prices fell so far since 2022, and what the cheapest useful models actually cost now
2️⃣ How open models like Llama on Groq change the cost curve, and why cheap still does not mean free
3️⃣ Why an agent runs as a loop rather than a single API call, and how that changes the whole bill
4️⃣ How real products price agents: per seat, per resolved task, or per usage, not per token
5️⃣ Why a support agent and a coding agent can run the same idea at wildly different cost
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⏰ Timestamps:
00:00 - The cost of AI is falling
01:15 - Open models has a different cost curve
01:50 - Why agents run as a loop
02:38 - Why agents price per seat, task, or usage
03:14 - The loop is what you pay for
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