𝐀 𝐧𝐞𝐰 𝐦𝐨𝐝𝐞𝐥 𝐬𝐡𝐢𝐩𝐩𝐞𝐝, 𝐚𝐧𝐝 𝐰𝐢𝐭𝐡𝐢𝐧 𝐡𝐨𝐮𝐫𝐬 𝐩𝐞𝐨𝐩𝐥𝐞 𝐡𝐚𝐝 𝐛𝐮𝐫𝐧𝐞𝐝 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐡𝐞𝐢𝐫 𝐭𝐨𝐩 𝐭𝐢𝐞𝐫 𝐩𝐥𝐚𝐧. 𝐓𝐡𝐞 𝐜𝐚𝐮𝐬𝐞 𝐰𝐚𝐬 𝐧𝐨𝐭 𝐚 𝐡𝐮𝐧𝐠𝐫𝐢𝐞𝐫 𝐦𝐨𝐝𝐞𝐥. 𝐈𝐭 𝐰𝐚𝐬 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥 𝐪𝐮𝐢𝐞𝐭𝐥𝐲 𝐬𝐩𝐢𝐧𝐧𝐢𝐧𝐠 𝐮𝐩 𝐭𝐞𝐧 𝐨𝐫 𝐦𝐨𝐫𝐞 𝐜𝐨𝐩𝐢𝐞𝐬 𝐨𝐟 𝐢𝐭𝐬𝐞𝐥𝐟, 𝐞𝐚𝐜𝐡 𝐝𝐞𝐟𝐚𝐮𝐥𝐭𝐢𝐧𝐠 𝐭𝐨 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐞𝐱𝐩𝐞𝐧𝐬𝐢𝐯𝐞 𝐨𝐩𝐭𝐢𝐨𝐧 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞. 😳
Nobody overspent. Something they delegated to did it for them, and nobody was asked.
This week on 𝐓𝐡𝐞 𝐀𝐈 𝐁𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧, Quentin and Stark sharpen the theme: 𝐓𝐡𝐞 𝐬𝐨𝐜𝐢𝐚𝐥 𝐥𝐚𝐲𝐞𝐫 𝐨𝐟 𝐀𝐈.
For two years the mental model was a meter that ran while you typed. Now there is a second meter, running while nobody is typing, started by something you delegated to. Your agent is hiring, and the invoices come to you.
Three signals your bill is now written by your tools: 📊
📊 A single task quietly launched 𝟏𝟎+ sub agents, each on the priciest model, enough to exhaust a top tier plan alone
📊 Two credible testers measured the same model the same week and got opposite answers: one found tasks 𝟕𝟎% more token heavy, another found them cheaper
📊 A new video analysis approach cut token use up to 𝟖𝟖%, putting long footage nobody watched within reach of a real question
The rule underneath: a cost per task number is a statement about the task, not the model. If you have not measured it on your own work, you do not know the cost.
Your move:
🔴 Pick the one AI job your business runs most often.
🔴 Run it twice, once on the cheapest model you have and once on the most expensive.
🔴 Compare the cost of a finished, usable output, not the cost per token. One job, not your whole stack.
Ten copies of the priciest model, working hard on a task nobody checked. Check the task. 🎙️
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Disclaimer: The information shared in this podcast is for general informational purposes only and should not be considered as direct financial or business advice. Always consult with a qualified professional for advice specific to your situation. This is an Independent commentary. Third-party excerpts are credited on screen or in the show notes. Some voices in this program are AI-generated.