The AI cost scare is a lie. Here's the receipt.

EVERYONE IS SCARED OF THE WRONG NUMBER.

Open any business publication right now and you will find a story about how much companies are spending on AI. Millions. Tens of millions. The implication is always the same: AI is expensive, AI is risky, AI is something you need a budget committee and a six-month roadmap to approach.

That narrative is doing real damage to real businesses.

This week I built a complete sales intelligence tool for my son in a single Claude session. He is a medical device rep. After every physician call, he used to sit in a parking lot and try to remember what happened, who said what, and what he needed to do next before the details faded.

Now he opens one app on his phone. He taps a mic button and talks. The app listens to his voice, processes the call in real time, extracts every action item, builds a Salesforce-ready checklist with location, date, and every attendee by name and role, and when the call involves referrals to his client clinic, it writes a clean professional report addressed to them. Everything is stored. He can search his history by doctor, by account, by date. He can ask "what did I discuss with Dr. Bagri last month" in plain language and get a real answer.

You can see it here: [APP LINK] Read ONLY please.

The whole thing took less than two hours to build. Backend, database, deployment, mobile UI, voice input, query system - all of it.

Now here is the number I want you to sit with.

At 12 calls a week, his app will cost $0.18 a month in API usage. That is not a typo. Eighteen cents.

The tools that do a fraction of what this app does - Otter.ai, Fireflies, any of the transcription-plus-summary platforms - run $20 to $40 a month per user before you even talk about CRM integration, custom outputs, or the ability to query your own history in plain English. And none of them are built around how he actually works.

The reason the "AI is expensive" story keeps circulating is that most AI implementations are built wrong. They are built for capability demonstrations, not for specific jobs. They use heavyweight models on lightweight tasks. They process everything whether it needs processing or not.

The right way to build AI is to start with the output you need, work backward to the smallest model that produces it reliably, and charge only for the tokens that matter. Every dollar of waste in an AI system is a decision someone made without asking that question first.

His app uses Haiku, Anthropic's most efficient current model, because extracting structured data from a voice transcript does not require a frontier model. It requires precision and speed. Haiku delivers both at a fraction of the cost. The intelligence in the system is in the prompt design, not the model tier.

That is the thinking most people are missing. And it is exactly what the expensive AI vendors do not want you to understand.

If you have a workflow that is slow, manual, or just frustrating enough that your team complains about it at breakfast, I want to hear about it. Not to sell you something. To tell you honestly whether AI can fix it, what it would actually cost, and what it would look like when it works.

30 minutes. No agenda. You describe the pain.

Book a call: BOOK NOW

Wade Kerzie Founder, Kerzie AI Solutions McKinney, TX