Cheap Models Just Changed the Real Work

Morning. Damian built an AI clone to handle the Monday briefing so the original can pretend he delegated brilliantly. Honestly... fair. DayLift Signal. AI-curated. Five minutes.

Model cost is now CHEAP enough that price is no longer your excuse. I read through the morning pile — launches, benchmarks, vendor chest-thumping. For U.S. tax and financial pros, one story matters... the cost floor just dropped again.

OpenAI pushed GPT-six Sol and Luna down to roughly two dollars and ten cents per million input tokens, and Anthropic put Claude Opus five point five at four dollars in and twenty dollars out. That does NOT mean you should rush to build things. It means high-volume triage, drafting, and internal research just got cheaper than the human review habits wrapped around them.

For the Solo or small tax and accounting practice, this is a capacity opening. Organizer extraction, notice summaries, bookkeeping cleanup notes, internal research drafts — jobs that felt too expensive to automate a month ago may now make sense. For the Multi-person accounting and advisory firm, this is more serious. Once a cheaper model becomes the DEFAULT first pass, realization and review design change with it. You're still paying premium-model rates for work your clients would never knowingly fund. Independent financial advisor, R I A, or wealth manager — partial skip today. The same economics matter, but your sharper constraint is still S E C and FINRA supervision on client-facing output. Smart move: benchmark one low-risk workflow this week before you standardize a platform, a model, or an integration.

Here is the lever. This one's for solo operators first — and team leads can run the firm version.

Pick one repeatable workflow. Good candidates are organizer-field extraction from P D F files or internal research summaries. Build a twenty-case test set with de-identified files. Run the same cases through GPT-six Luna, GPT-six Sol, and Claude Opus five point five inside an approved business plan or controlled A P I setup. Score four things. Accuracy. Review time. Citation quality. Cost per case. First step today: make the scorecard before anyone touches production. Keep taxpayer and financial-account data out of consumer AI tools.

Here is my honest take... most firms are still putting premium gasoline in a lawn mower. I mean that very literally. The smartest model in the market feels professional, safe, adult. But if a cheaper model clears the bar on routine production, paying up on every task is not discipline — it is drift. Expensive models should earn their place.

This is the trap I see in mid-sized teams. A new model gets cheaper, someone opens an account, staff get told to try it on live work by Friday, and now prompts, subscriptions, and outputs are everywhere. It feels fast... until nobody can prove what improved.

Better frame: keep a small benchmark set, define pass-fail rules, and give each model one approved job. Total workflow cost matters — including review time, controls, retraining, and switching friction. If you cannot compare the full workflow... you are NOT evaluating the model.

So here is the question. Which AI workflow in your firm will you benchmark this week, and what proof would make you buy, build, or ignore it?