Why AI Keeps Getting Cheaper for You

AI costs are falling in two directions at once. Cloud inference, the cost of running a model via API, dropped more than 280 times in two years. And this spring, NVIDIA announced hardware that runs capable AI workloads on a machine you own, inside your office, without a cloud account, starting around $1,800. If you are still evaluating whether to start, those two facts just changed the math. Not at the margins. Substantially.

AI Just Became Infrastructure

Electricity once required a generator on site. Then the grid made it infrastructure. Computing required your own servers. Then the cloud made it infrastructure. Each time a technology crossed that line, the question stopped being "whether" and became "how soon."

AI is in that transition now. From 2020 to 2024, serious AI meant a cloud account and someone else's server. Your data left your building. For any firm with privileged client files, that was a hard constraint. If you want to understand what agentic AI actually means for professional services, we covered what agents do for an advisory practice separately.

NVIDIA's Computex 2026 announcements moved the line. The RTX Spark is a chip going into laptops and compact Windows desktops this fall, running capable AI models locally without a cloud account, with estimated starting prices around $1,800. The DGX Station is a deskside machine that runs models up to a trillion parameters inside your office. Both run on hardware you own. Nothing crosses a third-party server.

The tech conversation focused on speed comparisons to cloud APIs. That is the wrong frame for a law firm or a family office. The relevant question is not which option runs fastest. It is which option keeps your client files inside your building. That question now has a real answer at a price professional firms can consider.

The Price Fell. The Cost of Waiting Did Not.

The standard argument for holding off goes like this: the tools are still improving and prices are still falling, so a better deal is coming. That argument was stronger in 2023. It is mostly wrong now.

Epoch AI's research on LLM inference pricing shows costs declined from roughly $20 per million tokens in late 2022 to about $0.07 by late 2024. The big drop already happened. The incremental savings from waiting another twelve months are real but modest. They are not what is holding any firm back.

What actually costs more every quarter is harder to put on an invoice. Firms that started building AI into their operations in 2024 now have two years of compounded knowledge: which tasks AI handles reliably in their specific context, how to structure the work, where the human review step matters most. Their staff has built the habits. None of that is available for purchase in 2027. You build it by running the work, and firms that waited are starting from zero on a foundation their competitors already poured.

I have seen security concerns kill more AI projects than actual incidents. Waiting for cheaper is the same pattern under a different name. It feels rational while the opportunity cost runs quietly in the background.

What This Means for North Shore Law Firms

For law firms, the hesitation has had a specific shape. ABA Model Rule 1.6 requires reasonable steps to protect client information. Sending privileged matter files to a third-party cloud server raises a question most attorneys would rather not answer. Courts are already fining attorneys for AI missteps, which makes the data question harder to defer.

On-premise hardware settles that by architecture. When the model runs on a machine in your office, the file is processed on site. Nothing crosses an outside server. The short version: the privilege concern disappears not because a vendor promised something, but because the data never moves.

The economics and the data obligation now point in the same direction. North Shore law firms are already adopting AI faster than most expect. Both say start.

Three Steps to Get Moving

AI is not waiting to become affordable. It already is. The tool is the cheap part. What gets more expensive every quarter is the gap between what your operation could be doing and what it is actually doing. The firms that started early are not ahead because they had better technology. They are ahead because they compounded two years of workflow knowledge that does not transfer and is not for sale.

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