July Repriced Intelligence. Most Family Offices Need To Reprice Their Thinking
Inkling, Kimi K3 and Jensen Huang's first post on X, and what a fast-moving, (increasingly open) model market means for how an office buys.
A barrel of crude went for about $81 last week. On CNBC twelve days ago, Chamath Palihapitiya priced a second kind of barrel: a million tokens, which he calls a barrel of intelligence. On his tally, a good model from Anthropic or OpenAI runs roughly $26 a barrel and the newest one around $56. Elon will sell you a barrel for a dollar. Zuck is heading for a dollar fifty, Google is at a dollar, and the Chinese labs will do it for fifty cents.
The math holds against the current published pricing from these firms. Claude Opus 5 sits at $5 per million tokens in and $25 out, Fable 5 at $10 and $50, GPT-5.5 at $5 and $30. Same input, a spread of roughly a hundred times, depending which pump you happened to pull up to.
And when used correctly, nobody buying should be able to tell you which barrel their office is burning on which job.
The opportunistic position that wasn’t sized
Consider how the same office would handle a commitment of comparable stickiness on the investment side. There would be a memo. A sizing discussion, a view on the exit, a line in the minutes. Somebody would ask what happens if the manager doubles fees or gets acquired.
Here, none of that. Somebody took out a subscription because it demoed well, a workflow formed around it, and within a year the workflow is how the office works. So the office now holds a concentrated, illiquid, unhedged position in a single supplier of a commodity input, and it took that position through the expense account.
The carrying cost is often less visible than the initial pricetag. Since usage is measured, the operational expenses will increase with activity rather than with the value generated, and invoices typically present a single total amount instead of breaking down spending by workflow. If you ask five different people about the monthly cost of running the reporting pipeline, you are likely to receive five puzzled shrugs as no one has established a way to measure it.
Chamath anticipates that a public company might miss earnings expectations by a few cents, which could be traced back to minor expenses that went unnoticed. In contrast, a family office doesn’t have an earnings call to spark such discussions. Instead, it faces a supplier it cannot sever ties with without incurring costs twice, once to dismantle the existing arrangement and again to rebuild it.
AI for family offices. Interested in what AI transformation looks like in practice? We have a framework, and we work with offices at different stages to help them focus on what’s important right now for AI readiness and over time, transformation. Head over to Simple’s framework.
Things are opening up faster than before
Three things happened between the 15th and the 24th.
Thinking Machines released Inkling, Mira Murati’s first model, with the full weights published. It is built to be customised rather than to win a leaderboard, and the company earns its money on Tinker, where you fine-tune it on your own material and walk away holding a portable adapter that belongs to you. Bridgewater did precisely that with financial data and ended up with a small model scoring 84.7% on financial reasoning benchmarks, ahead of the leading proprietary alternatives at under a tenth of the cost.
The next day, Moonshot released Kimi K3, a 2.8 trillion parameters open weight model. According to Arena it outperformed the commercial fronteir labs at a fraction of the cost and what’s more, in blind coding tests many developers preferred it to every American flagship.
Then Jensen Huang made the first post of his life on X, and it was not about chips. He shared an open letter arguing that open models strengthen security and enable sovereignty, and he was careful with the framing: the world needs both frontier closed models and frontier open models. Twenty-five signatories became about fifty inside a day.
But pricing moves fast. The barrel you bid on in the spring has been repriced more than once since, and with pricing moving quickly, large firms are expected to release more complicated models alongside less powerful options at cheaper prices. This trend clearly indicates where the market is heading.
Buy the car before choosing the fuel
Here is the sequence, and it actually works the other way around compared to how most offices have gone about it.
Choose the tools first. The workflows, the systems of record, the places where the work actually happens, selected for the job at hand and for where your data is obliged to sit. Then attach intelligence to them as a component rather than as a foundation.
In practice, this comes down to four things:
Select tools based on the specific work you need to accomplish, rather than solely on their AI features. Almost every vendor offers similar features, but very few align with your unique workflow.
Make sure that each tool will allow you to use a model of your choice, instead of being locked in to the default model it comes with.
Keep all your prompts, context, and corrections in external files that you control. This way, your accumulated insights and decisions will remain intact even if you switch suppliers.
Map and categorize tasks by complexity: use the less expensive models for standard jobs like reconciliation, extraction, and classification. When there’s a need for power, go for the more costly tools. Many engineers claim that for the majority of tasks, non-frontier models are entirely sufficient. For most offices, the latter usually represents a shorter list than what they’re likely using right now.
And measure. Try to understand the cost per workflow, not per person or org.
Chamath let the useful part slip almost in passing: buy the oil and you still need an engine, and the engine has to make you go faster. Barrels of intelligence are interchangeable by design, just like fuel. But you still need the car to drive, use navigation, cruise control, self driving, climate preferences and more.
Barrels of intelligence get used and reprice. The engine is the part you keep.
In a sea of solutions, you have to ask yourself, what problem you want to solve.