Every AI product you or your organization touches is a stack. There’s the application you type into.
Under that, a hosting platform.
Under that, a frontier model.
Each layer has its own terms governing what happens to your data: whether it trains future models, how long it’s retained, what jurisdiction it sits in, who it’s shared with. When you sign up for an AI writing assistant or a meeting transcriber, you’re accepting the entire stack’s terms, sight unseen. Almost nobody reads them all the way down.
My public benefit corporation, Q16, built a platform that does.
Frontier Watch scores AI products on a published six-dimension rubric at every layer of the stack, then composites the result on a weakest-link basis, the same way you’d assess any other supply chain. Twelve major applications and ten frontier-lab consumer products are rated so far, American and Chinese labs on the same ruler. Automated monitors re-check every policy document in the system daily, so when terms quietly change, the score gets flagged for review the same day.
Two findings are worth your attention.
First: in every single product chain we’ve rated, the weakest link is the application, not the lab behind it. The frontier labs’ API terms are actually strong. Anthropic’s enterprise terms score 9.0 on our rubric; OpenAI’s score 8.3. But the consumer apps wrapping those models score in the 1s to 4s, and since the user inherits the worst terms in the chain, that’s the score that matters. The industry’s privacy problem is not where the coverage says it is.
Second: the Chinese labs, measured on identical criteria, span a wide and mostly poor range — DeepSeek at 3.9, Alibaba’s Qwen at 4.1, Moonshot’s Kimi at 2.8, Zhipu’s GLM at 1.4, MiniMax at 1.2. These models are spreading through Western products right now, sometimes several layers down the stack, which is exactly where nobody looks.
All of it is public at watch.q16pbc.com, methodology included.
Now the ask. Independent scrutiny has an old funding problem: the people being scrutinized are the ones with the money. Q16 takes nothing from AI companies. We’re funded by me and by early subscribers, and subscription revenue, while real, doesn’t yet cover what the platform costs to run and extend. Adoption is accelerating and the monitors run every day. This is the wrong moment to slow down.
So I’m asking readers who want this work to exist to fund it directly. The goal is $25,000 by August 14 — four months of full operations, plus the next round of coverage: more applications, deeper tracking of the Chinese labs, and real-time alerts for subscribers. If we go past the goal, the surplus launches our Youth Signal program, which brings high school students into structured AI assessment alongside our expert respondents.
Contribute in any amount here: square.link/u/dkuZgSRc
A contribution of $1,500 funds a week of monitoring and analysis; $6,250 funds a full month. Or take the simplest route: a Pro subscription at watch.q16pbc.com is $70 a year, and you get the full ratings detail while backing the work. For larger support, or to talk first, just reply to this email.
Q16 PBC is a public benefit corporation, not a charity; contributions fund operations directly and are not tax-deductible.
Thanks for reading, as always.
— Jeff
