Facebook is not the Internet
But Claude is AI, right?
Most folk these days have internalized the idea that Facebook and LinkedIn or YouTube and Netflix are not the same thing as the Internet. Whether these cloud services are free on the basis of advertising and data harvesting or require a monthly subscription, we understand that, on a lower level, Internet access is a distinct prerequisite for using them. At the same time, we know there’s more you can do with Internet access than access a few brand-name services (even if those few services account for the vast majority of our usage these days).
Should a university procure every student a Netflix account and every employee a LinkedIn Career plan? Probably not, for so, so many reasons. It makes a lot more sense to offer everyone basic Internet access when they are on campus via convenient Wi-Fi. Individual users or smaller-than-a-whole-campus groups can opt into additional services on their own. Oh, and it would be nice for the university to provide some kind of hosting services so that specific members of the campus community have some capability of offering their services to others in the community.
There’s a similar picture we need to draw for Generative AI. This post is going to try to leverage your well-established familiarity with the Internet and the Web (even if you are mostly a casual user of it) to help you pull apart some ideas that are too easily collapsed in the current public discourse around Generative AI.
Let’s focus on retail Generative AI subscription services associated with brands like ChatGPT, Claude, and Gemini. Under problematic privacy terms and tight usage limits, you can usually access some version of these services for free. Or, if you decide to pay, you can get some kind of Plus or Pro subscription, perhaps $5 or $20 per month, offering more. For some folk, even that’s not enough, and there are Max or Ultra subscriptions, maybe $100 or $200 per month, that give you even more smarts or more messages per day. The privacy terms, usage limits, and per-user prices of the subscriptions available for universities and other large organizations are a little different than those for individual users, but we’ll ignore those distinctions for today.
Should the campus procure everyone a ChatGPT Plus account (or some close, institutionally-negotiated equivalent)? You can’t say it would be unrealistic: we’ve seen this done for individual UC campuses like UCLA and for the entire California State University system. But, augh, I can’t help but think these purchases were made without a clear understanding of alternatives. Was a campus-wifi-like design even considered?
What does a raw Generative AI service look like? It would be something that gives you access to the smarts (the text extruder, the spicy autocomplete engine, the stochastic parrot, etc.) without a brand-name chat interface. It might be a pay-as-you-go service like OpenRouter or a flat-fee subscription service like OpenCode Go. Typically, these are available under excellent privacy terms (usually zero data retention, ZDR) and with generous usage limits. Unless you are a software developer directly integrating your custom software with these services, you can’t make much use of these services by yourself. It’d be like trying to use the Web without a browser! (Your programmer friend, a fan of curl, asserts that this is a possibility, but they are suspiciously reluctant to show you how to do it yourself.) We often call this level of Generative AI service the API: the application programming interface. Specifically, we might call it an inference API providing access to pre-trained models (to distinguish it from APIs capable of training or fine-tuning new models).
So, what’s like the browser for Generative AI? The two places you’ll most often see Generative AI come to life (in the sense of making spicy autocomplete into something useful) are chat interfaces and agentic coding tools. In chat interfaces, your opening query and subsequent responses are valuable context that steer the underlying model to generate text that is relevant to your interests that day. In agentic coding tools, this context is typically thickened to include all of the code and documentation files for a project, and the generated text can be executed as commands in that project environment to have impacts on the real world (e.g. sending a message or deploying a new version of a web service). In 2026, we might generalize both of these with the term “harness.” We say a model can become an agent when it is connected to a harness and supplied with context.
Can you get a chat interface or a coding interface à la carte, without any bundled subscription smarts? Sure, Open WebUI offers a browser-based chat interface that feels a lot like the one you’d see at ChatGPT.com or Claude.ai. Importantly, you can plug in various different inference API services. Open WebUI is software that you can run on your own computer or have someone in your community run on a cloud service on your behalf. OpenCode is a terminal-based coding agent harness that feels like Claude Code, and OpenChamber is a desktop app that feels like Claude Cowork. But, again, they can be connected to any back-end API service. Harnesses are typically free to run directly on your own computer, or they can be run for your community on a cloud hosting service at very low aggregate cost. Setting up Open WebUI for your friends and family is only about as hard as setting up a self-hosted blog package like WordPress or Ghost. If the harness only needs to serve you as an individual, there’s often a convenient desktop app (or even mobile app!) package of that same software available.
Running your own harness (or having someone run it for you) doesn’t mean you need to fully disengage from big-name Generative AI providers and their flagship models. You can use a specific model like Claude Opus 5 inside of OpenCode or chat with the same model that powers ChatGPT from inside of Open WebUI. You can switch models or even back-end providers mid-session to explore alternatives or optimize your costs. You don’t need to export or import your data when you switch providers because your data is stored near you and your harness. Alternatively, you can fully disengage from the big-name providers if you like (perhaps for cost, privacy, ethics, politics, or other reasons).
You can use the Google Chrome browser with your Verizon home internet to watch a documentary on Netflix. I can use the Firefox browser with my Xfinity home internet to watch a documentary on YouTube. Such flexibility, very variety. Now, consider the idea of a university purchasing Google AI Pro for Education (like they’ve done at UC Berkeley). Students, faculty, and staff can only use one family of harnesses (Google-branded surfaces like Google Docs, Gemini Notebook, etc.) to access one family of Generative AI models (Gemini) with a cloud-only context storage model. In this design, there’s no way for me to have an off-the-record chat or try out a different harness or model because the API access layer has been erased. It’d be like an Internet service that only offered Facebook access, as if Facebook simply were the Internet.
BayLeaf is trying to make the alternative, the inference-APIs-and-agent-harnesses design, tangible. Students, faculty, and staff can access a wide variety of Generative AI models, all under zero-data-retention terms, via the BayLeaf API. We have a generous default usage limit (currently $5/day for most users) with the ability to make exceptions. We also run harnesses for chat and coding (using Open WebUI for chat and the Lathe toolkit to add sandboxed code execution abilities). We even have a smoother onramp for using BayLeaf in local-to-your-computer harnesses like OpenCode and OpenChamber. We don’t require your chat data to be stored on our servers (if you use your own harness), and we’ve even figured out how to offer you the smarts (the spicy autocomplete engine) without either us or even our back-end providers being able to access your data, even in flight, using E2EE (end-to-end encryption).
At this point in the blog post, I want you to be able to read retail Generative AI subscriptions (and their institutional equivalents) as if they were Facebook-only Internet access plans. I want you to be able to identify the harness as the thing that makes the underlying generative models useful. I want you to feel a creeping discomfort with the idea that California’s public universities are ceding control and making additional payments to access services with strictly less capability than API-level services from those same providers.
BayLeaf is my fairly hard-line take on what’s appropriate for an institution like UC Santa Cruz. However, I want to applaud the hundred-flowers approach taken by our sister campus UC Berkeley: https://ai.berkeley.edu/tools-training/licensed-ai-tools They offer harnessed and API-level access to models from various providers, some channels centrally funded while others based on recharge, some self-service and others by approval only. Beyond the big-name bundles like ChatGPT Edu and Google AI Pro, they have in-house offerings like their BearGPT and Campus AI Sandbox (roughly analogous to BayLeaf Chat and BayLeaf API). From this breadth of offerings, you can infer that a single big-name bundled-subscription service was not by itself sufficient to meet the needs of the campus community. You could also infer that the investment to explore in-house and open-source alternatives was affordable.
Before I close, I should say that inference API services don’t necessarily have to be things you access over the Internet. The hardware and software implementing the smarts of the API could also be run on your own computer. The responses from laptop-sized models might be a little disappointing, the text trickling out slowly as your laptop gets uncomfortably warm, but it is possible. Generative AI isn’t inherently an Internet technology. It’s just much more convenient (and energy-, time-, and cost-efficient) to treat it as one. We, a big-ass public university that already runs other IT infrastructure, could run the models ourselves, but I don’t think we should go that far. I can analyze the thermodynamics of on-campus inference in another post. For today, just hang on to this idea of separating inference APIs from harnesses. Keep control of your data (your context!), keep your options open, and, when possible, buy your spicy autocomplete in bulk.