EthicalAds Alternative: Disclosure as the New Privacy

EthicalAds built a reputation the slow way. It grew out of Read the Docs, stayed close to the developer and open source audience, and made a point of not doing the things most ad networks do. No third-party cookies. No behavioural tracking. No surveillance pipeline hidden behind the banner. The deal with publishers was clear, the deal with readers was clear, and the ad product matched the principles it was sold on.

That work still matters. It also was not designed for the problem AI developers are now trying to solve. A display network cannot render inside a chat, a voice response, or a generated image. The principles translate; the format does not.

EthicalAds' Approach

Credit first. EthicalAds pioneered a privacy-forward approach to advertising on developer websites at a time when most display networks were piling on tracking scripts. The targeting model was contextual. The creative was lightweight. The publisher reporting was straightforward. Read the Docs, the host for thousands of open source projects, used the network on its own infrastructure, which gave the product both credibility and a steady audience of developers.

Publisher economics were respectable. EthicalAds paid a majority of revenue back to publishers and stated the split publicly. Advertiser targeting worked by topic and site context, not by following individuals across the web. The whole design was built around a thesis: that privacy could be the defining axis of an ad network, and that developers, in particular, would reward a network that respected it.

That thesis aged well. Browser tracking protections got stricter. Third-party cookies have been steadily retired across major browsers. The advertising tech stack has been quietly rebuilding itself around first-party data and contextual signals. EthicalAds was ahead of that curve, and the work it did is part of why the curve bent.

What Translates to AI, and What Doesn't

The privacy principles translate almost directly. AI advertising that piggybacks on behavioural tracking is both legally and practically a dead end. Model outputs are often generated server-side, sometimes on device, and the ability to attach third-party tracking pixels to them is limited by design. A privacy-respecting default is the only default that actually ships.

What does not translate is the format. EthicalAds' unit is a display ad on a web page. That unit requires a page, a rectangle, a viewport. AI apps increasingly do not have those. A chat product renders a conversation. A voice agent streams audio. A code assistant writes into an editor. An image generator returns an image. Inside each of those surfaces, the place where paid content might belong is not a sidebar. It is part of the response.

There is also a measurement mismatch. EthicalAds sells impressions and clicks against a web page. Inside an AI response, the natural unit is whether a given result is surfaced at all, and whether it is disclosed correctly. Pricing becomes less about a fixed slot rate and more about whether the AI output included a sponsored mention at all.

The final translation problem is scale of inventory per interaction. A web page might have one or two ad units. An AI session might have a thousand answers, each of which either does or does not include a sponsored result. The logic that matches advertisers to opportunities has to run per-response, not per-page-load.

Disclosure as the New Privacy

Here is the argument worth making directly. On the web, the frontier of trust was privacy — whether the ad network was watching the user, and what it did with what it saw. That fight is not over, but it is largely settled at the default-settings level: most serious networks have moved away from third-party tracking, most browsers block it, and most regulators treat it as the baseline.

Inside AI, the frontier of trust is disclosure. The user is not browsing between content and ads. They are being told something — an answer, a recommendation, a piece of generated code — by a model they are being encouraged to trust. If parts of that answer are paid, the user needs to know which parts. Disclosure is no longer a visual label on a banner. It is a required field on the response.

Surfacedd treats disclosure as structural. Every ad object carries the disclosure metadata. The host app decides how to render it — inline text, spoken word, code comment, image caption — but cannot strip it. A network that lets disclosure be optional fails the trust test in exactly the way that behavioural tracking failed it on the web.

This is where the comparison with EthicalAds becomes interesting. Both products start from the same premise: that an ad network aimed at developers should have a clear, defensible story about trust. EthicalAds wrote that story in the privacy idiom the web understood. The AI-native version of the same story is written in the disclosure idiom the new surface requires. Same instinct. Different frontier.

When You'd Pick Each

EthicalAds remains a strong fit for developer websites. If you run documentation, a technical blog, a project homepage, or a site in the open source ecosystem, the product is designed for you. The privacy posture, the advertiser quality, and the publisher support all line up with that audience. If most of your readership is reaching you through a browser and reading articles, EthicalAds is a clean answer.

Surfacedd is the answer when your product is the AI output. If users arrive through your chat interface, your voice agent, your code assistant, or your image tool, and if the revenue question is "how do we monetise the answer," then the network has to run inside the answer. That is a different shape of inventory than display on a page.

Many teams run both. The documentation site that surrounds the product takes EthicalAds. The product itself takes Surfacedd. Neither of those decisions conflicts with the other.

The detailed side-by-side comparison is at Surfacedd versus EthicalAds. The longer argument for disclosure-first advertising is at honest AI advertising. If you are building an AI app and want to see what the integration looks like in practice, the monetise your AI app guide walks through the SDK end to end.