The other option is to target the ads to the publication. As long as the publication is on a niche topic or geographically localized, you can choose ads that will likely interest people (local restaurants) without needing to track them. Tracking is only necessary when the ad venue is incredibly broad across topics and geography.

As someone who used to work trafficking ads and spends a lot of time blocking them myself, I'm of the opinion that this is the approach to take.

ReadTheDocs and sites like BikeRadar are my favourite examples: If I go to a bike site, I expect the ads I see to be about bike stuff. If I'm on a technical site (like Stack Overflow or ReadTheDocs) I expect to see ads about related technical topics.

Unfortunately the advertising industry has gone down the route of hyper-invasive, user-data focused advertising, for what I consider to be very little benefit to either publishers or users. The only people who benefit are those who pedal the tech that enables this market.

With all the ML tools available to us now, I'm not sure why we couldn't build tools that accurately match ad content to site content. You don't need to invasively track users users this way either, because you only ever need to be concerned with the topic of the site/article and not who's seeing it.

We could build these things, but part of the "advertising host" optimisation metrics is "time to ad display" so the algorithms in use are frequently "deliberately dumb" for reasons of optimising the time involved in deciding what advertising should be displayed.

Somehow, televisions had the miraculous ability to display content-targeted ads with no user knowledge and zero latency...

It's much easier to target the publication than the individual user.

Web pages are far more static than users, your adserver/ML/analysis tool need only analyse the page once (not including updates for content changes) and then pass this information straight on to ad buyers.

Even if advertisers analysed the page content on seeing it, rather than the publisher, it would still be net faster than doing lookups/analysis per adserve (as you would when you target a user).

I don't think it would be hard at all to build something which analysed a page and made an adserving decision with a lower "time to ad display" than a similar process that used user data.

That's how old media has been doing it for centuries. But media is hard: you have to invest in personal relations with advertisers.

New media just outsources all the hard work to Google and other big ad placers. Easier but you lose editorial control over your work.

More like "for about 150 years".

Advertising, in the modern sense, is, well, modern. You pretty much simply did not have goods to advertise (or the means to advertise them) other than for very specific items -- think classifieds rather than bulk and mass advertising.

The rise of the factory system, of transport networks, and the capacity to create far more of a good than local demand could absorb created the first modern-style advertising in about 1860. The first prominently-featured adverts were the 19th century equivalents of Viagra ads: mostly-ineffective patent medicines.

Hamilton Holt has a delightful contemporary account of this, written (and presented as a lecture, it's short) in 1909, looking back on the previous 50 years or so of publishing history. I cannot recommend it highly enough.

https://archive.org/stream/commercialismjou00holtuoft#page/2...

In fact, that’s how Google started out with AdSense as well: It was called “content targeting advertising”, because Google scanned your webpage and then served ads that related to the content of your webpage.

I don’t know what happened along the road to lead us to the current tracking and spying mess.

What happened is the ad landscape shifted from targeting placements and content to targeting audiences and where they are in their journey to purchase. It simply performs better for advertisers and it's often more scalable. In turn, this makes the platform more money because sites that may have low value inventory can suddenly be sold for higher CPMs if an advertiser for example wants to bid on their retargeting data in the auction. It let's them price based on people.

I have been wondering why that isn't done more often myself. I mean,it isn't like tv ads didn't work.

Advertising is very much beset by the mindset of being "so preoccupied with whether or not they could that they didn't stop to think if they should" (Jurassic Park.)

Tracking and microtargeting just seem too attractive for advertisers to pass up. Same goes for large size, autoplay, multimedia ads.

These mechanisms would have to fail to perform in obvious ways (not long-term, subtle, whole-market ways) before people will stop using them.

I do not know you, so I won’t presume (certainly not on HackerNews, the least representative sample of the Internet) but I have seen many experiments proving that most people who think they don’t like targetted ads actually prefer them -- until they make a very informed but very wrong guess. Typical examples include:

- advertising for something shameful (underwear and dildo are my go-to examples) or that you otherwise want be kept secret (engagement rings);

- repeat ads for specific and unusual big-ticket items. I have told friends working at Amazon that no one I know ever started a vacuum cleaner collection, but they only response is “You would be surprised…” to which I can only assume the world is either weirder than I thought, or their analytics function has missed something.

What you (and most people) probably would prefer is the ability to edit what the ad engine knows or assumes about you: gone are dildos, rings and vacuum cleaners; come books about programming patterns!

I keep telling anyone close to any of those recommendation engines to allow me to do that, but they mumble things about design, feasibility and hacking that genuinely don’t add up to me.

Not true,sure I would prefer programming related ads but I still want it page-relevant and not based on my browsing history,habits or sensor data.

I would love to see more ads that instead target the content of the page. When I'm reading about machine learning, it should be books close to that topic, and other related things. I can't believe little that happens.

It has the added benefit of having a high probability that you'll be interested in the product, since you wouldn't be on the page if you didn't find the topic interesting. Instead we get tracking networks showing you ads for TVs because you bought a TV last week.

I think the problem with that is that most browsing is done on pages that do not target a valuable topic: news, weather, memes, etc.

For that reason, it's better to remind you about that Amazon product still in your cart while you're browsing for random stuff.

I used to to that with Amazon. About a decade ago, I added an Amazon banner, and the link included a search term. So I would add an appropriate search term (related to the top blog entry on the page, which is where the Amazon banner appeared). Sometime in the past few months, Amazon removed that and only allows generic sales banners to be presented.

Considering the sprawl of sponsored content, maybe because it's cheaper/easier to just buy the author?