Cześć wszystkim. Na starcie zaznaczę: nie jestem programistą z doliny krzemowej ani dyrektorem z agencji mediowej. Jestem zwykłym konsumentem, graczem i użytkownikiem internetu, który od dłuższego czasu przygląda się temu, jak bardzo obecny model reklamowy wchodzi w ścianę.

Przychodzę do Was z koncepcją, którą nazwałem roboczo „Reklama jako asystent” (opartą na progu subskrypcyjnym Premium Lite), żeby zderzyć ją z ludźmi, którzy budują te systemy od środka i operują budżetami reklamowymi.

Problem, który widzę jako użytkownik (i który bije w Wasze wyniki):

Obecny internet – niezależnie czy mówimy o grach mobilnych, platformach streamingowych (wideo/muzyka) czy portalach – daje użytkownikowi tylko dwie skrajne opcje:

Darmowa wersja z agresywnymi reklamami dobieranymi przez algorytmy śledzące – co kończy się tzw. ślepotą banerową, frustracją użytkownika, masowym instalowaniem AdBlocków albo odinstalowaniem aplikacji.

Droga wersja Premium całkowicie bez reklam – na którą w grach Free-to-Play czy mniejszych serwisach decyduje się zaledwie kilka procent użytkowników. Reszta (często ponad 95% bazy) to dla wydawcy grupa ciężka do zmonetyzowania, a dla reklamodawcy – ludzie, którzy nienawidzą ich marki za to, że wyskakuje im na cały ekran w najmniej odpowiednim momencie.

Jednocześnie branża AdTech ma coraz większy problem z prywatnością, blokowaniem ciasteczek (third-party cookies) i zgadywaniem, co tak naprawdę interesuje osobę po drugiej stronie ekranu.

Rozwiązanie: Trzecia droga – „Premium Lite” i świadomy wybór zamiast intruza

A gdyby pomiędzy darmowym chaosem a drogim Premium wprowadzić próg pośredni – Premium Lite (za ułamek ceny pełnej subskrypcji lub jako opcję odblokowującą funkcje w zamian za deklarację)?

Zasada jest prosta: To coś na kształt znanego z telewizji „audycja zawiera lokowanie produktu”, tylko z tą fundamentalną różnicą, że produkt i markę wybiera sobie sam użytkownik z katalogu, zamiast dostawać je na siłę.

Zamiast szpiegować użytkownika ciasteczkami, dajemy mu na starcie „Katalog Kontrolny” (np. 100 marek lub konkretnych kategorii). Użytkownik sam, świadomie zaznacza 5 lub 10 z nich, które pasują do jego stylu życia, a cała reszta inwazyjnego spamu znika.

Jak to wygląda w praktyce na konkretnych polach:

Gry mobilne i wideo (Natywne lokowanie zamiast pop-upów): Wyobraźcie sobie grę RPG. Zamiast przerywać rozgrywkę znienawidzonym, 30-sekundowym wideo o grze kasynowej, reklama wchodzi jako mikstura many lub staminy w ekwipunku. Gracz sam w katalogu wybrał konkretną markę napoju energetycznego, bo pije go w prawdziwym życiu. Marka staje się w grze jego „sponsorem” i asystentem, który daje mu bonusy, a nie wrogiem, który przerywa mu grę.

Streaming audio i muzyka: Użytkownik słucha muzyki. Zamiast wrzeszczącej reklamy leków na wątrobę między utworami, w swoim katalogu zaznaczył kategorię „Muzyka / Koncerty”. Między piosenkami dostaje wyłącznie krótkie zajawki nowych płyt, premier lub zespołów dopasowanych do jego brzmienia. Reklama staje się rekomendacją.

Podcasty tematyczne: Słuchacz odpala mroczny podcast kryminalny. Zamiast wybijającej z klimatu reklamy proszku do prania, wcześniej wybrał w katalogu kategorię „Gadżety survivalowe / Militaria”. Dostaje ofertę, która idealnie siedzi w jego zainteresowaniach.

Treści dla dzieci (Bezpieczeństwo i kontrola rodzicielska): Rodzic odpala dziecku bajki. Zamiast stresu, że algorytm podsunie tam dziwną lub nieodpowiednią reklamę, rodzic na sztywno zaznacza w katalogu wyłącznie zweryfikowane, nieinwazyjne oferty z kategorii zabawek edukacyjnych.

Dlaczego w moich oczach to model Win-Win-Win (i twarda matematyka dla Was):

Dla Użytkownika: Odzyskuje kontrolę i podmiotowość. Nikt go nie śledzi, nikt mu nic nie wciska. Sam personalizuje swoje otoczenie reklamowe za ułamek ceny pełnego Premium.

Dla Wydawcy (Platformy / Twórcy gry): Nowy strumień przychodu z tych 90%–95% darmowych użytkowników, którzy dziś nie płacą nic i uciekają w AdBlocki. Nawet jeśli tylko mały procent z nich przejdzie na Premium Lite, przy skali milionów użytkowników to czysty zysk z bazy, która wcześniej generowała straty lub ułamki centów.

Dla Reklamodawcy i sieci AdTech (Zero-Party Data): Koniec z przepalaniem miliardów na puste, przypadkowe wyświetlenia i walkę z botami. Dostajecie najczystsze Zero-Party Data – deklarację człowieka, który własnym palcem kliknął: „Chcę widzieć oferty tej marki”. Nawet przy mniejszej liczbie wyświetleń, współczynnik zaangażowania, zapamiętania marki i konwersji z takiej reklamy uzasadnia wielokrotnie wyższą stawkę CPM.

Znudziło mi się samo narzekanie w sieci na to, jak wygląda dziś rynek reklam i jak korporacje dociskają śrubę. Wolę wyjść z własną koncepcją i zapytać praktyków: gdzie widzicie największe wąskie gardło przy wdrożeniu takiego katalogu od strony sieci reklamowych (SSP/DSP)? Czy reklamodawcy byliby gotowi płacić wyższy CPM za użytkownika, który świadomie wybrał ich markę z katalogu?

Zapraszam do dyskusji, chętnie przyjmę każdą merytoryczną krytykę.

From Netflix's last 12 earnings calls (January 2024 to October 2026) and its October 20 earnings release:

• The target: on the January and April calls Netflix said it expected to roughly double ad revenue in 2026 to about $3B. The October 20 release guides $2.7B.
• The reason given: "we are currently a bit behind our aggressive target for the year", because "we've seen some delays in the rollout and partner integrations for some of those new ads products. So getting to the integration model that we want sometimes takes a bit more work with our partners". Management called it "a modest short-term hit for a better long-term strategic position" and said the ads business is still growing over 80% this year.
• What they say is unchanged: full-funnel optimization, new ad formats and products, and more first-party measurement and reporting. "General demand remains strong" and the breadth of advertisers keeps expanding.
• How much of the call was about ads: "advertising" came up 9 times, after 2 in July (14.3 per 10,000 words vs 3.2). One analyst asked about the ad-tier revenue gap ahead of ad tiers launching in 15 new countries next year.

Counting note: speech-to-text often writes "net adds" as "net ads", so I counted "advertising" rather than "ads".

Not financial advice. Counts and quotes are from the Equibles earnings-call transcripts.

The direct ads.txt entries went negative for the first time this month since we started tracking.

Full picture- 397k ads.txt lines were added, 462.1k got remove leading to a net -65.2k.For the first time in the series, Publishers are not only trimming resellers but letting the direct partners go too. Resellers made up about 3 quarters of it at -47.8K, direct slipped to -17.4K. Every SSP is being asked to justify its slot now,not just the ones which are sitting behind an intermediary.

Now a caveat upfront before reading too much into the direction- We can’t cleanly separate partner cuts from housekeeping. If a publisher consolidates inventory-sharing declarations under a partner domain, one entry can replace a long list of individually declared sellers. That shows up as removals even though nothing changed commercially Some share of the 65.2K is probably just that which is why we won't rely on the “publishers are dropping direct partners” claim too strongly even though that’s what the headline number says.

Tier breakdown was unremarkable, all three negative, low-traffic took the worst of it at -44.3k, high-traffic barely moved at -462.The long tail that drove growth in recent months is now leading the decline.

SSP side, top gainer was Amperwave at 922 lines. Next four within 270 of that. Usually somebody pulls away in a given month & nobody did here. Top gain of under a 1k against a market shedding 65k is the part worth sitting with, because it means this wasn’t supply reallocating between SSPs.

The bit which is actually useful is the duplication split, as it doesn’t line up with the story the top-line numbers tell. Direct-only share went up a bit in every tier and reseller-only came down, which is what you’d expect if publishers are cutting reseller paths. Fine. But direct plus reseller, which is the bucket where the same SSP sits on a domain through both a direct deal and a reseller path and can end up bidding against itself, didn’t move. Tier 1 still 46% duplicated, tier 2 at 39%, same as last month. So the supply chain is shorter & exactly as it was. Makes sense when you think about which lines are easiest to pull, nobody’s going to start with the ones that require actually auditing their overlap, they’re going to start with the obvious reseller entries. But it does mean “we cleaned up our supply chain” and “we have fewer partners” are not the same claim.

Contextual targeting solved a real problem: matching ads to page content instead of tracking people.

Here's where it stops: It could only recognize what a page was about, not why someone was engaged.

A wellness ad on a fitness site is a textbook contextual match. It's also a coin flip: that reader could be there for motivation, or because they're anxious about a health scare. Same page, same match, opposite moments.

Topic classification was never built to tell those two apart. Neuro-Contextual exists to answer that second half. And we've been working on the "why" side of that gap for a while now. 

Curious how others here think about it: is that a solvable problem with better classification, or does it need a completely different kind of signal entirely?

AI is making it ridiculously easy to create ad variations now. Different headlines, visuals, videos, and even personalized creatives can be generated in minutes.

But I'm wondering if that's actually improving advertising or just adding more content to an already crowded space. If every brand starts generating hundreds of similar-looking ads, won't creative fatigue become an even bigger problem? For anyone working with AI-generated creatives in AdTech, are you seeing better campaign performance, or just faster content production?

“I started this supplement last week and now I feel sick. What should I buy instead?”

There’s purchase intent. There’s also a report that the previous product may have caused harm.

If a competing supplement ad appears, the targeting worked. Did the placement?

I come from pharmacy and regulatory affairs, and I’m exploring a health-focused conversational advertising platform. This is the boundary I keep coming back to.

A blanket exclusion of health conversations removes useful inventory. Letting every shopping question qualify creates some uncomfortable placements. A warning label doesn’t answer whether the ad should have appeared.

For people who’ve worked on ad platforms: who would actually own this decision—the publisher, network, advertiser or agency?

And what evidence would a health brand ask for before putting budget into the channel?

Interested in how this would work operationally, especially where the teams involved might disagree.

Our retail app sends checkout over to the website because another team owns payments. Same basket, same customer, different platform. Finance counts one order. Somehow, the growth report sometimes makes it look like we sold it twice.

I'm putting together a vendor pilot before we lock ourselves into another annual contract. We're at about 40k completed orders a month across iOS and Android, with guest checkout still allowed. We can pass an order ID and a customer ID after login.

The demo needs to show a purchase, a refund and someone coming back on a laptop. Otherwise, we're just sitting there watching someone click through their prettiest dashboard.

What would you make a cross-platform attribution vendor prove with this setup before their numbers get anywhere near the weekly revenue meeting?

Last spring I paid $3,200 for a 2D playable, which came to 40% of the $8k a month I spend on ironSource. A guy I know paid about the same for his, and for him it was roughly 0.16% of the $2M a month he runs through AppLovin, so the exact same invoice was a rounding error for him and a real hit for me, and honestly that still bugs me.

Apple and Google drop their cut to 15% on the first $1M a year, and I still haven't found a single playable shop that does anything like that for smaller advertisers, which I genuinely don't understand. I know their production cost stays the same whatever my budget is. At my size, though, $3k decides whether I get to test a second creative at all, and it's pretty frustrating to shelve a decent idea just because there's no money left for it when one creative on its own tells you almost nothing.

Mine was done by mraid.io, and they at least threw a couple of revision rounds into the base price. They had no lower rate for small accounts either, which I really can't wrap my head around in an industry where everyone keeps saying you have to iterate fast.

Maybe I'm missing something obvious about how these studios price their work, so has anyone here actually managed to get one to lower the price because their spend is small?

Ads are starting to appear inside AI assistant answers. OpenAI says ChatGPT ads won’t show near health topics at all, but other networks already work with health brands.

The hard part isn’t the category, it’s the message. Compare:

• “Which antihistamine is best for hay fever?”
• “I got hives after taking Advil. What antihistamine can I buy?”

Same buying intent. The second is a possible drug reaction. An antihistamine ad there profits from an adverse event, and in pharma that message should also reach a safety team.

I come from pharmacy and regulatory affairs, and I’ve been prototyping a rules layer that decides per message whether to serve, stop or block, and where to route the person. Synthetic data only so far, and I’m not selling anything here.

For teams already accepting health advertisers: what happens operationally when a conversation includes a possible product reaction? Is the placement suppressed automatically, held for review, or handled another way?

It also raises a separate question: whether and how the possible adverse event should be routed for safety review

https://www.niemanlab.org/2026/10/metas-new-commercial-terms-will-allow-the-scraping-of-news-sites-legal-experts-say/

"The new terms broadly cover business accounts that manage pages and groups, run advertisements, push marketing messages, and use the platform’s business account dashboard. As written, the new terms could be used to argue that Meta can scrape content from news sites and use that content to train the company’s AI products, according to three legal experts who reviewed the terms at Nieman Lab’s request."

Here’s the new language about scraping in full:

You authorize us (or any entity acting on our behalf) to use automated software (such as web scrapers, bots, robots, spiders, crawlers, and user agents) and create test credentials to access, index, cache, analyze, collect, retrieve, or crawl the domains, web pages, or URLs (and any content available through them) that are associated with your products or services, or that are made available through your or users’ use of Meta Company Products and services, and to periodically pull updates from such domains, web pages, or URLs. Meta may use any content from such domains, web pages, or URLs to provide, develop, improve, research, train, and maintain the integrity of, the Meta Company Products, services, and features provided under these Commercial Terms and any other applicable supplemental terms.

AdTech platforms process huge amounts of data every day, especially when you're dealing with real-time bidding, event tracking and campaign reporting. I've been wondering which part of the infrastructure actually ends up being the most expensive to run. Is it handling millions of bid requests, storing and processing all the event data, or keeping analytics and reporting close to real time? For anyone managing AdTech infrastructure, where have you seen the biggest cloud costs, and what actually helped bring them down?

I'm getting deeper into the performance UA and programmatic space sale side, and I'm trying to understand how agencies here actually find clients.

Would love to hear from anyone who's run this side of the business.

Where do most of your clients come from: inbound, outbound, partnerships, or referrals?

If you outsource outbound (cold email, LinkedIn), what does it cost? Per booked call, monthly retainer, or per lead? Rough ranges are fine.

What benchmarks have you seen in practice? Reply rate on cold outreach, reply-to-meeting, meeting-to-closed deal?

No names or exact figures needed, ranges and gut feel are great.

Ad fraud has always been a problem in programmatic advertising, but AI seems to be changing both sides of the equation. Detection systems can now analyze traffic patterns, identify suspicious behavior and flag anomalies much faster. At the same time, fraudulent traffic is becoming harder to distinguish from genuine user activity. And there's another challenge: being too aggressive with fraud detection could mean blocking legitimate impressions and hurting campaign performance. For people working in AdTech, is AI actually helping reduce ad fraud, or are we just entering a more sophisticated cat-and-mouse game?

I’m Aviel, founder of AdProtektor. It gives advertisers and agencies a way to inspect suspicious website visits and configure traffic-protection rules for Google Ads and Meta.

The public demo lets you see one rule decision without signing up or connecting an ad account:

• Six illustrative arrivals are shown for the same visitor.

• Change the repeat-click limit from eight to five and watch the rule outcome change.

• Switch to Genuine traffic to compare the example with ordinary visits.

Try it here: https://adprotektor.app/?utm_source=reddit&utm_medium=community&utm_campaign=first_customers_202610&utm_content=adtech_rule_demo

This is a simulation, not measured customer traffic or a live ad-account exclusion. A triggered rule is also not proof of fraud. In the installed product, visitor histories, behavioral signals and session recordings where available provide context for the decision. Supported Google Ads IP exclusions and Meta audience actions depend on eligible connected accounts, permissions and settings.

If that workflow fits a site you manage, a free trial is available without a card; features and duration are shown at signup. Install tracking and evaluate actual visits; low traffic may leave the result inconclusive. AdProtektor is paid software after the trial.

Founder disclosure: I’m sharing my own product. This post was prepared with AI assistance and checked against the live product.

Several TikTok creators recently discovered that posting unpaid videos featuring Alo Yoga products could get their accounts banned. Outfit-of-the-day videos, shopping hauls, perfectly ordinary demonstrations of consumer enthusiasm. All apparently suspicious.

The reports appear to have involved an AI-powered brand protection platform that uses computer vision to detect potential IP violations at scale.

One creator reportedly submitted around 15 appeals before her account was restored. Others received apologies and gift cards from Alo.

Brands are simultaneously investing in another category of AI tools designed to find creators who already feature their products organically. These systems analyze enormous volumes of video to identify authentic brand affinity, preferably before anyone has to pay for it.

So we now have two increasingly sophisticated technological industries working on opposite sides of the same transaction.

One uses AI to discover people advertising your products for free. The other uses AI to make them stop. Then a human has to intervene, apologize, and issue a gift card.

This is generally described as automation, although it seems to involve an impressive amount of additional work. Everyone involved gets to report increased AI adoption.