Why Digital Marketers Desperately Need an Alternative to the Scale and Surveillance Marketing Tech Engine of Today
There is a now famous quote from Henry Ford about his first-generation mass production cars that reveals an instinctive understanding that customers like options. Yet his brilliance was knowing that his product was so seductive, customers were willing to suspend their normal predilection for choice to buy his one-color car.
In some respects, this is exactly where we are in adtech today.
For twenty years—the equivalent of a century in tech years—digital advertising has been delivered in one “color”: tracking and targeting people online with advertising at scale. This “push” model proved so seductive that marketers were willing to suspend their better judgment so they could drive this marketing car called adtech.
The adtech stack —tracking and targeting people at scale—has remained the same for about 20 years albeit with added flourishes around the edges. Even the planned phasing out of cookies failed because the current scale/ surveillance/ push model was so entrenched that tech platforms had little incentive to really come up with viable alternatives.
The adtech system as it is – is hugely profitable. Why fix what is not broken? Right?
Wrong.
It is time to reexamine this one-color adtech engine because with a monolithic “one-color as long as it is black” system, the downside issues are significant. As it is, a one-flavor system is…
- not good for marketers
- not good for publishers
- not good for people
The “Push” Model: Scale and Surveillance Should Not be the Only “Color” Available
Even as we add AI into the mix, let’s be precise in defining the current system for what it is: a “push” marketing engine. It pushes ads to people at every digital moment through a monolithic system of constant surveillance, targeting, and scale advertising.
This system rests on four, high-margin technical pillars:
- cheap content distribution platforms that put powerful broadcast tools in anyone’s hands thus expanding market demand beyond marketers;
- scale algorithms designed to push content and ads as fast and as broadly as possible for larger budgets;
- content monetization where algorithms reward engagement-driving content from any source;
- zero verification, where trust guardrails were deliberately dismantled.
The industry of roughly 15,000 firms across 49 categories—ad networks, social media platforms, AI marketing platforms, and a vast segment of publishers and profile data management companies—touches a market segment worth over a trillion dollars. Despite its relatively small size compared to industries like wholesale, its business footprint is broadly felt, not just by legitimate marketers but virtually every person on this planet.
This lack of choice for marketers is corrosive for the three groups at the center of the advertising ecosystem: marketers, consumers, and publishers.
Why Lack of Choice Hurts Everyone
For marketers. The push model forces reliance on impressions that can be faked, clicks that are faked, and content outlets devoid of journalistic value. In pursuing frictionless ad distribution and maximum revenue, the technology eschewed verification altogether that might reduce ad placements and revenue. From social platforms to digital data companies, impressions, clicks, and viewability metrics all defy virtually all attempts to verify what is real versus fake.
Marketers are buying in the dark and are told to be grateful for the reach. This is adtech’s equivalency to marketers: “You can have any type of adtech you want – as long as it is black.” No choice and no way to pressure the dominant players in this system to do better.
For consumers. The digital marketing and advertising ecosystem, form a massive infrastructure enabling marketers to reach global audiences at unprecedented scale, allowing messages to spread further and faster than ever before. At the same time, bad faith actors took full advantage of these new massive distribution systems, flooding our digital public square at unprecedented “scale” with no audience tools to cope with the tsunami of fabricated and often false information.
In the race to serve advertisers and maximize revenue, digital marketing tech adopted trust-busting practices and protocols to become the invisible infrastructure of manipulation, disinformation, and industrial-scale PSYOPs. Marketing technology contributed to democratic decline, rising hate, and the collapse of shared truth with profound albeit unintended consequences in the larger public domain. (For a deeper dive on the human cost of adtech, please visit: https://trustwebtimes.com/wp-content/uploads/2026/05/The_Trust_Web_White_Paper-v3-in-format-1.pdf.)
At the most fundamental level, the omni-marketing tech model of scalable reach was used by our adversaries to flood audiences with often false information designed to dissolve the trust glue that binds countries, communities, and culture. Worse, the lack of marketing tech choice meant the current adtech players had no incentive to close trust gaps because adtech currently monetizes:
- all types of content – good and bad;
- all types of users – good and bad;
- all types of engagement – good and bad.
Creating trust online was antithetical to adtech’s business model. The fallout is apparent for all to see.
For publishers. The scale game has devalued quality content. When low CPM outlets are valued over quality media reaching real audiences, the financial floor collapses under publishers who invest in journalism and verified reporting. The programmatic supply chain rewards volume over value, and quality publishers cannot compete with made-for-advertising sites churning out content designed to game algorithms.
The consequences are measurable and severe. Daily newspapers specifically have been in long-term decline: there were approximately 1,748 dailies in 1970; by 2018 that figure had fallen to 1,279, and by 2025 just 938 remained (Statista / Editor & Publisher). The local and community tier has been hit hardest. As of 2025, 213 U.S. counties have no local news outlet at all, up from 150 in 2005, and another 1,524 counties have only one remaining source — leaving roughly 50 million Americans with limited or no access to local news (Medill State of Local News Report, 2025). Newspaper employment has collapsed in parallel: the industry has lost three-quarters of its jobs since 2005.
Much of what is left in publishing are outlets that cater to mass audiences, covering complex topics in the most simplistic terms. This works against publishers being able to charge fairly for their content commiserate to the cost quality content requires.
The digital ad model did not just devalue content—it helped dismantle the economic foundation of journalism itself.
The Simple Insight Hiding in Plain Sight
The disruptive insight that can propel a new model forward has been hiding in plain sight: the Internet was built as a content-serving engine. It was never designed to track and target real people digitally, and it was certainly never designed to stalk profiles across the web, relentlessly pushing ads at audiences wherever they go.
A new opportunity is emerging from this realization. If the Internet’s DNA is fundamentally about serving content, then that architecture can serve as the foundation for an entirely different adtech paradigm—one that rides on content rails, not “people” rails. This type of system allows users to pull ads that are relevant to them in the moment instead of being passive recipients of push ads.
Marketers have actually known instinctively for decades that a content centric system where users pull the information for themselves is high performing. They dreamed of an efficient, pull system for many many years.
After all, virtually every marketer uses PPC since PPC is a content-centric marketing system. Google’s entire advertising machinery was built on the power of content—keywords—to convert audiences, all without demographic targeting. It has always been a powerful intent engine feeding the sales funnel.
Yet beyond keywords, the ability to harness content in digital marketing was never fully developed. Content-centric media opportunities do exist—sponsored content buys, influencer marketing—but these approaches are anemic in operational efficiency. And so, despite content’s proven conversion potential, the push model driven by profile targeting continues to dominate, even as its media-buying pipes overflow with fraud and hidden fees.
The result is that the Internet’s potential as a content-serving engine for marketers has been massively underutilized.
It’s not hard to see why. Once you adopt a content-centric mindset, it becomes clear that everything has to change—the data, the buying, the attribution modeling. And it becomes equally clear that enormous inertia protects the current system, which remains highly profitable for its incumbents.
But there is an exciting alternative: a system that gives users the agency to share their intent directly with brands, rather than subjecting them to a surveillance apparatus that pushes ads at them relentlessly. Marketers have dreamt of a pull system for decades. Now it can finally be realized.
An Action Plan for Marketers Ready to Adopt a Pull Marketing Engine
Changing from the entrenched push model requires concrete steps. Drawing on the Trust Web framework and hard-won operational experience, here is a practical action plan for marketers who refuse to keep buying the same black car.
1. Adopt New Data Focused on Topic Targeting
The fuel for every adtech engine today is data that tries to identify people as “cohort groups,” created in convoluted ways because digital audiences are not easily definable as real or with clear demographic attributes. Some firms develop complex ID resolution solutions. Others organize data based on behaviors by reverse engineering which people clicked on ads and then finding more “look-alike” audiences. No matter which method is used, this is where adtech gets into a heap of trouble because it is achingly difficult to know who is a real person online, much less who is in the right mindset to convert.
The alternative: topic data that identifies the topics an advertiser should invest in, (Topic Intelligence is one such topic data/ analytics platform). Rather than profiling individual humans, firms can deploy deep learning models to analyze the topical narrative of audiences’ content choices. This AI-driven approach identifies which topics have traction to convert audiences. By aligning brand messages with high-performing topics rather than following specific users, brands can achieve ROI while respecting privacy. No need to worry about audience privacy because you are tracking topics around the web—not people.
2. Execute Direct Media Buys
Adtech made no real attempt to follow true topic-based contextual matching because it would collapse the “scale” game rapidly. One can cook up scalable impressions that masquerade as real people, but adtech cannot spin up topic-based pages that advertisers actually want to be on and real people want to read. Real contextual matching simply is not in the interest of DSPs and exchanges that rely on selling “scale.”
The remedy is to push for direct buys with quality publishers and reject scale media buys as the primary paradigm. Agencies should be the front line here and significantly reduce programmatic media investment and execute more direct buys where advertisers know precisely what publishers their dollars support. This is a moment to invest in media channels that cultivate trust—local media, niche publishers, and outlets with real editorial value. Allocate media dollars based on topics rather than profiles by running in influencer platforms (such as https://www.becauseiloveit.com/) and in smaller publications with sponsored content buys.
3. Stop Using CPM and CTR as Measures of Media Buys
CPM—the cost per 1,000 impressions—matters too much in media decisions. A $10 CPM plan sounds more efficient than a $50 CPM plan, yet the lower CPM outlet could be a money loser compared to quality media reaching real audiences. As for CTR, in a perfect world clicks indicate interest, but we do not live in a perfect world. Clicks in digital media can represent interest, but no one can know whether a click is from a real person or a bot. CTR metrics provide some information but of little real value because too much of the data is too noisy to be helpful.
CPM and CTR have become the metric proxies for media buying efficiency—great for marketing tech firms but not for advertisers. Marketers should stop giving weight to these metrics. Instead, key indicators should revolve around quality outlets with value to real people and those demonstrating real outcomes.
4. Ensure Campaigns Are Technically Connected to Outcomes
The attribution gauges of today’s adtech engine are simply too crude and too complex to orchestrate systematically when trying to reconcile campaigns, channels, traffic verification, ID resolution, sales funnel management, and conversion. Imagine a more elegant analytics and attribution model that is simpler because it is topic-based—much like moving from gas-powered engines to electric.
Specifically, imagine being able to understand the “topic journey to conversion” across channels. With a topic-centric attribution platform, advertisers can understand which specific topics, in sequence, had the highest conversion outcomes. No need for last-click attribution models that are underpowered. No need to spend six figures on complex ID resolution attribution engines with less than 50 percent hit rates. Measurement should center on which topic-based campaigns were best at attracting the most convertible audiences—not potentially inconsequential impressions.
5. AI is the Change Agent (Pun Intended) to Realize a Pull Marketing System
AI is the change agent that can finally realize marketers’ decades-long dream of a true pull marketing system, where consumers seek out brands rather than being interrupted by them. Three capabilities make this possible now.
First, AI can contextualize content with extraordinary precision, dynamically tailoring messages to achieve best outcomes. By delivering topics driven by consumer’s journey to conversion – every touchpoint turns into something genuinely relevant rather than generically targeted.
Second, AI dramatically compresses the time and cost of building sophisticated marketing infrastructure; platforms like demand-side platforms (DSPS) that once required millions of dollars and years of development can now be developed faster and more economically. This lowers the barrier for tech firms to orchestrate complex, consumer-responsive “pull” systems.
Third, and perhaps most critically, AI can draw a tighter line between campaign activity and actual business outcomes, replacing the fuzzy attribution models marketers have long tolerated with more precise, granular measurement that proves what’s working and why.
Together, these capabilities make it structurally possible, for the first time, to build a pull marketing system that can realize the potential of content to deliver value at the exact moment a consumer is looking for it.
“Ok – I’m sold. Now what?”
“Pull” Marketing is A Rainbow of Content Colors
As was the case with the automobile industry, no one believed a new kind of engine could overcome all the technical and operational hurdles inherent in unseating an entrenched paradigm. But it happened.
A topic-centric adtech engine can herald a new era of transparency and performance that has eluded advertisers for too long—because the only color they could buy, until now, was adtech black.
The key to restoring a healthy Internet is embracing—not fighting—the content-serving DNA of the web through a verifiable model. Through a new “pull” adtech model that can be an option to the current “push” system, audiences have agency again. With empowerment, people can decide control their digital lives with clarity and confidence.
Step 1. Start with a new type of topic data to fuel marketing programs – from email and social to media buying.
Step 2. Allow for a different media buying paradigm that rejects scale in favor of audience precision. Ads can run in topic-based ad networks which will naturally be smaller than typical programmatic media buys.
Step 3. Migrate to an alternative attribution system where campaigns are measured based on topic efficiency – not profile targeting.
Happily, there is no need to ditch entirely the “push” adtech model advertisers participate in now. They can activate a “pull” adtech model strategically to diversify the risk of all eggs being in the one adtech black car.
The future belongs to the next generation of adtech “cars” where people have more choice beyond black. This rainbow of adtech colors can inspire marketers for the next twenty years.
