The AI advertising ecosystem is shifting from “neutral” to “transparent”: the ad tech business model is entering a period of restructuring

AI advertising ecosystem is shifting from “neutral” to “transparent”: the adtech business model enters a phase of restructuring

Introduction

Around Publicis’ acquisition of LiveRamp, the adtech industry has reopened an old debate: whether platforms are “neutral.” But after AI has been introduced into ad buying, audience modeling, identity resolution, attribution measurement, and automated optimization, neutrality itself is no longer sufficient to explain commercial relationships. The more critical question becomes: how is data being used, where is budget being consumed, and does first-party data ultimately strengthen a brand’s own capabilities, or is it absorbed into a larger ecosystem and retrained? For brands, agencies, DSPs, identity graph providers, and retail media networks, this is not an abstract ideological dispute, but a reassessment of profit distribution, data control, and competitive advantage.

Digital economy analysis

This Adweek article puts forward a highly typical judgment: in the AI era, the core of advertising systems is no longer simply “who owns the assets,” but “who can prove what the assets are doing.” This shift reflects a broader trend in the digital economy—when algorithms begin to dominate distribution, delivery, and optimization, value no longer comes mainly from a single product point, but from cross-stage data coordination.

The adtech chain used to consist of multiple independent layers: data provision, identity resolution, audience building, transaction execution, attribution measurement, and performance optimization. Every additional intermediary layer brings costs, signal loss, and reduced visibility. The article cites research by Cadent and Winterberry Group stating that for every $1 of media budget spent, 38 cents is absorbed by intermediaries and 47 cents is used for actual media buying. We cannot independently verify this data in this article, but the issue it reveals is very clear: value capture in the advertising ecosystem is increasingly shifting from the “content side” to the “infrastructure side,” and the efficiency and transparency of that infrastructure are determining who can secure brand budgets over the long term.

From the perspective of the digital economy, this means the basis of competition among adtech platforms is changing. In the past, platform competition emphasized scale, reach, and “independence”; today, brands care more about whether the data loop is auditable, whether the optimization logic is explainable, and whether first-party data truly becomes a durable asset. In other words, advertisers are not just buying reach, but a decision-making system that can be verified.

Business model observation

This change first affects the profitability logic of adtech. Traditional adtech business models are often built on matching, reselling, and multi-layer service fees, with platforms earning revenue by taking a cut at intermediary layers; AI-driven advertising systems, by contrast, are more inclined toward a “results-driven” and “unified stack” model, integrating data, identity, activation, and measurement into fewer technical layers to reduce friction and signal loss.

This will bring two direct consequences.First, the model of relying solely on “neutral narrative” to gain trust will fail. Brand owners are increasingly unwilling to pay for invisible middle layers, because AI is prompting them to demand explanations for every fee, every touchpoint, and every attribution result. Transparency is beginning to become the new basis for pricing.

Second, data-driven business models will depend more heavily on the efficiency of first-party data reuse. The article points out that when advertisers choose a technology vendor, they are not just choosing a tool; they are deciding where their data will “learn” the system. This is an important judgment: if data is encapsulated within a platform, it may bring better optimization results in the short term, but in the long run it may also strengthen the vendor’s own model capabilities, thereby weakening the brand’s bargaining power. For large advertisers, this is a typical trade-off of “efficiency for control.”

Therefore, the more competitive adtech companies in the future will not necessarily be the ones that emphasize independence the most, but the ones that can best prove how they reduce intermediary friction, improve traceability, and lower ineffective costs. Transparency is shifting from a compliance requirement to a business selling point.

Market Competition Analysis

This industry revaluation will reshape the platform competition landscape.

On one hand, integrated platforms with more complete data and distribution chains will benefit. The reason is simple: when advertisers want fewer redirects, fewer black boxes, and stronger explainability, platforms that can unify data, identity, and delivery within a more controllable framework are often more likely to win budget. This also explains why consolidation around retail media, cloud adtech, and first-party data networks continues to accelerate.

On the other hand, pure intermediary companies will face pressure. Agencies that mainly rely on identity brokering, repeated matching, or complex attribution fees will be reexamined in the wave of “transparency.” In the AI era, ad buying and selling place more emphasis on system coordination than on chain length. More layers do not necessarily mean more professionalism; instead, they may mean less transparency.

In the broader picture of platform competition, this change is also tied to the ecosystem advantages of giants like Google, Meta, Amazon, and TikTok. Large platforms naturally have stronger closed-loop data capabilities and can connect advertising, content, transactions, and user behavior. By contrast, smaller adtech players must prove that they are not “just another layer of take,” but infrastructure that can fill gaps in transparency, cross-platform collaboration, or efficiency in specific scenarios.

For agencies, this means their role is changing too. Agencies are no longer just media-buying executors; they are increasingly becoming data interpreters and system integrators. If they cannot explain how AI makes decisions, why budgets are allocated the way they are, and why performance fluctuates, their bargaining power will be further compressed by platform algorithms.

Data and Regulatory Impact“Transparency” has become a keyword not only because business demands it, but also because of regulatory trends. Under the frameworks of the GDPR, DSA, DMA, and the AI Act, the EU continues to strengthen scrutiny of data processing, platform responsibility, algorithmic explainability, and market dominance; the U.S. FTC is also intensifying enforcement attention on data use, consumer protection, and platform behavior. Although adtech is not the same as AI governance, the two overlap heavily in data flows, identity recognition, and automated decision-making.

This means that adtech may face three stronger regulatory requirements in the future:

1. Traceable data flows: How brand data is shared, transferred, and used across multiple vendors will require clearer disclosure. 2. Explainable algorithmic decisions: Automated optimization systems, how they affect campaign outcomes, and whether there is bias or unfair allocation will require greater explanatory obligations. 3. Transparent intermediary fees: The efficiency of how ad budgets are allocated at each stage may become a focus of regulators and industry scrutiny.

From a data governance perspective, cross-border data flows will also become a point of contention. Global brands often run ads across multiple jurisdictions, yet the boundaries of data compliance and model training are becoming increasingly complex. For multinational tech companies, future competition will not just be at the product level, but also in their ability to comply with data rules in different regions and in their governance credibility.

Global Trend Watch

What this article touches on is not just an internal debate within the advertising industry, but a longer-term global trend: the platform economy is entering the “verifiable era.”

Over the past decade, a platform’s core advantages have been scale, network effects, and algorithmic efficiency; over the next decade, what determines platform value may also include auditability, transparency, and governance capability. The stronger AI becomes, the more sensitive the black-box problem becomes. The more data there is, the more it needs to prove how it is used. The larger the platform, the more it must answer: “Who exactly does this system create value for?”

This also means that the evolution of adtech is aligning with broader digital-economy trends:

• AI Economy: moving from automated optimization to automated decision-making, but requiring explainability.
• Platform Economy: moving from connecting traffic to connecting data and transactions.
• Data Economy: moving from data collection to controlled and verifiable data use.
• Embedded Finance / Commerce: advertising, transactions, and payments becoming more closely integrated, with performance measurement moving closer to commercial conversion.
• Digital Sovereignty: countries’ sovereignty demands over data and algorithms will further drive localized governance.

Therefore, this is not a short-term shift in industry buzzwords, but a long-term structural migration. So-called “neutrality” will not disappear, but it will no longer be the only standard of trust. What matters more in the future is whether platforms can establish a new balance between efficiency, transparency, and compliance.### DigitalEcoNews Insight

The discussion around “neutrality” truly reveals that the center of power in the digital advertising economy is shifting: from the surface-level distribution of traffic to the underlying control of data, system visibility, and the right to interpret algorithms. For brands, what will matter most in the future is not just lower customer acquisition costs, but a clearer understanding of how data is transformed into growth; for platforms and technology vendors, competitive advantage will increasingly come from transparency rather than abstract promises; for regulators, ad tech will continue to serve as a frontline arena for testing AI governance, data governance, and platform accountability.

From a longer-term perspective, this means that the “trust mechanism” in the digital economy is being rewritten. In the past, the industry relied on neutrality narratives to build trust; in the future, what will truly win budget, capital, and regulatory recognition will be those companies that can clearly explain data flows, money flows, and decision-making logic. AI has not made ad tech simpler; rather, it has made business models, governance structures, and competitive boundaries more transparently exposed. Whoever can turn complex systems into verifiable systems will be more likely to gain an advantage in the next round of platform competition.

References

• Adweek: https://www.adweek.com/media/why-the-industry-needs-to-stop-talking-about-neutrality/
• European Commission: https://commission.europa.eu/
• OECD Digital Economy: https://www.oecd.org/digital/
• FTC: https://www.ftc.gov/
• Winterberry Group: https://www.winterberrygroup.com/
• Cadent: https://www.cadent.com/