What Changed: Transaction IDs Got Individualized
Until recently, Prebid.js would attach a single transaction ID, also known as a TID, to each auction. That same TID appeared across all bidders and exchanges/paths involved, allowing Demand Side Platforms (DSPs) to recognize when they were bidding for the same ad impression across different paths. This allowed buyers to deduplicate impressions, avoid bidding on the same inventory twice, and better manage frequency across channels. It also provided them with deeper insights into which supply paths were most cost-efficient, while enabling more intelligent budget pacing across streams.
But in late August 2025, Prebid fundamentally changed this logic. Now, each bidder receives its own unique TID for every bid request, fragmenting what was once a unified signal. The full technical details are available in Prebid’s official GitHub pull request.
In practical terms, buyers can no longer distinguish between two bid requests from different exchanges that are tied to the same impression. That single thread of connection, the foundation of efficient bidding, transparent optimization, and controlled costs, has been cut. And with it, the balance of the programmatic ecosystem begins to shift.
Under the Hood: How Prebid.js Handles TIDs
For those closer to the technical side, here’s what actually changed under Prebid.js version 10.9.0.
Before
Prebid exposed two ID fields:
- ortb2.source.tid: the auction identifier
- ortb2Imp.ext.tid: the impression/ad unit identifier
If enabled, Prebid generated one TID per ad unit per auction and sent the same value to every bidder. This aligned perfectly with how DSPs deduplicated requests.
After
Prebid now generates bidder-specific TIDs:
- ortb2.source.tid is now unique per bidder
- ortb2Imp.ext.tid is generated per bidder × transaction
- Adapter-visible .auctionId and .transactionId now reference these fields
- Publisher-provided first-party values are ignored for these identifiers
This means DSPs can no longer use a shared cross-exchange TID to identify duplicate requests, thereby removing the deduplication mechanism that many relied on for SPO and bid-collision control.
Why Prebid Made This Change
Prebid’s development team did this not to disrupt advertisers but to respond to growing concerns about data privacy. When a single TID is shared across exchanges, it can serve as a linking signal, allowing DSPs or data brokers to piece together how supply is moving, which publishers are involved, and even individual user behavior over time.
At a time of strict privacy regulation and heightened scrutiny, publishers and privacy experts saw shared TIDs as a hidden risk. Prebid responded by segmenting that ID by bidder, aiming to prevent unintended data stitching and protect user and publisher data.
What It Means: A Clear Shift in Power and Visibility
On the surface, this appears to be a technical adjustment. In practice, it disrupts several core pillars of programmatic optimization, shifting how value flows across the ecosystem.
It’s not surprising that Anthony Katsur, the IAB Tech Lab’s CEO, has voiced significant concerns in a LinkedIn post, emphasizing that the OpenRTB framework was designed for TIDs to remain consistent across all participants in an auction. They described Prebid’s new approach as “materially non-compliant” with the specification and recommended that any such changes be resolved through collaborative industry processes rather than unilateral decisions.
Let’s explore what caused the damage in real life.
1. Duplicate Bids and Higher Costs
Without a shared TID, DSPs may submit multiple bids for the same impression across different supply paths. While losing duplicate bids incurs no direct cost, bidding against one’s own strategy can push auction clearing prices higher when those bids are successful. As a result, overall efficiency decreases and CPMs may rise.
2. Reduced Transparency
The absence of a unified identifier limits the ability to connect bid requests across exchanges, making it harder to understand how auctions unfold. DSPs lose visibility into supply path dynamics and pricing signals, which weakens the basis for effective optimization and strategy planning.
3. Frequency and Reach Challenges
Managing exposure across channels becomes more complex when impression-level identifiers are fragmented. The same individual may receive multiple ad impressions from different SSPs, leading to potential oversaturation, inconsistent reach control, and reduced effectiveness of frequency management strategies.
4. Weakened Auction Intelligence
Auction forecasting, bid shading, and supply path optimization frameworks rely heavily on recognizing when the same impression surfaces across multiple paths. With bidder-specific IDs, these signals become less reliable, necessitating a greater reliance on manual modeling and alternative measurement approaches.
How This Reshapes Roles Across the Ecosystem
This update is reshaping dynamics across the programmatic ecosystem, with distinct implications for each stakeholder group. The impact varies for SSPs and publishers, DSPs and ad buyers, supply vendors and managed service providers, and brands, each facing unique challenges and opportunities resulting from Prebid’s change.
SSPs and Publishers
For SSPs and publishers, this could be a short-term win. If DSPs cannot deduplicate efficiently, auctions may see more bids per impression, driving up clearing prices.
However, it also increases pressure on SSPs to differentiate via quality, not just scale. As buyers seek cleaner paths, those who can guarantee low duplication and high transparency will benefit most.
Large DSPs and Ad Buyers
The most immediate effect is felt by The Trade Desk (TTD), DV360, Amazon DSP, and other large-scale buyers. These platforms have invested heavily in using TIDs for deduplication, auction intelligence, and cross-supply optimization.
Scoping TIDs per-bidder weakens one of the core pillars of TTD’s supply path optimization strategy. Without the ability to deduplicate across exchanges, TTD loses some of its historical advantage, making curated, pre-filtered supply paths increasingly important.
This shift, along with its dramatic impact on DSPs, also underscores the importance of collaborating with multiple DSPs and adopting a technology-agnostic approach. Relying on a single DSP creates dependency risks, while leveraging a diversified stack ensures better control, stronger optimization opportunities, and greater flexibility in adapting to ecosystem changes.
Supply Vendors and Managed Service Providers
This change has significant implications for both supply vendors and managed service providers.
For supply vendors, the shift places greater emphasis on controlled supply management and trusted partnerships. As DSPs face challenges with deduplication, SSPs that provide high-quality, low-duplication inventory become increasingly valuable to buyers seeking cleaner and more efficient paths to purchase.
For managed service providers, the impact creates both challenges and opportunities. Agencies and partners that rely on single DSP relationships will face greater inefficiencies, while those that take a multi-DSP, tech-agnostic approach are better positioned to adapt.
Here at AI Digital, the impact is twofold. For Smart Supply, our AI-powered supply management solution, we build deal IDs on top of SSPs, giving buyers access to clean, pre-optimized inventory pipelines while continuously monitoring performance to minimize duplication and inefficiency. At the same time, our managed service teams leverage Elevate, combining AI-driven deduplication modeling, advanced SPO capabilities, and first-party identity integrations to deliver stronger results across fragmented programmatic environments.
This approach is anchored in AI Digital’s Open Garden framework, which ensures vendor neutrality and technology agnosticity. By working seamlessly across multiple DSPs, SSPs, and identity partners, we retain the flexibility to select best-in-class solutions for every client. This positions AI Digital to deliver more transparent, efficient, and privacy-safe outcomes while helping brands and agencies navigate an increasingly complex programmatic landscape.
Agencies and Brands
For agencies managing large-scale campaigns, this shift introduces several critical challenges. Inefficiencies caused by duplicate bidding can result in higher CPMs, thereby increasing overall media costs.
At the same time, the loss of shared identifiers makes cross-channel attribution more difficult, resulting in increasingly fragmented measurement and reduced visibility into performance across platforms. Auction reporting also becomes less transparent, as the lack of unified signals removes granularity, making it harder to explain pricing fluctuations and optimization decisions to clients with confidence.
Broader Implication: Even Greater Fragmentation Ahead
This shift is not isolated. Instead, it deepens an existing trend: the move toward walled gardens, data silos, and fragmented signals. Privacy moves like this are necessary. Yet they impose tradeoffs: losing shared signals, fragmenting supply and demand, and creating inefficiencies for buyers and agencies alike.
This change marks a significant setback for the industry, creating deeper fragmentation and adding new layers of operational complexity. As transparency declines, the original promise of programmatic —a transparent, efficient, and data-driven marketplace —feels increasingly out of reach. The ecosystem is moving toward a more siloed future, where optimizing performance and maintaining control over costs will require greater innovation, stronger partnerships, and a shift toward more flexible, tech-agnostic strategies.
Unless we counteract this fragmentation with new cooperation models, agencies and brands will face rising media costs, fractured measurement, and a confusing patchwork of supply paths.
Why an Open Garden Framework Matters
The solution is not closing the doors. It is creating a model that strikes a balance between privacy and interoperability.
An Open Garden framework would enable stakeholders to share signals and insights in a privacy-safe manner. It would promote interoperable identity solutions, transparent supply path models, and collaborative measurement, all built around consent and openness.
At AI Digital, our strategy already embraces this approach. We seek:
- Better signal interoperability across partners
- First-party and consented identity solutions
- AI-powered tools to unify fragmented data, model frequency, and optimize paths
- Transparent supply agreements that respect both yield and privacy
With this mindset, we can turn fragmentation into a force for innovation, creating a programmatic ecosystem that is both privacy-safe and performance-driven.
Conclusions
Prebid’s update marks a pivotal moment for programmatic advertising. By prioritizing privacy, it reduces transparency, increases fragmentation, and reshapes the balance of power between DSPs, SSPs, agencies, and managed service partners. In this new environment, curated supply strategies—such as Smart Supply and AI-powered tools—become essential for navigating complexity and protecting efficiency. The future belongs to those who embrace an Open Garden framework, connecting signals and partners in a privacy-safe, interoperable way to deliver smarter outcomes for brands.
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