How can publishers reclaim online conversations from walled gardens and build thriving, brand-owned communities—while attracting advertisers and users alike? Jim Daily, CEO of OpenWeb, joins host Noor Naseer to discuss why moderation remains essential to fostering safe, productive dialogue for both consumers and advertisers.
From tackling toxic comment sections to creating decentralized, publisher-level social platforms, Jim shares how OpenWeb is pioneering a “social layer” for the open internet. He also shares how this approach helps publishers reduce reliance on search and social traffic, drive engagement and monetize responsibly.
In this episode, Ian Forrester—founder of creative effectiveness platform DAIVID—joins host Noor Naseer to discuss why traditional creative testing is too slow and costly for today’s high-volume ad world.
Ian explains how AI-powered creative data can measure attention, emotion, and memory at scale, helping brands quickly identify what works and link creative quality to business outcomes. He also shares his take on striking the right balance between data and intuition, the cultural nuances of emotional response, and how creative insights can unite brand and performance marketing.
In a volatile economy, marketers are expected to do more with less.
Year-over-year digital ad spending growth has dropped below 10% for the first time since 2009. And while the impact of tariffs has been less severe than many feared, budgets are still under pressure and every ad dollar must work harder. But the rise of AI slop—low-quality, AI-generated content that’s been flooding digital platforms—is making that more challenging.
It’s distorting performance metrics, eroding trust, and draining budgets in media environments that often don’t deliver. For marketing leaders, it’s critical to understand how, if left unchecked, the effects of AI slop can translate into real business risks.
AI slop content can garner huge reach without offering real value to audiences—or advertisers. Whether through social media algorithms that reward engagement with such content or ad networks flooded with AI content farms that masquerade as legitimate inventory, this inflated reach muddies the waters for marketers trying to measure success, making it difficult to separate strong campaign performance from inflated metrics.
The problem manifests differently across platforms. On social media, one AI-generated Facebook post racked up over 40 million views and 1.9 million engagements in a single quarter. Yet the pages that share such posts are often spam, using clickbait tactics to drive users to scams or fake storefronts. In the programmatic ecosystem, AI has led to the proliferation of made-for-advertising (MFA) sites filled with low-quality inventory that might appear legitimate, but delivers little meaningful value.
Running ads alongside such content wastes budget and obscures what tactics are actually working. Campaigns may appear successful on the surface, but if the underlying performance data is misleading, it’s harder to tie media activity to real business outcomes. In a high-pressure economy, where marketers are being asked to prove ROI with fewer resources, this risk can prove especially costly.
Amidst a crisis of consumer trust, AI-generated content is making things worse. The explosion of AI slop is fueling what has been described as a “perverse information ecosystem” that overwhelms and desensitizes users, making trust harder to earn and easier to lose.
As synthetic content continues to spread, so does public skepticism around the technology behind it: 43% of US adults think the increased use of AI will harm them. And a recent study found that 73% of UK consumers are concerned about the prevalence of AI-generated content online.
That skepticism affects how people interpret everything they encounter on the internet, including ads. When brands’ messages appear next to low-quality, synthetic, and misleading content, it puts their credibility at risk. Even well-intentioned campaigns may come across feeling manipulative or out of touch when the surrounding environment lacks integrity.
In an unpredictable economic landscape, that loss of trust can carry a real cost. Ads that don’t resonate—or worse, that backfire—drain budget and weaken long-term brand equity. For marketers under pressure to prove impact, protecting trust is essential to both brand health and business performance.
An often-overlooked danger of AI slop is its ability to quietly corrupt the data that advertisers rely on to make smarter decisions.
On MFAs, traffic is driven by content designed to lure casual clicks rather than build meaningful engagement. Meanwhile, social algorithms often amplify AI slop in a way that inflates shallow interactions and gives outsized weight to clicks and views with little real intent behind them.
This creates a cascading problem. When users interact with ads placed against AI slop content, those interactions pollute an advertiser’s own data. These contaminated touchpoints then get incorporated into audience segments, lookalike models, and retargeting pools, degrading data-driven decisions. In a turbulent economy where every targeting decision matters, flawed data can lead to costly misallocation of ad spend.
AI slop poses real risks to campaign performance, brand safety, and business outcomes. And during times of economic uncertainty, those risks are even more costly. As AI-generated content continues to proliferate across digital platforms, advertising leaders must make adapting to this new reality a strategy priority, not an afterthought.
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With AI slop on the rise, advertising leaders can’t afford to take a reactive approach. Check out How Advertisers Can Adapt to the Age of AI Slop to see how top teams are protecting performance, improving inventory quality, and maintaining trust in a turbulent media environment.
Greg Sherrill, VP of Demand at Magnite, joins AdTech Unfiltered to dig into the current state of programmatic inventory, transparency in fee structures, and smarter supply path optimization strategies.
Greg shares insights on streamlining partnerships, rethinking curation, and balancing performance goals with sustainability efforts. From addressing fragmentation to navigating live sports access to advancing data-driven targeting in a post-cookie landscape, this conversation offers perspective for anyone looking to simplify their programmatic stack and deliver better outcomes.
The 2024 Olympic Games marked a major inflection point in the rise of connected TV advertising.
After a slower year in 2023, the channel exploded in 2024, fueled by the successful monetization of live events—particularly the Olympics—as well as years of sustained investment in content, new ad formats, and user acquisition. Now, CTV ad spend is expected to see double-digit growth through 2029.
For many advertisers, the Games served as a proving ground for the brand visibility and audience engagement that live sports can deliver on CTV. And with streaming platforms racing to secure sports broadcasting rights, live programming is poised to become a central pillar of CTV strategy.
While the broader CTV market is maturing, the live CTV space remains relatively nascent. As marketers look to make smarter media investments, understanding the value of live content, how the ecosystem is evolving, and what it takes to activate effectively will be key.
From engagement, to reach, to relevance, live CTV offers advertisers unique conditions for forging strong connections with audiences in real time.
One of the biggest advantages of advertising on live content lies in viewer mindset. Live content audiences tend to be more focused and emotionally invested in what they have committed to watch at a very specific time. This heightened attention can translate to higher engagement and ad recall.
Live content also allows advertisers to meet audiences where they are. With digital sports viewership now outpacing traditional TV sports viewership, live CTV has become the new home for real-time fan engagement.
And, crucially, live CTV advertising gives brands the opportunity to be part of cultural moments that matter. Tapping into these shared experiences can help brands deepen emotional resonance and reinforce relevance with target audiences.
To make the most of the live CTV advertising opportunity, advertisers need to consider how the marketing funnel is evolving—both within connected TV and across the broader digital landscape.
Historically, CTV (like linear TV) has been viewed as an upper-funnel channel best suited for brand awareness plays. However, as shoppable and interactive formats mature and attribution capabilities improve, CTV is proving to be a conversion powerhouse as well.
Live CTV takes this a step further. Thanks to the heightened attention and engagement it commands, live content can prompt immediate action even more effectively, with ads shown during live programming driving 43% larger average transaction values compared to those shown during non-live environments.
This evolution isn’t limited to CTV. Across the board—from holiday shopping behaviors to the rise of social commerce and commerce media more broadly—the traditional marketing funnel is collapsing. In this context, live CTV’s proven strength in brand awareness and expanding potential for performance makes it well positioned to play a central role in the next era of digital marketing, as full-funnel approaches take center stage.
Driving impact with live CTV requires advertisers to tailor their strategies to the dynamics of the channel.
To start, marketing teams should lean into the relevance of real-time moments and align their creative with specific events—think sports- or regionally-themed ads during live sports events, award-themed ads during award shows, and messaging tailored to the time of day an event takes place. This could also look like leveraging dynamic creative optimization (DCO) to create content based on specific moments within those events.
The right collaborators and integrations are also essential. For example, Disney Live Certified DSPs empower advertisers with the ability to scale and respond to bids on live content in real time, adapt to spikes in live viewership, stay responsive to shifting demand, and more.
From a conversion standpoint, tapping into high-impact formats such as shoppable or interactive ads—like pause ads or ads containing QR codes—can help advertisers take advantage of live CTV’s lower-funnel opportunities.
And, as with any channel, live CTV must be thoughtfully integrated into an omnichannel approach. Pairing it with digital, mobile, and social touchpoints allows for cohesive re-messaging and deeper engagement. For instance, a viewer might see a brand during a live game on TV, then again in a post-game highlight on social, or in a mobile sports app that same evening.
Frequency capping is also key. Because live audiences are highly engaged, repetitive ads can quickly lead to fatigue and negative brand sentiment. Sequencing creative across channels and using tactics like dayparting can ensure brand messages stay fresh and timely throughout the customer journey.
By activating strategies intentionally in these ways, advertisers can craft dynamic, responsive campaigns that meet the moment and move the needle with audiences.
As live content becomes an increasingly central part of the streaming landscape, live CTV offers advertisers a powerful way to capture attention, build relevance, and drive action. By understanding how the channel is evolving and approaching it with strategic creativity and precision, marketers can turn live moments into lasting impact.
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With baseball, football, basketball, and more hitting the live streaming lineup this fall, now’s the time for advertisers to step up to the plate, tackle their biggest challenges, and set themselves up for a slam dunk marketing season. Check out Going Deep on Live Sports Advertising Opportunities to explore how brands can make the most of live sports on CTV and beyond.
In this episode, Ali Mack, VP of AdTech at Experian, joins AdTech Unfiltered to explore how marketers can future-proof their targeting strategies as cookies fade and consumer privacy expectations rise.
Ali sits down with host Noor Naseer to unpack the shift from a single-source identity model to a more flexible, multi-signal approach rooted in cleaner, more organized first-party data. As identity continues to evolve, this episode delivers practical guidance for staying adaptive in a fragmented ecosystem.
Over the last few years, the internet has been flooded with AI-generated content: from bizarre viral imagery like shrimp Jesus, to lifelike dog videos that blur the line between real and synthetic, to fabricated news headlines and disturbingly realistic depictions of violence and tragedy.
Where such content may once have felt fringe or experimental, it has now quickly become mainstream. And while its quality and intent varies widely, the collective impact is clear: It is reshaping what people see, share, and trust online. AI slop has become a defining feature of the digital media landscape, fueled by algorithms that prioritize engagement over authenticity and by bad actors seeking to monetize low-quality content at scale.
As AI slop floods digital spaces, it’s altering audience behavior, skewing performance signals, and complicating efforts around brand safety and media quality. For marketing leaders, navigating this new reality requires more than reactive efforts. It demands a proactive strategy that can adapt to evolving content ecosystems, prioritize brand safety and suitability, and ensure that campaigns continue to deliver meaningful business outcomes.
While AI has introduced powerful efficiencies for advertisers, the growing presence of AI slop has introduced new challenges. And recent data confirms that marketing teams are taking notice: 57% of advertisers now view AI-generated content as a key challenge for the digital advertising ecosystem, and 54% believe it has led to a decline in overall media quality.
“AI slop is diminishing the quality and trustworthiness of digital advertising as a whole,” says Mallory Chaney, VP of Integrated Client Solutions at Basis. “However, it also opens up opportunities for brands to cut through the noise by investing in authentic, differentiated messaging.”
One of the most significant concerns is the impact of AI-generated content on made-for-advertising (MFA) sites. With the widespread availability of gen AI tools, bad actors are increasingly using them to mass-produce low-value content with little to no editorial oversight. In one recent example, more than 200 AI-driven websites impersonating sports news outlets were found blending synthetic content with stolen reporting, creating an illusion of legitimacy while undermining the integrity of the media environment.
And, as evidenced by phenomena like shrimp Jesus, AI slop isn’t limited to MFA sites. It is showing up in nearly every corner of the internet. On Quora and Medium, for example, AI-generated material jumped from 1.77% and 2.06% in 2022 to 37.03% and 38.95% in 2024, respectively. Social media platforms have seen similar patterns, with AI-generated content increasingly filling feeds and recommendation algorithms. The rapid pace of this growth signals that AI-generated content is steadily becoming a widespread and dominant force across the online ecosystem.
Compounding these challenges is the way algorithmic systems reward this content. Social platforms, in particular, often promote low-quality AI slop even if users don’t follow the pages that share it, creating feedback loops that reinforce and scale artificial content. This algorithmic promotion can inflate impression volumes far beyond what that content would naturally earn based on user preference, making it difficult for advertisers to gauge authentic audience interest.
The implications for advertisers are serious. Low-quality AI slop increases brand safety concerns by putting campaigns at risk of running adjacent to misleading, plagiarized, or even harmful content that may not align with a brand’s values. It distorts performance metrics by inflating impressions and engagement on content that may not reflect authentic user demand. It can also contaminate optimization models—if campaigns consistently run against algorithmically amplified but low-quality content, the resulting engagement signals may not accurately represent true audience behavior, leading to flawed optimization decisions. And it wastes media dollars by funneling ad spend into inventory that delivers minimal value—and potentially even harm. These challenges are amplified in today’s climate of economic turbulence, where marketers are under increased pressure to prove the ROI of every dollar spent.
Though the proliferation of AI slop poses many challenges for advertisers, its ability to undermine brand safety at scale presents a growing risk. This is, in large part, because such content is often designed to pass as human-made. As such, detecting AI-generated content requires looking beyond traditional content signals. According to Chaney, performance monitoring can reveal telltale signs of low-quality AI content: unusual URLs, high impression delivery to sites outside campaign parameters, and metrics patterns like high impression volume paired with low click-through rates or elevated bounce rates.
This detection challenge is compounded by the fact that many platforms don’t yet offer advertisers the option to avoid AI-generated content altogether. On YouTube, for example, advertisers can specify the types of video content they want their ads to run alongside—such as avoiding sensitive or graphic categories—but they cannot filter based on how that content was created. As a result, campaigns may run adjacent to synthetic videos that lack editorial oversight, factual grounding, or alignment with brand standards.
To adapt, leading advertisers are evolving to adopt more intelligent, layered approaches that combine pre-bid verification tools, contextual intelligence tools, and curated inventory strategies to both detect and avoid low-quality AI content. “Contextual intelligence engines, in particular, are resilient against AI-generated content because they can understand semantic context rather than just keywords and terms,” says Chaney.
At the same time, many teams are working with partners that monitor supply paths dynamically and identify domains known to misrepresent quality or exploit optimization systems through mass-produced, MFA-style content. “Advertisers are also turning to more premium inventory like private marketplaces and programmatic guaranteed deals, which are generally less susceptible to fraud,” says Chaney. Custom blocklists and curated PMPs can add another layer of protection against low-quality content.
As content creation and monetization methods continue to evolve, teams that embrace this kind of approach will be better equipped to maintain brand integrity without sacrificing scale or efficiency.
As AI slop continues to reshape the media landscape, marketing leaders are under pressure to ensure their teams are prepared—not just to react, but to plan intentionally. Leaders who embrace a cross-functional strategy grounded in adaptability will be well-positioned to both deal with the current challenges and to adjust as the digital media landscape continues to shift.
To ensure their ads aren’t running alongside AI slop, marketing leaders should first assess exposure. Teams should audit recent campaigns for unusual traffic patterns, off-target site delivery, or inflated impression counts tied to weak engagement—common signals of low-quality AI content, according to Chaney.
From there, partner evaluation criteria should evolve. Questions that once focused primarily on brand suitability and safety should now include whether partners can identify AI-generated content, how frequently their detection logic is updated, and how easily their tools integrate with existing tech stacks.
Internally, leaders should align media buyers, brand safety stakeholders, and analytics partners around a shared response plan. This could include establishing clear protocols for identifying AI content and flagging questionable placements. Education also plays a critical role, as teams trained to spot the signs of AI slop are better equipped to escalate concerns. “Continuing to recognize AI-generated content as well as understand both the strengths and limitations of tech will help advertisers to balance available tools with human oversight,” says Chaney. With early investments in processes and training, marketing leaders will be far better prepared as AI-generated content continues to spread—and potentially becomes more difficult to detect.
In a digital media environment shaped increasingly by low-quality AI content, brand safety, performance accuracy, and trust are all on the line. Marketing leaders that adapt now won’t just protect their campaigns—they’ll build trust with audiences and maintain competitiveness in a rapidly transforming digital media landscape.
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Looking for more insights on how AI is transforming digital advertising? We surveyed marketing and advertising professionals from top agencies, brands, and publishers to get a pulse on how teams are using and feel about the technology today, and how it could change things going forward.
What media type offers advertisers access to emotionally-charged audiences and rivals the viewership of tentpole events like the Olympics? In this episode, Rob Christensen, Global EVP at Vevo, joins host Noor Naseer to explore the often-overlooked power of music videos in today’s advertising ecosystem.
Rob unpacks how music intersects with culture, the influence of artist fandoms, and the growing role of nostalgia and platform-specific behaviors in shaping CTV strategies and ad formats. This episode offers a fresh perspective on how music videos are reshaping video advertising and driving deeper audience engagement.
While Ari Paparo may not be the AdTechGod, he’s undeniably a defining voice in the industry—highly visible, well-regarded, and widely followed.
Ari, founder of Marketecture Media and former CEO of Beeswax, is now debuting his first book, Yield: How Google Bought, Built, and Bullied Its Way to Advertising Dominance. Ari joins host Noor Naseer to discuss the book and the impact of storytelling in adtech.
