BEYOND ‘PATHETIC’ CONTENT: USING SPY DATA TO BUILD CREATIVE SYSTEMS THAT SURVIVE THE AI CULLING

The commercial web is about to be buried under what Mark Schaefer bluntly labels “pathetic” content: AI-slop pumped out by the terabyte, as vendors brag about “killing busywork” while inboxes, feeds, and SERPs fill with auto-generated pitches and agent-written emails that even seasoned journalists now describe as “dumb AI agents” begging for attention on their behalf, as AdExchanger’s coverage of rogue AI outreach makes painfully clear.

In that environment, serious advertisers have a stark choice: get swept into the sludge as just another brand auto-churning creative, or use the kind of ad-intelligence and creative-effectiveness data that powers large-scale networks of AI-driven creators in campaigns like Unilever’s 300,000-influencer model to train your own AI systems so they consistently produce distinctive, on-brand concepts and variations that not only survive the coming AI culling, but also win conversions while everyone else drowns in sameness.

Most marketers know an arms race is coming. Few are ready for the casualty count.

As generative tools get cheaper and agents get more “helpful,” brands are spinning up content, ads, and emails at 10x–100x the previous volume. Vidmob’s CEO told Marketing Dive that both generative AI and creator-led media are driving a “10-times or 100-times expansion of the number of assets,” which is why Kroger now uses AI to analyze 1,900+ creative variants and predict which attributes actually move e‑commerce conversions, rather than just shoving more assets into the void of Meta and DV360 campaigns (Marketing Dive). That’s the first hint of the culling: volume alone is becoming a liability. Signal — evidence that your creative works — is the only thing that will keep you funded.

At the same time, distribution platforms are flipping from “any content is good content” to ruthless filtration. LinkedIn is already blocking hundreds of thousands of automated comment attempts every day and has reportedly stopped billions of posting attempts in recent months, while giving users the ability to report obvious AI slop and even showing authors when their posts are perceived as heavily AI‑generated, as Social Media Examiner explains. The platform has quietly killed its own “rewrite this in generic AI voice” feature and replaced it with a light proofreading tool that preserves the author’s original tone. Translation: if your content sounds like everyone else’s, it’s not just boring — it’s now a negative ranking signal.

This is happening across the ad ecosystem, not just in feeds. Meta is under fire for auto-generated creative that mutates products, like an REI ad showing a bike with two handlebars or a women’s networking campaign accidentally centering a man, errors that force advertisers into damage control while Meta shrugs and points to terms of service that say “AI can make mistakes” and the advertiser is responsible for reviewing outputs, as AdExchanger reported. Performance marketers still tolerate this because “Meta’s still the best platform” and “has the best data.” But that’s precisely the problem: if everyone leans on the same platform data and the same off‑the‑shelf models, the creative layer becomes a commodity. The only differentiator left is what you know that your competitors’ systems don’t.

Meanwhile, AI isn’t just generating content; it’s deciding which content appears at all. Agentic systems are already reallocating budgets, tweaking bids, and swapping creative on the fly without human intervention, delivering lower acquisition costs and faster sales cycles to early adopters, according to analysis in MarTech. As these agents absorb more decisions, they will also absorb more of the user’s attention. Search becomes “answer engines.” Recommendation systems become black‑box editors. The vast majority of generic, undifferentiated assets will never be seen by a human because they will never clear the agents’ internal thresholds for relevance, quality, or safety.

That’s the real AI culling: not some sci‑fi robot uprising, but a silent triage by platforms and algorithms deciding which pieces of content deserve to exist in front of people. Spammy, agent‑written pitches are already being flagged and ignored; journalists are publicly ridiculing the flood of “dumb AI agents” begging for coverage on behalf of lazy PR teams, as highlighted in AdExchanger’s roundup of complaints from 404 Media’s Jason Koebler about inboxes overrun with automated outreach (AdExchanger). Those messages don’t just fail — they become training data for smarter filters.

Underneath all of this is a brutal economic logic. Platforms and publishers are under pressure to cut costs, monetize AI traffic, and preserve what’s left of human trust. Some news organizations are racing to license their archives and become information suppliers for AI interfaces, while others are doubling down on their human creators as differentiated brands, a split that industry observers like Martin Schori and Brian Morrissey have described as an either‑or decision between serving bots or serving people, as summarized in AdExchanger’s coverage. Marketers face a parallel choice: become just another feedstock for machine learning systems, or build creative systems that are so grounded in proprietary insight and recognizable human voice that both algorithms and audiences learn to treat them as high‑value signals rather than disposable slop.

Generic automation loses in this environment not because AI is “bad,” but because everybody has the same AI. When every competitor can generate infinite average content, survival shifts to those who can feed their models with unique spy data — the behavioral, creative, and contextual intelligence that transforms automation from a content pump into a defensible creative system.

“Pathetic” content isn’t just low quality; it’s uncalibrated. It has no idea what actually works.

Most AI-slop exists in that blind spot. It’s generated from vibes: generic best practices, half-remembered frameworks, and prompts padded with adjectives like “high-converting” and “thumb-stopping.” There’s no feedback loop, no grounding in what real audiences have historically clicked, watched, shared, or bought from. In a world where models can spit out infinite variations, the scarce resource is not words. It’s evidence.

Spy data is what fills that gap.

By “spy data,” we’re not talking about shady scraping or corporate espionage. We’re talking about performance-grounded intelligence on what competitors’ and category content is actually doing in the wild: which hooks keep people from scrolling past, which visual patterns correlate with conversion, which tones trigger replies instead of eye-rolls. It’s the same logic that sits behind Kroger’s work with Vidmob, where AI analyzed more than 1,900 video and image assets to surface the specific creative attributes tied to e‑commerce conversion, making “creative quality” measurable instead of mystical, as Marketing Dive’s coverage of that initiative explained.

That shift—from guessing to pattern recognition—is the line between pathetic content and proven systems. Once you can see the patterns, you can codify them. And once they’re codified, they become the backbone of a creative engine your AI can actually be judged against.

Notice what Vidmob is really selling in that Kroger example. Not more assets. Kroger already has too many. They’re selling an upfront signal: predicted performance based on prior creative data. The company’s Vidmob360 product goes even further by piping those performance signals directly into tools like Claude and ChatGPT, turning creative performance insights into a form of proprietary training data that can guide future outputs, according to their description in Marketing Dive. It’s “spy data” on your own back catalog and, increasingly, on your category at large.

This is exactly the kind of infrastructure emerging in other corners of digital marketing. When DAIVID’s creative-effectiveness models were wired directly into ADIN.AI’s platform, they created a “live loop between creative intelligence and media execution” for Unilever’s 300,000‑influencer program, so marketers could pre-score assets, scale winners, and pause losers in real time, as Search Engine Journal’s reporting on the partnership detailed. The point wasn’t just to handle volume. It was to keep decision-making tethered to what was actually working across markets, formats, and creators.

That’s the essence of spy data: not static benchmarks, but a continuously updated picture of how the market is responding to specific creative choices.

And it’s not limited to ad creative. When Time quietly launched an AI-only version of its website, serving sponsored content directly to AI crawlers from companies like OpenAI and Anthropic, it effectively started “spying” on a different audience altogether: the models that increasingly mediate human attention. Those agent-facing pages, which included Ally Bank messaging and FAQ-style answers, were invisible to human readers but designed to shape what assistants say about brands, as MarTech’s coverage of Time’s “agent ads” strategy uncovered. If you can see what those crawlers are being shown—and how that influences generated answers—you’ve discovered an entirely new layer of competitive intel.

Put all of this together and the game changes. “Pathetic” AI content is what happens when you let the model hallucinate its own success criteria. Proven patterns emerge when you:

• Ingest large volumes of creative and content from your brand and category.
• Attach real performance outcomes—scroll depth, watch time, replies, conversion—to specific patterns in that creative.
• Feed those patterns back into your prompt systems, templates, and agents so they’re constrained by what’s historically worked.
• Keep the loop live, refreshing your pattern library as platforms, audiences, and formats evolve.

This is also where human creativity stops being a nice-to-have and becomes the differentiator. Social leaders like Cohesity’s Lisa Marcyes are already using AI for analytics, drafting, and research, but the truly memorable ideas—a drone show over Las Vegas that never happened, or a fainting goat in a ransomware explainer—still come from humans who understand their audience deeply, as she argued in a recent TopRank Marketing interview on creativity and AI. Spy data can tell you that “unexpected animal cameo” and “spectacle that blurs reality” outperform beige thought leadership. It can’t decide that a fainting goat is the right kind of chaos for your brand. That leap is still human.

The AI culling won’t punish automation; it will punish unproven automation. Spy data is how you move from spraying content into the void to running a disciplined, evidence-backed creative system—one where AI doesn’t replace judgment, but is forced to compete with what the market has already told you is worth paying attention to.

Most teams are still treating AI like an intern with fast fingers: “Write five variations of this,” “turn this webinar into a post,” “optimize this headline.” That’s AI-assisted copy, not an AI-native creative system. One makes more content. The other makes better, stranger, more effective content — at scale — because it is wired around feedback, not vibes.

An AI‑native creative system starts with a brutal constraint: every asset is guilty until proven effective. The default output of any model is “pathetic” until real performance data says otherwise. That’s why the most sophisticated advertisers are hardwiring spy data — the granular, attribute‑level feedback your campaigns generate every day — directly into their creative engines.

Consider how Kroger attacked the problem. Working with Vidmob, it ran more than 1,900 video and image assets through an AI system that tied specific creative attributes to e‑commerce conversion across Meta and DV360. As Marketing Dive reported, the goal wasn’t just “better ads,” but a predictive scoring model that could tell you, before you spent a dollar in media, which combinations of visuals, copy, and structure were likely to move product. That’s an AI‑native system: creative isn’t a sunk cost; it’s an input to a live model.

Architecting something similar inside your own stack means breaking the process into three tightly coupled layers:

1. Sensing layer (spy data capture).
   You start by instrumenting every creative asset like it’s a product experiment. That means going beyond CTR and ROAS to structured, machine‑readable tags: hooks used, visual motifs, pacing, emotional tone, narrative pattern, even weirdness level. Tools like Vidmob360 were built to expose this attribute‑level performance and then hand those patterns off to models via API, turning “creative performance insights” into proprietary data you can feed into Claude, ChatGPT, or your in‑house systems, as described in.

2. Generation layer (models that know your patterns).
Once your spy data exists in a structured store, you stop prompting from scratch and you start prompting from pattern. Instead of “write a high‑converting LinkedIn post,” your agents are conditioned on the attributes that historically earned saves, replies, or demos for your exact audience: “Use a tension‑led open, avoid overpolished ‘AI voice,’ lean into first‑person vulnerability, keep the CTA implicit.” That’s exactly the shift LinkedIn is signaling as it replaces its generic “enhance your post” tool with a proofreading‑only assistant intended to preserve the creator’s voice, a crackdown on AI slop that Social Media Examiner describes as the difference between content that builds authority and content that quietly gets buried.

Internally, this looks like a “playbook model”: you fine‑tune or heavily condition a model on your proven hooks, formats, and tones, and have it output not finished copy, but structured variations that conform to those patterns. Then you route those variations into different channels, each with its own safety rails and compliance checks.

3. Interpretation layer (humans as creative editors, not word monkeys).
As media buying, posting, and optimization get eaten by agents, the human role shifts toward judgment and escalation. In media, agency leaders already admit that buyers are becoming “decision makers and managers,” sanity‑checking what agents are doing and catching issues algorithms can’t see, like whether a sudden performance spike is brilliance or a measurement glitch, as AdExchanger’s reporting on agentic media makes clear. Your creative org needs the same posture: humans as pattern recognizers, chaos injectors, and brand stewards.

This is where originality lives. B2B social leaders like Lisa Marcyes are already using AI for analytics, copy, and competitive research, but the ideas that stop the scroll — a hyper‑real drone show over Las Vegas, or a fainting goat in a ransomware explainer — came from humans who deeply understood their audience. As she told the TopRank Marketing team, the content people remember “makes them feel something,” and AI didn’t come up with those stunts; a creative team did.

When you wire these three layers together, you stop shipping one‑off AI experiments and start evolving a living creative organism. Spy data tells you which weird bets actually worked. Models bake those patterns into reusable, channel‑specific templates. Humans keep pushing the edges — culturally, emotionally, conceptually — in directions no model would ever suggest on its own.

The result isn’t “more content with AI.” It’s a system that can survive the AI culling because it is continuously learning from real performance, ruthlessly pruning what doesn’t resonate, and doubling down on the rare, unmistakably human ideas that your models would never have generated — but can absolutely help you scale once they exist.

If Section 3 was the theory of AI‑native creative systems, this is where we put our hands in the dirt.

Your goal with Anstrex isn’t “steal winning ads.” It’s to reverse‑engineer why certain creative works in your category and then encode those patterns into a reusable “creative genome” your AI can build from — without collapsing into plagiarism or sameness.

Here’s a practical workflow.

1. Start With a Tight Question, Not a Vague Browse

Open Anstrex and resist the urge to just scroll. Instead, define a sharp question like:

• “What emotional angles consistently sell mid‑ticket SaaS trials on Meta?”
• “How do high‑spend ecom brands visually frame urgency without screaming ‘SALE’?”
• “What hooks keep showing up in health supplement pre‑landers this quarter?”

This aligns you with the kind of feedback‑driven systems being wired into platforms like DAIVID + ADIN.AI, where creative is evaluated in context of real outcomes, not vibes. Their integration creates a “live loop between creative intelligence and media execution,” letting brands pre‑score creative and scale what works in near real time, as Shelley Walsh explained when describing how Unilever governs a 300,000‑creator network.

You’re building a smaller version of that loop for your own brand.

2. Filter Ruthlessly for Signal

In Anstrex:

• Lock in your vertical and traffic source (e.g., Facebook, native, search).
• Sort by duration and spend/weight, not just recency. Creative that has run profitably for weeks or months is a better proxy for true performance than a flashy ad that appeared yesterday.
• Exclude obvious scams, cloakers, and irrelevant geos so your dataset matches your brand reality.

Your working dataset should be 30–100 ads, not 3,000. Remember: you’re trying to distill a genome, not wallpaper your brain with other people’s work.

3. Decompose Ads Into Structured “Genes”

Now you put on your lab coat.

For each ad or landing page, you’re going to break it into labeled components. A simple spreadsheet or Airtable works:

Columns for ads might include:

• Hook type – fear, status, curiosity, contrarian, relief, “seen & understood”
• Narrative frame – testimonial, founder story, before/after, “myth‑busting,” demo
• Human presence – talking head, UGC selfie, product‑only, animation
• Visual motif – screenshots, over‑the‑shoulder laptop shot, bold text cards, meme template, pattern interrupt gif
• Social proof – logos, review count, star ratings, named customers vs. anonymized
• Offer shape – free trial, discount, quiz, audit, “calculator,” waitlist
• CTA language – “Get started,” “See how it works,” “Check eligibility,” “Take the quiz”

You’re basically doing what Vidmob did for Kroger when it analyzed 1,900+ creative assets to identify the attributes tied to e‑commerce conversion; that study demonstrated that “creative quality is measurable,” which makes investing in better inputs rational rather than fluffy, as.

Do a quick first pass manually on 15–20 ads so you understand the patterns. Then, if you want to accelerate, you can paste raw ad text and screenshots into your model and ask it to populate the schema for you. The key is that you define the schema; the AI just fills in rows.

4. Extract Your Category’s Dominant Patterns — and Its Blind Spots

Once your sheet has a meaningful sample:

• Sort and count: Which hook types show up most? Which offer formats dominate? Which visual motifs are near‑universal?
• Note pairings: Maybe “myth‑busting” headlines almost always pair with text‑only thumbnails. Maybe testimonials nearly always lead to quiz‑style funnels.
• Spot absences: What isn’t anyone doing? No humor? No vulnerability? No product “fail” stories? These gaps are where originality can live without abandoning what works.

This is where you align with what B2B social leaders are seeing on the ground. When Cohesity’s Lisa Marcyes talks about a ransomware spot featuring a fainting goat and a drone show that looked real enough to fool people, she’s illustrating that ideas which “make people feel something” still originate with humans, even when AI saturates production and analytics, as the TopRank team documented. Your competitive edge won’t be another safe, on‑rails variant of the category norm; it will be a deliberate twist on it.

5. Encode the Genome as Reusable, AI‑Readable Rules

Now you translate the spreadsheet into a compact “creative genome” your AI can understand and remix.

Concretely, you create a playbook of rules and ingredients, not templates:

• Atomic elements: Lists of hook types, proof assets, visual motifs, and emotional tones that have proven durable in your niche.
• Combinatorial rules: “For cold traffic on Meta, combine one curiosity hook + one ‘seen & understood’ line + one specific outcome + social proof from peer identity.”
• Guardrails: “Never claim X,” “Always disclose Y,” “No generic ‘save time and money’ without a concrete scenario.”

Treat this as your brand’s proprietary version of the “creative effectiveness models” that AI‑native ad stacks plug into their systems to score and rank creative before media dollars hit, the way DAIVID’s models flow directly into ADIN.AI to guide which assets to scale or pause in real time, as.

This document is what you actually feed to your model: not “write a high‑converting ad,” but “using the creative genome below, generate 10 novel combinations that respect these guardrails and maximize these elements.”

6. Close the Loop With Performance — Even If It’s Crude at First

Finally, your genome can’t be static. Even a light feedback loop will keep it from decaying into fossilized best practices:

• Tag live assets with the same schema labels you used in Anstrex.
• Pull performance data weekly or monthly.
• Update your counts: which “genes” correlate with outliers? Which combos are consistently mediocre?

You don’t need full‑blown agentic optimization to start. But you do want to edge toward the kind of continuous experimentation and governance that modern AI‑native advertising requires — the same shift toward always‑on testing and clear guardrails that recent MarTech guidance on AI‑native advertising argues is now table stakes.

Done right, Anstrex becomes more than a swipe file. It’s your microscope. The creative genome you extract becomes the DNA your AI composes with — grounded in reality, tuned by performance

If AI-native creative systems are going to survive the culling, they can’t just be faster versions of what we already do. They have to protect — and amplify — the one thing the machines still reliably screw up: human weirdness.

Platforms are quietly telling you this in their own defensive way. LinkedIn is actively training its models — and its users — to sniff out “AI slop.” Members can now flag posts that feel automated, and those signals are feeding LinkedIn’s detection models while a new “proofreading-only” assistant explicitly prioritizes preserving the author’s voice over rewriting in a generic AI tone, as Social Media Examiner reported. That’s not a UX tweak; it’s a value judgment. The platform is saying: “Use AI, but if you sound like everyone else, you lose.”

The same shift is happening in paid media. As the volume of AI‑generated creative explodes, measurement vendors are racing to show that creative quality is not a fuzzy art project but a performance lever you can optimize. Vidmob’s analysis of thousands of Kroger assets connected specific visual and narrative attributes to e‑commerce conversion, then wired those learnings into tools that pipe creative performance data directly into models like Claude and ChatGPT, according to Marketing Dive’s coverage. The message is similar: everyone can generate more content now; what matters is the handful of outliers that actually move revenue.

But weirdness isn’t just a “nice to have” on top of this infrastructure. It’s the only defensible edge left when every competitor has access to roughly the same models, the same UI, and the same prompts. When DISH Media’s Liam Kristinnsson says he still wishes “the people in AI ads were real,” and nearly a third of Gen Z and millennials report negative feelings toward AI-generated ads, as AdExchanger documented, he’s expressing a broader fatigue: the experience of scrolling through content that all feels statistically correct and emotionally vacant.

So what does it actually mean to “preserve weirdness” inside an AI-native creative system?

First, you treat human ideas as the scarce raw material and AI as the refinery. Cohesity’s Lisa Marcyes uses AI across analytics, scripting, and production logistics, but the moments that punched through — a drone show over Las Vegas that looked too real, a fainting goat cameo in a ransomware video — came from a team with a deeply specific sense of what would surprise and delight their audience, as she explained in TopRank’s interview on B2B creativity and AI. That’s the pattern to encode: let AI explore, remix, and extend, but make sure the initial mutation in the gene pool is human.

Second, you formalize “strange” as a performance hypothesis, not an accident. The predictive scoring work around creative attributes isn’t just good for incremental CRO tweaks; it lets you test deliberately nonstandard choices at scale. You can ask your model to generate three on-brief variations and two “rule-breaking” ones, then send them all through a predictive layer like the systems Kroger tapped via Vidmob360’s data connection to foundation models. Over time, your spy data doesn’t just tell you what’s safe; it reveals which flavors of weird — pattern breaks, tonal whiplash, visual glitches, hyper‑specific cultural references — consistently earn attention and conversion in your category.

Third, you design your workflow so the human stays in the loop where it matters most: at the moments of taste. LinkedIn’s crackdown is already penalizing obviously automated comments and posts with low engagement and future algorithmic risk, according to AJ Wilcox’s analysis. The takeaway is not “hide your AI”; it’s “use AI where the audience can’t feel it.” Let the system crunch ad libraries, suggest creative angles, and mock up twenty storyboard options. Then force a human editor to make the uncomfortable call: Which one feels too clean? Which one is slightly risky but unforgettable? Which one would you text to a friend?

Finally, you make “creator energy” a strategic pillar, not an afterthought. Some publishers are explicitly repositioning their journalists as personalities and leaning into their accumulated trust and quirks as a differentiator, while others are racing to become commodity information suppliers to AI interfaces, as AdExchanger’s reporting on news publishers’ AI strategies laid out. Brands face the same fork. If you design your AI system solely to feed bots and algorithms, you will optimize away the very human idiosyncrasies that could save you. If you design it to scale the instincts of your strangest, most original people — and wire spy data in as a constraint, not a steering wheel — you can flood the zone with content that looks machine-made on the surface (highly personalized, relentlessly tested) but feels unmistakably human where it counts.

The AI culling won’t eliminate content; it will eliminate content that doesn’t justify its existence. Weirdness — disciplined, data-aware, audience‑obsessed weirdness — is how your creative system demonstrates that it deserves to live.