Happy Saturday!

From AI-generated creators disrupting social feeds to a brand getting dragged into court over misleading email subject lines to the avoidable mistakes marketers are making in AI-driven search, today’s lineup shows exactly where the industry is heading next…

Google Ads is testing a new PMax feature that automatically pulls in your social video ads, including assets from X, by sourcing them through Pathmatics. These videos appear as “Suggested” assets inside Google Ads, ready for use after you confirm ownership. It’s meant to simplify creative setup, but it also raises big questions about data sourcing and control. Why It Matters: This experiment signals Google pushing deeper into automated, cross-platform asset reuse, a future where PMax becomes “asset-aware” across the whole ad ecosystem. It saves time, but also blurs lines around creative rights and data permissions.

Meta’s Andromeda update now groups visually similar ads together before performance matters. If your creatives look alike (same face, palette, angle, vibe), Meta treats them as one ad, pushes spend into a single “winner,” and burns it out fast while starving the rest. The only real fix: heavy creative diversity, multiple concepts, formats, faces, and visual styles across testing and scaling. Why It Matters: This isn’t a small tweak to delivery; it changes how Meta “sees” your ads. Old-school testing (1 concept + tiny variations) and scaling (one hero ad + remixes) break under Andromeda. To survive, brands need modular UGC systems and more creators, where one concept = 5–8 visually different ads and scaling campaigns run like a playlist of distinct creatives and angles. 

A new study from Billion Dollar Boy shows just how fast AI influencers are entering mainstream culture. 76% of consumers trust virtual influencers for product recommendations, and 68% say they influence purchase decisions, a sign that virtual personas have moved from novelty to credibility. Meanwhile, creators are conflicted: AI competition is rising, digital twins are becoming common, burnout is driving adoption, and consumers worry these technologies erode authenticity. Why It Matters: AI influencers are no longer a future trend. They’re becoming culturally accepted faster than creators expected. Brands are already investing heavily in digital twins, while consumers are signaling trust gaps and authenticity concerns. 

Germany’s BVDW (German Digital Economy Association) just released its first official Market Landscape for the influencer and creator ecosystem, defining five standardized categories: agencies with portfolios, agencies without portfolios, management companies, platforms, and tech providers. The move reflects the creator economy’s rapid expansion, rising complexity, and growing demand for transparency, measurement, and professionalization. Why It Matters: Influencer marketing has outgrown the “creator + brand = campaign” era. As budgets scale and platforms tighten algorithms, brands need clearer partners, cleaner processes, and more reliable measurement. This new framework helps marketers navigate a fragmented ecosystem, choose the right partners, and plan creator strategies with the same rigor as media buying, especially as authenticity, attribution, and platform-driven AI tools reshape creator advertising.

Tommy Bahama is facing a new class action lawsuit in Washington state, accused of sending spam emails with false urgency in their subject lines, a violation of the Washington Commercial Electronic Mail Act (CEMA). The plaintiff claims these misleading “urgent” subject lines manipulate shoppers into quick purchases and distort decision-making. Similar lawsuits were recently filed against Macy’s and Discount Tire, signaling rising legal pressure on retail email marketing practices. Why It Matters: Regulators and now consumers are cracking down on deceptive urgency tactics in marketing emails. This lawsuit reflects a broader trend: brands that rely on aggressive scarcity messaging risk legal action, reputation damage, and tighter enforcement of state-level anti-spam laws. 

Similarweb’s Director of SEO released a new framework showing how brands can track and optimize AI citations, now the core visibility metric as zero-click searches surged to 69% and AI platforms send 95% less traffic than traditional search. The methodology reveals how ChatGPT, Gemini, and Perplexity choose citation sources, how volatile these citations are (changing 50% month-to-month), and how brands can strategically influence where AI models pull information by improving structure, factual depth, structured data, and presence on high-authority domains. Why It Matters: As AI Overviews and chatbot answers replace clicks, being cited is the new ranking. Brands that don’t appear in AI answers effectively “don’t exist in the session”, even if they rank well in traditional SERPs. This framework matters because it gives marketers a practical path to measure and influence AI visibility, understand platform-specific preferences, and adapt SEO strategies to an AI-first search environment. 

Many brands still fall into the trap of overselling, creating pitches that sound amazing but aren’t fully supported by the product. It works short-term, but it breaks eventually. A viral “chocolate scam” serves as a reminder of how easily marketing can cross the line. The real solution: reconnect product, marketing, and sales; align messaging to the buyer’s journey; use proof instead of promises; and adopt a “positioning floor” that forces teams to under-promise and over-deliver. Why It Matters: Customer skepticism has never been higher. When expectations don’t match reality, it’s nearly impossible to win back trust, even great marketing can’t fix that. 

Marketers are scrambling to adapt to AI-driven search, but many are making avoidable mistakes, like treating AI search the same as traditional SEO, relying on static tool prompts, or expecting performance metrics where branding impact is the real value. AI search requires new goals, new KPIs, and alignment with existing SEO, PR, and content processes. Understanding grounded vs. model-generated answers, plus optimizing for fluid user behavior across LLMs, is now essential. Why It Matters: AI search is transforming how users discover, evaluate, and trust brands, and optimizing blindly will waste time and budget. Companies that treat AI search as its own discipline, with its own metrics and visibility model, will gain an early advantage as LLM-driven discovery accelerates.

— Sam C.