Google finally said the quiet part out loud: good SEO is good AI SEO. For paid operators that moves where the work sits. Getting your product cited inside an AI answer is a feed and structured-data problem before it's a content problem. The model reads attributes, specs, and clean product data - the same Merchant Center discipline that wins Shopping. So the feed you already maintain for Shopping is the asset that surfaces you when a shopper asks an AI what to buy. One file, two channels, and the second one is growing fast. Most teams are about to find out their product feed was their SEO strategy the whole time.
HaHacking Growth by Ruslan Galba
Founder @ Tegra growth agency 🚀
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Facebook/Instagram/LinkedIn/Youtube Ads 🤑 Chatbots 🤖 Marketing Automation
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An agent built me a full advertorial funnel in a weekend. It also got two things dangerously wrong. I've been testing the thing half my feed is excited about. Point an agent at a product, and it spins up the whole funnel - advertorial, listicle, comparison page, a quiz. Work that used to take my team two to three weeks. It did a usable first pass in about two days. I want to be honest about both halves of that, because the demo crowd only shows you the first half. What it nailed: structure and speed. It produced a clean advertorial arc, a comparison table that didn't embarrass itself, a quiz with branching logic wired up. As a production tool, it collapsed weeks into days. That part is real and I'm not going to pretend otherwise. What it got dangerously wrong, twice: One. The offer. The agent wrote compelling copy around an offer that didn't actually make sense. It assumed a discount structure the brand couldn't afford and never questioned whether the math worked. It optimizes the words, not the economics behind them. A funnel that converts on an unprofitable offer is just a faster way to lose money. Two. The claims. It generated benefit statements that were, to put it kindly, ambitious. For a supplement brand that's not a copywriting flourish, it's an FTC problem. The model has no instinct for which claims get a brand a letter. Every line needed a human who knew the regulatory floor. So here's where I landed after the test. The bottleneck in funnel building used to be production - writing, designing, wiring it all up. The agent genuinely moves that. But the work just relocated. It now sits on the offer, the post-click math, and the compliance line, which was always the hard part and never the typing. I made a version of this mistake the expensive way a year ago, before any of these tools existed. Scaled a funnel with a beautiful front end and broken back-end economics. Cost me a real number to learn that production was never the constraint. These agents are a genuine upgrade to how fast I can build. They're also very good at confidently building the wrong thing. I run every agent output through the same two questions now before a dollar goes near it: does the offer math survive contact, and will a claim get us a letter. Those two stay human.
We waited three years for the PMax channel report. It shipped at Marketing Live, and it's the first time Google has shown where the budget actually goes. The first thing it reveals on most accounts is uncomfortable: a big share of that glowing PMax ROAS was branded search wearing a costume. Conversions the brand was already winning for free, billed back as PMax performance. I'm pulling the report on every account this week and re-running the math with brand stripped out. A few are going to look very different once the costume comes off. That stripped-down number is what should set next quarter's budget, not the blended headline.
AI shopping traffic to US retail jumped nearly 400% this year. Your product feed just became your most important API. Most operators still treat the feed as a Google Shopping chore. Titles, GTINs, the stuff you fix when disapprovals pile up. That mental model is about to cost people real revenue. Here's what shifted in the last two quarters. AI referral traffic to US retail is up nearly 400% year over year. Traffic to Shopify stores from AI search is up around 8x, and orders from it close to 13x. eMarketer pegs AI-platform retail spend at almost $21B in 2026. The buyer is increasingly a model, and the model reads your feed. Two rival rails are now live for this. OpenAI and Stripe shipped ACP - checkout right inside ChatGPT. Google's coalition is pushing UCP into AI Mode and Gemini. Shopify's Winter release syndicates one catalog out to ChatGPT, Perplexity, and Copilot. Same product data, many more surfaces consuming it. So I've started restructuring feeds for a reader that isn't human. A person scans an image and a price. A model parses structured attributes and makes a judgment call about fit. That means the fields most brands treat as optional are now the whole game: material, use case, compatibility, dimensions, what problem the product solves, who it's wrong for. The feed has to answer the question the shopper asked the AI, not just match a keyword. The discipline transfers almost perfectly. The same Merchant Center hygiene that wins Shopping - clean attributes, accurate GTINs, rich product types, structured data on the PDP - is what gets a product surfaced and cited inside an AI answer. One asset, two channels, and the second channel is growing triple digits. What I'm doing on accounts right now: auditing feeds for attribute completeness against the questions buyers actually ask, not against Google's minimum required fields. Adding the "who is this not for" data models use to qualify. Treating the PDP and the feed as one structured object a machine has to understand in full. The brands that win the next two years are the ones whose product data is legible to a machine deciding what to recommend. I'd rather rebuild the feed now, while the traffic curve is still early, than explain to a brand in 2027 why they went invisible to the buyer that grew 400% in a year.
Google's whole pitch at Marketing Live was "just tell the AI your goal and step back." Here's the part that doesn't make the keynote slide: the machine optimizes for Google's revenue, not your margin. Those two line up right until they don't, and the account can't feel the difference. I'll automate the boring 90%. The agent can build, bid, and report all day. What I keep my hands on are the few levers Google quietly designs out of the interface: margin-aware targets, brand exclusions, and where the budget is actually allowed to go. Hand over the work. Keep the calls that cost real money when they're wrong.