Every article we’ve published, newest first.
The paid media audit is sold on percentages, and chased to their sources most end at another agency blog. Here are six failures you can prove inside your own account in an afternoon, and the arithmetic that separates a finding from an assertion.
Roundups frame agentic commerce as a standards war between OpenAI and Google. Read both specifications and OpenAI’s retreat from Instant Checkout, and the two rails are converging on one journey, asking merchants for the same feed at a cadence the spec never demanded.
Two migration dates are circulating with the wrong campaign types attached. Google’s 7% and the independent 16% come from different verticals, baselines, and years, and the question worth testing is whether the added volume was ever new.
Claude’s newest models weave an invisible mark into their text, with no documented opt-out. In Anthropic’s explanation the mark measures one thing, how many words the model chose: faint on a proofread draft, loud on prose it wrote end to end.
The 31 markets that went live on August 24 are exactly the EEA plus Switzerland, the territory OpenAI carved out of personalization. Polish buyers get a contextual product with a 65 PLN daily floor and a CPC figure that is a suggested bid, not a market rate.
Teams running several European markets argue about splitting campaigns per country, and settle it with a number somebody half-remembers. Google publishes it as the sample you need to read a result, not the volume the algorithm needs, and that changes the decision.
Thirty conversions a month. Fifty events a week. Every consolidation argument in the industry rests on those numbers, and neither platform states them as structural requirements. Google says something quieter and more useful, and once you read what it actually says, the real cost of splitting a campaign turns out to be arithmetic you can do yourself.
Every team now has models writing, classifying, and summarizing at volume, and almost none can say whether the output got better or worse last month. The fix is an eval: a frozen set of real inputs, a rubric that discriminates, and a threshold. The research on how to build one is unusually clear, and unusually uncomfortable about the shortcut everyone takes.
The slide says AI traffic is up and to the right. Underneath it, some assistants send a referrer and some send nothing, the biggest AI surface of all arrives labeled as ordinary organic search, and the crawler that decides whether you get cited at all is a different bot from the one everybody blocked. Here is what is actually measurable, and what to stop claiming.
Two things break creative testing at once: the platform decides who sees what, and almost nobody runs the numbers on how much data a real answer needs. The arithmetic is not close. Detecting a 20% difference takes roughly 400 conversions per variant, and most tests are called at a tenth of that.
Your sales team knows which leads were worthless. Your ad account does not, because nothing ever told it. Closing that loop is not a reporting project, it is a change to the optimization target, and the hard parts are a seven-day window, a hashing spec the two platforms disagree on, and the double-counting trap nobody warns you about.
The number on your dashboard divides all the revenue by all the spend, which describes money you already committed. The decision in front of you is about the next increment, and that return is always lower than the average and falling. Here is how to find where your curve bends, and what to do at the point the two numbers disagree.
In Shopping and Performance Max you cannot pick keywords, audiences, or placements in any way that sticks. What decides which auctions you enter is a text file: the title, the attributes, and the labels you put on each product. Here is how the matching actually works, and the one Google rule about AI-written titles that almost nobody has implemented.
Google and Meta will optimize toward whatever a conversion is worth, and they take your word for it. Most accounts send the order total, which is not profit, then set a target on top of a number the business never earned. Here is what to send instead, and the refund pipeline that keeps it honest.
The whole path, in order: the Google Cloud project, the credential, the connection, and then the part that actually pays for itself. One scheduled pull becomes a dashboard, a cohort grid the interface cannot draw, a written weekly summary, and a Slack alert that stays quiet until something real moves.
From August 2, three separate regimes (the EU AI Act, New York’s synthetic-performer law, and Amazon’s marketplace policy) start forcing you to disclose AI-generated creative. The panic that the label tanks conversion is repeated everywhere and proven nowhere. Here is what each rule actually covers, and how to decide where a human face still earns its cost.
You can run Google Ads from a terminal now: read the account, draft the change, spend the money, all in plain English. This is the build guide. Which of the four paths to use (scripts, the official read-only Model Context Protocol (MCP) server, a community write server, or the raw v25 API), what v25 quietly broke, and how to wire a write path that fails safe instead of fast.
The June 2026 spam update set off a wave of “brands lost their own results” panic. No primary data supports it. What actually got demoted was the coupon and affiliate sludge on your brand-plus-modifier queries, and branded search is the one segment AI Overviews are making stronger.
Tymbre went from a clickable mockup to the App Store in 30 days, built by one person after hours, with AI. Here is the full product: 1,116 launch-period commits, a self-hosted core architecture in Warsaw, adversarial QA, and the lesson that the bottleneck was never the typing.
AI, Digital, Automation, Measurement. Every business already runs on all four, and is fragile in the exact one nobody is watching. ADAM is a diagnostic that plots you on a 4×5 matrix, names your weakest pillar, and forces one decision per quarter. Here is the whole framework, and the free playbook.
Claude Code already pulls last week’s numbers, drafts the negatives, and stages a budget change across Google Ads and Meta, wired in through the Model Context Protocol (MCP) and the platforms’ own APIs. Here is how the plumbing works, why Google’s own connector refuses to spend a cent, and where the guardrails still have to come from you.
Model Context Protocol (MCP) turns a large language model (LLM) from smart autocomplete into an agent that can touch your CRM (customer relationship management), analytics, and ad platforms through a single standard. How the handshake works, who adopted it, and the security perimeter it opens.
Seventeen percent of chief marketing officers (CMOs) have cut marketing jobs because of AI. Thirty-six percent expect to within two years. The gap between those numbers is where the restructuring is actually happening.
When a single AI agent hits its ceiling, the work moves to a fleet with an orchestrator on top. The 2026 problem is no longer model quality. It is coordination, guardrails, and reliability.
