In this month’s digital news, Google has been months behind schedule on delivering Gemini 3.5 Pro. The company has been taking its time trying to improve its capabilities, particularly in coding. Anthropic proposed a computing deal with Meta in June that could be worth as much as $10 billion over two years.
New research indicates that current AI visibility rankings are largely “statistical noise” rather than fixed performance metrics. Google Ads is rolling out AI transparency labels across Search, YouTube and Discover, indicating whether an ad was created or edited using AI.
More searches are now ending without a click as a result of AI search, but it’s clear that the traditional SEO and link building process, is just as important as ever. Google is running a small experiment adding AI-generated summaries directly within Search ad results, appearing below the ad description.
Google Gemini launch delayed as tech falls short of internal goals
Google has months behind schedule on delivering Gemini 3.5 Pro. The company has been taking its time trying to improve its capabilities, particularly in coding. The delay has frustrated Google engineers, AI researchers, and managers, many of whom are concerned that the company risks losing an edge. The model is currently being tested with partners, and Google is productively engaged with the US government on model testing and broader frameworks.
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Meta in Talks to Lease Computing Power to Anthropic in Potential $10 Billion Deal
Anthropic proposed a computing deal with Meta in June that could be worth as much as $10 billion over two years. Meta is considering the deal, which would involve monthly payments and the option to opt out of any agreement early. The talks show how demand for more computing power is still high. The deal could open up a new line of business for Meta and provide Meta with a new revenue stream until demand for its own AI services catches up.
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New research shows AI visibility rankings aren’t stable
New research indicates that current AI visibility rankings are largely “statistical noise” rather than fixed performance metrics. Because generative models inherently introduce randomness into their responses, a single “ranking” reading for a brand is effectively meaningless. The study highlights that visibility data only begins to stabilise after substantial sampling, often requiring between 33 and 94 data points, and even then, margins of error remain significant, rendering the difference between many competitors statistically indistinguishable.
For search professionals, this is a clear warning against treating AI dashboard citations as precise ranking data. Instead of chasing daily fluctuations, SEOs are advised to accept that AI reporting is evolving towards a probabilistic model, similar to audience analytics. Where results carry a margin of error. Brands should focus on long-term trends and robust sample sizes rather than overreacting to individual shifts, as the “rankings” observed today are often just one of many potential outcomes the model could have produced.
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Google introduces new AI labels for ads
Google Ads is rolling out AI transparency labels across Search, YouTube and Discover, indicating whether an ad was created or edited using AI. The disclosure will appear in the My Ad Center panel under a “How this ad was made” section, accessible via the three-dot menu on any ad. Where Google’s own generative AI tools are used, the label is added automatically. If advertisers use external AI tools, they can self-declare this manually. In certain regions including the EU, India and New York, a visible AI label will also appear directly on the ad itself. Google’s existing policy prohibiting misleading ads applies regardless of whether AI was used.
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Why unpaid media is now essential to AI visibility
More searches are now ending without a click as a result of AI search. But it’s clear that the traditional SEO and link building process, is just as important as ever. And these AI systems reward probability rather than popularity, cross-referencing specs, reviews and third-party sources to assign brands a confidence score, and they’re three times more likely to cite premium publisher content than brand owned content. For digital marketers. This means earned media and PR are essential signals that decide whether a brand gets recommended by AI at all, so paid, owned and earned strategies need to work as one joined-up system.
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Google Ads tests AI generated summaries under descriptions
Google is running a small experiment adding AI-generated summaries directly within Search ad results, appearing below the ad description. The test was spotted by Darcy Burk on X. With the ads carrying a disclaimer noting that AI responses are generated independently and may contain errors. Google confirmed it is a limited test “to see if adding AI-generated context to Search ads helps people make more informed decisions.” The development mirrors a similar test Google has run on organic search snippets. No further details on rollout plans have been shared.
Read more here.
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