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We know what AI can do; generate content, analyze data, recommend products, and handle complex tasks for us.
The more intriguing questions now revolve around what happens when we let them: What should AI be trusted to do? When should its involvement be visible? And who is responsible for evaluating the output?
These questions are emerging in product decisions, platform policies, and regulations. That makes this month's developments interesting beyond just another set of AI features.
1. Google wants to make the first pass on your data
Google has added new AI features across Ads and Analytics, including performance summaries, natural-language reports, and competitor benchmarking.
These updates aim to help users focus on what needs attention, with Analytics highlighting key changes, Ask Advisor assisting investigations, and Google Ads turning questions into reports and explanations.
The expectation is that these changes will reduce time spent searching dashboards, but also alter how and what decisions users make. If Google identifies unusual patterns and suggests reasons, teams must know when to accept the analysis and when to question it.
2. LinkedIn sets a boundary on AI slop
‘AI slop’ has been added to LinkedIn's reporting options.
Users can now flag posts as ‘Seems like AI slop,’ enabling LinkedIn to identify low-quality AI content. LinkedIn isn't targeting AI-assisted content itself but addressing issues from cheap production and poor quality control that affect user expectations.
3. ChatGPT Ads is rapidly becoming a serious performance channel
ChatGPT recently announced ads are coming; now they add conversion optimization, attribution, product feeds, and multi-product carousel testing.
Unlike ads interrupting a feed, the user may have already specified their needs and be refining a choice. This appeals to advertisers but places paid placements close to advice perceived as impartial. It’s a development to watch closely.
4. Europe's AI transparency rules come into effect
The EU AI Act's transparency rules took effect on August 2, requiring AI-generated content to be identified and users informed when interacting with AI.
Transparency is shifting from policy to practice, as content may pass through people, platforms, and models before reaching customers, with AI doing anything from tweaking sentences to full creation. This complicates answering the simple question – was this created by AI?
5. Anthropic aims to show where Claude was involved
Claude models launched in the EU on August 2 now support machine-readable marking with watermarks in generated text and source metadata in files.
‘AI-generated’ is becoming a broad term for a complex creative process. As transparency demands grow, businesses need clearer understanding of AI's contribution and how to share that with audiences.
6. Meta joins the AI assistant scene
Meta AI can now tackle objectives via multiple steps, including research, presentations, and recurring tasks, with connections to email and calendars for context.
Is this Meta’s catch-up move? Major assistants are shifting to more proactive approaches, so it will be interesting to see which platforms gain short-term traction with users.
7. Google lets your avatar do the talking
Google Vids now uses Gemini Omni to generate and conversationally edit videos, while new personal avatars create digital versions from selfies and voice recordings to deliver scripts.
Repeatable videos, updates, and variations can be produced without filming anew each time something changes.
However, generated clips will include SynthID markings. The same product easing video creation also helps identify that creation.
In the end...
We already know what AI can do. What's becoming more interesting is what happens when AI is used widely. More realistic generated content offers vast creative opportunities but also makes tracing origins more important. Businesses must reconsider trust levels in AI output and how they communicate with audiences about transparency and authenticity.
