Meta AI drives double-digit Instagram time growth as AI recommendations reshape user engagement

Reviewed byNidhi Govil

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Meta revealed during its Q2 earnings call that Instagram users are spending significantly more time on the platform, driven entirely by AI-powered recommendation systems. Global time spent on Instagram grew by double digits year over year in the second quarter, with Meta crediting AI-tuned recommendations in the main feed and Reels. The company now processes every public post through large language models to analyze content and match it to users, though this success arrives amid lawsuits alleging the platform is engineered to keep young users hooked.

Meta AI Transforms Instagram Engagement Through Advanced Recommendation Systems

Meta has confirmed that Instagram users are spending significantly more time on the platform, and the company attributes this increase directly to its artificial intelligence systems. During its Q2 earnings call, Meta disclosed that global time spent on Instagram grew by double digits year over year in the second quarter, driven primarily by AI recommendations in the main feed and Reels

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. The gains are measurable and substantial. Meta reported that a reworked Reels system delivered a 15 basis point increase in sessions, a figure that translates into millions of additional hours of attention captured across billions of users

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Source: The Next Web

Source: The Next Web

The technical architecture behind this shift is sophisticated. Mark Zuckerberg explained that Reels now combines faster inference with a new architecture that draws on a deeper history of what each user has done, allowing the system to predict more accurately what will keep them watching

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. This represents Meta's most significant Reels ranking update to date, with the biggest gains observed in reshares and time spent

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Large Language Models Analyze Every Public Post

Meta has deployed large language models across its platforms in a way that fundamentally changes how content reaches users. According to Chief Financial Officer Susan Li, Meta hit a milestone earlier this year when every public Reel and Feed post on Instagram started getting automatically processed through an LLM

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. These models analyze each post's topic and tone, then pair that understanding with a deeper read on each user's viewing history to predict what they'll watch next

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Zuckerberg described this LLM integration as "a key building block toward greater personalization," with plans to extend the same approach to Facebook

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. Meta has also started using a separate model family called Muse to analyze videos for automatic topic classification and summarization

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. The purpose is clear: understanding content more deeply lets Meta match it more precisely to each user, and more precise matching means more time spent, the metric on which its advertising business ultimately rests

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Business Impact and Ad Revenue Implications

For Meta's business model, increased user engagement translates directly into ad revenue opportunities. Every extra minute of scrolling represents another slot Meta can sell to advertisers, which explains why engagement gains matter so much to the company's bottom line

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. The AI-powered recommendation systems are delivering the clearest near-term payoff for Meta's enormous AI capital expenditure, which is often framed around future agents and superintelligence but shows measurable returns today

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Video analysis has become central to this strategy. Video time has surged on both Instagram and Facebook as short-form recommendations pull users from the feeds they follow toward an endless stream the algorithm assembles for them

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. This shift from chronological, follower-based feeds to algorithmically curated content represents a fundamental change in how people experience Meta's platforms.

Regulatory Scrutiny and Legal Challenges Mount

The same design Meta celebrates as personalization faces intense regulatory scrutiny. Meta booked a $2.4 billion legal charge in the quarter, and a group of US states is pressing claims that its products are built to keep young people hooked

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. The lawsuits turn "time spent" from a business achievement into potential evidence of engineered compulsion, especially concerning younger users

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Meta's response emphasizes user controls. Executives point to settings that let users tune their own algorithms and to teen-specific protections rolled out across its apps, arguing that more personalization need not mean less agency

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. However, critics argue these two claims are difficult to reconcile, noting that user controls sit on top of an engine fundamentally designed to maximize scrolling

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. The AI that lifts engagement serves as both Meta's best answer to investors asking where returns are and its most problematic evidence in cases questioning whether those returns come at users' expense

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