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2025 TRENDS: What Ur Algorithm Says About You

2025 TRENDS: What Ur Algorithm Says About You

a consolidated take on the impact of algos on consumer trends in 2025, following my chat with Maddie Schulz for Vogue Business last week.

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tariro makoni
Jan 14, 2025
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2025 TRENDS: What Ur Algorithm Says About You
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Sharing an amazing roundup of resources for those in LA (and for those of us who want to help). I’ve also leveraged this LA Times article to donate to several of these organizations, and

Tell the Bees
shared a great list of gofundmes (includes subcategories for displaced families of Black, Latinx, and Filipino descent).


I’d like us to get one thing really clear in 2025: the algorithm doesn’t create culture – it simply reflects it. This letter was inspired by a timely end of year convo with Maddie Schulz for Vogue Business, where we chatted extensively about the merits of the algo, and the true driver of the “sameness” (bland af) vibes we’re seeing mimicked relentlessly across culture (e.g., the jeans & white tee hegemony WILL be unpacked below). 

Analyzing ways that the algo impacts/drives younger consumers (Gen Z/Millennial women) to spend their VERY precious cash is a zone of my generative, theory-based expertise. Yet, I realized that while I’ve spoken extensively about the algo’s impact via paywalled, industry case studies and all modes of Vogue Business (panel, podcast, many articles written by Maddie), I’ve never shared a consolidated, point-in-time download on any of my actual hubs (insta, tiktok, substack). No time like the present! Let’s dig in :)

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It’s a mirror, not a magician 

A couple years ago (woah?) I had a viral video series on tiktok unpacking this concept of “identity-driven” consumption. This was largely predicted on an amalgamation of literature on consumer theory, and underpinned by Dr. Juliet B. Schor’s literature on Competitive Consumption. This crystalized my stance that algorithms are a mirror, serving as a tool for layered identity formation via this concept of the in-group/out-group (can be more simply distilled via the decision tree below): 

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