YouMoods

Six Years On

I wrote this one up six years ago, in the spring of 2020, as the capstone of an eight-week Product Management course. The question hanging over it now is the interesting one: does it still stand up?

Spoiler: yes. YouTube still hasn’t built it, but other people have, and I’ll show you the receipts.

I picked the product I knew best: the biggest living room on the internet. Two billion users, a business model most people couldn’t sketch, and — as you’re about to see — a complaint pile hiding a feature nobody had built.

Challenge accepted.

Six years on, the problem was still unsolved. Until now!

In a hurry? Skip straight to the proto →

Susan, the Backstory — a Small Amish Town Called Silicon Valley…

We’ll begin this YouTube story with Susan Wojcicki. She was the CEO in 2020, and without her, there might not be a YouTube story to share. And certainly no case study.

Back in 1998 she was working at Intel and renting out her garage (doesn’t that sound like the Apple story? — hold that thought) to Larry Page and Sergey Brin, who were tinkering with a little project called Google. A year later, she joined them as employee number 16.

YouTube arrived on 14 Feb 2005, launched by Jawed Karim, Steve Chen and Chad Hurley — three PayPal colleagues of one Elon Musk. The story goes that the founders started it out of frustration. They couldn’t share dinner-party videos via email because of attachment size limits. (Karim disputes the dinner party ever happened. Founding myths are like that.)

The first video ever uploaded: “Me at the zoo,” starring Karim. And some elephants.

Susan spotted its potential from inside Google and pushed hard for the acquisition — $1.65 billion in 2006, a price plenty of people called madness at the time. That same year, Time magazine chose ‘YOU’ as its Person of the Year, recognising millions of hobbyist creators with the revolutionary potential to “wrest power from the few” and to “change the way the world changes.”

And that Apple thing? Susan’s mother taught journalism at Palo Alto High — Steve Jobs’ daughter Lisa among her pupils. Jobs, of course, founded Apple with Steve Wozniak in his parents’ garage — in, surprise surprise, Los Altos of Silicon Valley.

Susan’s sister Anne co-founded 23andMe, the DNA-testing company once valued in the billions. The Wojcicki girls were raised in the heart of Silicon Valley — a place where everyone knows everyone, and everyone owns a garage they’ll rent to the founders of the next billion-dollar company. A small Amish town, basically. With better Wi-Fi.

By 2020, Susan was running YouTube itself — and had just declared “responsibility” the company’s number-one priority. This whole proposal was built to serve it.

The Platform, As It Was

2020. The world was indoors, and YouTube was the world’s living room. Fifteen years after launch, it had grown into something no category could contain: video platform, music service, ad network and social network all at once. Its mission was “to give everyone a voice and show them the world.” This rested on four stated freedoms: expression, information, opportunity, and belonging. Pin that third one — “people, not gatekeepers, decide what’s popular” — it’s about to matter.

The business model is famously unorthodox: the users aren’t the customers. YouTube sits at the centre of a symbiotic triangle. Creators upload content for free and share ad revenue (YouTube takes 45%). Viewers watch without charge while Google builds profiles from their behaviour on and off the platform. Advertisers pay to target these profiles through sponsored, display, overlay, and pre-roll formats. Premium had about 20 million subscribers, a 1% adoption rate. It was a promising sideline. Everything else, like Music, Gaming, TV, Originals, and Kids, revolved around that ad engine.

And here’s the point everything else in this case study hangs on: for most of its life, YouTube didn’t make money. Ads came in 2007, but the platform still hadn’t made a profit by 2016. Running costs of $6.35 billion a year will do that. Google didn’t even break out YouTube’s revenue until 2020. A giant that spent a decade in the red lives and dies by its ad engine, and that engine is tuned for watch time. It counts the minutes you watch, whether or not they were any good. That pressure explains the complaints in the next chapter. It’s why Susan ranked “responsibility” first on the agenda. And that’s the gap I went after: not more time on YouTube, but quality of time.

Three parties with one algorithm in the middle, and when it gets things wrong, all three get annoyed.

Reading 1,000 Angry Reviews

With no access to internal data, I turned to YouTube’s Trustpilot page, which had plenty to work with: 1,861 reviews, rated 2 stars — “Poor” — 41% of them one star. I read and logged all 1,000-plus reviews of 3 stars and below.

Once you tally them up, a pattern emerges. Set aside “too many ads” and one giant spike with a story of its own. BTS’s ARMY review-bombed in fury in February 2020, after YouTube’s systems wiped around 67 million views from the “ON” premiere overnight. YouTube called it routine bot-filtering; fans called it #YtBring67MBack. Beyond those, one word kept coming up: the algorithm. It was blamed for censorship, ad placement, and recommendations. The same theme ran across Complaints Board (one star from over 800 ratings) and a long-running Reddit thread, where a YouTube community manager eventually admitted “something weird is up” with recommendations, declared it fixed, and watched the complaints roll on regardless.

The Algorithm on Trial

Angry reviews told me where it hurt, but they couldn’t tell me why. So I went to the literature: nine research papers in all. Three of them shaped the product directly:

• A CSCW interview study on how YouTubers perceive the recommendation system.¹
• Research showing that users’ interests shift measurably month to month, so a fixed profile chases a moving target.²
• The Researching YouTube canon in Convergence,³ starting with the same Time ‘You’ cover from chapter 02.

A telling detail from the first: creators use “the algorithm” and “YouTube” interchangeably. The recommender is the company to the people who depend on it — and it headlined talk after talk at VidCon, the world’s biggest creator conference.

The CSCW study¹ has a title that says it all: Agent, Gatekeeper, Drug Dealer. These are the three conflicting personas creators give to the machine that governs their livelihoods. The Agent is a talent manager. It scans and promotes individual channels, and creators often talk about trying to befriend it. The Gatekeeper stands between creators and their audience, allocating views. Learn its rules, or get lucky. The Drug Dealer aims to keep viewers hooked on the platform, feeding them more of the same. Crucially, the study found these personas change what creators make — people choose content to please the machine. That’s exactly what creators were complaining about, documented in the peer-reviewed literature.

Then I came across Guillaume Chaslot, an engineer who worked on the recommendation system. He told TNW the AI wasn’t tuned to help users get what they want:

And his prescription, if recommendations had to stay? Keep them human-curated — or anchor them to the channels you’ve already subscribed to. So an engineer who helped build the thing was recommending, on his own, exactly what I was about to propose.

The same study’s final section — Algorithmic Wishes — asked creators what they’d want the algorithm to become. Two of their six wished-for personas read like a YouMoods spec: the Diversifier and the Advocator. The Diversifier would stop inferring interests from a single search, which can trap viewers in a rabbit hole. That’s exactly what YouMoods would dissolve. The Advocator would show smaller, original creators, the ones under 100k subscribers that YouMoods is built to help. Related work on YouTube’s rankings⁴ backed up the grievance: visibility can’t be explained by simple popularity, and niche channels that thrive on controversy systematically out-rank more mainstream voices. So the creators had good reason to complain.

One more piece of history mattered: how the algorithm got this way. Until 2012, views were king. Then the “Reply Girls” farmed clicks with suggestive thumbnails, and YouTube switched its north star to watch time. Critics, both in and out of the company, warned that the new metric would reward extreme and outrageous content. The platform’s whole recommendation history is a lesson in Goodhart’s Law: “when a measure becomes a target, it ceases to be a good measure.”

One Problem, Three Victims

Put the evidence together and it’s more than a user annoyance: it hurts viewers, creators and advertisers alike.

Viewers with short session windows get a feed tuned for a statistical average of them, not the person in the kitchen with eleven minutes before the school run. They scroll, refresh, try to remember channel names, and give up. Creators are a long tail of 31 million channels, mostly under 100k subscribers and without professional management. They often wonder if they’re making content for viewers or the algorithm, and to survive, many turn to clickbait. The ladder exists (the number of channels earning six figures grows 40% a year) but the algorithm decides who climbs it. Advertisers inherit the fallout: lower conversion rates than rival platforms, and brand-safety worries about what their ads appear beside. These issues were driving spend away, and had pushed ‘responsibility’ to the top of Susan’s agenda.

The Rule Out…

With the problem mapped, I went hunting for the solution — and I didn’t start with the viewer. Three constituencies means three places a fix could live, so I worked through the other two first.

First stop: the creator side. Meet Christine. Her content’s great, but the algorithm decides who gets to see it, and the only fix from her side is changing how YouTube recommends videos. The algorithm learns from what users watch through its recommendations, but it doesn’t check whether those suggestions were any good. Often, people just watch what’s shown to them, because they don’t have time to search, and better content gets missed. So the algorithm shouldn’t rely on its own recommendations. It needs retraining. Dead end.

Christine, 22

Makeup artist · London · beauty tutorials

Trend-setter building a channel towards full-time influencer work — community, moderate fame, financial security.

#Beauty#AdobePremiere#FashionWeek#Photoshop

PainBelieves her content beats bigger channels but can’t get views; has A/B-tested titles and thumbnails to no avail; refuses to chase trends just to feed the algorithm.

Why her side dead-endsFixing it for Christine means retraining what the machine knows — and only viewers can supply that.

Second stop: the advertiser side. Meet Anna. Better targeting tools might look like her fix, but targeting is only as good as the viewing data behind it. Her ads land best when someone’s actually engaged with what they’re watching, and unfortunately for Anna, that data’s a mess, or at best hit-and-miss. Better data would mean better ROI, but that’s not something she can fix from her side. Dead end.

Anna, 26

Marketer · New York · blue-chip accounts

Runs clients’ video campaigns against specific user types; works every hour god sends.

#Marketing#ROI#BrandSafety

PainLoves the granular targeting on paper, but conversion trails Instagram — and she worries what her clients’ brands appear next to.

Why her side dead-endsBetter tools can’t fix bad viewing data. The fix has to come from viewers, not from Anna.

Both roads led to the same place: the only party who can generate better viewing data is the person doing the watching.

So the fix had to start on the sofa, with the viewer.

Meet Yasmin

Every design decision from here on gets tested against one person. So meet the star of the show.

Yasmin is a working-from-home mum whose day runs on short windows and shifting moods — and the platform serves her an average every time. Sometimes, for her daughter, a dangerous one.

Solve it for Yasmin, and Christine and Anna get paid too. So the whole project came down to one question:

The Idea: Putting the ‘You’ Back in YouTube

The answer to that question arrived almost fully formed.

YouMoods lets users group their subscriptions into Moods — named collections with their own cover image, living in a strip at the top of the subscriptions tab. Just tap a Mood, get a feed of the latest unwatched videos from just those channels. Add a few to a playlist and let them run back-to-back.

A workout Mood before the school run. A safe, hand-picked Mood for her daughter in the afternoon — only the channels Yasmin herself put there. Some soothing jazz, or her favourite YouTubers, once the house goes quiet. And every tap tells the recommendation algorithm, in plain terms, who the user is at 7am versus 9pm. That’s high-quality viewing data it can retrain on. It brings together Chaslot’s fix (anchor recommendations to subscriptions) and the research finding that interests drift over time, in one feature that feels native to YouTube. Notice it’s also YouTube’s own third freedom made real: people, not gatekeepers, deciding what’s popular.

And the name came out of the research, not marketing instinct. A creator in that CSCW study¹ accused the company of “trying to take the You out of YouTube.” This feature is the literal answer: putting it back.

Scoping the MVP

What I’m proudest of in the 2020 work is what I left out.

The Design

The flow had to feel native, like something that could have shipped in the next app update, with zero onboarding. An eleven-minute session should spend zero of those minutes navigating:

Here’s 2026: The Remix

The 2020 version was enough for a five-minute pitch, the research, my findings and the idea. This time, there are no constraints, no course work, no presentation to make and no deadline looming — and I now carry the UX half of the toolkit that 2020 me didn’t.

So I went back and completed the vision: a 2026 remix, rebuilt screen by screen.

The emoji strip grows up into cluster icons assembled live from each Mood’s channel avatars — yet the promise stays the same: zero onboarding. Scroll the flow:

Go-To-Market

Moods doesn’t need a campaign. It lives on the tab bar, the way Shorts does, so it can simply be rolled out. The whole tab ships dark — switchable per user and per market, isolated from the core feed, killable in a second without an app update.

And it rolls out Premium first. An ad-free audience is a safe place to learn how the feature behaves. A curation tab is a natural Premium perk. And Premium’s power users are exactly the several-times-a-day crowd the feature was built for. Shorts rolled out in stages over seventeen months: an India beta, the US six months later, 100+ countries by month ten, and the beta tag off at month seventeen.¹⁰ Moods mirrors that arc, with Premium as its first stage:

Shorts got past its cold start by auto-converting existing vertical videos, and Moods does the same: the tab arrives with starter Moods auto-drafted from each user’s watch history and subscription clusters, ready to rename, delete or keep. So nobody starts with an empty tab, and there’s no need for a creator fund, because the videos already exist.

There’s a longer-term hope in here too. A Mood is a user telling YouTube what they’re in the mood for — something the platform currently has to guess from watch history. If the feature earns its rollout, that same information, handled transparently, could help show non-Premium viewers ads that actually match the moment. Moods set up for children would be kept out of it entirely. Starting in Premium means we learn whether any of this works before a single advertiser’s campaign is touched.

In short: no big launch, just a staged rollout, with the numbers deciding each next step.

The Numbers

These numbers cover year one, and they only count Premium subscribers, the 125 million-plus people who get the tab first.¹¹ Each stage of the rollout only goes ahead if the numbers hold up.

Base case 4 million daily; the pessimistic case halves trial and retention to land nearer 1.5 million; the optimistic one clears 8. Free-tier expansion through months 10–18 raises the ceiling from 125 million towards YouTube’s two billion. But that’s year two’s funnel, and it’s only earned if this one holds. Each gate reads the same dashboard: DAU of the tab, session length inside a Mood, playlists completed, and Day1/Day7 return — does anyone come back tomorrow?

The number that matters most, the north star, is weekly Mood-feed sessions per user. If Yasmin isn’t opening her Moods week after week, nothing else matters. Around it, the guardrails: NPS among feature users; YouTube’s Trustpilot score, which sat at 2 stars (“Poor”) when the research began (I’d want to see it creep up); Moods created per user; whether the several-times-a-day segment (38%) pulled the merely-daily (25%)¹¹ up into habitual use; and, on the creator side, whether channels under 100k subscribers saw improved view-growth ratios. That last one was the “responsibility” guardrail, and my favourite.

And one risk, named up front: Moods competes with recommendations for the same minutes.

Six Years Later: The Verdict

The great thing about waiting six years is that I can actually check whether I was right.

YouTube still hasn’t built it. As of 2026 there is no native way to group subscriptions into folders or collections — no grouping, no reordering, no per-group filtering. The subscription feed remains one undifferentiated firehose.

But the demand didn’t go away. It went third-party. A whole ecosystem of browser extensions and apps now does exactly what YouMoods proposed. PocketTube, for one, lets users group subscriptions into collections with custom icons, each with its own feed of the latest videos. It works across Chrome, Firefox, Safari, iOS and Android. Newer entrants like Groupify, launched in 2024 (four years after my 2020 proposal), even pitch collections grouped “by topic, interest, or mood”. In practice it’s clunky and not user-friendly: it only works in a browser, and you have to create an account and log in before you can use it. It’s not well reviewed either, with a 2.5-star rating from a few hundred users, and many saying it simply doesn’t work.

So how does the 2020 spec hold up against the tools that actually shipped? Line them up:

Feature sets from the tools’ official store listings, mid-2026. YouTube native: no grouping of any kind, so its whole column is dashes.

The extensions cover most of the features, but nobody outside YouTube can fill the bottom two rows.

Only YouTube itself can reach every device through the official app, or feed users’ declared moods back into the recommender. So the market has already shown people want the feature, and those last two rows are the reason for YouTube to build it anyway.

The public reviews of those tools double as free qualitative research on this exact concept. The praise is for the right things: users call grouping a must-have, and single out per-group feeds and play-all playlists, exactly the features I put in my MVP.

The complaints are even more useful: extensions break whenever YouTube changes its layout, large subscription lists slow to a crawl, and features drift behind paywalls. Every one of those problems comes from being third-party, so people clearly want this; it just needs YouTube to build it properly.

So Why Hasn’t YouTube Done It?

So if the demand is real, why hasn’t YouTube built it? I believe YouMoods may compete with the recommendation engine for the same minutes.

Recommendations drive watch time, and watch time drives revenue. The irony is that my own 2020 research contained the whole warning. YouTube had already switched north stars once, from views to watch time, after the Reply Girls gamed the old metric. And critics correctly predicted what optimising watch time would reward. Whatever YouTube measures, the platform ends up chasing. My pitch argued that explicit curation would feed the algorithm better viewing data. I still believe that, but it’s a genuine trade-off, and one YouTube would need to test.

A user who curates their own feed is a user the algorithm no longer fully owns.

In an ideal world, users who know their favourite creators would be watching those channels anyway. Some, like Yasmin, are wary of what the recommendations might play once her daughter’s favourite video ends. For them, Moods saves time by making the whole process manageable. And time is the key. Most of these users probably discovered their favourite channels through YouTube’s recommendations in the first place. When they have longer to watch, and as recommendations improve, they’re likely to go back to pulling the lever at the YouTube casino, hoping to hit 7-7-7 with a great recommendation. Having both Recommendations and YouMoods means everyone is catered for: the more established users, and those who still value Recommendations. This is something that can be tracked, of course.

Vibe Coded: Try It for Yourself

Reading about a feature isn’t the same as trying it. Give it a go! Et Voilà!