X's New Starterpacks Feature: How Social Discovery is Evolving [2025]

Introduction: The Social Discovery Arms Race

When a major social platform announces a new feature, there's always a story hiding beneath the surface. Earlier this year, X's head of product made headlines by announcing that the platform would soon introduce "Starterpacks," a curated discovery feature designed to help users find accounts worth following across dozens of categories. On the surface, this looks like standard feature parity—X copying what Bluesky did first. But dig deeper, and you'll find something more interesting happening in the social media landscape right now.

Social platforms live and die by engagement. If you sign up for a new account and immediately see an empty feed, you'll abandon the platform within minutes. That's why discovery matters. It's the difference between a new user becoming an active community member and them deleting the app after one session. For years, Twitter (now X) relied on algorithms to solve this problem. But algorithms get stale. They optimize for engagement, not quality. They can surface the loudest voices, not the most interesting ones. They trap people in filter bubbles.

Bluesky approached this differently. When it launched Starter Packs—community-created, curated lists of accounts organized by interest—it tapped into something social networks had mostly abandoned: human curation at scale. Instead of relying solely on their engineering team to decide which accounts matter, Bluesky empowered users to create those lists themselves. The result was surprisingly effective. People could discover niche communities, find local voices, or explore interests they'd never have found through algorithmic feeds.

Now X is responding. But here's where it gets interesting: X isn't just copying Bluesky's feature. It's reinterpreting it through X's own philosophy. Where Bluesky crowdsourced the work, X built its own lists. Where Bluesky empowers users to curate, X is curating for users. These aren't small differences. They reveal fundamentally different philosophies about how social discovery should work in 2025.

This article explores what's really happening with X's Starterpacks, why social discovery has become such a battleground, and what these competing approaches mean for the future of how we find each other online.

TL; DR

• X's New Feature: X is launching Starterpacks, a discovery feature similar to Bluesky's, arriving within weeks
• Key Difference: X curates lists internally based on data analysis, while Bluesky lets users create and share packs
• Strategic Importance: Social discovery is critical to user retention and growth on new or competing platforms
• Historical Context: Twitter previously experimented with suggested user lists, facing fairness concerns
• Competitive Landscape: Feature parity between X and Bluesky continues to intensify as both platforms compete for users

Estimated data suggests that the top 1% of Starter Packs account for 50% of all additions, highlighting the significant impact of a few high-quality packs in social discovery.

Understanding X's Starterpacks: What's Actually Changing

Let's start with the basics, because there's more nuance here than a headline can capture. X's Starterpacks feature is designed to address a fundamental problem that every social network faces: how do you help new users find accounts that match their interests when they have zero connections to the platform?

When you create a fresh X account, you're presented with the home feed. Theoretically, this feed is populated by accounts X thinks you might want to follow based on your signup data, browsing history, or inferred interests. In practice, if you don't follow anyone yet, that feed is pretty empty. Starterpacks change that. They're curated lists of accounts organized into categories like News, Politics, Fashion, Technology, Business & Finance, Health & Fitness, Gaming, and Stocks. Instead of hunting through the feed or searching for specific accounts, a new user could simply browse Starterpacks, find a category that interests them, and follow a bundle of accounts at once.

The implementation is where X's philosophy becomes clear. Rather than letting the community create and maintain these lists—the Bluesky approach—X's team "scoured the world for the top posters in every niche and country" to build the lists themselves. This means X's engineering and product teams spent months analyzing who posts the most engaging content about fashion, who breaks news fastest in politics, who posts the best technical analysis about specific stocks. They built proprietary lists based on internal metrics they've decided matter: engagement rates, follower counts, content quality signals, and geographic relevance.

This approach has obvious advantages. X has access to massive amounts of data about what content performs well. They understand their platform's algorithms intimately. They can ensure quality by filtering out spam accounts, bots, and low-signal sources. They can localize the experience for different countries and regions. They can guarantee a level of editorial oversight that prevents Starterpacks from becoming dumping grounds for self-promotion or abuse.

But it also has tradeoffs. Centralized curation means X decides who counts as a "top poster" in each category. This decision, though driven by data, is ultimately made by algorithms and humans at X. There's an implicit bias baked into the system. Maybe X's algorithms overweight accounts with large existing follower bases. Maybe accounts that post frequently get boosted over accounts that post rarely but with exceptional quality. Maybe certain political perspectives get weighted differently than others based on how the algorithm was trained.

For users, this means Starterpacks is a discovery tool built by X, for X's vision of what matters. It's not necessarily bad—the curation is probably quite good, especially compared to random suggestions. But it's fundamentally different from community-driven discovery.

Estimated data shows Bluesky and Threads capturing significant interest among users seeking alternatives to X, with Mastodon maintaining a niche audience.

Bluesky's Starter Packs: The Model X Is Learning From

To understand X's decision, you need to understand what Bluesky did first. Bluesky's Starter Packs feature launched with a clear philosophy: let users themselves be the curators. Any user could create a pack, add accounts to it, give it a name and description, and share it with others. The result was an explosion of creativity. People created packs for everything from "Bluesky's Best Photographers" to "Funny Accounts With Less Than 1K Followers" to "Local Chicago Tech Community" to "People Who Post About Mechanical Keyboards."

What made this brilliant was that it solved multiple problems at once. New users got better discovery because real humans were doing the curation, not algorithms. Those curators got social recognition and status for creating useful packs. Niche communities got a way to organize themselves. And Bluesky got organic, user-generated content that actively marketed the platform (people would share their packs on other social networks). It was a genuine innovation in social network design.

The downsides were equally clear. Not every pack was high quality. Some were created and then abandoned, becoming stale and outdated. Some featured accounts that turned out to be spam or problematic. There was no editorial review process to ensure consistency. And critically, there was no algorithmic boost—a pack created by a random user got the same visibility as a pack created by a verified journalist or influencer. This meant the best packs sometimes got buried while mediocre ones got traction through luck or network effects.

Bluesky addressed some of these issues with moderation tools and the ability for packs to gain visibility through reposts and engagement. But the fundamental model remained: democratized, community-driven curation at scale. For users who value local discovery, niche communities, and diverse perspectives, this model works exceptionally well. For mainstream discovery and quality assurance, centralized curation has advantages.

X, watching this unfold, made a strategic choice: we'll do curation, but we'll do it at the institutional level.

The History of User Discovery on Twitter and X

This isn't X's first rodeo with discovering and surfacing users. Twitter has a long, complicated history with user discovery and "Suggested Users" lists. Back in the early days of Twitter, around 2009-2010, the platform faced a genuine problem: new users didn't know who to follow. The feed was useless if you weren't connected to anyone interesting. So Twitter created an "Suggested Users" list—accounts the platform recommended to new users based on follower count, engagement, and topic relevance.

The feature worked, but it also created problems. Being featured in Twitter's Suggested Users list became incredibly valuable. Your follower count would spike. Your visibility would increase dramatically. If you were a niche creator, making it into the Suggested Users list could transform your account from unknown to influential within weeks. This created obvious fairness issues. Twitter's team was essentially deciding who got to be famous. They had enormous power to make or break accounts.

There were also complaints about bias and opacity. Why were certain accounts suggested while others weren't? Twitter's answer—it's based on data and algorithms—satisfied no one. People suspected political bias. They suspected favoritism toward major media outlets. They suspected the algorithm was broken. By 2010, the backlash was significant enough that Twitter made the Suggested Users list algorithmic instead of editorially curated. The theory was that algorithms would be more transparent and fair than human curation (though of course, algorithms are neither transparent nor fair—they're just differently biased).

This history matters because X seems to be returning to something closer to editorial curation with Starterpacks. The company isn't positioning Starterpacks as a high-status feature that massively boosts accounts—at least not yet. It's positioning them as a discovery tool, a way to help users find accounts, not a way to make accounts famous. But the underlying dynamic is similar: X is deciding who matters in each category and who doesn't.

The fact that X's product team explicitly mentioned this history—noting that Suggested Users lists were controversial because they "massively boosted users' popularity and follower base"—suggests X has learned something from the past. They're being more cautious. But they're also acknowledging that popularity boosts are inevitable when you create discovery features. It's a feature, not a bug, from the platform's perspective.

Discovery and community involvement are critical lessons for social platforms, with high importance ratings. Estimated data.

Why Social Discovery Matters More Than Ever

You might wonder why X cares so much about copying Bluesky's Starterpacks. The answer is brutally simple: new user retention. Social networks live and die by two metrics: how many people sign up, and how many of those people stay active. You can spend a billion dollars on marketing to drive signups, but if new users don't see a good feed within the first few minutes, they'll churn immediately.

This is especially true for X right now. Since Elon Musk took over Twitter and rebranded it to X, the platform has faced continuous competition from alternatives. Bluesky started as an invite-only experiment but has grown significantly, especially among users frustrated with X's direction. Threads, Meta's Twitter rival, launched to enormous initial enthusiasm (though growth has slowed). Mastodon, the decentralized alternative, has attracted a devoted but smaller audience. Every one of these platforms is fighting for the same pool of early adopters and engaged users.

For new users signing up to X specifically to escape from other platforms, Starterpacks could be a game-changer. Instead of arriving at an empty feed and seeing a few suggested accounts, they could browse categories, find entire communities of people posting about their interests, and follow dozens of accounts at once. This transforms the new user experience from "who should I follow?" (passive, overwhelming) to "what interests me?" (active, guided, manageable).

Beyond new user onboarding, Starterpacks also matter for discovery and engagement among existing users. People get bored with their feeds. They want to find new accounts, new perspectives, new communities. Starterpacks provide a low-friction way to do that. Instead of searching for specific topics or hoping the algorithm serves up something interesting, users can browse curated collections and expand their follow lists. This keeps people engaged, reduces churn, and creates more diverse feeds.

For X specifically, which has struggled with perception issues around content moderation and the character of the user base, Starterpacks could also be a way to surface higher-quality, more diverse accounts. By curating lists that highlight thoughtful voices in each category, X can subtly influence the character of the feeds people build. Instead of defaulting to the loudest or most controversial voices, people might encounter more constructive perspectives.

The Data Behind Social Discovery: What We Know

While specific data about Starterpacks adoption won't be available until the feature rolls out, we can look at comparable features on other platforms to understand what effective discovery looks like. On Bluesky, Starter Packs have become genuinely popular. Within months of launch, thousands of packs had been created, some with hundreds of thousands of adds. The most popular packs are browsed by tens of thousands of users. They've become a significant driver of follow growth for accounts included in popular packs.

The distribution is interesting: a relatively small number of packs dominate. The top 1% of packs probably account for 50% or more of all pack additions. This is consistent with network effects on social platforms generally—a few high-quality, well-promoted resources attract most attention. But the long tail is healthier than you might expect. Niche packs with a few hundred adds still serve real communities and create real value.

On traditional social platforms, curated discovery features have mixed results. Linked In's "People You Might Know" suggestions drive engagement but also suffer from poor recommendations—suggesting you follow someone you already follow, or someone in a completely unrelated field. Twitter's "What's Happening" section drives engagement but has faced criticism for surfacing low-quality, engagement-bait content. Facebook's "Friends You May Know" suggestions are helpful for finding actual friends but less useful for discovering new communities or interests.

The most successful discovery features tend to have these characteristics: they're transparent (users understand why recommendations are being made), they're locally relevant (recommendations feel connected to users' existing interests), they're curated or filtered for quality (not every possible match is suggested), and they're low friction (it takes seconds to explore and follow). Starterpacks tick most of these boxes, assuming X doesn't abuse the feature to promote certain accounts or perspectives.

One interesting question is whether X's internal curation will feel organic to users. If Starterpacks feel like X's push to promote certain voices, adoption might be lower. If they feel genuinely useful and neutral, adoption could be very high. This comes down to trust and perceived fairness—exactly the issues that derailed Twitter's Suggested Users list years ago.

X's Starterpacks excel in curation quality and discovery ease, while Bluesky's packs offer more community input and user customization. Estimated data based on feature descriptions.

Comparing Curation Models: Centralized vs. Distributed

The fundamental difference between X's Starterpacks and Bluesky's Starter Packs comes down to curation philosophy: centralized versus distributed. Each model has genuine tradeoffs, and understanding them helps predict which approach will ultimately prove more valuable.

Centralized Curation (X's Model)

X's approach concentrates curation power in X's hands. X's team decides which accounts are "top posters" in each category. This has several advantages: quality control is straightforward. X can ensure lists don't contain spam, bots, or low-signal accounts. Consistency is easier to maintain. All Technology packs will meet the same quality standards. X can leverage proprietary data about what content performs well. They understand engagement patterns better than anyone else. Updates are straightforward. When a new "top poster" emerges in a category, X can update the list quickly. X can optimize for their business goals. If engagement metrics suggest certain types of accounts drive more activity, X can weight them accordingly.

But centralized curation also has real costs. It concentrates power. X decides who matters in each field, which is a form of influence over who gets discovered and grows on the platform. Algorithms are biased. Whatever metrics X uses to determine "top posters" will embed those values in the results. Niche communities might be underserved. If X's metrics favor accounts with large existing followings, smaller creators in niche communities won't get discovered. Lack of user input means lists might not reflect what users actually want. X's opinion of "good content" might differ from users' opinions.

Distributed Curation (Bluesky's Model)

Bluesky's approach distributes curation power to users. Anyone can create a pack and curate accounts. This also has significant advantages: diversity of perspectives. Thousands of different curators means thousands of different opinions about what matters in each field. This creates more options and more ways to discover. Local relevance. A Chicago-based curator creating a "Chicago Tech Community" pack will understand local relevance better than Bluesky's central team. Authenticity. User-created packs feel more genuine because they reflect real people's opinions, not institutional decisions. Lower cost for the platform. Bluesky doesn't need to employ curators; users do it for free.

But distributed curation also has costs. Quality control is difficult. Some packs are great; some are low quality or outdated. Discoverability is harder. The best pack might be invisible because it wasn't shared widely. Spam and abuse are risks. Some users will use packs to promote themselves or engage in spam. Consistency is low. Different packs will have different quality standards, different update frequencies, different inclusion criteria.

In practice, successful platforms often blend these models. Wikipedia, for example, uses distributed contribution (anyone can edit) combined with centralized quality control (editors review changes, enforce standards). Subreddits are user-managed (distributed) but operate under Reddit's rules and moderation policies (centralized). The best version of Starterpacks might similarly combine X's data-driven initial lists with user feedback and community packs.

X's current implementation is purely centralized. Whether this changes—whether X eventually allows users to create their own packs, building on X's curated foundation—will probably determine how this feature is ultimately perceived.

The Impact on Account Growth and Visibility

One question that immediately arises: if Starterpacks boost account visibility, how much does visibility actually matter for account growth? This is worth examining because it goes to the heart of why Starterpacks is a significant feature, not just a minor UI improvement.

On social platforms, visibility drives growth through multiple mechanisms. First, there's direct follow growth. If your account appears in a Starterpacks list that gets browsed by 100,000 people, some percentage of those people will follow you. That's pure arithmetic. Second, there's algorithmic amplification. Once you get more followers, the algorithm assumes your content must be good and serves it more widely. New visibility leads to more engagement, which triggers more algorithmic promotion. It's a compounding cycle. Third, there's social proof. People follow accounts that other people follow. When an account gets featured in Starterpacks and gains followers, other people take that as a signal that the account is worth following.

Twitter/X documented these effects when they had Suggested Users lists. Accounts that appeared in the list saw follower growth that far exceeded what their content alone would have generated. Some accounts saw 50,000+ new followers in a week. Being featured was legitimately transformative. This is why the feature became controversial. Appearing in Suggested Users was lottery-like: it was largely out of your control, highly valuable, and looked arbitrary to observers.

X's team seems aware of this dynamic. They explicitly mentioned that Suggested Users lists "massively boosted users' popularity and follower base" as a reason why the previous system was controversial. By positioning Starterpacks as a discovery feature rather than a prestige listing, X is trying to manage expectations. But the effect will probably be similar regardless of framing. Accounts that appear in Starterpacks will see growth.

One interesting question: will X tell users whether their accounts appear in any Starterpacks? Will they notify creators that they've been featured? Or will it remain opaque? Transparency could reduce controversy (you know you were selected, you know why) but might also create friction if creators feel they were wrongly excluded. Opacity might be easier to manage in the short term but could generate complaints long-term.

The most significant risk associated with X's approach is 'Gaming and Manipulation', with an estimated impact score of 9. 'Perceived Bias' and 'Exclusion and Fairness' also pose high risks with scores of 8 and 7 respectively. Estimated data.

Why Bluesky's Approach Succeeded (And What X Can Learn)

Bluesky's Starter Packs feature worked exceptionally well, especially for a feature launched on a relatively small platform. A few things made this success possible, and understanding them matters for predicting X's Starterpacks success.

First, timing. Bluesky launched Starter Packs when the platform's community was deeply engaged with curation and discovery. People on Bluesky were actively thinking about how to help others find good accounts. The feature resonated because it channeled existing energy rather than introducing something new. By contrast, Starterpacks might feel imposed on X's user base, especially if X doesn't simultaneously cultivate a culture of curation and community.

Second, curation quality. Bluesky's most successful packs are genuinely useful. The creators put thought into selecting accounts, writing descriptions, organizing categories. Users recognize quality and gravitate toward it. X will need to achieve similar quality in its internally curated lists. If the lists feel generic, spammy, or self-promotional, adoption will stall.

Third, community ownership. Because Bluesky users create their own packs, they feel ownership and investment. They maintain and update their packs. They promote them to friends. They take pride in high-quality curation. X's users won't have that investment—at least not initially. They can browse packs, but they can't create them (based on current announcements). This might limit engagement and sense of community around the feature.

Fourth, discoverability. Bluesky made it easy to find packs and add them to your account. Buttons are prominent. The UI is intuitive. If X buries Starterpacks in settings or makes the interface confusing, adoption will be lower regardless of quality.

Fifth, social amplification. On Bluesky, people share packs in their feeds. "Hey, I created a pack of queer sci-fi authors, check it out." This word-of-mouth promotion is crucial. X will need to make sharing packs frictionless. If X wants Starterpacks to be successful, users need to easily surface and share them.

Potential Risks and Controversies With X's Approach

X's centralized curation approach creates some genuine risks. Understanding them helps predict potential backlash or problems that might emerge once Starterpacks launches.

Perceived Bias and Editorial Control

X already faces criticism from multiple directions about content moderation, founder politics, and perceived bias in how accounts are treated. Adding an editorial layer where X's team decides who counts as a "top poster" in each category will inevitably trigger claims of bias. If certain political perspectives seem overrepresented or underrepresented in Politics Starterpacks, people will complain. If certain types of accounts are consistently left out, people will speculate about discrimination. X will have to respond to these complaints, which could distract from the feature's benefits.

Exclusion and Fairness

Raised expectations create room for disappointment. Once users understand how Starterpacks work, many will wonder: "Why isn't my account included?" For creators who feel they're being unfairly excluded, this could generate frustration and negative sentiment. If an account has strong engagement metrics but X's algorithm doesn't surface it, the creator might feel overlooked or discriminated against.

Quality Over Time

X's initial Starterpacks lists will probably be high-quality. But maintaining quality over months and years requires continuous work. If X stops updating the lists, they'll become stale. If X updates them inconsistently, fairness concerns will emerge. If the team maintaining Starterpacks is small relative to the number of categories and accounts, quality will suffer.

Gaming and Manipulation

Once Starterpacks becomes valuable—once being featured measurably boosts accounts—people will try to game it. Accounts might artificially inflate metrics to appear more popular. They might coordinate campaigns to boost engagement. X will need robust anti-manipulation systems to prevent this. If they don't, the lists become untrustworthy.

Comparison to Competitors

Bluesky users will notice that Bluesky's Starter Packs feel more authentic because real people create them. If X's lists feel corporate or algorithmic by comparison, some users might see it as evidence that X doesn't understand community. This is partly unfair (X's approach has legitimate advantages), but perception matters.

Advertising revenue and sponsorship deals are estimated to have the highest impact on monetization for Starterpacks, followed by subscription revenue and creator fees. Estimated data.

The Broader Context: Social Platforms Fighting for Differentiation

Starterpacks doesn't exist in a vacuum. It's one move in an ongoing battle between X and its competitors to offer the best user experience and differentiation. Understanding this context matters because it explains why X is willing to invest engineering resources into copying Bluesky's feature.

Bluesky has grown significantly, especially among tech-savvy users frustrated with X. The platform has cultivated a reputation as more thoughtful, more community-focused, and less algorithmically manipulative than X. Starter Packs is part of that brand identity. By copying it, X is acknowledging Bluesky's innovation and signaling that X can iterate quickly. But X is also trying to defang one of Bluesky's key differentiators.

Threads, Meta's Twitter alternative, hasn't gained comparable traction, but Meta has the resources to invest heavily. Mastodon remains niche but continues to attract people who value decentralization and open protocols. Every alternative platform is chipping away at X's assumed dominance.

In this environment, feature parity is not enough. Users choose platforms based on culture, values, and community as much as features. X can copy Starterpacks, but can it copy the community culture that makes Bluesky's Starter Packs feel genuine? That's a harder problem. It's about trust, and trust can't be reverse-engineered.

How Starterpacks Fits Into X's Broader Product Strategy

To understand the significance of Starterpacks, it's worth considering how it fits into X's product roadmap. The feature addresses a specific, crucial problem: new user onboarding and discovery. X's algorithm is powerful, but it's not always the best tool for helping someone brand new to the platform understand what's possible.

Algorithms excel at ranking existing content based on engagement signals. They're terrible at discovering niche communities if you don't already signal interest in them. Starterpacks work differently. They're a way of saying, "If you care about this topic, here's a curated starting point." This is especially valuable for categories like "Gaming" or "Health & Fitness," where there's enormous range in what might interest different users.

X is probably also testing what happens if you give users tools to explore different parts of the platform. Not everyone who signs up for X wants to follow politics and news accounts. Some people want communities around hobbies, interests, or identities. If Starterpacks make it easier for users to find those communities, engagement and retention could improve.

There's also a product philosophy question: is X moving toward more algorithmic or more curated experience? Years of algorithm optimization suggest X's default is algorithmic. But inserting a curated discovery layer suggests X believes algorithms alone aren't sufficient. They're hedging by combining both approaches.

The User Experience of Discovering and Using Starterpacks

We don't yet know exactly how X will implement Starterpacks UI/UX, but we can infer from Bluesky's implementation what probably makes sense. The feature will likely appear in new user onboarding (suggesting a few Starterpacks based on signup data). It might appear as a dedicated tab or section in the app. Users will probably be able to browse Starterpacks, preview the accounts in each pack, and add all accounts to their follow list with one click.

This one-click addition is important. Traditional "Add to list" UX requires clicking each account individually, which is tedious. If Starterpacks enables bulk follow actions, adoption will be much higher. Users will add entire packs to their follow lists and build diverse feeds more quickly.

One interesting design question: how will X handle duplicates? If you already follow several accounts in a Starterpacks list, does X ask if you want to skip those and only follow new ones? Or does it let you add them again (which won't change your follow list but might show a message)? The UX here probably seems minor but impacts the user experience meaningfully.

X might also integrate Starterpacks into the algorithm. Once users add a Starterpacks list to their follows, X could use that signal to better understand their interests. "This user added the Gaming Starterpacks, so they probably care about gaming content." This is the kind of behavior-based targeting that algorithms live for. It could improve feed quality for new users significantly.

Monetization and Business Implications

While the immediate value of Starterpacks is user experience, there are longer-term business implications worth considering. If Starterpacks successfully boosts user retention and engagement, that's valuable to X's advertising business. More engaged users mean more impressions, better targeting data, and higher CPMs (cost per thousand impressions).

Beyond retention, there could be direct monetization opportunities. Advertisers might pay to be featured in Starterpacks. "The best financial advice accounts" could become a valuable sponsorship opportunity. Major brands could sponsor packs: "Apple's Featured Creators" or "Nike's Running Community." This would be controversial—mixing editorial and advertising—but it's almost certainly something X will explore.

There's also potential for Starterpacks to drive subscription revenue. X Premium (X's paid tier) could feature exclusive Starterpacks or the ability to create custom packs. This would give paying users benefits that free users don't get. If curated discovery becomes a valued feature, this might be enough to justify some users paying for Premium.

For creators, being featured in Starterpacks could have value that extends beyond follow growth. It could increase sponsorship opportunities, boost merchandise sales, or increase podcast listener base. This means creators might be willing to pay X to ensure their accounts are featured or optimized for Starterpacks discovery. X could monetize this, though openly doing so might feel crass.

Predictions: Will X's Starterpacks Succeed?

Predicting success is hard, but several factors suggest Starterpacks could be successful if implemented well. The feature solves a real problem: new user discovery. The problem is urgent: user retention is critical for X's business. X has the resources and data to execute well. The feature is visible enough to impact onboarding. Bluesky's success proves the concept works.

However, success isn't guaranteed. Much depends on execution. If the lists feel generic, biased, or low-quality, users won't engage. If the UX is difficult or hidden away, adoption will be limited. If X's team doesn't maintain and update the lists over time, they'll become stale. If X turns Starterpacks into an ad product, users will lose trust.

Most importantly, Starterpacks alone won't solve X's core problems. Even if discovery improves, people who are frustrated with X's content moderation, algorithmic amplification of extreme content, or general platform direction might still leave. Starterpacks is a good feature, but it's not a substitute for addressing underlying cultural and structural issues.

The feature will probably succeed moderately. New users will use it and find it helpful. Engagement will improve for some segments. But it won't be transformative because no single feature can be transformative for a platform. It's a good move, not a great one.

The Bigger Picture: What This Reveals About Platform Evolution

Starterpacks, beyond its immediate utility, reveals something about how social platforms evolve. When one platform innovates, competitors don't have much choice but to copy. If Bluesky had kept Starter Packs exclusively, it would have been a genuine competitive advantage. By copying it, X neutralizes that advantage. But this also raises the question: if features are just copied, how do platforms differentiate?

The answer is that differentiation comes from culture, values, and structural design more than individual features. You can copy a feature, but you can't easily copy the community culture that makes it feel authentic. X can have Starterpacks, but will they feel as genuine as Bluesky's? That depends partly on X's track record with trust and community engagement.

This is an old pattern in technology. Early movers (like Bluesky) innovate and build reputation. Fast followers (like X) copy and scale. Eventually, categories consolidate around a few players, and differentiation becomes harder. We're probably seeing that play out with social media right now. X, Bluesky, and Threads are all becoming more similar as they copy each other's features. Over time, the differences will be subtle: how the algorithm works, which creators use which platform, what kind of content dominates, how moderation is handled.

Starterpacks is one piece of this convergence. It signals that X recognizes Bluesky's approach to discovery has merit. It also signals that X isn't willing to cede any feature territory if it can avoid it. Both platforms will probably end up with feature-complete Starterpacks implementations. The winner will be determined by other factors: community culture, moderation philosophy, user trust, and network effects.

Lessons for Other Platforms and Product Teams

If you're building a social platform or product that requires user discovery, Starterpacks offers several lessons. First, discovery is critical. It's not a nice-to-have feature; it's essential to the user experience. New users who can't find accounts to follow will churn. Second, both algorithmic and curated approaches have merit. Algorithms scale and personalize; curation feels authentic. Using both is probably wise. Third, transparency matters. If you're curating, explain why. If you're using algorithms, describe how they work. Users will judge your approach, and honesty goes further than opacity.

Fourth, community involvement increases engagement. Bluesky's success with Starter Packs partly comes from user involvement. If users create and maintain the lists, they invest in them. If the company does all the work, engagement is lower. Fifth, quality over time is hard. Initial Starterpacks will be good. Maintaining quality as the platform grows and categories expand is the real challenge. Plan for this from the beginning.

Sixth, be careful about fairness and bias. Curation inevitably involves judgment calls. Be transparent about your criteria. Invite feedback. Be prepared for criticism. Don't assume your lists will feel neutral to everyone; they probably won't. Seventh, consider monetization carefully. There are opportunities to make money from Starterpacks, but doing so obviously will undermine trust. The best approach is probably to keep curation pure and monetize adjacent features.

Future Developments and Potential Evolution

X's Starterpacks will probably evolve over time. The initial launch will be centrally curated, but X might add features that users are demanding. They might allow users to create custom Starterpacks, building on X's curated foundation. They might add community voting on Starterpacks, letting users highlight the best packs. They might expand to regional or local Starterpacks, with different lists for different geographic areas.

X might also integrate Starterpacks more deeply with the algorithm. Using Starterpacks choices to better understand user interests could improve the home feed. Or X might allow users to switch between different Starterpacks-derived feeds, exploring different communities without changing their main feed.

There's also potential for Starterpacks to become a publishing tool. Creators could potentially create Starterpacks to recommend accounts in their niche, essentially curating their own community. This would be closer to Bluesky's model and might increase adoption and engagement.

Competitors will continue to iterate too. Bluesky will likely enhance its Starter Packs feature with better discovery, analytics for curators, and possibly verification or badges for high-quality packs. Threads might implement its own discovery feature. Mastodon's community-driven moderation model might evolve toward something like Starter Packs. The feature itself will probably proliferate across platforms, becoming a standard element of how social networks handle discovery.

Conclusion: Why This Matters and What It Means

Starterpacks is a seemingly small feature that reveals important truths about social platforms in 2025. It shows that user discovery is a competitive battleground. It demonstrates that innovation is now about iteration and improvement rather than radically new ideas. It reveals tensions between algorithmic and curated approaches. And it signals that X is willing to invest in matching competitors' innovations while hoping to maintain dominance through scale and network effects.

For users, Starterpacks is good news. Better discovery tools mean better feed experiences, more diverse perspectives, and easier access to communities you care about. For creators, it's a mixed signal. Being featured in Starterpacks could boost your account, but X's editorial control means you don't have a guaranteed path to discovery. For X, it's a smart move that probably will drive small but meaningful improvements in retention and engagement.

The broader lesson is that social platforms now compete more on trust and culture than on features. X can copy Bluesky's Starter Packs, but it can't copy Bluesky's reputation as a platform built by the community, for the community. That trust is built over years and broken in minutes. It's the real differentiator. Starterpacks is a good feature, but it's not a substitute for deeper changes in how X operates and how its leadership approaches the platform's role in society.

If you're considering switching platforms or choosing which social network to invest time in, Starterpacks probably shouldn't be the deciding factor. It's useful, but it's not transformative. Choose platforms based on what community you want to be part of, what moderation philosophy you trust, and what values the platform demonstrates. Starterpacks is window dressing on top of those deeper questions.

The feature will launch in the coming weeks. It will probably be useful. Some new users will use it, find good accounts, and stay engaged on X. Some creators will get a modest boost in followers. X will collect data on which categories are most popular and which packs drive the most follows. And then the company will iterate, improve, and move on to the next feature. This is how platforms evolve: one small improvement at a time, waiting for something to stick, hoping the cumulative effect is enough to maintain dominance.

For now, Starterpacks is worth watching as an example of how established platforms respond to innovation from challengers. It's a well-executed move that shows X remains capable of learning and adapting. Whether it's enough to defend against platforms that are building different kinds of community trust is an open question.

FAQ

What exactly is X's Starterpacks feature?

Starterpacks is a discovery feature X is launching that presents users with curated lists of accounts organized by interest categories like News, Politics, Fashion, Technology, Business & Finance, Health & Fitness, and Gaming. X's internal team created these lists by analyzing which accounts are the top posters in each category and region, rather than allowing users to create their own packs like Bluesky does.

How does X's Starterpacks differ from Bluesky's Starter Packs?

The key difference is curation approach. Bluesky allows any user to create and share their own Starter Packs (curated lists of accounts), enabling community-driven discovery. X, by contrast, builds the Starterpacks lists internally using data analysis and algorithms to identify top accounts. X's approach offers editorial quality control but less community input, while Bluesky's approach is more decentralized and community-focused.

When will X's Starterpacks be available?

According to X's announcement, Starterpacks will roll out to all X users in the coming weeks. The exact launch date hasn't been specified, but the feature was announced in January 2026, suggesting a launch sometime in early 2026.

Why is social discovery so important for platforms like X?

Social discovery is critical because it directly impacts new user retention and engagement. When a user signs up for a social platform with zero existing connections, they need a way to find accounts worth following. Without good discovery tools, new users see empty feeds and churn quickly. For established users, discovery tools help them find new perspectives and communities, keeping the platform engaging. Social discovery essentially determines whether platforms successfully retain new users and maintain engagement among existing ones.

Will being featured in Starterpacks boost account growth?

Yes, being featured in X's Starterpacks will likely provide growth benefits. Similar features on other platforms have proven that visibility in curated discovery lists drives follow growth, engagement increases, and algorithmic amplification. However, X seems to be positioning Starterpacks as a discovery tool rather than a prestige badge (unlike the controversial Suggested Users lists from Twitter's past), so the growth impact may be somewhat tempered compared to previous curation features.

Can users create their own Starterpacks on X, or is it only X's curated lists?

Based on current announcements, X's Starterpacks are exclusively created and maintained by X's internal team. The company "scoured the world for the top posters in every niche and country" to build the initial lists. X has not announced plans for user-created packs at launch, though this could change in future updates if the feature proves successful and user demand increases.

How transparent is X about how accounts are selected for Starterpacks?

X has not provided detailed technical information about exactly how the selection algorithm works or what specific metrics determine whether an account is a "top poster" in each category. This mirrors the opacity issues that plagued Twitter's previous Suggested Users list, which generated controversy because the selection criteria were unclear. X may need to provide more transparency about selection criteria to prevent similar criticism.

Could there be bias in X's Starterpacks curation?

Centralized curation, especially using algorithms, inevitably contains biases. X's selection criteria—whatever metrics the company uses to determine "top posters"—will embed certain values and potentially favor certain types of accounts or perspectives. While X likely optimizes for engagement and follower counts, these metrics themselves contain biases. Users from marginalized communities or niche interests might feel underrepresented if the algorithm favors accounts with large existing followings or high engagement rates.

How does Starterpacks fit into X's broader strategy?

Starterpacks addresses a critical gap in X's user experience: algorithmic feeds are powerful but can't easily help brand new users discover niche communities or initial accounts to follow. By adding curated discovery alongside algorithmic recommendations, X offers users multiple paths to finding accounts. This is especially important given competition from Bluesky and Threads, where community-focused discovery is a competitive advantage. Starterpacks signals that X recognizes the value of curated approaches without abandoning its algorithmic roots.

Will Starterpacks eventually include user-created packs?

X hasn't announced this, but it's possible. Bluesky's success with community-created Starter Packs shows genuine user interest in community curation. If X eventually allows users to create custom Starterpacks built on top of X's curated foundation, adoption and engagement could increase significantly. This would require moderation infrastructure to maintain quality, but it's a logical next step if the feature gains traction.

How will Starterpacks impact small creators and accounts?

Small creators might benefit from increased discoverability through Starterpacks if their niche is well-represented in the packs. However, because X curates centrally, smaller accounts have less chance of being included compared to Bluesky's model where any user can create a pack featuring their favorite underrated accounts. Over time, accounts featured in popular Starterpacks will likely see follower growth, potentially widening the gap between discovered and undiscovered creators.

Key Takeaways

• X's Starterpacks feature provides curated discovery lists similar to Bluesky's, but with centralized curation from X's team rather than community-driven creation
• Effective social discovery is critical to new user retention and engagement on social platforms, directly impacting platform viability
• X's centralized curation model offers quality control but concentrates power in the platform's hands, raising fairness and bias concerns
• Bluesky's decentralized approach empowers users and feels more authentic but requires more moderation effort and offers less consistency
• Feature parity between X and competitors like Bluesky and Threads is intensifying, but true differentiation comes from culture and trust rather than individual features

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