Table of Contents
Since Elon Musk's acquisition and rebranding of Twitter to X, the platform has undergone dramatic changes that offer valuable insights for product builders. This deep-dive analysis from Y Combinator experts Tom Blomfield (Monzo co-founder) and David Lieb (Google Photos creator) reveals why optimizing for engagement alone can destroy user value, how algorithmic feeds can backfire, and what founders need to know about building sustainable consumer products that users actually love.
Key Takeaways
- Optimizing for engagement metrics alone often leads to worse user experiences and lower long-term retention rates
- Algorithmic feeds that prioritize click-worthy content over user intent can destroy platform credibility and user satisfaction
- Product teams need clear vision from leadership to resist the temptation of chasing vanity metrics like time-spent
- Successful rebranding requires showing users the future vision before changing familiar names that have achieved verb status
- The best consumer products solve specific problems rather than trying to maximize dopamine hits through random content
- Founder-CEOs have unique moral authority to maintain product vision against metric-driven feature creep
- Social networks naturally evolve from tight communities to engagement farms unless actively constrained by design principles
- Breaking news and real-time information remain Twitter's strongest use case despite algorithmic changes
- User feedback tools exist but are often buried, requiring better UX design to help users curate their experience
Timeline Overview
00:00-01:22 - Coming Up: Introduction to the product analysis series and preview of key insights about metric optimization dangers
01:22-06:12 - Twitter vs X: Detailed examination of Twitter's transformation from chronological feeds to algorithmic content, including political content shifts and engagement quality concerns
06:12-09:25 - Why Were These Changes Made: Analysis of business model pressures driving algorithmic feeds and comparison with other social platforms like Facebook and Instagram
09:25-11:12 - Learnings: Core product lessons about balancing engagement metrics with genuine user value and the importance of clear problem definition
11:12-14:58 - When is Twitter Helpful: Discussion of Twitter's remaining strengths in breaking news, curated lists, and real-time information sharing
14:58-18:20 - Why Did the Name Change: Exploration of the Twitter-to-X rebrand decision and lessons about product naming and brand equity
18:20-22:27 - Rebranding: Practical advice for founders considering name changes and revenue impact analysis
22:27 - End - Outro: Summary of key principles for product leaders and invitation for future analysis topics
Twitter vs X
The most significant change Twitter users have experienced is the shift from a chronological feed of followed accounts to an algorithmic feed similar to TikTok. This transformation has fundamentally altered the user experience in ways that aren't immediately visible through interface changes.
• Content algorithm shifts have dramatically changed political balance - Users report seeing content that doesn't match their previous interests or engagement patterns, with the algorithm seemingly pushing more controversial or emotionally provocative posts regardless of user preferences.
• Engagement metrics have increased while user satisfaction has decreased - Many users report spending more time on the platform but feeling worse about the experience, creating a disconnect between traditional success metrics and actual user value.
• The platform now prioritizes dopamine-driven content over educational value - Random fight videos, car crashes, and clickbait content have replaced thoughtful discussions and learning opportunities that previously characterized many users' feeds.
• User control tools exist but are poorly designed and hard to discover - While Twitter provides "not interested" buttons and content curation options, these features require significant user effort to find and use effectively.
The fundamental issue isn't just algorithmic changes—it's the optimization philosophy driving those changes. "If you're optimizing for a single metric to the exclusion of all other factors, you're probably going to lead yourself down one of these rabbit holes," notes Tom Blomfield.
Why Were These Changes Made
Understanding the business pressures behind Twitter's transformation reveals broader lessons about how advertising-based business models can distort product decisions and user experience priorities.
• Advertising revenue models create pressure to maximize time-on-platform metrics - Unlike Google's model of getting users off the site quickly after finding what they need, social platforms must keep users engaged to show more ads, creating inherent tension between user satisfaction and business metrics.
• Social networks follow predictable evolution patterns from community to engagement farms - Facebook started with personal walls, evolved to newsfeeds of chosen connections, then gradually introduced algorithmic content as user bases expanded and engagement pressures mounted.
• Platform expansion dilutes original value propositions over time - As networks grow from early adopter communities to mass market platforms, the tight social connections that made them valuable get replaced by broader, less meaningful content.
• TikTok's success has influenced other platforms to adopt similar engagement strategies - The "skip straight to the endgame" approach of showing all possible content and letting algorithms decide has become the dominant model across social platforms.
The pattern is clear: platforms begin with specific user value propositions but gradually shift toward engagement optimization as they scale and face revenue pressures.
Learnings
The Twitter transformation offers crucial insights for product builders about the dangers of metric-driven development and the importance of maintaining clear product vision throughout growth phases.
• Define the specific problem your product solves before optimizing for engagement - Products designed to solve boredom (like TikTok) have different success metrics than products designed to facilitate real human connections or learning.
• Human community constraints should inform product design decisions - Dunbar's number suggests people can only maintain about 150 meaningful relationships, so social products might benefit from embracing rather than fighting these natural limitations.
• Different business models enable different optimization strategies - Ad-based models create pressure for time-on-site metrics, while other models might allow for user satisfaction optimization even if it means shorter session times.
• Algorithmic content curation can work when combined with clear user feedback mechanisms - The concept of algorithmically sorting content by user interest makes sense, but introducing random "entropy content" without clear user control mechanisms degrades the experience.
Founder-led companies have advantages in maintaining product vision because founders possess "the moral authority of like I started this company and this is what I believe in."
When is Twitter Helpful
Despite algorithmic changes and content quality concerns, Twitter retains specific strengths that highlight what the platform does exceptionally well and what other products might learn from these successes.
• Breaking news and real-time events remain Twitter's strongest use case - During major events like the Trump assassination attempt or Ukraine war coverage, Twitter provides unmatched access to first-party information from people directly involved or observing events.
• Curated lists provide high-quality, advertising-free content experiences - Topic-specific lists created by knowledgeable users offer focused, relevant content without algorithmic interference, though these features are poorly promoted and hard to discover.
• Community Notes have improved information quality verification - The crowdsourced fact-checking system has been relatively effective at identifying and labeling false information, representing a positive change in platform governance.
• First-party content creators can easily share information directly - The platform's low barrier to publishing allows people with direct knowledge or expertise to share information without traditional media gatekeepers.
However, when no major news is happening, the platform defaults to "constant drip feed of junk," suggesting that Twitter's value proposition works best for specific use cases rather than general social networking.
Why Did the Name Change
The decision to rebrand Twitter as "X" represents one of the most puzzling product decisions in recent tech history, offering lessons about brand equity and the risks of changing established product identities.
• Twitter had achieved the ultimate branding success by becoming a verb - "To tweet" entered common language, representing the pinnacle of brand recognition that companies spend decades trying to achieve.
• The X rebrand appears driven by personal obsession rather than user research - Elon Musk's long-standing fascination with the letter X, dating back to his pre-PayPal company, seems to be the primary motivation rather than any clear user benefit or business rationale.
• Future product vision remains unclear to users - While Musk has suggested X will become an "everything app" including payments, users haven't been shown what this means or why it requires abandoning the Twitter brand.
• Established naming conventions carry enormous value that's difficult to rebuild - Companies that achieve verb status (Google, Bump, Monzo in the UK) have created linguistic shortcuts that become part of users' daily vocabulary.
The rebrand illustrates how "taste is not transferable across domains"—success in one area doesn't guarantee good decision-making in another.
Rebranding
For founders considering name changes, the Twitter-to-X transition offers both cautionary lessons and practical guidance about when and how to approach rebranding decisions effectively.
• Early-stage rebranding is less costly than changing established brands - Companies should address naming issues when they have less market awareness rather than after achieving widespread recognition and verb status.
• Choose names that are easily spoken and spelled without explanation - The best product names allow people to say them clearly and type them correctly into search engines without asking for spelling clarification.
• Successful companies often bring meaning to meaningless names over time - Google, Amazon, and Monzo didn't have inherent meaning related to their products, but they created associations through consistent use and positive experiences.
• Revenue impact from rebranding can be significant - Twitter's advertising revenue reportedly declined substantially as advertisers became concerned about appearing alongside controversial content, though subscription revenue may have partially offset losses.
The key principle for founders is that names should serve users' ability to find, remember, and recommend your product rather than serving internal company vision or personal preferences.
What's Next?
Have you experienced similar metric-versus-satisfaction tensions in products you use daily? Share your thoughts on how founders can balance growth metrics with genuine user value.
Conclusion: Twitter's transformation into X demonstrates how even successful products can lose their way when engagement metrics override user satisfaction and clear product vision. Founders who maintain focus on solving specific problems while resisting the allure of vanity metrics will build more sustainable, beloved products that users actually want to use long-term.
