Why Everything Looks the Same Now
Open Instagram. Scroll through startup websites. Look at the last ten pitch decks you've seen.
Everything looks the same.
Same color palettes. Same fonts. Same layouts. Same messaging frameworks. Same aesthetic choices that feel like they came from the same template library.
This isn't coincidence. It's inevitability.
When the same tools, templates, and frameworks become available to everyone, something predictable happens: everything starts looking the same. Output naturally trends toward the mean because that's where the templates live, where the case studies cluster, where the "proven" approaches hang out.
The Mean Regression Engine
The platforms that democratized execution also standardized it. This creates what I call the “mean regression engine.” It’s the systematic pull toward generic that most people don't see coming.
Template Tyranny
Canva has millions of users creating designs from the same template library. The aesthetic choices that feel "professional" are literally the same choices being made by everyone else using the platform.
Squarespace themes determine how thousands of websites look and function. The design patterns that feel "modern" are the patterns that the platform optimized for broad appeal.
Social media templates standardize how content looks across platforms. The formats that "perform well" are the formats that everyone else is also using.
Takeaway: Templates aren't evil. They're incredibly powerful tools that solve real problems for real people. The issue is that when the codify taste at a moment in time and then scale that taste across millions of users, is it still taste?
Best Practice Homogenization
The same growth frameworks get taught in the same courses to the same audiences. Everyone learns the same "proven" strategies from the same case studies.
The same design principles get shared in the same communities. The aesthetic choices that get labeled as "good design" converge around the same references and influences.
The same messaging frameworks get applied across industries. The copy that "converts well" starts sounding identical because it's following identical formulas.
Takeaway: Best practices become "best" precisely because they work reliably across contexts. The homogenization isn't a bug, it's proof of effectiveness. But does effectiveness at the population level really guarantee effectiveness for your specific situation?
Algorithm Optimization
Platforms reward content that fits established patterns. The algorithms that determine reach are trained on what worked in the past, which creates pressure to replicate what worked in the past.
Social media algorithms favor familiar formats. SEO rewards predictable content structures. App stores promote interfaces that match user expectations. We see that as Instagram does away with the power of the #hashtag.
This creates a feedback loop where the platforms that enable creation also shape what gets created. The tools optimize for engagement, but engagement often correlates with familiarity rather than innovation.
Takeaway: Algorithms don't have the aesthetic preferences that you think they do, they have statistical ones. They optimize for aggregate engagement, which naturally favors familiar patterns. Are you really okay with the systematic bias toward the center of the distribution?
The Safety Paradox
Most people use these incredible capabilities to create safer, more predictable, more broadly appealing work. They optimize for the center of the distribution because it feels less risky. And even when they are swinging towards the outrageous, the method that they use remains the same.
Risk Aversion at Scale: When you can see what everyone else is doing, and you can see what's working for them, the logical choice feels like doing something similar. Why take creative risks when you can copy proven approaches?
Validation Through Conformity: When your output matches successful examples, it feels validated. When it diverges from established patterns, it feels uncertain. The templates provide psychological safety even when they limit creative potential.
Metrics-Driven Convergence: The same metrics get tracked across companies and industries. When everyone optimizes for the same KPIs using the same measurement frameworks, strategies naturally converge.
This creates a massive gravitational pull toward generic. Toward forgettable. Toward the middle.
The Opportunity in Opposition
But here's the thing about gravitational forces: the stronger the pull toward center, the more valuable it becomes to resist that pull.
When everyone zigs, zagging becomes exponentially more powerful. When everyone follows templates, breaking templates creates disproportionate impact. When everyone plays safe, calculated risks generate outsized returns.
Listen up because what we say next is key:
The brands cutting through noise right now aren't the ones with better budgets. They're the ones with stronger opinions about what should exist.
The Differentiation Multiplier
In a homogenized market, small differences create big impact. When everyone looks similar, being different in even subtle ways becomes exponentially more noticeable.
The companies that understand this are building competitive advantages through differentiation rather than operational excellence. They're competing on judgment and discernment rather than capability.
But differentiation without purpose is just randomness. The key is being different in ways that matter to the people you're trying to serve.
The Strategic Implication
The mean regression engine reveals something important about competitive strategy in the new economy.
When execution becomes commoditized, the advantage shifts to those who can resist the gravitational pull toward generic. Not through random differentiation, but through intentional curation of what matters.
This requires a different type of thinking than execution optimization. It requires the ability to see patterns across contexts, to understand what works and why, to predict what will work before it becomes obvious.
It requires taste.
But taste isn't random personal preference. Good taste is systematic pattern recognition applied to specific contexts. It's the ability to synthesize complex information about what people need, what they respond to, and what they're not getting from current options.
The companies developing this capability aren't just getting lucky with good guesses. They're building frameworks for judgment that compound over time.
This is Part 3 of "The Age of the Curator" series.