Type: Opinion
Authors: Kevin Owocki
Originally published: Allo Capital Research, June 2025
TLDR - The transformation of liberalism from meaningful philosophy to algorithmic manipulation reveals why we need better methods for distinguishing genuine importance from attention-grabbing content. Our systems have gotten incredibly good at measuring clicks, purchases, and engagement, but terrible at distinguishing between what captures attention and what creates fulfillment. We need new approaches — not just better technology, but better thinking about meaningful living in our connected world.
Understanding How We Got Here: A Story You Can Recognize
You've probably noticed different feelings between buying something you genuinely needed versus something an ad convinced you would bring happiness. Or spending social media time genuinely connecting with friends versus mindlessly scrolling.
That difference in feeling reveals something significant beyond personal preference. It exposes a fundamental problem with how systems operate. Technologies excel at capturing attention but often fail serving deeper needs.
This same pattern explains what happened to liberalism over centuries. Understanding that story illuminates why entirely new approaches are necessary for determining what matters in our connected world.
The Original Promise: When Freedom Meant Something Concrete
Consider what political freedom meant to someone in 1800. A farmer might own land instead of working as a serf for a landlord. Someone curious about ideas could read library books instead of having authorities control accessible information.
These weren't abstract concepts — they were concrete daily-life improvements you could feel directly. Political freedom translated into practical autonomy: making decisions about your work, your learning, your community voice.
Liberalism emerged from a powerful insight: rational people determine what's best for themselves better than distant authorities can. Instead of kings or priests dictating how you should live, you should pursue happiness on your own terms.
The First Shift: When Products Became Identity
Then something subtle yet crucial happened mid-20th century. Marketers made a discovery changing everything: they could sell far more by stopping focus on needed items and starting focus on who people wanted becoming.
Instead of "Buy this washing machine because it cleans efficiently," the message became "Buy this brand because it says something about your personality." Consumer choice evolved from meeting practical needs to expressing identity.
This transformation gave shopping malls organized around lifestyle categories, brands selling identity more than function, and the entire apparatus of consumer culture. The underlying liberal principle — individual freedom and self-expression — was supposedly still served, but through a completely different mechanism.
The Algorithmic Acceleration: When Choice Becomes Manipulation
Now we get to what you know intimately from technology experience. Every online action gets analyzed by increasingly sophisticated algorithms actively shaping, not just responding to, your preferences.
Here's a concrete example: You browse Instagram and look at a fitness influencer's post slightly longer than usual. Maybe you're curious, or perhaps reading something. The algorithm notices this micro-behavior and feeds more fitness content.
Within days, your feed transforms. You're seeing workout videos, supplement ads, and before-and-after photos. The algorithm decided you're fitness-interested and optimizes keeping you engaged with that content — regardless of whether this serves your well-being.
The crucial shift: the system isn't waiting for you expressing authentic preferences. It's making predictions about attention-grabbing content and actively making those predictions come true.
Why This Pattern Keeps Repeating
Here's the key insight explaining why this happens across many domains: meaning is really hard to measure, while engagement is really easy to track.
Consider meaningful versus engaging conversation. Meaningful conversation changes how you think about something important, helps self-understanding, or deepens relationships. But how measure that? It's subjective, delayed, and hard quantifying.
Engaging conversation is easy measuring. You track duration, response frequency, whether people share it. But engagement doesn't necessarily correlate with meaning. Some most engaging content — outrage, gossip, conflict — actively undermines deeper satisfaction.
Over time, systems naturally drift toward optimizing what's easily measured rather than what actually matters. This creates "Goodhart's Law": when a measure becomes a target, it ceases to be a good measure.
The Real-World Impact
This pattern manifests everywhere:
- Education: Teaching to tests rather than fostering curiosity and critical thinking
- Workplaces: Focusing on activity metrics rather than meaningful contribution
- Relationships: Optimizing convenience and compatibility metrics rather than depth and growth
Each example follows the same pattern: well-intentioned systems gradually losing connection to original purposes because they lack good methods measuring and preserving what actually matters.
Why Traditional Solutions Don't Work
Traditional authority structures — religious institutions, cultural traditions, hierarchical organizations — did provide shared meaning frameworks. But they often came with rigid constraints many found oppressive or limiting.
Pure market mechanisms seem promising because they appear letting people vote with wallets for what they value. But markets can be manipulated and often optimize for short-term preferences rather than long-term satisfaction.
Democratic processes aggregate opinions, but don't necessarily converge on wisdom. Sometimes what most people want momentarily isn't what serves their deeper interests over time.
What we need is something different: approaches honoring both individual diversity and collective wisdom, distinguishing between authentic desires and manufactured wants, preserving meaning as systems scale and become more complex.
Building New Approaches
So what would better approaches look like in practice?
Imagine social platforms designed optimizing for satisfaction-feelings after using them rather than time spent. These might include features helping reflection about whether time was well-spent, or connecting you with content and people based on stated values rather than just behavioral patterns.
Picture recommendation systems considering not just what you've clicked, but what you've told the system matters to you after reflection. Instead of just predicting attention-grabbing content, these systems would predict what you'll be glad encountering.
Consider workplace tools helping teams clarify shared purpose and track whether daily activities actually serve those deeper goals. Rather than just measuring productivity, these would help people staying connected to why their work matters.
The Role of Technology: Enhancement, Not Replacement
A crucial point: we're not talking about replacing human judgment with algorithms. The goal is creating technology enhancing rather than undermining our wisdom capacity.
Current AI systems are sophisticated at pattern recognition and prediction, but largely blind to value and meaning questions. The next frontier isn't just building more intelligent systems, but building systems participating in and supporting human meaning-making.
The key insight: technology is never neutral — it always embeds certain assumptions about what matters. Current systems often assume more engagement, more choice, and more efficiency are always better. New approaches would start from different assumptions about what actually serves human flourishing.
Social Processes: Learning Together
Technology alone isn't sufficient. We also need better social processes for collectively exploring meaning and value questions.
This might involve "citizens' assemblies" — randomly selected people spending time learning about complex issues and deliberating about shared values. It could include online forums specifically designed for thoughtful conversation about what makes life worth living. Or "Broad listening" — the opposite of broadcasting — using digital tools creating genuine two-way conversations at scale between leaders and citizens.
The goal isn't reaching universal agreement about what should matter to everyone. It's developing better methods for collective wisdom about conditions supporting human flourishing.
Starting Where You Are
This approach can start small and personal before scaling up to larger systems. You can begin developing what we might call "meaning awareness" in your own life — getting better at distinguishing between what captures your attention and what actually satisfies you.
Try this simple exercise: for one week, briefly note how you feel after different activities — not just whether they were enjoyable momentarily, but whether you're glad you spent time on them. You might notice surprising patterns.
The Bigger Picture
Understanding how liberalism got corrupted helps us see we're living through a crucial moment. Our technological capabilities became incredibly powerful, but we're still using relatively primitive methods for figuring out what to optimize for.
The stakes keep getting higher. AI systems are trained on human behavior at massive scale, but without sophisticated understanding of what actually serves human welfare. Social platforms are shaping information environments for billions of people, but mostly optimizing for engagement rather than wisdom. Economic systems are driving enormous productivity, but often at meaning and community costs.
The good news: we're not starting from scratch. Throughout history, humans developed wisdom about what makes life worth living. The challenge is translating that wisdom into approaches working in our networked, algorithmic world. That's the project ahead of us, and it's one of the most important challenges of our time.
