Meaning Awareness: We Need New Ways to Find What Actually Matters
TL;DR
One Sentence: The way liberalism went from helping people build meaningful lives to creating addictive apps shows exactly why we need better methods for distinguishing whatâs genuinely important from what just grabs our attention.
One Paragraph: Think about how Netflix recommendations work versus how your best friend recommends movies. Netflix optimizes for keeping you watching, even if you feel empty afterward. Your friend thinks about what youâd actually enjoy and remember fondly. This difference captures a much bigger problem: our systems have gotten incredibly good at measuring clicks, purchases, and engagement, but terrible at distinguishing between what captures attention and what creates fulfillment. The story of how liberalism transformed from meaningful philosophy to algorithmic manipulation shows us exactly why we need new approachesânot just better technology, but better ways of thinking about what makes life worth living in our connected world.
One Page: Remember the last time you spent three hours scrolling social media and felt worse afterward? That empty feeling isnât an accidentâitâs the predictable result of systems designed to maximize engagement rather than satisfaction. This same pattern explains how liberalism, which started with profound insights about human dignity and freedom, gradually transformed into something that often leaves people feeling manipulated and hollow.
Originally, liberalism meant practical freedoms that directly improved peopleâs lives: owning your own land instead of being a serf, reading whatever books you wanted, speaking freely in public, voting instead of being ruled by kings. These were what we might call âhousehold goalsââtangible improvements that clearly made life better.
But as systems got more complex, something subtle but crucial happened. Marketers discovered they could sell more products by targeting identity rather than needs. âExpress yourself through your purchasesâ became the new message. Political freedom evolved into consumer choice, and consumer choice became increasingly manufactured by sophisticated psychological techniques.
Today, every click you make gets analyzed by algorithms trying to predict and shape your next action. You look at something for half a second longer than usual, and suddenly your feed transforms. The system isnât waiting for you to express authentic preferencesâitâs actively creating them in real-time.
This isnât just about social media or shopping. The same pattern shows up everywhere: education systems optimizing for test scores rather than wisdom, workplaces optimizing for productivity metrics rather than meaningful contribution, even relationships being optimized for convenience rather than depth. Weâve built a world thatâs extraordinarily good at measuring surface behaviors but surprisingly bad at supporting the things that actually make life fulfilling.
The solution isnât going backward to traditional authorities telling us what should matter. Instead, we need new methods for distinguishing authentic meaning from manufactured significanceâapproaches that honor both individual diversity and collective wisdom. Think of it as developing better tools for the most important questions: What actually matters to you? How can you tell the difference between what you think you should want and what genuinely fulfills you? How can we build systems that amplify human wisdom rather than exploit human psychology?
Understanding How We Got Here: A Story You Can Recognize
Let me start with something youâve probably experienced yourself. Have you ever noticed how different it feels to buy something you actually needed versus something you bought because an ad convinced you it would make you happier? Or how different it feels to spend time on social media when youâre genuinely connecting with friends versus when youâre just mindlessly scrolling?
That difference in feeling points to something much bigger than personal preference. It reveals a fundamental problem with how our systems work. Weâve created technologies that are brilliant at getting our attention but often terrible at serving our deeper needs.
This same pattern explains what happened to liberalism over the past few centuries. And understanding that story helps us see why we need entirely new approaches to figuring out what matters in our connected world.
The Original Promise: When Freedom Meant Something Concrete
Think about what political freedom meant to someone living in 1800. If you were a farmer, it might mean owning your own land instead of working as a serf for a landlord. If you were curious about ideas, it meant being able to read books from the public library instead of having authorities control what information you could access.
These werenât abstract concepts. They were concrete improvements to daily life that you could feel directly. Political freedom translated into practical autonomy: the ability to make decisions about your work, your learning, your voice in the community.
Liberalism emerged from a powerful insight: rational people can figure out whatâs best for themselves better than distant authorities can. So instead of kings or priests deciding how you should live, you should be free to pursue happiness on your own terms.
As the Industrial Revolution progressed, this freedom naturally expanded to include new tools and appliances that helped people participate in modern life. But notice something important: the connection between the ideal (individual autonomy) and the reality (practical improvements) remained pretty direct.
The First Shift: When Products Became Identity
Then something subtle but crucial happened in the mid-20th century. Marketers made a discovery that changed everything: they could sell people far more stuff if they stopped focusing on what people needed and started focusing on who people wanted to be.
Instead of âBuy this washing machine because it cleans clothes efficiently,â the message became âBuy this brand because it says something about your personality.â Consumer choice evolved from meeting practical needs to expressing identity.
Think about how this plays out in your own life. You probably have strong feelings about certain brands that go way beyond their functional differences. Thatâs not shallowâitâs human nature to want our choices to reflect our values and identity. The problem comes when companies get really good at manufacturing those feelings rather than serving them.
This transformation gave us shopping malls organized around lifestyle categories, brands that sell identity more than function, and the entire apparatus of consumer culture. The underlying liberal principleâindividual freedom and self-expressionâwas supposedly still being served, but through a completely different mechanism.
The Algorithmic Acceleration: When Choice Becomes Manipulation
Now we get to the part you know intimately from your own experience with technology. Every action you take online gets analyzed by increasingly sophisticated algorithms. These systems donât just respond to your preferencesâthey actively shape them.
Hereâs a concrete example: Youâre browsing Instagram and happen to look at a fitness influencerâs post for a few seconds longer than usual. Maybe youâre just curious, or maybe you paused to read something. The algorithm notices this micro-behavior and starts feeding you more fitness content.
Within a few days, your feed has transformed. Youâre seeing workout videos, supplement ads, and before-and-after photos. The algorithm has decided youâre interested in fitness and is optimizing to keep you engaged with that contentâregardless of whether this actually serves your well-being.
The crucial shift here is that the system isnât waiting for you to express authentic preferences. Itâs making predictions about what will capture your attention and then actively working to make those predictions come true.
Why This Pattern Keeps Repeating
Hereâs the key insight that helps explain why this happens across so many different domains: meaning is really hard to measure, while engagement is really easy to track.
Consider the difference between a meaningful conversation and an engaging one. A meaningful conversation might change how you think about something important, help you understand yourself better, or deepen a relationship. But how do you measure that? Itâs subjective, delayed, and hard to quantify.
An engaging conversation, on the other hand, is easy to measure. You can track how long it lasts, how many times people respond, whether they share it with others. But engagement doesnât necessarily correlate with meaning. Some of the most engaging contentâoutrage, gossip, conflictâactively undermines deeper forms of satisfaction.
Over time, systems naturally drift toward optimizing for what can be easily measured rather than what actually matters. This creates what researchers call âGoodhartâs Lawâ: when a measure becomes a target, it ceases to be a good measure.
The Real-World Impact: Stories You Recognize
Let me give you some examples you can probably relate to from different areas of life.
In education, this shows up as teaching to the test rather than fostering curiosity and critical thinking. Schools optimize for standardized test scores because those are easy to measure and compare, even when everyone involved knows that test scores donât capture what makes education truly valuable.
In workplaces, it appears as focusing on activity metrics rather than meaningful contribution. You might recognize this if youâve ever worked somewhere that tracked hours logged or emails sent rather than actual impact or job satisfaction.
In relationships, it can manifest as optimizing for convenience and compatibility metrics rather than depth and growth. Dating apps optimize for matches and messages, not for the kinds of relationships that actually contribute to long-term happiness.
Each of these examples follows the same pattern: well-intentioned systems gradually losing connection to their original purpose because they lack good ways to measure and preserve what actually matters.
Why Traditional Solutions Donât Work
You might be thinking, âOkay, so why donât we just go back to the way things used to be?â But thatâs not really an option, and hereâs why.
Traditional authority structuresâreligious institutions, cultural traditions, hierarchical organizationsâdid provide shared frameworks for meaning. But they often came with rigid constraints that many people found oppressive or limiting. The liberal critique of these systems was legitimate: itâs not good for external authorities to dictate what should be meaningful to everyone.
Pure market mechanisms seem promising because they appear to let people vote with their wallets for what they value. But as weâve seen, markets can be manipulated and often optimize for short-term preferences rather than long-term satisfaction.
Democratic processes aggregate opinions, but they donât necessarily converge on wisdom. Sometimes what most people want in the moment isnât what serves their deeper interests over time.
What we need is something different: approaches that can honor both individual diversity and collective wisdom, that can distinguish between authentic desires and manufactured wants, that can preserve meaning as systems scale and become more complex.
Building New Approaches: What This Could Look Like
So what would better approaches actually look like in practice? Let me give you some concrete examples that you can imagine using or encountering.
Imagine social platforms designed to optimize for how satisfied you feel after using them rather than how long you stay on them. These might include features that help you reflect on whether your time was well-spent, or that connect you with content and people based on your stated values rather than just your behavioral patterns.
Picture recommendation systems that take into account not just what youâve clicked on, but what youâve told the system matters to you after reflection. Instead of just predicting what will grab your attention, these systems would try to predict what youâll be glad you encountered.
Consider workplace tools that help teams clarify their shared sense of purpose and track whether their daily activities actually serve those deeper goals. Rather than just measuring productivity, these tools would help people stay connected to why their work matters.
Think about educational approaches that help people discover their own sources of meaning while still developing practical skills. Instead of just preparing people for jobs that might not exist in ten years, these approaches would help people develop the capacity for ongoing meaning-making throughout their lives.
The Role of Technology: Enhancement, Not Replacement
A crucial point here is that weâre not talking about replacing human judgment with algorithms. The goal is creating technology that enhances rather than undermines our capacity for wisdom.
Current AI systems are incredibly sophisticated at pattern recognition and prediction, but theyâre largely blind to questions of value and meaning. The next frontier isnât just building more intelligent systems, but building systems that can participate in and support human meaning-making.
This might involve AI trained to help people clarify their values through guided reflection rather than just predict their behaviors. It could include recommendation systems that optimize for fulfillment rather than engagement. It might mean communication platforms designed to facilitate the kinds of deep conversation that people find meaningful rather than just viral.
The key insight is that technology is never neutralâit always embeds certain assumptions about what matters. Current systems often assume that 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
But technology alone isnât sufficient. We also need better social processes for collectively exploring questions of meaning and value.
Think about how scientific communities develop and test knowledge. They use methods like peer review, replication, and open debate to distinguish between better and worse theories. We need similar approaches for questions of meaningâways for communities to share insights and test different approaches to human flourishing without falling into dogma or relativism.
This might involve what researchers call âcitizensâ assembliesââgroups of randomly selected people who spend 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. It might mean communities of practice organized around different sources of meaning who help each other while remaining open to learning from other approaches.
Or âBroad listeningâ, the opposite of broadcastingâusing digital tools to create genuine two-way conversations at scale between leaders and citizens. Rather than politicians simply transmitting their messages to voters, broad listening involves using AI to visualize and synthesize public discussions, then continuously updating policy agendas based on what people are actually saying. Think of it as flipping the script on social mediaâinstead of algorithms designed to grab attention and create division, these systems are designed to help leaders truly understand what their communities need and find common ground among diverse voices.
The goal isnât to reach universal agreement about what should matter to everyone. Itâs to develop better methods for collective wisdom about the conditions that support human flourishing.
Starting Where You Are: Practical First Steps
This might all sound overwhelming, but the beauty of this approach is that it can start small and personal before scaling up to larger systems.
You can begin by developing what we might call âmeaning awarenessâ in your own life. This involves getting better at distinguishing between what captures your attention and what actually satisfies you, between what you think you should want and what genuinely contributes to your well-being.
Try this simple exercise: for one week, briefly note how you feel after different activitiesânot just whether they were enjoyable in the moment, but whether youâre glad you spent time on them. You might notice patterns that surprise you.
You can also experiment with being more intentional about the information environment you create for yourself. Instead of just following whatever the algorithms suggest, you might actively seek out content and conversations that align with your deeper values.
In your work, you might look for opportunities to clarify purpose alongside productivity, to measure satisfaction alongside efficiency. In your relationships, you might prioritize depth alongside convenience.
The Bigger Picture: Why This Matters Now
Understanding how liberalism got corrupted helps us see that weâre living through a crucial moment. Our technological capabilities have become incredibly powerful, but weâre still using relatively primitive methods for figuring out what to optimize for.
The stakes keep getting higher. AI systems are being trained on human behavior at massive scale, but without sophisticated understanding of what actually serves human welfare. Social platforms are shaping the information environment for billions of people, but mostly optimizing for engagement rather than wisdom. Economic systems are driving enormous productivity, but often at the cost of meaning and community.
We can continue down this path and hope things work out, or we can develop the epistemological tools needed to navigate toward something better. The choice is ours, but it requires exactly the kind of sophisticated thinking about meaning and value that can distinguish authentic fulfillment from empty engagement.
This isnât just an intellectual exercise. Itâs about building the foundation for technologies and institutions that genuinely serve human flourishing. Itâs about building our own meaning awareness. And it starts with understanding how we got here and what we need to build to get somewhere better.
The good news is that weâre not starting from scratch. Throughout history, humans have developed wisdom about what makes life worth living. The challenge is translating that wisdom into approaches that can work in our networked, algorithmic world. Thatâs the project ahead of us, and itâs one of the most important challenges of our time.