Have you ever read something that completely reframes how you view the world? Martin Gurri's The Revolt of the Public did exactly that for me. It wasn't just thought-provoking—it gave me an entirely new vocabulary to understand what's happening around us.

Gurri's central idea is simple yet profound: institutional authority is collapsing, and nobody has figured out what comes next. We're living through a messy transition where distrust isn't just a political talking point, it's become the default setting in how we relate to everything.

"The authority of every governing institution, from the presidency to the police, from Congress to the courts, has been steadily ebbing away."

What I found fascinating was Gurri's description of "the public" not as some unified entity but as this volatile, networked force that's incredibly good at tearing things down, but struggles to build anything lasting. Think about movements like Occupy Wall Street or even Anonymous. They could mobilize massive energy against institutions, but what happened afterward? Not much that was concrete.

This brings us to what Gurri calls the center-versus-edge dynamic. Traditionally, institutions (the center) filtered information, established cultural norms, and provided stability. Now, thanks to digital tools, the edges of society can challenge and sometimes overwhelm the center instantly. However the downside is that there's no shared method for replacing what gets destroyed.

"The information sphere has been overturned. Information flows horizontally now, among amateurs, rather than vertically, from institutions to the public."

It's not that people are inherently destructive. It's that the internet gives everyone the feeling of expertise without requiring actual expertise. We used to get information through channels that demanded some level of gatekeeping and rigor. Now everyone's their own broadcaster, journalist, and pundit. Authority has been flattened, but so has competence.

I loved Gurri's example of how old newspapers were literally constrained by their physical printing capacity. News content was directly shaped by the number of pages they could produce, leading to concise reporting and a focus on the most immediate news. This differs greatly from modern newspapers and digital platforms, where the amount of information available is far less limited by physical constraints, and seduces us into an endless scroll where information has no boundaries, proportion, or clear beginning and end.

Another insight that hit home was how governments have inadvertently created impossible expectations. Politicians promise more than they can deliver, the public demands even more, and when reality falls short, cynicism deepens. It's a cycle that's getting worse, not better.

"The public can demand anything because it is not responsible for anything."

What sets Gurri apart is his refusal to surrender to despair. Rather than pointing fingers, he advocates for individual responsibility, transparency, and humility as the starting points for rebuilding trust. In today's blame-obsessed media landscape, this approach feels refreshingly constructive.

"The remedy to the malady of distrust is not more democracy, but better citizenship."

This connects directly to what I'm exploring with The Performance Age. We're witnessing traditional authority eroding while the performance of legitimacy, expertise, and certainty often matters more than the reality these performances claim to represent. The conditions that sustained our institutions haven't just changed, they’re vanishing at an accelerating pace. We've moved from a centralized culture to a fragmented, networked one where confidence frequently substitutes for truth.

If I had to capture Gurri's thesis in one sentence, it would be this: "The dam has burst, and the flood has no direction." Authority collapsed under digital pressure, but no new channels have formed to guide the current. We're all wading through it, sometimes flailing, sometimes fighting, rarely building.

"If the center no longer holds, the future belongs to the networks—and to the borderlands of power."

The Revolt of the Public isn't just about politics or media, it's about the fundamental shift in how we establish what's real and true. If you want to understand why outrage dominates our politics, why trust feels increasingly impossible, and why movements rise and fall overnight, Gurri's work is essential reading.

If we're going to find our way through this new reality, we need new maps and new conversations. What books, essays, or thinkers have helped you reframe how you see the world lately? I'd love to hear what's been shifting your perspective, and maybe add a few more tools to the shelf as we navigate this strange, accelerating age together.

👉 Try the interactive tool now: Tokenizer →

In the age of ChatGPT, Claude, and other AI language systems, we often interact with these tools as if they understand us. We type something in, something intelligent comes out, and we move on. But there's a profound gap between how these systems process language and how humans do. This gap reveals much about both artificial intelligence and ourselves.

Today, I'm sharing Tokenizer, a project that lifts the veil on how language models actually process our words.

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What Happens When AI Reads Your Words?

When you type a sentence into a language model, four key transformations happen:

1. Tokenization: Breaking Language into Fragments

Language models don't process whole sentences. Instead, they slice your text into "tokens"—words, parts of words, or even individual characters. Each token gets mapped to a specific numeric ID from the model's vocabulary.

For example, the phrase "The young student didn't submit the final report on time" gets broken into tokens like "The", "young", "student", "didn", "'t", "submit"... each with its own ID number. This is the first abstraction away from human language.

For GPT models, common words might be single tokens, while rare words get split into multiple subword tokens. This affects how the model processes meaning; tokens are the fundamental units of "understanding."

2. Part-of-Speech Tagging: Assigning Grammatical Roles

Next, the system identifies the grammatical role of each token. Is it a noun, verb, adjective? Is it the subject of the sentence or an object?

The system maps out what linguists call dependency structure, which shows how words relate to each other in a sentence. It extracts subjects, verbs, and objects, creating a structured representation of who did what to whom.

In our example, tools like spaCy would identify "student" as a noun and the subject, "submit" as the main verb, and "report" as the direct object. These relationships form the skeleton of meaning.

3. Embedding: Converting Words to Vectors

Here's where things get fascinating. Each token gets transformed into a vector, which is a list of hundreds of numbers that capture its meaning and context. In the tool, we use BERT's 768-dimensional embeddings and visualize them in 2D through Principal Component Analysis (PCA).

Words with similar meanings, contexts, or functions cluster together in this mathematical space. "Dog" and "cat" would be closer to each other than either would be to "algorithm." This "distributional semantics" approach is the foundation of how language models simulate understanding.

The embedding space is where a model's "knowledge" lives, not as facts, but as geometric relationships between points in this high-dimensional space.

4. Dependency Parsing: Mapping Relationships

Finally, the system constructs a tree of relationships between words. This visualization shows how modifiers, subjects, objects, and clauses connect to form the complete meaning of your sentence.

These trees reveal the hierarchical structure of language: which words modify which others, how clauses nest within each other, and how the overall meaning is constructed from individual components.

Why This Matters (Beyond the Technical Details)

These technical steps reveal something deeper: Language models don't understand language the way humans do. They simulate it convincingly, but fundamentally differently.

When you or I say "dog", we might recall the feeling of fur, the sound of barking, even emotional responses. But when a model sees "dog", it sees a vector of numbers, shaped by how often "dog" appears near words like "bark," "tail," or "vet."

That's not wrong. It's statistical meaning. But it's also disembodied, ungrounded, and unaware.

So What?

Language models don't have beliefs or goals; they just predict what's likely to come next.

• Their understanding of "truth" is co-occurrence-based, not experiential.

• The ambiguity humans process instinctively must be explicitly encoded.

And yet: these systems now write our resumes, filter our content, and decide what's visible or valuable. The difference between performance and understanding is no longer philosophical trivia. It's infrastructure.

Try It Yourself & The Performance Age

Explore how these transformations work in real-time using the Tokenizer, an interactive visualization tool that reveals how AI parses and embeds your words.

This is part of a broader exploration I'm calling The Performance Age—investigating how truth, perception, and performance shift in the algorithmic era.

A System's Perspective

This tool shows how algorithmic systems convert rich, ambiguous language into structured data. Each transformation is a lossy process. We gain computational tractability, but sacrifice nuance. As AI systems increasingly mediate our lives, we must ask:

What knowledge is amplified? What subtlety is erased?

How do these algorithmic lenses shape the reality we perceive and the decisions we make?

Have thoughts about this project? Drop them in the comments below, or reach out directly.

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There’s a feeling that’s been following me for the last few years that I haven’t been able to shake.

It sits beneath my daily routines—when I’m scrolling my phone, listening to the news, or even just standing in a store watching advertisements flicker across mounted screens (how some of these perfume commercials attempt to portray the essence of their product that you can’t smell is quite a spectacle). It’s not quite dread, but there is a feeling of that too. It’s subtler. Like a quiet glitch in the atmosphere, a slow disorientation. A sense that something about the way we live—how we talk, how we argue, how we show ourselves to others—has changed. That something foundational is rumbling.

You might have felt it, too. The speed. The certainty. The performative layer that now coats so much of public life. It feels like we’re more connected than ever, but somehow less real. That the people we elevate are the ones who speak with the most confidence, not the most care. That attention has replaced depth. That charisma is now a stand-in for clarity. That truth is losing—not in some grand conspiracy, but by erosion. And I think that erosion deserves a name.

The Performance Age

I call it The Performance Age.

It’s not just that we perform—it’s that performance itself has become the organizing logic of the age.

We live in a time where visibility and attention are the currency. Where presentation overtakes presence. Where how something appears matters more than what it is. And where, slowly but steadily, our sense of what’s real starts to collapse into what gets seen.

This isn’t just a media story. It’s not just politics, or social media, or leadership. It’s a deeper cultural pattern: one where we increasingly mistake performance for truth, spectacle for substance, and confidence for expertise.

In politics, leaders perform strength while dodging complexity. In media, headlines are written for engagement before truth. In tech, founders posture as philosophers. On platforms, identity becomes interface. And in our own lives, we start to curate ourselves for an imagined audience—because we know, implicitly, that being seen is part of being believed.

These changes feel cultural. But they’re also cognitive. What we see shapes how we think. And in the Performance Age, we don’t just consume performance—we start to internalize it.

The Traits of the Performance Age

Here’s what that internalization looks like:

• Certainty over complexity

• Confidence over understanding

• Signal over substance

• Speed over slowness

• Simplicity over nuance

These traits aren’t new. But their dominance feels sharper now. The traits that travel best—the hot take, the viral clip, the emotionally charged monologue—aren’t the traits that help us think well. But they’re rewarded, so they’re repeated.

And eventually, we begin to think with those constraints in mind.

We start pre-editing our thoughts for clarity, speed, and shareability. We hesitate to say “I don’t know,” not because we know—but because uncertainty doesn’t perform well. And slowly, we begin to mistake what spreads for what matters.

The performance doesn’t just happen out there. It starts happening in here.

This is the tension at the heart of this project: not just how the world changes, but how we do—internally, subtly, and sometimes without realizing it.

What This Project Is (and Isn’t)

This isn’t a community. It’s not a media brand. It’s not a platform. It’s also not a performance.

It’s a space for me to think aloud. To slow down. To build clarity—not just in argument, but in attention. To explore what’s happening to thought, trust, identity, and reality in a culture increasingly optimized for speed, certainty, and visibility.

It’s not a space for hot takes. It’s not for proving a point. It’s for staying with questions—especially the ones that don’t resolve easily.

There will be errors. There will be doubts. That’s the point.

This is slow thinking—not as aesthetic, but as ethic.

What We’ve Lost

What gets lost in a culture built on performance?

Slowness, for one. Reflection. Nuance. Doubt. The ability to pause before reacting. The willingness to say “I’m not sure”—without losing credibility.

In the Performance Age, doubt is treated like weakness. Ambiguity is suspect. Uncertainty is penalized. But real thinking—the kind that deepens us—takes time. It moves against the current. And it’s not always visible while it’s happening.

Thinking well is a discipline, not a reflex. And that discipline—of making space to reflect, to reconsider, to slow down—feels increasingly rare.

What we’re missing isn’t just depth. It’s the very conditions that make depth possible.

A Philosophical Framework: Thinking in Layers

This is more than a cultural pattern—it’s also a philosophical shift.

Beneath the spectacle, the virality, and the optimization logic, there are deeper ruptures. And this project is partly an attempt to surface those ruptures, and think through them with care. The questions here will draw from three key lenses:

• Epistemology: What counts as knowledge when algorithms reward engagement, not accuracy? When charisma outpaces content? What happens to truth when visibility becomes its substitute?

• Metaphysics: What kind of reality are we building when our lives are curated, filtered, and aestheticized—when presence is replaced by presentation?

• Ontology of Self: Who are we becoming when we start to see ourselves as brands, interfaces, or products? What does it mean to live as a persona instead of a person?

These aren’t abstract questions. They show up in how we speak, how we post, how we lead, how we trust. And they’ll be explored not as academic exercises, but as living tensions.

The Performance Age isn’t just something we see. It’s something we feel. And the language of philosophy can help us name that feeling—so we can begin to understand it.

What This Will Explore

So where does this go?

In the coming months, I’ll be writing essays that explore:

• Attention – What we notice shapes who we become

• Certainty – Why we follow confident voices over careful ones

• Identity & Self-Performance – From authenticity to optimization

• The Algorithmic Mind – How platforms reshape thought

• Trust, Truth, and Thinking – What happens when institutions break

• Reclaiming Slowness – As a necessity, not a luxury

There will be thinkers in the mix—Baudrillard, Arendt, Foucault, Postman, Han, Gurri—not to signal depth, but to draw from it. These essays will be guided by what I’m reading, what I’m wondering, and what I’m learning to name.

Ultimately, this is a thinking project. Not a platform. Not a product. Not a brand.

Just a space to think more clearly, and maybe more humanely, in a world that pushes us toward performance.

An Invitation

If any of this has stirred something—if you’ve felt that dissonant hum I mentioned, or noticed yourself slipping into performance without realizing it—then I hope you’ll follow along.

I’ll post a 1-2 times a month. Maybe more, maybe less. Some essays will be long, others short. None of them will pretend to have everything figured out.

This is a space for me to slow down. To sit with ideas. To wrestle with a cultural condition that so often goes unnamed. And to ask the kind of questions that don’t fit neatly in a feed.

If that resonates with you, I’d love to have you here.

Not because I know where this is going. But because I think it’s worth finding out.

Think Slow. Stay Sharp.

**This is Part 1 of a new introductory series exploring how reaction, performance, and emotion are reshaping truth itself. Today’s piece begins with the core phenomenon: how reactions now precede events—and why that reversal is at the root of our emerging post-truth culture.**

According to researchers at MIT, falsehoods spread 70% faster than truths online and reach six times as many people. Humans haven't suddenly lost their ability to discern fact from fiction; the speed and volume of information have fundamentally changed our relationship with truth.

We've all experienced it: viral posts denounce scientific studies no one has actually read. Random accounts with 67 followers passionately critique thousand-page bills they clearly haven't read themselves. As AI becomes more ubiquitous, we may reach a point where even "authors" of such documents merely review AI-generated summaries rather than engaging with complete texts.

By the time you learn the actual facts, your perception of the issue has been shaped by a barrage of reactions. This creates an inversion of normal cause and effect, where reactions come before events, commentary comes before facts, and performance, the signaling of emotion and allegiance, replaces engagement with substance. Our first impressions are shaped not by the facts, but by how the facts are performed for us. By the time you encounter the reality underneath, you've already absorbed dozens of emotional cues telling you how to feel and interpret the event.

In the traditional flow of knowledge, events generated information, which trusted sources dispersed through established channels, allowing the public to react after absorbing facts. Today, this sequence has fundamentally reversed: reactions erupt before facts are understood, emotional anchors form first, and information arriving later gets filtered through these pre-existing narratives.

This reversal has at least one side effect, what might be called an "epistemic sunk cost fallacy." Once emotionally invested in a position, abandoning it may feel like a loss. Instead of updating our beliefs based on evidence, we filter incoming information to validate positions we've already claimed, bending facts to fit our emotional commitments rather than adjusting our views to accommodate new data.

“We are terrible at criticizing our own belief structures because we don't reason to find truth — we reason to defend what we already emotionally believe.”

—Jonathan Haidt, The Righteous Mind

Information Overload and the Barriers to Understanding

Today's information ecosystem presents a paradox: we have unprecedented access to information yet struggle to process it meaningfully. Legislation bills stretch thousands of pages while scientific journals hide behind expensive paywalls. We're left piecing together fragments from social media, like assembling a puzzle without seeing the complete picture.

This information density marks a stark departure from previous eras. Historical legislation was designed to be read and understood:

• Social Security Act of 1935: Around 64 pages. Established the entire Social Security system: retirement, unemployment insurance, and welfare.

• Homestead Act of 1862: About 6 pages. Gave settlers free land to encourage Western migration—one of the most consequential land policies in American history.

• Civil Rights Act of 1964: About 24,000 words (60-70 pages). Ended segregation in public places and banned employment discrimination.

• G.I. Bill (1944): Roughly 20 pages. Created education and housing benefits for returning WWII veterans that reshaped American society.

Contrast these with contemporary legislation:

• Affordable Care Act (2010): About 2,700 pages. Made significant healthcare changes but became symbolic of unreadable legislation ("We have to pass the bill to find out what's in it"—Nancy Pelosi).

• Dodd-Frank Wall Street Reform Act (2010): Over 2,300 pages. Created massive bureaucratic complexity with thousands of pages of follow-on regulations.

• CARES Act (2020): 880 pages plus hundreds more in guidance. Passed quickly for pandemic relief but packed with hidden provisions and special interest carveouts.

• Annual Omnibus Spending Bills: Typically 2,000+ pages. Combine funding for hundreds of agencies into unreadable documents passed hours before deadlines.

• Inflation Reduction Act (2022): About 730 pages. Title masked its actual contents spanning green energy credits, healthcare subsidies, and tax restructuring.

This proliferation of complexity reflects our broader information crisis. When documents become unreadable, public understanding depends on secondhand interpretations. The gap between what institutions produce and what citizens can realistically process widens, accelerating the inversion of understanding. Without direct access to original information, we increasingly rely on reactive interpretations, further distancing ourselves from source material.

The Ephemeral Nature of Modern Discourse

Martin Luther nailed his 95 Theses to a door, kickstarting the Protestant Reformation. This was a slow, physical, high-effort act. The document stayed up. People gathered, read, and discussed. It couldn't vanish into a feed. Today, the equivalent would be a viral thread forgotten in less than 48 hours. What once played out in town halls now unfolds through algorithms optimized for engagement over accuracy. Though, we are trying to keep up. Even the metaphor of public discourse was co-opted by Elon Musk, who justified his Twitter acquisition by calling it the world's "town square." We have immediacy but no staying power. The ephemerality of our information age prevents thoughtful digestion and deliberate processing. Our System 1 thinking has been hijacked.

“System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control.”
—Daniel Kahneman, Thinking, Fast and Slow

This acceleration of information flow contributes directly to the perception-formation process. When content disappears from view so quickly, we're left with impressions rather than understanding. The physical permanence of Luther's theses allowed for extended consideration and meaningful debate. Contemporary digital discourse, by contrast, rushes past before we can fully comprehend it, leaving behind only our emotional responses. This is precisely how reactions come to precede understanding, we simply don't have time to understand before we must react.

The Acceleration of Emotional Certainty

This is the reality-reaction inversion at work: emotional certainty hardens before the facts even have a chance to disperse and stabilize. In today's information ecosystem, feelings aren't reactions to knowledge, they are the foundation onto which knowledge must retrofit itself. Consider how quickly narratives crystallized when COVID-19 first emerged. Scientific understanding evolved daily, but public perception fractured into hardened positions within weeks. Before experts could establish consensus on masks or vaccines, millions had committed to immovable stances based not on whose evidence was strongest, but whose voice of certainty resonated with their existing beliefs regardless of whether the source was an expert, conspiracy theorist, or internet troll. Scientific knowledge's gradual, cautious progress couldn't compete with confident declarations flooding social feeds.

The ground reality is submerged under layers of reaction, each reinforcing not understanding but the illusion of certainty. Social media algorithms understand this reality better than we do. They measure precisely what content keeps you engaged—not what informs you or challenges you, but what makes you feel something strongly enough to keep scrolling. This creates self-reinforcing cycles where emotional resonance becomes the organizing principle of information, replacing accuracy, context, or usefulness.

Real-World Consequences

The ramifications of this inverted information process extend far beyond academic concerns, they reshape our daily lives in tangible ways.

In healthcare, patients form strong opinions about treatments before consulting medical professionals. Online testimonials circulate while complex medical literature remains inaccessible. When a celebrity endorses the hottest new health fad, millions commit emotionally before any evidence can be properly assessed. The result? People refuse effective treatments while embracing ineffective or harmful alternatives.

Financial decisions follow similar patterns. Investment bubbles form through emotional momentum rather than careful analysis. Crypto currencies and speculative assets rocket in value based on sentiment, not fundamentals. By the time cautionary analyses emerge, many have already suffered losses from decisions made when hype preceded understanding.

This isn't merely a shift in information consumption, it's a fundamental reordering of decision-making across every domain of life. We no longer live through events themselves. Instead, we experience layered interpretations that shape our perception before we've formed our own understanding.

Welcome to Post-Truth Culture

Martin Gurri spent years as a media analyst for the CIA, studying how information flows shape politics. In his book The Revolt of the Public, he frames our predicament not as mere information overload but as a fundamental crisis of authority. What we're witnessing isn't just an erosion of information quality but the emergence of a post-truth culture, where objective facts become less influential in shaping public opinion than appeals to emotion, identity, and tribal belonging.

Oxford Dictionaries recognized this shift by naming "post-truth" its 2016 word of the year, acknowledging a reality where what matters isn't verification but visceral resonance. In a post-truth environment, information rises based on emotional impact, not rigor. Debate becomes less about uncovering shared reality and more about projecting certainty, outrage, or emotional gravity.

"The public today is not a deliberative body, it is a loose collection of angry individuals searching for confirmation, not truth."
—Martin Gurri, The Revolt of the Public

Gurri argues that digital networks have fundamentally altered the relationship between authority and the public, creating "a world where assertions can fly without the traditional constraints of authority, expertise, or even logic," leading to a fragmentation where no institution can claim final interpretative power.

Post-truth emerges when reality-reaction inversion becomes the dominant operating system, when reactions consistently precede facts, emotional alignment outcompetes evidence, and signal strength becomes a proxy for truth. We no longer merely misinterpret reality; we replace it with competing performances of certainty, leaving no common ground, only overlapping tribal narratives performing conviction for their respective audiences.

When algorithms are designed to exploit our emotional responses rather than expand our understanding, whose responsibility is it to redesign our digital "town square", and what would that redesign prioritize?