Tag: Philosophy

Categories
AI AI: Large Language Models

The Echo Effect: Why Prompt Repetition is AI’s Best Kept Secret

In our relentless pursuit of complexity, we often overlook the elegant simplicity of a fundamental human habit: repeating ourselves.

We build colossal architectures, weave intricate neural networks, and throw mountains of computational power at our artificial intelligence systems, hoping to squeeze out a few more drops of reasoning and logic. Yet, sometimes the most profound breakthroughs require no new code, no additional latency, and no extra training data.

Sometimes, you just have to say it twice.

In a fascinating December 2025 paper titled “Prompt Repetition Improves Non-Reasoning LLMs,” researchers Yaniv Leviathan, Matan Kalman, and Yossi Matias uncovered an almost absurdly simple “free lunch” in AI optimization.

Their premise is straightforward: when you aren’t using a heavy reasoning model, simply copying and pasting your input prompt multiple times significantly boosts the model’s performance.

“When not using reasoning, repeating the input prompt improves performance for popular models (Gemini, GPT, Claude, and Deepseek) without increasing the number of generated tokens or latency.”

The mechanics behind this are elegantly pragmatic.

By repeating the prompt, you are moving the heavy computational lifting to the parallelizable “pre-fill” stage of the model’s processing. The AI’s causal attention mechanism gets to process the same tokens again, allowing the later iterations of the prompt to attend to the earlier ones. It effectively acts as a hack to simulate bidirectional attention in a decoder-only architecture.

What’s even more telling is the paper’s observation on why this works so well.

The researchers noted that models trained with Reinforcement Learning (like OpenAI’s deep-thinking variants) naturally learn to “restate the problem” in their internal monologue. They figured out on their own what these researchers are suggesting we do manually: repeat the question to focus the mind.

Reading this paper, I couldn’t help but draw a parallel to the human condition and the nature of listening.

How often do we assume that because we have articulated a thought once, it has been fully absorbed? We fire off a single, dense instruction to a colleague, a partner, or a friend, and then marvel when the nuance is lost in translation.

We suffer from our own attention bottlenecks.

Like a non-reasoning LLM trying to parse a complex query in a single pass, we are constantly bombarded with a stream of tokens—emails, notifications, conversations, fleeting thoughts. To truly understand, to truly digest and synthesize information, we need the grace of repetition.

There is a strange poetry in the fact that to make our most advanced digital minds smarter, we have to talk to them the way we talk to a distracted child or a busy spouse. The “microscope effect” highlighted in the study—where repeating a prompt drastically improved extraction tasks—shows that the failure wasn’t in the model’s capacity to know, but in its capacity to focus. Repetition forces focus. It creates a resonant echo in the context window, a digital highlighter that screams, “This matters. Look here again.”

As we continue to navigate a world increasingly augmented by artificial intelligence, this paper serves as a humbling reminder. The bleeding edge of technology isn’t always found in the most complex equation; sometimes, it’s hidden in the most basic principles of communication.

Whether you’re prompting a billion-parameter language model or trying to connect with the human sitting across from you, the lesson is clear.

Clarity isn’t just about the words you choose. It’s about giving those words the space, the resonance, and the repetition they need to be truly understood.

Say it once to be heard; say it twice to be understood.

Categories
AI Farming History

The Harvest and the Algorithm: What 1990s Farms Teach Us About AI

There’s a strange kind of wisdom hiding in dusty old books about agriculture.

When you’re caught in the middle of a technological revolution—and with AI, there’s no question that we are—it’s tempting to keep your eyes fixed on the horizon. But sometimes the most clarifying thing you can do is look back.

Tracy Alloway at Bloomberg recently pointed to something genuinely instructive from the past: Richard Critchfield’s 1990 book, Trees, Why Do You Wait? America’s Changing Rural Culture, which traced the collapse of the family farm as industrial agriculture swept through the Midwest.

The broad strokes are familiar. As machinery got more expensive and efficiency became everything, scale won. The 80-acre husband-and-wife operation got swallowed by the 2,000-acre neighbor with access to capital. It wasn’t complicated. It was just gravity.

But here’s the part that should make your ears prick up.

The Seed That Was Supposed to Save Everyone

In the late 1980s, agricultural biotechnology arrived with a very specific promise. The idea was almost elegant: if you could bake the magic directly into the seed, you wouldn’t need all that expensive machinery, all those sprawling acres, all that fertilizer. The playing field would tilt back toward the small farmer.

Critchfield quoted an Office of Technology Assessment report from 1986 that captured the mood of the moment:

“The Office of Technology Assessment in 1986 forecast that biotechnology in crops would be more quickly adopted by richer farmers… Others argue that the more that gets built into the seed itself, the more it means higher yields at lower cost… If it reduced farm income, it could work to the smaller farmer’s advantage. As it is with all new technology, it is hard to foresee the consequences.”

You can feel the cautious optimism in that language. Hard to foresee the consequences. The understatement of a century.

What Actually Happened

The biotech did raise yields. Nobody disputes that. What it didn’t do was leave the gains in the hands of the people doing the actual farming.

Thanks to intellectual property law, patent protections, and a level of corporate consolidation that would have seemed cartoonish if you’d predicted it in advance, the value flowed straight upstream. We didn’t get “more in the seed, less paid for inputs.” We got more in the seed, and vastly more paid for proprietary inputs. The tech giants of agriculture captured the surplus. The farmers got the risk.

Now Listen to How We Talk About AI

We are told AI will democratize expertise. That a one-person startup will be able to code like a ten-person engineering team. That a small business will generate world-class marketing copy. That this is, finally, the great leveler.

Sound familiar?

Alloway’s analysis lands hard precisely because it forces the uncomfortable question: who will actually capture this value? The ownership structure of AI looks eerily similar to the agricultural biotech boom—proprietary models, walled-off training data, and a handful of enormous tech companies positioned to act as tollbooths between everyone else and their own productivity gains.

She’s right to note that “the ultimate distribution of benefits isn’t determined by technology alone. Policy also plays a role.” That sentence is doing a lot of quiet work.

If the agricultural analogy holds, productivity gains from AI won’t naturally flow to the individual worker or the small business owner. Without a robust open-source ecosystem or some deliberate policy intervention, those gains will be captured by whoever controls the compute and the models.

Where the Analogy Might Break Down

Here’s where I think there’s room for genuine optimism—not naive optimism, but structurally grounded optimism.

You cannot open-source arable land. Reverse-engineering a patented biological seed is genuinely hard, legally risky, and practically difficult. Code and model weights are different. They’re infinitely replicable. The marginal cost of distribution is essentially zero.

The battle between closed, proprietary AI and open-source models is still very much live. That’s not nothing. AI is fundamentally more commoditizable than a physical farm, and the history of software suggests that open ecosystems have a real shot when the community is motivated enough to build them.

Who Owns the Harvest?

Technology can reshape daily workflows in months. Power structures take decades to budge, if they budge at all. The mistake would be assuming the former automatically changes the latter.

The question worth sitting with isn’t what can AI do—that list gets longer every week. The question is who decides how the productivity it unlocks gets distributed. That’s not an algorithm problem. It’s a political and economic one.

If we want the AI revolution to be a rising tide rather than another tractor paving over the family farm, we have to look past the technology itself. We have to decide, deliberately, who owns the harvest.

Questions to Ponder

On history and pattern recognition: The agricultural biotech optimists weren’t stupid—they were looking at the technology and making reasonable inferences. What does that tell us about the limits of predicting who benefits from a new technology by studying the technology itself?

On open source as a counterweight: The open-source AI movement (Llama, Mistral, DeepSeek) is often framed as a technical story. Should we be thinking about it primarily as a political economy story—a structural check on proprietary capture?

On the role of policy: Antitrust law, data ownership rights, compute access regulation—which levers, if any, seem realistic? And who has the incentive to pull them?

On the worker vs. the firm: If AI raises individual productivity, does the gain show up in wages, prices, profits, or somewhere else? What would need to be true for workers to actually keep a meaningful share?

On commoditization speed: Software and model weights can be replicated freely—but does speed matter? If proprietary models establish deep lock-in before open alternatives mature, does the theoretical commoditizability even help?

Inspired by Tracy Alloway’s analysis at Bloomberg and Richard Critchfield’s Trees, Why Do You Wait? (1990)

Categories
AI IBM

From Picnic to Workforce: The New Scaling

In 1977, Charles and Ray Eames released a short film for IBM called Powers of Ten.

The film opens with a couple picnicking on a blanket in Chicago and zooms out—every ten seconds, the field of view increases by a factor of ten.

We move from the intimacy of a lakeside lunch to the edge of the observable universe, then plunge back down through the skin of a hand into the subatomic architecture of a carbon atom.

The subtitle was “A Film Dealing with the Relative Size of Things and the Effect of Adding a Zero.”

It was a meditation on scale, suggesting that as we add zeros to our perspective, the very nature of what we are looking at transforms.

Today, with AI, we are living through a new kind of “Powers of Ten” journey, but the zeros aren’t being added to meters; they are being added to tokens.

I recently read a reflection by Azeem Azhar where he chronicled his shift from using 1,000 AI tokens a day to nearly 100 million. In the Eames’ film, adding a zero moved you from a park bench to a city, then to a continent. In the world of Large Language Models, adding a zero moves the AI from a novelty to a tool, then to a collaborator, and eventually—at the scale of 100 million—to something resembling a “workforce.”

“At 100,000 [tokens], a collaborator. At 1 million, I was building workflows. At 10 million, processes. At nearly 100 million – something closer to a workforce.”

This shift is more than just “more of the same.” It is a phase change.

When the Eames’ camera zoomed out to $10^{24}$ meters, the Earth didn’t just look smaller; it disappeared into a texture of galaxies.

When we scale our interaction with intelligence by several orders of magnitude, the “picnic” of human cognition—the way we think, draft, and create—is no longer the center of the frame.

At the 100-million-token-day scale, we aren’t just “using” AI. We are orchestrating vast, invisible ecosystems of thought. We are seeing companies like Spotify where top developers reportedly haven’t written a line of code in months, instead directing systems that ship features while the humans review the output from their phones.

We have added so many zeros that the “relative size” of human effort has changed.

The chilling yet beautiful thing about Powers of Ten was the realization of our own insignificance in the face of the cosmos, balanced by the intricate complexity found within our own cells.

As we zoom out into the “Token-Verse,” we face a similar existential pivot. If an AI can process a hundred million tokens of “thought” in a day—a volume no human could read in a lifetime—what does it mean to be the “author” of our lives?

The answer, I suspect, lies back on the picnic blanket.

The Eameses knew that while the scale of the universe is staggering, the meaning is found in the connection between the two people on the grass.

As we add zeros to our digital capabilities, our value shifts from the production of tokens to the intention behind them.

We are no longer the builders of the cathedral; we are the ones deciding why the cathedral needs to exist at all.

We are moving from the era of the “Worker” to the era of the “Architect” or maybe just the “Witness.”

Categories
AI AI: Large Language Models

The Architecture of Unpredictability

There is a special understanding that comes from looking too closely at a map of a massive network or a large city. There is a point where the individual components vanish, and something else—something “other”—takes over.

Niall Ferguson captures this beautifully in The Square and the Tower:

“Large networks are complex systems which have ‘emergent properties’ – the tendency of novel structures, patterns and properties to manifest themselves in ‘phase transitions’ that are far from predictable.”

We like to believe we are the architects of our systems. We build platforms, we codify laws, and we design cities with the intent of order.

But Ferguson points out that once a network crosses a certain threshold of complexity, it enters a state of “phase transition.” It’s like water reaching 100°C; it doesn’t just get “hotter”—it becomes steam. It changes its fundamental nature.

We see this most vividly today in the trajectory of Artificial Intelligence. An LLM is, at its core, a gargantuan network of weights and probabilities. We understand the math of the individual neuron, yet we cannot fully explain how, at a certain scale, these systems begin to exhibit reasoning, humor, or theory of mind. These are not explicitly programmed “features”; they are emergent properties—the ghost that moves into the machine once the network becomes sufficiently dense.

Dario Amodei, CEO of Anthropic, describes this phenomenon through the lens of scaling:

“The thing that is so surprising about these models is that as you scale them up, they just keep getting better at things you didn’t explicitly train them to do… there’s this sense in which the model is ‘learning’ the structure of the world just by being forced to predict the next word.”

This is the “emergent property.” It is the intelligence of the beehive that no single bee possesses. It is the sudden, viral revolution that no single activist could have ignited. These properties are far from predictable because they don’t live in the nodes of the network; they live in the relationships between them.

The philosophical weight of this is humbling. It suggests that our world is governed by a structural momentum that defies linear logic.

When we find ourselves in these moments of societal or personal transition, perhaps the goal isn’t to control the outcome, but to understand the new physics of the system we’ve helped create.

We aren’t just parts of the network; we are the medium through which the unpredictable manifests.

Questions to Ponder

  • If your own consciousness is an emergent property of your neural network, where does “you” actually reside?
  • In the social networks we inhabit daily, what properties are emerging that we haven’t yet named?
  • As AI continues its phase transition, are we creating a tool, or are we witnessing the birth of a new kind of physics?
Categories
Living Serendipity

The Architecture of the Unexpected

We spend an incredible amount of energy trying to build a ceiling over our lives, a structure made of spreadsheets, five-year plans, and trend forecasts. We convince ourselves that if we just gather enough data, the future will become a navigable map. But Morgan Housel, in Same as Ever, cuts through this illusion with a quiet, devastating observation:

“We are very good at predicting the future, except for the surprises—which tend to be all that matter.”

It is a humbling thought. We can predict the mundane with startling accuracy—the seasons, the commute, the steady inflation of a currency. But the events that actually shift the trajectory of a life, a business, or a civilization are precisely the ones that no model accounted for. We are experts at forecasting the rain, yet we are consistently blindsided by the flood.

This reveals a profound tension in the human experience. We crave certainty because certainty feels like safety. We want to believe that the “tail events”—those low-probability, high-impact occurrences—are outliers we can ignore. In reality, history isn’t a steady climb; it’s a series of long plateaus punctuated by sudden, violent leaps.

The problem isn’t that our models are broken; it’s that we are looking at the wrong thing. Instead of seeking total foresight, we must prioritize serendipity and resilience. If the future is defined by surprises, then the most valuable asset isn’t a better crystal ball—it’s a wider margin of safety.

We must learn to live with the paradox: we must plan for a future that we know, deep down, will not go according to plan. The surprises aren’t just interruptions to the story; they are the story.

Looking back at the last decade of your life, what was the single ‘surprise’ event that defined your path more than any plan you ever made?

Categories
AI Work

The Digital Beast of Burden

A friend of mine recently cut through the noise of the current AI discourse with a comment that felt surprisingly grounding. We were talking about the breathless predictions of AGI—superintelligence, sentient machines, the technological singularity—when he shrugged and said, “I don’t need any of that. I just want AI to do the donkey work.”

He wasn’t asking for a god in the machine; he was asking for a better tractor. He didn’t want a synthetic philosopher to debate the meaning of life; he wanted the next evolution of “Claude Cowork”—a reliable, tireless entity to handle the drudgery so he could get back to the actual business of thinking.

There is something profound in that phrase: donkey work. It evokes the image of the beast of burden—the creature that carries the heavy packs up the mountain so the traveler can focus on the path and the view. For thousands of years, humans have sought tools to offload physical exertion. We domesticated animals, we built water wheels, we invented the steam engine. We outsourced the calorie-burning, back-breaking labor to preserve our bodies.

“The ‘donkey work’ of the information age isn’t hauling stone; it is the cognitive load of bureaucracy, formatting, sorting, scheduling, and synthesizing endless streams of data.”

Now, we are looking to preserve our minds.

The friction that exists between having an idea and executing it is often composed entirely of this “donkey work.” When my friend says he wants AI for this, he isn’t being lazy. He is expressing a desire to reclaim his cognitive bandwidth.

There is a fear that if we hand over these tasks, we become less capable. But I suspect the opposite is true. If you are no longer exhausted by the logistics of your work, you are free to be consumed by the meaning of it.

We often talk about AI as if it’s destined to replace the artist or the architect. But the most valuable version of this technology might just be the humble assistant—the digital mule that quietly processes the mundane in the background. It’s the difference between a tool that tries to be you, and a tool that helps you be you.

We don’t need AGI to solve the human condition. We just need the “donkey work” handled so we have the time and energy to experience it.

What do you think?

  1. Is there a danger that in handing over the “donkey work,” we accidentally hand over the friction required to build mastery?
  2. If your daily cognitive load dropped by 50% tomorrow, would you actually use that space for “higher thinking,” or would you just fill it with more noise?
  3. Where exactly is the line between “drudgery” and the “process”—and are we risking erasing the latter to solve the former?
Categories
AI Business Work

The Curator of Intent

I have always found a certain comfort in the “clatter” of a digital workday. It’s that specific, rhythmic hum of a mind in motion—the clicking of a mechanical keyboard, the invisible friction of parsing a difficult paragraph or balancing a complex budget. For years, we’ve treated this white-collar grind as our intellectual sanctuary.

But Mustafa Suleyman, now steering Microsoft AI, recently laid out a timeline that suggests the sanctuary walls are evaporating.

From an article in the Financial Times:

“White-collar work, where you’re sitting down at a computer, either being a lawyer or an accountant or a project manager or a marketing person — most of those tasks will be fully automated by an AI within the next 12 to 18 months,” Suleyman said.

This isn’t just about efficiency; it’s about a fundamental shift in the “professional grade.” We are entering the era of the autonomous agent—AI that doesn’t just wait for a prompt but “coordinates within workflows,” learns from its environment, and acts. Just ask any programmer that you know how AI is impacted their daily grind.

If Suleyman is correct, the “knowledge worker” is about to undergo a forced evolution. When the “doing” is handled by an agent that can learn and improve over time, what remains for the human? Will the models actually be able to learn from each of us in a personalized way – like an intern learns from her mentor?

“Creating a new model is going to be like creating a podcast or writing a blog,” he said. “It is going to be possible to design an AI that suits your requirements for every institutional organisation and person on the planet.”

It seems like our primary job description shifts from “Expert,” but “Curator of Intent.” We aren’t the ones finding the answers anymore; we are just the ones responsible for asking the right questions.

The next 18 months won’t just be a test of our technology, but a test of our egos. We have to learn to find our value not in the work we produce, but in the vision we hold and the questions we ask. We are shedding the “task” to save the “craft.” I just hope we remember the difference.

As we move toward this curated future, I’m left with a few questions I can’t quite shake. I’d love to hear your thoughts:

  1. The Wisdom Gap: Can you truly be a “Curator of Intent” without having ever been a “Doer of Tasks”? If we skip the apprenticeship of the mundane, where does our intuition come from?
  2. The Metric of Value: If output becomes “free,” how should we measure a human’s value in a professional setting?
  3. The Line in the Sand: Is there a part of your workflow you would refuse to automate, even if an AI could do it better?
Categories
Living Productivity

The Architecture of Arete

In the modern landscape of productivity, we are drowning in “how-to” guides and “ten-step” frameworks. We treat our lives like machines that need oiling, rather than gardens that need tending. But David Sparks’ recent work on an updated productivity field guide brings back a much older, more grounded philosophy: the marriage of roles and arete. This is the third edition of his field guide with refinements that he’s made along the way.

To understand why this matters, we have to look at how we usually define ourselves. Most of us operate via a chaotic “to-do” list—a flat, untextured pile of tasks. “Buy milk” sits right next to “Finish the quarterly report,” which sits next to “Call Mom.” This flatness is where burnout lives. It lacks a sense of who we are being when we do those things.

“A role is not just a job title; it is a container for responsibility and relationship.”

This is where Roles come in. When we organize our lives by roles, we stop seeing tasks and start seeing stewardship. We aren’t just checking boxes; we are fulfilling a duty to the parts of our lives that actually matter. But roles alone can become burdensome—mere masks we wear—unless they are infused with arete.

The Greeks defined arete as “excellence” or “virtue,” but its deepest meaning is “acting up to one’s full potential.” It is the act of being the best version of a thing.

However, a warning from the 2026 guide: Do not treat Arete as a yardstick to beat yourself up with when you fall short. Instead, treat it as a compass bearing. You will never perfectly ‘reach’ North, but you can always check to ensure you are rowing in that direction . Success isn’t matching the ideal; it is simply making progress from who you were when you started .

When you combine a defined Role with the pursuit of arete, productivity shifts from a mechanical burden to a philosophical practice. You are no longer just “writing an email”; you are practicing the excellence of a “Clear Communicator.” You aren’t just “doing the dishes”; you are practicing the excellence of someone who “Values a Peaceful Environment.”

To keep these roles authentic, we must also identify their Shadow Roles. If your Arete is the ‘Present Father,’ you must recognize the Shadow Role of the ‘Distracted Dad’ who is physically in the room but mentally scrolling email. Identifying the shadow doesn’t make you a failure; it gives you the awareness to course-correct before you hit the rocks .

Implementing this requires what Sparks calls the Arete Radar. In a world demanding instant responses, we must cultivate a ‘meditative gap’—a pause between a request and our answer . In that gap, we ask a single question: ‘Does this commitment serve my Arete, or does it distract from it?‘. This turns the act of saying ‘no’ into a strategic ‘yes’ to your deeper purpose.

This framework rescues us from the “productivity for productivity’s sake” trap. It suggests that the goal isn’t to get more done, but to be more present and excellent in the specific seats we have chosen to occupy. In the end, we don’t need better apps. We need a better understanding of our station and the virtue required to fill it.

Finally, we must stop solving for speed and start solving for meaningfulness. Efficiency is the enemy of Arete internalization. Sparks suggests the ‘Blank Page Ritual’: rewriting your Arete statements from scratch every quarter rather than just editing an old file. This intentional slowness forces the values out of your computer’s storage and hard-codes them into your soul’s permanent memory .

Categories
Biology Creativity Living

The Compost of the Soul

There is a pervasive pressure in modern life to curate our experiences like a museum curator arranges an exhibition. We want to catalog our memories, label our skills, and display only the pristine, unbroken artifacts of our history. We treat our minds like archives—dusty, organized, and static.

But Ann Patchett offers a different, earthier metaphor, one that feels infinitely more true to the messy reality of being human:

“I am a compost heap, and everything I interact with, every experience I’ve had, gets shoveled onto the heap where it eventually mulches down, is digested and excreted by worms, and rots. It’s from that rich, dark humus, the combination of what you encountered, what you know and what you’ve forgotten, that ideas start to grow.”

This imagery of the compost heap is liberating because it removes the burden of purity. In a compost heap, you don’t separate the eggshells from the coffee grounds or the dead leaves from the fruit rinds. It all goes in. The triumphs, the heartbreaks, the books we read halfway, the conversations we barely remember, and the failures we wish we could forget—they are all just organic matter.

The magic, as Patchett notes, is in the digestion. We are not static repositories of information; we are active, biological processors. Time acts as the earthworms, breaking down the sharp edges of raw experience until it loses its original form.

We often fear forgetting. We worry that if we don’t hold onto a memory with a white-knuckled grip, it loses its value. But in the logic of the compost heap, “what you’ve forgotten” is just as vital as what you remember. The forgotten things are simply the matter that has broken down completely, becoming the nutrient-dense soil that supports new growth.

If we view ourselves as compost heaps, we stop fearing the “rot.” We understand that the difficult periods of decomposition are necessary to create the humus required for the next season of growth. We are not built to be archives; we are built to be gardens.

Categories
Probabilities

The Fiction of Certainty

There is a profound discomfort in the space between zero and one.

In her book Spies, Lies, and Algorithms, Amy B. Zegart notes a fundamental flaw in our cognitive architecture:

“Humans are atrocious at understanding probabilities.”

It is a sharp, unsparing observation, but it is not an insult. It is an evolutionary receipt. We are atrocious at probabilities because we were designed for causality, not calculus. On the savanna, if you heard a rustle in the tall grass, you didn’t perform a Bayesian analysis to determine the statistical likelihood of a lion versus the wind. You ran. The cost of a false positive was a wasted sprint; the cost of a false negative was death.

We are the descendants of the paranoid pattern-seekers. We survived because we treated possibilities as certainties.

The Binary Trap

Today, this ancient wiring misfires. We live in a world governed by complex systems, subtle variables, and sliding scales of risk. Yet, our brains still crave the binary. We want “Safe” or “Dangerous.” We want “Guilty” or “Innocent.” We want “It will rain” or “It will be sunny.”

When a meteorologist says there is a 30% chance of rain, and it rains, we scream that they were wrong. We feel betrayed. We forget that 30% is a very real number; it means that in three out of ten parallel universes, you got wet. We just happened to occupy one of the three.

Zegart operates in the world of intelligence—a misty domain of “moderate confidence” and “low likelihood assessments.” In that world, failing to grasp probability leads to catastrophic policy failures. But in our personal lives, it leads to a different kind of failure: the inability to find peace in uncertainty.

Stories > Statistics

We tell ourselves stories to bridge the gap. We prefer a terrifying narrative with a clear cause to a benign reality based on random chance. Stories have arcs; statistics have variance. Stories have heroes and villains; probabilities only have outcomes.

To accept that we are bad at probability is an act of intellectual humility. It forces us to pause when we feel that rush of certainty. It asks us to look at the rustling grass and admit, “I don’t know what that is,” and be okay with sitting in that discomfort.

We may never be good at understanding probabilities—our biology fights against it—but we can get better at forgiving the universe for being random.