Tag: learning

Categories
Living

The Compound Interest of Ignorance

There’s an emotional navigation system within all of us, an internal map of behavior and consequence. We navigate by way of kindness, curiosity, and empathy.

Most days, we manage to keep the car on the road. But there is a particular intersection on this map, one that rarely ends well for anyone who finds themselves there, either driving or just walking by.

It’s the intersection where Annabel Monaghan located a particularly difficult archetype in Nora Goes Off Script. She describes it, with a precision that feels like the pop of a bubble, as “the corner of arrogance and cluelessness.”

“At the corner of arrogance and cluelessness, you find the worst kind of person.” (Annabel Monaghan, Nora Goes Off Script)

Indeed.

It’s easy, and frankly quite satisfying, to point fingers. We can all summon the mental image of someone parked right at that corner.

Perhaps it was a micromanaging boss who had never performed the basic function of the department. Perhaps it was a self-styled intellectual whose confidence was inversely proportional to their subject-matter expertise. We’ve all felt that specific, teeth-gritting frustration when faced with the wall of certainty erected by the fundamentally uninformed.

Arrogance on its own is, of course, rarely endearing. But there’s a difference between earned arrogance—the abrasive confidence of someone who actually knows what they are doing—and this unholy alliance. Pure arrogance is often about results; it says, “I am the best, and here is my proof.” It’s difficult to live with, but it is at least based on a form of reality.

Cluelessness, too, has its own nuances. We are all clueless about something (a truth that keeps life interesting). There is an innocence to genuine ignorance, an implicit opening for growth. To be clueless and know it is a temporary state. It’s the raw material for humility and learning.

But Monaghan’s observation zeros in on the specific danger when these two states merge.

Arrogance and cluelessness don’t just coexist; they compound.

This isn’t just a simple mistake (cluelessness) or just a big ego (arrogance). This is a system where the arrogance actively prevents the realization of the cluelessness.

The arrogance acts as a sturdy shield, deflecting any data, any feedback, any reality-check that might reveal the cluelessness underneath. The clues are everywhere, screaming from the spreadsheets or the strained smiles of everyone around them, but the arrogance filters them all out. This person cannot learn because the primary tool for learning—admitting you don’t know—is precisely what the arrogance forbids.

When you find yourself arguing with a person at this intersection, you aren’t arguing about facts. You aren’t arguing about solutions. You are trying to breach a fortress that has decided that the external world must adapt to its inner perception.

The “worst” part of it, the thing that makes it so toxic, is the casual destruction it wreaks. The person at this corner is navigating with a map they have drawn themselves, one that ignores all existing roads, all traffic lights, and every standard convention of behavior. They crash through the lives and efforts of others, convinced all the while of their own perfect navigation.

The hardest truth to swallow, though, isn’t about them. It’s about us. Because if we find this so true of others, the final realization is that none of us are immune to the lure of that corner. It’s an easy intersection to drift into. Whenever our confidence outpaces our real-world competence, whenever we get a tiny bit of power and a tiny bit of success and we think we know, we are in danger.

We are all just a bad day, a stressful project, or a momentary inflation of ego away from parking right at that corner ourselves. The antidote to that specific, devastating brand of arrogance isn’t trying to become more right; it’s remembering how deeply, often, and completely we are wrong.

Stay humble, stay foolish.

Categories
AI

Beyond the Summary: Using AI to Find the “Friction” in Your Thinking

We’ve reached the “Summary Plateau.”

You see it everywhere. Every browser extension, every note-taking app, and every enterprise LLM now offers a “Summarize” button. It’s the ultimate promise of the efficiency era: Give us the 2,000-word essay, and we’ll give you the three bullet points. But there’s a hidden tax on this kind of efficiency. When we ask an AI to summarize, we are asking it to smooth out the edges. We are asking it to remove the “noise.” The problem is, in the world of ideas, the noise is often where the signal lives. The friction—the parts of an argument that make us uncomfortable or that we don’t quite understand—is where the actual learning happens.

If we only consume the summaries, we aren’t thinking; we’re just acknowledging.

The Mirror, Not the Maker

I’ve been experimenting with a different approach. Instead of asking the model to make the content shorter, I’ve been asking it to make my engagement with the content harder.

I don’t want a “Maker” to write my thoughts for me. I want a “Mirror” to show me where my thoughts are thin.

When I’m wrestling with a complex piece—perhaps a deep dive on the future of venture capital or a philosophical treatise on Arete—I’ve stopped clicking “summarize.” Instead, I feed the text into the LLM and use these “Friction Prompts” to find the sand in the gears:

The Essential Toolkit

  • The “Steel Man” Challenge: “I am inclined to agree with this author’s conclusion. Find the three strongest counter-arguments that this text ignores, and explain why a reasonable person would hold them.”
  • The “Recursive Logic” Audit: “Identify the three most critical ‘logical leaps’ the author makes—points where a conclusion is reached without sufficient evidence. If those leaps are wrong, how does the entire argument collapse?”
  • The “Blind Spot” Audit: “What are the underlying cultural or economic assumptions this author is making that they haven’t explicitly stated?”
  • The “Cross-Pollination” Filter: “Connect the central thesis of this article to a seemingly unrelated field (e.g., Stoic philosophy or biological ecosystems). How does the logic of this text hold up—or fail—when applied to that different domain?”
  • The “Analog Translation” Test: “If I had to explain the core mechanism of this abstract concept using only physical, analog metaphors (like plumbing or woodworking), how would I do it? Where does the metaphor break down?”
  • The “Socratic Sharpening”: “Don’t summarize this. Instead, ask me three probing questions that force me to apply the core logic of this essay to a completely different industry.”

Sharpening the Blade

Summary is about completion (getting it done). Friction is about cognition (getting it right).

When the AI points out a blind spot in an article I loved, it creates a moment of cognitive dissonance. That “click” of discomfort is the sound of a mental model being updated. It’s the digital equivalent of using a whetstone on a blade—you need the friction to get the edge.

As we move further into this age of “Flash-Frozen Cognition,” the temptation to automate our understanding will only grow. But discernment—that uniquely human trait we’ve discussed here before—cannot be outsourced to a bulleted list.

The next time you’re faced with a daunting PDF or a dense long-read, resist the “Summarize” button. Ask the machine to challenge you instead. You might find that the most valuable thing the AI can give you isn’t an answer, but a better version of your own question.

A Deep Dive (Further Reading from the Archive)

If you resonated with this piece on cultivating discernment, you might find these earlier synthesis experiments worth a revisit:

  • On Flash-Frozen Cognition: A foundational post discussing how LLMs are freezing the current consensus, and how we must resist it.
  • The Harvest and the Algorithm: Comparing 1920s ice harvesting to 2020s cognition—the critical shift from scarcity to abundance.
  • The Arete of Attention: A look at the Stoic concept of virtue as the intentional direction of our most scarce resource: focus.
  • Longhand Thinking: Why the physical act of writing is the ultimate antidote to digital velocity.
Categories
Business Living

From Know-It-All to Learn-It-All

Momentum is a strange phenomenon. In physics, it is simply mass times velocity. But in human organizations, it is tradition multiplied by ego. When a ship reaches a certain size, its sheer mass resists any change in direction. Microsoft, a little over a decade ago, was the ultimate corporate supertanker. It was massively successful, incredibly profitable, and dangerously stagnant.

When Satya Nadella took the helm, he inherited a culture defined by its own historic brilliance. They were the smartest people in the room, and they knew it. But in a world moving faster than anyone could comprehend, being the smartest person in the room quickly becomes a liability. It creates a defensive posture. You spend your energy protecting your status and proving your intelligence rather than exploring the horizon.

As the observation goes, Nadella had to turn this bigger ship. His mechanism for doing so wasn’t a massive restructuring or a ruthless wave of firings; it was beautifully, disarmingly simple. He told his organization that they were going to make a fundamental, psychological shift.

“We’re gonna go from being a know-it-all to a learn-it-all culture.”

This isn’t just a corporate soundbite; it’s a profound philosophical pivot. The “know-it-all” operates from a place of fragility and fear. If your identity is built on knowing everything, any new information that contradicts your worldview is a threat that must be neutralized. A “learn-it-all,” however, operates from a place of abundance and curiosity. Contradictions aren’t threats; they are invitations to expand.

Looking inward, it is striking how easily we slip into a “know-it-all” posture in our own lives. Competence is deeply comfortable. When we get good at our jobs, our daily routines, or navigating our relationships, we build a fortress of certainty around ourselves. We stop asking questions because we assume we’ve already mapped the territory. We begin to ossify.

To adopt a learn-it-all mindset requires something deeply uncomfortable: vulnerability. It means walking into a room and quietly accepting that you might be wrong. It means replacing the urge to provide a quick, authoritative answer with the patience to ask a better question. It means letting go of the ego’s demand to be the expert.

The turnaround of Microsoft wasn’t just about a pivot to cloud computing or new product pipelines. It was a quiet victory of humility over arrogance. It was the realization that in an ever-changing world, the ultimate advantage isn’t what you already know, but how fast—and how willingly—you are prepared to learn.

We are all steering our own ships through shifting waters. The moment we decide we have nothing left to learn is the exact moment we begin to sink.

Categories
Curiosity

Hunting for the “Why”

I’ve spent a lot of time watching people—myself included—hit what feels like a glass ceiling. We often chalk it up to a lack of “natural talent” or the missing spark of genius. We look at the high-flyers in our industry and assume they were born with a blueprint we never received. But lately, I’ve realized that the most successful people I know aren’t necessarily the ones with the highest IQ; they’re the ones who simply never stopped asking why.

Bill Gurley puts a name to this:

“The thing that will differentiate you more in your career than anything else is being the most hyper-curious person.”

For me, curiosity isn’t a personality trait; it’s an appetite. It’s that itch in the back of your brain when something doesn’t quite make sense. Hyper-curiosity is the willingness to be the “annoying” person who asks for the raw data or the one who stays up an hour late following a rabbit hole that has nothing to do with tomorrow’s to-do list—and everything to do with how the world actually works.

We live in an age where the “ivory tower” has been dismantled. The walls are down.

“I can’t make you the most talented person in your company or your field, but you have no excuse not to be the most knowledgeable person. The information is all out there.”

This hits hard because it removes our favorite excuse: “I just wasn’t born for this.” It shifts the weight from our DNA to our discipline. I’ve found that the moment I stop being a passive consumer and start being a hunter of information, my world gets bigger. Knowledge is the only asset that doesn’t depreciate; in fact, it compounds.

When you commit to being the most curious person in the room, you aren’t just “doing well.” You are building a life in high-definition.

“If you are the most curious person constantly learning in your field, you will do extremely well.”

But beyond the “doing well,” there’s a deeper peace that comes with it. You realize that you don’t need to be the smartest person in the room—you just need to be the one most willing to learn from it.

Categories
Living Writing

The Loop and the Pixel

There is a distinct muscle memory associated with the 1950s classroom. It smells of chalk dust and floor wax, but mostly, it feels like the cramping of a small hand wrapped around a pencil. We didn’t just learn to write; we were initiated into the discipline of the loop. The Palmer Method or Zaner-Bloser weren’t suggestions—they were rigorous architectures of communication. We made endless rows of O’s and l’s, tilting the paper just so, learning that language required flow, connectivity, and a certain deliberate grace.

Then, the world sped up.

By the 1990s, the loops began to unravel. As keyboards clattered their way into dominance, the efficiency of the printed letter—and eventually the typed pixel—took precedence over the artistry of the connected script. By 2010, the erasure was formalized; cursive was dropped from federal education standards (Common Core) to make room for “electronic literacy.” We traded the unique signature for the standardized font. We gained speed, certainly, but I often wonder what we lost in the translation.

“New Jersey this week joined a list of more than 20 states slanting in favor of bringing cursive instruction back to classrooms. Lessons on the looping letters were dropped from federal education standards in 2010, part of a shift toward focusing on electronic literacy.” — The New York Times

It seems the pendulum is swinging back. Proponents argue for its utility—the ability to read historical texts or a grandmother’s birthday card—but I believe the resurgence touches on something deeper.

In an increasingly digital world, cursive is an act of resistance. Typing is percussion; it is staccato and disconnected. Cursive is string; it is continuous and fluid. When we write in cursive, we are physically connecting thoughts, linking one letter to the next without lifting the pen. It forces the brain to slow down and the hand to dance.

As we stare into screens that demand our instant reaction, perhaps we are realizing that we crave the friction of pen on paper. We are bringing the loops back not because they are faster, but because they are human.

Categories
Curiosity

The Neutral Ground of Curiosity

We live in a time that demands certainty. We are constantly pressured to have a stance, to pick a team, to decide—right now—whether something is good or bad, right or wrong. It is exhausting. It feels like standing in a courtroom where you are forced to be both the lawyer and the judge.

But there is a quieter, more fertile ground we can stand on. Rick Rubin, writing in The Creative Act, describes it like this:

“The heart of open-mindedness is curiosity. Curiosity doesn’t take sides or insist on a single way of doing things. It explores all perspectives. Always open to new ways, always seeking to arrive at original insights.”

I love the idea that curiosity “doesn’t take sides.” It implies that curiosity is a neutral party. It isn’t there to win an argument; it is there to understand the argument.

When we approach the world with judgment, our vision narrows. We look for evidence that confirms what we already believe. But when we approach the world with curiosity, the lens widens. We stop asking, “Is this right?” and start asking, “What is this?”

Rubin reminds us that the goal isn’t to be correct; the goal is to be original. And you cannot arrive at an original insight if you are walking the same worn path of binary thinking. You have to be willing to wander off the trail, to listen to the opposing view not to defeat it, but to learn the shape of it.

I remind myself to try to drop the gavel. To stop judging the events of my day and simply witness them. To be the explorer, not the jury. Oh, and along the way, embrace serendipity!

I’m reminded of a couple of friends and colleagues. One seems to listen briefly but rapidly reach a black/white conclusion. Another seems to always want to explore further, asking questions to go deeper. One is much more enjoyable to be around. The other a lot less so! Which one can I be? Which one am I?

Categories
AI

Research Prompts

I recently came across a prompt in a post on X which has proven to be quite useful in brainstorming.

Here’s the prompt, tailored in this example to research the area of AI super intelligence:

You are a professional ghostwriter. Generate 15 high-signal content ideas on how AI labs will reach artificial super intelligence.

For each idea:
- Give me a hook line (<= 15 words, curiosity-driven)
- Outline the structure in 3 parts (hook, point, action)
- Include an example or analogy that will resonate with an audience of college graduates. 

Make them practical, non-generic, and designed to spark discussion.

To see how it works, just copy it and put it into your favorite large language model. I think you’ll be surprised and pleased with what results you obtain.

After the first pass, you can try this next:

Pick the best one and draft it.

You’ll get back a draft article about the model’s choice for the best among the 15 it first produced.

You can then steer the model using a prompt like this:

Tune the draft for the vc/tech founder voice. Also speculate that Google is most likely the winner.

It’s fun to see how the article evolves further with that voice and more speculation about the possible winner.

You can then ask the model to redraft it further:

Sketch two spicy counterarguments to the main thesis.

And so on. It’s fun to do a deep dive on a topic using this approach. The wide range of the first fifteen results narrows and deepens as you ask the model to refine the draft it has produced.

I’ve lost track of time exploring a topic of interest to me as I got back and forth with the model evolving my understanding. Some models will even assist you in that process by suggesting next steps along the way.

Let me know of your experiences using this kind of approach!

Categories
AI AI: Large Language Models

LLM Learning / Daydreaming

Following up on my earlier post about Dwarkesh Patel’s lament about LLMs not really learning, Gwern writes LLM Daydreaming.

I propose a day-dreaming loop (DDL): a background process that continuously samples pairs of concepts from memory. A generator model explores non-obvious links between them, and a critic model filters the results for genuinely valuable ideas. These discoveries are fed back into the system’s memory, creating a compounding feedback loop where new ideas themselves become seeds for future combinations.

Categories
Creativity Educated

Software for your brain…

Categories
AI AI: Large Language Models

Anterograde Amnesia

Recently Dwarkesh Patel shared some of his thoughts about one of the major challenges the current crop of large language models face: they’re not easily trained like a human assistant can be.

… the fundamental problem is that LLMs don’t get better over time the way a human would. The lack of continual learning is a huge huge problem. The LLM baseline at many tasks might be higher than an average human’s. But there’s no way to give a model high level feedback. You’re stuck with the abilities you get out of the box. You can keep messing around with the system prompt. In practice this just doesn’t produce anything even close to the kind of learning and improvement that human employees experience.

Today on X Andrej Karpathy replied to Dwarkesh and included the following which introduced a new term to me describing this weakness:

I like to talk explain it as LLMs are a bit like a coworker with Anterograde amnesia – they don’t consolidate or build long-running knowledge or expertise once training is over and all they have is short-term memory (context window). It’s hard to build relationships … or do work … with this condition.

I’m quite interested to see how this issue begins to be meaningfully addressed!