I loved Steven Johnson’s description of “flow”: “it is more the feeling of drifting along a stream, being carried in a clear direction, but still tossed in surprising ways by the eddies and whirls of moving water.”
Note: the following post was largely generated by Google Gemini 2.5 Flash. I prompted Gemini to draft it after reading Simon Willison’s post about the Claude 4 Opus system prompt and being struck by the notion of us humans also having our versions of system cards. I asked Gemini to probe and explore that notion along with the related notion of how our life experiences constitute the human version of reinforcement learning. Rather than avoid the use of and being critical of using AI to write for me, I’m enjoying exploring and learning more about its capabilities! One thing is clear: Gemini 2.5 Flash seems to be an impressive new model!
Simon Willison’s recent dive into the Claude 4 Opus system prompt got me thinking. He dissects the meticulously crafted instructions that define Claude’s core behavior, its ethical guardrails, and its fundamental operational parameters. It’s a fascinating glimpse into how a complex AI is given its foundational “personality” and purpose. But as I read, a parallel began to emerge in my mind, one that brought me back to something far more organic and familiar: ourselves.
Could it be that what we, as humans, are taught and learn from our parents and primary caregivers is, in essence, our own unique, individual “system card”?
Think about it. From the moment we are born, we are immersed in a world of instruction, observation, and subtle conditioning. Our parents, whether consciously or unconsciously, are constantly programming us. They instill values: “Always be kind,” “Honesty is the best policy.” They teach us social norms: “Say please and thank you,” “Don’t interrupt.” They guide our understanding of the world: “Look both ways before crossing,” “Stranger danger.” They impart their wisdom, their fears, their hopes, and their biases, all of which become foundational layers in our burgeoning minds.
This isn’t merely about rote memorization or factual knowledge. It’s about the deep-seated principles that govern our reactions, our decision-making, and our very perception of reality. Just as Claude’s system prompt dictates its default tone and its approach to difficult queries, our upbringing shapes our inherent optimism or pessimism, our tendency towards introversion or extroversion, our inclination to trust or to be cautious.
Consider the parallels more closely. A system prompt aims for consistency and predictability in an AI’s behavior. Similarly, parents strive to create a stable and predictable environment for their children, instilling routines and expectations that foster a sense of security and belonging. This consistency helps to solidify the early “programming.”
The “ethical guardrails” in an AI system prompt are designed to prevent harmful or undesirable outputs. Our parents, too, establish ethical guardrails. They teach us right from wrong, the consequences of our actions, and the importance of empathy. These lessons, often reinforced through discipline and encouragement, become our internal compass, guiding us away from behaviors that could harm ourselves or others.
Furthermore, a system prompt often defines an AI’s learning parameters and its ability to adapt. Our upbringing isn’t a static, one-time download. It’s an ongoing process. As we grow, we continue to learn from our parents through their reactions to new situations, their advice on navigating challenges, and their own evolving perspectives. This continuous input refines and expands our internal “system card,” allowing us to adapt to new information and experiences.
Of course, the analogy isn’t perfect. We are not machines, and our development is infinitely more complex and nuanced than any AI’s. We possess free will, consciousness, and the capacity for self-reflection in ways that current AI cannot. Our “system card” is not a rigid, unchangeable code. It’s a living document, constantly being rewritten and revised by our own experiences, our peer interactions, our education, and our personal revelations.
Yet, the foundational layers laid down in childhood are undeniably powerful. They form the default settings, the initial operating system upon which all subsequent experiences are built. Think about how ingrained certain parental phrases or beliefs become. Even as adults, we might hear our own parents’ voices in our heads when faced with a difficult decision, or find ourselves automatically reacting in ways that mirror their habits.
If our upbringing is our initial system card, then what about the rest of our lives? Here, the analogy to AI models becomes even more fascinating, specifically through the lens of reinforcement learning.
In reinforcement learning, an AI agent learns to make decisions by interacting with an environment, receiving “rewards” for desirable actions and “penalties” for undesirable ones. It’s a continuous feedback loop that refines the agent’s behavior over time, teaching it to achieve specific goals.
Doesn’t this sound strikingly similar to the human experience? Our formal education, from kindergarten to university, is a structured environment where we are rewarded for correct answers, for understanding concepts, and for demonstrating skills. Getting good grades, receiving praise from teachers, or excelling in a chosen field are all forms of positive reinforcement that shape our learning and our approach to intellectual challenges. Conversely, failing an exam or struggling with a subject provides negative feedback, prompting us to adjust our study habits or seek different approaches.
But it extends far beyond the classroom. Every social interaction, every career choice, every personal relationship is a mini-experiment in reinforcement learning. We try different communication styles, observe the reactions of others, and adjust our approach based on the outcome. A successful collaboration at work (reward) reinforces certain teamwork strategies. A relationship that falters (penalty) leads us to re-evaluate our emotional intelligence or our communication patterns. Even a simple act like trying a new recipe – if it’s delicious (reward), we’ll make it again; if it’s inedible (penalty), we learn what not to do.
This continuous stream of feedback, both positive and negative, constantly refines our “system card.” It strengthens certain neural pathways and weakens others. It allows us to adapt our initial programming to the ever-changing complexities of the world. We learn from our mistakes, not just intellectually, but at a deeper, almost instinctual level. The pain of a poor decision, the joy of a success, are powerful motivators that drive our personal “reinforcement learning” algorithm.
Think of it: Our early experiences are the initial dataset, our parents the initial trainers providing supervised learning. But then, as we venture out, we become our own agents in a vast, dynamic environment. We set our own goals, navigate unforeseen challenges, and receive a constant barrage of rewards and penalties, subtly (or sometimes not so subtly) adjusting our internal parameters. We optimize for happiness, for success, for connection, for meaning – whatever our individual “objective function” may be.
The beauty and the challenge of this human “system card” lie in its malleability. Unlike an AI whose prompt might be a fixed piece of code, ours is dynamic. We have the remarkable capacity to critically examine our early programming. We can identify limiting beliefs instilled in us and actively work to reframe them. We can challenge inherited biases and cultivate new perspectives. This introspection and intentional self-modification are what allow us to transcend our initial programming and forge truly unique identities. It’s our capacity for conscious reinforcement learning, where we can even choose which “rewards” and “penalties” we pay attention to, and which “policies” we decide to adopt.
This perspective also highlights the immense responsibility of parenthood. Every word, every action, every value conveyed, contributes to the shaping of a developing human being’s fundamental operating system. It’s a profound act of creation, far more intricate and impactful than any lines of code. And as we grow, the responsibility shifts, allowing us to become the agents of our own continuous learning and evolution.
Ultimately, the idea of our upbringing as a personal “system card” and our lifelong experiences as a form of reinforcement learning offers a compelling framework for understanding ourselves. It acknowledges the profound influence of our early environments while simultaneously celebrating our capacity for growth, adaptation, and self-determination. Just as AI developers meticulously craft prompts and then subject their models to iterative learning, our parents, with all their love and imperfections, craft the initial blueprint for who we become, and then life itself provides the ongoing, messy, and ultimately transformative training data. And that, in itself, is a truly remarkable feat of human design.
How often does creativity begin with no idea?…
“Extra details like this add even more tension by mimicking the way brains process peak moments of stress. When we realise our car is about to crash, say, the brain needs to temporarily increase its ability to control the world. Its processing power surges and we become aware of more features in our environment, which has the effect of making time seem to slow down. In exactly this way, storytellers stretch time, and thereby build suspense, by packing in extra saccadic moments and detail.” (Will Storr, The Science of Storytelling)
At its best, the sensation of writing is that of any unmerited grace. It is handed to you but only if you look for it. You search. You break your heart, your back, your brain and then and only then it is handed to you. From the corner of your eye, you see motion. Something is moving through the air and headed your way on two white wings. It flies directly at you. You can read your name on it. If it were a baseball, you’d hit it out of the park. It is that one pitch in a thousand you see in slow-motion. — Annie Dillard
Be careful hiring “experts” when what’s really required is “innovators”! The best people are able to do both. But they can be very hard to find and difficult to pick out of a crowd.
Lovely highlight this morning from Rick Rubin: “You might imagine that the outside world is a conveyor belt with a stream of small packages on it, always going by. The first step is to notice the conveyor belt is there. And then, any time you want, you can pick up one of those packages, unwrap it, and see what’s inside.” (Rick Rubin, The Creative Act)
I once found myself in a local restaurant filled with young professors and graduate students from a nearby university. They were clustered around a long table arguing about the nature of originality in a world where machines could now produce human-like text and code with a few keystrokes. I sat at a small table nearby, eavesdropping.
“I just don’t think it’s right,” said a woman with steel-rimmed glasses. “If you’re using AI to write your paper, you should be honest about it. It’s intellectually dishonest otherwise.”
Her companion, a man with unruly hair and a cardigan stretched at the elbows, shook his head vigorously. “But what about the code you’re writing? Aren’t you using GitHub Copilot? Isn’t that the same thing?”
The question hung in the air between them.
The border between human creativity and machine assistance has always been contested territory. When the word processor replaced the typewriter, did writers suddenly become less authentic? When compilers made it unnecessary to understand assembly language, did programmers become less skilled? Each technological advancement seems to bring with it a fresh anxiety about the dilution of human agency, a sense that we are somehow cheating if we don’t do things the “hard way”.
I recently visited a friend who works at a technology startup in San Francisco. His office was a converted warehouse with exposed brick and polished concrete floors. The ceiling was high enough that you could fly a small drone inside without hitting anything. Software engineers clustered around monitors, wearing noise-canceling headphones and drinking coffee from biodegradable cups. My friend showed me a tool called Cursor, which allows programmers to describe what they want a program to do in plain English, and then generates the code automatically.
“It’s called ‘vibe coding,'” he explained, showing me the interface. “You sort of… gesture at what you want, and the AI figures out how to make it happen.”
I watched as he typed a simple instruction: “Create a function that calculates the Fibonacci sequence up to the nth term.” The AI responded with a dozen lines of code, neatly formatted and commented. My friend nodded approvingly and made a few small adjustments.
“Did you really program that?” I asked.
He laughed. “Define ‘program.’ I told it what I wanted. It wrote the code. I checked it and made a few tweaks. Is that programming? I don’t know. But I’m still responsible for the end result.”
Tools like Cursor and Windsurf are all the rage lately among software engineers as they provide truly dramatic productivity boosts to those writing code.
The discussion reminded me of a conversation years ago with a group of master woodworkers. They were craftsmen who built furniture by hand, using tools that hadn’t changed much in centuries. I asked one of them, a man with fingers gnarled by decades of work, what he thought about power tools.
“People think using hand tools makes you more authentic,” he said, running his palm along the grain of a maple board. “But the old masters would have used power tools if they’d had them. The point isn’t the tool. It’s what you’re trying to create, and whether you understand what you’re doing.”
He showed me a dovetail joint he’d cut with a table saw and jig. “Is this less authentic because I didn’t use a hand saw? The joint is still tight. The wood is still joined. I still had to understand the properties of the wood and how the joint works.”
Writers and programmers alike are wrestling with similar questions. When does technological assistance become a crutch? When does it become cheating? The novelist who uses a thesaurus is not accused of intellectual dishonesty. The programmer who uses a library of pre-written functions is not condemned for laziness. But something about AI assistance feels different to many people.
Perhaps it’s the speed. A process that once took hours now takes seconds. Perhaps it’s the black-box nature of the technology. We cannot see how the AI arrived at its solution, cannot trace the path of its reasoning. We think they’re just dumb machines probabilistically predicting the next word. Or perhaps it’s simply that we are witnessing a fundamental shift in what it means to create.
My programmer friend has a different perspective. “The future of programming isn’t writing code,” he says. “It’s understanding problems and directing machines to solve them. The code is just an implementation detail.”
I wonder if writers will come to feel the same way. Will the future of writing be less about crafting individual sentences and more about directing AI to capture a particular voice or style? Will we come to see the arrangement of words as merely an implementation detail in the larger project of communication? How does this extend to other fields like film, movies and art?
The question of disclosure remains thorny. Should writers and programmers be required to disclose their use of AI assistance? Some argue that it’s essential for transparency and accountability. Others suggest that it’s no different from any other tool, and that the focus should be on the final product, not the process used to create it.
I think of the woodworker showing me his dovetail joint. “The wood doesn’t care how you cut it,” he said. “It only cares that the joint is tight.”
Perhaps the same is true of writing and programming. Many readers won’t care how the words were arranged, only that they resonate. The software user doesn’t care how the code was written, only that it works.
And yet, there is something deep within us that values the human touch, that finds meaning in the knowledge that another person’s mind and hands shaped the thing we’re experiencing. We want to know that somewhere in the process, a human being made choices, experienced frustration and triumph, poured their unique perspective into the creation.
As I left the restaurant I mentioned earlier the debate at the long table was still going strong. I caught a final snippet as I passed by: “It’s not about the tools,” someone was saying. “It’s about the intention.”
Perhaps that’s the heart of it. Not what tools we use, but how we use them, and why. Not whether we use AI, but whether we use it thoughtfully, with intention and understanding. Not whether we disclose its use, but whether we’re honest about our process, both with ourselves and with others.
There’s no question the AI tools are here and that they’re improving dramatically seemingly every day. They’re providing some powerful leverage to amplify our own skills – if we choose to use them wisely.
Note: this initial idea for this post was mine triggered by listening to a podcast interview with Dan Shipper of Every. I had help fleshing it out using Claude 3.7 from Anthropic. The post began with a couple of paragraphs I wrote. Then I used the following prompt: “You’re an expert writer and editor helping me with my personal blog. Write a 1000 word blog post in the style of John McPhee based on the following initial thoughts…” After that I rewrote portions of Claude’s response to add clarity and emphasis before sharing it here.
Note 2: all of this was done on my iPhone.
Ethan Mollick, a professor at the Wharton School, has written a popular book on artificial intelligence titled “Co-Intelligence“. Like I do with most books I read, I’ve collected a series of highlights of various passages I found interesting when I read Mollick’s book – a book I enjoyed very much. This morning I came across one of those highlights which sent me off on this exploration.
But first let me share a perspective. A few friends of mine – when exploring how they might use artificial intelligence – reject the use of AI tools for helping them write. They feel strongly that the words an AI creates aren’t their own and strongly reject simply using an AI-generated output to create articles for their blogs, etc. I certainly appreciate their point of view – but I’m finding a more nuanced perspective on this question is more appropriate for me.
What I’m finding is that when I invoke AI to help me explore a topic, an idea, etc. that the result will almost always open up my thinking by exposing me to things, ideas, etc. that I hadn’t considered. In so doing, I come away feeling “richer” in the sense that I’ve learned – and not simply written – in the process. How I use the work product created by the AI varies – sometimes it’s helpful in critiquing something I’ve written. Other times I use it to help me better understand something – or explore by stepping outside my own brain.
Here’s an example from some exploring I did this morning. During my morning review of my Kindle book highlights using Readwise, I came across a quotation from Mollick’s book that I highlighted:
“If you can link disparate ideas from multiple fields and add a little random creativity, you might be able to create something new.” (Ethan Mollick, Co-Intelligence)
I love the idea of how creativity is influenced by the often seemingly random interconnections between ideas – and this quote concisely made that point. I wanted to explore this further so I asked Claude Sonnet to help me by writing an article in the style of author John McPhee that’s based on the quote. Here’s the exact prompt I used with Claude:
You are an expert writer in the style of John McPhee. You are helping me write interesting articles for my blog. Create a 750 word draft article for my blog based on the following quotation: “If you can link disparate ideas from multiple fields and add a little random creativity, you might be able to create something new.” (Ethan Mollick, Co-Intelligence)
I’ve been finding that Claude often does a more creative form of writing as compared to other chatbots. The article that Claude returned in response to my prompt really surprised me – so much so that I thought I’d just share it directly here.
So here we go…the following wasn’t written by me – rather, it was co-created based on a prompt from me and written by my co-intelligent partner Claude.
In the verdant fields of central New Jersey, not far from where I sit typing these words, a peculiar ritual unfolds each spring. Farmers, their hands calloused from generations of working the land, don white protective suits that make them look more like astronauts than agriculturists. They move deliberately through their orchards, carrying small paintbrushes and vials of pollen.
These are the pear farmers of Mercer County, and they’re engaged in a practice as old as agriculture itself: cross-pollination. But why, you might ask, are they doing by hand what bees have done for millennia? The answer lies in the delicate biology of pear trees and the fickle nature of their reproduction.
Pear trees, it turns out, are notoriously poor self-pollinators. To produce fruit, they need pollen from a different variety of pear tree. But pear blossoms produce relatively little nectar, making them unattractive to bees. So these farmers, in their space-age suits, play the role of bees, manually transferring pollen from one variety to another, ensuring a bountiful harvest come autumn.
As I watched this process unfold on a crisp April morning, I was struck by a quote I’d recently encountered from Ethan Mollick, a professor at the Wharton School: “If you can link disparate ideas from multiple fields and add a little random creativity, you might be able to create something new.”
The pear farmers of Mercer County, in their own way, embody this principle. They’ve taken an idea from one field (entomology) and applied it to another (agriculture), adding their own creative twist (the paintbrushes and protective suits). The result? A thriving pear industry in a region where it might otherwise be impossible.
This concept of cross-pollination – of ideas rather than pollen – is not unique to agriculture. In fact, it’s a principle that underpins much of human innovation and creativity.
Consider, for a moment, the work of Frances Glessner Lee, often called the “mother of forensic science.” Lee was born into wealth in the late 19th century and, like many women of her social standing, was skilled in the domestic arts, particularly miniature-making. But Lee had a passion for criminal investigation, nurtured by her friendship with George Burgess Magrath, a medical examiner.
In the 1940s, Lee combined these seemingly disparate interests to create the Nutshell Studies of Unexplained Death – intricately detailed dioramas of crime scenes used to train detectives. These miniature crime scenes, built with the precision of a dollhouse maker but imbued with the gruesome details of a murder scene, revolutionized forensic training. Lee had taken skills from the traditionally feminine world of crafts and applied them to the male-dominated field of criminal investigation, creating something entirely new and profoundly useful in the process.
This principle of creative cross-pollination extends far beyond the realms of agriculture and crime scene investigation. The history of human progress is littered with examples of innovations born from the unlikely marriage of disparate fields.
Take, for instance, the development of GPS technology. The seeds of this now-ubiquitous system were planted when physicists William Guier and George Weiffenbach at the Johns Hopkins Applied Physics Laboratory realized they could track the Soviet Sputnik satellite by monitoring its radio transmissions. This was an impressive feat in itself, but it was their colleague Frank McClure who made the cognitive leap that would change the world. McClure realized that if the scientists could track a satellite from the ground, they could also use satellites to track positions on the ground.
This insight – born from the collision of rocket science, radio engineering, and a healthy dose of Cold War paranoia – led to the development of the Transit system, the precursor to modern GPS. Today, this technology guides everything from missile systems to the smartphone in your pocket, all because a group of scientists were able to flip their perspective and see their work from a new angle.
But perhaps the most striking example of creative cross-pollination comes from the field of medicine. In the early 1940s, a Scottish bacteriologist named Alexander Fleming was researching influenza. One day, he noticed that mold had contaminated one of his petri dishes – a common enough occurrence in a microbiology lab. But Fleming observed something unusual: the area around the mold was clear of bacteria.
This chance observation, combined with Fleming’s willingness to investigate rather than simply discard the contaminated sample, led to the discovery of penicillin. Fleming had linked the disparate fields of mycology and bacteriology, added a dash of serendipity, and created something that would revolutionize medicine and save countless lives.
As we navigate an increasingly complex and interconnected world, the ability to cross-pollinate ideas from different fields becomes ever more crucial. The challenges we face – from climate change to antibiotic resistance – are too complex to be solved within the confines of a single discipline.
So perhaps we should all take a cue from the pear farmers of Mercer County. Don our metaphorical protective suits, pick up our idea-paintbrushes, and venture into unfamiliar orchards of thought. For it’s in these unlikely places, where disparate ideas collide and combine, that the seeds of innovation are sown.
In a world that often celebrates specialization and highly values deep expertise, David Epstein’s words in his book “Range” come as a refreshing challenge to conventional wisdom. “Focusing narrowly on many fine details specific to a problem at hand feels like the exact right thing to do, when it is often exactly wrong,” he says, inviting us to reconsider how we approach problem-solving.
At first glance, this statement might seem counterintuitive. After all, isn’t attention to detail a virtue? Don’t we praise those who can dive deep into a subject, mastering its intricacies? While there’s undoubtedly value in specialized knowledge, Epstein’s quote highlights a potential pitfall: the danger of becoming so engrossed in the minutiae that we lose sight of the bigger picture. In my career, I was often considered the “expert” but I came to appreciate just how often the “tyranny of the expert” might apply to specific situations.
This narrow focus can lead to what psychologists call “functional fixedness” — an inability to see alternative uses for objects or ideas beyond their intended purpose. In problem-solving, this shows up as a tendency to approach challenges using only the most obvious or familiar methods, missing innovative solutions that usually lie just outside our immediate field of vision.
Moreover, an overly specific focus can blind us to valuable insights from other domains. Some of history’s most groundbreaking discoveries and innovations have come from interdisciplinary thinking — the ability to connect dots between seemingly unrelated fields.
Epstein’s quote also speaks to the value of generalism in an age of hyper-specialization. While specialists undoubtedly play crucial roles in advancing knowledge within their fields, generalists — those with a broad base of knowledge and diverse experiences — often excel at adapting to new situations and connecting disparate ideas in novel ways.
This isn’t to say that attention to detail and specialized knowledge aren’t important. Rather, Epstein’s words encourage us to balance depth with breadth, to zoom out periodically and consider the wider context of our problems and goals. It’s about developing what F. Scott Fitzgerald called a “first-rate intelligence” — the ability to hold two opposing ideas in mind simultaneously and still retain the ability to function.
In practice, this might mean deliberately exposing ourselves to ideas and experiences outside our comfort zones. It could involve collaborating with people from different backgrounds or disciplines. Or it might simply mean taking a step back when we feel stuck, asking ourselves if we’re so focused on the trees that we’re missing the forest. In a group setting, encouraging this kind of wide ranging thinking – without being unnecessarily critical too quickly – can often yield new insights. For example, as a board member, I was often struck by the power of group collaboration when trying to work through a difficult problem.
Ultimately, Epstein’s quote is a call to embrace a more holistic, flexible approach to thinking and problem-solving. By resisting the urge to dive immediately into the details and instead considering the broader context, we open ourselves up to more creative solutions and a richer understanding of the world around us.
So how can we put Epstein’s advice into practice? Here are five actionable steps to consider for broadening your perspective and enhancing your problem-solving abilities:
These practices can help cultivate a more versatile, adaptable mindset that helps us live and enjoy a better life.
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