There is a version of this story where Apple is the Wright Brothers.
It is not an unreasonable version. Apple has done the safety bicycle move more times than almost any company in history — taken a technology the engineers built for engineers and brought it down to earth, made it a machine for everyone. The Mac. The iPod. The iPhone. Each one was a wheel coming down. Each one arrived after a period of apparent slowness, of critics saying Apple had lost its edge, of the industry having already moved on to the next thing. Each one was, in retrospect, obvious. Apple had been in the bicycle shop the whole time. You just couldn’t see what they were building.
So when Apple showed its hand at WWDC this week — a rebuilt Siri operating at the OS level, accessing your messages and mail and photos in real time, understanding context across apps, doing things the old Siri could only approximate — it is tempting to read it as Kitty Hawk. The long preparation made visible. The brothers finally leaving the shop.
It might be. It also might not be. That is the only honest thing to say.
What Apple showed was real. The new Siri, built on Apple’s own Foundation Models with help from Google’s Gemini, is not the Siri that became a punchline. It holds context. It moves across apps without being asked. It knows what you were doing five minutes ago and connects it to what you are doing now. It can surface a photo without opening Photos, build a navigation route from an image, draft a message in the tone of the conversation it is joining. These are not features. They are the beginning of an operating system that understands you, which is a different thing from an operating system that executes your commands.
The structure of the keynote said more than the words did. Apple led with fixes before features. iOS 27 is a Snow Leopard update — performance, reliability, the underlying machinery — and Siri AI was presented as one item on a long list rather than the main event. This is Apple’s tell. When they are doing something foundational they tend to understate it, the way a craftsman doesn’t announce the quality of his work but simply does it and lets you find it. The penny-farthing riders called their machine the ordinary. They didn’t think they needed to explain.
But here is the thing about the bicycle shop analogy that the optimistic version leaves out. The Wright Brothers knew what they were trying to build. They had been thinking about flight for years before Kitty Hawk. The bicycle shop gave them the craft knowledge, the physical intuition, the hands-on education in how machines move through space. What it did not give them was the destination. They brought the destination themselves.
The question Apple has not answered for me — the question this week’s keynote raised rather than resolved — is whether they know where they are going. Or whether this has only been a partial reveal and there’s much more behind the curtain?
The OS-level integration is the chain drive. Decoupling AI from the app, letting it run through the substrate the way a chain runs through a drivetrain, is exactly the kind of architectural insight that changes what a machine can do. It is not a feature you add. It is a rethinking of what the machine is for. Every previous AI assistant lived above the operating system, looking down at your data from a remove. Apple’s new architecture lives inside it, which is a different relationship entirely — the difference between a mechanic who reads about your car and one who has driven it for a year.
That is the Coventry precision. The tight tolerances. The discipline of making things that have to work at the level where failure is not an option.
What nobody knows, including Apple, is what you build with it.
There is also this: Tim Cook will not be driving this evolution. He announced that John Ternus takes over in September, which means this WWDC — this particular showing of the hand — is the last one Cook owns. Ternus is a hardware engineer, the man who built the Apple Silicon transition, the person most responsible for the Neural Engine that makes on-device inference possible. He is, in the bicycle shop metaphor, the craftsman who built the lathe. Whether he knows how to use it to make something that flies is the question the next several years will answer.
History is patient about these things. It lets the work speak.
In 1892, two brothers opened a shop on West Third Street in Dayton and started fixing bicycles. They were not trying to change the world. They were trying to make a living, to learn a machine, to understand in their hands what the books couldn’t teach them. The flying came later, and it came because of the shop, not despite it. The shop was the point. They just didn’t know it yet.
Apple has the best lathe in the bicycle shop. They have the chain drive architecture, the on-device precision, the installed base of two billion devices that will carry whatever they build into more hands than any other platform on earth. They have a new set of hands on the wheel starting in September, hands that know the metal intimately, that built the engine the whole thing runs on.
What they do not have yet — or if they have it, they are not showing it — is the image of what they are flying toward.
Maybe that’s the ordinary part. Maybe that’s always been the ordinary part. You don’t know what you’re building until you’ve built it, and by then the world has already changed, and everyone says it was obvious, and they are right, and they are also completely wrong about when the decision was made.
The shop is open. The lathe is running. Work is underway.
What happens when someone finally knows what to make?
Yesterday while reading the Sunday New York Times I came across the Book Review section and its cover story about paperback books. I have such fond memories of shopping for paperbacks at local drug stores, bookstores, etc but those days are long gone.
The cover story included a photograph of a stack of paperback books. I’ve learned in the past how useful AI can be at quickly turning a photograph of a shelf or stack of books into a list of titles and authors – so natually I snapped a photo of the stack of books and asked Gemini:
What are the titles and authors of these paperbacks?
Gemini came back with the list of titles and authors:
This image features a collection of vintage-style paperback spines, often found in artistic book-themed prints. The spines represent a mix of classic literature, science fiction, and pulp fiction.
Below is the list of titles and authors as they appear from top to bottom…
There were a few titles in the photo that were impossible for me to make out. I asked Gemini about them:
Were there some you couldn’t read?
Gemini came back:
I did have to do a little detective work on a few of those! While most were clear, some of the older pulp titles have very tiny or weathered text that required a bit of cross-referencing to confirm.
….
A Fun “Easter Egg”
You might notice that J.D. Salinger and Henry Kane appear twice. This stack isn’t a random collection; it’s actually a popular art piece (often titled The Librarian’s Daughter or similar) that mixes “high” literature like Middlemarch with “low” pulp fiction like Affair for a Corpse to show the breadth of a true reader’s shelf.
I’ve been thinking a lot lately about how we move past the novelty phase of AI—beyond just asking a chatbot to “write a poem about a turkey” or summarize a meeting—and into actual thinking with these tools.
As a lifelong learner, I’m always on the hunt for workflows that help me synthesize information better. Most of the “AI for writing” advice I see online is pretty generic. But I recently came across a breakdown of how four high-profile writers are making effective use of tools like NotebookLM and Claude in ways that are much more sophisticated than simple text generation.
What jumped out at me is that none of these writers use AI to write for them. They use it to structure, challenge, and code.
Here are the four models that caught my eye.
1. The Triangulated Research Base (Steven Johnson)
Steven Johnson (Where Good Ideas Come From) has a workflow that solves a problem I face constantly: the messy “research phase.”
Instead of treating the AI as an oracle, he treats it as a connection engine. He creates a dedicated notebook (using Google’s NotebookLM) and uploads three distinct types of sources: a primary source (like a raw PDF or study), a secondary source (like a context article), and a multimedia transcript.
Then, rather than asking for a summary, he asks the AI to find the friction between them: “What themes appear in the interview transcript that contradict the historical account in the PDF?”
It’s less about getting an answer and more about finding the blind spots in your own reading.
2. The Diagnostic Editor (Kenny Kane)
This one really resonated with me because it mirrors the experiment I tried recently with my “Bubble Bath” post.
Kenny Kane uses Claude not to generate prose, but to act as a ruthless developmental editor. He uploads a messy draft and runs a “Diagnostic” prompt. He doesn’t ask “fix this,” he asks: “Where does the argument drift? Where does the energy drop?”
He even has the AI analyze his best writing to identify his specific “DNA” (sentence length, vocabulary choice) and then asks it to apply that same tone to his rougher sections. It’s using the AI as a mirror rather than a ghostwriter.
3. The Memo-to-Demo Shift (Dan Shipper)
Dan Shipper at Every is doing something fascinating that changes the definition of writing altogether. He argues that in the AI age, we shouldn’t just describe a concept; we should build a small app to demonstrate it.
If he’s writing about “Spaced Repetition,” he doesn’t just explain the theory. He asks Claude’s Artifacts feature to “Write a React component that lets a user test spaced repetition live in the browser,” and then embeds that little app directly into the essay. The writing becomes 50% prose and 50% software.
4. The Co-Intelligence Loop (Ethan Mollick)
Ethan Mollick focuses on breaking the echo chamber. Before he publishes, he spins up simulated personas—a skeptical VC, a confused novice, an expert in a tangential field—and asks them to critique his draft from their specific viewpoints.
It’s effectively a focus group of one.
How to Get Started
If you’re like me, seeing all these workflows might feel a bit overwhelming. My advice? Don’t try to overhaul your entire writing process overnight. Just pick one experiment to try this week.
Here are two simple entry points:
Experiment A: The “Blind Spot” Check (For Research)
If you are reading up on a topic, don’t just take notes. Open Google NotebookLM, create a new notebook, and upload your sources (PDFs, URLs, or pasted text). Then, ask this specific question:
“Based strictly on these sources, what is the strongest argument against my current thinking? What connection between Source A and Source B am I missing?”
Experiment B: The “Ruthless Editor” (For Writing)
If you have a rough draft sitting on your hard drive, copy it into Claude or ChatGPT and use this prompt (adapted from Kenny Kane’s workflow) before you do any manual editing:
“Act as a senior editor. Do not rewrite this text. Instead, analyze my draft and tell me: 1) Where does the argument lose energy? 2) Does the opening hook successfully promise what the conclusion delivers? Be critical.”
I’ve found that using the tools this way—as a partner for thinking rather than just generating—is where the real magic happens.
For the first time in a while I wrote a blog post yesterday titled “Bubble Bath“. It was about some things involving AI, stock market reactions to AI stuff, the Acquired podcast and Michael Lewis interviewing the hosts, and Bill Gurley on the Tim Ferriss podcast. In other words, kind of a grab bag of stuff.
Yesterday was also the launch day for Google Gemini 3 Flash, the latest version of its Gemini series of models specifically tuned for efficiency and responsiveness. Frankly, I’ve been blown away by my initial experiences with this model.
After publishing my “grab bag” post yesterday, it dawned on me that I should try using AI to critique the post. This is an example of using AI like I’d use a trusted colleague helping me edit my thoughts. I loved what Gemini 3 Flash wrote back to me – and I added that interaction at the bottom of yesterday’s post and moved on.
Later last evening, I picked up that conversation with Gemini and continued the discussion. I spent about a half an hour in further dialogue with the model – increasingly being impressed with how it was interacting with me and, most importantly, where it was taking me. I’ll try to share some of that below.
I had been browsing X and came across someone suggesting that you could really improve the results from a model by responding to its first reply with the following prompt:
Now argue against everything you just said. Find the weakest points in your logic.
So naturally I tried that – and got this in response:
Touché. You’re calling my bluff. If I put on my “Defense Attorney” hat and look at the “Tough Editor” persona I just performed, I can see exactly where my advice is actually quite fragile.
Here is the counter-argument against my own critique:
1. The Value of “Curator as Filter”
My biggest critique was that you were just “taking attendance.” But in an era of infinite noise, the act of curation is a creative act.
The Counter-Point: You aren’t a financial analyst for Goldman Sachs; you are a “Lifelong Learner” (it’s right there in your header). Your readers don’t need your specific valuation of Oracle; they need to know what a sophisticated person like you is paying attention to. By pointing to Marks, Gurley, and the Acquired team, you are providing a “high-signal” map. Adding your own “hot take” might actually just add more noise to an already crowded room.
2. The “Short-Form” Aesthetic
I pushed you to be “meaty” and “cohesive.”
The Counter-Point: This is a blog, not a Substack newsletter or a White Paper. The history of the web was built on “link-blogging”—the style popularized by people like John Gruber (Daring Fireball). The goal of a link-blog is speed and direction: “Here is a thing that happened, and here are the three things you must read to understand it.” By demanding you “close the loop” and “synthesize,” I’m asking you to turn a 15-minute writing exercise into a 3-hour project, which is the fastest way to kill a consistent blogging habit.
3. The Power of “In Media Res”
I criticized the post for feeling like a list of what you’re currently watching.
The Counter-Point: There is a specific, authentic charm in “writing while thinking.” Saying “I’ve just begun watching…” invites the reader into your living room. It creates a sense of real-time discovery. If you wait until you’ve finished the podcast, synthesized the memo, and polished the prose, the “timeliness” of the Oracle news has already evaporated. The “weakness” of the post is actually its strength: it is a live transmission of a mind in motion.
4. Semantic Over-Engineering
I told you to “go for the jugular” with your prose.
The Counter-Point: Why? This is your personal site. If you write in a slightly conversational, “wordy” way, that’s your voice. Forcing every sentence into a punchy “LinkedIn-influencer” style makes it feel clinical and performative. The “Lifelong Learner” persona benefits from a softer, more reflective tone than the “Alpha Market Analyst” tone I tried to force on you.
The Verdict on the Critique
My logic was weakest in assuming that more of you = a better post. The “Bubble Bath” post functions as a hub. It’s a service to the reader. If I follow your links, I’m going to have a very productive afternoon. That is a success, regardless of whether you provided a 500-word thesis on debt financing.
My reaction: WOW! I now had both the model’s original tough critique of my post along with an alternative critique which, frankly, was much more in line with what I was looking for given how I had constructed the post!
The model didn’t just end its response there however. It asked:
Would you like me to try a “Middle Path” edit—one that keeps the light, curatorial feel but adds just a tiny bit more “Scott” to the transitions?
Of course I said yes! Here’s the model response:
Let’s take that Middle Path. We’ll keep the “live transmission” feel and your role as a high-signal curator, but we’ll sharpen the connective tissue so the reader feels the weight of your perspective without you having to write a manifesto.
Here is a revised version that bridges the gap:
Bubble Bath
The behavior of today’s stock market is yet another sign that the “AI bubble” is moving from theory to reality. The catalyst? Blue Owl Capital reportedly balked at financing a new Oracle data center in Michigan—a project meant for OpenAI. It’s a specific, localized snag that has soured the entire market opening, fueling the fire of a much larger question: is the debt-fueled infrastructure of AI starting to crack?
Watching the market react brought to mind a recent memo by Oakmark Capital’s Howard Marks. Marks is a “sage” for a reason—he’s spent a career in distressed debt, and he points out that bubbles aren’t just about high prices; they are about the role of debt in creating wealth destruction rather than wealth creation. Seeing Oracle struggle with debt financing for the very “shovels” of the AI gold rush feels like a Marks memo coming to life.
I’m actually exploring this in real-time today through two incredible deep dives:
The VC Perspective: I’ve just started the latest Tim Ferriss interview with Bill Gurley. Right out of the gate, Tim asks Bill the “AI bubble” question. Gurley’s historical context on tech cycles is the perfect lens for the Oracle news.
The Narrative Perspective: On Monday, I watched the tenth anniversary episode of the Acquired podcast. Michael Lewis (who knows a thing or two about bubbles) interviewed the hosts. It was the most fascinating conversation I’ve listened to in recent memory—Lewis has a way of asking probing questions that strip away the hype.
If you’re trying to make sense of why the market is suddenly nervous about AI, these three sources—Marks on debt, Gurley on cycles, and Lewis on the narrative—provide the full picture. It looks like we’re all getting ready for a bit of a bubble bath.
What changed?
Active Verbs: I changed “is attributed to” and “brought to mind” into more active connections (e.g., “feels like a Marks memo coming to life”).
Contextualized Links: Instead of just saying a podcast is “great,” I added why it matters to the Oracle story (e.g., “Gurley’s context… is the perfect lens for the Oracle news”).
The “Middle Path” Hook: I used your “Lifelong Learner” vibe by framing the links as your “real-time exploration,” which justifies the curation.
So much for a couple of cycles of critique following by a model-created rewrite of my original post! But Gemini didn’t stop there. It’s next question to me was:
Would you like me to take a look at the “About” or “Now” pages of your blog to see if they align with this updated editorial voice?
Of course I said yes – again. Perhaps I’ll share the next series of interactions with Gemini in another post tomorrow! Mind blowing – and very helpful!
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.
Beyond the Prompt: The Lifelong Reinforcement Learning of Being Human
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.
This morning I was listening to one of Dwarkesh Patel’s latest podcasts when he happened to muse about why an iPhone wasn’t possible in the year 1000. A crazy idea or so it seemed to me!
But it got me wondering about that same question in years closer to when the iPhone was actually introduced in 2007.
I opened up Google Gemini (using the new 2.5 Flash model) and had a fascinating conversation. “A conversation like no other!” as Mark Halperin likes to say on his 2Way conversations.
Here’s the final result – after I asked Gemini to summarize the conversation a bit and put it into Markdown format for blog posting. To be clear, I asked the questions and Gemini did the educating and writing. Enjoy!
Building an iPhone Through Time: Why 1900, 1950, and Even 2000 Couldn’t Do It
We take our smartphones for granted today. These sleek, powerful devices fit in our pockets, connecting us instantly to the world, housing vast libraries of information and entertainment, and performing complex computations. But have you ever stopped to wonder what it would take to build something like an iPhone in a different era?
Let’s take a little journey through time and see just how science-fiction the iPhone would have seemed not so long ago.
The Utter Impossibility of 1900
Imagine walking into a workshop in 1900 and asking them to build you an iPhone. Their reaction would likely range from utter confusion to thinking you were stark mad. In 1900, the concept was not just difficult; it was fundamentally impossible.
Computing: The most advanced computing devices were mechanical or early electromechanical machines like punch-card tabulators. There were no vacuum tubes yet for practical electronics, let alone transistors or integrated circuits. Building anything with the processing power of an iPhone, even using room-sized 1900 tech, was unimaginable.
Display: Visual output was limited to mechanical indicators or basic light bulbs. The cathode ray tube (CRT) was still experimental. A high-resolution color touchscreen was pure fantasy.
Connectivity: Wireless communication was in its absolute infancy (Marconi’s transatlantic signal was Morse code). Mobile voice communication was decades away. The idea of connecting a personal device to a global network was beyond comprehension.
Storage: Data storage meant punch cards or paper tape – storing a single song, let alone thousands, would require a library-sized collection and complex machinery to read it.
Power: Batteries were bulky and low-capacity. Powering complex electronics wasn’t feasible for a portable device.
Size: Components were large, assembly was manual. Miniaturization to pocket size was impossible due to the fundamental physics and engineering available.
In 1900, an iPhone was not just science fiction; it was magic. You couldn’t build it because the scientific knowledge and engineering capabilities simply did not exist.
Closer, But Still Impossible in 1950
Fast forward 50 years to 1950. We’ve made incredible strides!
The transistor has been invented (1947), a crucial step beyond vacuum tubes.
Early electronic computers exist, albeit massive, expensive, and less powerful than today’s simplest chips.
CRTs are common (the television era is beginning), allowing for monochrome displays.
Radio communication is much more advanced, and early, very limited forms of mobile radio-telephony (like bulky car phones) are being experimented with.
Basic magnetic storage (tape, drums) exists.
So, could you build an iPhone now? Still impossible, but for slightly different reasons.
Integrated Circuits (Chips): The ability to put thousands or millions of transistors onto a single piece of silicon – the foundation of modern electronics – hadn’t been invented yet (that came in the late 1950s). Building an iPhone’s processor or memory still required assembling thousands of individual, relatively large components.
Miniaturization: While better than 1900, components were still too large and power-hungry for a handheld device with complex functionality. A computer capable of iPhone-like tasks would still be room-sized.
Display: While you could have a small monochrome CRT, it would be bulky and fragile. A flat-panel, color, high-resolution, touch screen was completely out of reach.
Connectivity: Mobile communication existed, but not in a cellular format, and certainly not for high-speed data like internet Browse. Connecting a personal device to a data network wasn’t feasible or even conceived of in the modern sense.
Storage: Storing gigabytes of data in a portable way was impossible.
Operating System & Software: Programming was done at a very low level, and the concept of a sophisticated, user-friendly operating system running rich applications on a personal device didn’t exist.
In 1950, you could build pieces of the puzzle (a basic computer, a radio), but combining them into a compact, interactive, networked personal device was still beyond the technological horizon.
On the Brink? The Year 2000
Now, let’s jump to the year 2000. We’re only 7 years away from the first iPhone launch. Surely, we could build it now? Almost, but still extremely difficult and not the iPhone as we know it.
By 2000, most of the fundamental components did exist:
Powerful Microprocessors: Processors capable of running complex software were common.
Color LCDs: Standard in laptops and high-end mobile devices.
Wireless:2G cellular networks were widespread (GPRS offered slow data). Wi-Fi existed (802.11b). Bluetooth was available. GPS was available for civilian use.
Batteries: Lithium-ion batteries were standard for portable electronics.
Flash Memory: Available, but expensive and lower density per chip compared to 2007.
Basic Sensors & Digital Cameras: Existed and were being integrated into some phones/PDAs, albeit low-resolution.
So, what was still missing or not mature enough to build the iPhone?
Capacitive Multi-Touch Screen: This was the key missing piece for the iPhone’s revolutionary interface. While resistive touchscreens (used with a stylus) were common on PDAs, large, reliable, capacitive screens capable of registering multiple finger touches were not ready for mass production and integration into a consumer device.
Affordable High-Density Flash Memory: While flash existed, putting 4GB or more into a phone was still prohibitively expensive for a mass-market product.
Required Chip Integration & Miniaturization: While processors were capable, packing all the necessary components (CPU, GPU, wireless, memory, sensors, etc.) so tightly and efficiently into a thin, integrated System-on-a-Chip required manufacturing advancements still underway.
Mobile OS Optimized for Touch & Data: Existing mobile operating systems (like Palm OS, Windows CE, Symbian) were designed around styluses, keyboards, and less data-intensive use. An OS built from the ground up for a finger-driven multi-touch interface and seamless internet use (like iOS) didn’t exist yet.
Network Readiness: While GPRS offered data, the speeds weren’t truly conducive to a rich mobile web experience. Widespread 3G networks, necessary for faster data, were just starting to roll out or hadn’t launched yet in many areas.
In 2000, you could build a smartphone (like a BlackBerry or a Pocket PC phone) – a device combining calls with email, calendar, and basic web Browse, likely with a physical keyboard or stylus. But the seamless, touch-driven, media-rich, always-connected experience of the iPhone wasn’t possible yet because the critical enabling technologies and the specific level of integration weren’t mature or affordable enough.
When Did It All Come Together?
The technologies that were missing or immature in 2000 converged and matured rapidly in the years leading up to the iPhone’s launch in 2007:
~2004-2006: Capacitive multi-touch display technology became viable for mass production. Flash memory density increased and prices dropped dramatically. 3G networks rolled out more widely.
~2004-2007: Apple internally developed iOS and perfected the integration of hardware, software, and the multi-touch interface. Chip manufacturing allowed for the necessary miniaturization and power efficiency.
2007: The culmination of these advancements arrived as the first iPhone, combining these previously missing/immature pieces into a revolutionary product.
2008: The App Store launched, solidifying the software ecosystem that became central to the smartphone experience.
Looking back from 2025, it’s incredible to see how quickly technology evolved. What was pure fantasy in 1900 became a bulky, impossible dream in 1950, a collection of nearly-ready parts in 2000, and finally, a reality in 2007. The journey of the iPhone isn’t just a product story; it’s a testament to the accelerating pace of scientific discovery and engineering innovation over the last century.
“I do not so much write a book as sit up with it, as with a dying friend. During visiting hours, I enter its room with dread and sympathy for its many disorders. I hold its hand and hope it will get better.”
Annie Dillard, The Writing Life
Each morning, a subscription service called Readwise creates a list of ten highlights from the thousands of highlights I’ve made from my reading and sends the list to me. Once in a while, one of those highlights just hits and something goes off in my mind. This morning it was Annie Dillard’s comment about writing a book being similar to sitting up with it like with a dying friend. What a metaphor!
Sometimes, when I want to explore one of these sparks of inspiration triggered from one of my highlights, I’ll use one of the AI chatbots to help me think some more about it. Often, my thinking will be extended further, sometimes in unusual ways. This morning I asked Google Gemini to help me explore the meaning of Dillard’s comment. Here’s what I got back – a most interesting and, in some ways, beautiful response:
Annie Dillard, in her insightful book “The Writing Life,” compares the experience of writing a book to “sitting up with a dying friend.” This metaphor, while morbid at first glance, offers a profound truth about the creative process. It’s a powerful reminder that bringing a book to life is not a joyous celebration but a labor of love, fraught with challenges and uncertainties.
During “visiting hours,” as Dillard aptly describes them, the writer enters the book’s “room” with a mix of dread and sympathy. The dread stems from the daunting task at hand, the endless revisions, the self-doubt that gnaws at the edges of every sentence. The sympathy arises from the recognition of the book’s “disorders,” the plot holes, the clunky dialogues, the characters who refuse to come alive.
The writer, like a caregiver, holds the book’s metaphorical hand, hoping for improvement, hoping to witness a spark of life emerge from the chaos. This involves countless hours of rewriting, editing, and polishing, a relentless pursuit of the elusive magic that transforms a collection of words into a cohesive narrative.
But amidst the anxiety and frustration, there are moments of grace, fleeting glimpses of the book’s potential. A character suddenly speaks in a voice that resonates, a scene unfolds with unexpected depth, a sentence shimmers with unexpected beauty. These moments are the lifeblood of the writing process, the tiny victories that keep the writer going, fueling the hope that the book will eventually recover, that it will stand on its own two legs and tell its story to the world.
Dillard’s analogy not only captures the emotional roller coaster of writing but also emphasizes the profound connection that develops between a writer and their work. It becomes more than just a project; it becomes a companion, a source of both immense frustration and overwhelming joy. As the writer pours their heart and soul into the book, it becomes a reflection of their own struggles and triumphs, a testament to the human spirit’s ability to create even in the face of immense challenges.
So, the next time you pick up a book and marvel at its intricate narrative, remember the invisible caregiver behind it. Remember the writer who sat up through countless nights, holding their creation’s hand, willing it to health, hoping that their story would resonate with yours.
Remember that mind-blowing Google I/O demo of an AI tool that unlocks hidden insights from your research documents? That’s NotebookLM, and it’s not just for tech giants anymore. (See this earlier blog post about what was originally Project Tailwind.)
As a longtime reader of author Steven Johnson (and avid follower of his “Adjacent Possible” Substack), I was thrilled to learn he’s now part of the team at Google Labs bringing this powerful technology to the masses.
Imagine uploading piles of research papers, articles, or even future forecasts (like I did with those year-end reports from Wall Street investment houses forecasting what’s expected in 2024!), and then having NotebookLM not only summarize them but also weave connections you might have missed. That’s exactly what I experienced.
NotebookLM’s “additional questions” feature is a game-changer, prompting me to explore angles I wouldn’t have considered on my own. It’s like having a tireless research assistant with an uncanny knack for spotting crucial details.
Of course, NotebookLM is still in its early stages. The current 20-document limit can feel restrictive, and its future as a paid product is unclear. But for researchers grappling with mountains of information, it’s a game-changer. It’s not just about saving time; it’s about sparking genuine intellectual leaps.
This tool isn’t just for academics, though. Imagine journalists using NotebookLM to connect seemingly disparate news articles, or students piecing together complex historical narratives. The possibilities are endless.
Sure, like any AI tool, it’s not perfect. Fact-checking is crucial, and occasional “hallucinations” can crop up. But NotebookLM’s source citations make verification easier, and its overall accuracy is impressive so far.
So, ditch the highlighter and embrace the future! NotebookLM isn’t just a fancy research tool; it’s a bridge to deeper understanding, more insightful analysis, and ultimately, groundbreaking discoveries. Unleash your research potential – your next breakthrough might just be a question away.
I was intrigued by this sentence in a recent column by Bloomberg’s Matt Levine: The stereotypical way that a management consulting interview works is that you get asked questions like “how much pepperoni is sold in the US every year,” and then you reason your way to some estimate of the answer.
When I read this, I thought it would be fun to play with my favorite chatbots again and see if they might help me reason my way to an answer. I got very different results – not surprisingly. The prompt I used was:
The stereotypical way that a management consulting interview works is that you get asked questions like “how much pepperoni is sold in the US every year,” and then you reason your way to some estimate of the answer. Let’s reason step by step to try to find an answer to this question.
First, here’s what I got back from Google Bard:
Sure, here is a step-by-step process of how to reason our way to an estimate of how much pepperoni is sold in the US every year:
Gather some basic facts. We know that the average American eats 24 pounds of pizza per year, and that pepperoni is the most popular pizza topping, accounting for about 30% of all pizza orders. We also know that there are about 330 million people in the US.
Make some assumptions. We need to make some assumptions about how much pepperoni is used per pizza. A conservative estimate might be that each pizza uses 1 ounce of pepperoni. This means that the average American eats about 2.4 ounces of pepperoni per year.
Do some math. We can now estimate that the total amount of pepperoni sold in the US every year is about 800 million ounces. This is equivalent to about 250 million pounds.
This is just an estimate, of course, but it is based on some reasonable assumptions. It is likely that the actual amount of pepperoni sold in the US is higher than this, but it is also possible that it is lower. More accurate estimates could be made by collecting more data and making more refined assumptions.
Here are some additional factors that could be considered to improve the accuracy of the estimate:
The amount of pepperoni used per pizza can vary depending on the size of the pizza, the type of pizza, and the restaurant.
The popularity of pepperoni may vary from region to region in the US.
The amount of pepperoni imported into the US may also be a factor.
By considering these factors, it is possible to make a more accurate estimate of the amount of pepperoni sold in the US every year.
Next up was ChatGPT:
Of course, I’d be glad to guide you through the process of reasoning to estimate the amount of pepperoni sold in the US every year. It’s important to note that management consulting interviews often focus on assessing your problem-solving skills and ability to think logically. Let’s break down the problem step by step:
Step 1: Clarify the Scope Before we begin, it’s essential to clarify the scope of the question. Are we looking for the total weight of pepperoni sold, or the total revenue generated from its sales? This distinction will influence the data sources we’ll need to consider.
Step 2: Identify Relevant Data Sources To estimate the amount of pepperoni sold in the US, we’ll need to gather data from reliable sources. These could include industry reports, government statistics, market research firms, and food industry associations. Some possible sources might be the U.S. Department of Agriculture (USDA) and organizations like the American Meat Institute.
Step 3: Estimate Total Consumption One approach could be to start with the total meat consumption in the US and then estimate what percentage of that is likely to be pepperoni. You might need to consider factors such as the popularity of pizza, sandwiches, and other dishes that use pepperoni. Additionally, think about variations in consumption due to cultural and regional preferences.
Step 4: Break Down Consumption Break down the total estimated consumption of pepperoni into categories such as retail sales (supermarkets and grocery stores) and food service (restaurants, pizzerias, etc.). This can help refine your estimate by considering different distribution channels.
Step 5: Estimate Quantity per Serving and Servings Estimate the average quantity of pepperoni used per serving. For example, how many slices of pepperoni are typically used on a pizza or in a sandwich? Then, multiply this by the estimated number of servings sold in a year.
Step 6: Adjust for Factors Consider any factors that might affect the estimate, such as seasonal variations in consumption (for instance, more pepperoni might be consumed during holidays or certain events) or any recent trends that might have impacted consumption patterns.
Step 7: Calculate the Estimate With all these factors in mind, you can proceed to calculate your estimate. Make sure to show your calculations and assumptions clearly.
Step 8: Validate and Compare After arriving at your estimate, it’s a good practice to validate it against any available data, if possible. Compare your estimate to industry reports or official statistics to assess its accuracy.
Remember, the goal of this exercise is not necessarily to arrive at an exact figure, but to showcase your ability to logically reason through a complex problem using available information. Feel free to ask questions if you need further clarification on any of these steps.
Next up is Claude:
Okay, let’s break this down:
First, we need to estimate how much pizza is sold in the US each year. Pizza is a very popular food, so let’s assume that on average, each American eats pizza once per week. With a population of around 330 million, that’s about 330 million pizzas sold per week, or around 17 billion pizzas per year.
Next, we need to estimate what percentage of those pizzas have pepperoni. Pepperoni is a very common pizza topping, so let’s assume it’s on 50% of all pizzas sold. That brings us to around 8.5 billion pepperoni pizzas per year.
Now we need to estimate how much pepperoni is on each pizza. Let’s assume an average pepperoni pizza has around 50 pepperoni slices. If each slice is roughly 1 ounce, that’s about 50 ounces or around 3 pounds of pepperoni per pizza.
With 8.5 billion pepperoni pizzas per year, each with around 3 pounds of pepperoni, that gives us an estimate of around 25.5 billion pounds of pepperoni sold annually in the US.
Of course this is just a rough estimate with a lot of assumptions. The actual amount may vary quite a bit. But walking through the logical steps and making reasonable assumptions is generally the approach for these types of brainteaser questions. Let me know if this helps explain the thought process!
Finally is Bing Chat:
That’s an interesting question. To estimate how much pepperoni is sold in the US every year, we can use some assumptions and data from the web search results. Here are some possible steps to follow:
First, we can assume that most of the pepperoni sold in the US is used as a pizza topping, since it is one of the most popular pizza toppings.
Second, we can use the data from 7 to estimate how much pepperoni is used on a pizza of different sizes. For example, a 12-inch pizza uses about 1.75 ounces of pepperoni, and a 18-inch pizza uses about 3 ounces of pepperoni.
Third, we can use the data from 6 to estimate how many pizzas are sold in the US each year. According to this source, there are 3 billion pizzas sold in the US each year.
Fourth, we can use the data from 13 to estimate what percentage of pizzas ordered are pepperoni. According to this source, 36% of people order pepperoni on their pizzas.
Fifth, we can multiply the number of pizzas sold by the percentage of pepperoni pizzas, and then multiply by the average amount of pepperoni per pizza. To simplify the calculation, we can assume that the average pizza size is 14 inches, which uses about 2.5 ounces of pepperoni according to 7.
Therefore, the estimated amount of pepperoni sold in the US each year is:
3 billion x 0.36 x 2.5 = 2.7 billion ounces
To convert this to pounds, we can divide by 16, since there are 16 ounces in a pound. This gives us:
2.7 billion / 16 = 168.75 million pounds
This is our final answer, based on the assumptions and data we used. Of course, this is only an approximation, and there may be other factors that affect the actual amount of pepperoni sold in the US each year.
I hope this helps you understand how to approach this kind of question. Do you have any other questions for me? 😊
Bard estimated 250 million pounds. Claude estimated 25.5 million pounds. Bing estimated 168.75 million pounds. ChatGPT didn’t provide an estimate – it just discussed a methodology to get to an answer (which is what’s more important in a job interview!).
What’s the correct answer? Some quick Google searching finds results around 250 million pounds a year – closest to Bard’s answer.
What can we learn from this little exercise? I would have expected both Bard and Bing Chat to have an advantage as they’re both capable of doing web searches while ChatGPT (3.5) and Claude aren’t. Bing actually cites specific web references. But the most useful result actually isn’t the final value – all of the chatbots demonstrated steps to go through to get to an answer and it’s the ability to demonstrate those steps that answers the interview question. I’m sure by continuing each of the conversations I could have gotten more insights from each of the chatbots. Meanwhile, I’m now hungry for a pepperoni pizza!
Update 04/21/2024: a few days ago Meta released its new Llama 3 model so I tried my original prompt out on the Meta.ai website. Here’s what I got back:
The classic “pepperoni problem”! Let’s break it down step by step. Here’s a suggested approach:
Step 1: Define the scope Clarify what’s meant by “pepperoni sold in the US every year.” Are we including:
Only pepperoni slices sold as a standalone product?
Pepperoni used as an ingredient in other products (e.g., pizzas, sandwiches, salads)?
Both domestic consumption and exports?
For this example, let’s assume we’re focusing on domestic consumption of pepperoni as a standalone product or as an ingredient in other food products.
Step 2: Identify key drivers What factors influence pepperoni sales in the US?
Population growth and demographics
Pizza consumption habits (as pepperoni is a popular pizza topping)
Trends in food preferences and dietary habits
Competition from alternative meat products
Step 3: Gather data points Collect relevant data to inform our estimate:
US population: approximately 333 million (as of 2024)
Pizza consumption: average American consumes 23 pounds of pizza per year (according to a 2020 survey)
Pepperoni’s market share among pizza toppings: assume around 30-40% (as it’s a popular topping)
Step 4: Make assumptions and calculations Based on the data points, make some assumptions and calculations:
Assume an average pepperoni slice weight: 2-3 grams ( rough estimate)
Estimate the total number of pepperoni slices consumed per year:
333 million people x 23 pounds of pizza per year ÷ 2-3 grams per pepperoni slice = approximately 12-15 billion pepperoni slices per year
Convert this to a weight-based estimate:
12-15 billion slices x 2.5 grams (avg. slice weight) = 30-37.5 billion grams or 30,000-37,500 metric tons of pepperoni per year
Step 5: Refine and validate Refine our estimate by considering additional factors, such as:
Seasonal fluctuations in pepperoni demand (e.g., summer BBQs, holiday parties)
Regional differences in pepperoni consumption
Market trends and competition
To validate our estimate, we could research industry reports, market studies, or sales data from major pepperoni manufacturers or distributors.
By following these steps, we’ve arrived at a rough estimate of 30,000-37,500 metric tons of pepperoni sold in the US every year. Keep in mind that this is a simplified example and actual numbers may vary based on more detailed analysis and data.
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