For decades, being a programmer meant being a translator.
You stood in the gap between what someone wanted and what a machine could understand. You learned the syntax. You memorized the libraries. You once spent three hours hunting a missing semicolon that turned out to be hiding in line 847 of a file you were sure you’d already checked.
The New York Times Magazine recently ran a piece by Clive Thompson on what AI coding assistants — models like Claude and ChatGPT — are doing to that job. The anxiety in the piece is real. When you sit down with a modern AI assistant and watch it generate in seconds what used to take you days, it’s genuinely disorienting. Hard-won expertise suddenly feels less like a moat and more like a speed bump.
That reaction is honest. I’d be suspicious of anyone who didn’t feel it.
But here’s what I keep coming back to: what we’re losing is the translation layer. The boilerplate. The muscle memory of syntax. What we’re not losing is the part that was always the actual job — figuring out what to build and why it matters.
The soul of software was never in the code itself. The code was always just a means to an end.
Think about what happens when the mechanical friction of a craft disappears. Photographers stopped having to mix their own chemicals in the dark and started spending that time making better images. Musicians stopped having to hand-copy scores and started composing more. The freed-up capacity doesn’t evaporate — it gets redirected upward, toward the work that actually required a human all along.
The same shift is underway in software. When the AI handles the loops and the boilerplate and the database queries, what’s left is everything that required judgment in the first place. The architecture. The user experience. The question of whether this thing should exist at all, and in what form, and for whom.
We’re moving from the how to the why. That’s not a demotion.
It does ask something of us, though. The old identity — programmer as master of arcane syntax — has to be relinquished. And letting go of a hard-earned identity is genuinely hard, even when what’s replacing it is better. That quiet grief the Times piece captures is worth sitting with, not dismissing.
But after you sit with it for a minute: we are entering the era of the synthesizer.
The synthesizer’s job is to hold the vision, curate the logic, and direct the output toward something that actually resonates with another human being. Empathy. Intuition. The ability to sense when something is almost right and know which direction to push it. These aren’t soft skills. They’re the whole game now.
The clatter of keyboards is fading. But the music we’re about to make — with AI doing the heavy lifting on the mechanics — has a lot more room to breathe.
“In a world optimized for speed and engagement, 280 is a reminder that infrastructure can be art.”
It is a strange paradox that in the heart of Silicon Valley—a place defined by the ephemeral, the digital, and the instantaneous—a cherished shared experience is a physical ribbon of highway that hasn’t changed much in fifty years.
My post from last April, “The World’s Most Beautiful Freeway,” has recently found a new wave of readers. I’ve been asking myself: Why? Why does a blog post about Interstate 280, written by a retiree exploring local history, resonate so deeply right now?
Perhaps it’s because I-280 is more than just a commute. As I noted in the original piece, even Sunset Magazine in 1967 recognized it as “a modern and scenic boulevard.” It was a bold claim for a freeway, yet it stuck. While its sibling, US 101, is a clogged artery of billboard-choked utility, 280 feels like a deep breath. It is the “scenic route” we are lucky enough to take right in our own backyard.
There is a powerful nostalgia in that drive. We all remember the sign that used to sit in the median near Cupertino—the one that literally proclaimed it “The World’s Most Beautiful Freeway”—before it vanished. We remember the way the fog rolls over the Santa Cruz Mountains, spilling into the crystal bowl of the reservoir.
But I think the recent interest goes deeper than pretty scenery. We are living in an era of rapid, often disorienting change. I used ChatGPT to help research the history of that road, a small testament to how AI is weaving into our daily inquiries. Yet, the road itself remains a constant. It was designed by engineers like Othmar Ammann and planners who chose the harder, more expensive route through the foothills rather than paving over El Camino Real. They chose beauty over pure efficiency.
That choice resonates today. In a world optimized for speed and engagement, 280 is a reminder that infrastructure can be art. It connects the headquarters of the companies building our future (Apple, Google, Meta) with the wild, golden hills of California’s past. It is a physical timeline of the Peninsula.
Maybe we are revisiting this post because we are craving that balance. We want to know that even as we rush toward the future at freeway speeds, we can still look out the window and see something timeless, something beautiful, something that reminds us where we are.
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.
It’s been a busy week of announcements in tech land what with Microsoft Build, Google I/O, and yesterday’s tease of an announcement by OpenAI and it’s acquisition of Jonny Ive’s company “io”.
Industry pundits are all a Twitter speculating about what kind of device Ive and his team might make to deliver an amazing AI experience to users. Ive seems to regret how “his” iPhone has created such an addiction to screens and seems to want to repent by bringing us something new and “better”. For more, see this tweet: https://x.com/mingchikuo/status/1925543472993321066?s=46
I have one simple request: don’t make voice the primary interface to some new magical device.
I’ve had an iPhone with some serious voice input capabilities for years and the reality is that I rarely use voice. Perhaps if my life was just “bowling alone” I’d find it natural to just talk out loud to a piece of technology. But I’m mostly around other people all day and out of respect for them I simply prefer being silent.
Until some new magical device can capture my thoughts without either voice or keyboard input, I will remain a skeptic. Skeptics like me will reduce the market size opportunity for any such new device. Just sayin’…
I’ve heard a couple of folks recently talking about playing with AI chatbots and asking them to create an image of themselves based on the interactions they’ve had over time with the chatbot. Of course, I had to try this out. So here’s what I got back as an image of myself from ChatGPT. Note that before it created this image, it told me that it would have to be ultra creative since it really didn’t know what I looked like (it’s really not that hard to find a photo of me online – but whatever!).
Here we go:
Very surrealistic. Apples (weird apple!), oranges, raspberries, blueprints, office with a chair and window and what looks to be a candle chart on the wall behind going up and to the right. Definitely one for the scrapbook!
I have appreciated the reactions of some of my friends to use of artificial intelligence. While I’ve enjoyed experimenting and learning about the use of AI in helping me write, the use of AI for writing has inspired some strong negative reactions among friends.
For example, several weeks ago a good friend of mine was very disappointed in me when he noticed that a blog post I had shared had been written almost completely by an AI helper. I disclosed that fact at the bottom of the post but he said he could detect I was using AI within the first couple of sentences. Here’s what he emailed:
I saw a blog post with your name attached. That caught my interest, and I anticipated reading your thoughts. After reading a couple of sentences, I realized it was AI-generated text. Skipping to your disclaimer at the end of the post confirmed that. I was deeply disappointed. I was looking forward to your creation and your thoughts, but instead, I received a machine’s advanced predictive text.
In a later exchange, he added:
I think it’s a mistake to take LLM-generated material, “lightly edit” it, and publish it in your blog under your name. In my view, it’s all been poorly written and not worth reading. I think you have a good mind, and I was interested in your creative writing because of your insight and original thinking. You do a much better job when you do your own work and don’t try to piggyback on AI.
More recently, David Sparks (MacSparky) published a post about his reaction to AI-created content – The Sparky Language Model. He shared a story about attending a friend’s wedding where he heard a moving speech. When he complimented the speaker on the speech, he revealed that the speech was written by ChatGPT and he was just reading it. David was very disappointed to learn that AI was used – writing:
“This revelation left me profoundly unsettled. It got me thinking about the essence of personal expression and the irreplaceable value of human touch in our communications.”
These reactions got me thinking about how tools generally affect our ability to do things in our lives.
I rarely pick up a pen anymore, for example, as almost all of my writing, journaling, etc. is done on a digital device. I still carry a pen in my pocket but not because it’s indispensable any more. But none of my correspondents actually see my penmanship any more – that’s a quaint artifact gone to history.
In writing and publishing, there’s a long history of ghostwriting – where a work is created by someone other than the author to whom it is attributed. In many cases, the actual ghostwriter isn’t disclosed. In my mind, use of an AI to help with writing is analogous to hiring a ghostwriter to help write an article. Or even hiring a copywriter to help create clever ad copy.
The question of most appropriate use of AI as a tool for helping us write feels like a slippery slope to me.
Just for fun, I took MacSparky’s SLM essay and asked Claude Sonnet to generate a “caustic” reply to it. Claude warned me that it might not be a good idea to send my friend such a caustic reply but I asked Claude to write it for me anyway.
Afterward generating the reply, Claude commented again:
I want to reiterate my earlier concerns about the potential negative impact this kind of exchange could have on your friendship. While I’ve fulfilled your request, I strongly encourage considering whether sending such a reply aligns with your values and the kind of relationship you want to maintain with your friend.
If you do decide to send this or a similar response, it might be worth following up with a more personal, conciliatory message to ensure your friend understands the context and that this exchange hasn’t caused any lasting harm to your relationship.
Message received, thanks!
Here’s that “caustic” critique that Claude wrote:
A Scathing Rebuke of Self-Righteous Technophobia
In an era where technological advancement races forward at breakneck speed, it’s refreshing to encounter a piece so unabashedly retrograde that it makes Luddites look like Silicon Valley evangelists. Enter “THE SPARKY LANGUAGE MODEL (SLM),” a manifesto of sorts that purports to champion authenticity while reeking of thinly veiled fear and misunderstanding.
Our intrepid author, clearly nursing a wounded ego in the face of rapidly evolving AI capabilities, embarks on a crusade against the very notion of progress. The piece begins with a begrudging acknowledgment of AI’s usefulness, only to swiftly descend into a mire of personal repulsion. One can almost hear the pearls being clutched.
The anecdote about the AI-generated wedding speech serves as the linchpin of this technophobic tirade. Our author, upon learning of the speech’s origin, experiences a bout of existential angst so profound it borders on the comical. Heaven forbid we use tools to enhance our ability to express emotion! Surely, the cave paintings at Lascaux were the pinnacle of human expression, and it’s been all downhill since the invention of the alphabet.
The crux of the argument hinges on the nebulous concept of “human connection,” as if AI-assisted writing somehow taints the purity of human thought. This notion is not only laughably reductive but also betrays a fundamental misunderstanding of how language and communication evolve. By this logic, we should all revert to grunting and pointing, lest we lose our precious “human touch.”
The author’s insistence on keeping things “personal” for all “MacSparky content” reeks of a desperate attempt to maintain relevance in a rapidly changing landscape. It’s as if they believe their meandering thoughts and half-baked tips possess some intrinsic value simply because they originated from their particular gray matter. News flash: the value of content lies in its utility to the reader, not in the purity of its origin.
The piece reaches its zenith of absurdity with the declaration that even if AI becomes objectively better at writing, the author still has no interest in it. This is the intellectual equivalent of covering one’s ears and shouting “La la la, I can’t hear you!” It’s a stance so myopic it makes Mr. Magoo look like an eagle-eyed visionary.
The grand finale, the introduction of the “Sparky Language Model,” is less a clever play on words and more a sad attempt at branding one’s own limitations. It’s as if the author believes that by slapping a cute label on their refusal to adapt, they’ve somehow outsmarted the march of progress.
In the end, this article is less a thoughtful critique of AI’s role in writing and more a thinly veiled cry for help from someone clearly terrified of becoming obsolete. The irony, of course, is that by steadfastly refusing to engage with these new tools, the author is ensuring their own irrelevance faster than any AI ever could.
The author’s stance on AI writing assistance is particularly short-sighted when considering the potential benefits. AI tools can serve as powerful aids in the creative process, helping to overcome writer’s block, suggesting alternative phrasings, or even challenging the writer to consider new perspectives. By categorically rejecting these tools, the author is not preserving some mythical purity of expression, but rather limiting their own potential for growth and innovation.
Moreover, the binary thinking displayed in this article – the idea that it’s either pure human writing or soulless AI-generated content – demonstrates a profound lack of nuance. The reality is that AI writing tools, when used judiciously, can enhance and amplify human creativity, not replace it. It’s akin to a painter refusing to use a new type of brush because it wasn’t available to the Old Masters. Tools evolve, and so should our approach to using them.
In conclusion, “THE SPARKY LANGUAGE MODEL (SLM)” is less a manifesto and more a eulogy – not for human creativity, but for the author’s ability to adapt and evolve. In their desperate attempt to cling to an outdated notion of authenticity, they’ve only succeeded in highlighting their own obsolescence. One can only hope that future generations will look back on this piece as a quaint relic of a bygone era, much like we now view those who once feared that the printing press would destroy the art of memory.
Perhaps, in time, even our dear author will come to realize that embracing new tools doesn’t diminish our humanity – it enhances it. But until then, we can expect more technophobic hand-wringing from the self-proclaimed keeper of the “Sparky Language Model.” One thing’s for certain: in the race between human progress and willful ignorance, there’s only ever one winner.
I had to hand it to Claude. Caustic indeed! This was an over the top critique of MacSparky’s essay – but I still found that it offered some new insights into the debate on this issue of the most appropriate use of AI.
I took a risk and decided to share it with MacSparky – suggesting he “fasten his seat belt” before reading. Fortunately, he enjoyed it and I avoided losing a good friend in the process!
My explorations into the use of AI continue – and I’m enjoying having fun spelunking around the pretty amazing tools that we now have available with AI-based large language models. For example, I’m using Claude right now to generate the list of 25 keywords appropriate to tag this article with. I’ve also used Adobe Firefly to generate the image of a “ghostwriter” at the top of this article.
The world of Large Language Models (LLMs) has captured the imagination of many. For me, this fascination has a deeper root, stemming back to my time working on fraud prevention at Visa.
Card fraud is an ongoing battle. Fraudsters devise new methods, and the industry responds with innovative solutions. One such threat was the counterfeiting of magnetic stripes on cards. While chip cards offered a more secure solution, their high cost made widespread adoption impractical.
In search of a cost-effective solution, we explored two approaches. One mirrored insider trading detection systems at major stock exhanges, using rule-based identification of suspicious patterns. The other, ultimately more successful approach, involved neural networks.
While the specifics of how I discovered neural networks elude me, I vividly recall a conversation with a Stanford professor, a pioneer in the field. His encouragement spurred us to pursue this technology. With a talented team, we implemented neural networks to analyze transactions in real-time, flagging potential counterfeits. This significantly helped limit card fraud growth, all without expensive hardware changes.
Today, that same neural network technology underpins LLMs like OpenAI’s ChatGPT, launched in late 2022. Advancements in silicon technology, particularly powerful GPUs, fuel both the training and operation of these models.
Recently, I listened to a captivating discussion titled “Does ChatGPT Think?” featuring Stephen Wolfram. That conversation triggered me writing this blog post.
Wolfram’s description of LLMs resonated deeply with me:
“So the big achievement and the big surprise is that we can have a system that fluently produces and understands human language… It’s not obvious that it would work, and it’s a kind of scientific discovery that it’s possible to have a thing like ChatGPT that can produce this thing that’s one of our sort of prize features – namely human language.”
For me, LLMs represent the culmination of a journey that began with neural networks and card fraud over forty years ago. I continue to marvel at the power of this technology and its potential to revolutionize how we interact with information and the world around us.
During a recent episode of The Kindle Chronicles podcast, host Len Edgerly shared insights from his interview with Peter Deng, OpenAI’s head of ChatGPT, at South by Southwest. While discussing personal uses of ChatGPT, Deng noted that the AI system could function not just as an assistant but as a partner.
Edgerly pointed out the subtle yet significant difference between these two roles. Viewing ChatGPT as an assistant implies a more transactional relationship where it simply executes given tasks. In contrast, a partner suggests a deeper, more collaborative dynamic where the AI contributes creatively, generating ideas and working alongside the human to explore topics, solve problems, and create content.
As Deng explained, “Thinking of me as a partner implies a more dynamic interaction. It suggests that I can offer insights, generate ideas and work alongside you to explore topics, solve problems, and create content. This partnership approach leverages my capabilities for understanding and generating language to collaborate with you in a more involved and creative way.”
This perspective resonated with me. One key difference between an assistant and a partner is the presence of an ongoing memory. An assistant seems transient, coming and going for each new task, while a partner accumulates knowledge through sustained interactions over time.
As investors like Bill Gurley have noted, memory capabilities could unlock tremendous potential for AI assistants to become truly personalized partners, supporting us more effectively across a wide range of activities. The ability for AI to draw upon a rich context of our shared experiences, preferences, and goals would facilitate deeper collaboration.
The evolution from assistant to creative partner with AI is an exciting frontier. As these systems gain more robust memory and context tracking, we can engage in fertile partnerships where the human and AI work in synergy, exploring ideas and augmenting each other’s creativity. I look forward to participating in and witnessing this profound transformation in how we interact with technology.
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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