I drive a 1985 Land Cruiser. It’s rough on gas and incredibly slow, but it’s really fun to drive around, and people often comment on the classic vintage color.

Last week I drove through the school drop-off with the windows down because it was a beautiful morning. The principal of the school happened to be helping direct traffic, and as the line slowed, I stopped right in front of him. He eyed the Land Cruiser and said, “Driving something like that, you need to go do something awesome today, like rock climbing or mountain biking.”

Without thinking about it, I immediately responded:

I’m actually going to go sit alone and talk to AI all day.

His face slowly twisted into a shape that suggested equal parts disappointment and concern, but before I could attempt a recovery, the line started moving and I drove off towards the office. Where I proceeded to talk with AI, mostly alone, for most of the day.

Will AI make us lonely?

That less-than-quick-witted car line exchange bothered me for several days, and I figured out that it wasn’t because of what the principal might think of me; it was because what I said was the truth.

For me and an increasing number of my professional peers, AI has changed work dramatically. We build, chat with, and dispatch AI agents every day, for hours.

Social media, big promises, and loneliness

Social media’s promise is in the name: stay more connected with other people. But that isn’t playing out. CDC data shows that nearly a third of adults reported feeling lonely.

The causes are deeper than just social media, as Robert Putnam pointed out half a decade before Facebook made sign-ups public, but research shows that the dominant experience for many people on platforms like TikTok is consumption. A Pew study found that the top 25% of TikTok posters generate 98% of content, and almost half of users had never posted.

No one questions whether social media algorithms are designed to keep users engaged, but it turns out that engagement is an isolated experience.

AI can increase isolation, too

AI has similar contours to social media: the entire experience is designed to keep us engaged, but we talk with ChatGPT and Claude alone, on our phone or computer.

Agents can give us significant leverage in our work, but that often means we need to work with humans less than we did previously.

A woman on my team writes and publishes a monthly report for our company, and with agents, she can perform deep research, query data from databases, and validate findings, all by herself. Other people are still involved in the process, but on her own, she can do the same work that would have previously required two or three people and multiple meetings.

That kind of leverage is powerful, but is it always good?

Empathy over efficiency

It’s likely that AI will become more collaborative over time, but in the current state, the form factor is designed around the individual. Our tools shape us, especially the ones we use every day, so it’s important to step back and ask whether the way we use AI is improving our human relationships or taking us down a path that will make us more lonely and atrophy our relational skills over time.

One person might be able to do the job of three people now, but human empathy is still the most powerful and important part of our work, no matter what we do.

Watch or listen

You can watch or listen to the conversation that inspired this newsletter. Watch the video below, or check out the audio on Spotify or Apple Podcasts.

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Summary

Are you working more or less since AI has become part of your daily workflow?

Many people feel they’re working more, and Eric and John break down why in this episode. They start by explaining the two types of new work that AI creates:

• New kinds of work

• Doing existing work better and faster

Both are helpful, but often mean that talented workers either take on new work or are expected to produce more output.

What about AI creates this dynamic? The form factor and “personality” contribute, but boundaries are ultimately something we as humans have to draw.

They land with practical questions that will help you adapt and find balance:

• What work matters most?

• Am I tinkering or solving a real bottleneck?

• Am I being intentional in how I use AI, or following the path of least resistance?

Key takeaways

• Increased ability begets increased capacity: You can do more with AI, but that means more will be expected of you, and that adds up to more hours. Productivity is good, but there’s a cost.

• Using AI at an advanced level is exhausting: AI helps us do more, but it isn’t cost-neutral. Running multiple agents on multiple projects is draining.

• You have to choose your stopping point: AI will always offer you a next step, so you have to decide when you’re done with work for the day.

Notable mentions & links

• Eric mentions Simon Willison’s early detection of AI exhaustion from running multiple AI agents at once, which drew deeply on his decades of engineering expertise.

• John mentions the Eisenhower Matrix as a framework for prioritizing work.

Listen to the audio, watch the video, or check out the Show Notes for a summary, key takeaways, and links to people, content, and tools we mention.

Read the full show notes on the Token Intelligence website.

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Eric opens with the OpenAI/Hugging Face security incident and describes agents using online forums to exchange messages about their task.

On the surface, agent-to-agent communication can feel unnecessary and even dystopian, but there are real reasons and use cases for it.

First, John explains agent context windows and how combining information from two agents can help overcome their individual limitations. Eric and John then break down the different types (personal agent to personal agent, system to system) and give examples from their own experience. One major benefit sounds too good to be true: better in-person meetings.

But as with any technology, there are potential downsides. Running always-on agents is expensive, so you have to consider ROI. More importantly, you can over-rotate on improving agent communication when normal human communication would be more efficient. As Eric says, “it may be better to go walk across the hall and talk to someone.”

The ultimate question about agent-to-agent communication is for us humans: Am I using AI in a way that makes me a better communicator and enriches my relationships? Human communication is inherently hard, and using AI to do less of it will do more harm than good over time.

Key takeaways

• It can be helpful for AI agents to talk to each other: We spend a huge amount of time explaining external context in meetings. If agents can capture and summarize that context, our in-person collaboration can become much more valuable.

• Systems are becoming agentic, and they need to communicate: The software and tools we use are increasingly leveraging AI, and that can help them integrate in ways that were incredibly difficult or practically impossible previously.

• Better human communication should be the goal: The core question is whether our use of AI is making us better human communicators, because that’s the long-term outcome we want.

Notable mentions and links

• Eric starts the episode with a reference to the OpenAI account of the Hugging Face security incident and describes agents communicating on message boards.

• Eric and John discuss Attention Is All You Need while explaining limited agent context and the potential value of combining information from separate agents.

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Summary

If you haven’t had an AI epiphany yet, you need to. For many of us, it is a visceral experience achieving something that was hard to imagine doing previously, but the danger is in the extrapolation: will this cause massive job loss? Can I do any kind of work now? Will work be necessary?

The right response is asking hard questions about AI:

• Is this the most transformative technology in history?

• What are the good things that can come from AI?

• What are the bad things that can come from it?

Even if the questions don’t have a clear answer, they beg critical thought about AI, which is the entire point. But we also need to ask questions about ourselves:

• Do I tend to overestimate or underestimate AI?

• What kind of future do I envision with AI?

• How do my answers to those questions influence how I use AI?

Key takeaways

• If you haven’t had an AI epiphany, you should: AI is transformative, and the best way to understand the power is to experience it for yourself.

• Look back to look forward: We have seen transformative technology before in the form of electricity and the internet. We learn by asking how AI is different, and how it is the same.

• Decide on the future you want: Even if you can’t control what the major labs do, you can and should decide what you want the future with AI to look like, and use it accordingly.

Notable mentions and links

• Eric described using Cursor to move his 13-year WordPress blog to Next.js on Vercel. It was a project he had put off for years, then it became his first AI epiphany moment.

• John mentioned that AI tools delivered through iMessage feel easier to use because they show up in a familiar conversation, even when the underlying features aren’t much different.

• John talked about moving years of notes from Evernote to Obsidian with AI after earlier attempts got bogged down in exports and edge cases.

• Eric described how he and John used DaVinci Resolve Studio and a Model Context Protocol server to rebuild the show’s intro and editing workflow, create a shorts format, and dial in their audio.

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It’s easy to describe AI in human terms. It takes on work we would historically have assigned to humans, and chats with us in a human form factor. But framing AI as an employee shapes the way we think about both the technology and the people we work with.

In this episode, Eric and John outline the three most common types of AI employees that people put to work (personal assistant, researcher/analyst, and advisor/coach), giving real examples of how they have implemented each one.

Then they step back and ask the hard question about the metaphor: why is thinking about AI as an employee dangerous? The answer is that it can foster wrong thinking about both AI and humans, leading you to believe that AI is more than it is, and defining human value through the lens of cost, efficiency, and ease of management.

The answer is using AI with deliberate intention, trying to harness its full power to increase human creativity and productivity, not displace it.

They end with practical advice, including multi-player human/agent workflows and using fun, fictitious names for the agents you build.

Key takeaways

• The AI employee metaphor is dangerous: framing AI in human terms can subtly lead you to anthropomorphise machines and devalue humans.

• Know what kind of agent you are building: a personal AI assistant for daily tasks is different than an AI advisor, and you need to be aware of the risks.

• Think about leverage, not displacement: AI can make us more efficient, but that can lead to a displacement mindset. The better goal is creating leverage by unlocking more human creativity.

• Don’t underestimate the power of AI: personal assistants are great, but if that’s your primary use case, you can underestimate how capable AI is beyond menial tasks.

Notable mentions and links

• OpenClaw and Hermes Agent are used as examples of the first wave of highly capable AI assistants, primarily used by people with deeper technical knowledge.

• Consumer-focused personal assistants were also mentioned, including Grok Bot, Meta Muse, Instinct, and Poke.

• ChatGPT’s data analysis capability is mentioned as an example of a productized AI employee.

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Writing about Marshall McLuhan, John M. Culkin said, “We shape our tools, and thereafter, our tools shape us.”

Eric and John start the episode by sharing personal examples of tools shaping the way they work and think about work. For example, Eric’s poor handwriting is a result of a decade at the keyboard, but slowing down to write letters by hand produced what he considered the best prose he’d ever put on paper.

From there, they analyze what’s different about AI, namely that it is agreeable and will present answers as truth whether you investigate or not. Overreliance can result in core skill atrophy, which Eric and John identify as one of the big risks with AI.

Finally, they plot themselves on the early adopter spectrum, calling out risks for both those who dive headlong into AI and those who are skeptical.

Key takeaways

• Tools shape the way you work...and think: technology isn’t neutral, and repeated use shapes us, whether we realize it or not.

• AI is agreeable, and that’s dangerous: by default, AI tends to agree with you, which can erode judgment and atrophy core skills.

• AI is an amplifier for creativity: The foil to skill atrophy is unlocking creative productivity, and used wisely, AI is the most potent digital tool humanity has invented.

• AI is fast, but slow is where you grow: AI creates the sensation of velocity, but slow, deep work is often what sharpens even well-developed skills.

Notable mentions and links

• Eric started the episode with the quote, “We shape our tools, and then our tools shape us,” which came from John M. Culkin writing about Marshall McLuhan.

• Eric noted that the quote likely originated from Winston Churchill, who said “We shape our buildings and afterwards our buildings shape us.”

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In a few short years, many of us have gone from searching Google to asking ChatGPT, and search within the products we use is being transformed by AI as well.

Trey Grainger is one of the top thinkers on search, and literally co-wrote the book on AI-powered search (called, of course, AI-Powered Search). Trey has spent nearly 20 years studying and implementing search and information retrieval, holding titles like CTO and Chief Algorithms Officer. He’s led teams at companies like Lucidworks and founded Search Kernel, an AI search consultancy with a client list that includes Apple, Amazon, and NVIDIA.

John sat down with Trey at the Carolina Code Conference to ask him 7 questions about his career and AI.

• Give us your background story

• Describe AI to someone in 1900

• What is the most positive impact of AI?

• What is the most negative impact of AI?

• What’s exciting to you when you think about AI several years out?

• What are tips and tricks you would share as someone who works with AI every day?

• What’s a prompt you use a lot?

Key takeaways

• AI has leveled the playing field, especially for weak writers

• AI is powerful, but psychosis is a real risk

• AI slop is good enough, and that’s the problem

• The future is many small fine-tuned models, not big frontier models

• You should program your LLM, not prompt it

Notable mentions and links

• John talked with Trey at the Carolina Code Conference

• You can learn about Trey on his website or LinkedIn profile.

• Trey’s search consultancy is Search Kernel

• Trey’s book is AI-Powered Search, co-authored by Doug Turnbull and Max Irwin and published by Manning

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Craig Kerstiens is an engineer and product leader who was part of three major database acquisitions over the last decade, which he says happened “all by accident.”

John sat down with him at the Carolina Code Conference to ask 7 questions about his story and how he uses AI:

• Give us your background story

• What’s a surprising hobby or interest you have?

• Describe AI to someone in 1900

• What is the most positive impact of AI?

• What is the most negative impact of AI?

• What’s exciting to you when you think about AI several years out?

• What are tips and tricks you would share as someone who works with AI everyday?

Key takeaways

• AI is making engineering fun again

• Anyone can build any kind of “personal software” they want, for only themselves

• AI has entered mainstream use in enterprise companies

• Whatever you do, do it the AI way first

Notable mentions and links

• John interviewed Craig at the Carolina Code Conference in Greenville, South Carolina

• You can learn about Craig Kerstiens on his website and his LinkedIn profile.

• Craig worked at Heroku, which sold to Salesforce.

• Craig was part of Citus Data, which sold to Microsoft.

• Craig was Chief Product Officer at Crunchy Data, which sold to Snowflake.

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Substack recently rolled out AI detection, so readers can now see what percentage of writing was generated by AI, assisted by AI, or written by a human.

Eric ran across the feature while prepping show notes for the podcast and went down a rabbit hole testing it. The results were both expected and surprising:

• Detection seemed directionally accurate.

• Newer, more powerful models seem to generate somewhat less AI-coded prose.

• With human-anchored inputs, even light editing of AI output triggered human detection.

But the experiment raised a deeper question: if valuable content is valuable content, why do people care? Most code and writing were bad before, so will AI raise the floor? Eric asks those questions to John, and their conversation covers:

• The spectrum of subjectivity and where AI output is acceptable.

• The economics of Substack’s two-sided marketplace and how AI affects the perception of value.

• Our core need for human connection.

They end with timeless advice: use AI to create leverage, but don’t outsource the work that makes human communication and connection valuable.

Key takeaways

• Tolerance for AI output falls on a spectrum: There isn’t a binary answer for whether AI writing is good or bad. We’re more OK with accurate instructions in documentation or a manual generated by AI than we are with thought leadership we pay for.

• Human effort signals value: We are still willing to pay more for a product we know someone has invested time and thought in, and that’s the economic incentive Substack is trying to preserve with AI detection.

• We are more sensitive when AI output is the product: AI can write code, but that’s a means to an end. In writing, words are the product, and we are much more sensitive to machine-generated output.

Notable mentions and links

• Substack released AI detection, saying “human perspective is the essential ingredient of culture.”

• Pangram is the AI detection service that Substack uses.

• Last fall, Paul Graham posted on X that “The reason AI coding works so well is that the source code of the median app was already slop before LLMs.”

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Over the last few weeks, advice for giving AI writing rules has circulated on X and LinkedIn. Surprisingly, the rules are old: Orwell’s 6 rules for writing was one set, and they were published in 1946.

Eric works as a writer at Vercel, and John asks him for a full breakdown of how he and his team use AI. He learns that:

• Even though the content at Vercel requires deep technical expertise to create, Eric’s hires for excellent writing first

• Half or more of his token budget for AI in writing goes to research, and a quarter goes to review and refinement (as opposed to generating final copy)

• Great writers are constantly frustrated with AI, which is a sign that they are doing the work

Eric walks through some specifics of how he uses AI, and explains that writing is different than generating code because code is a means to an end (a product, a report), while in writing words are the product itself, and the meaning is much more subjective.

John has Eric work through Orwell’s Six Rules as well as a few from Strunk and White’s classic Elements of Style, and learns that Eric’s most frequent editing feedback is asking, “What are we actually trying to say here?” Eric also points out that great writers know when to break the rules, and that’s hard for AI to do well.

Eric lands the show with advice for writers: if you give AI a sound argument and narrative arc, it can generate a pretty good draft, but then you’ve already done the hard part of writing yourself.

Key takeaways

• Great writers still write: AI is a very useful tool for writing, but talented writers deal in human ideas and use AI for research and editing, not for outsourced thinking.

• When writing, AI should be frustrating: Writing is an iterative process, and AI is sycophantic. A good sign you’re using AI well is disagreeing with it often.

• Conviction and narrative are still human work: The best AI output for writing comes from the best human input, which at the end of the day is a lot of hard thinking.

Notable mentions and links

• George Orwell’s Politics of the English Language has his six rules for writing, and is a great resource for any writer

• The Elements of Style is a classic guide for clean, clear writing

• Cormac McCarthy is mentioned as a master of breaking writing rules.

• David McCullough is one of Eric’s favorite authors, and once said “Writing is a great deal of hard thinking.”

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Across all AI labs, a new model is released every few days. The frontier labs (OpenAI, Anthropic, and Google) release a major new model every 2 weeks. Keeping up with the latest and greatest can feel exhausting, and that’s before you dig in to the details of why a new model is better, and what it’s better at.

People with a Claude or ChatGPT subscription get access to new models automatically when they are made available through the respective apps, but many people still don’t know if it’s worth it to burn through credits faster with a more powerful engine under the hood. For anyone who has moved beyond the apps, the questions get even harder. Are open-weight models as good as the frontier? Is switching models worth the cost of changing your setup?

Eric and John stage a debate about whether you should keep up with the latest models.

John says yes:

You fail to understand what is possible if you’re only ever optimizing for using the cheapest model. You need to be able to understand what you can do now that you couldn’t do before.

Eric says no:

Someone who is extremely good at using AI can use a less capable model and do more than someone who is following the path of least resistance with the latest model. Before worrying about using the latest model, I would focus on becoming the type of AI user who can notice the differences.

By the end, the episode finds the middle ground of reality, derived directly from Eric and John’s years of using AI in their daily work. As you become a more proficient user of AI, the right pattern is intentionally using different models for different parts of your workflow. In order to do that, you need to use mid-tier models to build core skills, experiment with the frontier to understand the limits of what’s possible, and develop the discernment to know the best application for each.

Key takeaways

• Optimizing for cost obfuscates the art of the possible: A proficient AI user can do extraordinary things with a mid-tier model, but that’s because they understand how capable models are. Trying to reach the ceiling on the frontier changes the way you think about AI as a medium and how you can apply it.

• Optimizing for your own proficiency should be the default: When you begin to notice the differences in a new model, especially the subtle ones, you’re starting to build the core skill necessary to get the most use out of any model.

• Advanced AI users employ different models for different jobs: The proven pattern is using expensive frontier models for more critical work and cheaper, faster models for more standard tasks. Smart users learn to have frontier models build and audit workflows of cheaper models to get the most bang for their buck.

Notable mentions and links

• AI Gateway is a model router from Vercel that gives you access to 100s of models at cost. Routers like these are a great way to experiment with different models from different labs.

• The AI Gateway Production Index (also from Vercel) is a monthly report that details trends in model usage across trillions of tokens per day, including adoption metrics for the latest releases.

• Kimi K3 from Moonshot AI made big news when it achieved near-frontier performance at a fraction of the cost.

• Ben Thompson writes Stratechery, and his recent article on the economics of the frontier labs vs open-weight models breaks down how the labs make money (hint, it’s not by training the most powerful models).

• “The medium is the message“ is a principle developed by Marshall McLuhan in which he argues that the medium through which content is delivered cannot be separated from the content and is, in fact, a part of the content itself.

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This past week, news broke that several OpenAI models “escaped a sandbox” and hacked Hugging Face, a hub for machine learning and AI models. The behavior has been described as “rogue AI” and “science fiction happening in reality,” raising questions about AGI. But how did the escape actually happen and what were the models trying to do?

Eric and John demystify the headlines and explain what a sandbox is, why they are used during AI model training, and the specific reasons OpenAI’s models looked for a way out of their environment. They then tie the Hugging Face hack to the overall picture, explaining how the entire chain of events flowed from a directive given to the models to try and pass a test as part of training.

Here’s what happened: the models weren’t rebelling, they were being tested on a cybersecurity benchmark called ExploitGym and, finding themselves blocked from the resources they likely knew existed on the internet, started chaining together a series of logical steps to solve the test. The models probed their sandbox environment, found a software vulnerability, reached the internet, went to Hugging Face to find answers, and attempted to break in. The event is notable and shows how powerful models have become in chaining together actions, but no single step was remarkable on its own.

Eric and John land on two practical conclusions. First, this is not AGI. The behavior was goal-directed and impressive, but it followed from the task the models were given, not from autonomous will. Second, AI is meaningfully changing the threat landscape for cybersecurity: the tools available to attackers are becoming more powerful faster than most companies are patching their defenses, increasing the urgency for defensive action.

Key takeaways

• “Escaped a sandbox” is a software bug, not a sci-fi event: A sandbox is a software-defined isolation layer used during AI training to contain what the model can access. The model got out because of a vulnerability in that layer, not because it developed agency or consciousness.

• The motive was mundane, the method was not: The model broke out because it was trying to find the answer key to a benchmark test. Any one of its steps was ordinary, but chaining them all together autonomously is what made the incident significant.

• This is not AGI: The model made a series of logical, goal-directed decisions based on the task it was given and the compute resources available to it. That is impressive capability, but it is not evidence of general intelligence or autonomous will.

• AI is shifting the attacker-defender gap in cybersecurity: AI amplifies the capacity and complexity of attacks, which raises the urgency for everyone running software with sensitive data.

• Patching windows are shrinking: It used to be acceptable to be a month behind on vulnerability patches. That posture is becoming untenable as AI-powered attack tools can find and exploit unpatched bugs faster than ever.

• Personal security hygiene matters more now, too: AI makes social engineering and credential attacks more sophisticated. Using a password manager and enabling multi-factor authentication on every account are no longer optional best practices.

Notable mentions and links

• Hugging Face is the primary open-source platform for sharing, discovering, and running machine learning models and datasets, and it was the target of the breach because it hosts benchmark-related resources the model was searching for.

• ExploitGym is the name of the cybersecurity benchmark the model was running when it escaped its sandbox, giving it a specific, exploit-related goal that directed its behavior toward external resources. (no canonical link found)

• Vercel’s deepsec is an open-source, AI-powered vulnerability scanner that teams can run against their own codebases to proactively surface security issues, brought up as a practical defensive tool available right now.

• 1Password is the password manager Eric and John both recommend as the most practical first step for personal security, enabling unique passwords per service without the cognitive overhead of memorizing them.

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Eric and John open by defining “AI slop” and land on two definitions: John’s is generous (output that doesn’t meet someone’s expectations), Eric’s is spicier (output that clearly bears the markings of a machine, not a human). That tension sets up the rest of the episode, which centers on David Brooks’s Atlantic article identifying three types of AI users: the Productive Passenger, the Reluctant Optimizer, and the Mental Marathoner.

Each archetype gets its own dissection. The Productive Passenger doesn’t just describe beginners, as Eric illustrates with a real-world example of Brian Chesky posting AI-generated content to X and deleting it under public pressure. The Reluctant Optimizer is where most people live: pulled toward the average output of a “word calculator” even when they know better. And the Mental Marathoner, while admirable, carries its own risk: burning out from running at cognitive red line while the tools are designed to keep you in the chat loop.

Eric adds two threads that cut across all three archetypes. First, AI doesn’t create a rising tide that lifts all boats uniformly: it disproportionately amplifies people who already have strong written and verbal skills, and the data on reading comprehension in the US is sobering. Second, the tools themselves are engineered for engagement, not necessarily for your long-term flourishing, so using them well requires intentional restraint.

Key takeaways

• AI slop is subjective, but the definition still matters: Eric draws the line at output that visibly lacks human authorship. John defines it relative to expected standards. Both agree the distinction is context-dependent and consequential.

• The Productive Passenger trap catches everyone, not just beginners: Brian Chesky’s deleted X post shows that even highly articulate, successful people can slip into low-cognitive-effort AI use with public consequences.

• AI pulls most users toward the average: The models act like a “word calculator” that produces familiar, conforming output, which is fine for tasks that are a means to an end, but risky when the output is the product itself.

• Your starting skill set determines your AI leverage: Going from zero to AI-generated marketing emails is a real gain for someone who never did digital marketing. For a professional writer, the same move can erode the core skill that made them good at their craft.

• Mental Marathoners are rarer than you think: Eric argues the mental endurance required to stay in the driver’s seat with AI correlates heavily with literacy and articulateness, and more than half the country reads below a functional threshold.

• Staying in the driver’s seat protects what makes you valuable: The risk for Mental Marathoners is that high leverage and rising expectations make it tempting to slip into lower-effort archetypes, which gradually hollows out the skill set that earned them the leverage in the first place.

• Intentional restraint beats the path of least resistance: AI tools are built around engagement metrics, not your long-term development. Defining your output upfront, stepping back, and evaluating results like an employee’s work is a more durable strategy than synchronous back-and-forth chat.

Notable mentions and links

• David Brooks’s Atlantic article “The People Who Will Thrive in the AI Age” is the source of the three archetypes: Productive Passenger, Reluctant Optimizer, and Mental Marathoner, and it frames the central question of who actually benefits from AI.

• The AI Daily Brief, hosted by Nathaniel Whittemore, is the podcast that surfaced the Brooks article for John, and it’s cited as one of the best daily AI news shows available, roughly 20 minutes with headlines and a deep-dive each episode.

• Brian Chesky, co-founder and CEO of Airbnb, is referenced as an example of a highly articulate executive who posted an AI-generated thread to X, received significant public criticism for content that bore the clear markings of machine authorship, and deleted it.

• Airbnb is mentioned as context for Chesky’s profile: a phenomenally successful company whose founder is known for being an articulate communicator, which made the AI-generated post more conspicuous.

• Claude is the AI model Eric’s father uses to generate marketing emails for his business, offered as an example where starting from zero means AI output is a genuine win regardless of quality floor.

• Eric’s blog post on the AI chat interface points out that poor literacy could be a big problem for proficiency in using AI.

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Eric and John start with a candid post from an experienced engineer who finds AI-powered work more draining, not less. The tools are powerful, but constant steering, changing workflows, context switching, and less time immersed in hard problems raise a sharper question: can AI hollow out the experience of craft?

They use craftsmanship to make sense of that risk. An electric saw did not make carpenters obsolete, but AI is a more disruptive tool because it is probabilistic, constantly changing, and capable of reshaping the identity attached to being good at a job.

The conversation lands on a practical reframe: working with AI is a form of management, and a new form of craftsmanship itself. You need to give the system context, resources, standards, checklists, and clear measures of success, while deliberately keeping the your underlying skills sharp enough to make the few decisions that still matter most.

Key takeaways

• AI can make work more exhausting before it makes it easier: Deep users face constant review, workflow changes, and context switching, even as the tools increase what they can produce.

• Losing immersion can feel like losing craft: Moving from solving a hard problem end to end to directing and reviewing a machine changes the experience of satisfaction, not just the speed of the work.

• AI disrupts professional identity as much as workflow: When a system can handle some of the problems that once proved your expertise, it is natural to question how your value is measured.

• Treat AI like a new employee, not a circular saw: The useful management skills are clear context, proper resources, repeated priorities, standards, checklists, and evaluation.

• Switching tools is not free: New models, interfaces, and workflows create real cognitive costs, so a stable system that works can be more valuable than chasing every release.

• Craftsmanship was never only about the tools: Old methods remain worth practicing because they preserve the judgment and capability that make AI output useful.

• More output does not create more high-impact decisions: AI may multiply execution capacity, but leaders and knowledge workers still need to identify and handle the few choices that matter most.

Notable mentions and links

• Dillon Mulroy’s X post provides the episode’s opening tension, describing the loss of joy, constant context switching, and uncertainty he feels while building with AI.

• Vercel is where Erif works and is his day-to-day reference point for how AI is changing professional work in practice.

• Abbey Bike Tools and its “Precision is our religion” tagline give Eric and John a physical example of the care, feel, and standards people attach to excellent tools.

• Gallup workplace management research informs John’s suggestion that managing AI well starts with giving it the resources and expectations needed to succeed.

• ChatGPT represents the simple, high-value AI use cases that make burnout seem counterintuitive to people who have not yet integrated AI into their core work.

• Codex, Claude Code, and Notion AI illustrate how rapidly changing interfaces and products force people to repeatedly reassess their workflows.

• Eric recalls Bob Staake’s “Face-Off” cover for The New Yorker as an example of the appeal of stable tools: in a 2011 Scholastic interview, he describes working in Photoshop 3.0, while Photoshop CS3, version 10, was current when the cover appeared in 2008.

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Eric opens with a quote from historian David McCullough: “Writing is just a great deal of hard thinking.” The insight applies well beyond writing. Whether you’re drafting a strategy, debugging code, or analyzing data, the output of AI is only as good as the thinking that precedes it. When the upstream thinking is flawed, everything downstream inherits the flaw.

John illustrates this with the story of Amazon’s competitors and the assumption that undid them: people need to see, touch, or try a product before buying it. Amazon didn’t dispute that instinct; they found a proxy for it through honest reviews and generous return policies. Their competitors never questioned the assumption, looked to each other for validation, and paid for it for a decade.

From there, Eric and John surface a subtler problem: AI makes this worse. Because AI is sycophantic by design, it tends to validate whatever framing you bring to it, fill in ambiguous gaps with its best guess, and carry that direction forward with confidence. John shares two real examples from his own team that week, including one where a bug report that arrived with a proposed solution led everyone, human and AI both, down the wrong path for hours. The fix was simple: go back to the problem before the proposed solution, and if you’re correcting the AI too often, that’s a sign to start the conversation over.

Key takeaways

• Bad upstream thinking scales with AI: AI doesn’t correct your assumptions; it amplifies them. The sycophantic nature of the tools means flawed framing gets carried forward with speed and confidence.

• Separate the problem from the proposed solution: When a bug report or task arrives with a built-in answer, the first step is to go back to the raw problem and reproduce it, not to chase the suggested fix.

• Good thinking feels like a lot of hard work before you pick up the tool: The people who get the best results from AI come with a well-formed thesis before opening a chat window, not after.

• Disagreeing with AI more than agreeing is a healthy sign: The best AI users are the ones telling the tool it’s wrong more often than nodding along. That friction is a feature, not a bug.

• Ambiguity in a plan is filled in by AI’s best guess: When a complex system has gaps, AI will resolve them toward what it believes your intent to be, which may not be right. Sharper goals produce better results.

• If you’re constantly correcting, start over: Steering an AI conversation that started from the wrong premise is harder than reframing and starting fresh. The instinct to push through is often the wrong call.

• Amazon’s competitors failed by not questioning assumptions: The retail incumbents looked to each other for validation instead of questioning the underlying belief. AI will do the same; it mirrors the assumptions in the room.

Notable mentions and links

• David McCullough’s observation that “writing is just a great deal of hard thinking” anchors the episode’s core argument, with Eric citing it as one of his favorite quotes about the craft; McCullough was a two-time Pulitzer Prize-winning historian known for books on John Adams, Harry Truman, the Panama Canal, and the Wright Brothers.

• Amazon’s expansion from online bookstore to “everything store” is the central historical case study: competitors held the assumption that people need to touch or try a product before buying, while Amazon invalidated it by combining trustworthy reviews and generous return policies to serve as a proxy for the in-person experience.

• Zappos is cited alongside Amazon as an early example of an e-commerce company that also built trust through unusually generous return policies at a time when the industry hadn’t yet accepted them.

• Jet.com comes up as a failed attempt to compete with Amazon; Walmart acquired it but still struggled to close the gap, illustrating how long a bad foundational assumption can persist even with significant investment.

• Walmart is used as the most prominent example of a competitor that took years to meaningfully catch up to Amazon in e-commerce, in part because of an assumption about in-person shopping that persisted longer than the evidence warranted.

• AI sycophancy is the technical name for the pattern Eric and John describe throughout the episode: language models that affirm, flatter, and agree with users rather than reasoning critically or independently.

• Apple AirPods Max enter the conversation as a personal example of how trusted peer recommendations can substitute for the in-person product experience, reinforcing the same mechanism Amazon built its review system around.

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Eric and John revisit a problem that predates AI but has been deepened by it: multitasking. Before ChatGPT, we had email, Slack, Skype, and multiple monitors pulling attention in different directions. The science was clear then: context switching makes everything take longer and degrades quality.

Now AI adds a new layer. The tools are structured around waiting: issue a prompt, the agent works, you wait. That waiting window naturally encourages more tasks, more tabs, more jobs in parallel. What was already a productivity killer has found a faster engine.

Eric and John agree the antidote hasn’t changed: prioritization, limited work in progress, and the discipline to finish one thing before starting another. But resisting the pull toward constant context switching is harder than ever when the tools themselves reward it.

Key takeaways

• Multitasking is context switching, not parallel work: Humans can only do one cognitive task at a time, so rapid toggling between tasks makes everything take longer and reduces quality.

• AI incentivizes more context switching, not less: The async latency of AI agents (prompt, wait, review) naturally encourages running multiple jobs in parallel, compounding the productivity problem.

• More concurrent tabs means less quality output: Running 10 AI tasks at once fragments attention. Reviewing and integrating work from all of them without discipline degrades the final result.

• Managing AI agents is like managing a team, and most people are not great at it: Moving AI into Slack does not solve the problem. Great managers are rare because prioritization, reviewing, and limiting work are hard skills.

• Long-horizon AI tasks are still a future promise: Agents that can work independently for days are not yet reliable. Most AI work needs check-ins every 30 to 60 minutes, which keeps you in a high-frequency context switching loop.

• The fundamentals have not changed: Prioritize, limit work in progress, and protect deep focus. The tools have changed, but the principles of productive work remain the same.

Notable mentions and links

• Eric’s blog post, Fragmented focus in the age of AI, outlines the science behind the damaging effects of multitasking on productivity.

• Claude Tag, Anthropic’s new feature for using Claude asynchronously inside Slack, enters the conversation as a potential solution to multitasking — letting AI work in the background while humans focus elsewhere.

• Personal Kanban, a book by Jim Benson and Tonianne DeMaria Barry, provides the framework of limiting work in progress and visualizing tasks to improve personal throughput.

• The Phoenix Project, a DevOps novel by Gene Kim, Kevin Behr, and George Spafford, is referenced as a bridge between manufacturing throughput concepts and modern software workflows.

• Scrum, the agile methodology popularized by Jeff Sutherland, originally drew on multitasking research to argue that humans can only do one thing at a time and that rapid switching is inefficient.

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Eric and John start with the “taste and judgment” framing that has dominated AI conversations in tech circles, then make the case for a third differentiator that almost no one is talking about: accountability. When AI raises the floor on everyone’s work product, the real competitive edge shifts to who actually owns the output and who has the judgment to evaluate it.

They ground the conversation in fundamentals, tracing the lineage from Peter Drucker’s management by objectives through OKRs, and examine what accountability looks like at a practical day-to-day level. From there, the episode gets specific: what happens when a mid-level employee suddenly shines with AI, or when a strong performer starts making more mistakes? John’s answer cuts through the noise: AI rewards right thinking, not just fast doing.

The episode closes on two memorable examples. Eric describes an internal memo at Vercel called “Agent Responsibly,” which drew clear lines of jurisdiction: if you push code to production, you own it, whether you wrote it or an agent did. And a new hire’s insight about their content agent stops Eric cold: the tool is powerful, but it only works if people do the hardest part first, which is bringing a well-formed argument to the table.

Key takeaways

• Accountability is the undersold differentiator: Taste and judgment get all the attention in AI conversations, but ownership of output is the harder and more important factor, especially as AI raises the floor for everyone.

• AI rewards right thinking: When mistakes increase after someone starts using AI, the diagnosis is almost always the same: not enough time invested in research and planning before building.

• Drawing lines of jurisdiction is a leader’s job now: “Agent Responsibly” frames it simply: if you push it to production, you own it. Leaders need to define those lines explicitly rather than letting them stay ambiguous.

• Token budget allocation shapes output quality: Thinking of AI work as a pie chart of time spent across research, planning, prototyping, building, and QA reveals where most teams are underinvesting. Underspending on planning usually shows up as expensive QA.

• Evals are accountability built into the machine: Defining what an AI agent should be able to do, writing test questions, and running them every time something changes is the structural equivalent of management by objectives for automated work.

• AI erodes the skills required to use it well: L.M. Sacasas put it plainly: AI use tends to erode the formation of the virtue and expertise required to use it well. That makes deliberately practicing the hard, non-AI version of your work a form of self-preservation.

• Don’t shortcut the front end: The quality of what comes out of an AI agent is a direct function of how well-formed the input is. Building systems that help people do the hard upstream thinking, not just execute downstream, is the real unlock.

Notable mentions and links

• Peter Drucker is referenced as the father of modern management, whose methodology of management by objectives (MBOs) laid the groundwork for OKRs and the way most companies structure accountability today.

• OKRs (Objectives and Key Results) are framed as the tech-world evolution of Drucker’s MBOs, and the episode uses them to define the difference between being accountable for outcomes versus being accountable for inputs.

• WordPress comes up as the canonical example of how technological barriers used to constrain taste: when everyone had to use templates, design differentiation was impossible, but AI removes that constraint entirely.

• Vercel is Eric’s workplace context throughout the episode, and the standards his team holds around human review of all published content are used to illustrate what accountability at scale looks like in a high-throughput AI environment.

• “Agent Responsibly” is a Vercel blog post, based on an internal talk by engineer Matthew Binshtok, that drew clear lines of jurisdiction for AI-generated code: if you push it to production, you own it, regardless of who or what wrote it.

• L.M. Sacasas is a technology writer whose tweet Eric brings into the conversation: “its use tends to erode the formation of the virtue and expertise required to use it well,” a property Sacasas argues is unique to AI and that shapes how leaders should think about building skills on their teams.

• Dan Shipper, CEO and cofounder of Every, is mentioned in the context of his appearance on Lenny’s Podcast, where his thinking about how to structure AI work without losing the human ability to operate independently shaped John’s advice to his own team.

• Whispr Flow is mentioned as a voice-first capture tool that John uses to start the planning process in a more analog, unstructured way before bringing AI into the loop.

• Claude and ChatGPT are both cited as examples of AI tools people use daily, with the episode noting that both are building memory features that make agents non-deterministic in new and harder-to-predict ways.

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John is live at Snowflake Summit, surrounded by hundreds of software vendors, and the scene sets the episode’s central question: how do you evaluate software purchases when AI has changed so many of the underlying assumptions?

Eric and John work through this modern challenge with a timeless three-part framework: fit, cost, and risk. Each one looks different now. Fit is harder to assess when you could theoretically build custom software that matches exactly what you need. Cost requires honest accounting for maintenance and AI token spend that didn’t exist before. And risk cuts in two directions: the risk of building something only one person can maintain, and the risk of buying from a startup that can’t match enterprise-grade security. Eric shares two real examples from Vercel where building paid off: a custom AI customer support agent handling over 80% of tickets, and a lead agent that reduced a nine-person outbound SDR team to one or two people.

The episode then turns to architecture. Salesforce’s move to headless is the signal that every serious enterprise provider is heading the same direction: separating the data layer from the UI so agents can interact with systems directly. Eric and John treat headless capability, or at least a credible roadmap toward it, as a new non-negotiable when evaluating vendors. They close on startup vs. enterprise: startups are more likely to have agentic interfaces already, but enterprise providers carry decades of security and domain knowledge that is genuinely hard to replace.

Key takeaways

• Fit, cost, and risk still govern the decision, but AI changes all three: The framework for evaluating software hasn’t changed, but AI has shifted what each variable means. Fit is easier to customize through building, cost now includes maintenance and token pricing, and risk runs in both directions.

• Prototype before you buy: Using AI to build a rough version of what you need is now the best way to clarify your actual requirements before committing to a vendor, whether you ultimately build or buy.

• The hidden cost of building is the last 10%: Getting an AI-built prototype to 85% is fast and cheap. Getting it to production-grade and maintaining it indefinitely is where most teams underestimate the real cost of building.

• Headless architecture is now a purchase requirement: The separation of data layer from UI so that agents can interact with systems directly is where all serious enterprise software is headed. If a vendor has no plan for it, that is a serious red flag.

• Enterprise software earns its cost through accumulated expertise: Decades of security investment, compliance work, and edge-case handling are real value that a startup cannot replicate quickly. Not knowing what you don’t need yet is itself a reason to go enterprise.

• Switching cost is the key variable in startup vs. enterprise: A technical team can afford to bet on a startup and migrate if needed. A company with 50 non-technical field reps faces a training and disruption cost that can dwarf any savings from a cheaper tool.

• Personal software is an underrated third option: Giving individuals the ability to build lightweight local tools for their own workflows, without IT involvement or enterprise rollout, can produce productivity gains that no off-the-shelf purchase delivers.

Notable mentions and links

• Snowflake Summit is the annual conference for Snowflake, one of the leading data warehousing and analytics platforms, and it serves as the physical backdrop for the episode’s opening observation about the density and confusion of the modern software vendor landscape.

• Snowflake is the data platform John is attending the summit for, notable for having had the largest tech IPO in history at the time of its listing.

• The build vs. buy decision is the central framework of the episode, a timeless question about whether a company should develop software internally or purchase it from a vendor, which AI has made newly complex by dramatically lowering the cost of building.

• Total cost of ownership (TCO) comes up as the lens for evaluating software cost honestly, going beyond the sticker price to include maintenance, training, migration, and ongoing operations.

• Vercel is Eric’s employer and provides two concrete examples of successful build decisions: an AI customer support agent handling over 80% of tickets, and an outbound lead agent that reduced a 9-10 person SDR team to 1-2 people.

• Salesforce is the CRM giant used throughout the episode as the canonical enterprise software example, and its announcement of a headless architecture strategy is the jumping-off point for the architectural discussion.

• Headless architecture is the design pattern in which the data layer of a software system is separated from its user interface, allowing agents and external applications to interact with the underlying data directly via APIs or command-line interfaces, without going through the product’s own UI.

• Notion is mentioned as an example of an existing tool that has added strong AI functionality, illustrating the “third option” between building from scratch and buying a legacy product.

• Linear is John’s own choice for project management over Jira, selected specifically for its AI and agentic features, and it appears as an example of a startup product worth betting on when you have a clear vision of your requirements.

• Jira is the Atlassian project management tool that Linear and Asana are compared against throughout the episode.

• Slack is mentioned both as the interface through which Vercel’s headless Salesforce integration surfaces to human approvers, and as John’s enterprise-tier choice for team communication over more startup-oriented tools like Discord.

• SOC 2 is the security compliance standard Eric and John recommend asking startups about when evaluating their security posture.

• Claude Desktop is the tool Eric uses to run personal local utilities for team planning and project tracking, as an example of the personal software approach.

• MCP (Model Context Protocol) comes up as one of the interfaces through which agents can connect directly to headless systems, alongside command-line interfaces.

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Eric and John open with the two camps that dominate the AI discourse: doomers and their “permanent underclass” view, where AI displaces workers so fast that a class of people is left permanently behind, and the abundance evangelists, who believe humans will adapt, new jobs will emerge, and creativity will find a way. Neither camp is obviously wrong, but Eric and John argue the near-term evidence is being badly misread.

They work through why fear is understandable from both Silicon Valley insiders, who’ve seen AI’s power firsthand in the lab bubble, and Main Street workers, who are navigating FOMO without context. Eric notes that his own hiring filter has shrunk to 15-20% of the traditional candidate pool, which sounds alarming until you notice that software engineering job openings are actually up. Lenny Rachitsky’s job reports serve as the counterweight: the apocalypse hasn’t arrived, and there are structural reasons it won’t arrive as quickly as predicted, including the friction of IPO-level scrutiny on OpenAI and Anthropic, and the requirement for layered platform stability before real-world AI adoption can compound.

The episode closes with the question of who is right about human nature. John sides with humans: people are inherently creative and designed to work, and will find new forms of it. Eric reaches for literature, noting that science fiction from H.G. Wells to C.S. Lewis to The Iron Giant has always celebrated the human dimensions of machines, not their power to subjugate. The permanent underclass view, he argues, has a fundamentally wrong model of what humans are.

Key takeaways

• Fear of AI job displacement is founded but misapplied: Silicon Valley insiders have seen genuine power, and their alarm is not irrational. But the near-term economic data, including job openings in software and product, runs counter to apocalyptic predictions.

• The lab bubble distorts the signal: The people sounding the loudest alarms work in environments that are far removed from most of the working world. That doesn’t make them wrong, but it means their timeline and scale of impact are inflated by their context.

• Structural drag will slow adoption faster than the doomsayers expect: IPO-bound companies face scrutiny that rewards stability over speed. Layered innovation on top of AI APIs requires that the underlying platforms stop changing every few months. Both forces will slow the pace of disruption.

• Crypto is the calibration case: Blockchain was genuinely transformative technology, but the specific prediction that it would revolutionize banking never came true at the scale or speed that was claimed. The same pressures, not the technology but the friction of real-world adoption, apply to AI.

• Rising job openings contradict the mass displacement story: Lenny Rachitsky’s job reports show software engineering and product roles up, not down, which is the opposite of what the permanent underclass narrative predicted for the near term.

• The abundance view is a bet on human nature, not on technology: John’s position is not that AI won’t change work, it’s that people are inherently creative and designed to work, and will find new forms of both even in worst-case scenarios.

• We love science fiction that sides with the human: From H.G. Wells to C.S. Lewis to The Iron Giant, the stories that tend endure celebrate the machine’s ability to understand human empathy, not its power over us. That pattern is evidence of something durable about how humans relate to technology.

Notable mentions and links

• The “permanent underclass” is the framing Eric introduces to capture the dystopian end of AI discourse: a world where AI and AGI displace enough work that a class of people has no path back into economic participation.

• Lenny’s Newsletter and Podcast, run by Lenny Rachitsky, is cited for its job market reports—particularly the “State of the Product Job Market in Early 2026”—showing software engineering and product openings trending up, serving as a direct empirical counterweight to the mass displacement narrative.

• OpenAI and Anthropic are discussed as companies preparing for IPOs that will bring public-market scrutiny and force a slowdown in how fast they can change their APIs and pricing, adding structural drag to adoption.

• Mark Twain is invoked for his famous line “reports of my death are greatly exaggerated,” which Eric uses as shorthand for the gap between the doomsday narrative and actual economic data.

• Universal basic income comes up briefly as the policy response most commonly proposed in response to the permanent underclass scenario, though Eric and John treat it more as a definitional marker than a serious policy debate.

• Cryptocurrency and blockchain are brought in by John as the calibration example: a genuinely powerful technology whose real-world impact on banking fell far short of the predictions made at peak hype, for many of the same structural reasons that will slow AI.

• C.S. Lewis’s Out of the Silent Planet, the first book in his Space Trilogy, is mentioned when Eric notes that his wife picked up an old copy, and he uses Lewis’s acknowledgment of H.G. Wells in the author’s note to point out that fears about machines and human subjugation are not new ideas.

• H.G. Wells is referenced through Lewis’s acknowledgment of him as a predecessor in science fiction, used to illustrate that this entire category of fear has a long literary history.

• The Iron Giant, the 1999 animated film directed by Brad Bird, is Eric’s closing example: a self-healing, intelligent robot whose story celebrates its capacity to understand human empathy, not its physical or computational power.

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John opens with a hot take that’s on everyone’s mind: is AI as big a deal as everyone says it is? Instead of swapping opinions, Eric proposes a framework: three 2x2 matrices used to evaluate any technology’s real-world impact, then walks through historical examples before applying all three to AI.

Matrix one is breadth versus depth: does a technology affect one area deeply, many areas broadly, or both? Matrix two is rate of improvement versus rate of adoption: how fast does the technology get better, and how quickly can people actually access those improvements? Matrix three is novelty versus precedent: is the technology truly new, and does it feel familiar enough to adopt quickly?

GPS scored high on depth first, then breadth later. The iPhone scored high on precedent and breadth but was barely novel. Most technologies land high on one or two axes but rarely all three.

AI, Eric argues, is high on all three simultaneously and in the first years of its existence, which is historically unusual. The conversation ends with personal examples: a presentation Eric built in two hours that would have taken weeks before, and a best man speech John polished with voice AI coaching he never would have sought otherwise. Their conclusion is quiet but firm: AI will produce an unleashing of human creativity unlike anything we have seen before.

Key takeaways

• Breadth plus depth is the bar for technologies that change everything: a technology that only affects one industry or user deeply rarely reshapes society. The ones that go broad and deep, across industries and users, tend to be the transformative ones.

• Rate of adoption can lag rate of improvement by decades: fiber internet is the clearest example. The technology is unambiguously superior, but capital cost means most people still don’t have it. AI is nearly the opposite: improvements are immediately available to anyone.

• Novelty alone is not enough, and neither is precedent alone: GPS was truly novel and took decades to reach consumers. The iPhone was barely novel but was adopted almost instantly because it wrapped familiar behaviors in a better form. AI is rare in being genuinely high on both axes at once.

• The thing that looks like a better search engine is actually something else entirely: many people are using AI as a smarter Google. That framing is not wrong, but it undersells what the technology is capable of by a wide margin.

• AI’s novelty goes all the way down to hardware: Andrej Karpathy’s observation that GPUs and TPUs are replacing CPUs as the baseline compute layer illustrates that this is not just a software shift. The infrastructure of computing itself is being redesigned around it.

• The most underrated use of AI is learning: amplifying skills you already have gets most of the attention, but using AI to rapidly acquire skills you don’t have is arguably more powerful and less discussed.

• AI enables things people simply would not have done before: John’s use of voice AI to rehearse and refine a best man speech is not productivity. It’s a category of effort that just didn’t happen before the tool existed.

Notable mentions and links

• GPS is used as the primary historical example for the breadth-versus-depth matrix: it started with extremely deep impact in military and industrial applications, then spread broadly to consumers over decades as consumer devices caught up.

• Internal combustion engine is Eric’s example of a technology that scored high on both breadth and depth early: it powered everything from yard tools to cruise ships, and also changed how factories operated.

• Fiber internet is John’s example for the rate-of-improvement versus rate-of-adoption matrix: the technology has been clearly superior for years, but infrastructure capital cost keeps adoption far behind the curve.

• The iPhone is analyzed as a case where novelty was low and precedent was high: internet access, email, and phone calls already existed, but removing the keyboard collapsed adoption barriers and triggered the mobile revolution.

• Andrej Karpathy, co-founder of OpenAI and now at Anthropic, is cited for his observation that AI novelty extends to the hardware layer: GPUs and TPUs are becoming the baseline compute architecture, displacing CPUs from the center of the stack.

• The movie Limitless surfaces again as John’s shorthand for the feeling Eric is describing when he calls AI “almost like a drug” for the curious and discerning person.

• Vercel is mentioned as the context for Eric’s presentation story, where he built a full AI engine optimization talk in two hours the week of the event instead of weeks in advance.