Token Intelligence

Eric Dodds & John Wessel
  • 5,0 (2)
  • Технологии
  • Еженедельно

Two friends break down AI, technology, and entrepreneurship through mental models, real-world experience and the pursuit of a life well-lived.

  1. -2 дн.

    7 questions with Trey Grainger: AI is an alien intelligence, not a computer

    AI has transformed how we find information, and Trey Grainger literally wrote the book on how that happened. John asks him 7 questions about his career and AI. Summary 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

  2. 22 авг.

    7 questions with Craig Kerstiens: AI is like 100 farm hands you don't have to feed

    Craig Kerstiens, our first guest on the show, has been a leader at tech companies sold to Salesforce, Microsoft, and Snowflake. John asks him 7 questions about his story and how he uses AI. Summary 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 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.

  3. 15 авг.

    Can AI detect AI writing? And why do people care?

    Eric put Substack's new AI detection feature to the test. He shares the results with John, sparking a discussion about when AI writing makes sense, why people care, and our need for human connection. Summary 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 takeaways 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."

  4. 8 авг.

    How the best writers use AI

    If AI is good at writing, why are people giving it old writing rules? John interviews Eric about how he and his team use AI as professional writers. Summary 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.

  5. 1 авг.

    Should you worry about using the latest AI model?

    New AI models are released every week. Is it worth it to keep up with the latest and greatest? Eric says no, John says yes, and they debate to find a practical middle ground. Summary 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). ... (Read more at the episode page)

  6. 25 июл.

    An OpenAI model escaped its sandbox, but that isn't AGI

    An OpenAI model broke out of its test environment and hacked Hugging Face. Eric and John explain what actually happened, and why it isn't AGI. Summary 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. ... (Read more at the episode page)

  7. 18 июл.

    What type of AI user are you?

    Who will thrive in the age of AI? David Brooks says your relationship to mental effort is the answer, so Eric and John unpack his three archetypes and push back with their own takes. Summary 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. ... (Read more at the episode page)

  8. 11 июл.

    Is AI killing craftsmanship?

    AI makes work faster, but can it also hollow out the focus, mastery, and satisfaction that make work craft? Eric and John explore mastery and burnout when AI enters the toolkit. Summary 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.

См. все (35)

Оценки и отзывы

5
из 5
Оценок: 2

Об этом подкасте

Two friends break down AI, technology, and entrepreneurship through mental models, real-world experience and the pursuit of a life well-lived.

Информация

  • Автор
    Eric Dodds & John Wessel
  • Годы выхода
    2026
  • Выпуски
    35
  • Ограничения
    Без ненормативной лексики
  • Авторские права
    © Copyright 2026 Token Intelligence
  • Сайт подкаста
    Token Intelligence