Token Intelligence

Eric Dodds & John Wessel
  • 5.0 (2)
  • Technology
  • Updated Weekly

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

  1. 5d ago

    Will AI make us lonely?

    AI can feel like a companion while making work more solitary. Eric and John ask how to use it to strengthen human connection. Summary Eric accidentally told the principal at his kids' school, “I'm actually gonna go sit in an office alone and talk to AI all day.” That's not the most inspiring description of a workday, but it's accurate, and that should give us pause. AI seems to follow the same shape as social media, with a grand promise that it doesn't always deliver on. Social media was supposed to increase our connection with each other, yet many of us feel more isolated. AI could follow the same path, and potentially be worse. The current user experience of AI is deeply individual, and it can give you a false sense of connection because it is conversational, but that could end up making us more isolated, not less. AI has become a core part of our modern technological toolset, but we need to be intentional in how we use it and how it shapes our thinking, so that we increase our capacity for human connection. Key takeaways Mind the gap between man and machine: AI can seem like a human, but it isn't. As we use it, we need to be careful about how the medium subtly shapes the way we view the technology and frame our relationship with it. Efficiency isn't the ultimate outcome: AI can often mean we don't need to collaborate as much as we did before, but that can mean we lose out on the best form of collaboration available, which is with other humans. Make human relationships the goal: How can you use AI to make your human relationships deeper, not just more efficient? Notable mentions and links John shares statistics from a Pew Research Center study about U.S. adults on TikTok. About half have never posted a video, and the top 25% by posting volume produced 98% of the publicly accessible videos from the users studied. Eric and John note that isolation isn't a new problem, citing Robert Putnam's classic book, Bowling Alone, on the decline of social connection before social media. John also cites CDC research about adult loneliness. In its 2022 survey across 26 states, 32.1% of adults reported feeling lonely always, usually, or sometimes.

    Will AI make us lonely?

  2. Oct 3

    If AI is doing our work, why aren't we working less?

    We're outsourcing major parts of our job to AI, but we're working more hours than we were before. Is the problem AI, or us? 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.

    If AI is doing our work, why aren't we working less?

  3. Sep 26

    Why are AI agents talking to each other?

    The idea of AI agents talking to each other can feel dystopian, but there are real, useful reasons for it. Just make sure you don't let it replace human communication. Summary 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.

    Why are AI agents talking to each other?

  4. Sep 19

    You need to have an AI epiphany

    An AI epiphany will convince you of the depth and breadth of its power as a technology, but you need to be careful about how that realization shapes your relationship with it. 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 & 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.

    You need to have an AI epiphany

  5. Sep 12

    You don't actually want an AI employee

    "AI employee" is one of the easiest metaphors to reach for to describe agents, but unchecked, it's dangerous, and both over and underestimates what agents are capable of. Summary 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 anthropomorphize 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.

    You don't actually want an AI employee

  6. Sep 5

    The subconscious impact of using AI all day

    If you use AI all day, it will shape the way you work, and think about work, for better or worse. This is an old problem, but staying sharp requires new strategies. Summary 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. Over-reliance 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."

    The subconscious impact of using AI all day

  7. Aug 29

    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

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

  8. Aug 22

    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.

    7 questions with Craig Kerstiens: AI is like 100 farm hands you don't have to feed
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5
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About

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

Information

  • Creator
    Eric Dodds & John Wessel
  • Years Active
    2026
  • Episodes
    41
  • Rating
    Clean
  • Copyright
    © Copyright 2026 Token Intelligence
  • Show Website
    Token Intelligence