Token Intelligence

Eric Dodds & John Wessel

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

  1. 3d ago

    Why are most people unimpressed with AI?

    Most people use AI as a basic chatbot. Others use it to automate entire parts of their job or do cutting edge research. Eric and John unpack why the gap is so wide, and what to do on either side. Summary Andrej Karpathy recently argued that the gap in how people experience AI is getting wider, fast. Eric reads Karpathy’s post, and he and John dig into why: cost, access, interfaces that hide what's possible, and how quickly the tools keep changing. Then they get practical. If AI has let you down before, it might be time to try again. Eric shares how rebuilding their podcast editing and organizing a music library with AI went from frustrating to genuinely useful. And if you're already deep into AI, John has a reminder: it's still a tool, and the results are the point. In this episode 00:56: What is the great disparity? 02:47: Karpathy's AI capability funnel 07:41: FOMO and the isolation of advanced AI use 14:34: Why the gap exists: access and cost 19:24: The Alexa problem: interfaces that hide what AI can do 26:35: If AI let you down, try it again 28:08: Rebuilding podcast editing and a music library with AI 34:07: Advice for power users: the results are the point Mentioned Eric reads Andrej Karpathy's post on the widening gap in how people experience AI. Karpathy calls his numbers napkin math. Eric's podcast-editing experiment runs on DaVinci Resolve and Model Context Protocol (MCP). Spotify's Web API rate limits come up in the music-library story.

    Why are most people unimpressed with AI?
  2. 6d ago

    Are agents human?

    Are AI agents human? Why do we even need to ask that question? Eric and John say no, then dig into why it matters and how to decide what work you should keep for yourself. Summary A bus ad in San Francisco recently declared that "agents are humans too." Eric and John start there, and discuss Anthropic's new usage policy prohibiting users from being mean to AI. The most important question is why we need to ask the question about agents being human. The short answer is no, agents aren't human. But AI is now powerful enough that the question changes how we work. Eric and John talk through real examples, from building software prototypes out of client conversations to reading, writing and data analysis, and land on a practical idea: pick the things you won't use AI for, because some of the effort is what shapes you. In this episode 00:52: Are agents human? (Spoiler: no.) 04:19: Anthropic's policy and why AI feels so human 08:27: Sci-fi expectations vs. what AI can actually do 13:38: Building software prototypes from client conversations 15:53: Deciding when (and when not) to use AI 18:45: Ethan Mollick and the work behind good writing 22:19: Why team chemistry still matters 25:24: Don't outsource your thinking Mentioned Eric and John discuss Anthropic's 2026 Usage Policy update and what it says about how people treat AI agents. Ethan Mollick's Co-Existence: The Next Phase of AI comes up in a conversation about which parts of writing you should still do yourself, alongside his earlier book, Co-Intelligence.

    Are agents human?
  3. Oct 17

    Congratulations, AI just made you a player/coach

    AI helps us do more work, and take on new kinds of work, but that also means expectations for managers are changing. Summary A player/coach is a manager that leads a team, managing other people's work, but also does that same type of work themselves, as part of the team. That's not a new management concept, as any founder or owner/operator knows, but there are also a lot of team leaders who haven't done on-the-ground work in a while, and AI is rapidly changing expectations for them. AI gives you the ability to do more work, and completely new types of work, even without prior experience. If you're not careful, AI can create a second job for you. But that also means leaders higher up than you in your company are developing new expectations about output, even if they don't understand the details of what's possible, and what's not, with AI. In this episode, Eric and John define the player/coach role, break down how expectations are changing and the danger that can create for managers who aren't prepared, then give practical advice on how to navigate leadership and individual work when AI is part of your toolset. Key takeaways You're probably a player/coach, even if that isn't explicit: AI creates leverage, and your boss will always want more leverage. You should plan to work like a player coach, even if your job description didn't change. You can (and should) choose where to apply the leverage: The good news is that as a manager, you can choose what type of work you want to do with AI, so choose something that interests you. You have to budget time for learning: AI changes fast, and you not only need to know how to use it yourself, but you need to know how your team is using it (or isn't). That requires time, so make sure to skip those optional meetings and block out intentional learning time. Notable mentions and links Eric and John note that only a few years ago, Cursor's auto-complete feature was the talk of the AI town. Now, cursor can deploy fleets of agents to do autonomous work. Things change fast. Eric uses the process behind Vercel's AI Gateway Production Index as an example of one person using AI to do an immense amount of work that would have previously involved multiple other people and meetings.

    Congratulations, AI just made you a player/coach
  4. Oct 10

    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?
  5. 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?
  6. 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?
  7. 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

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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.