Token Intelligence

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

  1. 1d ago

    How to buy software in the age of AI

    Buying software just got harder: AI changes what you can build, headless architecture changes what you should buy, and personal tools change how individuals work. Eric and Jon navigate all three. Summary 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 ... (Read more at the episode page)

    31 min

  2. Jun 6

    Why the AI apocalypse keeps getting postponed

    Insiders and outsiders worry about the economic impact of AI, and doomers predict a "permanent underclass." But data doesn't back the apocalypse, disruption is slow, and humans are durably creative. Summary 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. ... (Read more at the episode page)

    33 min

  3. May 30

    The three questions that tell you if AI will be disruptive

    Is AI actually a big deal, or just another hype cycle? Eric and John apply a three-matrix framework to cut through the noise and find a clear answer. Summary 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. ... (Read more at the episode page)

    30 min

  4. May 23

    You're probably paying too much for AI

    Most businesses are spending on AI without measuring the return. Eric and John break down the three factors that determine whether AI actually earns its cost. Summary Eric and John open with a question John raised over lunch: is AI actually too expensive for some businesses? It sounds simple, but the answer turns on three distinct problems most companies never separate: whether people actually know how to use AI well, whether you can honestly measure the return, and what you are actually paying versus what you think you are paying. They work through each one in order. On the usage side, they argue that buying licenses and hoping for adoption is a recipe for low ROI. Power users are rare, and the gap between someone who uses AI constantly but ineffectively and someone who uses it to think better about hard problems is enormous. On the ROI side, they draw a sharp line between cost savings (which are measurable) and revenue attribution (which is often fuzzy), and point to prospect research and faster creative iteration as two of the clearest paths to a direct revenue connection. The conversation lands on the cost structure itself. Most businesses default to the most powerful and expensive models for every task, without realizing that cheaper models handle routine work just as well and can cost orders of magnitude less. John's story about using a flagship model to rewrite prompts for a cheaper one captures the whole episode's argument: with the right approach, AI is rarely too expensive. Without it, you are paying full price for a fraction of the value. Key takeaways AI without adoption is just a sunk cost: Buying licenses does not create leverage. Most employees will not use AI well without deliberate training and incentives, and the power users tend to already be power users of other software. Using AI to think is the highest-leverage move: The biggest gap is not between people who use AI and people who don't. It is between people who use it to execute tasks and people who use it to think through bigger, harder problems. ROI has two sides, and cost is the easier one: Measuring hours saved and seat count reductions is straightforward. Attributing revenue gains to AI is harder because process improvements and business discipline often deserve as much credit as the tool itself. Start ROI tracking with use cases that have a clear line to revenue: Prospect research, faster creative iteration, and personalized sales demos are examples where the connection between AI effort and business outcome is concrete enough to measure. The default model is almost always the most expensive one: AI providers set flagship models as the default, and most business users never change them. Simpler tasks like reading a PDF or summarizing text work fine on models that cost a fraction of the price. You can use a smarter model to optimize for a cheaper one: If a task fails on a lower-cost model, asking the expensive model to rewrite the instructions for the cheaper one often solves it, and then you run all future instances on the cheaper version. Businesses on prosumer plans are sitting on a narrow window: Individual and small-business tiers are still heavily subsidized by providers preparing for IPO. That subsidy will shrink as these companies move toward profitability. Notable mentions and links Klarna is the go-to example of high-profile AI cost savings: the company announced its AI assistant had replaced the equivalent of 700 customer service roles, then later reversed course and began rehiring human workers, illustrating how easy it is to overclaim AI ROI. ... (Read more at the episode page)

    33 min

  5. May 17

    The honest scorecard for what AI can actually do

    Eric and John rate five AI use cases on a scale from 1 to 10: deep research, running an autonomous company, creative work, coding, and voice. The results are not what most people expect. Summary Eric and John open with a question they get constantly: what can AI actually do? It sounds simple, but the honest answer swings wildly depending on who's asking and what they're trying to accomplish. Before scoring anything, they work through how AI actually works, using Google Translate as an accessible entry point into why context is everything. Then John runs five use cases and asks Eric to react with a live score before he weighs in. Deep research scores an 8 from both. Running a fully autonomous company scores a 2. Creative work splits them. Coding lands at a 7. And voice, which almost nobody is using to its potential, scores a 9. The episode closes with an observation that cuts against most AI coverage: the most impressive capability on the list is also the most underutilized, and the use case everyone talks about, the autonomous AI company, is the one that works almost nowhere in the real world. Key takeaways AI's power scales with how specific your context is: the Google Translate analogy shows why; a vague prompt draws on everything, a specific one draws on exactly what you need, and the results are dramatically different. Deep research is genuinely an 8 out of 10, but only if you pay: the capability is there, but it requires a paid tier and an intentional mode most people forget to activate. The autonomous company works for one-dimensional content businesses and almost nowhere else: AI handles research-to-publish pipelines remarkably well, but real businesses are multi-dimensional, and context shifts too fast for full automation. AI raises the floor on creative and software work, not just the ceiling: the average quality of design and code will improve because AI lets skilled people iterate through more options faster, even if the best human work remains out of reach. Voice is the most underrated capability on the list: talking to AI while driving, walking, or thinking out loud is a 9 out of 10 experience that most people still haven't tried, and it is likely to become the dominant way people interact with AI. Your plan tier changes what AI can actually do for you: deep research, voice integrations, and enterprise features are meaningfully better at paid and enterprise levels, which means people on free tiers often form impressions based on a limited version of the tool. Notable mentions and links Google Translate opens the episode as Eric's preferred analogy for explaining how AI works: predicting the next word from an enormous dataset, which is accessible, accurate, and extends naturally to explain why context makes results better. The MacBook Neo is Eric's hypothetical research example, illustrating how an AI model issues 30 to 40 web searches, visits each page, reads the content, and returns a cited summary instead of making you do it yourself. ChatGPT and Claude are the two tools Eric and John use daily and reference throughout as the primary benchmarks for each use case scored in the episode. Grok gets a specific mention for releasing a new voice model the week of recording, which John calls out as genuinely good even though GPT remains his preference for voice. WhisperFlow is mentioned as a tool that can bridge some of the voice integration gap by cleaning up spoken input and feeding it directly into an AI model as a prompt. The reddit post about an AI-generated Monet which got millions of views and hundreds of comments critiquing what made it inferior to the original, only to turn out to be an actual Monet, becomes the episode's clearest illustration of how close AI image generation has gotten to professional-grade creative work.

    39 min

  6. May 10

    Can AI actually replace an employee?

    The headlines say AI is replacing workers. Eric and John dig into what's actually working, what isn't, and where the real ceiling is right now. Summary Eric opens with a viral post from David Cramer, founder of Sentry, pushing back on the idea that people are running fleets of AI agents doing real work overnight. John responds from firsthand experience, explaining that his company has run dozens of internal experiments, and the honest answer is that almost none of them are used to do real client work. They map the landscape by use case, from personal productivity tools to team-wide deployments, and find that the team tier is where almost everyone stalls. The tools are developer-focused, the adoption problem is real, and getting AI to work reliably across a group requires far more investment in guardrails and oversight than the demos suggest. The episode ends with guidance on what’s practical today. The most compelling near-term model is not a zero-person company but a "co": a single AI assistant that one person owns, trains over time, and stays responsible for. Key takeaways Impressive demos and production deployments are two different things: most agent experiments stay internal, and the gap between "kind of works" and "works with real clients" is larger than most AI coverage admits. What works at home does not automatically work at work: personal AI tools, team tools, and company-wide deployments each have different friction points, and almost everyone has figured out the personal tier and almost no one has figured out the team tier. AI tools are built by developers, for developers, and it shows: most frameworks default toward building and generating, with not enough support for planning, quality checks, and oversight, which limits what they can reliably do. AI will try to answer even when it shouldn't: agents respond by default even without enough context to be accurate, and building the guardrails to prevent that is harder and more expensive than it looks. Owning a single AI assistant beats managing a fleet: a one-to-one "co" that you prompt carefully, iterate over time, and stay responsible for is more practical and more trustworthy right now than trying to orchestrate autonomous teams of agents. AI helps analysts work faster, but it cannot replace what they know: giving AI access to data and asking it to run queries works well when a skilled human with domain knowledge is in the loop; without that, the answers are unreliable. Notable mentions and links David Cramer's post on X is the episode's opening provocation, in which the founder of Sentry argues that nobody doing serious work is running 20 agents overnight, and that the real benchmark is whether you can reliably ship one production-quality fix at a time. Block, Inc. is the financial services company behind Square and Cash App, and its high-profile layoff of over 4,000 employees in February 2026 became a recurring example in the AI-is-replacing-workers news cycle that frames the episode. OpenClaw is an open-source personal AI assistant that runs on your own hardware, connects to messaging channels like iMessage and Telegram, and can be given broad access to your computer, including, for those who push it furthest, credit cards and prediction markets. Zo Computer is described as a middle ground between OpenClaw and a consumer app: AI running inside a secure cloud computer with built-in limits, more powerful than a chat interface but without the security exposure of a fully local setup. Poke is a consumer-facing personal agent that works entirely through existing messaging apps like iMessage or Telegram, with no separate interface of its own. ... (Read more at the episode page)

    25 min

  7. May 3

    Fences, flagpoles, and the comeback of the generalist

    AI is removing the barrier of specialization, giving generalists the ability to span more domains and solve the most important problems faster. Summary Eric and John unpack a shift many knowledge workers can already feel: AI is changing which kinds of people create the most value. Their frame is the “fence-shaped” generalist, someone with broad range and multiple usable areas of depth, rather than one towering specialty. That kind of operator has always been valuable in startups and zero-to-one work, where bottlenecks move constantly and dependencies kill speed. But they also explore the risk of burning out, topping out, and getting trapped by taking on too many responsibilities. They land on the real shift: AI lets generalists execute across more domains without waiting on specialists, shrinking the gap between seeing the bottleneck and solving it. Key takeaways Breadth matters most when bottlenecks move: the best generalists keep shifting toward the current constraint instead of clinging to yesterday’s valuable work. The trap is taking on too much: range becomes a liability when a generalist spreads effort across many useful tasks instead of the highest-value one. AI deepens adjacent skills: tools now let broad operators reach workable depth in coding, analysis, and research without full specialization. Depth still matters for trust: organizations still reward visible expertise, even if AI lowers how much specialist help is needed to get real work done. Context beats syntax: AI can write SQL or Python, but knowing what to ask, what to filter, and what to trust remains the durable edge. Notable mentions and links T-shaped skills describe broad cross-functional awareness plus deep expertise in one domain, and they give the baseline model Eric and John are reacting against in this episode. X-shaped skills extend the older metaphor toward leadership and people skills, and they come up as an example of how organizations keep inventing new shapes to explain modern work. Zero-to-one projects inside larger companies also favor generalists because they can move quickly with fewer dependencies and get new initiatives off the ground. Regression analysis is the episode’s clearest example of adjacent expertise, because AI now helps non-specialists do work that previously required more dedicated technical support.

    28 min

  8. Apr 25

    Outshining the master is the silent career killer

    Why talented people stall out: going around your boss can break trust long before it creates opportunity, and the consequences simmer under the surface for a long time. Summary Eric and John start with a Reddit post from someone convinced he has been “outshining the master” for years, then reframe the idea in practical workplace terms: not just looking smarter than your boss, but stepping into authority above your level without clear approval. From there they unpack modern versions of the mistake, especially in startups and flat org structures, where skip-level access, cross-functional complaints, and ambitious side channels can feel efficient or principled while quietly breaking trust. They contrast insecure, kingdom-building managers with secure leaders who gladly create exposure for strong people and channel initiative instead of punishing it. The episode ends on blunt career advice: if you crossed the line, own it and repair the relationship; if your boss is blocking you, transfer or leave; and in either case, remember your boss usually sees more of the organization than you do. Key takeaways Define the line correctly: Outshining the master is less about looking talented and more about operating in authority lanes above your level without alignment. Trust is the real issue: The fastest way to look threatening is to make your manager unsure how you will handle information, visibility, and upward communication. Skip-levels are expensive: Going around your boss can feel efficient or principled, but it usually reduces the trust that creates real opportunities later. Great bosses channel initiative: Secure managers align first and then create exposure, which is far better than forcing ambition underground. Pursue craft, not ladder-climbing: Politics are unavoidable, but treating status games as the job will distort your work and your judgment. Bad managers create dead ends: If your boss is kingdom-building and blocking your growth, the realistic answer is usually a team change or an exit. Repair early and stay inside context: If you crossed a line, own it quickly, because your boss usually sees risks, budgets, and political context you do not. Notable mentions and links The 48 Laws of Power is the book that supplies “Never Outshine the Master”, giving the episode its core workplace frame. Circle of competence explains why bosses often see budget, staffing, and political context their reports do not, which makes unauthorized moves riskier than they look. Eric wrote a blog post about “pursuing craft, not politics,” which serves as shorthand for keeping organizational maneuvering in its proper place.

    47 min
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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
    2K
  • Episodes
    24
  • Rating
    Clean
  • Copyright
    © Copyright 2026 Token Intelligence
  • Show Website
    Token Intelligence