You're probably paying too much for AI

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)

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.

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)

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.

Notion won't build HubSpot, their users will

Eric flips his own thesis: Notion doesn't need to out-build HubSpot, it just needs to become the platform where everyone else does. SUMMARY Eric returns to his controversial take that Notion could threaten HubSpot, and after a new product development, expands it into something bigger. With the launch of Notion's custom agents and Notion Workers (running on Vercel Sandbox), Notion isn't racing to build CRM, marketing automation, or customer support itself. It's becoming the platform where its users, template creators, and developers build those tools on top of it. Along the way, John confesses that Notion stresses him out. He can't find what he creates, and he's migrated his own workflow into Git repositories and Granola-synced markdown files. That tension, approachable form factor vs. power-user control, frames the real debate: whether Notion's AI finally solves the "can't find anything" problem at scale, or whether the best survival strategy for the AI hurricane is still plain text files. They land by predicting that Notion's real play isn't replacing HubSpot feature-for-feature, it's turning the workspace into a business operating system, then letting a marketplace of agents, templates, and Workers fill in everything from CRM to eventually ERP. KEY TAKEAWAYS The platform beats the product: Notion's biggest advantage isn't shipping a CRM, it's giving users the primitives to build one themselves. Workers change the ceiling: once arbitrary code runs inside agents, the addressable surface area expands from "docs and databases" to "any workflow between any two systems." Form factor is the moat: Notion's approachable UI plus agents that clean up messy structure could finally make the "find anything" problem a solved one at scale. Git is the power-user escape hatch: for technical teams, plain text in version control remains the most durable substrate because AI reads and writes it natively. Integration quality is the real differentiator: deep, sanctioned partnerships with tools like Slack are what make agent workflows feel magical instead of brittle. Brilliant strategy beats brute force: rather than out-building HubSpot feature by feature, Notion is positioning to become the layer HubSpot alternatives get built on. NOTABLE MENTIONS AND LINKS Eric's original blog post framed Notion as HubSpot's biggest threat because AI changes competitive dynamics, letting a document tool expand into CRM, marketing, and support. Notion Calendar, built from the Cron acquisition, adds the time layer to the emerging business operating system. Notion Mail extends the workspace into communications, another piece of the HubSpot-style surface area. Notion's template marketplace, where some creators reportedly earn millions, is cited as proof the ecosystem can produce commercial products on top of the platform. Notion's custom agents, positioned as "the AI team that never sleeps," are framed as a more connected, integration-native successor to OpenAI's GPTs. Notion Workers let developers run arbitrary code inside agent flows to sync external data, hit APIs, and power custom automations. Vercel Sandbox, the compute primitive underneath Notion Workers, provides the isolated cloud environments needed to safely run third-party code inside enterprise workspaces.