CHAPTERS
Why MVP advice must change in the AI coding era
Michael and Dalton frame the core shift: building features is no longer the primary constraint, so the classic MVP mantra (“build the smallest valuable thing because building is expensive”) needs updating. The new environment makes it easy to build too much too soon, which can actively harm learning and adoption.
AI-driven feature creep arrives in days, not months
Dalton shares a recent launch experience where AI-enabled speed led to an overbuilt MVP. The surprising new work wasn’t building—it was deleting and simplifying to regain clarity.
Founders must become editors: cut scope to create value
They argue the key skill has shifted from implementation to editing—choosing what not to build. Broad surface-area products created by “vibe coding” often produce confusion rather than value.
The biggest trap: building for others without user reality checks
Michael explains how infinite build capacity is especially dangerous when the founder isn’t the user. Without tight feedback loops, it’s easy to rationalize imagined needs and postpone real user conversations.
Don’t implement the user’s feature list—dig for the real problem
They warn that AI makes it trivially easy to implement every requested feature, but that’s a path to a “tar pit.” Instead, founders must interpret requests and uncover underlying pain points and priorities.
User-pull, not tool-push: expand only when demand forces it
Dalton emphasizes adding features should be a response to strong user pull, not a reflection of what AI can generate. The goal is to avoid complexity until it’s clearly justified by real usage and retention.
Talking to users vs spamming them: AI amplifies the wrong behavior
They reaffirm the importance of talking to users, but note that AI also makes spam outreach and lead-buying easier than ever. High-volume outreach can create misleading signals and low-quality learning.
Fewer, deeper conversations create real insight and word-of-mouth
Michael argues startups aren’t just “ask users, build what they say.” Customers often don’t fully know what will make them successful, so founders need intimate understanding to actually move the needle—leading to authentic referrals.
Case study: 12 Zoom calls and the counterintuitive power of less
Dalton recounts conducting a dozen high-quality calls with potential users. The product was initially too complex to understand; removing most functionality made it immediately actionable and drove instant signups.
GarageBand metaphor: democratized creation doesn’t increase demand
Dalton compares AI coding to music production tools: it’s easier than ever to create “radio-quality” output, but demand and attention don’t scale. Most output becomes “slop,” while winners remain rare and highly skilled.
Where AI may help most: empowered insiders vs breakout startups
They note AI tools can be exceptionally powerful for people with clear goals inside companies—boosting productivity without needing to find external demand. Building internal tools/content has a larger addressable base than producing “blockbusters.”
Build in public, but don’t publish AI-generated slop
Dalton updates the “build in public” advice: AI makes it easy to flood LinkedIn/X with generic content, which blends into the noise. Distinctive, expertise-driven writing builds real trust and audience.
Avoid fake momentum: positive-looking loops can hide negative churn
Michael describes how AI-enabled spam can create the appearance of traction—more outreach, more signups—while the underlying product fails and users churn. This creates an opportunity for disciplined teams to differentiate.
The new MVP north star: clarity, focus, and ‘two features that work’
They close by arguing that in an AI world, the winning strategy is selective simplicity. A small number of features that clearly improve a user’s life beats a sprawling product that initially impresses but ultimately confuses.
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