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8 tactics from Eric Seufert
Why App Economy Disruption Won't Happen As Fast As You Think
Watch the full episode“as just you get more and more content being published it gets harder to stand out I mean that's just like a pretty fundamental premise I mean I don't think anybody would disagree with that right.”
Distribution has always been the bottleneck — easier building makes it harder to stand out, not easier
Seufert's core argument: every historical wave of easier publishing (blogging platforms, Unity for games, the App Store itself) has made discovery harder proportionally. AI-assisted vibe coding is inflationary for the app ecosystem — more supply competing for the same user attention, meaning distribution becomes even more valuable, not less. The winners are the ones who solve distribution, regardless of how the code gets written.
“name one successful company that where their product was built by lovable name a single one now maybe there's a bunch of people using their own recipe app that they built with Lovable can you name a single company.”
Vibe coding produces MVPs, not scalable businesses — nobody has named a single company built on Lovable
Seufert challenges the vibe coding hype with a simple empirical test: name a successful app company whose product was built entirely with AI coding tools. The result is silence. The breakouts still come from people with product intuition, code experience, and figured-out distribution — the same ingredients that always mattered. The tools accelerate, but they don't replace the underlying judgment.
“you know coding has never really been the core limitation of getting an app done right it's the specialization and it's managing the edge cases and it's having gotten user feedback I mean if you're just vibe coding something from scratch it's not going to be very good.”
Coding was never the real bottleneck — user feedback, edge cases, and distribution are what apps are built from
Seufert reframes the vibe coding debate: the hardest parts of app development were never writing code — they were gathering user feedback, managing edge cases, and maintaining a coherent scalable codebase. Successful apps have survived because they incorporated years of iteration signals. An app built purely from founder intuition in a weekend, no matter the tool, lacks that accumulated knowledge.
“you can use AI to scan the app store every day look for copycat apps we've built stuff at Fabulous to do this we look at the app store every day we're looking at stuff that's rising virally we're scanning it we're looking at the ads that they run and so we have defense mechanisms for this.”
Use AI to scan the App Store daily for copycats — the defensive use case is as powerful as the offensive one
Seufert flips the copycat conversation: AI is a defensive weapon, not just an offensive one. At Fabulous, he built automated App Store scanning to detect rising copies and analyze their ads in real time. The lesson from years of creative copying is that it accelerates the need for a repeatable creative production process — companies that continuously generate winning creatives pull away while copycats eat crumbs.
“the AI takes over everything bulls are underestimating the productization that takes place that all the people at Straa who've been building this product over years and have that knowledge and have new features they're continuing to add on in a weekly monthly cadence you just you don't get that.”
AI replacing apps requires productizing the use case — incumbents have a years-long runway to do it first
Seufert argues that productization is the underestimated moat against AI replacement. Running coaching via ChatGPT is technically possible, but it lacks the memory, training log, social layer, and weekly cadence of new features that Strava delivers. Replicating all of that in an LLM interface would require years of focused product work — giving incumbents a durable runway to integrate AI into their own products first.
“everything's an ad network on steroids right so now you've got everything running ads well okay well who's buying these ads all that companies that didn't exist before but can via the sort of miracle of AI you know launch a product and they've got to get distribution they've got to get eyeballs on their stuff.”
Race-to-zero software creates an ad economy on steroids — distribution winners capture everything
Seufert maps the macro economics of an AI-abundant software world: if building costs collapse, pricing follows, pushing more apps to ad monetization. But new AI-born apps also need distribution, so they become ad buyers themselves — creating demand that keeps CPMs from collapsing. The net result is a turbocharged ad economy where apps with strong owned distribution (brand, community, ASO) are insulated while new entrants fund everyone else's growth.
“all these frameworks for building apps very quickly have existed for a really long time cotland's existed for a long time unity 3D for publishing games has existed for a long time and you never like fully took the bar down to zero and even where you made it very very easy you invite the hobbyist class in who was just not really interested in trying to build a business out of it so you'll see disruptive change but it's going to happen a lot slower than people think.”
Disruptive change is real — but it will happen 5–10x slower than predictions from deep inside AI Twitter
Seufert closes with historical perspective: every 'this changes everything' tool — Codecanyon templates, Unity, early no-code builders — brought a hobbyist wave that didn't translate into commercially successful businesses. AI coding tools will follow the same curve. The change is real but slow, giving incumbent app businesses time to integrate AI before disruption materializes meaningfully.
“when this comes to fruition it will at some point is like yeah maybe the category winners just run away with everything because I'm not going to say hey Siri open up one of my ride sharing apps and book me a ride to the movies I'm going to say open up Uber.”
AI agents favor category leaders — users say the brand name, not a generic query
Seufert's counterintuitive take on AI agents: if voice or agent-driven interfaces become common, they'll dramatically favor category leaders because users specify brand names, not categories. 'Book me an Uber' routes to Uber; a generic query routes to whoever wins the platform's default. Dominant apps may actually benefit more than they suffer from agent interfaces — the risk falls disproportionately on second-tier competitors without strong brand recall.