Anthony: "Operational excellence" is a defensible moat. As more code is AI written, quality inevitably goes down. Most people using one-off apps are going to get burned, repeatedly, and they will try to offload that to someone whose only job is This Can't Blow Up.
@patio11
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Vibecoding Clashes With Team Collaboration in Software Development
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Anthony: Software which has collaboration within a single user (e.g. multiplayer mode in a company) is intrinsically resistant to "vibecoding" one's own solution on one's own desk. Me: Everyone loves vibecoding; nobody likes taking a meeting to synchronize vibecoders.
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AI Will Crater the Cost of SaaS Feature Development
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Anthony: We once considered feature development to be a core part of running a SaaS business. Required both some amount of taste and relatively large time/money due to engineers. Cost of features will *crater.*
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SaaS Founders Strategies for Thriving in the LLM AI Era
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Next up at MicroConf: @aeden of DNSimple (I use them for DNS and love them) on how SaaS founders can thrive in the LLM age of AI-generated apps.
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LLMs and Small SaaS Businesses as Early AI Revolution Winners
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My personal interest this year is the interaction of LLMs and small SaaS businesses. I suspect they (we?) are early big winners of the industrial revolution we’re presently going through.
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Using an LLM for real-world information retrieval and task assistance
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So many little wins, too. The insurance company asks for six fields about my primary care physician. Me to LLM: [uncommon name], Chicago, [institutional affiliation]. Google please.
LLM: Do you go see her at Main Street or Example Street?
Me: Example Street.
LLM: *gets it done* -
AI models performing real-world professional tasks
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Also note that ex-chatbot these things can do Real Work (TM) which doesn't look like systems engineering and you can check that Real Work (TM) in the typical ways that one checks Real Work (TM). They can compile a balance sheet from source documents.
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Expert adoption and risk management of LLMs in professional domains
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I think better calibrated experts use LLMs in e.g. adjacencies to their professional domain fairly pervasively these days, accepting some risk of a hallucination based on e.g. cost of checking evidentiary trail, how important that output is and where it is exposed, etc.
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The challenge of verifying LLM output accuracy
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Even accounting for hallucinations, there is substantial overlap between what LLMs are *manifestly* capable of doing and what a given user is *not conveniently capable of checking*, including when a relatively sophisticated user would say "I mean just check source URLs."
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Reflecting on the challenges of LLM alignment through analogies
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"There is substantial overlap between the smartest bears and the least-equipped humans" was once an observation on how it is difficult to engineer a bear-proof garbage receptacle for national parks. I think a spiritually similar thing happens with LLMs.