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Joined 3 years ago
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Cake day: June 14th, 2023

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  • Trying to sell consumers on “scaling solves everything” is going to be a hard sell.

    If we look at general purpose computation, which had decades of actual scaling-solves-everything growth, you had two influences that made the message resonate with customers:

    • Clear existing applications where more power made the experience straightforward better. Your spreadsheet took an hour to recalculate at 8MHz and 20 minutes at 25MHz. A lot of the “bigger model” stuff is plateauing with marginal or spotty gains. If I feed another 5 Internets of data to ChatGPT, will that summarized email be that much better?

    • New applications that could be demoed on specialised low capacity hardware and scaled down to consumers as more power became available. Think of early CGI on hardware costing tens of millions, and now you can run Blender on a $149 laptop. Since most commercial AI plays are hosted services, there’s not much opportunity to tease that way anymore.


  • The difference was that Amazon knew how to make a profit, but was reinvesting into infrastructure plays and bigger fish.

    If they had to, they could have been a modestly profitable bookshop in 2002. AWS and monster logistics might not have developed to put them in the 13-digit club though.

    Does any AI-centric play have that fundamental fallback? The services that seem to be most effective at direct monetization, the coding tools, are typically running at huge losses. If they raised costs to cover, precious few firms will pay basically the salary of a senior dev for an emulation of an enthusiastic junior dev with an affinity for footguns.

    The less enterprise-focused products-- parasocial toys, image and video gen, will likely try to dip into consumer subs and advertising, but can that generate the cash volumes these platforms demand?




  • It smells more like Facebook than Steam to me. they can print money for now because they have established scale and customer base, but it feels a bit slimy to where it might not be that appealing to new users. Dating services in general have a bad vibe-- bot problems, low quality matches, dark patterns, so authenticity is a big selling point, something AI drives a huge stake into.

    I’d expect that thr gay community, after decades of being a target for abuse, tends to be a bit more sensitive of red flags and looking for truly safe spaces. The Facebook comparison breaks down there, as it has 700 million Aunt Martha users whose most politically sensitive post is in defence of Miracle Whip on salads.







  • I’m surprised there isn’t more of a crowdsourced solution-- community maintained block/allow lists and pluggable tools.

    Part of the reason filters suck right now is that they’re sold to turboprudes and people pushing compliance solutions that will placate litigious turboprudes. So you get blocking all of Wikipedia and .edu/.gov because three pages have an anatomical diagram of a breast. The kids are frustrated, normal parents have to keep unblocking legit stuff, and nobody wins.

    If you could pick from easily managed lists sponsored by groups you personally trusted, with responsive appeals systems, people might be more willing to use them.

    The ad-blocker ecosystem has a lot of precedent for how to work this stuff.



  • IMO, the real use case for PayPal was really on the seller side.

    When it was 2002 and you weren’t a major business but just wanted to sell three old CDs on eBay or offer dog haberdashery online, it was by far the simplest way to accept a credit-card funded transaction.

    We’re still not a lot better there in 2025. Even with more modern platforms, you can’t really get from zero to accepting cards directly in 15 minutes.