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I find it hard to imagine nvidia's moat not drying up - the hyperscalers already have more cost effective silicon and the AI labs already use a mixture of all the capacity they can get their hands on.

And the big AI labs are starting to roll their own silicon too (ie OpenAI Jalapeño). Nvidia's moat won't last forever.

The strongest moat I see is ownership of training data - an area where Google seems to have a clear edge

Google has the internet data and user information. However the more they wait, the more the internet will fill with AI generated crap. That’s why there is a race to buy books and scan them (destroying them in the process). To get more data that’s not just barfed out AI slop. So they can win and be the ones spewing slop for others to ingest (human and robot alike)

They bill you per output token.


Never heard of work louder, but it sounds like an idea I used to joke with coworkers about, around making a clickly keyboard with an amplifier and speaker to passive-aggressively demonstrate how annoying the clicky keyboards are in a high density office environment.


They won't necessarily map out all the circuits but they will generally test them all with a tester to find wiring problems.


Becoming increasingly convinced that the pattern that will stick is not AI "integrated" into everything but personal agents. As in I will have one personal assistant that helps me in all of my tasks instead of using the little ones bolted on to every product.


Similar - had an HVAC tech out to diagnose mine (some intermittent electrical problem was killing thermostats randomly) and since it was intermittent they couldn't figure it out. I ended up using Gemini to narrow down a list of potential problem components and just replacing them all which fixed the issue.

Kind of a superpower to turn anyone with a bit of tech inclination and problem solving skills into an HVAC tech - not a very good one, but one with enough motivation to get the results you need


Same story, ended up just need to replace a fuse and clean out some filters.

I hate to be a "tech elitist" or whatever, but goddamn it seems like software is one of the most learning intensive careers you can pick. A little dabbling around the house has made me an amateur plumber/HVAC/handyman/gardener/etc. I should have gone into a trade, just for the comfort of having a skillset that doesn't need to be updated every 3 months. I guess my brain plasticity will thank me.


Kinda tired of being inundated with low quality AI slop absolutely everywhere.


Same - 13" macbook screen becomes less functional when you fill it with padding.


State of the art 7 months ago is good enough for a lot of use cases.


That was my thought. At my work, 7 months can be less time than it takes to get a project organized around a high-level plan. It sounds like nothing.


I suppose this is a more forward looking post though. It's about whoever gets to whatever is awaiting us in the future 7 months before the other.

And I guess the idea is is that there is this extreme inflection point in utility somewhere that makes it so getting there first gives you some incredible economic edge.

It might not exist though. Like either utility plateaus and its bubble crash of the century time or it just keeps going up but without any specific point where you can differentiate something.


Feels like that inflection point possibility passed a while ago since these models are starting to plateau in performance.


The GP is talking about recursive self-improvement.

What yes, it's clear by now it's way beyond the capacity of those AIs, and the odds are pretty good it's impossible to a large extent (but some limited version of it may be possible).


What makes you think they plateaued?


Exactly - for an average person's task, how different is AI today vs 7 months ago?


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