Which is funny because US industrial investment was on a tear pre-tariff as companies near/on-shored at historic rates. Not only were tariff's not needed, they've effectively shut down their intended goal.
I hate the fact that CI peaked with Jenkins. I hate Jenkins, I hate Groovy, but for every company I've worked for there's been a 6-year-uptime Jenkins instance casually holding up the entire company.
So Microsoft's definition of winning is being the host for AI inference products/services. Startups make useful AI products, MSFT collects tax from them and build ever more data centers.
I haven't thought too critically yet about Meta's strategy here, but I'd like to give it a shot now:
* The release/leak of Llama earlier this year shifted the battleground. Open source junkies took it and started optimizing to a point AI researchers thought impossible. (Or were unincentivized to try)
* That optimization push can be seen as an end-run on a Meta competitor being the ultimate tax authority. Just like getting DOOM to run on a calculator, someone will do the same with LLM inference.
Is Meta's hope here that the open source community will fight their FAANG competitors as some kind of proxy?
I can't see the open source community ever trusting Meta, the FOSS crowd knows how to hold a grudge and Meta is antithetical to their core ideals. They'll still use the stuff Meta releases though.
I just don't see a clear path to:
* How Meta AI strategy makes money for Meta
* How Meta AI strategy funnels devs/customers into its Meta-verse
1. Capital cost of AI only feasible by FAANG level players.
2. For Microsoft et. al., "winning" means being the defacto host for AI products- own the marketplace AI services are run on.
3. Humans are only going to provide monthly recurring revenue to products that provide value.
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Jippity is not my friend, it's a tool I use to do knowledge work faster. Google Photos isn't trying to trick me, it's providing a magic eraser so I keep buying Pixel phones.
High inference cost means MSFT charges a high tax through Azure.
That high cost means services running AI inference are going to require a ton of revenue in a highly competitive market.
Value-add services will outcompete scams/low-value services.
And we're seeing the result in real-time. Stupid shit doers have been replaced with hopefully-less-stupid-shit-doers.
It's a real shame too, because this is a clear loss for the AI Alignment crowd.
I'm on the fence about the whole alignment thing, but at least there is a strong moral compass in the field- especially compared to something like crypto.
I feel we hold up single-observability-solution as the Holy Grail, and I can see the argument for it- one place to understand the health of your services.
But I've also been in terrible vendor lock-in situations, being bent over the barrel because switching to a better solution is so damn expensive.
At least now with OTel you have an open standard that allows you to switch easier, but even then I'd rather have 2 solutions that meet my exact observability requirements than a single solution that does everything OKish.
If I understand correctly, the meat of the argument is "that is a system for every (∀) task, there exists (∃) a setting that gives the correct answer for that one task."
My understanding of this (correct me if I'm wrong) is that the scam is convincing users that GPT-X can do anything with say, the correct prompts.
This argument misses the mark for me. It's not that it solves all the problems, it's that the problems it does solve is economically impactful. Significantly economically impactful in some cases- obvious examples of call centers and first-line customer support.
Peter Zeihan's take is that urbanization leads to less children because there's less space, you don't need the free labor kids provides on the farm, and children are very expensive in the city.
This is coupled with the speed of urbanization for countries that industrialized after the second world war- the later you industrialize, the faster that industrialization happens, the more stark the transition to a childless economy.
As mentioned in the article, there is a demographic boon for that industrialized generation. Less money needed for schools, etc, more time your prime working age adults can contribute to the economy.
Except all those countries industrialized around the same generation. That generation is aging out of the workforce and there's nothing to replace them.
Zeihan posits this leads to demographic collapse, and that these countries just simply "go away" because there isn't enough children to keep the country functioning. I'm not sure how much I believe that, but I do know that nobody has a clue how to fix it. Japan has been front and center for this problem and still haven't found a way to reverse the trend.
Now Trump second round fixes it, but expires in next (presumably) Democrat administration.