71–80 of 121 posts
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That makes the fear worse, not better. It's clear that fake AI enthusiasm is a litmus test for how much shit you'll eat in order to toe the company line so that you can avoid layoffs in the worst jobs market since the GFC.
* toe not tow
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No developer job has yet been lost to AI. We are in a huge recession, and companies like to have an excuse to fire people that can be spun as a positive.
At my company at least four developer jobs were lost because of AI tooling, but keep spouting off about the entire economy at once.
How big is $344B? Apparently the total market capitalisation of the US stock market is $62.8 trillion. Shiller's CAPE ratio for the S & P index is currently about 38 -- CAPE is defined as current price / (earnings, averaged over the trailing 10 years) That suggests that over the last 10 years, the average earnings of the US stock market is about $1.7 trillion annually. So $344B of spending is about 1/5 of the average…
These days am looking at managing my own portfolio. I go off expected returns(using gdp as a component) plus dividend and adjust it for risk to compute which country/region has good expected returns. This i adjust a couple of times a year. If one would assume it's nearly all a bubble, How would you correct earnings for the US? I am interested in applying it to any investment that tracks AI heavy companies in the US.
One approach I've seen a few folks do is to fit a regression model of annualized real stock market returns over the next 10 years as some function of CAPE or 1/CAPE or log(CAPE).
It doesn't give a very good fit on training data, R^2 in the range of 0.2-0.3, i.e. it cannot "explain" most of the variation in 10 year returns.
CAPE based regression models like that have said the US stock market has been overpriced for the last decade! But investors in the US stock market have done pretty well over that period, with really good returns. Maybe these models are accurate but we've just gotten lucky? Maybe these models aren't very good. Hard to tell.
Elm capital publish estimates of expected returns of a few asset classes quarterly: https://elmwealth.com/capital-market-assumptions/
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$100k/year is literally nothing. Think of it as maybe $10k/employee, figuring a conservative 10% boost in productivity against a lowball $100k/year fully burdened salary+benefits. For a company with 10,000 employees that’s $100m/year.
That's literally not how the word "literally" works.
What's the path to recouping that money? Even if every major company in the US spends $100,000 a year on subscriptions and every household spends $20/month, it still doesn't seem like enough return on investment when you factor in inference costs and all the other overhead. New medical discoveries, maybe? I saw OpenAI's announcement about gpt-bio and iPSCs which was pretty amazing, but there's a very long gap between…
Wasn't the plan AGI, not ROI on offering services based on current gen AI models. AGI was the winner takes all holy grail, so all this money was just buying lottery tickets in hopes of striking AGI first. At least that how I remember it, but AGI dreams may have been hampered by lack of exponential improvement in last year.
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Wasn't the plan AGI, not ROI on offering services based on current gen AI models. AGI was the winner takes all holy grail, so all this money was just buying lottery tickets in hopes of striking AGI first. At least that how I remember it, but AGI dreams may have been hampered by lack of exponential improvement in last year.
I’m sure somebody believed that? But I never met them.
“Somebody” like… Sam Altman? Because he said that’s what he actually believes.
https://www.startupbell.net/post/sam-altman-told-investors-b...
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I think it's easy to forget how much low-hanging-fruit there still is, in terms of taking full advantage of this technology. People are still figuring out very basic integrations, and even now, at this early stage, the things I can do with LLMs are pretty incredible. For example, I was able to set Cursor up on finally dragging an old codebase out of the dark ages. It then built new features that I've long wanted. It…
"I think it's easy to forget how much low-hanging-fruit there still " Such as?
Just look at what Cursor (and similar) have done in terms of the tooling for LLMs. There's still tons of progress to be made there, but similar tooling can happen across a variety of industries and categories.
For example, I run a database of information that needs constant updating. I set up automated fact checking (with a human looped in), that enables nearly live updates, which would be incredibly expensive without an LLM. There are so many projects, big and small, just like that one, that are being created right now. The low hanging fruit is extremely abundant, for those who are able and willing to find it.
Most of us believed that crypto currencies were trash. Look how valuable its now . LLMs are a million times better than Crypto currencies.
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If LLMs could double the efficiency of white collar workers, major companies would be asked for far more than $100,000 a year. If could cut their expensive workforce in half and then paid even 25% of their savings it could easily generate enough revenue to make that valuation look cheap.
Ok but then another AI company would just offer the same thing at a lower cost.
This technology demos incredibly well and you can just see how everyone gets giddy with excitement around using it. I watch my colleagues and executives proud to show what they could do or make endless jokes about it. It reminds me of when people first got their phones and couldn't stop showing everyone how cool they were. This leads to an over rotation in the perceived value.. the value is significant just as the mo…
> how in anonymous forums there's a lot more people pointing out that they think this is hype whereas when we wear our professional hats many of us join in Different speeches for different audiences. On HN, for all its faults, people don't need to be told that yes, SOTA LLM can somewhat help you with code, parsing documentation, etc. A lot of people in the "real world" are still grossly underestimating this technolog…
Did you mean "overestimating"? "somewhat help" is putting it strongly, IMO.