141–150 of 248 posts
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What you are saying is not intuitive. Software engineers are a cost to software companies. With automation the profits would increase so I’m not sure how it can lead to recession.
More middlemen = more revenue/GDP, right?
AI is already so much better than 99% of customer support employees. It also improves brand reputation by actually paying attention to what customers are saying and responding in a timely manner, with expert-level knowledge, unlike typical customer service reps. I've used LLMs to help me fix Windows issues using pretty advanced methods, that MS employees would have just told me to either re-install Windows or send th…
It isn’t empowered to do anything you can’t already do in the UI, so it is useless to me. Perhaps there is a group that isn’t served by legacy ui discovery methods and it’s great for them, but 100% of chat bots I’ve interacted with have damaged brand reputation for me.
The trouble is when they gatekeep you from saying "I know what I'm doing, let me talk to someone"
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> it was not "They are soooo much better at all the things over there!" Though I think we should also disabuse ourselves of the idea that this can't ever be the case. An obvious example that comes to mind is the US' inability to do anything cheaply anymore, like build city infrastructure. Also, once you enumerate the reasons why something is happening somewhere but not in the US, you may have just explained how they…
Hence " had the skills" and " was not ". They are not making claims about the present day, they are talking about why the shift happened in the first place and who brought it about.
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This is no different from the personal computer, and it is to be expected. The initial years of adopting new tech have no net return because it's investment . The money saved is offset by the cost of setting up the new tech. But then once the processes all get integrated and the cost of buying and building all the tech gets paid off, it turns into profit. Also, some companies adopt new tech better than others. Some d…
> This is no different from the personal computer, and it is to be expected. What are you talking about? The return on investment from computers was immediate and extremely identifiable. For crying out loud "computers" are literally named after the people whose work they automated. With Personal Computers the pitch is similarly immediate. It's trivial to point at what labour VisiCalc automated & improved. The gains a…
It is well-documented, and called the "productivity paradox of computers" if you want to look it up. It was identified in 1987, and economic statistics show that personal computing didn't become a net positive for the economy until around 1995-1997.
And like I said, it's very dependent on the individual company. But consider how many businesses bought computers and didn't use them productively. Where it was a net loss because the computers were expensive and the software was expensive and the efficiency gained wasn't worth the cost -- or worse, they weren't a good match and efficiency actually dropped. Think of how many expensive attempted migrations from paper processes to early databases failed completely.
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> AI and robotics nullify any comparative advantages low wage countries had If we project long term, could this mean that countries with the most capital to invest in AI and robotics (like the U.S.) could take back manufacturing dominance from countries with low wages (like China)?
Manufacturing isn’t one uniform block of the economy that is either won or lost. US manufacturers focus on high quality, high precision, and high price orders. China excels at factories that will take small orders and get something shipped. The reason US manufacturers aren’t interested in taking small volume low cost orders is that they have more than enough high margin high quality orders to deal with. Even the smal…
The other factors are: In any sort of manufacturing, the only time you are making money is when the equipment is making product.
If you are stopped for a change over or setup you are losing money. Changing over contains risk of improper setup, where you lose even more money since you produce unusable product.
Where I live, the local machine shops support themselves in two way: 1. Consistent volume work for an established customer. 2. Emergency work for other manufacturing sites: repair or reverse engineering and creating parts to support equipment(fast turn around and high cost)
They are willing to do small batches but lead times will be long since they have to work it into their production schedules.
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This is no different from the personal computer, and it is to be expected. The initial years of adopting new tech have no net return because it's investment . The money saved is offset by the cost of setting up the new tech. But then once the processes all get integrated and the cost of buying and building all the tech gets paid off, it turns into profit. Also, some companies adopt new tech better than others. Some d…
> This is no different from the personal computer, and it is to be expected. What are you talking about? The return on investment from computers was immediate and extremely identifiable. For crying out loud "computers" are literally named after the people whose work they automated. With Personal Computers the pitch is similarly immediate. It's trivial to point at what labour VisiCalc automated & improved. The gains a…
Computing was revolutionary, both at enterprise and personal scale (separately). I would say smartphones were revolutionary. The internet was revolutionary, though it did take a while to get going at scale.
Blockchain was not revolutionary.
I think LLM-based AI is trending towards blockchain, not general purpose computing. In order for it to be revolutionary, it needs to objectively and quantifiably add value to the lives (professionally or personally) of a significant piece of the population. I don't see how that happens with LLMs. They aren't reliable enough and don't seem to have any path towards reasoning or understanding.
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There are many ways to attack this assertion. For example: 1. The stagnation or decline in real wages in the developed world in recent decades; 2. Increasing homelessness as a consequence of the housing affordability crisis; 3. How global poverty has increased in the last century under capitalism. This surprises some because defenders claim the opposite. China is singlehandedly responsible to massive decrease in extr…
Your first and last points are egregiously incorrect. A simple google search will tell you this. Extreme poverty throughout the world has dramatically reduced. In Western Europe it came down from 50% to less than 1% through the 20th century. India brought it down dramatically and is continuing to do it. A simple Wikipedia search can tell you this. Wages has been increasing in china, India as well as USA after account…
Dismissing people with arguments doesn't work either. It doesn’t eliminate the feeling of inequality or change people's perspective about absolute vs relative wealth.
Why? Because the promise used to justify labor - that hard work will be rewarded - was deeply believed. The contradiction becomes visible when the wealthy hold 36,000 times more wealth than the average person[1]. No one can work 36,000 times harder or longer than someone else, so the belief is no longer tenable.
That leaves us with two choices: either acknowledge that "hard work alone" was never the full story, or take real steps to fix inequality. Pointing to poverty reduction in other countries doesn’t resolve this. It simply makes people feel unheard and resentful.
Average billionaire has $7B in wealth. Median individual U.S. wealth $190,000.
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Hence " had the skills" and " was not ". They are not making claims about the present day, they are talking about why the shift happened in the first place and who brought it about.
Good point. When I commented, the sentence I quoted was the final sentence of their comment essentially leaving it more abstract. Though my comment barely interacts with their point anyways.
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"Only" kind of misses the benefit though. I'm very bearish on "AI", but this is an absolutely perfect use case for LLMs. The issue is that if you describe a problem in natural language on any search engine, your results are going to be garbage unless you randomly luckboxed into somebody asking, with near identical verbiage, the question on some Q&A site. That is because search is still mostly stuck in ~2003. But now…
Speaking of which, could we apply vector embeddings to search engines (where crawled pages get indexed by their vector embeddings rather than raw text) and use that for better fuzzy search results even without an LLM in the mix? (Might be a naïve question, I'm at the edge of my understanding)