I was also wondering if it will result in Helium shortage and even higher prices the next few years, then/if it will be all over, new fabs might also come online and it will be the opposite - a glut. I believe that follows the historic pattern of boom and bust cycles of chip production.
To put this into perspective, India has built 4000+ miles of new railway lines over the last five years including a Shinkansen style bullet train. That’s more than all the railway lines in the country of Switzerland for about $12 billion. The money being thrown around here is mind blowing.
Indeed! And the returns from such an investment in infrastructure will likely be profound.
I feel AI is a bit different, as in there is a spectrum ranging from “utterly stupid” (early ChatGPT), “very helpful” (kinda now), “SkyNet 2.0” (what good are humans!).
As algorithms and technology improve, AI will be both cheaper and more capable. Companies like Anthrophic wouldn’t be the vaulted celebrities as they are today. At that point, I’m not sure what value much of society can provide…
At one time, blacksmiths had a valued place in general society. Today, not so much…
NGL, I would gladly trade opus 4.8 in exchange for my city fixing the roads. Just imagine if even a quarter of the money being dumped into AI went to something that actually improved peoples lives. Man that would be a sight to behold.
Well, if everyone is out of a job, the government is going to have to do something so I’m not too worried about it. If I had to be an optimist about it, I would say that this might be the perfect catalyst to build out a lot of renewable energy (for data centers) and maybe actually implement UBI.
What type of projects you work on, in particular how rich it is in novelty, non-googlable data points and non-trivial project-specific deviations from industry standards?
> Defining exactly what the product is supposed to do is the hard part, writing code is the easy part.
> There is a massive difference between a spec, which defines what the product should do, and code, which defines exactly how it should do it.
He states: The difficult part is figuring out the details so LLM doesn't save much time.
You state: If LLM is able to correctly assume the details that saves you a lot of time.
Case 1:
Part of the spec describe some basic feature based on a popular framework and industry standards, everything is trivial.
You are right, he is wrong.
Case 2:
Part of the spec describe some niche feature and/or uses some not popular framework and/or require deviation from industry standards and/or cutting edge performance/latency requirements and/or uses a bunch of proprietary non-googlable data.
You are wrong, he is right.
The more senior engineer are the less time they spend on case 1, those are easy, they don't spend much time on it, it is the 2nd which is much more time consuming.
I'm also confused with that as well as the moral of the story as the whole. I get the sentiment but what is the lesson here, leave production capacity keep going as well as a "hiring pipeline" and just stock pile the output forever? Also, given article's take on the current situation of AI assisted coding, it seems to suggest that we need to apply that same logic to other industries too, so just don't let any industry/practice die and keep it alive? I would appreciate some inputs into some sort of actual solution or at least ideas of the solution but that is not present in the article.
Anthropic will kick and scream as those are often distilled from their latest models and is cutting into their margin. Though it is not like their hands are clean neither, it is just a different type of stealing, an approved one :-)
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