In other words, this is a statement on current US interest rates, not AI. The analyst just chose to single out the AI industry because they believe it has no ability to return on investment. But the actual numbers are not AI-specific.
There are many many examples of companies that make little money or are actually losing money, yet their valuations are sky high. In other words, valuations are not in touch with reality. I see a massive bubble in the public markets as well.
As of the time of this comment, it's a whopping 247. There is no possible justification for that kind of valuation. Even if Robotaxi is a huge success, they sell more rides than Uber, Lyft, and traditional taxis services combined, it wouldn't justify it.
When the dominant voices begin to claim, "This time it's different!!" that's usually a sign we're nearing the end.
Look this up. During the dot-com bubble, the popular notion emerged that the business cycle might have ended. "The end of the business cycle!!" Many media outlets pushed the narrative that humanity would experience endless growth thanks to technological advancements.
During the housing bubble, towards the end, multiple media outlets suddenly started repeating the idea that housing prices, in real terms, have never fallen.
When I heard that farcical argument gain steam, I got out of housing immediately—before the bubble burst.
So, for me, that AI-bubble skepticism is still the dominant opinion suggests that this bubble is far from over.
I think we're closer to the Netscape IPO than we are to the .com crash.
Maybe we're at the halfway point. Just one person's opinion.
To be fair, I think it's definitely a bubble, but it's hard to compare something like this.
Fortunately I'm employed but I have limited ideas on how to gain an edge for the next jobsearch, whenever that is.
1. Living in a tech hub like the Bay Area or Seattle. There may be tech hubs in RTP, Austin, Boston, etc but these tend to be inshoring offices and are oftentimes the first on the chopping block when offshoring is considered, because they never built the internal gravity needed to own P/L and roadmap, and those offices that did are few and far between.
2. Concentrate on doing a reputable online MSCS and concentrating on fundamental courses. Coding is commodified, but programming isn't. Just understanding how to glue together Python, C/C++, or whatever language and associated libraries is not enough. Technical complexity is rising, and skills like understanding OS internals, understanding the fundamentals of SGD, or truly understanding how to derive a path tracing algorithm matters.
3. Hyper-specialize in a specific industry. "AI" is broad, just like "Mobile" was broad. What matters is how these platforms are applied to a specific industry subdomain. If I'm a cybersecurity company working on building an AI SOC, I'd rather hire Engineers who understand both core fundamentals of AI and Cybersecurity.
The thing is, this isn't 2000 anymore. Eastern Europe, China, India, ASEAN, LATAM, and other regions of the world have fairly large and robust dev and tech scenes, and async+remote work has been proven out during COVID, thus removing one of the biggest barriers to offshoring.
I think as a mid-career engineer, you have the tools to survive this kind of a change. Any American SWE who graduated in the last 10 years is in a worse position because their universities failed them by watering down curricula to compete with bootcamps, reducing the business justification for building a domestic new grad pipeline aside from a couple top target programs.
Definitely interested to see where my thinking is off here, if anyone can help. This just doesn't seem much like a bubble to me.
Are there tons of uses for 2023 crypto farm hardware?
A trillion dollars of GPUs in various stages of obsolescence and/or shagged-outness in data centres might be useful to the right people but it's a depreciating asset.
Not old enough for the dotCom (36) but my educated guess is that because the internet was new and exciting, companies were prepared to train and take risks.
Training for AI now requires specialist skills which can be syphoned from within an existing organisation or from those with existing skill sets.
Unlike the latter where anyone can pick up a programming language. Not everyone can do quantum algebra mechanics or whatever AI/ML uses.
Plus the cost of running AI is expensive. SME's don't have the resources to hire those for new jobs; AI kit and training. So it's easier to hire from within then outside.
AI is a rich boys toy.
I never got given maths at school.
If we were still in ZIRP things would be insane.
I don't believe this to be the case.
Worst case it's mostly a bolt-on for most companies not a "core" money maker. It will be mostly be the hardware vendors like Nvidia and the AI as platform that will get hit.
By that I mean that the learning we should take from the dot com bubble is that the bubble should not have burst, not that we should not have let it go that high in the first place.
garganzol•57m ago