The AI Investment Boom

Reading this makes me willing to bet that this capital intensive investment boom will be similar to other enormous capital investment booms in US history, such as the laying of the railroads in the 1800s, the proliferation of car companies in the early 1900s, and the telecom fiber boom in the late 1900s. In all of these cases there was an enormous infrastructure (over) build out, followed by a crash where nearly all the companies in the industry ended up in bankruptcy, but then that original infrastructure build out had huge benefits for the economy and society as that infrastructure was "soaked up" in the subsequent years. E.g. think of all the telecom investment and subsequent bankruptcies in the late 90s/early 00s, but then all that dark fiber that was laid was eventually lit up and allowed for the explosion of high quality multimedia growth (e.g. Netflix and the like).

I think that will happen here. I think your average investor who's currently paying for all these advanced chips, data centers and energy supplies will walk away sorely disappointed, but this investment will yield huge dividends down the road. Heck, I think the energy investment alone will end up accelerating the switch away from fossil fuels, despite AI often being portrayed as a giant climate warming energy hog (which I'm not really disputing, but now that renewables are the cheapest form of energy, I believe this huge, well-funded demand will accelerate the growth of non-carbon energy sources).

I'm sure you are right. At some point, the bubble will crash.

The question remains is when the bubble will crash. We could be in the 1995 equivalent of the dotcom boom and not 1999. If so, we have 4 more years of high growth and even after the crash, the market will still be much bigger in 2029 than in 2024. Cisco was still 4x bigger in 2001 than in 1995.

One thing that is slightly different from past bubbles is that the more compute you have, the smarter and more capable AI.

One gauge I use to determine if we are still at the beginning of the boom is this: Does Slack sell an LLM chatbot solution that is able to give me reliable answers to business/technical decisions made over the last 2 years in chat? We don't have this yet - most likely because it's probably still too expensive to do this much inference with such high context window. We still need a lot more compute and better models.

Because of the above, I'm in the camp that believe we are actually closer to the beginning of the bubble than at the end.

Another thing I would watch closely to see when the bubble might pop is if LLM scaling laws are quickly breaking down and that more compute no longer yields more intelligence in an economical way. If so, I think the bubble would pop. All eyes are on GPT5-class models for signs.

Re: Slack chat:

Glean.com does it for the enterprise I work at: It consumes all of our knowledge sources including Slack, Google docs, wiki, source code and provides answers to complex specific questions in a way that’s downright magical.

I was converted into a believer when I described an issue to it, pointers to a source file in online git repo and it pointed me to another repository that my team did not own that controlled DNS configs that we were not aware about. These configs were the reason our code did not behave as we expected.

This is the main "killer feature" I've personally experienced from GPT things: a much better contextual "search engine-ish" tool for combing through and correlating different internal data sources (slack, wiki, jira, github branches, etc).

AI code assistants have been a net neutral for me (they get enough idioms in C++ slightly incorrect that I have to spend a lot of time just reading the generated code thoroughly), but being able to say "tell me what the timeline for feature X is" and have it comb through a bunch of internal docs / tickets / git commit messages, etc, and give me a coherent answer with links is amazing.

>they get enough idioms in C++ slightly incorrect

this is part of why I stay in python when doing ai-assisted programming; there's so much training information out there for python and I _generally_ don't care about if its slightly off-idiom, its still probably fine.

Yea, I was thumbs-down on ai-assisted programming because when I tested it out, I tried it by adding things to my existing C and C++ projects, and its suggestions were... kind of wild. Then, a few months later I gave it another chance when I was writing some Python and was impressed. Finally, I used it on a new-from-blank-text-file Rust project and was pretty much blown away.

As someone who doesn't generally program, it was pretty good at getting me an init.lua set up for nvim with a bunch of plugins and some functions that would have taken me ages to do by hand. That said...it still took a day or two of working with it and troubleshooting everything, and while it's been reliable so far, I worry that it's not exactly idiomatic. I don't know enough to really say.

What it's really good at is taking my description of something and pointing me in the right direction to do my own research.

(two things that helped me with getting decent code were to describe the problem and desired solution, followed by a "Does that make sense?". This seems to get it to restate the problem itself and produce better solutions. The other thing was to copy the output into a fresh session, ask for a description of what the code does and what improvements could be made)

The downside of this nvim solution is the same downside as both pasting big blobs of ai code into a repo, and, pasting big vim configs you find online into your vimrc: inability to explain the pasted code.

When you need something fast for whatever reason sure, but later when you want to tweak or add something, you'll have to finally sit down and learn basically the whole thing or at least a major part of it to do so anyway. Imo it's better to do that from the start but sometimes that's not always ideal.

When I’ve used AI for writing shell scripts it used a lot of syntax that I couldn’t understand. So then I took the time to ask it to walk me through the parts that I didn’t understand. This took longer than blindly pasting what it generated, but still less time than it would have using search to learn to write my own script. With search, a lot of time is spent guessing the right search term. With chat, assuming it generated a reasonable answer (I know: a big assumption!), my follow-up questions can directly reference aspects of the generated code.