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Comment by colejhudson | Hacker News Reader

Suspect that a core reason for Jerry's PoV is that the dynamics of the (zeitgeist-related) software industry do not currently favor small teams. That's counter to the ethos of Silicon Valley and it's guy-in-a-garage mythology.

Jerry would've been 14 around 1980, plus or minus a couple of years, which would've been in the heyday of personal computing. TRS-80s, Apple IIs, the IBM PC, its clones, etc.

Then, and for the next 40-odd years, software was king (read: capital-light). And anybody could write software.

LLMs are much less egalitarian.

Can anyone write an LLM? Yes.

Can anyone train an LLM that customers will want? A few. But most will need a few Ms, maybe even some Bs.

Hence the AppAmaGooSoft benefactors and Jerry's complaint about the status-quo.

Software-like economic dynamics will return, but it'll take a while, and until they do, it'll be much less morally satisfying to be a VC (in software) than it has over the last 40 years.

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Nah, you and the parent comment to you are not quite there.

It really isn’t exciting because the technological shifts have already happened. The “Industrial Revolution” has ended.

There’s no remaining industry that isn’t served by plenty of software and hardware solutions.

The Internet is done, it exists. Smartphones are done, computers are done, cloud computing is done, and that’s about as good as it gets.

AI is just the next tech industry house of cards that followed previous farces that were meant to stir up investment. When gathering data wasn’t profitable enough, big data came along. When big data didn’t lead to enough profitable insights, AI came along. Each new technology promises the moon but they really onmy prodiundly benefit the companies making digital pickaxes.

Now you have every company under sun releasing AI products where they have so little clue what problem they’re supposed to solve that they just call it “[Company Name] AI.”

That’s because the problems are solved. There’s a CRUD app for everything. It’s the end.

This video sums it up pretty well if you ask me: https://youtu.be/pOuBCk8XMC8

As an LLM-skeptic, I still think this is too pessimistic as an outlook for the future. I do think there is a good chance that LLMs as they exist will not change anything nearly as significantly as the hype is predicting. But I think there is scope for the technology, if it continues developing, to significantly help people.

The most interesting and clear AI-based product I can imagine is a true personal assistant, that can autonomously handle tasks like "book me a barber's appointment this week" (while taking into account my work days, my meetings, my other personal appointments, etc) or "I want to meet up with Joe, Susan, and Mary for a nice dinner, book us a table" (and your personal assistant would sync up with their personal assistants, or with themselves directly if they don't have one, to arrange this, and suggest some possible venues and so on). And it would help remind you of important upcoming events, help organize your shopping and so on. And of course, it would do all of this semi-interactively ("The best slot I'm seeing for your barber appointment would be on Tuesday at 3 PM, but you have that call with Andrew, can that be moved?").

All of these things seem relatively doable in principle, with existing technologies, as long as AIs improve on their reasoning skills and someone spends the time and effort to connect a single AI to several core technologies (in this case, email, calendars, web search, maybe some reservation apps, and some camera apps to help recognize various things).

I can understand why this person sees a natural language personal assistant as shopkeeping, though, compared with the invention (for example) of the internet or what it was like around the time personal computers started existing.

This solution seems like the kind of stuff that only Apple, Google, and maybe one or two other companies likd Microsoft will actually have any foothold in.

Nobody else is going to be the central point for enough data to make this kind of AI useful.

Example: Atlassian can’t make a product like this because they only have access to a small piece of your personal data in the first place. They don’t have access to your work calendar, emails, chat, conference calls, etc. Many companies who use Atlassian don’t even want to use them for source control or pipelines despite those products being a part of their suite.

Same deal with companies like Slack: they don’t have access to your email or your ticketing system. Notion doesn’t have access to your emails, conference calls, etc.

So realistically, truly helpful AI is going to be gated behind giants like Google and Microsoft, while the companies fighting for LLM scraps are going to all make their ShitGPT support chat bots.

The ultimate death knell for AI is that it’s such a unique technology that the average CTO can’t even conceptualize how it works and what it can do. I think they didn’t have that problem with previous tech industry hype trains.

It's become less capital-light over time - look at the games industry for example, AAA titles cost as much as a Hollywood movie. It's about a eightfold increase in real dollars per decade - and that's despite having a lot of ready-made game engine tech to build on.

Yes, there's still little in the way of overhead/operating costs or cost of goods sold - once you get a product to market, if you can acquire customers it scales perfectly up to market saturation. But the capital barrier has slowly become huge, and that suppresses innovation.

(Interesting counterpoint - when a new market opens up with low capital requirements, iPhone App Store being a case in point, it saturates rapidly and then becomes a race to zero - much potential innovation gets lost in the noise, or the market deflates so quickly that indie innovators struggle to sustain themselves financially).

I feel like with building computers, the hardest part was building the technology. When it comes to AI, the hardest part seems to be building the surrounding infrastructure: getting hardware to run it, data to train it on, and hardware to store the data. There are some startups working on data collection and compute markets, but until those are polished there's a barrier keeping smaller teams from moving fast and making things.

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