Comment by bubblelicious | Hacker News Reader

Comment by bubblelicious | Hacker News Reader

Really hard to believe articles like this and even more hard to believe this is the hive mind of hacker news today.

Work for a major research lab. So much headroom, so much left on the table with every project, so many obvious directions to go to tackle major problems. These last 3 years have been chaotic sprints. Transfusion, better compressed latent representations, better curation signals, better synthetic data, more flywheel data, insane progress in these last 3 years that somehow just gets continually denigrated by this community.

There is hype and bullshit and stupid money and annoying influencers and hyperbolic executives, but “it’s a bubble” is absurd to me.

It would be colossally stupid for these companies to not pour the money they are pouring into infrastructure buildouts and R&D. They know it’s going to be a ton of waste, nobody in these articles are surprising anyone. These articles are just not very insightful. Only silver lining to reading the comments and these articles is the hope that all of you are investing optimally for your beliefs.

I agree completely.

I work as a ML researcher in a small startup researching, developing and training large models on a daily basis. I see the improvements done in my field every day in academia and in the industry, and newer models come out constantly that continue to improve the product's performance. It feels as if people who talk about AI being a bubble are not familiar with AI which is not LLMs, and the amazing advancements it already did in drug discovery, ASR, media generation, etc.

If foundation model development stopped right now and chatgpt would not be any better, there would be at least five if not ten years of new technological developments just to build off the models we have trained so far.

Yes, HN discussions of LLMs are quite tiresome. I make indie apps, but it has been getting worse and worse over the years, as the API surfaces and UI variety of iOS and Android have grown.

Claude Code and ChatGPT brought me back to the early 2010s golden age when indies could be a one-man army. Not only code, but also for localizations, marketing. I'm even finally building some infrastructure for QA automation! And tests, lots of tests. Unimaginable for me before because I never had that bandwidth.

Not to mention that they unblock me and have basically fixed a large part of my ADHD issues because I can easily kickstart whatever task or delegate the most numbing routine work to an agent.

Just released a huge update of my language-learning app that I would never dreamed of without LLM assistance (lots of meticulous grammar-related work over many months) and have been getting a stream of great reviews. And all of that for only $100+20 a month – I was paying almost twice as much for Unity3d subscription a decade ago.

All that is fine. The bubble only happens if in your ecstasy you manage to think more of your indie apps, in which case Wallstreet has no qualms about taking any rando AI app public. When this is done at scale, you create the toxic asset that 401ks pile into.

In short, you and others like you will enjoy your time, but will care very little of the systemic risk you are introducing.

But hey, whatever, gotta nut, right?

—-

I don’t mean you specifically. Companies like Windsurf, Cursor, many, they are all currently building the package for Wallstreet with literally no care that it will pull in retail investment en masse. This is going to be a fucked up rug pull for regular investors in a few years.

We’re in a much wilder financial environment since 2008. It’s very normal for crypto to be seen as a viable investment. AI is going to appear even more viable. Things are primed.

Upvoted for a different perspective.

The thing to remember about the HN crowd is it can be a bit cynical. At the same time, realize that everyone's judging AI progress not on headroom and synthetic data usage, but on how well it feels like it's doing, external benchmarks, hallucinations, and how much value it's really delivering. The concern is that for all the enthusiasm, generative AI's hard problems still seem unsolved, the output quality is seeing diminishing returns, and actually applying it outside language settings has been challenging.

Yea a lot of this I understand and appreciate!

- offline and even online benchmarks are terrible unless actually a standard product experiment (a/b test etc). Evaluation science is extremely flawed.

- skepticism is healthy!

- measure on delivered value vs promised value!

- there are hard problems! Possibly ones that require paradigm shifts that need time to develop!

But

- delivered value and developments alone are extraordinary. Problems originally thought unsolvable are now completely tractable or solved even if you rightfully don’t trust eval numbers like LLMArena, market copy, and offline evals.

- output quality is seeing diminishing returns? I cannot understand this argument at all. We have scaled the first good idea with great success. People really believe this is the end of the line? We’re out of great ideas? We’ve just scratched the surface.

- even with a “feels” approach, people are unimpressed?? It’s subjective, you are welcome to be unimpressed. But I just cannot understand or fathom how

The way I've been thinking about this is that there is The Tech and The Business. The Tech is amazing and improving all the time at the core, then there are the apps being built to take advantage of the Tech, a lot of which are also amazing.

But The Business is the bubble part. Like all the companies during the first internet boom/bubble who did stuff like lay tons of fiber and raise tons of money for rickety business plans. Those companies went out of business but the fiber was still there and still useful. So I think you're right in that the Tech part is being shafted a little in the conversation because the Business part is so bubbly.

The community is divided about this. There's no one hivemind.

There's a general negativity bias on the internet (and probably in humans at large) which skews the discourse on this topic as any other - but there are plenty of active, creative LLM enthusiasts here.

I agree — probably my own selective memory and straw-manning. It just feels in my mind like the “vibe” on HN (in terms of articles that reach the front page and top rated comments) is very anti-AI. But of course even if true it is a biased picture of HN readers.

Would be interesting to see some analysis from HN data to understand just how accurate my perception is; of course doesn’t clear up the bias issue.

I'll take a shot at rationale for this perspective, which is similar to a peer comment:

The tech is undoubtedly impressive, and I'm sure has a ton of headroom to grow (although I have no direct knowledge of this, but I'd take you at your word, because I'm sure it's true).

But at least my perception of the idea that this is a "bubble" presently is rooted in the businesses that are created using the technology. Tons of money spent to power AI agents to conduct tasks that would be 99% less expensive to conduct via a simple API call, or because the actual unstructured work is 2 or 3 levels higher in the value chain, and given enough time, there will be new vertically integrated companies that use AI to solve the problem at the root and eliminate the need for entire categories of companies at the level below.

In other words: the root of the bubble (to me) is not that the value will never be realized, but that many (if not most) of this crop of companies, given the amount of time the workflows and technology have had to take hold in organizations, will almost certainly not be able to survive long enough to be the ones to realize it.

This also seems to be why folks draw comparison to the dot com bubble, because it was quite similar. The tech was undoubtedly world changing. But the world needed time to adapt, and most of those companies no longer exist, even though many of the problems were solved a decade later by a new startup who achieved incredible scale.

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