It would be fascinating to see numbers behind how many AI boosters are religious. Feels like a similar central belief. No matter the current state, boosters believe it will get better: essentially they have faith. Critics take it at current face value and don’t believe it can become AGI.
Although, people creating a digital god would be pretty blasphemous. Tough one.
> No matter the current state, boosters believe it will get better
How can it not, tho? Like it can't get worse (at least for the open models. You have them now. The underlying models don't change). So the only obvious answer is they either stagnate, or get better. And a brief look at history shows that nothing truly stagnates.
Keep in mind that we're not even 3 years into this new paradigm. We're still scratching the surface of how we can use these things, that are unreasonably good for being trained for NTP.
That's my main argument against an "AI winter". We have so many things to try that it'll take decades to fully realise all the potential of this tech, even if all research into foundational models would stop now. And it surely won't. There is just too much capital allocated here, and that attracts a lot of smart people working the problem. Yeah, it's reasonable to say things will get better.
> It would be fascinating to see numbers behind how many AI boosters are religious. Feels like a similar central belief.
Many of them appear to be somewhere on the rationalist/EA (rationalist as in LessWrong enthusiasts, not the literal meaning of the term) spectrum, and probably wouldn't consider _themselves_ religious, though many would consider those things to be religions in their own right.
A financial bubble has a clear definition. You're just changing the meaning to be a different strawman so you can refute that. Are there lots of companies that are overvalued because of their AI offerings, and will those companies fail when they don't provide acceptable returns? If yes, bubble.
These answers take a shockingly long time to resolve considering you can put the questions into Brave search and get basically the same answers in seconds.
The thing is, with Chat+Search you don't have to click various links, sift through content farms, or be subject to ads and/or accidental malware download.
In practice this means that you get the same content farm answer dressed up as a trustworthy answer without even getting the opportunity to exercise better judgement. God help you if you rely on them for questions about branded products, they happily rephrase the company's marketing materials as facts.
A counter example to this is that I asked it about NovaMin® 5 minutes ago and it essentially told me to not bother and buy whatever toothpaste has >1450 ppm fluoride.
Such is the nature of probabilistic systems. Generally speaking, LLMs read the top N search results on the topic in question and uncritically summarize them in their answer. Emphasis on uncritically, therefore the quality of LLM answers is strongly correlated with the quality of top search results.
This is why I am so excited about the way GPT-5 uses its search tool.
GPT-4o and most other AI-assisted search systems in the past worked how you describe: they took the top 10 search results and answered uncritically based on those. If the results were junk the answer was too.
GPT-5 Thinking doesn't do that. Take a look at the thinking trace examples I linked to - in many of them it runs a few searches, evaluates the results, finds that they're not credible enough to generate an answer and so continues browsing and searching.
That's why many of the answers take 1-2 minutes to return!
I frequently see it dismiss information from social media and prefer to go to a source with a good reputation for fact-checking (like a credible newspaper) instead.
> finds that they're not credible enough to generate an answer
The credibility is one side of the story. In many cases, at least for my curious research, I happen to search for something very niche, so to find at least anything related, an LLM needs to find semantic equivalence between the topic in the query and what the found pages are discussing or explaining.
One recent example: in a flat-style web discussion, it may be interesting to somehow visually mark a reply if the comment is from a user who was already in the discussion (at least GP or GGP). I wanted to find some thoughts or talk about this. I had almost no luck with Perplexity, which probably brute-forced dozens of result pages for semantic equivalence comparison, and I also "was not feeling/getting lucky" with Google using keywords, the AROUND operator, and so on. I'm sure there are a couple of blogs and web-technology forums where this was really discussed, but I'm not sure the current indexing technology is semantically aware at scale.
It's interesting that sometimes Google is still better, for example, when a topic I’m researching has a couple of specific terms one should be aware of to discuss it seriously. Making them mandatory (with quotes) may produce a small result set to scan with my own eyes.
A year ago I asked it to do deep research on Biomin F + a comparison to NovaMin & fluoride. It gave a comprehensive answer detailing the benefits of BioMin & NovaMin over regular fluroide.
What's incredible about that is that you are acting like that was a success story but it is a nuanced topic and it swallowed all the nuance and convinced you.
You're now here telling us how it gave you the right answer, which seems to mostly be due to it confirming your bias.
I like Brave but have found their search to be awful. The AI stuff seems decent enough, but the results populated below are just never what I'm looking for.
It seems like it would train users to ask questions that it can actually answer. (They might also need some examples of what sort of questions to ask.)
Mostly it would train users to not use their service and go to a service where the model outputs results they can copy paste to complete their assignment.
So these companies cannot do this, they would hemorrhage too many users and companies cannot go against the profit incentives in practice.
