I think code monkeys or journalists etc. have GREAT experience with LLMs.

In case of niche and more technical knowledge demanding task, LLM just fails. Eg. in my case, reverse engineering. It just spits BS all day long.

I don't think I am there yet. I got too confident about AI and was lazy to do some search instead or think through the problem solution. I wasted my 3 hours by not doing so. LLMs are getting better but search and stackoverflow still helps.

Its early days but for our B2B SAAS, we are seeing an uptick in traffic from Chatgpt and other similar AI tools. I even have prospects sharimg their prompts as i always ask them. Google search is going to have serious issues in next few years if they continue on their current path of literring page 1 with garbage "sponsored posts".

As opposed to 'ai' littering all of the response with more subtle ads that aren't even marked?

I'm wondering why, in this day and age, it is still difficult to search for a specific type of jeans, with a specific size, style and color, and availability. And filter it by shipping method/cost.

Same for other products. E.g. resistor 0805, 1/8W or better, etc. etc.

Why does every online shop have their own (often broken) filtering system? Why can't the information be gathered by an AI, and turned into a super filter?

Google had "Froogle" specifically for shopping. Not sure what happened there, as it looks like they renamed it to "Google shopping" (https://www.google.com/shopping) but it just redirects to broken google search page.

https://en.wikipedia.org/wiki/Google_Shopping

If you immediately find what you want you won't browse around and likely buy something with bigger profit margin or just more stuff.

It's not good for business to provide a good user experience, correct information and so on.

Probably for the same reason you can't ask in a Target whether the Wal-mart up the street has it in your size - the individual vendors are incentivized to avoid you going to another source, and hope you'll decide to go with something "good enough" rather than paying the cost of going to another place and comparing things.

That cost is much lower outside of brick and mortar travel times, but not 0, especially for people on crap internet connections or slow computers, and people tend to just want to get it done rather than shopping through N sites for the best option, so they pick "good enough".

That, plus the vendors don't want you to think they're just an Amazon frontend so they have their own UI, plus if they allow suppliers to populate the item fields, good luck getting the suppliers to provide consistent results...

Sounds like you are talking about Amazon? Which is a front end for online shops.

I'm talking about the web, not just Amazon. Amazon's filtering is limited too. I want a universal filter with many options. And not just shops that are captured in Amazon's sphere. And my search engine should have no incentives other than providing me with good search results.

Google’s Froogle was a bit like this. Obviously they had to cancel it.

Because most products are in their own ecosystem with no overarching standard way to share information. You have some standards but usually for specific niches, no product FHIR.

Just making sure every product has a unique ID would be a revolution. https://en.wikipedia.org/wiki/Global_Trade_Item_Number

Amazon's search is somehow even worse even though they already have a lot of the information in a structured format.

I suspect that's by design.

For the same reason some department stores have the up and down stairs/escalators on opposite sides.

https://ux.stackexchange.com/questions/49307/why-do-departme...

People quit Google Search to avoid "search-optimized junk" just to use AIs that answer using "Search" lol. Look closer at the sources your AI answers give you. They're not always pretty.

I've been kind of expecting to see the LLM impact on Google's 10-Q for a while now.

Even if LLM queries are just a small fraction of overall internet queries, I suppose they're stealing eyeballs from higher-value searchers.

Unless I need a map or want to see a picture of a celebrity, I use ChatGPT for pretty much everything.

This article uses an LLM to summarize itself into three key points. Those three points are milquetoast, making me much less interested in getting behind the paywall and reading the actual article.

Maybe this is a value add. Maybe the article is milquetoast and should be skipped. But either way it’s an example of how LLMs are not just competing with search they’re degrading the reading behaviors which make search more valuable (details, specifics, exact match).

It is an underrated point though that LLMs are competitive with Google search because they remove anti-user ads and SEO spam. In the long run LLMs will have that same problem though, because it’s a business dynamic not a technological dynamic.

I use Google's AI often by mistake. Even when I know it is wrong I will quote it here on HN and give credit to Google AI for the misinformation for posterity sake. Historians will want to see what output it was giving people especially if there should be any cases of public manipulation through these bots. I do not know where else to archive it. AI is still lacking fact-checkers. AI still can not provide any references to where it learned the misinformation often referred to as hallucinations.

I try to use different things depending on what I'm searching for. For techy stuff relevant Reddit group or stack is often better. For things that you end up with Wiki, their home page is there anyway to search. For news related search it's a problem with any of them as they usually show paywalled results.

As long as ChatGPT uses Bing for searching the web, Google Search is not going anywhere.