941–950 of 961 posts
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Do you think Walmart’s prices are higher than you’d pay if there were just ten competing stores with the same product mix, each of which did not have the leverage to squeeze suppliers?
Please please please learn the basics of monopolies.
x="https://www.nytimes.com/2026/05/19/business/google-seach-bar-ai-gemini.html?unlocked_article_code=1.jlA.95yh.ptfBUHf-rBtB&smid=url-share"
x=https://www.nytimes.com/2026/05/19/business/google-seach-bar-ai-gemini.html
links -source "$x" \
|egrep -o "(\"text\":\"[^}]+)|(\"textAlign\":\"LEFT\")|(\"url\":\"[^\"]+)|(\"__typename\":\"TextInline\")" \
|sed '/\"url\":\"/{s/??.*//;s/$/\">/;s/.\{7\}//;s/.\{24\}//;};
s/\"textAlign\":\"LEFT\"//g;/\"text\":\"/{s/.\{8\}//;s/\"$//;};
s/\\u002F/\//g;s/\\u0026/\&/g' \
|sed '1s/^//' > 1.htm
links 1.htm
#firefox ./1.htm
# firefox view-source:"$x" and "Save as" can be substituted for links -source
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Dont they all do that ? I know that deepseek has links for every chain it makes where you can read the source and it's actually a good thing to check on that.
Asking an LLM to cite sources just leads to hallucinated sources, same as any other attempt to make it explain its thinking process. It doesn't have actual visibility into its internal processes, just rationalizes an explanation.
I don't trust facts from LLMs. When I am searching for something, I usually want to find primary sources. As soon as a number is involved, I do my best to not even look at the AI output. Even though the result is often good and combines information from multiple sources, it can also get things wrong by combining information from different eras or just plain outdated advice. AFAICT, without primary sources, the result…
We’re often talking about something that the literature refutes, but the LLM was trained on a bunch of public content from resources such as whattoexpect.com, full of terrible parenting advice.
People didn’t bother with sources and research before, they don’t bother now, AI is just a magical thing for them.
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Yes, Google advertises its crawler IP ranges and it is quite easy to keep track of this and block them. But only if you control the infrastructure that your site runs on of course.
You think they'll stick to those IPs any more than robots.txt when it comes to that?
https://developers.google.com/crawling/docs/crawlers-fetcher...
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A couple of years back I worked with a company which maintained specific data which was the main traffic driver on that page. Google approached them and wanted to pay for the rights to get the data and display it on top of the search results, a feature which was fairly new back then. This was an interesting dilemma because it was very clear that the money was way less than the loss in ad revenue due to traffic drop,…
But like why would anyone make original content or websites anymore if google actively works to ensure you make no money
The web won't die. It will just become even more commercialized than it already is. You might recognize most of the things listed.
Selling things: Restaurants, hotels, local businesses; Ads: more of the same
Work: a) Projects people collaborate on and need to share information b) Writing articles to boost a resume
And maybe sharing what people do, they do it for free, see social media.
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> your product relies on the output of the industry it kills I disagree, Google doesn't rely on a massive amount of recipe websites; they rely on Google and exploit it. More importantly: These recipes exist from many sources, not just the website that exploited Google to be first in their rankings.
From the HN guidelines: "Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith." In this instance, the internet is more than top-ranking recipe websites.
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The other day I found a comment here on HN and I wanted to know if it's true. I asked Gemini and here is the conversation https://gemini.google.com/share/2c1089ac6fd6 You can't do something like this with search.
Yes you can. All you did there was paste in 9 search queries and get 9 results at once.
Sure, it's pretty low-friction to do this by opening a document on most platforms,[1] but using an LLM chatbot not only automates this but provides a synopsis of the findings, and if history's any guide, lowered frictions such as this tend to be the way people tend to move.
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Notes:
1. Though I'll note that opening a free-form text file on mobile OSes can be stubbornly difficult, let alone actually entering text into them. Even the simple act of copying and pasting text is remarkably higher-friction than on a desktop. In many ways the Web has gone vastly backward from text-based, CLI clients where I can 1) open my whole session in a terminal multiplexer (screen, tmux), 2) fire up a text-based web browser (w3m, lynx, etc.), and 3) just wholesale grab site metadata or result summaries and dump that to a textfile. Yes, you need a keyboard to do this efficiently, but keyboards and text-manipulation are just inherently so far superior to touch-based or speech-based interfaces it's not even funny. Touch and voice are convenient, for fast, very shallow uses. But not powerful.
I understand why they are doing this. My Google search usage is easily down 50%+. I doubt I am unique here. While there are times where I want pure search (Kagi, Old Google) I mostly use LLMs to search now and have them provide me links for source data. When I do use LLMs as a search engine I always want it integrated into my AI workflows with access to tools and scripts etc. I never want to have a conversation with…
It's tough for me to square the two things happening simultaneously in AI right now: 1. LLM Model providers are starting to charge real costs to users, revealing that AI usage is much more expensive than the subsidized rates we've been seeing for years. 2. Google is now using an LLM to answer every single google search that happens, for which Google bears the entire cost.
That is, it's training costs which are fixed, high, and amortised across all revenue potential.
In raising prices, pure-play AI chatbot vendors are facing the challenge that their only revenue option at present is pay-for-use. This is typical of numerous other information goods (software, comms networks, information services), but simply reflects the business reality that cost-basis and revenue-basis are largely disconnected, and that average cost pricing tends to be necessary despite not matching marginal cost provision. (That is, the charged per-unit price of use is going to be far higher than the actual marginal cost of provision for that use.
Google's situation differs from pure-play AI in several ways:
- The firm already has one of if not the largest corpus of Web data, as well as much offline / print material data (Google Books), of any entity in the world.
- Presuming Google are already using this for training AI for other purposes or general resource, the fixed costs are sunk costs already incurred, subsidised by the firm's existing AdTech monopoly, and which might as well be put to use.
- Similarly, Google's service costs (marginal costs) for traditional GWS are probably, within an order of magnitude comparable to those for an AI/LLM response.
- Google presently captures 90% or more of worldwide general web search (GWS) traffic, meaning it has an extant market for offering AI as a default search alternative.
- Complaints about declining Google search quality are nearly as old as Hacker News. Here's a seventeen year old submission on that topic, "Google's regression toward mediocrity (search quality & aggressive matching)" https://news.ycombinator.com/item?id=641145> (2009). There are many, many, many results in both stories and comments for related searches. If traditional Web search is going to die anyway, and LLMs replace it, Google might as well get up on the curve.
- Google has arguably the most advanced ranking data for websites, whether that's on the basis of quality and relevance (for good) or advertising/revenue generation (for bad, at least from a public-value perspective), and can and all but certainly does leverage this in training runs, as well as use that training to further refine its ranking systems.
- Google can directly attach its existing AdTech revenue model to LLM search. And probably has options for extending advertising into the LLM SERPs themselves.
- Doing all of this directly attacks pure-play AI firms' attempts to capture Web search from Google, as well as challenges other GWS challengers (DDG, Kagi, Marginalia, etc.), all of whom have vastly lesser revenue options and technical resources than Google.
I'm not saying this as a fan of Google (I think the firm should be broken up or destroyed, and it's not the only one). But I think it's a fair assessment of the firm's strategic position.