They get basically everything they want (keeping it all in the tent), plus a negotiating position on search deals where they can refuse something because they can't do it now.
Quite why the judge is so concerned about the rise of AI factoring in here is beyond me. It's fundamentally an anticompetitive decision.
I basically only use Google for "take me to this web page I already know exists" queries now, and maps.
Do you check all of the sources though? Those can be hallucinated and you may not notice unless you're always checking them. Or it could have misunderstood the source.
It's easy to assume it's always accurate when it generally is. But it's not always.
The type of search you are doing probably matters a lot here as well. I use it to find documentation for software I am already moderately familiar with, so noticing the hallucinations is not that difficult. Although, hallucinations are pretty rare for this type of "find documentation for XYZ thing in ABC software" query. Plus, it usually doesn't take very long to verify the information.
I did get caught once by it mentioning something was possible that wasn't, but out of probably thousands of queries I've done at this point, that's not so bad. Saying that, I definitely don't trust LLMs in any cases where information is subjective. But when you're just talking about fact search, hallucination rates are pretty low, at least for GPT-5 Thinking (although still non-zero). That said, I have also run into a number of problems where the documentation is out-of-date, but there's not much an LLM could do about that.
So like a lot of the internet? I don’t really understand this idea that LLMs have to be right 100% of the time to be useful. Very little of the web currently meets that standard and society uses it every day.
https://freedium.cfd/https://vinithavn.medium.com/from-multi...
At its core, attention operates through three fundamental components — queries, keys, and values — that work together with attention scores to create a flexible, context-aware vector representation.
Query (Q): The query is a vector that represents the current token for which the model wants to compute attention.
Key (K): Keys are vectors that represent the elements in the context against which the query is compared, to determine the relevance.
Attention Scores: These are computed using Query and Key vectors to determine the amount of attention to be paid to each context token.
Value (V): Values are the vectors that represent the actual contextual information. After calculating the attention scores using Query and Key vectors, these scores are applied against Value vectors to get the final context vectorA documentation for a specific product I expect to be mostly right, but maybe miss the required detail.
Some blog, by some author I haven't heard about I trust less.
Some third party sites I give some trust, some less.
AI is a mixed bag, while always implying authority on the subject. (While becoming submissive when corrected)