LLMs
11 statements across 4 episodes · 6 bullish · 2 bearish · 4 people on the record · first statement Oct 24, 2025 by Neil Patel · across every show →
Everything said about LLMs, oldest first
Oct 24, 2025 positive
Patel: Structured table comparisons help websites rank better in LLM citations
“And let's say if you want to compare best CRNs for construction companies and you write your own article for that on your own blog and you go super in depth, that's a great way for you to drive more signups, right? And you would do it in a table format. Pricin…”
Nov 11, 2025 positive
Nov 11, 2025 bearish
Nov 11, 2025 bullish
Nov 11, 2025 bullish
Nov 11, 2025 positive
Smith: TechRadar Is Among Most Cited LLM Domains Despite Affiliate Disclosures
“TechRadar is a good example. TechRadar shows up A huge amount. It's one of the most cited domains in LMs. And you'll see a snippet at the top saying we're an affiliate site and it does really well. There's no evidence that a paid for mention does worse than a …”
Nov 11, 2025
Ethan Smith: LLM video citations require only hundreds of views, not millions
“What I will say is that for many questions, the number of views is not millions or hundreds of thousands, it's hundreds or a few thousands.
So it's still pretty, it's still something that you could win without being an influencer.”
Nov 17, 2025 neutral
Singla: LLMs now index on-screen text in Instagram and TikTok videos
“So if you see most of Instagram and TikTok they've just become indexable on search, Instagram specifically. So which means LLMs can now actually go through your video and see all the text you have on your video and then prop that video up specifically. So it's…”
Nov 17, 2025
May 30, 2026 positive
Ries: LLMs may be the best teaching technology in history
“And what's cool about LLMs I think is not that they're artifact generating machines. They're okay at that, but like take them out of their distribution and all of a sudden you're in big trouble, but they're incredibly good teaching machines. There may be the b…”
May 30, 2026 negative
Ries: Correcting LLM errors in-chat increases likelihood of bad outputs
“And that has to do with the mechanics of the attention mechanism. As the contracts gets larger, you're literally spreading the attention over more and more and more data. But also, it's learning from all the bad examples it gave. So it's actually much more lik…”