What public figures publish and believe, in their own words.
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I think it's actually the opposite which is that everyone's kind of using the same data which is you need such an enormous amount of generalized text that the amount that Google has or that meta has is not actually enough to move to be a kind of fundamental difference in what you can train with.
Like it seems to me right now you could do like a double blind test of the same prompt given to Grock Claude Gemini um Mistral Deep Seek. Do a double blind test. I bet most people wouldn't be able to tell which is which.
I'm pretty sure people thought Microsoft had an advantage on the internet and Google and um Meta had an advantage on mobile and everyone thought IBM was going to win PCs. Like once IBM made a PC, that was it. It's all over now. And we kind of forget that like there were PCs before and then IBM made one and that kind of became the standard but then IBM lost it.
I initially started making Anki (well, Mochi) cards to keep track of Google DeepMind's LLM lineage—Gopher, Chinchilla, Gato, PaLM, Sparrow, Meena, LaMDA, Bard, Gemini, etc.
Yeah, these are great work that shows what's possible. The approach doesn't scale currently. Three days of Google's server time can solve one high school math format there. This is not a scalable prospect, especially with the exponential increase as the complexity increases.
So I do think scaling laws are working, but it's tough to get, at any given time, the models we all use the most, this maybe a few months behind the maximum capability we can deliver because that won't be the fastest, easiest to use, et cetera.
But the most important metric, and we carefully measure it is, like, how much has our engineering velocity increased as a company due to AI, right? It's tough measure, and we rigorously try to measure it, and our estimates are that number is now at 10%, right?
You can have AI give a lot of context, but one of our important design goals though, is when you come to Google Search, you are going to get a lot of context, but you're going to go and find a lot of things out on the web. So that will be true in AI mode, in AI overviews, and so on.
Look, I think news and journalism will play an important role in the future. We are pretty committed to it, right? So I think making sure that ecosystem, in fact, I think we'll be able to differentiate ourselves as a company over time because of our commitment there.
LLMs can write a large fraction of all the tedious code you’ll ever need to write. And most code on most projects is tedious. LLMs drastically reduce the number of things you’ll ever need to Google.
The all-in cost of operating the Google Play Store, stocking it, maintaining it, the software, the entire ecosystem is around 6% of revenue. So in a competitive market, would a company whose cost is 6% be able to charge 30%? Absolutely not.
You kind of Google and you try to solve a problem in the language based on all of your previous experience. And so you don't have what makes that language special. You have what all the other languages make special.
But Google has never had that DNA of like, "This is a product we should sell." The Google Cloud, which is a separate organization from the TPU team, which is a separate organization from the DeepMind team, which is a separate organization from the Search team. There's a lot of bureaucracy here.
I think it's even more impressive what OpenAI did in 2022. At the time, no one believed in mixture of experts models at Google who had all the researchers. OpenAI had such little compute and they devoted all of their compute for many months, all of it, 100% for many months to GPT-4 with a brand-new architecture with no belief that, "Hey, let me spend a couple of hundred million dollars, which is all of the money I have on this model." That is truly YOLO.
a good overview of SRE at Google. For those who worked at places with oncall, much of the first part of the book will likely be very familiar. Keep in mind that your mileage might vary: what works at Google scale, might not be the ideal fit for your use case.
They could have shipped ChatGPT for example, I heard, in 2019. And they never shipped it because they were so stuck in bureaucracy. But they had everything. They had the data, they had the tech, they had the engineers and they didn’t do it.
You can set out to build a good business and it’s still fine. Maybe the long-term business model of Perplexity can make us profitable in a good company, but never as profitable in a cash cow as Google was. You have to remember that it’s still okay.
We would rather take a more dramatic position, that the best way to actually make a dent in the search space is to not try to do what Google does, but try to do something they don’t want to do. For them to do this for every single query is a lot of money to be spent, because their search volume is so much higher.
In Google, even though we call it 10 blue links, you get annoyed if you don’t even have the right link in the first three or four. The eye is so tuned to getting it right. LLMs are fine. You get the right link maybe in the 10th or ninth. You feed it in the model. It can still know that that was more relevant than the first.
What is the weakness of Google is that any ad unit that’s less profitable than a link, or any ad unit that kind of disincentivizes the link click is not in their interest to go aggressive on, because it takes money away from something that’s higher margins.
Your product might be great, but how do you get it on the shelves of Walmart? Or how do you get it on the shelves of Google? Because if you're not on the shelves of Walmart, you're not on the shelves of Google, you might be invisible. And it's friction that kept you there, not some sort of merit contest.
But a lot of people have tried to just make a better search engine than Google and it is a hard technical problem, it is a hard branding problem, it is a hard ecosystem problem. I don’t think the world needs another copy of Google.
And oftentimes it's more important to them to have the public perception that they're good directors so they get the next best deal. If they have a reputation for taking on management too aggressively, word will get out in the small community of founders and they'll miss the next Google.
And so the only way to improve safety is to have an escape system. And historically, human-rated rockets have had escape systems. Only the space shuttle did not, but Apollo had one. All of the previous Gemini, etcetera, they all had escape systems.
Google’s the first — and often the last — place we go to for answers, so snagging top billing in a Google search results page is, approximately speaking, equivalent to being “true.”
Everyone who talks about AI talks about the button, the button to turn it off, right? Do we have a button to turn off Google? Is anybody in the world capable of shutting Google down?
the only ASIC that is remotely successful is Google’s TPU. The only reason that’s successful is because Google wrote a machine learning framework. I think that you have to write a competitive machine learning framework in order to be able to build an ASIC.
A korrent is a belief a person has stated in their own words: one
sentence stating the claim, backed by a quote and a source, kept at
korrents.com.
Under a name here, the quoted block is what they actually said.
The korrent beneath it is the claim those words support, in
korrents' wording — tap it to see the record, its source, and who
else holds it.
Nobody here wrote their own korrents. They are compiled from public
statements, and a person can change their mind, which is recorded too.
About the English under a post
Some people here publish in a language other than English. Where they
do, this site shows a machine translation beneath the post, in
this typeface — the site's own, not theirs.
The post itself is never changed, moved or hidden: what is set in the
serif above is exactly what the person published, and it is what to
quote them on. A translation can be wrong in ways that matter,
especially about tone.
Only the post's own words are translated. A quoted post, a linked
article and a belief on korrents.com
are left in their original language.