Jonathan Ross
speaker
170 appearances
2 recordings
2 series
first heard Jan 2025
last heard 20 Oct
Jonathan Ross’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Oct 2025 with 1.
Appearances
Yes, it's probably the most significant concern. There are other concerns that's probably the most significant because people don't think. They're so used to using these services. When you use one of these other services, you might be shocked to hear this. When you say delete... What they do is they write delete right next to your data. They don't actually delete it. They just mark it, delete it.
When you later come back and ask for your data, they give it to you with the word delete right next to it. It's still there. And these are well-meaning companies. Do you really think like the CCP doesn't have all your data and isn't going to look it up later? Some governments are more aggressive than others.
And if they have access to your data, not even your data, it could be your next door neighbor's data. Your next door neighbor might put something in there that accidentally gives information away that makes you more vulnerable. Now the CCP has something. Maybe you had some package delivered and they put a complaint somewhere and whatever.
Like you might not even do it yourself, but other people around you, the health data of a spouse, right?
Yes, but I don't think it's DeepSeek that's doing it. So you have to understand any company that operates in China and Hong Kong, the one country, two systems thing didn't quite work out as anticipated or maybe as anticipated, but not as stated. They have no choice. In 2016, when Grok started, we decided that we were not going to do business in China. This was not a geopolitical decision.
This was purely commercial. And what it was, was we kept seeing companies like Google, Meta fail over and over again, trying to win in China. The formula is actually pretty simple. You're not allowed to make net money. You're allowed to spend more money in China. But the moment that you start to become profitable or anywhere near profitable, all of a sudden there's a thumb on the scale.
Companies that manufacture a lot in China and send more money to China can actually be successful there. They can sell things there. Yeah, it's a pretty simple formula. You must send more money to China than you take out. But at the same time, they also require that you hand over all data. And not only that, they also require that certain answers be in a form that they find acceptable.
So, for example, one of the more common ways ones that you see about deep seek right now is when you ask about Tiananmen Square, if the temperature is low on the model and temperature, we don't need to get into that. It's complicated, but it's how like low means low creativity. Then it's actually going to give you an answer that basically says, I don't want to talk about that.
It's a sensitive topic, but you ask it about other things that are sensitive topics elsewhere in the world. And it'll just answer. But what happens if the CCP requires that they start to say, what about TikTok? Should it be banned? Absolutely not. Here's why. And it gives you a cogent reason. That's kind of scary.
Yeah. And worse. So we up until recently refused to run any Chinese models and we had to make a very difficult decision on DeepSeek. We now have it on our API at Grok.
So what it came down to was when we saw DeepSeek become the number one app on the app store, the realization was people were going to be putting their data in there. And what we want to make sure is that you actually have an option. So we store nothing. There is no like delete or whatever. Like there is just, we'd store nothing. We don't even have hard drives. We have DRAM.
And when the power goes off, everything goes away. So we wanted to make sure that there was an alternative where when you use DeepSeek's model, your data is not going to the CCP. Well, right now, the CCP is probably going to be taking the safeties off the weapons. They're going to be like, why are you making this model open source? Please direct your data towards us.
Go win a bunch of customers this way. But now we want the data. Right. And so they're going to change the strategy. But remember, DeepSeek is a real I mean, it's a hedge fund. They're doing this themselves and they're just influenced by the CCP. And the CCP, now that they've seen the success of this, might see it as yet another TikTok.
One question to ask is, are we going to be talking about R1 for the next six months? And the answer is absolutely not. We might be talking about R2 and R3 and R4, but R1 was one shot. The question is, are they going to keep coming up with very interesting things? Are we going to cat and mouse it? Is everyone going to learn from this? The biggest problem is we've
this has just made it absolutely nakedly clear that the models are commoditized, right? You've been asking the question, right? Like if there was any doubt before, that doubt's over. So what is the moat? And for me, I love Hamilton-Helmer's seven powers, right?
So marketing is the art of decommoditizing your product. And the seven powers are seven great ways to decommoditize your product. Scale economies, network effects, brand counter-positioning, cornered resource, switching cost, process power, right? The question is, who's going to do what? OpenAI, and you've got to give Sam Altman and that team credit.
They've got amazing brand power, like no one else in this space. And that's going to serve them for a really long time. But what you see Sam trying to do is scale, right? He's trying to go scale. That's why we hear about Stargate and $500 billion, right? That's the power he would like to have, but the power he has right now is brand. And he's trying to bridge that. But what about the others?
Actually, I don't think it's enough spending. And the reason is, so we saw this happen at Google over and over again. We do the TPU. So why did we do the TPU? The speech team trained a model. It outperformed human beings at speech recognition. This was like back in 2011, 2012. And so Jeff Dean, most famous engineer at Google, gives a presentation to the leadership team. It's two slides.
Slide number one, good news, machine learning finally works. Slide number two, Bad news, we can't afford it. And we're Google. We're going to need to double or triple our global data center footprint at probably a cost of $20 to $40 billion. And that'll get a speech recognition. Do you also want to do search and ads?
There's always this giant mission accomplished banner every time someone trains a model. And then they start putting it into production. And then they realize, oh, this is going to be expensive. This is why we've always focused on inference. And so now think about it this way. At Google, we always ended up spending 10 to 20 times as much on the inference as the training back when I was there.
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