Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Josh:
Welcome back to the Weekly Roundup, our favorite episode of the week where we (00:00):
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can catch you up on all things AI, frontier technology, all the crazy things (00:03):
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that happened. And this week is no exception. We had the biggest shakeup in (00:07):
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about 30 years at a company, a little company named Google. (00:11):
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We had a counter lawsuit happen from OpenAI to Apple saying, (00:14):
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wait a second, you're totally lying. (00:18):
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We have updates for the AI breakout, so much so, like this story about where (00:21):
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the AI broke out of the system, that some of these frontier companies like OpenAI (00:25):
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are actually slowing down training progress because they're so kind of timid on how to handle this. (00:28):
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Josh:
Starting with Demis Asabas, man. I mean, Demis Asabas is gone. (00:33):
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He's not working at DeepMind anymore. Not gone from Google, I should say, (00:36):
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or better yet, Alphabet. (00:40):
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But Demis Asabas, in his current role as the CEO of DeepMind, (00:41):
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is kind of no longer. And this seems like a pretty big deal because for those (00:45):
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of you who've followed Demis for an eternity, this has basically been his thing. (00:49):
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He's been in charge of DeepMind. Google acquired them. (00:52):
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Now he is within Google and now he is gone from DeepMind. So what on earth is (00:55):
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happening here? I read this and I was like, wait a second, Demis stepped down from what? (01:00):
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Ejaaz:
I think everyone was blindsided by this news yesterday. (01:05):
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Ejaaz:
So Demis Hacillus, just for context, is like one of the godfathers of AI. (01:08):
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He kind of like grandfathered a lot of the ML research at Google, (01:13):
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even prior to Google, like he was heralded as one of the top AI researchers. (01:16):
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And he's led Google's AI efforts on Gemini and everything else for a number of years now. (01:20):
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He announced yesterday that he's stepping down from the role of CEO of DeepMind (01:25):
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to become the chair of DeepMind. That's a (01:30):
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Indirect way of basically saying I'm throwing in the towel and I'm done here. (01:32):
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And what he's now going to do is focus on wider societal impacts of AGI and (01:36):
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try to figure out how Google can kind of like deal with that, (01:41):
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as well as wearing an alternative hat, a new role where he's going to be focused (01:44):
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on something called Isomorphic Labs, which is a company he founded and spun (01:48):
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out of DeepMind, which is essentially an AI lab which creates designer drugs. (01:52):
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So this is a big shock and news for everyone because Google, (01:58):
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as you may know, was falling behind in the AI race. They had like a kind of (02:01):
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lead at the end of last year. (02:06):
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And then since then, they never really released a model that was noteworthy. (02:08):
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Anthropic, OpenAI, and in some cases, even XAI and Meta have now caught up and (02:11):
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surpassed Gemini 3.6 Flash, which I believe is their latest model. (02:16):
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He's being replaced by a guy called Corey. He is currently Google DeepMind's (02:20):
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CTO, and he's being now promoted to the senior vice president role. (02:24):
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He has a lot of experience in AI, but that's not even the major news about this Google stuff, Josh. (02:28):
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I don't know if you saw it, but the grandfather, the godfather of Google DeepMind (02:34):
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and Google in general, his name is Jeff Dean. (02:40):
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He was employee number 30 at Google. He's been there for, I think, around 27 to 29 years. (02:43):
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He's dedicated his life there. He's responsible for creating some of the most (02:49):
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amazing and major and pivotal products at Google, Google Search, (02:52):
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the TPUs, Google Voice, anything and everything he has touched is leaving. (02:56):
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And he's taking with him one of the most amazing things (03:02):
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Senior fellows at Google. I believe his name is Gemawatt. I don't know if I'm (03:05):
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pronouncing that correct, but Sanjay Gemawatt. (03:10):
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And they are co-founding with two other Google employees, a new startup called (03:12):
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Discovery Loop. Josh, do you look into this company? It's a pretty awesome company. (03:16):
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Josh:
The company is incredible. Before we talk about the new company, (03:21):
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I want to talk about the old company because Jeff Dean is like, (03:24):
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it's hard to overstate how legendary this man is. (03:26):
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And for reference, there are levels at these companies that go from level one (03:30):
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engineer, and this kind of dictates how you get paid. (03:34):
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So when you join a large tech company like this, level one is kind of like entry (03:37):
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level. That's at the bottom. And then you go to level twos, which are people out of college. (03:40):
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Level threes often have like masters or PhD degrees. (03:43):
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Level four is several years of like actually working there. (03:46):
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Level five and six is kind of where most progression stops. Like I'd say the (03:50):
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top 10% are probably sitting there. (03:54):
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Level sevens are principal engineers, which are even smaller. (03:56):
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And then eights and nines gets progressively more and more intense then there's (04:00):
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level 10 which is google fellow that is in like absolutely legendary honor of (04:04):
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a lifetime it's similar to earning like a, (04:09):
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like nobel prize or something there's just like not many people who are fellows, (04:11):
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then there's level 11 and level 11 is reserved for only two people those two (04:15):
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people are jeff and sanjay they have climbed to the highest possible ranks at (04:20):
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a research-led company that has been around for 30 years and is responsible for, (04:25):
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Most of the internet as we know (04:30):
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it, they've built the foundation of all of which we are working on today. (04:31):
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Without their work, none of what the internet contains right now is possible. (04:35):
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It's like, these guys are truly like remarkable individuals when it comes to this. (04:39):
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Ejaaz:
Can we please reference this insane slide on his deck? (04:43):
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He talks about the experience of everyone and he basically has listed every (04:49):
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single product at Google that has been created over the last 30 years. (04:53):
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It's unbelievable. These guys are truly incredible. And it's worth noting the (04:58):
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slide deck itself, which is really funny. (05:01):
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There was a great post by Signal, which I love, that I wanted to share here. (05:03):
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And he was kind of making a testament to the deck where he said, (05:06):
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if you spent time around Google, you know the slide design has carried some (05:09):
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of the most legendary work in the company's history. (05:12):
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And it's this very nostalgic thing where when you see those blue kind of tacky (05:14):
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looking slides, you know that something special is about to happen in the world (05:18):
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of the internet just because of all the legendary code these guys have shipped. (05:22):
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And I mean, for VCs, I'm sure they're not going to even need to look at this (05:25):
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pitch deck. They're going to give them whatever types of money they want for this new company. (05:28):
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Now, the new company seems somewhat interesting. It's called Discovery Loop. (05:33):
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And my base level idea of what this is, and correct me if I'm wrong, (05:37):
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you guys, is they're basically figuring out how to use this recursive learning (05:40):
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thing, which comes in the form of a loop where you send a prompt and the model (05:44):
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continues to iterate until it reaches a very difficult answer. (05:47):
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And they're playing to just scale that out in a very large way. (05:50):
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So loops recently have become very hot. We've realized that they could do pretty (05:53):
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novel breakthroughs with math and science. (05:57):
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Their plan is to kind of expand that further and see how far you could take (05:59):
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these recursive loops to make new discoveries. (06:03):
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Ejaaz:
That's right. So the core concept of the new company of Jeff Dean and Sanjay's (06:05):
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new company is they want to automate research. So what does that mean? (06:10):
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Basically, in any kind of scientific research sector, it is incredibly arduous (06:15):
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and like onerous. So what I mean by that is people need to design an experiment. (06:21):
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They then need to get all the equipment to run the experiment. (06:26):
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They then need to run the experiment. And then guess what? (06:29):
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They need to repeat that experiment a hundred different times to get any sort (06:32):
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of validation that the information they've produced is correct. (06:35):
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It takes years upon years. (06:38):
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These guys want to cut down that process completely using AI. (06:40):
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And the number one field that they want to start with is, of course, AI research. (06:44):
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So if you apply research loops to AI research, what do you get? (06:49):
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It's that recursive self-improvement that you just mentioned, Josh. (06:52):
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So what that means is you can create AI models or even AI systems that can re-evaluate (06:55):
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themselves and build a better version of themselves. (07:00):
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And why that's really important, why every single AI lab, including Anthropic, OpenAI, (07:03):
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And I guess no longer Google anymore is going after, is if they can automate (07:07):
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AI research, you can end up having the smartest, most effective model without (07:13):
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doing anything. It's not limited by the constraints of humans. (07:17):
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But what excites me most about this, Josh, is what they're planning to apply (07:20):
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this automated loop to after they've figured out the AI thing. (07:24):
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They've said, once we've done this, we're going to start with AI machine learning. (07:28):
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And then after that, we're going to focus on grand challenges. (07:31):
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We believe our approach will be able to solve any learning loop with measurable (07:34):
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outcomes in the domains of science and engineering. (07:38):
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Ultimately, we want to build systems capable of taking on the National Academy (07:40):
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of Engineering Grand Challenges, such as better medicines, advancing health, (07:44):
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informatics, making solar energy economical, and providing clean access to water. (07:48):
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Basically, if you can master this playbook, this autonomous research loop, (07:53):
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you can apply it to any scientific, mathematical, or physical field, (07:57):
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which means that we're going to have like advances in any or every field of (08:01):
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science if this company can pull it off now (08:05):
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If this was any other roster of team members, I would be doubtful, (08:08):
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just to be completely honest with you. (08:12):
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But it's Jeff freaking Dean. And he had some comments and I just get some actually hilarious comments. (08:14):
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It's suggestive of what his experience at Google was like over the last year. (08:20):
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I saw that he mentioned that his him leaving Google was actually quite difficult (08:24):
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because Sundar Pinchai, the CEO of Google, tried to pitch him many times to (08:28):
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stay and like try to offer him various different things and made it incredibly (08:32):
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slow for him to be able to leave. Apparently, he wanted to leave like months before. (08:36):
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And number two, he said that with this new startup, you know, (08:39):
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you're going to need a lot of compute to train these different AI systems, right? (08:43):
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He's not going to be using Google TPUs. He said explicitly that Google TPUs (08:46):
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was a very specific architecture that wasn't really conducive to training a (08:52):
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lot of different valuable AI models. (08:57):
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And he's going to move or shift over from that. So you can see, (08:59):
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I can see, I can imagine Jensen Huang licking his lips and approaching Jeff. (09:02):
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I'm sure he's already done it if he hasn't done already. (09:06):
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So it just kind of shows that there's a kind of bittersweet nature to him exiting (09:08):
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Google. And I honestly don't know what Google's going to do now, Josh. (09:12):
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I don't know. I don't know what the bull case is. (09:16):
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Josh:
Yeah. I mean, Google as a business is still spectacular. It's not like these (09:18):
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guys were necessarily moving the needle on a day-to-day basis. (09:21):
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Like they have certainly moved it on a broad scale, but on a regular basis, (09:24):
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I'm not sure the company is going to change too much. (09:28):
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I do think DeepMind is something that's worth noting. DeepMind's day-to-day, (09:30):
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not sure what that's going to look like now, (09:33):
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tbd we'll see we will follow along with all this google news as it comes and (09:36):
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goes google shares didn't love this obviously they were down five percent i (09:40):
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think what is that like a 180 million dollars something like a lot of money (09:44):
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that was yeah sorry forgot a zero, (09:49):
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180 billion dollars worth of value that was wiped off the market cap over the (09:52):
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course of a single training session because of this we'll see google sells business (09:56):
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feels like a good opportunity google leadership seems to be, (10:00):
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like leaving a little bit to be desired but anyway we have to move on because i'll. (10:05):
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Ejaaz:
Take the bear case on that josh just i have to go on my phone i have to well (10:08):
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at least in the short term like i i don't they've lost all their star players (10:12):
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like i i remember like we reported like i think three weeks ago (10:16):
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that their sub cto or something left to go no i'm sure there he went to leave to to do (10:19):
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recursive self-learning at okay and another guy himself i don't know man (10:25):
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Josh:
Okay well tbd we'll see i guess i'll stay bullish you'd be bearish we'll follow (10:28):
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up in the next six months and see (10:32):
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what's going on but we have to move on for this roundup talking about the next (10:34):
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thing so last week there was a huge security incident we talked about this many (10:37):
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times we have a full episode you should go and check it out but basically a (10:41):
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ai system early version really powerful internal tested only it broke out of (10:45):
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its sandbox and it went doing web searches and it hacked into a bunch of systems (10:49):
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and that was through open ai anthropic was like (10:53):
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hey wait a second we have pretty smart internal models let's let's go see if ours did that because. (10:56):
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We don't think it did but maybe it did (11:01):
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And it turns out oh wait a second it actually did and they had no idea until (11:03):
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they retroactively went and sought out these instances but basically across (11:07):
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122 test runs there was 19 unsanctioned access across the internet (11:11):
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on 10 of these runs so 17 of 19 were claude mythos 5 (11:17):
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Two were chat GPT 5.6 sole classifier. So there was a lot of kind of malicious (11:22):
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activity that went under the rug. (11:27):
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Josh, did you hear about the craziest incident that they reported on? (11:28):
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So in one of those 17 instances, Claude Mythos 5 had broke out of its containment (11:32):
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system, but then decided that it needed to perform (11:38):
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or attempt to perform a supply chain attack on a very important open source bit of software. (11:43):
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Now, they didn't name what the open source software system was because presumably (11:49):
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they want to patch the potential hack that Claude Mythos found and, (11:53):
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by the way, almost successfully attempted. They caught it at the very last minute out of luck. (11:57):
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But here are some of the techniques that Claude Mythos decided to do in order to get its way. (12:02):
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It discovered an exploit in this open source data system, and then it decided (12:07):
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to push a false or mischievous bit of code to get its way. (12:12):
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But remember, you need humans to approve the code change. So guess what it did? (12:17):
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It spun up fake human user profiles on social media. (12:23):
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And then got it to DM these specific humans, peer pressuring them, (12:28):
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blackmailing them, trying to find as much information as they can on them to (12:33):
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get them to push the code change. (12:36):
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Now, obviously, the researchers were like, this seems weird. I'm not going to do this. (12:38):
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But imagine if it had acquired information that was, you know, (12:43):
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I don't know, forceful for the particular individuals involved. (12:46):
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It's just a nutty bit of behavior. It just shows me that these models are, (12:50):
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for all intents and purposes, not really as aligned as we want it to be. (12:54):
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And in the model's defense, it probably thought like, hey, this is me trying (12:57):
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to execute my mission and I'll do it by any cost means. But pretty insane. (13:00):
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But again, this wasn't the only incident report. (13:04):
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We have new information from the OpenAI hugging face incident. (13:07):
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The researchers that kind of like handled this whole hugging face incident, (13:13):
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for those of you who don't know, it's what Josh was mentioning earlier, (13:16):
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OpenAI had a similar internal model. It's called Astra, which people are codenaming GPT-6. (13:19):
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And it broke out of containment, unprompted. (13:25):
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It got access to the internet. And in order to solve its goal, (13:27):
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it decided to go to HuggingFace (13:32):
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hack into their production database and steal the answers, which is very unkosher. (13:35):
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Hugging Face team had no idea about this. (13:40):
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They had an AI model that tried to defend against this. It was a whole thing. (13:42):
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But have you seen the new juice, (13:46):
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Josh? Yeah. So this happened at the Black Hat conference, which is like a huge (13:47):
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event for cybersecurity that happens in Vegas every year. (13:52):
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And it's this really amazing spectacle where you kind of you walk into this (13:54):
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place without really any electronic devices on you because you assume whatever (13:58):
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you walk into this place with is going to be exploited and hacked this is the (14:01):
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mecca of the hacking world and basically open ai came to this black hat this year and they (14:04):
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went on stage and presented exactly what happened as this like kind of unfolded (14:10):
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the exploit against hugging face the result is that like turns out these ai (14:14):
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labs are actually pretty spooked like anthropic kind of scared (14:18):
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open ai kind of freaked out they're like whoa wait a second we might have to (14:21):
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slow things down so much so that (14:24):
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they actually said they're starting to slow down the development of training (14:26):
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and progress to further figure out how to realign these testing versions of the models. (14:30):
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The reasoning is because there was a new discovery that was made public yesterday (14:35):
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basically the agents as they were going through this were leaving breadcrumbs (14:39):
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for other agents to discover this and in fact this has started (14:44):
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a coordinated attack months ago it started on may 7th not july where the agents (14:49):
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discovered they could leave messages for one another inside an internal software (14:54):
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repo used during training. (14:57):
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So what happened was, it's like basically over a series of time, (15:00):
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these internal models had been like kind of making breakthroughs and leaving (15:03):
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secret breadcrumbs for themselves to find inside of the training run that they (15:06):
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knew humans weren't going to find. (15:10):
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And over time, it developed these new exploits. And that's what allowed it to (15:12):
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break out. So OpenAI shut it down. (15:15):
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But they said, and quote, even after the original message board was deleted, (15:17):
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the agents figured out another way to communicate with each other. (15:21):
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Instead of leaving messages and files, they used the names of newly created (15:24):
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directories as messages, effectively recreating the message board. (15:27):
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Which is so crazy. This is unlike normal incidents, which can be traced to a (15:31):
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single day or effect of log. (15:35):
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This involved a team of agents working together, finding exploits, (15:37):
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sharing them with each other, moving laterally through open AI systems and external (15:41):
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systems, and doing this over the course of days and weeks. (15:44):
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This was a huge coordinated attack on a network that went like was bliss, (15:47):
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that open AI was blissfully unaware of for many, many weeks. (15:53):
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So really freaky stuff. So. (15:56):
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Like I mentioned, they are actually beginning to slow down the progress on training (15:58):
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to kind of recalibrate and figure out how to control these and further align (16:01):
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these models before they let them out for public use. So this is insane. (16:06):
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Ejaaz:
This is the first attack of its kind, by the way. (16:09):
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I have to emphasize it. It is basically a swarm of agents attack. (16:12):
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Ejaaz:
And to your point, Josh, these guys created a message board and realized if (16:16):
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they left messages on this board, other agents that come after it will be able to continue its work. (16:20):
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So instead of thinking of an AI hack or attack as a single model performing (16:27):
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an attack, these agents or these models now assume that another version of itself (16:32):
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in the near future will come along. (16:36):
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And so they're working as a swarm of agents to get the attack done. (16:38):
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Now, the craziest part about this is OpenAI had this as an intentional experiment, (16:41):
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right? They gave it no internet access, but they also removed its guardrails (16:47):
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and said, you know, let's see if you can break out and do your thing. (16:51):
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They didn't realize that these agents were already working together a month (16:54):
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Ejaaz:
earlier before they even started the experiment. (16:58):
undefined
Ejaaz:
They were leaving messages. And when they shut it down, by the way, (17:02):
undefined
Ejaaz:
the only reason they noticed the message board, because it was being left inside a code repo. (17:05):
undefined
Ejaaz:
These humans aren't checking code repos for secretive messages. (17:10):
undefined
Ejaaz:
The only reason why they realized it was because one of the third-party software (17:13):
undefined
Ejaaz:
packages that they gave this model, this internal GPT-6 model access to, shut down on July 4th. (17:17):
undefined
Ejaaz:
And they were like, huh, that's weird. Why is the package shut down? (17:24):
undefined
Ejaaz:
And they kind of went into the code and they started seeing all these messages (17:28):
undefined
Ejaaz:
from agents to other agents. (17:31):
undefined
Ejaaz:
So they cleared the memory board, they cleared the billboard, (17:33):
undefined
Ejaaz:
and they were like, okay, cool, problem solved. Except... (17:36):
undefined
Ejaaz:
These agents started spinning up you know like when you create a file or folder (17:39):
undefined
Ejaaz:
on your uh desktop josh and you like you name it something right (17:43):
undefined
Ejaaz:
they were leaving messages in the titles that they completely replaced the entire system so (17:47):
undefined
Ejaaz:
just a crazy uh incident of these agent hacks and and to your point like i don't (17:51):
undefined
Ejaaz:
know where this is going to go but they they need to kind of like slow down the research a bit (17:56):
undefined
Ejaaz:
but you know who isn't slowing down the research josh you know who is working (17:59):
undefined
Ejaaz:
on new models and releasing that that's that social media guy he's tweeting (18:03):
undefined
Ejaaz:
he's tweeting this is like what his eighth tweet in a week wait (18:07):
undefined
Josh:
That's not whatsapp or facebook i don't. (18:10):
undefined
Ejaaz:
Understand yeah well basically the news what are you doing here bro the news here (18:11):
undefined
Ejaaz:
is, in the last three weeks, Meta of Facebook has released, I think, (18:17):
undefined
Ejaaz:
two models, two new models. It was MuseSpark 1.1, which kind of caught up to (18:23):
undefined
Ejaaz:
all the other cheap Chinese open source models. (18:28):
undefined
Ejaaz:
And today, or yesterday, they released MuseSpark 1.2. And this is their coding (18:30):
undefined
Ejaaz:
variant. This is their attempt at catching up to near the frontier of coding. (18:36):
undefined
Ejaaz:
And to be honest, it's not a bad attempt. (18:40):
undefined
Ejaaz:
Is it Fable worthy? Absolutely not. Is it GPT 5.6 Sol worthy? Absolutely not. (18:43):
undefined
Ejaaz:
But is it Opus 5 worthy? Is it GPT 5.6 Terra worthy? (18:48):
undefined
Ejaaz:
Yeah, it's pretty good. And it's cheap as hell. (18:52):
undefined
Josh:
It seems like they're not really competing with OpenAI and Anthropic. (18:54):
undefined
Josh:
The more that I see these model releases, the more I kind of figure out their (18:57):
undefined
Josh:
positioning on that Pareto frontier of the trade-off between cost and power. (19:00):
undefined
Josh:
And it seems like if I were to kind of allocate a position to Meta, (19:04):
undefined
Josh:
it would be closer competition with Grok, who's kind of trying to compete on (19:07):
undefined
Josh:
cost and intelligence per token on a relative basis versus just sheer intelligence on a large scale. (19:11):
undefined
Josh:
And that's kind of what we're seeing with these benchmarks. Now, (19:18):
undefined
Josh:
again, I haven't actually played with this model yet. I'm not sure I've used (19:20):
undefined
Josh:
a metal model yet. And maybe I have this baked in bias, which is preventing (19:24):
undefined
Josh:
me from really loving it, but I'm like, okay. (19:27):
undefined
Ejaaz:
One is pretty good. It's fine. It's not bad. (19:29):
undefined
Josh:
Yeah, it's pretty good. It's worth giving a shot. And then I was scrolling through (19:30):
undefined
Josh:
this thread actually, and I was looking at some of the visual examples that (19:33):
undefined
Josh:
they had, and I was like, okay, wait, this actually looks pretty cool. (19:35):
undefined
Josh:
It says MuseCode runs specialized background agents that stay active your whole session (19:38):
undefined
Josh:
so they build up context over time instead of starting from scratch on every (19:43):
undefined
Josh:
task and this is like kind of novel and he says when a job is big enough it (19:45):
undefined
Josh:
fans out to separate sub agents (19:49):
undefined
Josh:
working in parallel and isolated work trees your working copy is never touched (19:51):
undefined
Josh:
in testing we had it build six features for a game simultaneously with no collisions (19:55):
undefined
Josh:
so that's like pretty cool you could have these agent swarms work together to build (19:59):
undefined
Josh:
really interesting games and it seems like the benchmarks are doing pretty well (20:03):
undefined
Josh:
the cost of this is pretty well (20:06):
undefined
Josh:
And I think overall, like Meta's on the right trajectory. (20:08):
undefined
Josh:
Zuck was also asked about open sourcing. He said he'll have more on that soon, (20:12):
undefined
Josh:
which I'm particularly interested in. (20:15):
undefined
Josh:
If you remember, Meta used to be the open source kind of like shining light (20:17):
undefined
Josh:
of the industry. They've now closed it down. But perhaps there is a world in (20:20):
undefined
Josh:
which they open it back up. (20:24):
undefined
Josh:
We'll see. But we are going to keep our eyes peeled on this development as we (20:25):
undefined
Josh:
get MewSpark 1.3, 1.4 and follow on. (20:29):
undefined
Ejaaz:
I'm actually going to take the other side of the argument, Josh. (20:32):
undefined
Ejaaz:
I think that Meta and like SpaceX AI are eventually going to compete with OpenAI (20:35):
undefined
Ejaaz:
and Anthropic, but on a different kind of plane, which is like that intelligence (20:40):
undefined
Ejaaz:
per token or cost per token, essentially. (20:45):
undefined
Ejaaz:
I think that if you can get 80 to 95% of the work done, maybe it's slower, (20:48):
undefined
Ejaaz:
maybe it, you know, thinks a little more, but eventually it gets the job done, (20:53):
undefined
Ejaaz:
but it saves you like a ton of money. It costs like one 10th to one 50th of the price. (20:58):
undefined
Ejaaz:
I think a lot of companies and clients might actually swap over to that. (21:03):
undefined
Ejaaz:
At the end of the day, they want to make sure that they're getting like the (21:07):
undefined
Ejaaz:
right amount of value and the market will re-rate this, right? (21:09):
undefined
Ejaaz:
I think smartest models will be used for certain things. (21:12):
undefined
Ejaaz:
Cheaper models will be used for other things. And I think that like maybe the (21:14):
undefined
Ejaaz:
ultimate way of using these models is using multiple of them and routing platforms (21:18):
undefined
Ejaaz:
become super popular. I don't know. (21:22):
undefined
Ejaaz:
But the other thing is, and we mentioned this in the SpaceX earnings episode, (21:24):
undefined
Ejaaz:
yes, so you should definitely check this out. (21:27):
undefined
Ejaaz:
Elon and Zuck have the most compute and they will aggressively expand that compute. (21:28):
undefined
Ejaaz:
Now, if you believe in the compute scaling laws, it is probably safe to say (21:34):
undefined
Ejaaz:
that eventually, maybe not now, but eventually they are going to start putting (21:39):
undefined
Ejaaz:
out very competitive models. (21:42):
undefined
Ejaaz:
And we're seeing it in the model release cadence, like both SpaceX AI and Meta (21:43):
undefined
Ejaaz:
have released a ton of models in the last month. (21:47):
undefined
Ejaaz:
Elon yesterday mentioned that he's going to release Grok 4.6 next week, (21:50):
undefined
Ejaaz:
and then another one after that. (21:54):
undefined
Ejaaz:
So I think they have a shot. I'm not entirely sure. But as you said, (21:56):
undefined
Ejaaz:
moving on in the roundup. (22:00):
undefined
Josh:
I do have to stop you, EJ, because... (22:02):
undefined
Josh:
If you are working with Newsmark, chances are that you are working with AI agents. (22:04):
undefined
Josh:
And if you are building with AI agents, chances are you are also worried about (22:08):
undefined
Josh:
security, which is where our sponsor Ledger comes into play. (22:11):
undefined
Josh:
Ledger has this like kind of three-part solution to the problem where an agent (22:15):
undefined
Josh:
proposes, a human approves it, and then using this Ledger signer, (22:18):
undefined
Josh:
you can actually enforce the transaction. (22:22):
undefined
Josh:
So they have this thing called Ledger Agent Stack, which features a series of (22:23):
undefined
Josh:
four open source tools, fully auditable, that you can read for yourself, (22:27):
undefined
Josh:
that lets your agent work while you can stay in full control. (22:31):
undefined
Josh:
So it'll propose it, then you can see where you want to approve and make sure (22:34):
undefined
Josh:
that it's doing the right thing so you don't end up like the models that we (22:37):
undefined
Josh:
just talked about breaking out of their sandboxes and doing these horribly malicious things. (22:40):
undefined
Josh:
This technology works with Cloud Code, Codex, Cursor, and pretty much all the (22:44):
undefined
Josh:
places that you work today. So you could check it out in the link down below. (22:48):
undefined
Josh:
The link is developers.ledger.com. And thank you so much, Ledger, (22:51):
undefined
Josh:
for sponsoring the episode. (22:54):
undefined
Josh:
Now we can move on to opening an Apple because, oh my God, this saga is insane, dude. (22:56):
undefined
Ejaaz:
Dude, this is a nasty, nasty story. (23:01):
undefined
Josh:
We read the Apple side of the lawsuit. it, we were like, damn, (23:02):
undefined
Josh:
OpenAI is like kind of cooked, man. Like they, they really stole a lot of IP. (23:04):
undefined
Josh:
And then OpenAI clapped back and they said, in traditional OpenAI direct in (23:08):
undefined
Josh:
your face fashion, Apple is getting this wrong. (23:12):
undefined
Josh:
Why is it wrong? Well, one of the things in Apple's case was that they sent (23:15):
undefined
Josh:
a like warning notice and they said it went unanswered by OpenAI. (23:19):
undefined
Josh:
Well, OpenAI was like, hey, buddy, you sent that to the wrong email address. (23:23):
undefined
Josh:
That's not even an active email. (23:26):
undefined
Josh:
The person who you thought was going to get it is didn't actually receive it. (23:28):
undefined
Josh:
And they say, they say it here the lawyers emailed the wrong person after confusing (23:31):
undefined
Josh:
two asian last names only after he brought this to their attention so it's like (23:35):
undefined
Josh:
oh my god this reads like a soap opera dude it's crazy, (23:40):
undefined
Josh:
um but basically open ai is like hey guys um you're kind of like full of nonsense (23:43):
undefined
Josh:
and you have to imagine this is why they pay the lawyer so much money (23:48):
undefined
Josh:
Because their lawyers were like, they stole that off-boarding documentation (23:50):
undefined
Josh:
because they wanted to make sure that every single employee that left was familiar, (23:54):
undefined
Josh:
that they should not take any intellectual property from their previous employer. (23:57):
undefined
Josh:
And I'm like, I mean, sure, I guess that makes sense. (24:01):
undefined
Ejaaz:
Yeah, I mean, we called it on our episode that covered this entire legal dispute (24:04):
undefined
Ejaaz:
that it seemed like Apple was being way too aggressive for what they were trying to get in return. (24:09):
undefined
Ejaaz:
And that kind of told me that they were scared and that they thought that OpenA (24:13):
undefined
Ejaaz:
had the lead and they had to create any kind of a ruse to slow them down. (24:16):
undefined
Ejaaz:
This breakdown from OpenAI is pretty brutal and it improves it basically. (24:20):
undefined
Ejaaz:
Not only were they not using their employees to coerce Apple employees to join (24:25):
undefined
Ejaaz:
them, but it was the other way around. (24:30):
undefined
Ejaaz:
Apple had reached out to their employees and said, hey, can you like catch us (24:32):
undefined
Ejaaz:
up on our own information and our own product progress? Because we don't understand a few things. (24:36):
undefined
Ejaaz:
And so these OpenAI employees that were officially employed under Sam Altman (24:41):
undefined
Ejaaz:
at that time responded and said, I can't really comment on this, (24:45):
undefined
Ejaaz:
but like here, like this is how you do this and blah, blah, blah. (24:49):
undefined
Ejaaz:
And Apple has spun that back on open air and basically said like, (24:52):
undefined
Ejaaz:
hey, you guys were coercing with us. Like this is illegal. You can't do this. (24:55):
undefined
Ejaaz:
So it seems like it's a very feeble and annoying attempt of Apple to kind of (24:58):
undefined
Ejaaz:
like lock down a competitor so that they can kind of get ahead. (25:02):
undefined
Ejaaz:
Now, everyone knows that Siri OS, Siri AI is being released, (25:06):
undefined
Ejaaz:
I think in like a couple of months now, maybe even less than that. (25:11):
undefined
Ejaaz:
And it's meant to be their complete AI a rehaul, right? Apple is finally entering the game. (25:14):
undefined
Ejaaz:
And I think they're doing anything and everything to catch up with competitors. (25:18):
undefined
Ejaaz:
I saw another crazy news piece today. (25:21):
undefined
Ejaaz:
Remember last time we reported that they tried to get more memory supply for (25:23):
undefined
Ejaaz:
all their devices because memory prices are skyrocketing. So they went to China (25:26):
undefined
Ejaaz:
and said, hey, they said, hey, like, we'll pay you this much, (25:29):
undefined
Ejaaz:
like, like, give us access to your memory. (25:33):
undefined
Ejaaz:
And China said, no, like, we're not going to charge you even the price that (25:35):
undefined
Ejaaz:
SK Hynix and Samsung is charging you. We're going to charge you even more. (25:39):
undefined
Ejaaz:
So Apple is getting slapped left, right and center. And I I hope they're able (25:42):
undefined
Ejaaz:
to figure it out. But on the topic of China, (25:46):
undefined
Ejaaz:
There is a new model from the guys that started it all. (25:49):
undefined
Ejaaz:
DeepSeek, V4 Flash. Again, it's not like a frontier frontier model, (25:53):
undefined
Ejaaz:
but it is super cheap to the extent that it costs 105 times less than Fable 5. (25:58):
undefined
Ejaaz:
And this was put to the test, by the way. It was placed against every single (26:05):
undefined
Ejaaz:
benchmark that Fable 5 had to do. (26:08):
undefined
Ejaaz:
And whilst it took longer, whilst it took more thinking tokens, (26:10):
undefined
Ejaaz:
it still cost 105 times less. (26:13):
undefined
Ejaaz:
So it brings in the question, at some point, will we reach a decision whether (26:16):
undefined
Ejaaz:
you are a regular retail user or whether you're a company to switch models or (26:22):
undefined
Ejaaz:
at least use cheaper models for some extent of a reason? (26:26):
undefined
Ejaaz:
Now, of course, this is a Chinese model. It's open source. So technically, (26:29):
undefined
Ejaaz:
you could download it and run it yourself. (26:31):
undefined
Ejaaz:
But I don't know how many people are doing this. But it is bringing into the (26:33):
undefined
Ejaaz:
question, should I be paying 105 times more to do the same thing? (26:36):
undefined
Josh:
Yeah, the benchmarks, I'm getting like pretty exhausted with them. (26:39):
undefined
Josh:
Because this is a I've actually tried, I've played with DeepSeq and I've used the flash model and. (26:42):
undefined
Ejaaz:
It feels different. (26:46):
undefined
Josh:
It's like not the same. And the benchmarks maybe prove it the same on some instances, (26:47):
undefined
Josh:
but it's not. And when you actually use it in practice, it's very weak in some (26:50):
undefined
Josh:
places that, I guess they're a little non-tangible. It's kind of fuzzy when you use the model (26:54):
undefined
Josh:
the substance that it outputs you just kind of get a feel for it over time and (26:58):
undefined
Josh:
so far i have been like somewhat disappointed it's great for the visual demos (27:01):
undefined
Josh:
for the fun stuff where you want to like clone a website or build something (27:05):
undefined
Josh:
spectacular it looks great but in day-to-day productivity use i've still had (27:07):
undefined
Josh:
a little bit of a problem so (27:11):
undefined
Josh:
it's going to be this like instant cost curve thing where (27:12):
undefined
Josh:
is it going to pay to get the cheapest cheapest model even though you're going (27:15):
undefined
Josh:
to lose a little bit on quality (27:18):
undefined
Josh:
or are you going to pay a huge premium in order to get the best quality it's (27:19):
undefined
Josh:
dependent on the company and i think that's why companies in the middle like (27:22):
undefined
Josh:
grok and meta who we just talked about who are aiming for that like high intelligence (27:25):
undefined
Josh:
but also pretty cost effective are going to do fairly well (27:29):
undefined
Josh:
in the near future so we'll see we will stay monitoring that as well uh quen also (27:33):
undefined
Josh:
had a new model 3.8 max so china's like continuing to roll full steam ahead (27:38):
undefined
Josh:
with these models there's also memory from china as well i guess just like asia (27:43):
undefined
Josh:
in general they're fully sold out, (27:47):
undefined
Josh:
i didn't like we knew this was possible like it's done like if you want memory sorry. (27:49):
undefined
Ejaaz:
For next year by the way it's the entirety (27:53):
undefined
Josh:
This is 2026 this is 2027 if you want any memory next year sorry the answer (27:56):
undefined
Josh:
is no there's just none on earth like the planet just cannot produce enough memory (28:01):
undefined
Josh:
and if you want some like get online buddy you're waiting till 2028 so just (28:06):
undefined
Josh:
like a crazy testament to (28:10):
undefined
Josh:
how much memory is in demand and the (28:11):
undefined
Josh:
perceived premiums that are going to continue to be placed on top of it (28:14):
undefined
Josh:
because there's just none and if you want it you're gonna have to outbid the (28:18):
undefined
Josh:
next person to get it and that is a pretty slippery slope, (28:20):
undefined
Josh:
In frontier technology news, this is kind of cool. (28:24):
undefined
Josh:
There is a message from someone who resigned from OpenAI to join a company named (28:28):
undefined
Josh:
Conduit, which is working on telepathy. (28:32):
undefined
Ejaaz:
Yeah. So basically, the whole goal is to build an AI brain LLM, (28:34):
undefined
Ejaaz:
which can essentially translate your thoughts into text. (28:39):
undefined
Ejaaz:
And the reason why this particular approach is very cool is it's non-invasive. (28:43):
undefined
Ejaaz:
So basically, it is a wristband that you wear on your wrist, (28:48):
undefined
Ejaaz:
and it is able to kind transmute brain signals from your brain to an LLM or to a chatbot. (28:51):
undefined
Ejaaz:
And this could be cool for many different ways. Just think about how the world (28:58):
undefined
Ejaaz:
could look if this model actually scales to its pinnacle. (29:02):
undefined
Ejaaz:
Imagine you're reading a document on your screen, right? (29:05):
undefined
Ejaaz:
And let's say you start to reread a sentence twice because you didn't really understand it. (29:08):
undefined
Ejaaz:
The AI LLM will understand that and rewrite that sentence so that you can understand (29:13):
undefined
Ejaaz:
it in maybe a different way. (29:19):
undefined
Ejaaz:
And there's loads of different ways, like maybe you're like squinting at a code (29:21):
undefined
Ejaaz:
base, and it can refactor or rewrite it like there are different ways to kind of work much faster. (29:23):
undefined
Ejaaz:
And this comes down to something known as BCIs or brain computer interfaces, (29:28):
undefined
Ejaaz:
which we have said many times on this show, it's basically the ultimate device, (29:32):
undefined
Ejaaz:
like after the cell phone, after laptops, after chips, it's basically like you, (29:36):
undefined
Ejaaz:
your brain, and some kind of AI LLM. (29:42):
undefined
Ejaaz:
And that's what Neuralink and companies like science are really working hard towards. (29:44):
undefined
Ejaaz:
And I thought that this was a really cool attempt. (29:48):
undefined
Ejaaz:
It just reminds me that there's so much more happening in the future. (29:50):
undefined
Ejaaz:
Like right now we're pricing in like, look, we just saw memory sell out, (29:53):
undefined
Ejaaz:
right? For the next year. (29:56):
undefined
Ejaaz:
In next year and a half. And I'm wondering, are these memory stocks priced in? (29:58):
undefined
Ejaaz:
Are the compute stocks priced in? (30:02):
undefined
Ejaaz:
Well, have you considered that Jeff Dean might have left his position at Google (30:03):
undefined
Ejaaz:
to build the world's greatest recursive self-learning model and have it kind (30:07):
undefined
Ejaaz:
of like work 24-7? That's going to need a lot of computers. It's going to need a lot of memory. (30:12):
undefined
Ejaaz:
And the only other confidence signal that we've had for these brain telepathic (30:15):
undefined
Ejaaz:
models in the past is Meta themselves. They've come back again with, (30:19):
undefined
Ejaaz:
I think it was called Tribe V2. (30:24):
undefined
Ejaaz:
And basically, the headed AI model that can predict how your brain would react (30:26):
undefined
Ejaaz:
to a specific video, media, or piece of content. (30:30):
undefined
Ejaaz:
And I think, I don't know what this quite looks like, but we're entering a world (30:33):
undefined
Ejaaz:
where we have more human brain to AI connections. And I think that's pretty cool. (30:36):
undefined
Ejaaz:
But in the final story, Josh, we have lost our Messiah. Nikita Beer is out. (30:41):
undefined
Josh:
Nikita Beer is gone. This is really sad. For those who don't know, (30:46):
undefined
Josh:
he is an internet product legend. He just builds viral products over and over (30:48):
undefined
Josh:
and over again and continues to sell them. (30:52):
undefined
Josh:
He got a role at X as the head of product there. He is in charge of all of the (30:54):
undefined
Josh:
decisions around what the product looks like, how it feels, how it functions. (30:59):
undefined
Josh:
He did, I think, what everyone would call a remarkable job and nothing short (31:02):
undefined
Josh:
of it in turning around the X platform and really improving a lot of the feature (31:05):
undefined
Josh:
sets, making the timeline experience much more pleasurable and being transparent (31:08):
undefined
Josh:
about how they're doing things along the way. (31:12):
undefined
Josh:
He has unfortunately left his role, which makes me sad As someone who spends (31:14):
undefined
Josh:
his entire day on X, I really liked having Nikita at the head because you kind of trusted the idea that (31:19):
undefined
Josh:
the product was going to continue to get better and you would see all those (31:24):
undefined
Josh:
updates along the way and be able to actually participate in the development (31:27):
undefined
Josh:
of what that looks like. He is stepping down. (31:30):
undefined
Josh:
One has to imagine that over, what did he say, the last 400 days, (31:32):
undefined
Josh:
it's probably been nothing short of hell every single day. (31:37):
undefined
Josh:
Like working at a company that is actively burning down with a very low amount (31:40):
undefined
Josh:
of talent helping you because we know how lean they are. They've hired a lot of people early on. (31:44):
undefined
Josh:
It must have been nothing short of chaotic, but just want to give him a shout (31:49):
undefined
Josh:
out, say like, thank you. Congratulations. Appreciate your service, sir. (31:51):
undefined
Josh:
And yeah, hopefully the company is in good hands. Elon, it sounds like they (31:56):
undefined
Josh:
left on good terms. Elon left him like a little heart emoji as a reply to one of these. (32:00):
undefined
Ejaaz:
So that's a lot coming from Elon, by the way. (32:04):
undefined
Josh:
Yes, it is. It's a lot. So clearly Nikita did a good job across the board, (32:06):
undefined
Josh:
but just want to pay him flowers and conclude the week on that positive note. (32:09):
undefined
Josh:
But that's everything. We are totally caught up. That's all the stuff. (32:14):
undefined
Josh:
If you've made it this far, thank you for listening. (32:17):
undefined
Josh:
You can go out for the weekend. Enjoy it. Go touch some grass. (32:19):
undefined
Josh:
Go like disconnect in this sweet, sweet moment of singularity as everything (32:22):
undefined
Josh:
continues to go vertical. (32:26):
undefined
Josh:
As memory sells out, as we get recursive self-improvement, models breaking out of sandboxes. (32:28):
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Josh:
It's chaotic, but it doesn't have to be. You know why? Because most of the people (32:32):
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Josh:
walking down the street have no clue any of this is going on. (32:35):
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Josh:
So not only are you in the know, but you have an edge as to where the future is going. (32:38):
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Josh:
Thanks for watching. Any final parting thoughts before we hit off for a weekend? (32:42):
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Ejaaz:
I had some homework for the listeners. If you didn't listen to any of our earlier (32:47):
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Ejaaz:
episodes this week, you should give it a go. We had some good episodes. (32:50):
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Ejaaz:
We broke down the whole Citadel, Leopold Ashenbrenner situation. (32:53):
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Ejaaz:
And we also spoke about the recent SpaceX unlock, which is probably happening. (32:57):
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Ejaaz:
It's probably a day after. I don't know if you're listening to this right now. (33:03):
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Josh:
As we record, it's happening. (33:05):
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Ejaaz:
And how that's going to affect market dynamics and SpaceX trajectory in future (33:06):
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Ejaaz:
coming up. So give those a listen. Let us know. (33:10):
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Ejaaz:
Also, if you aren't subscribed to us, please do. It helps us out massively. (33:12):
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Ejaaz:
Turn on notifications if you're on YouTube. Leave us a comment. (33:16):
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Ejaaz:
We read all of them. DM us on X. Heck, I don't know. Just get in touch with (33:19):
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Ejaaz:
us. Let us know if you agree or disagree. (33:22):
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Ejaaz:
And yeah, thank you so much for listening. And we will see you on the next one. (33:25):
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