Transcript
Jeremy (0:00)
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John (0:02)
Today is Thursday, April 9, 2026. We are live, the TVPN Ultradome, the Temple of technology, the fortress of finance, the capital of capital. We have a great show for you.
Jeremy (0:13)
I was getting a little worried Monday. There were not a lot of fundraises going on. I was looking at the schedule and I was thinking, is it over? And today I'm happy to report that we're back. Andrew Dye is coming on from elorian, raised a $55 million seed round. We got Kaseva coming on with a $38 million Series B. We got $170 million Series C later and then $100 million Series E and then a nice little mango seed from the Enclave team to cap it off. But really fun show today. We got Sagar and Jetty from Breaking Points, our friend over there coming on, we got Joe Wiesenthal W
Guest Host (0:57)
and then
Jeremy (0:57)
CZ from Binance who is releasing Freedom of Money, detailing Binance's rise to crypto evolution and of course his US Legal battle memoir.
John (1:10)
And I'm excited to talk to Joe about the who is Satoshi Nakamoto? And I'm also excited to talk to CZ about who he thinks Satoshi Nakamoto is. You would imagine that he's in a position to potentially have a good take on that. Anyway, there is a ton of AI news. Andy Jassy released the 2025 shareholders letter. Amazon is known for fantastic shareholders letters dating back to 1997. I thought he did a good job of sort of resetting the AI narrative. There's this AI lab horse race going on, of course. We've been covering it all week. Anthropics, Mythos Preview and Project Glasswing launched on Tuesday, quickly followed by news today that OpenAI also plans to deliver a model with advanced cybersecurity capabilities to key Internet infrastructure providers. And there's this debate going on over how and when these models will rol I think this is going to be an ongoing trend. I don't think cybersecurity is the last model capability that will be slowly delivered to key companies. First, cybersecurity is a perfect fit for powerful coding agents. And people have been digging into exactly how some of these zero day exploits, some of these bugs and vulnerabilities were discovered. And it makes a ton of sense that if you have a model that's fantastic at coding, it can basically try every single coding exploit, try new coding exploits across a huge number of open source packages, submit pull requests, and generally harden the Internet infrastructure that we all rely on. So in general, it seems like the rollout of Mythos, although people are disappointed because they want to play with the latest and greatest model, even if it's very expensive. It seems like it's having a positive effect and should be a bit of a white pill for containing powerful models, having them have proper impacts, having them have a positive impact on the American economy, on our security, on all these different things. I do think I wouldn't be surprised if we see something similar happen in biosafety. Now, the biosafety AI research loop is a little bit longer because you might have to go to the lab. It's not entirely existing in a computer, in a virtual machine that you can spin up and just brute force reinforcement, learning against. But you could imagine if a model develops capabilities maybe in the next run, maybe middle of this year, if a model becomes powerful enough to design a harmful virus or something like that, you would want the lab that creates that model to deliver that to the scientific community and companies that can protect the population against the development of new harmful biological viruses, just like we're protecting the Internet against cybersecurity viruses. And so I'm not exactly sure where this all goes, what the other 10 steps are, but in general, it seems like there's going to be a pattern of a powerful model becomes capable of doing something that it makes sense to share with the particular community that can defend against that new capability. And then the entire community needs to make sure that that capability is carefully under control before releasing a version of that model that can still, in the biosafety example, you still want a model that can help you learn about biology, learn about how viruses are made, how they mut. This is important for education and augmentation of the, of scientists from, you know, high schoolers all the way to professionals. But the most advanced technology, it makes sense to put it in the hands of people that can actually take a real. Have a real impact on that immediately before rolling it out broadly. But Andy Jassy sort of zoomed out and was stepping back from the horse race because Amazon's a partner with basically everyone in the ecosystem and has their own models. And so he shared this shareholder letter, zooming out on the state of AI, the state of Amazon's plans, and he shares a bunch of very interesting anecdotes about his personal life. So we should read through some of this. He says, when I graduated from college, I wanted to be a sportscaster. After sending my resume reel to many small markets around the United States and only getting two nibbles. I settled on doing sports production at a major network to make extra money. I coached my former high school soccer team and worked at a retail golf store. Six months later, a college classmate convinced me to interview at the consumer products company where he worked and I spent three years as a product manager there. I left that job to try some of my own businesses after deciding these businesses weren't my calling. I tried short stints at sales and investment banking before going back to grad school and ending up at Amazon three days after my last final exam in May 1997. Not exactly a straight line, he says. AWS followed lots of squiggly lines too. And of course Andy Jesse is by and large the creator of aws. That was his major project during his tenure. Founder, yeah, deserves so much credit in building that business. The original vision included storage, compute, payments and human intelligence. They had a product called Mechanical Turk where you could go and dispatch a specific task. You would have to build sort of a web ui, but it was the original sort of data capture tool, but it could also be used for little things like manual translation tasks. People weren't really using it for customer support tickets, but data labeling, data labeling before you really needed mass data labeling, but it was that type of business. And there was always a question when scale AI started like is this just Mechanical Turk? Is this business process outsourcing? Obviously that went on to a fantastic outcome with Meta, but it became a different thing and quickly moved towards what we see in the expert networks and data collection that's much more nuanced than a single task on demand. But aws, this was in the vision and they wound up pulling away from that and sort of refocusing. So Andy Jassy says some of those, for example storage and compute, became linchpins in aws, others didn't, Others didn't succeed. We didn't initially plan a database service and when we built one, our first attempt failed to get traction. We went back to the drawing board and built new relational and non relational databases which have resonated well and become core to millions of AWS applications. When we launched EC2, our compute service, it was a single instance in one availability zone, Linux only, with no auto scaling, load balancing, block storage or private networking. Over the time we added those capabilities and hundreds more services and you see that when you go into the AWS dashboard, it is chock full of different products. AWS was initially attractive to startups. Companies like Doordash, Dropbox, Pinterest, Slack and Stripe were among many that built their businesses on aws. Pundits said that enterprises and governments would never use aws. Wait, what is going on here?
