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Season 2, Episode 32 · From First Principles

5,000-Year-Old Bacteria, Solar Storms, Dogs, and Meta’s AI War (EP. 32)

20 Mar 2026 · 38 min

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Hosted by Lester Nare and Krishna Choudhary , this is our first standalone rundown episode — a faster, looser format where we hit several stories we didn’t have room to turn into full deep dives. This week: bacteria revived from a Romanian ice cave after 5,000 years , a speculative but fascinating theory linking solar storms to earthquakes , new evidence that dogs and humans share genetic roots for personality traits , and the increasingly dramatic fight over the future of AI after Yann LeCun leaves Meta to build a new billion-dollar company focused on world models. Summary Ancient bacteria,…

Transcript

Read the transcript · about 6,190 words, follows along as you listen

Frozen for 5000 years, this ice cave bacterium resists modern antibiotics, which sounds a little bit scary. It's very speculative, but even even the fact that the sun could affect earthquakes is is a very interesting. That is Jan Lekun, who has recently left Facebook or Meta in early 2026 and is now raised a billion dollars to build AI that understands the physical world. Hello Internet, This is your captain speaking, Lester Naare, joined as always by my cohost and our resident PhD, Krishna Chaudhary. We are in our first stand alone rundown episode. We're going to cover several stories just at a high level, have a fun conversation.

These are stories we didn't get to to do a deep dive this week, but we still want to talk about it and get you guys into your weekend with some fun, exciting talking points with a special segment in the middle, which is going to be a surprise for our resident PhD. Today. We're going to cover microbiology on bacteria that was found in frozen caves 5000 years ago, solar storms and earthquakes and how they may be related. Some of the shared genetic routes between US and dogs.

And we will wrap up with an AI story about how the battle or control at Meta AI has now led to the start of a new billion dollar AI company coming at a different angle than large language models as we know it. We will be learning about the science not quite from the ground up, but at least from the top of the surface, because this is from first principles. Our first story for the rundown today is out of the Institute of Biology in Bucharest of the Romanian Academy, and it was in the frontiers in microbiology. Frozen for 5000 years, this ice cave bacterium resists modern antibiotics, which sounds a little bit scary. Yeah, dude, this one was pretty, pretty weird. There is a cave in Romania.

It is called the Skari Suara Cave. It's got this giant glacier that's about the size of 40 Olympic swimming pools. And that glacier began to form about 13,000 years ago. And what you can do is you can drill cores into that ice and then extract those samples. And there's bacteria that are preserved in that ice. And bacteria are notorious for surviving being frozen for thousands of years. So we can bring back to life bacteria that have been frozen for thousands of years.

This particular sample is from the 5000 year old layer of that glacier. They took it out, they revived it and they tested it on modern antibiotics. They tested 28 and this bacteria was resistant to 10. Not great. Yeah, it's really quite strange on the face of it because modern antibiotics are by definition modern, right. So we've developed these, you know, antibacterial medication such that it kills bacteria and these bacteria that are 5000 years old still have a kind of defense to stuff that we made today.

This is in line with a lot of the anti back antibacterial like resistance that we're faced with as humanity, right? We've been using antibiotics for hundreds of years now. The first real big one was penicillin by Alexander Fleming before World War One. And because we've been going at it, bacteria has evolved to fight against these antibiotics, right? They figured out ways to get around it. And the fact that ten of these 28 did not work on bacteria that were 5000 years old, right, suggests that these bacteria were already resistant in the 1st place, right? OK fine, 18 worked, but still the 10 is kind of weird. Yeah, yeah, yeah.

It's, it's it kind of dovetails with the the story we just talked about as it relates to bringing back samples from space. Yeah. And the the concern that arises because we don't know what will happen. Yeah, if there happens to be, again, that's a little bit that this is real world. We know that it's there. Yeah, we've tested it and they're basically pre. They have pre immunity like they were never our current antibiotic. They were never exposed to the current antibiotics and yet they're still already immune, right?

It's quite weird. I don't think we should be too worried about these glacial glacial bacteria because these are called psychobacters. Psychobacteria. Which means they're cold loving. They're not going to survive in the 989798 degrees Fahrenheit environment of the body of the body, right? But it's still quite a nice, you know, little problem in molecular biology that like, how could this even happen? And when when you look at like how bacteria actually evolve, they evolve together.

Bacteria are incredibly dynamic with their genomes. They swap genes from one to another all the time. That's actually how we transform bacteria. A lot of times, you know, in an undergrad lab, if you want to like make a bacteria glow in the dark or something with a GFP, which is this fluorescent molecule. What you can do is put in a plasmid, which is a circular piece of DNA that has the GFP code. You put that in and then you heat shock the bacteria.

There's something that we've talked about a lot where you just take the bacteria and you put it in hot water and then the bacteria kind of freak out and they're like, what are we doing? They ingest all sorts of DNA in there from their environment and they try to transcribe that and see if that that'll work. So bacteria are swapping DNA between themselves and the environment all the time. And perhaps through the swapping you get this kind of drug resistance.

What's really cool about this this story is that it actually the existence of this reveals how antibiotic resistance evolves naturally. So that's one thing. The other thing is that this ancient strain also showed the inhibit the so this ancient strain showed the ability to inhibit the growth of other modern superbugs. So kind of like in penicillin. When Alexander Fleming discovered penicillin, it was because it was a natural antibiotic that he had seen. There was a mold that was growing in his Petri dish and around that mold there was no bacteria and he was like the mold is creating something that is killing the bacteria. Well, in this case, this ancient strain is able to create compounds that will kill modern superbugs, right? So it possesses some kind of enzymatic gene that can hold existing biochemical potential, right? Existing pharmaceutical potential. And so it tells us that maybe like we need to look at these kinds of natural places, at isolated populations of bacteria to figure out how to beat modern day superbugs that are now resistant to RBAC, antibacterial compounds, antibiotic compounds. But maybe these guys have

something. Right that. We can use to defeat them. You know it's like that, Thor. I can't defeat you, but she can. Thing. Like but the she can thing is like these like 5000 year old bacteria. Right. With there is a we can look to the past to help inform us about how to fight in the present and in the future in a way that was maybe somewhat unexpected in that these bacteria that are 5000 years old contain sort of an active ingredient that is relevant to sort of a pharmacological pharmaceutical application today. And and historically, most antibiotics are developed from bacteria and fungi that live in the soil and like they produce natural compounds because over there, down there, it's like it's like D-Day every day, right? It's just World War all the time. Everyone is trying to eat everyone. And in recent times, those pathogens that we found from existing soil bacteria no longer work. Well, we can, you know, go back in time. Yeah, I think it's it's a pretty cool story at at the heart of. It and for it gives a new meaning to the phrase natural immunity.

Yeah. And if if we can scale up the same process that allows you to freeze bacteria and reanimate them 5000 years later, maybe Walt Disney has a chance. Because if I recall correctly, he's currently cryo frozen somewhere deep underneath Burbank's headquarters. That is, that is rumor. That's rumor, of course. I don't know we. Don't know anything about it. So not that to be true, but this is again, new research paper out of the Institute of Biology in Bucharest of the Romanian Academy. It was in Frontiers in Microbiology. Fun little microbiology story to get us started on our rundown #2 on our rundown today is can solar storms trigger earthquakes And scientists propose A surprising link. This is was actually covered by Science Daily elbows out of Kyoto University.

And there's there's a huge solar storm monitoring community on a variety of social media channels. Every time there is AI guess a coronal mass ejection or similar. There's this like ranking system at the Richter scale. And everyone's like this one is over 9000, you know, hunkered down prepper. But but the there's a there's a potential link here to actually like geological activity. Yeah. And that one, I was pretty surprised that there is even a theoretical argument that you can make here.

And this is mostly a theoretical argument. This is a theoretical paper. What they're doing is showing a surprising theoretical connection between space weather and earthquakes, geological activity on Earth. Now, how does that even what what is the mechanism there? Right, is what I always wonder. So here's the idea. Faults on the Earth are poised at a kind of critical spot where there's a lot of stress on them and they're ready to snap. OK, because they've, they've been pushed to their brink.

And like the faults on the Earth are these, you know, plates on the Earth's surface that are contiguous blocks of the Earth's crust that are moving around because magma underneath is like flowing in a liquid motion. And so you, you know, you move around just like how ice on a frozen lake sort of moves around based on currents underneath. So why would why would that be affected by solar activity is the idea? Well, solar activity is effectively a bunch of charged particles coming at us. That's effectively what it is, right? There's a bunch of electrons, let's say that come at us and those electrons are going to create a massive change in the ionosphere.

The ionosphere being the outside ions around the Earth's atmosphere that are built by the magnetic field that sort of, you know, shield us from space weather. Now that ionosphere is going to get changed up a lot because there's going to be a huge influx of charged particles from the sun that's going to change the electric field on the Earth. The Earth's crust, on the other hand, has all of these plates that are sort of at this critical point. And in between all these plates inside the Earth's crust, there's a lot of water that is in a super critical state because it's under high pressure, it's under high stress, and that water has a bunch of ions.

So the electric field on the outside is going to move around the electric field inside these cracks in the Earth's crust. And that could potentially create enough of a force to actually snap and create high earthquakes, you know, high damage earthquakes. The the idea is that there is these fields that are all around us all the time and interacting have impacts in the physical nature in which we see and we interact. But the tangible thing we can touch and feel. And at the scale of the sun, yes. And the scale of these solar storms, yeah. The amount of the change in fields in the ionosphere can potentially have a subsequent impact on plate tectonics. Yeah.

And and how they move. Which again, big enough solar storm means it would not only trigger things like frying electronics and all that other stuff, yeah, but could actually physically impact the the Earth itself. Yeah, and there's like weak correlation between solar activity and geological activity. If you go to 2.5, there's a weak correlation that was out in Scientific Reports, Nature Scientific Reports in 2020. Again, it's not that big. And this paper is not claiming this current paper out in 2026 is not claiming that, you know, solar flares directly cause all quakes.

But the research provides this like kind of new mechanism to explain maybe unusual ionospheric activity and that connection with major quakes, because in 2024, there was the Nodo Peninsula quake and that was preceded by a really large solar storm. And so there's these coincident sort of we don't have enough data to like, you know, the Sigma isn't down to five, but it's there. And this is exploring some of the mechanisms. I think that's, I think that was like pretty cool.

Yeah, that that, not that it's very. Speculative, but even even the fact that the sun could affect earthquakes is is a very interesting. That is, and I think the plot of a future upcoming Jerry Bruckheimer produced easy disaster movie Jerry. If you want a scientific advisor, I think I know someone I can suggest I've done it. Before SO. Great. So this is again out of Kyoto University, which is fine. I mean, not funny. It's interesting. Obviously, Japan has had very dramatic earthquakes. Yeah, if we think of Fukushima, etcetera. Yeah, so they have a very concerted effort to try to figure it out.

And this was in the International Journal of Plasma, Environmental Science and Technology. Yes, yes, makes sense. Makes sense. This is going to be fun. Here we go and we're going to try this. And if it doesn't work, you're not going to hear this on the final episode, but we are going to start a new game show segment called Are You Smarter Than a Scientist? Welcome, ladies and gentlemen, our resident PhD, Krishna Chowdhury, who will be our scientist of the day. And we will have one question, Family Feud style for you to answer.

And today's segment question, Are you Smarter than a scientist? Is can you name the 10 most abundant elements in the human body? We have 10 on this list. OK. All right. OK. So I'm not sure if when you mean abundant, if you mean by number or by mass. So let's test it out and start with hydrogen. OK. So we have you on the board with hydrogen. So hydrogen is #3, which means that you are going for by mass and not by number maybe. So let's go with oxygen.

That should be #1. Is it #1 Ladies and gentlemen, and. Yes, oxygen. So we got H2O. 70% of the body is H2O, so that tracks. Let's go with carbon. OK. Carbon is carbon on the board. We got the top three. OK. And then nitrogen. That should round up 4. OK, so you think that nitrogen is going to be #4 is nitrogen #4 you are on a roll. We have our top 4. OK, so we got our top 4 all right. That was the easy stuff. I expected to get that, yeah. So now let's do.

Now I don't know if this is going to be #5 but I know it's it should be phosphorus because nucleic acids ATP. OK. So phosphorus. Phospholipids of our cell membrane. Not #5 but still on the board. At number six, number six, so there's something in between nitrogen and phosphorus. Only thing I can think of is sulfur. OK, let's go with sulfur. OK. That's in proteins. We we may have sulfur on the board, but it's going to be at the number 8. Spot. OK, what am I doing wrong?

Wow, what is #5 nitrogen? So so right now we have oxygen #1 carbon 2, hydrogen 3, nitrogen 4, phosphorus 6IN, sulfur 8. We are missing 579 and 10. No cheating. All right, let's do so. Sulfur's already in there. Wow. Oh, calcium form our bones. We have #5 on the. Board there. We go calcium. All right, there we go. All right, now let's do. Reminder you have three strikes. Oh, I do have three strikes. OK, I don't want to use any though you. Know me? OK, so I think now we should get into ions. So I'm going to go with, you know, the bread and butter of our nervous system and ions in general. So let's go with sodium first.

That's why we like salt. Sodium is on the board at #9. We have two left number 7 and #10. Let's do potassium. Is potassium on the board? It is at #7. So we got sodium and potassium. That's our Nak channels. So you are the. Neuroscientists out there. One left are #10 This might be a tough. One this one's a tough one, this. Might be a tough one. OK, so. Give me oxygen, carbon, hydrogen, nitrogen, calcium, phosphorus, potassium, sulfur and sodium. We are looking for the 10 most abundant elements in the human body.

If you at home have gotten this before, Krishna, you may be smarter than a scientist. Yeah, I don't know. OK, I'm going to guess on this one. OK. You have to go with iron, OK, Because of our blood hemoglobin. Iron. Is it on the board? Iron is OK shit. Not on the board strike #1 got two more strikes in the chamber. We do not have phone a friend. We do not have any escape routes. We already got calcium. Put you on the spot today, I know this. One's a hard one. Let's go with zinc.

Zinc is heavy and I know that we need it, OK? I just don't know for what. I'm going to go with zinc. #10 so. Even if we don't need a lot. Is it zinc? No. Oh my. Gosh, it is not. Zinc, all right. One more strike left for the 10 most abundant elements in the human body. All right, let's, let's. So it's, I'm thinking it's either iodine because I know we need iodine, that's why we need iodized salt or magnesium. I know a lot of people take supplements of magnesium, but people also take iron supplements and that wasn't on it.

Let's go with magnesium. Final answer. Yeah, let's do it. Final answer for the number 10 spot 10 most abundance elements in the body is it? No, all right. What is? It it is not our number 10 spot. If you're at home, plug in your guesses. Now for our number 10 spot, the answer was how? Did I? How did I? Oh my God, I even said salt. Sodium chloride. Wow. So that's ridiculous. This was our first rendition of Are You Smarter Than a Scientist? If you got all 10s, you may have one out on this week 1.

So this was our first go around doing this. I think it was pretty, but that's just that's just my personal opinion. I now have also access to a soundboard. So this can get very outrageous very quickly. I'll be sure to hold it in as much as possible, but it looks like for the most part everything went smoothly on the board itself. I think this could be super fun. That would. Be really fun. So again, let us know in the comments if you are intrigued by are you Smarter than Scientist?

And would like to see us bring that segment into the rundown episode on a regular basis. We are going to now go into our third story of the day, which was the one about the link, the shared genetic link between humans and dogs, and this is about golden retriever genes linked to anxiety, aggression and intelligence in humans out of the University of Cambridge and the Morris Animal Foundation in the Proceedings of the National Academy of Sciences. Yeah, this was a really cool one. They studied 1300 golden retrievers and it turns out that not all golden retrievers have the same personality. I mean, anyone who has multiple golden retrievers or has seen multiple golden retrievers knows this. They're not like a one brand type dog. There's dogs that are shy, there are dog, there are dogs that are, you know, a little timid.

There's dogs that are very, very social. So there's enough genetic variation within golden retrievers where when the scientists studied 1300 golden retrievers, they could figure out genes that were related to certain traits. Each of these 1300 golden retrievers, the owners of those dogs got like, a little questionnaire about, like, you know, personality traits for your dog. And then they could correlate using the genetic data, certain genes with certain traits.

And then when they went and looked at humans and those human traits, the same genes mapped to the similar human traits. That's the idea. Interesting. So there's there's two kind of points of data. There's sort of a a quantitative data, which is the genetic and there's like a qualitative data point, which is the the survey. Exactly. And they there was the survey, there was a reference point both for dogs and the personality traits on the qualitative side. Yeah.

And humans. And when they mapped, they looked at the genes that were being expressed that mapped to the personalities in dogs based on the feedback from the owners. What's interesting is that if you point to those same genes in humans, the same qualitative data. Yes, matches up, yeah. And you can, you can map the genes together because even though obviously the the golden retriever gene and the human gene are not going to be identical. Sure, their dogs were humans, but you can look at the sequence of nucleotides, the ATGCATGC and you can find overlap where like this gene is like there's only very few mutations, right. So it's definitely the same gene. And then from that they can figure out these genes that are actually related.

There's one gene, for example, PTPN one. This was linked to aggression towards other dogs in golden retrievers and it's associated with intelligence and depression in humans. So not same same yes, but it's really kind of this meta behavioral state, right? Or personality type, yes, that these genes are controlling. That's what they were very careful about, like making that distinction that this is not like, you know, a one to one mapping, but it's rather like a shared biological route for overarching personality. It's it's more meant to be a framework versus like a purely like for lack of a bedroom like diagnostic. Exactly.

Great diagnostic tool. Yeah. And it helps create a a scaffolding by which to kind of architect the structures and then you can swap out the variables within those structures as a sense. Yeah, and it also, like, leads to, like, insights and training dogs, for example, because, like, certain dogs might just be a certain way because of the genetic makeup, right? And it has nothing to do with, like, their history or like, so on and so forth. Here's a quote from one of the lead researchers. If your golden retriever cowers behind the sofa every time the doorbell rings, perhaps you might have a bit more empathy if you know they're genetically driven to feel sensitive and anxious.

Love that from this researcher from the University of Cambridge, Ana Morris Nuevo. It's, it's funny as someone who has three dogs who have very, very, very distinct and different personalities. It it, it's an interesting just to think about. And, and also I as we continue to sort of in a variety because we've touched a lot of genetic stories, Yeah, especially in the last couple of weeks. And just the breadth and depth of the increase in our, in our understanding of like this fundamental, like, you know, the instruction manual for how our bodies and all like living beings, like create the stuff net the factories, right.

And the, the, the IP that generates it's as we continue to learn more, it, it, it's not only the understanding piece, which is the first step, but that understanding is also an already being applied because there's a whole variety of things we can now do because we actually know the map. Yep, we actually have that baseline reference point and we always love a dog story. Yes, always. Whenever I see dog stories and Science News, I'm like, Yep, we're going to cover it. The the corgi story continues to be, I think, our most popular dog story, yes.

And then the the whale, the pods attacking the, that's our biggest animal. That's actually our biggest story ever, which I still can't. It's still so surprising to me. We're going to wrap up the rundown today. Again, super casual Friday, almost summer Fridays here. It's 92° in Los Angeles, CA currently, which is. That's an average March Friday, so. I don't know. We're finally over the rain, I think apparently, which is great. And our last story is, is, you know, interesting because it dovetails with a lot of the AI stories we've talked about.

And there's both a human personality side to the story and sort of like a, an actual science part to the story. And this is related to Jan Lacun, who has recently left Facebook or Meta in early 2026 and is now raised a billion dollars to build AI that understands the physical world. And this is related to this was covered by Wired. And there's there's a lot of background to this story. I'll kind of just quickly paint a picture before we kind of get into the physical world AI piece, you know, in terms of if you've listened to any of the frontier model CEO's over the last 12 months. So this is Sardemus Asabus at Google DeepMind, who runs all of Google's AI products, Gemini everything that's integrated with Google Workspace.

You have Dario Amadei at CEO of Anthropic, who has the clod and clod code and Co work. They've been very much in the zeitgeist and in the mainstream because of their ongoing kerfuffle with the Pentagon over usage of clod within military applications, specifically autonomous weapons and mass surveillance primarily focused on civilian. But you could argue generally, and particularly with Davos, the Davos speeches that just happened, all all of them were there and talking about all the, you know, all the journals.

Like where is this going? And when are we going to get to quote AGI, artificial general intelligence or ASI, artificial super intelligence? And can simply scaling the current architectures, meaning putting more compute and more data into it, get us there? Yeah. Or do are we missing a fundamental architecture or concept that needs to be included in order to actually make that that jump? And Yan Likun has been very vocal about his perspective that the scaling laws are not going to get you yeah to AGI. Yeah, Yeah, he's, he's been very vocal about that. And he's had some real like really nice, like little analogies about why he thinks that is. Yes.

He he equates it to, you know, the models of Ptolemy and the epicycles. Yes. And how, you know, before Copernicus, everyone was trying to fit the Earth as the center of the universe. And so they came up with orbits around Earth, but obviously Mars does this weird retrograde motion where it goes backwards. And so Ptolemy decided, OK, I'm going to put an epicycle, which is Mars has a big circle around the Earth, but then around that circle, there's a little circle that Mars goes around.

So every once in a while on that little circle, it's going to go back. But then as you get closer and closer and the data gets harder and harder, you just start adding more and more epicycles. And it turns out because of something called Fourier transforms in mathematics for infinitely the more, the more epicycles you add, the closer and closer you'll get to the ground truth. And you'll never discover Kepler's ellipses if you don't have a fundamental change, right.

And and Jana Klun is saying that basically our scaling hypothesis, which is just make the parameters more and more we're just adding epicycles and fitting data, but the underlying parameter of Newtonian gravity and the 1 / r ^2 dependence, we're missing that entirely. I like that little. That's a great, that's a great. And now like reference point. Yeah. And just to briefly kind of touch on, so you know, people talk about ChatGPT and claw and all this and in the fundamental architecture that is undergirding them is this idea of large language models.

And you know, so the idea is you're optimizing language as a, as a, an abstract or as a construct. And both Demisosabus and Yan Likun in the last 6 to 12 months have discussed the importance of world models. This idea that language models are not actually having this fundamental understanding of the physics of reality, which is what has maybe made it a little bit more difficult to get image generation and video generation that out-of-the-box feels real.

What seems to have happened is these LLMS have sort of created their this this like abstraction of what they like, what they understand physics to be, but it is not actually grounded to like the real fundamentals. And so this has now become the new hot topic is world models. I actually believe Google has already introduced for public consumption a version of their world model where you can go into the interface and it's procedurally generating a 3D environment in real time as you move around it. And you can choose the POV of like a cat or, you know, a bird flying through the air.

And so it's not like a video game where all the levels have been explicitly coded by developers already. And then when you get to the edge, you can't walk deeper into the forest because there's an invisible fence in the Pokémon forest. It just continually will procedurally generate, generate on the fly as you move around. So as far as I'm aware, that's the first world model kind of thing at scale that's launched. But it seems like Jan agrees with this viewpoint and is trying to push forth.

And with his new company, Advanced Machine Intelligence, they're trying to focus on this idea of building AI world models to actually understand like physical 3D spaces, which becomes much more valuable in applications like having any kind of humanoid robotics do anything in a physical environment, whether that's in a manufacturing context. In a residential context, driving's may be a little bit different because it's such a rules based system. Whereas having a home humanoid robot at home where you want to be able to have it fold your laundry, do the dishes, pick up stuff off the floor, it's like unstructured tasks.

And I think just in general to even for language, right? And like producing text that makes sense or producing recommendations that makes sense, having a world model is a is a huge deal, right? It'll give you recommendations that actually make sense because it understands how fundamental physics works and so on and so forth, right? There's one other sort of interesting palace intrigue, corporate drama all right aspect to this story, which is Meta ended up acquiring. So Mark Zuckerberg ended up acquiring this sort of AI infrastructure company, scale AI, which was led by this 20 something, you know, super genius Alexander Wang, not that Alexander Wang, a different one.

And you know, with no E between the D and the R at the other, which is interesting. And they acquired and Meta acquired their scale AI for $14.3 billion. And the idea was it was they were building like a tagging and training kind of pipeline system that was supposed to accelerate, you know, O Llama and all of Meta's AI work. Now he Alexander Wang was brought in and became Meta's like first chief of AI, right, or our head of AI right? And you already have the legend Yan Lacun there, right?

And so. And Yan Lacun is not your head of AI. Right. And what seems to have been from the outside looking in, part of the drama here is, you know, Yan is more on the researcher kind of viewpoint. So you could probably categorize like Sam Altman and Mark Zuckerberg in the scorched earth philosophy, just spend money, get to the scale fastest because whoever gets their first wins. And then the Yan Likun and Demis Esabus are more on the if we have the deepest research bench, we will be the ones who win. And research is the driver of product, not product being the driver of research or product being the driver of implementation.

And Dario at at Anthropic is like kind of somewhere in between those two. So it seems to be the case that he was sidelined because he wasn't moving quick enough or Zuckerberg was worried about losing. And one of the reasons that Mark Zuckerberg is so motivated to not lose the AI battle against everybody else and why he was one of the first movers to really expect like do a huge amount of capital expenditure to build out metas infrastructure is when we were going through the transition from desktop to mobile in the early 20 teens, Facebook missed the opportunity to build the hardware device for mobile.

And what ended up happening is they became victim to Apple and Android as it related to getting distribution to their end customers. So there was a famous battle between Facebook and Apple where they were like, if you want to keep stealing users data, we're going to kick you out of the App Store. And Zuckerberg never again. He vowed never again to allow someone to be able to control his distribution in that way. That's why he went big on VR and Oculus because he's like, if that's the next medium, no ones going to beat me. That's why he went big on AI and all this because if that's the next medium, no one is going to beat. However, all Apple has to do to kind of really kill a lot of these.

And this is kind of the the bear case for a lot of the foundational models. If Apple finally decides to do something good and do a local only relatively high quality model on device that no longer needs you to have a ChatGPT subscription or any of these other things. And they have all they have billions of devices everywhere and become the first AI of choice. It is going to become very sticky for some of these model providers. So we'll see. But Yan Likun is out at Meta. He's starting his new thing.

I think he's gonna need more than a billion dollars. Yeah, he has a billion dollars already from the likes of Mark Cuban, Eric Schmidt and it values at 3.5 billion already already and like 6 months of starting and it's crazy. It's gonna and this is just the valuations of these AI companies, just outrageous. But again, people are viewing it as end game technology. So with that, we are going to wrap up our rundown for this Friday touched on a nice fun microbiology story, although it's not that fun if it ends up killing us all right, Solar storms and earthquakes.

Maybe this was not so happy and any but we had our are you smarter than a scientist? We got 9 out of 10 shared genetics between US and dogs. And the battle for AI continues as people throw money everywhere we are. We are going to ask again for feedback. This is going to be the third episode that comes out this week. You're splitting it out so that they're shorter, more bite sized, easier to consume and digest on your commute, during your workout, and it just becomes easier to share with friends if you want them to listen to a specific section to let us know If you do like that structure and that setup, we will continue on accordingly.

I am your host, Lester Naray, joined as always by my Co host and our resident PhD who may be as smart as a scientist, Krishna Chowdhury. We will see you all next week.

Transcript supplied by the publisher with the episode.

Podcast

From First Principles

Krishna Choudhary and Lester Nare · Tech & Science · English

From First Principles is a fast, funny, and rigorous breakdown of the biggest science stories of the week, hosted by Lester Nare and physicist Krishna Choudhary, PhD. We go past headlines into the actual mechanics: what happened, why it matters, and what everyone’s missing. Expect physics, space, A

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  1. S2 · E37 · 9 Apr 2026 · 1 hr 26 min

    Artemis II: Deep Dive on the Moon Flyby, Earthset, and Reentry (EP 37)

    Hosted by Lester Nare and Krishna Choudhary , this episode is a full deep dive on Artemis II as the crew returns from humanity’s first crewed lunar flyby in more than 50 years. Lester and Krishna break down the mission photo by photo, from launch and translunar injection to Earthset, Earthrise, the in-space solar eclipse, the science of lunar observations, and the skip-entry reentry profile bringing Orion home. Summary Why Artemis II is historic, what the crew saw on the far side of the Moon, and why this mission matters for the long-term return to the lunar surface.Why NASA relied on the…

  2. S2 · E36 · 3 Apr 2026 · 1 hr 1 min

    Artemis II, Claude Code Leak, iPhone Spyware & Project Hail Mary (EP 36)

    Hosted by Lester Nare and Krishna Choudhary , this rundown episode covers five new science and tech stories at a high level: NASA’s Artemis 2 moon mission, what actually leaked in the Claude Code incident, a new cancer genomics paper suggesting domesticated cats may be unusually useful real-world models for human cancer, two leaked iPhone spyware toolkits, and a science-focused review of Project Hail Mary . Summary Artemis 2 is finally flying — why this mission matters, why it is not landing yet, and why the moon race is back in geopolitical focus. Claude Code leaked, but not Claude itself —…

  3. S2 · E35 · 31 Mar 2026 · 1 hr 9 min

    Can AI Help Wake Coma Patients? The Science of Consciousness (EP 35)

    Hosted by Lester Nare and Krishna Choudhary , this episode is a deep dive into one of the hardest questions in neuroscience: what breaks in the brain during a coma, and can we figure out how to turn consciousness back on? We unpack a new paper from Daniel Toker et al. that uses an interpretable AI framework — not a generic black box chatbot model — to reverse engineer the biological mechanisms of prolonged unconsciousness, recover known features of coma, predict new ones, and propose a possible new target for deep brain stimulation. Summary Why diagnosis is so hard — disorders of…

  4. S2 · E34 · 27 Mar 2026 · 45 min

    AI Cancer Vaccines, Strange Fish, Ketamine, and Ancient Life (EP. 34)

    Hosted by Lester Nare and Krishna Choudhary , this episode is a fast-moving science rundown covering four remarkable stories from across AI, genetics, neuroscience, and paleontology. We dig into the story of a machine learning engineer who used AI tools to help design a personalized cancer vaccine for his dog, explore how an all-female fish species has survived far longer than evolutionary theory would predict, unpack new brain-scan evidence for how ketamine may rapidly relieve severe depression, and look at new research suggesting life rebounded shockingly fast after the asteroid that…

  5. S2 · E33 · 24 Mar 2026 · 1 hr 13 min

    Can Human Neurons Really Play Doom? The Science Behind Wetware (EP. 33)

    Hosted by Lester Nare and Krishna Choudhary , this episode is a deep dive into one of the strangest science stories of the year: a dish of human neurons allegedly learning to play Doom . We go back to the original 2022 DishBrain paper out of Cortical Labs, unpack how biological neurons can be read and written with multi-electrode arrays, and then compare the peer-reviewed Pong result to the much newer Doom claim. The result is a story that is both genuinely impressive and, in places, probably overhyped. Summary Wetware engineering — replacing artificial neurons with real biological neurons…

  6. S2 · E31 · 18 Mar 2026 · 56 min

    Optovolution: Teaching Proteins to Think Like Computers (EP. 31)

    Hosted by Lester Nare and Krishna Choudhary , this episode is a deep dive into a new synthetic-biology breakthrough out of EPFL: OptoEvolution . The big idea is simple but powerful — traditional directed evolution is great at making proteins that are always “on,” but biology is full of proteins that need to switch states , respond to stimuli, and behave more like logic gates than static tools. This paper takes directed evolution and couples it to light and the cell cycle , creating a new way to evolve dynamic proteins that can toggle, compute, and respond with far more control. Summary Why…

  7. S2 · E30 · 16 Mar 2026 · 54 min

    Can We Stop an Asteroid? The Physics Behind NASA’s DART Mission (EP. 30)

    Hosted by Lester Nare and Krishna Choudhary , this episode is a full deep dive on planetary defense . We break down NASA’s DART mission, why the goal was never to “blow up” an asteroid but to gently nudge it , and why the newest result is even bigger than the original headline: scientists can now directly detect that the Didymos–Dimorphos system changed not just locally, but in its heliocentric path around the Sun. Summary DART actually worked — not just by shortening Dimorphos’s local orbit around Didymos by 33 minutes, but by measurably changing the motion of the whole binary system around…

  8. S2 · E29 · 12 Mar 2026 · 2 hr 4 min

    Astrobiology’s Biggest Survival Test + A Vaccine Against Everything? (EP. 29)

    Hosted by Lester Nare and Krishna Choudhary , this episode starts in astrobiology with a fresh experimental challenge to one of the biggest objections to lithopanspermia : can life actually survive the violence of being blasted off a planet by an asteroid impact? Then, after a packed Rundown, we pivot hard into immunology with a radical Stanford paper asking whether we could build one nasal vaccine that doesn’t target a specific pathogen at all—but instead makes the lung itself a stronger fortress against whatever shows up. Summary Lithopanspermia gets less crazy — a Johns Hopkins / PNAS…

  9. S2 · E28 · 4 Mar 2026 · 1 hr 52 min

    Dark Galaxies, Fuzzy Dark Matter, and an Alzheimer’s Breakthrough (EP. 28)

    Hosted by Lester Nare and Krishna Choudhary , this episode has two main stories: an astrophysics update on a candidate “dark galaxy” in the Perseus Cluster (a halo that’s ~99.9% dark matter), and a major Alzheimer’s mechanism paper tracing how exercise protects the brain by repairing the blood–brain barrier —with an actionable drug-like path already emerging. Summary Candidate dark galaxy — Hubble + Euclid stacking and globular clusters reveal an ultra-faint halo that could test missing satellites and the cusp–core problem (and even “fuzzy dark matter”). Exercise → Alzheimer’s mechanism —…

  10. S2 · E15 · 26 Feb 2026 · 2 hr 12 min

    Dream Engineering, the Proton Radius Puzzle, and an ALS Breakthrough (EP. 27)

    Hosted by Lester Nare and Krishna Choudhary , this episode has three main stories: interactive dream engineering (yes, two-way “communication” during lucid dreaming), the proton radius puzzle finally getting resolved by a precision lab measurement, and a sobering but hopeful look at ALS —including a breakthrough “ALS-in-a-dish” model that could finally make drug screening translate to humans. Summary Dream engineering — targeted cues + induced lucidity → dream-content biasing and measurable next-day performance gains. Proton radius puzzle — precision hydrogen spectroscopy resolves the…

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