From First Principles - From Princeton to the Nobel Prizes — How FFP Started + 2025 Nobel Recap (EP. 12)

Episode Date: October 15, 2025

After a packed week of Nobel Prize coverage, Lester and Krishna look back on how From First Principles began and why they built it as an “ESPN for Science.” They revisit 2025’s Medicine, Physics and Chemistry winners and discuss why fundamental research and immigration policy are core to America’s scientific edge.Quick note: this week’s episode is in vertical format because of a technical hiccup during recording — back to widescreen next week!SummaryOrigin Story — Two Princeton friends from different continents unite around a shared love of science and storytelling.The Mission — Creating an “ESPN for Science” that celebrates research and the people behind it.Nobel Follow-ups — Medicine (Tregs and non-immune roles), Physics (macroscopic quantum tunneling and quantum supremacy), Chemistry (MOFs and industrial scaling).Funding + Immigration — Why public research grants and curating global talent are vital to scientific leadership.Show NotesNobel Prize Press Release (2025 Medicine)Nobel Prize Press Release (2025 Physics)Nobel Prize Press Release (2025 Chemistry)Nature Genetics (2001) — FOXP3 Mutation Causes DysregulationNature (1999) — MOF-5 Discovery (Omar Yaghi et al.)Google Quantum AI Lab — Quantum Supremacy (Nature, 2019)

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This is from First Principles, also known as FFP Pod. We are joining you here in our post-Nobel Prize Week episode. Unbelievable week. Super interesting. Amazing. We did three days in a row. We put out the full episode.

The engagement has been unbelievable. Clearly people liked our sort of, ESPN style coverage of the nobles. Waking up at 2.30 was worth it. Super interesting stuff. We're going to do a follow up this episode. But before we start, one of the questions that kept coming up in a lot of the comments as more and more people started watching the show was, who are you guys?

Which is a fair question given we just started the podcast. Our first episode was July 31st of 2025. So we're only a couple of months in. And we thought it would be a good time to kind of talk about our origin story, which is not just similar from any other podcasts, but it all starts at one place we talk about a lot on the podcast, which is Princeton. We both sort of were at Princeton at the same time, became best friends. And at that time, you were in one of the most illustrious programs on the planet in the Princeton physics program. Yes. Boy, that was a doozy, wasn't it?

So we met sophomore year, which was 2011. So we've been friends for about 14 years now. Our wives are very close at this point, which is another interesting variable. But at the time, you know, I played soccer and I was moving from economics to program two, which was Shirley Tillman's pet project for creating an arts program at the school. Right. And you were busy doing the hard sciences. Yeah, I was pretty set on physics right when I got there.

I remember asking you one day, oh, like, you know, physics, like, oh, was that hard? And you were like, mate. Me, you have no idea. Program started with like 200 freshmen being like, I'm going to be a physics major. And then slowly it just ended up being like the 20 of us being like, bro. This is ridiculous. So we have a long friendship and a long history. And a lot of the times, even back in the day, you know, I would always come to you

about, you know, one of the interesting overlaps in both of our lives is that we both come from immigrant families. Yes. Indian and Zimbabwein. Both of whom have two parents that are heavily involved in the sciences. That's right. Yeah. Between the two of us, we have three PhD parents. Yeah. We have physics, botany, parasitology, and clinical trials as the sort of verticals that our parents work in. And so I think part of the initial, like, substance of why this show works is we both grew up in science-based households that were very academic focused in terms of us. Exactly. And like the stories that I would hear growing up about like the heroes would for me would be like scientists. 100%. You know, my dad would tell me stories about

Feynman and Einstein and Chandra Shakar, you know, and so instead of sports, we got, I got at least a lot of just science heroes to look up to. I mean, it's very similar in Zim culture where, you know, your path out, a lot of times people don't know a lot about Zimbabwe. The number one thing most people know about Zimbabwe is we had a trillion dollar note because of hyperinflation in like the 2000 or 2001. But one thing that's not well known is we have an incredible academic infrastructure in the country.

And when you grow up, you're, you're, you're, you're, you're, built to be in the sciences, be a doctor, be in mathematics, because that was your way to sort of salvate. That was your way out of poverty. Same in India. I was going to say it's very similar. Education was your lottery ticket. Exactly. And so we have the sort of shared background, both family and college-wise.

You know, there's an L.A. connection as well. That's right. You born here, me moving here after college. I wasn't born here. Not born. Yeah. I was born in India, but I did grow up. grew up. This is the most I've grown up in.

This is the place that's the most grown up in. The most normal home. Yeah. And what's interesting is I remember when I was trying to decide between New York, San Francisco, and L.A., which is the classic three choices that you have after school. Yeah. I really wanted to come to L.A.

And you were so, you were just like, yeah, mate. Yeah. L.A. is the place to me. L.A. is the best. We both ended up back here. Yeah. A little bit of different paths. I went straight to the private sector.

I've worked in sort of entertainment and tech for the last 10 years, which is kind of where I've acquired a lot of the equipment that it now enables this podcast. It's been sort of 10 years of working in influencer marketing and gaming and now running a tech company. You stayed in academia for a little bit. Yeah. Went to UCLA for graduate school. Got my PhD there. ended up first in the physics department, of course.

And at Princeton, I got really interested in the emerging field of biological physics, which is the idea of trying to figure out how life even exists in a world where physics is king. In a world where the second law of thermodynamics is an axiom where everything needs to go to soup and everything needs, entropy needs to increase and everything needs to be uniform and that equilibrium. We are clearly like highly non-equilibrium beings that are just creating podcasts and not just us, but like plants, bacteria, all of these little things are hacking physics at a fundamental level to stay alive.

And that was always so fascinating to me that, you know, in a world where the second law of thermodynamics is an axiom, you can have so much stuff. that is just like visibly breaking that rule somehow. But it's not. You know, it's definitely not breaking the rule, but it's very clever in how it circumnavigates that rule, you know? And I thought that was awesome. So at Princeton, I did some research in like theoretical physics of DNA confinement,

like polymer physics, how DNA bends and curves and stuff like that. And protein aggregation in stuff like Alzheimer's, where these amyloid proteins like, aggregate and a neuron. So what are the statistics of that? And then finally for my senior thesis, I did a little bit on this thing called the omitted stimulus response, which is a way in which the retina in your eye literally acts like a little microcircuit controller that sends compressed data to your brain in a very cool way. So that's where I got really into neural computation. Right.

Of like how does a neural network that is biological and running at 300 Kelvin in this hot and soupy environment, able to compute stuff. And so that's where I got into that. And then when I came to UCLA, I did my PhD in neuroscience and physics. But it was in the physics department. And most of my scientific inquiry was physics-based, although the experiments were biological, because they had to do with like real neurons and real brains. But it was a really cool synthesis of like the two fields. Got my PhD in physics at UCLA. did a postdoc, and now I'm working as a scientist in the private sector. I think while you were, some of your work, correct me from wrong,

while you were at UCLA, did get some sort of press coverage as being pretty, pretty important. Yeah, yeah, that was cool. My first, like, first author paper came out in nature communications, and a lot of the press really, really took it up. You know, as the press do, there are some things I'm like, that's not quite right. But hey, I'm happy you're talking about it. Yeah. You got some international coverage, too.

That was really cool. There was like a news article in Iran. There's a news article in Japan. So that was pretty cool to see, like, you know, people responding to my work. So how do we get from this soup of backgrounds to deciding to do a science podcast? It's not our first rodeo. You know, in my work, I've previously done something. follow podcasts that were more focused in the tech space, given that I have a production background.

Again, all of the technical implementation of lights, cameras, switchers, editing, distribution. That was kind of the learning curve there. Around COVID, we had actually, you know, one of the things I'd always pushed on was even early on back when we were at Princeton, I was like, I think you have the gift of gab around science communication that has potential, you know, and we tried it with the Dark Matters podcast right around when COVID started. And I think in part the inspiration was the clear lack of like sort of the public health communication during that time period was maybe, let's say, lacking. Yeah. And there was not really an understanding of the basics around a lot of the

things related to vaccines, the immune system. We'll actually touch on the immune system in this episode a little bit. And so a little bit of the inspiration to try it then was that. It just didn't work at the time for a number of reasons. Then we tried again earlier last year, you doing a solo, me running production. And, you know, it just, there wasn't the, it just, the magic dust. The magic dust wasn't there. And then I finally decided, let me come out from behind the camera. I had actually done a little bit of a UAP, unidentified anomalous phenomenon, content creation, and podcasts over the last two years. Yeah. And in that process, you set up this studio as a part of that and found success there and it kind of figured out, I think, the way in which we need to structure a show.

So I came back and was like, let's try again. Yeah, let's do it. And that's how we got to where we are today with From First Principles. And so just a little bit of background on, you know, where we're coming from. And ultimately, we've built this show with sort of two component ideas. One is it's meant to be a love letter to the scientific community, academics, researchers, especially who give us the incredible life we have today where every device, every convenience that we take for granted is the result of some researcher toiling away years ago. Yeah. And ultimately that getting into industrial production and then into end consumer products.

And I think the second reason, and this is more of my reason, is when I look around and I'm looking for content to consume, I'm like, why is there not an ESPN for science? Why isn't there something that is doing the same kind of analysis and promotion and hyping around the thing that makes all of our lives so much better? Yeah. And so this combination of love letter to the scientific community and building. the ESPN for science is a lot of the like thesis and theory of the case for for why we've built the pod and with all of that being said one other question that came up um was uh how did you guys get sponsored by a beverage company if you're if you're so new so many of you probably have seen the show is presented by standard model beverages which has been conveniently placed

uh for those who are listening uh if you watch on the video it's always in the podcast as the drink of choice. That's right. And what is the history behind standard model beverages? So standard model beverages also has a history in science. It was started by me and a friend of mine, Cameron Bravo. He was a PhD friend from UCLA. He worked in particle physics at CERN, experimental particle physics.

And he got really good at making drinks. Oh, my God. I remember your bachelor party was incredible. the brew he made was incredible. I remember talking, like, are you going to do something with this? Yeah, exactly. At the bachelor party was when we started thinking like, hey, maybe we could like do this. And then after my wedding, he brought more beer.

And then it was a hit there. And then we were like, you know, let's just let's make a science-centric company, a beverage company that celebrates science and scientists and the experimental method of how we create these things. Because he's a man of science as well. And he uses the scientific method to get the best sort of ingredients and the best. best flavors. And so we called it the standard model beverage company after the standard model of particle physics, which we're huge fans of, and maybe we'll get into in some of the

later episodes. It's right now, we make non-alcoholic soda from agave sugars. And on the back of every can, you'll find a featured scientist that you can learn about. So this one has Galileo Galilei. It'll tell you all about his life and the science that he's known for. Other cans have other scientists. So it's really, again, just as this show is a love letter to scientists and science, this drinks company is a love letter to science and scientists. You can go on our website and learn about the quantum mechanics behind our logo, which is a visual depiction of the Duraq equation, which is the equation for an electron or any relativistic particle. And it gives you a prediction of matter and antimatter, the antimatter being the whole in a sea.

Again, something that we can go into in later episodes because that's one of the greatest achievements of theoretical physics is to literally predict antimatter based on the fact that a square root can be positive and negative. Right. And that's what sort of all of our artwork, all of our branding is around this love for science. And check us out. Our website should be on the links. We're working towards our alcohol license from the ABC Office of California.

we'll be offering more. And I think what's so funny is if you listen to this podcast, you'll be one of the few people that understands the secret of the logo that goes on the front of the can. And shout out to Snapple Facts, which if you grew up in the 90s and drank Snapples. You turned the cap and there was a little fun fact on the underneath, and that was a little bit of inspiration for featuring a scientist on every can. And so with all of that procedural nonsense out of the way, we can get back to regularly scheduled programming.

Also, not industry plants, by the way. We're not industry plants. Yeah, yeah, yeah. That would make my finances a lot easier to manage. Yeah, yeah. This is all self-funded. It's just the two of us. We don't have like a team.

There's no production crew. Everything is done, processed, edited, produced output just by the two of us. So all the support that you guys give us is really, really meaningful. Yeah. As we try to, again, grow this into a network. And again, build that ESPN for science, which in the times we live in today is more important than ever. Yeah. And with that, we will now pivot into our quick Nobel recap.

Yeah. So last week, we did coverage of all of the three Science Nobel's, chemistry, medicine, chemistry, and physics. Yep. And we want to do a quick follow up on a couple of the discoveries, starting with the medicine follow-up, non-immune roles in regulatory T-cells or T-Regs. Yeah. And so we'll start with that quick recap now. Yeah.

It's, you know, the, the 2025 Nobel Prize was given to three individuals, Shimon Shackaguchi, Fred Ramsdale, and Mary Bruncow. Shimon Shikaguchi was at the University of Osaka in Japan, and Fred Ramsdale and Mary Bruncow were two individuals at private research companies in Washington in America. they were given for basically discovering T-regs, which are regulatory T-cells. This is the, it's a cartoon that the Nobel Prize Committee came up with. It's got an alien, I guess, inside of a police ship. And the police ship is the T-Reg, which is the T-cell that is a regulatory T-cell.

And inside, the alien has a cap on, and the cap says Fox P3, because the Fox P3 is the gene that sort of creates and regulates the T-Reg itself and makes the T-Reg different from all the other T-cells in our body. And just as a quick review for what these T-Regs do, right? Their basic role is to act as the police of the police. The police being our greater immune system that targets viruses, targets antigens, targets other human cells that have been affected by a virus or an antigen and are now falling apart. So T cells are normally involved with identifying those guys and then gearing them up for

greater immune like therapy, greater immune attack from macrophages, from all these other kinds of things that come in. And once the T cell has identified that bad cell, the other guys come in and sort of take care of business. Right. But every once in a while, the T cell is going to identify one of your own as a bad cell because all it is is a lock and key method and sometimes the locks and the keys don't work like they're supposed to and so what these regulatory key cells do is they inhibit that immune response right and that's what these three individuals showed shakaguchi was the guy who found Tregs and and showed that they were completely different from normal Treg cells i mean from normal T cells and then fred ramsdell and mary brunkow found the

FoxP3 genome that actually makes these T-Regs what they are. So it was both the discovery of the function of this special class of T cells in addition to understanding how they are actually created at a genomic level. At a genomic level. It's both aspects. Yeah. And that's why there were three that got the Nobel Prize, right? But since then, it's been, you know, 40 years of research since this all, all of this has

been happening. And now we figured out the T-Regs aren't just about. about controlling immunity, right? They can also directly influence non-immune tissue function in a way that previously we hadn't really seen immune cells do. Okay? So one of the things, in today's topic, there's so many different ways that T-Rags are doing this, where they're doing this non-immune function. But the thing that we'll focus on today was discovered in 2013, and it shows that T-Regs are really involved in muscle repair and regeneration. We've got a, that's the paper that came out of Harvard Medical School by Burzine and others

in Nature Medicine in 2013. They're the ones who found a population of T-Regs in damaged muscle cell tissue that can actually promote repair of those muscles. Okay? And the way it works is in photo four, you go, these T-Regs, they actually, actually sit in your muscles. So they're tissue resident instead of sort of circulating in the blood. A lot of times we think of immune cells as just circulating in the bloodstream, right?

And they go and they find stuff to take care of. These are tissue resident T cells, Tregs, that are found in our muscle tissue. And what they do is they modulate the action of stem cells. Stem cells are the cells that don't really have an identity yet, but they lead to progenitor cells that lead to some kind of tissue cell. So in this case, they would become muscle progenitor cells that would then become new muscle cells and repair and damage the tissue there.

Does that make sense? Yes. Yeah, and these kinds of stem cells are found all over. They're found in our bone marrow. They're found in our muscles. They're found in our hair follicles. And that's actually the other thing that I wanted to highlight. That's kind of cool is another, this was pretty recent.

another way that these T-cell T-Regs are doing stuff that's outside of the immune system is they become on-site repair foreman for like stem cells in our hair follicles. So they promote hair growth in our hair follicles by actually influencing the stem cells that are in there that are called hair follicle stem cells. And those stem cells promote the growth of hair in those hair follicles, right? So without the T-Regs actually, like, mediating that interaction, you're not going to get hair growth. Hair growth in those hair follicles. So we can thank the cellular military police for our wonderful lock-in-air.

Yeah, they do a bunch of side jobs too, right? Right. Where they're like just maintaining a lot of the tissue resident stem cells for doing whatever job that they're doing. So they get deployed into local cities across your body to maintain. order. Yeah, there it is. There it is. Don't listen. Let's not get too triggering. But this was work that was done by UC San Francisco,

and this was a paper in cell in 2023 that showed that T-Rex can actually help hair follicle regeneration. They use something called the notch signaling pathway, which is just another, it's one of these other log and key signaling pathways where the T-cell locks in keys into a stem cell, and then

the stem cell is like, oh, okay, I need to start doing whatever I need to do. This is fascinating. So not only the Nobel discovery was around the existence of this specific T-cell type and its genomic origins. And then now, since those early discoveries, we've now begun to understand that these regulatory T-Cels, these T-regs, have multiple functions. Yeah. They're not just in the highway of the bloodstream. Yeah.

They can be in the tissue cells and actually facilitate other aspects of other processes that are independent of the immune response, which is its primary responsibility. Yeah, exactly. Yeah. And that's how they were discovered. But now it turns out they've got a much larger role in our body, right? So it really shows why something like that deserves the Nobel Prize. Like discovering this whole new class that is actually doing so much more than just an autoimmune response. Sort of, yeah, like curtailing the autoimmune response. Now it's doing all sorts of stuff. The other thing that's interesting, and this goes back to kind of what you always talk about with biology as compared to physics, where it's like the here, like, there's a lot of complexity still that we are continuing to better understand in terms of biological systems. Yeah. And there's complexity there. There's complexity at every level. It's insane.

Biology is somehow this like hodgepodge stack of cards that is just like staying alive because everyone is. is making sure the cards don't, you know, fall, right, at every level. At the molecular level, at the cellular level, at the organism level, then you can go into, like, ecosystems and there's population dynamics. It's an incredible, like, hierarchy of being. It's very cool. The combinatorial problem set there is one to behold. And so that was, again, the medicine, Nobel from last week.

If you haven't watched last week's episodes, we have both individual stories for each of the individual awards, as well as a collective episode with all of them. So you can zoom in to the subject you care about or zoom out and listen to them all. They're all fascinating. And then so day two, which was Tuesday, was physics. This one was for a macroscopic quantum tunneling. I will note, you know, there was some commentary about the use. of the word macroscopic. Yeah.

And understandably, in this context, it is macroscopic. But in our everyday lived life, when we say macroscopic, we don't mean laptop macroscopic. Yeah, yeah. It's still quite, quite small. This thing is still quite small, yes. But it's still, it's a nano-microscale. But it's still meaningfully different than where we were before. Yeah, it's still 10 to the 9, you know, one billion things, quantum tunneling.

That's not a joke, right? It was given to John Clark, Michelle Deverey, and John Martinez for their discovery of macroscopic quantum tunneling. It was a experiment that they had done in the 1980s at Berkeley. John Clark was the principal investigator at Berkeley. Michelle Deverey was a postdoc in his lab and John Martinez was a PhD student in his lab. I will be remiss to say, we also call it Cal on this podcast. Yeah, but, guys, Berkeley is fine. But for the sake of the international audience, we will refer to it as Berkeley.

Yeah, yeah. I mean, I think in science, I've almost never heard it called Cal. I'm sorry. I've been around circles in science. It's called Berkeley, okay. Go bears in athletics. Yeah. Cal, yeah.

In science, you know, we'll stay with Berkeley. I don't think anyone from Berkeley is pressed about it, honestly. Okay, anyways. Let us know in the comments. Yeah, let us know. And in any case, like what they did was, they showed that you could build and customize this kind of artificial atom in some sense, right? That even though it was 10 to the nine different things that were doing this quantum

tunneling, all of them collectively are obeying the Schrodinger equation. And so one could use this as a substrate to simulate things that do quantum mechanics, for example, atoms. And this is the central idea behind a quantum computer, right? It was actually first hypothesized by Richard Feynman, one of our favorite physicists in a 1981 talk. The talk became a 1982 paper in the International Journal of Theoretical Physics, and the paper was titled Simulating Physics with Quantum Systems, right? It's an incredible paper that's so forward-thinking, and it's something very much in the style of Feynman.

He builds it from first principles, and he asks like, okay, what if I want to simulate a quantum? quantum system. Right. Okay. What would that take? And here's the math that he does. He says, suppose I have an interacting system of n quantum particles, okay? And I want to simulate all of that. Can I do this with a classical computer? Pretty soon it gets way, way out of hand. Okay? And here's why. Consider just two quantum particles that can be in two states. Okay? They can be in a zero or or a one, right? In a classical computer, well, you would just have one thing be the first state and one thing be the second state. And then you can decide whether it's a zero or a one and you have four bits, right? But you can just keep track of each of the bits. And as you grow the number of bits, like it just grows linearly in terms of how much stuff you need to save. You need to store n bits. That's n different transistors that are storing a zero or a one. If you want to start doing quantum. systems now, commentatorically, it goes insane, right? Because just let's imagine two quantum systems,

right? Like two electrons that are spin up or spin down. You actually have to keep track of four different complex numbers. Okay. Each complex number is actually two numbers. One, how much, how much of the number is on the real axis, how much of the numbers on the imaginary axis. And what you have to do is you have to keep track of the spin up, spin down, you have to keep track of the spin down, spin down, spin down, spin up, spin down, spin up, right? That's four different complex numbers that you've got to keep track of. So that's eight different things. With three, now I've got to keep track of all eight of these spin systems.

Yes. Right? And with N, it becomes two to the end. Two to the end, different numbers that I got to keep track of to really fully characterize a quantum system. Because each of these individual spin states are themselves distinct. And so I need a number for each of these distinct spin states. And so pretty much what ends up happening is if you scale with the number of bits,

the amount of stuff that you can store in a classical computer becomes linear, but the amount of stuff you can store in a quantum computer becomes exponential. Right? Because a quantum computer by design is storing all of the little values for each of the superposition of these states. So it was a very simple, simple argument that Feynman made. And he did this back of the envelope of calculation. He's like, well, if you need two to the n numbers to keep track of a quantum state that has N things, each of those things having two states, then, you know, with if you want just like 40 interacting particles with two states, that's two to the 40. And one real quick trick to convert two to the something into 10 to the something is to know that two to the 10 is 1024, which is about 10 to the three.

So 2 to the 40 becomes about 10 to the 12. That's a trillion. That's a trillion bits that you need in your RAM, not in your hard disk. Right, right. You need in your RAM because you're messing with all of those numbers, right? In terms of computation, this isn't data that's being stored on a permanent basis. This is stuff in the middle of your computation that you're using to then calculate the new state and the new state and the new state. So that's a, that's a terabyte of stuff in your RAM, right, which is already a lot.

A terabyte of RAM is quite a bit. I wish I had a terabyte of RAM on the computer. We have to render the episodes. Yeah. That's reaching at the level of like supercomputers. Right. But 40 different things is not really what we're even talking about. We're trying to do like 200, 200 or 400 cubits, right?

Because in, in modern, like for a protein, for example, a protein will have like on the order of 100 to 400 atoms, that many interacting quantum states, right? for each of the little configurations that the electrons can do. And already at like 2 to the 200, you're getting to 10 to the 66. Which is an insane number. Which is an insane number because there's only about 10 to the 80 atoms in the universe. Right.

So like what? Each atom in your universe is now, you know? Like you can't make transistors. Yeah, it's not feasible anymore. So that was the main impetus. And it was sort of introduced by Feynman in the 1980s to start thinking about what if, what if we wanted to do. quantum simulations.

We would really need a quantum computer. Right. In order to do it. Classical systems don't have basically the horsepower to be able to sustain as you get to more complex systems. Exactly. And so we need a computer

that's made out of quantum bits. Right. And this is where these superconducting circuits that won the Nobel Prize, they sort of became a leading platform because the superconducting circuit itself can now have these quantum behaviors. And so you can use that as your two-state system that encodes a zero and a one. And that's what sort of I want to get into is just a little bit of background.

We can't cover all of it because that's going to be an episode in itself that maybe we can do. But I just wanted to give you a little bit of a starter on why superconducting cubits became like this sort of ubiquitous method of making a quantum computer. And this is the Joseph's injunction circuits. Yeah, yeah, this is exactly the Joseph's injunction stuff that we talked about on the episode that became a cubit in itself. So a good primer, if you haven't already watched our Nobel Prize episode on physics, would be to watch that because we go into very good detail from first principles about

like what is. Yeah, a Joseph's injunction. which is important to understand why this fundamentally is a workable solution to the problem. Exactly, yeah. Yeah, but we'll give you a tiny primer here. We'll start with just an LC circuit. An LC circuit is something that I think

anyone who's taken an undergrad physics class, the second semester you go into AC circuits, which is analog circuits. And an LC circuit is very simple. There's a capacitor and there's an inductor. A capacitor is a, is a configuration of metals, basically, that lets you store charge. And an inductor is like a coil of stuff that lets you create magnetic fields, and you can charge

it up, and you can decharge the magnetic field. It becomes a little battery in some sense, okay? And if you hook up a capacitor to an inductor, and you charge up the capacitor, and then you turn on the switch, what's going to end up happening is the capacitor is going to discharge, because it's storing a bunch of charge, but then the circuit's going to be like, oh, there's a way for me for the charge to rebalance. As it rebalances, that's going to create a current in my loop.

That current is going to turn on and then create a magnetic field in my inductor, which is going to become like a kind of battery. That's going to reverse the circuit, and the circuit is going to go the other way, and then it's going to go one way, and then it's going to go the other way. And this charging and discharging of the capacitor

and the charging and discharging of the inductor becomes a sort of simple harmonic oscillator. This is ubiquitous in physics. We love simple harmonic oscillators, okay? Because everything is a pendulum in some sense, right? Everything is around some energy minima, and it oscillates as it goes from one part to the other part of that energy minima. And that's what an LC circuit becomes. It becomes a simple harmonic oscillator. Now, you take anything at the classical level, in this case,

an LC circuit, which is a simple harmonic oscillator, and you cool it down. First of all, this circuit is going to become superconducting, meaning that there's going to be no resistance, which means this guy, this circuit is just going to keep going. There's no jewel heating. It's peak pollination season, and my business is scaling fast. To keep the nectar flowing, I need a phone plan with top priority data speed. That's why I chose GoogleFi wireless.

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When you want savings, not surprises. It matters where you stay. Hilton, for the stay. Involved, and so it's not going to lose any energy. So this thing can keep going as an oscillation, right? And in anyone who's taking first semester quantum mechanics usually goes through the Griffith's quantum mechanics textbook. And this is the cartoon they have. They show Schrodinger's cat going up and down a ladder. And all the ladders, the rungs of the ladders are equally spaced.

And what this guy is doing is going up and down the energy states of a quantum harmonic oscillator. In a classical harmonic oscillator, you can, in a classical harmonic oscillator, my pendulum can be in any state, right? It can go up. And if I push the pendulum more, it'll have a higher amplitude. This is like you want to swing, right? You can have arbitrarily low amount of energy. You can just sit on the swing stationary with no swinging. Or you can have a little bit. If you push a little bit more, you can have a little bit more.

In a quantum harmonic oscillator, though, quantum mechanics takes over, and there are discrete energy levels where the quantum harmonic oscillator will have a certain frequency, or it'll have a little bit more frequency, or it'll have a little bit more frequency. And each of those frequencies of oscillation is tied to a energy, just by H-bar omega, right? And the key with a quantum harmonic oscillator is the rungs of that ladder are equally spaced. Now, this is cool for a quantum harmonic oscillator. It becomes a problem if we want to make a quantum computer out of it.

Because what we want are just two states. And we want to be able to reliably go between one state and the other. But imagine if the energy difference between zero and one is the same as the energy difference between 1 and 2. When I'm poking it and this thing is at the 1, it could go to 2. Right. We don't want that. We want it to be fixed within 0 and 1.

And so what we do is we add a Joseph's injunction to our superconducting circuit. And what that does is, so on the left, we've got a perfect parabola. That's our omega x squared, which is a perfect parabola. That's our simple harmonic oscillator. Okay. That's like hook's law. you know, a spring and a mass on a spring, the energy of that thing is 1.5 kx squared.

Similarly, if you have a parabola, then you get a perfect pendulum. That's what we don't want. So when we add a Josephson junction to our LC circuit, then what ends up happening is you add a cosine term to that parabola. And that cosine is very much like a parabola in the bottom, right? If you zoom in close to its minima, it's going to be like a parabola, but it's going to add a little bit of wiggle on the outside.

And that's what's called an anharmonicity, which means we've gone from a harmonic, simple harmonic oscillator, to an anharmonic oscillator that has a tiny bit of a difference in that parabola. And what that does is now our ladders, it's still a ladder, but the rungs of the ladder are no longer equally spaced in energy. And so what I can do is now tune my poking, Right? So that it only resonates to that 0-1 transition.

Right. And it doesn't resonate with anything else. Above. And so I can reliably now control whether it's in a 0 and a 1, and I can switch it from 0 to a 1. And that's where this Josephson junction comes in. Okay. It adds that little cosine magic that turns my perfect quantum harmonic oscillator

into something that I can manage as a two-state system. Which is required in order to make a quantum computer operate in the way in which we needed to get like tangible versus like yes to have an operation yeah we want a two state system at all times for a quantum we want to i mean we like bits right yeah and um and a lot of the the simplest logic you can do is with a bit a two state system um this was first actually implemented at Tokyo by um nakamura and his colleagues in a nature paper in 1999 and that's what sort of started the field of superconducting cubits and it's come a long way right and now um one of the

one of the papers that I wanted to highlight was the big paper in 2019 by the Google team. John Martinez was involved in the hardware leadership. And they published in Nature in October 2019 with an experiment titled Quantum Supremacy using a programmable superconducting processor. This was a very big deal. They're claiming quantum supremacy. Quantum supremacy is the idea that we have done something now with a quantum computer that cannot be done with a classical computer

no matter how long you wait. Not no matter how long. They said something like the age of the year, like a million years, something crazy. And they were like, we did this in 200 seconds that would have taken a million years to do.

The point is, in a classical system, it would be so far beyond the life cycle of humans that it's irrelevant. Yeah, it's a million years. Yeah, yeah. It's like, It's something that we could not do. Now, what they did was they designed a random quantum circuit sampling task.

And this is where their claim met some scrutiny, okay? Because the task that they're saying they did that a classical computer couldn't do was itself a quantum task in some sense. So maybe you're gearing the problem for the hardware that you have. And that's not completely out of precedent, but you can understand if their competitors were like, yeah, you know. Doesn't count.

Yeah, it doesn't count. But in any case, they said that this is like, you know, the burden of proof is I've done something that a classical computer can't do, right? Quantum simulation is another thing that Feynman came up with

that is obviously by design something a classical computer can't do. Yes. Right? So it's not that like weird for me. they've got a 53 cubit computer, okay, 54 physical cubits. That's in a sort of nice chess board pattern. Each of the cubits talks to four of its neighbors using a coupler. And it's all in a 2D grid.

Their chip was called the Sincamore chip. It's 54 physical cubits, tiny, tiny chip that's inside of a giant dilution refrigerator. like those chandelier stuff that we were talking about. It's got to be cooled down to where these things can be superconducting and they can do all of those things. And their claim was that this quantum device could sample, i.e., could like produce these bit strings from a quantum distribution, from a probability distribution that was inherently quantum.

And in 200 seconds, they could produce enough bit strings to totally characterize this probability distribution. Okay? And they estimated that a classical supercomputer, like this is a big supercomputer, would require 10,000 years to do the same. Okay? And they did some cross-entropy benchmarking to show,

like for smaller number of qubits that this would work and how that thing would scale. Okay? And so that was their main thing. They were like, look, we did this in 200 seconds. This would require 10,000 years on a supercomputer, a million years on a normal computer, like blah, blah, blah. Okay.

it's it's sort of outperforming these classical algorithms right ibn claps back because ibn is the other big um competitor in the field of superconducting circuits so shortly after the google announcement they published this blog post that um you know it's it's not it's not that hard their first thing was like 10 000 years and they they made a better classical simulation they outlined a better way to do that classical simulation that would take 2.5 days, not 10,000 years. So they kind of undercut, they were like, well. Yeah, yeah. It's like, well, you didn't use the latest sort of classical algorithm to benchmark how good your quantum computer was.

You know, 2.5 days is still not 200 seconds, which is what Google did. Right. But it's also not 10,000 years. So they were saying, you know, you shouldn't say quantum supremacy. You should say quantum advantage. At this point, yeah, it's like they're, I mean, they're pressed because like this is a big deal still, right? And if you think about just also just economically from the business context, IBM has been struggling to survive both in chips in its clientele base in terms of being dominated. They are being pressed by Google, by Nvidia, by AMD in all avenues of their business.

So just from a purely- And they made a big push in the quantum- place, you know? And so they had to say something. Yeah, they had to. Yeah. And I mean, the other things that they did talk about is like, the paper itself is quite self-aggrandizing. Like the 2019 paper in nature says quantum processors have thus reached the regime of quantum supremacy. We expect that their computational power will continue to grow at a double exponential rate. I mean, that's, that's, you know, that's, that's a lot. That's saying a lot that maybe you shouldn't say in a scientific paper, and maybe the press should say that, right?

But so there's still ongoing scrutiny. There's some papers that say that they've improved the classical simulation algorithms to increase that, to decrease that gap even further. There's philosophical critiques about like, well, should we really say supremacy and all this other kind of stuff. So it's still an ongoing debate. And meanwhile, the field of quantum computing just like forges forward, you know, with these superconducting circuits. It's going to be very exciting. I think the implications of, you know,

stepping back from some of the corporate intransigence, the implications are that we now have computing systems that have a capability set to simulate massively complex concepts, environments at a degree that we don't have the capability for in classical systems. And there are a variety of use cases that you can imagine. A lot of times people talk about, oh, it's going to break encryption because you can just, you know, brute force hack, blah, blah, blah. That's like the low-hanging fruit of implications. But you can imagine, you know, simulating biological systems.

That's what I'm most excited about. Right. It's like the breaking encryption is like fine, I guess, from a national security point of view or whatever. But for me, it's like doing quantum simulation, the dream that Feynman had originally. Right. Right. Of like now, imagine you've got a new idea for a material. It's incredibly hard to simulate the physics of a material now because of this problem of exponential blowup. Well, now, what if we could just put in, we know where the positions of all the electrons are. We can create this molecular Hamiltonian that tells you how the physics of that molecule is going to work. And then we put that

into a computer and we try to see things like, okay, what is the, what is the base energy? What is the conductivity of this thing? Imagine finding a room temperature superconductor by just hypothesizing all of the different materials that it could be, putting it into the quantum computer and having it tell us, hey, is this thing superconducting or not? There's huge implications in material science and drug discovery in personalized health care. Like, you can, it's hard to like make a list. You know, it is as fundamental as, you know, vacuum tubes giving us computing for the first time. It's as fundamental as the move to mobile, as the Internet.

It just in terms of you can only, it's hard to even imagine. Yeah, all the things that we could do. We could do with it because it's sort of an infinite canvas of opportunity. I always find the quantum space so fascinating. Yeah. And then I guess the last one we did was chemistry. Yes, the last follow-up was for chemistry. This was on metal organic frameworks.

And the, you know, it was our third day of waking up at 2.30 in the morning. But it was equally as interesting. And it was one that I had the least amount of basis for understanding. I think someone made an interesting comment that was like, chemistry never gets the love that it deserves for how important and crazy the stuff we do and it is. So we're making sure that shout out to all the chemists out there. Yeah. That chemistry gets... Some of the most important sort of Nobel Prizes have come in chemistry.

We're going to make sure you guys get your just desserts here. And so, yes, so chemistry was our last prize that we covered last week. Yes, this was for metal organic frameworks. It was given to Susum Kitagawa at Kyoto, Richard Robson at Melbourne, and Omar Yagi at Berkeley. Omar Yagi is someone that I called. Yes. I totally predicted that. And next year, we're going to do a more crazy prediction show.

We're going to try to get some partnerships with, I don't know, maybe we'll do, what is it, polymarket. Yeah. Maybe we'll get Kalshi, one of the prediction markets out there. Yeah, that'll be hilarious. But Omar Yagi specifically, we're going to focus on some of his follow-ups. He made M-O-F-5, which was the really big deal, metal organic framework, right? this thing had a single gram, had several football fields worth of surface area. And we got a lot of questions in the comments, actually, about like, what does that even mean for a molecule to have surface area?

And this was something that I sort of had an idea what they were talking about, but I wanted to dig deeper. So, you know, my initial inclination was that they're basically talking about how much area is available for interactions at a molecular level. When we think about adsorption of molecules, this is a 2D surface interacting with molecules and absorbing it and making it stick, right? Instead of absorption with a B, which is a 3D way of absorbing molecules. So at a 2D level, how much surface area is available for interaction with other chemistry? That's the idea. And the analogy you can use is like a parking garage, right? It's a single building, but it has an insane amount of surface area.

It's packing like five or six parking lots into a single 3D structure, and so you can park a bunch of cars there, right? And in this case, we're calculating the surface area. We're using how much surface area is available for parking other chemical stuff. Okay. One of the ways that they do this to calculate the actual surface area is by using liquid nitrogen and nitrogen gas absorption. So here what they do is they take your sample and they cool it down with liquid nitrogen so that it's super cold. And then they run nitrogen gas over it. And they see how much nitrogen gas is gone from the sample and gone into the sample.

And from that, you get this curve where on the x-axis you vary the amount of nitrogen that you're putting in, that you're exposing that MOF to. and on the y-axis you get, okay, how much nitrogen gas volume-wise was absorbed by this compound. And then you can fit something called the Browner-Emett-Tler equation, the BETT equation, which then calculates how much volume has been absorbed to create a mono layer, a single layer of N2 on this thing, right? Which would it be the equivalent of a single level of the parking garage? Yeah, exactly. It's like a single, it's the amount of stuff, exactly.

And then the more pressure you put in, the more molecules are going to get absorbed. And then from this, you can figure out, okay, what is the constant amount of surface area that is needed to show this dynamics of how much is being absorbed? And that's where you get in, get this, you know, 3,000 square meters per gram. Per gram. Like several football fields per gram. It's because the amount of stuff that this one gram of thing is absorbing, the amount of nitrogen, the amount of nitrogen that, this thing is absorbing would be equivalent of if I had a football field of nitrogen absorbing area and that's how much nitrogen was absorbed. And so part of the idea here is like, you know,

we've maxim, we've engineered the space of the parking garage to be maximally efficient for the number of cars that can park at any given level at any given time. So you can imagine a parking garage with double wide spaces. Yeah. And we've created the minimal amount of space for any individual molecule car to be able to park to maximize the amount of cars that can be parked at each level. Yeah, exactly. And sometimes maybe you do want the double wide spaces.

So then there's MOFs that do that. Right. That only work for larger. Right. Like for CO2, right? CO2 is slightly larger than N2. But you want to absorb that and not the N2. So then there's other ways of doing it.

Exactly. It's tunable. The space of the parking garage is tunable. It's very cool. And the other question that we were getting a lot, and this is something that I was also interested in, is, you know, how do you make these things in a lab? How do you make these things? And I was briefly in a chemistry lab at UCLA when I was doing rotations from one lab to the other to figure out what I wanted to do my PhD physics in. This was a lab that was actively doing cryo electron microscopy to find, you know, the structures of giant proteins. And in that, my advisor at the time, she said, you know, chemistry is a lot like cooking, okay? In that there's only, there's a few techniques that you have, and you have to be extremely good at figuring out which techniques you want to do and the order in which you want to put them in order to actually make your chemistry happen. But it is really like cooking, because you've got standard tools, right?

you've got heat. You can heat up stuff. You can cool down stuff. You can mix stuff together and you can stir stuff together. Every once in a while you have like fancy techniques. Like if you want to do creme brulee or whatever that thing is with like a blow torch, right? And you're like, you know, every once in a while or like you want to electrically shock stuff for some reason. But most of it is basically mixing, heating, letting it sit, all of the techniques that we use in the kitchen to make a really good meal.

Right? And that's what's happening in chemistry. Like in the lab, that's what you have. And so MOFs are created by this process called solvothermal synthesis. Okay. Effectively, what you're doing is you're mixing together, metal salts, think like zinc nitrate, you've got copper, zirconium and chlorine together. You mix that in with your organic linker, which is the linker between the things that makes that cube happen. And these can be like, carboxylates, organic molecules. You've got some kind of solvent. Usually that's DMF, which is dimethyl formamide. This is pretty toxic solvent. You can also now, there's techniques that are using ethanol or water. And then what you do is you mix them up. You heat them in a sealed vessel for 250 degrees Celsius.

You can heat them up for hours to days in these vessels that are like teflon lined autoclaves. autoclaves are like ways to make high pressure, high temperature reactions happen. And then you can cool them slowly. The idea is to cool them very slowly so that that promotes the crystalline growth. As you cool, the chemicals are now going to start forming in this energetically favorable way where the organic linkers are going to find the metal, and then the metal are going to find more organic linkers, and you're going to grow this MOF out of a tiny seedling of stuff.

It's the mix has the prerequisites such that when you apply heat and then apply cooling, the end result is what is planned for based on the amount of heat applied, the period of time the heat is applied, the amount of cooling that's applied, the period of time that the cooling is applied. So it's going to fall into place based on just the mix, how it's mixed, the proportions of each component part and that sort of heating and cooling cycle.

Exactly. And that's why I'm telling you it's like a lot like cooking, right? Because like if the bread is like underproved, if people watch the Great Burys Bakeing show, which I love. You know, if the bread is underproved or overproved, it becomes like stodgy and whatever. So, so everything has to go perfect for all of this to happen. Right. And so it really is like a cooking thing. And after all of that's done, now you've got these crystals.

And what you have to do is now wash out the solvent. And then you have a naked MOF. Right? And you get these crystals, which they kind of look like this under the microscope. They're tiny, tiny amounts, you know, grams at a time. The real challenge now is scaling this thing industrially. I do remember one comment saying, like, well, can you produce it at industrial scale? Yeah, yeah. And it's difficult. There's a lot of difficulty here. First of all, the DMF, which is a common solvent that's used. This thing is toxic and it's expensive. So we'd rather use stuff like ethanol or water. The yield is pretty small. We're getting like, you know, grams of stuff at a time. And if you scale up, then you can imagine the crystals are less good because the environment is not completely uniform. The bigger the vessel, there's going to have, you're going to have like edge effects, right, where the edge temperature is different from the central temperature.

The pressures are different. You know, and removing that solvent is pretty hard at larger and larger scales because what you have to do is basically vacuum pump all of that stuff out and, you know, producing a big enough vacuum pump for industrial. scale is pretty hard. I did find a company. There's several companies, but there's one company I wanted to highlight, Novo MOF. They're a company out of Switzerland. We're not sponsored by them or anything, okay, but it's a cool company that I found. They made 300 kilograms of their MOF, and that 300 kilograms has 240 kilometers squared of surface area, which is like the size of cities. Right. And they're using this for carbon capture in factories, right? But they've, they, on their website, they talk about the challenges that comes with trying to scale these up at an industrial

level and some of the techniques that they're using to go through and actually mitigate those things. So what's interesting is the concept of metal organic, like most of the Nobel's, the discoveries have been made quite some time ago. Yeah. And, you know, with metal organic frameworks, we're at a place now where they've been experimentally proven. There's a variety of different methodologies that have kind of been identified. There's been also the rule book on how to make stuff, right? The cookbook is now pretty

well established. And so now the challenges is going from making a meal for me, you, the spouses. To like McDonald's level. To McDonald's. Yeah. How do you make this meant, you know, enterprise, industrial grade, volume, size, scale,

which again is non-trivial, but is the tail. end of this sort of fundamental research to end consumer product cycle. Yeah, exactly, exactly. And it really shows how science benefits us as a society, right? It's like the process is always fundamental science needs to happen first to show the base case. It's possible and it's worth it. Once you have that, then you go into, okay, how do I industrial scale this thing? How do I make it better? how do I make it cheaper, blah, blah, blah, blah, blah. This is an important context in understanding

how the public and private sector work in terms of innovation. A lot of times, especially in the modern tech era, innovation is framed as the private sector is the only location in which innovation can arise because in the public sector there's too much bureaucracy and all this other stuff. And the challenge with that viewpoint is

so much of the innovation, big eye innovation, that gets talked about has a basis that was publicly funded for fundamental research that dictates it. There are some exceptions to this rule where the private sector will look at an area and usually it'll be a founder-led company where the founder has super voting shares to be able to dictate what happens and it doesn't matter. And they have a conviction about something. And then they'll go and do that fundamental research. They'll acquire all.

We've seen this particularly, for example, in AI, which, again, is still based on. decades of fundamental research. Every private company that is doing this, fundamental research, they're standing on the shoulders of giants, right? And those giants are always funded by public institutions and by our taxpayer dollars. Because there's not a profit incentive for the private sector to do fundamental research where they can't tie it

to quarterly earnings or shareholder value in a time cycle that is reasonable for the sort of stock. market system that we have today. If we look at AI, right, like John Hopfield in the 1980s working on neural networks was NSF funded. Everybody thought he was just like doing some weird biophysics like stuff. Okay, like you made you made an icing model out of the brain. Cool.

And then, you know, Jeffrey Hinton saying that guys, neural networks are the future and everybody else was going for the algorithmic AI. He's the one who was like, no, let's just make it bigger and bigger. and with back prop, it's going to work. No one believed him, but he got funding because some people were like, eh, it might work. Let's give him some funding for it.

And now we have all of AI, right? The PhD students of Jeffrey Hinton that went and started OpenAI and all this stuff, they were supported by grants from taxpayer dollars. And now it's a multi-trillion dollar global industry that is. Neurrelink, the PhD students that started Neurilink were supported by grants, from the American government.

100%. Right? Like, there's no world where this isn't happening because of funding from our taxpayer dollars. And this is one thing that I really feel really strongly about is like when I pay taxes, I want it to go for this kind of stuff. One of the things that has created this strategic advantage for the U.S., where we have been a dominant global power driven not only by our military,

but by our technology, both of which are fundamentally where they are because of our investment in fundamental science research. Yeah. We would not have dominance in air and sea with the Navy and the Air Force. We would not have dominance where all of the biggest technology companies on the planet. Yeah. Are here. We would not have that without the early investments in these arenas that were perceived as not being commercially viable at the time.

Yeah. Yeah. It's like, it doesn't make any sense to me because this used to be, this used to be a bipartisan thing. That's what's so interesting. This used to be a very bipartisan thing. We're going to be the best when it comes to science. George Bush funded the NASA's re going to Mars and stuff like that. There's so many, so many things that have happened now, especially with the current administration,

that is just like totally, totally bankrupting us when it comes to our supremacy in the sciences. And it's really, really rather unfortunate. We have sort of photo seven here, which is, you know, we're leading in Nobel Prizes. Yeah. Our universities are at the top. But the current administration is just running scientists out of our country.

Which is like another problem, which is like, if you look at the Nobel Prizes from the U.S. Yeah. Even this year. And who led? and who they are. Yeah. And how they got to be American.

Yeah. Omar Yagi, he was an immigrant from Jordan, Palestinian refugee, came in, embraced this country, was supported by the DOE, was supported by the NSF, did an NSF Postdoc fellowship at Harvard, and the NSF Postdoc fellowship is now completely gutted. The NSF Postdoc fellowship used to be this thing that would take graduate students that were stars and put them at these top institutions. to do postdoc research and gear them up for becoming top faculty members at our leading institutions.

And now that thing is gutted. You know what's funny is the only reason that I am able to be in the U.S. is, you know, my dad did his postdoc at Harvard and was funded. He not only got in through a newly created visa program. That was specific to Zimbabwe. Okay. Right. And it was the first round and he won the lottery the first round.

He similarly got to be able to do, that's how he got to McGill. where he did grad school. And then he got to do the Harvard Postdoc because of a similarly, federally funded grant program around postdoc research. And like, you know, he's been in science and nature,

published multiple times, has multiple patents that are accruing value to multiple American companies. You know, he's been a director of biology at a biotech that's sold for millions of dollars. And like all of that value creation is because of these systems that exist.

Yeah, I mean, same from my dad. My dad came on a, I think it was an NOA fellowship or an NSF fellowship. We first came to Huntsville, Alabama from India. We stayed there for two years. He was working at the Marshall Space Flight Center for NASA, and then he got a tenure track position here in L.A., and that's how he moved here. But even throughout his position as a professor here in L.A., he's gotten funding from the NSF. And now all of that is going down. It's just really, really quite insane to me that this is happening at a countrywide scale. And it's not something that I think we should take lightly. Because, you know, Germany, I hate to bring up Germany as an example, but it really is such a

great example because before the 1920s and 1930s, Germany won almost every Nobel Prize in chemistry, in biology. And then afterwards, they haven't won a lot. It took them a while to crawl back. And we're seeing that happening with American institutions. the UC system has a hiring freeze. Right. JPL has let go of 500 of their staff.

Right. And that's just right around the corner from us here. I mean, it's just nuts, dude. Like, it's going to take us, I fear that it's going to take us decades to come back from this. And I think, I think, you know, we should really be thinking about this as a nation, about, like, what is important to us. Like if American exceptionalism is important to us, which it should be, certainly important to me. Like, you know, I got my American citizenship and I love the fact that America is exceptional at a lot of things, right? And it's perhaps more so than any other profession, I think scientists understand what that means to have American exceptionalism, right?

We have the greatest research infrastructure in the world and we shouldn't be so casually letting that go. 100% I mean and it's one of those things that takes a long time to see transpire and it's why ringing the alarm sooner rather than later matters because the Nobel prizes that went out last week were for discoveries from decades ago and so there's like an inertia yeah right exactly show up in the system yeah yeah and so we don't want to be in the 2050s now and then all the Nobel prizes are being won by countries outside of the U.S. There's several stories of scientists now leaving the U.S.

because of what's happening, right? Other countries are salivating. At the opportunity. At the opportunity to just poach all of our scientists. And this is happening now in real time. This is something that needs to stop. This is an important subject we have covered before. Yeah, but I really, yeah, I think the recent Nobel Prize

is like really made me think about it, right? Because, you know, Omar, like, we're also doing this whole anti- immigration thing, which I totally get, right, like with the illegal immigration and all that. Like, there's arguments to be made there. But I'm, I think both sides of the aisle should agree that curating exceptional international talent is something that America should be doing. It's what we've been doing for hundreds of years. Yeah. And it. We've done it through multiple world wars. Yeah, we've done it since the 1800s. Andrew Carnegie was a, was a immigrant. and look what he did for our nation, right?

Albert Einstein was an immigrant. Enrico Fermi was an immigrant. Like, oh, come on, guys. Tesla was an immigrant. If you look at most of the top companies in the S&P 500, if you look at Fang, most of these, you know, Facebook, Amazon, Netflix, Google, etc., etc., the biggest companies that the U.S. has right now,

the majority of them are founded by immigrants. Yeah, yeah. And I mean, even this year's Nobel Prizes, right, four out of the six Americans are immigrants. You've got, actually, no, sorry, three out of the six. John Clark, he's an immigrant from the UK, did his Ph.D. at Cambridge, and then came here to Berkeley. Michelle Deverey, did his Ph.D.

at Paris Sude, came here to Berkeley. We've got Omar Yagi, obviously, from Jordan, came here. So it's worked, guys. Even if you look at our Math Olympiad team, I think it's 100% Chinese. Yeah, no, there's an Indian guy and there's an American guy. There's a US guy, like a white guy. That's fair. But I mean, I love that, dude. Like, I love the fact that like our country curates talent from all over the world and we just, we just create the A team. Yes. Right? Yes. I absolutely love that. And we should continue to do so. Yeah. So we're getting the plus ones, right? It's our scoreboard.

Right. 100%. This is an important issue. We need to continue to fund fundamental science in the United States. We need to continue to bring the best and brightest from the world here to continue to facilitate the life and the lifestyle that

we have facilitated and also give the gift of what we create to the rest of the world. We have the infrastructure to do so. We have the sort of cultural dynamics to do so. We're irrationally confident.

Yeah. And it works. And it works. And it works. We are irrationally confident and we just do it. And we just do it. And if it doesn't work, we try again and we try again. Because we're so confident that it's going to work. You know, and that's how we get stuff like a MOF5 that has a football field size surface area in a gram. Or LIGO. Everybody else was like, what are you talking about?

He's like, no, this thing literally has a football field. It's been a really fascinating two weeks with the Nobel Prize. We want to thank all of you who are listening, whether it's on YouTube, Spotify, Apple Podcasts, TikTok, Instagram, X, Facebook. You guys really are powering the show. Continue to send over questions. We'll try to touch on them as much as possible. We may introduce a listener questions. section. Yeah, we might.

In future episodes. But this is one of the most important subjects to all of our lives. Our everyday lives, our family, our careers, and everything. And we're going to try to continue to do it justice by covering it

from First Principles. I'm your host, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chowdari. This is from First Principles. We'll see you all next week. Relax and let Ralph's delivery handle your grocery shopping this week. We start with only the freshest items, then review your list and carefully choose each one.

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