Latest / From First Principles / Interstellar Visitor 3I/Atlas, Human Longevity Plateau, New No-Sort Plastic & Analog AI (EP. 7)

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Interstellar Visitor 3I/Atlas, Human Longevity Plateau, New No-Sort Plastic & Analog AI (EP. 7) · From First Principles

• 0:00
  Hello Internet, this is your captain speaking Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chowdhury.
• 0:08
  This is From First Principles.
• 0:10
  We have some great stories for you lined up this week, starting with an update on the interstellar visitor 3i Atlas as we've seen four new instruments capture images as it makes its way towards Earth, followed up by a story about longevity.
• 0:25
  If you were born after 1939, you may not live to see 100 years of age.
• 0:30
  We'll talk about that and Brian Johnson, aka the man who wants to live forever.
• 0:35
  Our third story is about a new no-sort plastic that will allow us to recycle more efficiently.
• 0:41
  And we will end today with an interesting analog AI story coming out of Microsoft.
• 0:47
  This is FFP.
• 0:48
  Let's get after it.
• 1:04
  My friend.
• 1:05
  How's it going?
• 1:06
  We're back again.
• 1:07
  Yeah.
• 1:08
  Another one.
• 1:09
  I think we have a little something here.
• 1:10
  People seem to be excited about talking about science.
• 1:13
  Yes, dude.
• 1:14
  In the weeds.
• 1:15
  I'm really enjoying seeing all the feedback that we're getting.
• 1:17
  100%.
• 1:18
  Yeah.
• 1:19
  We're going to start with a very popular science story that has been all over the headlines.
• 1:22
  That's right.
• 1:24
  About 3A Atlas.
• 1:26
  The headline this week, four powerful telescopes agree, interstellar comet 3I Atlas really is bizarre.
• 1:31
  This is from a variety of outlets, but from space.com is where we sort it.
• 1:35
  And the new information here is both NASA and ESA, the sort of North American and European space agencies, have used their instruments, Hubble, Sphear X, JWST, and TESS to capture images of the object as it makes its way towards our sun.
• 1:49
  Yeah.
• 1:50
  So what's new here?
• 1:52
  So what's new here is we've actually gotten really nice data from the James Webb Space Telescope, from Hubble, and from the VLT and from a lot of these things.
• 2:03
  And they're converging on trying to identify really what this object is actually like.
• 2:09
  Okay.
• 2:10
  And some of the, you know, nitty gritty characteristics.
• 2:13
  So just to recap our viewers about what 3i Atlas is.
• 2:17
  Right.
• 2:18
  It was discovered on the 1st of July of this year by the ATLAS program, which is the Asteroid Terrestrial Impact Last Alert System.
• 2:29
  And this is a system that is part of University of Hawaii's Institute of Astronomy.
• 2:36
  There's basically a bunch of telescopes all around the globe.
• 2:39
  There's two in Hawaii.
• 2:40
  There's one on Maui, one on the Big Island.
• 2:42
  Then there's one in South Africa, Chile, Australia.
• 2:46
  and Spain in Tenerife in the Canary Islands.
• 2:49
  This particular one was discovered in the one in Chile.
• 2:51
  Okay.
• 2:53
  The whole point of this guy is just to peruse the sky looking for transient objects and objects that move around.
• 2:59
  Okay.
• 3:00
  So it was discovered there and then it turns out that
• 3:02
  You know, other telescopes had already seen it but hadn't identified it as this thing.
• 3:07
  We'd talked about the Zwicky transient facility in Palomar Mountain near San Diego, right?
• 3:11
  Yes.
• 3:12
  That thing actually had already found it in May but hadn't, like, pinpointed it as, like, a thingy that we should be worried about.
• 3:18
  Yep.
• 3:19
  So once we identified it with the Atlas program…
• 3:23
  everybody all over the globe was like, oh, let's just peruse the data because we know where it is, right?
• 3:27
  And where it would have been.
• 3:28
  And so from that, we could then get the trajectory of this thing.
• 3:32
  Yes.
• 3:32
  And once we calculated that trajectory...
• 3:35
  It was like, oh, whoa, whoa, whoa, whoa, whoa, whoa, whoa.
• 3:38
  Okay?
• 3:38
  It's moving really fast, 61 kilometers per second, all the way out in Jupiter.
• 3:44
  Yep.
• 3:45
  Okay?
• 3:45
  And if it's moving that fast all the way out there, that means it's not going to stay inside the solar system.
• 3:49
  Right.
• 3:50
  You can do the Newton's laws and do the calculation.
• 3:52
  It'll go right past the sun and then on its way.
• 3:55
  Mm-hmm.
• 3:56
  Then we got into these things about people who had these really blurry images.
• 4:00
  They're like, is it aliens?
• 4:01
  It's never aliens.
• 4:03
  Much to my chagrin.
• 4:04
  Yeah, yeah, yeah.
• 4:05
  We always hope, right?
• 4:06
  One can always hope.
• 4:07
  But time always tells that it's not aliens.
• 4:09
  And it turns out to be the third interstellar object, right?
• 4:14
  Which is an object that is not part of our solar system, that is literally going from star system to star system.
• 4:22
  around the galaxy of the Milky Way, right?
• 4:24
  Because the idea is there are these large gaps of void space in between, you know, star systems.
• 4:29
  And so it's from a probabilities perspective to because of the vastness of space.
• 4:35
  Yeah.
• 4:36
  And our ability to detect both of those reasons.
• 4:39
  We haven't seen a lot of these types of objects before.
• 4:41
  Yeah, we haven't seen a lot.
• 4:43
  Now we will be able to because of things like the Vera Rubin coming online and things like that, right?
• 4:47
  Yes.
• 4:47
  This is our third one.
• 4:48
  That's why it's called 3i.
• 4:50
  The i is actually for interstellar.
• 4:51
  Oh, there you go.
• 4:52
  And 3i Atlas is because Atlas was the program that discovered it.
• 4:56
  Right.
• 4:56
  Very.
• 4:57
  The first one is one eye Oumuamua.
• 5:00
  That was the really weird elongated thingy.
• 5:04
  And that one we caught on the way out, so we couldn't really get good data on it.
• 5:07
  The second one was two eye Borisov, which turned out to be an interstellar comet.
• 5:12
  And then now this was three eye Atlas.
• 5:14
  And so a key difference between Oumuamua, let's say, and Atlas is that we were able to catch Atlas on its way in, not when it was on its way out, which gives more time to capture more data.
• 5:28
  Yeah.
• 5:29
  As well as being able to see how it behaves over longer time periods.
• 5:32
  Yeah, exactly.
• 5:33
  It's like that scene from Office when I was like, don't panic.
• 5:36
  I said, stay calm.
• 5:37
  Like, OK, now it's like it's happening.
• 5:38
  Right.
• 5:39
  So now everyone's like scrambling to figure out how to image this thing, because once we've got it, like every astronomer who's a planetary astronomer, who is, you know, or planetary origins or an exoplanets astronomer trying to understand how solar systems form.
• 5:53
  This is a very big deal.
• 5:54
  Okay.
• 5:54
  Because the other thing is if you turn back the clock, this thing came from above the Milky Way, which is where there's an old population of stars that are much older than our sun.
• 6:03
  Right.
• 6:04
  Right.
• 6:04
  So this thing could be something like 7 billion years old.
• 6:07
  Our solar system is tops 4.6, 4.7.
• 6:11
  So this thing is older than, you know, anything that we've really had a close encounter with.
• 6:15
  Right.
• 6:15
  Now, the...
• 6:17
  The problem is when this thing gets close to us, we're going to be on the other side of the sun.
• 6:22
  Right.
• 6:23
  So it's coming in.
• 6:24
  We're on the front side of the sun right now and it's coming in towards us.
• 6:27
  But we're going to be ordering.
• 6:29
  It won't be here.
• 6:30
  It'll still be coming this direction and we'll be blocked by the sun.
• 6:33
  By the sun, right?
• 6:34
  It's like during –
• 6:35
  If you wanted to see it, it would be during daytime behind the sun.
• 6:38
  Yep.
• 6:39
  Right?
• 6:39
  Which, because of the atmosphere, we're not going to see anything.
• 6:43
  The sky is going to be blue, and the air glows more than any stars or this 3i atlas.
• 6:49
  This is why Vera Rubin is doing a survey, sky survey at night.
• 6:53
  Yeah.
• 6:54
  And not during the day.
• 6:55
  Yes, exactly.
• 6:56
  And furthermore, we can't really point the James Webb Space Telescope towards it either.
• 7:00
  Because, like, then the sun, the whole point of the James Webb Telescope, the reason why it's so nice and, like, sensitive is because it's got this giant shade.
• 7:11
  That shades it from the sun so that the instruments can be kept at like four Kelvin.
• 7:15
  Right.
• 7:15
  Like colder than like actually no even less than like there's like a literal like dilution refrigerator on that thing that is like keeping it colder than outer space.
• 7:25
  Right.
• 7:25
  Because then you get these instruments that are super cold.
• 7:28
  The instruments the atoms aren't jiggling.
• 7:30
  Right.
• 7:30
  So then the only signal you're getting is not from the instrument itself.
• 7:33
  Right.
• 7:34
  But from like the starlight.
• 7:35
  Right.
• 7:36
  There's less or no noise.
• 7:37
  Yeah, yes.
• 7:38
  But if you wanted to point it towards that thing, that defeats the whole purpose.
• 7:42
  That makes sense.
• 7:43
  So you can't really do that.
• 7:44
  That makes sense.
• 7:45
  So what we'd like to do is to observe it right now with our terrestrial-based telescopes like the James Webb.
• 7:53
  Yes.
• 7:54
  Now when it's sort of away from the sun.
• 7:56
  Yes.
• 7:56
  Get as much data as possible.
• 7:58
  Yes.
• 7:58
  And then as it goes through the solar system and it reaches Mars.
• 8:02
  Yes.
• 8:02
  Yes.
• 8:03
  It's going to be on the other side of the sun.
• 8:04
  It's going to be near Mars' orbit.
• 8:05
  Yes.
• 8:06
  We can have all of our orbiters that are orbiting Mars.
• 8:10
  Right.
• 8:11
  Sort of... And then point towards this thing and try to get as much data as possible.
• 8:16
  So the idea is we have orbiters around Mars that have a certain mission right now, right?
• 8:20
  A Mars-based mission.
• 8:23
  They can take a break.
• 8:24
  This is one of those, like...
• 8:26
  Not that often in the lifetime opportunity.
• 8:28
  It's like we can take a couple hours a day, reorient, use some of our fuel to do so, to capture, to provide that data when we on Earth, as well as JWST, don't have the capability or the angle of viewing to be able to actually properly capture it as it gets closer to our orbit.
• 8:49
  Yeah, and exactly.
• 8:49
  And with comets, you really want to look at the thing when it's close to the sun.
• 8:54
  Because that's when it's doing the most cool, crazy stuff.
• 8:57
  Right.
• 8:58
  Okay?
• 8:58
  When the comet is far away, it's just a chunk of ice.
• 9:01
  But as it gets closer to the sun, it's going to start interacting with the solar wind.
• 9:05
  It's going to start interacting with the photons that the sun is giving out.
• 9:08
  So then there's going to be chemical reactions and physical reactions happening on top of the comet.
• 9:13
  And that's what we want to see.
• 9:14
  Right?
• 9:15
  In the very, like...
• 9:17
  Way out there, when we tried to look at 3i Atlas, it was really bright for how far away it was.
• 9:24
  And that's what gave rise to a lot of these alternative theories about it's artificial and all this other kind of stuff.
• 9:29
  The light's not reflecting from the sun.
• 9:31
  It's generating its own light.
• 9:32
  It's generating its own light because it's so bright.
• 9:34
  How is that possible?
• 9:36
  Now, as it got closer, actually, so August 6th is when James Webb looked at it.
• 9:41
  And the preprint came out on August 25th.
• 9:43
  They were fast about this because they know that people want it.
• 9:46
  Right.
• 9:47
  So the preprint came out like literally they had like what, like 20 days to do all of the people were upset about how long it took.
• 9:54
  Yeah.
• 9:54
  Yeah.
• 9:55
  But dude, like 20 days to like massage data is kind of like create a story.
• 10:00
  Right.
• 10:01
  It's not just like you're analyzing the data.
• 10:02
  You have to like interpret the data.
• 10:04
  Try to create a story about like what you're trying to say.
• 10:07
  Right.
• 10:07
  Science is all about stories at the end of the day.
• 10:09
  It's not just numbers.
• 10:10
  Right.
• 10:10
  You have to have some kind of narrative.
• 10:12
  Right.
• 10:12
  So.
• 10:14
  They analyzed the data from James Webb Space Telescope.
• 10:17
  It used one of the instruments that is a spectrograph.
• 10:20
  So it can tell us things like the composition of this thing.
• 10:23
  And what they found was a really high mixing ratio for carbon dioxide and water.
• 10:29
  Okay.
• 10:29
  So usually the mixing ratio is quite low.
• 10:33
  Here it's eight to one.
• 10:34
  Okay.
• 10:35
  Okay.
• 10:35
  There's eight times as much CO2 as water.
• 10:37
  Right.
• 10:38
  That's like a lot.
• 10:39
  Yes.
• 10:39
  Okay.
• 10:39
  And that's very unusual for comets in our solar system.
• 10:43
  Correct.
• 10:44
  Okay.
• 10:44
  So that means that this thing, there's two possibilities.
• 10:47
  Okay.
• 10:48
  One is that when this thing was born, Mm-hmm.
• 10:51
  it had a very different birth environment than the comets in our solar system.
• 10:56
  It could be that this thing was born in something called the CO2 ice line.
• 11:00
  Okay.
• 11:00
  You can imagine when a star is forming and the solar system is forming around it, the star starts...
• 11:07
  making light and thermal energy, that starts going out, right?
• 11:10
  There's going to be...
• 11:11
  The disk is going to have a mixture of silicon, hydrogen, CO2, water, and all the dust from that primordial nebula that sort of became the star, right?
• 11:24
  So all of this stuff is going to be in the disk.
• 11:28
  There's going to be a line...
• 11:29
  Where inside that line, close to the star, there's going to be enough radiation to put CO2 in a gaseous form.
• 11:38
  Okay.
• 11:38
  Okay?
• 11:39
  But afterwards, there's not going to be enough radiation, not going to be enough heat, and that CO2 is going to become dry ice.
• 11:45
  Okay?
• 11:46
  So there's going to be this ice line where after a certain distance away from the star, you just have dry ice and before you have gaseous ice.
• 11:53
  Because also... Gaseous CO2.
• 11:55
  Carbon dioxide does not have...
• 11:56
  There's no liquid form.
• 11:57
  Yeah, I mean...
• 11:58
  So it goes straight from gas to solid.
• 11:59
  Yeah, yeah.
• 11:59
  I mean, there is a liquid form, but at, like, very peculiar pressures, right?
• 12:04
  Okay, that's fair.
• 12:05
  Like, in the low pressure of space, and even at, like, one atmosphere, there's... Like, at one atmosphere, it'll go directly from dry ice to CO2, right?
• 12:15
  If you put in high enough pressure, you can liquefy CO2.
• 12:17
  Okay, that makes sense.
• 12:18
  Right?
• 12:19
  But, like, out here, it's just going to go ice to gas.
• 12:22
  And back and forth.
• 12:23
  Right?
• 12:23
  Back and forth.
• 12:24
  So...
• 12:25
  You have this like dry ice line now you could be that like the comet was forming in this right past that ice line, right?
• 12:33
  So it's like gathering up a lot of co2 not a lot is a lot of water right because water the water line is elsewhere at right right right so
• 12:43
  That could be one reason why 3i Atlas has a lot of CO2 versus water.
• 12:49
  The other thing could be that it formed or it was subjected to a really harsh radiative environment.
• 12:57
  Okay.
• 12:58
  Okay.
• 12:58
  And then what can happen is you can have these reactions between carbon monoxide, which is also fairly common.
• 13:04
  Yes.
• 13:05
  Carbon monoxide and water with this high energy radiation to create CO2.
• 13:10
  Got it.
• 13:10
  And that's what happened, right?
• 13:11
  Got it.
• 13:12
  So there's these two possible scenarios, but in any case, it's still very, very different from any comet that we've seen.
• 13:18
  Which is, I think, what's interesting is there's this leap, you know, that folks like me who are not as educated always make where it's like, oh, we have no reference points, so that must mean it's aliens.
• 13:30
  And I think kind of what the through line you're drawing here is...
• 13:33
  We do know that it was birthed in an environment that is different than the environment where we have these data sets.
• 13:41
  Yeah.
• 13:42
  Yeah.
• 13:42
  And so those initial conditions matter.
• 13:44
  Yeah.
• 13:46
  And the data sets, to your point, the data sets that we have,
• 13:49
  on what is normal is like from our solar system.
• 13:53
  As you say, right, there's millions and millions of solar systems out there with different types of stars, different galactic environments.
• 14:01
  Like we are a tiny little sample size, right?
• 14:06
  And one of the great things and why everyone is so excited about interstellar objects is because it gives us a sampling of what's out there.
• 14:14
  Yes.
• 14:14
  Right.
• 14:14
  And so that's why everyone is like scrambling to try to get as much data as possible on this guy.
• 14:19
  Right.
• 14:20
  Because from that data, like we kind of talked about at the beginning of the story, there are multiple insights that can be gleaned, not only at time of capture, but also afterwards when we learn other things.
• 14:32
  And it's like, oh, let's go look back at the Atlas data.
• 14:35
  Yeah.
• 14:35
  Now that we have some different context about how these highly radiative environments work.
• 14:42
  work yeah or any number of other yeah options yeah exactly we can like start i mean you know we work we're kind of scrambling because like this thing's going to be out by september right right so right now we're just in the observe phase right okay after this thing gets out and we get all the data then i'm pretty sure there's going to be a big cohort of people that are going to go into the modeling phase right of trying to understand okay theoretically how is something like this possible right like if i were to model it this way would it match the data no well then that's not
• 15:12
  That's probably not what happened.
• 15:14
  And so, yeah, at this point, it's just like everyone's just like just pointed at the thing and get as much light as you can.
• 15:20
  What's so funny about this is, as I mentioned in a previous episode, I get the pleasure of being able to work with Dr. Avi Loeb on a regular basis, who is a professor of astrophysics at Harvard.
• 15:33
  you know, one of the things we'd reached out to him to do was to do a white paper on this new update from 3i Atlas.
• 15:40
  And to go with what you're just saying, his response was almost a more expanded version of what you just said, which is like, we need to wait for the dust to settle.
• 15:50
  Right now, it's just an observational stage.
• 15:53
  We need to focus on capturing as much data and information as possible.
• 15:57
  Once it's gone and that opportunity has left, then there's going to be time to actually settle in and do analysis on what it is that we just captured.
• 16:08
  So I think there's an important distinction to identify here, which is like even if it was aliens, right, we don't have any real baseline to reference for it, to identify it as such.
• 16:17
  And if it doesn't land on the White House lawn, there's going to be this two-stage data capture and observation, which is where we are now, and then sort of analysis and modeling, which happens after.
• 16:27
  And we will get more clarity, generally speaking, once the observation period is over and we move into this analysis and modeling timeframe, which is, again, how science works.
• 16:38
  Yeah, exactly.
• 16:39
  But there's a big public desire for answers, which is understandable.
• 16:48
  And like many things, it's just a process.
• 16:51
  So it's interesting you say that because it makes me think of that.
• 16:53
  Yeah.
• 16:53
  That letter we got.
• 16:54
  And like, and you know, it's definitely not aliens at this point, but it's still like one of the coolest objects that's ever visited us.
• 17:03
  Right.
• 17:04
  And so, I mean, I don't think people should be disappointed.
• 17:08
  Right.
• 17:09
  It's kind of like.
• 17:10
  A cop-out.
• 17:12
  I mean, you know, as the Buddhists say, like, don't have these expectations.
• 17:15
  Because the world itself is a wonderful place.
• 17:19
  Right.
• 17:19
  Right?
• 17:20
  Imagine this thing, like, going through the Milky Way for seven billion years.
• 17:25
  Right.
• 17:25
  Visiting, like, who knows what, right?
• 17:27
  This isn't... We're not the first star system that it's seen.
• 17:30
  Mm-hmm.
• 17:30
  Imagine from its point of view.
• 17:31
  It's gone from one star system to the next.
• 17:34
  The fact that it's going so fast means that it's had a pretty close encounter.
• 17:38
  Yeah, right.
• 17:38
  With something pretty massive.
• 17:40
  Right.
• 17:40
  To like be zooming through like this.
• 17:42
  Right.
• 17:42
  The sun is not really going to change its velocity that much.
• 17:46
  Got it.
• 17:47
  Right.
• 17:47
  It's just, it's sort of like redirecting it in one direction.
• 17:50
  Yep.
• 17:50
  But like this thing had a very close encounter.
• 17:53
  Like this, you know, like imagine from its perspective, it's been around...
• 17:58
  for like half the age of the universe.
• 18:02
  That's a long time.
• 18:02
  That's a long time.
• 18:03
  We've only been around for a third.
• 18:05
  So I think it's an incredible story, right?
• 18:08
  And to get back to the idea of we're trying to observe this thing as much as possible, right?
• 18:13
  So that's where the European Space Agency and this new stuff is coming in, right?
• 18:17
  Where the European Space Agency has a bunch of orbiters.
• 18:21
  around mars that they're like okay we're gonna we're gonna repurpose for this one of them is um the mars express and the other one is the exo-mars trace gas orbiter both of these both of these things have cameras that are pretty good this thing is still going to be pretty far away from mars but you know getting any sort of anything getting anything when it's on the other side of the sun and we can't put anything there
• 18:42
  And it's going to be the most volatile when it's on the other side of the sun.
• 18:45
  So getting any data at that point of its orbit, of its trajectory, I should say, not orbit because it's not orbiting, is going to be incredibly valuable.
• 18:54
  And the guys at the ESA are definitely tempering everyone's expectations.
• 18:57
  It's like, guys, it's going to be far away.
• 18:59
  Like, our stuff wasn't designed for this.
• 19:01
  It's like, okay, dude, like, no one's going to blame you if you get shit data.
• 19:06
  Like, just get whatever you can.
• 19:08
  And then the other thing is, so we also talked about the Europa Clipper in one of the previous episodes.
• 19:13
  So on its way back to Earth, it's actually going to cross through where the comet was.
• 19:21
  So as a comet goes across the solar system, it leaves behind...
• 19:26
  a trail of particles, of debris in its wake.
• 19:30
  That's actually how we get meteor showers.
• 19:32
  Meteor showers are literally just the Earth moving through cometary paths that have these particles.
• 19:40
  And just like planets, the particles are going to be going through the sun.
• 19:46
  They're orbiting the sun as well.
• 19:48
  So as the Earth goes through these cometary paths...
• 19:53
  any sort of particles that get trapped in the Earth's gravity fall down to Earth, and then that's how you get the Perseids and all of these meteor showers, right?
• 20:01
  But you can now imagine the Europa Clipper probe is going to be going through this thing, right?
• 20:07
  So we could be getting observations about
• 20:10
  the particulate matter that, you know, was a part of that.
• 20:13
  Yeah.
• 20:13
  Yeah.
• 20:13
  And it's not just new Europa Clipper.
• 20:15
  I think there's other places like Hira and Lucy, they may fly through the commentary tail.
• 20:19
  They're all NASA.
• 20:21
  Yep.
• 20:21
  So that's going to be super exciting to get like actual, like, you know, data.
• 20:26
  It's going to be both optical, like imagery.
• 20:28
  capture as well as like physical matter analysis.
• 20:32
  And I think this sort of, you know, this brings up the, when we build these really expensive, highly precise instruments for observation and data capture, they're not necessarily single purpose in this exact context.
• 20:47
  Yeah, yeah.
• 20:47
  It's like we send a bunch of stuff to look at Mars, but we can still use those same instruments for other use cases, such as,
• 20:54
  Hey, go just look at this thing that's coming through and let's see what we can get.
• 20:58
  Yeah.
• 20:58
  Yeah.
• 20:59
  It's pretty crazy because like I'm pretty sure when the ESA and when NASA like made these things, we didn't – you know, Oumu'a wasn't even a thing.
• 21:08
  Right.
• 21:08
  Right?
• 21:09
  When they were planning like – These were made well before we even knew.
• 21:11
  Well before we even – and then now it's like – now it's kind of nice that humanity has a solar system-wide presence.
• 21:18
  Right.
• 21:18
  Right.
• 21:18
  You know, where even though Earth is in a crappy part of its orbit and we're kind of handcuffed in that way, we can still get that data from other parts because we have this presence that's so big.
• 21:31
  That's kind of cool.
• 21:32
  It's really cool.
• 21:33
  It's why both near and deep space matter quite a bit.
• 21:37
  And while it may be the case...
• 21:42
  based on our second article, that we may not live past 100.
• 21:46
  Yeah.
• 21:47
  Let's hope that these tools and instruments maybe one day do capture alien life in our local neighborhood.
• 21:53
  Oh, that would be dope.
• 21:53
  That would be fantastic.
• 21:54
  That would be fantastic.
• 21:56
  But our second story is a similarly interesting one about longevity no longer increasing.
• 22:03
  Headline...
• 22:03
  Studies find generations born after 1939 unlikely to reach age 100.
• 22:09
  This is covered in the Independent.
• 22:12
  But the research was out of it was an international collaboration between the Max Planck Institute of Demographic Research, the Institut National d'Etudes Démographiques,
• 22:23
  And the University of Wisconsin-Madison, who we've mentioned on this podcast a couple of times now.
• 22:28
  And they published their paper in PNAS, which is the Proceedings of the National Academy of Sciences.
• 22:34
  And so people are probably most familiar with longevity as a concept or studies, either from the Netflix documentary, The Man Who Thinks He Can Live Forever, which is about this hundred millionaire, Brian Johnson.
• 22:46
  What is he doing?
• 22:47
  So he's doing all, he's getting the best doctors in the world.
• 22:49
  Okay.
• 22:50
  To like give him a regiment.
• 22:52
  So he's now allegedly decreased his actual age from his mid-40s, where he's in his 40s, to the bodily age of a 20-something-year-old.
• 23:02
  Right?
• 23:02
  Wow.
• 23:04
  So his whole thing is, unless I die in a freak accident, my body is literally...
• 23:09
  Is he one of these guys who gets blood transfusions?
• 23:13
  So I don't want to say specifically.
• 23:15
  Yeah, okay.
• 23:15
  But the only two angles are either the Brian Johnson story or the idea that all these billionaires are vampires.
• 23:20
  Yeah.
• 23:20
  Getting their young employees to give them blood.
• 23:22
  Yeah, because that's like a real thing.
• 23:24
  I saw news stories about that.
• 23:26
  I'm not sure with Brian specifically.
• 23:27
  Yeah, maybe Brian Johnson isn't doing that.
• 23:29
  Okay.
• 23:29
  But this is how in the pop culture.
• 23:31
  Everyone's obsessed with it.
• 23:32
  Right.
• 23:33
  People want to live longer, all this stuff.
• 23:35
  It's always been the case over the course of the year that like as generations go by we get older We are able to live longer.
• 23:41
  Yeah, this study is sort of suggesting not anymore.
• 23:44
  Not anymore.
• 23:45
  We've yeah We might have come to the end of the road with the low-hanging fruits.
• 23:48
  That's what this thing is suggest.
• 23:50
  Okay.
• 23:50
  Okay So longevity there's been this thing called the longevity revolution over the over the past century, right?
• 23:55
  life expectancy has been increasing like crazy over the last century and
• 24:00
  And that has everything to do with advancements in science.
• 24:03
  100%.
• 24:03
  Right?
• 24:04
  We've, like, figured out biology at a nitty-gritty level.
• 24:09
  We figured out, hey, like, sickness actually comes from little tiny pathogens.
• 24:14
  And we figured out ways to advance public health with vaccines, with antibiotics, with all kinds of, like, revolutionary technologies.
• 24:22
  technology, medical innovations.
• 24:25
  We've also had a lot of socioeconomic development.
• 24:27
  So in sanitation, in the eradication of infectious diseases, smallpox is no longer a thing, which is an incredible thing to think about, dude.
• 24:38
  Smallpox doesn't exist in the world.
• 24:41
  Which is controversial to bring up the fact that that's an incredible feat for humanity.
• 24:46
  For humanity and science and everything.
• 24:48
  Dude, I mean, and
• 24:50
  You know, I can't imagine it happening today, which is so sad, which is so sad, because like back when in the I think it was in the 1980s when, you know, we were at the last stages of eradicating smallpox.
• 25:04
  And there was just a tiny little bit of smallpox in Africa.
• 25:07
  OK. And in the middle of a civil war.
• 25:10
  Both sides decided to have a ceasefire so that health workers could go in and because there had been a small, smallpox outbreak there and health workers could go in and vaccinate everyone there.
• 25:23
  And then they started fighting each other.
• 25:25
  It's like, it's like, just stop fighting for like, for like a few days.
• 25:28
  Give us the time here.
• 25:30
  And then everyone's like, probably like.
• 25:32
  planning how to restart the fighting.
• 25:35
  But even in the Civil War, they respected the fact that, okay, nobody wants smallpox.
• 25:39
  Right.
• 25:40
  Like, I want to win the Civil War, but not at the cost of a nation with smallpox.
• 25:47
  I can't imagine that happening today.
• 25:49
  There's no – I mean, a funny story that someone always brings up to me about this is the former Senate Majority Leader, Mitch McConnell, I think when he was younger, had –
• 25:59
  Smallpox.
• 26:00
  That's crazy.
• 26:01
  And, you know, leader of the Republican Party in the United States for a long period of time, he's been very vocal about the defense of like science in this particular isolated use case.
• 26:13
  Yeah.
• 26:14
  And to your point, imagining any of the current global conflicts going on pausing to do a public health.
• 26:20
  No, I can't.
• 26:21
  It's hard.
• 26:22
  It's hard to envision.
• 26:23
  Yeah, it really is.
• 26:24
  It's hard to envision.
• 26:24
  Yeah.
• 26:25
  Which is sad.
• 26:26
  It really is sad.
• 26:27
  But like those are the advancements that got us this longevity revolution, right?
• 26:30
  Where we had an insanely increasing lifespan, average lifespan.
• 26:37
  Okay.
• 26:39
  And one of the things that happened as a consequence was policymakers just took it for granted.
• 26:45
  Yep.
• 26:46
  Okay.
• 26:46
  Where, yeah, of course, we're going to just keep living longer.
• 26:50
  Right.
• 26:50
  You look at the, you look at the, the trend of like how average life expectancy is increasing and it looks like a line.
• 26:58
  And so like any good high school student, they get three data points.
• 27:02
  They're like, oh, it's just going to go that way.
• 27:06
  Right.
• 27:07
  And so, and, and this, this paper is really putting that claim to rest.
• 27:13
  Right.
• 27:14
  A lot of people have been sounding the alarm, though.
• 27:17
  Because anyone who's reasonable is like, okay, just because it's linear for now doesn't mean it's going to keep staying linear.
• 27:24
  There's plenty of systems that go linear and then they plateau off.
• 27:29
  But there's been sort of two camps.
• 27:31
  One camp has been there is kind of an inherent...
• 27:35
  limitation to how long humans can live.
• 27:37
  Okay.
• 27:37
  Okay.
• 27:38
  Based on just evolution, mutation rates, the ability of our cells and our organs to keep up.
• 27:45
  Yep.
• 27:45
  Right.
• 27:45
  And then there's another camp that's like, well, you know, we could have enough science to,
• 27:51
  To circumnavigate.
• 27:53
  Science.
• 27:53
  Like inherent evolution, right?
• 27:56
  And just like replace bad cells and do all of this bioengineering to just make us live forever, right?
• 28:03
  So those are the two camps.
• 28:04
  This is sort of adjacent to the transhumanism kind of movement where they're like, there's going to be the symbiosis between all of our science and stuff.
• 28:12
  And then that way we'll be able to be beyond, we'll be this next thing that is...
• 28:16
  Yeah, yeah, yeah.
• 28:17
  We'll merge with AI or some nonsense, right?
• 28:21
  And it's like, okay, yeah, maybe in like 100 years, right?
• 28:25
  But not me.
• 28:26
  I ain't doing that.
• 28:28
  No, ain't no way, boy.
• 28:29
  But these are the two camps.
• 28:30
  But those are the two camps, right?
• 28:32
  And it's been hard to sort of justify one or the other without hard data.
• 28:37
  And that's what this group is doing from Max Planck, right?
• 28:41
  The paper that's come out in PNAS, it's...
• 28:46
  sort of taking a very rigorous approach to this problem.
• 28:50
  Okay.
• 28:50
  Because this problem is a hard problem to get good data and good analysis on.
• 28:55
  Okay.
• 28:55
  Okay.
• 28:56
  There's, let me, let me give you a sense of why it's hard.
• 28:59
  So there's two approaches to how we quantify life expectancy.
• 29:04
  Okay, there's the period life expectancy and then there's the cohort life expectancy.
• 29:08
  Okay, the period life expectancy is the easier one to quantify.
• 29:14
  It's basically the idea that reflects the average mortality risk in a given year.
• 29:19
  Right.
• 29:19
  Okay, so it's kind of like saying like, okay, like if a newborn is born now and you say the life expectancy is 80 years, but that's a period life expectancy.
• 29:27
  What you mean is that like given the climate right now,
• 29:33
  This newborn, given the mortality climate right now, this newborn would be expected to live 80 years, right?
• 29:38
  Given the chances of dying on a random person.
• 29:41
  Yep.
• 29:41
  Right?
• 29:42
  But that is a very local, local in time measure.
• 29:46
  Right.
• 29:46
  Right?
• 29:46
  Because if the COVID pandemic happens, that period life expectancy goes down.
• 29:50
  Right.
• 29:51
  If the Spanish flu happens in 1918, the period life expectancy goes down.
• 29:56
  But, like, it's kind of a live metric of life expectancy, okay?
• 30:00
  Then there's something called the cohort life expectancy, which is, like, saying, okay...
• 30:05
  The people born in 1950 in that birth cohort, the average lifespan of that individual is 80 years.
• 30:13
  Okay?
• 30:14
  That's different because that is taking into account this giant time span of 80, like 1950 plus whatever 80 years.
• 30:21
  Yes.
• 30:21
  Right?
• 30:22
  Yes.
• 30:23
  In principle, that's really hard to quantify unless everyone has died from 1950.
• 30:28
  Right.
• 30:29
  Right?
• 30:30
  Then you take everyone who died from 1950, you average it out, and you say that was the cohort life expectancy.
• 30:35
  So it can be useful in a retrospective context when you have a generation or cohort that's already...
• 30:40
  Yeah, that's already done.
• 30:42
  But it's not predicting.
• 30:43
  It's hard.
• 30:44
  You can't play it forward as like, well, we have a generation that's like halfway.
• 30:48
  Yeah.
• 30:49
  Yeah.
• 30:49
  So like maybe predict.
• 30:51
  Yeah, exactly.
• 30:52
  These unknowns about the future.
• 30:53
  Exactly.
• 30:54
  Yeah.
• 30:54
  Yeah.
• 30:55
  So you see now the struggle, right?
• 30:57
  That like scientists have in defining a cohort life expectancy of a generation that's still alive.
• 31:02
  Yes.
• 31:02
  And that's what these guys are trying to forecast.
• 31:04
  Yeah.
• 31:04
  Okay.
• 31:05
  Okay.
• 31:05
  And so the way they did it was really cool.
• 31:08
  They got six different types of models.
• 31:10
  So, you know, in social science, if you just do like a single model, lots of confounds, you want to do a bunch.
• 31:18
  That's why I like this paper, right?
• 31:19
  They were pretty rigorous about it.
• 31:21
  You do six different, different approaches, and you see if they all give the same answer.
• 31:26
  If they do, then it's a pretty robust finding.
• 31:28
  Got it.
• 31:28
  Right.
• 31:29
  On the other hand, if it's a giant spread, that means that the nitty gritties of the model and the initial data that you put in, that's what's messing it up.
• 31:35
  Right.
• 31:36
  So they did six different models, like the Lee Carter model, the cohort segmented transformation, age at death model, all of these like different kinds of models.
• 31:44
  They're basically like fancy data analysis techniques where what you do is you take a time series of data.
• 31:51
  of different age groups and how they're doing in terms of mortality.
• 31:55
  And then from that you project forward and try to get like a sense of, okay, when is the time series going to end?
• 32:01
  Okay.
• 32:02
  You try to do this for six different things.
• 32:04
  And what they came up with the same, they came up with the same endpoint, which is that this life expectancy is going up, but it's not going to get to a hundred.
• 32:12
  It's going to plateau.
• 32:13
  Okay.
• 32:14
  To validate it, you might be thinking, okay, well, these are just models, right?
• 32:17
  It's nice.
• 32:17
  To validate it, they forecasted life expectancy for people from 1919 to 1938 who are all dead.
• 32:25
  Okay?
• 32:25
  Everyone born in that time period are all dead.
• 32:27
  So you can take that data, right, as your test data in some sense, right?
• 32:32
  And be like, okay, I'm going to do the same analysis for this data from people born in 1919 to 1938 and then try to project when they're going to die.
• 32:42
  And it worked.
• 32:42
  And we have the answer for that.
• 32:43
  And we have the answer for that because we have the data about when they died.
• 32:46
  So we built a sort of predictive model to be able to work for current cohorts that are still alive.
• 32:50
  Yeah.
• 32:51
  And we validated it.
• 32:52
  And it worked across six models.
• 32:54
  And then we used a historical cohort who, where everyone's dead to put it in to see if it mapped onto the actual results we see saw.
• 33:03
  from that cohort that's now all.
• 33:05
  Yeah, and it was a little bit off, but not enough to justify what they were seeing with the present cohort.
• 33:11
  And it might be a little bit off because like, you know, when you're trying to do, like obviously the environment of the data back then is different from the environment now, right?
• 33:20
  But then you can make arguments about how off the results should be.
• 33:24
  And the results should not be that off compared to what we're finding now, which is that this thing is definitely plateauing.
• 33:30
  Okay?
• 33:30
  Yes, yes.
• 33:32
  The cooler part of this is they actually did a kind of causation of what is causing this plateau.
• 33:38
  Right.
• 33:39
  And what's causing this plateau is the fact that we have exhausted all the low-hanging fruits when it comes to longevity of human lifespan.
• 33:48
  We've taken all the low branches on the tree for extending life, and we've actualized.
• 33:53
  We've actualized them all because young people are now living longer, right?
• 33:57
  No one's, I mean, people, obviously people are dying before the age of five, but much less.
• 34:02
  Infant mortality is way down, okay?
• 34:04
  People dying before the age of 20 is way down because infectious diseases are way down, right?
• 34:10
  Like the problems that you have as a young person.
• 34:14
  are no longer the kinds of problems that you used to have before.
• 34:18
  Okay?
• 34:19
  Like, because of vaccinations, because of fundamental science, because of all of the advancement that we've been doing over the past century.
• 34:27
  The hard stuff is now the actual aging.
• 34:31
  Okay?
• 34:32
  That's the cancer.
• 34:34
  Right.
• 34:34
  That's the Alzheimer's.
• 34:37
  That's these higher order things that are notoriously hard.
• 34:43
  Because they're no longer...
• 34:44
  They're no longer agents that are trying to kill you.
• 34:48
  Okay?
• 34:48
  They're yourself.
• 34:50
  That's just like sort of at a cellular level giving up in some sense.
• 34:55
  Right?
• 34:55
  Like the neurons are sort of dying.
• 34:57
  Right.
• 34:57
  And the nervous system and the brain is in some sense deteriorating just because of...
• 35:03
  Like time.
• 35:04
  Right.
• 35:04
  Right.
• 35:04
  And in cancer cells, it's like the mechanisms that control the growth of cells is sort of giving up.
• 35:11
  Right.
• 35:12
  And then now you have these tumors growth.
• 35:13
  Right.
• 35:14
  So so it's a much more complicated problem now.
• 35:17
  Right.
• 35:17
  That.
• 35:18
  We haven't really had the kind of progress that we have had in cases like polio and smallpox and all of these other sort of, you know, quote, easier problems.
• 35:30
  They weren't easy at the time.
• 35:32
  But now that we've looking in hindsight, it's like, OK, they were all like agents.
• 35:36
  And now we have these like modular mechanisms to like solve that.
• 35:38
  Right.
• 35:39
  Oh, just make a vaccine.
• 35:40
  Coronavirus.
• 35:41
  Like within six months, we had a vaccine.
• 35:43
  Easy.
• 35:43
  Now we have to solve for our own body trying to kill us over time.
• 35:48
  And the open-ended issue with this is it's not clear that in the short or midterm that we're going to actually make this sufficient or significant progress that would lead to outcomes that were at the rate of progress.
• 36:06
  Exactly.
• 36:07
  Yeah, yeah, yeah.
• 36:08
  It doesn't seem that like it's not going to – we're not making progress at the speed that would like temper this plateau.
• 36:16
  You know what this reminds me of, funny enough, is in technology, there's this, you know, markets and cycles are defined by this S-curve, right?
• 36:25
  Yeah.
• 36:26
  Where you start, you know, with a low rate of change and then you have this explosive growth.
• 36:30
  And with every technology cycle, eventually it tapers off.
• 36:33
  Mobile phones is the perfect example, right?
• 36:35
  We went through years of Apple innovating and doing new things and combining all this stuff.
• 36:39
  And then all of a sudden everyone catches up and then we kind of get to the place where we are now where...
• 36:44
  Is a foldable versus, is that really, you know, or are we now just kind of making things for marketing?
• 36:50
  Yeah.
• 36:50
  And so this similar idea of sort of the S curves that you see in technology cycles.
• 36:56
  Yeah, it's happening in fundamental science when it comes to biology.
• 36:58
  When it comes to this longevity issue, it's like we kind of have reached the end of the first S curve.
• 37:02
  Yes.
• 37:02
  And so in technology, the idea is S-curves move, right?
• 37:05
  So it's like you first have like vacuum tubes, all this stuff.
• 37:07
  Then you get to microprocessors.
• 37:09
  Then you move to mobile, PC, mobile, et cetera.
• 37:12
  The open question is arguably we just went through the first, let's say, longevity cycle.
• 37:16
  Yes.
• 37:16
  S-curves cycle.
• 37:17
  Yes.
• 37:17
  And it's not clear.
• 37:19
  what the second S-curve cycle may or may not look like, and whether we can traverse it or not.
• 37:25
  Yes, exactly.
• 37:26
  And if we get to that S-curve, what is the real upper limit?
• 37:30
  Because right now, the upper limit, I think the record is 122 years for someone to live.
• 37:35
  Okay?
• 37:36
  And...
• 37:38
  Is that the real limit?
• 37:39
  Right.
• 37:39
  Is it at that point?
• 37:41
  The body is just like, nah.
• 37:42
  Right.
• 37:43
  Right.
• 37:44
  Evolution has literally programmed us to be like, no, no, no, we're still evolving.
• 37:49
  Right.
• 37:49
  The human genome has a really high mutation rate compared to like, like, for example, sharks.
• 37:54
  Right.
• 37:54
  Sharks have like a insanely low mutation rate, which is why their fossils 300, 300 million years ago look exactly the same.
• 38:01
  Right.
• 38:01
  There's no humans like even 10 million years ago that look like us.
• 38:05
  So the mutation rate is really high.
• 38:07
  On top of that, we're incredibly agile creatures.
• 38:13
  We're mammals at a high body temperature, high metabolism.
• 38:18
  So all of these things sort of start contributing to tempering an infinite lifespan.
• 38:24
  Right.
• 38:24
  The limits.
• 38:25
  We have these limits that exist that are fundamental to the existence of our being.
• 38:30
  Right.
• 38:31
  And it is not clear that they are necessarily surmountable by a human manufactured solution in human time spans.
• 38:38
  Exactly.
• 38:38
  Yeah.
• 38:39
  And what I liked, I mean, I guess not what I like about this story, but what concerns me about this story is that I think we need to, again, start taking this very seriously as a policymaking substrate.
• 38:54
  Right.
• 38:54
  Right.
• 38:55
  As like now a scientific fact that we have learned and urgently need to act on.
• 39:03
  Okay.
• 39:04
  Because from a policy perspective, I mean, to extrapolate this, there's huge arguments right now over the debt in the U.S. Yeah.
• 39:13
  Being at untenable levels.
• 39:15
  And one of the biggest line items in that is Social Security.
• 39:19
  Yeah.
• 39:20
  And it makes assumptions about retirement age and who gets Medicare.
• 39:24
  And what happens if you're the line of average life expectancy shifts or moves.
• 39:29
  Yeah.
• 39:30
  It's no longer...
• 39:31
  I mean, everybody has made this policy based on an increasing life expectancy forever.
• 39:36
  And now this paper is saying that actually, no, life expectancy has plateaued.
• 39:41
  We're at the end of the road when it comes to how old we can get.
• 39:44
  Right?
• 39:45
  Like all these...
• 39:46
  With pension funds, with social security systems, there was a riot in France the other year when the government tried to raise the retirement age.
• 39:57
  The reason why they're raising the retirement age is a pretty simple logic, which is if people are living longer, then they can work longer.
• 40:06
  And if they can work longer, we should make them work longer before they get their retirement age.
• 40:10
  Well, this isn't the case anymore.
• 40:11
  People aren't actually living longer.
• 40:13
  They're living the same as they were like 10 years ago.
• 40:16
  Right.
• 40:17
  Right.
• 40:17
  We're not actually making it...
• 40:21
  Right.
• 40:22
  And the worst part about these kinds of policies that sort of raise the retirement age is it actually impacts those of worse socioeconomic background.
• 40:32
  Right.
• 40:32
  Because the rich people, rich people can just retire.
• 40:34
  Right.
• 40:34
  You could retire at 50.
• 40:36
  You probably already did.
• 40:37
  Right.
• 40:37
  It's the poor people that are still going to have to work until like 70 now.
• 40:41
  And and then what die at like 72.
• 40:44
  Right.
• 40:44
  Like.
• 40:45
  You know, hundred.
• 40:46
  No, it's a really, it's a really important point about how a lot of this very fundamental science research.
• 40:53
  historically, especially in the United States, has both informed like military and intelligence posture as well as policy-making posture.
• 41:00
  That's right, yeah.
• 41:01
  And there's just been this disconnect where for whatever reason, there's no longer the through lines from fundamental research and the insights that are gleaned from that into bleeding into these areas that are in the control of nation states in terms of how do you sort of organize society around it.
• 41:22
  Yeah, dude.
• 41:22
  This is like a meaningful and important point because people do still generally do still think like, oh, yeah, we're just going to live longer than our parents.
• 41:31
  Much like every other aspect, at least in the U.S. of life, right?
• 41:34
  We are less like the millennials and younger.
• 41:37
  Yeah.
• 41:37
  We're less well off financially than our parents.
• 41:39
  Yeah.
• 41:39
  Everything is less affordable than it has been for our parents.
• 41:42
  And we're the first generation.
• 41:45
  to be less well off than our parents.
• 41:47
  And so the idea that we're going to also now live just as long makes it like you can kind of see like all these things are like aligning.
• 41:55
  Yeah.
• 41:55
  And then and then when they when when, you know, our parents generation retires,
• 42:00
  The population pyramid is now basically inverted for all of these developing countries where this is true.
• 42:05
  Right.
• 42:06
  The Japan problem.
• 42:06
  Right.
• 42:07
  Like it's it's now there's fewer and fewer young people to support this older population for Social Security.
• 42:13
  Turns out, given that population pyramid and the mathematics of just two plus two equals four and it's never five.
• 42:20
  Right.
• 42:20
  Like this might just be a scam.
• 42:23
  Compound on top of that.
• 42:24
  Right.
• 42:24
  This idea that like actually the fundamental premise that like the younger generation is going to live longer.
• 42:30
  Yes.
• 42:30
  And so can contribute more.
• 42:32
  Right.
• 42:33
  To the social security pile.
• 42:34
  Yes.
• 42:35
  Is now no longer correct.
• 42:36
  Right.
• 42:36
  And we need to start asking questions like is this like it's there's a fundamental shift now I think in my mind from is this a tenable strategy.
• 42:46
  Right.
• 42:46
  To is this even a correct way to do business in a developed society.
• 42:51
  Right.
• 42:52
  Right.
• 42:52
  Right.
• 42:52
  Right.
• 42:52
  I don't want to sound communist because I swear I am not a communist.
• 42:57
  Okay.
• 42:57
  But like at some point you got to start asking questions about fundamental things like the social construct.
• 43:05
  Right.
• 43:06
  Like is social contract.
• 43:08
  Sorry.
• 43:08
  Yes.
• 43:08
  Like, you know, is this true?
• 43:10
  Like this is the social contract of.
• 43:13
  the post-war era still applicable today.
• 43:16
  Right.
• 43:17
  And the fundamental variables in that equation have changed so drastically.
• 43:21
  And we're still saying the outcomes are going to be the same, even though all the variables on the other side of the equation are fundamentally very, very different.
• 43:29
  Yeah, yeah.
• 43:29
  Like, this is a reality now, and it's, like, going to catch up with us as we grow older.
• 43:33
  Yeah.
• 43:33
  This is a really, really important story.
• 43:35
  I'm sure we're going to come back to the subject of longevity because I think the nexus you brought up from longevity studies to policy is a really important note, especially when we're seeing not only in Western, let's say, quote-unquote, Western developed nations, but any developed nation, this similar issue of generational friction that arises from 50-year-old to 80-year-old people.
• 44:02
  policy conceptions that are based on fundamentals that are now statistically, data wise and scientifically are proving to not necessarily be the same.
• 44:14
  And yet we've not changed any of our posture.
• 44:18
  Yeah, yeah, yeah.
• 44:18
  And then we're just going to run into a brick wall at some point.
• 44:20
  And this is it's a problem.
• 44:22
  Yeah.
• 44:23
  Yang gang.
• 44:24
  Yeah.
• 44:28
  It's interesting because it does dovetail somewhat to our third story.
• 44:31
  One aspect we didn't talk about the factors of longevity that changes, you know, climate is a big part of that.
• 44:38
  It's getting bad.
• 44:39
  And our next story is about a new no-sort plastic recycling being near.
• 44:44
  The headline, New Nickel Catalyst Enables No-Sort Mixed Plastic Recycling.
• 44:51
  This is a new, brand new breaking research coming out of Northwestern University.
• 44:56
  They put their paper out in Nature Chemistry.
• 44:59
  Yeah.
• 45:00
  I just went to Japan earlier this year, and one of the interesting things about in Japan, unlike in the US, is they have different recycling bins for different types of recycling, glass, cardboard, et cetera.
• 45:09
  In the US, we just put it all in the same thing.
• 45:11
  So my assumption is this no-sort plastic is saying like there's no need to,
• 45:18
  X, Y, Z.
• 45:19
  This seems like it's on the sort of pollution and our need to be more effective with our resource users.
• 45:26
  This might have impact.
• 45:28
  But tell me what we need to know about this story.
• 45:29
  Yeah.
• 45:30
  Plastic.
• 45:31
  We love it.
• 45:31
  Yes, we do love it.
• 45:32
  We do love it.
• 45:33
  As humans, we are cheap.
• 45:36
  And we want things.
• 45:39
  We want things to be easy.
• 45:41
  We want them to be cheap.
• 45:43
  And so we love plastic.
• 45:45
  Okay.
• 45:45
  Plastic is really bad for the environment.
• 45:49
  It sticks around for like millions of years or something like that.
• 45:52
  Right.
• 45:53
  And we are producing a lot of it.
• 45:55
  Something like 400 million tons a year.
• 45:59
  Something like 11 million tons go into the ocean.
• 46:03
  Which, you know, I'm very...
• 46:06
  I really don't like that.
• 46:07
  Okay.
• 46:08
  But I'm also a lazy, selfish American.
• 46:12
  And...
• 46:15
  I like my things, right?
• 46:18
  I like my mustard and ketchup bottles in plastic and my milk jug that comes in a gallon in plastic.
• 46:25
  I don't want to go with a glass bottle and refill the same milk jug.
• 46:29
  It's just so convenient to just go to the store and grab a new thing and then throw away the old one and just not worry about what the old one is doing, right?
• 46:38
  So as Americans, we are very selfish and we're very lazy.
• 46:43
  And, you know, it's not just something that Americans are.
• 46:46
  It's something that the whole world is.
• 46:48
  This is a world problem.
• 46:50
  And, you know, everyone who says that, like, we need to start using less.
• 46:55
  It's just simply not a practical way to save the planet.
• 47:01
  Okay?
• 47:02
  Mm-hmm.
• 47:02
  Like, we love our planet, but we love ourselves and our comfort way more.
• 47:07
  And that is just not going to change.
• 47:08
  Okay?
• 47:09
  Even the developing, especially the developing countries, right?
• 47:13
  Like China and India and the continent of Africa that are...
• 47:18
  also contributing immensely to this chaos of plastic and consumables and all this stuff.
• 47:23
  They're looking at the developed countries being like, no, you guys did that.
• 47:26
  Well, you guys just had 50 years of doing that.
• 47:29
  And now we're just starting.
• 47:30
  And now it's like, oh, I'm sorry.
• 47:32
  I can't use coal.
• 47:34
  You know, when that's the one resource that's super cheap to use to make electricity, which is something I need so I can like educate my kids.
• 47:41
  Yeah.
• 47:42
  So this idea that like, you know, using humans doing less to save the planet is not going to work.
• 47:49
  Yeah.
• 47:49
  Okay.
• 47:50
  The solution that is going to work is scientific innovation to make our current lifestyle better for the planet.
• 47:57
  And more sustainable.
• 47:57
  More sustainable for the planet.
• 47:59
  I know this is a hot take.
• 48:00
  I know.
• 48:01
  About like how we need to deal with human directed climate change because it is real.
• 48:06
  Right.
• 48:06
  Humans are changing the planet.
• 48:07
  Yes.
• 48:08
  But my fundamental take is like we're not going to stop.
• 48:11
  I mean, at the end of the day, the idea that, you know, people using paper straws instead of plastic.
• 48:18
  But then every corporation and manufacturing entity and these large people who are actually producing the largest volume of the problem is really where the problem is.
• 48:30
  Yeah, yeah.
• 48:31
  Exactly.
• 48:31
  In terms of having the most impact for our efforts.
• 48:34
  Yes, yes.
• 48:34
  We need a bulk strategy.
• 48:36
  Right.
• 48:37
  Because even if you made every human, like individual human, like feel bad, corporations who are apparently also people –
• 48:46
  They're not going to feel bad because the mob is crazy, right?
• 48:49
  No, exactly.
• 48:50
  And so, no, they're going to be like, oh, yeah, just dump it right there.
• 48:53
  It's fine.
• 48:53
  Exactly.
• 48:53
  You know?
• 48:54
  So we need a strategy from fundamental science perspective that helps us recycle efficiently and in a better way.
• 49:01
  Yes.
• 49:01
  Okay?
• 49:01
  Plastic recycling is a key problem that we as humans, I think we need to solve.
• 49:06
  Yes.
• 49:06
  Okay?
• 49:07
  And this is a really big step in that direction.
• 49:09
  Okay.
• 49:10
  Okay?
• 49:10
  Okay.
• 49:10
  So let's talk about plastic.
• 49:11
  Plastic is really bad.
• 49:12
  I just told you like something like 11 million tons go into the ocean every year.
• 49:17
  It's going to just stay there for thousands of years.
• 49:19
  It's not going to decompose.
• 49:20
  Plastic is essentially chemically.
• 49:23
  It's just polymer chains of hydrocarbon.
• 49:26
  So carbon, hydrogen, hydrogen, hydrogen, carbon, hydrogen, hydrogen, hydrogen, just chains of carbon and hydrogen.
• 49:32
  Okay.
• 49:33
  Like that's all plastic is.
• 49:34
  It comes from like petroleum, which is also hydrocarbons.
• 49:39
  So another like, you know, it's another use of petroleum in that case.
• 49:44
  And in order to recycle it, there's basically like a few ways that we do it right now.
• 49:48
  And they're all pretty bad.
• 49:50
  Okay.
• 49:50
  One of them is the thermomechanical method, which is what you got to do is you got to basically materially degrade this thing.
• 50:00
  You've got to like heat it up.
• 50:02
  You've got to make it into like lower, worse plastic and then use that plastic somehow for some other purpose.
• 50:10
  Okay.
• 50:10
  It's a pretty bad way of doing things.
• 50:13
  But we do it 9%.
• 50:14
  9% of the plastic globally is done this way.
• 50:18
  It's really expensive.
• 50:19
  Nobody really wants to do it because you've got to sort all these plastics for all their different types of plastic.
• 50:26
  Right.
• 50:27
  Because they break down to different fundamental parts and you don't necessarily want those mixed.
• 50:30
  Yeah.
• 50:30
  And then if they mix, then they make even worse stuff.
• 50:34
  And then they just go to the landfill.
• 50:35
  And it's like, okay, what did you even do?
• 50:37
  We just wasted a bunch of money.
• 50:38
  You just, yeah.
• 50:40
  So that's one way.
• 50:41
  The other way is pyrolysis, which is just heat it up.
• 50:46
  Heat it up, degrade this thing.
• 50:47
  And then you get some kind of low value type of GHG, which is like some kind of like low value fuel.
• 50:56
  Right?
• 50:56
  The problem with that is, okay, fine.
• 50:57
  Like, how much energy did you use to, like, heat this thing up?
• 51:01
  To get a low value fuel.
• 51:03
  You're getting, it's like nuclear fusion, right?
• 51:06
  It's like, why are you even doing that?
• 51:07
  You're putting more energy in to actually get, like, some kind of fuel out.
• 51:11
  Right.
• 51:12
  On top of that, if oil prices are super cheap, then, like, why would you even do this?
• 51:16
  Right.
• 51:16
  Right.
• 51:16
  So...
• 51:20
  And whenever you're doing anything like this, there's also all of these contaminations that happen, right?
• 51:26
  Because plastic has like food products.
• 51:28
  Sometimes they have PVC, which is polyvinyl chloride.
• 51:32
  And if that gets in here and as it's degrading, so this polyvinyl chloride, it's found in like pipes, flooring.
• 51:39
  And if that gets in there, even in a batch, a small thing in a huge batch, right?
• 51:44
  That whole batch is done because you get this hydrochloric acid.
• 51:48
  All the equipment is done.
• 51:50
  And at the end of the day, you're dumping it into the landfill anyway.
• 51:53
  Okay.
• 51:53
  So it's really kind of bad.
• 51:56
  We have a lot of bad options.
• 51:58
  We have really a lot of bad options.
• 52:00
  The only good option is to take the plastic.
• 52:02
  Usually it's in this thing called polyolefin, which is a CH2.
• 52:08
  Carbon, two hydrogens attached to a C and a H and then some other thing in our group, a residual group.
• 52:16
  Those are the things that are basically two thirds of the global plastic chain.
• 52:22
  This is like, you know, your milk cartons and plastic spoons and like plastic wrap.
• 52:27
  and all this other kind of stuff.
• 52:30
  Single use, really short lifespan, just gets used, gets thrown.
• 52:35
  Okay?
• 52:35
  That's what we're trying to, at the end, ultimately, we're trying to recycle.
• 52:40
  We're trying to repeal and replace it.
• 52:42
  Yeah.
• 52:43
  Yes, exactly.
• 52:44
  And what I just told you, right, the options are pretty bad.
• 52:48
  Right.
• 52:48
  Okay?
• 52:49
  There's one option that has a little bit of hope.
• 52:53
  And that is this thing called hydrogenolysis.
• 52:59
  It's basically breaking it down in the presence of hydrogen.
• 53:04
  And what it does is the hydrogen sort of comes in along with some kind of catalyst that helps this reaction.
• 53:10
  And that hydrogen comes in and with the carbons and the hydrogens there, it creates hydrocarbons.
• 53:16
  Okay.
• 53:16
  that can then be used for either fuel or like waxes or like these higher sort of lubricants, things like that that you can recycle them to, right?
• 53:27
  So that's one way of doing things.
• 53:29
  That's like kind of a promising way of like recycling this plastic and...
• 53:35
  putting it back into simple hydrocarbons, right?
• 53:37
  And that's something that's like much less worse for the environment, right?
• 53:41
  Like animals aren't choking on it and shit like that.
• 53:44
  So that's what we want to do.
• 53:45
  Now, usually this hydrogenolysis takes catalysts, and those catalysts require like higher order elements, rare earth elements like platinum, palladium,
• 53:58
  We don't got a lot of that stuff.
• 53:59
  Like, the amount of plastic we got compared to the amount of platinum and palladium.
• 54:03
  Like, this is the stuff that Iron Man used to make his, like, new element.
• 54:08
  Okay?
• 54:08
  So, that's how rare it was that Marvel was using it in a movie.
• 54:11
  So, we can't be dependent on platinum and palladium to, like, recycle...
• 54:18
  the tons and tons of plastic right how many mines we gonna get right so so the idea is you want you want to have a catalyst that is not that rare that's what this team is doing this okay this this fundamentally okay yes fundamentally they're they they found a way to catal catalyze hydrogenolysis using a catalyst made out of nickel which is very abundant and
• 54:43
  Furthermore, this catalysis only targets a specific type of this polyolefin.
• 54:50
  Okay?
• 54:50
  Because as I said, this is the CH2, CH2, right?
• 54:53
  And then there's the R group.
• 54:54
  That R group can be sometimes H, which makes it polyethylene.
• 54:58
  Or it can be sometimes polypropylene, which is the other group is another complex thing, like another CH2.
• 55:06
  So you get these like, with polyethylene, you just get this chain of carbons with hydrogens attached.
• 55:11
  Yes.
• 55:11
  Like a caterpillar.
• 55:12
  Yes.
• 55:13
  Or you get for polypropylene, you get the chain of carbons with carbon sticking out.
• 55:19
  Yep.
• 55:19
  Okay?
• 55:20
  And so what you want to do is you don't want to recycle these together.
• 55:25
  Okay.
• 55:26
  And usually that requires separation.
• 55:28
  And that's really, really just a pain.
• 55:31
  Yeah, yeah, yeah.
• 55:32
  Yeah.
• 55:32
  All right.
• 55:32
  Nobody wants to do this.
• 55:33
  Both time and cost prohibitive.
• 55:34
  Both time and cost prohibitive.
• 55:36
  And more importantly, it's cost prohibitive, right?
• 55:38
  And the consumer is not going to do it because, again, we're lazy.
• 55:41
  Yeah, yeah.
• 55:41
  We don't want to do that.
• 55:43
  Yes, I love the earth, but I don't want to check under the bottle what kind of plastic it is before I throw it in the garbage.
• 55:50
  This goes back to the sorting issue, which is like, ideally, this is why you want to have the humans who have the products put it into different buckets so that when it gets to the facility to recycle, it is pre-sorted.
• 56:01
  Yeah.
• 56:02
  But yeah.
• 56:03
  And it's like, even if you got the humans to do it, though...
• 56:07
  Do you really trust that human?
• 56:08
  That's the point.
• 56:09
  Like, I don't know if this human can read.
• 56:12
  Right.
• 56:12
  Right.
• 56:13
  So it's like, I don't know.
• 56:14
  Right.
• 56:15
  So you would rather have a way of doing it within the chemical reaction.
• 56:20
  Right.
• 56:20
  That targets one type of plastic and not the other.
• 56:23
  Right.
• 56:23
  And that's what this thing is doing.
• 56:25
  So they developed a catalyst out of nickel.
• 56:28
  It's got aluminum, nickel.
• 56:30
  the usual carbon and all this other kind of stuff.
• 56:32
  And one of the cool things they did was this catalyst, you can imagine it as, again, a Lego block, okay?
• 56:39
  But this Lego block has only a single piece of, like, active site, okay?
• 56:46
  Okay.
• 56:46
  Whenever you have a catalyst, you have something called an active site, which is the part that does the locking and the moving around of the atoms, okay?
• 56:55
  This thing has a single active site, okay?
• 56:58
  And what that gives it is...
• 57:00
  the benefit of being extremely specific to what shape it wants to actually attach to, right?
• 57:08
  A lot of catalysts have different shapes, which means that when you try to put it into something like polypropylene or polyethylene, they're going to attack every single carbon-carbon bond, okay?
• 57:17
  And then you're going to get a bunch of methane, which is fine, I guess, but like it's not like great.
• 57:22
  Methane is like super cheap.
• 57:23
  It's like this low-
• 57:24
  What you'd like to do is with polypropylene, I told you, right, there's these carbon and then there's these carbons that are sticking out.
• 57:30
  You want to just take out the carbon that's sticking out.
• 57:33
  And then this thing just becomes like polyethylene.
• 57:36
  And then you can then do those two together in some other sort of process.
• 57:41
  So you want to take those out and then only target specific bonds.
• 57:46
  And that's what this new catalyst does.
• 57:48
  Very, very clever.
• 57:49
  So it's doing the sorting within the chemistry.
• 57:52
  At the facility where they're going to be doing the recycling, regardless of the input,
• 57:57
  The chemical process itself is doing the job of separation.
• 58:04
  Exactly.
• 58:04
  That we otherwise are, in some cases, in some places, depending on humans, to put things in different buckets.
• 58:10
  Yeah, exactly.
• 58:11
  And the results are, they speak for themselves.
• 58:14
  So they tested this catalyst with isotactic polypropylene, which is this thing that I was telling you about with the branches coming out.
• 58:23
  And 99.5% conversion.
• 58:25
  into lighter compounds in 20 minutes.
• 58:28
  And then when they did the same thing, they did the same thing with the polyethylene.
• 58:34
  Yes.
• 58:35
  Nothing happened.
• 58:37
  Right?
• 58:37
  Yes.
• 58:38
  And it took four hours.
• 58:39
  Yeah.
• 58:40
  And then they finally got a little bit of signal.
• 58:42
  So what you can do is you can have a mixture of this stuff.
• 58:44
  You can apply this catalyst.
• 58:45
  Yes.
• 58:45
  And then you can just let it run.
• 58:48
  Yes.
• 58:48
  And then within a short amount of time, all the polypropylene will be,
• 58:52
  decomposed, but the polyethylene will be in this native state, and then you can go forward.
• 58:56
  And now you have these two buckets of distilled compounds that you then have functional use for in any number of other downstream or upstream, depending on what way you want to look at it, industrial processes.
• 59:07
  That's right, yeah.
• 59:08
  And then the best part was that, you know that PVC that I was talking about?
• 59:11
  Yes.
• 59:11
  The polyvinyl chloride, which is a contaminant for all of these plastics, that basically makes plastic unrecyclable in these previous strategies?
• 59:20
  Yes.
• 59:22
  Somehow, and I don't think they fully understand how this works.
• 59:25
  We love that.
• 59:25
  This made it better.
• 59:27
  Really?
• 59:27
  Yeah.
• 59:28
  The introduction of PVC made the catalysis reaction better.
• 59:32
  No friggin' way.
• 59:35
  Yeah.
• 59:35
  So then they were like, oh, that's free.
• 59:37
  That's great.
• 59:38
  Just like free upside.
• 59:39
  It's like, oh, that's great.
• 59:40
  Yeah.
• 59:41
  Which then removes the cross-contamination issue.
• 59:44
  Yeah.
• 59:44
  Which goes back to this no sort.
• 59:46
  Like, yeah.
• 59:47
  So then we don't have to sort it.
• 59:48
  We don't even have to worry about the PVC.
• 59:49
  The PVC could be in there and it's just going to make it better.
• 59:51
  Yeah.
• 59:52
  It's just like fine.
• 59:53
  So I think this is a really cool use of like fundamental chemistry to try to like tackle a very, very real problem.
• 1:00:01
  This is really good.
• 1:00:02
  That's actually a great explanation because I actually had no context for how the recycling process really worked.
• 1:00:09
  Yeah, I didn't either until I started researching this.
• 1:00:11
  And this is like a clear like it's a clear problem set.
• 1:00:14
  We don't have good solutions currently implemented at scale.
• 1:00:17
  No.
• 1:00:18
  And we.
• 1:00:19
  But it's something that I really do care about.
• 1:00:21
  Right.
• 1:00:21
  Just, you know.
• 1:00:22
  Right.
• 1:00:22
  But do it at the factory.
• 1:00:23
  Yeah.
• 1:00:24
  Yeah.
• 1:00:26
  I'm lazy.
• 1:00:26
  I'm sorry.
• 1:00:27
  As are the majority.
• 1:00:29
  Again, it also should not be incumbent.
• 1:00:32
  The commons is a concept.
• 1:00:33
  We all should invest in the commons and trying to upkeep.
• 1:00:37
  But at the end of the day, the biggest kind of issue is the industrial grade, industrial level.
• 1:00:42
  Exactly.
• 1:00:43
  It's hard for me to do anything.
• 1:00:47
  And we have to address it at that level.
• 1:00:48
  And one of the ways you can do so is when they bring the stuff to the facility, it can actually repurpose it in a way that creates...
• 1:00:54
  functional, valuable output that is not the current state of affairs where it's either the output sucks and we wasted a bunch of energy, time, and money.
• 1:01:04
  Yeah.
• 1:01:05
  Or it just went to the landfill.
• 1:01:06
  It just went to the landfill anyway.
• 1:01:08
  Or the output doesn't have real value in terms of the concept of recycling it back into society.
• 1:01:15
  Exactly, yeah.
• 1:01:16
  And your point about the commons, I think, is important, right?
• 1:01:19
  There is a commons that I am heavily invested in.
• 1:01:22
  I pay taxes, and I want those commons, my tax dollars, to go into this kind of research.
• 1:01:27
  Because that is the future of saving this planet.
• 1:01:30
  Saving the planet is not making humans somehow be less comfortable, okay?
• 1:01:36
  That's not going to work.
• 1:01:38
  I know humans.
• 1:01:39
  I know several humans.
• 1:01:40
  They're all like me, okay?
• 1:01:43
  And what we want is solutions that actually make sense, that don't compromise my state of life.
• 1:01:51
  And yeah, we need fundamental science research to do this.
• 1:01:56
  I think we've had enough innovation on our mobile phones.
• 1:01:58
  Yeah, I'm totally fine.
• 1:02:00
  Yeah, with the current state of my iPhone, it's sufficient.
• 1:02:03
  Maybe let's not try to iterate on an incremental version for next year and spend that brain power, time, and energy.
• 1:02:10
  on things that will create a better commons environment, which then drives the consumerism that capitalism loves.
• 1:02:17
  Great, great story.
• 1:02:18
  Yeah, I like the story.
• 1:02:20
  Speaking of the private sector and multi-trillion dollar companies, we're going to end today with a story about analog AI coming out of Microsoft.
• 1:02:29
  We almost cannot avoid AI every week.
• 1:02:32
  Nope.
• 1:02:32
  Headline on the story, Microsoft's analog optical computer cracks two practical problems and shows AI promise.
• 1:02:40
  This was published on Microsoft News' blog.
• 1:02:42
  But they did put out a research paper associated with this in nature.
• 1:02:47
  So it's not just marketing.
• 1:02:49
  They did sort of go through the peer review.
• 1:02:52
  And so I don't really actually even understand what they mean when they say analog optical computer.
• 1:02:57
  Yeah.
• 1:02:58
  And there's several problems to crack, but they cracked two of them.
• 1:03:00
  So let me know.
• 1:03:01
  What are we talking about here?
• 1:03:02
  That's right.
• 1:03:04
  It's an analog computer instead of a digital one, which is very cool to me.
• 1:03:09
  Digital, you got your zeros and ones, right?
• 1:03:13
  And we talked a lot about digital computers in the past and some of their problems, right?
• 1:03:17
  The fact that Moore's Law is slowing down because we're sort of reaching this limit of
• 1:03:23
  of how small we can go before the capacitors and the transistors that we're using start leaking off so much power that we just have to constantly keep powering it in order for the data to actually stay alive.
• 1:03:35
  We also talked about the von Neumann gap, right?
• 1:03:40
  The von Neumann bottleneck, which is the separation between your processor and the memory there, which is your RAM,
• 1:03:49
  and your hard disk or your actual memory yes right and the where you're storing stuff is different from where you are doing the computation yes right this is a thing that plagues cpus but it also plagues gpus okay and um as ai becomes increasingly big
• 1:04:11
  There's going to come a point where we're going to really have to start having the conversation of, again, you know, what we were talking about last in the last story.
• 1:04:20
  Is this worth the environmental impact?
• 1:04:23
  Right.
• 1:04:23
  Right.
• 1:04:24
  And of course, the corporations are going to be like, yes.
• 1:04:26
  And they're going to make bigger and bigger data centers.
• 1:04:29
  For example, the energy usage has tripled from 2014 to 2023.
• 1:04:33
  And it's projected to be 20% of global electricity by 2030.
• 1:04:39
  Okay, the data centers and the AI, all that AI computations for you making a cat that wears a hat or whatever.
• 1:04:47
  Like that's 20% of global electricity.
• 1:04:50
  Now, I'm not saying that we shouldn't be using AI.
• 1:04:52
  Like because I use AI, AI is great.
• 1:04:56
  But I think all of these companies, including Microsoft, are starting to realize that it's no longer going to become tenable, right?
• 1:05:04
  100%.
• 1:05:06
  there needs to be an alternative way to do the computation that they are seeking.
• 1:05:11
  I'll just make a brief note here, which is that every single one of the either Frontier Model Builders
• 1:05:20
  or sort of hyperscaler cloud computing platforms, Google Cloud Product, AWS, they're all spending a ton of their capital on directly investing into power infrastructure.
• 1:05:37
  Yeah, because they know that that's going to be a bottleneck.
• 1:05:40
  Every single hyperscaler or frontier model company is investing, if not hundreds of millions, billions of dollars in...
• 1:05:48
  into partnerships with these mini nuclear reactor companies.
• 1:05:53
  They're literally returning on the big power facility that's just north of us in California.
• 1:05:58
  The nuclear one, Facebook has a little alcove there.
• 1:06:01
  So they all know the story.
• 1:06:03
  Yeah.
• 1:06:03
  Like it's not.
• 1:06:04
  They see it.
• 1:06:04
  They see the writing on the wall.
• 1:06:05
  It's not controversial for any of them.
• 1:06:07
  Yeah.
• 1:06:08
  And on top of that, there's a bunch of these companies that are investing in alternative computing technology, right?
• 1:06:14
  100%.
• 1:06:15
  Like we were talking about Spintronics the other day.
• 1:06:17
  Yes.
• 1:06:18
  This is an analog computer.
• 1:06:20
  It's doing sort of the same type of deal.
• 1:06:22
  Not the same physics, but the same sense of let's go away from transistors and physical stuff.
• 1:06:31
  And let's try to find a different, completely different way to store data and manipulate data.
• 1:06:36
  Okay.
• 1:06:36
  Okay.
• 1:06:38
  If we go back to the von Neumann bottleneck, right, from John von Neumann, he championed this idea of that early computer where there was going to be a separation between the processor and memory, right?
• 1:06:49
  That's become sort of the bedrock for modern computation.
• 1:06:53
  But it's really become a huge handicap for AI, okay?
• 1:06:59
  Because when we want to do AI, there's two core operations that we have to do.
• 1:07:03
  OK, one is matrix multiplication, right, where we take the weights of your neural network and you multiply it by some vector and then you multiply by another matrix and another matrix.
• 1:07:15
  And all of these are weights that you've stored.
• 1:07:18
  And the other thing that you have to do is you have to do a nonlinearity, which is this idea that if if my input is above a certain threshold, only then do I let it through.
• 1:07:29
  If it's below a certain threshold, then it's a zero.
• 1:07:31
  Okay.
• 1:07:33
  That's one of the types of non-linearity that you have to do.
• 1:07:35
  Those are the two fundamental things that you have to do as someone who wants to implement a neural network.
• 1:07:41
  Okay.
• 1:07:42
  Then there's other things like momentum and annealing and all this other stuff that you have to do.
• 1:07:46
  But fundamentally, if you really want to do neural network stuff, it's matrix multiplication and implementing a non-linearity.
• 1:07:53
  Okay, so now in order to do these things in a traditional computer, what you have to do is you have to store your weights in a certain spot, okay?
• 1:08:01
  And then you got to load them up in your processor.
• 1:08:03
  Right.
• 1:08:03
  And then you got to multiply the weights to this thing.
• 1:08:07
  And then you got to load up new weights.
• 1:08:08
  And then you got to multiply to this thing.
• 1:08:10
  And, you know, now we've gotten clever where maybe we can load up all of the weights to this thing and, you know, do a forward pass and things like that.
• 1:08:18
  But fundamentally, a lot of the source of latency that comes from like you waiting while you've typed something into ChatGPT and it's like thinking is doing this, right?
• 1:08:28
  It's like loading up.
• 1:08:30
  And then there's like copper wires in whatever server that is like transferring data.
• 1:08:36
  And the latency is a huge part of it too, right?
• 1:08:39
  So there's a lot of power that's being dissipated for no reason.
• 1:08:42
  And then there's also a lot of latency.
• 1:08:44
  This thing is solving both of those.
• 1:08:45
  That's very interesting.
• 1:08:46
  Okay.
• 1:08:47
  This thing is solving both of those by creating a computer out of light, lenses, and filters.
• 1:08:54
  Very interesting.
• 1:08:55
  Okay?
• 1:08:55
  Okay.
• 1:08:55
  It's very cool to me.
• 1:08:56
  Yeah, yeah, yeah.
• 1:08:57
  Because it's like fundamental physics used in a whole new way.
• 1:09:00
  Okay.
• 1:09:01
  Okay?
• 1:09:02
  So, to understand this, let's look at the two things that we need to do in order to do a neural network implementation.
• 1:09:11
  Okay?
• 1:09:12
  The first one I talked about was matrix multiplication.
• 1:09:14
  Okay?
• 1:09:14
  That's this idea of you take a vector of numbers, which is, let's say, a row of numbers, and then you apply a matrix to it.
• 1:09:20
  So this this number gets applied, gets multiplied to all of these.
• 1:09:24
  This number gets multiplied to all of these.
• 1:09:26
  And then you sum it up and you get a new vector, kind of like things like that.
• 1:09:28
  Right.
• 1:09:30
  What these guys figured is.
• 1:09:33
  We could do that with like light and filters.
• 1:09:37
  Okay.
• 1:09:38
  Okay.
• 1:09:39
  So the vector is an array of numbers, right?
• 1:09:42
  We could represent that with the brightness of LEDs.
• 1:09:48
  Do you see where I'm going now with the whole thing?
• 1:09:50
  I already see where you're going.
• 1:09:51
  Okay.
• 1:09:51
  I could represent that with a brightness of LEDs.
• 1:09:54
  Okay.
• 1:09:55
  Then I've got a matrix that I need to multiply it with, right?
• 1:09:59
  I could represent that with a bunch of filters.
• 1:10:01
  Oh, no.
• 1:10:02
  Right?
• 1:10:03
  That each light goes through.
• 1:10:04
  I'm so mad.
• 1:10:06
  It's so nice.
• 1:10:06
  Yeah.
• 1:10:07
  It's so nice.
• 1:10:08
  Right?
• 1:10:09
  And then I've got an output vector, right?
• 1:10:12
  That output vector could be a CCD that senses how much light went through.
• 1:10:19
  Okay?
• 1:10:20
  So completely analog.
• 1:10:22
  Right.
• 1:10:22
  Right.
• 1:10:23
  Right?
• 1:10:23
  There's no zeros and ones.
• 1:10:25
  It's like something, there's a continuous value here of the brightness, there's a continuous value of the filters, and then there's a CCD that captures whatever comes out.
• 1:10:34
  Right?
• 1:10:34
  And then now, okay, the second thing I have to do is implement a nonlinearity.
• 1:10:37
  Right?
• 1:10:37
  In order to implement the nonlinearity, they've got...
• 1:10:40
  like a bunch of filters that they can use, like the bipolar difference filter.
• 1:10:44
  So they've got, so now they've got these CCDs, right?
• 1:10:46
  That converts it into current.
• 1:10:47
  The current now goes through analog.
• 1:10:49
  It's still analog.
• 1:10:51
  A lot of these implementations, what they've done is they've done a hybrid approach of digital and analog.
• 1:10:55
  This is all analog.
• 1:10:57
  Pure analog, it's wonderful.
• 1:10:59
  You got these, you got the light that's coming out like this.
• 1:11:03
  It is now going to go through this sort of filter block.
• 1:11:06
  Okay.
• 1:11:06
  It's going to go through a bipolar differential pair, which is going to do the non-linearity.
• 1:11:11
  So if the light, if the current is below a certain amount, it's going to give it zero.
• 1:11:16
  But if it's above a certain amount, then it's going to let through a bunch of current.
• 1:11:19
  You also want to do this thing called annealing, which is this idea of like...
• 1:11:24
  Taking the current representation and sort of changing it a tiny bit so that you don't get stuck in like a state of the neural network where you're not like moving and you're getting the wrong answer, so to speak.
• 1:11:37
  So you can do that using these things called variable gain amplifiers.
• 1:11:41
  Which are VGAs used a lot all over tech.
• 1:11:45
  You can also do this thing called momentum, which is something that you use in neural networks.
• 1:11:49
  If you're like converging on an answer, you want to keep going in that direction.
• 1:11:53
  That's something that you can implement with VGAs, which is, again, something that everybody uses.
• 1:11:58
  And then you can feed it back into the thing and have this loop.
• 1:12:02
  Now, this is a fundamentally different architecture, not just for the physics of it, right?
• 1:12:07
  Because we're not using GPUs.
• 1:12:08
  We're using light and matrices.
• 1:12:10
  Right.
• 1:12:11
  in the form of filters and things like that.
• 1:12:12
  But also you can imagine it as like, there's a single sort of neural network that we're like passing over and over again.
• 1:12:19
  Right.
• 1:12:20
  So what these things are implementing is something called deep equilibrium models.
• 1:12:25
  There are different types of neural network where you've got a neural network and instead of a forward pass that gives you a right answer, what these models do is they call the model over and over again,
• 1:12:37
  until the model doesn't change its output.
• 1:12:43
  So what it's doing is, it's in the space and it's going down, kind of like a gradient descent algorithm, where it's going down this sort of energy landscape, until it gets to this minima, where as it moves, it's not really moving anywhere.
• 1:12:55
  Before it was moving here, then it's moving here, it's trying to find the answer.
• 1:12:58
  But once it gets to the answer,
• 1:13:00
  the more you apply it all the way to infinity, it's not really going to move.
• 1:13:04
  Right.
• 1:13:04
  Right.
• 1:13:05
  That's what this thing is doing.
• 1:13:06
  The problem with deep equilibrium models on GPUs was, it was like extremely, like how many times am I going to, right.
• 1:13:13
  But this, the physics itself is geared towards these deep equilibrium models.
• 1:13:18
  Yes.
• 1:13:18
  Right.
• 1:13:19
  And so you've got this, you've got this entire like system that is a digital analog.
• 1:13:27
  I mean, sorry, it's an analog computer.
• 1:13:30
  Right.
• 1:13:30
  Right.
• 1:13:31
  Right.
• 1:13:31
  Which is which is, I think, very cool.
• 1:13:33
  And because part of the this goes back to like the energy and cost issue with, you know, GPUs are both expensive on their face to produce at the scale we need to to do these like chain of thought reasoning level models, these frontier models.
• 1:13:47
  And then the power usage, not only to acquire the physical hardware is cost prohibitive, but then to run them to be able to get to that level is also cost prohibitive.
• 1:13:58
  And this sort of solves both issues.
• 1:14:01
  Again, accounting for the fact that this is obviously not an industrial manufacturing scale and all these other things.
• 1:14:08
  This is not industrial manufacturing, but they're not using like crazy tech.
• 1:14:12
  That's okay.
• 1:14:13
  They're using LEDs.
• 1:14:15
  They're using the matrix that they use for the multiplication.
• 1:14:18
  That's just like the filter in your projector.
• 1:14:20
  That's very interesting.
• 1:14:21
  Okay.
• 1:14:22
  And then the CCD is like the sensor in your phone.
• 1:14:25
  And then you got VGAs, which everybody knows how to, like everybody has.
• 1:14:29
  We know how to make it super cheap at this point.
• 1:14:31
  You don't need like an insane fab to do any of this stuff.
• 1:14:35
  Which this becomes very interesting because of the, I mean, there's so many implications here, not only from just from the fundamental perspective, but if you extrapolate both like geopolitically and economically.
• 1:14:46
  You know, one of the issues with the whole AI debate is NVIDIA has now become a trillion dollar company in record speed because they're the only ones that can build these chips, these GPUs, sorry, these GPUs that can really run the frontier models effectively.
• 1:15:02
  And to be able to get to NVIDIA scale because of the complexity of the manufacturing process and the amount of proprietary knowledge necessary.
• 1:15:09
  There's like no IBM has tried.
• 1:15:11
  They can't.
• 1:15:12
  And there's no one else.
• 1:15:13
  Yeah, because they've done 20 years of work to get to where they are.
• 1:15:17
  Right.
• 1:15:17
  Right.
• 1:15:17
  And what's interesting is it sort of upsets the apple cart in the sense of if there is a low cost, high efficacy hardware substrate.
• 1:15:28
  that you can utilize to do the same type of work that you're seeing out of a GT5.
• 1:15:33
  Faster, cheaper, with less power.
• 1:15:36
  Like one caveat is this thing is only going to work for inference.
• 1:15:40
  Okay.
• 1:15:40
  It's not for training.
• 1:15:41
  Which is... Because in training, you'd have to change the weights and things like that, right?
• 1:15:45
  So in order to train, what they did was they created a digital twin, which they then trained.
• 1:15:50
  Yep.
• 1:15:50
  And the digital twin matched the hardware's... Yep.
• 1:15:53
  Performance.
• 1:15:54
  So then when the digital twin said, okay, you want to set your filters this way and you want to set your nonlinearities this way, they could go and implement that with the hardware and then they could like do the inference time stuff on this.
• 1:16:06
  But to be fair, like all of the...
• 1:16:10
  Like the reason why all of these companies are building all of these data centers is not to train.
• 1:16:15
  It's for inference.
• 1:16:16
  It's for inference, right?
• 1:16:17
  The training happens in like San Francisco in their facility or, you know, whatever.
• 1:16:22
  In like some like tiny, like the hundreds of millions of users of ChatGPT and Gemini are just using it for inference.
• 1:16:29
  It's all inference.
• 1:16:29
  I was going to say like where the usage is going.
• 1:16:32
  Like the power is going for inference.
• 1:16:34
  Correct.
• 1:16:34
  As more users start using, you know, LLMs and other versions of AI in everyday life, and we get to the order of billions, billions, billions of users doing so, what they're using is the inference.
• 1:16:47
  And so this can be a whole – you can still have your proprietary training stacks with all these fancy GPUs and all that stuff.
• 1:16:54
  But now you don't need to have that high-cost infrastructure.
• 1:16:57
  in order to serve the end user of the models.
• 1:17:00
  Yeah, and if you can miniaturize this thing, then you're good to go.
• 1:17:05
  I mean, again, we're extrapolating here, but you can imagine I have my own little analog.
• 1:17:08
  Yeah, yeah, and it's just, and the great thing is this uses LEDs, it's not using lasers.
• 1:17:13
  Right.
• 1:17:14
  So I don't need coherent light.
• 1:17:15
  This is just like normal LEDs.
• 1:17:17
  So that's so clever.
• 1:17:19
  Yeah, that's so I love.
• 1:17:20
  I love that.
• 1:17:21
  Yeah, I think I think they did a really good job.
• 1:17:23
  I quite like it.
• 1:17:24
  Creativity is alive and well.
• 1:17:26
  Yeah.
• 1:17:26
  Fundamental science research.
• 1:17:28
  One of the ways you get breakthroughs is by being creative.
• 1:17:31
  Yeah.
• 1:17:31
  Dogmatic.
• 1:17:32
  Yeah.
• 1:17:32
  Um.
• 1:17:33
  That is really, really.
• 1:17:35
  Yeah.
• 1:17:36
  Coming from the tech space, this obviously.
• 1:17:37
  Yeah.
• 1:17:37
  And then my light bulbs are going off.
• 1:17:39
  Yeah, dude.
• 1:17:40
  And it's like 20 nanoseconds per search per iteration of this thing.
• 1:17:46
  The idea is called a fixed point search because you're searching for a fixed point to get to the answer, right?
• 1:17:51
  What I was telling you earlier about.
• 1:17:54
  Yes.
• 1:17:54
  Yeah.
• 1:17:55
  20 nanoseconds per fixed point search.
• 1:17:57
  And this thing is projected to be 100 times more efficient than leading GPUs.
• 1:18:03
  Going at 4.5 tera operations per second per watt.
• 1:18:07
  Right?
• 1:18:08
  That's 100 times more efficient than GPUs.
• 1:18:10
  That's actually crazy.
• 1:18:11
  Yeah.
• 1:18:11
  No, that's actually crazy.
• 1:18:12
  It's a lot.
• 1:18:14
  Like, in terms of per watt, like, yeah.
• 1:18:17
  Having 100x decrease in watt, like, usage.
• 1:18:21
  That's not trivial.
• 1:18:22
  I mean, like, that's not so much.
• 1:18:25
  Yeah, yeah.
• 1:18:26
  That's so sick, actually.
• 1:18:28
  Yeah, yeah.
• 1:18:28
  And they proved it, you know, with their validation.
• 1:18:31
  Obviously, you make something like this.
• 1:18:33
  Okay, how are you validating that something like this works?
• 1:18:36
  So they got their digital twin to train on the MNIST data set, which is the classic handwritten digits data set to identify, like, two, three, four.
• 1:18:45
  Yes.
• 1:18:46
  Did really well on that.
• 1:18:48
  Train it on the MNIST fashion data set.
• 1:18:49
  Did really well on that.
• 1:18:51
  The other thing they did was they can really do.
• 1:18:53
  So this this was A.I.
• 1:18:55
  inference.
• 1:18:55
  The other thing they can really do is combinatorial optimization, which is this like this class of NP hard problems like the traveling salesman problem and things like that.
• 1:19:04
  that they can efficiently, they're not solving P equals NP, but they can efficiently solve NP hard problems using this technique.
• 1:19:13
  And one of the things that they were saying was, as I was reading, was like, you know, in MRI scans, the way the MRI scans work is like the patient has to go in and be there for like half hour to an hour.
• 1:19:24
  Because...
• 1:19:25
  What you got to do is you got to take a bunch of different little puzzle pieces of effectively what you're doing is you're taking like scans in frequency space because of the way that the physics of the MRI works.
• 1:19:40
  You're trying to look at the precession of hydrogen nuclei.
• 1:19:45
  the patient as you like disturb it.
• 1:19:47
  And then there's a magnetic field.
• 1:19:48
  So then the proton is going to relax.
• 1:19:51
  And as it relaxes, it releases light.
• 1:19:53
  And you were trying to gather this light.
• 1:19:55
  And that'll tell you some, it'll give you like a frequency map of the patient.
• 1:20:00
  And then from that frequency map, you go into like a positional map.
• 1:20:03
  Right.
• 1:20:04
  So in order to do that, it takes a lot of scans in this Fourier space to then reconstruct the actual thing.
• 1:20:10
  Right.
• 1:20:10
  Right.
• 1:20:11
  But there's a way to do it with something called sensing.
• 1:20:17
  Right.
• 1:20:17
  Like and what you can do is you can.
• 1:20:22
  very selectively probe this frequency space.
• 1:20:27
  And do it in a very smart way with this compressed sensing.
• 1:20:30
  It's like a zip file for sensing.
• 1:20:33
  And you can then reconstruct
• 1:20:37
  The image.
• 1:20:37
  Yes.
• 1:20:38
  Right?
• 1:20:38
  So they could do that.
• 1:20:40
  That's an optimization problem.
• 1:20:42
  Yeah, I get what you're saying.
• 1:20:42
  At the end of the day, right?
• 1:20:43
  I get what you're saying.
• 1:20:44
  And the compressed sensing is an optimization problem that they could then figure out.
• 1:20:48
  And they projected that, you know, instead of a 30-minute to an hour-long MRI, it'll take a patient five minutes.
• 1:20:55
  And then this thing can just, like, reconstruct it pretty quickly.
• 1:20:57
  What's an important point here is that the analog...
• 1:21:02
  infrastructure, the analog computer infrastructure that they've built, has this application as sort of a replacement for GPUs at inference time in the AI use case.
• 1:21:14
  Because it's good at one aspect, but it's also good at this combinatorial piece, which gives it implications in the medical field, for example, in decreasing MRI times.
• 1:21:24
  Because it's fundamentally so much faster and better at this very specific...
• 1:21:29
  function that is a part of the mri process that currently is using digital computers and if you just swap out the hardware infrastructure and a variety of other details to get it integrated into the mri workflow you're now just able to actually just do more with less yeah yeah and that's like super huge right yeah like now i can do like 12 patients instead of one right right that's
• 1:21:53
  I love.
• 1:21:54
  Yeah, so innovation is happening.
• 1:21:58
  For everyone who said innovation is dead, watch From First Principles because we will dispel that rumor quickly.
• 1:22:05
  We touched on four very, very fascinating stories this week.
• 1:22:09
  We started with 3i Atlas.
• 1:22:10
  Yep.
• 1:22:11
  We had some new imagery.
• 1:22:13
  It's not aliens.
• 1:22:14
  It's still very interesting.
• 1:22:16
  We may not live to 100, so Brian Johnson, watch out.
• 1:22:20
  But longevity studies are showing that there is a real plateau being reached in this sort of increase in life expectancy.
• 1:22:26
  There's huge implications for policy and economics related to how we shift the zeitgeist opinion about that to be connected to the fundamental frontier research in that area.
• 1:22:39
  We don't have to sort plastic anymore.
• 1:22:41
  We talked about this new nickel catalyst that enables this.
• 1:22:45
  That was a fascinating one.
• 1:22:46
  And last but not least, analog AI from Microsoft, which has potential to solve for the energy issue with AI.
• 1:22:55
  Yeah.
• 1:22:56
  But also has other applications.
• 1:22:58
  Yeah.
• 1:22:58
  We want to make AI better.
• 1:22:59
  Right.
• 1:23:00
  It's good.
• 1:23:01
  And cheaper.
• 1:23:02
  And cheaper, faster.
• 1:23:03
  Better for the environment.
• 1:23:04
  Yeah, who knows?
• 1:23:05
  Maybe it'll solve, like, the problems.
• 1:23:08
  Right.
• 1:23:09
  Right.
• 1:23:09
  If it also solves the NP problem, it might solve other problem types that we didn't have the tools for before.
• 1:23:14
  This is what we love to do here at From First Principles.
• 1:23:18
  Talk about this breaking science research.
• 1:23:20
  Break it down so you don't need a PhD to understand it.
• 1:23:23
  The implications here are crazy.
• 1:23:25
  My name is Lester Nare, joined, as always, by my co-host and our resident PhD, Krishna Chowdhury.
• 1:23:33
  all next week.