Live-Cell Imaging and the Limits of Structural Biology | Eric Betzig on Super-Resolution Microscopy

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Almost everything you learn in biology textbooks is a hallucination. You guys have probably seen on the web. You know, there was those beautiful things of here's a cargo on a chin walking along a microtubule like this and it's all like in this vast empty space. I don't know any cell that's a bunch [laughter] of vast empty space. I'm sorry. It's crowded as [ __ ] There's 10 billion protein molecules. There's 10 billion carbohydrates. It's by far the most complex matter in the known universe. We understand the interiors of neutron stars far better than we understand the interior of cells. There's a reason why only 9% of the drugs that enter phase one come out phase three cuz we don't know what the we're doing.

We don't know the real mechanisms that are going on. And when you start to [ __ ] look at the dynamics, not just the structure, you realize that you had it all wrong. And you realize that so many of the things that they thought they knew, they you can't be sure that they know. We have to reinvestigate all of it. This week, Mike and Misha sit down with Eric Betsig, Nobel Laurate and UC Berkeley professor who pioneered super resolution microscopy. Eric traces his path from Cornell to Bell Labs, including two stretches of unemployment that produced some of his best ideas. He reflects on his work in the auto industry in the living room, where he built a microscope to beat the defraction limit.

Now he's looking ahead to his new cell observatory, using AI to turn pabytes of live cell imaging into a queryable model of biology. Please welcome Eric Betsy. So Eric, you've been working like battling with defraction limits throughout your career. What is the defraction limit? How would you explain it? Well, it's was first figured out around the end of the 19th century is and it kind of makes sense that if you're going if light is waves and you're trying to get an image through light, then you can think of the light colloquially as like little fingers trying to feel the sample, right? And if the fingers are too fat, you're not going to feel the structure below the level of those fingers, right?

And so basically about half the wavelength of light is what Ernst Abbe mathematically determined is the limit. You know he did it but you know as soon as people understood the wave nature of light going back to Huygens or or certainly Maxwell all of this could have been easily deduced. Um but he was the guy who really realized from a from a real imaging perspective that that was a fundamental limit. you know, in in in many fields, of course, you know, when I first started getting in in the 80s in graduate school, there was obviously already semiconductors and other structures that were pushing those same limits in terms of their manufacturer and so forth.

So, it was already known as an issue. And of course, this is why the electron microscope was invented in the 30s with Ruska and others, right? Is to get around that limit. Um uh in biology it was perhaps less appreciated that that you'd necessarily want to go further but you know the defraction limit is such that it's roughly 100 times smaller than a cell. So you can still learn a lot but it's 100 times larger than the molecules that make up the cell. So obviously there's an incentive to try to do better. Maybe if you could give us like some taste of like maybe some numbers in terms of let's say there's like available off the shelf microscope someone can get like with objectives and how how small those numbers are.

So yeah, so with visible light you're going to be down to around 200 nanometers. Okay. So again a protein molecule might be four nanometers. So you're 50 times too coarse to get to that level. But a cell might be 20 microns, which is 20,000 nanometers. So then 200 nmters means you're you're roughly, you know, can see 100 spots across the the width of a cell. Um, but you have to have a good microscope. I mean, the kind that you'll have in a high school is probably not going to cut it, right? You need good objectives. It's it it an art and a science for those guys.

And that was really the thing is in fact there's an interesting story is that when when um Abbeby came up with with the understanding of this limit not only did he do that but he also understood exactly how to create objectives um lenses that would actually reach that limit. He was working he was a professor at University of Yana, but he was working with Carl Zeiss who had his microscope company in Yana. And um and so he had all these prescriptions about how many lenses, how to grind them, you know, how to space them exactly to get to the defraction limit. Um Zeiss goes and follows those exact rules.

And it was crap. Um and they're like, [laughter] why is this the case? And they realized was because the quality of the glasses were not good. Okay, that that um that uh you know this is often the way as experimentalists. You have a mental picture in your head but reality bites you in the ass. And so they went to auto shot who was making um glasses particularly in that time there was a lot of street lamps that were gas lamps and so he was making the the bora silk glass that was the the covers the lamps and they went to him and say hey can you make us better glasses and so basically shot figured out exactly how to make exactly the types of refractive indices they needed.

Once they did that bang they were right to the defraction limit. And so, you know, uh, Abbeby, he did okay. Zeiss, he made a fair amount of money, but the real winner was shot because shot is today still one of the largest makers of glasses for every application in the world. Shot and Corning are the two big names in this stuff right to this day. And so he became uber rich because of his ability to make these glasses. And was it like impurity impurity issues? was part of it, but but also al also because you have to introduce impurities as well specifically to get certain refractive indices, right?

You need to go anywhere from, you know, glasses is is normally 1.5 index and they would go down to below 1.4 all the way up to 1.9, right? And so you have a lot more tools at your disposal as an optical engineer if you have all these different refractive indices to make your lenses from. Why did it matter that much? because they could also use like, you know, the curvature. It's not enough. You don't have enough enough knobs to tweak to get to where you need to be with that alone. You actually need to be able to have different indices of glass to make it work.

So, where are we now with objectives? Oh, I mean, so there's there's many many um uh fringe benefits of winning the Nobel Prize. So um one of them is I've had a long relationship with Zeiss because they licensed our patents on palm they licensed our patents on lattice light sheet but um there normally I go to Yana but one trip I got to go to Obercockin and Obercockin is where they make the big EUV lenses that then go into the ASM ASML machines that then go into making Nvidia chips and so forth. And um that is the closest I've ever seen to alien technology in my life.

I mean these lenses are are you know taller than me. They're this fat around. They have um they're all reflective lenses because you can't use glasses there, right? But the coatings to make it reflective at EUV wavelengths. They have like 30 layers and because of the curvature even though it it's like sub a monotomic level of coating thicknesses varying across the diameter and then they have to put little impurities in so the so the atoms don't diffuse across the boundaries between the different layers and that this whole thing is literally gigantic and it's precise down to the atomic level in what they do. It's just absolutely, you wouldn't believe that this would be physically possible, but it they do it and it's it's incredible.

Have you thought about a biological imaging microscope based on these giant mirrors? You could well if if a living cell could live under EUV wavelengths, life would be great, wouldn't it? But sadly, that's not the case. But I guess you could image like cryo cryosamples. Yeah. Yeah. Yeah. Yeah. But well, one of one of one of my main points that I make in all my talks today, right, and one of the reasons I pivoted from from structural super resolution is it's my firm belief that you cannot understand life without looking at it live. Mhm. And I I feel like a big problem in biology today and in pharma today is that there's been um in the because of the defraction limit because we reached this wall at the end of the 20th century and it was 100 times too small to see the molecules.

Science pivoted from this holistic look at cells with that they could look at live cells to doing reductionism. And EM is one of those reductionist tools. You're doing structural imaging. But you will never understand life without looking at it live. Okay. So having high resol Everybody was thinking what's the point of a microscope? High resolution. No, it's one of the things you're interested in, but you're also interested in in speed because the cell is moving. You're interested in non-invasiveness. So what's the point of looking at a cell if you're killing it while you're looking at it? So all of these other metrics matter too. And so UV is not the path to glory to study living things.

In fact, while we've learned a lot from the reductionist tools like like EM and and particularly like structural biology like Cryomm and Alphafold and all of that, there's a whole slew of companies five miles across the bay here trying to do drug discovery based on protein structures determined by Alphafold. the those proteins and that stuff is such an infantessimal part of the whole dynamic complex system that creates life. They're burning money for nothing. I I guarantee that unless they pivot to pull in other types of information, biological information, those companies will will not be here in 5 years. Okay. So what kind of information do you see needs to be there to like develop to be able to see the dynamics and to see the interactions that actually happen in living matter?

That's the only imagine you try to reverse engineer an internal combustion engine. And if all you have is the reductionist tools that that rule biology today and ruled in the 20th century, those are biochemistry, how different proteins and other molecules interact with one another. Um structural biology such as um alphafold or or um or em or stuff like that. And molecular biology, the central dogma, knowing DNA makes RNA makes proteins and all of that. But those are are incredibly reductionist tools and would be like trying to under to reverse engineer life from that would be many many orders of magnitude harder than having a random pile of internal combustion engine parts and trying to figure out how internal combustion engines work.

Okay, that's where we are today. That's where most of the field is. And it's it drives me insane because I keep making this this point over and over again that there com it we need to go back to a holistic understanding of this complexity that exists in the cell instead of being focused on just little parts. So you mentioned icon is is a company we have u so the head of ICON is um is Roger Permutter who used to be head of research at Merc. is one of the most prolific and successful drug discoverers of of the 20th century and he has he has a line which is you know it's a miracle if we ever find a drug that works because we have no idea what we're doing and that is accurate because of this focus on such a crazy level of reductionism instead of understanding the system holistically.

Do you know we were discussing this yesterday like uh I mean obviously we've kind of skipped a lot of the what what is super resolution microscopy or anything but is it do you have you ever thought about or like have have you ever kind of like come across an idea of somehow making a cell where it's genetically engineered to be friendly to microscopy. So meaning like you genetically engineer as many proteins, as many structures as possible in the cell to be easier to image so that you can image like all of them at on the in the limit it would be like they're all fluorescent in different wavelengths or something.

Right. Right. Well, that's that's exactly the way we do engineer cells today, right? Is to put fluorescent tags on specific proteins so they'll light up. And that's one of the most limiting parts of optical microscopy still because in in visible wavelengths there's a limited number of colors that you can do. and there's 20,000 different types of proteins in the cell. It would be great if we could see them all at once, but that technology does not exist. And that is a nobel waiting to happen if somebody can directly interrogate proteins in some way and deduce them without having to put those tags on. But more generally, if you start modifying a cell in order to make it tractable to image it, you're not looking at real cells anymore.

[laughter] You're looking at the thing you created, right? and and so uh yeah that's I I don't think that's the path to go is is there have people tried this where they try to like make the proteins more absorbing or more some more maybe like I mean the thing is like if you want to add let's say a green fluorescent protein that's a big protein so it's going to start changing of course no kidding it's a bowling ball but maybe two kilo dolton bowling ball added to added to your protein that's right yeah is there is there any has that actually y has that type of approach of actually genetically engineering the cells yielded any kind have like interesting results yet.

Well, I I mean again, we genetic engineer all the time to put those labels on to put you you you do all sort all sorts of as many controls as you can to try to show that the cell is still behaving physiologically with that particular tag on. And a lot of times you'll find it isn't okay. And a lot of times it's a black art about the linker. for example, the the little the little peptide linker between the protein and the bowling ball and and all sorts of crap that you have to just there it's it's an art, not a science in terms of figuring out what works and recapitulates as much as we can tell the native physiology.

But there's it's it's just it's just like you know the uncertainty principle. Anytime you observe anything, you're going to perturb the thing. And so it's really really important to make sure that you you do as many controls as you can to to believe your physiological. Yeah. So what kind of like cells or objects organism you are targeting? So many many many things. So we start of course with with cultured cells because that's the easiest right. Um and certainly with palm and so forth, that's where we had had to start super resolution started with with single culture. In fact, at first it starts with dead cells, fixed cells, chemically fixed so that so they're not wiggling around because the super resolution techniques at least initially and still in many cases today are slow and so things would blur out if you tried to look at super resolution of something that's wiggling and moving real fast.

So you chemically lock it into place and then you image that, right? Um, so that's where you start. But those fixation protocols which started with electron microscopy are incredibly perturbative to the ultra structure at the nanocale you want to see. It's fine if you're looking at at regular optical resolution, but when you when you dig down to super resolution, you're looking at artificiality. And that was one of the reasons I pivoted from super resolution because most of the time people electron micros have known this for years. And so their gold standard for them is not to chemically fix cells. It's to high pressure freeze cells so that in in milliseconds you're locking in the structure in vitrius ice, not crystallin ice and then look at it that way.

But that's a lot of work to do. Harold and I have done it at Janelian done correlative em and super resolution. But it is not it's not a protocol for the faint of heart. It takes a lot of technology and a lot of skill to make that kind of thing work. Do you try to combine both microscopes system? Yeah. Yeah. Yeah. So, well, you go from one to the other, right? So, so you vitriusly freeze the cells. Um, then you put them in your super resolution cryostat that's now at 4° Kelvin. And so, you do cryo cryopalm we call it, right? And you and you bleed out all the molecules, find out where they are.

Then you take that and then you take it out while it's still frozen vitriusly. You do freeze substitution and you end basically end up putting it in resin in a way that still preserves that ultra structure. Then that gets sliced and put into Herold's three-dimensional focused iron beam milling electron microscope. So then you can get the three-dimensional profile that way. And then you take the two data sets, which of course have all sorts of like weird little distortions and stuff. And then you do kind of a a warping and and register them all together. And then you got your correl that's this one right up there.

You see that? That's correlative superresolution. That orange thing with overlapping them there. Yeah. They're exactly mapped on one another right there. Yes. So you have to slice basically the in the in the EM. So there's a a focused ion beam like a gallium beam that comes and takes a nanometer or two off. How thin is the layer? It's like a couple nanometers. Really thin. And then you image that. You image that. and Herold is the god of fibsim. So, so connetoics is a big thing in neuroscience right now. So, um one of the first connetos was made possible by Harold's FIBSM at Genealia where I used to work.

So, and that has to be done couple nanometers at a time. You couple nanometers image that surface. Couple nanometers image that surface. And it's it's less crazy than those lenses that I talked about at Zeiss, but it's pretty crazy. Yes. Not very scalable. Uh it's not terribly scalable. That's [laughter] great. It's not going to be a consumer product anytime soon. No. No. But none of what we're talking about is yet. Uh may maybe we can go back to like uh to your jumping all over the place. You're a physicist, right? So how did you get into all this like wet, you know, fluffy stuff? Yeah.

So So curiosity, right? So um yeah, I started as a physicist and you know as a kid I wanted to be an astronaut cuz I grew up with Apollo and Star Trek, right? And uh but by the time I was in in college and by the time I graduated from college um the the shuttle was coming online and I knew at the time the shuttle was the biggest mistake you could possibly make. It was a horribly engineered system, a complete waste of money. nothing but a nothing but a a um a political gift to the legacy aerospace contractor. Did you like the Soviet rockets better?

Uh well the Soviet this the Soyos has been flying for what 50 freaking years or something, right? That's pretty good, right? But obviously SpaceX has completely changed the nature of the game, right? So this is very exciting to me because I think that uh you know I honestly believe there is a finite chance that I could still reach my dream of making it to space before I die and that is the number one thing on my bucket list is is to make it to orbit. Yeah. So take you had to take a break from that dream. Yeah. But but anyway anyway so getting back to your point, how did I get into the wet and squishy stuff, right?

So, I went to graduate school. Um, and I've always wanted to be like I wanted to do physics not because I wanted to be a fineman or anything. I wanted to do engineering physics. Okay? I wanted, you know, my dad was an engineer. I wanted to be an engineer. I like making things. Um, and so at that time there were only two um applied physics departments in the country. One was at Stanford and I [ __ ] hated California. I wanted to get the hell out. And then the other was Cornell. And Cornell was a lot more like Michigan where I grew up. So I went to Cornell.

And there was two professors. One was a guy who was an electron microscopist. And the other guy was a raman spectroscopist. And um they had this crazy idea that if you could use your electron microscope to drill a hole smaller than a wavelength in a black film, then you could press it against a cell and you would have a little nano flashlight that would only illuminate one spot. You drive it around, you get a super resolution image. And you know the idea is what if we could make an a microscope that could look at living cells with the resolution of electron micros. All you have to do is say that sense and you go, "Oh my god, that would be incredibly revolutionary, right?"

And you say, "Okay, that's the kind of thing I'd like to engineer." So that was my entrance into working in super resolution for my thesis. And so I did near field microscopy as it was called because you're in very close to the aperture. You're not in sort of the sort of farfield optics limit. you have to get into the details of the of electric fields that decay exponentially away from the thing. So that's called near field optics. Did you use a fiber or I and not at that at that time we used um so one of the things that was new at that time was some something in electrophysiology called patch clamping where you could basically look at single ion channels in a membrane by pulling glass pipets down to the nanometer scale and then pop that thing as an electrode on top of the ion channel actually measure sort of single ion currents going through pores in in cells.

Okay. And so we realized that well if we coat that thing with metal then the hole in the end of that glass thing is going to be our little aperture and that's a lot easier than having to go to the electron microscope and drill these holes in this in this thin film. And the thin film if you looked at it the wrong way would shatter and so um the pipets were a lot more robust and simpler. And so that's what I did. And then I took that technology uh in my own lab at Bell Labs and then realized once I was there that we could get much better delivery of light by instead taking optical fibers and pulling them like a piece of taffy and then it would taper down and break with cleave with a flat end and then illuminate the sides and then it would be optically guided in the wave guide down to that aperture.

So we'd have a much brighter light source. And then that was where I developed my reputation as a super resolution guy. And the experiments I did there, although I didn't know it at the time, kind of set the stage for what would become palm and other super resolution. How small can you get in terms of the 20 nmters? I see. And that was the your like resolution limit essentially. Yeah. Yeah. Yeah. Yeah. it tough to get the tr the the the key thing that killed um near field for biology is that again because it is near field the light that comes out of that hole spreads incredibly rapidly.

So if you're even 20 nanmters away from the surface, you know, you've lost much of your resolution. And I didn't know a lot of biology at the time, but I knew cells were a lot rougher than 20 nanometers, right? And so there was there really wasn't any way I was going to be able to follow even get the surface and follow the contours of the surface with this this big ass probe and and and get the resolution that I needed. And there was no way I was going to get to the interior of the cells. So, I did some experiments on cells then, but I picked cells that were known to be crazy flat, right?

And and not the whole cell, but just out at the periphery where it's just kind of, you know, and demonstrated that I could do it. But, um, it was really for biology pretty much a dead end. By the way, related maybe to defraction limits. Seems like it's a good demonstration that if you have a tiny hole like 10 20 nm like 10 times smaller than the wavelength that the light wants to expand. Is there really really fast I mean I can derive it like let's say from yeah it falls from the formulas but it's an effan field it's exponential right right is there intuitive u explanation for uh I I don't know if I don't not really unless you want to think of it as like a fire hose right or something like that right if that's your level of intuition but generally speaking it you know it was it was something that that was a concern of mine from day one in fact the first thing I did when I got in there as a graduate student is I I I couldn't do a round hole, but I developed um a theory for figuring out how fast the defraction is from a from a slit.

Um and I did that on an IBM the an IBM PC, the 8088. [laughter] And so I was able to do do that calculation said, "Yeah, it's pretty fast." But I didn't necessarily have enough confidence to think that well let let's just do the experiment and find out how bad it is. How were you getting the probe so close to the cell without like like how do you you like that's the other right? So what you need is an independent feedback mechanism to regulate the height of the aperture above the surface. So um there's there's two answers to that question. The first is um if you at Bell Labs I had lots of hits.

I had a number of you know science papers you know at at one you know I I I became I became well known as a scientist at Bell Labs but if you look at all of the applications I published they have one thing in common the samples were really really flat [laughter] okay so we did like high we had the world record for high density data storage at one time with near field optical data storage because you're looking at very flat you know ferroelectric film that you can switch the bits in, right? Um and uh but still it was it was uh limited to very flat things.

But so your samples were the magnetic samples or Yeah. Yeah. Yeah. Yeah. Exactly. Seems like it was a big theme uh to develop data storage systems. Oh yeah. Yeah. I you know back I you know flew out here and had talks with Seagate about commercializing and [ __ ] like that. None of it none of it ever went anywhere, but at least at the time it was it was something that was worthy of thinking about, right? But but to get back to your question, in order to get that regulation that I developed at Bell Labs and it turned out so this was nearfield was a form of what is called today scan probe microscopy.

The most famous example of that is scanning tunneling microscopy, right? And that's the one that that won the Nobel in '86. That was while I was um where was I? I was still in grad school in ' 86, right? And so at first we were trying to you know because our our tips were our nearfield tips were coated with metal in order to make them opaque. Well, now I can try to use that to do tunneling microscopy against the sample if I have a conductive sample. That didn't work so well because those tunneling distances are not nanometers, they're angstroms [laughter] which gets even harder, right?

But another technology that developed out quickly out of out of scanning tunneling microscopy was something called atomic force microscopy where instead with a with a tip that's attached to a spring you can kind of feel the forces at at maybe not the atomic level but sort of at the nanometer level you could kind of do that. And so the trouble was that was done with a tip on a can lever. So the canal lever is very floppy, right? And my probe has to go this way and it's stiff this way, right? And so what I did instead is I dithered it in the floppy direction.

And then as I came close to the surface, there would be enough forces between the tip and this that it would slightly change the resonant frequency of that thing, you know. So damping it. Damping. Exactly. And so you could see that slight change in the damping frequency and use that as a regulatory. That worked really well. So that worked on cells, worked on everything. So sheer force feedback is a thing today. And that was developed specifically for Nearfield first. Yeah. When did you realize that like Nearfield wasn't going to do it? Because eventually I got to the point after six years in which I had pretty much exhausted every flat thing worth [laughter] looking at.

Okay. But at the same time uh and this gets now into sort of philosophy of science and and why I'm not really a scientist right is um is um I I I I dropped near field is in part because I dropped science altogether. I got really fed up with science. Um because you know when I when we first started in graduate school to do near field everybody told us we were nuts. there's no way fraction limit blah blah blah this will never work. Um and um it was enough to to convince the people at Bell with the results I had to do it. And um and it just took off.

I mean again it was it was a terrific best time of my life was at Bell. Um and and things were going great, but I knew the limits, right? I and and uh and they were real, but at the same time, you know, it's it's how science is very fattish, okay? And every new thing just people jump on, right? Just tons and I don't mind that. That's fine. But they have to be careful and they're not. Okay? And most people who jump you there's first off if you're willing to drop what you're doing as a scientist to chase something else. What was the value of the thing you were working on in the first place?

Right? What is motivating you in order to do this? It's fine if there's an opportunity particularly if that opportunity reads upon what you were doing. But if you're if you're just like whoosh, let's do this all of a sudden, right? So the field blew up overnight. It was it was part of the blow up of scan probe microscopy in general with STM with AFM with near field all of this. But the trouble is is that most of the people are not careful. And one of the things I one of my stock phrases from that error was it's very easy to get an image and very difficult to get a meaningful image.

So it's very easy to see artifacts in your images all the time and thing and this was just endemic and I kind of felt like every good paper that we published was the justification for a 100 pieces of crap that followed in its wake and everything that I was doing was a net negative to society and a waste of the taxpayers's money. Um so that's the way I still feel about most of science. Do you have here an image taken with that with nearfield? No, I don't I don't think I have a nearfield one anywhere. Uh Nope. Sorry. Yeah. I I mean I have them somewhere.

I have some Oh, actually, you see those two big ass black books right right above where he is there. That's my thesis. One of the biggest thesis in the history of applied physics at Cornell University. If later on if we want and I can give you there's plenty of images in there from my nearfield microscope at Cornell. Okay. So, and it was written without LLMs. It was Yeah, it was, you know, you know what? It was written on a Wang word processor. There wasn't even, you know, didn't even have a a PC to do it on then. So, yeah. Yeah. I think this complaint about the like people publishing essentially artifacts, right?

Because like I've heard it before actually from an AFM guy that I'm friends with that he worked on um he had a job for a while doing like hero experiments at Brooker. So, they want to show like the power of the microscope. Exactly. So he would put a lot of effort into like let's say imaging the helix of DNA or whatever. But then he was saying that like yeah if you look at papers like you're often just looking at artifacts. Absolutely. Yeah. What's pro microscopy is loaded with artifacts. Is is there like a do you have like a hack that would you know help reduce the amount of artifacts in literature?

Have you ever thought of like a if you if you could like wave a wand? So, one of my favorite lines and I and I only learned it a few years ago, but I think it explains everything in the world is from Charlie Mer. Show me the incentives and I'll show you the outcome. Okay. So, the incentives in science are not incentives to to to increase the the store of knowledge. there to get grants, to um get awards, to do things of this sort, right? And if one can do that, receive the Nobel Prize. Yeah. Whatever. [ __ ] Yeah. Yeah. [laughter] Award. I I can rant forever about how toxic I think awards are to science.

Um every award that's ever given out, there's 50 people who think they should have gotten it and are bitter about it, right? And it's all subjective. It's a [ __ ] beauty contest, right? Who's to science is a collective work of many many people. Who's to say one guy should get all theing credit for for what an entire field has done? Or worse, a guy who runs a lab of 50 people, right? These giant super groupoups, right? Where it's all like, you know, field marshal at the top and lieutenants and this and that and that all the way down. Some some kid down at the bench has some ID and the guy up here gains all the credit, right?

I mean, what the [ __ ] is that, right? The whole system is is frankly corrupt. Were you already [laughter] Were you already Is this what you were thinking when you were leaving IBM? Like when you No, sorry. Bell Labs. Yes. I was thinking this when I was in graduate school. Are you kidding me? The scales fell off my eyes pretty damn early, right? But I felt that that's this was a lot of the reason I I didn't just leave Bell Labs. I didn't just leave nearfield. I left science, right? I mean, I was done with it, right? So, yeah. What did you do? Where's your I worked for my dad.

So he he uh timing was good because um uh he uh um worked for a machine tool company in Michigan which um so a machine tool is so Michigan of course is home of the big three in the auto industry and they were producing millions of cars a year. So that means you need to make millions of brake calibers calipers a year and you may need to make a million you know intake manifolds a year. That's a lot. Okay. You don't do that by monkeys typing out Shakespeare by having a thousand guys each on its own milling machine and personally doing that. You have to have automation.

So CNC machines existed then but they were not robust. Okay. You could you could not produce the million parts a year with the technology with CNC computer numerically controlled machine tools at that time. So you what what my where my dad worked is you you actually design the machine specifically for that part. So I two years before the car ever is in the showroom the big three says okay we've designed this this is this is for example the brake caliper. Okay, they go to these different companies and they say, "Quote for me a machine customized specifically to make a million of these brake calipers a year."

And so they do that and that machine will be, you know, 10 times the size of this room. Okay? And because it has and it's working in parallel doing here it's doing a milling operation here it's doing a drill here it's tapping or at different stations as it goes around and every 30 seconds pop part comes up pop comes up. So these machines even back then cost hundreds of thousands to million dollars and you have a year in order to get that machine running and it's got to run 247 on the floor or you are out of business because they will never buy from you if you stop the assembly line because they don't have that brake caliper.

Okay? So you have to make really really robust machines. So my dad my dad did that and then um and then he decided to make his own company doing that. And so that happened while I was at Bell. Um right when right when I started Bell he had left the other company and started a competing company. So that was 1988. You guys are probably too young to know but 1990s was the era of us boomers becoming real consumers. The minivan was invented. you know, the SUV was invented. It was boom time for the auto. So, my dad's timing could not have been better. And so, his company, so by the time of 97, he had 300 people and was doing 70 million in sales from zero, right?

and like you know that's great and so he could af when when I left so therefore in 95 when I left Bell they were already on their upswing and he could afford a leech like me to come work there and try to see if there was some way I could use you know my physics experience and like that to to make machine tools better. And so, you know, when I was completely fed up with science, I always always kind of knew I would have this as a potential backup cuz he was always saying, "I want you to come work for me." And eventually was like, "Okay, Dad.

Yeah, I'll come work for you, but can I just kind of try to be the guy who's your R&D department? Okay. I'm I'm going to try to think if there are other ways of doing things or other markets we could do." And so he was willing to humor me with that, right? And so and so I did that and I did that for six years and and I I developed two projects in that time. The first one was um when the when the parts come off the machine, they had damn well better bolt together at the rest of the parts. Well, right. And so they couldn't inspect every part, but what they do is they take every one in a zillion parts and they go to a clean room where there's there's something called a coordinate measuring machine, this thing that's on a gantry, and it has this little sapphire ball at the end and it touches the part here, touches the part there D, and it kind of feels its way around and figures out to, you know, sort of 10 microns or so precision where everything is, right?

But you're only looking at one out of a zillion parts and then you hope statistically you look at enough parts and you hope the variability of the process is good enough. It wasn't. It it clearly wasn't. Okay. And so what I came up with was because I was still filled with optics and still filled with super resolution, right? um uh you know I knew in fact one of the two experiments I did at Bell which led to the idea for the Nobel Prize was I was the first person to see single molecules um at room temperature and furthermore I was able to localize their positions to about a 50th of the wavelength of light because I knew what the energy distribution inside of the near field aperture was.

So I could fit a profile to that and therefore figure out where the molecule was to much better than the width of the aperture itself by doing that fit. Right? Just like you could find the center of a Gaussian to much better than the width of a Gaussian if you have enough signal noise and the nature of that Gaussian. Did you do it with near field microscopy? I did that with near field microscopy. How do you also prove that it's a single molecule? Oh, that's easy because they two there were two things that were really conclusive on that. I loved that. That was that was probably one of the best papers of my life.

Um the the first one is is is it bleaches it doesn't go down slowly where the molecules bleach. They're there and then the next instant they're gone. Okay. So you fried it. Okay. The other is is that fluorescent molecules are dipoles. That's how they work. And so it had a dipole orientation. So actually what happened is because of that dipole orientation it was actually mapping out the electric fields in that subwavelength aperture. So I could actually map the field the fields in the aperture to almost one nanometer precision as I drove it along that molecule and I compared it to a theory that Hans Beta developed in the 40 for what defraction would be through a subwavelength hole and it matched up exactly.

It was an awesome paper. So you could see the the double near field distribution and and and I could turn that around and since I knew from Beta's theory what the electric field profile was like I could not only determine the position of the molecules I could determine the orientation of every [ __ ] dipole. [laughter] So it was really cool. Is it the single dipole or single dipole? No no no it's the floor fours are generally just a single dipole. And how do you collect the fluoresence in that situation? Is it through the same fiber just coming? You can either come back up or you can have a detector on the opposite side if it's on a transparent substrate.

Got it. Yeah. Um the other key thing then then was um having these um uh avalanche photo dodes. So before that was all photo multiplier tubes which sucked at both in terms of quantum efficiency and noise. But the avalanche photo diode absolutely changed imaging when that came on the scene. And so again, it was being in the right place at the right time. And one of the other things I would say is one of the reasons I've been successful as a scientist is I've always been one of the first adopters of new technology. I'm always got my, you know, my sniffing around for whatever is the latest widget that I can and h and how combinatorically it adds with all of the other widgets and all the other experience I have to do something new.

How would you explain it? How do you do it? Like to be in the right Well, again, it's it's just it's you I hate going to conferences, but I go to conferences to go to the trade shows, right? Because that's where you kind of learn what's the new and you talk to the people. You even learn things that aren't quite they're not quite ready to release yet and so forth. And so, yeah, I started actually to do the same. [laughter] Yeah. Okay, there you go. Yeah, absolutely. Yeah, I I learned way more from those guys than I ever learned in any talks at at, you know, something like, you know, the optics conferences in Moscone or whatever, right?

Yeah. But what uh how did this uh you were going to you were going to talk about how that microscopy work translated to the like CNC? Oh, yeah. Yeah. Yeah. We got off the that thread. Exactly. So, um, so instead of just measuring one part every so often, I developed this thing that had about 30 little cheap CCD cameras that were all around the thing and then I could measure to sub pixel precision where say the edge of a part was or where a hole was located or all of that and I could do it in a fraction of a second. That's it could be done for every part for so therefore we can look at every part that comes off the machine and we can reject those parts that don't that don't fit the the the uh tolerances of the of the of the blueprint.

Right? And so I built one of those um we put it on a machine that we had shipped to New York. Uh and um and what happened it starts rejecting parts, right? Because what happened is um it the the tolerances that the companies would spec this was the era of of GE and the six sigma [ __ ] um with Jack Welch and all of that, right? Six sigma is crazy. You know what six sigma means in terms of precision? It's it's like it's like 10us 6 or something. [laughter] It's it's crazy [ __ ] right? I mean, nobody can can achieve that type of stuff, right?

So So basically it's rejecting parts because of this, right? And um and tacitly everybody knew that, right? And so with the old method of just using the the coordinate measuring machine to to statistically get some parts, as long as the [ __ ] things bolted on to to the axle, life was good. Okay? And and so they speck them so tight that even if you were a factor of five off in terms of tolerance, it was still plenty good that it would end up bolting onto the thing. Right? That was the way the world worked. And so what they did is because it it rejected parts, they turned it off, right?

They turned it off and then they let the parts go through and then they measure them the old way. And so so that was my first lesson is that okay, well the thing that is king is is productivity. I I developed something which hampered productivity, right? So instead for my second thing I did with my dad is can I do something that increases productivity. Okay I've learned my lesson. Okay. So there was an opportunity there as I learned is that you see in order to have the robustness that these machines needed to have. They were incredibly big. Okay. Um there's you're talking about each each you would and you it would do stuff in parallel.

So if you have like on one surface eight holes are being drilled at once, you make a spindle that has eight spindles together linked together and in the exact positions of where those holes will and it all goes in in parallel together and it drills all of those holes at once, right? And so um that's the only way you get the speed. Um and so um so you're moving masses for these in order to make it robust, in order to make aggressive milling cuts across, you know, cast iron or something, uh you need to make things that that are really heavy. So each station around these machines might be a ton a piece, right?

Um maybe more because again, you're you're making three axis moves, right? So you have three different one axis on top of the other on top of the other to make these moves. And so it's like a rocket, right? Where you have three stages, right, in order to make this this type of move. And so the bottom stage is moving all the upper stages, right? In order to do all that. So it has to be even bigger and more more robust in order to do that, right? So that's how it worked. Um and um and in that day, uh you had two options for trying to move this stuff.

You could use a big electric motor which was tied to a ball screw and the ball screw would then move the stage forward or you could use hydraulics and use a hydraulic cylinder that would just push it forward. So you you you could use either a hydraulic cylinder or ball screw. The trouble with the hydraulics was it was cheap but it wasn't very precise. Okay. So generally what you do is you just move it forward until you come up against stop but you couldn't really make milling moves with this. just kind of drilling moves. And the trouble with the ball screw stuff is it was really limited in speed because, you know, you're putting all of the force of this tons through a screw, right?

And so the screw could get ripped up if you don't do it slowly enough, right? So it meant that a lot of the most of the time in which you're making these parts, you're not making chip. You're just moving the masses to the point where it's going to make the chip. And there's an old saying in the business that if you're not making chips, you're not making money, right? And so the duty cycle of actually making chips was small. And so, but I had learned enough control theory while I was in grad school in Bell Labs that I realized that there was no need to do all this openloop hydraulics.

And there were some new, again to the things that are just new, there was some very impressive servo valves developed by Bosch in Germany. And so I was able to do some nonlinear control theory coupled to those servo valves, coupled to hydraulic cylinders, coupled to the final thing, which is which is energy storage. So normally when you're doing an electric move, right, is is is if if I wanted, for example, move that whole big column of metal forward in a certain given of t period of time. If I wanted to to do it in half the time, that means I'm going at twice the speed.

Okay, but twice the speed means I've got four times the kinetic energy, MV squared, right? That I have to put in there. But I have to put that energy in in half the time. So the peak power goes up as the cube. So that's ruinous with an electric motor, right? Because now I have to have electric motors that are eight times bigger to do half the time and their mass goes up eightfold to have that much extra horsepower. And so now it's like having a rocket in which your rocket fuel is really shitty and your mass fraction is really bad. Okay, so that was not the PE.

But hydraulics, hydraulics, you can fit a 100 horsepower hydraulic motor in the palm of your hand because basically it's just delivering what's the original source. And with hydraulics, they have accumulators. Basically short-term batteries for hydraulics. And it's nothing more than a big ass cylinder which stays remote, right? And it's got a little nitrogen bladder in the top. and you use your 20 horsepower electric motor to drive a pump to to basically compress that gas up there and now it's a spring, right? And then when you want to make your move, boom, you can blast that out at hundreds of horsepower through the hydraulic lines and move the load.

So I made this thing, it's somewhere here, somewhere on one of the walls. Do you do like acceleration? There it is right here. That's it. Okay, that's fast flexible adaptive servo hydraulic technology. So you see that thing there? See that? See this big ass mass here? A human sits like right here. See this big thing here? That's a couple tons. I could move that at 8 gs of acceleration and position it anywhere within a meter cubed to 5 micron precision. That's sick. Yeah, it was sick. It was sick. It's been taken over since by linear electric motor technology has this is why you know Starship started with hydraulics but they quickly pivoted you know for the for the uh flaps and so forth and and and um moving the Raptors around to electrics but um it was only long since I left that electrics could get to that point but man it worked great.

It worked great. It was just amaz I was so proud of that machine and so I spent three years developing it and two years trying to sell it and in the end I sold two [laughter] two because it was you well couple reasons a there's a lot of a lot of push back from the UAW because we had to use 3,000 PSI hydraulics instead of the 300 PSI they were used to. Now, aerospace at that time was already using 10,000 PSI hydraulics, and there's no problem with 3,000 PSI, but that doesn't mean that they're not going to fight it anyway. The UAW was definitely not on board with that.

Um, was it because it would produce more parts per person, so there's like fewer jobs? That's possible, but I I think it was just the bottom. It wasn't just the UAW. It was also So, so this gets into the into the human aspect of how orders are placed and so forth, right? is is that typically what happens is again we the the big three say hey we're going to uh make uh this particular new car here are the parts here's the part print and what happens is um the uh the the guy who is responsible for getting that part a million parts per year to the company he's up and coming he's probably about 40 years old he's a middle manager this is going to help define his career Right.

So, should I go with what's always worked or should I put my neck out [laughter] and and do this crazy thing that this crazy kid who Well, he's not a kid anymore, but he's wildeyed and nutty and talking about and I look at his machine and and you we would bring people by and and they would look at it and say, "Okay, here we're going to make this part for you." And and they would literally jump out of their skin when it first moved because it was it was it was like a hummingbird, you I mean, and it had to be tied not just to the concrete floor, but into the rebar in the in the in the thing for for the for the back reaction, you know, of the inertia, right?

And so, um, it just scared the [ __ ] out of people, okay? Particularly the people who were going to sign the check in order to buy it, right? [laughter] And and so yeah, it and and I I you know, I I'm a good scientist, but man, am I a horrible businessman and an even worse salesman just just it's just not I cannot I cannot make people feel comfortable, right? In terms and that's still a problem today in trying to go to philanthropy and and raise money is because I come across as too [ __ ] crazy, right? and and even philanthropists who claim that they're that they're risk tolerant are fundamentally they don't want to look stupid, right?

And so uh this is a problem. But yeah, and so what what happened next? So did you go back to back to science or Yeah. So that's where we then get into the palm story, right? is is so after six years of that, I apologized to my dad for wasting a couple million dollars of his money and so forth, but the company was still doing fine. And and um you know, of course, he wanted me to stay in in what he always wanted. He wanted his son to take over his business someday, but I did he was just he was so good. He was the inverse of me.

He was he was so people friendly and he could make everybody feel comfortable and like him and um he he just he just was really terrific. There's no way I [laughter] was ever going to fill his shoes, nor did I want to, right? It it was I I wanted to do some I still had in my head I'm a scientist. I'm a technologist. I I want to make the warp drive. Okay. I I this it's not my dream to run a machine tool company. I wanted I want to build a warp drive. Okay. So, what happened to the business? Oh, that's a sad story. So, um uh so it went fine again for several years after I left, but um then uh um uh you know, my dad got old.

Um the other technologies, remember I said eventually um particularly CNC's started to take over more and more of the business. They were still doing fine, but my dad was getting old. Okay. And um and the business was changing. And of course through the '9s, not only were the minivans doing well, but there was encroachment of the Japanese and so forth. It was putting pressure on the big three. And um and so the company the company was, you know, they got up to 70 and then they stayed around 70 for most of the time until my my dad retired. 70 million. And um and then it was time for him to retire.

And um uh so he ended up selling the company and then and then this was 2005. And then 2008, what happened in 2008? GM goes bankrupt. The great the great financial crash. You you got it guys. Okay. It was it was it was a bloodbath in Michigan. A bloodbath. unemployment hit over 30%. Okay. Um my dad's company, the the people who went into receiverhip, 300 guys, 300 guys, many of them who I knew out out of a job. I was by then a Janelian and living high on the hawk. Okay. Um but a lot of those guys and it was a scramble for those guys.

A lot of them, you know, just odd jobs here and there, whatever. Um, we never got the reckoning we deserved for what happened in 2008. There are not enough heads who rolled for that wholeing thing. Basically, you know, moral hazard went out the window and allowing people to actually [ __ ] take take the penalty for what theying did never happened. Um, it's one of the things that I I I kind of feel like the United States lost the threat of what it was starting in 2008 and has not recovered. that there is no there is no reckoning for for um for bad behavior financial or otherwise.

And I think it really started right then. Um and again, you know, eventually those guys found things, but but you know, it was it was it was brutal. Really brutal. Um so anyway, let's let's switch topics from that. But yeah. Yeah. Coming back to the poem. Yeah. I just want to say one more story about that, right? Is is another another thing that In some ways they they I while certainly I I I I believe the financial types have primary responsibility for this, the big three in the unions did not help themselves. And a story for that was on one trip when I when I put in one of my vision systems.

Um uh this is a a a um a transmission plant in Cooko, Indiana. Um, Cooko, Indiana is literally in the middle of nowhere. It's in corn fields. It's right next to Seymour, Indiana. Do you guys know John Melanchamp, the singer, at all? You guys, but I I did my page at Purdue, so we had a lot of Yeah. Right. So, Cooko is, trust me, Urbana Champagne is New York City [laughter] compared to Cooko. Okay. So, so anyway, um, so I made the grievous error, you know, because I went with some of the service guys who were doing other things on that machine. I made the grievous error of taking one of our, we had a fleet of cars that the service guys would take out, right?

Um, when they went on jobs, I took a Ford to a Chrysler plant. Okay, so I'm working all day trying to get my machine up and running in this Chrysler plant. I have never seen a closer approximation to hell in my life. It was August. It's 120 degrees inside of this plant. The plant is probably like 250,000 square feet, chocked full of machinery and people. Probably 120 dB in there. Every frequency from subsonic grinding to supersonic milling. Um stuff for the brain. There there's coolant mist in the air everywhere. Chips. Um there were there were a thousand people working in that plant and I worked there from 6:00 in the morning until 11:00 at night and and um and I go out to my car bone tired ready to go to the hotel and there was it was it was 90° outside that day and so there was somebody had poured chili all over my windshield that had dried because I took afford to a Chrysler plant.

Can you imagine if I took a Toyota [laughter] but but the thing I learned from there and this is something which I'm sorry still stick. I am never going to be an academic. I I am an academic. I have the [laughter] first the first class of the day I have to teach this. There's no way I would ever be at this place if it were not for my wife who wanted to be here. Okay. You're trying so hard not to be an academic. Yes. But it pulls you back. What? [laughter] But but but but the but the but the thing is is um those people work their ass off and they work under just miserable conditions, right?

Eventually that that's gone. That transmission plan is also gone. All of those people were thrown out of work in the end, right? As as the every as everything changes and and more and more stuff gets done overseas, right? I get it. I believe in competition, okay? But um those people worked really really hard and then all of a sudden skipping past the palm I'm at Janelia working for Hughes a1 billion dollar building that's just luxuriously appointed the first thing that happened when I was the first group leader on staff and when the first six of us were together they took us in a room showed us six different executive desk chairs and said pick the one that you like the best when I was in my dad's company.

I worked on a nogahhide stool that had the juke poking out of it into my ass for six years because it was good enough. It was good enough. Money mattered, right? And all of a sudden, I'm in La La Land. And then I start going to conferences again, right? And I go to these conferences. They can't be put the conference in Cooko, Indiana. It has to be in [ __ ] Creed or someplace, right? So, everybody flies to Creed. They're eating their lobster and they and they talk about the dumb rubes and the flyover states who vote for Trump. Well, why the [ __ ] do you think they're voting for Trump?

This is this is Marie Antuanette all over again. They have no most academics have no appreciation, no gratitude for the grant money that they get and not an understanding [clears throat] that that money comes from those people in Cooko, Indiana and a 100 million other people just like them all around the country who scrabble every day and they think that their tax money is being well used to support research. So anyway, sorry there was many people. Is that the reason why you don't apply for apply for the grants? I I have never never wr written a grant in my life. I will go to my grave never never writing a grant.

And it that's only part of it. The other part of it, of course, is is that is that peer review is toxic, right? It it enforces conformity. They never want to take a risk, right? Because you if you stick out, your peers are never going to support that. They they all they all want to just support the stuff that everybody else already does, right? That's how it works. Again, show me the incentives and I show you the outcome. They are not incentivized to support crazy ideas, right? So, you don't get crazy ideas. you get. But the lack of gratitude of academics towards the money they have or or a willingness to realize that every penny they spend is off of off of the labor of people doing all sorts of [ __ ] I I I go I have a a a vacation house near and I cross the the central valley every couple every couple weeks.

the people working so hard in the fields there, particular again in the summer and and the smell from the from the the pesticides and the and the and the fertilizers and so forth. And Jesus, I it it it it just it just drives me crazy that that people don't realize the basis of of of what everything of of of of the comforts that we have, where the hell it comes from and and or or an appreciation of of of all of the technology behind. I I wrote I wrote a tweet just the other day about uh about SiriusXM, right? and how much I love being able to to not have to be just limited to FM and CDs and tapes like when I was younger, but have this infinite variety of musical sources and so forth and the technology in order to do that, right?

in or in order to in order to have 10,000 satellites going on in orbit and the switching from satellite to satellite and a phased array antenna I have in my backyard for just a couple hundred bucks and all of this stuff and people just don't realize that there's magic all around us, right? And and and they're completely oblivious to that magic, completely un ungrateful or un unappreciative of that magic. they expect it and it's so easy to lose it if we don't if we don't have gratitude for it or an understanding of it and so it's just nuts. Anyway, I'm going off topic. We haven't even gotten to Palmer or anything [laughter] and we're way off the thread of microscopy and have been for quite some time.

So, [laughter] this is good. I I actually I I uh I worked in Michigan a bit uh where? So I was I had like built a factory in China and I was then directed by the CEO of this company that had acquired my company and I built the factory for them in China and then he was like a he was from Michigan and he wanted to bring manufacturing back there. Oh, cool. So we tried to move the factory to Detroit and this was like 2015ish 2016. It was probably 2016 and that was like a I when I tried to do it in Detroit I was like yeah I think some of these things had to go out of business.

Yeah. So we ended up we failed to do it in Detroit and we ended up doing it in West Michigan which was much better. And the explanation or uh it was Grand Rapids. The explanation that I got which I'm curious like what you think of this is that the flavor of Christianity in West Michigan this is just what I was told so I never verified this but it doesn't allow you to join a union because you can only be in one organization which is like your I think it's like some type of Baptist church or whatever. I don't know. And so because of that, they never had like the same penetration of unions.

And so people were just much more reasonable. Like when I worked with them, they were flexible, you know, just the typical like like reasonable. Whereas like in Detroit, everybody was kind of there was this like goo you would move through, you know, when you tried to do anything. You would just be in a room with people. I would call it solid cement, something [laughter] like that. And I was like, "Yeah, I kind of get why this didn't work out." kind of like, you know, the whole thing felt very hard to get through. So, and and I I think the Ohio plants that started and and the ones in Tennessee, it it was a new story then, but there was a lot of legacy stuff obviously in southeastern Michigan, right, that made it extremely difficult.

Yeah. I think it's just hard to keep a culture like obviously Detroit was incredible for whatever 50 years or however long, but it's hard to keep that culture, right? people got. When I was born, Detroit was the fifth largest city in the United States. You know, it had 3 million people. Okay. Now it has 600,000. Yeah. Yeah. Yeah. Wow. It's crazy. Yeah. The 632 Nm podcast doesn't have any sponsorships. It's an art project and we're going to keep it that way. But we wanted to do an unpaid advertisement for Tesla Autopilot. Both of us use it. We've both done probably over 1,000 miles on autopilot now.

And it's just an incredible technology. It's gotten amazing over the last two years and I'm personally very passionate about it because I got into, you know, was hit by somebody who ran a red light and my pregnant wife was in the car and it's just something that really shouldn't happen anymore. Like we have the technology to stop this. You know, if that person was driving a Tesla, it wouldn't happen. So, luckily it all, you know, it all turned out fine. Driving in Boston is a hell and I think that definitely helps with finding the right way. It's mindbending and life-saving technology. I highly recommend. You have your hands free, your mind free, you can think.

Yes, you have to like pay attention to the road to an extent, but it really drives itself. But they really improve it all the time. I don't think I've had to take over. Probably in the last 6 months or something like that and does literally all of my driving is on autopilot. Anyway, go out and try this. I I think every car should have it, not just Tesla. Like this software should be in every car on the road. If you are a fan of the 632 nm podcast, you most likely love quantum computers. And now I'm in the lab at Quera Computing where we build the most advanced neutral atom quantum computers.

We have plenty of job openings and I'm hiring for the position of the quantum machine builder. If you have a relevant skill set and want to contribute to the race for building the first fall tolerant quantum computer, join us and enjoy the rest of the episode. How did you end up building a microscope in a living room in a living room? All right. So now we'll pivot. All right. So, so picking up that thread. So, I failed it for my dad, right? And I left and and so I was unemployed, you know, I was unemployed for about a year after I left um after I left Bell and I was unemployed for two years after I left uh my dad's company.

And the first it's like, well, what am I going to do? I blown up my scientific career and I've blown up my backup plan of working for my dad. Is it good to be unemployed to get some fresh ideas? That's a major understatement. By far the best periods of my life were my two periods of unemployment. So for example, getting to the palm, the first key idea that led to Palm and in fact the Nobel committee cited two papers for why I was going to share that prize. Both of them, one each was from each of those two periods of unemployment and they were done by me and me alone.

Well, not know the second one was with Harold, but you know the the the key was that um that I was unemployed in both cases. So, so the first one is um after I left Bell and I was trying to think of well, am I going to work for my dad? What am I going to do? My wife, my first wife was at Bell and still working. So, at least we weren't going to starve. And we had had a baby by that time. And so, the baby's like 6 months old. And um so I'm a house husband. And so, I was pushing the baby in a stroller.

And there were two remember I said I just had this hit of streak of hits with with with uh nearfield. Um, one of the I mentioned the single molecule one already, but another was one I did with my friend Harold where we looked at um, again because Harold was a low temperature physicist, we were looking at semiconductor lasers at cryogenic temperatures to understand exactly where in these quantum well layers the light emission was occurring. And um, people have studied that optically for ages and understood the spectrum of the emission and like that. But when we did it with a near field probe, we found out that that spectrum, which normally looks like a normal spectrum, you know, it's got little humps on them like that.

When you look at it with nearfield, it completely broke up into sharp spectral lines. Boom, boom, boom, boom, boom, boom. And every time you moved the probe by even 50 nmters, the lines would change dramatically. And so what we found was that um that the light wasn't formed anywhere. It was formed in discrete spots which were usually just different points of roughness, single monollayer changes in the roughness like potholes in the quantum wells that would change the quantum confinement slightly and hence change the the spectrum of the emission. And even though even with our nearfield probe that was 50 nanometers, you know, those those potholes were too close together to resolve, but because the emission from them was so narrow, you could still resolve them in a multi-dimensional space based on wavelength is the additional dimension.

So you could individually study them because you had enough spectral resolution in the one dimension coupled with enough spatial resolution thanks to the near field in the other dimensions to resolve all of them. Interesting. So I was pushing and then the second one was the fact that I was the first guy to be able to see single fluorescent molecules and then localize them to subways and dimensions. So while pushing the daughter in the stroller, it just I wasn't really even thinking about it, but it literally just popped from nowhere that you could combine those two ideas. And so if I had some way, say the molecules I know I knew fluorescent molecules at room temperature had broad spectrum, but say they didn't.

Say that I was at a cryogenic temperature and they were equally sharp, right? Well, then I could then at low temperature with a spectrograph see these single molecules and with my nearfield tip I could see just a few of them at a time underneath the thing and they'd be isolated because they're different wavelengths. So I isolate them. But now I can find the center of the emission even though it's 25 nanometers big with the near field. Hell, I could have done it with with regular light, you know, defraction limited light, but I can still find the center of emission to much better precision than the width of the emission.

Okay? And so then I could find the position of every molecule to nanometer precision. So this would be a path to super resolution, okay? Is that you could do. So I published that paper in optics letters and I described how you might do this at cryogenic temperatures and so forth. But [clears throat] it would have been a hero experiment. I could have gone back to Harold who was still at Bell. I was still in the neighborhood. I because my wife was working there. I could have tried to do that experiment but it would have a been a hero experiment to do it. And the and the second thing is you're [ __ ] at four degrees Kelvin.

there's not a lot of biology, live cell biology to study at Fort Kelvin, right? And so and so uh um and so I just published the paper and left it at that. And then that's when then I went to work for my dad. Okay. So fast forward six years, we talked my dad's story, I'm back unemployed. Okay. And um and so it's like no one really take it like seriously. Actually, there were there were a couple papers. So there was um this guy Brackenhof um in the Netherlands used a con focal microscope around 2000 or so to cryogenically look at um like six florores inside a volume and and and see them there.

Right? So that was really like the first and there was a couple other things where people used like blinking of molecules to see two molecules in one spot but not at the you know if in order to have super high resolution you need to have lots and lots of molecules to decorate your sample or else it's just a bunch of dots right so this is something called the Nyquist limit right is that you have to sample at at least half of the finest resolution you want to have a complete picture what's going on so that means you need a mole if I want to have 50 nm ter resolution.

I need a molecule every 25 nanometers localized to a few nanometers to have resolution at that level. So you need to have lots and there was no good way to get lots in the same defraction limited region at the time. How would you like sample them to like uniformly cover the space? Again, you would you decorate them at high density. But at at the original concept, it's I I generalized the concept even that paper to have any sort of discriminating third dimension. If there's sufficient resolution in that discriminating third dimension, whatever that means is it could have been the lifetime of the molecules. It could have been the polarization of the molecules.

It could be then you can discriminate in that space but you but you have to have the more the higher the density of molecules the more resolution you need in that third dimension to discriminate them. Okay. Was there like a possibility to make a single layer or they were just like stack on top of each other? No, no, even in a single layer there's, you know, the defraction limit is big, uh, you know, 100 times bigger than the molecule, right? And so, and so you need to be able to discriminate, have resolution of a 100 times better than whatever you had in the third dimension in order to do that discrimination to say that this molecule is different from this one within that same spot.

Right? So, um, so there wasn't a great way to do it. maybe cryogenically could have done it. But then fast forward and during my second round of unemployment. Um well, first it's why go back to science? I hated science. I hated everything about academia, right? Is I think I've made that clear by this point. Um and um but the the the key for me was um I realized that I really miss science. I [laughter] It's like love and hate. It definitely It's love hate. It's definitely I really missed doing science. Okay. Um and I wanted to see if there was some way somehow I could get back to doing science and so so I reconnected with Harold and dur after I left in in ' 95.

Um this was this was the time period in which um uh you know in '84 is when they broke up the monopoly that that provided the financial underpinning for Bell Labs and and so they they continued to support it through the mid90s but um by then they were starting to think they need to be uh financially relevant and contributive to AT&T's bottom line. And so um then that so they among other things they purchased national cash register right and then so so the the the the environment changed so quickly at Bell that when I started in 88 in in in 90 I had another scientist another friend come by and said you know what you are doing is technology and that doesn't really matter here at Bell the only thing that matters is how many fsrev letter papers you publish Okay.

So, fast forward two years after that after the NCR purchase and the head of of Bellab's research, Arnold Pensas, who did the the background theory for the Big Bang and won the Nobel, every year gives a gives a um a State of the Union address or something. And he used my work as the sort of applied work that we should all aspire to because I had had my first few hits then, right? A couple years after that with the National Cash Register purchase a right after I left, Harold was asked to spend some fraction of his time not doing low temperature STM, but instead see if he could figure out how to use spectroscopy to figure out which fruit was in the shopping cart so he wouldn't have to put those little fourdigit stickers on it and would automatically know.

That's how quickly the culture changed. Okay. [laughter] So, so anyway, so there was this diaspora out of Bell and by 2000 The Henrik Shonne scandal was the final nail in the coffin. And so everybody was gone after that, right? And so Harold had gone into working for a company that makes test equipment for the disc drive industry in San Diego. But so I reconnected with Harold cuz he's been my best friend forever. Um and uh and you know, he was feeling dissatisfied to a degree, right, with that. and we was thinking, well, he'd like to get back into science, too. So, we started going to different national parks where we would meet up and, you know, hike around and think about ideas and and [clears throat] I developed I decided, of course, I couldn't get away from microscopy.

I I knew I didn't want to do microscopy, but then I started to think about it and and uh the thing that changed my mind was starting to read the scientific literature for the first time in a decade and I ran across the paper on green fluorescent protein. Green fluorescent protein came out the exact same time I quit Bell Labs and I did not look at the scientific literature at all. The idea that you could snip a piece of DNA from a glowing jellyfish and have that attached to any protein of interest in a live cell. My jaw was down on the ground for a week after learning that in 2003.

Okay. I was like, "Oh my god." Because one of the hardest I tried so hard to do near field biology, but in addition to all the problems with the microscope, you just couldn't decorate the fluorescent molecules onto the proteins at high enough density to to do super resolution. Nor because you were bringing them in exogenously. A lot of times they'd stick to the things that aren't the protein you want. The idea that finally you have 100% certainty that that glowing spot is the protein. It was like, "Oh my [ __ ] god, this is going to revolutionize imaging." Okay. I said, "Shit, I got to do imaging."

So I tried to come up with an idea and I came up with an idea about how to interfere multiple light beams from different directions to create different types of optical lises. This is used now a lot in in the AMO field, right, to do stuff. But at that time it wasn't. And so I came up with these theories of optical latises to make a massively multif focal exitation field that I could try to do s you know parallelized three-dimensional imaging of living cells. And so I tried to get Harold to come in with me on that idea. Um, and and he said, "It's it's a nice idea and I'd like to help you, but I'd be chewing your cut cuz it's your idea."

Um, but he tried to help me. And so, um, he there was so part of that diaspora out of Bell Labs was a lot of good people were everywhere, people I knew. I had contacts everywhere because I had made a name for myself at Bell and these people had all gone to academia or or government labs or whatever. And so, man, did I use that contact network. [laughter] So, so um one of them was was Harold was friends with this guy Greg Boinger who had been at Bell and had since become head of the National High Field Lab in Tallahassee. And so, Greg had been trying to recruit Harold to become a scientist at the Magnet Lab.

And um in an earlier visit, he met this weird dude, Mike Davidson. Um, and Mike's job was to use microscopes to look at the grain boundaries in the wires that would go into the magnets because if they weren't the right type of structure, the magnets would just blow apart when you're running at 30 Tesla, right? Um, and so, but Mike's real love was live cell imaging. And so Mike had made himself independently wealthy by using his microscopes to look at cocktail mixes under polarized light microscope and print those on neck ties and then sell them online. And so he made millions off of this. Okay.

And so then Mike um was uh um uh used that money to follow his dream of doing live cell imaging. And live cell imaging meant GFP in that era, right? Green fluorescent protein. And so Mike became a cloner. And in fact, he because Mike is was kind of self-taught, barely made it through college, self-supported all the time. He took all these kids who were flunking out of Florida State and hired them as techs to be cloners to start knocking in fluorescent proteins on everying protein under the sun. So he had the world's biggest library of fluorescent protein fusions, about 3,000 different knock-ins by that time.

And so we went to visit and so Harold thought, well, you know, Mike's into live imaging. Maybe he'll give you some space in the lab to do this. So we went there, we we hit it off. Great. And um and he then told us, you know, I I said how much I love fluorescent proteins. And and he said, "Yeah, well, there's this kind of weird one that just came on the scene. It's what happens is when you when you shine the normal 488 laser on it, it doesn't glow green. Nothing happens. But if you first shine 405 light on it, it activates it. And then it glows green and so okay well that's interesting.

So we finish the trip and Harold and I are in the airport in Tallahassee and we and it hits us both at the same instant. Oh my [ __ ] god. If you turn down that violet light really low only a few photons at a time are going to hit the sample. You're going to only photoconvert a few of the molecules. Statistically they'll be separated. Their fuzzy balls will be separated by more than the fraction limit even in a regular optical microscope. And then you can find the center of those fuzzy balls. Then you turn those molecules off, turn on another subset, do that again and again and again, and you get a super resolution image with a standard microscope.

No funny fancy tricks at all other than having this photosw switchable fluorescent protein. And so we said, well, [ __ ] that optical lattice [ __ ] Um, let's do this. Okay. And now now Harold's fully invested because it's our shared idea, right? And so um and so we were terrified. We were like my god that paper is like is like four, five, six months old. Why hasn't anybody done this already? I I I pitched that idea back in '94. How how this would be possible if you had something like nobody had connected the dots. Um it was like we were, you know, we're nothing.

We have no lab, nothing, right? And anybody who had a reasonable lab, this would be trivial to do. And it's like holy, we got to do it now. And so, um, and so, uh, you know, we figured we'll do it at Harold's place because when Harold left Belle, he took all of his equipment with him because Belle didn't have any use for it. So, a lot of that was a lot of the spectroscopy and optic [ __ ] we did when we did our our quantum well experiment back in the day. So, we had some lasers and detectors and other [ __ ] and and then we had to put about 50k each of our own money into it for other [ __ ] that didn't happen and machining the microscope.

And there it's that guy right there. That's Harold's living room right there. That's where we did it. Okay. You said EMCCD was the the EMCCD was the big ouch because that was 30 grand and I I struck a deal with Andor to at least return it to them at half price if it didn't work out. But that was the whole thing. We put that together and in under we went from the idea to shipping it. So that was the other part of it is we didn't know [ __ ] about biology. We didn't know how to clone a cell to get fluorescent proteins in or anything.

We needed a [ __ ] biologist to work with us. Mike was willing to help and that was great. Um, but I had another in which is that. So, who invented this photoactivated fluorescent protein? Two biologists who were at the National Institutes of Health. Well, guess what? I had a friend from Bell Labs who had gone to NIH, [laughter] Bell Labs Network, Bell Labs Network. So, so I I I I I called up I called up uh uh him, Rob Tiko. He was an STM guy um and who had pivoted to to other things and and I said um uh um you know uh uh I'm trying to find a job.

Um can I come give a would you host me to give a talk? And um and in in fact I had set that up before I I I I misspoke because actually I spoke to him before we had the idea for this because it was one of the many ways I was trying to sell my optical lattice idea and get into a lab is I asked him could I give a talk to try to pitch my lattice idea. Okay. And so, um, when the day came to give that talk, um, I I I said, "Rob, would you please ask these two people, George Patterson and Jennifer Lipincot Schwarz, to come to my talk because I'd really really really [laughter] like to meet him."

And so they came to the talk and and I I went to them afterwards and can I take you guys to lunch? [laughter] And I and I said, "Okay." And took him to lunch. I said, "My buddy and I have this crazy idea and we need you guys. we need you guys badly for your for your photoactivated flare surprise. And Jennifer said, "Fantastic. Sure. Bring it by." And so, um, we were already building the scope then. And so, from conception to shipping that thing out to NIH was 3 months within Oh, so you you ship the We ship that whole thing to NIH, right? We built it there.

Why not? Just to get the the Because there's because there's a lot of biology and [ __ ] you got it. It's easier to take the microscope to the biology than take the biology to the microscope. Okay. So, we did that and um and within less than a month, you know, we we had a sample in we uh we Jennifer's people would rather than taking a whole cell, we did cryossectioning to take thin sections, plate them out because you're worried about the third dimension, right? There'd be a lot of out of focus stuff like that. So, we wanted to make sure we were really two-dimensional.

So we had these these slices that were then on on the sample. Could you use conf focal approach? Uh not really. Again, just you want to see like the whole field of view. Yeah. Yeah. Exactly. Exactly. And so um and so uh we did that and um uh so we had some slices through uh through some through some loss. That photo is somewhere in here I think. Yes, right there. There it is. Okay. Um that was one of the first but in the first so we turned on that the for the violet light and then turned on poof these molecules came on we said [ __ ] we got it it works it's going to work and then we just raced like hell we had only a 10 m laser so we spent so we worked round the clock just babying the microscope because the focus would draft [laughter] because it's just this cheap little thing that we put together right and so we would it was like it was like you November and it was cold and it was a concrete it was an old dark room that was converted and we we kept it secret.

Her lab was big but we kind of kept what we were doing secret except for George. Uh and so uh um they would see us going back and forth and they didn't know who these two old dudes were because you know everybody's just a posttock or a grad student, right? We're these old guys talking to George, right? They called us the gruesome tsum. [laughter] And so we were doing this and uh um and uh and once we had that everything went like so so yeah we we went within 3 months we had all the data that was in that science paper. So you realized that before you did the post-processing of the Oh yeah.

Oh yeah. Yeah. The post-processing was easy. Yeah. So um yeah it was it was obvious we had it. Um what was the most difficult challenging part in building the setup? Nothing. It was it was like a gift from God, right? It was just trivial. Um everything was easy. Um it was it was just the ripest plum you could imagine [laughter] that was just waiting to be plucked. Why did it take so long to actually collect the data? What was the Well Well, because because we were unemployed and had to shell out of our own pocket for a 10 m 561 laser to excite the fluorescent protein.

Okay. So we didn't have much. So so we dribbled those molecules out. Nowadays you you know you got even f you know the the SCOS cameras are much faster than the CCDs back then. The the lasers are far more powerful. You can do it just you know Icon takes a [ __ ] pabyte of data today. Okay. So it's a different world than it was back in 2005. How did you control the lasers? Because you need like switch on and off. Yeah. Yeah. There's just TTL pulses that would go to to control the laser from a PC. And so we had, you know, there's a PC somewhere in that, you know, you can at least see the screen of the PC, whatever.

So they had like the kind of triggering. Yeah. Yeah. Yeah. You could you could trigger the lasers, the 405 and the and the 561 and so forth. Yeah. It was really simple. How did you build the the software part to to just just ourselves, you I mean, you know, you just have National Instruments cards and, you know, [ __ ] and you control it through, uh, either Lab View or Lab. Lab View was around. I I I just did it with Mat Lab, but uh, yeah. So, yeah. And also the localization code was done in Mat Lab and Yeah. So, yeah. And for the objective, special objective.

But yeah, you need a good you need a good We used a high NA turf objective and that was another one of the ouches. Okay. In terms of that was probably at least 10 grand, something like that for that objective. Yeah. What was So that those images proved that the method could work. What was kind of the first use of the method that generated like biologically interesting results? That depends on who you talk to, [laughter] right? I I would argue that that most single molecule super resolution hasn't revealed much anything. In fact, I would argue that most super resolution in general has not revealed much of anything.

Okay. Um we can go deep down that rabbit hole if you want, but my feel my feeling is is that um even back in 2014, I felt like it was incredibly premature to give a Nobel Prize for that. I think the only reason they did is because well they broke this fundamental limit but I think anybody who who knew anything um from an optics point of view would know that it's not a fundamental limit in the same way that the uncertainty principle is fundamental right it was a practical limit that could be you you know circumvented by by a trick right and the trick is you can localize to better precision than the wavelength Right.

And that's the trick, right? Um, so anyway, where was where were we going with this? What was the biological first thing? Yeah. So, so again, so overall, I the first in my opinion, the first real hit and the one that led to ICON was when we started not to look at dead and fixed samples. The reason you look at dead and fixed samples is you're only looking at a few molecules at a time. And it takes a [ __ ] long time to bleed out every molecule to get that Nyquis criterion of having every molecule on the thing decorated to put together that whole image and you're throwing a lot of light at the sample and if it were alive it would be cooked with all of that light on it for that time.

So um so yeah I know we're never going to make it through everything [laughter] at the rate I'm talking but um it's a great story you know was like where else can you find it? Exactly. This is like what it's not written in any paper. Yeah. Well, most of this stuff is you can find somewhere if you dig hard enough. But um but uh the uh the first real hit was and this this really kind of set the stage for the whole rest of my career up to the present which is that um at at um when I was at Janelia um the president of Janelia is funded by the Howard Hughes Medical Institute and at that time the president of the Howard Hughes Medical Institute was uh Bob Teen who is famous as um as a biochemist who was one of the key guys to unravel the methods of transcription, how different proteins come together to um recruit the uh polymerase which is necessary to unzip DNA and then produce RNA.

Okay. Um and uh uh Tee had of was a biochemist but he understood the potential of trying to see at a single molecule level how this transcription is actually occurring and so um so we started to use palmlike techniques in order to do that. Um and uh and so the in their models in their biochemical models they had come up with this idea that many different transcription factors their proteins that come together at the start of the gene first before it recruits the polymerase. And so there's a sequence of events that has to occur with different proteins. And they believed it formed this large larger call it micromolecular complex but multiple proteins dissimilar proteins that had to start at the beginning of the gene before the polymerase would come in.

And it was believed that this would take minutes to hours to happen. when we started to look at the indiv by by palm like stuff in in live cells um at these transcription factor molecules none of them were binding to the DNA for more than a second or two and so it was like holy [ __ ] our whole model of how this how transcription works is completely wrong and in fact this is the take-home story of this whole talk okay of this whole morning all right is that almost everything you learn in biology textbooks is a hallucination because they it is it is it's because or at least cell biology because what they're doing is they're taking three reductionist tools biochemistry, molecular biology and structural biology and then hypothesizing what how those little pieces tiny tiny little bits come together both structurally, stoometrically and dynamically to create the cell.

They have no direct knowledge of the stoeometry or the arrangements, spatial arrangements or the dynamics. All of that is hallucination. Okay, they little bits. I'm I'm exaggerating, but largely speaking, you you guys have probably seen on the web, you know, there's those beautiful things of of you know, oh, all these molecules coming together. Here's a here's a cargo on on uh a kines walking along a microtubule like this. You know, if you've seen these things and it and it's all like in this vast empty space. I don't know any cell that's a bunch of vast [laughter] empty space. I'm sorry. It's crowded as [ __ ] There's there's there's there's there's 100 trillion water molecules in every cell.

There's there's 10 billion protein molecules. There's 10 billion carbohydrates. There's 10 billion uh uh um lipids. There's metabolites. There's it's by far the most complex matter in the known universe. We understand the interiors of neutron stars far better than we understand the interior of cells. It's it's crazy complex. Um and yet we've got a whole industry of farm. That's back to Roger's point. There's a reason why only 9% of the drugs that enter phase one come out phase three because we don't know what the [ __ ] we're doing. We don't know the real mechanisms that are going on. And and and this is the real hit from single molecule stuff was the realization that that's the case.

That when you start to [ __ ] look at the dynamics, not just the structure, you realize that you had it all wrong. And you realize that so many of the things that they thought they knew, they you can't be sure that they know. we have to reinvestigate all of it, right? And so that's where I pivoted to live imaging, right? How did you what was the like improvement in the method that actually allowed you to look at transcription live? Like well, you were saying you had to have there was no improvement. There was it was basically applying palm type techniques to photoactivate a subset of these transcription factors and look at them in a live cell instead of trying instead of having to get to that Nyquis criterion of trying to get every molecule right in order to look at a structure.

I didn't care about that. I just want to understand their kinetics. Okay? And so if I want to understand their kinetics, I can do a subsample, right? And if if that if that principle was right that they would form these these stable complexes, I should be able to see that. But they weren't they weren't they weren't stabilized anywhere. They were they were staying for a second or two. And so the model they've built up is that basically you it's the mitochondria is not the powerhouse of the cell. Brownian motion is the powerhouse of the cell. That's how everything happens is statistically ever, you know, molecules diffuse at, you know, on the order of 20 micron squares a second.

That means you'll go across a whole cell in in like 3 seconds. Okay? But in that time, you're not going in a straight line. You're bouncing off of literally one trillion other molecules in those two seconds to get to that other side. Your instantaneous velocity based on Maxwell Boltzman is like a 100 meters a second. Okay? But you're going That's what makes it all work, okay? Is that it's it's all stochastic, but you're throwing the dice so many times that eventually you collide with something where it's energetically good for you to stick to that other guy. That's how order comes out of disorder. Okay? And that's how you build up successively larger structures.

And so it's that bouncing around getting to that start code on on the gene for that first transcription factor. Then the second one comes along and he sticks to him. and then the third one comes along and and the first one's gone by then but the second and and so you get a cascade and then eventually polymerase comes in and then it scoots along in the DNA right so that's the picture that emerges so then how do the pharma companies like you said they succeed in like 10% one of those cases yeah it's when it works it's like blindfold [laughter] now there's more to it than that there's all sorts of there's there I'm not saying that there's no utility to the methods they use but it's incredibly inefficient.

It is and and um and all they really think about is is the mechanism of action at the molecular level and thinking of it in terms of the the binding, right? Because that's really what's key. Well, again, single molecule tracking is great for studying binding because you can get on times, off times, diffusion rates, all of that stuff. That's what Icon is all about, right? Is is having an assay to really look at that, right? Um but um uh it's it's it's still a crapshoot because among other things there's multiple the living matter involves emergence from many different levels starting from the stochcastic motion of single molecules to macular molecular assemblies to membrane bound organels to cells to tissues to populations to the wholeing biosphere.

Everything about life is emergence from single molecules to that level. Okay? And and if you just focus on that molecular level alone, you're going to be really limited in your ability to discover drugs, right? Is that you have to be thinking about multiscale mechanisms of action. Okay, this protein has to be here in order for me to conquer this disease. What if the protein can't get there, right? I mean, what good is it that you have, you know, that that you have a drug that will interact with this protein when it's there if the protein never gets there, right? What happens in terms of if that drug affects some other of the 20,000 other types of proteins that creates an offtarget effect over here, right?

So maybe it's doing what you want, but it's doing a thousand other things you don't want, right? They don't look for that directly. That doesn't happen till you get to clinical trials and then either you have ineffective stuff or you have dead people on your hands, right? So um so gee, wouldn't it be nice if we actually looked at all length scales if possible while we're doing initial drug discovery in order to find these offtarget effects? How do you do that? by using all the other microscopes that we've developed since. Right? So, we have microscopes that look at all length scales from the molecular up to the whole organism.

Right? And they all have a different niche that they serve and they all have a purpose. Um and but the trouble is is now that now we can look anywhere from milliseconds to days. Anywhere from nanometers to millime to centime. What does that mean? You do that in three spatial dimensions. One dimension of time. You're covering seven orders of magnitude of time and about 14 orders of magnitude of volume. And and we have the ability to do all that with our microscopes. What does that mean? voxels. Five-dimensional voxels. A [ __ ] lot of five-dimensional voxels. Pabytes and pabytes of five-dimensional voxels. Now, you're making the full cycle to now we're coming now.

Well, but but actually what we're coming to is I know we're running out of time. I'm trying to get to the present day. Okay. So, the point is is that we have enormously powerful tools now. Um, but you can see all of these pictures on the wall are from are from many of these tools. Right behind your head right there, that's what a neutrfil actually looks like when it's moving inside an organism. This is a zebra fish. What you're seeing at the top is the skin cells. What you're seeing in that cavity is a mezenymal space. What you're seeing at the bottom that that funky thing is the neutrfll.

They're in crazy dynamic. That's that's what fights that's part of the uh uh part of the uh immune system. immune system. Yes. Right. So So uh um if you watch the movie of that thing, it'll blow your mind. Okay. In terms of how it works and how it and how it moves. Do you have one to show? I have a load of movies I can send you. Right. I mean more movies than not space in the disc. More movies than you could ever could ever want. Okay. So, so um but but the but the thing is is that is is that we evolved to see in 2D plus time.

Okay. Um when you're looking at that, you're looking at 2D. It it looks 3D, but you're again you're not seeing the interior that of that thing. We the data is there. It's it's blocked by all the other cells in front of it in that particular view. Right? Um life happens in five dimensions. XYZT and molecular species, the 20,000 proteins, all the lipids, the carbohydrates, all the rest, right? Um, we can't even with the microscopes that take our pabytes of data at all of those link scales. It's [ __ ] bits on a drive and it does nothing. It's so [ __ ] frustrating to have pabytes of data on drives that are completely worthless because there is no scalable way to look and understand that data.

What we need is to build a fivedimensional mind, right? That can look and see in five dimensions. A fivedimensional vision transformer. That's what we need to be able to crack this nut. I was the last human on earth who ever wanted to have anything to do with AI because I hate doing what everybody else is doing. But I am forced in this direction because I think it is the only possible scalable way to really extract meaning at scale from the data that we can take in the right location close to all these companies. God, you don't know. You don't know how over the last 18 months of pitching this, you don't know how many times I've pitched.

How many close calls I think you know how the it it is the right place and it's the wrong place. The reason it's the wrong place is at [ __ ] Kale, how much do you think I can hire an AI engineer for compared to what he can get 5 miles away from here? Right? That's problem number one. You have to find the crazies. The crazies like me and Harold who don't give a [ __ ] about the money. they give a [ __ ] about the problem, right? They're hard to find. Okay. Um, have you when you talk to AI people about this, do they think it's like a tractable problem in terms of, you know, the type of data it is, the size?

I wish we It's a great question. Even I and I'm not an AI guy feel like this is at the very bleeding edge of what's tractable. It is a big scale. It would make Alpha Fold look like a picnic. Okay. So it is you know building a vision language model on this level is really bleeding edge. Okay. So we would need you know one of the big hyperscalers to bite in the end. But we but we could but to triage that risk there's a lot of initial ablation studies and so forth we could do to try to better answer that question but we can't get our paws even on enough GPUs to do that.

Right. What's the maybe just to like go into the detail a little bit. So what is the data set versus the like what are you training towards? So are you trying to predict the next change? No prediction. So well no I I I mean you'll do things like like you know next token well not token but prediction which pixel or like not not that but the first task that you have to do is robust 40 segmentation. All right. The fundamental unit of life is the cell, right? You would like to be able to see the cells individually. Beyond that, you would like to see the organels inside of the cell.

So, we need robust 4D segmentation. In this modern age with everything that we have, even 2D segmentation is imperfect. Okay? Biological 2D segment you know you you know uh meta had had SAM, you know, the segment anything, right? And they have SAM 2 and other things. by segment it just very specific find the boundaries of objects okay tell me where this is this organal this is that or and where does this cell begin and the next cell end right this kind of thing right and and the machine have some understanding of that because if it doesn't have that understanding of where cells begin and end it's not going to get very far in being this sherpa that I want to lead us I want to be able to ask through an LLM interface what happens when um when when uh uh a a um TE-C cell is through immuninocology engaging with the tumor.

What particular proteins are expressed at the surface? Um what is the course of its its uh motility in order to get to the tumor? And if I I have all of that data on [ __ ] drives, okay, but I can't access it because I can't find out exactly where it is. I need something that can recognize that, right? So, and we have labels to label the T- cell. So, we can train the model to understand what a T- cell looks like and all of that. But we need to build the model to be able to be able to do that. And and segmentation is that first step is to know what is a cell to understand what one a different cell type from that cell type from a mitochondrian from a from a a uh endopplasmic carticulum or whatever, right?

I mean all of that is doable okay but again it requires it requires compute on a vast scale to get to that point but the first step is segmentation and I believe if we had enough GPUs and enough people working on the problem we could get robust segmentation because nobody's really tried you know so much of the imaging that that has been used has been two-dimensional imaging right so so that's why meta has like segment anything it's a two-dimensional tool tool and people try to apply that by doing plane by plane the 2D tool. That's not the way to do it because you're missing a prior.

The prior is there's reasonable continuity between successive planes. And then likewise, there's reasonable continuity in time. The cell doesn't go like to this right away. It moves continuously, right? So you need to build an inherently native 4D model that takes use of those products. When you collect the images, you are are you collecting them in slices? Yeah. Yeah. But we do it so fast that Oh, I see. So it's like yeah it's it's like a snapshot right. Do you think that the autopilot uh like let's say the the self-driving cars because they have like 3D plus time plus you have hit on on the closest analogy to what we need exactly is self-driving.

But even like Tesla right I mean they've got what dozen cameras something it's not true 3D right in the sense that they're they're sort of interpolating the third dimension but it is by far the closest analogy. And trust me, I tried to get XAI interested in this among many many others and it's just everybody's got their own thing. What do you think is the first if you if you grew this tree, what is the first fruit it bears? Like what's the first interesting thing that comes out of this? Let's say if you put I don't know billion dollars of computer into it and and you know labeling and people and so we we estimate the whole we we call this thing cell observatory, right?

And we're we're doing all we can to develop the the biological reagents, knocking in all these fluorescent proteins in dozens, hundreds of different tags, cell types, and like that. Um, we've got the microscopes. I'll show you guys later. And doing uh to take the data and all of that. Um, but but uh um where was I going with this one? Well, my question is, let's say if you took a billion dollars or something, put not a billion. We estimate it would take us 50 $50 million. But what what's like the first kind of the first thing we're I mean it's always hard to know but like what would be like the first kind of result that you might imagine could come out of it.

So the first thing would be just robust segmentation right which would be valuable in many contexts. But the other is again if you had the ability to identify cell types right if the model could do that and that would be trivial once you have a robust segmentation. Now you can ask all sorts of [ __ ] interesting biological questions, right? That that gets back us into the pharma and the offtarget effects, right? Like I mean I can look through, you know, we use zebra fish as a model organism because it's transparent and it's 70% genetically homologous with humans and it's a vertebrae. But to the extent that those things are recapitulated, if we use that as an organism, we could create an entire company that's nothing but a contract research organization for pharma everywhere where if they got something they're about to put in phase one, they bring it to us, we put it into the fish, and we see if bad [ __ ] happens.

Okay? Because we can holistically look practically down to the molecular level. We have a baseline of what normal activity is like across all organs, all cell types, and whatever. and we see what the [ __ ] this drug does, right? How many if you have a zebra fish, what how many how much of it can you look at at a given moment? Like what's like the Yeah. Again, obviously the bigger the field you could do, the the the the less of the less speed you have, right? Because you have to cover it. But um you know, again, you you would look over small fields of view of about 50 microns at maybe 100 millisecond intervals, which is pretty fast, right?

um you can look over larger organs or half the organism maybe every 10 minutes if you cover you know a young adult right so um so there's there's obviously a huge dynamic range of application depending but a lot of this stuff yeah I mean I I not not only the basic biology but the but the farm implications I think are immense And it it it just it drives me crazy that I haven't as tried as hard as I've tried, I have not been able to to interest any philanthropist. Not not even H I'm still HHMI. I still have a lab at Genealia. I haven't been able to interest them.

They they have their own things they're doing in AI, which I think are stupid. Um but but uh but [laughter] but apparently they think what but they apparently apparently they think what I want to do is stupid, too. So lately this can happen sometimes. [laughter] Oh man. I'm too old to have any sensor circuit left at all. Okay guys, but yeah that's that's interesting. It's uh but I wonder so I mean for for the these problems where where you have like um that people have like cracked with AI a lot of times there's a lot of like good label data. So for example in the car they have the person intervening and driving the car and so Tesla has collected a ton of that data of course.

So I I'm just thinking like what's the labeled what what what is the labeled data for in this case the labels from us comes out of the fluoresence right no but what I mean by labeled is like what is let's say you want to predict like it has to be annotated like this cell is attacking that cell right so that has to be human annotated initially or like how would you see that not necessarily so so again once for example if I had a different fluorescent label for my target cell and my T- cell right the model itself just needs to know that that merged or something like that.

Exactly. So that's interesting. So and this is why you have to do something through a transformer because it has to be self-supervised with minimal annotation thereafter. Right. That's the only way it's going to work because you just can't use human annotation at scale. Are there any existing efforts? I feel like you hear sometimes about efforts to like model the entire cell. Are there [laughter] that's another pet peeve of mine. The virtual cell. We're back to these reductionists, right? So they do spatial transcrytoics or or alpha fold or things like that and think that they're going to predict response to perturbation on the basis of just that.

That's crazy talk. It's it's it's it's one part in 10 to the what? One part in 10 to the tenth of what's going on in the cell and they're going to recapitulate all of cellular behavior on the basis of that. It's it's naive to the point of craziness in my opinion. What is the advantage of transcrytoics over microscopy? Is it just transcrytoics easier to spatial transcripttoics is a form of microsh because it's spatial also. You're you're doing the sequencing. So what what do they get out of it? They get oh well okay well these transcripts are here in this cell these so they they determine where cells of different cell types are.

Mhm. It's valuable to an extent whether it actually it certainly won't tell because they're basically looking at where what cell types are where they're not telling you fixed cells. This is all totally fixed tissue. Another huge pet peeve I have about modern biology is there's so muching stamp collecting going on and spatial transcripttoics is part of it, connetoics is part of it. Everybody's creating huge atlases and so we're getting all sorts of data and no understanding no fundamental understanding just collect collect collect collect collect you know that's where we are right now but at least I understand the limitations of it and I want to get understanding out of it so what do you think needs to be happen so that um those [snorts] uh AI people would be would would feel former jump I I I ask you that question I've tried everything I can for I you don't believe how many doors I've knock and I've never had problems in the past getting getting uh money and philanthropy to to support me, but on this one it's been a bridge too far.

Um I'm still trying. I'm you know, as long as I'm alive and kicking, I'll I'll still swing it swing at the pitch. But um is the data publicly available like in pieces? Right. But again, all you're again all all they can make sense of is the movies. I can show you a crapload of movies, right? But those movies again are two-dimensional movies, right? Cuz they're projections of No, but let's say the thing you were saying that there are these pabytes of data that are generated by the microscopes. Is there a single place like let's say I'm a I'm a researcher in France. Okay. And I happen to have access to $50 million.

All of our data is a data warehouse somewhere. Oh yeah, absolutely. How would I access it? Like is there a practical way to access? Yeah, you could you could you can download the data and we have metadata that goes with it that says this this particular file has got this cell taken under these conditions and da da da. You have to have all of that if you're going to actually build any understanding out of it. All that's available but again most people don't even have the tool. First they don't have the tools to download the data. Then they don't have the tool Yeah. Exactly. Then they don't have the tools.

You know we we have two 100 gig pipes going out of this building right now. Right. But it took us forever to get to you to give us those 200 gig pipes going out of this place. So there's a real limit. There's like a physical limitation even to handling the quantities. Oh yeah. Most most people are not are not are nowhere near capable. How many places are generating data in this amount like are there other microscopies? Maybe astrophysicists or Well, yeah. I mean Yeah. Exactly. I mean in the cell world nobody really nobody like us. We we collect I I wouldn't be Well, no, that's not true.

the people doing the connetoics now are taking data at at big scale. Okay, that would be the only other application I can think of where they're really going balls to walls with the Do you think that transformers even has the teeth to grind through this data? It's it's our best option now until something better comes along. Okay, if there's you this is maybe offtarget, but since you know I love space, right? And I I think maybe I might get there. And if I get there, well, because of data centers in space, which I'm still not sold about, is is making much sense. But but um but uh uh if if that happens, one one of the things I'm I'm hoping for, remember my munger, show me the incentives and I'll show you the outcome.

Um we're on this crazy buildout of AI right now and data centers, right? crazy. Um, but I can think of two things that would change that. Um, one is people come up with new model architectures that are orders of magnitude more efficient than the model architectures. Obviously, there's a huge incentive to do that. Some nuts somewhere will be thinking about do I need this many nodes and this many layers in order to get the same result. Right? If they could if they could reduce that by an order of magnitude, what would that mean in terms of all of this capex that's there might be some spare compute like there might be more than enough spare compute.

There might be there might be a blood letting right and and and and then the other one is is is what if there's a new architecture? What if there's a hardware architecture that would be much more efficient? What if somebody could, you know, that we don't need to wait 10 years from an A100 to a B300, but somebody comes up with something that's poof, you know, a different way of doing things that's that's an order of magnitude faster all. Do do we have in the world somewhere like resources for this kind of scientific compute. So let's say like have a you know a data center which would be dedicated for like I don't know astrophysics process or Yeah.

Right. Right. Right. Now not really right. And I mean there's there there's big one up on the hill, you know, half a mile from here in LBL. You know, there there's the prom motor supercomput has like 30,000 A100s in it, right? And now they're building the the the what what is the next one? The DNA one that's going to have like 30,000 B200s in it, right? But do you know how many people want a piece of that? Yeah. You never get 30,000. You don't get a hundred. You get tiny tiny little bits for tiny bits of time because it's all diffused to a billion different projects, right?

So, I would kill to have 128 B200s. Okay, we just, you know, we have limited money, you know, haven't been good with philanthropy recently, but we just shelled out for 32 B200s. A, it took us nine months to get them. They just came in now. B, we had to fight like hell to get the power and the cooling to put them in 32 [ __ ] B200s, right? And but it it was a huge hit for us, but we need something we can use to do more ablation studies to get back to your question of what is actually physically possible at the state-of-the-art, right? And what's an ablation study?

Oh, just try different different tests about you know how again how many nodes you need how much of this you need whatever what is the size in order to and again you know be able to do prediction right and make sure that you're making accurate predictions as you take away stuff or you add stuff or you do different types of tools right so um but uh uh it's you know it the just the demand for the GPUs now is such that it's you know it's very interesting just as The technology is becoming broadly applicable. The it's hard to get to get your hands on it.

Yeah. Yeah. [laughter] It's very It makes sense for the same reason, right? It's supply and demand, right? Yeah. Are the opportunities [clears throat] in people countries or people need cat videos. It's sad, but it's true. Well, it's fine. It's fine. It's I I mean, come on. I mean, it's already transformed everything. I you know, all the all the optics stuff I do in microscopes now, I mean, I used to do in Zmax, now I you know, now I just gro it, you know? I mean, and I I ask I ask, you know, yeah, I I need I need to have a, you know, a telescentric lens that takes me from here to here with this focal length and please build it and tell me what what the spacing of lenses are.

Tell me the sources of components and out it comes in five minutes, right? So yeah, how do you now just the data to build the uh videos? What's that? How do you how do you now just poorly? So there's various software packages that will do renderings right of the data of fourdimensional data is three-dimensional images glacially slow just just loading it up loading the data into a even a good quality workstation and then rendering those images in the view it's just it can you know a a a 10-second movie can take days I there's at least a 100 to one difference between the time it takes to take the data and the and the time it would take to visualize the data maybe sometimes 10,000 to one difference.

So to start with you like at least like accelerating the the rendering would already scientist look into the movie. So this is another area that of course we investigate is can you do some kind of dimensional reduction to to save right so basically you know so we're working with Gaussian splatting for example is as a way of trying to have an alternative reduce space or reduced data representation that still recapitulates most of the structure and the dynamics that's in the data right if that's a heavy area of interest right now because of our poverty right in terms of compute in order to try to make something that not not just for the AI but also for the visualization is we may make a different type of renderer based on a Gaussian splatting model that would be much more efficient than than what exists today but I would love it if somebody out in the world did it instead last thing I want to do is reinvent the wheel if there's people better at this type of [ __ ] than than us neophyites right but super interesting um yeah it's uh I the The idea of modeling the entire cell is definitely very it's just it's very uh I don't even want I don't even

want to talk about and go there. I I I haven't gotten through to you guys howing complex it is. Okay. We're not ready to model. We have to observe. We have to be tao brahe and guys like that, right? We have to be looking at the thing and seeing these epicycles. We don'ting know the inverse square law yet, but we have to see the epicycles first before we ever get to the inverse square law. Right? So it's science starts with observation. We haven't done enough observation of these types of systems to sensibly even think about virtual cells or prediction or [ __ ] like that.

What I'm asking for is to help us do the observation. Putting it as bits on drive is not observation. having having understanding in quotes either through a machine or through humans or best humans and machines working together is the only path forward. What do you think besides So obviously you have a strong opinion about applying AI to this. Is there something? What's the future of the actual microscopes, the hardware? Uh well, they get better all the time. Like I say, I have my my ears to the ground on anything new that comes on. So, I'll show you some microscopes. We have one that's coming online now.

They'll be more performative than anything we've had before. Um because we need to we think our best guesstimates is we'll need on the order of 20 to 50 pabytes by the time we're done. And we'd like that to happen in two years instead of 15 years. Okay. So, that so we want to get an order of magnitude more out of our scopes, right? And so, we're building scopes, take multi-well plates and we'll be automated in order to do this. do perturbation experiments and all the rest. So, so we're developing that. Is it mainly automation or is there is there like fundamental biology or optics that that's still needed?

I I mean the optics you you have to be clever. Um but most most of what we most of what I've been doing for I'd say the last eight years is is more about just mixing together. You know, I take this off the shelf of what I know in optics. I take this off the shelf and I mix them together in different ways and and put it together. So it's more about how you put it together and get the pieces to work. Um you know is is uh if I if if someone is like a let's say synthetic biologist or cell biologist is there something that uh like something missing that if somebody built in biology would really help with these experiments many many things.

What are like some some things people could well you know we talked about the bowling ball before right? um if there was some smaller type of the other the other big thing that helped particularly single molecule is colleague of mine at Genealia Luke Levis made a brighter uh floraphor called generes right they allow us to track much longer they allow us to uh look at any organle small molecules so they need to be then attached to the thing and that can be done with nowadays reasonable specificity by using bowling balls called like halot tag that are attached to that and then there's a lian on the dye that then attaches to to get the dion on.

So and then so it's fluorescent proteins are more flexible but in terms of in terms of the photoics the the the JF dyes are are much better. Now if again somebody can make a better fluorescent protein or smaller bowling balls or um or again if somebody could particularly make this would be a dream is if you could make floorors you know ions have very very narrow emission widths right if you could somehow have a caged ion that maintained its narrow emission width in a you know a bucky ball or I don't know right and get that to to attach and you can get that to attach with a thousand different ways of attaching.

Then we could look at a thousand proteins at once because all of their spectra would be narrow. So someday that you know after I'm dead and buried maybe that kind of [ __ ] will happen. But that would be another frontier, right? Narrow line with floor narrow line with I I always have my my my ear to the ground trying to look at has anybody come up with anything clever in that regard. And how is it now? Like what's the density? It's it basically you have about 50 nanometers of you know of emission spectra you can do you can let them overlap and unmix right if you you know again you turn your lasers on and off for each one individually you you put them on multiple cameras that have look at each spectral band and although they overlap yeah it's it's doable but you excite them with just like white light or No no no no lasers all lasers right um so you're we're narrow in that regard right at least we can say uh say that but uh but yeah the the exitation spectra are just as broad by vibrronic bands as as the emission spectra and you can temporarily also separate the signals maybe.

Yeah. Yeah. To a degree. Yes. Yeah. Yeah. That's that's what we do. Anyway, has there been any like anything useful out of like plasmonics or resonators or anything like that? Has anything come? That was another area I I was looking at early on, right? little silver resonators and so forth as to whether I could do something like that. But so far, no. Uh again, you and delivery is a pain, right? Um you know, you're now in an environment which isn't a vacuum anymore. And so the electric fields around where you are can also perturb your lines, right? And so forth. So uh you know, just stark shifts or whatever else from what else is around there.

All sorts of [ __ ] can happen, right? It's messy. Um but uh uh yeah so you know incredible to see all those microscopes and and kind of the full stack of everybody working on even just trying to process the data coming out. If you weren't doing this what would you be like something what would you do like something completely different like what is the thing that you wish you know if you had two lives like what would you be working on right now? Yeah. Um I actually did during the pandemic when it was difficult to do much. Um I went down two rabbit holes.

Um, one was um one was uh uh nuclear uh space propulsion um and tried to see if that was feasible and worthwhile as you know since Musk was already talked about Mars and so forth is maybe use starships for people but maybe there's cargo or something that might be better transported by other means. um uh went down that rabbit hole and I kind of decided that based on orbital mechanics and so forth it probably Starship is is the best um solution to that. Um the other rabbit hole was energy um that I really want to understand the energy economy. I I read this book from from an expert on it called backlov schmill called how how the world really works which is about the energy economy and you know what is the right mix of fossil fuels nuclear solar wind etc.

I went pretty deep down that rabbit hole um and came up with the conclusion. It's obvious that the right long-term answer is nuclear. Just no doubt about it. Um it it will be scalable. It's um it's uh um done right by far the cheapest method and it's it's basic physics, right? Because the costs and everything come down to energy density, right? And because energy density is materials and materials is cost. Um and the energy density of nuclear is orders of magnitude better than hydrocarbons which is orders of magnitude better than wind and solar. Okay. So um anybody who thinks that we're going to replace hydrocarbons with wind and solar are completely delusional.

Absolutely delusional. So the reason that nuclear has stag stagnated so much is because the nuclear regulatory commission. Okay. is that you know so it was formed in 72 out of the AEC and in that time 50 years there's been hundreds of proposals for new reactors in the United States only two just last year came online which were the vocal reactors in Georgia two out of hundreds of proposals over 50 years the regulations are insane they have certain things like what in fact there was a review of this just under the new administration to try to see if they should modify by this called Aara as low as reasonably achievable.

Right? What does that mean? What that means is no matter how much you spend, if you have another dime, you should spend more to get the background radiation from the plant even lower than the the native background radiation. What what kind of sense does that make? Or the vocal reactors where they had already poured the concrete and rebar for the thing and then they changed the specifications and made them rip it up and do it all over again. How are you? They're intentionally sabotaging the ability of nuclear to be cost competitive. And so you'll see study after study by solar and wind proponents that that nuclear is unaffordable.

They've made it unaffordable. The energy density is such that done right it will be by far the cheapest by far. And if you look at, you know, if you look at our world in energy or things like that, in terms of deaths per per kilowatt hour produced, nuclear is orders of magnitude safer than any other technology that exists. Any reasonable, rational person, economic or or physicist would realize that this is the right answer, but the hysteria and the politics have just absolutely killed it. Did you when you did your deep dive, it's kind of funny because I actually did one of these also. I had like a week off.

Uh we shut down our startup every every year during Christmas. So a lot of people are from Europe and they go home and everything. And I I read like a nuclear textbook and talk to a lot of nuclear friends, right? Uh and I kind of came away with from it thinking finding kind of similar feeling like it's not really about the technology that much. It's really like you could just build the PWR reactor. There's so many good reactors. You could do PWR but it's not the best. Yeah, they're better ones, but even the PWRs are pretty good. You you should have a you should have a high temperature reactor.

Okay. Like like again you know TISO with with uh with helium cooling like X energy is doing or Radiant. In fact that the good news was uh Radiant which is also using TRISO um just got an award from the Army to do some initial reac micro reactor projects. Right. So if they get off the ground that's there there's there's one by a guy here at Berkeley um per Peterson uh uh that's I can't remember the company but in Alama they're also using tricopellants in sort of a you know molten salt when you were looking into this did you have besides the regulatory aspect was there kind of like a oh if this piece of technology turns over suddenly a lot of things become possible was there like kind of a technology insight or I I really feel like the technologies really exist It's all there.

Okay. And and I particularly like the high temperature designs because a huge amount of our energy economy is process heat, right? And if you have a lightwater reactor, you don't have the temperatures necessary for for a lot of the process heat applications you'd like to do. So if we can do high temperature reactors at scale, you know, that's the thing that would that that would make fossil fuels basically just a materials thing as opposed to an energy thing, right? And um but yeah, but the wind and solar has just been proven wrong. You look at Germany, you know, they're over 40% solar now. It's the stupidest place on earth except maybe maybe the North Pole to to go all and and so what happens is is you know the They they they have their in the winter there's no wind and there's no sun, right?

Because it's just a big cloud cover over it. So So they buy electricity from from either the hydro parts in Norway or from the nuclear reactors from the Mesmer project in in in uh France. Now France is still 70% nuclear, right? But those reactors are crazy old and they're ending nearing end of life and they haven't thought anything about how they're going to replace them. Um, and that's going to come up. You know, we keep extending the life of the reactors we have in the US and they're good, but they won't last forever. Okay. And so it's it's the most insane. There are many things that like I I said before, I kind of feel like there was sort of whether it was 2008 or before, kind of an inflection point where it felt to me like things kind of went nuts.

Um and and not being rational about energy policy is is really really high on my list. I mean you know as a Nobel laureate you get invited every year to this Lindow thing and on Lake Constance to talk to 600 odd young scientists about whatever you want. So, I gave an energy talk there, you know, but again, I just it was great because I got attacked left and right, but because a lot of the Germans are very pro- solar, right? And so, I took them out all for beers afterwards so we could debate it, right? And I don't think anybody changed anybody's mind, but uh but I tried.

Okay. But it it just it's it's not science that's holding nuclear back. It's it's uh it's sociology and politics. Yeah. So earlier in your conversation, you said that uh the best time of your life was in Bellabs, right? Yeah. Um can you tell us what's the magic about this place? Do we want to bring it back? And what do we need to do? Uh it was magic. It would be wonderful if we could bring it back and I doubt we ever will. Um so the the magic was um it it understood how science is done. Um, creativity is not done by committee. Creativity comes out of the individual human mind.

It's useful to have other people to bounce ideas off of, but it's the guy waking up in the middle of the night with where just the different pieces fall into place. And in order to be creative like that, you have to be fully immersed in a problem. You can't be like at a university where in a couple hours I'll be teaching a class. You don't have you can't be writing grants. You can't be doing all of this other extraneous stuff and still focus your mind completely. This is how Shannon worked. This is how Hamming worked. This is how towns worked. This is how 12ing Nobel prizes out of one institution.

Okay. The reason is is they let scientists do science all the time. And furthermore, in the physical research division where I was, there were a hundred PIs. Okay, we were called members of technical staff. Didn't matter whether you were a new hireer or whether you had a Nobel prize, you could not have in your group more than one posttock and one technician. That was it. Okay? You could not build empires. Okay? So, as a result, how do you get stuff done? Well, you collaborate. And furthermore, there was no focus. There was no departmentalization. There was no What's the idea of having a chemistry department over here, a physics department over here, and a mathematics department over there?

That's insanity. Siloization of academia is nuts. You need to put people together because the advances happen at the interface between disciplines. Belle understood that. So, we were all in one corridor of a massive building that was one straight line with labs on either side going all the way down. In order to get lunch, you had to pass 50 other people. And those stochastic interactions you would have with other people either going to lunch or in the lunchroom was what made the whole damn thing work. Okay. And everybody understood because after a while once you have a reputation, everybody understood a lot was expected from you, right?

I mean it was you you felt you felt like do I belong here when you first go into the door there. there's a lot of history there, right? And you're like, "My god." Um, can I measure up? And so, and so you bust hump in order to try to prove yourself, right? And um when Harold and I were there um we, you know, we would come in at 4 in the morning and to and we would always fight for the first spot in the parking lot. And if he beat me, I would put my hand on the hood of his car to guess by temperature how many minutes he beat me by, right?

And then we would work until the sun rose. Then we would play tennis and we come back to the lab. Then we work till 6:00. Then we go to the same Chinese restaurant two blocks away for dinner. Then we come back from the lab and we work to 10. And we did that seven days a week. Boom, boom, boom, boom, boom, boom, boom. You get a lot done if you do that. A lot done. Okay. And and again, when you're playing the tennis or when you're having the Chinese dinner and your mind is off of it, that's the subcon or when you're sleeping, the subconscious mind is turning over all of the [ __ ] that has been put into your mind by your conscious effort during the day and the conversations you had with other people and the answers come out justing out of nowhere.

The answers just pop up out of nowhere. Um and uh the fact that there was no focus you know in my time at near field what did I do I did highdensity data storage I did looking at fiberblast cells in their cytokeleton I looked at single molecule imaging I looked at profiles of um of emission from from fibers I worked on on fiber lasers I worked on tissue sections I worked on a zillion different things with so many different people And I gained not just in the application but from the technology. I I started going with with with my pulled uh uh um pipet tips.

And then once I learned about the fibers and how I could exploit those, I pivot to the fibers and oh well the fibers in order to pull the tips, well that's silica glass. That's way too hot for to pull with a with a with a heated filament. Well, guess what? The guy who invented the carbon dioxide laser is down the hall. So I just go and buy borrow carbon dioxide laser from him which then absorbs enough in the glass that I could then melt the glass and do that. I don't know how many times I was stuck on something and it turns out that the guy I needed was or I'd start reading the literature about something and it's the [ __ ] guy down the hall that I've I've nodded to a hundred times and and never really had a conversation with.

Um it was magical. Um to be to be uh um what is it? Um uh not politically correct though it had other aspects to it and that was um uh it was not an environment for a happy family life. Okay. Um it was not about work life balance at all. Okay. So, it was in the era where most of the guys had stay-at-home wives. Okay. I think that was I think those people deserve the Nobel prizes as much as the guys who got the Nobel prizes because they wouldn't have happened without those people. It would not have happened. Okay? We live in a world now where it doesn't happen.

Okay? Where it's not expected that you're going to have a stayhome wife and you're going to be working 16 hours a day, seven days a week. Okay? It's that's a unicorn now. It doesn't happen. So that's the trade we've made. Okay. But don't think that you can just put a building up like Bell Labs and have people work, you know, even 50hour weeks. And most people don't want to even work 50 hours these days, least wise, 100 hours like we did, right? But there is no substitute for just full immersion in something. And you can't do that when you have a family. So that's that's the price.

Yeah, that's interesting. So it kind of the implication is basically that it's it's not just about the environment or the freedom or anything. It's also the the fact that people were working non-stop basically. It's difficult. You think you think that's important enough that it would be difficult to replicate without that that aspect? Absolutely. Impossible. Literally impossible. Yeah. you it's to me it's an immutable law of the way the human brain works is that you have to be fully immersed in a problem fully immersed any distraction your mind will latch on to instead of doing the job it's supposed to do right so um also you know with all the distractions of of that damn box in the corner that you type into right and and you spend your life in front of a screen you spend spending your life in front of a screen unless you're a theorist is not the way to get science done.

Okay? So, um you know, when I work, I intentionally make sure I turn my Wi-Fi off. Okay? So, that if I had any interest to like click on something, I'm just going to come up with a blank web page and it's a reminder myself focus, dude. Don't don't mess around. Okay? Um so, uh yeah, it's you there's a cost to everything. Okay. Um, and you know, call it the sort of again, you're fighting back sort of an entrop. You're trying to create order out of nothingness when you're creating some new scientific instrument or principle or whatever. Creating that ordered structure requires enormous energetic input. Okay?

If you don't put the energetic input in by working insane hours, um, it ain't going to be built. Okay? So yeah, I [snorts] mean I think I wonder if you know there probably are some people working that way today, but some of it is also like maybe you get more of them in places where it's more commercial. That's one. Startups is is the lo, you know, if you have your back against the wall in a startup, some of those people work insanely hard. Yeah. And the other one is that even people working very hard in academia might be spending half their time on paperwork. So it's like you might be working that hard, but like half of your work is paperwork.

And as as soon as there there are times particularly when I'm focused on onto something and I know the path that you have to tune out the rest of the scientific community. You don't listen to the scientific community. You you focus, right? I mean you don't need those distractions there. So I I talk about blinders on, blinders off. There are times in your life when you have to have the blinders on. When you know what your goal is, when you know what you have to do and you do it. And anytime you take the blinders off, it's just temporary to find the missing piece to kind of go on, but you're focused.

And then there are times when, you know, my unemployment are like that. You have to learn to take the blinders off. And I could do anything. I could go and become a chef in a diner or whatever, cook, you know, but so you need to take the blinders off to survey widely. But but yeah uh the you know academia is is just almost the worst possible system one could create if the goal is to get science done be and the other problem is so many things in AC not not only is it about having to get the money. Not only is it the peerreview causes everybody to have to think the same way in order to get the money.

So it it punishes creativity. Um it's it's it's also um and and the teaching and the interruptions that you get from that. Um it's it's and and that what again show me the incentives and I show you the outcome. What are the incentives for academics? The incentives are to publish papers doesn't necessarily mean they have to be good papers. They have to be papers and to train the next generation. And so and the more grant money you get in, the more postocs and graduate students you have to have in order to administer that stuff and to do that work and so and the more that you're going to move up the tenure ranks and so forth if there's more overhead going to the university because you have more grants.

So it's all incentivized to grow large groups. Okay. Until I came here and started Cell Observatory, I never had in my entire life more than three people working for me at one time. Okay? Because I viewed every project as a 50-50 collaboration between me and that other person. But as soon as you start to do that and you grow these groups all the throughout my entire career, the since I was in grad school, people were talking about science is in crisis. We don't have enough money. Okay? It's now now louder more than ever under the Trump administration, but they haven't hardly done anything. Okay? The NH budget is just about the same as it was last year.

Okay? Yes, they're slow pedaling some of the stuff, but overall science has grown at an enormous rate. And why has it grown? Because you hire all these grad students and posttos who want to become PIs. And so more and more this this whole structure gets bigger and bigger and bigger. But there aren't enough universities for all of these people, right? There's a lot of there's a lot of wash outs and fling outs and unhappy people who spent the better part of their 20s, very productive years being just slaves at crappy salaries to work for some PI. The PI gets all of the glory and they end up either starting again as an assistant professor at some cow college in the middle of nowhere or else they flame out and they do something else.

Right? The system is seriously broken. There's nothing wrong with academia that getting rid of 95% of it wouldn't cure. Okay. But that's how professors like you or like some other groups to find a way around and succeed. Right. I I say that I use my HHMI money and the other sources of money we have to put a force field around the third floor of Barker Hall to keep the rest of Cal out. That's the way I view it. Right. I I'm trying to create my own little Janelia Bell Labs within this microcosm that I have here, right? But yeah. Yeah, that's interesting. If what's like the if you were 20 years old today, where would you go to try to get that type of environment?

I know exactly where I would go if 20 years old today. I would beg for a job at SpaceX. SpaceX is the new Bell Labs. Okay. They have a narrow somewhat narrower focus, but do you know how many technological problems they are solving? You know, they've created new steels for the starship in the engines. Um, they probably understand more about computational fluid dynamics than anybody ever in history. Um, if they're going to be building, um, uh, you know, a moon colony or a do you know all the human factors, the environmental factors, all the other stuff? They already know a lot of this. Hell, they they've got they've got the the um the Dragon capsule, right?

There's so much to be able to do space travel is such an interdisciplinary problem. It's at least as interdicciplinary as anything. Oh, the control algorithms to do a hover slam for the Falcon 9 rocket, that was non-trivial, too. Okay. the compute that they do, the real- time compute that's necessary. There's so much so much immense science and technology happening within that organization now and they're justing getting started. I would tell any young person who's listening this in any field of endeavor that if you want to have a really [ __ ] exciting career and you do like the idea of working your ass off cuz those guys work their asses off every if you ever watch those launches though.

Have you ever seen anybody happier than than those guys watching their their rocket do what it's supposed to do like on flight 13 recently? It brings tears to my eyes to see how happy those young people are doing that. That's the way that's the way you should live. You should you should be making a better world. And these people are making a better world. I I just can't believe how much [ __ ] that company gets and how much [ __ ] Musk gets for basically pulling us up, kicking and screaming to a better a better world than we've had before. How much of an impact has Starlink had in third world countries already?

I mean, come on. [laughter] Yeah. Yeah. It's insane. absolutely insane that that you know he gets the [ __ ] he does for for the impact that he's had and his whole team you know they paint the same brush for all of those young people doing that work and and they're they're producing miracles daily. Yeah. I think is there a SpaceX of biology? Like I guess if you're a biologist if you're a biologist where are you going? Well, as a biologist, I don't know. I every you know, the the thing about biology is is it's it's a wonderful, you know, I this is the last stage of my career and and I'm never going to be a biologist, but I but I study biology and the wonderful thing about it is it's the last it's the last frontier.

It's mystery everywhere. We understand so little. We're we're again in the in the pre-Kepler era. We're we're in the floistan era, okay, of understanding biology. It's there are so many things we think we know that are just [ __ ] you know, that it's going to and the it's it it really is the final frontier because it is the most complex matter in the known universe. And it's going to take it's going to take generations, if not eons, if ever, to have a to have enough of a mechanistic understanding to really do a virtual cell like they say they want to do. Now people just don't understand exactly what big of a mystery it is.

Yeah. I mean maybe in that sense it's more it's a little bit more like a better it's an easier field to do some interesting work in even if you're not at a SpaceX because there's just and there's I think a lot of opportunities to do it on a small scale. You don't need to build an empire in order to do that stuff, right? Mh. It's It's not like trying to do fusion or quantum computing or something where you're going to need, you know, bigger resources or whatever. There's still opportunities to do it on a small scale. Yeah. Nice. Okay. I think should we wrap it up?

Okay. This this was awesome. Thanks for Thanks for your time. Yeah. Well, thank you guys. I appreciate you coming out. Awesome.