Ben Bloom (5:44)

So I think in five years, what you'll see is quantum computers attacking problems for national labs, for defense, for all these kind of esoteric industries, you could call them, that want calculations done that cannot be done on classical computers. It's probably going to Be something like materials simulation. So how electrons behave in materials and

Jacob Goldstein (6:10)

why are national labs interested in that?

Ben Bloom (6:14)

Ooh, I think that's a funny question. I think the Department of Energy is really interested in understanding materials at their extremes. And I think that's true to both make it more efficient for the US Government to deal with all of the equipment they have. And also really true, I mean, obviously for the Department of Energy main mission, which is understanding nuclear weapons and doing that without having to test nuclear weapons.

Jacob Goldstein (6:44)

So five years from now, quantum computers are where classical computers were in the 1940s, right? A few giant crazy expensive ones being used by the government or a couple governments. Two things people talk a lot about with quantum computing are drug discovery, discovery of new drugs and energy. For some reason, people talk about energy, they talk about batteries, that kind of thing. So if things go relatively well in quantum computing, like, are we going to be seeing meaningful impacts on those field in what, in 10 years? And if so, how will they be different?

Ben Bloom (7:18)

Yeah, I do think we will. I mean, I think 10, 15 years, we will see new drugs on the market that were designed on a quantum computer.

Jacob Goldstein (7:25)

Okay.

Ben Bloom (7:25)

I think we will see batteries that can do more recharge cycles because we understand the materials and understand the various properties that, you know, cause them to lose charge over recharge cycles.

Jacob Goldstein (7:37)

And more recharge cycles means cheaper in the long run. Importantly. Right, like batteries are constrained by cost now. And if one battery can last longer, that effectively means it's cheaper per cycle.

Ben Bloom (7:49)

Yeah. And I think another example of this is, you know, the one that people throw out a lot is kind of understanding how you actually do fertilizer production. Like, it turns out a few percent of the world's energy is spent on fertilizer production. And so if you can find a new catalyst that could do that same process with some slightly lower temperature or slightly less pressure, marginal savings in that process turn into gigantic global savings in an energy budget sense. And so all of these problems kind of have this general idea behind them, which is that chemicals and materials and understanding the physical processes which we kind of have built our world on top of understanding them to a point where you could engineer around them or engineer them to work better, could have these giant lever arm effects, basically.

Jacob Goldstein (8:41)

I mean, A, more and better drugs and then B, just efficiency gains. So that's fewer emissions, more power, lower cost. Like those are the dreams, broadly speaking. Okay, so where are we today?

Ben Bloom (8:57)

Yeah, we're building toy systems. I mean, there's no question. I think that over the past, I Would say two years. A lot of technologies, including the one I work on, neutral atoms, have kind of crossed a threshold where you can build better and better qubits by taking that quantum information, spreading it around and doing error correction. And one of the examples that you know is probably what, what in some sense started this entire race is it turns out you can factor numbers on a quantum computer efficiently, which sounds trivial

Jacob Goldstein (9:29)

to the uninitiated, but it turns out, among other things, if I have this right to allow you to crack most of the encryption on the Internet. True or not true?

Ben Bloom (9:38)

It's true. The kind of encryption we have now was, was a choice. And in some sense right now it's even a choice in your browser or in your operating system. And the likes of, you know, Apple or, you know, the Linux maintainers or Microsoft, they can literally just, you know, quote unquote, flip a switch and it can switch to what they call as post quantum cryptography.

Jacob Goldstein (10:00)

So people have come up with new systems that are quantum resistant, that can work to secure our data in the post quantum world.

Ben Bloom (10:09)

Exactly. And so I think that the future is bright in that sense. Now, the funny thing that governments around the world are a little probably freaked out about is the past, which is that we've been using for decades upon decades we've been transmitting information between people and businesses and government enterprises and stuff like that, that if someone stored it and waited until they had a quantum computer, they can just go and read in the future when quantum computers are available. And, and so I think that is the kind of issue that I think most people think of when they think, oh, they're going to break encryption. How bad is that going to be? It's not going to be, you know, five years from now that people are worried about how do you pay for something online? It's more going to be these kind of, you know, big government issues of, okay, you know, 20 years ago we sent a cable to so and so and it said this.

Jacob Goldstein (11:02)

I mean, we can. Let's talk about geopolitics here. Now, when people say let's talk about geopolitics in the U.S. they basically mean let's talk about China and specifically what happens if China gets there first. Right. Like, I do feel like, I know that's a crude way to ask the question, but I also know China is putting a lot of money into quantum computing, right? Like, is there something of a race here? Is that a way to think about it?

Ben Bloom (11:23)

I think it is a race. I mean, I think that they are doing it differently than the U.S. i mean, at the U.S. you know, my company Atom Computing is one of many companies in the United States who are pursuing quantum computing, are trying to build a quantum computer. I think it's all private. There is some public funding which we're accessing. There's DARPA programs and things like that, but it's mostly private funding. Whereas you talk about what's going on in China and I think there are big governmental initiatives and I also think there are in some sense state backed companies that exist.

Jacob Goldstein (11:57)

This is very much in keeping with both of our countries. Right. This is exactly what you would expect. Perhaps it's a test of our various systems.

Ben Bloom (12:06)

Yeah. So I mean, I think that the question of is it bad if China builds a quantum computer and we don't have one? I mean, potentially I think that a lot of the advances we were talking about earlier, yeah, they're performance gains, efficiency gains and things like that. But those are the kinds of things that really, I would say, drive an economy in the sense where the numbers associated with them are so, so large that it's hard for me to wrap my head around. Like if you can decrease the energy budget of your country by 5%, that's a huge number and I think it can actually make big changes possible.

Jacob Goldstein (12:44)

Yeah. I mean, in a way it makes you much richer. Right. It allows you to do things you could otherwise not afford to do or you wouldn't otherwise have the resources to do. Yeah. I mean, presumably it gives you some kind of cryptographic power.

Ben Bloom (13:00)

Well, I mean, it goes back to this idea that there's this store now, decrypt later philosophy which I've heard espoused by various government officials as others are doing this and so on and so forth, which is that they're storing all the communications and things like that. I think that is a problem. There's no question. If we're successful and we build quantum computers, you will be able to decrypt later. I guess the question is how useful is old information? That would be the problem. And the question is, how bad is that going to be? Maybe it's not so, so bad on individual scale, but on a kind of company scale or a country scale, maybe it could be quite problematic.

Jacob Goldstein (13:44)

So let's talk a little more about the difference between quantum computers and regular computers, classical computers. And maybe one way to do that is to talk about classical computers. The kind of computers we have now, the kind of computers that underpin AI are bad at. Like what is their meaningful limitation in this context?

Ben Bloom (14:03)

So say you have an electron. And you want to understand the properties of that electron. And because you're designing a drug, you're looking at a material, you're doing something like that.

Jacob Goldstein (14:13)

A particular electron on a particular molecule.

Ben Bloom (14:16)

Yeah, yeah, yeah. And all you want your classical computer to do is write down the state of that electron very, very precisely.

Jacob Goldstein (14:24)

Where is it and what is it

Ben Bloom (14:25)

doing and its spin and things like that. Yeah, yeah. So now you try to write that down, and it turns out you use a lot of binary data, you use a lot of classical data to do that. Now, if you want two electrons, you have to use more classical data to describe it. But it turns out, and this is where quantum mechanics starts mattering, it turns out you don't just care about the individual electrons in their states, you about care. You actually care about all the correlations, all the ways the two electrons are interacting with each other and are linked together. And that turns out to require more classical data to write down. And very, very quickly, with tens to less than 100 electrons, if you tried to write down all the classical data you needed to describe that situation, you would need, I think it was more bits of information than there are, like atoms in our universe.

Jacob Goldstein (15:21)

It would be impossible.

Ben Bloom (15:23)

It would just be impossible.

Jacob Goldstein (15:23)

It would be impossible for a classical computer to solve. And so it is the case that once you get to the really fundamental level of what's going on with energy or what's going on with matter, with materials, it behaves in a quantum way. And classical computers can't understand what's going on once you get to that level. And quantum computers, at least the theoretical quantum computer, if you could solve the engineering problems, sure could do it.

Ben Bloom (15:53)

Yeah, exactly. I mean, I think that the cool thing about quantum computing, we kind of do have north stars. We know that if you can build a quantum computer, you can factor large numbers. We know that if you can build a quantum computer that, you know can deal with billions of operations, that you can find the structure of some molecule or something like that. And so we know that the goal is go from the short little calculations you can do now to longer and longer and longer calculations to the point where you can reach these goals.

Jacob Goldstein (16:26)

Yes. And so maybe there's one sort of problem, broadly stated, that seems particularly interesting and worth discussing. Let me see if I have this right. Is it right that each bit, each qubit of a quantum computer needs to be entirely isolated? It needs to be sort of not in touch with anything in the world? No. Not a photon, not another molecule, not anything. Right. Because it is sort of sitting in this, what, quantum superposition. I'm reaching here. And if anything, if anybody sees it, if any light photon hits it, if anything happens to it, it of breaks, it decoheres and it doesn't work. Right. And is it fair that it's extremely hard for maybe obvious reasons, Everything is touching everything else all the time. Is that like a central problem? Is that a problem worth talking about?

Ben Bloom (17:28)

It is, because I think it's actually the fundamental challenge of building a quantum computer is having that isolation, but then making sure that you have complete control over that quantum system.

Jacob Goldstein (17:40)

Right. And like, how could you have both at once? Right. If it's completely isolated, how can you control it? So, I mean, let's talk a little bit about your approach. You are working with Microsoft to build a thing for the Novo Nordisk foundation and what is part of the Danish government.

Ben Bloom (18:01)

Yeah, yeah.

Jacob Goldstein (18:02)

So what are you building for the Danes?

Ben Bloom (18:06)

Yeah, so it's a 1200 qubit system.

Jacob Goldstein (18:10)

Okay.

Ben Bloom (18:11)

And that is kind of our part of this, which is that we're building the hardware. So it's a system that has all of the classical control around it, all of the quantum control around it so that they can trap.

Chase for Business Announcer (18:21)

Cool.

Ben Bloom (18:22)

Manipulate 1200 physical qubits.

Jacob Goldstein (18:25)

Okay. And each one of those is one atom?

Ben Bloom (18:27)

Yes, each one of those is one atom. Yeah.

Jacob Goldstein (18:29)

It's a very small number of atoms.

Ben Bloom (18:31)

Yeah. And actually, the quantum part of our system is very, very small. Like it's like half a millimeter by half a millime. It's very, very.

Jacob Goldstein (18:38)

There's like a box or something. What is. What is it?

Ben Bloom (18:41)

Yeah, yeah. No, it is pretty much a box.

Jacob Goldstein (18:43)

A box that's half a millimeter by half a millimeter, which is, I don't know what, like a size of, like a hair or something? Like, it's so small.

Ben Bloom (18:52)

It's a little bigger than a hair, but. Yeah. What is it?

Jacob Goldstein (18:54)

But it's bigger than, like a little fingernail trimming, right?

Ben Bloom (18:56)

Yeah, yeah, exactly. Fingernail trimming. That's a good way.

Jacob Goldstein (18:58)

Fingernail trimming. And that's where all of the action is. That's it. That's all of your, I guess, 1200 atoms.

Ben Bloom (19:07)

It doesn't take up a lot of room. Yeah.

Jacob Goldstein (19:08)

And what's going on in that tiny box? What do you. You start with? It's a metal, right. What's the element?

Ben Bloom (19:15)

Ytterbium.

Jacob Goldstein (19:16)

Okay. So you start with this element, Just a thing that exists in the world called ytterbium. You don't need very much of it. What do you do.

Ben Bloom (19:24)

So first off, we have to take. We have a chunk of it, so we have to heat it up. So you go from a solid to a gas. That gas kind of streams down our system. We have lots of lasers kind of hit that gas so that it goes from hundreds of Kelvin down to about a microkelvin. So 10 to the minus 6 Kelvin. So very, very cold.

Jacob Goldstein (19:45)

And 0 degrees Kelvin is absolute zero. It's as cold as anything in the universe can ever be. So you're making it very, very, very cold. And is that because you don't want it moving around, because you want it isolated? That's.

Ben Bloom (19:57)

Yeah, exactly.

Jacob Goldstein (19:58)

Okay.

Ben Bloom (19:58)

Yeah, yeah, yeah. Pretty much like the temperature is pretty much the velocity it goes at and things like that. So we get the atoms, we get them down to a microkelvin, and then we kind of shuttle them using a laser. We kind of push them up into that tiny little area that's half a millimeter by half a millimeter.

Jacob Goldstein (20:15)

Tiny little box. That's gotta be a little box. Computer sort of. Yeah.

Ben Bloom (20:19)

And then all of a sudden, we get to this little area, and what we do through that microscope objective is we put a big beam into the back of that microscope objective. And the reason why we put a big beam, when I say big, it's like 20 millimeters. Yeah. Of light.

Jacob Goldstein (20:32)

Okay. So you're shooting light through the microscope. Lens. Let's say lens.

Ben Bloom (20:36)

Lens.

Jacob Goldstein (20:36)

Okay.

Ben Bloom (20:37)

And when you do that, you put a big light beam into the back of that lens. What it does is it focuses down really, really tight and it creates an optical tweezer.

Jacob Goldstein (20:46)

Okay.

Ben Bloom (20:47)

And this won the Nobel Prize in, I think it was like, 2019 or something like that.

Jacob Goldstein (20:53)

Okay.

Ben Bloom (20:53)

The cool thing here is that this optical tweezer at the very focus of that lens gets really, really tight and diverges really, really quickly.

Jacob Goldstein (21:02)

Okay.

Ben Bloom (21:02)

And for some light. And we very, very carefully choose what kind of laser we're doing this with. Atoms are attracted to the point of highest intensity.

Jacob Goldstein (21:12)

Okay.

Ben Bloom (21:12)

And so the atom kind of gets sucked in to the optical tweezer. And then it wants to sit at the exact center of that focus.

Jacob Goldstein (21:20)

So it's called an optical tweezer because it is grabbing a single atom.

Ben Bloom (21:25)

Yeah.

Jacob Goldstein (21:25)

Okay.

Ben Bloom (21:27)

So what we do is we do a bunch of tricks so that we don't just create one optical tweezer, but generally speaking, we build display technology that creates the image of many, many optical tweezers and just stuffs that into the back of the lens.

Jacob Goldstein (21:44)

So you basically have, like, what, 1200 optical tweezers. And each one grabs one atom and sticks it in the box.

Ben Bloom (21:52)

Exactly. No, no. That is exactly what we do.

Jacob Goldstein (21:54)

So now there's this box that's ready to help someone figure out something about the world. What is something about the world that it might actually help them understand?

Ben Bloom (22:05)

Yeah. So say there's a user in Denmark, in Copenhagen, who wants to understand the ground state of some molecule. They write down an algorithm that says, I am going to simulate many, many electrons, and I am going to use it such that it kind of gets down to the lowest energy state. And then I will read out the qubits in their various positions and things like that so that we can actually understand what was the state of the electrons in this simulated system.

Jacob Goldstein (22:34)

So they're sort of saying to the quantum computer that you have built, act like you are this molecule. Act like you are the electrons in this molecule. Exactly in their lowest energy state and tell me what state you're in.

Ben Bloom (22:48)

Yes, exactly.

Jacob Goldstein (22:52)

We'll be back in just a minute. Hey, it's Jacob. And I want to tell you that I am hosting a new show called Business History. It's about the incredible innovations and massive failures and unbelievable characters in the history of business. And, and I hope, I think the show provides insights about how business works today. At the end of today's episode of what's yous Problem? We're going to play you a clip from Business History. It's the story behind the video game company Atari and their surprising early hire of a young hippie named Steve Jobs. The show's called Business History. You can listen to it wherever you're listening right now. And we'll play that clip at the end of today's episode. Small businesses are the pulse of every community. They bring people together, create opportunities, and drive growth. With a widespread presence in communities across the country, Chase for Business supports small business owners at a local level that makes it possible for you to connect, learn from each other, and grow together. There's a real commitment to seeing small businesses succeed. The Chase for Business team has knowledge and expertise that span a wide range of financial areas. They can help you make more informed choices as you navigate the complexities of running your business. They'll help your business grow with individual guidance and convenient digital tools all in one place. With that guidance and your determination, you can take your business farther and help build a brighter future for your community. Learn more@chase.com business chase for business make more of what's yours. The Chase mobile app is available for select mobile devices. Message and data rates may apply JPMorgan Chase Bank NA Member FDIC Copyright 2026 JPMorgan Chase Co. Run a business and

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Jacob Goldstein (26:16)

One of the big problems in quantum computing is detecting and correcting errors. This is a hard problem for a few reasons. One One of those reasons gets at the heart of quantum weirdness, and it's this. When a quantum computer is working, the qubits are essentially in many states at once. But if at a given moment you look at a qubit, try and figure out what state it's in, it will instantly snap into a single state. All that beautiful quantum weirdness of multiple states at once will suddenly disappear and your quantum computer will not work. There's been a lot of progress on this in the past few years, but for the most part, quantum error correction still only works at a scale too small to be useful for practical real world problems.

Ben Bloom (27:01)

I asked Ben, why all the classical stuff we're building, all the laser projectors, all the spot makers, all these things? As you scale those numbers up, if you know you're stray light or whatever it is you're doing for your quantum computer, if that increases, then all of a sudden, your quantum error correction doesn't quite work as well as you want it to.

Jacob Goldstein (27:22)

It seems very hard.

Ben Bloom (27:24)

Yes.

Jacob Goldstein (27:25)

I mean, I talk to a lot of people who do things that seem hard, but this seems like wild hard.

Ben Bloom (27:32)

I would say that the last 24 months, there have been so many amazing advances. I would say everyone in the industry is more excited than they've ever been, because I think you go back two years ago and no one had demonstrated quantum error correction at any scale. Now, there's us, there's Google, there's lots of other companies that have now demonstrated quantum error correction. Yeah. At a small number of qubits. But I think that is a key milestone, which is to say, look, that theory is right. Like, your errors go down as you spread this quantum information across your quantum processor. And I think that's a huge, huge boon because it says no. All you have to do is just get to larger and larger and larger numbers.

Jacob Goldstein (28:15)

So there's this phrase, I think I heard you use it, or maybe I read it, which is commercial advantage. Right. You've talked about sort of governments being initial users. What's the universe where a quantum computer becomes practical for a private company?

Ben Bloom (28:32)

Yeah, I mean, I think that like a pharmaceutical company, like Novo Nordisk or Eli Lilly or something like that, I think they're going to have simulation frameworks that engineers are going to sit down at their desks and use those simulation frameworks, and it's going to be constantly sending jobs to a quantum computer.

Jacob Goldstein (28:49)

Tell me about this molecule in its lowest energy state.

Ben Bloom (28:53)

Exactly. Yeah. Or dynamics or something like that.

Jacob Goldstein (28:56)

Like, what are the dynamics if this drug binds with this part of a cell?

Ben Bloom (29:02)

Exactly. No, that's exactly it. So I think that most companies will view quantum computers in that lens where it's just another cloud computing resource that they're spending money on. And I could even imagine a future where, you know, this is so ingrained in kind of simulation frameworks, it's not even clear, you know, you're spending it on a quantum computing, you know, hours on a quantum computer. You're actually just, you know, buying time on your simulation framework that you get from another company or so on.

Jacob Goldstein (29:31)

Yes. We haven't talked about AI. We've gone a while. We haven't talked about AI. And there's like a couple of sides of AI, Right? There's like, AI helping to make quantum computers. There's also AI being able to do things, you know, on classical chips that we didn't think maybe classical computers could do. Like, I'M thinking of, of the protein folding problem, right? This famous hard molecular level problem that AI solved that nobody could solve for a long time. Seems like the kind of thing that you might have looked 10 years ago and been like, oh, here's a thing quantum computers can do. They can predict the shape of a protein.

Ben Bloom (30:08)

Yeah, I think that's true. I will say when we go and talk to the experts in the field, I think one of the things we hear an awful lot about is the idea that the AI is only ever as good as the data you give it. And a new use case for quantum computers that no one talked about more than three years ago was using quantum computers to just pump out data for AI to train on.

Jacob Goldstein (30:34)

It would be amazing if that was the killer use case. It's like, oh, we're just making the AI better. We kind of a bummer at some level, not to be silly, but like, you know what I mean? It'd be a little bit of a letdown.

Ben Bloom (30:45)

Interesting, let's put like that. But I do think it's this idea that, you know, fundamentally like if all of your AI data is coming from classical models or classical, you know, algorithms spitting out data to go and train alphafold or whatever it's called on, you only get it so good. And if you can go a step further and start getting the AI to understand the kind of quantum pieces of the puzzle, that all of a sudden your AI is going to start including those in its predictions. But I do agree the flip side of this is building a ton of quantum systems whose sole job it is, is to just pump out data for AI is a little surprising, a little

Jacob Goldstein (31:29)

bit of a sad trombone end to a 30 year journey. I mean, is there any way in which AI is helping you or helping the field figure things out?

Ben Bloom (31:39)

Yeah, I mean, I do think it is like, I think that actually in control, I think there's a huge, huge ability for AI. I think that when we talk about scaling up systems and how do you tune things and how do you kind of get control across many, many, many qubits, AI is much, much better at finding correlations in data and things like that. And actually having an understanding of how you turn the control knobs you have into the outputs you want. AI is actually fantastic at that. I also think the other thing is true, which is just that, I mean, we operate faster because people write software faster now.

Jacob Goldstein (32:13)

Oh, right.

Ben Bloom (32:14)

Like even in the last like six months, I would say there is more and more and more software at atom computing that is being Written with AI, and it would be done, you know, I don't even know. 10 times faster with AI than it was possible before.

Jacob Goldstein (32:26)

Speaking of simple productivity gains with profound consequences. Yeah. So we started out talking about a relatively optimistic scenario. Five years, 10 years. Why might quantum computing not get there or take much longer than you think?

Ben Bloom (32:48)

I think it's all a challenge of how many resources are you willing to devote to get quantum computers to work? And what are the use cases? I think that a lot of the kind of chemistry use cases and stuff we're excited about, but like you said, there's always going to be new techniques and things like that. And if we can find new techniques that get us far enough, then the thing that we only have to fall back on is decryption. And the question is, there's only one customer that wants to decrypt. At least in the United States, at least two.

Jacob Goldstein (33:23)

I was going to say the US there's every government in the world, right?

Ben Bloom (33:28)

Yeah. But I think it's a question of resources and resource allocation, which is that if that's something the government's very, very excited about still, then of course, you know, I think we can keep scaling and we can make bigger and bigger quantum computers. And this is the use case.

Jacob Goldstein (33:42)

You say it's just money.

Ben Bloom (33:44)

I mean, I think that we have, and I think this is true of a lot of different kinds of quantum computing modalities. Now, the science has now been demonstrated. Error correction works. We need, like you said, a thousand times more qubits. And we want to go and do that by building network machines and scaling up the number of qubits inside each one of our systems and so on. And I'm sure our competitors have seen similar stories as well. And so the question just becomes, you know, is there the capital? Is there The. The excitement around building it?

Jacob Goldstein (34:13)

So you, you mentioned your competitors. You are a private company, right? Your competitors include some public quantum computing companies, but they also include, like, Google and IBM, right? Like these giant companies with huge revenue streams and kind of all the money they want. Not literally, but a lot of money. How do you compete? How do you. How do. How does that, how does that work for you?

Ben Bloom (34:45)

Oh, I mean, I think that. I mean, like any industry, I mean, I think there's, you know, venture investors and stuff who are excited about, you know, first of all, building funding technology that's different than the technology that's found at those giant hyperscaler, you know, big companies. And second, who want to invest in that technology. Because if that technology Wins, they actually get significantly more back from their investment than if they, you know, invested in Google. Like if Google has a quantum computer, I actually don't think the Google quantum stock is going to double overnight. Whereas if all of a sudden we have a universal fault tolerant quantum computer at atomic, yeah, our value could go up by 10 or 100x or something like that. So I think there's just like an asymmetry there and returns, things like that. Yeah.

Jacob Goldstein (35:33)

So one of the interesting things about the field is there are a bunch of different companies using very different approaches, like physically different approaches. And I recognize that maybe more than one will work or some will be good for some things and some will be good for others. But there might be something of a binary outcome. Right. Like somebody might win, somebody might get there first. What do you think are the chances it'll be somebody other than you?

Ben Bloom (36:02)

I mean, I certainly think there's a chance. I think that who wins in the end is going to just come down to dollars per unit compute. Like, I think there's someone will get there first. I hope it's Adam, but you know, someone's going to get there first. I think there will be a few years of multiple people then getting there, multiple modalities getting there. And I think at the end of the day what wins is just dollar per unit compute. And all of a sudden if there is a 10x difference in price between running on this system versus running on another system, people are just going to gravitate towards the cheaper one. And I think that will be the one that actually turns out to be the one that an entire industry is built around.

Jacob Goldstein (36:44)

Where do you think we should end the main part of this conversation? If you sort of sit back and think big thoughts and gaze off into the distance, where do you land thinking about this today?

Ben Bloom (36:57)

I mean, I think quantum computers are more of an inevitability than they've ever been. I think that we've made such amazing progress as an industry that everyone is on board with the idea that no, no, this is just going to happen. Like someone's going to have a universal fault tolerant quantum computer. People are going to be able to use it over the cloud, they're going to be able to do all the problems they want to do and so on, so forth. And I feel like even, I don't know, three, four, five years ago, I don't think people would have said that across the board.

Jacob Goldstein (37:30)

We'll be back in a minute with the lightning round.

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Jacob Goldstein (40:10)

okay, let's finish with the lightning round. It's gonna be a little more random but fun. I hope Albert Einstein Overrated or underrated?

Ben Bloom (40:21)

Underrated.

Jacob Goldstein (40:22)

Yes, very highly rated. Richard Feynman Overrated or underrated?

Ben Bloom (40:34)

I think underrated. I think that actually seeing him give lectures, I didn't get to see him in person, but I mean, I think seeing recordings of him giving lectures, I think he was probably the most amazing physics teacher there was, so.

Jacob Goldstein (40:45)

Is that right? Did Feynman come up with the idea of the quantum computer?

Ben Bloom (40:49)

I read that also, yeah. And he has this amazing sentence about how it would be so simple to do this with a bunch of atoms that you kind of rearrange and you kind of move around and stuff like that. Someone showed that to me many, many years after I had started a company to do this with Adam, so.

Jacob Goldstein (41:05)

And was he right that it would be so simple?

Ben Bloom (41:07)

Absolutely, yeah.

Jacob Goldstein (41:08)

Wait, what? No, is it right that you made or helped to make an atomic clock that's Precise to the second. Over 5 billion years? The most precise clock ever.

Ben Bloom (41:22)

Yeah.

Jacob Goldstein (41:25)

So, okay, a few questions following on that. Precisely how early do you get to the airport to catch a plane?

Ben Bloom (41:32)

Oh, actually this is a great thing that someone taught me. If you don't miss a flight every like, you know, 50 times, you're always getting to the airport too early.

Jacob Goldstein (41:40)

Yes, that is a theory. That's an optimization theory. So what for you is the optimal fraction of flights to miss?

Ben Bloom (41:48)

I think like 1% or something like that.

Jacob Goldstein (41:50)

Okay. When was the last time you missed a plane?

Ben Bloom (41:53)

Probably like 30, 40, 50 flights ago, something like that.

Jacob Goldstein (41:56)

Okay, so you're doing okay.

Ben Bloom (41:57)

Yeah, yeah.

Jacob Goldstein (42:06)

Ben Bloom is the co founder and CEO of Atom Computing. Today's show was produced by Gabriel Hunter Chang. It was edited by Lydia Jean Cott and engineered by Sara Bruguerre. You can email us at problemushkin fm. I'm Jacob Goldstein and we'll be back next week with another episode of what's yous. Jacob Goldstein. I'm Jacob Goldstein, and right now we're going to play you a clip of a new show that I co host. The show's called Business History. My co host is Robert Smith. And this clip is from an episode we did about how Nolan Bushnell, a stoner turned entrepreneur, created the video game company Atari and hired a young, inexperienced Steve Jobs. I really hope you like the clip. And if you want to hear more, you can find Business History wherever you're listening to this show right now. This like 19 year old hippie kid walks in and he says he won't leave until they give him a job. And the receptionist calls the head engineer and she goes, yeah, we got a hippie kid in the lobby says he won't leave until we hire him. Should we call the cops or let him in? And the engineer says, bring him in. It's 1970s in Silicon Valley, and this guy wanders in and he is. Whenever you tell a story like this, you know who it is. It's Steve Jobs. It's Steve Jobs. So fun. It's so delightful and perfectly. Steve Jobs is very good at his job. Yes. And very unpleasant to work with. Completely unsurprising. He tells Bushnell that, like, everybody's soldering wrong, right? They're actually putting together the hardware. Soldering the hardware. He's probably right. And Bushnell's like, yeah, he was right. He keeps calling his manager a dumb shit. Probably was, but not kind, not necessary. Bushnell winds up putting Jobs on the night shift, partly so he won't bother so many people, and partly because he knew that Jobs liked to hang out at night with his buddy Steve Wozniak, who was a great engineer. It would be great to have hanging around Atari. And in fact, Jobs and Wozniak helped to make Breakout, a great Atari game. Remember Breakout? Yes, of course. You're trying to knock down the bricks in a wall. There's still games like that where you got a little paddle at the bottom of the wall, and there's a moment when it goes through the wall and then goes, so good. So Jobs worked at Atari for a little while and then decided he wanted to go off to India to find his guru. Perfect. Asked Atari to pay for the trip. Nobody ever said he liked moxie. And wound up making a deal with Atari where they'd pay him to go part of the way there. They had exported some games to Germany, and there was some kind of problem with the games in Germany. And they're like, we'll send you to Germany to fix the games, and then you can get the rest of the way to India. And, you know, the Germans said Jobs was terrible to work with, but he fixed the games. I was thinking about, like, the link between Atari and Jobs and what did he learn there? And it felt like maybe a little over determined. But I do think, you know, clearly he had this profound sense of aesthetics and of delight, right? Like think of the Macintosh, right? This breakthrough Apple machine in the 80s. It was round, and instead of squares, it was rounded, and it cost them more money. But it was beautiful and it was fun.

Ben Bloom (45:23)

Fun.

Jacob Goldstein (45:23)

And you were engaged with it like a game. It almost looked a little bit like an arcade game with the curves. But more than that, I think that especially at Silicon Valley, at the time when they were dealing in actual silicon. Right. If you're making chips for somebody, you're thinking about the future and the computers. You're not thinking about the psychology of the customer, because the customer was another electronics company. Right. And so Atari and the strength of it was really the first time where they're just like, how will a regular human being who has no training whatsoever, interact with technology? Yeah. So Atari now mid-70s, they're selling all the machines they can make. They need more space. And so they rent an abandoned roller rink and they turn it into this office slash, video game factory. I'd been 10 years older, I would have loved to have worked in a roller rink. Video game factory. Yes. Everybody smoked weed. Yeah. There was a hot tub, There was a pool party where everybody ended up naked in the pool. Bushnell himself looked back on it later and said, if that isn't a horror show for any HR person today, I don't know what is.

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