Time to believe the quantum computing hype?

Summary

The Vergecast – "Time to believe the quantum computing hype?" (July 9, 2026)

Episode Overview

In this episode of The Vergecast, executive editor Jake Castronakis hosts an in-depth discussion on the current state of quantum computing amidst growing hype and political attention. Joined by science writer Sophia Chen and tech editor Marina Galperina, the episode examines the promises vs. the realities of quantum technology, debates about corporate claims, the US–China “quantum race,” and which applications might finally bring quantum computers out of the hype cycle. The conversation also addresses public misunderstanding and incremental scientific progress.

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Key Discussion Points & Insights

1. Why is Quantum Computing So Hot Right Now?

[03:36]

• Quantum computing is getting intense interest because of a "Cold War-esque framing" pitting the US against China, and massive investments from big tech (Google, IBM, Amazon, Microsoft) as well as startups.
• Many announcements are filled with "gobbledygook," but excitement remains high.
• Sophia Chen: "There's this Cold War-esque framing where it's China versus the US over quantum computing." [03:55]

2. What Is a Quantum Computer and How Is It Different?

[04:45]

• Quantum computers are fundamentally different from classical computers (laptops, GPUs) that operate with binary (ones and zeros).
• Quantum bits (qubits) represent probabilities (an “in-between” state), like a flipping coin that hasn’t landed.
• They rely on quantum mechanics, allowing types of math impossible (or extremely hard) for classical machines.
• Qubits can require special physical conditions (some near absolute zero), but newer designs might relax those requirements.
• Sophia Chen: "A qubit is also in this in-between state, like the coin flipping, it's neither a one or a zero, but a probability of both." [05:34]

3. Physical Realities and Challenges of Quantum Hardware

[07:26]

• Not all platforms need the same extreme conditions. Superconducting qubits (IBM, Google) demand cryogenic temperatures; atom-based approaches may not.
• Currently, quantum prototypes have only hundreds of “physical” qubits—each logical qubit (useful unit) often requires many physical ones due to error correction.
• Quantum “data centers” rather than personal devices are the likely model.

4. Are Quantum Computers Good for Anything Yet?

[09:12]

• According to Chen and her sources: “No, they do not think they're good for anything yet. The current, the existing quantum computers are not good for anything yet.”
• Closest useful application: molecular simulation for pharma and materials science; distant applications include logistics optimization and financial modeling.
• Key quote, Sophia Chen: "The physicists have written like a very beautiful symphony and they just have... a couple crappy clarinets." [10:52]

5. Hype vs. Reality: Navigating Corporate Claims

[12:05, 16:03]

• Companies (especially Microsoft) often announce grand achievements. Microsoft’s Majorana chip press releases cite a "path to a million qubits," while having only eight.
• Sober voices (like IBM) publish detailed roadmaps but the gap between claims and practical capability is vast.
• Jake Castronakis: "They have a path. Majorana 2. Right. Once again, the team is on a path to achieve a scalable quantum computer." [13:01]
• Sophia Chen: "When it comes to overhype, Microsoft is in a league of its own." [13:36]

6. What Does “Scalable” or “Practical” Actually Mean?

[25:41]

• “Practical” implies an application only feasible on a quantum computer with commercial value.
• “Scalable” means increasing size without increasing error rates—a major technical hurdle.
• Researchers' predictions for when scientific (not necessarily commercial) problems might be solved range from 2028 to “a couple decades.”

7. Encryption and Security: The Real Quantum Race

[18:40]

• The greatest motivation for the “arms race” is the potential for quantum computers to break RSA encryption, due to Shor’s algorithm (1994).
• Current quantum computers cannot break modern encryption yet, but governments are proactively migrating to “post-quantum cryptography.”
• Sophia Chen: “We have what's known as post quantum cryptography... One of the executive orders that Trump signed recently, he was ordering that the government computing systems migrate... by 2030 or 2031.” [19:08]

8. The US, China, and the Geopolitics of Quantum

[20:45]

• China is making major advances, especially via physicist Pan Jianwei, using photons and quantum satellites.
• What it means to “win” is unclear, but both countries are pouring resources into quantum innovation.

9. Press Announcements vs. Real Progress; The Microsoft Debate

[22:25, 22:50]

• Accusations have surfaced that Microsoft’s claims about its Majorana particles (a foundation for its quantum computer) are overstated or unsupported by the science, according to physicist Henry Legg and others.
• Sophia Chen: "The critique was that, yeah, Microsoft did not create a Majorana particle, which is the basic building block of their quantum computer." [22:50]
• IBM and Google are focused more on meeting engineering milestones, like error correction and scaling.

10. Incremental Progress and Future Roadmaps

[23:41]

• Google is running "quantum advantage" experiments: proofs of principle without immediate practical value.
• IBM aims for a data-center-sized quantum computer (200 “logical” qubits) by 2029, with applications still unclear.
• The entire field is working on error correction and making larger machines with more useful qubits.

11. Industry and Expert Perspectives on Timelines

[25:49, 27:16]

• Expert guesses on roadmaps vary wildly, from a breakthrough as soon as 2028, to at least “a couple decades.”
• Each year sees similar hype and reassessment. The general consensus: progress is real but extremely slow.

---

Notable Quotes & Key Moments

• Sophia Chen [03:55]: “A big part of what's driving the interest is there's this Cold War-esque framing where it's China versus the US over quantum computing... 80% of it is like gobbledygook. But it seems like everybody is really excited.”
• Sophia Chen [05:34]: "A qubit is also in this in-between state, like the coin flipping, it's neither a one or a zero, but a probability of both."
• Sophia Chen [10:52]: “The physicists have written like a very beautiful symphony and they just have... a couple crappy clarinets.”
• Jake Castronakis [13:01]: “They have a path. Majorana 2... the team is on a path to achieve a scalable quantum computer.”
• Sophia Chen [13:36]: "When it comes to overhype, Microsoft is in a league of its own."
• Sophia Chen [19:08]: “We have what's known as post quantum cryptography... One of the executive orders that Trump signed recently, he was ordering that the government computing systems migrate... by 2030 or 2031.”
• Sophia Chen [22:50]: "The critique was that, yeah, Microsoft did not create a Majorana particle, which is the basic building block of their quantum computer."
• Sophia Chen [25:49]: “By practical in my definition it means something that they wouldn't be able to do with a non quantum computer that also has some sort of commercial value.”
• Sophia Chen [27:16]: “Yes, and actually I think it'll be very similar. And I think the only thing that will change is that there will be like one little section in there about the nerdy incremental stuff... because it's pretty [dense].”

---

Timeline of Major Segments

• [03:36] – Public and corporate quantum enthusiasm, press releases, and “cold war” framing
• [04:45] – Quantum computing vs. classical computing: What is a quantum computer?
• [07:26] – The hardware realities: qubits, error correction, and scaling challenges
• [09:12] – Are they good for anything? Current and speculative applications
• [12:05/16:03] – Dissecting hype, press releases, Microsoft vs. IBM/Google approaches
• [18:40] – Encryption as a motivator: Shor’s algorithm, post-quantum security, policy changes
• [20:45] – China’s advances and the question of “winning” the quantum race
• [22:50] – Technical drama: disputes over whether Microsoft actually created a Majorana particle
• [23:41] – Updates from IBM and Google; industry-wide focus on error correction and scaling
• [25:49/27:16] – Definitions of “practical” and “scalable”; expert predictions on timelines; expectation of continued incremental progress

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Tone & Final Takeaways

• The episode is skeptical yet fascinated by the field’s staggering complexity and the gap between scientific reality and marketing language.
• The hosts and guests repeatedly stress just how hard quantum computing is and how much remains unknown, despite vast funding and public promises.
• There’s value for listeners in understanding what quantum computers won’t do (run email/Word/live-streaming), and what their eventual revolutionary applications could be.
• Anticipate another similar analysis next year—with perhaps another increment of “crappy clarinets” added to quantum’s orchestra.

---

Further Reading

Sophia Chen’s article on quantum computing development is available at theverge.com, offering deeper insight and source interviews.

Transcript

[Jake Castronakis]

Hello and welcome to the vergecast, the flagship podcast of probabilistic computing. I'm Jake Castronakis, executive editor of the Verge, and today we are talking about quantum computers. Quantum is hot right now. Trump just issued an executive order saying that America, quote, stands at the cusp of a quantum revolution. The order demands that America accelerate deployment and commercialization of quantum computing. Meanwhile, his science advisor promised that the US Government will develop a computer powerful enough for scientific discovery by 2028. Meanwhile, Microsoft just unveiled Majorana 2, its second generation quantum computing chip. Last year, Microsoft CEO Satya Nadella said that its team had created a new state of matter that had only been theorized 100 years ago. This year, the new Majorana 2 chip has apparently, quote, put the team on a path to achieve a scalable quantum computer that is commercially valuable by 2029. These are big, ambiguous, ambitious goals. Like I said, Quantum is hot right now. There are a lot of big promises and a lot of money flowing into the space. We were really interested in what's real and what's the hype, so we had science writer Sophia Chen dig into this for us. She's joining me today along with Verge tech editor Marina Galperina. But first, here's what's happening on the Verge today. This is 90 seconds on the verge for Thursday, July 9, 2026. It's only July, but Anthropic has launched what my colleague Hayden Field is calling Claude Wrapped. It's a new dashboard that shows how much time you're using Claude, your top days, how much time you're spending, that sort of thing. If you're using Claude enough to bother checking this feature, you might want to take a break. Fortunately, Anthropic is also adding a feature that can send you reminders to sign off, so maybe take a look at that. Meanwhile, OpenAI is finally upgrading ChatGPT's voice mode with a new model that it says is far more capable. I don't want the needles to be too big. What do you think?

[Sophia Chen]

If you go up, it's going to get kind of baggy.

[Jake Castronakis]

Although Baggy is very popular now, don't you think? ChatGPT's voice mode has been on a two year old model until now, and it's been pretty bad. You might have seen that one guy on TikTok who gets chatgpt to give dumb or bizarre responses. The old model is part of the reason why. I think that guy might have single handedly embarrassed OpenAI enough into upgrading it. OpenAI even gave him early access to test the new model, so we'll see if he can break it. Finally, Character AI, the AI chatbot role playing service is getting into AI generated vertical video microdramas. I watched a couple episodes. It only took a couple minutes, you know, and they completely break my brain. I think everyone should watch these just to understand how bewildering they are and to recognize what the end state is of TikTok iFIed attention spans. You can read [e-mail] that's 90 seconds on the verge for Thursday, July 9,

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All right, this seems to happen every few years. Noise around quantum computing has reached a fever pitch. Everybody is super excited. There are press releases left and right about new quantum computing developments, and then nothing happens most of the time. So just to start us off, Sophia, why is there so much attention on quantum right now?

[Sophia Chen]

I think that a big part of what's driving the interest is there's this Cold War esque framing where it's China versus the US over quantum computing. And then in addition to that, the big tech companies are investing billions of dollars into this new technology. You've got, you know, Google, IBM, you've got these smaller startups, you've got Amazon, all these big names. And then periodically they'll come out with like this real flash press release about what they've done and like 80% of it is like gobbledygook. But it seems like everybody is really excited.

[Marina Galperina]

This is a question that we get from commenters all the time who are complaining they don't understand what we're writing about. What is a quantum computer? How is it different than a classical computer?

[Sophia Chen]

A quantum computer is a fundamentally new type of computer, and so they already exist in an experimental form. So Google, IBM, Amazon, and academic physicists have already made quantum computers. But they're these very small scale prototypes, prototype devices, and they're not meant to be consumer gadgets. And it's very likely that actually a quantum computer is going to be this specialized data center that you log into via the cloud. What makes a quantum computer different from a classical computer? So by classical computing, I mean like your laptop, what's in your phone, like GPUs that train AI. These are all classical computers. And so for a classical computer, the basic mathematical language is binary. So ones and zeros. And so to run any piece of software, your classical computer has to translate the commands into ones and zeros. The computer represents the ones and zeros using transistors, where a 1 corresponds to a transistor that's on or 0 corresponds to a transistor that's off. A quantum computer, on the other hand, doesn't use binary bits at all. It uses quantum bits or qubits, which are probabilities of 0 and 1. And as an analogy, I like to tell people to imagine flipping a coin before the coin lands neither heads nor tails, it's a probability of both. It's in this in between state. So a qubit is also in this in between state, like the coin flipping, it's neither a one or a zero, but a probability of both. And so quantum mechanical objects like electrons and atoms, they exist in these probabilistic states, which is why we call this a quantum computer when it deals with probabilities like this. It's a computer that obeys the math of quantum mechanics. And when you represent information this way, you can do different types of math that are difficult for a classical computer.

[Jake Castronakis]

This is what I think is like so fascinating about these. I think our current model for computing is so based around consumer electronics, right? Obviously these high powered data centers exist today, but ultimately it's a lot of the same technology just scaled up. And this idea of having a specialized computer that operates on its own is sort of a different model, right? But these things, they operate at like deep, deep, deep, like close to absolute zero temperatures is my understanding. And I think we are entering a situation where we only need a few of these to actually work to get the benefits. Is that right? And then the question is, what are those? What are we trying to achieve here?

[Sophia Chen]

Okay, so actually that's a common misconception about the cryogenic temperatures. So some quantum computing designs do require absolute zero temperatures, but then it really depends on the qubit hardware. So the stuff that you're thinking of is made by IBM and Google. They're these superconducting qubits. And you know the picture of that chandelier, like though it's those Things that need to go in the, you know, cryogenic temperatures, whereas there's other emerging platforms. So like using atoms. So like those have different requirements. And my understanding is that those do not need cryogenic temperatures. That's an argument for the companies that are doing that, that, you know, that'll be easier to scale. And actually Google has recently made an announcement that they were going to try doing neutral atoms in addition to superconducting qubits. Definitely. Like in terms of the metrics. So like, you know, when we talk about silicon chips and transistors, we're Talking about like 100 billion transistors in the size of a fingernail. Like it's like a totally different scale. So like when we're talking about qubits, like we don't need that many. But actually one of the big questions is how many do we actually need to make something useful. So right now companies are making things in the hundreds of physical qubits. Qubit is both the unit of information and also the name of the hardware. And what they're finding is actually a unit of information needs multiple qubits because that's their way of reducing errors. And so, yeah, that's something that people are working on figuring out right now. And they're also trying to figure out, you know, what applications are good for the computers that they currently have.

[Marina Galperina]

So you've interviewed a lot of physicists for this story. Have they told you if quantum computers are good for anything yet?

[Sophia Chen]

No, they do not think they're good for anything. The current, the existing quantum computers are not good for anything yet.

[Marina Galperina]

Right. So who's invested in this? Who's hoping to benefit?

[Sophia Chen]

So people are so excited about quantum computing because it's a completely different paradigm for computing and they've come up with all these algorithms which are simply too difficult to implement now, but they are very exciting. I guess the most near term application, I would say is in molecular simulation. So using the quantum computers to simulate complicated molecules and to simulate chemical reactions. And these are really interesting to the pharmaceutical industry industry for developing new drugs. It's also interesting for maybe EV companies, like people who are making batteries, different material science applications. And so that's one goal application. And then another goal application is they seem to be good at optimization potentially. And so that could be useful in any sort of system industry that, you know, does logistics. And then also the banks are interested in it for financial forecasting.

[Marina Galperina]

But how close are we to that actually happening?

[Sophia Chen]

So, okay, so we're, that's a difficult question. It's we're probably a while from a lot of these applications. The way that I like to think about it is like these physicists have written like a very beautiful symphony and they just have, they just have like a couple crappy clarinets, like they can't play what the beautiful music that they've prepared. They have to improve the hardware. And I want to give them some credit because it's actually really, really difficult. It's quite different from like working in classical computing because they have to build everything from the ground up. They have like basic material science problems they have to solve, like how to make the chips, like what combination of metals and semiconductors to use for different parts. They have to use customized lasers. Like there's little like off the shelf equipment that they can use to make quantum computers. Like, and you mentioned cryogenic cooling earlier. Like some of them have, they have to have these special refrigerators and they also have to figure out lots of basic engineering questions, like how do we want to arrange the wires so that they don't interfere with each other, like very basic things like this. And so it's really, really difficult and they are making progress. But, but, but a lot of these applications, they just aren't going to happen anytime soon.

[Jake Castronakis]

The thing that I think is so hard to untangle is the difference between the hype and excitement that these companies come out with whenever they have any little advancement. Which, in fairness, I'm sure these are very, very hard problems and it is very exciting that they're trying to tackle them. But this is like two years in a row now. We've had Microsoft come out and be like, we have this new chip. It's exciting. It's getting us so close to quantum. And then when you dig into what they've done, you read these quotes where it's always revealing just how far they really are. So with the Majorana one, there was this line where they said this architecture offers a clear path to fit a million qubits on a single chip. They're currently at, I think it's eight qubits. Right. So they have a path. Majorana 2. Right. Once again, the team is on a path to achieve a scalable quantum computer. So the Majorana one was eight qubits and they're trying to get to 1 million. And I guess my question is like, how do we weigh these two different statements, right. Where they're putting out these very aggressive dates. Right. Trump wants a Quantum computer by 2028. Microsoft wants something by 2029. Right. Is a scalable quantum computer actually just some technology that they say can scale by 2029 or is it a functional thing that can be used by 2029? And I think this is where it gets hard to for the layperson to understand what is happening here.

[Sophia Chen]

I think in this conversation it's important to say that when it comes to overhype, Microsoft is in a league of its own. So basically Google and IBM, I mean, I would definitely say they also have hyped things, but the thing that people complain about Microsoft about is whether or not their quantum computer even exists. Whereas for Google and IBM, people don't dispute that.

[Jake Castronakis]

Yeah.

[Sophia Chen]

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[Jake Castronakis]

I think it's less a question and more, you know, how do we. How are we supposed to understand this distinction between the hype and the real achievements they're making and the. The realities of the fact that they have eight qubits and they want to reach a million in like two or three years?

[Sophia Chen]

I guess in the case of. I'll just use IBM as an example because I think they are more of a realist. They have come up with a roadmap where they plan to make this Data center by 2029. And they. It's unclear whether or not this, this size of computer is going to be useful. One of the big problems with previously was that the more qubits you attach to each other and use for computation, the more errors there were. And so they've figured out that problem at a small scale where they're like, okay, we can build a more complex quantum computer and not have it create more errors. Instead, there are fewer errors the larger we make it. So they have a roadmap where they talk about this. I think for the layperson, one important thing to keep in mind is just like, while they say they're scaling this up, to always ask, what are the applications at that stage? And so for IBM, by 2029, they want to make this data center and they want it to be made of 200 logical qubits. So basically it can encode 200 units of. Of quantum information. When I talked to other experts, they were like, we're not sure what that would be good for. But at the same time, there's these algorithm developers who are also trying to figure out, like, is there a simpler algorithm that might be interesting for industry? And so, like, there's a lot of development all happening at once. I think one of the biggest misconceptions is that a quantum computer is a classical computer that is faster. That is just not the case. And it's not going to be good at a lot of things classical computers are good at. Like I mentioned, like, it's not going to be good at email. It's not going to be good at word processing. It's not going to be good at, you know, this podcast recording, like live streaming. It's not good for any of that stuff. It's supposed to be good at molecular simulation, which supercomputers, like, require a lot of compute to do right now. And these simulations take a really, really long time or they're prohibitive. And so, yeah, so it's a completely different class of computer.

[Jake Castronakis]

The one thing we haven't gotten into yet is encryption. And it's funny because that feels very different to me from molecular biology and, like, interesting and important science research. But I get the impression, and Sophia, I'm curious from your perspective, but my impression is that the encryption issue is a lot of why there is this arms race around quantum.

[Sophia Chen]

Right now, the encryption issue is very convoluted to talk about because what. Basically, it was an argument to make quantum computers, but also it was also a reason to be scared of quantum computers as well. So in 1994, this computer scientist, he developed a quantum computing algorithm. This was before any quantum computers even existed. So his name is Peter Shora. He came up with this algorithm for factoring prime numbers. And so the current encryption system that we use, the RSA family of algorithms, they rely on computers being really, really bad at factoring prime numbers. And so the fear was that once quantum computers existed, that they would be able to break all RSA encryption. And currently none of the existing quantum computers can do this. Basically, that was a real motivator for cryptographers to develop better encryption. We have what's known as post quantum cryptography. So these are algorithms, encryption algorithms, that quantum computers are not supposed to be able to break, and they're not in widespread use yet. One of the executive orders that Trump signed recently, he was ordering that the government computing systems migrate to post quantum cryptography by 2030 or 2031.

[Jake Castronakis]

Is that going to be necessary? Do you really think that that's, like, a possibility?

[Sophia Chen]

I do think that it's probably a win for encryption. I don't know what it means for quantum computers, but, yeah, I think that. Yeah, I think it's probably a good thing for encryption.

[Marina Galperina]

Of course, the question everyone's been wondering about is us going to be China.

[Sophia Chen]

Well, China is doing quite well. The guy to watch in China is this guy named Pan Jianwei, and they are using photons to make a quantum computer. And they also had this quantum satellite that they were doing things relating to a related technology, quantum sensing and also quantum cryptography. And I mean, I don't know. I don't know. I don't know what it means to win.

[Jake Castronakis]

I mean, it is this interesting thing, right, where I think because this hardware is so specialized, we don't need hundreds or thousands of quantum computers. We need a handful of capable ones. And if you can crack encryption, then that's great. The government probably just needs one of those computers. Getting there first seems really important. If that's the case, and it's so interesting that The US Government is putting out these, I would say, outrageously ambitious timelines to get to a quantum computer. It still, from the sound of it, feels like we are at this R and D phase where perhaps there actually has been some meaningful movement over the past few years, but it seems like there is still a large gap before this stuff is actually implementable and usable in a meaningful way.

[Marina Galperina]

Well, there's a confusion because the flashy announcements don't cover the really incremental esoteric things that Sofia addressed in the article because there are. They're not as interesting to the layperson because they're confusing.

[Jake Castronakis]

Well, should we talk about that? About the fact that there's at least one physics researcher who all the drama. Right. They think that Microsoft maybe didn't even create what they said they created.

[Marina Galperina]

Right. Is it the same thing that happened last year? The girlies are fighting again.

[Jake Castronakis]

Yeah. Sophia, is that something that we should, you know, take seriously?

[Sophia Chen]

Yeah, I think so. I like. So basically the critique was that, yeah, Microsoft did not create a Majorana particle, which is the basic building block of their quantum computer. In June, Henry Legg, this academic physicist, he published a peer reviewed critique and it was a criticism of Majorana 1 from last year. But he says that the, the same critique applies to Majorana too. And he's not the only one. I've talked to other physicists as well who have a lot of criticisms about Microsoft's approach and in particular that what they write in their papers does not match their announcement or their plans for scaling.

[Jake Castronakis]

So that's the Microsoft situation. Google and IBM, you said, are a little bit more sober in their approach. What are we expecting from them in the next couple of years?

[Sophia Chen]

So for the last couple of years Google has been doing these, what they call quantum advantage experiments where they're doing these algorithms which don't have much practical use or any practical use, but they are trying to show that they can do it better than a supercomputer. And at the same time this year they also announced that they are going to make a neutral atom quantum computer. So their current quantum computing chip, Willow, is made from superconducting circuits. And so now they're branching out to a different hardware platform. With IBM, they are planning on making a data center sized Quantum computer by 2029 with 200 logical qubits. And I think at the same time everybody's just, they're working on improving error correction. So this idea that, you know, they need a special technique to correct errors in a quantum comput computer. And so being Able to use fewer qubits to represent one piece of information and then also figuring out how to do that at scale. So yeah, I think in the rest of the industry they are working on figuring out how to do error correction better and make things bigger, you know,

[Jake Castronakis]

and I should say Microsoft of course disagrees with those researchers criticisms of their, of their work. I think what's really interesting right now is for, for so long I think this stuff has been really hyped as just around the corner and we are actually seeing hard dates this time and we will kind of just be able to answer those questions in a couple years time. Right. Like I, I think before it was always this stuff is right around the corner. But when, when is around the corner and it sounds like there is progress happening. I'm not sure if I believe how aggressive they are.

[Marina Galperina]

Well, they're using terms like practical and scalable, both of which have very specific meanings, right, Sophia?

[Sophia Chen]

I don't know that. I think it's. Well, I guess like by practical in my definition it means something that they wouldn't be able to do with a non quantum computer that also has some sort of commercial value. And by scalable it means, I think by scalable what they mean is that they can build them bigger without running into this problem of more errors. There are some bullish researchers. Like these are someone I spoke to in academia who was like, I think that we could do a scientific simulation by 2028 that was scientific, interesting. Might not be a commercially interesting simulation. And so she was studying like a simplistic model of photons interacting with electrons, which applies to photosynthesis and it also applies to solar cells. And so she was very optimistic. I talked to some other people who were like maybe by 2030, 2035. And then I had another guy who was just like, I feel that they have totally underestimated how difficult it is to scale. Like, I think it's gonna be a couple decades. So the responses to that are all over the map.

[Marina Galperina]

So you think we're gonna write the same article next year and the end?

[Sophia Chen]

Yes, and actually I think it'll be very similar. And I think the only thing that will change is that there will be like one little section in there about the nerdy incremental stuff because you probably won't let me write more than that because it's pretty.

[Jake Castronakis]

Sophia, I really do want to have you back every single year to write this. So did they do it again? And I know it's going to be the same for a while, but I find this stuff really, really fascinating and I think the upside is like this stuff is exciting as you get closer I think the possibility of even these quote unquote scientifically interesting experiments that's very meaningful and that is a story start and but cutting through I think their very excitable language about how close they've gotten is very difficult particularly with a subject this naughty and dense. So I appreciate you coming on and speaking with us. The story is fantastic. If you all haven't read it yet, check it out. It's on theverge.com all right. Want to say thanks to Sophia and Marina for joining me today and thank you all for listening. If you like what we do here and want ad free podcasts you can become a paid subscriber to the [e-mail] the Vergecast is produced by Josh Kahas, Eric Gomez, Brandon Keefer, Travis Larchuk and Aaron Locasio. David is back tomorrow or is he? See you later.