This Week in Startups - AI’s Next Leap: Hyper-Realistic Agents & Unlimited Power | E2094

Episode Date: March 7, 2025

Today’s show: AI is evolving at lightning speed—smarter assistants, faster models, and now, the massive energy demands that come with it. In this episode, Alex dives into two cutting-edge companies from the TWIST 500 that are shaping AI’s future in very different ways. First, Alex interviews Zap Energy, where CEO Benj Conway explains how their breakthrough fusion power technology could provide unlimited, clean energy to fuel AI and beyond. Then, we talk to PolyAI, where CEO Nikola Mrkšić shares how their conversational AI is revolutionizing customer service with ultra-realistic, human-like voice assistants. If you're excited about where AI is headed—from hyper-realistic agents to a future powered by fusion—this is an episode you don’t want to miss!*Timestamps:(0:00) AI's impact on business and technology; Fusion technology; AI powered voice assistants(1:51) Power demands of AI; Fusion energy's potential and commercialization(6:50) Zap Energy's approach to scaling and efficiency in fusion(9:58) Oracle. Try OCI and save up to 50% on your cloud bill at https://w⁠⁠⁠⁠ww.oracle.com/twist(12:11) Zap Energy's Sentry project and the future of fusion power(18:02) Competition in fusion development and importance of liquid metal walls(20:10) Squarespace. Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www.Squarespace.com/TWIST(21:38) Thermal management in fusion energy and Zap Energy's milestones(24:19) PolyAI's CEO on conversational AI and generative AI(28:39) Founding PolyAI and advancements in AI technology(30:30) Atlassian. Head to https://www.atlassian.com/software/startups to see if you qualify for 50 free seats for 12 months.(32:30) PolyAI's technology stack and deep learning advancements(39:13) Growth, competitive landscape, and AI's impact on employment(46:16) Startups M&A in applied AI and conclusion with Nikola Mrkšić*Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcp*Links from the show:Check out Zap Energy: https://www.zapenergy.com/Check out Poly AI: https://poly.ai/*Follow Benj Conway:X: https://x.com/Energy_Zap*Follow Nikola Mrkšić:X: https://x.com/nikola_mrksicLinkedIn: https://www.linkedin.com/in/nikola-mrksic*Follow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm*Thank you to our partners:(9:58) Oracle. Try OCI and save up to 50% on your cloud bill at https://w⁠⁠⁠⁠ww.oracle.com/twist(20:10) Squarespace. Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www.Squarespace.com/TWIST(30:30) Atlassian. Head to https://www.atlassian.com/software/startups to see if you qualify for 50 free seats for 12 months.*Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland*Check out Jason’s suite of newsletters: https://substack.com/@calacanis*Follow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.com*Subscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

All right, guys, welcome back to Twist. This is Alex. AI is absolutely everywhere. We have been talking about it in terms of new models, faster reasoning, who has the best inference engine? The list goes on. But AI is not just about the core technology. When we think about AI, we have to think about how we're going to power the future and

how AI is going to change business today. So we have two interviews today with amazing Twist 500 companies. The first one is with ZAP Energy. Now, in this case, CEO Benj Conway. and I talk about why they're pursuing Z-Pinch technology in their approach to fusion and how this is going to get us all the way to commercialization. I'm very excited about fusion technology, not just in the AI context. And it's chats like this, they really give me the confidence to say, hey, we are going to figure this out. Then we're talking to Poly AI and its CEO, Nicola Miersch.

Now, we break down how AI-powered voice assistants are taking on the customer service game and are changing it very, very quickly. So if you care about how AI actually interacts with the real world, how it impacts jobs, how it impacts workflows. That's the chat for you. We're going to start with our ZAP Energy interview. So here's my chat with Binge Conway, all about Fusion and why it's coming sooner than you think. This weekend startups is brought to you by Oracle. Oracle Cloud Infrastructure or OCI is a single platform for your infrastructure database application development and AI needs. Save up to 50% on your cloud bill at oracle.com slash twist. Squarespace, turn your idea into a new

website. Go to Squarespace.com slash Twist for a free trial. When you're ready to launch, use offer code Twist to save 10% off your first purchase of a website or domain. And Atlassian. From MVP to IPO, Atlassian for startups provides your team the right tools to plan, track, and collaborate on work. Head to atlassian.com slash software slash startups to see if you qualify for 50 free seats for 12 months. If you've been listening to Twist for the last couple of months, you've heard more about AI than I'm probably sure you ever wanted to. And one theme in that conversation has been enormous power demands. Because AI compute requires so much power, people are talking about massive solar arrays, D, mothballing nuclear reactors. There's a lot of fun

approaches out there. But one that I am personally most excited about is the progress being made by commercial fusion companies. We have a couple of these on our Twist 500 list, but one that I am most excited about is a company called Zap Energy. Now, I am not a physicist, have some in my family, but I was the family idiot. So I have brought their seat. EO and co-founder Benj Conway onto the show today to help explain to me how their approach to fusion works and how close we are, hopefully very close, to seeing commercially viable fusion power. So please welcome Benge to the show. Ben, how are you?

Nice to see you. Thanks for having me on. I'm good because I'm excited because prepping for this chat, I learned more about the different approaches to fusion than I ever thought I was going to be able to. And frankly, I think what Zab is up to is fantastic and very exciting. But for folks out there who are a little bit less red-end bench, I was hoping we could start with just something basic. How does, from a very high level...

The toroidal-na-camera magneti-matim and catushkimi... Oh, you're Russian so much better than mine. I'm so glad you said that. A toroidal chambers surrounded by magnetic coils. The idea being that you have big magnets to confine and compress plasma. And then bucket number two, which is build a big laser. And your listeners may be familiar with the NIF experiment, the National Ignition Sicility experiment at Lawrence Livermore

that achieved a scientific. of energy break even in late November 2022. So we don't occupy either of those two buckets. We use a twist on a well-known bit of physics called the Z-Pinch. So Z-pinchers were understood long before nuclear fusion was understood. A couple of scientists in 1905, Pollock and Barrowclough, in Australia, looked at a lightning rod that had been struck by lightning in some tin mine in southern Australia and saw that that lightning rod had been crushed down.

It looked like it'd been crushed down the length of that lightning rod. And that's because if you remember from high school, your right-hand grip rule, a current going through a conductor creates a magnetic feel that curls like your fingers. And if you squeeze your fist, that's the force that gets exerted on that lightning rod. You sometimes see it in a science museum. You put a current through an empty can of coat, and that kind of coat goes crunch under its own self-generated magnetic feel. So this Z-pinch was actually the earliest approach to fusion.

And then what was the secret 1950s fusion program called Project Sherwood. It was called Project Sherwood because the guy running it was called Dr. Tutt. The Z-Pinch was actually the first attempt. So the idea was they would take some plasma, they would put a big electric current through it, it would create this magnetic field, and it would press and crush just like that lightning rod

or their empty can of Coke. The problem was it was like taking a water balloon in your hands and doing that. it hit the walls in a nanosecond. And so these Z-Pinch plasmas became unstable very, very quickly, and they then abandoned this idea and built magnets and lasers for the next 70 years. What we've worked out how to do is to stabilize that water balloon. And we do that with something called sheer flow.

So imagine that water balloon is not static, but is now flowing. And it's flowing in a way that it's flowing faster on the outside than on the inside. When you're in fast-moving traffic, we all know it's very hard to move, change lanes into fast-moving traffic. So if you have this column of plasma that's flowing faster on the outside than on the inside and ever sort of concentric rings going towards the center, and then you put electric current through it, it compresses just like that lightning rod or like that empty can of poke. But when it tries to destabilize, it can't change lanes because the traffic's moving really

fast. In fact, the traffic's moving really fast on all sides of it. And it compresses a bit further and the traffic tries to change lanes, but the the traffic's moving really fast. And so you end up now with a really stable compression that lasts for 10,000 times longer than that normal instability. So for now, for several microseconds, this thing is stable. And that gets to the point where you have fusion conditions.

So on these two-meter devices that cost single-digit millions and that we can build really, really quickly, we're now doing fusion in a way that rivals some of the biggest experiments in the world. In fact, in the last 12, months, we published an electron temperature experiment result. There really only a handful of fusion topologies in the last 70 years have achieved. We did it on something that you could literally fit in the chunk of your car. So going back in time, we've known about Z-Pinches for a long time.

They were an initial approach to thinking about fusion. They went away for 70 years. You guys have brought them back and you think you are on, from what I can tell, the cusp of making them viable in a net positive power generation environment. So what's exciting about our approach is that the scaling is really significant. The relationship between the amount of electric currents, so the amount of lightning bolt that we put through that shift flow-stabilized Z-pinch, and the fusion reaction rate is an 11th power relationship,

meaning if you double the current, you two to the power of 11, so two times two times two-tenth times the fusion reaction rate. So it's a really strong lever. So what we've been doing really successfully over the last few years is driving more current through our shear-flow stable, Z-pinch, not by building ever-increasing complex and enormous machines that cost billions of dollars, but on that same small device where we can iterate really fast, driving more current through a shift-lis-stablish-stablish-z-pinch and increasing plasma parameters, for example,

neutron yield and other things that demonstrate we're getting to a hot-dense plasma. So I'm going to play a clip here from you guys that shows what we're talking about, I think in a much more illustrative format than just words. But when you talk about your comparison to systems that cost billions of dollars, to me, what Zapp is building is kind of like the anti-I-T-E-R, the major fusion reaction over in Europe, because it just seems so much simpler, smaller, modular, and easier to tweak because you don't have to spend 20 years building a magnetic array that can bend space and time, you know? Right.

Why hasn't this approach been more popular bench? because it seems very logical to me to approach it in this way because there's so many advantages from my layman's perspective. I believe that the reason that we don't have fusion energy has got nothing to do with the science, and I can hear my 160 scientists next door suck air through their teeth as I say it. But the reason that we don't have fusion,

I believe is nothing to do with the science is because we've been building these billion-dollar experiments that take several years to design, several years to build, several years to commission, several years to do science. these 10-year time scales, we've been building these enormous devices where it's just impossible to rapidly iterate. So, you know, imagine spending a billion dollars on an iPhone prototype and building one iPhone prototype every 10 years. You know, you would never, ever achieve a commercial

product. You know, to go from Windows 3 to an iPhone in 15 years, that kind of iteration just isn't possible. So it's one of the key differentiating factors of ZAP. It's our sort of superpower, the ability to build devices with single-digit millions, so orders of magnitude cheaper, an order of magnitude faster, so we can build a new device in a year, less than a year. We spend, you know, single-digit millions, not hundreds of millions or billions, which allows us to iterate really fast. The fusion electricity that we produce is going to be competitive. And I think that's one of the things that the fusion communities has largely ignored over the last

70 years, which is how much is this going to cost? And if fusion electricity or fusion heat can't compete, there's going to be one fusion power plan in the world and kids are going to go look at it on their school field trips. And they're going to say, this is a fusion power plant. It's not going to scale. Okay. Even if you think it's a bit overhyped, AI is everywhere from self-driving cars, medicine, and just business efficiency, right?

We're all using it all day long. And if it's not in your industry yet, it's obviously coming and it's coming fast. You need to get ahead of this giant wave of computing and innovation. But as you know, you're going to need a lot of compute power. You're going to need a lot of speed. So how are you going to compete without your cost spiraling out of control? Well, it's time to upgrade to the next generation of the cloud. That's Oracle Cloud Infrastructure, or OCI.

OCI is a blazing, fast, and secure platform for your infrastructure database application development plus all your AI and machine learning workloads. That's key. So save up to 50% on compute and up to 80% less on networking, according to our friends at OCI. So you're going to save a pile of money. Thousands of businesses have already upgraded to OCI, including Vodafone, Thompson, Reuters, and Suno A.I. Right now, Oracle is offering to cut your current cloud bill in half if you move to OCI for

new U.S. customers with a minimum financial commitment. The offer ends March 31st. See if your company qualifies for this very special offer at oracle.com slash twist. That's oracle.com slash twist. You're about to show an image of one of our devices. Yeah, so this is actually one of the things that helped me really understand the difference in the ZAP approach. If you're on the audio version, I'll play the audio for you. If you're watching the video of Twist today, you're going to get a better show for the next 43 seconds.

Let's look at how ZAP Energy creates Z-Pinch Fusion within an experimental core. First, a puff of gas is injected into a vacuum chamber. As the gas expands, an intense pulse of power ionizes the gas into a plant, Its currents and magnetic fields cause it to accelerate down the chamber. As it comes past the inner nose cone, the plasma collapses into a thin column. The Z-pinch magnetic field powerfully compresses the plasma for a few fractions of a second, producing extreme temperatures and densities. As this happens, a wave of fusion reactions produces highly energized particles,

which can be harvested to make heat and electricity. Soon after the pinch disappears, the cycle will begin again. So I want to double-click on the cycle beginning again. Yeah. Because I know that you guys are working on Century, which will get to in a second, which is all about doing more and more of these pulses. And you said they last for the microseconds. So how many pulses do you guys expect to have eventually in a minute time frame?

I'm not quite sure what the correct question there to ask is, but how frequently do you spin the cycle? Yeah. So what you showed there is our fusion core. It's what will power the center of our power. power plant. But that's not enough. You need to surround that with something that then turns neutrons into electricity ultimately. So what you see there is what our R&D team are working on day and day out. What we've done in Century, which hopefully we're about to show, is really that

first integration of power plant relevant technologies that you need in addition to your hot dense plasmas. So Century includes repetitive pulse power, this liquid blank. kit, durable electrodes, all integrated into something that is about the size of a double-decker bus. It's the size that ultimately it'll be in the power plant, which shows you that this fusion module is incredibly compact. But I think Century is probably the most important demonstration of some of these key enabling technologies around fusion that's ever been achieved by a fusion company.

Sure. And it demonstrates, I think, also for Zah, our technology. is highly differentiated, but our strategy of doing plasma physics alongside systems engineering is also highly differentiated. When I set up Zat, I, and again, I'm one of that sort of rare fusion CEOs who's not a nuclear scientist, I'm not a nuclear engineer. And it seemed barking mad to me that the focus for 70 years had been on plasma physics only, and all of this other technology that you need to make fusion happen had basically

been ignored. And I think people were hoping that Siemens or Hitachi would deliver invented it at some point. At Zapp, we've got almost as many people focused on these key enabling technologies as we have on the plasma physics side. And Century is a really incredible demonstration of those. So Century is much more than just the Fusion Core. Right.

Is it essentially a working, for example, if you had perfected the Fusion Core technology, would the Century system you're currently building function as a power plant, or is it still a portion of the pieces you eventually use? need to build this fusion plant you're discussing. So it's the first generation of all the pieces that we're going to need in order to do in order to do a fusion plan. So, you know, the repetitive pulse power was Century. We pulse every 10 seconds.

In a power plant, it needs to pulse 10 times a second. Okay. And everything is, everything will need to be scaled up. But it really is the first. Actually, it's not even the first generation. It's sometimes a second or third generation of the pieces that are needed to put together for an eventual power plant. So Century was shown off.

October when you guys also announced $130 million in new capital. It's a lot of money. It's been a couple of months. How has progress been on the Century Project? Have you reaching new kind of key milestones that provide extra confidence in your approach? Yeah, we have. We hit a key milestone that we'll be announcing soon, and that meshes with a DOE milestone that I don't want to get too far over my skis. Oh, that is such a tease. That's brutal. Okay, fine. And also on the R&D side,

we bring on some really new, new, exciting configurations that we think we're going to push plasma physics pretty successfully this year as well. So yeah, no, a lot of progress. When you're iterating this fast, it's really amazing how quickly you do progress in all of these different programs, whether it's on the R&D side or whether it's on the systems engineering side.

Now, $130 million. It sounds like a lot of money to folks out there who have $130, but in the realm of big science projects, you've mentioned the cost of some fusion installations runs into the billions. So what I don't have a good idea, Ben, is how much money is $130 million for you guys? I know you've raised a little over $300 total,

but does $130 get you all the way to commercial liability? Does it get you just through the end of the century project? I'm just not sure about scale of capital versus work to be done. We're probably the most capital-efficient fusion company out there. So $130 million is, you know, is worth, a lot to us versus perhaps other fusion approaches. Are we going to need much more capital to get to a commercial product? Yeah, we're going to need several billion dollars, I would have thought, between now and launching a commercial product. Not because the fusion component of our power plants

cost billions of dollars, but because ZAP is unique in that we'll be building our modules in a factory. So we'll get the sort of economies of scale that you get when you build a fusion module in a factory, a power module in a factory, some of the Tritium Cycle modules in a factory, to then install them on site. Whereas other topologies, you're building almost like a conventional nuclear power station on site. With that, we are going to be building up manufacturing capabilities in order to be able to deploy and scale. And everyone knows, setting up large factories for complex machines is incredibly easy.

It doesn't cost any money at all, and you can do it overnight. Right. Yeah, exactly. me. So on the competition point, though, there's, there's a lot of people out there who are doing cool stuff. I mean, I've been familiar with Tokomax accelerators for a while. This app approach was new to me. I know helium energy is also doing a kind of a different approach to it. Do we eventually reach a point in which someone gets their first and then kind of owns the market? Or are we going to see eventually several different approaches to fusion power generation

become kind of the norm around the globe, just like we have different types of nuclear the reactors. Predicting fusion is really hard. I mean, I don't, I, I, I can't imagine, I, I don't know another sector where so many smart people have got predictions wrong over the years. So I, I predict, I predict humbly the fusion, the fusion future. I believe there is room for multiple players. It would be like saying, well, you know, one person, you know, one, one group launches a,

a reasoning LLM and that's it. We just need one. there's going to be there's going to be more I do think that the idea that there's going to be when it comes to commercial fusion multiple approaches I suspect not

I suspect there's going to be one way of doing fusion in the most economical way and that will be the version of fusion that scales I don't think we'll be in a world where we have Tokomak fusion power plants and Shiflow Stabilized Z-Pinch power plants and Stellarator power plants and laser power

plants. I think there'll be one way where this is the cheapest. And that will be the technology that scales. But it is very difficult to know. Why do you have circulating liquid metal walls inside of the technology? Because that to me sounds like science fiction in the best possible sense as a big sci-fi guy. But explain liquid metal walls to me and why they're important. I'm just curious. I'd love you to come and see it. It's really incredible. And I think Zapp is, I think, one of the only fusion companies has actually ever done these liquid blankets. You need something in order to interface with your neutron.

So a liquid wall replenishes. You can circulate heat out of it quite easily. And so it's the blanket and the material which interfaces with your neutron output. And rather than being solid, it's a liquid, which has all the properties, which enable that to, you know, as I say, transfer heat and with complicated sort of thermal management that allows that to get hot

and to breathe the tritium that you need in order to produce one of your fuels. All right, founders, let's talk about your website. Yes, I know it's embarrassing. Yes, I know you're too busy to upgrade it. Well, if you're going to launch something new or you need to give your brand to refresh, which I know you do, you need Squarespace.

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enthusiasm and the cutting edge technology of a startup founder. We love you, Swarespace. Given the my knowledge of thermal management is opening my gaming PC's case to let more air in, I'm going to leave the physics to you. But just to make sure I understand this, when the neutrinos are fired out from the Z-Pinch fusion moment of fusion, they're collected by the liquid metal, which then heats up. And then you can extract that heat from the flowing, liquid metal, and then essentially do you take that out to do thermal power generation? Right, exactly. So neutrons, not neutrinos, just important correction.

But yeah, exactly. They're just different things. Exactly. No, no. Sorry. But you're exactly right. So you need to somehow get the heat out, and you need to turn heat into electricity. And, you know, humans are very, very good at boiling water and spinning turbines.

And so, yeah, everything from boiling water onwards looks very much like a conventional power plant. We've been doing a feasibility study on a decommission coal fire plan, not far from we are, we're not far from where we are in Seattle. You know, obviously, you burn coal, boil water, turn turbines. You know, fusion does fusion, boils water effectively turns turbines. Everything from sort of boiling water is very, very similar. So we think there may be some efficiencies. in terms of scaling commercially by retrofitting some legacy energy infrastructure like old coal-fowl power stations.

Well, if all power generation is just making a tea kettle blow, then why not just take your fusion power and attach it to an existing tea kettle? I mean, that makes perfect sense to me. That's the rational. Okay, so, Ben, I have to let you go, but I'm curious, when should we sync back up with you to see the next milestones you're talking about that you can't quite share yet? Just what's the timeline for the next big piece of Zapp news? We'll be publishing later this year. I mean, we really believe in the peer-reviewed process. We'll be doing that more this year.

But I would really encourage you to come visit. We'd love you to see what we're doing in person. It'll fulfill all of your preconceptions of what a fusion startup looks like. So, look, we'd love you to come visit later in the year. And, you know, we'll be announcing some progressing. Not an impossibility given that I grew up in Oregon and a family on the West Coast. And I got to go see the student reactor at Oregon State University when I was a kiddo. So I've gotten to see some of related to technologies.

But Bench, thank you so much. ZAP Energy on the Twiss 500, one of the coolest companies in the United States today and at the forefront of fusion energy production. Thanks, Alex. And that, my friends, is why I consider nuclear energy very important, but merely a stopgap on our path to fusion. I can't wait. It's going to be amazing to have unlimited, clean, free energy. All right. Next up, Polly AI and its CEO, Nicola Mierstitch, we're talking about AI, but pay a special attention to the difference between conversational AI and generative AI, how the CEO thinks about it and what that means for the market. All right, let's go. Hey, everybody, welcome back to Twist.

My name is Alex, and we have another Twist 500 interview for you today. Now, on the podcast, we spend a lot of time talking about models, new models, faster models, reasoning models, how new models are built, what they cost. But what might matter more is how AI is being used today in a business context. And for that reason, I'm really excited to have Polly AI on the show today, and we're going to bring up CEO and co-founder Nikola Mershitch. Nicola, hey, how you doing? I'm doing great. Thank you for having me. And thank you for one of the best pronunciations of my last name that I've ever heard. I did much better before we hit record. And I kind of butchered the second time, but hey.

No, you do great. You're a great. Honorary Serbian citizenship to you. I'll take it. My country's going through some weird times. So never bad to have one of those. We've always been in weird times. So, yeah. What a strange time we live in. Putting aside geopolitical jokes, though,

you are in London. Thank you for being up so late to record this. No problem at all. I'm excited about Poly AI because I'm a big fan of, I think, where AI is going, which is going to be me talking to it. both a personal and a business setting. But I thought to start, before we get into exactly what your company does and its history is to define conversational AI, because you guys differentiate that from generative AI in a very useful way.

So, Nicola, if you don't mind, can we just start there? Yeah, look, I mean, I think conversational AI is to do with using technology, AI or not, but really, it always is AI to build conversational systems. So to build technology that will allow us to speak to machines. And then, you know, well, generative AI is just, you know, are you. using generated models to apply and applying them in different fields of work. So it's kind of the question there is like, are you using electricity, right? So yeah.

So then I guess it's simpler than I thought, but I was going to ask when I do use, for example, just chat GPT in the consumer context and I'm using it in a me speaking, it's speaking back to me thing, I'm using conversational AI, which is just undergirded or supported by generative AI technologies. Yeah, I mean, look, I would basically think of it as one is like a foundational model layer, you know, kind of like if you're thinking of computer networking one, it's like a cable, right, it passes packets through it, right? And the other is more of an application layer thing. So generally, an AI for charge of a PT or for our systems, et cetera, is used to build a

conversational application, right? So by a large conversational AI is about applications and it lives in the application layer. Yes. And for folks who don't know, I know this is a simple question, but can you define application layer for folks? We use that term a lot, but I think it's good to hear from the expert exactly how you and your company define it. Well, I mean, I think the application really blows down to like, are you doing something useful for people, right? You know, one is just a technology and the other one is like something that you use as an application, right, like the same way to use an app on your smartphone or on your computer,

right? So similarly, these are maybe applications used by enterprises to give them a voice on the phone that allows them to speak to their customers anytime of their night and to do a really good job, right, to open them up to the world and to contact. Well, I think anyone who's been stuck in a call waiting line to talk to a human, there's like three of them. It seems that every single major credit card company can understand why this might be faster and simpler and better.

But let's go back in that to 2017 when you founded the company. I think a lot of people didn't think about AI in this type of context until Chad CTP came out, which was 2022 of memory serves. You guys founded the company the same year as the attention is all you need paper that kind of launched Transformers and LLMs into the technology context. So I'm curious, when you found it, did the technology exist that you needed to actually get to where you are today? Or did you found it almost an anticipation of technology having a couple of breakthroughs?

That would make your vision feasible in the market. Yeah. So I think like it's a moving target, right? Technology is advancing and we're doing increasingly impressive things. I had an incredible fortune in my life path where I ended up doing a PhD at Cambridge, starting in 2014, with a professor called Steve Young, one of the most cited guys in speech recognition and a believer in deep learning. So deep learning really starts kicking off around 2012 when people figure out how to, you know, Jeff Hinton, his students and others, figure out how to basically pre-train deep neural networks, right? at that point, we start getting much more powerful machine learning technology that allow you to generalize, to understand people, you know, without using one of three words to say, you know,

balance check. You might be able to say, look, I want to check my balance, or I want to know how much money I have in my account, right? My PhD was largely about that, how you move away from, like, exact matching of sentences and patterns to, like, what someone said, into, like, mathematical representation of a sentence where, and you know, that sort of looks at it and goes, well, Is this the right response for this or not, right? So we pioneered a lot of that stuff.

And now we measured by distance in vector space. Yeah, yeah. I mean like similarity, right? So cosine similarities typically used to kind of like say, these two things are similar and then you have different neural nets that learn to look at two things that might be in different spaces. So it might not be distance. It might be like learning and mapping between them.

There's a lot in there, right? It's gone pretty complicated in terms of how it's implemented and these things get bigger and more powerful. And it's getting harder and harder to. really see what they're doing behind the scenes, right? It's not like they're writing code. They're learning to have an intuitive understanding of, you know, is this sentence the right answer for this, right?

Or once you say that sentence, what do I say next, right? Because that's really how they're trained to think, to reason, right? All right, if you're shipping a product or rolling out an update, you're building a company, you need to be organized, right? We know that. Atlassian has exactly what you need to streamline your work and smash her goals. And the Atlassian for Startups program is packed with all the tools you need, like Jira, where you can track every task, sprint, and bug. That's the industry standard. Confluence, another industry standard for team collaboration and documentation.

And of course, Lume for quick video explainer creation. Now, Lume is really brilliant. My team started using Lume on their own. They started paying for it on their own. Why? They wanted to get credit on the investment team for communicating to me, the general partner of the firm, why? they wanted to invest in a company. So they would do a loom where they recorded over a recording of an interview they did with a founder or visiting their website and going through why they want to invest in a company. And this was so great for me. I would be skiing in Japan. I would be on a flight to New York to see my parents. And all of a sudden I get a notification for one of my team members. Hey, watch this loom. And I get the link for the loom. I click it. And then I can put comments at any time. So it's like doing a conference call, but on my time, a synchron. and I can communicate right there on the video.

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or scaling to the big leads, Atlassian is here to accelerate your startups growth. Check out Elassian for startups where eligible startups get up to 50 seats for free. for one full year. That is absurdly generous. Why can they be so generous at Atlassian? Because they're the standard. Atlassian is the standard. And they are generous to startups because they were once a startup. I remember meeting them 20 years ago in Australia. What a great company. Head to atlasian.com slash startups slash twist for complete details. This is one of the things that impress me about your company because when I was learning more about the space and what you guys are up to and just how to go

about it, the number of pieces that you had to put together to build your in-market product were interesting. So I'm going to attempt here a kind of breakdown of how this works in your context, and then I would love for you to correct me and tell me where I got it wrong if you're up for it. Okay. So ASR is automatic speech recognition. And essentially what that does is takes me talking and converts it into text that an LLM or some other kind of technology system can ingest. From there, we need natural language understanding, which takes those now text-based words and converts them into meaning. And I believe you use an LLM for that.

And then after you have an output, you use speech synthesis to turn that back into spoken words. That would come back to me the customer. How do I do? For kind of like the pre-LLM era, pretty well, right? Because that meaning, like whether you're breaking down that meaning in the middle and then writing logic to say, you asked for an Italian restaurant in, I don't know, Northwest London.

Like, I'll go to a database and figure out what they are. Like, yeah, that's kind of how it worked. Kind of like when we started the company, right? Now, already there, it's always been about adding data to make the whole thing work with less logic written and with more just data passing through. Right? So the more data you see.

And like the culmination of that really is things like modern, large language models where basically you don't write any logic. It's just it flows. It predicts the next word and through that it actually in the background. it does a lot of reasoning, it's able to call APIs and you can kind of like train it to do a lot of these things. So increasingly now,

like you're able to avoid a lot of that ontology-based, kind of specific use case-based encoding, and you get to the point where you just have a single model. At this point, like the end-to-end voice really stands from models that take voice in and produce voice-ad, subsuming all the components in one. And deep learning has always been about just doing that, being focused on creating intermediate representations that don't have to have, like, humans saying for this application, pull out this and then do this.

And instead just having a single thing that from data learns how to do it all. Right. So we have, you know, my co-founders, my work during our PhDs has always been about having fewer components, right? So some versions of that dialogue stack have like five components, right? And really, you don't have to have all that anymore, right? Now you can kind of like prompt an LN and say, hey, you're a restaurant concierge. Like, if you need to find a list of restaurants, here's this function. And the function is give it a location and it'll return like the list.

And when you see that list, well, you know, keep talking to the person. Then you might see how it talks, right? So as LLMs have gotten better, you've been able to do more inside of one specific model, say. And I know you guys have the, you have a convert model. I think that's the name for it. So Convert was our kind of like pre-isdient transformer model. It was, is 600 megabytes. So puny in comparison to a lot of the market stuff.

That's insane. Yeah, yeah. I mean, like, a lot of people these days are like small, large language models. It's like, what does that even mean, right? Like, but everyone was just writing code that was kind of saying like, let's just figure out how to, you know, have these intense entities, sort of database searches. And that stuff was a nightmare. That's how that first generation of voice IVR is built, right? So, like, the one step forward was not exact matching of words and stuff like that.

That happened around like 2014, 15, 16, right? Then you got a bit more expressive, but it was still like, you know, those loops where you get stuck? You're like, no, do you want a credit card, debit card? You're like, no, I'm calling you because of my mortgage. Ah, was it debit or credit? You're like, no, no. That's when I take my phone and I throw it across the room. That's exactly where violence happens, exactly.

And, you know, really, like, we've kind of gotten further and further away from that. Now, we'd convert, and we no longer use it much, right? You were able to just say, like, look, hey, you, system, the user will say something. And you can say one of these 500 things. Pick the best one, right? So it's kind of like an LLM, except an LM just generates a sentence all flat out, right? That's where you have hallucinations, all those things. So this was, like, you know, a bit less expressive than an LM, but very safe for the

enterprise. It's a good approach. And these days, you know, we use LLMs. We tune our own, right? We have a lot of data. We have many customers that take, you know, millions and millions of calls with us. So we have a serious data mode that allows us not to be better than OpenAI or DeepSeek or who else, right, at like general LLMs. And we don't want to be that, right? but it allows us to tune open source LLMs so that they're better at customer service over the fall. Similarly, that speech recognizer. We still treat separately.

A lot of people hope they will be able to kind of like do these N2N models and, you know, we're excited about it. That's ideologically where we've always been going. We're experiment with it. But for the enterprises there, you know, we still kind of like keep two separate things that we tune for them. And that's really needed to drive that performance higher, right? And the truth is it's better than it's ever been.

It's not solved. Do you know this intuitively from the fact that if you tell Alexa to set a timer for 15 minutes? I don't use Alexa as the state of the art for anything because Alexa is dumber than my dogs. Well, we can talk about that. It is, it is. But the one thing which is, I think, a good illustration is you say set a time for 15 minutes and you're just as likely to get 50 as an outcome. You know that everyone who uses it as aware of this, right? And that's just like stuff that has to do with speech technology.

it's not at the cost of working 100% of the time. And that's something that you must build into how you build the systems. And that's what I think the markets mostly, these newer people working on it, they don't really get it because they just, you know, they started working on it three months ago. And they think that that jump happened in three months. Right, versus it taking seven years or eight years since the Transformers paper came out. Transformers are not the beginning of deep learning history, right?

I'm just going back to 2017. I'm just going back to 2017 when you founded the company in that paper came out, going back to 2012 is 12 years. Yeah, yeah, and look, I mean, that was just like, you know, there are people who would tell you that it was 90s, and then there was one winter and another, and we might yet slow down, but I think society as a whole has not put so many great minds

and resources into AI that I think we're kind of like all in now. So it's going to take a lot for us to slow down. Well, what matters in the business context is that polya AI has been in the market now for some time, and you guys raised a series C last year that was quite large. I think it was a $50 million round, and I know that a coastload was in there, and I think Nvidia's venture capital arm was in there. Two questions about the stated things. One, how fast is your technology improving? We talked about the market itself overall, but I'm curious how much better you guys are getting at the kind of in-market task of handling customer support calls

for your customers. And two, how quickly is the business itself growing since you raised that hefty, sizeable, CREC last year? The technology is improving really fast, right? And that's to do with getting the opportunity to work on really important big problems for very large companies, right? If someone trusts us with, you know, 20, 30, 40 million of their annual calls, like, we take it pretty seriously. And, you know, we've been building up to this challenge for years. And there's a lot to handling that, right? With some of these companies, we're able to do 90% of the calls in a fully automated way with like a custom satisfaction score at comparable and sometimes even better levels than humans, right?

Nicola, that 90% number, what was that three years ago for PolyAAAA? Yeah, it's hard to talk about it generally. But, you know, three years ago, we had like maybe the highest one might have been in the low 70s. Okay, so you've gone from pretty darn good to almost all of them. That's probably harder than going from 0% to 20%. Yeah, but I'll tell you something else about the business context that I think that I'm listening and thinking of applying this needs to know, right?

Like, if LOMs right now are outstanding, they're really good, they might improve still, right? Yeah. But the reason that we don't see like a 20% annual GDP growth of all countries that have access to it is because we need to connect it to the world in meaningful ways. You need to find a conduit for it to excel at what it does and produce value from it, right? So for us with enterprises, it's really about like helping them navigate their transformation of their processes and to work with people, of their context. so they can reap the benefits. It's really hard for a company to structure their processes in ways that they're comfortable with enough

to then entrust it to AI. That's a journey that we help them navigate that's as difficult as implementing the AI itself, right? Because it involves their IT teams, their people leaders, their contact center leaders, their chief operating officer, risk officer, brand, everything, right? Companies doing this are still, I think, early adopters. But one thing I've also seen is that there are other companies

working in the broader voice AI or conversational AI space that are also raising a lot of money and making a lot of noise. Eleven labs just raised a bunch of money, play AI as well. And to me, I read that as investors noticing the momentum that you're describing in your business and the market itself and just placing multiple bets because it's going to be a market big enough for multiple winners. But I'm just curious from your perspective as the leader, how much competitive pressure are you seeing in the market when you go out to try to land an account?

Do these other names show up? Is it still kind of a greenfield opportunity? You see everyone, right? I think there's like, the recent style was that 30 to 40 percent of all white combinator companies are working on voice agents. So this is the new world rush, right? And I feel extremely privileged with kind of like when we started, how we started and everything we've done, right?

Because unlike a lot of those companies, we have roastered clients. We have a bench of people in every department, right? Yeah. And just the cache, and the data that allows us to have, you know, we have full model strategic autonomy. I mean, we're not a rapper, right? So do we see competition, hell yeah, more than ever? That marketing map came out. It's like 20 different verticals.

It's like, you know, healthcare, restaurants, hospitality, travel and logistics, financial services, obviously. In all of those, I think we probably have more revenue than most of these new challengers, times probably. like five to ten, right? That's a formidable advantage, but it's also like something to, you know, I think, you know, you think of all the companies that have gone the way of old favorites, you know, your Netscape, sales, others, right? You know, I don't think we can get arrogant here, right? I think there's a lot to do.

We fight Google left and right. We fight, like, hyperscalers. There's C-cast companies deploying things. It's a gold rush because everyone understands that, you know, between North America and Europe, there's a trillion dollars a year of labor costs in this. And, yeah, listen to this. people don't want to do those jobs. That's why in my notes, I don't have,

Nicola, what are you going to do with all the people you put out of business? Because calls into jobs. I don't think I've ever put anyone out of business. Oh, I'm sorry, that's not what I meant. What are you going to do to people that you displace who are doing these rote jobs? But I just don't think we need to have humans answering these questions for other humans. It's a waste of human potential, in my view. Yeah, yeah, no, no, no, I heard you.

I mean, like, the real thing is I don't think that we ever had like a large-scale deployment where we implemented this, where someone was happy with what they're spending on the contact center and they had enough people. Like, that is a fictional noun at this point, right? And there isn't really like this, you know, proverbial private equity-backed CFO

who's like, I'm going to fire a thousand people. That doesn't happen, right? Because people who don't have a problem don't tend to go and fire a thousand people, right? And if they have to fire a thousand people, that company has already let those people go and their service levels have gone to hell. Right?

So now they need technology to dig themselves out of the grave. Right? So that's really the business we're in, the only people we hope to put out of business are all our competitors, right? But, yeah. But, yeah, no, it's a really fun time.

The other thing that's really important to say is that we're creating a whole new generation of highly paid knowledge workers in the contact center, right? And many ways we think about it. Like, we're turning the contact center into a command center, right? because where you previously had, you know, this department that is there as a band-aid for failure management, and you do something wrong as a business. You roll out a product that needs to be recalled.

People call the contact center. Your product doesn't work, context center. You'll overcharge someone in your back office. They call the contact center. And everyone points the finger in them and they're like, hey, it's your fault. You didn't pick up the phone. You're not working hard enough. Oh, trust me, I've spent a lot of time in contact centers.

the number of empathetic people that are doing their best, while one after another, callers are being incredibly bad to them. They're terrible. I don't know why people think that being mean will get them something better. Their thing is going to get them refund or whatever. People behave terribly on average. Like, I don't think the context of their leadership talks enough about what their people are going through, right?

Well, I don't think they want to admit it. They just want to put that onto someone else and pay them, you know, $14 an hour. This is, by the way, if you're listening to this, here's a hack. The next time you talk to a person on the phone who's helping you with your credit card miles or whatever, be nice. Because, one, selfishly, they'll go to bat for you. But two, they didn't do it. Whatever your problem is, they didn't cause it. Be nice to them.

One last tiny question in that I promise I'll let you go. There's a lot of money sloshing around the world of AI. And you mentioned all the YC companies that are out there trying to kind of follow in your footsteps. How often do someone try to acquire? Poly AI. Yeah, we've said no many times. Is it more frequent that people show up and try recently? I'm trying to get an idea essentially for what's the state of startup M&A in the realm of applied AI today?

Well, I think post-election and stuff, the imbalance and stuff have increased. And I think that in general, the state of the markets is such that I think a lot more is expected, right? I don't, like, I mean, the previous period was pretty quiet, right? I think when we started, it was left and right. Several companies were really, really keen. And that was, like, the first golden age of, like, deep learning aquifers, and we have a pretty strong team. So that was no surprise, right?

Then kind of, like, when COVID hit and, like, the whole thing, like, kind of, like, afterwards slowed down, like, we were busy building. Like, I didn't even, like, respond to those emails, right? And I still don't, right? Because I think that what we're doing is, like, really, really, really exciting, right? And with all these companies, like, do want to buy you, you know, you want to partner? Let's do a deal. Well, okay, whatever your next ARR milestone is 25, 50, 100, whatever it is.

Come back on the show, tell me about it. And I want to ask that question again to see if you've turned the tables and are you now going to buy other companies. But in the meantime, Nicola, thank you for coming by. We appreciate it. And just hit us up with all your news because Twist is always here for more startup stuff. Appreciate it. Yeah.

Thank you for having me. It was very fun. I love talking to founders. And that's why I'm so glad we're doing this Twist 500 project. If you don't know, the Twist 500, which is at Twist500.com, is a list of the 500 most important and potentially most lucrative private market companies out there. Essentially, the goal is we want to find the top 1% of startups and then talk to them. So these interviews are going to keep coming as we add companies to the list.

We're in the back half of building out the Twist 500 now, so expect a lot more to come. In the meantime, Twist does go live Monday, Wednesday, Friday at about noon central, 1 p.m. Eastern over on YouTube, LinkedIn, and every other social media application you can name. We're also out there on podcast platforms wide and far. And if you want even more for me, well, I write over at cautious optimism.com. But in the meantime, I'll see you on Monday.

Bye, everybody.