AUTOMATING HEALTH CARE, THERMODYNAMIC COMPUTING, AND PRODUCT VELOCITY | E2155

So I go to a pcp, they say, alex, you need to go talk to a nephrologist. And then what happens right at that moment and how does it break?

So, so the, it's like a, you kind of had to think about it as like a funnel, right? If, if, if you clicked on an Instagram ad for the incredible, you know, shirt that you have on there, right? That funnel is super, super tight. All your data is going to neatly get passed on over to the store that's trying to sell you that shirt. And you know, you're going to enter some information and man, if you don't buy that shirt right then and there, they are going to hound you and they're going to find you on every inch until you buy. Because what they really care about is capturing you as a lead, right? So most people think about patients as patients and not enough actually as a lead that really needs to go be serviced and worked to go and actually get their attention.

This week in Startups is brought to you by Squarespace. Turn your idea into a beautiful website. Go to squarespace.com 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. Northwest Registered Agent. Starting your business should be simple. With Northwest Registered Agent, you can form your entire business Identity in just 10 clicks and 10 minutes. From LLCs to trademarks, domains to custom websites, they've got you covered. Get more privacy, more options and more done. Visit northwestregisteredagent.com twist today and AWS Activate. AWS Activate helps startups bring their ideas to life as you build and scale your business. Activate Credits grow with you to support your changing needs. Apply to AWS Activate today and receive up to $100,000 in credits. Visit aws.Amazon.com startups credits. Hey everybody, and welcome back to this week in Startups. My name is Alex and we have a special episode for you. Today we are once again going to go talk to some of the founders who are building the companies of the future. If you're not Familiar with our Twist 500 project, we are going out into the world to find the 500 startups that have the potential to have the largest financial impact, which is a good corollary for how much disruption they're going to bring to the world of business and technology. We're having a lot of fun. We have about 400 names on the list so far and are racing to close. But also right now we are going through and talking to people as often as we can. So today we are going to talk to the founders from Tenor and Xtropic and Beehive. So we have applied AI, next generation chips and then a software company that we've talked to before and we know and love here at Twist. So first up, we're going to talk to Tenor. What are they and why do we care? Well, it's important to keep in mind that medical care is one of the largest markets in the world today, period. The US spends around 18% of GDP on healthcare, just to pick an example. Now, inside those trillions of dollars that we spend on medical care each year are billions of dollars worth of technology spend, and a lot of that goes to software. In fact, software for the medical practice management and administrative automation spaces alone are worth about five or six billion dollars this year. And even better, healthcare spend is rising faster than gdp. So that means startups like Tenor are chasing large expanding markets that are often pretty much recession proof. It's really not hard to see why. Tenor recently raised over 100 million doll venture investors are simply betting that the medical world will spend on software to help control costs and also to ensure better patient care. Given how much spin is up for grabs, it's not a hard wager to understand. Let's talk to the company. Here's Tenor. All right. Here on Twist, we talk a lot about technology. Companies of all shapes and sizes, from drones to robots to AI models to people that are trying to reinvent business software yet again. One space we don't spend enough time really looking at is our health, caring for ourselves. It's an enormous part of the local economy, tens of percentage points of gdp, and it's pretty inefficient and it's pretty slow and it's often not that great for patients. Some companies are finding real success, though, attacking certain areas of the current health care market, bringing a little bit of that technology magic dust to helping us all get care faster, easier, and with less disruption. One of those companies is a firm called Tenor that I've been tracking for a little bit now. And after they raised an enormous Serie C just last month, I thought, hey, let's get him on the show. Let's talk to them and figure out what they're doing. So I'm going to bring on a guy named Trey Holterman here in just a second. And oddly enough, of all the CEOs I've spoken to the last 12, 18, 24 months, he's the only one who has a strong AI background but wants to focus more on who he's helping than how he's using AI inside of his business. Trey, welcome to the show.

Hey, Alex, Great to have. Great to be here. Great. You didn't use the word luddite. I was expecting it. But I appreciate it. I appreciate the intro nonetheless.

Before we started, I. I gave Trey a joking intro and called him a luddite just because of the AI thing.

I thought that was gonna be the real intro, but either way, no, no.

I. I try to not flame guess until we're at minute two or three into the. Into the chat, and then, you know, loosen them up and then go for it.

Well, I'm fully clenched, so I'll stay that way for a couple more minutes then.

I knew this was gonna be a fun chat. The moment you got on. Your energy is fantastic. All right, Trey. Seriously, though, Tenor, looking at the medical referral market and its problems, if people listening this haven't had a medical issue that required a specialist, they may not know what that means. So can you tell me why you built the company and the problem inside of health care today that you guys.

Are attacking so super simply, basically, if you want to go to a variety of specialists, you really just can't. And why is the reason that you can't? You can't because you are not the one paying, right? There's an insurance company or the government is actually paying for your service. So what do they do? They say, look, you've got to go to a gatekeeper, a frontline, a primary care physician, urgent care hospital. And these people are allowed to refer out into these specialists. And it turns out, right, when they refer out to these specialists, one of the big problems in the US Healthcare system is you refer a patient out to the US Health, out to a specialist to do, you know, a drug, a device, or, of course, a more specialized consult. And the patient never gets contacted, never actually gets there or shows up and doesn't have the paperwork necessary to, you know, get the work done effectively. And that's a big problem. It's a huge problem. In fact, over 50% of patients do not get captured along that process. So they just go, you know, on with their lives. And oftentimes, disease get on, go untreated, patients get unseen. That's the problem in a nutshell.

I want to know what counts as a specialist, because to me, the doctor is the doctor, and sometimes they have different job titles, but I kind of go into a room, there's a white coat, they chat to me, whatever. So what is the difference in a specialist and A pcp, for example.

So typically, right when you go to medical school, right, you're focusing, you're specializing in one of many areas. And the, the, you know, some of the most incredible doctors are the frontline. And oftentimes they are generalists in doing family medicine. They're treating a whole host of things. And you go to them as really that first line of defense, saying, what's going on? But listen, they're not the ones that are going to give you knee surgery or diagnose a complex issue with your stomach or heart. They're not the ones that are going to go and do your imaging for you. They're going to refer out. And so those specialists are, they're all doctors, they're all providers, the way we would refer to them. But we really only focus on the providers and the businesses really, that are getting sent patients.

All right, so I go to a pcp, they say, alex, you need to go talk to a nephrologist. And then what happens right at that moment and how does it break?

So, so the, it's like a, you kind of have to think about it as like a funnel, right? If, if you clicked on an Instagram ad for the incredible, you know, shirt that you have on there, right, that funnel is super, super tight. All your data is going to neatly get passed on over to the store that's trying to sell you that shirt. And you know, you're going to enter some information and man, if you don't buy that shirt right then and there, they are going to hound you and they're going to find you on every inch of the Internet until you buy. Because what they really care about is capturing you. Most people think about patients as patients and not enough actually as a lead that really needs to go, be serviced and worked to go and actually get their attention. And the problem is when that provider sends you, Alex, as a patient, they don't just send a nice clean data blob of data. Oftentimes they're sending all your chart history, bunch of documentation, labs, studies, their notes, their determination of what they think is going on. And so you end up sitting on a backlog. And that backlog is heinous. And what we help people do is get through that backlog while engaging the patient every step of the way. So they know what they're going to pay, if they're going to be denied for medical necessity reasons, and really what the roadblocks are to actually getting in. And it turns out basic human psychology, if you're able to contact you Alex within five minutes saying, look, this is what it's going to cost you. We're missing this one piece of documentation. If you can go back or go get imaging done for this one thing, you're going to be able to get seen and serviced. It's just tremendous the impact that it has on actually converting patients into real visits.

So we talk a lot about in the e commerce world about reducing friction in the checkout flow. Like every time you can remove a step, your overall conversion rate improves by X percentage points. It sounds like the current referral system is just like a 50% fail funnel to getting people from you need this to then actually getting it. So using Tenor system as it exists today, how much better is the successful rate from referral to care?

Well, you're going to hate this answer, but the reality is it totally varies by specialty. Right. 50% for gastro, where somebody's going to stick something where the sun don't shine is actually an incredible conversion rate. Right. So you're trying to get more towards.

Is that because people don't want to actually go get those appointments?

Yeah, there's certain, right, there's certain care, pieces of care that people do not want to go and get. There's other care. Right where you are going to get that care. It's just a matter of whether or not you go to the first place that you were actually sent. And so in that place you as a patient you're going to get service one way or another. It's just a matter of if you have to go and hound down the door yourself. And so you as a patient may convert personally at like an 80% rate but the provider that received you. For us, we're dealing with a lot of folks in the 50 to 70% range trying to get into the 70 to 90% number. I mean it's really astounding actually the sort of impact that you can do on a per provider basis.

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Let'S just say the United States does this apply to? Because, you know, as a person not in the medical field, my exposure to the doctors is whenever something breaks in my body, I go talk to them. But past that I don't think about it much. So how many people do you think are impacted each year by this broken linkage between primary doctors and specialists?

Yeah, so it's in the tens of millions from an individual's basis in the United States. But what's interesting about that is that unfortunately a lot of the folks, it's not like it'll happen to one person one time. For a lot of these people, they're seeing different specialists every month. And even for a single sort of patient journey, where this gets really quite a bummer, to be honest, is oftentimes you'll have sort of six links in the chain to the point that you're actually getting treated. And at every single point you're dealing with this insane friction and the same sort of paperwork obstacle. Going from really point A to the time that a patient's actually getting treated. It can feel like you're trying to get an expensive, rare, packaged good through multiple adversarial countries, through different domestic ports. And honestly from timing delays, sometimes I, I actually think Ryan Peterson over at Flexport would do a better job at getting some of these patients into Congo or something. So it can be months for a whole patient journey, but we see oftentimes patients that are not getting contacted by A single provider on the order of five days to two to three weeks.

So we joke about delays and timelines and working with our friends over at Flexport to speed things up. But on a non joking side of this, this is bad for health. Right? These delays are not just administrative issues. They're not just, you know, paperwork headaches. You could die if you don't get the care you need at times.

Yeah.

I mean, what really ends up happening, right, is you go to the hospital, you have, you know, something that's really wrong. They say, hey, you got to go to the specialist. They send you, the specialist, specialist doesn't contact you. So you think, hey, this must not actually be that serious. Or maybe the specialist contacts you, you're freaked out about how much it costs. Right. Number one reason people don't convert is time. Number two reason people don't confirm is price. And really just not having financial transparency to what it's going to cost. And then where do they end up? They end up back in the hospital. So it's one of these problems that it's bad for the patients, it's bad for the system, it's bad for the payers, it's also bad for the providers getting sent these patients. So it's just like a problem, you know, it's a clean definitional problem that should not exist.

All right, now let's tilt this towards what you're building. Because Tenor didn't just raise a nine figure series C on the back of wanting to help people strictly only out of the goodness of your hearts. There's a business here. And so what you guys have done, as far as I understand it, is train a in house AI model, which I believe you were working on before the ChatGPT explosion. So you're kind of an AI OG, if you will, and you took essentially the documents from doctors and referrals and kind of created your own data set to then teach a model to not only find out the right information, but also to just decipher the famously convoluted Dr. Skrawl that we've all had to deal with at least once.

Yeah. So this is where I think a lot of people kind of end up slightly misunderstanding, kind of the reason. It's like tricky. It's not about really reading the document as much as actually interpreting it against these complex sort of payer guidelines. Right. Everybody knows that, you know these astounding rates of denials from commercial payers, they actually publish these guidelines. So they say, look, we are not going to cover that visit for You, Alex, unless this, this, and this is true about your history, right? If you got a back surgery 11 months ago, we're not going to give you another back surgery or we're not going pay for it at least. And so somebody has to go in to the clinical history of Alex and determine, hey, have you had a back surgery in the last 11 months? And so we have to go through insane amounts of documentation on you. Now, when people see documentation, yes, there's an insane amount of this documentation that literally travels via fax. And we do a bunch of fun marketing around the fact that like, you know, the primary means that most patients are getting sent over is the fax. But the reality is actually there's a massive volume of patients that we process that are actually just like they're in EMRs. They exist there as notes, you know, sometimes even ambient listener notes. But it's complex documentation that, you know, if you, if, if you don't have the payer guidelines, if you don't have a model that can really interpret them against those guidelines, and you don't feel really, really com confident in what you're determining, you cannot triage patients.

Okay? So just, just to help people that are listening along. EMR is electronic medical record systems like epic, essentially the, it's the CMS of the health care world, more or less. It kind of keeps track of each person and what's happened. If you need an analogy, okay, so that helps a lot. Where do you get all the information that you need from the insurance companies to know for a particular, let's say back surgery, you need to have these four things. Is that all accessible and then therefore ingestible by you guys? Or do you have to go out there and kind of like beat down the door and say, please give me the secret recipe to approvals?

I mean, Alex, this is where like, you know, part of the reason I'm always happy to talk about this problem is like, it is such a schlep problem. These guidelines, the mainstream ones, get posted to websites every six months. That, and they change. And so we have a massive, massive, massive operational overhead of converting these different guidelines into language, modelable, interpretable formats for ourselves. And then, guess what? Codes get reprinted all the time with different guidelines that update before they even publish online. So you have to be calling. So we are basically this like, massive supply chain of bringing these guidelines in and then converting them into a workflow software that can take a patient, match it to the guidelines, and basically convert if they're good or not. We also, of course, Though, like stand on the backs of giants. We've got a great partnership with a couple of folks in that specifically focus on the policy game. But we also have to do quite a bit of work ourselves to keep them up to date. So they're changing constantly. And, you know, there's thousands of different plans in this country, so it's a bundle of fun. It's a bundle of fun to do this.

Yeah, it does sound like a particularly thorny and naughty problem. Just given like, my insurance plan and my spouse's is different than the one we had a year or two ago. We had different guidelines for different things, different pay points. Honestly, I don't even know who my insurance provider is right now.

You got to get that checked out. I mean.

Well, no, I'm. I'm on her insurance and it's good. That's all I. Yeah, yeah, as long.

As you can take care of.

Okay, so when I said that you guys trained an AI model to ingest the data from individual records, I was missing the backstory on all the work you have to do to interpret that information. But when this works the way you guys want it, to the patient, who goes to get the back surgery referral, to keep with that example, when the doctor sends out the referral, how does tenor step in? And then what from the patient side is the lived process?

Yes. So that's actually part of the sort of big product that we released called the network product.

But I was working my way towards that.

Yeah, sorry. I'll get to the patient experience side then in a little bit. But we really, primarily, our customer is the receiving provider. And that receiving provider today, people, obviously, they love to talk about us as an automations platform because we do automate a lot of work. But it's really, it's. It's work that's so laborious on the receiving side, where you're taking in a patient, reading through their chart and trying to interpret all this information, trying to figure out where to triage them, try to figure out if. Oh, man, they're already in our system. We've got to update them, we've got to create a new one. We got to contact them. What do you do? So we are a SaaS platform that lives right before their EMR. So like you gave a good example. It's like their CMS or their CRM, Right. Or their billing platform, what have you. So we exist by interpreting all the patient flow right before them, and then we get them in to that system at the end of the day. And that's where they live to go and be billed and stuff like that.

So your customer is the specialist provider who wants to have an easier onboarding and ingestion of incoming patients that previously would have arrived with a binder full of faxes.

Yeah, and I'll be slightly. A little bit of a pain. The specialist wants one of three things. They want to convert more patients, they want to reduce denials, and they want to have better FTE efficiency. A lot of people get super excited, like, oh, this crazy technology. We get to automate a bunch of work. Well, what's the point of automating work? Because sometimes you see some of the most insane behaviors where people are like, oh, we automated this thing and our conversion rate went down 8%. You should have thrown more humans if you were going to lower your conversion rate. This is ridiculous. It turns out, though, of course, that, like, automation is a key piece of increasing conversions. And so you want to automate certain things, but it's always a fraction of the story.

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All right, so now let's. Let's get to the new product, which is the Tenor Network, which, as far as I can tell, because this is the first time you and I are talking, so I didn't have the advantage of running this by you before, but it appears like it's a visibility network on top of the mechanics of tenor. So you guys do all this work and then you've essentially made like a, like a lens into it that both providers and patients can use.

Yeah, no, I'm kind of laughing at hearing you describe it. Not because you're doing a bad job, you're doing a great job, but actually because it's like the, you know, so much of sort of our mantra is to keep things dead simple. Maybe I'm not so much a tech luddite as maybe like a tech marketing Luddite. And, you know, yet I hear myself using enough jargon I could tie a rope with. But anyways, the simple way to think about it is you're a sender of a patient and tenor can be doing all the great work in the world, but you still have no clue what the hell happened to that patient. They exist in a black box. As a patient, you could have no clue where you are in that process. So we built this incredible patient experience and this incredible referral source experience so that everybody knows what's going on. So you as a patient have this like really sort of lovely experience where you know what it's going to cost, you know what the gaps are, you know what services you're eligible for and things like that. And so it is cool because it's like the first sort of foray for us into the actual direct patient experience.

And how has reaction been? What do people think?

So it was rolled out across, you know, a number of beta customers and it's been the number, it's been the biggest. This is why we talk about like there was no, there's no automation in that.

Right.

Like that's just, it's just software. Right? It's just an experience. But it's been one of the best things we've seen for conversion lifts. It turns out if you give patients an experience that is digital and friendly and logical as all hell, even if their records are moving back and forth on a fax, who cares? They're getting this incredible experience. They're going to show up to the doctor at much greater clips.

Trey, do you eat pizza?

I do, yeah.

Do you ever seen the Domino's Pizza tracker?

That's like the 95% of the inspiration. Just give people a Domino's Pizza tracker. We don't need to reinvent the wheel here. The problem I guess for us was really just to create the Domino's Pizza tracker experience. Everybody, it's like Ronnie Coleman, everybody wants to be a bodybuilder, but nobody wants to lift anybody heavy ass weight. So everybody wants a pizza tractor. Everybody wants a pizza tractor, but nobody wants to Build the integration into the 100 plus effects systems, the 50 different email inboxes that are going to come in. And that was really why it was easy for us, relatively speaking, to get to a point that we could offer visibility to both sides.

Yeah, no, you're. I do not want your job because it's a lot of work. Everyone that I've talked to who deals with anything in the insurance space deals with a huge plague of complexity as far as I can tell. So I'm glad you're doing it. So what's next? What can Tenor do next? After you fix referrals, what's the next thing you can go do with a similar mindset and bent, if you will, to make this system work a little bit better and suck a little bit less?

I just, you know, I'm so allergic to kind of like the oversell or the overhyping or anything like this, but if you look at like where very medically specialized models are going and where this stuff is going to get super, super interesting is when a doctor refers you for one service, you often actually have like a ton of sort of other issues potentially going on. Now what prevents you from actually going and getting treated for those services is that it's a pain. You don't know how much it's going to cost. And what's cool about this patient experience is in the very sort of alpha stages we're seeing that. Like, if I'm getting sent over for something like a cgm, there are on average two or three other services that I as a patient am eligible for and actually can go and be receiving. The insurance company is happy to pay for it because they know it's a good return on investment. And yet I'm not taking advantage of as a patient. So the big thing for us is actually creating like net new experiences for patients. Well, that sounded sort of Airbnb esque, but not quite as sexy. You know, more like net new service is for, you know, patients that are getting sent over for thing A. They, they are qualify and eligible for thing B and C and making it dead simple for them to go and get referred for thing B and C when relevant.

I just really dispute your, your framing of constant glucose monitors as not sexy because if you need one, I bet you there's nothing more beautiful in the world than that exact piece of modern technology which we didn't have not that long ago. So I like all that and I appreciate you not overselling it. But I do think that if you've done all the work to Learn this element of the internals of how healthcare actually works. You've already done so much of the intellectual lift to figure it out. I hope in time, no pressure, that it can expand. All right, before I let you go, I'm curious about building in your market. One thing that I've heard from founders and venture capitalists over the years is don't sell into government. Don't sell into highly regulated industries because sales cycles are longer. There's just more stuff to take care of. It's tough. In other words, your company, you guys had tripled between series B and series C, which is incredibly quick. And I'm really happy to hear that. So do you think more founders should. Should dive into your side of the market, or do you have words of warning?

You know, when we were. When it was like 2022 and nothing worked and everything was completely hellish, you know, we had a bunch of VCs trying to convince us to go into crypto. And it's nothing against crypto, but basically at that phrase. I don't know where I learned it, but Buffett talked about how in the 1960s, everybody was talking about the efficient market theory. All of his competition thought it was worthless to try investing because the markets were already efficient. I guess the learning for myself there was, if everybody is saying that you shouldn't be doing something, it's like, okay, that actually gets me quite excited. Conversely, if everybody's saying you should do something, that's kind of lame. And maybe that's the reason we never said AI as a company. We don't say agents, even though theses will say to our face that we are the only agentic AI thing that's actually working. And we just don't do that because it's like, I'd rather just talk about the problem, rather focus on the problem. Did we solve the problem better today or tomorrow? And actually, if there was some crazy technology that came out tomorrow that helped us solve the problem better, we would do that to solve. We would use it to solve the problem in the. In two seconds.

Well, what I like about your company is that when people think about AI in the healthcare space, they mostly think about, you know, medical imaging and finding more tumors and that sort of thing, as opposed to, you know, automating document flow. But if that's the real issue that needs. That's broken, I mean, I'm glad we're using everything we can to smooth things out. You give me a modicum of hope, Trey, because if you guys can make this better, there must be other things that we can improve and probably quickly. You know, I mean, you guys are moving fast. The company was only founded four years ago, and you're already as big as you are now. I think Fortune said you're in the eight figures of revenue, so clearly you can build a relatively sizable company and attack a big problem here quickly. So I hope other founders listen to this and ignore the part where you said hellish and only talked about the good bits and are encouraged to follow up.

Well, yeah, the only. I guess the last soapbox being on the soapbox a little bit. The last soapbox for me is like, the cool thing is if the scorecard is a problem that you're trying to solve and you can solve it for one after customer after another, you almost get to a point where you talk to a customer like, just let me solve the damn problem for you, because I know you have it. I see that you have it. And I'm not trying to sell you software. I don't care about selling you software. I want to solve the problem for you. If you don't have the problem, I'll run away faster from you, you know, than you can chase me. That makes life easy because you're just not selling anything you want to buy. And you either get really good at solving a problem or you don't exist. And that's made my life a lot less stressful because I don't have to, you know, I don't have to think about everything I could possibly sell somebody, you know.

Well, I'm really excited about it. I want you to come back on the show in a couple of months when you've had a little bit more time to spend the money to let me know how fast growth is going. And I want to learn more about what you guys are going to do in terms of how to use the money. But you just raised it, so left you alone. Trey, where can people find the company on the Internet? And also, what is a job that you're having a hard time hiring for?

Oh, great question. So, first of all, that'd be. That'd be awesome. Hopefully I have something to say. I can't believe LinkedIn is probably the best place. Listen, we are always. One of the reasons we're in New York is we think there's a great more number of great software eng talent that wants to be out in New York, that doesn't have great opportunities to be out in New York, that is always universally in a world where the efficient market theory of today is everybody thinking you don't need engineers. I hope desperately for any competition to believe that you don't need engineers, because engineers are still. That's our stable of, of incredible talent here. And you know, we keep obsessing over it, so it'll always be hard.

So if you're an engineer and you want to work in the Chelsea neighborhood of Manhattan, Trey Holtman, look him up. Go talk to him. In the meantime, Trey, we appreciate it and we'll talk to you in a couple months.

Talk to you guys soon. Thank you so much for having me.

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The most valuable company in the world today is not a software company. Instead, Nvidia's $4 trillion market cap puts a hardware company at the absolute top of the corporate world. Why? Well, because building and selling chips to power compute is simply an enormous business. For perspective, Nvidia generated revenue of about $44 billion in its most recent quarter, and that was growth of 69% on a year over year basis. Naturally, startups are circling service systems and GROK are building chips to handle inference at scale, while companies like Rebellions are reducing power demand for AI Compute, the list goes on. But there's a startup called Xtropic that is not simply trying to build a faster or more efficient GPU using known principles. Instead, the startup has an entirely new approach to crunching AI numbers that could consume far less power and potentially topple the most viable corporation in the world. That's why we're excited about Xtropic and put it onto our Twist500 list. The startup is gunning for the richest vein in the modern economy with hundreds of billions of dollars worth of potential yearly revenue up for grabs. So please join me in welcoming Gil Verdant, the founder and CEO of Xtrompic. Gil, how you doing, man?

Doing good, doing good. Thanks for having me.

Happy to be here.

My absolute pleasure. And if folks don't know you're also based Beth, Jesus. Over on Twitter, people might know your Twitter account and not have made that connection. Great to have you on the show. I want to start with what the problem is. A lot of folks out there have seen AI models improve. We keep hearing stories about bigger and bigger data centers. It seems that to some degree, the current model of GPUs and LLMs works. So, Gil, what are we running up against and why does Xtropic want to fix that?

Yeah, I mean, you know, it's been, it's been a highly successful sort of scaling of AI in the past years, but I feel like we, I think every hyperscaler is feeling the pain of trying to keep up with the demand. I think the, the thesis is that, you know, as you have more compute, you have more intelligence, and if you have more intelligence, has more value in the market, you get more revenue. And so the pull of the market is nearly infinite for more intelligence. And that sort of transduces to the pull for more compute. And we've clearly seen that. And a few years ago we would warn, you know, I used to warn that intelligence will scale and we're going to run out of power. And everybody would laugh. Three years ago, why are you working on a paradigm of computing that's far more energy efficient? And now no one's laughing because they have to build five. They have orders to build several data centers with five nuclear reactors per data center, and they're pulling their HAO out. And so essentially what we've seen is that over the past few years, there was scaling. The training worked really well. You got to GPT 1, 2, 3, 4, 4.5, and then they went back to 4.1. That's kind of the top end of that sort of scaling. And now what we're seeing is Test time compute they call it, or reasoning. Right. The models reason for a very long time and you use the same model instead of a bigger model with more parameters. You use the same model kind of recurrently. You make it think for a while and then you optimize that sort of thinking trace with reinforcement learning often on, for example, at the moment it's mostly on math problems because we can check whether it was right or wrong. And really that's kind of the future scaling law, at least our thesis, and I think the thesis of opening Eye and of of Google is that scaling, reasoning, scaling test time compute more generally is the way to go to have smarter models without having to keep scaling the training compute by 10x to get diminishing returns in terms of the performance. Then again, the test time compute scaling law tells you that the more compute both at test time and when you're training this reasoning system, you get more performance. I think it's about quadratically more performance. I think if you put in 10x more compute, you get about at reasoning, you get about 100x, the equivalent of 100x more compute at training. And the sort of old school method.

If inputs go quadratically. When we compare training versus test time compute and just doing more inference work, what you're saying is your method will be more efficient inherently because you're focusing on the more efficient area of AI improvement today.

Yeah, so what we're seeing is that if you make your AI algorithm more probabilistic, so instead of having just a single forward pass that's deterministic and it starts looking like a Monte Carlo algorithm, you're kind of probabilistically thinking, probabilistically searching over responses, then you get more performance. And that was the thesis of the company from the get go, is that the algorithms will become more probabilistic. But then, and you know, we're catering to that because we are building probabilistic computers. But also if you look on the hardware side from the bottom up, as you try to make your transistor smaller, you try to use less current, less power, the transistors become probabilistic as well. So then we saw an opportunity. Well, hey, most workloads are going to be probabilistic. The hardware naturally wants to be probabilistic. Why don't we just cut the Gordian knot here and run probabilistic algorithms on probabilistic computers? Right. And that sounds like heresy. You know, we've been doing things a certain way for many decades. You know, A lot of people rooting us on, a lot of people are skeptical, totally natural. It's very disruptive. It's a very fundamental change in how we do computing from the ground up. But what we've demonstrated really is that we can build these new probabilistic primitives in silicon using mass scale manufacturable techniques. So using one of the large fabs, and that these are very real. They're here, they're in the lab. We submitted a bunch of papers to peer review and now we're going to put this chip and this product, this is our test board with our first xtropic silicon chip. We're going to put it in customers hands, some early customers, for people to try out what we call thermodynamic computing.

I don't think everyone is familiar with deterministic versus probabilistic computing. So if you could just break that down for the layman, I think that'll help people understand the rest of our conversation.

Absolutely, absolutely. So computers, they're in a deterministic state. You have a bunch of transistors, there are zeros and ones, and they're definitely not in a state that you don't know. Because if you don't know the state of your computer, it crashes. In current architectures, essentially, you get a blue screen, it's over. And then I came from a different world, originally from quantum computing, where there we're using quantum physics, which is like superpositions and you have any noise there crashes, same thing, right? So it seemed to me like, okay, well to scale quantum or scale deterministic computers, the bane of our existence is noise. So randomness, just from the jitter of matter, of electrons, of radiation, whatever it is, sources of noise come in, seep into your system and add entropy. They add uncertainty as to the state of the computer. And that's usually a problem. But if you use the uncertainty and you shape it, that's actually the algorithm we're trying to run in machine learning. You're just trying to shape uncertainty into a blob that is the same shape as the data set blob. And that is machine learning. And so that's what we've been doing using essentially the natural jitter of electrons that occurs at very low current. Because if you have very few electrons, then whether a handful of them are oscillating does matter to the population average. Right? So you could think of it as a mosh bit of electrons and we're kind of steering it. Right. And that's kind of the art that's the very real challenge there. And we're creating software and compilers to compile well known textbook algorithms onto the physics of this device, onto the physics of this mosh pit of electrons.

Okay, so deterministic compute, everyone knows, is what we use today, quantum computing. Everyone knows that we're working on it. All the majors are, you worked on this at Alphabet, I know Microsoft, Amazon, everyone's working on quantum super low temperatures and an entirely different set of problems. But in between those is where you guys seem to exist. And so I think it might be time to bring up energy based models and how those tie to the underlying structure of neural nets and other AI techniques that matter. So talk to us about that and then I want to get from there into production.

Yeah, for sure. So energy based models are both very old and cutting edge at the same time. There's kind of some founding fathers of deep learning and AI that are big fans of them. Yann Lecun has an active working group working on energy based models at Meta. But originally their back propagation algorithm we use to train neural networks came from Geoffrey Hinton, who was working on how to train networks of these energy based models. And if you look at averages of energy based models, averages of their inputs and outputs, because naturally they're more of a distribution, they're a probability distribution over outputs. But if you just look at the average, then that's how we actually got neural networks. And then if we looked at how to train deep networks of these energy based models using the averages, that's how backpropagation came about. So in a way, we're going back to the ancestors of deep neural networks and running them natively in hardware. That's pretty awesome from a theory standpoint. But at the same time, the energy based models have been known to be far more parameter efficient, again because you kind of have to use more test time compute to use them. Traditionally, sometimes people didn't want to throw more compute at test time to actually use them. But if you do, then you theoretically have the most parameter efficient model, and hence data efficient, because the more parameters that you have, the more you got to chisel away using gradient descent. So you need more hits of the chisel, which is data points every time. Right. So unlocking the ability to run energy based models scalably and tractably is what we're working on. And that's going to unlock these models that are more data efficient, more parameter efficient, they use far more compute. But if compute is 10,000x cheaper for us in terms of, then why do we care.

Why not just use 100,000 x 10,000,000 x?

Exactly. Right. And so we've been not only working on the hardware, but we've also been working on the algorithms in order to get people to use more energy based models. Right. So how to modify diffusion models to use more energy based models is some research we've been doing. For example.

Yeah.

So I want to get to software in a second, but I want to go back to the underlying work that you're doing here to make chips more efficient, because you guys talk a little bit about brains and how they are incredibly efficient, but also deal with noise in an efficient way. So does the brain provide a bit of a map for the company or is it more of an analogy for how you guys are approaching dealing with noise inside a signal, inside a compute?

I mean, these energy based models were kind of a physicist model of how the brain works. That's why they were then called neural nets. Right. But again, it's like a loose inspiration. But we're not obsessed with sort of biomimicry. This is not a neuromorphic processor. But for us, unlike quantum computing, there is a proof of existence of a large scale thermodynamic computer out there that is very performant for AI and runs on 20 watts. And we're using it to converse right now. It's our brains. Exactly right. So that's very encouraging. And again, the brain to us, instead of we build it with electrons sloshing around in a mosh pit. The brain is really a mosh bit of neurotransmitters, which are bigger chemicals, they're heavier and slower. So to some extent you could imagine someday we might exceed the brain in terms of energy efficiency because we're using kind of particles that are lighter and they're less energy, they're less costly energetically to slosh around. But you know, I don't think we're at brain energy efficiency yet, or in the near future. The manufacturing process is going to have to improve. But we want to end up in a 10x 10x away from the brain. So if you were to scale, if you were to scale this chip, this tiny, tiny chip right here to a whole wafer, it would run on 20 watts and have about 1.5 billion p bits and 17 or 20 billion parameters per layer. And then you could do multi layered programs. So you get a 100 billion parameter model. And because these models are 10 to 100x more parameter efficient, that effectively should be the intelligence of a trillion parameter model. And that's what we're getting for. Right. And so if you have a trillion parameter model for 20 watts, that seems pretty valuable.

That's insane. You can run that on the edge. I mean, shit, you could run that on a smartwatch.

So obviously we have to scale our system, right? We're aiming to scale thousand x year over year, which is really crazy. We started with three, now we have nearly 1,000 degrees of freedom in the, in the devices we're going to hand customers. And then next year it's going to be on the order of a million. Right. And then we keep scaling.

So people are definitely familiar with qubits in the quantum case. Explain for people what a P bit is and how it might relate intellectually to a qubit.

Yeah, I mean, you have a classical bit, it's definitely zero or definitely one, and then you have a quantum bit. It's actually, people used to say 0 and 1, 0 or 1, it's not, it's not. And, or, or it's a complex valued superposition. Right? So they have a phase, they have a complex number. And the value of that phase matters because zero plus one and zero minus one are completely different states. A P bit, for example, you can have it be 20% of the time sitting in the state one, and it's dancing between zero and one and 80% time in the other state. And then you could flip that, you could go from 20% 0, 80% 1 to 80% 0, 20 percent 1. And that's sort of a fractional bit flip. So we can do fractional operations and that's how we save power. Right. Because there's fundamental limits for any operation you do in terms of your edit distance, how much are you editing, how much compute you're actually doing. But because we maintain the computer in this uncertain state, we can technically do fractional bit operations. So this is really useful for low precision neural networks. Right. And sort of any sort of probabilistic algorithms. It's not necessarily the best for super high precision scientific computing.

No, no. God no. But it's great for AI, which is inherently probabilistic versus deterministic.

Exactly. And we're seeing that low bit quantization works pretty well in practice. And that's what the labs do to scale their product. Right. Otherwise they couldn't afford the inference. And so, yeah, that's what we're targeting from the ground up. So it's a Herculean effort to reimagine, understand the physics of the transistor and of silicon, reimagine the whole Architecture from the ground up for AI, which is needed because we're investing, I mean, we're at the point of investing trillions of dollars into building out nuclear reactors and GPU based data centers. I think we can afford to have some startups with tens of millions of dollars taking a different bet in order to save the planet.

Right?

I mean, yeah, so that's actually a great segue into what I want to get to next, which is just growth of the company as a commercial entity. So you guys put out this, this slide here which shows essentially your first superconducting prototype back in 24 this year. The test chip that you've been holding up and showing us. Thank you for bringing that. And then next year, first production chip. So much more capacity on that thing. As far as I can tell from public information, you guys have only raised 15, $16 million. This all looks to me incredibly expensive. I'm used to, as you said, you know, GPU spends being in the budgets of billions, tens of billions and so forth. So yeah, how much more capital will Xtrapic need to get out into the world at commercial scale? And is that capital hard to access? VCs have been known historically to be a little bit skeptical of hardware companies.

Yeah, yeah, no, I totally understand that. I think VCs have been burned by, you know, hardware plays. Because if you're not ambitious enough, you're in the Nvidia kill zone. Right. If you're trying to make a better GPU and competing with Nvidia, you're in for a world of pain. But if you create something that's entirely alien to them, they're going to dismiss it at first and you're going to keep scaling it and then when you're at scale, it's going to be too late. So you have far less market risk there. Yeah, in our case, I mean, we've raised actually $38 million so far. We haven't burned at all, obviously. We're in a very good position. We've burned only about 20, 20 million so far, but we have a very high talent density team. A lot of physicists, a lot of applied mathematicians, electrical engineers, and we've been building the whole stack from the algorithms compilers to the hardware itself. And we've done several tape outs, we have done all these experiments in superconductors. We took our learnings, brought that to semiconductors and now we're taping out our much bigger chip and we're aiming to make it available next year. But this chip, turning it into a product A dev kit is what we're working on right now. And in the coming weeks we're going to be able to ship it to the very first enterprise customers. So we're pretty stoked about that.

Yeah, summer 2025 is the time this stuff actually reaches the customer. And that's something I'm very curious about because you can take Nvidia GPUs and put them into many different data centers because they kind of have some compatibility. I have no idea what bringing Xtropics chips into any existing data center might look like. Maybe the right question, Gil, is can you do that or will people have to build? Oh, okay. So talk to me about that.

Yeah, yeah, yeah. So people, people, you know, saw our first experiment. So the thing with thermodynamic computers is as you make them smaller, they can run hotter. And we started with, you know, the chip was small, but the P bits were macroscopic. You could almost see them with the naked eye. They were pretty, they're pretty large. But to have that operate in a thermodynamic regime, we had to super cool it. So it was in a big fridge. We put out this movie and everybody was like, ah, that's never going to scale. It's like we're just learning here. This is our experimental platform.

And this is the 2024 superconducting prototype. You were working at quantum temperatures.

Quantum. And then a bit above, we're going hotter. Right. So essentially we cracked how to do it at room temperature. So originally we thought we were going to have to cryo cool it with some liquid cooling or some crazy pro gamer overclocking setup.

I see you've seen my gaming rig. All right.

But we figured out how to do it room temperature in silicon. Right. Using the right process. And so now it's just like this. You could have a card, you could slap it in into your server and that's what we're going to sell in the early days. But over time you could imagine you want to have it as integrated as possible with regular forms of computing. And so I think people, what people should plan is for their build outs that there will be heterogeneous compute. You're going to have some GPUs or you're going to have some Grox, some Cerebra, some etched or whatever, and you're going to integrate some thermodynamic computers and then they're going to work together. Right? So just like a diffusion model is like a big neural net and then it proposes a jump, a probabilistic jump for how to denoise the image. You're going to collaborate between a thermodynamic computer for that proposal and a big neural net running on a traditional processor. But that sort of collaborative algorithm, you get about 100x less compute on the classical side needed. And of course, that's most of the energy consumption. The thermodynamic computer consumes very, very little. But if you were to figure out how to run a whole algorithm on the thermodynamic computer, again, it's going to look like a different architecture. So far, what we've been able to do, CIFAR, you know, kind of benchmarks that are kind of 2012. Again, we're going back to the drawing board in terms of the architectures. So we have to redo 10 years of deep learning from scratch. Hopefully the community helps us with that as we release it. But with CIFAR, we were able to get 10 to the 8 times greater energy efficiency. 10 to the 8, that's pretty significant. That's 10 million times greater energy efficiency than diffusion models running on GPUs. And so our hope there's something between 100x and 10 millionx, right. Where some architectures that run really well, either mostly on a thermodynamic computer and partially on a classical one, or fully on a thermodynamic computer, sit somewhere in the middle. And anywhere in that middle is awesome because that means we have 100 to 1,000 to 10,000 acts less nuclear reactors to build to get the same intelligence and performance. And to us, frankly, I don't think robotics and embodied AI will be tractable unless you have a paradigm like this that is at scale. Right, because right now you have a whole cloud being the brains of these robots. And even with the biggest clusters running these video models, these video world models, they're extremely expensive. They're not even close to being at the edge. And ideally we want them at the edge. And so you need that many orders of magnitude for us to unlock robotics and American.

Are you saying that eventually robotics will not be essentially tethered to remote data centers to handle compute, but it'll be literally on device thanks to lower cost, power demand, AI chips.

I mean, that's the goal, right? Like to have a fully autonomous robot that's not necessarily tied to the fleet. But I would imagine the manufacturers were always going to have some connections for all sorts of other reasons. But you know, in practice, you could have a brain that runs on the, on the robot's battery and doesn't take all the power and is very highly performant and can help do motor control and reasoning in a 3D environment. And that's the goal. It's also very important for AR and VR, right? And AR and VR, they're kind of, you know, Apple Vision. I have an Apple Vision. It's pretty heavy, right? Like Apple have their own silicon, they have some of the best team in the world, but it's too heavy. So it needs to miniaturize, it needs to have way more compute with less power, right? And so that's the sort of stuff we can unlock with much denser intelligence and thermodynamic silicon.

So really briefly, before we go, is your target customer the hyperscalers? Is it the foundation AI model companies? Is it companies that just want to run their own inference in house? I'm just trying to figure out who you're going to sell to to start.

Yeah. I mean, you could look at who buys GPUs, right? You have companies that put them in devices at the edge, you have companies that put them in their clouds, you have developers that slap them in their computer at home. And I mean, that's what we're going for, right? We're going to sell cards and dev kits. So the dev kits you're going to be able to have on your desk and you can have one or two chips and then the cards are going to have more chips than that and then you could build servers. And so it's a bit of everyone, but frankly, everyone needs to migrate because there's a sort of ecosystem effect when everyone's using the same software tools. And the same researchers can go from a robotics lab to an ML lab in the cloud for OpenAI or whatever. And the same researchers, their skills carry over because they are using the same chips and the same tools. So ideally we kind of spread to all these verticals. But are you referring to kind of.

Like a Nvidia Cuda system, but for Xtropic processors?

Yeah, yeah, absolutely. So we have our own compiler and everything like that. And for some background, co founder And I, Trevor CTO originally we built TensorFlow for quantum computers, right? How to integrate quantum computers into deep learning compute graphs and how to compile to them. And so that's what we worked on at Google as basically kids like eight years ago. And so we have experience basically creating a compiler from scratch for a new type of weird alien computer that we don't know how to program. Right? That was kind of our. We've done that and now we're doing it for an entirely new form of computing, that there was basically no community that was pre existent. I think really there was some DARPA research on like, hey, neuromorphic is kind of dead. We should think more like thermodynamic. One of our advisors, Todd Hilton, worked 20 years at DARPA on Neuromorphics and he's kind of the godfather who coined the term thermodynamic computing. So we met up and I was like, hey, I'm leaving Google X. I'm going to do this, I'm going to build the paradigm. And he was just over the moon. And we've done it now and we have the components. Of course, as we scale it, it's going to have more impact in the world. But right now it's going to be the early adopters, the early believers, and also kind of the companies that can't afford to be disrupted by this next year. You could think of a hedge fund, a defense company, or even this big AI labs. Now they're in such a tight race, it's almost like hedge fund. They're looking for alpha. They're trading researchers for $100 million a pop. It's getting crazy out there. So any sort of edge they can get, they'll be interested.

Right.

So I think if your guys's technology risk goes all the way to zero, your market cap is going to scale to essentially infinity. And that's why I was really excited to have you on, because I really do think that we're going to have to really go in a different direction to get to what we need to go. Gil, an absolute pleasure. I could keep you here for two hours. You have to let it go here. When you do get the dev kits out, let us know. And oh, what are those going to cost?

Oh, that's a good question. It's, it's a, it's going to be a case by case basis. There's not that many for me.

Five dollars? That's what I heard.

Something like that. Well, we'll see. All right, well, thank you so much for having me on. I really appreciate it.

Pleasure.

Cheers.

Okay, everybody. Your pitch is the face of your company, right? What does everybody say when you ask them for money or they're going to join your company? They say, send me your deck. And some founders, no offense. You're walking around with the little schmutz. You got the schmutz on your face, right. It's messy and people judge a book by its cover. So you need a clean, easy to follow, beautiful pitch deck. And not just to let investors know about your company. It's also to show them you're capable of making a beautiful deck.

Right.

It's just kind of table stakes. Let's call it what it is. That's why we've joined forces with our friends at Gamma G A M M A for a Twist pitch deck competition. Why am I doing this? I want to see better decks. So go to Gamma app G A M M A dot app. These decks come out so beautiful because you use their AI powered platform to create stunning, stunning two to three minute pitch presentations. And you can import your existing deck from whatever incompetent, ugly, disgusting platform you've built your deck on. And you can get started from scratch with just simple text prompts. Right. AI first. It makes sense. And Gamma is working with 20 of the top AI models. That means you're going to be able to use one of their hundred pre made themes and templates to help you elevate your ideas and your script and your designs. So here's what I want you to do. I want you to submit your finished pitch deck@gma twist.com Gamma twist.com Go to gamma twist.com we set up that special domain and you submit it there. Ten finalists will pitch me right here on this week in startups and I'm going to give the winning company $25,000 in cash or I'll even make it an investment. If you want my name on your cap table, you choose. If you take the $25,000 a surprise, it may have a certain tax treatment. If you take it as an investment, it may have a different one. So talk to your accountants, make your deck today at Gamma Dot app and then go to gamma twist.com to submit it. And hey, get the schmutz off your face. All right. Clean it up a little bit.

All right. So last week on Twist, we dug into Substack's massive $100 million Series C. It was a big event for the newsletter and online creator space. The investment pushed the value of Substack up north of $1 billion and was, I believe, the largest round in its space since Beehive raised $33 million last year. Our question, after chewing on Substack's latest fundraise was pretty simple. What's the 10x case for both Substack and Beehive over the next 10 years? Venture investors backing the two companies really do expect material upside. But can that be achieved when media economics are in tatters? Now, we've had the founders of both companies on the show before. But with new capital flowing in and new revenue milestones being announced. Talk about that in just a second. It's time to look to the future. So please welcome back to the show, it's Beehive's co founder and CEO Tyler Dank. Tyler, how you doing man?

It's great to be back, Alex. Thanks for having me.

It's been, it's been almost three whole months, so it's about time we got back together. And since we last spoke, you dropped some news that in early July I believe beehive scaled to 20 million in ARR. But that's not the full revenue story. Tell me about where Beehive is today.

Yeah, so 20 million ARR as of a few weeks ago. And then we also have an ad network and a tertiary monetary revenue stream called Boost, which is like a co registration network. And the combination of the ad network and Boost accounts for another 10 million in revenue. So altogether about 30 million revenue run rate as of today.

But just to be clear, this gorgeous chart that you shared, I believe it was in your newsletter, Big Desk Energy. This chart is not inclusive of Boost and the ad network. This is just the software revenue here.

Yeah, if you, if you multiply that by 12, that would get you the 20 million ARR. And yes, that's just the SaaS revenue.

I wanted to put this chart up because it's beautiful. It's very, very. Yeah, I was going to work of art. Beautiful, impressive. It's pretty much rock solid. And I want to talk a little bit about how this chart came to be because one thing I have seen is that Beehive has incredibly quick product velocity. You guys are always launching new stuff. So can you just for folks out there who are building, try to connect for me product velocity and shipping things and MRR growth or ARR growth.

Yeah. So the product velocity I think is kind of like sounds cheesy to say, built into our DNA. But we entered a very competitive industry back in 2021 where there was Mailchimp who just been acquired for 12 billion. There's tons of email, 1.0 platforms. Substack has been around for four or five years, there's other platforms in the space and we entered with an MVP prototype type product where we knew that it was very rudimentary relative to the competition. And so the only way we can possibly catch up or the philosophy we took is let's just ship as many valuable features as quickly as possible. And what that does is one catches up to the market that we entered very late, relatively speaking. But it also creates this narrative that we can ship quality product quickly that addresses our users needs. And so that gives users or potential users the perception that yes, they don't have what I want today, but because they ship so quickly, maybe they will solve my use case next month or in a few months. And that buys you a lot of patience with these early adopters of the platform. And we really just haven't taken our foot off the gas since.

So I was thinking about Beehive and Substack and people like to talk about Uber and Lyft and kind of other traditional well known technology rivalries. But I think the best analogy, or perhaps analog for what Beehive is building is Ramp. Because when Ramp came out they were targeting a similar ish market to what Brex was doing. They had a particular angle on it, they were focused on saving you money versus kind of corporate cards for startups. And since then they've managed to grow from what was immediately or initially dismissed as an also ran into being one of the most valuable companies in in Silicon Valley. So I guess the Beehive story saga, if you will, is that you can start with something basic and then iterate incredibly quickly and catch up to any incumbent or just one that was only a couple of years in the market.

Like substance, I mean Substack is a formidable competitor for sure. I think the market's massive, right? And like we, like I just said, Mailchimp got acquired for 12 billion. There's dozens and dozens of other email platforms. As of two weeks ago, we just launched a website builder as well. So you could argue that we are going deeper into the creator or just builder space, competing with a WordPress webflow framer as well. And so I guess we're going to make the pitch of where we can 10x from here. I think there's a lot of opportunity both in email but also in the website space which is growing very competitive. But I think the wedge of being vertically integrated with a website builder that has a full CMS and newsletter and the growth and monetization of your audience is a really compelling offer that other website builders don't offer. Also I'd say your analogy of Brex vs. Ramp is interesting. I think the way that I typically look at it is I think us versus Substack is pretty analogous to Amazon versus Shopify where Amazon has millions of third party sellers, but to access their products you have to go through the Amazon website or the Amazon app. The product shows up in an Amazon box and Amazon owns the customer relationship very Similar to how Substack owns the distribution and reader relationship with its users. Whereas Shopify is more similar to Beehive in the sense of it's the infrastructure and tools that kind of sit behind the scenes to empower the success of our users. And we don't take a cut of revenue, unlike Substack. So I think the, the Shopify and Amazon analogy is, is pretty interesting.

So just pulling up the numbers here. Shopify currently valued at about 161 billion. Amazon at 2.41 trillion. Stretching out the time horizons here, do you think that in time Shopify's model will generate a larger company than Amazon just based on market value? I'm trying to understand why you're taking this side of the bet when Amazon has done so well with its centralized model and frankly, owning the customer relationship, as you said.

Yeah, but you could also probably make the. Granted I'm not an like a financial analyst viewing both of these.

You're not, but that's why I had you on Tyler.

But. But you could also argue that Amazon benefits from us and they own Twitch. Not that that's a massive revenue stream, but they have a diversified product suite outside of the pure e commerce play where Shopify has really owned. Like, if you want to build your own brand and your own product, they are the storefront, they're the infrastructure, they will facilitate payments. So I think that is kind of what we are trying to do. We don't want to be. We don't say sign up for my Beehive. Right. Doesn't sound great. And like that's not the purpose of what we're building. We want you to create your blog, your website, your newsletter, your storefront, whatever that looks like. We want to sit in the background and empower our users. I think Substack has very intentionally created the alternative, or taken the alternative approach of sign up for my Substack. Download the Substack app. If you want content created by our users on our platform, you get that by substack.com and find different content that suits you. So I think it's like a philosophical difference in how we're approaching the business.

So as I was thinking about the future of Beehive, actually one of the questions I wrote down was central application question mark. And it sounds like you're saying that that's not something that Beehive would want to build because you're not trying to aggregate demand. But notably in the case of Shopify, they have put out an app. Just sticking to your analogy there, that does Centralize shopping demand for shoppers, Sorry, for sellers on the Shopify network. So does the analogy to Shopify fall apart when it comes to centralized mobile applications in the case of Beehive? Or am I just taking that analogy too far?

I mean, it's a great point, right? I think what we've seen time and time again is when platforms get in between their users and their audiences, it usually doesn't end up so great for the users. And so in 2018, when Facebook revamped or overhauled their news feed, much to the detriment of publishers and brands on the platform, in 2022, Twitter deprioritized external links in the newsfeed, which hurt traffic to publishers. And then just recently, as of a few weeks ago, you've seen publishers claiming 40 to 50% decrease in traffic due to Google's AI summaries. And so I think that there's something around aggregators and trusting a large platform that has historically always come to the detriment of users and publishers. And I think one of the winning narratives for Substack is distribution and algorithms that can boost quality content. And just know when that distribution works in your favor at times, it also can work against you. Just like Facebook used to give tons of free traffic as they googled all of these publishers and users until it didn't. And so I think actually it's like a cheap dopamine hit to get that type of traction early. But I think the more sustainable businesses have their own foundation and can stand alone on their own. And that's what we're trying to empower our users to do.

I mean, in one sense, I think that the dopamine hit the short term sugar rush is the right analogy because it never feels bad back in the days of Google being a great traffic source to have Google send you 5 million people, but if it's going to send you zero the next day, it's just simply not sustainable as a model. So that brings us all the way back to kind of the core Beehive product, which is email newsletters. You and I both write one on a regular basis, so we're in the trenches here. I also write the Twist 500 newsletter, which is on Beehive, and I'm curious about how much that market can grow because I think you and Substack have done a good job of getting all the folks like myself and yourself who want to write for a regular audience on our own terms, ish onto these platforms. But what I'm not clear about, Tyler, is just how Many more folks out there in the market who might want to sign up for Beehive, launch a newsletter, either use the ad network or sell subscriptions and just kind of join the party, if you will. Are we halfway there? Are we a third of the way through the market? How much is left?

Yeah, it's interesting because when I raised our seed round three and a half years ago, that was the number one point of feedback that investors would give me. They're like, how big is the market? Because everyone kind of looks at, oh, newsletters are at the time, Morning Brew, the Hustle, Axios, the Skim. And they're like, how many more? How can you build a business? Like, how many more of those exist? And I think the narrative you just gave is like, yes, there are writers, there are people who want to build their. Who write content and build their audience, but how many of those, like journalists and like written word first creators exist? And I think the one, the answer is a lot more than people give it credit, which I think we've proven over the past three and a half years. Two, I think you're seeing that content creators that are YouTube, native, Instagram, TikTok, or just businesses are seeing the value in communicating with their audience directly via email. Whether that is a customer comms email that you send weekly, whether that is an investor update. I think we've all come around to like, email is not going anywhere. It's actually, it has a ton of staying power. And so I think that there's more opportunities to bring these types of net new creators on board. But I also think there's a lot of markets that are not email first yet, but can diversify and leverage email as an additional channel to engage with their audience. And I still think we're pretty early.

On that email is the form, but the content can be variable. You and I write a lot. You also do a lot of graphics and illustrations. Your newsletter is in the. What is like Windows 3.1 historical style or something?

Yeah, Windows 98.

Oh, that's. Well, I just dated myself there a little bit. But one thing I'm curious about is emails and newsletters as essentially a way to send stuff that isn't words, videos or podcast transcripts or audio or whatever. So how does Beehive think about expanding support for other types of content inside of email to hopefully get those TikTok first creators possibly onto your kind of economic engine, as you call it?

Yeah, and so what you're pushing up against is like the limitations of email where like native video is just not supported. Which is one of the downsides of email. Right. It's antiquated and has staying power, but it isn't as dynamic as a web page. What I think we are hinting at is where I see a lot of opportunity in the business. And so because I know that the preface for the conversation is like how does it be? Hive 10 100x from here. And so I'll paint the picture of what I think what we're building is so interesting and like the three pillars that overlapped like a multibillion dollar opportunity. One is email, two is website and then three is monetization. And I don't think there's a platform that exists that does all three of those as well as what we're trying to build in a way that's a truly one of one opportunity. So focusing on email, we own the newsletter space now. So newsletters are bread and butter. I would argue that email and content based email is actually the smallest segment of email. So e commerce and marketing emails that you get for 20% off Black Friday, like there's a reason why Mailchimp sold for 12 billion and why Klaviyo is a public company. So I think not saying we are definitely going into marketing emails, but that is an expansion opportunity. If we are great at email, what would be stopping us from expanding there? And another type of email is like transactional email. So whether it's SendGrid, Mailgun, basically if you are creating a mobile app or a website that needs to fire forgot password or login like transactional emails is a whole different segment of emails that we could potentially explore not tipping our hat that we're going into either one of those. But I think there is a wide range of email type companies beyond just content, which we are suited to be able to expand as opportunities present themselves. So let me explain the two other pillars. So the website builder. We acquired a website builder company a year ago called typedream. We've been building in beta for the past six months. We launched it two weeks ago. The website as a key differentiator that's vertically integrated with the email is like a great selling point, right? So you can have a website on WordPress, but if you have an email newsletter you're copying and pasting into mailchimp or to beehive two different systems, two different logins, two different pieces of infrastructure that you have to manage. We've just gone vertical with the website builder. And where website leads to is kind of like the classic creator play of link in bio community Courses, digital products. And so I think there's a whole roadmap that can be built and will be built on the website side that allows us to onboard coaches who want to sell their time and they have a storefront of booking time with them directly. And rather than paying a 10% fee on a lot of these other platforms that do booking, you can have all of your booking is done on our website product that is fully integrated with the email product so you can communicate with your users. So I think website as something that we made a big bet on is bearing fruit already. And I think there's a huge opportunity and roadmap to be had on the website front. And then the last pillar is monetization, right? And so we offer paid subscriptions just like Substack, Patreon and other platforms. We're the only platform in the space that doesn't take a cut of revenue. So you can charge your readers 10, 20, $30 a month and you keep the 10 minus the stripe fees. You keep what you charge. We have an ad network which has brands like Nike, Netflix, HubSpot, AG1. And so as a publisher and focusing on the ICP of publishers, you receive ad opportunities every week. You see that AG1 wants to sponsor your newsletter for $5 CPM. You don't have to run it if you want to. It's a high quality ad. And you've never spoken to anyone at AG1 yet they're paying you 150, 250, up to a few thousand dollars for running the ad in your newsletter. And I think we're very early on the ad network front and in a way you could argue we could maintain our user base. We have now scale the ad network and that is zero marginal cost, an additional revenue as long as we're filling ad rates in these different newsletters. So I guess going back to your initial question of like, why not jump into marketing Yet I think we have owned this ICP of like content first email newsletters and if we can help them grow faster, build a website, build products on top of their website and monetize, I think that's a really compelling offer and I think there's a lot of room to run with that.

So you know, we used to think about companies getting to 100 million in revenue and then going public. And I know that's a 1997 perspective on things, but I still think about 100 million in revenue as like, like here is a company that will last for a very long time. It has reached essentially unkillability in Some way. It sounds like, Tyler, that with the addition of websites, which I believe will unlock incremental software or ARR for you, software revenues and growth on the advertising side, you have enough components just there to grow the business from a $30 million run rate today to 100 million in some amount of time. That answers the question of how do you 10x? Because if you can get to that scale, your last valuation is going to appear tiny in comparison to what you're doing now. So maybe the right question then is to ask what could go wrong? What could disrupt you? Because I've seen the revenue charts, I use the product, I've seen the releases, things seem to be going pretty well. What's the potential turbulence on your horizons?

Yeah, I mean, it's worth calling out for all of the revenue projections too. We just launched three and a half years ago, so I think we are just getting warmed up and really just beginning to hit stride. Up until two months ago, we had a team that was manually provisioning ads in the ad network to our publishers. Now we have a machine learning model that's using data and optimizing both campaigns for advertisers and for publishers. So it's very nascent. It's successful, but very nascent. And the website builder has been live for two weeks. So I'd say these are big bets that we've been making and investing in for years. But we're three and a half years old and I think just starting to see a lot of these things go live. What could go wrong is I just pitch you eight different business models and eight different ICPs of we could do transactional, we could do marketing, we could diversify here. And I think it is very hard what I have learned to build a single startup that does one single thing very well, that customers will come back to and pay. We are trying to build almost like three companies in one with the website builder, with the email newsletter and core email platform and then an ad network that is globally scaled with thousands of advertisers across all of these different publishers. And each one of those is almost like a startup in itself and very difficult. So can we thread the needle and do all three equally well and have them compound where 1 +1 +1 equals 10 is like the bet that we're making, but each of those has tons of complications and I could see ourselves if there was an area of concern, it's that they all need to work well together in lockstep and it's very hard to do all three at once.

But it does seem like you have a pretty clear conception of your ICP or ideal customer profile. If people don't know we're not talking about the Insane Clown Posse. And if you stick to knowing what your ICP is, it seems like you're going to have the right direction to follow to make sure that you do thread the three parts together into kind of one cohesive whole. Maybe I'm underselling how hard this is, Tyler, but to me it sounds like what you're describing is doable with your team. Just given what I've seen you put together before, you don't need to be modest. We're on a podcast. You can, you know, you can beat your chest a little bit.

It's okay. Yeah, no, I mean, again, because we've been here for three and a half years. I remember like yesterday, the days that we had $10,000 of revenue and everything feels so fragile. Right. So we're in a hyper competitive space. A competitor just raised $100 million. We like the valuation, though. That's good. And yeah, I mean, it's great.

It's great for you guys to have that comp. And they're jumping into advertising, which is something that you guys really kind of showed a new way forward for. For folks who don't know, you usually don't get a $5 CPM from an advertiser. You're often selling banner ad space at a very low effective cost per thousand or CPM rate. Which is why I started off this little chat with the media economy is in tatters because the old models of advertising don't work and yours seems to work, at least for newsletters. So I don't know, I'm pretty bullish. Tyler. But just to kind of close things up for today, Tyler, why is Beehive and Substack doing so well when traditional media is in such an absolute dire situation? Because to me, the split screen between Beehive's growing newsletters are doing so well. And also, you know, my old home of TechCrunch has cut huge chunk of its staff. It's now down to the absolute bones. So what's driving that? And is that a long term opportunity for Beehive?

Yeah, I think the underlying thing there is the owning your audience and distribution. So grouping media as a whole is struggling. I think is partially incorrect. Morning Brew had an amazing exit and post Exit continued to crush it. Axios had a great exit. Politico had a great exit. The thing all three of those have in common is they were newsletter first and they own their audience distribution, where if you flip it to the other side of the equation, you see a lot of publishers who outsource traffic to Facebook, Twitter, Google, they monetize with a few pennies on a page view, on banner ads and Google Adsense. And when Twitter, Facebook stops prioritizing external links and Google now is leaning into AI summaries, your traffic dries up, as does your revenue. So the underlying business model, I think, struggled. You're seeing new business models like Puck and other publishers kind of lean into subscription first, and they're having a lot of success as well. So I think it was, again, going back to that easy dopamine high that you can get from network effects and distribution, I think it feels good. But those who can own their own distribution, build their own moat, have their own brand and product, and have distribution directly with their audience, I think those are like the tried and true media companies and individual content creators that stand to benefit from this.

All right, so the 10x wager from Beehive is email's not going away. Building websites and vertical integration inside your service is going to have a big effect on helping people do more. The advertising system can scale well. You're going to have lots of software revenues. And as the media falls apart, well, the people are going to be building and wanting to own their own audience. I can see that being a 10x wager. So, Tyler, when you do raise more money, you call me and I have five, $5 ready to go into the next Beehive round. So watch out, world.

We'd love to have you.

I'm going to tell you thank you so much as always for your time. I love hearing you explain this stuff. And when you do hit the next ARR. Milestone, call me.

Yeah. Or subscribe to my newsletter. It's the first place where you'll find any of the milestones that we have.

It's actually true. I had to go back through your newsletters to pull stats for today's chat. For folks who want to find Big.

Desk Energy, where can they just mail.bigdesk energy.com it's not a podcast appearance if I don't promote the newsletter.

I worked it into the first, like, two minutes of our chat. I know, I respect that.

I appreciate it. Of course.

All right, Tyler, Thanks a lot, man. We'll talk to you soon.

Cool. Thank you.