Are Brain-Computer Interfaces Actually Ready for Humans? | E2263

Summary

This episode features three distinct interviews covering cutting-edge hardware and wellness startups. First, Paradromics CEO Matt Angle explains how brain-computer interfaces work, from reading neural action potentials to controlling robotic arms with thought alone. Paradromics recently received FDA investigational device exemption and is now enrolling clinical trial subjects in Ann Arbor, Michigan, with a device that achieved the highest data rate BCI in the world by a factor of 20. Beyond restoring movement for paralyzed patients, their Tempo product aims to continuously monitor brain states for mental health treatment.

The second segment features JetZero CEO Tom O’Leary discussing the company’s blended wing body aircraft — essentially planes that are all wing, providing dramatically better fuel efficiency while fitting into existing airport infrastructure. JetZero is working with the US Air Force and major aerospace suppliers to develop what could be the most significant advance in commercial aviation design in decades, using digital twin technology as a core strategy.

The episode concludes with Nutrisense CEO Dan Zavorotny explaining how continuous glucose monitoring combined with AI-powered coaching can reduce metabolic issues without pharmaceuticals. Nutrisense has an AI assistant called Nora that synthesizes dietitian conversations, glucose data, and food tracking, and the company is notably profitable — a rarity among health tech startups.

Highlights

”We demonstrated the highest data rate BCI in the world by a factor of 20”

“So last year we demonstrated the highest data rate BCI in the world by a factor of 20. Now we’re bringing that into the clinic in an area that is really quite exciting.” — Matt Angle, 20:08

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Controlling Robotic Arms with Thought

“Right now in clinical trials across the country, there are people controlling robotic arms by thinking about moving their arm.” — Matt Angle, 6:09

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BCI as Moore’s Law for Neurons

“Or you could think of it as Moore’s Law. You know, in Moore’s Law, it was how many transistors can we pack into a given area? In BCI, it’s about how many neurons can we listen to.” — Matt Angle, 15:24

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The All-Wing Aircraft Revolution

“Simplest understanding of an all-wing is that it is lift across the entire wingspan. So you have a fuselage that’s blended into the wing.” — Tom O’Leary, 27:24

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Glucose Monitoring Without Drugs

“This is no prescriptions of drugs, pharmaceuticals. This is purely just understand your glucose, understand how you respond to things.” — Dan Zavorotny, 1:00:32

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Key Points

• Paradromics implantable BCI (1:43) - Matt Angle describes their brain-computer interface that records neural action potentials directly from the brain surface
• How BCIs read minds (3:00) - Neurons fire action potentials lasting about 1 millisecond; BCIs detect these voltage changes via electrode arrays
• AI decoding brain signals (4:53) - Modern AI is being used to decode neural signals, improving BCI accuracy and capability
• Controlling robotic arms (6:09) - Clinical trials already have paralyzed patients controlling robotic arms by thought alone
• Recording dreams (7:14) - Matt confirms dreams are fundamentally neural activity that could theoretically be recorded with enough electrodes
• FDA IDE approval (8:09) - Paradromics received FDA investigational device exemption in November 2025 for clinical trials
• Surgery description (10:15) - Procedure involves scalp incision, skull window, and electrode placement on brain surface
• Tempo mental health product (13:45) - Second Paradromics product to track moods and mental health states in real time
• 20x data rate record (20:08) - Demonstrated highest data rate BCI in the world, 20 times better than competitors
• Clinical trials enrolling (25:58) - Now enrolling participants within 2 hours of Ann Arbor, Michigan
• Blended wing body explained (27:24) - JetZero’s all-wing aircraft provides dramatically better fuel efficiency
• Digital twin strategy (37:38) - JetZero will be first large jet manufacturer with full digital twin modeling
• Continuous glucose monitoring (55:43) - Nutrisense was first company to bring CGMs to general wellness consumers
• AI dietitian Nora (1:03:26) - AI assistant that synthesizes dietitian conversations, glucose data, and food tracking
• GLP-1 limitations (1:06:43) - People regain all weight when stopping GLP-1s because the underlying metabolic patterns weren’t addressed
• Nutrisense profitability (1:10:59) - Company is profitable, a rare achievement among health tech startups

Mentions

Companies

• Paradromics (0:49) - BCI company with FDA-approved clinical trials, highest data rate BCI in the world
• JetZero (26:34) - Aviation startup building blended wing body aircraft for dramatically improved fuel efficiency
• Nutrisense (54:41) - Continuous glucose monitoring startup with AI coaching, profitable
• Neuralink (0:00) - Elon Musk’s BCI company, referenced as another player in the space
• US Air Force (39:30) - Working with JetZero on blended wing body development
• Boom Supersonic (0:00) - Referenced as a different approach to next-gen aviation (supersonic vs efficiency)

Products & Technologies

• Brain-Computer Interfaces (1:25) - Technology to read and interpret brain signals via implanted electrodes
• Tempo (13:45) - Paradromics mental health monitoring product
• Continuous Glucose Monitors (55:43) - Wearable devices tracking blood glucose in real time
• Nora AI (1:03:26) - Nutrisense’s AI dietitian assistant
• Digital Twin (37:38) - JetZero’s virtual modeling strategy for aircraft development
• GLP-1s (1:06:19) - Weight loss drugs discussed in context of glucose management

People

• Matt Angle (0:49) - CEO of Paradromics
• Tom O’Leary (26:34) - CEO of JetZero
• Dan Zavorotny (54:41) - CEO of Nutrisense
• Mark Page (39:30) - JetZero co-founder, aerodynamicist

Surprising Quotes

“If you had electrodes in the right part of the brain, you could in principle record a dream.” — Matt Angle, 7:14

“Or you could think of it as Moore’s Law. In Moore’s Law, it was how many transistors can we pack into a given area? In BCI, it’s about how many neurons can we listen to.” — Matt Angle, 15:24

“This is no prescriptions of drugs, pharmaceuticals. This is purely just understand your glucose, understand how you respond to things.” — Dan Zavorotny, 1:00:32

“For GLPs the biggest issue is that when people get off GLPs they regain all the weight. Because what it does is suppress your appetite, but it doesn’t change the underlying metabolic patterns.” — Dan Zavorotny, 1:06:43

Transcript

Jason Calacanis: 0:00 Hello everybody and welcome back to TWIST. This is Alex. Today is Monday, March 16th, 2026, and we have three amazing interviews with world-class, world-changing founders coming your way. First up, we’re going to talk to Paradromics. They are building brain-computer interfaces. Yes, BCIs really are coming. Then we’re talking to JetZero, which brings me to a question: what if you had a plane, right, and it was just one big giant wing? Then we have Nutrisense. Yes, I know, health tracking is all the rage because it’s fair enough. Knowing what’s going on in your body is a pretty important thing if you want to health max, and maxing is a big deal these days. I mean, just look at me. I’m looksmaxing right now. Nutrisense, I think, is on the cutting edge of consumer health, and so I wanted to learn more. It’s a great show. I learned a lot. I hope you enjoy it. We’re back on Wednesday. Let’s have some fun. If you are an AI fan, you must have heard about the big hype in talking to your AI, talking to your agents, using your voice. Well, that’s well and good if you have that capability, if that’s in your personal capacity. But what if your body doesn’t quite do what you want it to do? You’re going to need a little bit of help. And that’s why I’m really excited that companies like Paradromics are working on taking BCIs out of the lab and putting them into our brains, not only so we can do cool stuff, but also so that everyone can participate in the future digital and AI revolution. So, please join me in welcoming to the show, it’s Matt Angle from Paradromics. Matt, how you doing?

Matt Angle: 1:23 Good. Thanks for having me.

Jason Calacanis: 1:25 Dude, my absolute pleasure. I love brain-computer interfaces because although I can type very quickly, I really do think though that my hands slow me down and as AI gets faster, I just want to be able to do more with my mind. So you were the exact person to talk to. But I’m getting ahead of myself. Tell me about your brain-computer interface and how it works, if you don’t mind.

Matt Angle: 1:43 So we’ve built a brain-computer interface that is implantable. And so one of the reasons why you want to implant a device in your brain is that it has access to a higher level of signal resolution if it’s close to the brain and particularly if we have—our device has little tiny microwires that are smaller than human hairs that can get close to individual neurons. And that’s the highest level of resolution that you can have when you’re recording from the brain. The more neurons you can record from, the higher the data rate of the device and we’ll talk a little bit about that later, I think. But the way our device works is that it records from large populations of neurons and transforms that neuronal information into something actionable by a computer. So in the example of a person who can’t speak anymore, take Stephen Hawking, the late physicist. He was paralyzed. He couldn’t move his muscles anymore, but his mind was completely intact. With a Paradromics device, he could have spoken again through a computer by attempting to speak and then having the little device in his brain read out what he’s trying to do and inform the computer.

Jason Calacanis: 2:43 That’s how I understood it. But I’m curious about how the mechanics actually function. Because to me, if you put some wires next to my neurons, they’re going to record what? Electric impulses, essentially a series of data points?

Matt Angle: 2:55 Individual neurons you can—they—you can think of them like tiny electric eels. They undergo these binary signals… events called action potentials where for about 1 millisecond they change their voltage a lot. And then that results in them sending signals to up to 10,000 other neurons. And so if you go into the brain with tiny little electrodes and record from a lot of neurons, you can see when each neuron is firing if your electrode is within about a tenth of a millimeter of the neuron you’re recording from. And you’re basically recording that electric field around the neuron when it undergoes its signaling event.

Dan Zavorotny: 3:29 So by electric field, you’re more keeping tabs of an area of electric activity versus just what that one neuron is doing, because you said they’re connected to 10,000 apiece.

Matt Angle: 3:41 So we don’t record from individual—well, we don’t go inside of the neurons, but we sit in the surrounding space, and it’s almost like dropping microphones into a crowded cocktail party.

Dan Zavorotny: 3:54 Got it. Okay, so that helps a lot. Now I get now how you can get the data from the brain, but the thing that I’ve always been a little bit uncertain on is: cool, we listen to the brain, we drop the mics into the cocktail party, we have this background hubbub. How do we turn that into what the person wants? What’s the translation mechanism from signal into: ‘Okay, this person wants to say X or do Y’?

Matt Angle: 4:15 We put the device into the area of their motor cortex that tells their body what to do. So there’s enough information in this area of motor cortex for us to understand what they were trying to do with their muscles. And so the way the training works is that someone is looking at a computer and it says ‘Try to say this sentence,’ and they’ll attempt to say a sentence. And then they’ll get another sentence, and another sentence. And eventually we have enough paired recordings of neural activity together with the sentence we know they were attempting, and so we can build a decoder that can detect what they were trying to say based on the neural activity.

Dan Zavorotny: 4:53 That’s really interesting. Is this a place—and I hate to bring up AI in a non-AI conversation really—but like, is this a place where modern AI tools and technologies have increased the pace at which we can decipher the microphones at the cocktail party to better understand more quickly what people are trying to do with their brains to improve our ability to read the signals?

Matt Angle: 5:08 Absolutely. So the most popular forms of AI right now are large language models. And a language model is exactly what you want to do if you have a noisy signal of what someone’s trying to say. It allows that you to use the system even faster, incur some error, and then go back and fix the error, or even sort of predict prospectively what someone’s going to say. And so, you know, language models even before the era of OpenAI had their, you know, a lot of what they were being used for was speech recognition. When we talk to our phone and we get speech-to-text, so those are language models that are cleaning up what you’re saying as you’re saying it. And we can use those same things in a thought-to-text or thought-to-speech model.

Dan Zavorotny: 5:54 I know Connexus, your BCI is working on helping people talk, so we’re talking about translating signals about speech. Does the same concept…

Jason Calacanis: 6:00 Beside other things, you know, moving my body or expressing a thought or a concept if we put the sensors on a different part of my brain that was more used for that activity?

Matt Angle: 6:09 Right now in clinical trials across the country, there are people controlling robotic arms by thinking about moving their arm. And in general, this idea of using AI, you know, to predict sequences of events. You know, in the case of language, it’s really easy to understand because we all have examples and experience with large language models. But you can also have a motor language. You know, there are predictable sequences of motor events. Like for instance, if I want to reach over here and pick up my cup, at a certain part in that sequence it becomes very predictable what’s about to happen. And so we can use those kinds of predictive models for motor decoding as well. Even as we move kind of up the chain into higher cognitive areas, we can start to develop new frontier models to understand essentially the statistics of thought in different areas of the brain.

Jason Calacanis: 7:03 So I’m a, I’m a big science fiction dweeb. And so this next question’s just for me, not for the startup community. But can we use this sort of technology, maybe we need multiple sensing areas, but to essentially be able to watch people’s dreams?

Matt Angle: 7:14 Yeah, I mean, to the extent that you experience your dream, it is fundamentally just neural activity. And so if you had electrodes in the right area, you could in the same way that we can reconstruct if we’re recording in a sheep’s brain, this is actually how we test our data rate. We put devices in sheep primary auditory cortex and play it sounds. And then we build decoders to understand what sounds the sheep is hearing. So we can, when we’re recording from a sheep’s auditory cortex, we can know what the sheep is hearing by decoding the brain activity. And that could be the case for someone who’s sleeping as well. Because the same substrate for, you know, dreams is the substrate for sensory experience.

Jason Calacanis: 7:58 Matt, did you just let me know by accident that you can speak sheep?

Matt Angle: 8:03 Yes.

Jason Calacanis: 8:04 That’s incredible. I raised sheep. Sheep are dumber than dirt. I would love to know what they’re thinking.

Dan Zavorotny: 8:09 November of 2025, Paradromics gets the FDA to grant it what’s called investigational device exemption, or IDE, to essentially start using the Connexus BCI in people. And you were shooting for a clinical study this quarter with two people with impaired speech and limited extremity movement. Where are we in progressing from taking this technology out of the lab and out there into the real world testing?

Matt Angle: 8:30 We’re talking to potential patients right now. We haven’t implanted the first patient yet, but that is, I would say at this point, eminent.

Jason Calacanis: 8:33 So in the world of biotechnology, eminent can mean something very different than in the world of, like, startups or software. So eminent as in, like, is that weeks, months, years? I don’t know how to read that.

Matt Angle: 8:46 Yeah, yeah. It’s, let’s say it’s in the next four to eight weeks we’re expecting to implant.

Dan Zavorotny: 8:52 How long does it take to get data back from those early implantations of your BCI to begin to iterate and improve and learn from this? Is it right away or does it take a while to kind of…

Matt Angle: 9:00 It’s usually about three to four weeks after the surgery that we begin the rehabilitation component.

Jason Calacanis: 10:01 Politely, how invasive is the implantation process?

Matt Angle: 10:02 Invasive is a weird word. It’s been bandied around, but it’s not actually a medical term of the art. I’ll say this because, you know, there are a lot of devices that are touted as more or less invasive, but then that doesn’t correlate with actual safety outcomes in surgeries. So I’ll describe what the surgery is, which is an incision in the scalp, a window in the skull. There’s a thin skin called the dura that covers the brain. We open up a flap in the dura and then we place a device that’s smaller than a dime on the surface of the brain. Then we replace the dura, replace the skull, replace the skin, and we have a wire that tunnels under the skin down to the chest and we have a pacemaker type device in the chest. So everything is completely under the skin and wireless.

Jason Calacanis: 11:03 And the pacemaker, is that… or the pacemaker like device, is that simply to provide electric power or to record signals or is that actually doing something with my cardiovascular system?

Matt Angle: 11:11 It provides power wirelessly through an inductive link that’s worn on a vest. So the power comes wirelessly to the device and then the data comes out wirelessly as an infrared signal through the skin.

Jason Calacanis: 11:23 That’s awesome. If someone wasn’t… if the wearable vest is the only visible part of the system, you know, if someone’s taking a shower and you wouldn’t be able to tell they have a device. But it’s… it’s charging by proximity and data transfer via IR because you want to keep the stuff inside or underneath the skin without a lot of open ports. Okay, look, I know you’re doing this to help people speak and so forth and that’s all very lovely and high-minded. But when it comes time for tests outside of that just for dweebs who want to play with it, can I volunteer to be one of your early guinea pigs? Because I want the in-body BCI, it sounds awesome.

Matt Angle: 11:58 Not today, but I mean the direction that we’re moving. is that every capability that we build in the clinic can become very interesting for people outside of the clinic. So even the first participants to get a speech BCI, they can now prompt AI models directly with their thoughts. When we start building devices for hearing, you know, a hearing prosthetic, you know, think of the applications that you can run in the loop for hearing prosthetic, you could do real-time translation.

Jason Calacanis: 12:24 Exactly.

Matt Angle: 12:25 Like someone hears Mandarin and Mandarin in, English to the brain.

Jason Calacanis: 12:30 It sounds a lot like what you’re doing is by helping people who have a particular, I’m probably butchering the polite terminology here, but let’s say a deficiency in their hearing or movement or whatever, by tackling those specific tasks you’re also creating at the same time the capability for people to do quite a lot more with their minds than they could otherwise. So it seems to be kind of like a, you help people, but at the same time you also open up this amazing aperture of enhancing human capabilities.

Dan Zavorotny: 12:57 Our goal is, you know, we’re giving these devices to people who have disabilities, unmet medical needs, but we want them to come out of the operating room not just with some restored function, but like, if we can give them superpowers on the other side, that’s great too.

Jason Calacanis: 13:10 Yeah.

Matt Angle: 13:12 And then the process, you know, every three years the technology’s going to get better. It’s like a semiconductor play in the same way that Intel got better every cycle, Nvidia gets better every cycle, Paradromics is going to get better.

Jason Calacanis: 13:17 Yeah.

Matt Angle: 13:18 Every device that we put out is going to have twice as much data as the device before it. We’re going to have more health data, you know, the devices get safer. And so this risk-benefit calculation changes. Right now, you know, the initial markets we’re focused on are people with severe disabilities, but we want to build out capabilities that are so compelling that one day anyone would consider getting them.

Jason Calacanis: 13:43 Yeah. This brings us to Tempo, which as I understand it is the second kind of major Paradromics product and this is to track my moods, mental health? Tell me about that.

Tom O’Leary: 13:54 One of the things about the Connexus system, it was designed specifically for a user population that is, they live in a wheelchair. And so a lot of the design decisions revolve around the idea that it’s going to be powered off of the battery on their wheelchair. There’s actually no implantable battery in the Connexus. We didn’t, medical batteries are like a mess and we didn’t want to have to spec a battery and decide like, how long is this going to last versus how big is it going to be. At the time we didn’t like the trade-offs and we said, well these people have a battery on their wheelchair all the time, let’s just continuously power it inductively through the wireless link.

Matt Angle: 14:29 But we want to start moving into patient populations that are mobile, they’re walking around, they’re not in a wheelchair, and so we need to move, we need to move to a battery-based form factor. And so we’ve spent a lot of time reducing the power consumption of the entire system so that we can use a relatively small battery to power it for more than 12 hours a day. And so we’re, you know, we’re moving into a, we’re moving into a world where we want to have these devices be smaller and smaller and more and more mobile. Applicable to a larger population. At the same time, we’re pushing to make the data rate higher. Right now, each individual cortical module has 421 microwire electrodes. The next generation will have 1024. And so, each—as we multiply the number of electrodes, it essentially reflects how many neurons you can record from, and so that—that kind of—

Jason Calacanis: 15:21 Higher resolution, the analogy here is pixels, I presume?

Matt Angle: 15:24 Or you could think of it as Moore’s Law. You know, in Moore’s Law, it was how many transistors can we pack into a given area? In BCI, it’s about how many neurons can you record from in the right areas. So, you know, there’s some—there’s some nuance there, like if you put 50,000 electrodes in visual cortex but you’re building an auditory prosthetic, that won’t be very helpful. You know, you have to put them in the right areas. But it’s really about getting a high density of electrodes in the areas that you really care about. That’s what determines your ability to transfer information in and out of the brain.

Jason Calacanis: 15:58 And does this higher data rate allow the Tempo idea to kind of, quote, ‘capture brain states in real time’ to inform mental health treatment? Does that take a lot of—a lot of data to—to do well?

Matt Angle: 16:10 Exactly. So, we’re—we’re very excited about, um, as we move into sort of mobile patients, having maybe not just motor restoration, but we know that you can read out cognitive states, you can read out mood, um, using a BCI. And so imagine what it would be like, a world of mental health where there were objective signals that you could optimize against. Right now, you go into a neurologist’s or a psychiatrist’s office, they have checklists, they have a big book called the DSM and they’re like, checking your scores against that, they’re trying to fit you in a category, and then they have like a—a bag of possible prescriptions that they give you one and say, like, ‘Come back in a few weeks.’ Like, this is a very data-impoverished way—

Jason Calacanis: 16:52 Medieval.

Dan Zavorotny: 16:53 Yeah, it’s medieval. My—and my wife is a psychiatrist, so I hear a lot about this.

Matt Angle: 16:56 So compare to, you know, what um the treatment of diabetes was like 25 years ago. You know, before continuous glucose monitors, the treatment of diabetes revolved around blood sugar from a pinprick, and you had some uh scarcity of data to try to optimize your treatment of diabetes. Now with continuous glucose monitors, you have constant access to your blood glucose, and that’s changed, even though CGM doesn’t treat diabetes, it’s changed the treatment of diabetes. Um, and even people who don’t have diabetes are interested in it because measuring your blood sugar can be great for bio-optimizers. Absolutely. And the same thing is gonna happen in mental health. We’ll develop more sophisticated ways of reading out brain states, and that will change how people take medicine, it’ll change how traditional neuromodulators um work in the brain. Like if someone gets a deep brain stimulator, instead of using that in open loop, now they have real-time data to close the loop and help that treatment be better. Data basically makes medicine better.

Jason Calacanis: 17:54 Oh, for sure. I’m just thinking about how we have, you know, if you go to the hospital they put the little heart rate— Whether on yours, they want to keep track of what what your heart’s doing. It’s almost kind of odd that we don’t have more and better brain signal monitoring in general, because I feel like it’s another amazing dataset. I’m just thinking through what you’re telling me.

Matt Angle: 18:11 Absolutely. I mean, during COVID, everyone learned about pulse ox and then you’re like, ‘Oh, actually that makes a lot of sense.’ Like to know if you have pneumonia or not with a little, like, 10 dollar thing you can have at home and put on your finger, like that’s that’s really helpful to know if you’re getting enough oxygen. And the brain is so much more complicated than those other systems, and yet, you know, the way we interact with it is so crude.

Jason Calacanis: 18:32 Okay, so talk to me about the next couple quarters. So you’re going to get the people signed up, you’re going to get these systems in place, you’re going to start learning. Um, the last time that Paradromics raised, according to the data I could find, was, I think, 2023. Uh, and it seems like everyone just wants to fire their money into GPU clusters. So is the company going to have access to enough capital to fund itself through this next stage of of work to get this out into the market as a real commercial product? And I hope the answer is yes.

Matt Angle: 20:08 So last year we demonstrated the highest data rate BCI in the world by a factor of 20. Now we’re bringing that into the clinic in an area that, I mean, let’s—Neuralink stock is trading on the secondary market at 20 billion, at a 20 billion dollar valuation. We have a number of companies that are raising now at the near or or unicorn level valuation, and, you know, we have the best BCI in the world. So we’re pretty confident about our upcoming fundraise, which will be on the heels of the data that we’re collecting over the next couple of months. There’s a lot of interest in BCI. So BCI is considered, you know, by a lot of people in the AI space, to be kind of an adjacent companion technology to AI.

Jason Calacanis: 20:47 No, I think it makes a lot of sense, because if we’re building synthetic brains and we have physical brains, we probably want to have a way to have them talk and link to one another. So to me, this is a step in a direction towards me being able to computer— without having to open my eyes. And as someone who spends too much time in front of screens, bring it on. Okay, before I let you go, Matt, because I’m really loving this, but I do want to ask about like five years from now. Where do we get in like a half decade? If you had your optimist hat on, I’m not going to call you back in and beat you over the head with predictions, but I’m just curious, like when you’re at night just thinking about the future, where are we in half decade with BCIs?

Matt Angle: 21:22 We’re going to have a lot of trials right in what are called early feasibility studies, which means that people in all different application areas in mental illness, degrees of paralysis, will have next generation devices, which at that point will be even better than what you’re seeing right now coming out of Paradromics and Neuralink. It’s likely to be Paradromics and Neuralink five years from now that are leading because we’re the two companies that are building wireless high channel count, what are called intracortical systems. And those are the most scalable systems. Five years from now, you’ll see these people will be using BCIs to control exoskeletons, robotic prosthetics, you’ll be reading out cognitive states associated with psychiatric conditions, reading out mood states associated with mood disorders. To some extent those things exist already in the clinic, but we’ll have much better hardware. You’ll have in market devices or the first generation of devices for communication and computer control. So you’ll see people who are paralyzed and have lost the ability to communicate getting devices to speak, getting devices to control computer cursor, computer keyboard, the, you know, I think you’ll have on the order of maybe a thousand people with really exciting devices of different types. I think what you’ll have is just this menu of offerings for patients and the suggestion of very, very big markets which have been anticipated right now in theory, but will then be clinically proven out.

Jason Calacanis: 23:00 You know, there was that famous moment in the history of, I think it was insulin, when they began to inject people that were like in comas and they all kind of started to wake up because suddenly their bodies started to work. I wonder if we’re going to have a similar moment in which people who are currently disabled in speech or sight or mobility all kind of become brought back to full interactivity in a way. And I wonder what that’s going to look like about in terms of how we approach it societally because once we have that capacity, I feel that we have a moral requirement to bring it to our fellow humans because how could we not? Which I know sounds like a weak argument, but I really mean that from my heart. So I’m hoping that even though it’s fun to talk about the sci-fi elements of this, that we do get some way to pay for all the healthcare we can do with this. And I— is this going to be price exclusive? Is this going to be like prohibitive?

Matt Angle: 23:56 You’re definitely hitting on the right question, which is that to date, I would say— Regulators have been extremely engaged with the BCI community. The FDA is really smart and really on top of it and has, I would say, not significantly slowed down the industry. Like, they’ve worked hard to make sure that safe devices are coming through this process and try to find safe opportunities to accelerate. CMS has less staff than FDA does and they’re not up on all of the different possibilities that BCIs have to offer the patient population. And so like, probably the next factor will be political advocacy, education of CMS, because they are the payer for most of the patients that’ll get first generation BCIs, and private payers, insurance companies usually follow the Center for Medicare and Medicaid Services in determining what they’re going to cover. And so like, it sounds like bureaucracy, but like probably the most important decisions in understanding how quickly these devices will get to patients and how quickly the field will thrive have to do with how easy it will be to get good reimbursement for BCI devices.

Dan Zavorotny: 25:14 Yeah. That’s why I wonder if the Veterans Affairs is going to be a big, a big potential early customer because they can make that decision for themselves.

Matt Angle: 25:25 There are a number of researchers in the BCI field that are already affiliated with the VA in some way. And DARPA, Defense Advanced Research Projects Agency, was the initial funder for most of this research. So it’s absolutely the case that the military is looking at this as a way to help warfighters.

Jason Calacanis: 25:40 Well, I do love to start my week off with a bit of good news and some optimism and a nice ramp to the future. So Matt, an absolute pleasure. It’s paradromics.com, P-A-R-A-D-R-O-M-I-C-S dot com. You can check it out. And then just before we let you go, is there a job you’re looking to hire for where you can’t find the right candidate and you want to shout it out into the void in case someone here watching is the perfect person for you?

Matt Angle: 25:58 Well, for anyone who’s listening, I will say that if you live within two hours of Ann Arbor, Michigan, we’re now enrolling in our clinical trial at Michigan. And so you should definitely, you should definitely look at that if you have someone close to you that could benefit from this device. We’re always looking for awesome technologists, and of any ilk. If you are, if you’re incredible at what you do and you really want to get into BCI, please, you know, shoot us an email. We’re excited to talk to you.

Jason Calacanis: 26:31 All right, appreciate it, Matt. We’ll have you back on in a couple months to see how things are going. Until then, peace.

Matt Angle: 26:33 All right, thanks a lot.

Tom O’Leary: 26:34 I have a question for you. Have you ever taken a flight? If you have, I presume it was commercial and therefore you were on a plane that you could probably name. A Boeing 787, 777, or perhaps an Airbus A320, A330, A350, or if you’re having a lot of fun, maybe an A380. But no matter what plane you were on, it was probably a tube with a couple of sticks coming out the side called wings. That’s one way to build an aircraft, but it’s not the only way. And one company, JetZero, wants to take on a very…

Jason Calacanis: 27:00 Radical approach to airline design, airplane design if you will, that could save lots of fuel and really change the entire game of getting people and stuff through the air. So please join me in welcoming Tom O’Leary, co-founder and CEO of Jet Zero. Tom, welcome to the show.

Tom O’Leary: 27:13 Hello.

Jason Calacanis: 27:14 So glad that you’re here because I get to ask the expert to explain what a BWB or a blended wing body or an all wing aircraft is. The people out there don’t know, Tom. Teach us.

Tom O’Leary: 27:24 Absolutely. So, simplest understanding of an all-wing is that it is lift across the entire wingspan. So you have a fuselage or a that’s blended into the wing, also known as a blended wing body. And these these terms are basically a physics first, first principles physics way of solving for the most optimized airframe that delivers the lowest fuel burn, the lowest emissions, the lowest noise, and once the engineers go to work on it, it’ll produce the best cabin with the best characteristics for the market.

Jason Calacanis: 27:58 I want to get to that in a minute, but I’m just perplexed because prepping for this I was looking into different airframe designs and it seems that all-wing aircraft are awesome on paper at least. And I know it’s not a new concept, we’ve been kind of tinkering this as a species for a while. So what has changed in, I don’t know, material science or design tools that allows this idea to go from cool idea but not practical to we’re going to build a demonstrator and get it in the air by next year?

Tom O’Leary: 28:29 Yeah, there’s really just three key enabling technologies that take you from a tube and wing to an all-wing. It’s the aero, that’s first principles physics, lower drag, higher lift. And then it’s the flight controls because you have to have stability and control of that particular shape. And then it’s the structures, the composites. So actually NASA’s been working on this for 30 years. They’ve put over a billion dollars of research into this whole, this form. And so really that’s what we are taking those three key enabling technologies and different from what we did at Tesla back in the day where we verticalized the supply chain, we’re just taking those three things, making a new airframe, and then using existing supply chain, existing parts to speed the introduction to the market because it’s a great big task.

Jason Calacanis: 30:26 Now, on the lack of vertical integration, definitely different than Tesla, but my, my guess here, my understanding is that there are already very strong suppliers that can give you what you need to help accelerate the process versus back in the earlier days of electric vehicles in the US, there was less of an existing supply chain to tap into.

Tom O’Leary: 30:44 Yeah, absolutely. And it’s just a challenge, but aerospace, there’s a great supply chain. We’re working with companies, some of whom have invested in JetZero, companies like RTX, 3M, Northrop on the military side. They’re an incredible partner and enabler. They’re actually through their subsidiary Scale Composites building our demonstrator, which is a full-scale demonstration of this, this whole capability.

Jason Calacanis: 31:12 Now, you guys have raised some money from the Air Force, you’ve raised some money from United, so clearly there’s a dual-use affair going on here. You can use these airplanes for all sorts of things. And now for people out there who have only ever flown in tube-and-wing aircraft, is this a plane, the one that you’re building, that’s going to be able to, to function at normal airports? Because one thing I recall is when the A380 came out, um, people had to retrofit their airports and that made it, I think, less commercially viable. So does this thing slot in to, um, you know, my beloved TF Green or JFK?

Tom O’Leary: 31:30 TF Green! Love it. I’ve flown in and out of there many times. Went to high school in Rhode Island where TF Green is. Yeah, Barrington. Barrington High. So, TF Green was just over the bay from us. And it’s like a lot of airports, it’s a smaller airport that’s grown up from the 90s when Southwest came there. And so it’s, the question then becomes like, will this plane work within the existing infrastructure? We’ve been working with 15, now 16 airlines that come to our hangar twice a year for a couple of days and we go through all of the parameters: gate access, baggage, seats, bins, everything that you would think of from a commercial standpoint. The short answer is yes. If it’s in a group five gate, um, and it’s basically set up to be, it’s, this is the solution as opposed to the problem because it’s a shorter plane. So it can actually pull up to any gate and fit in. And that’s something that I think is underappreciated about the blended wing because we think in one dimension, width, as opposed to two dimensions, length and width.

Jason Calacanis: 32:47 I want to get back to the technical stuff here, but I’m now very curious. One thing that everyone has to suffer through is airline onboarding and offboarding. Yeah. Does the fact that the plane is a little bit shorter and a little bit fatter perhaps… And does that allow us to get on and off faster? Or will that not actually impact that element of what I would call passenger sanity?

Tom O’Leary: 33:07 Your hunch is correct. It will mean faster boarding because you’ve gone—

Jason Calacanis: 33:13 Yeah, exactly.

Tom O’Leary: 33:14 For the win. Just think about a single aisle. We know what the biggest single aisles take, about 45 minutes to board when full. And then you get on a widebody which has two aisles, or if they board from the middle it’s effectively four aisles, and all of a sudden you’re like, wow, I was expecting a logjam but it’s not here. Well think for this plane it’s actually six aisles, four in the back, two in the front, you board from the middle. So we anticipate the full boarding time for this plane is going to be something more on the order of 10 to 15 minutes as opposed to 30 to even 45 minutes. And that’s just a whole revelation, right? I mean that that’s a significant pain point. But more importantly our design has a place for everyone to have their own carry on bag at their own seat in a space so that there’s no like, you’re not sitting outside the gate wondering like I better get on board so that I can you know be assured that I have, you know before you even walk on the jet bridge that there’s a place for your luggage, anxiety diminishes, and like that’s the future that we all want and that this plane and this shape can deliver.

Jason Calacanis: 34:21 Well I think if that’s the case Tom, you’re gonna harm some incidental airline incomes because they do love to charge me for a checked bag because I worry that I won’t have space, so I cough up that extra money. So I wonder if you’re solving some of their kind of marginal revenue by accident by making a plane that does away with some of those pain points.

Dan Zavorotny: 34:36 What we see in the market right now is the friction is more do they have enough premium seats? When we talk to airlines now, their number one business problem is that they have frequent flyers who are not getting upgrades because people have bought all the premium seats post-COVID. Now that’s a different problem to have, like they’re making more money from premium than they are from coach because people are upgrading themselves. We finally come to the point where people have, they’re voting with their feet and they’re buying the premium tickets. They love that. So it’s really that’s where the market is today and you see like Southwest just went to assigned seats. I mean we’re reading those tea leaves, right? We’re reading those tea leaves and you’re seeing that people are demanding a more premium experience when they fly. It shouldn’t have to be heartache. It should be a great experience and we can just forward that. It’ll be change but we’ll adapt to the change. It’ll be great.

Jason Calacanis: 35:30 I have a… I could talk our entire time about my views about the airline industry but I’m going to bring us back to the progress of technology here. Okay, so you guys built a scale model, the Pathfinder. That’s gone through design, building, and testing. What were the learnings from that? Did it go as planned? And then I want to talk about the demonstrator after that. But first, take me to the miniature version.

Matt Angle: 35:54 Yeah so there’s multiple. Pathfinder is a program not a plane.

Tom O’Leary: 36:00 Um, we—we’ve done 4%, 6%, 12%. Right now we’re doing rapid iteration in the—in the middle. The Goldilocks point for us is the 6% because we don’t have to certify it when it’s done. It’s just under the—the drone limit. So we can do rapid iteration, we do it on truck tests. We learn tons. It’s basically like the cheapest wind tunnel on the planet. Do a change to your—your sub-scale model, put it on the back of a—the truck on a stick and run it down the airport runway. I mean, this is—this is a great—I mean it’s fan—I mean we do—we do wind tunnel tests too. We have a glorious shining stainless steel wind tunnel model which—great learnings from that. But sometimes we get more learnings just from that rapid iteration cycle with our—our pathfinders either on the back of a truck or out in the desert, which has been a little bit problematic this year because the dry lake beds are lakes, right? You know, that’s—that’s a challenge.

Jason Calacanis: 36:59 Darn rain falling and improving reservoir access. What are we going to do? It’s slowing down our… on these, you know, truck stick demonstration—pathfinder and then, you know, running it around. I’m almost surprised you’re saying that because when I think about what JetZero is building, I’m thinking about, you know, the future. We’re taking an old concept and we’re updating it for the modern world with new technologies and you’re not using digital twins so far as I can tell, you’re not doing as many digital simulations as I expected. You’re doing real world out there with I presume, you know, high-vis paint. So, why did I—why am I wrong in my expectations about the best way to design aerodynamics today?

Tom O’Leary: 37:38 Well, it’s actually all of them. So digital twin is core to our strategy and we will be the first large jet—jet manufacturer to have a system that’s built on a digital twin. And where the digital thread ties through from design to test to certification to manufacturing to service. That is absolutely core to what JetZero is doing. But it’s—it’s all of the things, right? And it’s the right tool for the—the right time and the right job. So we’re absolutely using quantum compute, computational fluid dynamics is—accelerates us in—in lots of ways, but sometimes there’s just nothing better than putting a scale model, dynamically scaled, on the back of a truck and running it down the runway and you learn things in—in real world that—you know, it’s wild. And it’s actually there’s—there’s nothing that gets the team more excited when you’re just around the hangar and then all of a sudden here goes the—the scale model team is out on the runway and everyone like runs to the window and sees the thing. They’re—they’re calling the tower and asking them to hold flights. We’re in a commercial airspace here and they’re super—

Jason Calacanis: 38:48 Down in Long Beach, California.

Tom O’Leary: 38:50 Down in Long Beach. And the airport here is super helpful with us and they’ll integrate—they’ll be like, ‘We just closed taxiway Kilo for you, go for it’.

Jason Calacanis: 39:00 And it gets everybody it’s just excitement, right? Like when there’s a new CFD run, people aren’t like going around someone’s computer to say like, oh my gosh, but when that thing hits the runway, and then the plane spotters come out and they post it on Reddit and stuff and it’s it’s a thing. Yeah. It’s great. Yeah. It’s fun. I will say that with a wind tunnel and, you know, operating knowledge of CFD, you could also just branch out and build an F1 team. You’ve all the ingredients, you know, just just an idea. Just throw it out there for the future.

Tom O’Leary: 39:30 You know what’s amazing is, uh, my co-founder, Mark Page, who’s the aerodynamicist who ties all the way back to the beginning of this type of work back in the in the nineties. Uh, when Boeing and McDonald Douglas merged, his boss was like, you’re an innovator’s innovator, you you fly my pretty one, you need to leave the nest and go innovate. And the first thing that he did was he started to work on Indy cars. So open wheel open wheel racing. It was back in, you know, 99, 2000 when aero started to become now it’s like a controversial like one little tweak and everybody’s in an uproar. But back then it was it was, you know, it was in a sense in its infancy compared to where it is today with CFD. So that’s that was one of the places where he learned a lot. Uh, and it actually had a surprising amount of contributions to his understanding of the blended wing. So it’s it’s pretty pretty interesting background.

Jason Calacanis: 40:29 And people say that racing is a waste of money. No. You see, it’s actually how we get better better airplanes for the future. Uh, okay. Let’s move to the demonstrator, this is the uh the project you guys have had pitched for 2027, since you first uh, you know, raised the money from the Air Force. So, uh, are we on target and 2027 is a big time zone, so when should I expect to hear from you guys about the demonstrator taking off and uh flying away.

Tom O’Leary: 40:54 Yeah. Second part of ‘27. The beauty of this team is extremely extraordinarily capital efficient. We’ve we’ve made huge amounts of progress with relatively small amounts of money from an aerospace context or perspective. The plane will be built this year, it’ll go integration and then into series of tests, right? So you’re testing your ground testing and then you get into flight test. Second half of ‘27 is the expectation and everything has been up until this point on time under budget, we’ve hit all our milestones, we’ve hit them in time. We have NASA and the Air Force, it’s an Air Force program, but a whole a whole host of NASA folks are on loan, people from Air Force Research Labs, industry partners, the FAA has has a few people that are in that program. They’re vetting everything we do, we don’t clear a milestone without their say-so and their approval and we’ve we’ve hit the time and the budget uh to a T so far. And that in and of itself is pretty major win.

Jason Calacanis: 41:57 But that’s an enormous win. Nothing in the world of mega projects goes on time and on budget.

Dan Zavorotny: 42:00 So, points. Now you mentioned a whole host of people that are interested in what you’re building, investing in it, helping out, lending people, whatever. And that’s because the way that I see it, if your plane, the Z4, will end up as efficient as hoped, you could reduce the per passenger, the passenger mile, no, I’m going to butcher this.

Tom O’Leary: 42:20 Fuel burn per passenger mile.

Dan Zavorotny: 42:22 Fuel burn per passenger mile. That’s a mouthful. But by up to 50%, which is a staggering amount of money. Operating costs for airlines are often predicated on fuel costs and that profitability too. So do you think that you’ll get up to that 50% savings in the first iteration or is that more of a long-term goal for the technology and maybe the second version of?

Tom O’Leary: 42:42 Yeah, and a first iteration, well, it’s all about what you’re comparing to. So that, that 50% is comparison to things that have been in the market for a while and that are flying. But, United and Delta are still flying a 767 cross-country. And so against that aircraft—those are pushing into their 30s now—and so they’re replacing those aircraft, but as we compare to those, it’s in that up to—that one plane—up to 50%. Way to think about this is it’s 30% aerodynamic efficiency above a tube and wing. That’s the L over D part. So that’s still a pretty staggering piece because as you, as you point out, it’s about a third of the cost for, for an airliner is fuel.

Jason Calacanis: 43:33 Yeah.

Tom O’Leary: 43:34 So if you can make a massive dent in that, you know, even at 33%, you’re still cutting something like 10% of their cost structure. That’s massive, right? And when you just think about the billions of dollars that they spend even just in fuel costs.

Jason Calacanis: 43:47 Right. And is that also why the military is interested? Tankers, getting freight around… The US military has bases everywhere, so logistics is a big part of the task. Um, is the fuel savings the core thing or is there something else that your plane can do that is particularly appealing to the DoD?

Tom O’Leary: 44:04 It’s the flip side of the coin, right? So, lower fuel burn for them… I mean, yeah, they want a lower cost and lower emissions and all those things. But keep in mind, we’re working at the behest of the Air Force and Air Mobility Command, right? Dominance is fighters and bombers, and air mobility is tankers and transports. Their motto is, ‘you can’t kick ass without the gas’, right? So…

Jason Calacanis: 44:27 I thought that was a tanker motto.

Matt Angle: 44:29 It is.

Tom O’Leary: 44:30 It is basically, right? But this is the deal, right? So that’s the tanker motto and then so their focus is, but what is that efficiency that has lower fuel burn, lower emissions… what is that to them? It’s ‘how do I take a huge amount of payload across huge distances?’ You think about Operation Midnight Hammer, you know, that was, you know, a handful of B-2s supported by a couple three dozen tankers, right? So… Yeah, that’s what it takes. And so if you can lower the fuel consumption then you’re actually increasing the range. It’s just the flip side of that coin. So, same reason, different kind of a benefit.

Jason Calacanis: 45:15 Okay. So, the question that I had pinned down about this entire idea of greatly more fuel efficient aircraft is, you know, can all-wing aircraft take over the US or global airline industry? And one thing that I found you guys talking about was the underserved middle market. So, help me understand the economics of different plane sizes in the airline context and how the Z4 might fit into that and unlock something perhaps as opposed to just replace the aging 767s of the world.

Tom O’Leary: 45:43 It’s just a beachhead, right? It’s like if you’ve got this amazing technology and you’re going into an existing market as opposed to opening up a new market, you’re just a question of product market fit. We all know what product market fit looks like. It looks like a product that the customer can’t not buy.

Jason Calacanis: 46:00 Ding ding ding.

Tom O’Leary: 46:01 Who doesn’t have one of these? Right?

Jason Calacanis: 46:03 Only smart people.

Tom O’Leary: 46:04 Right. You can’t not buy a smartphone and operate in today’s world. So that’s product market fit in a nutshell. And then so when you look at the airliner market, for example, there are no planes between 200 and 250 passengers. So the single aisles top out three class. So you gotta normalize so you’re doing an apples to apples comparison.

Jason Calacanis: 46:14 You gotta normalize for what class.

Tom O’Leary: 46:15 It’s three class, you top out at 200 passengers in in that biggest narrow body. But then the bigger, the smallest wide bodies, you don’t start coming in until 250 or even more, right? And so we just looked at that and said how could there be a better product market fit? We can take everything that the airlines want, lower fuel burn and all of the rest, and we can bring it to a place where they have no plane. And this just seemed like the most obvious product market fit beachhead for me having worked in tech, you know, I worked at eBay, Tesla, dealer.com, then got into aerospace over a decade ago, worked with Beta Technologies. Worked on product market fit with all those companies. When I saw the potential for product market fit here, it just blew me away. There was no I’ve never seen anything like it. There’s literally no product in the middle of a very mature market and the TAM is trillions, right? But you just want to be able to have that beachhead, prove that thing and then grow from there. And it will eventually, by 2050, all planes will be all wing of a certain size and greater because how how will you how will you compete? And we’re seeing that, right? Like Airbus is Airbus is already saying that publicly. They’re like, yep, this plane is inevitable and we’re not doing it just yet and we’re probably gonna do it with bigger planes, not smaller planes, but they’re clearly messaging it.

Dan Zavorotny: 48:00 …ing that that this isn’t an inevitable future, it’s just a question of who can absorb the risk and take it to market.

Jason Calacanis: 48:08 I just find it really, really amazing that Boeing is too afraid to make a new airframe, and here you are with the financial equivalent of like eight dollars and two sticks doing that and building the future at the same time. Kind of an indictment of Boeing, I’m not trying to pit you against them, but that’s the way that I think about this. Okay, so…

Tom O’Leary: 48:25 I think it’s more, I think let me just because I would not indict Boeing and we have many people here who came from Boeing and who have great affinity for that company. We we have a phrase around here which is respect your elders, right, where we’re providing an alternative but ultimately this is what startups are meant to do. It’s not an indictment of the company, it’s basically saying let’s remember sometimes when a change is needed and a paradigm needs to be shifted, it’s not the incumbent who can do that, the risks are too high, they’re not set up for it. We can provide that and they will ultimately benefit I believe.

Matt Angle: 49:05 Yeah, there’s a book written about this, something about a dilemma and people who make change, I think? Yeah, maybe you’ve heard about it.

Tom O’Leary: 49:10 Innovator’s Dilemma.

Jason Calacanis: 49:11 Yeah. Okay, I know you guys are doing manufacturing in the United States, long term it’s going to be based in Greensboro, North Carolina. My question is, what does this thing cost? Now, commercial operations are supposed to be early 2030s, so clearly demonstrator to commercial is going to take a little time. But what do you think it’s going to cost and how will that be in kind of a seat-to-seat comparison with, I don’t know, the 737 MAX or the 787?

Tom O’Leary: 49:36 Yeah, so cost is proprietary. It’s going to cost billions for us to put it together and these planes are, you know, coming into the upper mid-market where planes are selling pretty deep into the nine figures, right? It’s not, these aren’t $100 million planes, they’re more like $200 million planes out the door, and list prices are a lot higher than that. It’s very common to discount in the space. It’s not common, it is, you know, de facto heavily discounted. And so prices are very closely protected. Our feeling there is is pretty straightforward. We we can project the costs of the program and of the development of the manufacturing facility and we’re using existing supply chain so we’re not guessing, right? When we’re looking at, hey, we’ve got to produce a clean-sheet airframe, if we’re leveraging existing supply chain for those parts, then those are known commodities and so we have a a really good handle on the cost and the idea that we can create margin, particularly because we’re lowering the cost. So that’s the way we look at the cost picture, but the TAM is huge and our ability to sell into that TAM is going to be amazing. It’s just about, people want to see that proof point, that’s why we’re building a full-scale demonstrator.

Jason Calacanis: 50:54 Absolutely. And for anyone curious about the math on TAM here, when United invested in… 2025, they had something like a loose agreement to buy 100 with another 100 maybe down the road. 200 times 200 million adds up pretty quickly and that’s what we’re talking about here, not, you know, selling software seats for 10 bucks a month for 20 people. It’s an entirely different category of TAM. All right, so last question for you Tom is, is there any remaining technology risk that you can see or is this just simply build the demonstrator, get it out there, test it, learn from it and iterate, but there’s nothing enormous you still have to leap over, just no chasms in the road?

Tom O’Leary: 51:30 There are known knowns, right? There are known knowns that you have to burn down the risk in terms of your test on flight control, stability and control, on composite structures. It’s like when we’re building this full scale test model, there are known knowns. That’s why we build a pressurized cockpit test because we’re going to put humans in the cockpit to fly it. So we build one, we destroy it, and, you know, we do a destructive test and then we move on and build the final article. So those are the - those are the kind of things, but as - as we were pointing out earlier, we’re talking about decades, we had not one but two X-planes, X-48B and X-48C that NASA did and the chief engineer of that program is here working at JetZero. So these are, you know, there’s a lot of - there’s a lot of known knowns. It’s just about execution.

Jason Calacanis: 52:31 I feel like we’re all - we’re dancing around the old Rumsfeld quote about, you know, unknown unknowns and known unknowns. Um, slightly off for a question, but I’m really glad to see the progress you’re making. I - I am desperate for some sort of innovation in the airline space. I would love to have a better experience as a commercial flyer. And as a United guy myself, I’m going to be flying on your planes. So Tom, will you come back next year when the demonstrator is ready to go and tell us how it’s going?

Tom O’Leary: 52:46 Oh you bet. We’ll show you - we’ll bring some - a picture book.

Jason Calacanis: 52:52 Actually, last question about capital because I’m - I’m really curious about the venture world. Right now AI is just hoovering up all the money, it sounds like. Now I know you raised from B Capital and a bunch of, like, RTX ventures and so forth, but you will need more money. Do you think that the venture world is a fit for JetZero moving forward or are you going to look for other sources of capital because there’s a lot of ways you could finance this, I know.

Dan Zavorotny: 53:12 It’s been challenging up until now and the momentum right now is - is really strong. I think people are starting to - to see that hardware is a great buy, dual-use hardware is an even better buy, and if you’ve got a really solid use case for both of those, then you can do what we’re doing now and you see that in our series B, we’ve turned that corner. Early on, there was a lot of ‘wow, this looks incredibly risky, I don’t - I’m not sure how can you…’. It’s very small. We’ve been in business for five years and we’ve gotten to the point where we’ve proved that there’s traction with supply and there’s traction with demand and the full-scale demonstrator gives people that proof point. So what we’re hearing from growth capital folks right now, which is the round that we’re moving into…

Tom O’Leary: 54:00 This this year is putting together our growth capital round. You know, B is kind of the end of your venture style rounds, and it’s going really well because, you know, what happened in the market a couple of weeks ago, right? Where it’s like everybody’s wondering if AI is just going to completely demolish software, and people are saying, ‘Aha! Hardware, hardware, hardware. This is a this is a solid place for capital right now.’

Jason Calacanis: 54:26 All right, Tom, we gotta leave it there. Come back next year, I want to hear all about it. And also, I cannot wait to take a Flight 1. I’m going to guess 2032, I think that’s my guess for you guys right now. So we’ll see how that bears out. Tom O’Leary, we’ll have you back. Thank you.

Tom O’Leary: 54:40 All right, thanks so much.

Jason Calacanis: 54:41 We’re going to take a little bit of a detour into a part of the startup world that we don’t spend enough time on, which is wellness. This is a topic that is near and dear to my heart because I used to be very, very unhealthy, but a little bit of rehab, a little bit of exercise, and some actual food has put me back onto the right course. But I have been a very unhealthy person and I know what that feels like. It makes you slow, it makes you feel stupid, you don’t take good care of yourself, you don’t do good work. And that’s why I wanted to talk to a company called Nutrisense. They are working on helping people track their own glucose, make better goals for themselves, handle their stress, and generally live better and more productive lives. So please join me in welcoming to the program, it’s Dan Zavorotny, the co-founder and CEO of Nutrisense. Dan, hey, how are you?

Dan Zavorotny: 55:19 Hey Jason, how you doing? Great. Thanks for having me, appreciate it.

Jason Calacanis: 55:23 So, Nutrisense. When I think about technology startups and founders, I do not think healthy people. I think a lot of late-night Red Bulls and pizza, but I presume that you are here to change that for everyone out there. So first of all, tell us what Nutrisense is and then target demographic to start.

Dan Zavorotny: 55:43 Sure, sure, sure. So when we first started, we were the first company to take continuous glucose monitors—and these are devices that you put on your arm and historically use glucose to make sure that you understand how you respond to food, stress, sleep, and exercise, right? This is in some ways like the Oura Ring or the Whoop of nutrition, right? However, it’s been used for type one diabetics who take insulin.

Jason Calacanis: 55:59 Yeah.

Dan Zavorotny: 56:00 So we said, what if we give people who don’t have type one diabetes, but type two non-insulin, pre-diabetes, weight loss, PCOS, Hashimoto’s, longevity—everything you could imagine? And everyone said, ‘You guys are crazy, no one is going to put this in their arm, it’s going to penetrate skin, why would anyone want this?’ So we launched anyways, we said why not, and it turns out a lot of people want it. And after that, the mass market flooded and there’s more and more people entering the space. So that’s how we started. However, we said, you know, that’s not enough. You need to create infrastructure, you need to create the ability to understand what this means. And so then we added a layer of software on top, which helped you put in—you actually track your carbs, macros, your micro-macronutrients, pull in all the data from your other wearables. And then the third piece is: how do you have another human coming in—a doctor, a dietitian—and help you interpret this data? Because it’s very, very complicated. And so that’s what we’ve been doing for about seven years now. We’ve worked with over, I think, something like 120, 130,000 people at this point.

Jason Calacanis: 57:00 Wow, that’s more—that’s a lot. Now for folks out there who are not as familiar with how your glucose moves or your blood glucose levels in relation to food, can you just explain that relationship and why it matters?

Dan Zavorotny: 57:11 Sure. So glucose is one of the most important metrics for your health, basically. When you eat something, your body takes, especially if it’s a carbohydrate food like a cheeseburger or a burger, your body takes that carbohydrate and it breaks it down in your system and converts some carbohydrate to glucose. That’s what spikes glucose in your system and your bloodstream. And then that glucose is usually either absorbed into a, into your liver or b, into your muscles and converted to glycogen, right? And the way that works is your pancreas releases insulin, that’s what pulls it out. And we unfortunately in the US and most of the world have become incredibly pre-diabetic. Right now, a third of Americans are pre-diabetic. And the reason for this is we’re eating so many carbohydrates and so much saturated fat that our bodies cannot produce enough insulin to pull it in the right places. And so it ends up just floating around your blood instead of going into your liver and your muscles. And that’s a bad thing. And so when that happens, you have all these cardio-metabolic issues like heart disease, diabetes, weight gain, fatigue, stress.

Jason Calacanis: 58:23 Yeah, yeah, yeah. So essentially our blood is too sugary and it’s not good for us and we need to take better care of that. Now this brings us to monitoring. I’ve never had diabetes, pre-diabetes, anything like that, so I’m not as familiar with glucose monitoring and what goes into it. So can you tell us, in the Nutrisense context, how you help people monitor their glucose and how, if I can be impolite, intrusive that is?

Dan Zavorotny: 58:43 So it’s not that intrusive. It’s a little microneedle that you put inside of your—on top of your arm. It penetrates your skin a couple millimeters, but you feel nothing, right? And it lasts about 14 days. And what happens is that data gets sent directly to your phone. And what you’re doing is you’re just talking to an app and you’re saying what you’re eating. And I can show it to you guys, by the way, as well if you’d like to see an app, it might be easier.

Jason Calacanis: 59:08 Yeah, well I mean we do love a demo here on Twist, Dan. So if you want to, if you want to show it off. And also as a big fan of using voice dictation to both write and also to interact with AI, this is right up my alley.

Dan Zavorotny: 59:22 So here you have a glucose chart. And it shows you your glucose, exact glucose from minute-by-minute that shows you what your glucose is responding to everything in your day, right? And so then if you scroll down here, if you across, you can actually see what is driving a glucose response. So here this person worked out and they had a glucose spike after the workout. And the reason for that is your muscles release glycogen in your body to help you work out. And that’s positive, right? Versus here this person had a dinner and they recorded they had, you know, they had avocado, cucumber, broccolini, arugula, pretty healthy person, and some tuna. And again, their response was 93 milligrams per deciliter, right? So it’s a lot of data. And for someone like you or me, we’re probably going biohacking our health, but it’s kind of complicated, right? And so how do we— How do we simplify that, right? The way we did it is twofold: we basically added dietitians so you could just grab, grab a time with dietitian, press ‘I want to talk dietitian’ and insurance will cover it. Means that it doesn’t hit the deductible, it doesn’t do anything, just a free call. That data will be reviewed by the dietitian and she will, she or he will give you feedback on how to improve and what does this data even mean, right? And so what we’ve seen is pretty fascinating. Like, we’ve seen people that stay around for 12 months or longer with us, they’re losing something like 20 to 30 pounds, right? And this—

Jason Calacanis: 1:00:31 That’s a lot.

Dan Zavorotny: 1:00:32 It’s a lot. This is no prescriptions of drugs, pharmaceuticals. This is purely just understand your glucose, understand how you respond to things. So for example, we’ve seen certain factors, like there are people who respond very well to potatoes, but you give them rice and they’re spiking their glucose in diabetic range for two, three hours.

Jason Calacanis: 1:00:51 That’s very interesting. So essentially even if I’m eating foods that don’t always spike other people’s glucose, I could be accidentally pushing myself towards pre-diabetes without even knowing it.

Dan Zavorotny: 1:01:01 Exactly, right? And it’s those repetitive spikes over and over and over. It’s kind of like if you think about, you know, if I did you, give you a little light tap on your shoulder, you won’t feel any pain. But if I give you a light tap on your shoulder 30,000 times, sooner or later you’re going to have a bruise. And that’s exactly what’s happening in glucose and diabetes, right? When you have a glucose spike once in a while, it’s not a big deal. But when you do consistently for 30 years, that’s the issue. And so if you identified these issues ahead of time, you could prevent it.

Jason Calacanis: 1:01:25 Dan, one thing I’m really curious about is the inclusion of dietitians and humans in this, in this loop. Because, you know, here on TWiST, we’ve been talking so much about AI and automation, and to me, what you have is a large data set of people’s glucose levels and how they react to food, and then individuals. And it, it seems to me like a place where I would kind of expect you to be pitching an AI solution versus a, you know, licensed dietitian. So talk to me about that choice and why humans matter in this particular context.

Dan Zavorotny: 1:01:52 I wish overnight everyone said AI takes over the world. But when it comes to health, there’s too many differentiation between all of us, right? Uh, we have our historical disease states, we have how we’re feeling. Like, I think one of your colleagues before said, like, you broke up with your husband or your wife, or your boss is yelling at you. It’s too hard for AI to understand that context. But when you talk to a human, they’re able to track all that context and put it into the system so they remember it over time. So what we did is, uh, we added the contextualization through the individual. However, we took that contextualization and we added AI to it. And so now you can have—do two things. One, you can actually make the interactions between dietitian calls way more impactful with AI. And two, you can make the dietitian way more impactful as well. So historically, it’s taken an hour for a dietitian to come and talk to you so they know everything about you. Now they press two buttons, we created a report, they review the report, and now they talk to you about what you did three weeks ago and they remember it. So it feels more personalized. So it’s not me taking notes or me remembering what I—we talked about a month ago, but going directly into you and you specifically, so you feel heard and understood. So essentially instead of having—

Jason Calacanis: 1:03:00 AI jump in and do an entire process start to finish where instead using AI to uh to allow and extend humans to do what they do best, which is understand fellow humans and therefore not only can the dietician probably see more people and therefore do more business if you will, but also me the user gets personalized information without massive waits, which in the healthcare world I mean that that almost sounds like a magic trick. I mean try to get a dermatologist appointment, you know?

Dan Zavorotny: 1:03:24 Exactly, it’s fascinating.

Jason Calacanis: 1:03:25 We call her Nora, right?

Dan Zavorotny: 1:03:26 Okay. And so what Nora does is she takes all the conversational data that a human has with the dietitian and she puts it in here. She takes all the glucose data, all the nutrition, your workouts, everything you can imagine and puts it in here. So all of a sudden now you know a lot more things. So if I just press review my day for example, it’ll populate- there we go, populate pretty quickly. So it’ll tell me already a summary of what happened, right? But what’s fascinating here is as it goes through, it talks about your strengths and it sees what you’ve been eating and how it’s responding. So remember, in theory people say, oh well OpenAI could do this. You have to feed data into OpenAI every single day and from so many different sources. This is accumulating all that source of data all together, you know? And so it has all that context about what you currently eat, your current BMI, you know how you’re sleeping, how you’re exercising, right?

Jason Calacanis: 1:04:14 And Dan, can I use the Nutrisense app to like take a picture of my food and have it like auto scan that for me? Okay good, because I’ve tried MyFitnessPal in the past and I’m not going to lie, I’m not going to log my food. I just- I have learned that about myself. The answer is I won’t do it.

Dan Zavorotny: 1:04:29 Exactly. So what we do is you basically take a screenshot. You could also just- when you press that meal, you could also just talk into it and you just tell it what you want. Uh and it’s way easier, right? You just talk into whatever you ate and it’ll type it for you and it’s 100% accurate. It’s fantastic. So because logging is annoying, logging is very very annoying, right?

Jason Calacanis: 1:04:45 Yeah, and also it would probably wreck your retention because then I feel guilty that I didn’t do it and then I don’t want to open the app and then I don’t want to engage with it because I just feel- I feel shame that I failed to, you know, point out that I actually had three scoops of ice cream instead of just two that I wrote down.

Dan Zavorotny: 1:05:00 And that’s what makes it exciting. So because it’s so annoying to track food, when I talk to a dietician live, she’ll ask me what did I eat for the last week? And now the AI will pull that and it’ll put it in the system as well. Yeah, so what’s really cool is because people hate tracking food, it’s really annoying, right? But when you talk to a dietician, maybe talk once a week, once every two weeks, you talk about the things you ate and because it’s conversational you don’t really notice that you’re sharing information, but it’s also pulling to the app right away. And it’s populating it for you inside that app and inside of the AI. And so immediately like when I ask what should I eat today, like if I just type in like what should I have for breakfast- what should I eat for breakfast- it’ll literally say it- like talk about- it’ll load in a second, but it’ll say like based on what you like and what you don’t like and your preferences here’s what you should, right?

Jason Calacanis: 1:05:44 I- I love that. That’s fantastic.

Dan Zavorotny: 1:05:47 Yeah and it’s just fascinating. It’s like, you know, it’s talking about- I mean just look how fast that was and it’s saying based on all the stuff you’ve eaten and you talked about- you stuff- you ingredients you hate and hey don’t like, right? Uh which is so so specific to the individual.

Jason Calacanis: 1:06:00 Right. One of my children was recently diagnosed with celiac disease. And so like one thing we’ve had to do is throw out our recipe book literally and just kind of restart. So it would be great to have that kind of baked into this, that way I know that if I’m prepping food for not just myself but the family, that I’m going to be hitting the right ingredients and so forth. So I absolutely love that. One thing I’ve heard a lot about though recently is GLP-1s. And you know people use these as a way to control their appetite, control weight if you will, and there’s a bit of an overlap I think with the Nutrisense products. So do you consider GLP-1s to be an accessory, a competitor? How do they fit into the Nutrisense vision? Yeah, that’s it.

Dan Zavorotny: 1:06:43 Yeah, so for GLPs the biggest issue GLPs have is that when people get off GLPs they regain all the weight. Because what it does is suppress appetite a lot of times and you basically just lost weight, but you didn’t learn new habits. You didn’t understand what caused this issue. And so when you use Nutrisense, two things happen. One is you lose weight faster, number one. Number two, you learn things that are beneficial to you when you get off. And so we’ve actually seen a lot of data on people stop using GLPs and they keep using Nutrisense, they don’t regain the weight. They stay at that positive weight, number one. And number two is, GLPs actually make you lose not just fat but also muscle. And that’s a big issue.

Jason Calacanis: 1:07:21 Which is very, very bad, right?

Dan Zavorotny: 1:07:22 It’s very, very bad, right? So you might lose 30 pounds, but 15% of it’s fat and 15% muscle when in reality you just want to lose 100% fat. And so what we’re doing is we’re teaching you to make sure that you’re not losing the muscle and you keep the muscle around and that’s really the key attribute to both long-term health and longevity over time, right?

Jason Calacanis: 1:07:31 Right.

Dan Zavorotny: 1:07:32 Those two things are very valuable together.

Jason Calacanis: 1:07:33 Right. Talk to me about what this costs for the average person. I’m curious. I know it’s a subscription-based product, there’s also some sessions that I can get about stress and diet, so just for folks out there who aren’t familiar, business model.

Dan Zavorotny: 1:07:43 Yeah, so historically when we first started company, we were taking these devices buying them off the shelf and then adding software and nutrition to it and that cost anywhere from you know 150 to 250 dollars. Since then, we’ve gotten partnerships for getting insurance coverage and then basically we give people if their insurance covered for free. They go to our app and they just download it, they pick a dietitian only and they get inside our app for free, that dietitian gets calls gets for free through insurance, and so it doesn’t cost anyone anything. And over time more insurance is covering the devices as well, so that will come into the market as well over time, but right now if they want to just go through dietitian and the app, it’s free for insurance.

Jason Calacanis: 1:08:22 And if I do want to use your guys’ glucose monitor what does that cost?

Dan Zavorotny: 1:08:25 Usually like anywhere from 100 150 bucks a month, but what we tell a lot of people is like use it for a month get data and then from there use the free version because we want you in there to get the value as much as possible for as little as possible.

Jason Calacanis: 1:08:35 And just because you know this is a startup show, we do have a lot of founders that come on we talk to a lot of people who are building stuff, has this had a particular target audience in the founder community? Because not the healthiest demographic by definition, I talk to a lot of folks who are staying up late working all the time eating like crap, and so I’m kind of curious have you found that founders also kind of need and want this in their own lives?

Dan Zavorotny: 1:09:00 into their like, oh, that sounds neat, it’s another app. But when they get into and they see the data set, founders are very data-driven people, right?

Jason Calacanis: 1:09:08 That’s what I was thinking. This feels designed for them.

Dan Zavorotny: 1:09:10 It’s designed for them. And so when they get into the data, they go, oh my god, I love it. And they themselves, a lot of tests, and they try to understand about it, right? So we have these early folks who are just obsessed with data points. And by the time they’re done with it a month, they’re like, I know exactly to the point of grams I can eat, of what, when, how, and why, to the point I can optimize everything in my life. And then people either they can work longer, they can work smarter, and they get better sleep. Nutrition is one of the most critical parts there is.

Jason Calacanis: 1:09:30 I really feel like you just hit me right in the gut because my nutrition’s not very good and my sleep’s terrible, and those are like the two things that I are on my list of to work on this year, as I think they were last year as well.

Dan Zavorotny: 1:09:46 They impact each other. They impact each other. Sleep impacts nutrition, nutrition impacts sleep.

Jason Calacanis: 1:09:48 Oh, for sure. Yeah, this is why I shouldn’t eat three scoops of ice cream and then try to go to bed right away because, shockingly enough, Dan, it does not work that way. All right, one last question for you about the the market you’re in. You know, clearly the last 18-24 months have been pretty AI-heavy. How is wellness been going as a category with consumers and also, if it’s okay to ask, with the venture capital community?

Dan Zavorotny: 1:10:01 Sure. So, with the consumers, good, right? I think it is growing. I think there’s a lot of companies that have started offering products adjacent to us, and they may, we as a category have opened up the floodgates. And so the more and more people enter the space, the better it gets for everyone because people are starting realizing what exists. Historically, when I mentioned seven years ago, people said, what the heck is this? Now most people know what a continuous glucose monitor is; they don’t know what to do with the data. So it’s the next step of evolution, right? So we’re moving forward at that step. And so from that perspective, it’s been very positive. And from the venture community, there’s been a lot of people in our space raising a lot of money. We’re in a different path now. We’re basically close to profitability. And so our goal is to keep going in this path without having—

Jason Calacanis: 1:10:51 Thank you.

Dan Zavorotny: 1:10:52 Without having to keep raising money forever, but keep scaling the efforts we’re in right now.

Jason Calacanis: 1:10:59 The profitability point, I didn’t think we were going to get to this at all because I didn’t know that. But you know, some founders, especially in the post, I don’t know, the post-kind of-COVID era, when things got a little bit calmer for a bit, really discuss profitability as a way to keep things kind of on the right track for their business. Why? Why is it the right fit for Nutrisense? Because to me it feels like, you know, growing market, there’s an AI thing built into this, it’s an app, it monetizes well. I mean, it feels like you might want to go out and raise, I don’t know, $50 million and, you know, shoot for the moon here. So talk to me about profitability as a choice.

Dan Zavorotny: 1:11:25 Yeah, I mean, I think we’ve seen all that. We started the company in 2019, and so we’ve seen the COVID craziness, then we’ve seen the crypto craziness, and then the AI. And I think we’ve gotten to the point where we like the way to build and the tooling that we have exists, right? Our engineers are now 5 to 10x faster, right? Our product developer is way more efficient, our designers are doing faster. And so we need the money theoretically to grow faster or more, and I think the issue’s not the money at this point, it’s how do you get distribution as much as possible, right? And I think a lot of that comes from partners, right? Effective partners, because the product we’ve built with the team we have is just way more efficient than we ever needed. And so— So, I don’t think the money is required anymore to grow, uh, because of the, you know, technology and the place we’re in now.

Tom O’Leary: 1:12:07 Well, that explains quite a lot about why we’re seeing people hire fewer folks. I mean, you know, back in the old days, the rift was you raise money, you took up your burn rate by adding head count, but if that’s no longer the case, then the value of marginal cash to a startup goes down, I think.

Dan Zavorotny: 1:12:20 Dramatically, right? And so why dilute yourself more if you don’t need to? Basically.

Tom O’Leary: 1:12:25 Well, that’s the most gangster thing I’ve heard on TWIST in a while. I really appreciate that Dan. Um, for folks who want to learn more about the company, nutrisense.io, and we love to ask founders who come on, is there a role you’re looking to hire for that you wanted to shout out into the void? But given that I just talked to you about the not-the-need to not hire, probably not is my guess.

Dan Zavorotny: 1:12:43 Well, I mean we all… I mean there’s always talented people and talented people always help, and so if there’s any good product managers out there, we’re always looking for good product managers to come on board.

Tom O’Leary: 1:12:51 All right. Well, we appreciate it Dan. Thanks so much and continued success and good luck.

Dan Zavorotny: 1:12:54 Thank you so much. Appreciate it.