New Limit closes $130M Series B to advance epigenetic reprogramming medicines for liver disease and aging

Uh but I mean it's a fantastic company. Uh Brian Armstrong has been working on it for a while. Uh brought in a fantastic team and they've been doing a bunch of stuff. So, Jacob, welcome to the stream. Thank you so much for joining.

Uh, could we start with a quick introduction on yourself and the company just for those who don't know? Yeah, absolutely. Thank you all for having us today. So, my name is Jacob Kimmel, one of the co-founders and president of New Limit.

So, New Limit's working on a new type of medicine to try and extend human health span by which we mean we want everyone to have more happy, healthy years in their lives than they do today. And we're doing that using a technology called epigenetic reprogramming, which we're happy to unpack if it's of interest.

Absolutely. Let's unpack it. Let's unpack it. Okay, let's unpack it. So, I think it's an interest of our our human audience. There might be some AI agents listening that the ape audience could also benefit if there are any who are currently tuned in.

Um, so all the cells in your body, they've all got the same DNA code, which is both kind of trivial. You probably learned that in like third grade biology class. But it's also presents a wonderful question, which is then, well, how do your eyeball and your kidney and your tongue all do different things?

They have the same code base. It turns out on top of your DNA and some of the proteins your DNA wraps around, there are these chemical modifications called epigenetic marks.

Um, I think of these kind of like the control flow in software where not every line of your codebase runs every time a user hits your application service. You use some pieces of the codebase sometimes and others other times. And so your genome's the same way.

Some cells express some genes from your genome, others express a different set. And that's how they do different jobs. Turns out as you age, all of those epigenetic marks get, for lack of a better technical term, messed up. And so your cells stop using the right genes at the right time.

They use the wrong genes at the wrong time. And then they degrade in function.

And that actually precipitates a number of the diseases and pathologies that arise with age, including things that we don't even traditionally define as disease, but that you or I would certainly like to remedy about our experience if we could. Is is the plan here kind of traditional FDA biotech?

I'm thinking like some research, then some mice, then dog or monkey, then human trials, then phase one, phase two, phase three. Maybe you're a public company at some point or is there some sort of Silicon Valley twist or different approach that you're taking? Yeah.

So, all of the downstream steps are pretty much what you just laid out. Ultimately, in order to get a medicine into humans, we have to first do no harm, demonstrates the medicine has some reasonable chance of actually helping a person and then definitely isn't going to harm anybody when you put it into them.

Um, so we do need to do animal trials. We then eventually need to go into the clinic.

I think the Silicon Valley twist here is that the problem we're trying to solve of how to basically rewrite these epigenetic marks back to the state you had when you were young really wouldn't have been tractable even 10 years ago without basically two technologies. One of those is something called single cell genomics.

So we can actually take a cell out and sequence all of the different RNAs in that cell. Basically telling you which parts of the genome it's using. And then the second, which I'm sure your listeners are intimately familiar with, is AI.

There are more ways we could try and rewrite this epiggenome than we'll ever be able to test in the lab. It doesn't matter how clever you are. The number here is about 10, which is a stupidly large scientific notation number to put it put it in context. That's like 10,000 Milky Ways worth of stars.

You just can't test all of those things. Yeah. And so by building AI models, we're able to actually prioritize which of those potential reprogramming medicines we even go bother to test in the lab. And that helps us get to these discoveries much faster.

I really don't believe this company would have been a good idea prior to the advent of really those two key technologies. And so we're trying to parallelize as much as we can early on.

So that by the time we get to that traditional drug discovery process, we have a lot more conviction around the molecules, we're moving forward than you might otherwise.

Can you talk about uh the different kind of milestones that you sort of uh that that have been important to you and important to your to your partners to get to this point?

It's it's you know much different than SAS where maybe you have you get funded with an idea in a deck like every you know company does and then you get to maybe 2 million of AR you do a series A 10 million you do a series B.

I imagine uh the milestones have been uh much different here but I'm sure you guys hold yourself to a pretty extreme you know standard around you know continuously derisking the opportunity and getting more and more convicted. But I'm I'm curious what those have been to date. Yeah, absolutely. Oh, lovely.

You let that out. One of the challenges with our technology and biotech in particular is the milestones are not quite as legible as revenue for quite a while. You know, famously, some of the the largest companies in the space took quite a while to even get to revenue positive.

And the reason these bets still make sense is that once they do have revenue, you really start printing it. There are more drugs making a billion dollars in revenue today than I believe there are software companies. Um, so it's truly amazing to recognize like a single product is transformational.

So all of these hard bets make sense even if there is a challenge in making them legible along the way. So for us really there were a few key milestones when we started out and built the company. All of this was effectively an idea.

There was an existence proof where we knew it was possible to reset the epiggenome of an old cell back to a younger state. But that existence proof was something that in the field we often call a tool compound. It tells you that the biology is tractable, but it's not a medicine you can put in people.

It basically works, but it's not safe. And so, we had to first build all of the technology that would even let us run these experiments in the first place, just to give you a sense of scale.

Before New Limit, people had really only tried 19 different ways to do this sort of epigenetic reprogramming intervention, make an old cell look young. 16 of those I had done in my old lab with my own hands. Like really, this was just a gold mine that not many people were digging in.

It's just like me there with like a tiny little plastic shovel. Um, and so at New Limit now, we've tested about 14,000 of the different ways to reprogram cells. So we like to say it's six or 700x depending on if you let me round up or not.

And we had to build all the molecular tools that even let us run those experiments. That's sort of step one. Step two is we then have to demonstrate that actually even though I'm really proud of the fact that we can run 14,000 experiments. It's way smaller than 10 to the 16.

We can build AI models that are sufficient to actually prioritize which of those experiments are worthwhile. If you imagine that there's some gold out there, but there's just way more places to dig than than we can really uh exhaustively search through, we need a way to find where those spots are.

So, we're able to demonstrate that now. We think we have the most performant models in the field really thanks to just the scale of data we've generated. We have hundreds more than anyone else does to train these types of tools.

And then one of the final pieces, and this was the last unlock that actually came a lot faster than we thought it would, is we demonstrated what in the in the biotech community is known as a pre-clinical proof of concept. In layman's terms, we've built some molecules that are actually drug-like.

So, you can actually hold these in your hand. You can hold a tube of a potential reprogramming medicine, put that into an animal model of a disease and rescue the function of that diseased animal. We can do this both with human cells.

In our case, we're able to take old liver cells from old people and restore them back to a youthful function so they can regenerate and repair a tissue the same way young ones do. And then likewise, we can put these molecules into animal models. And one of the diseases we're going after is alcohol-related liver disease.

It's some damage that accumulates from alcohol exposure over the course of your life. Not only can we make those cells regenerate better, but we can also just make them much more resilient to alcohol.

Um, it turns out that as your hypatocites, the cells in your liver age, they become much more vulnerable to alcohol when you drink. That's an experiment that I've run in my own life. And so I believe the data even without having to replicate it as many times as we have.

And we're able to really improve the protection they have against that type of damage in a way that we think will really benefit patients. So to our knowledge, that's the first time anyone's really made a reprogramming medicine.

you can hold in your hand that demonstrates efficacy, something you could imagine carrying forward to the clinic. Uh, can you talk about the series B $130 million? I'm uh I want to know about the round, but then I also want to know about what do what's the use of funds look like? Because you're doing a lot with data.

Is are you going to spend $100 million in some sort of like large H100 cluster to crunch all these numbers? Are we at that scale yet or is it just R&D salaries for engineers, scientists? Are there legal folks putting together FDA applications? Is it a bunch of money on like lab mice and monkeys?

Uh- like what is the shape of the business right now? Yeah, absolutely. So, everything you noted is some expense. Um, thankfully our compute budget quite isn't $100 million yet, though I hope we're generating enough data to get there soon. Jensen can donate some GPUs if he's feeling so kind.

If he's serious about living to 150. Yeah. I mean, it's in his own interest. He he wants to be running Nvidia for the next 40 years. Why not? It's it's true. Um, so yeah, the breakdown is basically the main costs are R&D. So we have a couple we have three different therapeutic programs.

Our most advanced program I told you a little bit about is focused on restoring function in the liver. We hope that can treat some advanced liver diseases and eventually treat everyone whose liver ages. You know, you probably have a little bit less energy than you did when you were 21. I certainly do as well.

And that turns out that as we age, over half the population starts to suffer from something called metabolic syndrome. Basically, your rates of heart attack, diabetes, obesity all shoot up. We see this in our own lives with people we know. It's just we don't necessarily have the language for it normally.

And we think those medicines could treat a very broad swath of patients.

We also have programs in immunology trying to restore youthful functioning your immune system and more recently vasculature trying to basically fix your kidneys and other portions of your body that that degenerate when you can't really move blood around the way you did when you were younger.

So that's a large fraction of the spend. And then another big fraction of use of proceeds is really to take these molecules toward the clinic. As you start to do experiments in humans, things get very expensive very quickly.

It turns out even just manufacturing the molecules that you need to go take into a clinical trial has to be done in a really controlled way, you're also just making very large masses of this stuff and it's very expensive to do.

And so because of that, you know, we try and really upfront derisk everything before we get to the clinic.

But we still have to reserve a fair amount of the dry powder to make sure we can do right by the patients we're going to treat, produce the drug in a sufficient quantity, run a sufficiently sophisticated trial to really gain evidence that it's working.

Can you talk about the pressure of working on technology that so many people are effectively betting on and and in somewhat in some ways feel is inevitable?

I feel like if you ever have a conversation with a loved one, you know, a parent, you know, you're talking with, let's say, a parent, they'll be like, you know, maybe a young person is thinking on a 30-year time horizon and the parent is thinking, well, I don't, you know, I don't know if I'll be around then.

And then the the the the you know uh younger younger uh person says oh well by that point like we'll have life you know I just feel like there's a something that comes up constantly that people I like maybe maybe it's um you know maybe it's misguided but I I feel like there's a general sense of inevitability with these types of technologies but when you guys are actually doing the research and and fighting these you know um fighting to make this possible.

I imagine that at times, you know, maybe in the fullness of time, you feel like it's inevitable, but it's it's certainly not, you know, pre-programmed, right? It's it's something it's it's knowledge that needs to be earned.

But I'm I'm it's a much different level of pressure than, you know, somebody raising $130 billion for a SAS company where if it fails, there'll be another note-taking app that just sort of like takes the place and will still be able to organize, you know, words on, you know, digital documents. Yeah.

Maybe for your note-taking app, Jordy, my note-taking app is going to be entirely unique and irreplaceable. Yeah. Yeah. Exactly. Um Yeah. I I love the way you set this up. It is something we think a lot about which is the responsibility we have as we're trying to work on these tools.

You know, I think in biotech, you don't get many opportunities to take really audacious bets like this. The science we're working on is earlier stage than you'd typically start a biotech on. There's more inherent tech risk here than many companies are willing to underwrite.

And so because we do have one of the unique opportunities to take a shot on goal at potentially a thousandx outcome, a medicine that everyone would benefit from over a certain age, we feel really a burden to make sure we're using that opportunity responsibly.

And so I do think there's some amount of stress in it, but I think it's mostly positive in that I feel like often we're presented with challenges where if we shrink the scope of the ambition a little bit, we can make our lives a lot easier.

Well, certainly we can get to the clinic faster if we're comfortable just having a medicine that maybe doesn't apply to as many people or doesn't work quite as well.

And having that burden to say, no, we've got one of the few rare opportunities in the industry stewarding this capital, working with these generational investors to be able to try and take a shot where really we are optimizing for the scale of the outcome rather than just the marginal basis point on probability of success, which I think is something that biotech has maybe forgotten over the past decade or so.

we've really optimized on probability of success but forgotten the other half of the EV equation. If you multiply even a high probability by a low potential value, your outcomes aren't generating as much as you'd hope for anyway. Um, so it is something that's always front of mind for us. Makes sense. Yeah, makes sense.

How how do you think about uh the potential impact of you know improving the function of uh of the kidney by 10% could have some you know additional externality in the body that could actually have a really meaningful you know so how do you think about kind of complex systems and and the sort of unintentional impacts of certain sort of effects of of the type of drugs that that you guys will develop over time.

Yeah, it's a great question. It's something like we don't have a super rigorous framework for this because it's these questions are very difficult to answer quantitatively. I guess in short, we like to treat those sorts of knock-on benefits as pure upside.

So before we launch into a program, we need to convince ourselves that just making one type of cell in your body younger is actually going to benefit you. And that might sound kind of obvious, like sure, I definitely want younger cells.

But if you then step back and realize your body's hundreds of complex cell types, they're all interacting. Well, maybe one really solid link in an otherwise pretty busted rusted chain isn't actually all that useful.

So, we try and find examples where if we're able to demonstrate that restoring function in just that one cell type helps individuals, helps especially helped humans, then we can get conviction around starting a program. And then everything in terms of the knock-on systems benefit.

Maybe if you had a healthier liver or a healthy kidney, we'll see benefits elsewhere in your physiology that maybe we haven't even directly measured yet. We just treat as upside potential for the long term. Yeah. I have a little uh lightning round.

I want your uh reaction to three kind of biotech projects or technologies and I want to know uh if they're at all relevant to the work that you're doing, if you're learning anything from them or just the overall impact on health and just general biotech community. Uh Ozmpic, Crisper and Alphafold.

Uh so I'd love to start I guess with Ompic.

uh it feels like you know it's not necessarily life extension but in terms of health span could be very good but um how have you been processing the story of GLP1s and are there any learnings from GLP1s that you've been able to kind of port back or update your philosophy or strategy at new limit absolutely I think you're entirely right in saying these are some of the first health span medicines I'd argue we actually have a couple already this isn't like a totally crazy idea there's another type of medicine called statins a bit older brand name ones called Livore you've probably heard of and between statins and GLP1s now I think it's quite plausible if you do some napkin math we're adding several healthy years to the median Americ's life which very few other medicines can claim you know if you look at the rest of therapeutics development over the past decade and add up how many years could you I maybe expect from these benefits it's it's actually quite small so I think one one of the things we've internalized is that you know the scale of the opportunity really does matter the difference between the GLP1s and many other drugs drugs is that the axis of biology they're targeting actually is dysfunctional in a huge swath of the population at least 30% of Americans probably more depending on exactly how you define that and that when you strike those really high value opportunities I think exactly as you alluded to you start to see knock-on benefits maybe you didn't even plan for initially you know GLP1s are now showing benefits in cardiovascular diseases there's early data in neuro that I don't think anyone quite understands but does seem to be reasonably consistent And so trying to keep your mind stuff specifically like the addiction and gambling stuff you're referring to.

No, no. So there's actually even neurodeeneration. So there are some early trials that like folks on glip ones are having lower the uh sort of slower disease progression for Alzheimer's and Parkinson's. Wow.

And as of my understanding right now, it's like basically no one knows how that works, but you're starting to see results like that pop up just because these drugs are out into such a broad swath of the population. We're kind of running these experiments without even necessarily intending to.

Um, and so I think really we've just tried to internalize the scope of the opportunity really matters and then also the strategy they took to get the grip ones into the market is one we're trying to emulate. They didn't start by trying to dose the glip ones into a huge population.

You start with a small patient pool that you know will benefit a huge amount. So in the case of GLIP ones certain type of diabetics eventually you expand to the obese. Eventually you expand to other indications from there.

And so we're really trying to mirror that step-wise with our programs where maybe as an example we're starting an alcohol-related liver disease for our metabolism drug. But that's the beginning. We don't intend it as the destination.

And we've tried to plan this in such a way that with you know in uh steadily increasing quantum of capital you can unlock larger and larger opportunities rather than just trying to shoot for the home run right off the bat. Yeah. Do you want to stay on before we move on to crisper and alphafold?

Oh yeah we yeah I I I want to get your take on crisper obviously it was extremely hot technology about a decade ago. uh is that at all relevant to what you're doing or have you learned anything from uh the story of crisper and how that was ultimately commercialized and and baked into the rest of the biotech community?

Yeah, absolutely. Maybe two things. I think the impact of crisper is somehow both over and underhyped in different places. It's maybe overhyped a bit in terms of direct application to therapeutics.

Um, I think if you go out and look at sort of how the paniply of different crisper companies are doing, there are a few really stellar examples like like beam therapeutics that we really admire who have managed to make compelling medicines and others have struggled.

And so maybe there's a bit of hype there, but it's really undervalued on the research side. So we've used a lot of crisper tools on the discovery end to be able to ask questions at the level of very basic discovery experiments. What happens when I turn this gene on or off?

And maybe we're not going to take those crisper tools and trying to actually put them in a person as the medicine itself, but they certainly help us accelerate the discovery of other therapies.

And so I think it maybe the lesson to draw out of that is like when we have breakthrough technologies, often people rush to what is the most direct application therapeutically but undervalue sort of the knock-on benefits of that technology underlying and accelerating a lot of trends that are already at play.

And then similarly, Alphafold uh massive breakthrough in terms of AI and bio didn't really move the biotech markets when it when the news broke.

And my interpretation of that was that maybe protein folding as a cost center for biotech development was actually not that much of a cost driver in the sense that it's mostly just farmed out to uh grad students maybe.

Um but but uh what uh I'm sure there's things where you look at that and you think this is going to this is on the path to really really powerful AI and bio working together.

Um but are you using alphafold or anything like it or are you learning anything from it and overall do I have a right understanding about the impact of alphafold on biotechnology broadly? Yeah. Yeah. I think you've hit the nail on the head with the direct impact.

You know, I think solving protein folding itself, like actually taking a sequence, predicting a structure, hasn't been rate limiting for drug discovery in the traditional sense.

It is painful to get structures, but usually we work on few enough molecules that getting the structure is not really what slows you down for for most traditional therapeutics. That said, I do think it demonstrates we're able to learn much more powerful representations of biology than others thought possible.

And so maybe by analogy to crisper the direct application is predicting structures itself really going to solve therapeutic development. No, probably not. Is learning the language of life in a way that we can actually work with tractably going to accelerate everyone in the ecosystem? Absolutely yes.

And so to your question of are we using something similar? We definitely do. So one of the the tools that we've built, the models we've built, they try and take in some notion of the genes we're manipulating transcription factors.

We needed to learn an embedding just like an embedding in natural language processing we're all familiar with that you can get from LLMs and then predict what will happen to a cell's age as a result.

And it turns out some of the best embeddings we found are from these protein language models that really are often deriving from that basic premise of the alpha fold like tools.

And so I don't think if you see the alpha fold announcement you'd ever intuitit, oh solving protein folding is going to make it easier to find reprogramming drugs that make old liver cells act young.

But it turns out that that underlying substrata of learning these representations actually does accelerate a whole bunch of science you don't expect. And so I think often the headline is a little overhyped, but the subtext is underhyped and probably more important. That's an awesome framework.

How much does bio the the biotech industry broadly pay attention to biohacking and citizen science? I know I know you can't read too much into what one ex account is experimenting with on any given day whether it be seal seialis or or uh sleeping sleeping nine hours a day.

But the other one that was interesting recently was a guy I think it was last Friday who was let he let like hundreds of snakes bite him. Oh yeah. Over time. And so I I feel like there there there is alpha in citizen science, but uh there's also, you know, 90% noise. Yeah.

And it's also not, you know, they're not running. You got to take it with a with a spoonful of cobra venom. Anyway, the guy's a legend. I I wish I had the opportunity to shake his hand because he's definitely underappreciated.

Um, so at large, I think the pharma and biotech ecosystem are actually fairly receptive to like using the results of citizen science because it's so hard to get data in humans that even if the data you're collecting in a human is confounded in some way, yes, this person didn't do a perfect controlled trial, etc.

, it can still be better than having no data at all. So, I think the two qualifications on this and maybe the examples you gave are are good ones. One is when you've got a very small trial, like a single person, but you've got a huge effect size like this guy who's immune to like basically every snake bite.

That is such crazy outlier biology that I think that wakes people up to, okay, there's something to learn here. And the other is when you actually have this replicated maybe unintentionally.

So a good example I think you alluded to earlier is the GLIP ones and sort of alcohol use where this wasn't something that the companies were exploring Eli Lilly or Novo. It's something that started getting reported anecdotally from patients. Hey, I just like don't want to drink anymore.

And so the amount of drinking I've done has gone down a lot. And so that was in a way a type of citizen science people recording reporting on their own behavior. And now those are actually going through clinical trials in a more formal process. And so in the the industry people call this reverse translation.

And it often has a lot of fancy words dressed up on it. But the basic idea being that the place we can learn the most biology in humans is in humans and we should try and treat every piece of data we have preciously. I think most people would appreciate.

Um there's probably not as much active work trying to collect this sort of citizen science data as there could be, but I think there are a few strong examples where it's really changed drug discovery. No, this is fantastic. This is fantastic. We can go a long way. This is great.

Uh I I I do want to let you kind of give an overview of the company like I imagine you're hiring. What type of roles are you hiring for? What's uh next on the agenda for New Limit? Yeah, absolutely.

So over the next few years, part of the use of proceeds here is actually to take these prototype medicines we have today and move those into the clinic, actually take them to patients for the first time.

And so we're hiring across the board really for drug developers and therapeutic area leaders to help run some of our therapeutic programs, for scientists who are skilled in functional genomics. And on the computational side, we have a growing ML team.

And for those sorts of individuals who might be excited about AI and bio, the carrot we can offer is we have some very differentiated data. We have the largest data set of this kind by hundreds of fold. You really need to get to this scale before you can train meaningful models.

And we think new limit is one of the few places you can go and and expend your valuable talents where the predictions of your models are actually going to get tested in a real laboratory fairly soon. So you've got real contact with the universe. You're not too far from the metal.

Um so would love to to be in touch with anyone who's interested in any of those verticals. Amazing. Well, thank you so much for taking the time. Uh this fantastic conversation doing this work. Thank you for saving my life in the future because I'm putting the car before the horse on this.

We're making, you know, our our sons are are about, you know, under five and we're making succession plans with them in about 30 years. They'll be taking over the microphones, but if we could get another, you know, 30 years uh at a new limit, that would be ideal.

So, the world needs parallelized technology brothers podcast. Um, it's required. So, we'll try and do our best to make that happen. Thanks. Great talking, Jacob. Congrats to you and the team. We'll talk to you later. Thank you so much. Bye. Uh, let's run through some posts and get out of here.

Uh, thank you to Brian Via who went ahead and started a list for uh, following everyone that has ever been on TBPN. I thought this is really cool. Working through the TBPN feed slowly to add all the guests they've ever had. Uh, he said, "Give me a few days so you can go follow this list. " Uh, we'll quote post it.

Um, but uh, we were thinking about doing this and he's just doing it for us. So, thank you. citizen journalism there. Citizen podcasting, you know, we think journalism. Journalism. We think of ourselves as corporate podcasters, but corporate media, but citizen. Very cool. I'm going to go follow the list.

Uh, also Eric Dunan says, "I swear TBPN has me wearing a nice watch and buying DuPont registry at the airport. If I start wearing a suit, then they won. " Oh, it's great. Cracking open the And he's got the wedding ring on. You know, he's a family man. You love to see it.

I can't clock what watch that is, but it looks fantastic. Eric's a day one fan. Uh, great having you as a technology brother, Eric. And it's nice to have a have a watch on. Uh, it looks great. And DuPont Registry, I'm a big fan. I follow them on X pretty much every day. I'm always tagging my friends in it.

This is a good option for you. Yeah. Uh, anyway, uh, we saw Greg Brockman at the uh, Met Gala, which we talked about. This was a fun one.

Uh, Brex had just launched a podcast with the with the CEO of Deal and I feel like they must have recorded this before all the drama and then they just decided to release it once things had kind of cooled down, but uh anyway, I invited Alex from Deal on the show.

Uh, he got back to me and said he can't come on right now. Obviously, things are a little chaotic, but uh, we would love to have him. Uh, there's a bunch of interesting questions. I want to know about the business. I also want to know about the drama.

Uh, and it's a live show, so we won't edit you, Alex, if you come on the show. We can't um you can you can deliver whatever message you want. Uh always interested and it would be a great comeback story. Certainly a dramatic way to launch a podcast. It is a crazy episode one. Yeah.

Like of all the people like this is this is who people want to hear about. I wouldn't be surprised if this is a really really popular episode. Um although it seems like it's more focused on uh the run rate, the scale, the employees, the business. But uh hadn't really heard much from Alex before all the all the drama.

But uh interested to hear about him now, you know. Yeah. Anyway, uh Schwetta says, "Never underestimate the power of one good tweet. Ror's founders were $15,000 in credit card debt. Been there and sleeping on a friend's mattress when Matt's post went viral. " 15 minutes later, Austin Red wired 100K.

By the end of the day, they closed 350K leading to a $2. 8 million seed round led by A6Z. Conviction is crazy. And Matt says, "My jaw dropped. Ror lets you create entire iOS apps just by describing them. Zero code required. " This changes everything for app development. ROR blows Bolt out of the water.

And yes, I invested immediately after trying it. Watch this. It's crazy. Uh what a great viral market entry.

I mean, this really does seem like just part of the standard playbook of launching a seedstage company at this point is like figure out a way to go viral and just fill up that first, you know, batch of customers to test on. Yeah. Might not be a sustainable growth mechanism.

It it's almost getting to the point where if people people will judge you as a founder CEO if you can't nail a sales call, right? It's extremely bearish. Totally. Even if you're not um every every founder should figure out how to sell the product, right?

Because you have to sell employees, partners, investors, you know, the list goes on. uh being able to manufacture virality and just figuring out how to go viral y is is getting to the point where it's going to be required in the same way that just being able to nail a sales call is right.

Y um so uh 100% well speaking of that watch we saw earlier, if you're looking for a new watch, you want to join the ranks of our listeners with fantastic watches, head over to getbzzel. com. Your bezel concierge is available to now to source any watch on the planet. Seriously, any watch. Head over there.

Uh Keon at Nucleus Genomics, friend of the show, is over at the Ramp office, another friend of the show, doing a little collab. All Ramp employees can use their wellness benefit to get the world's most advanced DNA health test. He'll be at Ramp's office for lunch.

Love how he's just setting up a little stand selling to Ramp employees. I love it. Uh pretty nicely designed little uh little partnership there. I thought that was cool. So, congrats to him on that. Uh, Michael Dempsey says, "Okay, maybe the best venture swag of all time from Hoody R.

" And, uh, uh, I It has a badge here that says, "What? " Hasho Capital Fund LP. Uh, what do you think of this? You're You're the merch guy. You have uh, put together some fantastic drops. I think it's pretty cool. I like the pockets. I think it's a different look.

You get a lot of t-shirts and, you know, t-shirts are great, but you know, they fabric out. This is fabric. Very nice. Something a little different. It's It's got some weight. Didn't Bane Capital get in trouble for doing the car heart and it was like a little bit too LARP store stolen dollar?

This feels like it evokes like the workman. You could you could do some work in this but it's not trying to be like perfectly aligned with another uh you know cohort of individuals and so it's just kind of its own thing. I thought it was cool. Uh so congrats to those.

And we'll close with this post from Paula over at XAI says who recently joined X. Yeah, dramatic move. Congratulations Paula Rambles. Uh, so who's building the app at the intersection of AI and astrology?

I actually uh told Sean uh over at My First Million when I went on over a year ago that this was a prime area for LLMs because there's something called the Barnum effect. Yes, PT Barnum.

So the Barnum effect is essentially there are a set of phrases that you can say to someone that sound hyperspecific but in fact apply to everyone. So, an example would be like you have a complicated relationship with your family.

Sounds like I know you, but really everyone kind of has a complicated relationship with their family in some one way or another. Yeah. Or uh you think you're meant for more. Sounds really specific, but really everyone thinks that they could do more. You know, everyone has these aspirations.

What's your uh what's your astrological sign, John? Maurus bull. Okay. I'm asking three. my business partner is a Taurus. How should I communicate? How should I backstab him today?

Um, but yes, I I I think that I think that astrology and AI go hand in hand in the sense that you can you can download this app and with only knowing a little bit, you can start producing really uh really tailored responses that feel very specific to the individual.

create this uh and even if you're just regurgitating regurgitating generic life coaching advice, everyone needs to hear uh you got this. You know, you can do it. Your astrological sign says that it's fortune cookie, you know. Yeah.

I mean, the the thing here is I think there's an opportunity to build a thin a relatively thin wrapper on top of whatever your preferred model is. Yep. I believe that many people will just use their preferred LLM for a lot of this stuff. Right. Totally. Like I just asked 03. Yeah.

we might not need to go get a separate app but at the same time if you're if you design the app with the right aesthetics right functionality you do the right customer acquisition on on Facebook and then you get in the app purchase as a subscription like I think a lot of apps that have raised venture have actually done very well uh so here's my dues for communication with John today what to do open with appreciation hey John you know I think you did a really great job on the podcast today everything from the prep to your zingers to your interview questions were fantastic, but we got to talk after the show because uh present No, no, no.

Present concrete tangible data. You know, John, the number of times you said hit the soundboard, you know, it it meant a lot to me. So, thank you for that. You're welcome, Jordy. Frame ideas as we projects. You know, John, the prep you did for the show today, I think we did a great job. Common.

And again, I think this is like the the the challenge here is this is like probably very general advice. Uh, but it can all work out. If you're into Who knows if you're into astrology, go check it out. Go let us know. It's funny. It says here, "No rush deadlines, but you're actually very good with deadlines.

" I love deadlines. Oh, we have to go live in 30 minutes and we don't have crap. Done. Done. No problem. Done. You're the kid who waits until the last 30 minutes. Always do your homework same day in the hallway before you go into the class.

Anyway, uh thank you to Ramp Poly Market Public Numeal AdQuick, Eight Sleep, Wander, Bezel, Linear, Figma, and Vanta for the incredible partners of TBPN, more than sponsors. And we're going to be up in San Francisco with the Figma team tomorrow at Config. Config Config Config Config Config Config.

Uh we're excited to be up in the Bay tomorrow. If you were in SF, yeah, if you're if you're at if you're at config, let us know. Seriously, hit us up. Uh we'd love to have you on. Talk about design, talk about Figma, talk about uh vibe coding, vibe designing. We're going to do it all. Anyway, uh thank you for watching.

Fantastic show. We will see you tomorrow. Have a great rest of your day. Can't wait. Bye. See you.