Podcasts about vcs

Beer, dry ice, and concrete all have one thing in common... they require CO2. Between demand for CO2 rising, and new gas plants taking a billion dollars to build, shortages are becoming more common. So Sonny built a modular plant the size of a fridge, making it cheaper and easier for companies to control their own supply. Will the VCs buy that a box this small can crack a problem this big? This is The Pitch for Ventrix. Featuring investors Elizabeth Yin, Jesse Middleton, Mike Ma, Rohit Gupta, and Michelle Kwok. Watch Sonny's pitch uncut on Patreon (@ThePitch) Join us for our fall live shows and Season 17 taping: pitch.show/events Subscribe to our email newsletter: insider.pitch.show Learn more about The Pitch Fund: thepitch.fund *Disclaimer: No offer to invest in Ventrix is being made to or solicited from the listening audience on today's show. The information provided on this show is not intended to be investment advice and should not be relied upon as such. The investors on today's episode are providing their opinions based on their own assessment of the business presented. Those opinions should not be considered professional investment advice. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Produced by Foundersuite (for startups: www.foundersuite.com) and Fundingstack (for emerging manager VCs: www.fundingstack.com), "How I Raised It" goes behind the scenes with startup founders and investors who have raised capital. This episode is with with Matt Ober of Social Leverage, a San Diego-based venture capital fund that invests in FinTech and Vertcal AI startups. Learn more at https://socialleverage.com/. In this episode, Matt shares his journey from working at a quant hedge fund to becoming a VC, trends in FinTech and Vertical AI, tips for using Claude and MCPs for raising capital, how they use content to attract the best founders, advice for emerging VC managers, tips for founders, and more. How I Raised It is produced by Foundersuite, makers of software to raise capital and manage investor relations. Foundersuite's customers have raised over $21 Billion since 2016. If you are a startup, create a free account at www.foundersuite.com. If you are a VC, venture studio or investment banker, check out our new platform, www.fundingstack.com

What does it actually take to win in venture capital?Harlem Capital co-founder and Managing Partner Henri Pierre-Jacques joins Maria to unpack a decade of lessons. They talk about how to identify exceptional founders, why the best VC deals take years to build, the growing importance of an investor's personal brand, and why founders should reference-check their VCs.Henri also shares Harlem Capital's approach to investment decisions, what it takes to make partner, how AI has changed the way he reflects and creates, and why he believes “winners want to be around winners.”

Jeff Mains sits down with Elie Bouzaglou, founder of Fish Tank, a video-first crowdfunding platform built for a generation that discovered entrepreneurship on their phone. Elie built an AI voice-call analysis tool as an internal sales tool at his web agency, only to have his team push him to bring it to market. After getting rejected by Republic, Wefunder, and every major crowdfunding platform, Elie didn't blame his product — he blamed the format. Pitch decks are boring, gatekept, and built for VCs, not consumers. His answer: a platform where founders pitch like content creators and everyday people can watch and actually invest, Shark Tank style. The conversation covers building in public, going "all in" on a risky content bet that proved his thesis before launch, why the next Mark Zuckerberg is more likely to be an ex-UGC creator than a programmer, and his "carefully reckless" framework for tackling the thing founders are most afraid of.Key Takeaways3:48 — How Elie's internal AI sales tool became a product because his team saw value he didn't.6:15 — Why the tool wasn't VC fundable, and the pivot toward crowdfunding.10:30 — The core insight: crowdfunding's real problem isn't quality, it's that discovery is boring.13:01 — Why people will binge-watch Shark Tank but can't invest in it — and how Fish Tank closes that gap.17:10 — The "pushing a car" metaphor: starting is the hardest part, momentum does the rest.19:46 — His trick for beating camera-shyness: film it and tell yourself you won't post it.28:54 — Why the next Zuckerberg probably won't be a programmer — it'll be an ex-UGC creator.36:42 — The "carefully reckless" framework: name the thing you're avoiding, write it down, do it.37:31 — The story of betting nearly his last dollar on a content shoot with a VC-turned-content-creator — and how it proved his thesis before launch.40:19 — Why investors are just people, and being seen with flaws beats not being seen at all.41:59 — On dealing with online hate: most of it comes from jealousy, not dislike.Tweetable Quotes"The worst possible case scenario is no one knows who you are. It's not bad PR — that's where you're at right now, and it can only get better." — Elie Bouzaglou"You have to be carefully reckless. What is the thing you are delaying the most? What is the thing you're most afraid of? And just do it." — Elie Bouzaglou"It's always easier to build than to start from zero." — Elie Bouzaglou"I believe the next Mark Zuckerberg will not be a programmer. He'll probably be an ex-UGC person." — Elie Bouzaglou"The greatest success is just on the other side of that fear." — Jeff Mains"When someone hates on you and takes time out of their day to comment, it's because they're jealous — not because they actually dislike what you're doing." — Elie BouzaglouSaaS Leadership LessonsShip it even if you don't see the value. Elie almost never brought his internal tool to market — his team had to convince him. Don't assume "anyone could build this" means no one wants it.Question the format, not just the product. When rejected by every platform, Elie didn't fix his pitch — he concluded the entire crowdfunding format was broken and built a new one.Momentum beats planning. His "pushing a car" philosophy: the hardest part is starting; perfect names, decks, and plans can wait.Break big fears into small, reversible actions. Film it without posting. Schedule it with the option to cancel. Small, low-stakes steps unlock big behavior changes.Distribution is a founder skill now, not a marketing afterthought. Building in public and mastering short-form content may matter more than technical pedigree for the next generation of founders.Bet asymmetrically on your scariest move. Spending nearly his last dollar on a content shoot was terrifying — but the asymmetric upside (VC access, proof of concept, investor DMs) made it the right kind of reckless.Guest Resourceselie@fishtank.vchttps://www.fishtank.vceliebouzaglou.comeliebouzaglou.com/linkshttps://www.linkedin.com/in/elie-bouzaglo/https://www.instagram.com/ftnk.elieEpisode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/https://jeffmains.com/books/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

Can new regulations finally fix the broken token market? This week, we debate whether the SEC's new crypto framework meaningfully changes token design, capital formation, and investor protections. We dig into token cash flows, Hyperliquid's institutional moment, if CLARITY is actually dead, regulatory first movers, and whether founders should ignore their VCs. Enjoy! TIMESTAMPS: 00:00 Intro 00:28 Jackson Hole & SALT Symposium 04:16 Can Regulation Revive Crypto? 16:23 The Four-Year Cycle & Is It Time To Nibble? 18:23 Hyperliquid's Institutional Moment 25:22 Who Loses From Regulatory Clarity? 32:48 Which Projects Go First? 39:10 Token Transparency Hits Bloomberg 42:13 Travis Kalanick & Peak Founder Mode 51:01 Content Of The Week FOLLOW THE SHOW › Empire – https://x.com/theempirepod › Jason – https://x.com/jasonyanowitz › Santi – https://x.com/santiagoroel › Rob – https://x.com/HadickM › Telegram – https://t.me/+CaCYvTOB4Eg1OWJh › Blockworks – https://x.com/Blockworks EVENTS › Join us at Digital Asset Summit 2026 Asia October 7th & Digital Asset 2026 London November 10-11th https://blockworks.com/events DISCLAIMER Nothing said on Empire is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only. Any views expressed are opinions, not financial advice. Hosts and guests may hold positions in the companies, funds, or projects discussed.

Natalia Salcedo opens season 16 with a pitch for Pitz, her voice AI that helps mechanics repair cars in under 24 hours. She's proven the market in Mexico and Brazil. Can she convince VCs the same playbook will work in the US? This is The Pitch for Pitz. Featuring investors Elizabeth Yin, Jesse Middleton, Mike Ma, Rohit Gupta, and Michelle Kwok. Watch Natalia's pitch uncut on Patreon (@ThePitch) Join us for our fall live shows and Season 17 taping: pitch.show/events Subscribe to our email newsletter: insider.pitch.show Learn more about The Pitch Fund: thepitch.fund *Disclaimer: No offer to invest in Pitz is being made to or solicited from the listening audience on today's show. The information provided on this show is not intended to be investment advice and should not be relied upon as such. The investors on today's episode are providing their opinions based on their own assessment of the business presented. Those opinions should not be considered professional investment advice. Learn more about your ad choices. Visit podcastchoices.com/adchoices

https://youtu.be/jPTlkjF8M-c Tanner Taddeo, CEO and Co-Founder of Stable Sea, is driven by a mission to bring Wall Street-grade financial services to Main Street while embodying the principle Stay Put in Your Convictions. By combining blockchain technology, stablecoins, tokenized capital markets, and AI advisory services, Tanner helps businesses access investment opportunities, put idle cash to work, and move money globally with greater speed, transparency, and capital efficiency. In this conversation, Tanner introduces The Lionel Messi Startup Framework—Develop a High-Level Thesis, Talk With and Learn From the Market, Run 30-Day A/B Tests, Iterate Your Offering, and Stay Resolute With Your Convictions. He explains why founders should observe patiently, validate their ideas with customers, and act decisively when market opportunities emerge. Tanner also discusses balancing long-term conviction with continuous experimentation, unlocking 24/7 liquidity through tokenized capital markets, reducing friction in cross-border payments, and finding urgent “morphine” problems that customers cannot afford to leave unsolved. — Stay Put in Your Convictions with Tanner Taddeo  Hello everyone. Steve Preda here, and my guest today is Tanner Taddeo, the CEO and Co-Founder at Stable Sea, an autonomous treasury management platform that helps finance teams and global businesses access capital market products and move money around the globe to 40 currencies with the cheapest FX rates. Tanner, welcome to the show.  Steve, thanks for having me. Excited for the conversation today.  It’s very interesting that this is how you position your business because most businesses in your industry, as I see them, position themselves with low transaction fees, but really their money is made on the FX. So if you do preferential FX rates or cheap FX rates, that can be a very transparent way of getting business. So I don’t know if that connects to your personal why, but I’d love to learn about your personal why and how you manifest it in your business.  Yeah, definitely. At Stable Sea, we’re very mission-driven in terms of everything that we do. The team itself comes from Block, which was formerly known as Square. Yeah. And everyone on the team has been focused on building products for the real economy, for consumer use cases, for business use cases, et cetera, over the course of everyone’s career.  And so when we started at Stable Sea, our primary thesis was, with blockchain, with stablecoins, with some of the tokenized capital markets products like money market funds, bonds, equities, et cetera, that are coming on-chain, how can you really take Wall Street-grade financial services and provision them out to Main Street for businesses that need them the most? And so the why for Stable Sea, for myself, for the team, is really around helping businesses drive greater capital efficiency in their operations. And we service businesses in the real economy that typically make widgets or some sort of physical hardware devices, and they need to send them around the world.  We help them because we give them access to different types of capital markets products, so money markets and private credit and fixed-income products, et cetera. And then we help them move their money around the globe a little bit more efficiently than they could with either their state bank or their credit union or some third-party cross-border payments provider. Because our firm thesis has always been, if you and I ran Coca-Cola or a large organization, we would have the best-in-class transaction banks helping us put our idle capital to work at every point in time during the day.  If you and I ran a steel manufacturing company in Missouri, you typically have a checking account and QuickBooks, and that’s about it. And so for us, it was always about helping businesses grow, save more money, and then operate more efficiently with some of the new technologies that are out there today.Share on X  So that means, presumably, that what you focus on is more about the investment side of the business rather than crypto and blockchain, and helping people access financial products through the blockchain. Help me understand a little bit what you do and how it is different from what people can get from banks?  Yeah. So everything that we do, all the technology that we build and provision, is on-chain. So all of the capital markets products are tokenized. So tokenized bonds, tokenized equities, tokenized fixed income, tokenized money markets. All of the payment services and settlement services that we offer are through the use of stablecoins, and we can send that around the globe, settle it instantly, and then have low FX rates off the back of that. And then we have some of our AI advisory services.  But from a broad paintbrush perspective, at Stable Sea, you’ve got three products that hang off of our platform. You’ve got capital markets, you’ve got global settlement, and you’ve got advisory services. And then with all of that, we share a common architecture, and that architecture is built across many different blockchains. And then we utilize stablecoins and we utilize RWA tokens, or real-world asset tokens, to provision those use cases.  So everything that we do is stablecoin-native, but we don’t lead with that from a messaging perspective. And the reason we don’t lead with that from a messaging perspective is that if you and I ran a bakery here in Brooklyn, New York, and we had a point-of-sale terminal that just got offered RTP access from the Fed for instant settlement, the bakery owner doesn’t really care about the technology underneath it.  They just care, “Do I trust it? Is it going to get me my money quicker, and is it going to be cheaper than my current alternative?” How it happens, not very many people care unless you’re in the industry and you’re a builder, product manager, et cetera, and you want to nerd out on the actual mechanical nature of how the product works. But for us, it’s always been leading with the narrative of, what is the value proposition and how can we drive greater value to the businesses? So that’s how we lead. But to your point on what the difference is, with any new technological paradigm that occurs, rarely is it so disruptive in nature that folks can’t recognize it.  Everything that happens in terms of the innovation paradigm is typically you stand on the shoulders of giants and you make things incrementally better. And so for us, what we do with capital markets is, the first value proposition is that many businesses in the United States just don’t have access to a diverse array of capital markets products. So the first thing that we have done is just provision access, which is an innovation in and of itself because in the traditional markets, if you want to access a money market fund or a fixed-income product, you typically have high hurdle rates, meaning that as a business, you need to invest at least $10 million at the asset manager in question.  You need to hold that there so then you can get access to all these products. With us, you don’t. There’s only a $1 minimum to clear, so I think most folks can handle a $1 minimum. And then secondly, as things go on-chain, the value proposition there is that you have 24/7, 365 liquidity and tradability. And so what that means is that, just from a money market fund perspective, the interest accrues daily and it pays out daily.  So you get this interest that is dripped into your account daily as opposed to waiting for a month. You also have the ability—so let’s say that you and I run this bakery in Brooklyn. Let’s say that we close our business on Friday, and we’ve got $100,000 sitting in our checking account, and we’re closed on Saturday, Sunday because it’s the July 4th holiday. So we know $100,000 is just going to be sitting in our checking account Saturday, Sunday, not being put to work. With Stable Sea, you can put that to work in a tokenized money market fund because it operates 24/7, 365.  So what we see is businesses now that close their books on Friday can just do an auto-sweep into a money market fund, generate yield Saturday, Sunday, get back to U.S. dollars for their open of business. And again, it’s one of those things where it might not sound like the most revolutionary concept in the world, but if you can help businesses, especially in the mid-market, lower mid-market, operate a little bit more efficiently, I mean, saving an additional $20,000, $30,000, $40,000 a year is a big value-add to them in the real economy, right? If you’re a large Fortune 100 company, you probably don’t care, or it’s not as valuable. But for us, the companies that run on us, these small increments, standing on the shoulders of giants, a small derivation in innovation is actually really valuable for the end user.Share on X  Well, I think it is because, looking at the inverse of it, I used to be in banking, and I know that one of the biggest moneymakers for banks is float. Yeah. So it’s basically the money that doesn’t earn interest, which they have access to just because they cash the check a day later or make the wire two days instead of one day. And essentially, what you’re doing is you’re taking this money from the bank and you’re giving it to the company that actually should have it in the first place, right? Yep.  Then the question is, how are the banks going to survive if you take away their bread?  Yeah. That is the debate that’s happening right now. I think if you’re one of your G-SIBs, your major banks, you’re going to be okay. So the top 25 banks in the U.S. are going to be just fine, and they make money in tons of different ways, and you’re not going to disrupt that trust ultimately. In the long tail is where I worry because a lot of credit unions and a lot of state banks, they just don’t offer—they’re smaller banks, right?  So they’re not managing—they don’t have a ton of money by virtue of assets under management. So with the deposits that they receive, they need to turn around and recycle that because it’s fractional depository lending, meaning that if I have a checking account, I put 10 grand into it, the bank is then turning around with that 10 grand, making money on it somehow. And you have to think, how does the bank actually make money on that? Well, they typically make it through debt facilities, so mortgages, auto loans, student loans, cards, et cetera.  They’re putting it to work in high-margin financial products back into the economy. They’re not taking that and then buying some money market fund from an asset manager where they make 10 basis points and provisioning that out to the businesses, right? There, I think that we’re seeing a lot of companies move off. They’re taking their money from their checking account, moving it to Stable Sea because we can put it in these capital markets products.  I think that overall, that’s a net positive for the business because the business now has a higher degree of operating capital on hand that they can make money with. But by the same token, if the state banks and the credit unions don’t wake up and respond to this, their depository base will be, if not fully eroded, tarnished and diminished. And what that means for local community health, I’m not sure because banks do play a very important role, especially credit unions and local banks.  You know your local community the best, and so you lend back into that community with the deposits that you receive from that community. So there’s a cyclicality to it which has some poetry in it. And so it’s not apparently clear to me that some of this stuff is going to be a net positive. But at the same time, living in one of the most capitalistic countries and markets in the world, there’s a clear demand for this, and if the banks aren’t going to wake up and serve it, we’ll be there to help businesses do what’s best for them.  Yeah. It’s the invisible hand, right? You increase the efficiency, which will force the banks to also increase their efficiency. And yeah, the smaller banks might have to be more innovative. But they are more nimble, so maybe there are other ways that they can serve the community.  So I’d like to switch gears here and talk a little bit about frameworks. So this is a podcast of frameworks, and 350 episodes in, I’m always looking for some kind of a framework, shortcut, a mental model that you have come across or developed yourself that helps you make more sense of the world around you, get something done. It can be explained in three to five steps, something like that, which the listeners might get some ideas out of and be able to improve their businesses. So what comes to mind for you?  Yeah, two things. I’ll start with a high-level analogy and then go a little deeper. It’s the World Cup right now, so I don’t know if you or any of your listeners are following the World Cup. But if you watch Messi play, his playing style is a great analogy for startups. And whether that be a startup externally where you raise venture capital, or even just intrapreneurship if you’re inside of a big company and you’re on an innovation team, et cetera.  From the outside, it looks like startups are always building things and they’re always moving fast, et cetera. But in reality, if you watch Messi play, Messi really doesn’t move that much on the pitch. He just sits around, he observes, he watches, and then when a hole opens up and some opportunity opens up, he breaks for it, and then he goes and executes. But he spends the vast majority of time just sitting there, tinkering, observing, watching. And then if you’re watching him, you’re like, “He’s not working that hard. He’s just sitting around.”  And then he goes and executes. But he’s always observing, he’s always watching, and there’s a real learning in that. I feel like Silicon Valley, as it relates to startups, there’s this pressure that you always have to be building, you always have to be shipping, you always have to be constantly grinding. I think that wisdom is actually counterintuitive because you want to have a thesis in the market, and then you want to be able to test that thesis quickly. So in some respects, you do want to be shipping all the time.  But you don’t want to be working for the sake of work. You want to have a thesis in the market. You want to be building towards that thesis that will happen in the next six months, 12 months, two years. And then you always want to be learning and talking to the market because when that hole does open up, you’ll have the right product at the right time to go and execute on. So I think that's something that we have learned: being patient and staying resolute in your conviction that what you're building is right.Share on X And it can’t just be a gut feeling. It has to be validated by the market.  So we do a bunch of A/B tests every 30 days where we have an idea about a feature or a product or a direction we want to take it. And the thing is, if you can’t get five CEOs on the phone in 30 days to validate if a product is going to be interesting or not, then that’s a signal in and of itself, right? So for anything that we do, we always have a thesis on the market, and then we spend 30 days testing it. And at the end of those 30 days, we get some feedback.  The reason why we do these A/B tests, just to drill down into one level further, is that the idea of a startup or a product that you have in your head, it’s a living entity. It’s always evolving on the basis of who you talk to, what your team is thinking, what you’re reading in the market, et cetera.  And then you’re trying to take that living concept and plug it into a market. But the market itself is also living, right?  You’ve got regulations, you’ve got different macroeconomic cycles, you’ve got companies that have budget, don’t have budget, people getting laid off in different organizations. The market itself is living and evolving. So you have this idea that is living and evolving, and you have a market that is living and evolving, and you need those two things to stick together. And so for us, we’re always wedded to this concept that product at time A is not going to be product at time Z. You need to constantly be doing A/B tests to figure out what that right fit is.  And then when you have that fit, you need to double down on it and grow it into a line of business. But you also need to recognize that there are very few businesses in this world that have been around for more than 200 years, if at all. So whatever your original product idea is, or whatever the feature that gave you product-market fit is today, you have to consciously be aware that, “Hey, that’s not going to be the thing that gets us to IPO in five years’ time.” So you can’t be lulled into this false sense of security. You always have to be waiting, observing, testing, experimenting, growing, and then if you see opportunity, you strike.  Yeah, this is fascinating. Especially now, things are moving very fast with AI creating capabilities all the time for people to test products or to create capabilities that then get disrupted in a couple of months. So it’s interesting that you say that you have to stay resolute in your conviction. So there is a tension there. You build a thesis and you stay resolute, but then you’re testing and the market might tell you not to be resolute.  And then you also told me that companies don’t live forever. So how do you resolve this tension of being stable with your thesis and not letting your conviction be upended, but also being nimble in the changing market dynamics and everything to respond to? So how do you manage the tension?  Yeah, it’s a good question. There has to be a high-level thesis, right? So for us at Stable Sea, it is as simple as: In 10 years from now, will more finance teams and businesses be on-chain or off-chain than today? And so our high-level conviction is, in 10 years’ time, more businesses will be running their treasury stack on-chain. So that’s our conviction. We know, come hell or high water, that is going to be where the puck is going to be in the future, and we’re going to skate to that future.  So if you start with this high-level conviction that more companies are coming on-chain, that is what we’re building for. Now, how they come on-chain is a matter of debate, which is where the A/B test comes in, right? We originally thought it was going to be for payments. So we built all the stablecoin infrastructure to do global payments in 40 different markets.  Turned out to be not the case, actually. And then we started tinkering as we saw the data coming in and were like, “Okay, some companies are using stablecoins for payments, but there’s a bunch of inefficiencies. That world’s still going to take two or three years to wake up. Where is the wedge in the market today?” And so when we started experimenting with capital markets products, we found that there was this massive opportunity that businesses just didn’t have access to a diverse array of yield-bearing strategies, and they wanted that.  And so that was where we were like, okay, let’s get businesses into the on-chain economy through capital markets. And then what we’re finding is, as folks come onto the platform, everyone uses us today for capital markets, and then 20, 30% of our companies say, “Actually, I do have a cross-border payment need, and I already hold money with you. Can you facilitate that payment or that settlement to Mexico, Colombia, Brazil, South Africa, et cetera?”  So for us, when I say you need to stay resolute in your conviction, our why is always: We want to take Wall Street-grade financial services and provision them out to Main Street.Share on X The conviction behind that is that you can do that through on-chain technology. And then in 10 years from now, more businesses will be on-chain than off-chain. How we get to that future in 10 years, who knows, right? And that’s where the fun of the startup is.  You’re always testing. And so for us, we’ve waxed and waned on different product strategies, primarily because the market has changed. And as people start to educate themselves on what the value props are, you see where folks find value, and then you build to that value. And in theory, in three, five, seven years, we should be living in a world where more companies are operating on-chain, and then they might use that full product suite.  But out of the gate, it’s kind of like, where is that value, that wedge? You charge as hard as you can into that wedge, and then you continue to expand your product set over time. All with that high-level conviction of, in 10 years from now, we believe that more businesses will be on-chain than off-chain.  So basically, you want to find the point where you can penetrate that market opportunity, and then it’s a land-and-expand kind of thing. And then you expand from there as the market opportunities evolve over time. But you already have a customer, you’re already building trust with them, and now they’re going to be more disposed to buying from you.  Yeah, that’s right. And I think it’s interesting from a mental place being a startup because you’re forced to think so short-term because you just need to generate revenue, get to the next capital round, et cetera. So you’re always building for the moment. But what we try to do at Stable Sea is we try to think as if we were already a Vanguard and a large company, to the extent that we have the luxury of planning for 10 years.  If you think about it in that regard, it takes a lot of the day-to-day anxiety away. It’s a little bit like, if you listen to Warren Buffett, any time that there’s volatility in the market, he’s like, “Well, it doesn’t really bother me because I’m investing for 50 years.” So, is it up 20%, down 20%? Who cares? In 50 years, it’s going to be up 200%, so that’s all I’m worried about, right? And there’s a real luxury when you come and think about it that way. So that’s why I think if you’re founding anything, or if you’re starting something inside of a company as an intrapreneur, you need to have a strong conviction on where the market’s headed in five or 10 years, and then you need to test towards that future. But that also makes the day-to-day operations of the business a little bit more palatable.  So often, you can get caught up in this whipsaw of, “Big Company A launched this product. Regulation came down, wiped out this company. This competitor raised a Series C, and they have way more money in the bank than we do.” And so you can get caught up in all this minutiae, but it doesn’t really matter if you sit back and you say, “I know that I’m going to find a way to make this business exist for the next 10 years.” In 10 years’ time, what does the future look like? Do I feel strongly that that’s going to be the case? Cool. I’m going to build towards that future. And then whatever the headwinds are in the interim, they’re just short-term temporal problems that kind of come and go along.  Yeah. I mean, I totally agree with you. And interestingly, 20 years ago, or 25 years ago, I didn’t feel like I had enough time to think that long term. But now that I’m older, I actually am more patient to have the long view, which is very counterintuitive.  And Dan Sullivan, who is a coach and the founder of Strategic Coach, he is now, I think, north of 80, and he has this thesis that even at his age, he has a 25-year plan, and that allows him to actually create more value. So that’s fascinating. So switching gears here, what drives growth in your business right now?  Yeah. So we govern the business with an assets under management model. So we have USDC, we’ve got money market funds, we’ve got fixed-income products, we’ve got Bitcoin on platform. So we just look at overarching platform balance. And so that’s the primary, very simple heuristic for how we define success: Is that thing growing month over month, quarter over quarter? That’s how we define growth and measure our growth.  But again, the value prop in terms of what drives that, why do companies actually sign up to Stable Sea? Primarily because they just don’t have access. Almost every business that we have talked to so far, and honestly every business that I’ve interacted with, has idle cash sitting in a checking account someplace. Full stop. And that idle cash could sit there for the weekend, i.e., two days, or it could sit for a quarter. If you’re gearing up for quarterly bonuses in Q1, you will escrow a million, $2 million in Q4 so you can pay out in Q1. Not just the U.S. economy, but every economy, there’s just cash sitting around at a bank, and it’s being underutilized.  And so for us, when we go and finally chat to businesses in the mid-market, lower mid-market, even SMBs, we have a customer on platform that invests $2,500 every week. It almost looks like a checking account, or almost looks like retail behavior in some ways. But they do it because they say, “Hey, I don’t make a lot of money with my business, but if I can eke an additional two, three grand at the end of the year, that’s valuable to me.” And there’s a real poetry to that because they’ve never had access to it. They’ve always wanted it.  But banks, large and small, won’t go build for the long tail of the economy. And so finally, we show up and we say, “Hey, here’s your menu of investment options. Here’s the risk profiles. Here’s how you should think of it. Based on the seasonality of your business, we can get you into the right products.” There’s real utility there, and that’s what kind of drives the value proposition and the growth of the business and the business’s assets under management overall.  So you’re looking for opportunities where you can be additive to customers, where there’s a situation where maybe there’s a gap in the market or there’s friction that they are experiencing with investing their money, and you can be the wedge in that situation and offer them a 3X better solution.  Yeah. Correct. Correct. And again, our tagline internally is, “Keep your bank, upgrade your capital.” Because we really don’t want to compete with the checking account. Where you run payroll, where your invoices land if someone pays you, your day-to-day spend, keep your banking relationships because it’s very difficult to usurp that. And also, we don’t want to get into that. That puts us squarely in this neobank realm where you’ve got great companies like Mercury and Rho and Ramp and Brex and a thousand other companies there.  We don’t really want to go compete with that. We’re more of, if you had the privilege of working with some of the largest transaction banks in the world, that’s what we’re trying to be and essentially provision those services out to the real economy, which is typically access to capital markets, access to global foreign exchange for payments and settlement, and then advisory services, tax reporting, et cetera.  Almost like a democratized private banking service.  Yeah. Yeah. All of us at Stable Sea, we’re trying really hard to steer away from the banking narrative, but yes, in the future, if you take that 10-year perspective, yeah, we will most likely be a private banking solution, a democratized version of that.  Yeah. Fascinating. So what’s one thing that you’re actively trying to figure out right now in your business?  Yeah, it’s a great question. I mean, the one thing that we’re actively trying to figure out is two things, really. One is, so we build directly into ERP systems like QuickBooks or NetSuite or Oracle or SAP, and we have advisory services. So we take a lot of that data, we build our own model weights on top of it, and then we offer that out to our customers so that they can essentially query their own transaction data and use it for different services.  Now, we’ve got strong signal on the first value proposition for that, but I’m curious mostly for owner-operators in the real economy: What are their biggest back-office pain points? And that’s something that we’re trying to figure out because we hear a lot, “Yes, we don’t have access to savings products.” Okay, we can solve that today. “Yes, cross-border payments are frustrating, slow, and expensive.” Yes, we solve that today.  So we’re looking for that third pillar. One of our VCs always talks to us about morphine versus vitamins, where it’s kind of a crude analogy, but if you go to the hospital and you’re in dire pain, you don’t want to be sold vitamins. You want some morphine, and that’s what you’re going there for, right? And when you’re in a startup and you create products, you’re really looking for that morphine of, people just cannot live without this product. And then you can sell all the value-added services around it, which are essentially the vitamins.  And so for us, we’ve found two morphine-like products where there’s a real pain point for accessing capital markets. Primarily, there is no ability to access that today. And then second, cross-border payments: slow, difficult, expensive, opaque, all the things. Solved that. So the third one that we’re trying to figure out now is: How do we A/B test quickly enough to figure out—we have a treasure trove of data building into ERP systems—what is the highest signal-to-noise product that we can build using a diverse data set to help owners operate their back office a little more efficiently?  So you say highest signal-to-noise. Is it the ratio of signal to noise? So what is the product value which you can detect as being a need in the market? Is this what you mean by that? Yeah, yeah. It’s like, what is that one pain point that is so resolute that people are like, “I would do anything to have this thing solved”? There’s all these value-adds like cash flow reporting and automating some of your tax stuff at the end of the year, which are all nice-to-haves. We’re curious. We’re trying to figure out what it is that folks will say, “I’ve got all this data in my ERP system. I would love to know one, two, three things and have A, B, C automated so my back office can run a little bit more efficiently and my accountant doesn’t have to ask me every quarter-end, ‘Where is X, Y, and Z statement?'”  Yeah. I mean, I’ve got some ideas, but I’m sure that you’ve already thought about most of it, so I’m not going to share them. So if someone is listening to this who is a small business or medium-sized business, and they’ve got some cash just sitting around, or they’d like to invest, but they don’t have big enough balances or the transaction costs are prohibitive for their size of investment, whatever the reason, but they are curious about exploring how to have access to better FX rates, more investment products, where can they learn more, and how can they connect with you?  Of course. Well, connect with me on LinkedIn, Tanner Taddeo, pretty easy to find. And then the platform is stablesea.com. So, free to sign up, no cost whatsoever. Also, no cost to use the platform at all. So feel free to sign up right online, and then, yeah, typically it takes us two days to run through the KYB document requests, and then you’re up and running. So, pretty simple. Stablesea.com, free to sign up and start putting your capital to work. Awesome. We try and make it as seamless as possible.  So I’m just wondering, the name of the company, is it something to do with stablecoin? Is it a sea of opportunities for stablecoin?  It was stablecoin for sure. So we started with the word “stable” and then “sea” because we wanted to provide a sea of liquidity. Both for FX, because we do B2B settlements, which are typically large transactions, low volume. You’re not doing twenty $10 million transactions a day. You’re typically doing one $10 million transaction a week or every other week. But you need a deep pool of liquidity to service that.  And then also, from a capital markets perspective, we wanted to be able to provide a sea of liquidity there for different investment options that companies could access based on the seasonality of their cash flow or the risk tolerance that they have as a business. So stable meets sea, so Stable Sea.  Okay. Well, if you want to keep your bank but upgrade your capital, then reach out to Tanner Taddeo, the CEO and Co-Founder of Stable Sea. He’ll get you more investment opportunities that maybe you have not had access to. And if you enjoyed this episode, make sure you subscribe and follow us on Apple Podcasts. Do not miss any episode with exciting entrepreneurs like Tanner. So thanks, Tanner, for coming, and thank you for listening.  Thank you, Steve. Important Links: Tanner's LinkedIn Tanner's website

Most founders think you need a $250,000 VC check to get funded, but angel investors will write you a check for $5,000, actually get to know you, and stick around as a mentor long after the money lands.In this episode, Chris, the new director of 412 Angels, explains the real difference between angel investors and VCs, why founders should start building investor relationships long before they need the money, the most overrated (and underrated) traits he sees in early-stage founders, and what's next for 412 Angels — including plans to launch a fund and lead their own deals.In this episode:✅ The real difference between angel investors and venture capital✅ When founders should start reaching out to investors (hint: earlier than you think)✅ Where to actually meet investors in Northwest Arkansas✅ The most overrated trait in early-stage founders✅ Chris's advice to his younger, first-time-founder self⏱️ CHAPTERS00:00 – Why 90% of a founder's job is networking 00:21 – Meet Chris Ehrhardt, back after 10 years01:24 – From Germany to Arkansas: Chris's origin story 02:23 – Building a startup and moving to Canada on a startup visa 05:22 – What is 412 Angels? 06:28 – Why keeping funding local matters for founders 08:20 – Angel investors vs. venture capitalists 10:24 – When founders should start talking to investors 11:51 – Where to actually meet investors in NWA 14:21 – The most overrated trait in early-stage founders 15:36 – The most underrated trait: coachability 18:09 – How 412 Angels pays it forward 19:50 – What's next for 412 Angels 22:53 – Advice to his younger self 25:26 – Where to find Chris and 412 Angels—Connect with Chris & 412 Angels

Jeff Mains sits down with Logan Yonavjak, a nearly two-decade veteran of impact investing and venture capital, who built the Founder Readiness Institute after watching a promising founding team unravel post-investment. Logan explains why "people risk" — not market or product risk — is the leading cause of startup failure, and how her Founder Readiness Level assessment uses AI-driven quantitative linguistics (rooted in adult developmental psychology) to measure a founder's capacity for complexity, resilience, and coachability. The conversation covers her pivot from selling to VCs to selling to mid-market companies (200–2,000 employees), why she favors partnerships over "owning the whole stack," what building the tool taught her about her own leadership blind spots, and where people analytics is headed as AI reshapes what's left for humans to do.Key Takeaways4:46 — The deal that started it all: a founder who "buckled" after investment closed, and the lesson about charisma bias.7:16 — Why investors spend more time on the pitch deck than on the person who has to execute it.9:18 — The data: ~65% of startup failures trace back to people problems (Noam Wasserman / Harvard Business Review, 10,000 founders studied).10:18 — How the Founder Readiness Level differs from personality tests: it measures developmental stage, not static self-reported traits.13:27 — Why AI made scoring open-ended, scenario-based responses possible at scale (versus hand-coded transcripts).17:30 — What "managing complexity" actually looks like in high-level leaders: holding multiple interdisciplinary frameworks at once.19:15 — The go-to-market mistake: mistaking "this is interesting" for a real, fundable pain point among VCs.21:50 — Repositioning the ICP: C-suite and L&D leaders at Series B/C+ startups in high-innovation industries.29:02 — The Steve Jobs thought experiment: high strategic complexity, likely lower relational intelligence early on.35:04 — Build vs. partner: why "ecosystem builder" is a higher-complexity strategic move than trying to dominate a category.38:32 — The question every founder should be asking themselves: "How coachable am I?"39:17 — Where to find Logan and the assessment (readinessengine.io, LinkedIn).Tweetable Quotes"Charisma is a remarkable camouflage. A great storyteller can make a structurally fragile leadership team look like a dynasty, right up until the growth pressure hits.""We bet on the horse, not the jockey... we say we're betting on the jockey, but we don't give them any tools.""It's not about being better or worse, it's just that it depends on where you would be best situated professionally.""AI has taken the bottom out of [junior-level work]. So what's left for humans to do is actually more complex tasks.""It's a higher level strategic complexity move to be more of an ecosystem builder than someone trying to dominate a market.""How coachable am I? ... that growth mindset to success — everyone should be asking themselves that on a regular basis.""Sixty-five percent of startups don't die because the product was bad. They die because the founder couldn't see themselves clearly when things got hard." — Jeff MainsSaaS Leadership LessonsPeople risk beats market risk. Roughly two-thirds of startup failures trace back to human/leadership breakdowns, not product or timing.Charisma is not a leadership metric. The most compelling storytellers in the room are often the hardest to accurately evaluate.Self-reported assessments have a ceiling. Static, forced-choice tests can be gamed and don't track development over time — open-ended, scenario-based signals are harder to fake.Position around infrastructure, not features. Framing the product as "developmental intelligence infrastructure" rather than another HR tech point-solution avoided category fatigue.Partner before you build or acquire. Complementary players (e.g., behavioral analytics firms) can expand value to shared clients faster than trying to own the entire stack.Coachability and flexible identity predict survival. The founders who pivot fastest under negative market feedback are the ones who don't over-identify with a single idea.Guest Resourceswww.founderready.iohttps://www.facebook.com/loganyonhttps://www.linkedin.com/in/loganyonavjak/https://www.instagram.com/loganyon/https://x.com/LoganyonEpisode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/https://jeffmains.com/books/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

The Learning Leader Show with Ryan Hawk www.LearningLeader.com The Price of Becoming is a USA Today, LA Times, and Publishers Weekly Bestseller! www.LearningLeader.com/Becoming My guest - Mark Pincus is the founder of Zynga, the social gaming company he built from zero to $12.7 billion, pioneering a category that generated over a billion app installs and introduced hundreds of millions of people to online play. He's a serial founder who has started ten companies, taught product development at Stanford, and made early seed investments in Facebook and Twitter. His new book is Life at the Speed of Play. Key Learnings Coaches are cheat codes. Mark has hired a surfing coach, a tennis coach, a chess coach, a life coach, and the legendary Bill Campbell. None of them were assigned to him. He went and found them. "You throw to where you swing. You don't swing to where you throw." His tennis coach, Jorge, on learning how to serve. Commit to the natural swing first. You'll miss. You'll look stupid. Eventually your brain realizes you're serious and puts the ball where it belongs. You play to get better. You don't play to win. Not this point. Not this match. What am I doing right now that makes me better in the next game? Kill the ego so it gets out of the way of you being humble and curious. Don't contort the organization around one talented jerk. Colleen McCreary, Zynga's chief people officer, kept a sign on her wall: "No jerks allowed." She once vetoed an acquisition because she believed the founder was toxic. When the organization sees you refuse to bend just to win one point, they build muscle and confidence too. You'll have periods out of alignment. That's okay. Sometimes the person is in a crucial seat and you can't swap them tomorrow. What matters is that you're aligned with the philosophy. Bill Campbell's two lessons: intellectual honesty and courage. Be committed to the deep truths above everything else, and say them to your team even when it hurts. Then have the courage to stand up to your own team, your own board, and your own investors. A well-run company is a democratic dictatorship. Campbell leaned heavily on the dictatorship half. Unapologetically. The flip side of ambition is sacrifice. Everyone says they're a ten out of ten on ambition.  Mark's test: would you toil in obscurity for the next ten years, nobody respecting what you're doing, for an 80 or 90 percent chance at something bigger than your wildest dreams? Or take the 80 percent chance at a respectable single and a pat on the back? Nobody believes in us as much as we do. That's why we become founders. At 41, VCs who had already backed Mark twice told him he was too old, too rich, and too settled to go all in on Zynga. Mark isn't all in until he is. He calls it a lazy on-ramp to curiosity. Lots of projects. Looking for signals in a lot of places. Processing. Then it flips. "When the fish are running, we're up all night throwing nets until they're done running, not until we're tired." The chess lesson: play boring. His chess coach told him if you want to beat a player rated much higher than you, play boring and conservative and let them make the mistake. He started doing it. He started winning. Your kids don't follow what you say. They follow what you do. Mark set out to be a different kind of dad than his own, who measured people by their résumé. Then he watched his 15-year-old daughter spend every waking minute with math tutors before leaving on a service trip. "How did we get here? Oh, I know how we got here. They're not listening to what Dad says. They're following what Dad does." Happiness comes from feeling useful to a community you care about. Not from being useful. From feeling it. That's Alfred Adler, by way of The Courage to Be Disliked. There are two levels of success. A life well lived is a life in alignment with what only you can bring to the world. You don't have to build Google to feel like you went for it. The ultimate success is the greatest instantiation of your talents. Use money as freedom, not lifestyle. When Freeloader sold and 28-year-old Mark had more money than he'd ever imagined, he made a list of everything he was going to buy. It totaled about $9,000. He put leather interior in his Pathfinder. He still has the vehicle.  He set a $500 million goal that had nothing to do with buying things. At a net worth around $15 million, working with a life coach, he wrote it down because that's the capital required to run a studio of teams chasing many ideas without going to anyone else with hat in hand. "Creatively, that's freedom." Retirement is spiritual death. The stretches between building things are what Mark calls the abyss. Once you've felt the high of building with a great team and shipping something into users' hands every week, it ruins you. You can't be happy with less. Over-fund the things that matter. Not every day is the same. Not every life moment is the same. When one of those moments comes, put all your best players on the ice. The Allen & Company investor tour. They told Mark it was a dog and pony show. Just shake some hands. He told his team: this is our IPO, this is our roadshow, and we are going to use every minute of it the way we want. He gave the biggest public market investors a full presentation, handed them his numbers, and told them to judge him against those numbers in a year. He met with them a year later and had crushed them. King for a day. Carol Bartz brought Mark in to talk to Yahoo's entire senior management. He spent two weeks building a presentation on how he'd run Yahoo if he were king for a day, stood up, and took over the room for an hour. They got a deal done and one person in that room came to work for him. He ran the same play on the CEO of AMEX with a credit card that competed on fun. Amex spent $75 million with them. Seven minutes with Obama became 45. Mark was told the President would ask about his kids and then the meeting would be over. He walked in with a PowerPoint on the ten bold objectives he'd run on if he were president. Obama went through the entire thing and then asked what else he had. Prepare as hard for everything going right as you do for everything going wrong. Mark had under-prepared for the version of where Obama said, "Yeah, I'm buying. What else you got?" We sleepwalk through our own biggest moments. The question isn't what's the agenda or why they want to meet with you. It's: I have five minutes with the king. What is theoretically possible here? The Book of Life. Every year during the Jewish New Year, Mark writes in the same book. Only during that window. It isn't a journal. It's a spiritual board meeting with himself. He reads back through every previous year first, then writes. Partner with your future self. "What will Mark 2030 thank me for doing right now?" It started with quitting smoking. October 19, 1994. "I pulled my own power back. If I can quit smoking and really commit to it, what else can I do?" A year later he quit his job and did something bigger. Even if you missed the big goal, ask what you actually did toward it. Did you talk about it? Did you do real things? Did you turn the boat toward it? If not, maybe you don't believe in the goal. When Mark is at his lowest, he's humbled, he picks achievable goals, and he finds the most grit. When he's winning, he picks stratospheric goals and does the worst. After his biggest successes, he isn't humble enough to succeed again, and he has to go through failure to reel himself back in. Mark's champagne moment a year from now: that the book connects for hundreds of thousands of people, and that he's built a product people find real meaning in. Reflection Questions If you went and hired a coach, who is the person who could most change your life right now?  What is your next high-stakes moment, and are you treating it like the dog and pony show or like your IPO? What is the one habit your future self would most thank you for making this year? What would it unlock if you actually committed to it? More Learning #688: Dr. Henry Cloud - Your Desired Future: 5 Steps to Take You Where You Want to Go #689: Eric Ries - Why Good Companies Go Bad, and How Great Companies Stay Great #687: Jim Collins - What to Make of a Life & The 3 Types of Luck Episode Chapters 00:00 Meet Mark Pincus 01:40 Why Mark Hires So Many Coaches 02:25 Lessons From Tennis In Leading People 04:56 Refusing to Bend the Culture for One Great Player 07:03 Lessons From Bill Campbell 09:17 On Being Called a Control Freak 13:38 The Ambition Question Mark Asks Founders 14:53 What High Standards Do to Your Kids 20:55 How Mark Defines Success  23:26 Money as Freedom, Not Lifestyle 29:45 Why High Achievers Can't Retire 32:39 Over-Fund the Things That Matter 34:35 Treating a Handshake Tour Like an IPO Roadshow 35:36 King for a Day at Yahoo and Amex 38:14 Seven Minutes With Obama That Became 45 41:39 The Book of Life 45:21 Why Mark Does His Best Thinking at His Lowest 46:48 The Champagne Question 48:40 EOPC

Nvidia just announced partnerships with some of the world's biggest financial institutions to mobilize more than $500 billion of capital for AI infrastructure.But there's an important distinction:Nvidia isn't investing $500 billion.The initiative is about bringing institutional capital into the financing of AI data centers, compute infrastructure, and related projects.In this episode, we break down what Nvidia's financing strategy really means—and why it could be one of the most important developments yet in the next phase of the AI buildout.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comKey topics we explore:– What Nvidia's $500B financing initiative actually involves– Why Nvidia wants institutional investors to finance AI infrastructure– How compute could increasingly become an investable infrastructure asset– Why this could accelerate AI data center and GPU deployment– The potential beneficiaries across Nvidia, data centers, power, and networking– The risks if AI demand or GPU utilization doesn't meet expectations– Whether this creates a potentially circular financing ecosystem around AI– Why Wall Street is becoming an increasingly important participant in the AI buildoutThe bigger question:Are we simply finding new ways to finance the AI infrastructure boom—or are we watching the emergence of an entirely new institutional asset class?For investors, the answer matters. The next constraint on AI may not be GPUs or power—it may be the enormous amount of capital required to build everything around them.LINKSPrashant Choubey - ⁠https://www.linkedin.com/in/choubeysahab⁠Subscribe to VC10X newsletter - ⁠https://vc10x.beehiiv.com⁠Subscribe on YouTube - ⁠https://youtube.com/@VC10X⁠Subscribe on Apple Podcasts - ⁠https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986⁠Subscribe on Spotify - ⁠https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQ⁠VC10X website - ⁠https://vc10x.com⁠For sponsorship queries reach out to prashantchoubey3@gmail.comThis channel is for asset managers, allocators, and investors who want analysis that holds up—not headlines dressed as insight.Subscribe for weekly data-driven breakdowns of the forces reshaping capital markets.#Nvidia #AI #ArtificialIntelligence #AIInfrastructure #DataCenters #GPUs #Investing #TechStocks #VC10X #BlackRock #Apollo #Blackstone #GoldmanSachs #KKR #AIInvesting #Semiconductors #CloudComputing #CapitalMarkets #Finance #WallStreet

Andrea Hippeau is Head of Portfolio Management at Lerer Hippeau, an early stage venture firm based in New York. She has been at the firm for twelve years and recently moved into this role from Partner, shifting her focus from sourcing new deals to supporting the existing portfolio at scale. Lerer Hippeau invests at pre-seed and seed, leads rounds, and writes checks between one and four million dollars.This is Andrea's second appearance on VC10X, and a lot has changed since the last one.We get into what Series A investors are actually screening for now (hint: it isn't revenue), why AI efficiency is pushing some companies to raise more instead of less, the founder trait Andrea says has quietly overtaken sales, why Lerer Hippeau runs 70 to 75 percent enterprise despite its consumer reputation, and what twelve years of investing taught her about patience.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comWe talk about:- What Series A investors actually screen for now, and why an exceptional team can raise with no revenue- The efficiency paradox: AI lets you do more with less, so why are companies raising more?- Why Lerer Hippeau runs 70 to 75 percent enterprise despite its consumer reputation- Storytelling replacing sales as the founder trait that predicts everything else- Why a Partner deliberately stopped chasing deals after twelve yearsConnect with Andrea:LinkedIn: https://www.linkedin.com/in/andrea-hippeau-64658227/Lerer Hippeau: https://www.lererhippeau.com/Connect with Prashant Choubey:LinkedIn: https://linkedin.com/in/choubeysahabSubscribe to VC10X newsletter - https://vc10x.beehiiv.comSubscribe on YouTube - https://youtube.com/@VC10XSubscribe on Apple Podcasts - https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986Subscribe on Spotify - https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQVC10X website - https://vc10x.comTimestamps:(00:00) - Teaser: The Changing Landscape of Venture Capital(01:58) - The Evolving Bar for a Series A Investment(04:36) - How AI is Redefining Compounding Growth in Startups(06:02) - Lerer Hippeau's Investment Focus: From Consumer to Enterprise(08:05) - Why AI Makes Consumer Brands a More Exciting Investment(10:03) - The Double-Edged Sword of AI on Startup Funding Needs(12:29) - The Impact of AI on Paid Advertising and Customer Acquisition(15:51) - Balancing Portfolio Support and Sourcing New Deals in a New Role(18:05) - Using AI to Manage Portfolio Data at Scale(20:30) - The Shifting Profile of a Modern Founder(21:58) - Sourcing Growth Capital for Consumer Brands Today(25:06) - The Dangers of the Venture Capital Hype Cycle(27:07) - What Founders Need the Most Help With (But Don't Always Ask)(30:36) - Why Storytelling Has Replaced Sales as the Most Important Founder Trait(32:18) - What Makes a Compelling Founder Story?(34:30) - Startups vs. Incumbents: The Battle for the Workflow Layer(36:43) - Regulated Sectors Ripe for Disruption(39:21) - Evolving the Investment Decision Process in the Age of AI(43:13) - Are Big Tech's AI Investments Boosting Startup Productivity?(46:27) - How a Decade in VC Shapes Investment Instincts(49:45) - Strategies for Seed Investing: Go Early, Be Contrarian, or Be Flexible(52:12) - Rapid Fire Round

“By operating in secrecy, they're able to avoid or evade accountability — and, in many instances, engage in anticompetitive behavior or even fraud.” — Renée M. Jones on unicorns Twelve years ago there were 39 unicorns — private companies worth a billion dollars or more. Today there are over 1,400, collectively valued above $7 trillion, with the twin beasts of Anthropic and OpenAI at the front of the herd, driving the entire American economy. A good thing, surely? Not according to Renée M. Jones, the SEC's chief regulator of corporate finance from 2021 to 2023 and author of Untamed Unicorns: Why Startup Finance Is Broken and How to Fix It. The former SEC big game warden worries that this stampede of wild unicorns might be driving the entire American economy off a cliff. Her problem isn't that these private companies exist. It's that we know almost nothing about them. That's because of changes in the law since the Nineties that have lifted the hundred-investor cap on private funds, thereby enabling them to mushroom from under $1 trillion to $17 trillion. Add secondary markets where insiders quietly cash out, and the IPO becomes optional. And so we know almost nothing about companies like Anthropic and OpenAI with private valuations in the hundreds of billions of dollars. The result is what Jones calls the founder-friendly model of Facebook, Uber or Airbnb. With super-voting shares at ten votes apiece, founders effectively choose their own bosses, thereby stripping investors of the power to discipline anyone. Think Travis Kalanick and Mark Zuckerberg. Think Theranos, WeWork and FTX. Unicorns are named, of course, for their impossibility. Not so long ago, nobody could imagine a private company worth more than a billion dollars. However, with $7 trillion now on the table, Jones is concerned about the health of the American startup economy. On the brink of the OpenAI and Anthropic IPOs, I fear Renée Jones might be right about the dangers of a real crash triggered by the stampede of these mythical creatures. Jurassic Park is now playing in Silicon Valley. Pass the popcorn. Five Takeaways •       The $7 Trillion Secret. The unicorn was named for its rarity: 39 existed twelve years ago. Today there are more than 1,400, worth over $7 trillion — roughly 1,100 in America, nearly 300 in China — and the biggest of them shape the economy while disclosing essentially nothing. That is Jones' target: not the billion-dollar valuations but the secrecy. A billion-dollar private company faces neither the disclosure rules nor the governance requirements of a public company its size, which means accountability arrives only by accident — a scandal, a frustrated investor, a whistleblower calling a reporter. Everything else stays dark.•       How the IPO Died. Startups once went public within five to seven years, for two reasons: growth capital lived in public markets, and the 500-shareholder rule forced large private companies to register — it's reportedly why Google and Facebook held their IPOs at all. Both reasons were legislated away. NSMIA (1996) uncapped private funds, whose assets exploded from under $1 trillion to $17 trillion; the JOBS Act (2012) moved the trigger to 2,000 shareholders with employee shares exempt; and secondary markets — Forge Global, Nasdaq Private Market, EquityZen — let insiders cash out without a prospectus. The IPO became a liquidity event rather than a necessity. Only AI's bottomless capital hunger, Jones notes, is pushing OpenAI and Anthropic toward the public markets at all.•       Founders Choosing Their Bosses. The founder-friendly model gives startup founders super-voting shares — ten votes to one — letting them control the board that supposedly controls them. Venture capitalists lost their traditional power to discipline or dismiss a misbehaving founder: Uber's investors, lacking the votes to oust Travis Kalanick, had to stage a coup via press leak. And the VCs are conflicted anyway — exposing fraud destroys the exit they're invested in. Jones' answer to the Google-and-Facebook counterargument is historical: dual-class structures were invented at those companies precisely to coax their founders into IPOs, and they now arrive by the second or third funding round — so the governance rot starts earlier and, as Zuckerberg demonstrates, persists indefinitely after the public offering.•       The Fraud Files — and the Social Bill. FTX. Theranos, which hid parts of its lab from inspecting regulators. WeWork, whose IPO filing finally told the truth about the spending and self-dealing — whereupon the public refused to buy, the company limped through a SPAC into bankruptcy, and employees who had borrowed money to exercise options and pay taxes were left holding worthless paper. (The VC money lost, Jones notes, is substantially public pension money anyway.) Beyond the frauds lies the social bill of the below-cost blitzscale: taxi drivers destroyed and then prices raised; passengers assaulted under lax background checks; Airbnb's uncollected occupancy taxes, underinvested security, and name-based discrimination. A culture of outrunning the law, Jones argues, gets baked in — and firms powerful enough simply change the law, as Uber and Lyft did to driver-classification rules in California and Massachusetts.•       Not Teddy — Franklin. Asked whether the coming reckoning demands a new Teddy Roosevelt — Casey Michel's prescription on this show days earlier — Jones reaches a generation later: Franklin's New Deal securities acts of 1933 and 1934, which made disclosure the price of other people's money and worked for ninety years. Since the 1980s the architecture has been chipped into optionality, and the SEC is now dismantling Sarbanes-Oxley and Dodd-Frank protections while deregulating public markets too. Her remedies: disclosure to employees paid in options they cannot value, and disclosure in the largest private offerings — because investors of any sophistication cannot make responsible decisions while investing blind. Andrew's closing verdict: I hope she's wrong. I suspect she's right. About the Guest Renée M. Jones is Professor of Law and Dr. Thomas F. Carney Distinguished Scholar at Boston College Law School, where she has taught corporate and securities law for nearly a quarter century. From 2021 to 2023 she served as Director of the Division of Corporation Finance at the U.S. Securities and Exchange Commission — the nation's chief regulator of capital formation. A graduate of Princeton University and Harvard Law School, she is the author of Untamed Unicorns: Why Startup Finance Is Broken and How to Fix It (Harvard University Press, August 4, 2026). References: •       Untamed Unicorns: Why Startup Finance Is Broken and How to Fix It by Renée M. Jones (Harvard University Press, August 4, 2026). Jennifer Taub: “This essential book, replete with details and drama.”•       The National Securities Markets Improvement Act (1996) and the JOBS A...

Jeremy Au joined Rohit Malhotra on Life Self Mastery to talk about the moves between founder, VC, and operator, and what each seat actually teaches you. Now leading Cosmetic Physician Partners Asia after Bain, CozyKin, Monk's Hill Ventures, and Lucence, Jeremy is candid about what does not transfer between roles. They cover the three things every founder has to get right (the product, the team, and the business), the Southeast Asia story that still is not told at ground level, why angel investing is an Olympic-level race rather than a pass-fail test, why coaching someone and investing in someone are two completely different prisms, and the two-by-two he uses to advise emerging fund managers who have deployed capital without returns. Support the original show: Life Self Mastery with Rohit Malhotra: https://www.youtube.com/@LifeSelfMastery  Watch, listen or read the full insight at https://www.bravesea.com/blog/navigating-southeast-asia  BRAVE is Southeast Asia's leading tech podcast, hosted by Jeremy Au. Honest conversations with the region's top founders, investors, and operators on building startups in Southeast Asia. New episodes every week. Subscribe so you never miss one. Listen & Subscribe YouTube (English), YouTube (Bahasa Indonesia), Spotify (English), Spotify (Bahasa Indonesia), Spotify (Chinese), Spotify (Vietnamese), Apple Podcasts Follow BRAVE LinkedIn, X (Twitter), Instagram, TikTok, WhatsApp Follow Jeremy Au LinkedIn, X / Twitter, Instagram, TikTok, Facebook, Threads, Twitch Resources Get transcripts, startup resources & community discussions at www.bravesea.com #VentureCapital #SoutheastAsia #TechPodcast #Founders #Singapore #AngelInvesting #Startups #CareerPivot #EmergingManagers #Leadership #SEAstartups #Malaysia #Vietnam #Indonesia #Philippines #Harvard #Entrepreneurship 00:00 Intro 01:19 Founder to VC to operator, and back again 03:10 The three things: product, team, business 07:33 Scaling a clinic group from the US into Asia 10:54 The Southeast Asia story nobody is telling 18:30 Why a regional thesis is too broad a brush 22:20 Angel investing is an Olympic race, not a pass-fail test 25:41 Coaching someone vs investing in someone 28:08 Advice for emerging VCs who have not returned capital 35:07 The thread: high-performing teams 41:59 The comfort crisis and the 2% idea 46:54 What he would tell his younger self 50:20 Why Flow Club is his favourite tool 52:37 Where to find Jeremy

What's the difference between a founder who pivots and one who just quits with extra steps?In this episode of KP Unpacked, KP Reddy sits down with Andrew Ackerman, Zero RFI's Head of Special Projects ringing a fresh perspective on what it actually looks like to go from AI-adjacent to AI-native, why corporate AI rollouts fail before they start, and why the best onboarding experience isn't a training deck, it's a video game tutorial.The conversation gets real about the state of construction tech startups: corporates are running pilots, paying five grand, and building the same tool in the background. Vibe coding is pickleball. It's approachable, it's fun, and it's not software engineering. But it's changing how CEOs think about procurement, stretching sales cycles by months, and quietly killing companies that haven't figured out whether their customers actually love them or just tolerate them. Then KP and Andrew break down the missionary versus mercenary test, when VCs should tell founders to walk away, and why the next three years define the next thirty.Key questions answered:What's the difference between a missionary founder and a mercenary one?When should a VC tell a founder to shut down and move on?Why are corporates paying startups for pilots while building the same tool in-house?Is vibe coding a real threat to SaaS or just pickleball for software?Why does "we're on Copilot" tell you everything you need to know about a company?How do you onboard an entire organization into AI without losing them at the first button?What's the right pivot versus the wrong one?Why do customers love Tesla but not Salesforce, and what does that mean for founders?Should startups switch from SaaS to software as a service to survive?How did KP's 30-year mission stay the same across five completely different companies?Why is streetball the best analogy for early startup hiring?What's happening at AEC Summit's 10th year in Brooklyn?If you're a founder wondering whether to pivot or shut down, a VC trying to figure out when to give a founder permission to walk away, or a corporate innovation team quietly building behind a startup pilot, this episode will force you to ask whether you're a missionary or just waiting for a better offer.Listen now.Join our AEC Summit for a full-day event featuring developers, innovators, operators & business leaders and the massive new tech transforming the built environment

I am trying something a little different with this episode. I am heading back to 1977 to take a look at the original titles for the Atari VCS. I have been a lifelong Atari fan and collector and while my top games are not from the first year of the console, I am very much intrigued by the idea of a time when the console only had 9 titles. It would take me a while, but eventually I would get to collect ‘em all. Support the Retroist on Patreon On the show, I start off by talking about this time in Atari's history and how I have loved the start of any product line. Variety is great, but a small checklist is very satisfying. In addition to the original games, I talk about Sears, cartridge technology, the art and artists, and the original cast of programmers who made these first few games. These trailblazers would all go onto other things. Its is amazing that Star Wars and the VCS landed in 1977. Two things among many that helped to define a lot of childhoods. This episode helped to remind me why I really loved early Atari games. I sat down to play them and tried to put myself into 1977. Other consoles had been trying to do what Atari was doing, but the Atari VCS with its joysticks and paddles feels so much more intuitive. The color, graphics, and sound might have been simple, but it showed what the future was going to be like. Games would grow by leaps and bounds over the next few years, but most of these games remained very playable.

This week, one tweet from Elon Musk reignited one of the biggest debates in AI investing.After AMD reported strong earnings, Musk posted that SpaceX has committed to using Nvidia GPUs exclusively because they are the best.At the same time, xAI continues to deploy both Nvidia and AMD GPUs—raising an important question for investors.Is Nvidia's lead in AI becoming even stronger, or is the AI infrastructure market simply becoming large enough for multiple winners?In this episode, we break down what AMD's earnings and Musk's comments tell us about the competitive landscape in AI chips.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comKey topics we explore:– What AMD's latest earnings reveal about AI accelerator demand– Why Elon Musk said SpaceX will exclusively use Nvidia GPUs– Why xAI is taking a different approach by deploying both Nvidia and AMD– Nvidia's competitive moat beyond hardware: CUDA, networking, and software– Whether AMD needs to beat Nvidia—or simply capture a growing share of the AI market– What this means for the broader AI infrastructure investment thesisThe bigger question:Is the AI accelerator market a winner-takes-all industry, or will explosive AI demand create room for multiple winners?For investors, understanding where Nvidia's moat remains strongest—and where AMD is making meaningful progress—is key to evaluating the next phase of the AI infrastructure buildout.LINKSPrashant Choubey - ⁠https://www.linkedin.com/in/choubeysahab⁠Subscribe to VC10X newsletter - ⁠https://vc10x.beehiiv.com⁠Subscribe on YouTube - ⁠https://youtube.com/@VC10X⁠Subscribe on Apple Podcasts - ⁠https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986⁠Subscribe on Spotify - ⁠https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQ⁠VC10X website - ⁠https://vc10x.com⁠For sponsorship queries reach out to prashantchoubey3@gmail.comThis channel is for asset managers, allocators, and investors who want analysis that holds up—not headlines dressed as insight.Subscribe for weekly data-driven breakdowns of the forces reshaping capital markets.#Nvidia #AMD #AI #ArtificialIntelligence #GPUs #ElonMusk #SpaceX #xAI #Semiconductors #TechStocks #Investing #VC10X #DataCenters #CUDA #WallStreet #Finance #ChipStocks #AIInfrastructure #Markets #Earnings

Hey, can Go be good for a version control system? André Bianchessi is building twigg and we discuss about why to build a VCS in 2026 and how Go can help for certain thing and where it fall short.Links:Twigg.vc

This Week In Startups is made possible by: Vanta https://www.vanta.com/twist Agree https://agree.com Odoo https://Odoo.com/twist Today's show: *Airtable just sold for $2.25 billion, an 81% drop from its peak of $11.7 billion. On this week's TWiST VC Roundtable, Aditya Agarwal (South Park Commons), Niko Bonatsos (Verdict Capital), and Rick Heitzmann (FirstMark Capital) break down why the venture world sees this as a good outcome, not a financial disaster. By declining the deal, would Airtable's team have just been delaying the inevitable? Is the fact that they reached $400M+ ARR on its own a reason to celebrate? Find out why our investor panel prefers unwinding a stuck situation rather than chasing a growth rate that's no longer sustainable. PLUS Robinhood's booming prediction market business, the secondary market flap over Anduril shares, why so many VCs shy away from "vice" categories, and a glimpse at how insiders are talking about the Apple-OpenAI lawsuit. Guests Aditya Agarwal on X: https://x.com/adityaag South Park Commons: https://www.southparkcommons.com/apply Niko Bonatsos on X: https://x.com/bonatsos Verdict Capital: https://verdictcap.com/ Rick Heitzmann on X: https://x.com/rick FirstMark Capital: https://firstmark.com/ Relevant Links Airtable: https://www.airtable.com/ Bending Spoons announces Airtable acquisition: https://investors.bendingspoons.com/newsroom/bending-spoons-agrees-to-acquire-airtable Constellation Software: The Anti-Conglomerate: https://www.eaglepointcap.com/blog/constellation-software-the-anti-conglomerate Introducing Robinhood Ventures Fund II: https://robinhood.com/us/en/newsroom/introducing-rvii/ Quartz: Robinhood posted record quarterly revenue: https://qz.com/robinhood-record-revenue-prediction-markets-earnings-073026 Riot Games: https://www.riotgames.com/en AngelList's USVC Fund: https://usvc.com/ Baseten: https://www.baseten.co/ OpenEvidence: https://www.openevidence.com/ Hermes Agent: https://hermes-agent.org/ Granola: https://www.granola.ai/ Timestamps: 0:00 VC intros & Bending Spoons buys Airtable 9:19 Why growth is the only metric that matters 9:45 Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist 20:52 Agree.com - Stop chasing invoices and automate your entire contract-to-cash stack. Go to https://agree.com and tell them Jason sent you to get 50% off for life! 26:25 When should VCs sell in secondary markets? 30:55 Odoo - The all-in-one business platform. Get started for free at https://Odoo.com/twist 35:38 Robinhood's prediction markets are exploding 43:13 The USVC-Anduril secondary controversy 46:18 How VC firms use open source models 58:43 Telling the real founders from the grifters 1:02:38 Why Apple is suing OpenAI Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp   Follow Lon: X: https://x.com/lons   Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm   Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis   Check out all our partner offers: https://partners.launch.co/   Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland   Check out Jason's suite of newsletters: https://substack.com/@calacanis   Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

40% of PE firms and 60% of credit funds still track their portfolios on spreadsheets. That's now a way to monitor a few trillion in AUM. Private equity (and credit) are about to get way more transparent as retail investors get access to PE, and the tools you need to monitor and report your exposure must evolve. The products are way ahead of the plumbing.  Devin sits down with Kevin Hsu, founder of Lumonic, a portfolio monitoring software company now part of PitchBook. We discuss Kevin's early career in private markets technology and what that taught him about the difference between the needs of VCs and PE firms. We talk about the coming convergence of public and private markets, and why the old ways of doing things (in spreadsheets) won't cut it in the new world of heightened transparency and regulation. ParkerGale is in the middle of automating much of its internal workflows with new AI-powered tools, so this conversation will help other funds think through the options. Should you roll your own (Kevin has a surprising opinion on this), stick with what you've got, or make a switch? What should you expect from your software provider and how do you handle those edge case portfolio companies? We cover all this and more with Kevin. https://www.lumonic.com/  

Victor Penev, Founder and CEO of Edamam LLC, is on a mission to help people make healthier food choices by organizing the world’s food knowledge, while building a Zero-Spend Marketing Engine that fuels sustainable growth through a proprietary semantic food database. By combining food science, nutrition expertise, and AI-powered technology, Victor has built Edamam into a trusted food intelligence platform that enables businesses to develop innovative health, wellness, and nutrition applications while making reliable food data accessible at scale. In this conversation, Victor introduces The Democratic Decisions Framework—No Pre-Judgement, Freedom to Speak Up, Intellectual Environment, Robust Discussion, and Work Towards Consensus. He explains why removing preconceived opinions encourages better ideas, how open dialogue and diverse perspectives lead to stronger decisions, and why working toward consensus builds lasting commitment across teams. Victor also shares how partnerships, referrals, and search authority fueled Edamam’s sustainable growth, and why proprietary data and continuous human refinement remain the company’s competitive advantage in the age of AI. — Zero-Spend Marketing Engine with Victor Penev  Good day, dear listeners. Steve Preda here with the Management Blueprint Podcast. Today, my guest is Victor Penev, the Founder and CEO of Edamam LLC. Edamam is helping people eat better by making daily food choices simple and easy, eventually organizing all the food knowledge in the world. To that end, the company has built a proprietary semantic food knowledge base and is creating, on top of it, a range of consumer and business applications to solve real-world, everyday problems. Wow. Victor, welcome to the show.  Thank you. Good to be here. I’m very excited.  So I was shocked to learn that you’ve got 900,000 foods in the system and 2.3 million recipes. I mean, I don’t know who is even able to create so many recipes. So how did that whole thing come about? How did you come up with this idea? Are you a foodie?  I am a foodie. Yeah, that’s kind of the origin story. I’m a serial entrepreneur. I’ve done a few startups. I had a successful exit about 15 years ago. A friend of mine and I built Bulgaria’s largest internet company. Then I started looking for what to do next. I was actually going to start a news organization, but then I realized one day, on a beach in Thailand, that I think about food five hours a day, and I cook every day.  So I might as well do something around food that helps people. I married my passion for food with my passion for technology and built a company.Share on X The problem we set out to solve back then is still a very valid problem. People need the right information, just in time, to make the right food choices. The data is always inconsistent, incomplete, and lacking. So we set out to organize the world’s food knowledge so we can help people make the right food choices.  Wow, this is fascinating. So what is your personal ‘Why’ that you’re manifesting in this business?  So, in terms of philosophy—and my philosophy changes. Everybody’s personal philosophy changes over time. But where I am right now, I think there are a couple of things that really matter if you want to live what one would call the good life—a meaningful life. One is to be really present in the moment. You know, be here now. The other is to help people. I think my business falls into…Share on X For us, the measure of success is not revenue or profit.  It’s how many people we ultimately reach through our data. It’s worth noting that we’re a business-to-business company, so we don’t reach people directly. But we have partnerships with companies like Nestlé, Microsoft, Amazon, and Food Network. Through those partnerships, we think we reach at least a billion people. For us, that’s the real measure of success.  Helping everybody eat better and live longer, healthier lives. My vision is that everybody can live to 120 without chronic illness or mental conditions. A big part of that is food. So we're trying to help people. That's my 'Why'.Share on X  Yeah, I love it. Obviously, eating healthy is a big thing. You know, garbage in, garbage out. This is fascinating. Do you find that certain cultures have better eating habits than others? And what drives it?  I think history and geography, to some extent. Everybody knows about the Mediterranean diet. And it’s not only the countries around the Mediterranean. Vietnamese and Japanese cuisine are Mediterranean in the composition of the food. A lot of it comes from being close to water, living in a certain climate, and having a huge variety of fruits, vegetables, and fish. But I think the biggest change over the last hundred years hasn’t been culture. It’s been technology—quote-unquote—and the processing of food so it’s shelf-stable, can be shipped, and sold at a lower price.  I think that’s the biggest problem. It’s changing eating habits everywhere in the world. It’s very well known in the United States and, to some extent, in Western Europe. But even places like Japan are starting to eat a lot more processed food. Mexico is starting to eat a lot more processed food. This is more technology-driven than anything else. If I had to say one thing that would help people, it would be this: Go back to your roots. Get food directly from the soil. Cook it at home. It's a question of time and effort, but if you value your health, that's one of the best investments you…Share on X  Yeah, as we’re getting busier and busier, the opportunity cost of cooking our own meals is becoming higher. And that’s the real problem.  It is a problem. I mean, it’s a question of priority. I cook every day. I’m very busy, but for me, it’s an important enough thing. Food, apart from nourishment and nutrition, is also a very social thing. People connect through food and spend time together, and that’s another thing that correlates very well with longevity and a well-lived life. So there’s a lot more to be said about having good food.  Yeah, I think Woody Allen said in one of his movies that eating together is the second sexiest thing to do, or something like that.  Yeah, there you go. I’m not going to ask what the first one is.  Yeah. That’s awesome. So let’s talk about frameworks. This podcast is all about frameworks. What’s a framework that you’ve discovered along the way that helps you think about your business, create outcomes in your business, help other people be productive, or whatever it is that’s driving results in your business?  A couple of things come to mind. I’ll start with the non-obvious one. Some people speak about it, but I’ve practiced it, and I think going slow gets you further. You know, the hare and the tortoise. I am definitely against growth for growth’s sake. That drives a lot of decisions: who you raise money from, how you run your business, and so on. Over the years—and I’ve been an entrepreneur for probably close to 40 years—I’ve discovered that every successful product has its timing and its soul.  You just have to let it come to fruition. Sometimes that means slowing down instead of rushing forward. For me, being deliberate about the end goal without having time constraints is a framework that’s always helped me. It’s not for everyone. Definitely not for the modern world, where VC money is trying to get you to grow very fast, and everybody competes based on how fast their revenue or customer base has grown.  But I find that this does not lead to lasting results in terms of impacting humanity and having a meaningful life—both for yourself and for everybody in the company. So that’s one framework. The other, which is also very simple, though not too many people execute it, is just: treat people like people. This means there’s no hierarchy in my company. I’m super proud to say that in my last company, I never had an employee leave. And that was over 15 years.  It’s because I treat people as people. Going back to the Latin origin of the word companion, companio, which means “to break bread together.” We are a band of people breaking bread together on a journey. That’s how it works. That means doing things that are not intuitive in business. For example, having no advance notice requirement for vacations. If you want to take a vacation, take it now. Giving responsibility to people and treating them well. Those are the two frameworks that I think have helped me. In addition, making decisions democratically, which is a very contentious thing in business. Okay.  So how do you do that?  There is always, ultimately, an arbiter, which is oftentimes me or some kind of board. But that means having a robust discussion around a topic without preconceived notions of what the end decision or result should be. Creating a culture where everybody can speak up, argue, and tell you they disagree with you. Having that freedom creates an intellectual environment where people really debate.Share on X  At the end, more often than not, the obvious decision emerges. It takes a long time, but the reality is that once a decision is made, everybody has bought into it, and it’s usually the right decision. So I don’t make wrong turns. Now, there are times when it’s a coin flip. There are two equally good—or equally bad—options, and somebody has to make the call. Then it falls to me. But for the most part, it’s democratic decision-making.  So how do you cultivate that? How do you foster it? How do you make sure people contribute to it?  I’ve had the luxury of starting a company from scratch. It’s a lot easier to do when you start from scratch because you establish the culture with the first hire. It’s a lot harder to change a culture and instill new values. For me, it's been a very deliberate choice about what I want the company to be. People working together toward a common goal. People who don't get overworked. People who always have something exciting to do and an amazing group of people to work…Share on X  So it’s about hiring people who are reliable, self-sufficient, and want to move things forward. Then really showing that any conversation and any intellectual argument is absolutely allowed. There is no hierarchy where you can say something and I’ll just shut you down because I’m the boss. You have to demonstrate that in practice. You have to actively solicit everything a person has to say. After the first couple of hires, it becomes easier with the next ones because they see what’s going on. That’s how you do it. It’s a deliberate choice to build the culture through day-to-day interactions. There’s no easier way, unfortunately.  So how scalable is this flat, democratic culture? Does it impact scalability in any way?  It is scalable. My current company is small, but my previous company had about 150 people. Even when we sold the company and had to find a replacement CEO, the candidates had about 50 interviews each. At the end, the whole company voted on who the CEO would be. It is scalable. It slows down the decision-making process, but it speeds up the progress of the company.  An individual, even the smartest individual, is more likely to make mistakes than the crowd. Now, there are exceptions. I’m pretty sure Steve Jobs would have done it differently. But for the most part, I think people make fewer errors through a democratic process than by making decisions on their own. So yes, I think it’s scalable. It’s really a matter of deciding which decisions belong at which level. You don’t have the entire company of, say, 10,000 people voting on one thing. For a particular problem, there might be 10 people who are the relevant decision-makers. They get together and sort it out, and everybody else accepts what those people decide.  So it’s more of an approach to tapping into the minds of the people who can potentially bring diverse opinions.  Yeah. Diversity of opinion. They all have a different angle from which to look at the issue that needs to be resolved. So different angles. Because they’re all impacted, everybody can contribute. There’s respect in listening to everybody else. It’s not that one person establishes the ground truth and everybody else has to agree. There is actual debate.  Yeah. Interesting. By the way, I just want to comment on this bootstrapping aspect. I saw your post on bootstrapping. You spoke at a recent conference on it. So what are your thoughts on bootstrapping? What are the critical ingredients there?  So bootstrapping is the way business has been run for a very long time. It’s only recently—in historical terms—that the venture capital industry emerged and allowed businesses the luxury of building without thinking too much about the bottom line. But I think the disciplining effect of constantly thinking about the bottom line is super important. To some extent, it takes a certain kind of individual to do it, which I call the true entrepreneur. Those are the people who are risk-takers and can live with a very high degree of ambiguity.  When you don’t have money in the bank to cover next month’s expenses, you have to be comfortable with that. To me, that’s the right entrepreneur. It has become too easy for people to become “entrepreneurs” by raising tens of millions of dollars. That’s not to say they might not be brilliant people who execute very well and create amazing companies. I think those are probably one in a thousand. The majority get the money, spend it in two years, and they’re gone. So for me, bootstrapping is the proof in the pudding. If you do it day in and day out and keep moving forward, you're actually building something that's valuable.Share on X  Yeah, I agree. Some people say it’s akin to being an employee when you raise money and just have to get to the next fundraising round, and then the next fundraising round. It’s almost like executing the business plan your board has given you.  Exactly.  Worst case, you go take a job somewhere else if things don’t work out. But you’re not losing your livelihood, and your family isn’t going to starve, right?  It is a certain type of personality, and I don’t think it’s for everyone. But my personal belief is that if you’re going to build a business, you’re better off building it with a little bit of hunger. Not always having enough. Having just enough so you can keep moving forward.  Yeah. Necessity is the mother of invention, right? If you don’t have too many resources, that’s a constraint you can push against and come up with better ideas. It’s a forcing function.  Correct.  There you go. So, Victor, what drives growth in your business?  Like I said, I’m not pro-growth per se. This was also a deliberate choice for the company. Just as a parenthesis, the last business I ran before this was a media business. We sold advertising. I personally don’t believe that spending money on advertising is a good idea. So I built a business with the explicit desire not to spend any money on marketing. That meant I had to build mechanisms for referrals, inbound traffic, and so on.  A lot of our early customers—including companies like The New York Times and Food Network—received very high discounts, but with the requirement to display “Powered by Edamam,” linking back to us. Search engines like that. It builds authority and so on. Over the years, we’ve built enough authority that most of our traffic now comes from search engines and chatbots. That’s what’s driving our growth.  The other thing is that we try to stay nimble. We build technological assets and products, but the market changes. Who needs the data? For what use cases? It’s important to stay aware of the market and adjust to wherever the opportunity is. That’s how I’ve built the business. I’ve built it to generate lots of inbound traffic and then adjust very quickly if the market changes. The rest is we’re almost like a spider waiting for the flies to come in.  So you say you’re not pro-growth. Do you mean you don’t want to make this a bigger company?  No. I’m not for growth just for the sake of growth. Let’s grow 300% this year in top-line revenue. For me, as I said, what drives the business is how many people we reach with our data. That’s the metric I’d like to grow as much as possible. If possible, I’d like to reach every person on the planet. That’s the aspiration. When I say I’m not for growth, I mean that  sometimes growth becomes its own incentive. You grow without asking, “What’s the ultimate goal here?” Why do you have to grow?  Oftentimes, the answer is because you want to sell the company at a big profit. That’s why VCs put money into your company. You’re feeding the VC business model. But if you’re not feeding the VC business model, why do you have to grow the company? What’s the point? You may have investors, but you have patient capital, and they’re aligned with you. Eventually they’ll get their money back, but it doesn’t have to come at the expense of the company’s mission. When I talk about growth, I mean the pressure to constantly grow top-line revenue, which has become very popular over the last couple of decades.  So when you say you eventually want to organize all the food knowledge in the world, it sounds a little like Google organizing the world’s information—just for food. Isn’t that a big vision that requires growth?  No. Again, I don’t think growth is necessary to execute that vision. I think you can organize the world’s food knowledge, and we’re already well along the way. There’s always more to do. Even with very limited resources, we’ve been able to do it. I think that having more customers and more revenue would probably help, but only on the margin. It’s not a prerequisite.  The prerequisite is having the right technology and the right setup to constantly ingest new information about food, pass it through our pipeline, clean it, verify it’s accurate, structure it, organize it, and link it to other data. Food is a relatively limited universe. You started by asking whether people really create 2.3 million recipes. There are only so many ways to combine food items into something people eat. So it’s a limited universe. What’s exciting is the depth of food.  There are macronutrients, micronutrients, allergies, diets—we all know about those. But there’s much more. There are many more molecules in food. Eventually, even the soil it was grown in and the amount of sun exposure could become valuable data. There’s always more work to do, but we focus on what’s actually doable right now.  So do you envisage that the growth of technology, especially AI, means you can keep the company the same size as it is and accomplish your ultimate mission?  I think so. Similar size. To some extent, AI is helping us because it’s bringing a lot more customers. People are building all kinds of applications with large language models and agents, and they need accurate, deep data, so they come to us. We are also using AI to build tools and meet the demand, so our engineers are becoming more effective. So, to some extent, that helps keep the team small.  That being said, in terms of the speed with which AI moves, it’d be nice to have a few extra people. That doesn’t mean doubling the size of the company. I think it’s probably having 20 to 30% more people would be sufficient to actually leverage the technology—which is AI—to the best effect. So that’s kind of my view now. Ask me in two months, I may have a different view.  So what is one thing that you’re actively trying to figure out in this business right now?  Well, AI is throwing a wrench into everything, so it is a very fast-moving environment. Our clients are constantly changing their demands. The profile of our clients is changing. There’s a lot more new health, wellness, prevention, and weight-loss companies that are showing up and starting because they can now build with AI. So all of that makes for a very choppy sea. We can’t figure out exactly where the wind is blowing from and where we should head.  So for me, it’s kind of like having the North Star of, “Okay, we are a data company. We leverage our data asset, and we just keep doing that. Then we’ll innovate on the technologies that we need to offer, but stay there, as opposed to trying to change what the company does.” That’s kind of what’s keeping me steady. But again, it’s a very choppy sea, so I don’t know what’s going to happen. One big worry is whether Anthropic or OpenAI are eventually going to replicate everything that we’ve done. I don’t know. I don’t think so. But artificial superintelligence is something that nobody knows about.  Yeah. So the technology evolves very fast. So how do you avoid being commoditized by AI? I think the data—our moat is the data, right? It’s taken us so much time to actually clean, organize, and structure the data, and that’s not a process you can do easily just by throwing people or AI at it. We’ve used what used to be called AI since the beginning of the company: machine learning, natural language processing, and so on. Now we use generative AI. But we’ve used that, plus super-smart engineers, food experts, nutritionists, and week in, week out, we’ve been improving the data, improving the algorithms, refining it, and so on. That is not something you can just throw resources at.  It requires that constant iteration between humans and technology in order to get there. So for us, I think that’s an important moat because if somebody wants to build it and commoditize us, they’ll have to replicate that. I think the more likely scenario is they may end up buying us. Like I said, artificial superintelligence could be a completely different ballgame. But with the technology that exists right now, with large language models, I don’t think that’s replicable. We know it because enough people have tried.  Yeah. Would you be okay with someone buying you? Is this your…  Sure.  Yeah?  Yeah. The way I think of companies is the following. They’re like children. At some point, children become 18-year-olds. They can earn their own bread. They can walk on their own two feet. You kind of have to let them go. I think this company has gotten to that stage. It’s a teenager that can walk on its own. It doesn’t need me. And I have ideas for 10 more businesses. Who knows? If I’m healthy, I think I could probably build another four or five businesses in the next 40 or 50 years. Why not?  Yeah. I love it. So who do you want to connect with you, go on your website, check things out, and what’s the best way to engage with what Edamam is doing?  Two categories of people or entities. One is companies that are building something around diet management, nutrition, health, or food. I think they’ll find that we have valuable resources that can speed up whatever they’re developing. So that’s one category. The other is like-minded individuals. They could be investors, but they could also just be people who really care about healthy eating and the prosperity of humanity.  Those are the types of people I’d like to talk to because aligned minds often come up with new ideas. It’s kind of the Y Combinator thing—lateral thinking. If we have an aligned goal and we come from different fields, we may come up with something new.  Okay. So that’s a way for you to find people worth brainstorming with, I guess, who have the same ideas: healthy eating, helping people live longer—to 120 years. And other companies that could be using the data you’re processing and organizing. So if you’re out there and you’re in the food business or the healthy living business, then definitely pay attention.  Check out Edamam LLC‘s website. Talk to Victor Penev on LinkedIn. So, Victor, thank you for coming on the show and sharing your wisdom. And if you enjoyed this conversation, stay tuned because we have wonderful entrepreneurs like Victor every week. Anything else, Victor, you want to share? Any famous last words?  Yeah. I just want to say thank you. I appreciate the opportunity. Keep up the good work. I really enjoyed the conversation.  Thank you, Victor. And thanks for listening. Important Links: Victor's LinkedIn Victor's  website

Jeremy Au joins an episode of The Accidental VC podcast. Hosted by seasoned founders and VCs from ANZ and SEA, this show reveals how unlikely beginnings can lead to extraordinary outcomes. Whether you're building, backing, or just curious—tune in for stories, strategies, and sharp takes from those who've been there.  In this episode, we explore the thought that “Everyone wants a unicorn. But what if that's the wrong goal?” Mohan Belani and Jeremy Au join Milan Reinartz to challenge what founders and investors think they know about building in Southeast Asia, from venture capital and AI to startup exits, global expansion, and the future of entrepreneurship. Support The Accidental VC: https://www.youtube.com/@TheAccidentalVC  Watch, listen or read the full insight at https://www.bravesea.com/blog/stop-chasing-unicorns  BRAVE is Southeast Asia's leading tech podcast, hosted by Jeremy Au. Honest conversations with the region's top founders, investors, and operators on building startups in Southeast Asia. New episodes every week. Subscribe so you never miss one. Listen & Subscribe YouTube (English), YouTube (Bahasa Indonesia), Spotify (English), Spotify (Bahasa Indonesia), Spotify (Chinese), Spotify (Vietnamese), Apple Podcasts Follow BRAVE LinkedIn, X (Twitter), Instagram, TikTok, WhatsApp Follow Jeremy Au LinkedIn, X / Twitter, Instagram, TikTok, Facebook, Threads, Twitch Resources Get transcripts, startup resources & community discussions at www.bravesea.com #VentureCapital #SoutheastAsia #StartupFunding 00:00 Intro 02:44 What 2026 actually looks like in Southeast Asia 07:10 Big problems, small wallets: who in the region can actually pay 10:00 Why a $50 million exit is a real outcome 11:14 Power law investing vs micro private equity 17:13 Block lays off 40% and blames AI 18:43 Is Southeast Asia facing a K-shaped economy? 26:21 Future shock: a Waymo, a Costco chicken and no jobs 27:59 What founders should focus on now 31:03 Moat building in the age of AI 36:48 Quick fire: biggest wins and fails in VC

The Bible is by no means a picture book. But, for its lack of illustrations, it still presents many powerful images. A burning bush. A giant fish. An angel army. A pillar of fire. A snake. A harp. A wall. A fruit tree. A lamb. A tomb. A fishing net.The Bible seizes the visceral realities of what it means to be human—in all that gore, grief and glory—by connecting its ideas and its characters to natural elements we interact with daily (even if, in some cases, those elements are acting very differently than how we typically experience them).So, to bridge this gap between the descriptions of the world found in scripture and our physical experience of it, we have artwork. Christian artwork has spanned every generation of believers, with Christ himself being depicted through a multitude of ethnicities, time periods and artistic mediums.But is “Christian” artwork the only art through which Christians can see reflections of scripture? Ben Quash, Director of The Visual Commentary on Scripture doesn't think so. The Visual Commentary on Scripture is an online resource from King's College London which combines theology, art history, and Biblical scholarship to pair artwork from all across the stylistic and historical map with different sections of scripture. These pairings are called exhibits and feature high-quality scans of art pieces tied together with Biblical commentary.After over a decade working on the project, Quash is taking a step back. He was interviewed on this show 6 years ago when the project was still relatively young. So we wanted to have him back on the show as his time ends to talk through what he accomplished, why the internet needs the VCS site, and what's next.You can find the Visual Commentary on Scripture at thevcs.org. You can also sign up for their new newsletter called Bible and Art Daily, which will send artwork and an audio scripture commentary straight to your inbox every day. You can also find them on Instagram, TikTok, Facebook and X.

David Epstein is General Partner at USF Ventures, the venture fund backing companies connected to the University of San Francisco. He was previously a General Partner at Crosslink Capital and has held management and CEO roles at more than half a dozen startups. He also teaches entrepreneurship and finance, and began his career at Data General as a computer designer, on the project chronicled in Tracy Kidder's Pulitzer Prize winning The Soul of a New Machine.In this episode, Dave argues that the real AI bottleneck isn't chips, power, or capital. It's data. We get into why frontier labs backing open weight models is a defensive move rather than a principled one, where early stage startups can still win, and why he thinks jobs will disappear faster than they get created.⭐This episode is brought to you by Podcast10x. We help founders and investors turn one podcast episode into a full month of content. Strategy, production, and distribution handled end to end. Learn more at https://podcast10x.comWhat we cover:→ Why "AI company" is no longer a category, and the pitch deck claim that has become his pet peeve→ Why AI isn't a tool anymore, and what makes this cycle different from the dot com era→ The real bottleneck: why we've exhausted the internet's data and what comes next→ Money as the constraint nobody prices in, and the circularity in the current data center build out→ How Chinese open weight models pull revenue out of token charges and subscriptions→ Why big lab support for open models is defensive positioning→ Who survives if open weights take share, and why consolidation is coming→ Where early stage startups can still win: drug discovery, financial services, legal→ Why the likely exit is a sale, not an IPO→ Ethical investing as a return rather than a tax, and why it's tough to work with jerks→ Why self-regulation rarely works, and what 2008 tells us about the current AI alliance→ Why layoffs are just the beginning, and the Industrial Revolution parallel everyone forgets→ Where the jobs actually are: management, human facing care, and the trades→ What top tier VCs get right, and why VCs are also lemmings→ Quantum computing as a data center accelerator, and the password problem it creates→ Physics AI vs physical AI, and the validation problem sitting on top of both→ Five year predictions: AGI, commonplace robots, and why consciousness doesn't matterConnect with Dave Epstein:LinkedIn: https://www.linkedin.com/in/thedavee/USF Ventures: https://usfventures.comConnect with Prashant Choubey:LinkedIn: https://linkedin.com/in/choubeysahabSubscribe to VC10X newsletter - https://vc10x.beehiiv.comVC10X website - https://vc10x.comTimestamps:(00:00) - Preview(00:56) - Introduction to David Epstein and the Episode's Topics(02:48) - How the AI Startup Landscape Has Fundamentally Changed(05:08) - Comparing the Current AI Boom to the Internet Boom(06:25) - Identifying the Next AI Bottleneck: Chips, Power, or Data?(09:30) - Why Money is an Overlooked Bottleneck for AI Development(11:14) - The Cyclical Nature of AI Investments and Financing(12:46) - Analyzing Big Tech's Support for Open Source Models(15:12) - Winners and Losers: Open Source vs. Frontier Models(18:01) - How Early-Stage Startups Can Compete and Win in the AI Space(20:53) - The Role of Ethics in AI Investment Decisions(23:31) - The Challenge of Upholding Ethics in a Competitive Market(26:21) - Implications of the OpenAI Model Escaping(29:46) - The Future of AI-Driven Job Disruption(32:46) - Where to Find Employment Opportunities in the AI World(37:43) - How Top-Tier VCs Evaluate Founders and Make Decisions(40:21) - The Most Exciting Emerging Areas of Innovation(43:18) - Explaining Quantum Computing's Potential and Impact(48:39) - An Ambitious AI Prediction for the Next 5 Years(51:35) - Rapid Fire Round: USF Ventures' Investment Strategy(53:06) - Conclusion

Most people assume serious AI silicon can only be built by a giant, in the cloud, burning hundreds of millions of dollars. Ravi Annavajhala did the opposite. Kinara built two AI chips for under $50M, 90% of it out of Hyderabad, and sold to NXP, one of the world's largest semiconductor companies for $307M,  all cash. It's one of the largest deep tech exits India has seen, and one of the least talked about.In this episode, Vikram Vaidyanathan sits down with Ravi to take apart how it actually happened, and what it says about where AI is heading next.You'll hear: 1. Why the disruption playbook that minted the NAND-flash exits is the same one that explains Kinara's sale to NXP 2. The clearest plain-language explanation of training vs. inference you'll find, and why inference is moving off the cloud and onto the edge3. The four reasons edge beats cloud: cost, latency, privacy, reliability 4. How you actually shrink a frontier model to run on a chip: distillation, compression, purpose-built retraining 5. Why India's real AI edge isn't talent or capital, but the industrial data sitting in its factories — and why it can't afford to become a "data colony" 6. Ravi's distinction between frugal and cheap, and why low cost has stopped meaning low capability▶ If you're building, investing in, or just tracking India's deep tech story, this is the episode to watch.About Intelligent Indians Intelligent Indians is Z47's thesis podcast on the people building India's technology future, for builders, operators, and investors who want the operator's view, not the press-release version.

Noah Hopton, CEO and Founder of Finvisor, helps startups and growing businesses simplify operations by building integrated back-office teams that combine accounting, finance, payroll, HR, insurance, and technology. By combining experienced financial professionals with modern technology, Noah enables businesses to streamline operations, stay compliant, and focus on sustainable growth.  In this conversation, Noah introduces The Adjacent Extension Framework—Earn the Trust, Build the Relationship, Listen for Other Problems, Connect Other Specialists, and Empower the Team with Tech. He explains why proactive service creates lasting client relationships, how solving adjacent business challenges leads to sustainable growth, and why integrated back-office teams outperform disconnected vendors. Noah also shares how AI is reshaping finance operations by automating repetitive work, empowering finance professionals to focus on strategic decision-making, and helping businesses leverage technology to enhance—not replace—human expertise. — How to Outsource Your Back Office with Noah Hopton  Good day, listeners. Steve Preda here with the Management Blueprint Podcast, and my guest today is Noah Hopton, the CEO and Founder of Finvisor, helping seed and Series A companies that have outgrown spreadsheets and part-time bookkeepers but aren’t ready for a full-time finance team yet. Their job is to give you the financial clarity to make good decisions at every stage of growth. Noah, welcome to the show.  Yeah. Pleasure to be here, Steve.  Well, great to have you here, and I’m very curious about your career and your business and what you built here. I’m particularly curious about your personal ‘Why’ and how you manifest it in your business.  Personal ‘Why.’ That’s great. Well, I’ll be honest, I didn’t go in thinking I was going to be an accountant or run an accounting firm. You know, I studied accounting in school. Eventually, I thought I was going to probably be more in a kind of front-of-house sales relationship because I enjoyed the people part—making relationships and meeting people.  But I was very fortunate that I found the consulting, fractional CFO world, where I got to discover a love of problem-solving, creating relationships, and creating value for clients. For me, it was kind of this love of helping clients understand their business, helping clients understand what to think about around the corner, where it's not just being in-house with one set of books that you're closing.Share on X  When you’re at Finvisor, my day-to-day, at least when I started, was probably working with 10 to 12 clients a month and helping them understand, “Okay, how did they perform last month? Can they hire a certain number of people? And what’s the plan going forward?”  Yeah, I mean, that’s super helpful. I started life in accounting as well with KPMG, and what attracted me was to essentially have that language of business so that I would be able to understand how a business works and have this confidence of not flying blind, right? That’s really, really cool. So how did you evolve from a CFO into a founder? What was the trigger point for you?  So I was very fortunate. I actually was at a prior firm at one point when I started my career, and they were a little bit like the cobbler with bad shoes, where eventually they decided they had to close shop, and clients were going to be given notice. I, myself, was given notice saying, “Hey, in a week, you’re not going to have a job, Noah.” And so I was really given this moment in life, saying, “Hey, if I enjoy what I’ve been doing, do I think I could do it better than the firm I’d been at?  And do I want to make this leap into being a founder and starting a business?” And so my co-founder and I both talked to each other and said, “Look, we love our clients. We love what we’ve been trying to build. I think we just need to do a little bit of a refresh and restructuring of how this operates.” And so we started our own company. I was very lucky that I started with about, I had about 30 clients and a team of four on day one, which I think is unusual.  Most people in the accounting space start off as a one-person shop, trying to grow from one to two, and having to double their clients or double their size to get there. We were fortunate to have five team members and 30 clients on day one. Originally, our vision was just, “Hey, let’s help with the fractional CFO and the bookkeeping,” but that really evolved over time as we added additional services and really understood where our clients were having problems in their back office.  What are the areas where maybe the insurance brokers they’d been working with weren’t very hands-on and kind of came in once a year? Our clients were asking us, as their CFO, “Hey, can you help us select our health insurance?” And we’re like, “Well, we’re kind of doing the broker’s job. Why don’t we build out our own team?” So that was one of the first verticals we moved into and added by building an insurance brokerage. From there, we kept building, where now not only do you have your CFO and accountant helping you, but you also have them with the ability to go out to market, help you compare quotes, and help get your insurance in place. So you’re essentially expanding the array of virtual services that you’re providing, or fractional services that you’re providing, to your clients?  Correct. Yeah. We really try to own the full back office end to end because I think a lot of people deal with, “Okay, great, I have a bookkeeper, I have a tax accountant, I have an R&D tax provider,” and they’re dealing with four or five different vendors that don’t really communicate. The client is the person playing telephone between the two, and we’re like, “Wait, stop. Why is this the solution?” We should just build a different business where it’s all under the Finvisor umbrella.  It’s all full-time team members who are actually working together on behalf of the client, even if fractionally. Some of our clients only need five hours of a payroll specialist, but they need someone to own that role, and they need that person to be able to talk to their sales tax team because it’s like, “Oh, we hired someone in a new state. Is sales tax applicable there?” And connect those dots because, when you have these disconnected providers, you have a lot of things that can drop because they’re not in people’s field of view.  Yeah, I mean, it’s a great service. If you can get a competent team that will take care of your back office, then you can focus on figuring out message-market fit and then essentially scaling revenue. You don’t have to worry about it, and you don’t have to babysit inexperienced people that maybe you can afford to hire, but who would not be able to own the job.  Yeah, exactly. I mean, it’s kind of the, “Do you want to…” You know, I think at least when we started in 2014, there was more of a generalist bookkeeper. That’s kind of the typical solution people went with. Nothing against that, but it’s kind of nice to have dedicated specialists in the different back-office areas that you need. I mean, bookkeepers are great.  They’re usually not your best payroll and HR people. They’re not thinking about California final-paycheck laws, or whether you need to offer a 401(k) if you hire someone in California. Whereas, if you have someone whose entire job is payroll and HR, and you need Finvisor to help run your payroll, they’re going to be thinking about those edge cases and helping you along so that you can just build your business, get to the next milestone, and not worry about tripping yourself up because of compliance, taxes, or a lack of visibility in your reporting. Yeah, that’s great peace of mind. So this podcast is about frameworks, and I wonder, what is your framework? How do you help your clients, or how do you figure things out? What have you developed? We’re about 400 frameworks in, so I’m looking for something unique that helps you and is easy to explain—three to five steps maximum.  Yeah. I mean, one of the ones that comes to mind for us is what we’ve really called the Adjacent Extension Framework. So, first, do really good work and earn your client's trust in one area. Makes it easy for them to approach you.Share on X For us, it’s historically been accounting. People think, “Great, get my books put together.” But for us, it’s really about creating a relationship and earning the client’s trust.  Then, as step two, listen for the other problems they’re having. What are the adjacent problems they’re asking you to solve? And then for us, what we’ve really done is double down in those other areas by building specialists in those verticals. Once you’ve earned the client’s trust, if you’re doing their accounting and all of a sudden they’re struggling with invoicing or collections, you can say, “Hey, we can also help you with accounts receivable and collection efforts because we see your AR balance increasing on your financial statements.” At that point, they’re already thinking, “Great, I like working with this person.  Let’s give their team a try and help us solve another problem.” So, for us, it’s really been about finding those adjacent problems, building a team that specializes in them, and then connecting the client with the right expert. The last piece that’s really coming to market now is using technology to empower the team. Historically, a lot of our value came from having experts who could handle the edge cases or the gray areas between payroll, accounting, taxes, and sales tax. Now, with technology, you can also build the data infrastructure to highlight what’s happening for the client while helping guide the team as they manage those clients.  Love it. So what I’m hearing is, number one—or maybe even number zero—is do a great job, right?  The trust.  Okay. So that’s maybe another way of saying it: earn the trust. But is doing a good job enough to earn that trust, or is there more to it?  I mean, I think in any service business, you want to be proactive. A lot of bookkeepers, accountants, and even legal professionals are usually waiting for the client to ask a question before providing an answer. I think the goal should be to think ahead for the client and proactively provide guidance. That came naturally for us because we sit in the fractional CFO seat.Share on X  But even if you’re just doing bookkeeping, you can still catch these things for clients and help them out. Or if you’re selling P&C insurance and helping clients with their general liability coverage, you can think about what other types of coverage they may need. So I’d say the more proactive you can be, the better. The other thing is meeting clients where they already are.  For us, a lot of our clients are on Slack, so we connect with them on Slack. We chat with them as if we were full-time employees because we don’t want the experience to feel different. We don’t want you to feel like you’re emailing a generic support inbox and not knowing when someone is going to get back to you. If you only need fractional-level support, it shouldn’t feel like you’re getting fractional value or a fractional level of communication.  I love it. So you actually own the function inside the organization, so it feels like you’re part of the team, or your people are part of their team. So that builds the trust. So, do a great job, or earn the trust, number one. Number two, build the relationship. Number three, listen to other problems that they might have. Number four, connect them to other specialists. And number five, empower the team with technology.  Yeah. That’s a lot of it. I mean, as an advisor, we’ve grown… I mean, 60% of our growth comes from client referrals. So I think you know you’re doing something right if clients are recommending you to their friends and network. And so hopefully, if someone’s listening to this and you’re not getting referrals, you should be thinking about, “How do we either create more trust for our clients to be referring us, or how do we become more top of mind when clients are having these conversations?”  That’s great. So 60% of your growth comes from referrals. What’s the other 40%? How do you drive growth? What drives growth for you? What’s the other way to drive growth besides referrals?  Yeah. I mean, I think it’s also being connected with the ecosystem that you’re in. In our space, there are a lot of technology partners. Think about Xero, which is an accounting software, QuickBooks Online, NetSuite, payroll software like Rippling, Bill.com. They all have accounting partnerships, and the more you can build with them and grow your team alongside them, clients will reach out to them and say, “Hey, do you have someone who can help us set up Bill.com or help us set up Rippling?  We don’t have a payroll team to do our state tax registrations.” So we’ve seen a lot of good momentum as our software partners start sending us clients to help us grow. I think the other area is trying to figure out where you can have partnerships that will do introductions. We’ve been very fortunate in partnering with a number of VCs. Obviously, the VCs have worked with us because we’re on the board, or we had a mutual client. A lot of them will start to build partnership channels, and it’s a great opportunity.  They’ll say, “We just invested in this company, and you should go talk to Noah’s team to help with your accounting or your fractional CFO.” So it’s really about finding those tangential operators or entities that complement whatever you’re doing.  So are these primarily personal relationships that need to scale, or do you have a way to scale this across other people in your organization—this ability to develop partners? Or is it mainly you?  It depends on the role. A lot of our fractional CFOs on the team continue to build relationships. I would say probably 40% of our new clients come through a channel that’s not through me. There’ll be other people on our team who have built relationships with another VC or another software company. I think one of the key things we’ve always focused on is hiring people who are very, I would say “doers” might be the wrong word, but people who can self-manage and be project managers.  If you find the right people who can take a step back and look at the bigger picture, I mean, sometimes people come to Finvisor and they don’t realize that we ourselves are a business. Yes, you’re doing accounting like you were in-house and getting the books closed, but if you do good work and you realize clients are having problems, you have to think, “Hey, how can I help clients more and also help Finvisor create a win-win?” A lot of times, when we’re hiring, we’re trying to find people who have that type of drive to continue building and helping us internally, and not just do one part of the puzzle they’re responsible for. That might not be the most direct answer, but I would say a lot of it is hiring—making sure it's not just me leading the growth, but me building a team that can help lead the growth outside of just me.Share on X  Yeah. So how do you share the context so that your team members can connect the dots as well as you can? What’s your approach to that?  There’s a couple of ways we’ve done it. One way is we use a note-taker that then feeds into our CRM. For all client communication, whether they meet with us on Zoom or Google Meet, the transcripts are put into a centralized hub for us. It also connects to our CRM in terms of what we’re doing for the clients. At any point in time, someone can ask, “Hey, what’s going on with this client?” They can understand, “Great, this is what the payroll team talked to them about this week.  This is what the CFO team talked to them about last month. These are the problems they’ve been bringing up.” So we can capture that information without it having to be provided orally every single time, and without having to rely on a chat or an email to the team. There are some moments when it’s useful to give the team a larger update, but in general, it’s good to figure out a way to capture the essence of what you’re doing for your clients so that the team can then, in an AI chat-specific way, talk through, “Hey, great, what’s going on with this client? What are their needs? What has changed in the last six months? Who’s working on the client?”  I’ll have a VC that we’re talking to say, “Oh, we’re looking to invest in the CPG space and this type of vertical. Do you have any clients?” We’re at a point now where I don’t know every client. I usually have an idea about most clients, but there are definitely clients where I don’t know everything that’s happened in the last six months because I don’t talk to all 200 clients.  But I can go to our central hub to gain that information and understand, “Okay, great, which client is looking to fundraise and might want to be connected to this VC?” It’s a nice way to connect the dots. They’re looking to invest. The client is looking to raise. We also do brown-bag sessions. We’re a distributed team, so I think you have to be a little more intentional about how you educate the team. We’ll have weekly meetings where we walk through new technology, new changes in what we’re offering, new positioning, and continue educating the team in a more structured format.  The other thing we’ve done to help the team understand what’s going on is to make information as accessible as possible, similar to how we communicate with clients. So the team doesn’t have to log in to a pretty outdated CRM to pull information on a client. It’s either available directly in the Slack conversation or in a more modern tool like Notion, where you can easily search and find the information you want. So basically, you’re managing and harvesting your data and using that to feed people information about how they can develop partnerships. Is that what I’m hearing?  Yeah. And I think a lot of it is also figuring out which playbooks and processes are repeatable, documenting them better, and then educating the team around them. For example, with our fractional CFOs, we want to be in the board meeting. If we can be in the board meeting, A, we can help clients answer questions about their finances more easily, and B, it’s good to have visibility into what the board is saying about the business and where they want to go.  Then, obviously, the VCs are going to say, “Oh, great, this is Ian at Finvisor.” If he reaches out to me about a partnership, they’re going to have a better understanding of what we do because they’ve been in the room with us—or they’ve been in a virtual or in-person boardroom with us.  So you’re basically sharing the playbook so that they have a better understanding of what they can refer you for. Correct. Yeah.  So, switching gears here, Noah, what’s one thing that you’re trying to actively figure out in your business right now?  I mean, the question everyone is trying to figure out, at least in my space, is how they’re going to use AI in some fashion. That’s the kind of million-dollar question everyone keeps talking about—AI in accounting, AI in finance. Right now, we’re really structured in how we’re trying to use it and apply it. But the question I have is, what’s the next year going to look like? What’s five years going to look like as this technology gets more legs and more trust behind it? We’re pretty intentional about what we’re building and how we’re using some of the newer technology with AI. But I think there’s a lot that, at least for me, you have to continue to iterate. The world today feels different than it did three months ago. I’d say for most of Finvisor’s history—and this has been 12 years—it hasn’t felt like that, where a year later things might feel marginally different because we’re maybe 20% bigger or whatever might have happened.  Now, I think there’s a lot more excitement and unknown around technology and how it can either make people more efficient or help highlight and surface better issues that clients need to talk through. But I also feel like we’re in a moment where everyone’s trying to throw AI into every technology. So we're also trying to stay true to who we are, which is people first, relationships first—technology powering us, not being the solution.Share on X  So as you’re scaling AI to improve the information that your people have, your CFOs have, that presumably is going to lead to people doing less of the mechanical, repeatable tasks and more of the judgment tasks. So how do you scale judgment as you’re scaling the impact with AI?  On our side, I think it’s A, trying to organize and structure the data coming in. B, trying to create tooling that isn’t unique to one client but is built in a way that can be customized for each customer. A lot of the firms I talk to that are in the Finvisor space just take a blanket approach—turn Claude on for every fractional CFO, let them connect it to QuickBooks, and try to figure out their own playbooks.  That’s not how we’ve ever run the business. We don’t just hire accountants and let them run the accounting and see how the output turns out. We’re more focused on figuring out what is actually useful for review. Right now, I think AI has been most helpful around quality. It can definitely check that things are consistent and make sure edge cases are being caught.  I think we’re going to get to a future state where it’s not only making sure quality is at the 95th percentile of confidence, but also giving visibility into metrics like CAC, LTV, and churn—things that would normally take longer to pull together. Your fractional CFO might currently spend hours reviewing Stripe data or Shopify data to come to a conclusion. AI can cut out maybe 40% of that data-cleanup layer, where it’s like, “Okay, now they have the tools to dig in and understand what the underlying problem is,” instead of spending so much time cleaning up the data and getting everything organized.  So currently, at least my thesis is that it’s going to allow us to manage more clients because some of the day-to-day—I don’t want to call it busy work—but the work you have to do before you get to the exciting parts of the job will become more automated and less manual, like pulling data out of Stripe, Shopify, your CRM, or NetSuite.  So does that mean you’ll have a different type of people, maybe higher-level thinkers? Or do you think you can elevate your current team to that level?  Yeah. I think you’re… Sorry, I know I was originally answering this through the fractional CFO lens. Most of our fractional CFOs are already at the top of that organizational pyramid. For them, it’s really about helping them have cleaner data, better visibility into the actions they need to take, and better insight into what they should be reviewing and discussing with the client.  If I think more broadly about the back-office finance team, I do think a lot of the more generalist staff accountant and AP specialist roles won’t be spending as much time on the day-to-day blocking and tackling. If a client has 1,000 transactions a month flowing through their bank and credit cards, historically that accountant would sit in QuickBooks Online clicking “Okay, okay, okay,” reviewing every transaction and coding it. Eighty percent of those transactions will simply be coded automatically in real time as they come in. That leaves them to focus on the 20% that actually requires human judgment.  For me, the question is, can we continue to empower those people to be more impactful with that 20%? Are they the right people for that 20%? We’ve always tried to hire people who are proactive and broader thinkers, so I think we have the right team to step into that. If we’d built a traditional BPO model with an outsourced accounting team made up of people who were really just coding transactions at a basic level, I’d be more worried because getting those people to step up and handle edge cases is difficult.  But that’s not how we’ve historically built Finvisor. We’ve always tried to find people who are a little more… I’d rather hire an A-plus player than a B-player just because there’s some savings in the cost structure. I’d rather have the right people who can perform 80% of the time when they’re at bat than just hire someone because they’re cheaper.  Yeah.  Wrong baseball analogy there, but yeah.  Yeah, I understand. So you have A-plus people. Maybe the people who are doing more bookkeeping-type services—their jobs may become automated—but your A-players are going to have best-in-class information, and they can serve more clients that way.  Yeah. I still think that if you think about the typical accounting structure—if you’re working in-house and you have a bookkeeper and a controller—it’s still helpful. Depending on the size of the company, if you’re a small company, you probably won’t need that bookkeeper.  The controller can handle the edge cases and close the books. But at a certain scale, you’ll still want that junior resource supporting the controller so the controller can focus on the higher-level, more strategic work. I think people will simply be able to do more with less if they’re the right person. There will be people who, if they aren’t good at staying on their toes and figuring out edge cases, won’t be the right fit.  AI will probably replace some of those roles. But I think there’s a great opportunity for people who can think more strategically. They don’t have to be a CFO. They can just be a really smart bookkeeper who’s good at handling edge cases. They’ll simply be able to manage three times as many clients as they could when they had to code every single transaction.  Okay. If you had a magic wand and you could fix one thing in your business over the next 12 months, what would it be?  One area that we probably haven’t prioritized enough because of growth is SEO, AEO, and our overall sales build-out. Our paid advertising hasn’t been the strongest part of our business because it hasn’t been the top priority. If I had a magic wand, I’d have someone clean up our SEO and AEO visibility because I know clients love us and we do great work, but I don’t think we’re showing up the way I’d like from an SEO and AEO perspective. So that would be it. Yeah.  Yeah. Yeah. Love it. So, who are your ideal customers? Who do you want knocking on your door? Is it venture-backed companies primarily, or do you also work with private company founders? Who are your sweet-spot customers?  A lot of our clients are going to be in that 5-to-50-employee range, where they don’t need a full-time back office, a full-time accountant, a full-time CFO, or a full-time payroll specialist, but they need someone to own those roles. That way, we can put together the right Finvisor team to support them. We’ve intentionally made ourselves pretty modular, so while the largest group of our clients is in the tech VC world, we also have a lot of SMBs—law firms, beauty businesses, and other professional services businesses.  I would say that, if you looked at the Finvisor client base as a whole, you’d probably see a lot of startups. But we’re also starting to see more SMBs and more traditional businesses that don’t have VC funding but still need help with their accounting, bookkeeping, and modernizing their back office. So it’s a bit of both. Most of our clients are going to be in that 10-to-50- or 100-employee range, where they’re complex enough that they care about their financials and want to understand what they spent last month, where they’re going, and how they’re going to get there.  Earlier-stage companies are sometimes just a little too early. If you’re a one- or two-person company with just an idea, there’s a reason people think about their financials on more of a cash basis. They can think about the five clients they’re working with. Their bank balance ties pretty closely to their financials. There’s not a huge difference between the two when you’re a sole proprietor.  But as you start to evolve, that’s where Finvisor can provide more value. For all of our clients, we do accrual accounting, so we’re recognizing your revenue and your costs over the life of the service. As you start to grow and build, that’s really helpful. Obviously, if you’re at day one, it’s less impactful because you’re living more day to day, week to week, and month to month.  Steve Preda: Okay. So if we have those kinds of companies—which we do among our listeners—and they hear about this and want to fix their back office and outsource it to a reliable partner who can help them own those functions and give them good advice, what’s the best entry point? Where should they go, and how can they connect with you personally as well?  Yeah. hello@finvisor.com comes to me and the sales team. There’s probably a 95% chance you’ll talk to me if you reach out because I still love connecting with most new businesses that come through the door. The other area I wanted to call out that could be helpful for businesses is PEOs. PEOs are great, but I think at some point clients need to graduate from the PEO, and Finvisor is uniquely positioned to be both your insurance broker—helping you quote large-group plans—and your payroll and HR team to help you leave the PEO.  For a lot of our clients, once they pass that 100-employee mark, it’s like, “Great, we now qualify for a large-group plan,” which might have better rates than what they’re getting through the PEO. They just don’t have the team or bandwidth to get off the PEO. We’ll come alongside those larger companies and say, “Great, let’s quote a large-group plan for you. We’ll also put together a transition plan to register you in the 20 states where your employees are currently located.  We’ll make sure you get your workers’ compensation and employment practices liability insurance in place so there’s really no difference—apples to apples—from being in the PEO to running your own payroll.” We help with that transition because I’m always surprised to see companies with hundreds of employees still on a PEO, where the savings could be in the hundreds of thousands of dollars if they left. They just don’t have the internal team because they’ve always been on a PEO. They’ve never had to do state registrations, so they don’t know how to do them. Because of that, they’re usually not looking for an alternative path to get off that structure. We can at least review it with them and help them out if it’s a good fit. And just to remind our listeners what a PEO is, in case they don’t know.  Oh, sorry. Yeah. A PEO is a Professional Employer Organization. If you’ve heard of companies like TriNet or Justworks, they’re PEOs. In the health insurance space, there are four primary ways you can get health insurance. Most companies start with small-group plans in the early days because they’re state-mandated. For example, in California, if you’re under 100 employees, the rates my company gets would be the same rates Steve’s company gets if we’re both under 100 employees and we’re asking Blue Shield for a quote from the same ZIP code. That’s small-group insurance.  Then there’s level-funded, where carriers quote specifically based on your employee group. There’s large-group, which is somewhat similar but designed for larger organizations. Then there’s the PEO. Let’s say you’re a 10-person company. You don’t have enough employees to qualify for large-group health insurance, which is usually discounted because the risk is spread across hundreds of employees. The PEO says, “We’ll employ your team. Instead of you directly employing 10 people and buying health insurance for only those 10 people, we’ll employ your team and give you rates based on the 10,000 employees we already have.” PEOs are really popular in places like California and New York, where health insurance is very expensive.  But once you get above about 100 employees, you can usually qualify for your own large-group rates, which are similar to what the PEO is getting. The difference is that the PEO is generally marking up those rates because they need to make a margin on the plan. You can often get those rates directly yourself.  Yeah. That makes perfect sense. Okay. So if you’re listening to this and you’re building a venture-backed startup, or you’re the founder of a professional services firm, a law firm, or another small business with 10 to 100 employees, and you don’t yet have the budget—or maybe you simply don’t need—a full-time CFO, insurance advisor, HR leader, and other functional specialists, then reach out to Noah and Finvisor.  Check out what they have to offer and see what services might be a good fit for your business. Thanks, Noah, for coming on the show and sharing your expertise. It’s fascinating to see how this field is evolving, how you’re tapping into technology, and how you’re focusing on the highest-quality CFOs to help your clients. If you enjoyed this conversation, stay tuned.  Follow us on YouTube, Apple Podcasts, or wherever you get your podcasts. Make sure you don’t miss an episode. Every week, we bring you exciting entrepreneurs and their best management frameworks. Thanks for coming, Noah, and thanks for listening.  Thanks, Steve. Appreciate it. Important Links: Noah's LinkedIn Noah's  website Noah's email: hello@finvisor.com

Our guest in this episode is Kyle Hanslovan, Co-Founder and CEO of Huntress. When Kyle started Huntress in 2015, most investors believed protecting small and mid-sized businesses wasn't a venture-scale opportunity. The contracts were too small, customer churn would be too high, and the market simply wasn't attractive enough. Kyle heard “no” more than 60 times from VCs before raising his first institutional round.Ten years later, Huntress has just crossed $250M ARR, protects more than 270,000 businesses, and has become one of the defining cybersecurity companies serving the 99% of organizations that don't have dedicated security teams. Even more remarkably, Huntress achieved this growth without abandoning its original mission or moving upmarket.On Inside the Network, Kyle shares the unconventional story behind building one of cybersecurity's fastest-growing companies. We discuss why he ignored investor advice, why customer revenue mattered more than venture funding, how Huntress built a partner-first go-to-market strategy that became a lasting competitive advantage, and why serving the SMB market turned out to be a much bigger opportunity than most people realized.We also dive deep into AI and what it means to build an AI-centric cybersecurity company. Kyle explains why Huntress didn't start as an AI-native business, how years of automation and data science laid the groundwork for adopting modern AI, where large language models are genuinely changing security operations, and why he believes technical founders with deep research DNA will have a significant advantage in the AI era.Finally, Kyle shares candid lessons about leadership, founder psychology, and the personal cost of building venture-scale companies. He reflects on growing up “pretty darn broke,” what drove him to become an entrepreneur, why founders often underestimate the sacrifices required to build enduring businesses, and how he has learned to balance relentless ambition with building a company around a mission larger than himself.

Capital is rotating, the line between real estate and infrastructure is blurring, and the deals getting done today look different from those of even three years ago. In this episode of CRE Exchange, Omar Eltorai sits down with Tim Bodner, who leads PwC's US and global real estate deals practice, to discuss the firm's 2026 midyear CRE outlook. They cover the real assets convergence thesis, why operational prowess is replacing cap rate compression as the primary value driver, where REIT consolidation goes from here, and how private capital is redefining the dealmaking landscape. Key moments02:10 Midyear outlook key shifts03:35 Real assets convergence07:18 Operating prowess and AI09:07 VCs owning hard assets10:56 Where AI gets used today13:50 Industry bifurcation and new entrants18:09 REIT discounts and consolidation24:26 Private capital and capital solutions28:27 Themes for the back half of 202629:51 Policy uncertainty and data centers31:06 Lightning round and wrapResources mentionedTim Bodner - https://www.linkedin.com/in/timbodner/Navigating the capital rotation and AI-driven convergence reshaping real estate and real assets dealmaking - https://www.pwc.com/us/en/industries/financial-services/library/asset-wealth-management-real-estate-deals-outlook.html

Some of the biggest names in AI—including Nvidia, Microsoft, Meta, IBM, Palantir, OpenAI, and Google—came together this week to support open-weight AI models.In an industry defined by intense competition, that level of alignment is rare.So why are these companies pushing for broader access to AI? And why did Anthropic choose not to sign the letter?In this episode, we break down what this industry-wide initiative means for the future of AI, innovation, regulation, and the companies building the AI ecosystem.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comKey topics we explore:– What the open-weight AI letter is actually calling for– Why so many leading AI companies backed the initiative– The difference between open-source AI and open-weight AI– Why Anthropic took a different position– How open-weight models could accelerate AI adoption– What this means for Nvidia, Microsoft, Meta, Google, OpenAI, and the broader AI infrastructure ecosystemThe bigger question:Will open-weight AI create a larger, more competitive AI ecosystem, or will safety and regulation eventually limit how widely these models can be deployed?For investors, this debate isn't just about AI policy. It's about the future structure of the AI industry—and who stands to benefit as adoption continues to accelerate.LINKSPrashant Choubey - ⁠https://www.linkedin.com/in/choubeysahab⁠Subscribe to VC10X newsletter - ⁠https://vc10x.beehiiv.com⁠Subscribe on YouTube - ⁠https://youtube.com/@VC10X⁠Subscribe on Apple Podcasts - ⁠https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986⁠Subscribe on Spotify - ⁠https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQ⁠VC10X website - ⁠https://vc10x.com⁠For sponsorship queries reach out to prashantchoubey3@gmail.comThis channel is for asset managers, allocators, and investors who want analysis that holds up—not headlines dressed as insight.Subscribe for weekly data-driven breakdowns of the forces reshaping capital markets.

VCs taking on interim roles at their portfolio companies is something that's happened for a long time — as is VCs making the permanent switch to the operator side. But a new trend seems to be gaining steam in Europe: VCs adding a CEO or operator title alongside their investor role, and doing both at the same time. Judith Dada, general partner at Visionaries, for example, has made that move this month, taking a role as co-CEO at AI startup Langdock, while Carmen Alfonso Rico, who founded UK-based angel investment firm Cocoa, announced she's joining her portfolio company, British chip startup Fractile, as VP of business operations. Both are retaining investment roles at their firms.Joining host Freya Pratty to discuss the trend is senior reporter Anne Sraders. The pair chat about why these moves are happening now and whether they could bring up issues for the firms further down the line. They also discuss further changes at Visionaries, as a number of partners transition from their full-time investment positions to entrepreneurial roles. Sign up to Sifted's weekly newsletter about VC here: https://sifted.eu/newslettersThis episode was brought to you by HSBC Innovation Banking.

Lara Nuchowicz is the Principal at AR Capital, a next-gen allocator at the family office, where she manages the private markets portfolio. She started attending investment meetings with her father at fifteen, sitting across from Chase Coleman at Tiger, Steve Cohen at Point72, and Jim Simons at Renaissance. In 2017 she deployed her first capital, and today she owns sourcing, diligence, and monitoring across a portfolio of more than 80 venture funds, with the family screening around 500 managers a year. She has also launched her own fund-of-funds platform, now on its second vehicle, and hosts an invite-only family office retreat twice a year in Spain and Switzerland. Almost every manager she backs runs a fund under $100M and is under thirty.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comWe cover:→ Why there was no single moment she knew, and what actually changed in 2017→ The family philosophy: back people, back them early, stay twenty years→ Why venture doesn't scale, and what that forces you to do→ Funds vs directs, and why the two are not a trade-off→ Running 80+ funds with a three-person team→ The invite-only family office retreat, and why there's no membership fee→ Building a fund-of-funds that isn't a blind pool of capital→ How mixing directs into the vehicle answers the double-fee critique→ Why she'd rather back an emerging manager than write a check to Sequoia→ Fund size as the return lever: under $100M, ideally under $50M→ Tracking talent before thesis, and backing managers under thirty→ The consumer fund she passed on that's now at 7x MOIC and 3x DPI→ What emerging managers get wrong when they pitch family offices→ Why a family office is a resource, not a check→ The next gen's real edge, and where most of them go wrongLinks:Connect with Lara: https://www.linkedin.com/in/lara-nuchowicz/Connect with Prashant: https://linkedin.com/in/choubeysahabVC10X newsletter - https://vc10x.beehiiv.comYouTube - https://youtube.com/@VC10XApple Podcasts - https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986Spotify - https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3kVC10X website - https://vc10x.comTimestamps:(00:00) - Why emerging managers are a better fit than brand-name funds like Sequoia.(00:45) - Common mistakes emerging managers make when approaching family offices.(01:02) - Introduction to the episode and guest, Lara Nuchowicz.(02:28) - Lara's gradual journey into the family's investment business.(04:42) - The family's core investment philosophy of backing people early.(06:51) - Balancing fund investments vs. direct investments in companies.(09:33) - Lara's role and autonomy within the family office.(10:45) - The exclusive, invite-only family office retreat Lara hosts.(13:29) - Managing a lean team and internal processes at a family office.(14:55) - Launching a fund of funds platform with a family office mindset.(18:23) - Addressing critiques of the fund of funds model like double fees and liquidity cycles.(21:48) - How family offices handle tax implications in venture investing.(24:10) - The strategic reasons for backing emerging managers over established brand-name funds.(27:14) - Investment Strategy: Prioritizing talent over a specific thesis.(29:49) - The right and wrong ways for emerging managers to approach family offices.(32:43) - The story of a fund she passed on that returned 7X and the lesson learned.(34:41) - The evolving role of the next generation in family office investing.(37:17) - Lara's approach to raising her child in a family of investors.(41:04) - Final advice for emerging managers on building long-term relationships.(42:43) - Start of the Rapid Fire round.(43:02) - Investment focus: Regions and sectors.(43:21) - Typical check size for fund investments.(43:38) - Ideal investment stage for fund managers.(44:06) - How to connect with Lara.

Small businesses represent nearly half of all American jobs and 45% of all technology spend, yet less than 5% of venture capital goes to building technology for them.In this episode, Tim Metzner joins us to share how Fireroad, his Cincinnati-based early-stage venture firm, is betting that AI is changing that math. A serial entrepreneur who co-founded Coterie Insurance ($70M+ raised) and Differential (the studio behind Cincinnati's first unicorn, Astronomer), Tim returns to the show four years after his Episode 190 appearance with an entirely new chapter.We dig into the "silver tsunami" of retiring business owners with no succession plan, why Fireroad targets AI-resistant categories where technology supercharges rather than replaces, the flywheel of having business owners as LPs who become his founders' first customers, and why Tim believes staying small as a fund is the alpha most VCs are missing. Hosted by Logan JonesMiddle Tech is proudly supported by:KY Innovation → kyinnovation.comAwesome Inc → awesomeinc.org

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

Everyone Has the Same AI Tools Now. So where's the Moat?In this episode of Tank Talks, host Matt Cohen sits down with Jacob Jackson, partner at Julian Capital and founder of Deep Checks. A physicist turned SaaS founder turned deep tech investor, Jacob brings a unique perspective on the shifting landscape of venture capital. He shares his personal journey from developing MRI techniques at UBC to founding MedStack, and why he made the full-circle pivot back to backing companies building real, hard technology.Jacob offers a frank assessment of the defensibility crisis in software, explains why AI is democratizing superpowers across every industry, and makes the case that deep tech offers faster exits and higher unicorn density than its reputation suggests. He also breaks down how Julian Capital supports founders with growth and go-to-market muscle, and why the team behind the idea matters more than ever in today's market.Whether you're a founder building hardware, an investor looking for the next generation of venture returns, or just trying to understand where technology is headed, Jacob Jackson delivers the kind of straight talk that cuts through the hype.Why Software's Moat Is Gone (04:00)* Jacob's firsthand experience building MedStack, where Amazon, Google, and Azure built competing products almost overnight* Why the defensibility crisis in SaaS is making it harder than ever for new companies to get off the ground* The shift from competing on product to competing on capital and go-to-marketRedefining Deep Tech: What Everyone Gets Wrong (06:44)* The biggest misconception: that deep tech requires 20-year fund cycles* How SpaceX-style private market liquidity proves milestone-based exits work* Why the hottest sector of the year is never where the biggest company gets seededHow AI Accelerates Hardware Development (08:23)* AI as a democratizing force that makes everything faster and more competitive* From CAD design to patent strategy: how AI is becoming a founder's essential tool* The commoditization of vertical robotics and what it means for defensibilityBuilding Moat in Deep Tech: Talent, Networks, and Network Effects (10:30)* Why network effects and talent density matter more than patents alone* How to pull the “ladder up” behind you as you scale* Incumbents vs. upstarts: why everyone has access to the same tools nowThe Rise of Deep Checks (18:01)* How Deep Checks became the world's largest network of deep tech founders and VCs* The platform that helps founders run a tight, accelerated fundraising process* Over $100 million deployed and 5,000+ founders submitted in just two yearsWhat Jacob Looks for in Founders (24:50)* The 50/50 split between team and idea* Why obsession and speed matter more than business experience* How to spot the founders who will learn to sell, recruit, and negotiateFrontier Tech vs. Deep Tech (29:20)* Why Julian Capital backs “picks and shovels” companies in spaces like fusion and quantum* The importance of market pull and meaningful near-term milestones* Why a 20-year fund cycle makes financial math nearly impossibleThe Data That Surprised Everyone (32:58)* Deep tech exits are 25% faster with more unicorns per dollar invested* Why historical data shows deep tech outperforming software* The role of capital flooding and dilution in venture returnsAbout Jacob JacksonJacob Jackson is a partner at Julian Capital and the founder of Deep Checks, the world's largest network of deep tech founders and VCs. A physicist by training, Jacob pivoted from academic research to founding MedStack, a privacy and security SaaS platform, before returning to his engineering roots as an investor. He now backs founders building hardware and tackling the world's hardest problems. Jacob is known for his data-driven approach, obsession with team quality, and candid takes on the future of venture capital.Connect with Jacob Jackson on LinkedIn: https://www.linkedin.com/in/jonlovekingsett?originalSubdomain=caVisit Julian Capital's website: https://www.julian.capital/Submit to Deep Checks: https://www.deepchecks.vc/Connect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Google's latest earnings weren't just about another strong quarter.They offered one of the clearest signals yet on the health of the AI investment cycle.From resilient Search revenue to continued Cloud growth and massive AI infrastructure spending, the results suggest that AI is becoming deeply embedded across Google's business—not just as a product, but as a platform.In this episode, we break down what Google's earnings mean for the broader AI thesis and why investors across the semiconductor, cloud, and infrastructure ecosystem should be paying attention.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comKey topics we explore:– What Google's earnings reveal about AI adoption– Why Search remains more resilient than many expected– How Gemini and AI are being integrated across Google's products– Why Google Cloud remains a key beneficiary of enterprise AI– What continued AI infrastructure spending means for Nvidia, TSMC, ASML, Micron, and the broader supply chain– The key metrics investors should watch over the coming quartersThe bigger question:Are Google's results evidence that AI is already generating real business value, or is the market still too optimistic about long-term returns?For investors, Google's earnings provide another important data point that the AI infrastructure buildout—and enterprise adoption of AI—remains firmly on track.LINKSPrashant Choubey - ⁠https://www.linkedin.com/in/choubeysahab⁠Subscribe to VC10X newsletter - ⁠https://vc10x.beehiiv.com⁠Subscribe on YouTube - ⁠https://youtube.com/@VC10X⁠Subscribe on Apple Podcasts - ⁠https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986⁠Subscribe on Spotify - ⁠https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQ⁠VC10X website - ⁠https://vc10x.com⁠For sponsorship queries reach out to prashantchoubey3@gmail.comThis channel is for asset managers, allocators, and investors who want analysis that holds up—not headlines dressed as insight.Subscribe for weekly data-driven breakdowns of the forces reshaping capital markets.#Google #Alphabet #AI #ArtificialIntelligence #Gemini #GoogleCloud #Nvidia #ASML #TSMC #Micron #Semiconductors #Investing #TechStocks #VC10X #Finance #CloudComputing #BigTech #Earnings #WallStreet #Markets

Less than 1% of U.S. businesses that ever raise outside money end up raising venture capital, and most founders chasing it don't actually need it.In this entrepreneur interview, Jeff Amerine — a venture capital investor and longtime advisor in the startup ecosystem — breaks down the real landscape of startup funding: when to bootstrap, when to raise, and why most companies burn time chasing the wrong kind of capital. He and co-hosts Daniel Koonce and Grace Gill also dig into what VCs actually look for, the most common fundraising mistakes founders make, and the difference between convertible notes and SAFEs.Whether you're bootstrapping your first idea or getting ready to pitch investors, this conversation will change how you think about who to raise from and when.What you'll learn:The three main ways founders fund a business and how to know which one fitsWhy raising venture capital has only gotten harder, and what VCs are really evaluatingThe most common fundraising mistakes founders make (and how to avoid them)Convertible notes vs. SAFEs, explained in plain EnglishWhat a strategic investor actually offers and when it's worth taking their money⏱️Chapters 1:26 Welcome to the Startup Junkies Podcast 2:03 The Three Ways to Fund a Small Business 3:43 What Is Bootstrapping? 5:33 Does Venture Capital Only Fund Tech Companies? 6:46 The Truth: Less Than 1% of Businesses Raise VC 7:33 When Is a Founder Ready to Raise Money? 9:59 Why Raising Venture Capital Has Gotten Harder 11:15 Inside a VC Firm: From 1,000 Deals to 3 or 4 12:43 How to Actually Find Investors 14:19 Common Fundraising Mistakes Founders Make 16:43 Convertible Notes vs. SAFEs, Explained 21:27 Why Startups Actually Need Money 23:35 What Are Strategic Investors — And Are They Worth It? 27:16 The Deal Jeff Wishes He'd Invested In

On this episode of Run the Numbers, CJ sits down with Dave McClure and Aman Verjee of Practical VC to talk about the evolution of startup investing — from the PayPal mafia and Founders Fund era to 500 Startups, YC-style scale, and today's secondary market.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjMaximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer cut their close in half, took audit findings from seven to zero, and cut back-office costs by 70% in six months. You pay for outcomes, not seats. See it at https://www.maximor.ai/Brex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAnrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtnRightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comPulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetrics—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuests:https://www.linkedin.com/in/davemcclure/https://www.linkedin.com/in/aman-verjee/Company:https://practicalvc.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and Intro3:00 Writing as a distribution strategy5:17 How Dave's blog led to Founders Fund7:24 Aman: writing at PayPal and law school9:29 PayPal: the red pen of David Sacks11:44 Sponsors — Rillet | Maximor | Brex14:59 500 Startups: the original thesis16:22 The volume strategy: more shots on goal18:44 Twilio, Lyft, Sendgrid, Credit Karma20:15 60x returns: right place, right time23:07 Sponsors — Anrok | RightRev | Pulley25:58 Accelerator ecosystem evolution28:42 500 vs. YC: scale as a weapon32:52 Globalizing the accelerator model35:04 Seed to secondaries: how it happened37:00 Scratching their own itch for liquidity38:10 The secondary market explained40:50 Types of secondaries43:29 Why would a VC sell a winner?46:07 Managing DPI as a late-stage manager47:02 The five horse framework49:15 Skipping the J curve51:44 Imperfect information as a feature55:22 Landmines: fraud, mismarked valuations57:00 VCs lie about valuations three ways57:58 Forward contracts and counterparty risk1:00:29 Credits

Most founders treat fundraising like a necessary evil — something to endure, survive, and move past so they can get back to building. Jason Fishman, founder and CEO of Digital Niche Agency, has spent a decade proving that's exactly backwards.With over 500 deals under his belt and campaigns that have collectively generated hundreds of millions in revenue and capital raised, Jason breaks down how modern founders are using regulated investment crowdfunding (Reg CF, Reg A+, and Reg D) not just to fill their bank accounts, but to build armies of shareholders, brand advocates, and strategic partners. The real asset isn't the money — it's the 20,000 investors who now want you to win and will tell everyone they know.This episode is a masterclass in treating your capital raise as a full-blown marketing campaign.Key Takeaways4:14 — How Jason discovered fundraising is a marketing exercise. Working at a social gaming company in LA, he created 75 versions of a pitch deck and saw firsthand the inefficiencies — and the upside — of a well-executed raise.7:01 — Why the warm-intro VC mindset is outdated. The traditional approach limits founders to who they know. Reg CF and Reg A+ let you target anyone — including non-accredited investors — and build a shareholder base of tens of thousands.8:48 — The planning principle most founders ignore. If you need funds in a year, start today. Fundraising isn't a sprint — it requires seeding relationships and building infrastructure long before you launch.10:48 — The traffic math behind a successful Reg CF campaign. You need 50,000–100,000 visits to an offering page to generate ~1,000 investments. Understanding digital marketing metrics — not just dollars raised — is the real measure of momentum.14:37 — Reg D vs. Reg CF vs. Reg A+ — how to choose. A clear breakdown of all three exemptions: who can invest, minimum investment levels, filing complexity, timelines, and when each makes sense for your stage.22:05 — How crowdfunding creates negotiating leverage with VCs. A graphene-industry client hit their full $5M Reg CF raise in 43 days — then used that crowd as a "waiting list" to walk away from unfavorable VC terms.23:52 — Which industries work best for community-driven raises. It's not just consumer brands. B2B companies, biotech, modular homes, and AI companies are all succeeding — what matters is a compelling market narrative.28:34 — Storytelling is the real conversion lever. The 3-1-3 method: break your pitch into 3 sentences, compress to 1 sentence, then distill to 3 words. If someone can't repeat your idea at a coffee shop, they won't invest or refer.36:55 — The #1 mistake founders make when marketing a raise. Not starting early enough — and assuming the offering page will do the work. The top 10% of deals get the majority of investments; the bottom 50% do no marketing at all.40:28 — The right vs. wrong way to use AI in your raise marketing. AI slop is rampant. The rule: don't use it unless it's better than human. AI accelerates experts — it doesn't replace them.42:47 — The future of capital formation is large crowds, fast. Prediction markets, digital communities, and A-list endorsements point toward a world where raises fill overnight — whoever builds the audience first wins.Tweetable Quotes"Twenty thousand investors isn't twenty thousand line items on a cap table. It's twenty thousand people who now want you to win — and who will tell everyone they know." — Jason Fishman"The community, the audience, is actually the most valuable part. It is a marketing exercise well beyond the funds raised." — Jason Fishman"If you fail to plan, you plan to fail. Look at fundraising as already accomplished — then figure out the steps to get there." — Jason Fishman"The fewer words used, the better. People need to be able to understand what you do so well that they feel comfortable explaining it to someone else." — Jason Fishman"AI slop is far too prevalent. Don't use it unless it's better than human — for any software, any tool, any AI." — Jason Fishman"If I scroll your offering page and the headlines don't sell me, I'm gone. It could be the most amazing company ever — but the storytelling sold you short." — Jason Fishman"Raise money like you're building a following, not begging for a bailout." — Jeff MainsSaaS Leadership Lessons1. Your investors are your first growth channel — treat them that way. The companies winning with community raises aren't just collecting capital. They're recruiting advocates. Every shareholder is a potential referral source, customer, and word-of-mouth engine. Build your raise strategy like a customer acquisition funnel, not a one-time event.2. Plan your raise 12 months before you need the money. Fundraising has a long cycle — regulatory filings, audience warming, relationship seeding. Founders who wait until they need capital have already lost the game. Start building your investor community before your runway demands it.3. The offering page is your highest-stakes landing page. Optimize it like one. You need 50,000–100,000 visits to generate ~1,000 investments. Apply e-commerce conversion thinking: glance test, bold headlines, social proof above the fold, and clear immediacy (time-limited share prices, investment bonuses). If the headlines don't convert, the product never gets a chance.4. Simplicity isn't dumbing down — it's the highest form of clarity. Use the 3-1-3 method: 3 sentences → 1 sentence → 3 words. If your investor can explain your company at a dinner table, they will. If they can't, they won't invest — and they definitely won't refer anyone. Complexity kills conversion.5. Crowdfunding creates leverage — don't give it away too early. A crowd of investors is a negotiating asset. Founders with a demonstrated ability to raise from retail investors can walk away from unfavorable VC terms and return to the crowd. This only works if you've built the infrastructure before you need the leverage.6. Diversify your capital-raise strategy the same way you diversify your marketing stack. Don't put all your eggs in one basket — not in SEO, not in one broker-dealer, not in one VC relationship. The founders who succeed build multiple traffic sources, multiple audience touchpoints, and multiple investor pipelines working simultaneously. One channel is fragility. Multiple channels are momentum.Guest Resourcesjfishman@digitalnicheagency.comdigitalnicheagency.comhttps://www.linkedin.com/in/jafishman/Episode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/https://jeffmains.com/books/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

What separates the investors and founders who thrive in moments of radical change from those who don't? According to Alec Litowitz, it isn't intelligence or emotional maturity - it's adaptability.Alec is the founder of Magnetar Capital, one of the most respected multi-strategy hedge funds in the world, and the founder and managing partner of QStar Capital, his single family office and investment platform. Over a 30-year career that began at J.P. Morgan, continued as a founding partner and global head of equities at Citadel, and culminated in building Magnetar from scratch, Alec developed a framework he calls the Adaptability Quotient - AQ - for making decisions under genuine uncertainty. His book, The Adaptability Quotient, publishes September 15th.Today, through QStar, Alec invests with no fund mandate and no LP constraints - thematically across both public and private markets, in everything from CoreWeave and SpaceX to top-tier VC and PE managers. That unconstrained vantage point, combined with three decades of pattern recognition across market regimes, gives him a distinctive lens on where venture capital sits inside the current moment of change.Nick and Beezer dig into the core distinction Alec draws between risk and uncertainty - a difference he argues most investors collapse at their peril - and how the AQ framework maps directly onto how founders build, how VCs back them, and how the venture ecosystem itself needs to adapt. They also get into what he calls the second cognitive revolution: why AI isn't just a new tool but a system-level regime change, what that means for the capital stack and liquidity timelines in venture, and why the answer for smaller players isn't resistance - it's remapping.Quotes"What entrepreneurs get paid for is not risk. They get paid for uncertainty, for resolving the uncertainty. People may stay at some stranger's house or they may not, but I don't know the probability. If it's high, I have a business. If it's zero, I don't have a business. Let's go resolve that probability. And when someone does a startup and tests it, raises money, probes around it, and gets feedback loops - the answer is yes. That's what they get paid for, for resolving that uncertainty."Time Stamps00:00 What Entrepreneurs Actually Get Paid For00:31 Introducing Alec Litowitz: Citadel, Magnetar, and QStar02:49 Three Career Chapters and the Through Line: A Systematic Approach to Uncertainty06:09 The Book: Why Alec Wrote The Adaptability Quotient07:29 AQ Defined: Why IQ and EQ Aren't Enough When the Frame Itself Changes9:40 Why QStar: No Constraints, No Mandates, Just Mapping the Moment12:22 QStar's Investment Framework: Thematic, Top-Down, Technocentric and Anthrocentric14:51 Why Venture Still Matters: The Venture 20, the Mag Seven, and Where Disruption Lives17:03 Risk vs. Uncertainty vs. Black Swan: The Framework Most Investors Get Wrong22:30 Applying AQ in Venture: MVPs as Probes, Pivots as Feedback Loops22:51 A Case Study in Failing Without Feedback Loops24:33 The Second Cognitive Revolution: Why AI Is a Regime Change, Not a Tool29:20 Is SaaS Uninvestable? What Becomes Abundant and What Becomes Scarce32:53 Mapping the Venture Ecosystem: Capital Intensity, New Entrants, and IRR Pressure37:08 The Liquidity Problem Reframed: DPI, TDPI, and Timeline Mismatch40:12 Secondary Markets as a Structural Response43:48 Final Advice: Upgrade Your Operating SystemLinksConnect with the guest and hosts on LinkedIn!Alec LitowitzBeezer ClarksonNick ChirlsLearn more about:The Adaptability Quotient (pre-order on Amazon)QStar CapitalMagnetar Capital⁠⁠Early Adapters Newsletter⁠⁠Asylum Ventures⁠⁠OpenLP

In this GrowNLearn episode, host Zorina Dimitrova speaks with Travis D. Hahler, Senior Director of Global Strategy & Transformation at Salesforce, founder of The Neurological Nomad, and author of Rethink Resistance: Embracing Neuroscience to Lead Transformational Change. Why do so many well-funded, well-communicated transformation initiatives still fail? Travis brings a neuroscience-informed perspective to one of the biggest challenges in business today: why people resist change, why AI transformation creates fear and uncertainty, and how leaders can stop treating resistance as opposition — and start reading it as useful information. The conversation explores why 70–80% of transformation efforts fail, how the brain interprets change as loss, why AI adoption must be handled as a group experience rather than an individual survival test, and how executives can reduce anxiety without lowering ambition. You'll learn: • Why traditional change management frameworks often miss the human layer • How AI transformation changes the way leaders should support adoption • Why “all change equals loss” — and what leaders should do with that insight • How exclusion, uncertainty, competence loss, control loss, and relationship loss shape resistance • Why resistance is not something to fight, but something to investigate • How leaders can distinguish neurological resistance from real strategic or technical blockers • Why successful transformation depends on adoption, not just implementation • How executives can think about the ROI of change through the cost of failed adoption This episode is especially relevant for founders, executives, transformation leaders, HR leaders, change managers, consultants, and investors evaluating whether a company can actually execute its growth strategy. Guest: Travis D. Hahler Website: https://travishahler.com/ Book: https://travishahler.com/book/ LinkedIn: https://www.linkedin.com/in/travisdhahler Instagram: https://www.instagram.com/theneurologicalnomad/ Host: Zorina Dimitrova GrowNLearn / Grownlearn — Strategic Growth & Capital Advisory Website: https://grownlearn.org/ GrowNLearn, led by Zorina Dimitrova, connects select VCs, Family Offices, and Strategic Investors with precisely matched high-growth ventures across Europe and the U.S. We also support founders with strategic growth advisory — helping them transform their business model, increase valuation, and prepare for investment or exit. Listen to GrowNLearn Apple Podcasts: https://podcasts.apple.com/at/podcast/grownlearn/id1515759956 Spotify: https://open.spotify.com/show/1bUIDGbSQl4BlHeNeeJfva SoundCloud: https://soundcloud.com/grownlearn All platforms: https://grownlearn.onpodium.com/ Follow GrowNLearn LinkedIn: https://www.linkedin.com/company/grownlearn X / Twitter: https://x.com/grownlearn1729 TikTok: https://www.tiktok.com/@healnlearn_grownlearn Instagram: https://www.instagram.com/healnlearn_grownlearn/ Business Model Transformation Playlist https://www.youtube.com/playlist?list=PLTJlaeHk03J0lwEsQYIZ1wpaqJrxJYa28 Tools for Investors & Founders Tax Optimization for Real Estate Investors: https://mavencostseg.referralrock.com/l/ZORINA/ FREE LinkedIn Profile Audit: https://ps.linkedvanow.com/r5wgf AI-Powered Lead Generation for Dealmakers: https://ps.linkedvanow.com/tjnia9mrkt85

This Week In Startups is made possible by: Digital Ocean - do.co/twist Agree.com - agree.com Every.io - every.io.   Today's show: How many startups matter in tech? Fewer than you think. That's why venture capitalists are tripping over themselves to get onto their cap tables, no matter the cost. Why? Footwork's Nikhil Basu Trivedi argues that the Valley has never been more "power-law-pilled" than it is today. Basu Trivedi joined Cendana Capital's Michael Kim and TWiST's Alex Wilhelm to go deep on secondary markets, the state of startup M&A, why the SaaSpocalypse may be temporary, and what could trigger a retrenchment of the AI trade. It's Wednesday, so it's time for our venture capital roundtable to go deep on how VCs are investing today, and where on the horizon they have their eyes fixed! Guest links: Nikhil Basu Trivedi https://x.com/nbt Footwork https://www.footwork.vc/ Michael Kim https://x.com/MKRocks Cendana Capital https://www.cendanacapital.com/ Show links: The USVC-Anduril blowup https://x.com/ankurnagpal/status/2072701195714531398 Kline Hill Cendana Partners https://www.secondariesinvestor.com/kline-hill-and-cendana-raise-400m-for-second-vc-secondaries-fund/ GPTZero's exit https://gptzero.me/news/preserving-whats-human/ Salesforce buys Fin https://www.salesforce.com/news/press-releases/2026/06/15/salesforce-signs-definitive-agreement-to-acquire-fin/ Vercel buys Better Auth https://vercel.com/blog/vercel-acquires-better-auth Figma buys Bud https://techcrunch.com/2026/07/07/figma-acquires-team-behind-a-vibe-coding-app/ Protoge https://withprotege.ai/ Windborne https://windbornesystems.com/ Etched https://www.etched.com/ Lovable's reported raise https://sifted.eu/articles/lovable-300m-13-2bn-valuation Josh Browder https://x.com/Joshuabrowder   Timestamps: 0:00 Introduction: Nikhil Basu Trivedi (Footwork) & Michael Kim (Cendana Capital) 1:59 The Anduril vs. USVC secondary market blowup 4:08 Why Silicon Valley is 'power-law-pilled' 8:23 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off! 9:37 Information asymmetry in the secondary markets 9:45 Every.io — For all of your incorporation, banking, payroll, benefits, accounting, taxes or other back-office administration needs, visit https://every.io 15:02 Is SPV fraud smoke or fire? 16:32 Superhuman acquires GPTZero 19:54 Agree.com - Stop chasing invoices and automate your entire contract-to-cash stack. Go to https://agree.com and tell them Jason sent you to get 50% off for life! 21:10 The M&A wave 27:19 The SaaSpocalypse debate 29:59 DigitalOcean - Head to https://do.co/twist to start building on DigitalOcean's AI-Native Cloud today — and cut your AI workload costs by up to 50%. 30:44 Data's moment in the energy → compute → data loop 35:01 Where will AI value accrue? 40:04 What could cause an AI correction? 42:17 Why some companies are "too big to miss" 46:23 China's possible open-weight model ban 53:28 Young founders: Etched, Thiel Fellows, Z Fellows, Neo 55:33 Portfolio spotlight: WindBorne's weather balloons and data moat 58:47 Michael's favorite fund manager: Josh Browder Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp   Follow Lon: X: https://x.com/lons   Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm   Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis   Check out all our partner offers: https://partners.launch.co/   Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland   Check out Jason's suite of newsletters: https://substack.com/@calacanis   Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

In this episode of Business Brain, you’ll rethink everything you assumed about failure. Shannon and Dave dig into why America’s culture of embracing failure, from bankruptcy protection to at-will employment, fuels entrepreneurship in a way most other countries can’t match. You’ll hear how low switching costs mean low experimentation costs, why VCs invest in founders instead of ideas, and why a business failure here doesn’t follow you for life the way it does elsewhere. The big idea: failure isn’t a punishment, it’s tuition, and outliving it is what keeps your charmed life on track. Then the conversation takes a sharp turn into the awkward economics of small talk. You’ll laugh at Dave’s confession that he’d pay extra for a silent haircut, and you’ll dig into the introvert-extrovert divide, the rise of silent salons, and even a pitch-black Vegas restaurant where waiters wear night vision goggles. Whether you’re the type who bonds with your barber over rental properties or the type who just wants to sit in silence, you’ll walk away with a sharper sense of how comfort, connection, and customer experience shape the businesses you build and support. 00:00:00 Business Brain – The Entrepreneurs' Podcast #768 for Wednesday, July 8, 2026 July 8th: National Blueberry Day 00:02:17 How we perceive failure in the USA We are surrounded by opportunities Mistakes are tuition Bankruptcy and business failures can be outlived Failure is baked into venture capital business The power of at-will employment The pivot is revered 00:12:28 SPONSOR: Shopify: Stop waiting for permission to build something. Your next revenue stream starts free at Shopify.com/BusinessBrain. 00:13:53 SPONSOR: Bitdefender. Keep your small business safe with Bitdefender Ultimate Small Business Security. Save 30% when you go to https://bitdefender.com/BRAIN 00:15:25 Can I pay extra for the silent haircut? 00:19:55 Blackout, the dark restaurant in Las Vegas 00:23:05 Business Brain 768 Outtro This Episode's Big Takeway: Business Failures Can Be Outlived Check out Business Brain Blueprints Tell Your Friends! Business Blueprints Review Business Brain Subscribe to the show feedback@businessbrain.show Call/Text: (567) 274-6977 X/Twitter: @ShannonJean & @DaveHamilton, & @BizBrainShow LinkedIn: Shannon Jean, Dave Hamilton, & Business Brain Facebook: Dave Hamilton, Shannon Jean, & Business Brain The post What Have You Failed at This Week? Business Brain 768 appeared first on Business Brain - The Entrepreneurs' Podcast.

What if everything you've been taught about investing is missing the most important distinction? In this episode of WholeCEO with Lisa G., Lisa sits down with investor and entrepreneur Joseph Argiro to unpack the hidden dynamics shaping today's private investment markets—and why many investors don't realize the risks until it's too late. They explore the critical difference between exposure and ownership, why that distinction can determine whether you build lasting wealth or simply take on risk, and how misunderstandings around it continue to cost investors. Joseph also shares his candid perspective on the crypto venture capital landscape, explaining why he believes many crypto VCs operate more like predatory hedge funds than long-term partners—and what that reveals about the growing trust problem in private markets. Finally, he tells the story behind Fish Network, the platform he is building to democratize access to private investments, giving everyday investors opportunities that have traditionally been reserved for insiders. In this episode, you'll discover: Why exposure and ownership are not the same—and why the difference matters. The trust crisis affecting private markets and crypto investing. How Fish Network aims to make private investing more transparent and accessible. What true democratization of private investing could mean for the average investor. If you're an investor, founder, CEO, or simply curious about where private markets are headed, this conversation will challenge conventional thinking and offer a fresh perspective on the future of investing.

Most founders treat product-market fit like a feeling. Mark Roberge thinks that's as absurd as calling profit a feeling.The founding CRO of HubSpot, Harvard Business School lecturer, and Stage 2 Capital co-founder joins Josh to break down his new book, The Science of Scaling. The argument at the center of it - the decision of when and how fast to scale shouldn't be a gut call. It should be gated on retention.Mark shares the leading indicator of retention (the one metric that predicts churn in a customer's first month), the 5-50-500 playbook a Microsoft leader used to launch new products, why Drift's founder flew across the country to onboard $50/month customers, and what "AI-native sales team" should actually mean in 2026 (with numbers attached).If you work in Customer Success, this episode makes the case that you own the most strategic metric in the company. All proceeds from Mark's book go to McLean Hospital for mental health care.---Want the playbook, not just the conversation? Subscribe for deep-dive, actionable breakdowns from every episode at unchurned.substack.com.---What You'll Learn- Why we scale haphazardly, not scientifically- A quantitative definition of product-market fit - How to design a leading indicator of retention- Real LIR examples from Slack, Harvey, Facebook, and Gainsight - The three maturity levels of an LIR- How a Microsoft leader used the 5-50-500 rep model to de-risk new product launches- How to pick your threshold based on the blitzscale risk in your category- Why product-market fit and go-to-market fit must be sequenced, not pursued at once- How to know when to move past founder-led sales - How to bring unit economics into board meetings - Why blitzscaling fails more companies than it saves (and why VCs push it anyway)- The two metrics that define an AI-native sales team in 2026- Mark's four phases of the AI revolution in go-to-market ---Timestamps0:00 - Preview & Intro1:30 - Meet Mark Roberge3:07 - The Science of Scaling (All book proceeds go to McLean Hospital)7:30 - We scale haphazardly, not scientifically9:06 - Microsoft's 5-50-500 playbook13:19 - Is product-market fit a feeling?15:48 - The leading indicator of retention, explained17:30 - Time to value vs. recurring value19:42 - Designing your own LIR24:04 - Why go-to-market fit comes after PMF26:06 - Pitching this framework to VCs28:15 - How to know when you have go-to-market fit31:15 - Bringing the science to larger companies33:00 - When to move beyond founder-led sales35:51 - AI and the four phases of the GTM revolution37:20 - What "AI-native sales team" means in 2026---Josh is writing a book on building customer relationships. Follow his journey and insights at www.joshschachter.com---Where to Find the GuestMark Roberge: https://www.linkedin.com/in/markroberge/The Science of Scaling: https://a.co/d/0boDpDUcStage 2 Capital: https://www.stage2.capital/---Where to Find the Host: Josh's LinkedIn: https://www.linkedin.com/in/jschachter/Unchurned Substack: https://unchurned.substack.com/

Unlocking billions in cloud marketplace revenue. Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ This powerful panel discussion featuring leaders from Google, Tackle, and dbt Labs dives deep into the explosive growth of cloud marketplaces and the radical shift toward AI-driven go-to-market strategies. With hyperscaler backlogs nearing half a trillion dollars, the conversation unpacks how top-tier organizations are transforming their compensation models, aligning executive buy-in, and navigating the complexities of co-selling to capture committed customer budgets. From the rise of AI agents acting as metered SaaS to the essential operational investments required to scale marketplace revenue from 10% to over 50%, this session provides an actionable roadmap for software companies ready to dominate the 2026 partner ecosystem. https://youtu.be/LSj49f5FEII Key Takeaways Hyperscaler backlog commitments represent a massive, nearly half-trillion-dollar addressable market that completely changes the budgeting conversation. Successful marketplace selling requires complete executive alignment, right down to the CFO, and strategic adjustments like spiffing sales teams for marketplace transactions. The AI category is experiencing staggering 18x year-over-year growth, forcing companies to pivot toward an “agent-first” go-to-market model. Shifting from traditional channels to cloud go-to-market demands a multi-year, intentional investment in operations, people, and technology. System integrators are evolving into software companies as they build orchestration agents to manage fragmented, end-to-end workflows. Leveraging cloud commitments bypasses standard 12-15 month budget cycles, allowing for significantly faster deal closures and larger initial lands. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags: Google Cloud Marketplace, hyperscaler backlog, cloud commitments, co-selling strategies, AI agents, metered SaaS, product-led growth, rev ops, B2B sales transformation, ecosystem shift, channel strategy, system integrators, Deal registration, private offer APIs, digital transformation, software procurement. Transcript: Insight to Revenue- The State of Cloud GTM [00:00:00] Dai Vu: These are all things everyone has to do to get to that first five to 10 deals, and then 10, 20, 30% of your business through Marketplace. [00:00:09] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way Hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:21] Vince Menzione: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi, own your host, and each week I sit down with leaders at the intersection of technology. Partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:43] Vince Menzione: It is the strategy because being in the room changes [00:00:46] John Janke: everything. Let’s start. [00:00:52] Vince Menzione: And we have an incredible session. The way that we wanted today to, to, to start the day up was like, let’s talk about what’s happening right now and let’s get three leaders in this space to come up and talk about the world and how it’s a rapidly evolving. So I want to invite to the stage dvu from Google is a great friend of Ultimate Partner. [00:01:14] Vince Menzione: Are you guys ready? Are you guys micd up already? Okay, good. Good. John Yanke, the CEO and Founder of Tackle, and Sean Todo, who is an incredible leader with DBT, but also an old friend of mine. We worked together on Microsoft Days. Good to see you gentlemen. Thanks Sean. Great to have you with us. [00:01:37] John Janke: They stuck me on the side ’cause they said I’d block the screen if I sat in the middle. [00:01:41] Shawn Toldo: You still block it a little bit. [00:01:42] John Janke: And that picture’s from like 1985. I, I, we do have to get that. I had way darker hair. It was, uh, 10 year, 10 years at a startup. Makes you turn white. [00:01:52] Shawn Toldo: Mine’s the exact same right now. So it’s all good. [00:01:55] Shawn Toldo: Mine’s AI generated. Yeah. [00:01:57] Vince Menzione: Well, you know, guys, I just took it all off at that point, you know, it’s like good. Yeah, but you lose enough of it. You pull it out over the years. Yeah. So, uh, some really exciting times. Uh, you, we gotta spend some time at you at our breakfast. That’s right. A couple weeks ago. [00:02:13] Dai Vu: A lot of folks here, too. [00:02:14] Vince Menzione: A lot of folks that are here were at that breakfast, and I thought we’d spend a few moments with you talking about all the exciting things that have been happening at, at Google. I mean the, yeah, the businesses just to, first of all, the numbers were house. Outstanding. Congratulations. [00:02:28] Dai Vu: That’s right. [00:02:28] Vince Menzione: Yep. [00:02:28] Vince Menzione: Really, some really great numbers. Commitments are off the charts. [00:02:32] Dai Vu: Yes. [00:02:32] Vince Menzione: Crazy off the charts. [00:02:33] Dai Vu: Yes. [00:02:34] Vince Menzione: Yes. Uh, and then there’s a lot happening in this little world called ai, which makes a ton of sense. Yep. I was critical about Google in the beginning because you had all the assets, but Microsoft leaned in first. [00:02:45] Vince Menzione: Uh, but now it’s like things have evolved, uh, quite a bit since those first days. Absolutely. In, in November of 2022. So, uh, take us through a little bit. Let’s, let’s go through [00:02:56] Dai Vu: it. Yeah. I could talk for quite a bit of time because obviously we came out next, yeah. At the end of April, and then we had our earnings announced, but shortly thereafter. [00:03:03] Dai Vu: But, but real quick on next, uh, for folks who attended, uh, you know, the way they framed, uh, the discussion was they showed this AI integrated stack, and that’s how they frame the keynote because we position ourselves as being the only vendor that provides this. Fully integrated stack from custom silicon all the way to the apps and agents. [00:03:23] Dai Vu: And a lot of the announcements were, were focused in those areas. Um, uh, I won’t go through the, the long list, but I think the big ones coming out of next were, uh, certainly the eighth generation TPU we announced, so we actually split this into two specialized chips for training and inference. Uh, so that’s, uh, that was a big piece. [00:03:41] Dai Vu: Uh, but the big one that we announced was this, uh, Gemini Enterprise. Uh, agent platform. So think of it as the comprehensive platform for companies to basically build scale, govern and optimize their agents. And of course, once they have that, they can bring that into, uh, what we call a Gen Gemini enterprise app, which is really the front door for AI for. [00:04:03] Dai Vu: All customers and all employees to manage a mix of agents, um, as part of their daily workflow. And, uh, and a big part of it is, you know, certainly they’ll have some custom agents, but we think a lot of the agents will come from the ecosystem. And obviously there was a big announcement around what we’re doing there. [00:04:21] Dai Vu: Um, and in fact, one of the things that’s interesting is this shows the evolution of, of marketplace in our, in our partnership, which is we’ve taken a lot of the marketplace experience. And brought it into Gemini exp uh, Gemini Enterprise app, right? So search, discovery, uh, the ability to invoke agents, uh, in context. [00:04:39] Dai Vu: I think that’s gonna be very powerful as we think about the evolution, uh, of, of go to market. And then the last thing maybe I’ll highlight is this, um, is. 750 million, uh, investment fund that we’re gonna drive with the broad partnership. So this cuts across all partner types, global system integrators, uh, uh, you know, AI, pure plays, uh, ISVs, uh, the big management consultants as well, uh, because we recognize that partners are gonna be critical to drive business transformation with our end customers. [00:05:08] Dai Vu: So we’re investing around things like. Technical enablement, access to our product teams, access to our FDE for deployment engineers, and then a lot of incentives to drive usage and deployment. So, um, so a lot of, a lot of activity and obviously the ecosystem’s gonna be very critical for us to drive that impact’s. [00:05:25] Dai Vu: Fine. And the last thing, I know we’ve going on and on fine, but the last thing I’ll just mention is just on the earnings announcement, uh, Vince touched on the backlog, so people have been tracking Yeah. Two quarters ago. We were 155 billion on the backlog, and then a quarter later we were 240 billion. And then in the last quarter, just recently, 462 billion. [00:05:46] Dai Vu: So obviously that’s a, a massive signal of customer intent, but more importantly, it’s a, it’s, it’s a addressable market for this ecosystem to go after as well. [00:05:54] Vince Menzione: Yeah. Almost a half a trillion dollars. Yes. In commitment. So a lot, a lot of reason why we should be on the marketplace. [00:06:01] Dai Vu: Absolutely. Absolutely. [00:06:02] Vince Menzione: Um, each of these gentlemen have some things to talk about as well, about their companies and the exciting things that have been happening. [00:06:08] Vince Menzione: I’m gonna start, John, I’m gonna start with you because Tackle has, has transformed quite a bit since the last time you were on stage with us. I thought maybe introduce the company. Take us through the transformation and then we’re gonna do the same thing with Sean with his organization. [00:06:21] John Janke: Yeah. Thanks. Uh, thanks Vince. [00:06:23] John Janke: Great to see everybody. Uh, John Yanke, GM of Tackle at App Direct. So the big news there is Tackle was acquired in Q4 by a company called App Direct, and I think the why behind this app, direct Powers, marketplaces, they run 400 marketplaces around the world for telcos, for ISVs, for system integrators, channel partners. [00:06:42] John Janke: And we were talk like, when you build a marketplace and diagnose this, stocking the shelves is actually really hard. Uh, and we were talking to them about how could we connect the dots between the hyperscaler marketplaces, the iscs we support, and these additional routes to market. Uh, and that became more strategic and we ended up joining forces in December. [00:07:00] John Janke: And since then, the other part that’s really hard when you build a marketplace is how do you generate demand? Uh, so four weeks ago we acquired a company called Partner Stack. And Partner Stack does affiliate content. They have an affiliate content platform that allows you to connect with 150,000 content providers to be able to start to tell your story to drive leads to. [00:07:23] John Janke: Marketplace. So we think there is a tremendous opportunity to continue. We’re in the earliest days. I think the, you know, Jay, I was with Jay at Channel Partners a few weeks ago and he is like, we under called it, he didn’t say this on stage yesterday, but he is like, uh, the 82% growth. He’s like, we totally under called it. [00:07:40] John Janke: Uh, and I think just listening to dies commit level increase mm-hmm. Reinforces the fact that we’ve under called it. But I also think we’re at this tipping point in the market where all of the new capabilities coming out, we have to all rethink our better together stories. So I think the challenge to all partner leaders, it’s like, how do we. [00:07:58] John Janke: Figure that out. So it’s, it’s a, it’s a fun time. As we transform the way we worked. We wrote the first helping people kind of list, launch and sell through the marketplaces. And now to be able to take that to the next level to hopefully unlock the next a hundred billion of marketplace throughput. [00:08:13] Vince Menzione: And are we at a hundred billion? [00:08:15] Vince Menzione: ’cause that was the number, right? [00:08:16] John Janke: I mean that’s, that’s, that’s the number that’s talked about. I mean, we’re seeing the data signals we see, I mean, we will process 20 billion plus this year. Uh, and that number’s growing faster than Jay’s stated number. So I think we’re excited to see where this year lands. [00:08:30] Vince Menzione: We’ve come a long way from three years ago and we all got on stage and talked about marketplaces together. Right. It’s been, it’s been amazing. And then Sean, let’s talk about DBT. You’ve had some excitement. I know some things maybe we can’t even talk about yet on stage. [00:08:43] Shawn Toldo: Uh, yeah, go ahead. [00:08:44] Vince Menzione: No, I was saying I, I could, I’ll pre-announce things, but No, I’m just, uh, tell, tell us about DBT for those who don’t know in the room, sure. [00:08:49] Vince Menzione: Mean Yeah, that might help. [00:08:51] Shawn Toldo: So, uh, Sean Todo, I lead the partner business at DBT. I’ve been here about 18 months. Um, DBT really started as an open source tool. That help data engineers be successful in SQL transformation with cloud data warehouses? Right. And so back even to the Redshift days now into what I would call more the BigQuery, snowflake, Databricks fabric led days, um, DBT is the tool of choice amongst the data engineering community in terms of how they wanna drive SQL transformation. [00:09:21] Shawn Toldo: And so more recently, we kind of jumped into this kind of paid world. Which is why we needed to bring in additional experience leadership around go to market product, sales, et cetera. And so when I walked in the door, one of the things I noticed really quickly was we were running on AWS, which was great. [00:09:40] Shawn Toldo: We were doing some AWS marketplace stuff. We were running on Azure in Europe only. And one of my first strategies was we have to be everywhere, right customer. We have to meet customers where they are. And so we, uh, made some major investments to be on Google Cloud platform to then be able to really take advantage of marketplace, to then really be able to take advantage of the co-sell opportunities that exist in the field from a day, day-to-day AI perspective with Google. [00:10:07] Shawn Toldo: And it has been a hell of a ride. We launched on, uh, Google Marketplace in July of last year. We went to Google next and we were Google Partner of the Year. Wow. For data and analytics in a very rapid way. We’re now in three, uh, data centers around the, the world. So we’re here in the us, we’re in Frankfurt, we’re in uh, uh, UK as well. [00:10:30] Shawn Toldo: And so it’s been a pleasure to work with D and the broader team. Because the enablement we’ve had and the support we’ve had from that group has really helped our growth be up and to the right. The data point I would give is that when I walked in the door, we were 10% of our business from an A RR perspective was transacting through marketplace. [00:10:48] Shawn Toldo: Last quarter we cracked 40%. Whoa. We will be at north of 50, uh, next quarter. [00:10:53] Dai Vu: Wow. [00:10:54] Shawn Toldo: The other piece that Vince was talking about is we’re getting ready to merge with a company called Five Tran. And so there will be a new company name at some point down the road. Uh, pay attention on June 1st for a public announcement around that merger. [00:11:06] Shawn Toldo: Uh, but we’re really looking forward to what we’re gonna be able to do with folks like DI and the Google team as well as others in the ecosystem. Um, ’cause I think in this data world that we’ve played for so long. This trusted foundational element of data and what it’s gonna mean to context in the AI world. [00:11:23] Shawn Toldo: We’re in a very interesting place to really continue our growth rate at a high level. [00:11:28] John Janke: Yeah, that maybe just a comment something there. Start there. I think we, we used to hear people say we wanted to be strategic with cloud, go to market and get to say 10 or 20% of revenue. I think this like 40, 50%. Yeah. Th that’s where people are setting the bar these days. [00:11:43] John Janke: Yeah. So the numbers are getting really crazy. Yeah. Uh, and people are showing up and being like, I have to go big. Mm-hmm. So a huge change over the last few years. [00:11:52] Vince Menzione: Yep. What’s the experience you’re seeing as well? I mean, it, it was a huge amount of buzz at next. [00:11:57] Dai Vu: Yeah. I mean, so interestingly, um, you know, typically when, when people get started on the, on the marketplace in Cosal journey, I always try to caution them and say, this is, uh, this is like a multi-year. [00:12:07] Dai Vu: Yeah. Uh, process. You have to be very intentional. You have to invest. It’s not gonna be a thing where you just list and, and, and, and, and, and sort of this channel opens up. So in some ways, Sean is describing an acceleration that is not common, right? Uh, so they’ve done, we’ve done some amazing things together and we hope to keep that acceleration going. [00:12:22] Vince Menzione: What does that require, by the way? Is it engineering resource? I mean, there’s, I talk about executive commitment and maniacal focus. Yeah. But it’s all those things, right? [00:12:29] Shawn Toldo: Well, all of it. But we went to a QBR in Austin, and I put up a slide and I said, we have to do this. And everybody in our ETE agreed. So when you have a chief financial officer that’s bought into the partner business. [00:12:43] Shawn Toldo: Yeah. And I guess qualifying coming into this role at this company, I qualified the C-level staff. Uh, like are they really serious about partner or not? And it’s one of the reasons I took the role. So I think executive commitment was one thing. I think the second thing is we were really well supported, um, by the Google team across the board, right? [00:13:02] Shawn Toldo: Yeah. So folks, Indy’s team that we would work with regularly on, these are the things you need to do to have an effective marketplace offering. Here’s what you need to do operationally with folks like John and team and others that are in the market, right? That helped us a ton to be able to scale. And then the other thing that we did is we changed comp. [00:13:20] Shawn Toldo: So from our VP of sales levels down, we have a 5% kicker for everything that goes through marketplace. [00:13:26] Vince Menzione: Hear [00:13:26] Shawn Toldo: that everyone. So as soon as we incented the sales team, I love that, right? We, we created the foundation on the partner side, but then from top down on the sales side, they were all in. And as a result of that, the question would become, okay, which marketplace stage two sales cycle are we gonna go use? [00:13:42] Vince Menzione: Yeah. [00:13:43] Shawn Toldo: Who’s the right partner to go partner with? And then my team is reaching out to make sure that co-sell connection happens. [00:13:48] Vince Menzione: That is such a best practice, Sean, to, because there is, as a seller out in the field and we talk about, you talk to John, talks about rev ops all the time. But getting rev ops eng getting the field engaged in the right way. [00:14:01] Vince Menzione: ’cause it feels like it’s more work for them. ’cause they have to think, they have to have more conversations with their customer about their cloud commitments and things like that. Mm-hmm. And then getting them incentive to do the right things. The right behavior. [00:14:12] John Janke: Yeah. It’s a strategy process. People, technology problem. [00:14:17] John Janke: Yeah. It’s not just some flip API automation, go list something if you don’t like that top down view. I think the other thing. Like there’s a, there’s a theme in startups where VCs fund second time founders. I think Sean and team have done this before and they took a lot of learnings over the years and reapplied them, which I think helps them go faster. [00:14:36] John Janke: It’s like that second time. Yeah. Second time cloud go to market Founder theme. [00:14:41] Vince Menzione: Yeah. Yeah. Um, so we could talk about the platform and all the changes there on the. The, the commitments and everything. Mm-hmm. Uh, what separates ISPs generating real incremental revenue on your, in your marketplace? What, what do you see? [00:14:58] Dai Vu: Yeah, so I mean, I, I think there are a couple things. Number one is, uh, the, the foundation has to be, uh, this better together story, uh, with Google Cloud. Um, so this idea that what, you know, what do you bring, what does the Google platform bring and how does that drive impact with customers? And I think this is the reason why Sean and DBT Labs has been very effective. [00:15:16] Dai Vu: ’cause our field recognized they, they can recognize that better together story and communicate it to their customers. So I think that’s the foundation. For everything. Right. And I think as you get started, uh, you know, we do tell partners that they probably need to lean in a little bit, uh, in terms of focus, uh, you know, pick a vertical, a customer segment, um, you know, a geography where they’re particularly strong and, you know, get that momentum going. [00:15:39] Dai Vu: And once you do that, the field knows about it and starts to pull you into deals. Um, so I think that’s the other big opportunity. And then the other thing I just mentioned. Which, uh, the panel already touched on, which is be very intentional around all the things you need to do to invest. Whether it’s like, uh, you know, the business functional alignment, uh, the policies around like, uh, pricing and, and comp, uh, making sure you have the operational capabilities. [00:16:02] Dai Vu: These are all things everyone has to do to get to that. First five to 10 deals, and then 10, 20, 30% of your business through marketplace. And not to, not to top you Sean, but our very top partners are driving 80 to 90% of their business on marketplace. And in fact, some of these partners are actually only marketplace first, uh, uh, because they started out that way. [00:16:21] Dai Vu: Obviously it’s the bigger challenge if you have an existing channel, you’re trying to shift that. But, uh, the aspiration to be more marketplace focus, uh, is up there. [00:16:28] Shawn Toldo: So I just set a new goal for the business plan for me. So that’s exciting. I love it. Looking forward to seeing you in six months on that. [00:16:35] Shawn Toldo: It’s good. [00:16:36] Vince Menzione: I love [00:16:37] Dai Vu: it. Work together on that. [00:16:38] Vince Menzione: Well, di I’m just gonna add, add this because I, I got to see operationally with some of the things you do. Mm-hmm. You, you have an overlay organization. [00:16:45] Dai Vu: Yes. Yes. [00:16:46] Vince Menzione: And so you put accelerants in place within your own organization Yeah. To drive the ISVs into the, into the lines of business. [00:16:54] Vince Menzione: Right. You have, you, you do some of that to accelerate. [00:16:57] Dai Vu: Yeah, I mean, I think, I think this is somewhat unique. I don’t, I don’t wanna speak to the other [00:17:00] Shawn Toldo: hyperscalers, [00:17:01] Dai Vu: but we do have, um, uh, you gotta know the field roles, right? [00:17:04] Shawn Toldo: Yeah. So [00:17:04] Dai Vu: obviously at Google Cloud in the regions, we have, uh, ISV sales specialists who are effectively quoted on marketplace revenue, right? [00:17:12] Dai Vu: So they’re a hundred percent focused on that. And, uh, in addition to that, uh, we also have these, uh, co-sell teams, partner teams where, you know, opportunistically if there’s an opportunity, uh, in a, in a, in a particular area. This team is responsible for connecting the regional sales leadership, uh, the regional, uh, sales teams with, with the partner on the opportunity. [00:17:32] Dai Vu: So there’s a lot of things we’re doing to sort of accelerate that. And of course, the foundation for all this is, you know, our, our, you know, registering deals. And as you definitely get started on that, it’s very important to be very mindful around when you register deals. Uh, be very clear around what the ask and the engagement is with the field reps. [00:17:51] Dai Vu: But once you have that going and get the right rhythm, it becomes sort of a natural way to sort of register all your deals and get that engagement. And then, um, and then maybe the last thing I would say is it isn’t always the sales specialists. It’s, you know, the FSR, our field sales rep as well as our customer engineers are also very motivated. [00:18:08] Dai Vu: To work, uh, with, uh, with our partners because they know that this, you know, whether it be solution completeness or it’s part of a bigger workload or helps unlock greenfield opportunity, they really are motivated to engage with the partners. [00:18:21] Vince Menzione: Nice. [00:18:22] Shawn Toldo: Yeah. I’ll just add, I’ll just add to that statement too. I think, um, it’s one thing to have a story as it relates to. [00:18:30] Shawn Toldo: Google Cloud and what you do with marketplace. It’s another thing to have a story in terms of how you impact data and analytics in our world. And there’s a set of specialist sellers inside of Google mm-hmm. That really care about us because we drive a lot faster consumption of big query. And our ability to tell that story across the world effectively has really created a pull now. [00:18:54] Shawn Toldo: And so I, I would say it’s almost, you know, back to, you know, being 12 years at Microsoft and watching kind of that. Phase and how that went. As we went to the cloud and we picked specialty areas, um, Google is doing that as well and they’re doing it extremely fast in a very, very productive way with partners. [00:19:12] Shawn Toldo: And so, you know, I’ll get comments from like Levi who runs west in north region for us, and he’s a, he was at Google next and he was like, I, I gotta, I, I just gotta go to bed. I’m tired. Like we wore him out over two days with their sales team and gave him a host of follow ups and actions related to specific sales areas as well as specific accounts. [00:19:34] Shawn Toldo: And I think that’s the other thing that, um, Google’s done a good job of, but we’ve pushed and we’ve had to work really hard to earn that seat at the table. To help make those people successful from a comp perspective inside of Google as well. [00:19:45] John Janke: Yeah, and this is a huge failure zone for partners with the clouds because they think enablement’s a one and done thing. [00:19:51] John Janke: Like I did a training for the field and I told them the better together story. That doesn’t work. Like you have to literally. Have consistency around this message every day. Oftentimes you need experts who can partner with your reps to give them the confidence. ’cause they may be able to ask the first line question, but someone asks a follow up and they fold up ’cause they know your product. [00:20:11] John Janke: That’s right. They don’s don’t understand all of the nuances of Google and the clouds and the questions that may come back. But if you do that well, it is a huge unlock. [00:20:20] Vince Menzione: Talk about the coaching you provided on the tackle side of that as well and kind of helping. Through this maturity model? [00:20:26] John Janke: Yeah. I mean we, we, over the years, I mean we started as a pure SaaS company and over the years our customers would consistently ask us for more help and we would struggle to figure out how to do that, and we had to invest in services and we actually acquired a company. [00:20:42] John Janke: Five years ago now, that was the foundation. Aaron Feiger, who’s in the room. The core consulting was the foundation of our services business. And that continues to evolve with us. And you know, we see customers at scale saying, I wanna operate my cloud, go-to market really consistently, and I want you to do all the backend operations so my teams can be outselling our products, selling the better together value with Google and others, and not have to figure out how to run the machinery. [00:21:09] John Janke: So we’ve invested a lot there. We have services around strategy, like how to help people think about their business strategy and translate it into a better together story and able to get executive buy-in. And then we have coaching, which is really a phone, a friend, because I think these things get complicated. [00:21:24] John Janke: And I had a customer who was doing the largest deal in their company history. It was the end of the quarter and it was Friday, and they’re like, this is going to be the most complex transaction we’ve ever done and we have no idea how to do it. Our team gets on the phone with them, they work through, what are you selling? [00:21:40] John Janke: How are you selling it? Is your listing set up the right way? Can we actually create all the offers? In a way you have confidence to execute. ’cause those are failure modes. You try to build a cloud, go to market business, and you mess up the largest deal in the company. On the last day of the quarter, uh, that’s something you can’t recover from. [00:21:55] John Janke: So we try to really wrap support around our customers to help them have the confidence to grow. [00:22:02] Vince Menzione: Di you’ve seen tremendous growth in marketplace. Mm-hmm. We don’t publish the numbers specifically. Yeah. We kind of try to figure it out on the back end, but [00:22:09] Dai Vu: Yep. [00:22:09] Vince Menzione: I know you’re accelerated. Your, your marketplace numbers are astounding. [00:22:13] Dai Vu: Yes. I can share some numbers, if that’s [00:22:15] Vince Menzione: okay. Please. Yeah, let’s go. [00:22:18] Dai Vu: So, um. I would say that for a few years now, we’ve been talking about growth. So we’ve been consistently, uh, you know, north of a hundred percent year over year growth. Uh, for the last few years we’ve been processing, uh, what I say, uh, billions of dollars, uh, annually and, uh, uh, millions of transactions. [00:22:36] Dai Vu: And again, that’s for a few years now. Now for 24 to 25, that full year we also doubled. Wow. Uh, which is, uh, which is amazing when you think about the scale in which we operate. But more importantly, if you look at specific category areas, right? So, you know, historically, marketplace has always cater to, uh, those solution pillars that are tied to cloud migrations, like, uh, like security and data and analytics. [00:22:59] Dai Vu: And those continue to be very strong areas for us. But the biggest growth area is, uh, is in the areas of business app. So obviously, you know, the, the ServiceNow workday, uh, Salesforce of the world, as well as the AI category. So one number that we threw out next was 18 x. Year over year growth for the AI category. [00:23:17] Dai Vu: Wow. So in one year now, a lot of it is models, right? So foundational models with our, with our ecosystem. But a lot of that is around agents. So this whole agent go to market model is gonna be, continue to grow and it’s gonna be a huge focus area for, for the coming years. [00:23:32] Vince Menzione: Fantastic. Yeah. Fantastic growth. [00:23:34] Shawn Toldo: Yeah, and, and I’ll add, Diane and I talked about this at Google next. This is a. Very complex thing for DBT, where today we sell seats. [00:23:42] Vince Menzione: Mm-hmm. Yeah. [00:23:43] Shawn Toldo: To data engineers. [00:23:44] Yeah. [00:23:44] Shawn Toldo: And now we have all these agentic things that are hitting our engine. And di and I are talking and we’re like, okay, so how does this work in an ag agentic marketplace? [00:23:54] Shawn Toldo: Yeah. Kind of a scenario. And what should we build? Where should we play it? ’cause we’re gonna spin the meter in a different way, so to speak. [00:24:01] Dai Vu: Yep. [00:24:01] Shawn Toldo: And so candidly, we got stuff to figure out related to that. Um, I think what’s been fascinating for DBT is our partner ecosystem changed overnight. So now it’s like I talked to x.ai on Monday. [00:24:15] Shawn Toldo: Mm-hmm. We got time with open AI on Thursday and we have a call with Anthropic and our, uh, CEO and co-founder and uh, chief Product Officer next week. [00:24:26] Vince Menzione: Mm. [00:24:27] Shawn Toldo: We don’t have anybody managing those partners. [00:24:29] Vince Menzione: Right. [00:24:30] Shawn Toldo: Today our focus is on managing the large, uh, hyperscalers plus Snowflake and, uh, Databricks. [00:24:36] Vince Menzione: Mm-hmm. [00:24:36] Shawn Toldo: And then the SI ecosystem and some tech partners. So we’re having to like, to your point on Agile yesterday. Yeah. Mm-hmm. Like we’re having to change our strategy, operating model and organizational model to support that. And candidly, we don’t have all the answers yet, so we have a lot of things to figure out fast, which is a little bit scary. [00:24:54] Shawn Toldo: And challenging, but it’s also a huge opportunity we have to kind of embrace and get into. Yeah. [00:24:59] Vince Menzione: And they’re figuring out as well. ’cause they’re, they’re new to partnering as well. Yeah. As organizations [00:25:03] John Janke: and these AI agents. I think to demystify for a lot of people, and what Sean said is totally right. [00:25:08] John Janke: They’re disrupting everyone’s business model. But in reality from a marketplace standpoint, they’re metered SaaS. This is a thing that’s existed for a long time. Yeah. They look like product-led growth products. There is a lot of patterns around how product-led growth products work in marketplace. Mm-hmm. [00:25:24] John Janke: But you have to bring your business strategy, your product and pricing strategy to those two categories. Metered SaaS and product-led growth. Put that all together to get cross-functional alignment. So we are seeing like. A lot of people get tripped up here and it really does go back to more of the company strategy, product strategy questions, and a lot of partner leaders are not in the room for those conversations. [00:25:48] John Janke: So I think at, at this point in time, as you see big pivots with the partners to go all in on agents, you have to go elevate. Those discussions to be like, what is our plan here? ’cause I, I mean, pricing and packaging will be the thing that trips almost everyone up. [00:26:02] Dai Vu: If I could, if I just build on what John John mentioned, um, so I do agree. [00:26:06] Dai Vu: P it looks a lot like POG, but, uh, but the difference I think is POG has. More historically been in like the data and developer space, now it’s like the general business user, right? So this idea that you want a business user to be able to search and discover, um, agents that could actually be part of their like everyday workflow is going to be very critical. [00:26:26] Dai Vu: And uh, you know, I do think that when we think about the ecosystem building agents. Uh, you know, a lot of the ISV partners aren’t necessarily gonna own end-to-end workflows, right? They’ll, they’ll have a very specific, uh, domain and scope area, but you have to enable yourself to be orchestrated and managed by, you know, orchestration agents or, or, or meta agents that are gonna span end, end workflows. [00:26:49] Dai Vu: And sometimes that includes system integrators and, and others who can stitch that, that automation. So I think, I think that’s, that’s one piece of it. But the other area that I think is gonna be different is, um. There’s going to be a lot of agents. I mean, literally you’re gonna have a very fragmented set of, uh, uh, of players, right? [00:27:07] Dai Vu: It’s not just gonna be the incumbents, it’s gonna be a lot of disruptors and, and, and, and startups. And so the, uh, for the incumbents in the room, it is a mandate that you need to, to innovate because if you do not identify and go to like an agent first, go to market model. Uh, you’re gonna be, you know, disintermediated. [00:27:25] Dai Vu: Somebody’s gonna go build an agent that’s going to leverage you as a dumb database. Um, and they’re gonna own the workflow. So you have to, you have to push the, the, the, the limits here. And I think it’s creates a big opportunity for everyone in this room. [00:27:39] John Janke: I’m going off script. I’m curious. Let’s do it. I’m curious on your take on the system integrators. [00:27:44] John Janke: ’cause I think this, this puts like they’re all, a lot of them are creating agents for people and I think that’s turning them almost more into software companies than they’ve ever been. [00:27:53] Dai Vu: They are, and I think they’re, you know, obviously they’re being, uh, impacted from like, you know, typical like, you know, SOW you know, time and materials type type business models. [00:28:02] Dai Vu: But I do think they play a big role because a lot of the system integrators are bringing, um, you know, vertical and business process expertise. And, um, like I said, I said before, a lot of the ISVs are not gonna necessarily have big enough scope in their area to own end-to-end workflows. And that’s really the promise of agents, right? [00:28:20] Dai Vu: You really need. This cognitive, you know, reasoning, planning, executing across end to end workflows. And I think, you know, the system integrators are gonna bring that capability either, either through, you know, these custom, uh, orchestration or meta agents or if they’re able to productize that and bring that to a model, they can also sort of go through the marketplace model as well. [00:28:41] Dai Vu: So who knows is how it’s gonna evolve. But you know, we’ve always been talking about. Marketplace being a broader opportunity for all partner business models. And I think that will extend to not only, uh, you know, traditional sort of, uh, sell and services partners, but also some of these system integrators as well. [00:28:58] Shawn Toldo: If I could comment on that, please. Yeah. I, I was in London two weeks ago and we did an SI partner day. Mm-hmm. We had 25 sis in a room, probably about 50 people. We had no, um, hyperscalers or cloud data warehouse providers. And when we started talking about open data infrastructure. The role that they can play. [00:29:17] Vince Menzione: Mm-hmm. [00:29:18] Shawn Toldo: Cross platform in a cost efficient manner for customers and the advisory orientation of that. They all leaned in and we, we stopped talking and they started talking. [00:29:28] Vince Menzione: Right. [00:29:28] Shawn Toldo: So they’re all facing this kind of same problem, which is actually causing a little bit of a shift, I think, in how they think about, I’m a Databricks partner. [00:29:38] Shawn Toldo: Uh, you sure you wanna do that? [00:29:39] Vince Menzione: Yeah. [00:29:40] Shawn Toldo: So this, this whole thing that’s kind of evolved in the last six to 12 months, when you kind of pick one horse to ride, I, I would tell you be cautious about what that means. You may pick a horse to lead with mm-hmm. But you’re gonna have to flank yourself a bit in terms of other providers that can help you be successful with that, that that partner you’re gonna roll with. [00:30:00] Vince Menzione: So you’re suggesting data vendor agnostic. [00:30:04] Shawn Toldo: I’m suggesting you really have to think about your strategy. Yeah. Because I think the AI, AI disruption is gonna make you think about that strategy. [00:30:13] John Janke: Yeah, I mean there’s, someone mentioned anthropics First Partner Summit. I was not there, but I’ve heard from a bunch of people were there. [00:30:20] John Janke: You know, they had a hundred partners in the room. 95 of them were system integrators. Five were technology companies, the three Clouds, Databricks and Snowflake. Like if you just think about the, the one of the major disruptors in ai, ISVs, were not in the mix. So I, I think, are they trying to disrupt all of us? [00:30:40] John Janke: Uh, do they need us? And they haven’t figured out how to work with us. I, I think. It’s, it’s, [00:30:44] Vince Menzione: and I’ve heard they only have five people in their partner organization, so I just, it’s, [00:30:49] Shawn Toldo: it’s 11 now, but it’s 11, [00:30:51] Vince Menzione: so it was five [00:30:51] Shawn Toldo: last growing fast in the, in the new company I have 50. So like, to put it in perspective, they have to make some pretty big priority. [00:30:59] John Janke: Yeah. And everyone’s been there a hot second, [00:31:00] Vince Menzione: like, right, exactly. Yeah, they, well, we will talk about the learnings we’ve had over the years, getting to where they need to get to. It’s exciting times. We got a lot to talk about here. Um, I, you know, we have about 15 minutes. I I, I want to kind of gauge, ’cause we could talk, we, we have a few things we could talk about, I could ask about, but I want to see if there’s an, like, an interest in opening up to the room for questions. [00:31:25] Vince Menzione: ’cause I feel like we’ve got a very interesting group here. [00:31:28] Shawn Toldo: You got a hand here? [00:31:29] Vince Menzione: Uh, are there hands that wanna Yeah, there’s some people that wanna ask some questions. So Yeah. We have a mic? Yeah, [00:31:37] Dai Vu: we have [00:31:37] Shawn Toldo: a mic. We, [00:31:37] Vince Menzione: we [00:31:38] Shawn Toldo: got one here. [00:31:38] Vince Menzione: We got one here. One here. Thank you. Sorry we went off script, but [00:31:44] Shawn Toldo: that’s fine. [00:31:45] Vince Menzione: It’s fine. [00:31:45] Dai Vu: Off [00:31:45] Vince Menzione: script. Better is good. [00:31:46] Shawn Toldo: I’m sure you planted the questions outta anyway. It’s okay. We [00:31:48] Vince Menzione: did, we did. [00:31:55] Audience Guest: Okay. All Eva, Sean Lightner, quick question to your, uh, increase on the marketplace, and you said you spiff the salespeople by fifth percent. 5%. Mm-hmm. So, and that obviously drives a very large adoption of, uh, marketplace transactions. How are you accounting for the margin you’re losing on, uh, you know, going through the marketplace? [00:32:14] Audience Guest: And also have you done analysis? I’m sure you have, how much is, uh, shape shifting or shifting from existing versus incremental? [00:32:22] Shawn Toldo: Yeah, it’s a great question. Um, um, lemme make three points. Number one, the backlog statement makes the margin statement not matter. So do you wanna play in that space where a customer’s already bought or not? [00:32:36] Shawn Toldo: Yeah. Or do you wanna force a budget conversation that you have to drive on your own in a direct model? That to me, I think it was 484 4 62 [00:32:43] Dai Vu: 4 6 [00:32:44] Shawn Toldo: 2. [00:32:44] Vince Menzione: That’s new Tam available to you? [00:32:46] Shawn Toldo: Yeah. That, that’s just with one. Right. And we are, we are, uh, running on four marketplaces. So that just increases our tam and makes our, our sellers lives easier. [00:32:55] Shawn Toldo: So on that piece, yes, there’s an expense, but we believe it’s right for growth. So there’s a balance there. Um, I think the, and then the second part of your question again. Sorry, [00:33:05] Vince Menzione: shapeshift. [00:33:05] Shawn Toldo: Oh, shift. We, we actually don’t think we would’ve won the business. So if I go back to our Q4 and I can probably point to three or four deals that went, um, Google Marketplace, we would not have won those deals because we couldn’t have created the budget cycle and that quarter. [00:33:23] Shawn Toldo: To make it happen. Generally a budget cycle is gonna take anywhere from 12 to 15 months. Bingo. Because of the spend that was available to us, we were able to close it in that quarter, and we had the largest Q4 in company history. [00:33:35] Vince Menzione: That is such an important point. I’m sorry. [00:33:37] Dai Vu: Okay. [00:33:38] Vince Menzione: But I, I just wanna, that is such an important point of the budget cycle. [00:33:42] Dai Vu: Yeah. [00:33:43] Vince Menzione: Being a year to a year and a half versus being able to tap into a commitment that’s already been made. Yeah, so I just emphasize that [00:33:51] Dai Vu: I was, I was just gonna add real quick, even, even when we see sort of a, uh, a channel shift renewal, which is, you know, it’s on partner paper and it moves to marketplace as part of the renewals, we do consistently see that the, uh, renewal rates on marketplace and the incremental a CB on the expansion and new opportunities tend to be better when it’s on the platform marketplace than than offline. [00:34:12] Dai Vu: And that’s why partners choose to continue to drive renewals on marketplace at a reduced to rev share. But uh, because they see that that growth, [00:34:20] John Janke: we, we, sorry. [00:34:22] Shawn Toldo: We see that as well. Yeah. And I would also make the statement on our land business, when we go through marketplace, we are two x higher across marketplaces. [00:34:30] Shawn Toldo: We’re three x higher with them. [00:34:32] John Janke: Yeah, I think separate new from renewals and then instrument deeply. [00:34:37] Shawn Toldo: Yeah, [00:34:38] John Janke: go proactively talk to your CFO and your head of rev ops to understand their mindset. Because I was with a billion dollar seller a couple weeks ago, their CFO still creates friction in the process, even though they’re selling a billion dollars through these channels. [00:34:52] John Janke: But when they broke it down, their deals are three times bigger. They do them faster. They use more components of the product, which I thought was a really cool one. So customers who buy this platform, many component platforms through a marketplace, end up using six components of the product. Versus a normal land customer who uses two increases gross in net retention. [00:35:12] John Janke: So you have to get to the point where you have the data and you can tell that story real really clearly to your finance team to get support ’cause that they will trip you up if you don’t get them on board. [00:35:23] Vince Menzione: And you’re saying there’s friction in that company. I’m just kind of curious ’cause a billion dollar company. [00:35:27] John Janke: There’s a billion dollar marketplace seller [00:35:29] Vince Menzione: market marketplace company. That’s what I meant. Yeah. But, but the fact that this, their CFO friction, like, is it, is it because they’re not doing a good enough job or? [00:35:37] John Janke: Uh, in, of educating, I, the root of the question is from this person is, would they win without it? [00:35:44] Vince Menzione: Yeah. [00:35:45] Shawn Toldo: Oh, and is it worth the three points? [00:35:46] John Janke: Right. It’s, it is And, and I think some pe like to me, it’s the cheapest channel in the world. Yeah. Like with committed budget and people to support you winning. Like the, that formula, the math is so simple. [00:35:57] Shawn Toldo: Yeah. For, for a company of our size to go to like the classic resell ecosystem, I gotta walk in with 30 points. [00:36:02] John Janke: Yeah. [00:36:03] Vince Menzione: Yeah. [00:36:03] Shawn Toldo: It, it’s an illogical conversation. Outside of public sector and growth, you know, geos around the world. And so I, I’ve been lucky to have a CFO that I haven’t had that challenge with, at least at DBTI should say. [00:36:19] Vince Menzione: Really great insights. I think we have, we have another hand up here. [00:36:28] Audience Guest: Yeah. Thanks Susan. The question is for Dai. Uh, my name is Latif Hamani. I’m the founder of Partner System ai. Um, so what we’ve done is we’ve built a, a co-sell AI agent mm-hmm. That your partners can use to Yeah. Reduce all the friction in the co-sell with you. Uh, the questions that I have is, I guess I should back up, so XAWS Madison with a very large alliances, and then I worked, went on the other side. [00:36:55] Audience Guest: For software companies, and even though I had an operational team, I was spending two to three hours on on the keyboard, right? Mm-hmm. Deal registration, emails that can’t be automated, et cetera. So the question that I have for you is, I’d love for you to validate that. You know, unless you are one of the big companies, one of the big enterprises, if you go to the lower end of the enterprise or the mid market, uh, would you validate that there is a challenge? [00:37:20] Audience Guest: There’s a lot of friction for a smaller company. Mm-hmm. Uh, ’cause these marketplaces are complex. Yeah. The cosell is complex. Uh, that there’s an opportunity to really break down that friction with some automation and ai. [00:37:33] Dai Vu: Yeah, absolutely. So, um, we have already been, uh, part of the journey to remove some of the, uh, the friction as part of that selling and purchasing journey. [00:37:43] Dai Vu: Uh. We’re not quite there yet. But, uh, we’ve done things like we have, uh, you know, private offer APIs. We, uh, we have co-sell, uh, registration automation. Um, you know, we have tools like, uh, propensity to buy, tooling to help, uh, partners do, uh, more targeted efforts. Um, but the a i piece is still coming. Um, so I think, uh, the idea here is that we have launched a number of agents as part of our, um. [00:38:08] Dai Vu: Uh, part of our, uh, Google Cloud Partner network, partner hub. Uh, so these are, uh, agents that are gonna do a bunch of things to help partners as part of their workflow, but we’re gonna extend this to the marketplace and ISV area as well. Uh, so I think there’s a lot of opportunity. So, uh, I know there’s probably a lot of feedback in friction, uh, in, in certain parts. [00:38:29] Dai Vu: So we can, we can go tackle together. [00:38:32] Vince Menzione: Hey. There you go. There was a little [00:38:34] Dai Vu: plug [00:38:34] Shawn Toldo: there for tackle. Exactly. [00:38:37] Dai Vu: Uh, and I wanted, and just to be clear, I want to take a look at it from the end to end, uh, uh, flow, right? It shouldn’t just be just marketplace. It should be all the way from like, you know, top of the funnel, demand generation, all the way to like post transaction follow up. [00:38:51] Dai Vu: So we really need to take a look at, at the, the end, end flows and figure out a way we can remove some of that friction [00:38:56] Vince Menzione: three sense. [00:38:57] Dai Vu: Yeah. [00:38:59] Vince Menzione: Any more questions [00:39:00] Audience Guest: back here? Hey. Hey guys. This, this is a really good discussion. Uh, di this question’s primarily, uh, from, I’m interested in the hyperscaler response. [00:39:09] Audience Guest: Yep. Uh, but all of you, uh, can you talk about the patterns or say more about the patterns between. Um, the consumption of just platform capabilities versus industry workflows. Mm-hmm. And how industry where I, I mean, I, I, my sense is that industry workflows are becoming more [00:39:27] Dai Vu: Yeah. [00:39:28] Audience Guest: Uh, the easier thing for enterprises and SMBs to buy. [00:39:33] Audience Guest: Yeah. Especially SMBs, I think. Um, but say more about those patterns that you’re seeing develop and kind of what is. Uh, who are, where, where are those kind of, where is the demand being driven? Is it, is it, yeah. The search and discover in the marketplace, or is it being led by field sales of mm-hmm. Either GCP or partners? [00:39:55] Dai Vu: Yeah, so let me, I’ll mention a couple, a couple areas where, where it’s growing. So I think number one I mentioned before about some of these large horizontal business apps that we’re partnering with, right? Um, and, uh, and of course the fact that we’re, we’re, we’re transacting them through marketplace is, is a huge. [00:40:14] Dai Vu: Evolution from a few years ago. So who would’ve thought you would be buying like, you know, a hundred million dollars a CB deals, uh, through, through marketplace with like a Salesforce or a ServiceNow workday. But it’s happening now. And to be clear, all these. Horizontal business app. They’re not doing this in a very, you know, opportunistic, transactional way. [00:40:32] Dai Vu: They basically see marketplace and cloud go to market as a strategic growth lever for them. So that’s one big area. So from just a pure large deal perspective. Okay. Then you mentioned before around sort of corporate and SMB. Well, we find that a lot of the big opportunities are mostly around as they scale their business, uh, they’re not necessarily looking for things in the traditional sort of infrastructure space, but they’re looking for, you know, full SaaS applications to help scale their business, right? [00:40:58] Dai Vu: So it would be CRM, finance, hr, these types of solutions to become very attractive for some of this, uh, downstream market. And then lastly, as I mentioned before, which is, uh, when we think about this gentrification and owning, um. Uh, driving, uh, this business process and vertical, the ISVs become very important along with the services partners who bring that domain expertise to drive the end to end workflow. [00:41:25] Dai Vu: So I think that’s gonna be increasingly important. So those are three areas I think we need to watch out for. We. Okay. [00:41:30] John Janke: Maybe one thing, like as the cloud commit grows inside of companies, it’s shifted from being an engineering department, IT department budget line item to a corporate finance budget line item. [00:41:40] John Janke: Typically one of the top five to 10 expenses in a company. So that has shifted. Who is thinking about optimizing? The cloud commit with marketplace contracts. And that opens, that’s really opened up the avenue in addition to like these biz apps, vertical apps players. Yeah. Like having success. So I, I do think even inside your own company, evaluating where your cloud commits are, who owns them and are they thinking about the intersection of marketplace? [00:42:06] John Janke: ’cause I, I think it’s smaller companies, they’re still figuring it out. I run into engineering leaders who still own the commits, uh, but in medium to large companies. Very different. [00:42:16] Vince Menzione: Really good point. Because it, you know this, the optics change dramatically, right? This large commitment is now at the board level, [00:42:23] John Janke: right? [00:42:23] John Janke: And then you do have to teach your sellers as a vertical or business application player how to ask that question. ’cause the first resistance everybody says is, oh my, my person, my stakeholder, we. Manufacturing vertical application provider talking at an event last week, and they’re like, the shop floor manufacturing owner doesn’t know anything about the cloud commit. [00:42:43] John Janke: But if they ask the question, be like, Hey, do you guys have a strategic relationship with Google? Would it be easier to buy our product on the bill? Eight out of 10 times they get a yes. So [00:42:52] Vince Menzione: which is why the 5% comes in And that really accelerates the conversation happening. Yeah. We’ve got three more minutes. [00:43:01] Vince Menzione: Um, if we don’t have any other questions, I ha I have one for each of you really about the maturity model and partners are in the room that are not committed yet, right? We’ve talked about some very significant DBTs doing some incredible things, right? So we, there’s maybe a sense that like we, you, you are working with the be the biggest and the best out there, but what about everyone else that’s in the room that maybe isn’t committed yet? [00:43:23] Vince Menzione: And maybe they’re in motion, but they need some help and advice on what to go do next. What? What would you say die first? [00:43:30] Dai Vu: So they’re early stage, [00:43:31] Vince Menzione: early, early stage or not, they’re not on board yet. They’re not, yeah. They’re not with you yet. [00:43:35] Dai Vu: Yeah. So I’ll, I’ll go back to my earlier comment, which is that as you go into the journey, just be very intentional about what you need to do from an operational, investment people, uh, technology perspective. [00:43:47] Dai Vu: Uh, because it could be, it could be a multi-year journey. Um, uh, so I’d say go into it with the right expectations as opposed to thinking it’s going to be some accelerated six month thing that Sean has been driving here. It’s, he’s the outlier. [00:43:59] Shawn Toldo: But, but the reason for the outlier, [00:44:00] Dai Vu: yeah. [00:44:01] Shawn Toldo: And just to add to the intentional point Yeah. [00:44:02] Shawn Toldo: Is, you know, hire the right people. Right. So, somebody told me a long time ago, uh, hire slow, fire fast. That’s a really, really, really good principle that I take. Mm-hmm. I don’t like the fire part, obviously, but just for context, I, I am very lucky to have a great set of leaders that we were able to add people in. [00:44:24] Shawn Toldo: When I walked in the door, we had a person that was leading the Snowflake and AWS partnership. I had nobody on GCPI had nobody on Microsoft. I had nobody on Databricks. And then we made prioritization decisions on where we’re gonna go next. And so we hired people that had the experience and could drive the outcome in the right way. [00:44:43] Shawn Toldo: But we were very thoughtful about when we made those decisions on a quarterly basis, not a daily basis. So who you’re gonna bet on and then who you’re gonna put in the seat to make that bet come to life, I think is a really important thing as well. [00:44:58] John Janke: Yeah. [00:44:58] Vince Menzione: John, you worked with the be biggest and the best out there, so Yeah, sorry. [00:45:01] John Janke: Well, I think there’s the, like there’s the bottoms up and the tops down. Like seven years ago, this was all bottoms up. It was a partner leader who thought launching a marketplace would be good and they would go figure out how to do some deals and then sell their way up. Today there’s a lot more top down where people get it. [00:45:17] John Janke: But you can evaluate top down pretty fast. ’cause if you go talk to your CEO, you talk to your head of product, you talk to your CFO, and they have an allergic reaction to these concepts. You know, you have to go bottoms up. But there also are success story examples in every single ISV category that exists. [00:45:33] John Janke: Like this is not just security and data and DevOp like the, I think the ServiceNow. Salesforce workday. Examples are really great, like the marketing tech examples, more and more business of vertical apps every day. So I do think you can look at those people who’ve been successful. Maybe they’re your competitors, maybe they’re people you aspire to be and reference them as you’re trying to figure out how to do top down. [00:45:55] John Janke: But like you need both. You can’t win long term unless you get top down and bottom up aligned. [00:46:01] Shawn Toldo: And, and when I, when I would go ask for resourcing, I would always get the question, do, could you go faster with more? And I’d say, no. Gimme the one or two humans here, let me go prove it out and I’ll come back. [00:46:13] Shawn Toldo: So there’s a little bit of a strategy in doing that, that you’re gonna get more over time when you’re, you know, very measured in how you go ask for investment and resource. And so I would just add that point also. [00:46:27] Vince Menzione: Was, was hiring a significant component of your executive commitment, Sean? I mean, [00:46:33] Shawn Toldo: yes. So when I walked in the door at DBT, we had eight people in the partner organization. [00:46:38] Shawn Toldo: Today we have 25, and that was 18 months ago. But that did not happen. I didn’t go in and ask for, you know, that 16 people. Right. I asked over time in a very measured way with, you know, the programs and strategy team, like, what can we also support? You don’t want to bring somebody in to go do something and you don’t have the programs and operations side to support it ’cause they’ll fail. [00:47:01] Shawn Toldo: So we’ve been very thoughtful about how we’ve done that as well. [00:47:04] Vince Menzione: Die from you. I know you had something. [00:47:06] Dai Vu: No, no, no. I, I was good. [00:47:08] Vince Menzione: What is the one thing that people in this room need to go better and differently? Is there one, is there one specific thing other than what we’ve already discussed, did we miss anything? [00:47:16] Dai Vu: No, I would just, the whole identification. So obviously, uh, identifying this is not just like slapping a chat bot, but more around thinking all the things we talked about, product commercials, but also go to market where it’s agent first, where you can surface your agent in a workflow like Gemini Enterprise app. [00:47:34] Dai Vu: That’s gonna drive high alignment with how we work and go to market with Google. [00:47:38] Vince Menzione: Awesome. [00:47:38] Dai Vu: Yeah. [00:47:40] Vince Menzione: Wow. Good stuff. Yeah. Very good session. [00:47:44] Dai Vu: Thank [00:47:44] Vince Menzione: you guys. What do you think? Everyone? Thank you very much. [00:47:47] Shawn Toldo: Thanks for listening to the Ultimate Partner Podcast. [00:47:50] Vince Menzione: If today’s conversation resonated, share it with a partner leader in your network. [00:47:55] Vince Menzione: Subscribe where you listen, and head over to the ultimate partner.com. For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, [00:48:11] John Janke: October 26th through October 28th. [00:48:14] Vince Menzione: Until next time, keep showing up in the rooms that matter because being in the room changes everything [00:48:22] I.

Produced by Foundersuite (for startups: www.foundersuite.com) and Fundingstack (for emerging manager VCs: www.fundingstack.com), "How I Raised It" goes behind the scenes with startup founders and investors who have raised capital. This episode is with with Kane Hsieh of Root Ventures, a deep tech seed VC fund that funds and supports investors at the earliest stages. Learn more (and check out their very cool website) at Root.vc. In this episode we discuss why chaos and disruption is good for founders and VCs, why it's critical to find your edge to raise capital, how being a VC is similar to being a Hollywood agent, how geting intros to VCs / LPs is the first "meta test", ways that Root is applying AI to the venture game, and much more. How I Raised It is produced by Foundersuite, makers of software to raise capital and manage investor relations. Foundersuite's customers have raised over $21 Billion since 2016. If you are a startup, create a free account at www.foundersuite.com. If you are a VC, venture studio or investment banker, check out our new platform, www.fundingstack.com

An anti-MEV activist spent weeks building 66 fake contracts to trap the sandwich bot jaredfromsubway.eth. Then jared's operators did the one thing nobody expected. ======================================================== Thank you to our sponsors! Cape: Your biggest crypto vulnerability isn't your wallet, it's your phone number. Cape is America's privacy-first mobile carrier that rotates your SIM identity daily and blocks SIM swaps before they happen. Get 33% off your first six months at https://cape.co/unchained (use code: UNCHAINED). ======================================================== A new R&D lab called Ethlabs has split from the Ethereum Foundation, backed by Bitmine and Joe Lubin. Its first stated goal is solving a '15 minute finality problem' that none of the hosts can quite explain the point of. Kain Warwick, Taylor Monahan, and Luca Netz ask whether a breakaway staffed largely by ex-EF people can really escape the EF's habits, or just rebuild a smaller version of them.  Then the conversation turns to fomo's $75M raise from non-crypto VCs, and why a trading app that never calls itself a wallet may have cracked the onboarding flow the rest of crypto keeps getting wrong. The hosts also trace a CryptoPunks judge ordering a self-represented plaintiff to handwrite filings to stop the AI slop, the anti-MEV activist who trapped sandwich bot jaredfromsubway.eth with 66 fake contracts, and the WSJ's claim that Polymarket paid creators to stage fake winning bets. Hosts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Kain Warwick⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Founder of Infinex and Synthetix ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Taylor Monahan⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Security Expert ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Luca Netz⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, CEO of Pudgy Penguins Timestamps

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Monica Cornitcher. Entrepreneurial journey, the inspiration behind Medase Cocktails, and the realities of launching, funding, and scaling a premium nonalcoholic spirits brand in a highly competitive market. Purpose of the Conversation The purpose of the episode is to: Educate aspiring entrepreneurs on how to build a differentiated consumer brand Demonstrate the importance of storytelling, market clarity, and operational discipline Highlight the growth of the nonalcoholic / zero‑proof beverage movement Inspire founders—especially founders of color—to own their niche, seek capital strategically, and scale intentionally. Key Takeaways 1. Business Built from Personal Need and Purpose Medase Cocktails was co‑founded by Monica and her lifelong friend during her friend’s battle with breast cancer, a time when alcohol was no longer an option—but celebration still mattered. The brand was created to allow people to celebrate authentically without alcohol It carries emotional depth rooted in friendship, gratitude, and loss Monica continues the mission after her co‑founder passed away in 2024 Lesson: Purpose-driven businesses create deeper emotional connection and long-term brand equity. 2. Differentiation Is Everything Monica deliberately rejected the “sparkling water with flavor” model common in nonalcoholic drinks. Her differentiators include: Authentic cocktail taste (Old Fashioned, Margarita, Moscow Mule) Organic juices, not artificial flavors Bold packaging that stands out on shelves Drinks designed to smell, taste, and feel like real cocktails Lesson: Competing on authenticity—not cost—is how you carve out market share in crowded spaces. 3. Brand Names and Stories Matter The name “Medase” means “thank you” and reflects gratitude, friendship, and emotional support. Monica emphasizes: Every flavor name, color, and product decision has a story A strong brand narrative creates curiosity, loyalty, and investor interest Lesson: People invest in brands they feel—emotionally, not just intellectually. 4. Venture Capital Is Not Just About Numbers While financials matter, Monica stresses that VCs also invest in founders and stories. What helped her secure venture capital: A compelling personal story Relevant founder skill sets (M&A, law, operations) Clear understanding of the market opportunity Lesson: Early-stage funding often depends on who you are and why you’re building, not just revenue. 5. Research, Planning, and Discipline Before Launch Unlike many food startups, Medase did not begin in a kitchen. They: Conducted a feasibility study Built a formal business plan Worked with a Black female food scientist Set strict personal funding limits before seeking capital Lesson: Preparation reduces risk and builds long-term sustainability. 6. Scaling Requires Operational Maturity As sales increased—especially on Amazon—Monica emphasized the need to move from “hustle mode” to operational excellence. Key scaling principles: Understand unit economics Track ROI for events and activations Adjust pricing as volume increases Build strategy across marketing, operations, and distribution Lesson: Hustle starts the business; operations grow it. 7. Niche First, Expansion Later Medase does not try to be “everything to everyone.” Core customers include: People seeking a break from alcohol Health-conscious consumers Black men looking for alcohol replacements Consumers wanting cocktail taste without hangovers Lesson: Strong niches create loyal advocates who fuel organic growth. 8. Smart Distribution Strategy Rather than rushing into retail, Monica prioritized direct-to-consumer channels: Amazon (top-performing channel) Brand website TikTok Shop Only after 6–7 months of traction did retail expansion become viable. Lesson: Control your margins and demand before entering expensive retail environments. Memorable Quotes “I wanted an authentic cocktail without compromise.” “Everything we do has a story behind it.” “Sometimes it’s not about the financials—it’s about the founder and the story.” “Don’t be everything to everybody. Find your market and stick with your market.” “Hustle starts the business, but operations give you scale.” “If it tastes too much like alcohol and you gave me a one-star review—thank you. That means I did my job.” Overall Message This episode is a real-world entrepreneurial blueprint showing how clarity of vision, emotional authenticity, disciplined planning, and niche focus can turn a personal idea into a scalable national brand. Monica Cornitcher exemplifies the modern founder:visionary, data-aware, emotionally intelligent, and unapologetically authentic. #SHMS #BEST #STRAWSee omnystudio.com/listener for privacy information.

(0:00) Brad Gerstner, Gavin Baker, and Kelly Rodriques join the Besties! (0:47) Secondary Markets are Booming & Competing with IPOs (3:10) Why Companies are Staying Private So Long? (9:22) SPVs, the Forge-Schwab Deal, Democratizing Private Market Access (13:28) Secondary Markets as Exit Liquidity for VCs (27:00) The Private Market Bubble? (32:03) Hottest Secondary Companies Right Now Thanks to our partners for making this possible! EY - Agentic AI is introducing a new investment discipline. As AI shifts to consumption-based models, EY connects spend to enterprise value. https://www.ey.com/en_us/insights/ai/agentic-ai-token-costs?WT.mc_id=3501318&AA.tsrc=sponsorship NYSE - Thank you to our partner, the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE. https://www.nyse.com Plaud - Never miss a moment. Plaud, our official wearable AI note-taking partner at All-In Liquidity Summit, captured every insight. https://www.plaud.ai Follow Brad: https://x.com/altcap Follow Gavin: https://x.com/GavinSBaker Follow Kelly: https://www.linkedin.com/in/kelly-rodriques-9b49418 Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg