Podcasts about series c

Eno Reyes is the co-founder and CTO of Factory, the agent-native software development platform building autonomous "Droids" for enterprise engineering teams. Factory has raised $220 million, most recently a $150 million Series C at a $1.5 billion valuation, from investors including Khosla Ventures, Sequoia Capital, 20VC, NEA, Blackstone, Insight Partners and Nvidia. Before founding Factory, Eno worked as a machine-learning engineer at Hugging Face, training, optimizing and deploying large language models for enterprise customers. AGENDA: 00:00 Are We Underestimating AI by an Order of Magnitude?  06:35 Why Can the Smartest AI Model Be the Cheapest?  18:51 Is Anthropic's Coding Business Really Worth $2 Trillion?  33:41 Will Continuous-Learning Models Help or Hurt Factory?  40:43 Will 80–90% of Neo-Labs Die in the Next 18 Months?  44:33 Should American Enterprises Work With Open-Source Chinese Models?  55:42 Must AI Founders Radically Rethink What a Great Outcome Looks Like?  1:04:30 Do Pedigree and Credentials Still Matter in AI Hiring?  1:19:17 Which Is the Biggest Threat: Claude Code, Codex, Cognition or Cursor?  1:24:20 What Seems Crazy Today but Will Be Obvious in Five Years?  

Who carries responsibility when an AI agent begins reviewing contracts, applying regulatory rules or making commercial decisions on behalf of an organization? In this episode of Tech Talks Daily, I speak with John Nay, founder and CEO of Norm Ai, about Agentic Law and the attempt to redesign legal work around AI agents, legal engineers and experienced attorneys. John has worked across AI, law and public policy for approximately 14 years. His early research adapted neural network methods to legal and government text before large language models became a commercial force. The company information supplied for this episode states that Norm Ai recently raised $120 million in Series C funding at a $1.2 billion valuation, bringing total funding to over $260 million. Norm also says organizations representing over $30 trillion in assets under management use its technology for legal and compliance work. Those figures provide useful context for the scale of interest, while our conversation concentrates on how the model works and where responsibility remains human. John describes Norm Ai as automating the first pass of legal and compliance tasks. One example involves an in house team using an agent to review communications against relevant rules before a person finalizes the decision. Another involves Norm Law receiving transaction documents, assigning the first analysis to AI agents and then presenting the output to an experienced attorney. The attorney decides what happens next, communicates with the client and supervises anything leaving the firm. That division of labor matters because legal reasoning contains several layers. Some work can be handled through deterministic rules. Frontier models can then apply guidance and precedent to new situations. Human judgment remains responsible for high stakes advice and the review of agent output. John also stresses that the model is not making an isolated request to a generic system. Legal judgment is embedded in the way agents are designed, tested and called before live matters enter the workflow. We discuss legal engineering as the bridge between software and professional practice. Norm's legal engineers are trained attorneys who spend much of their time building, testing and validating agents. They work with practicing lawyers, clients and AI engineers to translate preferences, policies and matter specific requirements into systems that can operate within real workflows. Pricing is another part of the model. Norm Law prices selected matters around outcomes rather than hours. John acknowledges the limits. Some complex work can be scoped with enough confidence for a fixed price, while highly unpredictable litigation is much harder to price upfront. The opportunity is to give AI the incentive to examine additional documents and identify inconsistencies without increasing a client's bill for every human hour. The conversation then moves to the proposed Delaware AI Company initiative. John describes it as a regulatory sandbox for a legal entity managed by an AI agent while humans remain involved in its creation and supervision. His argument is that autonomous agents will take increasingly consequential economic actions, so policymakers must decide whether those activities happen within a recognized legal order or outside it. The proposal is designed to test questions around liability, disclosure, capitalization and government oversight before any broader model is adopted. John also believes companies deploying agents today should consider supervisory AI. If an operational agent can act faster and at greater volume than a person, a human team may be unable to inspect every decision. A second agent can evaluate the first against laws, regulations and company policies, with people retaining authority over exceptions and consequential outcomes. Does adding a supervisory agent create stronger accountability, or does it introduce another system whose reasoning must also be tested and questioned? Listen to the episode and share your thoughts with me.

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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

Axios reported that Crusoe is in talks about a potential IPO as AI data center demand strains power supplies. Crusoe, founded by CEO Chase Lochmiller and president Cully Cavness, builds modular data centers powered by stranded energy at oilfields. The company raised a $350 million Series C in 2022 and launched Crusoe Cloud to offer Nvidia GPU compute. EPA methane regulations finalized in December 2023 increase incentives for flare mitigation partnerships that can supply Crusoe with power inputs. Competitors such as CoreWeave, Equinix, Digital Realty, Applied Digital, and Iris Energy are expanding capacity through debt, construction, or pivots to AI hosting. Investors would assess site counts, megawatts deployed, utilization, GPU procurement, capex per megawatt, and power sourcing economics if Crusoe files an S-1.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Dr. Karen Litzy speaks with Hannah Kim, CEO and co-founder of Pocket, about the hidden operational burden that happens before a patient ever reaches the clinic door. They break down why front office teams get stretched too thin, how insurance verification and scheduling create revenue leakage, and what it looks like when a clinic automates the full pre-arrival workflow without losing the human touch. In this episode: ·       Karen frames the episode around the pre-arrival patient journey and why it matters for physical therapy clinics. ·       Hannah Kim explains Pocket's focus on automating intake, insurance verification, scheduling, and patient communications. ·       The biggest front office inefficiency is overload: one team is often handling phone calls, insurance verification, scheduling, onboarding, and patient greetings. ·       Insurance verification is more complex than checking a portal - clinics often need to call payers, confirm authorization requirements, and verify visit limits. ·       Manual verification can take hours and includes documentation, EMR entry, calculating patient responsibility, and explaining benefits to patients. ·       Growth exposes weak systems: workflows that work for one or two locations often break when a clinic scales. ·       Slow or incomplete pre-arrival responses can cause patients to book elsewhere, creating missed visits and revenue leakage. ·       Pocket aims to own the full process from start to finish, including extracting patient info from the EMR, verifying benefits, making calls when needed, creating documentation, and uploading it back into the EMR. ·       Beyond insurance, Pocket also supports intake, onboarding, case creation, first-visit scheduling, and automated patient communications. ·       Hannah shares that the best transformations free front office staff to spend more time with patients, reduce burnout, and support clinic growth without adding staff. Timestamps: (00:01) Intro to the pre-arrival patient experience (00:52) Hannah Kim's background in healthcare and scaling Clipboard Health (02:10) Front office inefficiencies that show up again and again (03:20) Why insurance verification eats so much time (04:15) EMR documentation and the scheduling "Tetris" problem (05:53) The red flag that front office is quietly bleeding money (07:39) What front desk time could be used for instead (08:55) Why problems get worse as clinics scale (09:33) Why the pre-arrival journey affects first impressions and revenue (11:51) The biggest patient-loss window: weekends and after hours (14:08) Why insurance verification is still so manual (15:59) What a clearinghouse is and why it is not enough (17:11) The cost of sloppy or incomplete verification (18:20) How Pocket handles the full verification workflow (21:02) Expanding beyond insurance into the entire pre-arrival journey (23:48) What transformation looks like after automation (26:09) How clinics get comfortable letting go of manual processes (28:16) Why technology should reduce burnout, not replace people (29:47) Lightning round: front office assumptions, coordination of benefits, and advice from banking (34:30) Where to find Hannah Kim and Pocket Key frameworks ·       Pre-arrival journey: everything that happens before the first visit ·       Full-process automation: not just clearinghouse checks, but calls, documentation, EMR updates, and patient communication ·       Coordination of benefits: verifying primary, secondary, and tertiary coverage before problems show up later Notable quotes ·       "The pre-arrival journey is just so important. It's the first impression that patients have from your clinic." ·       "Nobody wants to be drowning in admin stuff." ·       "Technology implemented in the right way really helps a lot of clinics and really helps ease some of that burnout." Resources & Links: ·      Hannah on LinkedIn ·      Pocket Website ·      2048 Ventures More About Hannah: Hannah Kim is the CEO and Co-Founder of Pocket, an AI-powered pre-arrival platform that automates patient intake, insurance verification, scheduling, and patient communications for physical therapy clinics. She devoted her entire career to healthcare - began her career in healthcare investment banking at Centerview Partners before joining Clipboard Health, an "Uber for nurses" that built a marketplace connecting healthcare facilities with nurses for on-demand staffing. As Chief of Staff, Hannah helped scale Clipboard Health from Series A to Series C, supporting the company's growth into a billion dollar company. Jane Sponsorship Information: Book a one-on-one demo here Mention the code LITZY1MO for a free month Follow Dr. Karen Litzy on Social Media: Karen's Instagram Karen's LinkedIn Subscribe to Healthy, Wealthy & Smart: YouTube Website Apple Podcast Spotify SoundCloud Stitcher iHeart Radio

In a world where 21,883 software companies are all chasing the same narrow pool of buyers, automation isn't a competitive edge — it's the noise. Neal Goyal, who has closed $41M in software revenue with 81% of it sourced from LinkedIn, makes a compelling case for slowing down to speed up. This episode breaks down why trust is the only moat that can't be replicated, how LinkedIn is actually a stage where your ideal buyers are sitting in the audience, and why the kindergarten rules you already know — give before you ask, show up for others first — are the most powerful GTM strategy available right now. If you're over-automating and under-relating, this one is a wake-up call.Key Takeaways[0:00] — The counterintuitive edge: doing things that don't scale is the most powerful thing you can do in a world where everyone has the same automation tools[6:09] — The ecommerce SaaS explosion: from 5,000 to 21,883 software companies chasing the same TAM — and why that kills trust by default[8:47] — You're not competing against direct mail competitors; you're competing for the finite bandwidth of a 3–5 person marketing team[13:33] — Why 100% inbound pipeline is a "cancer" — it feels great but attracts everyone, not the right ones[16:46] — 81% of $41M in closed revenue sourced from LinkedIn — what the first 8–9 months of posting with zero engagement actually looked like[18:48] — The theater analogy: your buyers are in the seats, but only 1 in 100 sellers ever gets on stage[21:15] — The lurker phenomenon: LinkedIn engagement is low because it's public and professional — and that's exactly why the relationship value is high[21:18] — Why your LinkedIn connect request is like asking for someone's phone number at a bar — and what to do instead[26:37] — The bank account model: you can't make a withdrawal from an account you never opened. Deposits (engagement, value) must come before asks (connection requests, pitches)[32:47] — Email as a trust eroder by default — and why "who sent it" matters infinitely more than any subject line[34:45] — "Relationships beat algorithms" — why building rapport on LinkedIn before hitting the inbox changes the open rate entirely[36:46] — How to get organizational buy-in for a long-game strategy: lead from the front, be the best BDR on your own team[40:38] — What to do when your target prospect isn't posting on LinkedIn: write about them, spotlight their work, and watch what happens[44:09] — The founder question almost nobody is asking: where is your moat beyond technology? Care at scale is the answerTweetable Quotes"Automation takes away the most valuable skills we learned in kindergarten — give to others before you ask for anything in return." — Neal Goyal"Trust doesn't scale. That's exactly why it works." — Jeff Mains"You're not competing against direct mail companies. You're competing for the limited bandwidth of a 3-person marketing team alongside 21,000 other software vendors." — Neal Goyal"Nobody remembers who liked their post. They remember who left a comment that showed you actually read it." — Jeff Mains"Every cold pitch you send is a withdrawal from an account you never opened." — Jeff Mains"Only 1 in 100 sellers posts on LinkedIn — but your buyers are there 7, 8, 9 times a day. That IS the stage." — Neal Goyal"If you post 3 times a week, you move to the top 1% of content creators on LinkedIn. That's how low the bar is — and how big the opportunity is." — Neal Goyal"Care is going to be the thing that stands out above everything we talk about with AI. If you can demonstrate it, you're going to win." — Neal GoyalSaaS Leadership Lessons1. Do the things that don't scale — on purpose. When everyone has access to the same AI tools, the same sequences, and the same targeting data, doing what everyone else is doing makes you invisible. Genuine human attention is rare enough that when a prospect receives it, it stops them cold. That's your competitive edge.2. Trust is the only moat automation can't replicate. With the software landscape growing 4–5x in a few years and churn becoming a top threat, the relationship you build before the sale is what keeps the customer after it. The companies that invest in their customers the way they invest in prospects will win the retention wars ahead.3. LinkedIn is a stage, not a social app — and almost no one is using it that way. Your buyers are on LinkedIn every day. Only 1 in 100 sellers posts. If you post three times a week, you're in the top 1% of creators on a billion-person platform. Stop thinking about it as a channel and start thinking about it as the most accessible stage you'll ever have.4. Deposits before withdrawals — always. The bank account model isn't a metaphor, it's a system. Comment authentically on your prospects' posts before sending a connection request. Connect before pitching. Build before asking. This sequence flips connect acceptance rates by 3–5x and transforms cold email into warm email.5. Lead from the front to change a team's culture. Philosophy alone doesn't move teams. Results do. When Neal steps into a new org, he operates like an IC first — showing, not just telling. When the team sees the long game producing pipeline, they buy in. You can't coach trust-building from the sidelines.6. You're not competing against your category — you're competing for attention. Whether you're at seed stage or Series C, the real battle is for a limited-bandwidth buyer with 3–5 people on their team and 21,000 vendors in their inbox. The question isn't "are we better than our direct competitors?" It's "are we worth their attention right now, and are we earning it?"Guest Resourceshttps://www.linkedin.com/in/nealgoyal/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

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we delve into recent transformative events that are shaping this dynamic industry, from strategic mergers to groundbreaking drug approvals. Starting with the merger between Supernus Pharmaceuticals and Indivior Pharmaceuticals, this all-stock deal valued at $2.2 billion is set to create a powerhouse focused on central nervous system (CNS) disorders. By combining their resources, the new entity is expected to enhance its capabilities in neurological disorders with a robust portfolio of approved drugs. This merger allows the companies to leverage economies of scale, optimize research and development, and expand their market presence, offering promising prospects for advancements in treating CNS-related conditions. In regulatory news, Novartis has secured FDA approval for an expanded indication of Pluvicto (lutetium vipivotide tetraxetan), a radioligand therapy originally approved for PSMA-positive metastatic castration-resistant prostate cancer. This therapy can now be used for metastatic hormone-sensitive prostate cancer, marking a significant step forward in prostate cancer treatment. By targeting prostate-specific membrane antigen (PSMA) with precision, Pluvicto offers the potential for improved patient outcomes and highlights the growing role of radioligand therapies in oncology. Globally, Pharmamar's Zepzelca (lurbinectedin) has been approved in Canada, Qatar, and South Korea as a first-line maintenance therapy for extensive-stage small cell lung cancer. This approval signifies a potential shift in how aggressive cancer types are treated, particularly when combined with PD-L1 inhibitors, opening new avenues for effective combination therapies. Industry partnerships continue to drive innovation, as seen with Fujifilm and Taiho Pharmaceutical's collaboration to develop next-generation antibody-drug conjugate (ADC) manufacturing technologies using the Aralinq platform. With ADCs becoming increasingly pivotal in targeted cancer therapies due to their precision in delivering cytotoxic drugs to tumor cells, advancements in manufacturing could significantly enhance production capabilities and therapeutic efficacy. Financial dynamics within the industry remain robust. AbbVie has raised its 2026 revenue forecast to $67.6 billion, citing strong performances from its immunology drugs Skyrizi and Rinvoq. These therapies have shown substantial success in treating autoimmune conditions, reflecting their impact on AbbVie's financial health and reinforcing confidence in their commercial viability. On the clinical trial front, Ratio Therapeutics has successfully closed a $70 million Series C funding round to support its radiotherapeutics pipeline and initiate the ATLAS trial. This infusion of funds underscores the continued interest and investment in radiopharmaceuticals with promising applications in oncology. The landscape of mergers and acquisitions remains active as AstraZeneca and Bristol Myers Squibb reportedly engage in early-stage merger discussions. Such a merger could create an oncology giant valued at approximately $400 billion, potentially reshaping competitive dynamics and accelerating innovation across therapeutic areas. Regulatory changes are also underway with HRSA advancing a revised 340B rebate model pilot program despite hospital opposition. The implications for healthcare providers are significant as this could affect operational efficiencies and financial strategies within participating entities. Overall, these developments reflect ongoing trends toward industry consolidation and strategic partnerships that foster innovation in drug manufacturing technologies. Regulatory approvals continue to advance precision medicine through targeted therapies, showcasing the sector's dynamic nature as it addresses unmet medical needs while navigating complex regulatory environments. Meanwhile, Sandoz's settlement of nearly $500 million for antitrust claims in the U.S. highlights ongoing scrutiny of industry competition practices. This settlement signals potential shifts in market dynamics as companies seek to resolve legal challenges while maintaining operational integrity. Cybersecurity has emerged as a critical concern following Amgen's reported breach compromising sensitive patient data. This incident amplifies the need for enhanced data protection measures to safeguard information integral to patient trust and competitive integrity. In leadership news, BioNTech has appointed Guido Oelkers as CEO amid its continued innovation in mRNA technology post-COVID-19. This strategic move underscores BioNTech's commitment to leadership capable of navigating advances in mRNA therapeutics. Lastly, Novo Nordisk faced setbacks with its investigational therapy ziltivekimab failing a phase 3 trial targeting inflammatory pathways for cardiovascular outcomes. Despite such challenges, these high-stakes trials highlight both risks and opportunities inherent in pharmaceutical innovation. As we reflect on these stories, it's clear that scientific advancements, regulatory developments, and strategic business moves continue to shape the trajectory of the pharmaceutical and biotech sectors. These efforts promise significant implications for future drug development and patient care as companies strive to harness breakthroughs while adapting to evolving industry landscapes.Support the show

PODCAST EPISODE | An Analog Brain In A Digital Age With Marco Ciappelli Fifteen years ago, Rose Ross brought a client an idea for an awards program built specifically for enterprise tech startups. The client passed. She built it herself — and the Tech Trailblazers have been running ever since, independent, judged by practitioners, and open for entries until 3 September.

Osvald Nitski is the Chief Product Officer at Mercor, the AI-training and expert-data marketplace powering frontier-model development. Mercor last raised a $350 million Series C at a $10 billion valuation, and is reportedly in discussions for a new round at a $20 billion valuation. Mercor crossed $2BN in ARR in June; doubling from $1 billion in only four months. AGENDA:  00:04:00 Will open-source models kill the data-provider business? 00:07:00 Are enterprises still terrified of working with frontier model companies? 00:09:00 Does every company end up with its own specialised AI model? 00:10:00 Do enterprises actually have an AI ROI problem? 00:11:00 How should founders balance AI performance against exploding token bills? 00:14:00 Does AI mean product teams build 10x more—or ruthlessly simplify? 00:15:00 What does it now take to be a great product manager in an AI-native world? 00:20:00 Is the boom in AI services and forward-deployed engineers here to stay? 00:37:00 Can Mercor escape its dependence on a handful of frontier-model customers? 00:47:00 Are AI-generated code and agents creating a cybersecurity arms race? 00:56:00 When will robotics have its real "ChatGPT moment"?  

How is DeFi evolving as crypto matures? What role will AI play in blockchain security? And why is Japan becoming an important market for digital assets?In this episode, Gauntlet CEO and co-founder Tarun Chitra joins David Sencil at WebX Tokyo to discuss: The evolution of DeFi and on-chain risk management Why DeFi's adversarial environment could strengthen security How AI is changing crypto security assumptions Gauntlet's $125 million Series C led by SBI Holdings Japanese stablecoins and credit assets The growing role of RWAs in DeFi Why New York remains a leading hub for crypto builders

The latest episode of the Cambridge Tech Podcast features a fascinating panel discussion on international trade recorded live at this year's Cambridge Wide Open Week.Steven Lynch, Director of International Trade at the British Chambers of Commerce (BCC), opens with a sobering statistic: whilst only 10% of British businesses export, the BCC's network achieves a remarkable 45%. This disparity represents a significant untapped opportunity. As Lynch points out, "If we increased UK exports by 2%, we could improve UK GDP by 0.6%."The problem isn't a lack of innovation or ambition; it's a delivery gap. UK founders and scientists often lack the business expertise and market clarity needed to confidently enter international markets. This is where the new trade accelerator comes in.Rather than offering generic webinars about exporting, the BCC has created something fundamentally different: an end-to-end, process-driven accelerator specifically targeting life sciences companies interested in Singapore.Singapore might not be the obvious first choice for UK startups - the US typically dominates - but it's positioned as a strategic gateway to Southeast Asia. With a population of 6 million, Singapore punches well above its weight as a hub for healthcare innovation and life sciences.Jay Sadanandan, a Singapore life sciences expert, highlights the market's unique advantages:The Health Sciences Authority recently launched a close collaboration with the UK's MHRA, enabling regulatory expediency for innovative technologiesAccess to world-class clinical research infrastructure and patient populationsSingapore's ambitious government vision to solve prevention through treatment across healthcareThe 10 selected companies span pre-seed to Series C stages and focus on areas where UK strength aligns with Singapore opportunity: genomics, molecular diagnostics, advanced therapeutics, and enabling technologies like biologic stabilisation. Importantly, companies aren't required to relocate or abandon UK operations. Instead, they use Singapore as a commercial testbed, a lower-risk environment to validate their business model before scaling across Asia Pacific, the US, or beyond.What's Next?The pilot launches in September 2026, but the ambitions extend far beyond Singapore. Lynch reveals plans to scale to Hong Kong, the Gulf, and other strategic markets. The ultimate goal? Moving from 15 businesses entering one market annually to 15 businesses entering different markets each month.For UK startup founders and investors watching the export opportunity, this episode offers genuine insight into how the next generation of British life sciences companies might achieve global scale, and why Singapore might be the launchpad you haven't considered yet.Listen to the full episode on the Cambridge Tech Podcast to hear the complete discussion, including audience Q&A and practical advice for companies considering international expansion.Headline sponsor Holden Polestar Produced by Cambridge TV #CamTechPod Hosted on Acast. See acast.com/privacy for more information.

What if Bombay Shaving Company's biggest opportunity is already in front of it, but the founders are not seeing it clearly? In this episode, Shantanu Deshpande (Founder & CEO, Bombay Shaving Company) sits down with Aditya Sehgal (Founder, Asgard. World and Ex-President & COO, Reckitt), along with Deepak Gupta (Co-Founder & COO, Bombay Shaving Company), to unpack what it really takes to build a large Indian consumer brand for the next decade. At the centre of this conversation is Bombay Shaving Company's next big question: Can a grooming brand from India become a ₹4,000 Cr business, and eventually build products strong enough to compete globally? Aditya breaks down why India may still be behind China in innovation velocity, why local manufacturing is not just a financial decision but a strategic one, why AI will completely change how teams are built, and why money follows opportunity, not the other way around. What you'll learn from this episode: - Why 10-year-old startups learn and adapt faster than 100-year-old FMCG giants - How the "XEMES" innovation formula works and why India's "jugaad" mentality might be holding it back - What the 2x2 AI Decision Matrix is (Repeatability vs. Sensitivity) and what tasks to automate - Why taking a lower margin to build local manufacturing is a massive strategic moat - How a non-tech executive built a fully functional app in 8 days just by using AI If you're building a consumer brand, D2C business, retail company, or India-first startup, this episode gives you a sharp look at what it actually takes to build beyond the next quarter and create something that can compound for years. Navigate your way through these chapters: 00:00 Coming up 01:20 Introduction 02: 40 How They Built the Business 08:17 Breaking Down the XEMES Framework 16:15 Scaling Without Losing Quality 21:49 The Playbook for Building a ₹4,000 Cr Brand 27:42 How AI Will Transform Every Company 37:08 Closing Thoughts

Yasir Arafat, Co-Founder, CTO, & President, stops by the Energy News Beat podcastOn July 4th, 2024—America's 250th birthday—something extraordinary happened that most people missed. Allo Atomics turned on a brand new advanced nuclear reactor for the first time in 50 years. But here's what makes this truly revolutionary: they designed, built, and achieved criticality in less than 12 months. In this episode of the Energy Newsbeat Podcast, host Stu Turley sits down with Yasir Arafat, CTO and President of Allo Atomics, to explore how a company that was just two people 2.5 years ago is now rebuilding American manufacturing muscle and solving one of humanity's greatest challenges—powering the AI revolution while ending global energy poverty.From factory-based mass production to autonomous reactors powered by AI, from 36-day construction timelines to modular power plants that rival grid reliability, this conversation reveals how American innovation is about to transform energy forever.This is the story of how nuclear went from a decade-long project to a factory product—and why it matters for every country on Earth.Buckle up, as this was a huge discussion, and I hope that it is the first of many with Aalo Atomics. They are going places, and we need them to be as successful as they seem likely to be. They have the nuclear market and are designing the entire supply chain, from standardized fuel to go-to-market strategies, controlling it along the way.Secretary Chris Wright was at their offices and personally signed their approvals for critical mass testing. This is huge, and they were one of four reactors that kicked off our 250th anniversary in style.This is just about as cool as it gets - Well done Aalo Atomics and Secretary Chris Wright!1. Advanced Nuclear Reactor Development & AchievementYasir Arafat, CTO and President of Allo Atomics, discusses their groundbreaking accomplishment of building and turning on a brand new advanced nuclear reactor on July 4th—the first time this has been done in 50 years. This was achieved in less than 12 months as part of an executive order challenge from President Trump.2. Rapid Company Growth & Manufacturing Scale-UpAllo Atomics grew from a 2-person company 2.5 years ago to nearly 200 employees. The company is expanding its manufacturing facility from 40,000 square feet to 200,000 square feet this year, with plans to reach a million square feet within three years—positioning it as the largest nuclear reactor manufacturing facility in the United States.3. Factory-Based Mass Production ModelRather than traditional project-based nuclear construction, Allo is pioneering a factory mass-production approach similar to the automotive and aerospace industries. This includes automated welding systems and modular component design that can be transported and assembled like “Lego blocks.”4. AI Data Center Power DemandsThe podcast highlights the massive energy needs of AI data centers, which require 60 gigawatts of power over the next five years. Nuclear provides the ideal solution as a 24/7, clean baseload power source that doesn't depend on weather.5. Fuel Supply Chain & EnrichmentDiscussion of uranium enrichment levels, with Allo's reactors operating at 5% enrichment using commercially available uranium dioxide fuel—avoiding the need for exotic fuel forms or military-grade materials that would complicate supply chains.6. Modular Power Plant Design (AuloPOD)Allo designed a 50-megawatt power plant with multiple reactors and turbines providing N+1 redundancy, achieving 99.9% availability. This allows data centers to operate independently from the grid while maintaining reliability.7. Accelerated Construction & DeploymentThe company built its first nuclear reactor building in just 36 days—reducing construction costs by one-fifth. Their goal is to compress the entire deployment process from order to operation in less than 12 months, compared to the traditional 6-10+ years.8. Vertical Integration & Supply Chain SolutionsAllo manufactures the majority of hardware in-house while partnering with 100+ suppliers. The philosophy is “no unobtanium”—every component must be obtainable from existing suppliers or manufactured internally.9. AI Integration in Nuclear Design & OperationsAllo uses AI extensively for engineering design, licensing acceleration, and autonomous reactor operations. They have 62+ major AI projects running, with human validation at every step to ensure quality and safety.10. Global Energy Impact & Future ApplicationsThe conversation extends beyond data centers to broader applications: ending energy poverty worldwide, powering remote locations, military bases, marine vessels, and even lunar bases. The vision is to transform the U.S. into an energy exporter and uplift nations through reliable, low-cost nuclear power.11. Series C Funding & IPO PlansAllo is currently raising Series C funding and plans to go public only after demonstrating its ability to execute at scale with licensed products and generate revenue.This is a compelling discussion about how American innovation and manufacturing expertise can solve the global energy crisis through advanced nuclear technology.Check out Aalo Atomics here: https://www.aalo.com/Connect with Yasir on his LinkedIn here: https://www.linkedin.com/in/yasiraalo/A shout-out to Steve Reese and the Reese Energy Consulting group for sponsoring the Podcast https://reeseenergyconsulting.com/.Data2 if you have any business systems, can you trust A? Well, they have the patent on validation. . https://data2.zoholandingpage.com/energyAnd we have WellDatabase rolling in as a new sponsor. https://welldatabase.com/

Fabrice Haiat serves as CEO and Co-Founder of YOOBIC, the AI-powered platform helping frontline and deskless teams in retail, hospitality, and logistics communicate, train, and execute. YOOBIC has raised a total of $80 million in funding, including a $50 million Series C round in 2021, and is used by more than 350 global brands, including Ralph Lauren, Levi's, and Puma, to support over 3 million frontline employees worldwide. The company's investors include Highland Europe, Insight Partners, and Felix Capital.AGENDA:00:01:04 - Who is Fabrice Ayat? Building Yoobic from Scratch00:09:52 - How Yoobic Was Started and the Problem It Solves00:12:45 - How to Find a Real Customer Pain Point00:15:40 - How to Get Your First B2B Customers00:18:50 - Enterprise Sales Tips: How to Sell to Large Companies00:21:22 - Enterprise Security, Compliance & Winning Customer Trust00:24:32 - How Yoobic Scaled Sales and Marketing Globally00:27:37 - Best AI Tools Every Startup Should Be Using00:30:17 - How AI Is Changing the Way Companies Work00:32:56 - The Future of AI User Interfaces00:34:36 - AI Agents: The Next Big Business Opportunity00:36:38 - Scaling a Startup Team Without Losing Focus00:38:29 - When Should a Startup Expand to the US?00:41:58 - How and When to Raise Venture Capital00:44:42 - The Future of AI, Robotics & Automation00:45:38 - Entrepreneur Habits That Built a Billion-Dollar Company00:48:37 - Biggest Startup Mistakes and Lessons Every Founder Should Know

Today I'm delighted to welcome Andy Parker, CEO of Step Pharma.With over 25 years of experience across AstraZeneca, Shire, Zealand Pharma, andventure capital, Andy has led Step Pharma since 2019. The company is pioneeringa targeted approach to cancer and blood disorders by inhibiting the enzymeCTPS1. Their lead candidate, dencatistat, blocks this pathway that certaincancer cells and activated immune cells rely on, while sparing healthy cellsthat use the related CTPS2 enzyme.In this episode, we'll dive into the science behind this mechanism, explore StepPharma's expanding pipeline from lymphomas and solid tumours to essentialthrombocythaemia, and discuss their recent €38 million Series C financing.We'll also look ahead to the future of precision oncology.01:17 Meet Andy Parker06:12 The biotech ecosystem around Geneva07:51 The CTPS1 enzyme and why cancer cells depend on it14:08 Pipeline-in-a-product strategy across three indications20:14 The series C: €38 million raise25:41 Partnering with big pharma: possibilities and limits28:11 The future of precision oncology and metabolic targetingInterested in being a sponsor of an episode of our podcast? Discover how you can get involved here! Stay updated by subscribing to our newsletterTo dive deeper into the topic: Step Pharma and Concr to partner on cancer treatmentStep Pharma moves into oncology clinical trialsStep Pharma announces promising pre-clinical cancer data

We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li

.entry-img img{ display:none !important; } .single .hentry .entry-img{ display:none !important; } https://open.spotify.com/episode/6raW3lf3gJuwTrYNbdkf0F Too many organisations are pouring time and money into AI only to find that the promised efficiency gains and cost savings never materialise, leaving CFOs struggling to justify the investment. Understanding why most AI projects fail to deliver ROI, and what finance leaders can do differently, is now a critical skill for anyone responsible for steering strategy, systems, and spend. In this GrowCFO Show episode, host Kevin Appleby sits down with Sinohe Terrero, CFO and COO of Envoy, to explore why so many AI initiatives fall short and how finance leaders can change the outcome. Drawing on his experience as a serial startup CFO and operator in high-growth tech companies, Sinohe reframes AI as a practical toolkit for augmentation, task automation, and application development, and explains how confusion between these use cases leads to poor deployment and weak returns. Throughout the conversation, Sinohe shares real examples from Envoy's finance function, from AI-powered reconciliations and automated interview workflows to custom dashboards that bring data together in one place. He also dives into AI governance, describing the AI council he leads and the data policies that allow innovation while protecting sensitive information, ultimately positioning the CFO as a hands-on AI leader focused on both value creation and risk management. Key topics covered: Companies misunderstand what AI can do, deploy it inappropriately (e.g., trying to “fully automate everything”), and often lack in-house application developers who can tailor solutions to their actual workflows. Sinohe breaks AI use into augmentation, task automation, and application development, arguing that most ROI today comes from targeted task automation and small, purpose-built tools, not sweeping end-to-end automation projects. Envoy's finance team used AI to automate health insurance and other reconciliations, identifying about $40,000 in recoveries and turning tedious, quarterly work into a largely automated process. Sinohe personally builds AI-powered applications to reconcile accounts, summarize emails and Slack, prep and debrief interviews, and create a “morning coffee” dashboard that consolidates operational and financial insights into a single pane of glass. As head of Envoy's AI council, Sinohe has helped design a data governance matrix that clarifies what data can be used in which tools, allowing experimentation and creativity while strictly protecting company and customer data. Sinohe is bullish on increased data accessibility (e.g., via banks and platforms like Salesforce) and predicts a shift toward custom, CFO‑designed dashboards and tools, with legacy point solutions being displaced by in‑house applications that do exactly what the business needs. Links Sinohe Terrero on LinkedIn Kevin Appleby on LinkedIn GrowCFO Mentoring Timestamps:  0:01:36 – Sinohe explains Envoy as a workplace technology platform focused on managing physical spaces (visitor check-in, security, emergency notifications, desk allocation) with 6,000+ global customers and around 250 employees. 0:03:35 – He shares how timing, a tight investor story, and demonstrating strong cash flow and operational discipline were critical to a successful Series C raise during a turbulent market. 0:04:47 – Sinohe lays out the core reasons AI fails in many organizations and introduces his three-part framework: augmentation, task automation, and application development. 0:07:11 – He describes teaching himself to build AI-powered applications, including an asset-account reconciliation tool that cut a two-hour monthly process down to about two minutes. 0:12:21 – Using tools like Scribe to document workflows, Envoy's finance team identifies automation candidates; a payroll-led AI skill for health insurance reconciliations surfaced roughly $40,000 owed to the company. 0:17:53 – Sinohe explains Envoy's AI council, clear AI policies, and a data governance matrix that defines what data can be used where, enabling safe experimentation at scale. 0:21:17 – He details his personal AI setup: automated interview briefing/debriefing via Granola + Claude, daily digests of emails/Slack/meetings, and automated summaries of operational metrics and customer activity. 0:24:58 – Sinohe predicts job disruption in large teams (e.g., 100 accountants potentially shrinking to 60) but sees smaller teams using AI to focus on higher-value, advisory work rather than basic reconciliations. 0:26:30 – He describes replacing tools like Flowcast, Asana/Monday, and other SaaS products with custom AI-enabled applications that do 75% of what generic tools do—but 100% of what Envoy actually needs. 0:33:36 – Sinohe forecasts greater bank and platform data accessibility, more automated reconciliations, and a shift that frees CFOs from operational drudgery so they can focus on higher‑value strategic work. Find out more about GrowCFO If you enjoyed this podcast, you can subscribe to the GrowCFO Show with your favorite podcast app. The GrowCFO show is listed in the Apple podcast directory, Spotify and many others. Why not subscribe there today? That way, you never miss an episode. GrowCFO is a great place to extend your professional network. Join GrowCFO as a free member today and participate in our regular networking events and webinars. Premium members can also access our extensive training center and CFO Digital Toolkit. You can enroll in our flagship Future CFO or Finance Leader programs here. You can find out more and join today at growcfo.net

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we're diving into a wave of exciting advancements and strategic movements shaping the landscape of this ever-evolving industry. Starting with a significant breakthrough in cell therapy, the FDA has granted approval to Orca Bio's Tregzi, a novel treatment aimed at preventing graft-versus-host disease (GVHD) in blood cancer patients undergoing allogeneic transplants. This approval emphasizes the potential of regulatory T cells in mitigating the severe complications that often accompany such transplants. The success of Tregzi not only marks a pivotal moment for cell therapy but also heralds a new era in transplant medicine where cellular therapies can significantly improve patient outcomes by reducing GVHD incidence. In other strategic moves, Ipsen's acquisition of Memo Therapeutics for up to $796 million highlights the industry's focus on addressing unmet needs in transplant medicine. Memo's work on monoclonal antibodies targeting the BK virus—a significant threat to kidney transplant patients—reflects a growing trend towards personalized medicine and targeted biologics. This acquisition is poised to bring much-needed therapeutic interventions to market, underscoring the sector's commitment to innovative solutions. Meanwhile, Anthropic is making waves with the introduction of Claude Science AI Workbench, accompanied by an internal drug discovery program aimed at neglected diseases. This initiative underscores the transformative role of artificial intelligence in drug discovery, particularly in areas previously overlooked due to limited commercial incentives. By accelerating the identification and development of novel therapeutics, AI-driven platforms promise breakthroughs in treating rare and neglected diseases. In clinical trial news, Abivax has reported promising Phase 3 safety data for obefazimod, a miR-124 enhancer targeting ulcerative colitis. By alleviating previous cancer-related concerns, obefazimod stands out as a potential small molecule oral therapy for autoimmune conditions. This advancement demonstrates ongoing innovation in small molecule therapeutics designed to modulate immune responses, expanding treatment options for ulcerative colitis patients. On the financial front, several companies are bolstering their pipelines through significant funding efforts. Alvotech has secured a $75 million term loan to enhance its biosimilar pipeline, reflecting the increasing importance of cost-effective biologic alternatives. AB Science has raised approximately $2.62 million focusing on oncology and rare diseases like acute myeloid leukemia (AML) and amyotrophic lateral sclerosis (ALS). Additionally, Flare Therapeutics' $85 million Series C round emphasizes precision medicine approaches targeting transcription factors, illustrating the industry's dedication to precision medicine. Regulatory landscapes are also shifting with Sarepta Therapeutics' FDA acceptance for full approval bids on Amondys 45 and Vyondys 53 despite confirmatory trial challenges. These therapies leverage exon skipping technology for Duchenne muscular dystrophy treatment, highlighting the intricate balance between accelerated approvals and robust clinical evidence requirements. In mergers and acquisitions news, Kimball Electronics' acquisition of Helvoet Polymer Technologies expands their drug delivery systems capabilities across Europe and India. This strategic alignment reflects broader industry trends towards integrated service offerings and global expansion efforts. Regulatory challenges continue with leadership changes at the FDA as Vijay Kumar steps down amid ongoing turbulence within gene and cell therapy sectors. Additionally, Angitia Biopharmaceuticals' termination of its Phase 3 trial for its BMP-6 candidate illustrates inherent risks and strategic pivots often necessary in drug development. Turning our focus globally, China's approval of its first CAR-T therapy for solid tumors signals a pivotal moment in oncology. This milestone could spur similar advancements worldwide, particularly drawing interest on when such treatments might receive approval in other regions like the United States. Moderna's expansion into in vivo CAR-T therapies for autoimmune diseases further marks a strategic divergence from traditional approaches, aiming to create off-the-shelf solutions that redefine treatment paradigms. Gene therapy continues to gain traction as Uniqure navigates regulatory reversals from the FDA regarding its Huntington's disease gene therapy filing for accelerated approval. This development hints at a broader shift within regulatory bodies towards fostering innovation in rare disease treatments under new leadership directives. Industry-wide trends also reveal a notable shift towards automation in cell therapy production led by companies like Cellares and Ori. This reflects growing demands for scalable manufacturing processes that enhance production capabilities while reducing costs. Overall, these developments illustrate a dynamic landscape where scientific innovations, strategic partnerships, regulatory adjustments, and technological integrations drive progress across various medical fields. As these sectors evolve, they hold vast potential for transforming patient care through groundbreaking therapeutic options and improved healthcare delivery systems worldwide.Support the show

We talk the foldable iPhone, Michael Jackson's death, and Trump passports. Some other notable news:Micron delivered one of the most stunning quarters in semiconductor history, reporting roughly $41.5 billion in fiscal-Q3 revenue, up about 346% year over year, and guiding next quarter to about $50 billion with an 81% gross margin. The read-through is that AI has turned high-bandwidth memory from a boom-bust commodity into a scarce, contracted input for the next generation of compute.SpaceX's record IPO unwound almost as violently as it launched, with the stock falling 31% in four sessions from its June 16 peak and erasing more than $600 billion of market value. A 4.2% public float, a $20 billion bond sale, newly listed options, August lockup risk, and a $4.9 billion 2025 net loss all collided in one of the clearest market-structure lessons of the AI trade.Oracle disclosed about 21,000 job cuts and directly tied the reductions to AI adoption in its own annual filing, even as it expands AI cloud capacity through major data-center deals linked to OpenAI and Meta. China also reclaimed the world's-fastest-supercomputer crown with LineShine, an all-domestic CPU-only system that hit 2.198 exaflops under U.S. export controls.A fatal Tesla crash in Katy, Texas, reopened the self-driving accountability fight after the driver said he had been using Tesla's partially automated driving system and both NHTSA and NTSB opened investigations. Robotics funding added the other side of the physical-AI story, with about $55.8 billion raised so far in 2026 and Figure banking a $1 billion Series C at a $39 billion valuation.The runner-ups: FedEx beat expectations and completed the FedEx Freight spin-off, onsemi agreed to buy Synaptics for about $7 billion to push deeper into edge and physical AI, and UN Secretary-General Antonio Guterres pressed AI companies to disclose data-center emissions, water use, land use, and energy sources. The 30,000-ft view: Q1 GDP was revised up to 2.1%, PCE inflation ran at 4.6%, markets repriced toward possible Fed hikes, Nvidia's Vera CPUs entered full production, and the mega-IPO pipeline still has Anthropic and OpenAI queued. If you want a prize, send us a DM: instagram.com/rickerandbon tiktok.com/@rickerandbon youtube.com/@rickerandbon

In this episode of The Product Podcast by Product School, Carlos González de Villaumbrosia sits down with Cristina Cordova, Chief Operating Officer at Linear, the product development system built for teams and agents. Linear raised $82 million in a Series C round in June 2025 at a $1.25 billion valuation. The company has been profitable since 2021, and serves over 20,000 paid business customers, from seed-stage startups to Fortune 100 enterprises, with a team of just 140 people. Before Linear, Cristina joined Stripe as one of its first employees, and led Platform and Partnerships at Notion.What you'll learn:Why keeping headcount intentionally lean is a strategic advantageReplacing traditional interviews with paid two to five-day projectsWhy PMs are the fastest-growing power users of agentic toolsKey takeaways:A small team is not a small business. Revenue, customers, and growth rate matter more than headcount.If you fully delegate your AI thinking, you lose your native understanding of how these products actually workAgentic workflows are now the default, not a feature. The companies that treat them that way will pull ahead.Credits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Cristina CordovaSocial Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

Every founder gets a version of the same advice: don't pick a fight with an entrenched industry. The incumbents have the relationships, the regulatory cover, the deep pockets - you'll bleed out trying.But some of the most interesting companies of the last decade were built ignoring that advice, winning over markets that were nearly impenetrable.In this episode, Yaniv Bernstein is joined by Brandon Weber - co-founder and CEO of Nava Benefits, a Series C-funded AI-powered health benefits brokerage. Before Nava, Brandon co-founded Hightower, a commercial real estate startup that merged with VTS and went on to run over half of all office buildings in the United States. Brandon has now done this twice in two completely different industries, and has developed a repeatable playbook for breaking into entrenched markets and using AI as a structural advantage.Yaniv and Brandon dig into what actually makes a market 'broken', why the entry point needs to be far narrower than most founders think, and how to build the conviction to keep going when a thousand people tell you it won't work. In this episode, you will:Understand the 'burning platform' signal - what makes a market 'broken', but worth spending a years breaking intoLearn why your entry point needs to be far narrower than feels comfortable, and how Brandon went from targeting 'the health insurance market' to 'employers with 50-500 employees who can't afford a dedicated benefits team'Hear why 'disrupting from within' is often smarter than disrupting head-on - and how Nava built a broker-shaped entity that the industry's immune system couldn't rejectDiscover how to design a human-AI system (what Brandon calls a 'cybernetic' service model) where agents handle 80-85% of the work and licensed professionals operate at the top of their licenseTimestamps00:00 Coming Up…00:45 On Today's Show: Brandon Weber on Fixing Broken Industries01:43 How To Spot Broken Markets03:59 Why Most Healthcare Startups Fail (Distribution)05:35 Lessons From Building Hightower and VTS08:41 How Do We Think Smaller? Finding the 'Narrow Wedge'10:57 What It Means To 'Disrupt From Within'16:53 Choosing the ICP18:35 The Innovator's Dilemma and Moving Upmarket22:57 Scaling with AI: A Business in Two Phases26:29 Service as a Software34:02 Attract and Hire Industry Insiders36:44 When to Acquire39:06 Closing AdviceResources mentioned in this episodeNava Benefits (Brandon's company): https://www.navabenefits.comGary Lo's previous TSP episode: https://youtu.be/jtMgd7Nv_HYThe Innovator's Dilemma by Clayton Christensen (framework discussed at length): https://www.amazon.com/Innovators-Dilemma-Revolutionary-Change-Business/dp/0062060244The PactHonor the Startup Podcast Pact! If you have listened to TSP and gotten value from it, please:Follow, rate, and review us in your listening appSecure your official TSP merchandise at https://shop.tsp.show/Follow us on YouTube for full-video episodes: https://www.youtube.com/@startup-podcastGive us a public shout-out on LinkedIn or anywhere you have a social media followingKey linksThis episode of the Startup Podcast is sponsored by .tech domains. Forget weird prefixes and creative misspellings; the availability for .tech domains is simply way better than .com. For a clean name that highlights your tech credentials, get a .tech domain at your favorite registrar.The Startup Podcast website: https://www.tsp.show/episodes/Learn more about Chris and YanivWork 1:1 with Chris: http://chrissaad.com/advisory/Follow Chris on Linkedin: https://www.linkedin.com/in/chrissaad/Follow Yaniv on Linkedin: https://www.linkedin.com/in/ybernstein/Producer: Justin McArthur https://www.linkedin.com/in/justin-mcarthurAssistant Producer: Steph Hefferan https://www.linkedin.com/in/steph-heff/Intro Voice: Jeremiah Owyang https://web-strategist.com/

https://novacut.ai/  https://genaimeetup.com/  Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM     Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h  Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/  OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens  Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d   Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/    https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073    Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales.   https://www.minimax.io/models/text/m3  MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops.   https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/  Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/  JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8    https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html      Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal.  https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench  ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%.   Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/  The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we're diving into some of the most significant advancements in scientific research, clinical trials, and regulatory landscapes within the industry. These developments are shaping the future of patient care and drug development significantly. Starting with Legend Biotech's LB2501, which achieved an impressive 100% response rate in a Phase 1 study for non-Hodgkin lymphoma using in vivo CAR T-cell therapy. This breakthrough highlights the transformative potential of CAR T-cell therapies in oncology, especially for B-cell lymphomas. Such success opens the door for accelerated regulatory pathways, offering hope to patients with limited treatment options. In another key development, JJP Biologics shared positive interim data from its Phase 1b trial of nebaprubart targeting CD89 in linear IgA disease. This monoclonal antibody is promising in treating autoimmune conditions by targeting specific disease mechanisms. Meanwhile, GSK's Velzatinib (IDRX-42) achieved a 61% response rate in Phase 1/1b trials for gastrointestinal stromal tumors, showing efficacy against cases resistant to treatments like imatinib. Johnson & Johnson's Nipocalimab met its primary endpoint in a Phase 2 study for systemic lupus erythematosus, underscoring the potential of FcRn blockade in managing autoimmune diseases. Zenas Biopharma's Phase 3 data for Obexelimab targeting CD19/FcγRIIB in IgG4-related disease further emphasizes the role of targeted therapies in managing complex autoimmune disorders. On the regulatory front, Foundation Medicine's FoundationOne Blood Test received FDA approval as a companion diagnostic for Pfizer's Talzenna (talazoparib) to treat prostate cancer with homologous recombination repair gene mutations. This approval underscores the growing importance of precision medicine and companion diagnostics in tailoring cancer treatments based on genetic profiles. Additionally, Lupin and Natco Pharma secured FDA approval for their generic version of Eribulin Mesylate Injection, essential for reducing healthcare costs and improving patient access to vital therapies. Eli Lilly's collaboration with Ascidian Therapeutics focuses on RNA exon editing for kidney diseases, potentially revolutionizing treatment approaches by correcting genetic errors at the RNA level. This partnership reflects a burgeoning interest in RNA-based therapies and their capacity to address unmet medical needs. Regeneron expanded its pact with CytomX Therapeutics to develop conditionally active bispecific antibodies, emphasizing innovation in oncology drug discovery. Such collaborations combine expertise across companies to expedite cutting-edge therapies' development. In terms of funding, NewLimit's successful $435 million Series C round aims to advance epigenetic reprogramming medicine towards human trials. This initiative highlights the burgeoning field of aging biology and its implications for extending healthy human lifespan through innovative therapeutic approaches. Similarly, Immu Biosciences raised $53 million to enhance its immunology platform using AI/ML technologies, underscoring AI and machine learning's critical role in accelerating drug development processes. Turning our gaze towards China's expanding influence on the global biotech stage, Akeso's presentation at ASCO 2026 marked a significant milestone as it became the first-ever Chinese dataset featured in a plenary session. This achievement underscores China's growing prominence in biotechnology and highlights its commitment to advancing innovative medical solutions globally. Simultaneously, Gilead's strategic partnership with Cencora aims to enhance access to CAR-T therapies like Yescarta and Tecartus by expanding their network of treatment centers. CAR-T therapies represent a paradigm shift in cancer treatment by offering personalized options for certain types of cancer. Despite challenges such as Roche's setbacks with its oral SERD drug giredestrant in breast cancer trials, innovation continues unabated. Zevra Therapeutics' launch of Miplyffa for Niemann-Pick disease type C exemplifies efforts to transform rare disease markets by improving patient outcomes through increased access and tailored treatment strategies. Finally, Eli Lilly's acquisition spree reflects broader trends where pharmaceutical companies increasingly integrate Chinese innovations into their development pipelines. This period marks a transformative phase characterized by collaboration between global pharma giants and Chinese biotechs, signaling an era where innovation is globalized and aimed at addressing critical healthcare challenges worldwide. These advancements reflect a dynamic period of innovation within the pharmaceutical and biotech industries. The focus on personalized medicine, targeted therapies, and groundbreaking technologies like RNA editing indicates a shift towards more precise treatment modalities. As these discoveries transition from research phases to clinical applications, they hold the potential to transform patient care significantly. Strategic partnerships and substantial funding initiatives illustrate a robust ecosystem supporting these innovations' rapid advancement. As regulatory bodies continue approving novel therapeutics and diagnostics, the emphasis on personalized healthcare will likely drive future developments, ultimately leading to improved patient outcomes worldwide. As we continue navigating these developments, it's clear that the pharmaceutical and biotech sectors are on the cusp of transformative breakthroughs that promise to redefine healthcare delivery across multiple domains. Thank you for tuning into Pharma Daily; stay informed and stay ahead.Support the show

Four stories today — starting with the most importantweek in NIO's 2026 delivery story so far.NIO delivered 37,705 vehicles in May 2026 — up 62.3%year over year and up 28.4% from April's 29,356.Three brands all firing: 20,013 NIO brand, 12,029 Onvo,5,663 Firefly. Year-to-date deliveries hit 150,526 —up 68.7% year over year. Cumulative deliveries reached1,148,118. The ES8 has been the number one sellingvehicle above 400,000 yuan for five consecutive months.The ES9 has over 50,000 pre-orders — with deliveriesonly starting May 28th. June is when the ES9 volumereally arrives.William Li is reportedly preparing to take Mirattery —NIO's battery asset operator — public as his eighth IPO.Mirattery owns the batteries in NIO's 3,846 swap stations.It recently closed a Series C total of nearly 2 billionyuan with state-backed institutional investors. A MiratteryIPO gives the battery swap infrastructure its own publicvaluation — separate from NIO's stock price. NIOshareholders already own a piece of that business.The market hasn't priced it in yet.The May Chinese EV delivery rankings reshuffled.Leapmotor delivered 81,569 units — record high for thesecond consecutive month. Zeekr delivered 34,377 unitsup 81.81% year over year. Huawei HIMA delivered 46,122.Xiaomi exceeded 30,000 for the month. BYD delivered376,990 — ending its 8-month year-over-year declinestreak. The market is forming a barbell — premium brandsand value brands winning, the middle getting squeezed.NIO is firmly on the premium side of that barbell.The Iran 60-day MOU is still waiting for Trump'ssignature. Hormuz remains largely closed. Oil at $92.56.June brings Gen 5 swap stations, Onvo L60 launch, andES9 volume ramping. The setup for Q3 is building now.

Founded in 2020, Axiado deploys hardware-anchored, AI-driven platform security by embedding silicon directly on the rack, protecting AI and cloud infrastructure against cyberattacks in real time. Latham represented Axiado in its oversubscribed US$100+ million Series C+ funding round. In this episode of Connected With Latham, Haim Zaltzman, Global Vice Chair of Latham's Emerging Companies & Growth Practice, sits down with Gopi Sirineni, Founder, President, and CEO of Axiado, to discuss the company's proximity-based security approach, the evolving cybersecurity landscape for AI infrastructure, and India's growing role in the global semiconductor ecosystem.   This podcast is provided as a service of Latham & Watkins LLP. Listening to this podcast does not create an attorney client relationship between you and Latham & Watkins LLP, and you should not send confidential information to Latham & Watkins LLP. While we make every effort to assure that the content of this podcast is accurate, comprehensive, and current, we do not warrant or guarantee any of those things and you may not rely on this podcast as a substitute for legal research and/or consulting a qualified attorney. Listening to this podcast is not a substitute for engaging a lawyer to advise on your individual needs. Should you require legal advice on the issues covered in this podcast, please consult a qualified attorney. Under New York's Code of Professional Responsibility, portions of this communication contain attorney advertising. Prior results do not guarantee a similar outcome. Results depend upon a variety of factors unique to each representation. Please direct all inquiries regarding the conduct of Latham and Watkins attorneys under New York's Disciplinary Rules to Latham & Watkins LLP, 1271 Avenue of the Americas, New York, NY 10020, Phone: 1.212.906.1200

SOND, a startup led by Bose's former head of sleep products, emerged from stealth with $7M in funding for its AI-powered sleep earbuds. Also, WeRoad, the Milan-based group travel startup, has raised a $58 million Series C round led by Airbnb as it prepares for its first major expansion outside Europe Learn more about your ad choices. Visit podcastchoices.com/adchoices

The FBI warns attackers are abusing Microsoft OAuth authentication. India pushes faster patching as AI speeds up cyberattacks. Iranian hackers blend phishing with SEO poisoning. Anthropic's AI finds thousands of open source flaws, while AI also reshapes bug bounties and fuels supply-chain attacks hitting thousands of GitHub repos. Plus, a new LMS zero-day, bulletproof hosting arrests in the Netherlands, FTC action over bogus “active listening” claims, and another busy week for cyber funding and M&A. Our guest is Kurtis Minder, author, joining us to discuss his book "Cyber Recon: My Life in Cyber Espionage and Ransomware Negotiation.” Please disregard all searches for disregard. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today we are joined by Kurtis Minder, author, joining us to discuss his book "Cyber Recon: My Life in Cyber Espionage and Ransomware Negotiation." Selected Reading FBI warns of Kali365 phishing service targeting Microsoft 365 accounts (Bleeping Computer) India's CERT-In Sets 12-Hour Patch Deadline for Exposed Flaws (Infosecurity Magazine) Iran-Linked Hackers Target US Aviation with Phishing and SEO Poisoning Campaign (Infosecurity Magazine) Anthropic: Mythos Detected 23,000 Potential Vulnerabilities Across 1,000 OSS Projects (SecurityWeek)  HackerOne takes an axe to its bug bounty rewards (The Register) Automated 'Megalodon' Campaign Spreads GitHub Repo Backdoors (GovInfo Security) Hackers Exploited KnowledgeDeliver Zero-Day for Web Shell Deployment (SecurityWeek) Admins of Bulletproof Hosting Service Used by Russian Hackers Arrested in Netherlands (SecurityWeek) FTC to Require Cox Media Group, Two Other Firms to Pay Nearly $1 Million to Settle Charges They Deceived Customers About “Active Listening” AI-Powered Marketing Service (Federal Trade Commission) Socket raises $60 million in Series C funding. (N2K Pro Business Briefing) You can no longer Google the word 'disregard' (TechCrunch) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Europe’s startup ecosystem is maturing rapidly, with companies like Revolut, Lovable, and Legora demonstrating that world-class technology businesses can be built and scaled on the continent. While the US remains the dominant force in venture-backed software as home to the largest markets, the deepest capital pools, and the most ambitious exit culture, a growing number of European founders are choosing to build at home. Edward Keelan is a Partner at Octopus Ventures, one of Europe’s largest and most active venture capital firms, where he has spent over 16 years leading the B2B software and enterprise AI fund. His portfolio spans seed through Series C, with a focus on European founders building in AI, vertical SaaS, and enterprise software. This long-view experience gives him a rare perspective on what it takes to build enduring technology companies in Europe. In this episode, Edward joins Elena Boroda to discuss what separates great founders from the rest, how AI is reshaping the software landscape and threatening established players, the state of the European startup ecosystem and what it needs to compete globally, and what engineers and founders should be thinking about as the industry enters a new era. Elena Boroda focuses on GTM for developer tools and AI startups, with experience in observability and building tools for MCP servers. She is based in Berlin. https://www.linkedin.com/in/elena-boroda Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post The European Startup Scene appeared first on Software Engineering Daily.

Europe’s startup ecosystem is maturing rapidly, with companies like Revolut, Lovable, and Legora demonstrating that world-class technology businesses can be built and scaled on the continent. While the US remains the dominant force in venture-backed software as home to the largest markets, the deepest capital pools, and the most ambitious exit culture, a growing number of European founders are choosing to build at home. Edward Keelan is a Partner at Octopus Ventures, one of Europe’s largest and most active venture capital firms, where he has spent over 16 years leading the B2B software and enterprise AI fund. His portfolio spans seed through Series C, with a focus on European founders building in AI, vertical SaaS, and enterprise software. This long-view experience gives him a rare perspective on what it takes to build enduring technology companies in Europe. In this episode, Edward joins Elena Boroda to discuss what separates great founders from the rest, how AI is reshaping the software landscape and threatening established players, the state of the European startup ecosystem and what it needs to compete globally, and what engineers and founders should be thinking about as the industry enters a new era. Elena Boroda focuses on GTM for developer tools and AI startups, with experience in observability and building tools for MCP servers. She is based in Berlin. https://www.linkedin.com/in/elena-boroda Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post The European Startup Scene appeared first on Software Engineering Daily.

Europe’s startup ecosystem is maturing rapidly, with companies like Revolut, Lovable, and Legora demonstrating that world-class technology businesses can be built and scaled on the continent. While the US remains the dominant force in venture-backed software as home to the largest markets, the deepest capital pools, and the most ambitious exit culture, a growing number of European founders are choosing to build at home. Edward Keelan is a Partner at Octopus Ventures, one of Europe’s largest and most active venture capital firms, where he has spent over 16 years leading the B2B software and enterprise AI fund. His portfolio spans seed through Series C, with a focus on European founders building in AI, vertical SaaS, and enterprise software. This long-view experience gives him a rare perspective on what it takes to build enduring technology companies in Europe. In this episode, Edward joins Elena Boroda to discuss what separates great founders from the rest, how AI is reshaping the software landscape and threatening established players, the state of the European startup ecosystem and what it needs to compete globally, and what engineers and founders should be thinking about as the industry enters a new era. Elena Boroda focuses on GTM for developer tools and AI startups, with experience in observability and building tools for MCP servers. She is based in Berlin. https://www.linkedin.com/in/elena-boroda Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post The European Startup Scene appeared first on Software Engineering Daily.

The man who helps finance Europe's defence: Robert de Groot, vice president of the European Investment Bank There is a particular kind of power that comes with someone who decides, quietly, which ideas get funded and which don't. Robert de Groot, and his team, holds that power over an extraordinary range of things: military bridges in Poland, rocket launchers in Spain, satellite-to-smartphone startups in Luxembourg, drone intelligence software in Estonia. As Vice President of the world's largest multilateral lender, the EIB sitting on the Kirchberg plateau, his brief covers security, defence, space, and innovation. It is, as he puts it with characteristic understatement, "quite a new direction" for a bank that, not long ago, wouldn't touch defence at all. That has changed. Dramatically. Since Russia's invasion of Ukraine, the EIB has rewritten its mandate, opening five distinct financing pillars across the defence and security ecosystem, from large-scale infrastructure to venture equity for startups building things that didn't exist five years ago. De Groot has spent the last two years touring every European capital, sitting down with defence, finance, and interior ministers, and asking “What does Europe actually need, and can we finance it?” "The urgency I hear in private is far greater than what you see in public." What he found on the road was a continent with a perception gap. The Baltic states are operating in a different psychological reality from much of western Europe. For Estonia, Latvia and Lithuania, the threat from the east is not geopolitics but geography. However, de Groot is cautiously optimistic. Germany has made a near-complete reversal on defence spending in three years. The Nordics have joined NATO. Ministers of Interior are now showing up to defence finance meetings, because the boundary between military security and civil security has dissolved. Cyber attacks, compromised energy grids, sabotaged undersea cables are happening now. The physical problems, meanwhile, are startlingly concrete. Bridges that cannot carry battle tanks. Ports unable to defend against unmanned underwater vehicles. Roads along NATO transit routes from Antwerp through Germany deep into Poland that haven't been maintained to handle today's military hardware. "It sounds absurd," de Groot says, "until you realise it's a multi-billion euro problem." The financing exists. The fixes are underway. But getting three countries to agree on a shared corridor before one of them goes its own way remains the harder challenge. For innovators and entrepreneurs building the dual-use technologies that now sit at the heart of European defence strategy, de Groot offers a map through the financing ecosystem. Early stage? Venture capital funds backed by the European Investment Fund. Series A and B? Venture debt, a product barely known in Europe five years ago, now scaling fast, with Luxembourg companies OQ Technology and Artec 3D among its beneficiaries. Series C and beyond? The European Tech Champions Initiative, designed explicitly to stop European unicorns from decamping to California. And for defence tech specifically, a new Defence Equity Facility of up to one billion euros: real, patient, European capital, with no American relocation clause attached. "The companies I meet across Europe mostly want to stay. We need to make sure the financing is there when they do." On the day of interview, a loan was signed for the Luxembourg Fire Brigade's logistics infrastructure. Security exists at multiple scales simultaneously, from orbital launch capability to the speed at which a fire engine reaches a crisis. Both matter and both require investment. Both represent the same underlying bet: that Europe, if it chooses to move with enough conviction, is more than capable of defending and financing its own future. De Groot, for his part, seems to believe it. The question, as ever, is whether the institutions can move as fast as the moment requires. Robert de Groot is Vice President of the European Investment Bank, responsible for Security, Defence, Space and Innovation Finance.

The window for government contractors, especially those in defense and space technology, to go public is open again as several listings over the past 12 months show and SpaceX's own offering this year will illustrate. Dave Khalsa, head of mid-cap defense and government technology investment banking at J.P. Morgan, works on transactions of many different types and observes all of them to help companies in the market figure it all out. In starting out this episode, Dave explains what all companies can take away from the handful of initial public offerings over the past 12 months and SpaceX's listing. This is true of whether they plan to go down the IPO path or not. The rest of the conversation between Dave and our Ross Wilkers focuses on how government priorities shape merger-and-acquisition activities by companies under different ownership models, including private equity and venture capital. Public offerings put GovCon in a new spotlight as SpaceX's listing looms HawkEye 360's public offering hauls in $416M AEVEX fetches $320M in IPO proceeds Firefly captures $868M in IPO proceeds York Space Systems raises $629M in public offering Merlin Labs' public offering collects $200M to build an AI autopilot for any aircraft L3Harris to spin off its rocket motor business with the Pentagon as an anchor investor AeroVironment's tech and business blueprints with BlueHalo now in the fold Veritas Capital's ninth fund grows to $15.3B OceanSound Partners hauls in $3.4B for third fund Arlington Capital fetches $6B for its seventh fund Government equity investments open a new frontier for industry Venture investing is part of the M&A conversation too Anduril hauls in $5B for Series H round Shield AI closes $1.5B Series G round and moves on acquisition Saronic wraps up $600M Series C round Sierra Space and Vast detail their Series C investment rounds

The FCC eases restrictions on foreign-made routers. Shiny Hunters hit Canvas and Zara. SailPoint discloses unauthorized access to its GitHub repositories. TrickMo Android banking malware has more tricks up its sleeve. Polish officials warn of increased targeting of ICS and public infrastructure. A federal judge orders $10 million in restitution for stolen zero days. German authorities takedown the Crimenetwork marketplace, again. Monday business breakdown. Dan Lorenc, Chainguard CEO and co-founder, is talking about a recent wave of supply chain attacks. Malware gets signed, sealed and delivered.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Dan Lorenc, Chainguard CEO and co-founder, is talking about how the recent wave of supply chain attacks is fundamentally different – and more dangerous –than previous incidents, as well as immediate steps organizations should take as this continues to unfold. Selected Reading US: FCC Relaxes Foreign-Made Router Ban to Allow for Security Updates (Infosecurity Magazine) ShinyHunters Escalates Canvas Extortion (Infosecurity Magazine) Zara Data Breach Impacts Nearly 200,000 Customers (Infosecurity Magazine) SailPoint Discloses GitHub Repository Hack (SecurityWeek) TrickMo Android banker adopts TON blockchain for covert comms (Bleeping Computer) Polish ABW warns cyberattacks shifting from espionage and data theft toward physical disruption of critical infrastructure (Industrial Cyber) Trenchant Exec Who Sold Zero Days to Russian Buyer Ordered to Pay $10 Million in Restitution to Former Employers (Zero Day) Resurrected 'Crimenetwork' Marketplace Taken Down, Administrator Arrested (SecurityWeek) XBOW secures an additional $35 million in Series C funding. (N2K Pro Business Briefing) Hackers Trick DigiCert Into Issuing Certificates Used to Sign Malware (Hackread) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

In 2026, startups age like milk. Josh Foreman's solution is a radical one - step down as CEO, go back to basics, and refound the whole company. Yaniv Bernstein discusses this decision with Josh, founder and (for now) CEO of InDebted - the AI-native debt resolution business he scaled to an $80M revenue run rate, a Series C raise, and operations across 8 markets. Just days before recording, Josh publicly announced he's hiring a new CEO so he can step back into the business as a hands-on operator and refound the company for the agentic AI era.In this conversation, Josh and Yaniv discuss 'refounding' in practice, what it takes to rebuild the company's processes from the ground up, and why technical founders who don't go back on the tools right now are setting themselves up to be outbuilt by a smaller, faster, leaner version of themselves.In this episode, you will:Learn why Josh believes the highest-leverage role for a technical founder in 2026 is no longer CEO, and how to structure a founder-CEO partnership that actually worksUnderstand why 'feature patching' an established business is a losing strategy, and what it really means to rebuild your company function-by-function from a clean slateDiscover how revenue-per-employee has become the metric that matters most when raising capital and competing with AI-native upstartsHear why services-as-software and performance-fee models are suddenly the bull case for investors who hated them 12 months ago - and why the SaaS seat fee is on the way outFind out what it looks like to unbundle your product into agent-ready primitives, and why owning the eval for a narrow domain may be a bigger moat than your full-stack UITimestamps00:00 Coming Up: Refounding00:41 Josh Foreman, CEO (for now)01:41 What Refounding Means04:53 Rebuilding the Factory07:38 Bringing the Team Along10:51 No Choice but Change14:56 Aligning the Board and Investors16:52 Putting Founders Back on the Tools26:24 'Corporate Ozempic' Shrinking Teams32:57 Unbundling and Products for Agents38:39 Hiring a CEO When Refounding44:11 Closing ThoughtsMentioned in this episodeJosh Foreman on LinkedIn: https://www.linkedin.com/in/joshforeman/InDebted: https://www.indebted.co/Scott Galloway on 'Corporate Ozempic': https://www.profgalloway.com/corporate-ozempic/Surviving the AI SaaSpocalypse with Scotty Allen: https://youtu.be/j84LF4aru8I 'Paranoid Optimism' with Yaniv: https://youtu.be/FGqbdzr0-PM The PactHonor the Startup Podcast Pact! If you have listened to TSP and gotten value from it, please:Follow, rate, and review us in your listening appSecure your official TSP merchandise at https://shop.tsp.show/Follow us here on YouTube for full-video episodes: https://www.youtube.com/channel/UCNjm1MTdjysRRV07fSf0yGgGive us a public shout-out on LinkedIn or anywhere you have a social media followingKey linksThis episode of the Startup Podcast is sponsored by .tech domains. Forget weird prefixes and creative misspellings; the availability for .tech domains is simply way better than .com. For a clean name that highlights your tech credentials, get a .tech domain at your favorite registrar.The Startup Podcast website: https://www.tsp.show/episodes/Learn more about Chris and YanivWork 1:1 with Chris: http://chrissaad.com/advisory/Follow Chris on Linkedin: https://www.linkedin.com/in/chrissaad/Follow Yaniv on Linkedin: https://www.linkedin.com/in/ybernstein/Producer: Justin McArthur https://www.linkedin.com/in/justin-mcarthurAssistant Producer: Steph Hefferan https://www.linkedin.com/in/steph-heff/Intro Voice: Jeremiah Owyang https://web-strategist.com/

Progress Software urges customers to patch a critical MOVEit authentication bypass. Washington worries about limited access to advanced AI tools. Paid influencers promote pro-American AI. CISA warns Copy Fail is under active exploitation. The Canvas educational platform suffers a data breach. The Lazarus Group uses ClickFix to target high-value enterprise users. U.S. and Chinese authorities raid scam centers in Dubai. Monday Business Brief. On Afternoon Cyber Tea with Ann Johnson: Tony Sager, Senior VP & Chief Evangelist, Center for Internet Security, joins Ann to discuss the accelerating pace of technology, AI, and global software dependencies. May the Fourth be with your firewall.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. Afternoon Cyber Tea On this segment of Afternoon Cyber Tea with Ann Johnson: Tony Sager, Senior VP & Chief Evangelist, Center for Internet Security, joins Ann to discuss how the accelerating pace of technology, AI, and global software dependencies are reshaping the cybersecurity landscape. To hear the full conversation, check out the episode and subscribe where you get your favorite podcasts to listen to past episodes. The show is going on hiatus. Stay tuned for the next chapter soon. Selected Reading ⁠Progress warns of critical MOVEit Automation auth bypass flaw⁠ (Bleeping Computer) ⁠What Was Discussed at Google's White House Meeting About A.I. ⁠(The New York Times) ⁠US Military Reaches Deals With 7 Tech Companies to Use Their AI on Classified Systems ⁠(SecurityWeek) ⁠A Dark-Money Campaign Is Paying Influencers to Frame Chinese AI as a Threat⁠ (WIRED) ⁠CISA says ‘Copy Fail' flaw now exploited to root Linux systems⁠ (Bleeping Computer) ⁠Edtech Firm Instructure Discloses Data Breach Amid Hacker Leak Threats⁠ (SecurityWeek) ⁠Lazarus Targets macOS Users With New “Mach-O Man” Malware Kit⁠ (GB Hackers) ⁠US, China partner on scam center takedown in Dubai⁠ (The Record) ⁠Cloudsmith raises $72 million in Series C funding.⁠ (N2K Pro Business Briefing) Microsoft for Startups (N2K Networks) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

The U.S. military doesn't have enough pilots—and automation may be the only way to scale airpower. At the same time, Skyryse is formally launching its new defense unit, bringing its software-defined flight system, SkyOS, into military applications. On this week's episode of Valley of Depth, we sit down with Mark Groden, CEO of Skyryse, to unpack how the company is building a universal operating system for aircraft that can dramatically simplify flight, reduce pilot burden, and enable fully autonomous operations when needed. The goal is ambitious: turn helicopters and airplanes into flexible, optionally piloted systems that can shift between crewed and uncrewed missions—unlocking a new model for force projection, logistics, and survivability. The conversation spans the tragic accident that inspired Mark to start Skyryse, why aviation's biggest safety problem is really a technology problem, how SkyOS works across platforms from Robinson helicopters to Black Hawks, and why defense demand for autonomy is accelerating faster than most people realize. We cover: How SkyOS transforms aircraft into software-defined systems Why helicopters are so difficult and dangerous to fly today What Skyryse Defense is building for crewed, uncrewed, and autonomous missions How optionally piloted aircraft could reshape military logistics and ISR How Skyryse's Series C positions the company for scale Why the future battlefield requires simpler, more adaptable systems …and much more.   • Chapters • 00:00 – Intro 01:34 – The accident that changed Mark's life and mission 04:10 – A PhD in sensor data fusion 06:54 – The evolution of Skyryse 10:09 – Product stack 15:30 – New business unit 17:12 – Skyryse's partnership with the Army 19:39 – Why even build for humans? 21:35 – The software distribution of SkyOS 26:40 – Guinness World Record for autorotation 30:58 – Training commercial helicopter pilots with Skyryse 33:52 – Commercial picture for Skyryse 37:43 – Addressing the pilot shortage in the military 42:22 – Commercial regulations 45:39 – What certification unlocks for Skyryse 47:19 – Military regulatory process 48:53 – What Skyryse plans to do with their Series C funding 51:27 – How people's lives change if Skyryse is everywhere in 20 years 53:30 – Can you buy the Skyryse helicopter? 54:05 – What Mark does for fun when he's not building helicopters   • Show notes • Skyryse's website — https://skyryse.com/ Skyryse's' socials — https://x.com/skyryse Mo's socials — https://x.com/itsmoislam Payload's socials — https://twitter.com/payloadspace / https://www.linkedin.com/company/payloadspace Ignition's socials — https://twitter.com/ignitionnuclear /   https://www.linkedin.com/company/ignition-nuclear/ Tectonic's socials  — https://twitter.com/tectonicdefense / https://www.linkedin.com/company/tectonicdefense/ Valley of Depth archive — Listen: https://pod.payloadspace.com/   • About us • Valley of Depth is a podcast about the technologies that matter — and the people building them. Brought to you by Arkaea Media, the team behind Payload (space), Ignition (nuclear energy), and Tectonic (defense tech), this show goes beyond headlines and hype. We talk to founders, investors, government officials, and military leaders shaping the future of national security and deep tech. From breakthrough science to strategic policy, we dive into the high-stakes decisions behind the world's hardest technologies. Payload: www.payloadspace.com Tectonic: www.tectonicdefense.com Ignition: www.ignition-news.com

Latin America is the third-largest fintech and payments market in the world — bigger than India, behind only the US and China. Gastón Irigoyen is Co-Founder and CEO of Pomelo, the fintech infrastructure company powering card issuing and processing for banks, fintechs, and global enterprises across eight markets including Brazil, Mexico, Argentina, Colombia, Chile, Peru, Puerto Rico, and Panama. Pomelo is backed by Index Ventures, Insight Partners, Kaszek, Monashees, and most recently Adams Street Partners in their first-ever Latin American investment.In this episode of The J Curve, Gastón unpacks the contrarian playbook behind Pomelo: why the team went regional from day zero on a $10M seed round instead of nailing one market first, how they built a "plug and play" hiring engine that's stayed at 90%+ since founding, why they tripled revenue without adding headcount, and what it actually takes to win enterprise customers like BBVA, Santander, Bci, Bancolombia, Binance, and Bybit when nobody trusts an infrastructure startup. He also shares the Series B-to-Series C lessons most founders never document — including the end-of-year memo that turned rejections into investor trust — and his framework for the AI transformation a five-year-old company is now being forced to run.This is a masterclass on regional-by-design strategy, B2B fintech go-to-market, founder-led fundraising in down markets, and building world-class companies from Latin America for the world.Subscribe to The J Curve Insider newsletter for deeper insights and follow Olga on LinkedIn and Instagram.

The Great private Capital Reset is upon us. Markets are volatile and driving new economic imperatives. Are VC funds still VC funds, even if they raise billions per fund? What happened to the rest of the market? What is driving VC investments? What do Limited Partners think? What is on their minds? This and more, in episode 76 of Tech Deciphered. Navigation: Intro The State of the Reset: The Hangover from the Party? LP Fatigue and VC Differentiation What Really Matters: Performance.. Returns The Mega Fund Question The Case for Smaller… Rightsized Funds What Comes Next? Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Bertrand Introduction Welcome to episode 76 of Tech Deciphered. This episode will be about the great private capital reset. As you know, or you have probably heard, there is significant structural transformation in the world of venture capital, and we are probably witnessing a fundamental reset of the private capital stack. We got a huge bubble in 2020, 2021. Fueled by near-zero interest rates. We got inflated fund size, compressed due diligence, and now a generation of zombie funds and zombie startups. Now that rates have normalized, exits have not been as much as expected. LP patience is a warning sign, and I guess the industry is being forced to confront an uncomfortable truth: most VC funds raised since 2017 might not return what their LPs expected. You know, how do we start?   Nuno This is going to be a relatively nuanced episode. Obviously, there is going to be a lot of haves and have-nots, both in terms of VC funds, also in terms of startups. And so I want to start with that. This is going to be more nuanced than all transformational and disruptive.   Bertrand It’s not the end. It’s not the end.   Nuno State of the Reset: The Hangover from the Party? It’s not the end. There’s still huge mega funds that are raising more and more. It’s clear that the music has stopped, right? So if we’re playing the game of chairs, the music has stopped. Around ’22, ’23, we started seeing the first signals that funds had raised way too much money. Firms collectively raised around $669 billion globally in 2021 alone. If we fast forward now to last year, 2025, depending on the sources, we did some internal analysis at Chameleon. We came up with $75.6 billion was raised last year by 493 funds, right? So That’s a significant drop, right, in terms of fundraising. Other sources would say a little bit more. There’s a little bit of a discussion around how much did the top 30 funds capture. If you believe some of the stats out there, they would say that actually top 30 funds captured 75% of all capital raised last year. We did again some internal analysis at Chameleon, and the conclusion we came to, it was closer to 50 to 55%. So not as dramatic as some of the sources out there, but still pretty dramatic. There’s a lot of capital concentration on the top funds. Again, the top 30 funds would’ve raised 50 to 55% of capital or up to 75% according to other sources. So definitely a tremendous amount of concentration. There was a lot more fragmentation in terms of capital raised if we’re looking at the years from 2010, 2011, all the way through 2021. So 2021 would’ve been sort of the peak of non-concentration if you look at that. And that again, now we are getting more and more concentration. There’s more and more of this arbitrage around, I’ll give money to the top funds, I will not give money to the smaller funds, or I’ll give less money to the smaller funds. There’s a little bit of a movement around concentration. We’ll talk about it later and what that means. Are mega funds really better? Are the small funds still the way to go? We’ll talk a lot about that later in today’s episode. There seems to be a little bit of a bifurcation. We could say it’s either bifurcation around top-tier VCs or larger VC funds versus smaller VC funds. My perspective is the bifurcation that we’re seeing right now is more of a bifurcation between funds that are no longer just stepped into the VC space, but they’re actually becoming more and more private equity firms with full asset management range from early stage all the way to late stage. Think of it almost like a private equity hedge fund, quasi, versus classic VC funds. And I think what we’re seeing is the Andreessen Horowitzes, the a16zs of the world, the NEAs, the Sequoia Capitals, just to name a few, becoming more and more broad asset class managers across private equity, whereas you have more classic VC happening in earlier stages. And so that’s the real bifurcation that I think is actually happening.   Bertrand And maybe not really hedge fund, because they are always still long-only funds. So there is no hedging happening, at least as far as I know.   Nuno Well, some of these guys have become RIAs, like A16z has become an RIA, so they can do secondaries.   Bertrand That’s true. Yeah.   Nuno And they can also sell stuff, etc. So I don’t know how aggressive they’re going to be in terms of secondaries and selling and actually doing other kinds of services you can do if you’re an RIA. But it’s not, I think, out of the realm of possibility that they would sort of acquire and sell stock more rapidly. In that way, to your point, Bertrand, maybe they actually become beyond just long guys, right?   Bertrand Yes. Another trend I have seen is some of the larger VC funds seems to have no problem investing in multiple competitors. This was not possible before. I mean, if you’re a VC fund, you had some sort of duty not to invest in the competitors, but now some invest OpenAI, Anthropic at the same time. Do you see that as part of this evolution?   Nuno For sure. And I think there’s a lot of people like the ostrich putting their heads below the ground and it’s like, “Eh, no, no, nothing to see here.” But that does constitute a conflict of interest. And if I’m a startup raising, this assumption that you will not invest in one of my competitors is no longer there, certainly for the mega funds, because of that notion of deployment of capital. Now, some funds will still hide under the notion, actually formally from a fund perspective, we’re not investing in competitors. It just happens that different types of our funds are investing in competitors. Like maybe my growth fund is investing in a competitor to my early stage fund, right? But our funds are relatively independent. So I think there’s a little bit of hide and seek that will go on if you talk to some of the fund managers. Well, they say, well, we’re not investing out of the same fund into these competitors. But between you and I, as we know, a lot of these partnerships actually do a lot of stuff together at the general partnership level. So are there really actual Chinese walls between the funds? Well, it really depends on the partnership. And to be honest, most of the partnerships don’t have very significant Chinese walls between the funds, right? The managing general partners sometimes actually occupy investment committee roles across different funds. So I think the conflict of interest is there. So that’s why I say there’s a little bit of ostrich behavior. Put your head behind the ground or below the ground and just pretend nothing is happening. Just sharing maybe a couple of interesting stats. Global fund closings for 2025, according to our numbers at Chameleon, 1,098 closed. In 2025. Closed is when you start deploying capital, right? Whereas— so it’s not closed down, it’s closed like we start deploying capital. And that number, 1,098, is dramatically down from 1,600 in 2024. And it’s actually the lowest number of closings that we saw since 2014. So again, this is bad, right? It means there’s less funds doing fund closings and deploying capital in the market than since 2014 and dramatically below the 2024 numbers, right? Where we already saw some market readjustments. The number of active VC firms in the US that did 2+ deals, which is not a huge bar, has dropped 38% back to numbers in 2023. So we don’t have numbers that are a little bit more up to date, but basically in 2023, those numbers are already dramatically dropped. So there’s less and less active funds. So there’s funds that might be in the market, but they’re not actually deploying that much capital, not doing that many investment. They’re sort of either zombie funds or relatively passive funds that have passed their investment period. For those listening to us, the investment period for a VC fund is normally between the first 3 to 5 years of the fund, which is when you build your portfolio, when you can invest in new companies. After that time period, everything that you do up to normally what would be year 10 is follow-ons. You put more money into the companies that you’re already invested in, that you already constructed portfolio with during those 3 to 5 years.   Bertrand Yeah, that’s a pretty scary change. And obviously, I guess we’ll come to it, but the time it takes to fully liquidate investments is getting longer and longer. In the old days, we used to talk about VC funds having a 10-year life, maybe a +1/+1 in terms of extension of the fund life. But it looks like it’s taking 16 to 18 years actually to get full liquidity from a fund investment.   Nuno LP Fatigue and VC Differentiation And I think that’s the scariest piece. I mean, just to share some numbers, we in venture capital talk about vintages, right? Which year did your fund start in? Normally when you did your first close onto the fund, as we were saying before, close is when you get all your investors at that moment in time to come in and you do your first close so the next fund starts running. 2018 vintage funds, right? This is now almost 7 years ago. So you should start having— actually 8 years ago almost at this point in time. You should start already getting distributions or you start getting cash back if you’re a limited partner and investor in those funds, you should start getting cash back. Half of all 2018 vintage funds have returned $0 to their LPs. So they’ve had no distributions to their LPs. 2020 vintage, which was a very hot vintage, only 42% have begun any distribution. So 58% have distributed $0, right? 2021, only 25% have done any distributions. Now, I happen to have a 2018 vintage fund and a 2021 fund. My 2018 fund has already distributed over 3x net of fees in distributions, and my 2021 fund’s already over 10% distributed back in distribution. So we’re very proud of that. But in general, the numbers are awful. There’s no liquidity back to LPs. And to your point, that’s kind of a big deal because some of these funds have been going on for 7, 8 years, and where’s the liquidity going to come from? On the other hand, if you look at TVPI, so DPI is distributions to paid-ins cash on cash. But if you look at TVPI, which is total value to paid-in, which also includes the book value or the value that you’re marking it on your books, basically the paper value as we call it for the company, even on that, the median 2017 fund, so 2017 vintage fund has a TVPI, total value to paid-in, of only around 1.76x, which is well below what should be, which is sort of the 2 to 3x benchmark of a really good performing fund. So the median funds are doing very, very poorly overall. So if you add that to the fact of what’s happening and distributions are taking a long time, back to your point, Bertrand, it’s taking like— this should be a 10-year asset class, maybe 11, 12 years, and now it’s looking a little bit like a 15, to 18-year asset class, which is not what most limited partners sign up for. Part of this dynamic, I think, is that we’ve had tremendously overvalued private companies over the last few years, right? Secondly, these companies have just stayed private longer. And I was having a discussion recently with a friend of mine, it’s like, hey, what’s this thing about companies are staying private much longer? Is there some dynamic around secondaries? And the reality is there is a dynamic around secondaries, right? Because if I’m a very large fund and I can get away with doing secondaries on my portfolio, I will get liquidity at some point, right? But someone else is stuck with private stock, which hopefully will IPO, but who knows, right? And so there’s this funny dynamic right now of because of secondaries, because of a couple of other things that are happening in the market, actually a lot of these startups are staying private for tremendous amounts of times, and some of them will IPO and they’ll be huge deals. Some of them might not and might not warrant the latest private valuations that they’ve exercised. And so there’s this tremendous noise that we’re seeing in the mid to late funnel of privately held companies where some are just waiting to be public. Some of them might not be able to go public at anything that is an up round versus private valuations that they’ve had in previous moments and in previous rounds.   Bertrand And obviously the 2 to 3x returns that funds are targeting, and obviously more 3x than 2x, I mean, that was good and nice if it’s a 10-year fund, but if it’s the same 3x for 15 to 18 years, it’s not at all the same rate of return annualized. So it’s a really, really, really big issue if you keep the return the same, but you extend the duration of the fund. Concerning going IPO, there is a lot of complexity going public, the IPO process itself, but also after that when you’re a public company. It changed how you can run the business. Some would argue that we have had an issue with more companies delisting than companies listing on the public market. So I think there might be also separate issues about the efficiency of the public market and maybe a need for change. We went very strongly in one direction for the public market, have post and run, but was it really ultimately the right thing to do? I’m actually not so sure.   Nuno Yeah, I mean, just to be clear, this is anecdotal, but when we tell prospective LPs at Chameleon about our returns, the last few funds, 2018, 2021, the first reaction is, “You must be lying, right? Surely you can’t have distributions already for 2021,” et cetera, et cetera. So clearly there’s almost a state of disbelief right now from limited partners. And liquidity does matter. So clearly you have to move forward. So how did we get to this point where we had this bubble 2021 all around that time space and now things don’t look so good. Well, the macro conditions have changed dramatically. I mean, rates when they were near zero, safer assets yield nothing or yield nothing. So basically you had to push capital into longer duration risk assets like venture capital. And so you had to push it. So the opportunity cost of capital also has fundamentally shifted. Obviously a 3x VC return in 15 years over 10 actually competes very poorly against 5% annual credit returns over several years. So there’s been a readjustment of stuff. And then the public equities in particular, the tech public equities have had a lot of volatility, but some of them have done extremely well, right? Chipsets, things like NVIDIA, the Amazons of the world, Alphabets, et cetera, et cetera. They’ve done very, very well. So why would I invest in a long-term illiquid asset that takes now longer to give me money back, and in some case doesn’t give me back, if I can invest just in public equities, and a variety of other things. The venture debt costs have increased dramatically. The burn rates that were sustainable back in the day with sort of the addition of venture debt, private credit, et cetera, now are overblown at this moment in time. At the end of the day, there’s been a lot of movements also overall in the pipeline in terms of valuations, et cetera, et cetera. Now, I would put a grain of salt into all the numbers I just told you. There still is a little bit of the haves and have-nots in startup land. Certainly in early stage where if you’re a hot AI company, you can get away with raising a Series C or $480 million. This is actually a true story. Series C, right? Not Series C, a $480 million at $4 billion pre-money valuation. Whereas if you are maybe in a space that’s less hot, you’ll have more difficulty in raising money at this point in time, might not be able to even raise a Series C, right? So there’s a little bit of the haves and have-nots happening on the VC side in early stage that has been really amplified by the macro regime and where we’re at, which is actively zero-rate era is done and now the new regime is quite different. And so I can get better returns by doing something else.   Bertrand Kind of makes sense. I mean, if you have some ways the SaaSpocalypse in the public market because there is that fear that AI is going to completely change the game for especially for the more typical software companies. Good luck raising private money to quote unquote just build traditional software companies. You cannot expect a warm embrace from the private market if the public markets are completely destroying that category. I’m not saying that this is there forever, uh, things might change over time, but for sure what’s happening on the public markets always have a very strong impact on the private market.   Nuno Indeed. So what’s happening in this relationship between limited partners and VCs, the general partners? Again, limited partners are the people that give venture capital firms and venture capital funds their capital to actually deploy. And they are a variety of different players, right? Could be endowments, like university endowments, pension funds, family offices, very high net worth individuals, fund of funds, et cetera, et cetera. I mean, in particular, if you look at the institutional investors, the endowments, the pension funds, the fund of funds, they have allocations that they do to different asset classes typically. And the feedback that we’ve received from the market is they are increasingly frustrated with what’s happening in terms of distributions. They’re not getting capital back. It’s like, I gave you capital 8 years ago, 9 years ago, 2017, 2018 vintages, and I’m not getting any capital back. So what the hell’s happening? On paper, it looks maybe the fund’s doing okay or it’s doing great in some cases, but where’s my money? And so that creates a little bit of wait-and-see kind of game on portfolio allocation. As we’re thinking through their re-ups, putting more capital into funds that they’re already actually put capital or putting in capital into new slots, into new fund managers that they want to put money into. They’re like, well, let’s wait and see. I want to get my money back or get some money back first before I redeploy it. Again, this is a little bit the haves and have-nots because we’ve seen, for example, a couple of top-end LPs in terms of returns that have a little bit the opposite problem, right? Because they are into funds that are performing extremely well. They actually are over that period and they want to actually redeploy. But to be honest, the average in the industry right now is a wait-and-see game. It’s like, I want to wait and see, which leads to what can only be characterized— I was hearing someone the other day, one of the top advisors in the LP community, saying this is the worst fundraising environment ever for venture capital. Not the last 20 years, 30 years, like ever, right? Since this became an asset class more institutionally in the late ’60s, early ’70s, Pulse Robo 2 as it was created, this is the worst fundraising environment ever. Oh, wow.   Bertrand And concerning TVPI, let’s not forget that typically it’s not mark-to-market. So the metrics in terms of TVPI, correct me if I’m wrong, you know, but the metrics in TVPI are based on typically the last fundraise. So if the valuation went down but there was no additional fundraise, we wouldn’t know by looking at the TVPI metrics. It will only be updated if there is a new Financing, equity financing, or an exit.   Nuno Yeah, normally most funds act like that. Some funds are a little bit more aggressive and do do mark-to-market, but normally funds would be conservative and say, hey, I’m being conservative, it’s whatever is the last known valuation of the company. And if there wasn’t a priced round, it’s a little bit more obscure than that, right, Bertrand? Because it might actually be the company has raised money on a note, or either convertible note or a SAFE note, and that wouldn’t count as a priced round. So I would say actually, even if it was a cap that’s below with a significant discount, I won’t recognize the assets as a down round. I won’t recognize the asset with a lower valuation because formally it wasn’t a price round. So it’s on the one hand conservative, on the other hand, it’s only relating to price rounds or exits to your point. So it’s sort of, you can be like, hmm, well, we opt to do that because we think it’s actually the most conservative route. Mark-to-market is extremely difficult to do. And who would do the mark-to-market for you, right? It’s like it’s some valuation firm, et cetera.   Bertrand I’m not saying a mark-to-market is easy, but I’m not sure I would call using the last valuation something conservative in the context that most startups will fail. So it’s not clear.   Nuno Well, in some cases it is, some cases it’s not, right? Depends on the startup situation, to be honest. Yeah, yeah.   Bertrand But yeah, at least that’s how it’s done. So for instance, to evaluate the impact of the SaaS apocalypse, it’s tough to know. We will have on the private market. I mean, we will see that in a few quarters. Because if companies still exist in that environment, if they still do additional truly price rounds after that, that’s when I will start to know.   Nuno I mean, just to share a little bit more data, like VC fund close time stretched to 15 months. Basically, it’s just taking a long time to raise money. It’s taking a long time to do your first close, get your fund running. When entrepreneurs complain to me that their fundraising is difficult, I always say, you have no clue how difficult it is compared to ours. First-time funds have collapsed. We had some numbers that only 77 first-time funds actually closed. I assume this is in 2025 versus 215 in 2023. So that’s a huge number. We did some internal analysis on our side and we did some analysis that emerging fund managers, emerging fund managers are normally people that are in their first one or two funds. Basically emerging fund managers gained some ground until 2017. Reaching by then a slice that was 63.7% of all capital raised in 2017. But since then, the capital deployed to emerging managers has been largely reduced to actually 24.2%, right? So it’s gone from 63.7% in 2017 to 24.2%. So this has been a culling of sorts on emerging managers and almost like a slaughterhouse of emerging managers. Compared to previous situations, which is obviously incredibly concerning if you’re an emerging manager starting your VC firm, et cetera, et cetera. So really tremendously problematic for those. We think capital’s not leaving VC. I think we see a lot of the institutionals saying— there’s some numbers as high as 33% of institutional investors plan to invest more in venture in the next 12 months. So I don’t think capital’s leaving VC. I think it’s really concentrating. We’ll come back to the concentration issue later in the episode. And part of that concentration comes from a topic that has been widely spoken in venture capital recently, which is differentiation. How do you differentiate in venture capital if you’re talking to a limited partner, right? How does my firm differentiate versus the firm next to mine? And that’s incredibly, incredibly challenging. Bertrand, what are your thoughts on that?   Bertrand Differentiation is always a question. I mean, if you’re an entrepreneur, Typically, you think fully about the best possible partner for your stage and for your type of business model. You want a VC who understands fully your business model, because if they don’t, then it’s going to be troubled down the line. But that’s true that another piece of the puzzle is that the best VCs help you get more visibility in terms of achieving potential customer deals, in terms of attracting the best talent. And that’s where VCs’ brand names can help. If you can say you have backing by some of the top, most visible names in the industry, and usually these are the mega funds because others have trouble to be as visible, then they have some sort of unfair advantage compared to others. So I can see that there is some level of concentration happening naturally, especially in the later stage from Series B onwards.   Nuno What Really Matters: Performance… Returns Yeah, I mean, we did some analysis internally about What are the top funds that invested in the top performing companies in early stage, Series C, Series A? And we looked at it by size of fund and the top performing normally are funds below $100 million, but in some cases very closely followed by funds between $100 and $500 million. And actually funds above $500 million, so $500 million to $1 billion and then $1 billion and above are actually tremendously underperforming. So this notion of the industry that says, well, the mega funds still see The top investments early on, because they still deploy in Series C and Series A opportunistically, in some cases even spray and pray if they have their own incubation and acceleration programs, is not true. Actually, we verified that over the last 12 to 13 years. It is not 12 to 13 years in vintage, right? So up to a 2021 vintage fund. So we went basically 12, 13 years back from there. And it’s not true. Actually, the most performing are 0 to 100 and then 100 to 500. And as I said, there’s 100 to 500 in a couple of years actually are a little bit better. Than the $0 to $100 million ones. So that’s the first thing that’s a conclusion. And actually, that’s not shocking. If we remember back in the day, Kleiner Perkins used to raise funds up to $600 million, Benchmark raised their $425 million funds. It seems like the sweet spot for a VC fund would be around $500 million at the top end, like maximum. And now somehow people are saying, well, I’m raising a $3 billion VC fund. It’s like, well, it can’t be a VC fund. The return profile is totally different, right? You can’t deploy that capital just based on early stage investing. And by the way, you’re not seeing the guys at early stage, all that you’re seeing, you’re going to make your returns in mid to late stage, right? Back to what we said at the beginning of the episode. So there’s a little bit of the haves and have-nots there. The big guys are raising more and more money, but they’re no longer venture capital. And I think limited partners that are a little bit more evolved, that are a little bit more conscious of this, that have been in the market longer, are realizing that shift. So it’s like if they want to have the alpha of venture capital, they need to deploy to the sub-$100 million funds or the sub-$500 million funds, right? That’s where they need to actually focus their VC capital. They can still deploy to mega funds, but they’re deploying to a different asset class. They’re deploying to a private equity, mid to late stage asset class, which looks maybe a little bit more like a growth fund or something like that. The second part of differentiation is the honest truth is most VC funds are like, I have proprietary network access, right? I’m ex-Stripe or I’m ex-Google or I’m ex-Facebook or whatever, and I have access to that. I mean, we know proprietary networks from that standpoint are no longer true. The whole thing that created Silicon Valley back in the ’70s of what I used to call the country club deals where there were a few people coming out of the big companies, the Fairchilds of the world, later on the Intels of the world, et cetera, et cetera, that made some money along the way that sort of bootstrapped their next companies, were well-known quantity to the existing VCs and raised money relatively easy on ideas, that doesn’t work anymore. Someone was telling me the other day one interesting thing that I wasn’t quite aware of, a lot of it had to do with the NDAs. I don’t know if you knew this, Bertrand, but like the fact that in California, it was sort of the Silicon Valley community sort of imposed this, we don’t sign NDAs thing and Boston continued signing it. And this whole NDA enforcement issue and non-compete, actually not the NDA thing, but more strongly that California did not enforce non-competes. I could leave Fairchild and start a company that magically was doing something that could be considered competitive to Fairchild. And that was sort of part of the acceleration actually of venture capital in California versus, for example, Boston, which was sort of hand in hand at the beginning.   Bertrand Yeah, I mean, I’m a big, big believer in California success coming from not enforcing or banning non-compete agreements. I think it’s a key part of the game. If you lock people into not doing something similar in the next 6 months to 24 months. And the industry has always been moving fast. So this is a significant time where you are blocked to do something very similar. I think it was really an issue. So I think it’s a key part of the game and it has been there. I don’t know how it started, but I think that non-enforcement of non-compete has been a key part of the success of California. I’m actually pleased to say that Washington State is going in the same direction. They are just signing a non-compete ban. And you might remember that at the federal level, I think in 2024, there was also a ban that was put in place to ban non-compete, but this has been reversed by the courts. So this is not there anymore. So that’s why we see a state like Washington State putting their own ban, and we might see more state by state moving in that direction. I think it was not helping at all, this non-compete. I mean, there is obviously stuff that needs to be done, like you cannot steal secrets, you cannot steal IP.   Nuno Yeah.   Bertrand Even stealing employees, there should be some restraints. We need to find the right balance, but you have to be careful there. That was key for the success of California, and I’m glad to see that this is a trend that’s going to go beyond California. And I hope most states will have a ban on non-compete.   Nuno Maybe just to close on the differentiation process, two things. One, I think there’s this notion When you talk to some LPs, that seems to be a little bit ingrained, some LPs that prefer specialized funds. We’ve also done some significant analysis internally and have talked to a couple of datasets other than our own, or people that own datasets other than our own, and the feedback has actually been not so fast. Actually, generalist funds over time cannot perform specialist funds. There seems to be a little bit of a sweet spot around generalist funds. We like to call ourselves multi-specialized at Chameleon, but ultimately from the perspective of specialized versus Generalist funds, the picture’s not as clear as specialized funds outperform generalists or generalists outperform specialized. We’ve seen there are pockets where actually generalists outperform specialized, in other pockets where specialized of a certain size can outperform generalists. So that’s one topic on differentiation that is a little bit broader. And then the final topic on differentiation, it’s really an industry that hasn’t innovated dramatically on where it creates the most value, which is really the picking stage, right? So it’s having great deal flow, very optimal, productive, efficient due diligence with very few resources and the ability to then get into those deals. That’s where most of the value is created. And then hopefully liquidating the asset if there’s an opportunity to do so at the right time, either through secondary trade sales or an IPO or something else. And what we’ve seen is the industry has innovated very little. I mean, the only thing I could point out in terms of core innovation at the top of the funnel has been the creation of the mega funds, the well-known funds, right? Like a16z, Union Square Ventures, et cetera, et cetera. But there needs to be more innovation on that cycle. And that’s why we certainly at Chameleon believe that the future is to have quant and AI-native VC firms that develop their own tooling, their own platforms. We have Mantis in our case that allow you to have this unfair advantage in how you source deals and how you do due diligence, how you get into the deals, et cetera, and how you take it to the next level. And we think that’s the beginning of the next stage is that the industry becomes more tech-enabled, shockingly enough, an industry that has made all its returns on tech or almost all of its returns on tech. That we need to be more tech-enabled ourselves. But I think the writing is on the wall there, and that will be a source of differentiation certainly over the next 3 to 5 years.   Bertrand One thing the industry has innovated somewhat and maybe could innovate even more is providing liquidity beyond trade sale and an IPO, because it’s clear that if VCs want more liquidity without waiting 18 years, you need that liquidity at different stage, not just when it’s time to do an exit, a full exit for the business. And for employees as well. I mean, it’s one thing to stay for a company for 4 years, which is your typical vesting. Maybe you extend that to 6 years, to 8 years, you have a great time at the company. But to think that maybe you have to stick around for 15 to 20 years in order to get liquidity on your stock options. I mean, that’s too much to ask for most people. I mean, people have a life, they have other things to do, other plans, they might want to move, they come at a different stage of life. So you need to provide them liquidity. The new game is we are not going to exit until 15 to 20 years, else it’s truly unfair. It’s not just unfair, but people will say, you know what, I’m going to go across the street, go work for Amazon or Google. I will have RSUs at best regularly that are liquid, and why bother? I mean, we need to find pathways to liquidity for both investors but also employees. There has been a change in that direction, but I think we need more of this change, and maybe not just reserved for the absolute biggest, most successful companies like OpenAI or SpaceX, but also us as well. Hopefully we can find a way.   Nuno Well, now we have these AI companies that actually grow so fast that they will IPO in one year. Now, isn’t that what’s going to happen? They raise They raised $500 million in Series C or $1.4 billion in Series C, and they’re going to IPO in 2 years. No? Is that not the new reality? I’m being facetious.   Bertrand At the same time, I mean, there are rumors that some of them are going to IPO this year. I mean, we talk about OpenAI, about Anthropic. I mean, OpenAI is quite old, but Anthropic is a relatively new business, quote unquote. So I think it’s a good time.   Nuno The Mega Fund Question So maybe it will be true after all. Moving to the next section, are mega funds still venture capital, Bertrand? Are they still venture capital funds?   Bertrand Yeah, I guess venture capital is a term that can encompass from small to very big funds. I truly don’t know. I mean, once you reach a growth stage, are you truly a VC fund? I don’t know. I think some of these definitions are kind of arbitrary from my perspective. What is clear is that you as a business need different providers of capital. And as we just discussed, you as a business, probably need to keep going and stay private for longer. One reason being, again, there is a tremendous cost to being a public company. There are some true strategic disadvantages. And at the same time, just practically, I mean, you need to get bigger and bigger in order to have a chance of a successful IPO. So you cannot just go IPO at a $500 million valuation. I mean, that’s like committing suicide, at least in the US market on NASDAQ. So my point is, you truly have no choice. You need to extend and If you need to extend, then you need to have capital providers that are there at later stage and therefore have more money. Is it still true venture capital? Is it true venture? I don’t know. At some point, it makes sense that from the startups to the capital providers, everyone adjusts to a reality where the life cycle is getting longer.   Nuno We don’t think it is. We don’t think mega funds are venture capital. We have actually some data that shows that they’re not in terms of actual returns. The alphas you can generate, the IRR that you can generate is actually not comparable. We did some analysis again with some of our datasets and from 2012 to 2022, so that’s the datasets that we used so that we had actual distributions and stuff we could take into account and so on and so forth. And looking at IRR, just to share some numbers in terms of IRR over those 10 years on sub-$100 million funds versus above $1 billion funds, the differences are incredibly stark. And this is true for global and US IRR, right? So just to quote some numbers in terms of average, sub-$100 million funds, global IRR of 22.9%, US IRR of 21.6% versus above $1 billion, 9.1% and 9.0%. Median IRR, if we just looked at median, 7.3% and 16.6% for sub-$100 million funds, 7.5% and 8.1% above $1 billion. Top quartile IRR, sub-$100 million, 31% versus 30.4% US IRR. And then above $1 billion funds, 14.7%, 15.5%. So it’s very clear if you sort of cut this in different ways, averages, medians, top quartiles, et cetera, over all these years that sub-$100 million funds are in a very different asset class than above $1 billion funds. They’re in different alpha that you can generate and so on and so forth. Now to the point you made, Bertrand, I don’t fully disagree with the point you made of the bigger funds should become bigger. I just think they’re becoming different things. Now, again, some of these funds will hide under the facts like, well, wait a second, we have all these assets under management, but they’re over different funds. Sequoia, we’re still raising small early-stage funds, $500, $600 million funds. And then we have larger funds for growth, et cetera, et cetera. Andreessen Horowitz, a little bit less clear what they’re actually doing. We heard that they’ve raised $15 billion across funds. I’m not sure if that’s the exact number at the end of the day. But the point is, if I’m a multi-asset class manager, like early growth, et cetera, et cetera, then it still applies what Nunu is saying. I’m still going after the $500 million, $600 million early-stage funds. Well, not so fast, right? Because you still have all this capital with managing general partners that are maybe across funds for which their incentives in particular, both carry and management fees are coming from the larger funds. Et cetera, et cetera. So there’s necessarily conflicts of interest. In many cases, the funds are just straight up big, right? And so they are above a billion. And so I don’t think a lot of these guys are in early-stage investing anymore, right? It may appear that they are, but I don’t think that’s where the returns necessarily are going to come from. And so if you are a limited partner, if you’re looking at your asset class allocation, again, you’re absolutely free to put money into mega funds because that’s the kind of asset class you want to play in. In terms of a blended private equity asset class that has a little bit of growth, a little bit of whatever, or actually a lot of growth, a lot of late stage, and maybe a little bit of early stage. And I want something that’s a little bit more blended, right? But if I still want the alpha venture capital, I need to deploy to funds that are early stage, right? And that’s like up to $100 million, up to $500 million. I think that’s my two cents on that topic. We see crossover things coming around, like guys who do both public and private markets. Again, that starts feeling a bit like a hedge fund. A lot of these funds have also become RAs, as we discussed earlier. So I feel the writing’s on the wall. The mega funds are going more and more after either some mechanism of edging or a mechanism that’s a little bit more blended in terms of private equity than classic venture capital.   Bertrand Yes, I think a few things. One, if you’re an LP, I can imagine that dealing with multiple $100 million funds might be more difficult. You, you need to know the partners, you need to have some background, uh, visibility. You need potentially to change regularly of VC investments. So I can see some level of simplicity if you just focus on the bigger ones, especially if you have a lot of assets you have to put to work. Another piece of the puzzle, I would guess that the bigger funds are able to return money faster because they are at later stage of the cycle. So instead of that 15 to 18 years, maybe they are more in a 5 to 10 year range, while the smaller funds being there more early might be the one who are taking longer to deliver. So I can see that Yes, there is an IRR picture, but there is also time to liquidity that is not the same. So that can probably also influence. And in terms of crossover PE hybrid model, I mean, for sure we have seen some of the public equity investors doing crossover, meaning going into private equity firms like Coatue, like Tiger Global and others. And for companies that are preparing for IPO, there is a lot of value to work with these firms because they have very good visibility and understanding of the public markets. And their presence in the cap table is also a sign of quality, typically for public market investors. So there is a lot of value and logic for them to be there on both sides of the puzzle. But again, the fact that firms keep delaying IPOs, that the market is not so much startup-friendly, makes this model a bit more difficult. But personally, I think there is value there.   Nuno Yeah, I think on the mega fund, just so that I’m not boo-booing everything, I mean, but there’s definitely angles in terms of the asset class that make a lot of sense. And there’s the scalability of the model. The ability to go after Series B, Series C, as well as mid-stage, as well as late-stage, even secondaries over time, to your point, in some cases even public equities. And that level of skill I think matters. We’ve also seen, as we’ve known, we won’t mention any brands, but people will know who they are, that late-stage hedge funds and investors, even if they’ve done okay-ish in growth in private equity, don’t necessarily do well in venture. So it’s clearly a very different asset class, right? So once you start getting venture teams together, The returns are not quite the same. Actually, sometimes they’re not even quite the same as the growth investments. So clearly they’re very good at the growth side, but not so good in early stage. But definitely there is a case for it. The Case for Smaller…Rightsized Funds But if we switch gears maybe to the small, or I would call right-sized funds, maybe just to quote a couple of numbers and then open up the discussion. Small funds do seem to outperform larger funds. There’s a lot of data in the market that shows some of that dynamic outperformance frequency. All the Very historical numbers from Cambridge Associates from 1981 to 2010. 19 out of 30 vintages were won by sub-$150 million funds. We did our own analysis as I was sharing before. Funds between $0 and $100 won most years between around 2010 and 2021. And the years that they didn’t outperform in terms of investing in the top-performing companies in early-stage Series C, Series A, they were outperformed by the $100 to $500 million funds. The $500 to $1 billion funds and $1 billion or above were never even in the same league in terms of performance, of having identified those top performers in terms of quantity over those early-stage investments. Top 10 funds by vintage, 2004 to 2006, 2016 numbers. Top 10 funds, 73% were sub-$100 million. 2004 to 2016, top 10 funds by vintage, 73% of those were sub-$100 million. So there seems to be a little bit of a case that actually smaller funds, sub-$100 million, sub-$500 million in some cases, are outperforming the larger funds over time. Now, these funds are complex in and of itself. The positive of it is small fund GPs like myself, we are deeply invested in our own funds. We’re not there to just make management fee monies. I mean, we’re not making $1 million, $2 million a year in management fees of salary ourselves, like some of the larger funds. So we are there to really get the carry and be less focused on management fees. And so I think there’s a little bit of alignment around that and really taking that kind of perspective on portfolio construction and liquidation, being also more aggressive on the individual time that we spend with our startups. On the negative side, obviously a lot of these smaller funds, not the case of Chameleon, but others out there are single GPs, very little teams or very small teams. And so it’s sometimes difficult to actually do a lot for portfolio companies as well. And this is where the mega funds, for example, a16z notably would say, hey, we have 600+ people that can support you, right? On market development, business development, communications, talent recruiting, all this stuff. Question mark whether that’s the right way to do it in terms of operating model, if technology is not a better way of supplying that value back to your portfolio companies, or if there’s no better way of doing it. But still, that’s one of the appeals of actually dealing with a larger mega fund if you’re a startup, right? That they will have the resources, also the financial resources to put more capital in you. But also, again, if there’s entrepreneurs listening to this right now, and hopefully there are, it’s a two-edged sword, right? Because if you have Andreessen Horowitz putting money in you, or NEA, or General Catalyst, or whatever, putting money in you on a Series C and then not doubling down on the Series A or the Series B, there will be questions, right? Because like they have the capital, they have other funds, so why the hell are they not putting more money in? Um, so, so it’s a little bit of a two-edged sword.   Bertrand Yeah, I think that one is a pretty big one. And on top of it, as we discussed, some of these big firms have multiple funds managed technically by different teams. So you might have convinced the early-stage teams, they have investors, they’re happy, but you don’t convince the growth-stage firm. As you say, it might raise questions because people might think that there is some communication between the early-stage team and the growth-stage team. So why the heck are they not deciding to invest? And as we also discussed, even worse possible situation, what happens if the growth-stage team has invested in your competitor? It’s even more trouble. So I think trying to understand how firms behave, what’s the reputation of the firm, what’s the reputation of the partner you are working with, I mean, can have tremendous importance and impact. When it’s time for you to work with a firm.   Nuno Indeed. I mean, at the end of the day, we still believe that the smaller fund— we at Chameleon discuss the notion that our limit should be $500 million per fund, right? And that’s the logic of it. We think that model is the model that works well in venture capital. We do recognize, as I said before, why mega funds keep raising more and more money, right? It becomes a harm’s race at that end of the market. As I said, probably a slightly different asset class, or if not a significantly different asset class as well. So seeing a little bit both sides of the market, I mean, we often compete with the mega funds, but honestly, a lot of the mega funds are kind to us and they let us in. And this whole notion of elbows out, we haven’t felt it that much in the market. And people see our value at the table. And in many cases, I, I do see the larger funds more and more seeing the value of smaller funds coming in on the same rounds and even in some cases co-leading early stage rounds like Series C. So it’s not like elbows are out everywhere across the board. So I don’t mean to say this is like an all-out war between small funds and big funds and the small funds need to win or the big funds need to win. I think actually there’s a lot of potential for coexistence. My point is more that the asset classes and the returns are quite different over time, and that’s how I would think through it. And if you’re an entrepreneur, you should think about that as well, right? What are the implications of taking money from certain funds versus others in terms of the expected returns, expected time allocated to you? For example, if you’re not doing very well as a as a company, right? Will the big funds spend the same amount of energy on you if you’re not doing great and all of that? So it’s a little bit sort of a beware, open your eyes, both for limited partners and for startups. What do you actually want, right? What do you want from your VC firm if you’re a startup? And what do you want from your VC firm if you’re an LP?   Bertrand I must say, as an entrepreneur, uh, a board member, I have seen some situations where the bigger funds are actually trying sometimes to elbow out the existing investors. Like, uh, we have that much money to put to work, we cannot do less. And you’re like, yeah, but I don’t need that much money. And then they’re like, okay, just don’t let your existing investors do their pro rata. I don’t think it’s great because an entrepreneur, if your investors, your VCs, trusted you earlier stage when it’s more risky, and when it’s becoming less risky, you don’t give them the right to their pro rata because you have to let this big guy come in. That’s not great. Or even if there is not this pro rata issue, when an investor tries to put more money to work than it’s really necessary, it’s also not a good idea as an entrepreneur to take more capital than you could use. It will dilute you more, it will set higher expectations in terms of valuation, it will push you to use that capital faster than maybe would be reasonable. So I think that’s something you want to be careful with the bigger funds. So don’t talk to funds that are in some ways beyond your stage and try to make it work in that context. Or don’t accept to have your strategy change dramatically for no good reason by funds that just want to put too much money to work in your business. And that for me is surprising because it should also be in their best interest not to invest in businesses that are not ready to accept that much capital. But as we have seen, there were in the past some funds that believe that capital is a moat. Was a good idea. So hopefully, I guess we’re a bit behind that. But yeah, I would say entrepreneurs, be careful, find partners that are the right partners for you at your current stage. Sometimes some big names look great, but at the same time, if it comes with a lot of issues, from too much capital to also taking the risk that these partners don’t understand the stage of the business you are in or your industry, Just be careful. There is a lot of value to have firms that are very focused on your stage, on your industry, are finely attuned to that situation.   Nuno What Comes Next? Maybe to end in terms of sections, what comes next? And maybe we can come up with some predictions that are a little bit provocative on what’s going to happen to the market. You, if you’re listening to us, feel free to interact with us on LinkedIn, on X. If you have our email address, shoot us an email as well. We’d love to hear from you if you think these are the right predictions or if we’re totally off. Maybe I’ll throw in the first one, Bertrand, and we’ll go one by one. So we’ll each put one at the table and see where we head. My first one is that we’ll have a huge culling of VC investors. We had this rapid expansion of the VC asset class with arguably at least tens of thousands of firms globally, maybe even over 10,000 in the US. I think we’ll have a culling and the culling will continue and we’ll have several firms sort of getting eliminated over the next couple of years that will have either because they’re having tremendous difficulty doing their first close in their next fund, or the returns are not there, or it’s a firm that has done 3, 4 funds, but for some reason the returns have just gone out of whack in the last few years during the bull years. And so therefore, actually they can’t justify to raise more funds out there. So I predict there will be a significant elimination of active firms in the next at least 2 to 3 years. So maybe by 2028, and we’ll be below, I don’t know, 30% of number of active firms that we are today. The other side of it is I do think if we look beyond that, 2029, 2030, and so on, we’ll have the reemergence of not micro funds, but nano funds where people will start deploying capital very, very early and writing small angel checks, but doing it in a way that it’s sort of not this cottage industry that we’ve had of angel investors. So I think angel investment will be disrupted by people that will use more and more of the AI toolification out there to actually manage their portfolios of 10, 15, 5K investments in a way that is a lot more professional, creating sort of an advent of nano funds.   Bertrand Yeah, makes sense. On my side, in terms of prediction, I think there is a possibility that the mega fund model keeps expanding and looks more similar over time to some PE models. So do we have the top 10 VC firms that look more like a Blackstone than a Kleiner Perkins or Sequoia used to be? That for me will be an interesting question and development. I think that there is some possibility that it keeps going in that direction. A lot of incentives are pushing things that way.   Nuno My next prediction is that DPI, distributions to paid-in cash on cash, just cash back, will become essential for limited partners. I think TVPI, total value to paid-in, that also has in there, as we just said, paper valuations. There’s a lot of disbelief now around the TVPI metric if there isn’t distributions going alongside it. For those who, again, don’t know what TVPI is, it’s total value paid in, but it also includes DPI. So it’s cash on cash component plus a remaining valuation to paid in, an RVPI. And the problem is the RVPI really, in reality, it’s that kind of on-paper valuation that never gets attributed. I think LPs, they’ve seen the writing on the wall and they’re like, dude, just show me your DPI numbers. I don’t care about TVPI. Some LPs will still ask about TVPI just to make sure that the rest is sort of looking in order. Like, show me the money, show me the cash. Actually, it’s not money, show me the cash, right? I want money back.   Bertrand But that’s an issue. I mean, if you’re supposed to raise financing every 3 or 4 years, good luck getting DPI to show for that. So you need to be at least on your third fund in order to be able to show DPI, I guess.   Nuno I mean, my corollary to that, Bertrand, is if you allow me just to have a corollary kind of prediction, is that we’ll see certainly for funds like $50 million and above, $100 million, $200 million, et cetera, even increased concentration, right? I really need to have anchors that believe in me over time. And we might start having, again, the advent— we had it some decades ago, the advent of cap table kind of VCs, right? Like Sutter Hill Ventures, right? Where they’re not really raising funds anymore. And so we might have the advent of that, that we’ll have structures that are created that have more permanent capital allocated to them, or at the very least more concentrated capital by very few players.   Bertrand Interesting. Me on my side, as I shared before, I believe secondaries are, are important and here to stay. Um, in the past, some could argue, is it a distress signal or something? I, I don’t think it’s true anymore. In a world where your average startup might take 15 to 18 years to exit through M&A or IPO, we need to have other options. For funds, for employees, they cannot be expected to stick around for so long and have no liquidity. I mean, it’s just pure madness. It’s just bad alignment at some point to do that. So I think secondaries are becoming the third liquidity pathway for VCs, for employees, and it should be more and more a key part of the game, a key infrastructure in the VC/startups tech industry.   Nuno I mean, on specialized versus generalist funds, I believe we’ll continue seeing the coexistence of those two models where the specialized funds will in many pockets actually outperform generalist funds, but where we’ll continue seeing that the large franchises, the tier one franchises will likely be generalist funds. I mean, we just saw it in the cycle. The AI cycle went upon us. We had a 2021 fund. We could easily adapt and go into AI and figure out that AI was growing very fast. I mean, if you have an ultra-specialized fund and that’s your remit and that’s the only thing you can invest on, very difficult to change even during our investment period. I will put a caveat on that. We don’t call, for example, ourselves at Chameleon generalist. We call ourselves multi-specialized because our scoring models for the verticals that we track are specialized within Mantis. Because the partnership is specialized, we all focus on different areas. And because we have the Kin network that allows us to tap into that level of expertise, Again, I think the world will be specialized coexistence. Some pockets specialized will do very well, certainly on the smaller fund size, but the big franchises will likely look a little bit more generalist. And as I said, multi-specialized from our perspective is the future. We’ll start seeing more and more funds that are multi-specialized like ourselves. Do you want to talk about AI and how it’ll distort the metrics? No.   Bertrand Yes. I think AI is an exciting moment in the tech industry. It feels in some ways that the same way we had a big distortion coming with COVID and work from home in 2020, 2021. 2021, where suddenly everyone and their mother will build a SaaS company or invest in a SaaS company. AI feels a bit of the same. I mean, to be clear, I truly believe it’s deserved. I mean, we are facing a dramatic shift in how computing is being done in terms of value you can get from software. So at the same time, AI will probably distort this matrix for a long time. We clearly see a split where investments are going, in what startups are being created. So I think, yeah, we will see some distortion. And we know that maybe 50% of all deal value is going to AI in 2025. We have seen single rounds reaching 40 billion, like to OpenAI. We have seen, as you discussed, some seed stage investment of 400 million. So AI investing and AI startups are definitely a beast on their own. And will distort VC metrics for a long time. And we might need two sets of metrics in parallel, you know, AI versus everything else. So that would be an interesting bifurcation in the industry in some ways. I would say it’s fair to separate AI versus non-AI. We reach a point where it’s two different beasts.   Nuno Conclusion So in conclusion, AI has changed the world and it’s changing VC as well, as we discussed earlier in the episode. We have a tremendous momentous occasion for the asset class where venture capital is really bifurcating into very large funds, which no longer are in venture capital or seemingly may be distributed between different asset classes, and the smaller funds, sub-$500 million and sub-$100 million, that keep having the better returns, but also with much smaller scale. We’re seeing a culling of the industry where the industry is definitely getting smaller and smaller and more concentrated at both ends, number of VC firms, as well as a number of limited partners per fund and the interest that some of these limited partners have of being more and more concentrated in their own portfolio allocations. And last but not the least, the discussion around specialized versus generalist, where it seems like there’s some clear winners on some asset classes, on some sizes, in some industries, but on others, there’s other kinds of winners. And so maybe the future is multi-specialized, as I framed at the end. Thank you so much for listening. If you want to check us out and if you want to comment, feel free to send us messages on X, LinkedIn, to both myself and Bertrand, as well as send us an email. Thank you so much, Bertrand.   Bertrand Thank you, Nuno.

Users are fed up with misinformation and AI slop cluttering their feeds. SaySo is a new short-form video app that delivers news from vetted creators and journalists. Also, Loop, the San Francisco startup, closed a Series C funding round led by Antonio Gracias' firm Valor, which is a major backer of xAI. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Shoot us a Text.Episode #1319: Dealers prove growth doesn't require more rooftops, Amazon inches into car sales with real-world friction, and Slate Auto raises $650M to bring its affordable EV vision closer to production reality.Show Notes with links:Forget “grow or die”—2025 proved you can win without adding rooftops. Many Top 150 groups drove serious gains through operational discipline, not acquisitions, signaling a shift toward smarter, not just bigger, dealership strategies.52 groups grew new-vehicle sales with zero footprint change, pointing to stronger same-store execution.High performers leaned into used-car ops, inventory availability, and internal GM development.Great Lakes Auto Group climbed 19 spots to #88, boosting volume 28% while holding steady at nine stores.Late-year acquisitions (Q4 closings) meant organic performance—not M&A—drove most gains.“We think that scale helps… but I don't think it's absolutely necessary,” said Hudson Automotive (#11) CEO David Hudson.Amazon is upping its new-car retail platform, and yes, you can now buy a Corvette there. What started with Hyundai has expanded to include multiple brands, bringing digital-native shopping into a $1.3T dealership market.Amazon Autos now features Hyundai, Kia, Mazda, Subaru, Chevrolet, and Jeep in 130+ cities.Customers can browse, price, finance, and start paperwork online, reducing time in-store—not replacing it.Dealers pay to list inventory, gaining high-intent traffic from Amazon's massive audience.Early friction like inventory sync issues and incomplete deal structures highlights the complexity of auto transactions.“Customers have a level of comfort with Amazon… but it's definitely just in the starting phase,” said dealer Matthew Phillips.Slate Auto just locked in $650M in Series C funding, keeping its low-cost EV truck plans on track—and putting a spotlight on its next big milestone: production.The funding supports next-stage development and production ramp at its Indiana facility.Slate just crossed the 160K reservation mark and still targets late 2026 deliveries, with preorders expected to open in June.The truck will start at a mid-$20K starting price, using a stripped-down base model with modular add-ons that let customers upgrade into things like a 5-seat SUV or fastback configuration.The company plans to invest $400M in its plant, creating 2,000+ jobs.“We will deliver Slate Trucks at nearly half the cost of the average new vehicle—as promised,” said President Chris Barman.Join Paul J Daly and Kyle Mountsier every morning for the Automotive State of the Union podcast  as they connect the dots across car dealerships, retail trends, emerging tech like AI, and cultural shifts—bringing clarity, speed, and people-first insight to automotive leaders navigating a rapidly changing industry.Get the Daily Push Back email at https://www.asotu.com/JOIN the conversation on LinkedIn at: https://www.linkedin.com/company/asotu/

On today's Technology Report, Ed Mehr, the co-founder and CEO of Machina Labs, joins Defense & Aerospace Report Editor Vago Muradian to discuss the innovative Chatsworth, Calif., firm's AI-driven intelligent factories approach to increase commercial and defense production volumes at lower costs that attracted $124 million in Series C funding from investors including Lockheed Martin Ventures and Toyota to underwrite a new 200,000 square foot facility; the imperative to adopt AI as well as cutting edge machining technology and greater robotization to increase production; scaling the business as a manufacturer and technology provider; importance of designing for production and greater component flexibility to speed manufacture; how to better manage supply chains; how to manufacture at the edge; and the critical role of allies and partners in delivering capability.

What does it actually take to make fractional work sustainable, not just for a few months, but as a real career path?In this special solo session, Ben Erez shares everything he's learned about fractional product work from two distinct perspectives: as Head of Product at Continuum (a Series A marketplace for fractional executives that eventually shut down) and from his own two years doing fractional work. He walks through the foundations that set you up for success, how to actually find work and structure engagements, and most importantly, how to make it sustainable and not die. Ben is ruthlessly honest about what worked, what failed, and why intentionality is the difference between loving fractional work and hating it.He covers the counterintuitive truth that narrow positioning beats broad positioning (using Phil Carter as a case study), why you need to learn to enjoy marketing yourself or you'll burn out, and the three non-negotiable requirements for any engagement (relevant expertise + urgent need + budget). Ben shares real revenue data showing the spiky, unsteady income reality, explains the “feast or famine” trap that kills most fractional careers, and why he's skeptical that demand aggregators will ever crack this market at scale. Plus, tactical advice on pricing, the transition from being helpful for free to asking for money, and why referrals beat every other channel.If you're exploring fractional work as a side hustle, between full-time roles after a layoff, or considering it as a long-term path—this session is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

It's been a week since missing episodes of The Daleks' Master Plan revealed themselves to the public to great excitement and joy and indeed a Film of Fabulous! screening of said episodes on April 4 sold out near-instantly upon its announcement. We also have news of an ex-Google chief possibly taking the helm at the BBC, The War Between still being MIA from Disney+, the usual Big Finish over-examining, and our feature interview with The Long Game and Pull to Open author Paul Hayes, here to discuss the production of 1956 CBC production Flight Into Danger, starring one James Doohan and produced by future (at the time) Doctor Who producer Sydney Newman! CanCon, baby! Links: Support Radio Free Skaro on Patreon Film is Fabulous statement about the Doctor Who recovery Film is Fabulous short restoration clips Film is Fabulous Daleks' Master Plan screening Apr 4, sold out Gallifrey One 2027 tickets on sale BBC Close To Hiring Ex-Google Chief Matt Brittin As Next Director General The War Between the Land and the Sea not on the Disney+ April schedule Paul Cornell's Saucer Country comic optioned for TV series Big Finish: David Tennant returns as the Tenth Doctor, new 12-part series coming 2027 Big Finish: The First Doctor Adventures: Beware the City of Illusions out now Big Finish: UNIT – Brave New World: Knightfall out now Blake's 7 Series C available to preorder, release date TBD Interview: Paul Hayes Flight Into Danger Doctor Who: Star Flight by Paul Hayes

This week's Espresso covers news from Meddi, MetaBix, Handle, and more!Outline of this episode:[00:30] – Azos raises $24M Series C[00:44] – Meddi raises $7.8M from GNP Seguros[00:59] – Deeptech startup metaBIX raises $1.3M[01:10] – Handle raises $6M seed round led by Andreessen HorowitzResources & people mentioned:Startups: Azos, Meddi, metaBIX Biotech, Handle,VCs: Kaszek, Kevin Efrusy, GNP Seguros, Médica Móvil, Dalus Capital, EWA Capital. Andreessen Horowitz

Has your hiring process kept up with the industry's AI leaps, or are you still interviewing like it's 2022?Today's AI-driven landscape means the skill gap between a good engineer and a great one widens every day, and the great ones can be difficult to find. But how do you choose – and hire – the best in the business? How do you find those elusive engineers who can skilfully handle multi-agent workflows, ship in hours what used to take weeks, and add an AI-focused competitive edge to your startup?In this episode, Chris Saad and Yaniv Bernstein are joined by Matt Cook, an expert in hiring the best engineers in the business, and co-founder of Scouut – one of Australia's most respected engineering recruiters for early-stage startups. Matt works with pre-seed through to Series C companies and has a front-row seat to how AI is reshaping what ‘great' looks like in engineering, and how founders and hiring teams alike can keep pace.They cover what's fundamentally changed in engineering hiring, what hasn't, and how to build a small, elite team that punches well above its weight.In this episode, you will:Understand why AI is widening the gap between great and average engineersDiscover the three questions to ask any Big Tech candidate to determine if they'll thrive in a startupFind out why much more is expected of ‘senior' engineers, and what that means for foundersHear how the best startups are now testing for AI competency in interviews, not just coding abilityLearn why smaller teams, higher salaries, and generous token budgets are the new arbitrage for attracting elite engineersUnderstand why you should be designing the role for the people you want, and making your AI-forward culture clearly visibleResources mentioned in this episodeScouut (Matt Cook's engineering recruitment firm for early-stage startups): https://scouut.com.au/Matt Cook on LinkedIn: https://www.linkedin.com/in/matthewmarkcook/Loki (Australian startup referenced for their public AI-first hiring stance): https://www.itsloki.com/Solid (lightweight vibe-coding tool mentioned by Chris): https://trysolid.com/The Pact Honor the Startup Podcast Pact! If you have listened to TSP and gotten value from it, please:Follow, rate, and review us in your listening appSubscribe to the TSP Mailing List to gain access to exclusive newsletter-only content and early access to information on upcoming episodes: https://thestartuppodcast.beehiiv.com/subscribeSecure your official TSP merchandise at https://shop.tsp.show/ Follow us here on YouTube for full-video episodes: https://www.youtube.com/channel/UCNjm1MTdjysRRV07fSf0yGg Give us a public shout-out on LinkedIn or anywhere you have a social media followingKey linksThis episode of the Startup Podcast is sponsored by .tech domains. Forget weird prefixes and creative misspellings; the availability for .tech domains is simply way better than .com. For a clean name that highlights your tech credentials, get a .tech domain at your favorite registrar.This episode of the Startup Podcast is sponsored by Vanta. Vanta helps businesses get and stay compliant by automating up to 90% of the work for the most in demand compliance frameworks. With over 200 integrations, you can easily monitor and secure the tools your business relies on. For a limited time offer of US$1,000 off, go to ⁠⁠⁠⁠https://⁠www.vanta.com/tsp⁠⁠⁠⁠⁠Get your question in for our next Q&A episode: https://forms.gle/NZzgNWVLiFmwvFA2A The Startup Podcast website: https://www.tsp.show/episodes/Learn more about Chris and YanivWork 1:1 with Chris: http://chrissaad.com/advisory/ Follow Chris on Linkedin: https://www.linkedin.com/in/chrissaad/ Follow Yaniv on Linkedin: https://www.linkedin.com/in/ybernstein/Producer: Justin McArthur https://www.linkedin.com/in/justin-mcarthurIntro Voice: Jeremiah Owyang https://web-strategist.com/

Today, I'm speaking with Richard Craib, the CEO and founder of Numerai.If you've heard of Numerai before and thought of it as an interesting experiment at the intersection of data science and crypto, it's worth updating that mental model. Over the last few years, Numerai has quietly grown from roughly $60 million in assets to over $600 million. JPMorgan has invested and secured $500 million of capacity, and Numerai recently raised a Series C at a $500 million valuation led by top university endowments. This is no longer a toy project. It is a real, institutional-scale market-neutral hedge fund with a very unconventional engine.In this conversation, we go deep into how Numerai actually works. Richard walks through the core insight behind Numerai's design: that crowd-sourced alpha only works if incentives are aligned, not just participation. Simply opening up data and ranking models creates incentives to game the system, not to produce durable signals. That realization led to the introduction of the Numeraire token. By forcing researchers to stake real capital behind their predictions, Numerai shifts from a leaderboard-driven experiment to a capital-weighted signal engine. Instead of rewarding activity, the system rewards conviction, accountability, and uniqueness, creating a self-filtering model that naturally reduces noise and discourages the behaviors that caused earlier crowd-sourced platforms to fail.We also talk about portfolio construction and risk management, including how Numerai neutralizes common factor exposures, what went wrong during the 2023 drawdown, and how those lessons reshaped their approach to diversification and concentration. Finally, we look forward, covering the limits of crowd-sourced modeling, the next frontier for Numerai's research ecosystem, and how Richard sees AI agents reshaping model development.Please enjoy my conversation with Richard Craib.

Aman Verjee, Founder and General Partner at Practical Venture Capital, shares his view of how venture capital has evolved over the past two decades and why secondary markets now play a critical role in the ecosystem. Drawing from his time at PayPal, eBay, and Sonos, Aman explains how companies today stay private far longer than they used to, what that means for early investors and employees, and how thoughtfully structured secondary transactions can reduce friction and misalignment on the cap table. He also challenges popular narratives around tech bubbles, walking through historical examples to explain why today's AI-driven market looks fundamentally different.In this episode, you'll learn:[01:11] Aman's journey from Wall Street to Practical VC[03:40] What made the early PayPal team exceptional[06:32] Follow the customer, not the original plan[10:44] Why are startups staying private longer today?[11:17] What secondary transactions actually are[18:41] How founders should handle secondary requests[26:11] Are we in a tech bubble today?The nonprofit organization Aman is passionate about: AYSO (American Youth Soccer Organization)About Aman VerjeeAman Verjee is the Founder and General Partner of Practical Venture Capital, a secondary-focused fund providing liquidity to early investors in late-stage private companies. Before launching Practical VC, Aman spent over a decade in finance and operations roles at PayPal and eBay, joining PayPal in 2001 before its IPO and witnessing its transformation from a money-beaming mobile app to the dominant payment platform for eBay. Earlier, he worked in investment banking in New York after studying economics at Stanford and constitutional law at Harvard Law School. Aman was recruited to PayPal by Peter Thiel and worked directly for David Sachs during the company's pivotal early years. Now partnering with Dave McClure, he focuses on Series C and D investments in SaaS and FinTech companies with $200M+ in revenue and clear paths to liquidity within 5-7 years. He's also writing a book on the history of financial bubbles and co-hosts the Trading Places podcast, analyzing private company valuations.About Practical Venture CapitalPractical Venture Capital is a secondary-focused venture firm that provides liquidity solutions for early investors, employees, and funds. Operating with a 7-year fund structure instead of the traditional 10-15 years, Practical VC targets 20-40% discounts to last-round valuations in Series C and D companies with $200M+ in revenue and clear paths to exit. The firm specializes in SaaS and FinTech but has made exceptions for exceptional opportunities like SpaceX, now their biggest winner despite violating their typical investment criteria. Founded by Aman Verjee and Dave McClure, Practical VC evaluates roughly 50 companies at any given time, making 5-10 investments annually. The firm also offers SPVs for deals that don't fit their main fund and covers LATAM opportunities through an operating partner in Argentina. Their approach recognizes that modern venture capital requires new liquidity solutions as companies like SpaceX (23 years private), Airbnb (17 years), and Palantir (20 years) redefine what "patient capital" means.Subscribe to our podcast and stay tuned for our next episode.

Bobby and Alex open by discussing (you guessed it) the Los Angeles Dodgers signing Kyle Tucker to a jaw-dropping four year, $240 million contract. They discuss whether this has any impact whatsoever on the impending lockout and what, if anything, we should read into this about the state of baseball economics. Then, they're joined by Good Friend of the Show Jen Ramos Eisen to discuss, among other things, private equity in minor league baseball, the Joker, Series C funding, and the nascent Women's Professional Baseball League.Links:Follow Jen on BlueskyThe WPBL Has Teams and Players Now⁠⁠⁠⁠⁠⁠⁠Join the Tipping Pitches Patreon ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tipping Pitches merchandise ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Call the Tipping Pitches voicemail: 785-422-5881Tipping Pitches features original music from Steve Sladkowski of PUP.