The Philippines’ used car market is booming, but the dealers driving that growth have been constrained by how they provide financing to their customers. Many rely on their own capital or slow, traditional lenders that are not built for the speed and flexibility of the industry. Now these dealers can use OneLot, the Manila-based fintech that provides working capital and digital tools designed specifically for used car dealers. Its AI-native platform underwrites businesses in hours instead of weeks, allowing dealers to move faster, buy more inventory, and scale their operations with less friction.

Harm-Julian Schumacher, co-founder and CEO, grew up around his family’s Mercedes-Benz dealership in Germany, which gave him early insight into how car retail and financing work in practice. He did some undergraduate studies in Southeast Asia and developed a strong personal affinity for the region before eventually moving to the Philippines. He met co-founder and COO Tommy Campos, a former operator at Uber and Postmates, through their first local investor. Subramaniam Srinivasan, CTO, joined later as technical co-founder after they connected through a mutual contact in Indonesia. The three previously built a quick-commerce company together, an experience that helped them build trust, pressure-test ideas, and establish a strong working dynamic before starting OneLot.

What’s OneLot’s North Star?

As a lending business, our North Star is net interest income. It’s effectively our gross profit after cost of capital and loan losses. It’s easy to grow a loan book by handing out cheap capital, but that doesn’t mean you’re building a healthy company, so we focus on growing in a clearly profitable way. That metric forces us to balance growth, risk, and unit economics instead of chasing vanity volume.

Tell us about some recent milestones OneLot crushed.

The first few years were about building the foundation by onboarding dealers, building the platform, and proving the model. In 2026, we launched pilots for our consumer financing product, which lets us finance both dealers and the customers buying their cars. We’re also integrating AI agents across the company to automate sales, customer support, credit analysis, underwriting, and internal operations. This is making the business faster, more scalable, and more efficient.

Why will OneLot win?

It really comes down to two things. First, we’re digital and AI‑native, which lets us move faster, be more data‑driven, and take better, quicker credit decisions while always being available for customers. Second, we’re obsessively focused on a single vertical (used cars) while most competitors are horizontal lenders or marketplace players. That focus lets us build highly tailored underwriting models, specialized processes, and very specific products for used‑car dealers, all wrapped in a deeply vertical, AI‑enabled platform.

How do you think about work–life balance for yourself and your team?

For me, work–life balance doesn’t mean 9‑to‑5. In a startup leadership role, that’s just not realistic. I typically work six or seven days a week, but I make sure I have anchors like sports and time away from the laptop to clear my head, and I want everyone in senior roles to have their own version of that. At the same time, I’m very conscious about burnout: we work incredibly hard, but I don’t believe in burning through people. If I see someone online late in the evening too often, I’ll tell them to shut the laptop and go home early another day, because in the mid to long term, protecting our human capital is non‑negotiable.

Any favorite books?

I often recommend Yuval Harari’s Sapiens and, even more so, Richard Dawkins’ The Selfish Gene, which explores how potent genes shape evolution and extends that as an analogy to ideas, or “memes” that spread through societies when they help those societies function and grow.

Fun fact:
I’m really into water sports and anything with a bit of adrenaline: kitesurfing, sailing, freediving. I enjoy those activities a lot, even if my enthusiasm (and the occasional accident) doesn’t always make my mum very happy.

Listen to Anshu Jain with Scott Hartley, on the Venture Everywhere podcast: Outmarket the Market. Now on Apple & Spotify. Check out all our past episodes here!

Insurance remains one of the largest industries in the world, but many brokerage workflows still depend on manual processes, disconnected tools, and institutional knowledge.

Outmarket AI is working to change that by building an AI platform purpose-built for insurance brokerages.

The team announced its $17 million Series A, led by Permanent Capital Ventures, with participation from SignalFire, Fika Ventures, TTV Capital, Dash Fund, and strategic investors across the insurance ecosystem. The round brings Outmarket’s total funding to $21.7 million and reflects growing confidence in the company’s vision for the future of insurance operations.

Outmarket’s platform connects directly to agency management systems and transforms structured and unstructured brokerage data into an intelligence layer that powers key workflows across commercial, benefits, personal lines, and specialty insurance.

The company is already seeing strong market adoption. Since launching in March 2025, Outmarket has grown ARR 5x year-over-year and is now used by more than 250 leading insurance brokerages. Its platform processes millions of quotes, policies, applications, and loss runs documents, helping customers save more than a decade of manual work each month.

The impact extends beyond efficiency. Customers have reported up to a 65% reduction in errors and omissions through AI-assisted policy comparison and gap detection. Brokerages are also using Outmarket to improve win rates, identify cross-sell opportunities, and deliver better service to clients in less time.

The broader shift across insurance is clear. Brokerages are moving from manual, fragmented processes toward connected, AI-enabled infrastructure that can support every stage of the brokerage workflow.

The funding will support continued platform expansion, including new AI-driven workflows for complex, high-stakes insurance processes. Outmarket also plans to more than double its product capabilities in 2026 as it expands across commercial lines, benefits, personal lines, and specialty insurance.

For Vishal, Anshu, and the team, the opportunity is to build the operating system for the modern insurance brokerage and help define how the industry works in the years ahead.

Read the full article on PRNewswire.

In episode 119 of Venture Everywhere, Scott Hartley, co-founder and General Partner at Everywhere Ventures, talks with Anshu Jain, co-founder and CPO/CTO of Outmarket AI, an AI-native platform that helps insurance brokerages grow revenue, reduce errors, and close more policies. Anshu shares how years at IBM Research, Meta, and Ethos Life revealed that insurance agents were drowning in manual data entry, buried exclusions, and uncompared quotes, with zero tools to help them. Outmarket’s vision is to become the AI infrastructure layer for insurance distribution, modernizing a chronically underinsured market by giving every agent the intelligence of a team behind them.

In this episode, you will hear:

• Closing America’s chronic underinsurance gap with smarter agent tools

• Catching policy exclusions and coverage gaps before claims surface

• Evolving pricing from seat-based to usage-based to revenue-share

• Turning policy data and agent workflows into proprietary AI advantage

If you liked this episode, please give us a rating wherever you found us. To learn more about our work, visit Everywhere.vc and subscribe to our Founders Everywhere Substack. You can also follow us on YouTube, LinkedIn and Twitter for regular updates and news.

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Transcript:

00:00:04 VO: Everywhere Podcast Network.

00:00:14 Jenny Fielding: Hi, and welcome to the Everywhere podcast. We’re a global community of founders and operators who’ve come together to support the next generation of builders. So the premise of the podcast is just that, founders interviewing other founders about the trials and tribulations of building a company. Hope you enjoy the episode.

00:00:32 Scott: Hi, everybody. I’m Scott Hartley, co-founder and general partner at Everywhere Ventures. Everywhere Ventures is a global pre-seed fund, and I’m super happy to be here today with Anshu Jain.

00:00:43 Scott: Anshu is the co-founder and CTO of Outmarket AI. Outmarket is transforming the insurance industry by bringing AI native intelligence to brokerages so that they can grow revenue, make things more efficient, scale smarter.

00:00:57 Scott: He’s also a programmer by training who’s found his way into the heart of insurance over a storied career. But I know that prior to founding Outmarket, you worked at Meta, Ethos Labs, IBM, SAP. So, Anshu, welcome to the podcast. Thanks for being here with us today.

00:01:13 Anshu: Thank you, Scott. Really appreciate the opportunity.

00:01:16 Scott: Given you’ve been living in AI land for much longer than most of us, give us the 60,000 foot view of what you’ve seen over your career studying and working in AI and how fast the world is changing today with this new evolution that we’re all experiencing.

00:01:32 Anshu: I appreciate that question because it takes me 20 years back into my life journey when I first joined IBM Research, which is when I got into natural language processing and text analytics. That allowed me to be a part of the original AI commercialization effort, which was the IBM Watson effort.

00:01:47 Anshu: Around the constraints of computing at that time, took a very different approach, which was a rule-based approach to AI. Things, of course, evolved significantly different when deep learning and neural networks came.

00:01:58 Anshu: But even the best of us who were deep into deep learning and neural networks, except for the few who had the real vision long back, could not see it coming until it almost started tipping over in early ‘21. That this seems to start to have the appearance of a brain of its own.

00:02:17 Anshu: I think it took everybody by surprise as to how good it got, how the act of next token generation can start to define and generate what are very intelligent sounding reasoning tokens. Everybody had to react.

00:02:32 Anshu: Even though we understand that it is in the end token generation, it is not true intelligence. The technology and frontier has come so far that it’s great and good enough to change an industry landscape for the next decade, even if it were to stop all model innovation today.

00:02:46 Anshu: If there was nothing beyond Claude Opus 4.7 and GPT 5.5, it will take 10 years for the industry and world to realize all the gains from what has happened. It’s been overwhelming.

00:02:56 Scott: It’s pretty remarkable. I’m midway through reading the book by Sebastian Mallaby. He was one of our speakers at our annual meeting a couple of weeks ago in New York City. Sebastian wrote The Power Law, but his most recent book is a deep dive into Demis Hassabis and the evolution of DeepMind. He’s actually gone 10 years of interviews with Demis.

00:03:15 Scott: What’s so fascinating, as you alluded to with IBM Watson, the deterministic rote form of AI, and really his background in neuroscience and thinking about non-deterministic ways to build these neural networks that were moving in the direction toward where we are today.

00:03:33 Scott: On the investor side, I don’t think we’ve seen a fundamental shift like this since the advent of the iPhone and the App Store, and maybe before that, the internet.

00:03:42 Scott: The last 15 years, we’ve been living in sort of a rinse-repeat SaaS world of very specific growth curves, very specific KPIs to manage and navigate and scale very predictable pathways to IPO. We’re completely in a sea change that we probably haven’t seen since 2007, and maybe before that, 1995.

00:04:02 Scott: So it feels like a fundamental restructuring of venture capital the way startups are scaling. You guys predicted this going after a huge market like insurance two or three years ago when the writing was not totally on the wall, not everybody was a believer in AI. What gave you that confidence?

00:04:19 Scott: Was it the fact that you were in these worlds 20 years ago and you saw the evolution, how quickly things were changing? And you said, “Gosh, we got to get ahead of this and where are the biggest opportunities in the market? Well, let’s go after insurance.” How did you guys arrive on this particular industry, this particular go-to-market?

00:04:37 Anshu: I call this the Medici effect, where it was the intersection of different streams of thoughts which happened for us. You could always call it accidental when you connect the dots back. The fact that me and both Vishal, my co-founder, we spent our lifetime in technology.

00:04:52 Anshu: Vishal had done a startup in doing sentiment mining from social networks more than a decade ago. I was at the frontier of this at IBM Research. He was at the frontier of it in the social landscape, and we knew this technology, and we were following it very closely.

00:05:06 Anshu: But then what happened was we both were at Meta. So Vishal joined Meta, I joined the same team there. That’s where we started serving some of the largest businesses of Meta, and that’s when insurance first happened to them.

00:05:18 Anshu: I started managing the FinTech and insurance vertical, and we were serving their ads worth hundreds of millions of dollars. We started working closely with the insurance industry, and I understood, hey, how much the insurance industry spends to do distribution, acquisition.

00:05:31 Anshu: One thing led to another, we started helping Ethos Life, which was a direct-to-customer life insurance MGA reseller. We said, “Hey, we could help you do this.” And when they saw the impact our background brought into this insurance world, they said, “Hey, why don’t you just join?”

00:05:47 Anshu: So we joined Ethos Life. Vishal went there first. He headed the product there, and then I joined to head the platform team. That opened our eyes completely in two ways.

00:05:57 Anshu: From the technical side, for me, understanding how much of a big data problem insurance is was mind-blowing, because I believe that insurance is the original big daddy of big data, even before there were computers.

00:06:09 Anshu: 200 years ago, they were having these thick registers where they were handwriting the probabilities and computing the stats to insure the ships which were traveling through the sea to get to India and America. More than that, it was also about complex natural language, which is contracts.

00:06:27 Anshu: All these contracts are essentially natural language, very hard to understand by machines, at least until recently. They are the things that make or break the actual policy. They can have multi-million or even hundreds of million dollars of consequences.

00:06:40 Anshu: Understanding that technology landscape of it, that there are some technical limitations which have stopped this industry from being benefited, but they are on the cusp of being solved, was one thing.

00:06:50 Anshu: The other thing, even the consumers for life insurance would not be inclined to buy the life insurance unless there’s an agent who they can call. The single biggest experiment which increased our return on ad spend was putting an agent phone number there.

00:07:02 Anshu: So we decided to build an agent business at Ethos. Towards building that business, both Vishal and I participated in a lot of interviews where we literally shadowed them. “Hey, just show us what you’re doing.”

00:07:12 Anshu: Many of these agents were actually PNC agents who were trying to get into life. But when we saw the actual work which they do every day, it blew our minds because they had zero tools which are helping them. They just had a monitor which was allowing them to do data entry.

00:07:27 Anshu: But their actual work, it’s deep underwriting work which agents were doing without a law degree, without a data science degree. They need help. This is a technology which is ready for revolution.

00:07:38 Anshu: At that time, I had enough understanding. I was already using ChatGPT to write code. This was using the browser ChatGPT 3.0 and ChatGPT 3.5, I was using to write code. I could see this going in that speed and scale.

00:07:50 Anshu: Three years ago, I was shouting at the top of my voice on LinkedIn that “Guys, this is bigger than we think. This is not 10 to 20% productivity improvement. You could be writing three times the code with this very soon.” And we knew that this would happen in other domains.

00:08:03 Anshu: I also look back at this and say we were fortunate to choose the timing right. Because to me, when the internet was happening, I was still in high school. To me, this is the next big or as big or bigger than internet.

00:08:13 Anshu: We are at the cutting edge of this and in that confluence of we being technology providers who understood insurance as a business first was the biggest driver of the fact. We’re just lucky to have that sequence of events happen to us.

00:08:25 Scott: It’s really fun, having spent time on the ad team at Google 20 years ago, on the platform team at Meta 15 years ago, sometimes being in those positions where the day-to-day mechanics of the role maybe weren’t that interesting.

00:08:38 Scott: But getting to see the 60,000 foot view into an industry, I remember in my early 20s being absolutely perplexed by the amount of ad spend in some industries that I didn’t know were that big.

00:08:48 Scott: You would say, “Gosh, these guys are spending this amount. They must be generating 10 times this amount of revenue.” And it’s some obscure business that you had never conceived of being at that scale.

00:08:59 Scott: So part of that 60,000 foot view of you guys being at Meta, seeing the ad spend from insurance coupled with that age old idea that insurance is really an underwriting, is an understanding of data and probabilities and risk, which are maybe the things that machines can do infinitely better than humans. So it’s a natural combination of effects.

00:09:19 Scott: Taking a step back to what Outmarket is doing as a company, walk us through how you guys are baking AI into some of these agent roles and helping supplement. Because in legal, for example, there’s the black letter law of precedent of what the mechanics of the law. But then there’s also a very commercial layer that’s much more human-driven, that’s much more nuanced-based on a client risk profile, based on a client goal.

00:09:45 Scott: So I imagine there’s both agentic machine part of this, and there’s also the human layer part of this of leveraging Outmarket and some of the intuitions, but also baking in these human elements that a good agent knows how to structure a contract or how to push a deal forward. Walk us through a little bit of the business of Outmarket and how you guys are enabling these agents.

00:10:06 Anshu: You absolutely called out a very unique aspect of this industry, where the same function/role is so significantly a relationship-based role, which has so many touch points with the customer, and then so significantly a data processing and information processing role. Because they’re not the underwriters, but they have to do a lot of work, which is in the same vein of understanding risk and exposure.

00:10:29 Anshu: The legal parallel is great because they’re effectively acting as the lawyers for their clients. Some of the largest corporations, one of the biggest job of the lawyers there is actually to have airtight insurance policies.

00:10:40 Anshu: But most businesses cannot afford that set of lawyers, and that’s why they depend on their agents to do that. That’s an actual big aha for me that, “Hey, these guys are actually doing complex legal work. The precedents of law also apply here.”

00:10:53 Anshu: But also there is this notion of endorsements and exclusions, which is what makes the policy. So there is the deck page of the policy, which says, “Hey, this is the amount of coverage you have for these different sections.”

00:11:05 Anshu: And then there is a bunch, not necessarily hidden, but deep, embedded into the 400 page policy on page number 262 that, “Oh, by the way, digital theft is excluded from this.” Or, “Oh, by the way, you cannot be performing service in a specific industry if you want this insurance.”

00:11:23 Anshu: The agent, if they don’t look at it deeply, may not realize that the client I’m giving this insurance to actually is in that industry. This is a true story that the first insurance we got for our cybertech liability had an exclusion in it that you cannot be in professional services in finance.

00:11:40 Anshu: The agent failed to notice because it was buried on some last page that we are actually in the insurance industry. For six months we were exposed. And when we were putting it through our own policy review system, building that AI, we actually found out that this was a gap.

00:11:54 Anshu: This is a huge nuance of this industry, exclusions and endorsement, which make or break the industry. And we have intersected there as the first thing. So going back to your question of where are we addressing the problems of this space? It starts with a touchpoint, which is how do you gather the context of the client.

00:12:08 Anshu: Because that’s necessary to be able to tell them what’s the right risk exposure profile for them and what kind of markets I can bring them. We start from the point they have a lead. I’ll also touch upon that.

00:12:18 Anshu: Why do we start from the point we have a lead? Because bulk of this industry today is not stuck at lead generation. They have more leads than they can process on their desk, but they cannot get through enough of them.

00:12:28 Anshu: Even though our background was distribution and lead generation at Meta, we said, “Hey, let’s park that problem for now. Let’s focus on the lead to close problem and then the operations problem.”

00:12:38 Anshu: So we say from the time you get the lead, understanding the customer context, filling up the forms, taking your last two policies, and then instead of having to do data entry, use that information to automatically fill up the forms.

00:12:50 Anshu: From that point, having the quotes, application forms being submitted to comparing the quotes which come back from multiple carriers. That’s a very complex task. You’re not just looking at premium, which is what people do because they don’t have time.

00:13:01 Anshu: But buried under those 40-page quotes is some exclusion which talks about the same coverage, the same premium, but this one is much better for your client. Or in fact, higher premium, but this one is much better for your client.

00:13:12 Anshu: They fail to see it because the client themselves are also indexed on the premium and the coverage, but not the details. So now every day, our agents are coming back and saying, “Hey, Anshu, I was able to sell a policy which was the higher premium because the conversation never went to premium. It went to the details of what’s covered in these quotes.”

00:13:30 Anshu: From that point, the quote comparison to then actually the policy being issued… in these steps, there are so many issues which happen in terms of fat fingering, human errors. You had eight vehicles you wanted to cover in the input application, but somebody missed it and there are only seven vehicles, or the VIN number got wrong.

00:13:46 Anshu: Figuring out all of these, which are hidden errors and exclusions. So until the actual accident happens of that vehicle, nobody looks into the policy to say that, “Hey, it’s entirely missing in the policy.”

00:13:54 Scott: We have a number of cybersecurity companies that we’ve invested in. And thinking about pen testing, of looking for vulnerabilities in the code base or vulnerabilities in the potential threat mapping to what you’ve built, in some ways, it’s doing that in reverse.

00:14:10 Scott: What’s so fascinating about insurance is you get the benefit of the premium in the short run, but you have potential catastrophic costs of the payout or the claim in the long run. And there’s a timing mismatch where you really don’t know that you’re wrong catastrophically for a number of months or a number of years.

00:14:24 Scott: This was a pushback on some of the companies that went public, like the Lemonades of the world that were up and to the right on premium. But there’s this long tail of risk that may be baked into those companies with claims that may be poorly underwritten out a few years. You guys are effectively enabling real-time pen testing almost in between risk exposure and client archetype.

00:14:47 Anshu: 100%. That takes me to the 100,000 feet vision for the company. America is underinsured. Be it businesses, be it individuals, be it homes, we are underinsured.

00:15:00 Anshu: As an example, very recently with some of our own friends in Los Angeles, the risk was clear for many years making. Even then, most houses were underinsured. When it came to the actual recovery and rebuilding, they’re now suffering because of lack of information, lack of time, lack of attention.

00:15:18 Anshu: It’s not like there was no intention of anybody to do this right. But because of the lack of tools and information and sheer time, there was not the right advice coming to the final insured. In this case, it was individuals. In some cases, it is businesses.

00:15:31 Anshu: We want to remove that entire inefficiency in multiple layers of this ecosystem so that agents can go back to focusing on what is the most important thing: advising their clients to be appropriately insured and giving them enough reasons to say, “Hey, a higher premium is better for me.”

00:15:47 Anshu: Ask the folks in Los Angeles, they’ll pay double the premium if they have to now. I think that’s the grand vision here. And that’s the opportunity that because everybody is underinsured.

00:15:55 Anshu: This is not about automating this world and removing the efficiency. It’s about increasing the TAM of this industry. The same agents will not be losing their jobs. In fact, they will be having more because they can actually do more.

00:16:08 Scott: As somebody that has to buy insurance and pay for insurance, the biggest risk is that you pay a high premium and you’re still not covered for the catastrophic things that you want to be covered for. So it’s kind of this mismatch of I’m fine paying a high premium as long as that I’m legitimately covered for the highest risk events that I may experience.

00:16:27 Scott: But there’s probably an information asymmetry there where sometimes I’m paying a low premium for the amount of risk coverage I’m getting and other times I’m paying an extremely high premium for very low risk coverage and helping kind of align those incentives.

00:16:40 Scott: To your point, I would maybe spend more money, therefore increasing the TAM of the market if I knew for a fact that there were real risk mitigation elements that I was paying for.

00:16:51 Scott: One of the most interesting things we’re seeing in this new AI driven world is an evolution of pricing. It used to be you kind of sell off the shelf SaaS for a fixed per seat per month model.

00:17:01 Scott: We’re really starting to see where you can drive asymmetric growth and revenue or asymmetric savings and cost, a pricing based on the Delta, the increase in revenue or pricing based on the efficiency of cost savings. How are you guys thinking about pricing in this new AI led market?

00:17:19 Anshu: This is the most active thread of go to market for us right now, Scott. We’ve experimented already. We’re starting to arrive at a very good place here. We started with a seat-based model in the early days because two years ago, nobody knew how to price this based on outcomes.

00:17:33 Anshu: Now that we have the credibility in the market and we’ve been the first to arrive in some sense, what we are starting to see is that the seat anxiety is actually a much bigger problem.

00:17:42 Anshu: I’ve never seen a product market fit like this in my lifetime of building software. Once somebody uses it, they cannot unuse it. They can never get back to not using it. The validation from our customers is that, “Hey, if we drop Outmarket, I’m quitting.”

00:17:56 Anshu: Now everybody wants a seat. The end customers and the CEOs are realizing that, “Hey, I need to give a seat to everybody.” But now the traditional seat model starts to sound very expensive for them also. So it’s been a great boon for us that adoption makes the traditional seat economics drive more anxiety for the customer.

00:18:12 Anshu: Then we were able to quickly experiment and pivot to a model where I think the innovation we ded was we normalize it to a very simple and understandable price point, which is per page of data ingested.

00:18:23 Anshu: Instead of trying to make it hit this workflow, this module, so many credits of usage, we said, “Hey, this doesn’t matter how many of ever times you want to use this. Bring in the data, bring the policy pages, the code pages, and we will just price it by page.” So it became a usage-based model.

00:18:38 Anshu: Within months of launching, we’ve done several deals now on that usage-based model, especially including the larger deals, because now a large insurance agency can say, “Hey, I can put it on all my 2000 employees and then just pay for usage.”

00:18:51 Anshu: The more pricier outcome for them is a happier outcome, which means it’s being adopted. So if they exhaust their entire usage, it just means that they are hyperproductive as an organization. That model is starting to play out.

00:19:01 Anshu: And then we are slowly now, from there, also evolving to a revenue-based model because even the page-based model has an uncertainty and an anxiety of, “Hey, what if I start to use it a lot? Will my budget just go double because I’ve used double the pages?”

00:19:13 Anshu: So from there, we are now able to move to a model which is revenue-based that, “Hey, this is your revenue.” If we can just say that, “Hey, this is the percentage of revenue. So forget counting pages, counting seats, nothing. It’s really tied to your outcome. The happiest case for you is the happiest case for us, that your revenue is doubling and our remuneration from that revenue is doubling.”

00:19:30 Anshu: We are evolving there, but I think the evolution cannot happen as a step function. It has to happen gradually as you build credibility. In some cases, we might still start with seat-based model, let them build trust into the environment and then say, “Okay, now that you know us, let’s do the revenue-based model.”

00:19:46 Scott: That’s a great learning that you kind of need a blunt go to market in the form of per seat per month, followed by maybe a volume-based pricing as you gain scale and then concluding with an outcome-based pricing as you’ve built credibility.

00:20:01 Scott: But a lot of people are trying to jump straight to outcome-based pricing. It’s hard. You guys have developed a simple model, but sometimes these modules with tokens and it’s very complex pricing. That’s a great intuition around seat to volume to outcome-based pricing, which I think would apply to a lot of different businesses that we see.

00:20:19 Anshu: Everybody using AI is struggling because the cost of gas in any SaaS, which is run by AI, is much higher than it used to be. Compute does not scale linearly. And for 100X usage, compute probably scales by 5X. But with AI, for 100X usage, AI usage scales 100X and that’s a big problem.

00:20:39 Scott: As you think about being on the inside of AI, a lot of the stuff that I read about and hear about is the coming crunch of compute,

00:20:58 Scott: Do you guys foreee a world in the coming couple of years where demand really outpaces the ability to keep up with compute supply? What does that do for the underlying cost structure of a business like yours? How do you think of those risks under the hood?

00:21:13 Anshu: My technology view as an AI researcher in the past, and as somebody who’s watching and reading every new paper which is published, especially in the compute efficiency domain, I believe there is a lot of hype going on in terms of this capacity issue.

00:21:28 Anshu: That’s the job of the industry to sell that hype so that they can capitalize on it right now. But I think there is a lot of people who are working on advances which drastically reduce the compute needed and the energy per token needed to do the same token generation.

00:21:44 Anshu: In a matter of a couple of years, it’ll start to show up in the actual technology landscape. In a matter of five, six years, it’ll also start to be in the chip and the silicon. That’s happening on one end. So I believe the long term, I don’t have to worry about pricing too much.

00:21:58 Anshu: In the short term, I feel like I can actually use it to my advantage. There is potentially this demand supply gap which will be there. That demand supply gap will reveal itself when, let’s say, the head of data or technology of a large insurance company says, “I’m going to build this myself using Claude and price to do it.” They will see that it’s actually hyper expensive to do it.

00:22:19 Anshu: It’s not easy to scale it out at their scale of economies. While at my scale of economies, when I am serving hundreds and thousands of those customers, I can manage that better as well as because I am having a deep technology team which is doing the token compression. When I say token compression, it’s not literal.

00:22:35 Anshu: Because of the knowledge graph I’m building, because of the knowledge compression I’m building, I don’t have to go back to AI for everything. I have actually been able to deliver the same outcome by using much fewer tokens because I’m not depending on AI to do everything.

00:22:50 Anshu: I have been able to break down the problem into many small steps, optimize each step, and then reuse the step, cache the step, whatever, to have a much higher outcome to token ratio.

00:23:02 Anshu: Then what? Somebody who tries to build the same thing themselves might still get the outcome somewhere in the vicinity. It will not still be the same accuracy, but will be paying much bigger price. So I’m going to use that to my advantage.

00:23:13 Anshu: I think that is the reason why people should not even try to do that. They should lean on somebody who’s specialized to do that. As a technologist, I feel it’s a short-lived problem and people are hyping it up more than necessary. But from a GTM, I can actually use it to our advantage.

00:23:27 Scott: What you said just there is so fascinating because one of the existential conversations that’s always bubbling around Ventureland is with the frontier models, “can’t Claude do that? Can’t Claude do that?”

00:23:38 Scott: You know, that’s the trope that constantly bandied about is that there will be no vertical specific solutions because there’s this one super model which can do many things.

00:23:49 Scott: But to your point, if you’re tapping that one model in a very blunt way and utilizing it for every bit of knowledge that you need to extract from it, you’re very quickly going to get to a price point that’s untenable as a business.

00:24:01 Scott: You’re then going to look for cost-effective alternatives. Which to your point, as a vertical specific solution, you can really do a lot of knowledge compression before ever tapping into AI so the outcome per token price or the outcome per ping to AI is actually much more economical for the business.

00:24:20 Scott: In the short run, while people may do off-the-shelf Claude builds to try to figure something out, I think they’ll very quickly eclipse a cost threshold that is tenable for the business and then be shopping for vertical solutions like Outmarket in the space of insurance, for example.

00:24:36 Anshu: Absolutely. This is really just something that any vertical stack should take into account as they build and as they start planning for this. I’ve lived through this crisis in my own head and I’ve come out on the brighter side of this that… can Claude do this.

00:24:49 Anshu: I’m going to get a little bit of flak for saying this out loud right now, and I’m happy to take the criticism that what is Claude under the hood? It’s just a model which is providing enough data centers to execute. It’s a model which is running on the chips and providing the inference.

00:25:05 Anshu: If you look at the evolution, Claude’s 4.0 technology, which is where we were starting to get very accurate results, is already there in open source right now. So Claude’s 4.7 will be there in open source in six months from now or maybe ten months from now. If you really peel under the hood, they have a great execution engine, but there is nothing proprietary about it at some point.

00:25:24 Anshu: What is the hard things Claude will never be able to do? The amount of calls I’m taking from insurance agents on a daily basis to support them. I have a big customer success team who’s saying, “Hey, these people are coming and saying, hey, my proposal needs to look like this. My output needs to look like that.” That can be done only by somebody who understands the vertical deeply and is able to invest in that.

00:25:44 Anshu: So I feel like what Claude has today is replaceable. I can actually literally replace Claude with OpenAI, but what I have today is not replaceable. I’m living much more peacefully with that concept now, now that I’ve seen it in action. And I, in fact, think that we can actually remove these technologies and these as a commodity.

00:26:00 Scott: Interesting. It’s almost the opposite of the platform reliance trope that’s often thought of in the sense that as these models get eclipsed and open sourced, there’s almost like a platform openness rather than reliance.

00:26:12 Scott: It is actually the vertical specific application that you guys are building, the deep domain expertise, the ability to service the customer, the ability to understand that nuance and that pen testing between buyer and underwriter, that really gives you guys a durable advantage.

00:26:27 Scott: We’re close on time. So I want to shift to the lightning round of just a couple of fun questions for you, Anshu. I know you’re in the Bay Area, been in Silicon Valley for a long time. If you didn’t live in the Bay, where in the world would you choose to live?

00:26:41 Anshu: I think of two places. So I’m going to put both the places which are extreme contrast of each other. I love warm beaches because you cannot get a warm beach here in the Pacific. So Hawaii would be that place. I could live and work on the beaches of Hawaii for life, not just one year.

00:26:55 Anshu: And then the other place, which I’ve always been fascinated by, I wish I grew up there, is New York City. The other place is if I could be on the Upper West Side overlooking the Central Park and spend my life there – because the energy that city brings is just next to nothing. I would definitely want to go back in New York and live there for a year.

00:27:13 Scott: That’s great. You could be Jenny’s neighbor on the Upper West Side.

00:27:16 Anshu: Yes, I know.

00:27:19 Scott: With all this stuff that’s happening in AI, you alluded to reading all the frontier papers. Are there any books or podcasts or ways that you keep up with how fast the world is changing?

00:27:29 Anshu: I’m a big podcast guy. That’s something I live and breathe by with all my rounds of dropping my kids to different places. On the AI side, Dwarkesh’s podcast is the most fascinating one. Dwarkesh speaks to most of the industry leaders in this space. He’s been doing that for many years. So that’s been a big help for me keeping pace with the future.

00:27:48 Anshu: I’m an absolute fan, if I was to not keep the AI lens on it, of the Acquired podcast, Ben and David. They literally do a podcast which is a book on a company.

00:27:59 Anshu: As an entrepreneur, I’ve actually made some choices in my startup which have been very helpful based on the learnings from that podcast because they talk about companies and how they evolve and their thinking.

00:28:10 Scott: Oh, yeah. You need a good three-hour walk or a full doubleheader soccer game with your kids or something to get through a three-hour podcast.

00:28:19 Anshu: The whole podcast lasts for a week for me, but it’s so educating in some sense. It’s my favorite. I would recommend that to every entrepreneur listening to this podcast. Acquired should be in your must-have listens always.

00:28:31 Scott: I know you guys are building productivity hacks for insurance agents and brokers. How do you guys use it internally? What do you use? How do you time block your calendar? Are there any AI hacks that you use to be more efficient as a CTO?

00:28:44 Anshu: My two biggest hacks is… one, of course, connecting Claude to my calendar and all my systems. So now I actually ask Claude a lot of questions. So Claude can connect to your Gmail, Claude can connect to your calendar, Claude can connect to your CRM and all of that.

00:28:58 Anshu: I start asking questions to my Claude rather than going into these individual tools and even ask it, “Okay, what’s my day looking like in the morning,” and scripting some of those things.

00:29:07 Anshu: The other one, from a technologist perspective, we’ve deployed in the company something called Claude Conductor. Conductor actually conducts and orchestrates a lot of parallel activities.

00:29:17 Anshu: So even as a person who’s spending all my time in client meetings and strategy and discussions, I’m able to, in parallel, have many development tasks. That is made so easy because now you’re moving out of the IDE. You’re not doing any more IDE.

00:29:30 Anshu: It’s all happening directly on the Claude Conductor console and it just manages so many threads. I’m sitting in a meeting, I think of an idea, I start that thread. It starts actually implementing it and I can come back to it tomorrow and see what’s happened.

00:29:40 Scott: Even just what you said there is a fundamental shift from the role of CTO 5, 10, 15, 20 years ago in the sense that it was sitting in the IDE, it was writing code, it was managing engineers.

00:29:52 Scott: And the fact that you’re able to spend half your day in client meetings, in strategy discussions, that is a fundamental shift already in productivity, the ability to be doing both at the same time.

00:30:03 Scott: Finally, where can listeners find you online?

00:30:05 Anshu: I’m most active on my social networks on LinkedIn. It’s my first name, last name together, Anshu Jain and always be able to answer questions or just connect.

00:30:14 Scott: Amazing. Thanks so much. Congratulations on the $17 million Series A you guys just closed and announced. I know that the business is better than ever and you guys are just in an incredibly exciting pole position on a huge market at the right place at the right time.

00:30:29 Scott: So hats off to you and Vishal for having the foresight and the conviction to be building in this space two or three years ago. We’re super excited to be a small player in the company.

00:30:40 Anshu: Thank you for this podcast, some amazing questions you asked. I mean, I’m going to go back and listen to it myself because we had some really engaging conversations. And thank you for being one of the earliest backers when me and Vishal were just two guys with not even a deck actually and just a mission. You took a bet on us and hopefully it’ll pan out in the right direction in the future as well.

00:30:59 Scott: Absolutely. I love it. All right. Thanks, Anshu.

00:31:01 Anshu: Thank you, Scott. Take care.

Read more from Anshu Jain in Founders Everywhere.

I’ll admit, I’m someone who’s been exposed to NASA since the 1980s, and have a little sister named after astronaut Anna Fisher. I grew up with framed Apollo 11 sketches signed by Neil Armstrong in the hallway, and baby dedications from Bruce McCandless on his 1984 Shuttle Mission STS-41-B framed next to soccer trophies. My childhood home was one as infused with space as it was an obsession with Steve Jobs and Apple, someone who later became my college graduation speaker.

I’ve been an investor in space technologies since 2014, and through that time have had the chance to work with companies like Spire, Relativity, Umbra, Ascend Arc, Mirala, Starcloud, Azora, and many others, across LEO and GEO, Earth Observation (EO), Launch, Signals Intelligence (SigInt), Synthetic Aperture Radar (SAR), Satellite Communications (SatCom), alternative precision navigation and timing (PNT) systems for alt-GPS, and optical ground stations. I delivered a keynote address to 3,000 people at the 2018 GEOINT Conference, and in 2020 I wrote about how Elon Musk was building the “Railroad to Space,” not too dissimilar from how Leland Stanford connected the American West with the trans-continental railroad in 1869. The knock-on effects, and new economic power off of those rails, transformed our economy.

The newfound velocity around hardware today is being driven by a few factors, but particularly artificial intelligence as the base-layer accelerant. AI is powering hyper productivity in software, and we’re all aware of Anthropic’s Claude, and the vertical specific applications and many agentic incarnations. But this same productivity accelerant is additive to hardware development as well. Companies like VXB Aerospace are helping accelerate the ideation to production speeds for all types of complex component manufacturing around aerospace and defense.

The defensibility of software as a service (SaaS) is under perennial threat, with founders and investors alike asking the existential question, “Won’t Claude do that?” And so many are impelled by the dual tailwinds in hardware around increased prototyping and production efficiencies through AI modeling, as well as the perceived moat around building a physical product that can’t be immediately obviated.

The 2026 hardware resurgence is being driven by a:

• Proliferation of talent spin-outs from Tesla, SpaceX and others

• Perceived defensibility of a product vs merely a software service

• AI-enablement layer driving cost-efficient prototyping and development

• Renewed defense and government procurement system focused on commercial partnerships, fast-tracking contracts, and re-domesticating manufacturing. This is challenging the notion that “hardware is expensive” (meaning dilutive)

• Compounding of hardware components becoming more modular, akin to what happened with software stacking, SDKs, and the explosion of APIs

To Peter Thiel’s delight, innovation is finally moving to atoms, not just bits. In 2013 he and Marc Andreessen faced off at Milken about this exact topic. The nuance however, is it’s probably those bits that are driving the change in atoms. Both right.

Each week I see a former SaaS fund post their latest deal in robotics, space, or physical infrastructure. We need not be surprised. If anything, those who are doing this have recognized that the venture frontier has shifted. Years ago I wrote about how lived experience helps us build patterns and heuristics which inform frameworks. Our frameworks are backward-informed and time-delayed. Frameworks necessarily lag the reality we already live in. So rather than root in yesterday’s outdated thesis, or point fingers at some manager we deign to think has gone “off thesis,” we might accept that AI is driving change in not only software, but hardware as well, and markets like space, robotics, and physical infrastructure are no longer “deep tech.”

These are categories impelled by AI, fueled by talent spin-outs, and compounded by the very popularity of hardware itself. As more founders begin building hardware, component stacking becomes even easier, more modular, more assembly-block. Just like SDKs and APIs made app development an assembly project in 2008, with the Apple iPhone as distribution, today commoditized platforms like K2 make the raw satellite ingredients accessible, and SpaceX offers distribution just like Apple.

AI is driving the most significant technology wave since the Internet, and we’d be remiss to overlook hardware founders as major beneficiaries of AI. While Claude may erode software moats in SaaS, it might also bloom 1,000 flowers in hardware.

—

Scott Hartley is Co-Founder and General Partner at Everywhere Ventures, a $100 million-dollar early stage venture capital fund that has backed over 250 companies.

Every day, marketplaces need to move money to thousands of people at once: hosts, creators, sellers, contractors, and distributed workers across different markets. The workflow sounds simple, but behind it is a complex web of banking integrations, compliance requirements, reconciliation, and local payment infrastructure.

Radar is focused on making that infrastructure work.

The company automates financial operations for platforms that need to pay large groups of people simultaneously. Its specialty is payment dispersion, helping companies like Airbnb, TikTok, and Booking move money across Latin America more efficiently and reliably.

In a region where many fintechs are competing for the largest markets, Radar has gone after countries that are often underserved by global payment infrastructure, including Ecuador, Bolivia, and Paraguay. These markets may be smaller individually, but together they represent a meaningful opportunity, especially for global platforms looking to operate across Latin America without rebuilding payment rails country by country.

That focus has shaped the company’s operating discipline as well. Founded in 2022, Radar has raised just $1.5 million, became profitable within three years, closed 2024 with $2.9 million in revenue, and is projecting $12 million by 2026.

Early on, when banks would not give Radar direct access to their systems, Herbert Schulz and the team built their own workaround using a physical bank token, a webcam, and AI to read authentication codes in real time. It was scrappy, imperfect, and exactly what the moment required.

Today, that shoebox is no longer needed. Radar now connects directly into banking systems and processes more than one million transfers per month.

From our lens, this is where durable infrastructure companies are often formed: in the messy operational gaps that incumbents are too slow to solve and larger players are too broad to localize.

As marketplaces, creator platforms, and cross-border commerce continue expanding across Latin America, payment infrastructure will need to become more embedded, more flexible, and more regional by design.

Radar is building in that direction, toward a world where global platforms can move money across overlooked markets with the same speed and reliability they expect in the largest ones.

Read the full article on Diario Sustentable

Real-world evidence is becoming a critical layer of modern drug development, but much of the clinical data needed to generate it remains fragmented across specialty care settings.

Century Health is working to change that by building a platform that accelerates drug development and commercialization by applying AI to real-world data.

The team announced their oversubscribed $5 million raise to expand its platform and accelerate adoption across the healthcare ecosystem. Co-founder and CEO Vish Srivastava and co-founder and CTO Sanjay Hariharan are focused on building infrastructure that allows life sciences teams to better organize, analyze, and generate insights from complex clinical data.

The need for this infrastructure is growing rapidly. Providers are sitting on valuable clinical insights, while life sciences teams need better access to actionable data for clinical insights, market access, and outcomes analysis.

Century’s proprietary AI abstraction engine, CHARM, extracts clinical variables from unstructured clinical data with validated accuracy and traceability to the source. This helps turn fragmented records into datasets that can support research and decision-making.

The broader shift happening across healthcare is clear. Clinical data is moving from siloed and difficult to access toward connected, disease-specific, and AI-ready infrastructure.

The funding will support continued product development, expansion of the company’s network, and broader deployment of its platform as Century continues building tools for providers, researchers, and life sciences organizations.

For Vish, Sanjay, and the team, the opportunity is to create a stronger foundation for real-world evidence generation and help accelerate medical breakthroughs.

Read the full article on MobiHealthNews

Nori is a personalized AI health advisor that captures the full picture of your health. It combines data from wearables like Apple Watch, Garmin, and Oura Ring with medical records from more than 70,000 healthcare providers across the United States. It takes into account your sleep, workouts, labs, and even how you say you’re feeling. Instead of offering generic recommendations, Nori learns over time, remembers what works, adapts to your habits, and proactively reaches out through iMessage, WhatsApp, or SMS to help keep you on track. The result feels like an always-on personal health coach, using AI to deliver personalized guidance at the right moments and help people build healthier habits, make better decisions, and ultimately live longer, better lives.

Co-founders Dave Zohrob and Harish Agarwal have been building together for over a decade. They met while working at AngelList, then left to start Chartabale, which was acquired by Spotify. After taking a step back and reflecting on what mattered most, health emerged as a top priority for both of them. They also shared a frustration that much of the standard advice (calorie counting, Mediterranean diet, “move more”) felt like advice written for someone else, and it always wore off, which led them to want to build something that would not only help them become healthier, but also help others trying to make lasting, meaningful change.

What’s Nori’s North Star?

Our North Star is to add a billion healthy years of living to humanity. We’re nowhere near that yet, but it’s deeply motivating when you talk to users and see how having always-on support changes their relationship with their health.

Tell us about a recent milestone that Nori crushed.

We’re a small team, but shipping fast and we stay very responsive to our users. We just launched Nori 2.0, which is a pretty major rewrite to make Nori more personalized, with better memory and more proactive behavior. It’s based on hundreds of user conversations about what they actually care about. Since launching the new version, active usage has grown to the thousands, and daily active users are up about 65% month‑over‑month. More importantly, users now tell us this is something they use every day and would be genuinely sad to lose.

How does Nori inspire ‘customer love’?

Health is messy and complicated for everyone. There is the generic advice like “sleep more, drink water, workout.” Then there is the reality of daily life, where sleep, stress, exercise, family history, routines, and setbacks all interact in unpredictable ways. What people love about Nori is that it does not reduce them to a generic patient or treat every conversation like a fresh start. Instead, it builds a continuous understanding over time, remembering what matters to you, what has worked or not, and how your context is changing. Users connect with the feeling that Nori is holding their entire messy picture in mind and turning it into something clearer and more actionable, meeting them where they are rather than where an idealized version of them is supposed to be.

What is your motivation to build Nori?

On the personal side, I’ve seen people in my life with both serious health issues I want to avoid and very healthy lives I’d like to emulate, and that contrast has been a big motivator. After we sold Chartable to Spotify and I finally took a breath, I went to the doctor for a check‑up and the results were worse than I expected. That was a real wake‑up call, especially as a parent who wants to be around for his kids, and I decided I couldn’t keep ignoring this part of my life. On the professional side, I have this somewhat naive but persistent belief that we’re under‑using the incredible data from devices like Apple Watch and Oura Ring, and that if we connect that data to increasingly capable AI, we can build a positive feedback loop that actually helps us, instead of just building the next doom‑scrolling machine.

Favorite books or podcasts?

Recently I really enjoyed The Infinity Machine by Sebastian Mallaby. I happened to be reading it on the train to the Everywhere Ventures AGM where the author was speaking, which was a nice surprise. Outside of tech, Elena Ferrante’s Neapolitan novels are deeply lodged in my brain. I’ve reread them a couple of times, and they’ve influenced how I think about people and relationships more than most business books. My two must‑listen podcasts are Conversations with Tyler and the Dwarkesh podcast.

Fun fact:

I used to play in multiple bands. I was the singer in a dance‑punk band that played at raves in San Francisco. While that chapter has closed, it’s still a big part of my personal story and a very fun era of my life.

Listen to Mickael Roger with Jenny Fielding, on the Venture Everywhere podcast: Zero to PropHero. Now on Apple & Spotify. Check out all our past episodes here!

In episode 118 of Venture Everywhere, Jenny Fielding, Co-founder and Managing Partner at Everywhere Ventures, talks with Mickael Roger, co-founder and co-CEO of PropHero, a data-driven real estate investment platform that helps everyday investors identify, buy, and manage high-return properties globally. Mickael shares how a painful personal experience losing money on an investment inspired him to build a company that takes the guesswork out of real estate. He discusses how PropHero uses machine learning across 25 years of data and hundreds of variables to consistently deliver 16% average annual returns across 4,000+ properties, outperforming every major index, by pairing machine learning with the local insight that no dataset can fully replace.

In this episode, you will hear:

• Predicting property performance with 25 years of machine learning data.

• Managing the full investment lifecycle through a single digital platform.

• Deploying AI for 24/7 support while keeping humans in high-stakes decisions.

• Launching PropHero Academy to deepen client investment knowledge.

• Building a lifetime wealth partnership over a transactional real estate model.​​​​​​​​​​​​​​​​

If you liked this episode, please give us a rating wherever you found us. To learn more about our work, visit Everywhere.vc and subscribe to our Founders Everywhere Substack. You can also follow us on YouTube, LinkedIn and Twitter for regular updates and news.

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Transcript:

00:00:04 VO: Everywhere Podcast Network.

00:00:14 Jenny Fielding: Hi, and welcome to the Everywhere Podcast. We’re a global community of founders and operators who’ve come together to support the next generation of builders. So the premise of the podcast is just that, founders interviewing other founders about the trials and tribulations of building a company. Hope you enjoy the episode.

00:00:33 Jenny: All right, everyone, welcome to Venture Everywhere, where today, I’m very excited to have our special guest, Mickael Roger, who is the co-founder and co-CEO of PropHero. I’m particularly interested in talking to him, because he’s built a data-driven real estate investment platform that helps users identify, buy, manage high-return property investments globally.

00:00:54 Jenny: As someone who has a bunch of properties, never really knows how to manage it, and is interested in the category, but really doesn’t have the skills, I was super excited to be able to talk to you and support you guys. Welcome, Mickael, to the program.

00:01:08 Mickael: Hey. Thank you for having me.

00:01:09 Jenny: Awesome. I’d love to start off a little bit at the beginning, because you have such an interesting background. Half Australian, half French, grown up across cultures. But you also started a little bit less traditional than some of our founders in that you worked at big companies, finance, places like McKinsey. Just talk a little bit about your background before we get into PropHero.

00:01:28 Mickael: When I was 16, I had a big master plan. I’ve always wanted to launch my own business and make it successful. I felt that I need to know how the world works before, and especially in terms of finances, strategy, management, etc.

00:01:44 Mickael: And so the ideal path was to study maths, because maths runs the world. Even before AI, that was already the case 20 years ago. Learn about maths, then go to banking, then go to management consulting. That was my plan and feel ready before launching my own business.

00:02:00 Mickael: What happened is that about 10 years ago, I started to fall in love with AI and being absolutely convinced, so way before OpenAI, that AI was going to change humanity. I launched the McKinsey AI practice with other colleagues. Things went really well doing that, and then I thought, “Wow, the world is changing. I have to be part of it.”

00:02:20 Mickael: And so that was five years ago. Found the business idea that I believed in, and we launched PropHero. We are celebrating our fifth birthday next month.

00:02:27 Jenny: That’s awesome. You talk about PropHero. What was that like going from more big company? I know maybe you felt entrepreneurial, and I had a similar experience. I felt very entrepreneurial. My parents worked for themselves.

00:02:38 Jenny: There was a lot of that type of ethos in our family, but I went to law school. I worked at J.P. Morgan. I’d worked at big companies before I launched my first startup. So what was it like taking that leap?

00:02:48 Mickael: Learning that in real life nothing is ever perfect. When you work in investment banking at McKinsey, people get crazy for a comma missing somewhere. In startups, you can’t get mad about this part, otherwise you just become completely insane.

00:03:03 Mickael: I must say that. When I resigned from my corporate job, I remember dropping my son at daycare at like 7:34 a.m. back at my place, sitting alone on my couch thinking, “Wow, where do I start?”

00:03:17 Mickael: This feeling of like, “Wow, no one cares about me.” I have to learn how to create a company, create an email, create a website, find my first clients, build our tech, find money. Well, this is a huge mountain to climb. And the thing is just to go step by step. Because if you only look at the top of the mountain, you will panic. Just go step by step.

00:03:39 Jenny: I’d love to jump in and just hear really where the idea came from and what was the problem you were trying to solve? Again, I I mentioned that I’ve always been fascinated with real estate. I grew up in New York City, seeing booms and busts and have participated in a very inefficient way because I have a day job. So I’d love to hear where this idea came from.

00:03:58 Mickael: From my own painful experience. 12-15 years ago, I was at McKinsey at the time. So making good money, but not that much time. I wanted to invest my money across multiple asset classes, including real estate. Because I had no time, I hired a buyer’s agent. Paid her about $50,000.

00:04:16 Mickael: I remember telling her, I’m into AI. I built this model. I’m predicting that right now in Australia, I should invest on the gold coast. I think prices are going to more than double in the next three years.

00:04:28 Mickael: “Ah, Mickael, you know nothing about real estate. I’ve been in Sydney for 20 years. Buy there. Okay?” So I spent one and a half million seven years ago. As of last week, as of CBA, my investment is worth 1.4 million. So actually, I lost money in seven years.

00:04:44 Mickael: Terrible investments, but amazing business idea. What if real estate investment was done by serious people who know about data and who make the experience ultra simple and digital and use data and local knowledge to outperform the markets? That was our vision from day one.

00:05:04 Mickael: Not thinking in real estate in transaction, okay, I just want to sell you something. But no, I’m going to help you for the rest of your life to build wealth through real estate. We’re going to use data and an amazing digital platform to make it super simple and super profitable. That’s how it started from a very bad investment, which in the end was maybe the best one.

00:05:24 Jenny: That’s an amazing story. A similar story. My investment turned out well, but it was buying when people just thought it was crazy. I bought an apartment in San Francisco in about 2010, and San Francisco was in this real estate slump.

00:05:38 Jenny: Everyone was like, “That city is dying.” I just was like, “I have a view of the Golden Gate Bridge. How can this city be dying?” But you get a lot of bad advice is my point. So can you talk a little bit how PropHero is using data as opposed to vibes?

00:05:53 Mickael: We don’t rely on gut feeling. We are relying on mathematics. If you think about it, what drives real estate? Growing demands, lack of supply, gentrifying population, growing economy, lack of land, et cetera, just like business sense.

00:06:09 Mickael: What we do, we convert these business principles into data. So evolution of land available, new build, time to market, liquidity. We’ve got hundreds and hundreds of variables over 25 years of data.

00:06:24 Mickael: We run a machine learning model on this to see – which I’m simplifying but which indicators are predictive of future price and rent growth. This is surprisingly not used by so many people.

00:06:37 Mickael: I was talking to one of the biggest trillion dollar company recently in London, and somehow they still use old school macroeconomic analysis. We use machine learning to predict where to invest and what to invest in.

00:06:50 Mickael: What’s great is we also measure our accuracy. So we know how good we are, and we know also how good we are over time during bullish and bearish times as well. Our performance is ultra consistent. So we’ve bought over 4,000 properties at PropHero over the past five years.

00:07:06 Mickael: Our average annual total return is 16% per year, 1-6, which is outperforming any index that you can look at. Great results and not because of luck, honestly. It’s just mathematics.

00:07:18 Mickael: Mathematics, and if I may just add, we have local people on the ground who find the best deals and data does not see everything. We are very well conscious of what the data doesn’t see. So we have people on the ground who know and see what the data models don’t see.

00:07:34 Jenny: Amazing. What’s been something that has surprised you most about building a startup in the property space? You hear a lot about real estate people being cutthroat. You hear about all the problems with management. What’s been something that’s actually surprised you?

00:07:49 Mickael: It will sound maybe a little bit arrogant, but how much people love us. I wanted to create a cool company that was useful for people, but in my previous life, clients didn’t really love us. It wasn’t really the point.

00:08:01 Mickael: Some of our clients are just like fanatics. My analysis on this is that most people are not passionate about real estate, but it brought them a financial independence and a peace of mind for which they feel proud of themselves for having made the decision and taken the risk.

00:08:17 Mickael: And also they feel, for many of them, grateful. I wasn’t expecting so much of that. I’ve got very often clients reaching out to me on LinkedIn, on Instagram, some even have my WhatsApp and say, “Hey, Michael, we don’t know each other. But FYI, I’m one of your clients. And thank you. We’ve done so well.”

00:08:32 Mickael: It’s one of the perks of this job. Apart from that, it’s a very fascinating industry. It’s much more complex than it seems, and it’s got a depth that I was not suspecting.

00:08:43 Mickael: While now as a business owner, I see it as a defensibility barrier. Because what we’ve built in all these country, all these revenue verticals, all these types of assets start to replicate. It’s actually a pretty deep industry.

00:08:56 Jenny: Interesting.

00:10:09 Jenny: I was looking at your website before this call. You had a lot of properties in Spain. I’ve always dreamed of owning an apartment in Barcelona, but I saaw the interesting properties and probably the best value was kind of in the South, Alicante and some of these other places. So I was like, “Gosh, that’s so interesting.” So what makes a good customer and a good high return property? How do you define that?

00:08:57 Mickael: When you join PropHero, first of all, you talk to an investment advisor and this person will understand your profile and firstly, is PropHero the right company for you? If you’re looking for a purely emotional asset, don’t go with PropHero.

00:09:13 Jenny: Right, right. That’s the Jenny apartment in Barcelona. Don’t do that.

00:09:18 Mickael: We’re super honest. Yes, we could make money, but actually we don’t want to. Our investments are purely based on where we believe you are going to build wealth, not emotions at all.

00:09:27 Mickael: Then depending on your profile, we’ll build your investment strategy. And then once you sign up, we will start sending you properties to buy, either full properties or club deals. So buying a share in a building.

00:09:38 Mickael: Then on the app, you can buy this property or this club deal ticket completely digitally. And then you can, from your phone or computer, track your wealth growth over time. So all your properties are all connected in one place.

00:09:49 Mickael: And for us, a great investment is actually super simple. It’s the sum of capital growth and net rental return. The denominator is how much the investment, the property costs you: cost to buy, to renovate, to furnish in some markets, all the fees like stamp duty, etc.

00:10:06 Mickael: And then all the money you make is capital gains plus net rental return. So rent minus all the rental expenses. Super simple. Depending on your strategy, you may want to favor more capital growth or rental yield. And we’ve got different asset classes depending on how much of each you want. But, in short, we optimize for capital growth plus net rental return.

00:10:26 Jenny: And you guys manage everything. Is that correct?

00:10:28 Mickael: Everything. What you have to do is to decide which assets you want to buy, sign a few documents and make a few bank transfers. 99.9% of our clients have never seen the property with their own eyes.

00:10:40 Mickael: Me, myself, I invest almost all of my savings into PropHero properties. I know in which cities they are. I couldn’t tell you the neighborhood or how many rooms. I’m just seeing the returns. That’s it. Pure investments.

00:10:50 Jenny: That’s really cool because I think many of us have bought some properties. It’s just a hassle to manage. And then if you buy like a REIT, you’re so disconnected from the product that it’s just a financial product.

00:11:02 Jenny: And so I love this. That was why we got interested in being involved, in investing is I love this middle where you actually get to pick. You get to understand. You get to learn about how you guys are underwriting this. But you take care of everything, which is really cool.

00:11:15 Mickael: Speaking of this, what we launched a few months ago is the PropHero Academy, courses for those of our fans who want to learn more and become much more knowledgeable about the industry.

00:11:26 Mickael: We got a few thousand people who signed up almost overnight. We’re surprised to see how interested people were. You got some people who get really, really into this.

00:11:34 Jenny: That’s amazing. Can you talk about macro trends? Interest rates in the U.S. have gone up a lot, and that’s causing some interesting bumps here. Things like inflation, AI. What are some of the macro trends that are either headwinds or tailwinds for you guys?

00:11:49 Mickael: The big thing is that at any point in time, there are always good assets to buy and bad assets that you shouldn’t buy. At any point in time. The data is ultra clear. There has never been a time where you should not buy anywhere.

00:12:03 Mickael: Maybe 2008, I didn’t check this in detail, but I’m actually pretty sure that, yes. It’s even better when the market goes down is when you make the best deals. I’m not stressed about macroeconomics ever.

00:12:13 Mickael: Personally, I prefer buying when the market is going down. Some people prefer the opposite. When you get data on a 7 to 10 year holding period, it doesn’t change much.

00:12:23 Mickael: People tend to overpanic on the real estate changes. But what happens if you buy in the right areas, rents keep going up almost always. And when they go down, it’s only a few percentage points. And by the time your lease will not be over anyway, so it doesn’t affect you anyway.

00:12:41 Mickael: Our yields are constant or increasing. And property prices in the areas where we invest, the biggest fall we’ve seen are maybe 10 percent within one year, but then with a sharp catch up. And over a 7 to 10 year holding period, doesn’t change much.

00:12:57 Mickael: And so just like the stock market, don’t try to time the market. It’s just not working. If you can try to not put all your eggs into one basket. Try to buy one asset every two to three years. And if you do this, one will be bought at a small peak, one at a low, etc. But over time, you will flatten the timing risk and will just benefit from the long term trend.

00:13:18 Mickael: And as a fact, I was saying where I sold, so we’ve bought thousands of properties for our clients. So 100% have outperformed the S&P and all other indexes. And all of them have outperformed the real estate market.

00:13:30 Mickael: Yes, interest rates can be a pain, especially in markets where interest rates are viable. So you should really be careful on your cash flows. But apart from that, as long as you can manage your cash flows, don’t worry too much about it. Based on thousands of properties bought.

00:13:43 Mickael: One mistake I see, which I think is such a shame, is people who buy at their limit borrowing capacity. And then when interest rates go up, massive trouble.

00:13:52 Mickael: My sincere advice is that brokers will often tell you, “Hey, you should borrow one million, two million streaming.” No, don’t borrow at your maximum capacity, borrow much, much less and try to invest in multiple assets in different locations to hedge your timing and location risk.

00:14:08 Jenny: Great advice. I think I read that you studied engineering. That’s your background. You just said that you were obsessed with AI before the LLM models made an appearance.

00:14:17 Jenny: Can you talk about a little bit of that journey going from V1, which was more about machine intelligence, machine learning to how you’re able to really utilize AI now and how that’s reshaping real estate in general?

00:14:30 Mickael: When we launched PropHero, AI was mainly machine learning so predicting what to buy. That’s how we started PropHero and how our clients got great investments.

00:14:40 Mickael: Now, AI, of course, with LLMs is a whole different game. What we launched about one year ago is brand new. We call it the ecosystem ERP software. It’s like a big software that runs all real estate for the entire industry for PropHero, for real estate agents, mortgage brokers, notaries, renovators, property managers, etc.

00:15:02 Mickael: So we’re building this big software to run the industry. And this, of course, we’ve got some massive help from vibe coding. What we are building now in one month before would have taken us one year. And I think we can still go even much faster.

00:15:16 Mickael: That’s one big area. So building the platform, a much more robust and comprehensive platform with LLMs. The big dream we have, which was only a dream five years ago and which is now becoming a reality, is using generative AI to have your own real estate wealth advisor.

00:15:33 Mickael: And so we are connecting LLMs to our data sets, connecting tens of thousands of properties and hundreds of hours of our call recordings, so of our advisors talking to clients. Retrain the model with all this data. And now we’re starting to advise clients with an AI model.

00:15:52 Mickael: What we’re seeing is that for simple queries, this is really amazing. Because clients can get answers 24/7 straight away. What we see, however, is that when the time comes to make the big decision, writing a big check to buy a property, our clients still want to talk to their human wealth advisor.

00:16:09 Mickael: And this, I’m actually not seeing it change because it’s such a massive amount of money. People feel safer talking to a real human being. And so that’s why we keep our wealth advisor team, who are complemented by our AI wealth advisors.

00:16:23 Jenny: That was actually one of my questions I wanted to ask you is like, what parts of the investment decision do you think will never be fully automated? So I guess you answered that.

00:16:32 Mickael: Making the big check, making the big bank transfer. This is always the big moment where we think, “Hey, am I really trusting this company? Is it really a great deal?” This is where things get serious.

00:16:43 Jenny: All right. Well, humans still have a fighting chance. This is kind of more of a macro question, not just about PropHero, but do you think that traditional real estate agents or brokers are going to be disrupted in general by AI or empowered or both?

00:16:56 Mickael: Real estate agents will remain for owner occupiers, so for emotional purchases. I don’t really see a world where they are necessary, for investors, to be honest. Mortgage brokers, apologies to my friends who are brokers, but I know they agree with me.

00:17:11 Mickael: I actually just bought a house… I don’t see why the broker was necessary. It’s the bank’s job to improve their processes and automate their processes to provide instant mortgages or mortgage approvals.

00:17:25 Mickael: I’m certain this will come. I was expecting them to be faster to deploy this, to be honest. But I think with AI development and AI checks now, I think this will accelerate. I’m not watching mortgage brokerage business.

00:17:38 Jenny: Good to know. What’s one idea that experts in your field say that you just totally disagree with?

00:17:44 Mickael: People, especially venture capital funds, always tell me, “Oh, it’s a transactional business.” Absolutely not. It’s a wealth business focusing on customer lifetime value. We are our clients’ lifetime wealth partner.

00:17:58 Mickael: I don’t care if they buy today or tomorrow. What I want is to help them throughout their lives, helping them buy, sell, renovate, manage, finance, whatever they need to build real estate wealth. And so it’s a lifetime wealth business, not a transactional business.

00:18:14 Mickael: And I think that is one of the reasons why real estate agents have sometimes some bad press, because they push you so much to buy, not procure. We are not here for the short term and just to have you make one transaction. We are here to help you for the rest of your life. Especially for investments, it’s a lifetime value optimization game, not a transaction game.

00:18:33 Jenny: For sure. So tell us a little bit just more about your team. Are you still spread across continents or mostly concentrated in Spain now?

00:18:40 Mickael: We used to think that we would hire people anywhere, just hire the best talents. That was our people and culture policy for about three years.

00:18:51 Mickael: What we realized that for the most senior roles, being able to be in the same room at least twice a month changes everything. So now the most senior roles are all in Spain. Not necessarily in the same city or Spain but at least 45 minutes flight or one hour flight from each other to be able to see each other once or twice a month at least.

00:19:12 Mickael: All the other roles can be anywhere. And what we try is for the top talents to have them see each other once or twice a year. So we went back from we don’t care where you are to well, actually, for some role, we do care. Our biggest offices now are in Spain. We still have people all around the world.

00:19:28 Jenny: Got it. So what’s next for PropHero? What should we expect next? What are some of the things that are on the horizon?

00:19:34 Mickael: So we’ve got big dreams. To be honest, when I launched PropHero, for me, success was just having a good business, maybe 10 employees, just making some revenue, being profitable, being sustainable. That was success.

00:19:49 Mickael: Well, now we are 250 people, profitable, four countries. My first dream is already achieved. So now the next one is how do we build a global sustainable institution?

00:20:00 Mickael: And so we’ve got many, many big dreams. One of them is to build this software that will run the entire real estate industry. It’s a big ERP or CRM for all the industry. It’s crazy, but I know we’re going to get there.

00:20:13 Mickael: I really, really want to launch a real estate investment fund, a listed investment fund, because I think we have the best supply. And for so many people, it would be so much easier. I know if you’re institutional, also, if you cannot have at least 50 or 100K to invest, this would be so helpful for so many people.

00:20:30 Mickael: Many dreams. Keep enjoying the ride, opening new countries and having a lasting impact on our client’s lives and our employees.

00:20:37 Jenny: I love it. Okay, speed round. So just quick answers here. Is there a book, newsletter, podcast or some type of media that you’re enjoying right now?

00:20:44 Mickael: You have to listen to A Short History on Spotify. For the past 100 years, history has accelerated like never before in human history. Understanding the past helps you so much understand what’s happening today. So A Short History Of. I listen to dozens of history podcasts. It’s by far the best one.

00:21:03 Jenny: I love it. If you could live anywhere in the world for just one year, where would it be?

00:21:08 Mickael: So I’ve lived in 12 countries, five continents already. When I could, I will do a one year road trip across Africa. So I’ve lived in Morocco and Tunisia in the past. I miss Africa. I love this continent so much and it’s so big. I want to discover the rest.

00:21:22 Jenny: I love it. Favorite productivity hack. What keeps you super efficient?

00:21:26 Mickael: If you’re anyone doing some form of corporate job, if you don’t have Claude Cowork open 24/7 on your laptop doing 80% of your work, something is wrong. Claude yesterday did the work that I would have done myself in one month and did it in one afternoon.

00:21:44 Mickael: So honestly, whatever you do, as long as there is a computer needed, have Claude Cowork open 24/7 and force yourself to use it and not do things. I know it’s not crazy. Most people are now doing it. But if you’re not doing it already, you have to.

00:22:00 Jenny: Love it. Okay, Claude Code. Where can listeners find you? Where is the best way to get in touch with PropHero?

00:22:05 Mickael: Airport lounges is where you will most likely find me. Otherwise, go to www.prophero.com, book a call with one of our wealth advisors and they will walk you through our investments and what we can do for you.

00:22:18 Jenny: Amazing. This was so fun, Mickael. Might be your next customer, so we can talk about that. But excited to see you in Spain this summer. So thanks so much.

00:22:25 Mickael: Thank you. See you. Have a great week. Bye bye.

00:22:29 Scott Hartley: Thanks for joining us and hope you enjoyed today’s episode. For those of you listening, you might also be interested to learn more about Everywhere. We’re a first check pre-seed fund that does exactly that, invests everywhere. We’re a community of 500 founders and operators, and we’ve invested in over 250 companies around the globe. Find us at our website, everywhere.vc, on LinkedIn and through our regular founder spotlights on Substack. Be sure to subscribe and we’ll catch you on the next episode.

Read more from Mickael Roger in Founders Everywhere.

The creator economy has long been dominated by influencers with massive followings, but Noise is drowning out the old playbook. Their double-sided marketplace connects brands with everyday people, turning anyone with a smartphone into a social media creator. By tapping into the interest-based algorithms powering TikTok, Instagram Reels, and YouTube Shorts, Noise makes it possible for college students, stay-at-home parents, and side-hustlers to generate real income through organic content. No previous experience or high follower counts required. Brands, in turn, get a cost-effective, highly diversified alternative to traditional advertising, sometimes at up to 90% lower cost than conventional channels.

Co-founders Diego Kafie, CEO, Nic Weber, CPO, and Stu Feldt, CTO, have been building together for over a decade. They met at Answers, a St. Louis startup that shaped their careers and gave them the playbook they’d eventually use to launch companies of their own. The idea for Noise emerged organically from their previous consumer app company, where they discovered the effectiveness of nano-creator marketing while scaling that app. After that business was acquired by a competitor, the team saw an opportunity to productize the strategy that had driven their growth within a new company. Diego shares more about how what started as an internal growth tactic quickly revealed itself to be a scalable business opportunity: helping brands unlock authentic, high-performing creator content at scale while giving everyday people a new way to earn income online.

What’s Noise’s North Star?

We actually have two, because we serve two very different sides of the marketplace. For brands, we aim to become a permanent, must-have line item in every brand’s marketing budget by being the most reliable and ROI-positive channel for growth. For creators, we want to build the world’s largest gig-economy platform by making flexible income through content creation more accessible than traditional gig work, allowing anyone with a smartphone to participate.

Why is Noise going to win?

People are tired of the overly polished Hollywood-type on social media. They want to see real people. The structural shift in social media is working in our favor as modern algorithms don’t care about followers anymore, they’re interest-based. That means anyone with a smartphone can reach an unfathomable amount of people. We’re also riding a craving for authenticity that AI has accelerated.

For brands, the model is simple and flexible: they pay per view, there are no upfront charges, contracts, or lockups, and brands can set their own prices. If something doesn’t perform, they can even strike it and get a refund. There’s absolutely no reason not to try it. If you’re interested in talking about viral growth, sign up for free or book a call!

The internet is vast. The only way you win is by making a lot of NOISE!

How does Noise inspire “customer love”?

What inspires customer love at Noise are the stories from creators whose lives have genuinely changed through the platform. We’ve seen creators pay off student debt, stay-at-home parents build meaningful income, and families afford experiences they never thought possible. Creators are paying medical bills, getting their first apartments, funding Disney trips for the whole family, and all kinds of other fun things. Those stories remind us that we’re building more than a marketing platform. We’re creating new economic opportunities for people everywhere. On the brand side, customer love comes from consistent results, with brands seeing measurable growth in installs, sales, engagement, and ROI.

How has your background shaped you as a founder?

I’m from Honduras and I moved to St. Louis at 18 to go to college. That shaped the way I approach everything as a founder. Navigating the immigrant experience, fighting for a work visa, and fighting to stay in the country gave me a deep appreciation for the opportunity I’ve been granted to live in this country, and a strong sense of urgency to make the most of it. Early in my career, I was fortunate to join a company that invested heavily in a young kid with no real experience and gave me every tool to grow. My co-founders and I all came up through that same environment, and it shaped how we think about building teams and creating opportunities for others.

Any favorite podcasts or books?

I consume as much as I can, but I have a rule for myself to make sure I’m relaxing every now and then: all the visual content I watch (TV, movies) is purely for entertainment. All the audio I listen to is purely educational. So when I’m driving, working out, or heads-down at the office, I’m listening to podcasts and audiobooks and just constantly learning from people sharing their experiences. Some of my favorites are Acquired, Pivot, Invest Like the Best, and How I Built This. For books, I really loved Bob Iger’s Ride of a Lifetime, a full tell-all before he came back to Disney, so he probably has a few more chapters to write now.

Fun fact:
I’ve always loved soccer. I’m in my 30s now but still play 3 times per week. A big part of what I enjoy about it is the leadership and teamwork aspect, which naturally carries over into entrepreneurship.

Listen to Su Sanni with Kameale C. Terry, on the Venture Everywhere podcast: Worth Every Dollaride. Now on Apple & Spotify. Check out to all our past episodes here!

The host of episode 117 of Venture Everywhere is Kameale C. Terry, co-founder and CEO of ChargerHelp, an EV charging maintenance and workforce development company keeping charging stations operational across the country. She talks with Su Sanni, co-founder and CEO of Dollaride, a clean transportation company electrifying small urban fleets in New York City. Su shares how growing up in East New York with limited transit access shaped his conviction that underserved communities deserve better transportation options. He discusses how Dollaride evolved from a digital app for dollar van riders into a B2B electrification platform, positioning small fleet owners as key players in New York’s clean transportation transition.​​​​​​​​​​​​​​​​

In this episode, you will hear:

• Digitizing dollar van payments and routing through a consumer app.

• Pivoting from B2C rideshare to a B2B fleet electrification platform.

• Winning a $10 million NYSERDA grant to electrify New York’s dollar van industry.

• Offering EVs, charging, and parking as a turnkey solution for small fleet operators.

• Balancing government contracts with a commercial ground game to sustain cash flow.​​​​​​​​​​​​​​​​

If you liked this episode, please give us a rating wherever you found us. To learn more about our work, visit Everywhere.vc and subscribe to our Founders Everywhere Substack. You can also follow us on YouTube, LinkedIn and Twitter for regular updates and news.

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Transcript:

00:00:04 VO: Everywhere Podcast Network.

00:00:14 Jenny Fielding: Hi, and welcome to the Everywhere podcast. We’re a global community of founders and operators who’ve come together to support the next generation of builders. So the premise of the podcast is just that, founders interviewing other founders about the trials and tribulations of building a company. Hope you enjoy the episode.

00:00:32 Kameale: Hello and welcome to the Venture Everywhere podcast. I’m Kameale, the co-founder and CEO of ChargerHelp!. I am so excited because I’m here with Su Sanni, who is the co-founder and CEO of Dollaride.

00:00:47 Kameale: And this is a bit of a treat because one, I’ve been on this podcast before. But two, I have gotten the pleasure to meet Su in a lot of places while building my company, ChargerHelp!. Let’s hear more from him. Su, introduce yourself to the people.

00:01:02 Su: Thank you so much, Kameale, for that great intro. Hey, everyone. My name is Su Sanni. I’m the founder and CEO of Dollaride. I’m calling in today from Brooklyn, New York, where I was born and raised and where Dollaride is also headquartered.

00:01:18 Su: For those of you who don’t know, Dollaride is a clean transportation company that focuses on bridging transit gaps in New York City by electrifying fleets. Happy to be here and happy to talk more about what we’re doing and our journey through the clean tech startup ecosystem.

00:01:37 Kameale: I think this is my first time interviewing a founder. And the one thing that I am always interested in, because it’s a very interesting story for me, is what sparked your idea? How did you even know to start Dollaride? Let’s start there.

00:01:51 Su: Dollaride is actually the family business for me. I say that because what inspired me was my two uncles who were immigrants from Nigeria, moving here to New York in about the 70s or early 80s. The first jobs that they were able to get were jobs as, quote unquote, “dollar van drivers.”

00:02:12 Su: For those of you who don’t know, in New York City, we have this underground network of drivers who own their own shuttle vans and buses. They’re known around town as the dollar vans. That’s because back in the 80s, it literally cost only a dollar to get on these vans. They would take you wherever you needed to go within a certain neighborhood.

00:02:34 Su: My uncles were among the early pioneers of this industry. Their story in moving to New York, starting this little business of theirs, and then it growing into a little bit of a phenomenon was what inspired me to start Dollaride.

00:02:50 Su: The name is piggybacking off of them. The problems that we’re solving have a lot to do with transportation access in communities that the dollar vans serve today.

00:03:00 Kameale: That’s so incredible. I’m from LA and anytime I learn more about the dollar rides and even your company, I was like, “Oh, my gosh. That would have been something so cool growing up in LA,” because we didn’t necessarily want to catch the bus per se. But I could imagine us getting into this really cool, sleek electric van and getting around.

00:03:20 Kameale: Could you talk a little bit about how it was for you growing up in Brooklyn and how did that shape your perspective on mobility, access and underserved communities?

00:03:29 Su: Yeah, sure. And this is an integral part of my story and like lens on building businesses and transportation, especially in New York. I grew up in East New York, Brooklyn, which is right next door to Brownsville.

00:03:42 Su: I always tell people Brownsville is the home of Mike Tyson. These are some of the most challenging neighborhoods in Brooklyn. Especially growing up in the 90s, not only was it dangerous oftentimes, but we had very limited access to public transit.

00:03:58 Su: It was common in my neighborhood for kids as young as six, seven, eight years old like I was to walk about a mile from our apartment buildings to the nearest subway where we could finally hop into public transit and then get to school.

00:04:13 Su: My earliest memories of taking public transit always started with a lonely, cold and sometimes dark walk to the subway. That always left an impression on me about why is my commute so difficult or different from other people who live in New York?

00:04:31 Su: That pretty much shaped my view on transportation, and it just made me have a soft spot for folks who live in areas that are far away from public transit or even people who might not be able to take public transit because they might have a physical disability. These transit access issues were really prevalent in my neighborhood.

00:04:49 Su: By the time I became an adult and felt like I was empowered or had the agency to do something about it, I was really driven by that experience that I had as a eight-year-old trying to get to school every day, having to start with a 25, 30-minute walk to the subway.

00:05:06 Kameale: That’s so impactful and just dope to see just how our lived experiences can really shape what we build. When did you start Dollaride again and who was your first customer and what did the business first look like when you started?

00:05:21 Su: I first started Dollaride… you can say officially in 2019. At that time, it was just me and my co-founder. His name is Chris Coles. He’s our CTO. We essentially just built a website and a prototype app. The idea at the time was to create the Uber for New York City dollar vans.

00:05:43 Su: This was an app-based service where the driver from his phone can see any nearby passengers that wanted to hail a dollar van or his particular vehicle. At the time, this was novel, very unique because the way people take dollar vans in New York City, even today, is they’ll get out onto the street and hail it just like you’re hailing a yellow cab.

00:06:07 Su: You have to physically know where the vans are driving each day in order to see them. And then you physically wave your hand so that the driver can pull over, open up his door and let you into his van or his minibus.

00:06:21 Su: Our idea initially was to make this experience digital and more transparent to both the driver and the passenger and also enable them to transact digitally. We introduced credit card payments and paying digitally into this dollar van service, which was primarily cash-based at the time.

00:06:42 Su: That’s how we started in 2019 and what the business and the service looked like then. But after COVID in 2020 and a bunch of iterations, the company has definitely evolved from that initial vision.

00:06:55 Kameale: That makes total sense. Before we get to just where you all are at today, as someone who started the company in January of 2020, it’s always interesting to hear what was your biggest challenge back in 2019? What was keeping you up at night in 2019? Because I think that probably what was keeping you up then is different than what’s keeping you up now. Tell me about that a little bit.

00:07:19 Su: In 2019, the biggest challenge that we were faced with then… it was a couple of things. I remember we were really struggling with this idea of somehow allowing passengers to still pay in cash. Believe it or not, even in 2019 in New York City, amongst people who are going to work every day, there are still a significant number of people who wanted to pay for their rides in cash.

00:07:47 Su: We just found that roughly 35% of passengers preferred to pay in cash even though they had credit cards, they have bank accounts and they were using services like Uber and Lyft. They just still had the habit of using dollar vans and paying the driver with two or three dollars in cash.

00:08:05 Su: We often had that request and we were oftentimes arguing internally about whether we should change our technology or make some new features that would account for drivers receiving cash but still providing their service digitally to the passengers who wanted to pay with credit card.

00:08:23 Su: Another challenge that we were faced with then had to do with routing. In 2019, we had a bit of a technical issue. This just was a reflection of our team’s ability to get access to real time routing data that would enable us to show a driver the most efficient route to the passengers that were in their geography.

00:09:47 Su: Traditionally, dollar vans operate on fixed routes like a bus service would. But when you can actually see everyone’s geolocation through their phones, it behooved us to give drivers the ability to pick up passengers that weren’t directly on the fixed route, but that might be only one or two blocks away.

00:09:09 Su: But we realized that when you were doing that, you do have to also take into account if there’s traffic or if there’s obstructions that you can’t really see through the Google Maps API, but would ultimately affect the driver’s ability to get to that passenger on time or within a reasonable amount of time.

00:09:27 Su: I remember we spent a lot of time just trying to optimize our routing algorithm so that it could take into account real time traffic signals or predict if there would be some traffic congestion based on weather patterns.

00:09:42 Su: Needless to say, we do not take for granted the magic that the Uber and Lyft apps have. There’s a reason why those apps work so well today. They basically have invested a lot of engineering time and many millions of dollars into making their service flawless. For us in 2019, that was not easy to do. We spent a lot of engineering cycles trying to figure that out for our startup.

00:10:08 Kameale: Thank you for sharing that. I know that this podcast is one that has a lot of founders and VCs, so it’s always super interesting to think about what kept you up at night when you first started. You guys are almost a decade in.

00:10:22 Kameale: Let’s come to the future. Talk a little bit about how Dollaride has transformed, how you guys operate now and the intersection of mobility and climate. How is the business evolving into an EV fleet solution?

00:10:35 Su: Today, Dollaride operates more so like a B2B platform as opposed to a digital rideshare service. In 2021, maybe even early 2022, we started realizing that as good as our digital product could be, it would be really difficult for us to make an impact on our customers, particularly the drivers and the fleet owners, with only a digital app.

00:11:05 Su: I’ll share an example of my uncles. My uncles became my first customers. They onboarded their drivers into our app and we started using their drivers, their vehicles as the supply in our marketplace to pick up passengers.

00:11:22 Su: But at the end of the day, my uncles and other business owners like them, whenever they would complain about the issues that kept them up at night, it wasn’t our app, it was things like the cost of a vehicle or the cost of gas or getting more customers.

00:11:40 Su: We realized that although our app was getting better and we were getting more and more users, it wasn’t really making a difference in our customers’ true day-to-day operations.

00:11:50 Su: That was when I had a light bulb moment and realized that we could probably be making a bigger impact by taking on more responsibility in the operations of our customers. That means dealing with more of the physical world that they’re in as opposed to only operating a digital app service.

00:12:10 Su: At that time, we pivoted and began providing a B2B service where Dollaride would help our fleet customers. These are the business owners who employ the drivers or who own the vehicles.

00:12:26 Su: We started working directly with the fleets and provided them services like identifying contract-based opportunities to grow their businesses, learning a little bit more about the cost of insurance and how they can optimize insurance so that they can lower their operating costs. Again, it was my uncles and then some of their competitors who became my earliest customers.

00:12:50 Su: But in early 2023, we reached an inflection point and had a huge opportunity to go much deeper in building out a platform that truly helps the business owner. Dollaride eventually won a large contract with New York State. In particular, it is an agency called NYSERDA. Which is the New York State Energy Research Development Authority.

00:13:16 Su: NYSERDA provided Dollaride with a $10 million grant and contract to electrify dollar vans. The general idea here was that Dollaride already knows the dollar van ecosystem very well. We had over 400 drivers already using our app at the time. But all of these drivers and their vehicles emit a ton of carbon or greenhouse gas emissions as they’re providing transportation around Brooklyn, Queens and the Bronx.

00:13:49 Su: As the state wants to decarbonize transportation, they chose Dollaride as an engine for decarbonizing small urban fleets. They wanted to use us as a service provider to the dollar van industry for electrifying dollar vans.

00:14:07 Su: This was an opportunity, but also an inflection point where we had to decide, do we want to get into clean energy as part of our business model in our service offering? It was an opportunity that we embraced with open arms.

00:14:20 Su: This is what Dollaride does today. We are a turnkey solution for small businesses that provide transportation. We provide them with access to an electric vehicle, a charging station and even dedicated parking so that they can electrify their fleet, whether they’re starting with one vehicle or electrifying a fleet of 50.

00:14:45 Kameale: That’s incredible. And it’s so beautiful to see this full circle moment of where you started out before, but also understanding the customer, understanding the market and then bringing something to market that people needed and then being recognized by an entire state that they saw that you all were the best folks to drive this solution. That’s incredible. Hats off to you, Su.

00:15:06 Su: Thank you, Kameale.

00:15:07 Kameale: You’re welcome.

00:15:08 Su: I want to say one thing, too. I’m condensing the story because I just want to give people the highlights. But what led to getting the contract with the state was proposing this actual solution based on the feedback that we were getting from our customers.

00:15:24 Su: We ended up getting over 40 letters of intent from small business owners who are all complaining that the cost of gas and insurance and replacing their vehicles is a big inhibitor on their growth. That gave us the framing and the narrative to propose to the state of New York that there needs to be a solution to this.

00:15:49 Su: Because if we want to electrify and decarbonize our cities and our state, we need to actually think about small businesses who are typically cash strapped and have very limited resources, but make up the majority of the vehicles on the road, especially commercial vehicles. That’s how we really bridged the gap from our experience with customers to a concept and a pro solution that even governments could get behind.

00:16:17 Kameale: I’m so glad you brought that up. You have to truly, one, utilize what you know about your customer in order to create the opportunity. It sounds like, Su, you did that so well and even did it in such an organized way to get those letters. Some people may think, oh, that may take a lot of work, but honestly, it’s totally worth it. So that’s super impressive.

00:16:36 Su: Thank you. Thank you.

00:16:38 Kameale: You’re welcome. Just revisiting the question we had before, what was your biggest challenge back in 2019, super interested to hear what is one of your biggest challenges today? What is keeping you up at night today?

00:16:52 Su: This is an easy answer. It might be a bit of a nod to you, Kameale, and what you’re doing with ChargerHelp!. What keeps me up at night today and the biggest challenge we have is simplified by two words: charging infrastructure. In short, there’s just not enough chargers in New York City to support the electric vehicle fleet transition.

00:17:16 Su: We have policies, even in New York City, that require rideshare drivers and other for-hire vehicles to convert to electric by 2030. But there’s not enough public chargers. There’s not enough chargers that work consistently and reliably.

00:17:34 Su: It’s very common that a driver, whether they have a Tesla or an electric van, will pull up to a charging station and the station doesn’t work or they have to wait in line for over an hour before they can actually start charging, which takes them another half an hour to an hour anyway.

00:17:52 Su: This is a huge problem if you’re a small business owner and you make money off of driving that vehicle. Time is money and the money lost in sitting, waiting for a charger for an hour versus the amount of time that it takes to fuel up if you go to a gas station becomes a really challenging value proposition for my company to get over, as well as something that everyone who’s considering an EV has to really deal with.

00:18:19 Su: At the end of the day, we spend a lot of time right now at Dollaride trying to figure out how to build or procure better charging solutions. It’s something that is inevitably an issue that we deal with every customer and something that keeps me up at night literally every single day.

00:18:37 Su: This is not supposed to be a plug for ChargerHelp!, but I just have to say this. I kid you not, literally today we’re dealing with an issue at one of our charging hubs. We don’t own this particular hub. All the chargers are not owned and operated by us, so we’re relying on third parties.

00:18:54 Su: From time to time, the chargers don’t work or they’re having malfunctions. It’s the worst when we learn about this the morning of when the customer is supposed to take their vehicle out and do their routes in order to earn money.

00:19:08 Su: I’ve already talked to my director of fleet ops about ChargerHelp!. We should definitely talk because we need more of your services and your technicians out here in New York City, for sure.

00:19:17 Kameale: I appreciate that, Su. The other call out here is as you build, sometimes your partners and your customers may be other startups and figuring out how to do that well. That’s a really big deal.

00:19:30 Kameale: I’m going to interject this sales cycle question. So you guys won this really big contract. I’m sure that’s not the only deal that you all have. But then also, I’m sure with transportation, at least from outside looking in and as somebody that’s been in transportation, especially working with government, it just sounds like a very long sales cycle.

00:19:47 Kameale: Talk a little bit about how has the sales cycle been, how have you guys been working towards improving your sales cycle and then, just any tidbits that you could give to founders out there that are trying to build in the transportation, working with government space?

00:20:01 Su: Flat out, working with government is definitely a long sales cycle, especially as you get into larger six figure and seven figure deals, if not more. We’ve experienced that and we’ve dealt with those challenges in a couple of different ways.

00:20:17 Su: Firstly, with the NYSERDA contract where we won $10 million, that in and of itself was at least 18 months of, quote unquote, “sales.” It started out with an RFP, but then there were many milestones and interviews and different steps along the way until we actually received that award and email that we finally got the deal done.

00:20:41 Su: That at least can give the audience some view of how long deals like this can actually take. We also had a similar experience with another contract that also took about 12 to 15 months. With this particular one, once that contract landed, it took another year before we got paid.

00:20:59 Kameale: That should be a whole segment. Just because you win the contract, don’t mean you’re going to get the money in your bank.

00:21:04 Su: This becomes a lesson for us founders about managing cash flow and figuring out how to capitalize your business properly, because you might have a huge contract from a government customer, but they pay late. They don’t necessarily pay reliably or in a predictable way.

00:21:23 Su: You still need to keep the lights on. You still need to pay payroll while you wait for the checks to come in. This is something that we had to learn the hard way, but hopefully folks can learn from our mistakes.

00:21:34 Su: But what I would encourage other founders to do if they are in a highly regulated industry, where government becomes your customer and these contracts are attractive to you… what we do at Dollaride is we do two things at once.

00:21:49 Su: We pursue government as clients. We’re always responding to RFPs and finding ways to pitch our services as a small purchase agreement, which is typically under a certain cost or threshold that might be outside of the RFP process. But we use that as a way of getting our foot in the door and then building up that relationship to a much larger contract agreement over time.

00:22:14 Su: We do that, which is like our sales approach with government. But then at the same time, we’re constantly still building the commercial side of our business, which is selling B2B. In combination, the B2B sales have a much smaller or shorter sales cycle. There’s a lot more customers out there that we can sell to.

00:22:35 Su: I use a football analogy. While you’re throwing for the big Hail Marys, which are the government contracts, you still have a ground game and you’re running these short plays with your B2B customers.

00:22:47 Su: That approach is balanced and can help you weather these long spurts of time where government pays late or they don’t pay on time. You still have a commercial business that is sustainable.

00:22:58 Kameale: That’s really smart. Everybody’s just trying to get their first contract. Once you get your first contract, you do have to start really looking at, okay, what was this process? What do I need to change? How do I go faster?

00:23:08 Kameale: I’m going to ask this last question and then we’ll go into the speed round. If you could wave a magic wand and fix one urban mobility problem tomorrow, what would it be?

00:23:21 Su: Sometimes I think about incentives as a more systematic way to influence customer behavior or markets. So if I could wave a magic wand to fix an urban mobility problem, I would want to somehow create another revenue stream that is durable for urban transportation and local transportation.

00:23:50 Su: What I mean by this is all around our country, public transportation is actually subsidized by government as much as up to 80%. The fares that we’re paying as consumers when we ride the bus or the subway or the light rail, that barely covers the true cost of transportation. It’s our taxes and loans that are covering the rest of it.

00:24:12 Su: But this makes it really challenging if you’re running a for-profit private business or a small business in this sector because the passengers can’t sustain you. You have to somehow have a revenue stream, and this is typically with contracts with government or larger institutions, to help you build a business.

00:24:31 Su: If I could wave a magic wand to solve a problem in urban mobility, it would be to create another revenue stream that helps public transit become more affordable, but also less reliant on government to actually fund its operations and its expansion. If we did that, it’d be easier to build more subways and to expand bus lanes and bus service so that we have fewer transit deserts and more mobility options overall.

00:25:03 Kameale: I love that, and that makes so much sense. Thank you for that. This has been so much fun. I’m going to close this out with a speed round. Are you ready?

00:25:12 Su: I think so. Well, let’s do it.

00:25:14 Kameale: What’s a book you’re reading or a podcast you’re enjoying right now?

00:25:18 Su: I love, love, love The Founders podcast. It’s by David Senra. He interviews founders, but he also reads biographies and autobiographies of famous founders and he talks through his CliffNotes.

00:25:33 Su: It’s a super engaging podcast. It’s helped me learn a lot about building businesses based on the stories and the tactics and techniques of founders before me. So I would highly recommend that one. And that’s one that I listen to nearly every day.

00:25:49 Kameale: Oh, I’m definitely going to have to look that up. I’ve never heard of that. Thank you. All right. Next question is, if you could live anywhere in the world for a whole year, where would it be?

00:26:00 Su: I would go with Tokyo, Japan.

00:26:03 Kameale: Oh, have you been?

00:26:04 Su: I have been. My wife is Japanese, so our family goes to Japan. We try to go there at least every year or two to see her family so that our kids can also be as close to Japanese culture as possible.

00:26:18 Su: Every time I go there, I am floored by how organized and clean and easy it is to live in Japan, especially in places like Yokohama. That’s a place that would be very easy for me to live for a whole year. Hopefully, I’ll get that opportunity at some point.

00:26:36 Kameale: That’s amazing. All right. Last two, favorite productivity hack.

00:26:42 Su: I ran a business almost 10 years ago called WeDidIt, which was a fundraising platform for nonprofits. I’ve had a decent amount of success winning RFPs and government contracts and these opportunities where you have to first know about it and then you have to apply before you actually have a chance.

00:27:05 Su: It occurred to me that the reason why I find these things so often is because I have built a little bit of a system. The productivity hack here is I literally spend an inordinate amount of time subscribing to every government agency and philanthropic foundations newsletter where they will announce when they have an RFP or some grants based opportunity.

00:27:30 Su: And then in my inbox, I filter the newsletters so that they only show me the most relevant opportunities to my business. Over time, when you are subscribed to 30, 50 or hundreds of newsletters, your inbox becomes this consistent magnet of relevant opportunities that you can apply to within your city, state, or region that might be coming from a government agency or a philanthropic foundation.

00:28:01 Su: This is something I’ve done over the years, but you could literally hack together something like this in a week and it tends to pay for itself very quickly.

00:28:12 Kameale: That’s amazing. Last question is, where can listeners find you?

00:28:17 Su: Listeners can find me on LinkedIn. That’s super easy. Just type in Su Sanni, S-U, first name, S-A-N-N-I. And on X @thesusanni.

00:28:30 Kameale: Amazing.

00:28:31 Su: Likewise Kameale. In fact, I should ask you, where can listeners find you if they want to learn more about you and your story, or follow along the journey of Charger Help!?

00:28:42 Kameale: Oh yeah, on LinkedIn as well.

00:28:43 Su Sanni: Excellent.

00:28:44 Kameale: This has been so much fun. Thank you so much for sharing with us and I hope that listeners enjoyed.

00:28:50 Su: Likewise. Thanks for your time today, Kameale. This was fun.

00:28:54 Scott Hartley: Thanks for joining us and hope you enjoyed today’s episode. For those of you listening, you might also be interested to learn more about Everywhere. We’re a first-check pre-seed fund that does exactly that, invests everywhere. We’re a community of 500 founders and operators, and we’ve invested in over 250 companies around the globe. Find us at our website, everywhere.vc, on LinkedIn, and through our regular founder spotlights on Substack. Be sure to subscribe and we’ll catch you on the next episode.

Read more from Su Sanni and Kameale C. Terry in Founders Everywhere.

Recognition often follows companies that are quietly reshaping everyday categories. Wagmo is one of them.

Christie Horvath, founder and CEO of Wagmo, was named a finalist for the EY Entrepreneur Of The Year award. The recognition reflects not only her leadership, but also the company’s growing role in redefining how pet care is accessed and managed.

Wagmo focuses on preventative care and pet insurance, offering a model that helps pet owners better plan for both routine and unexpected expenses. Rather than treating care as reactive, the platform encourages ongoing wellness through structured coverage and reimbursement.

Horvath’s approach is grounded in a simple insight. Pet owners increasingly view their pets as family, but the systems supporting their care have not fully evolved to match that shift. Costs can be unpredictable, and traditional insurance models often lack flexibility or clarity.

By building a more accessible and transparent platform, Wagmo is helping address that gap. The company’s offering supports everyday care while also providing coverage for larger medical needs, creating a more continuous and manageable experience for pet owners.

The recognition from EY highlights a broader trend. As consumer expectations rise, categories that were once considered routine are being reimagined through better design, clearer pricing, and more user-friendly experiences.

For Horvath, the focus remains on building a product that aligns with how people actually care for their pets. The goal is not just to offer coverage, but to support healthier, more proactive care over time.

As Wagmo continues to grow, its model reflects how even established industries can evolve when approached with a clearer understanding of user needs. Recognition like this signals that the shift is already underway.

Read the full article on Citybiz

Wildfires rarely begin as large-scale disasters. They start as subtle shifts—drier vegetation, rising temperatures, changing wind patterns—signals that, if understood early enough, can change outcomes entirely. But increasingly, the systems designed to monitor and interpret those signals are under strain. Research stations are closing. Data pipelines are thinning. And in many cases, the people responsible for managing risk are left operating with incomplete visibility.

That’s the gap Rhizome is stepping into.

Rhizome works with utilities and grid operators—organizations that sit directly in the path of wildfire exposure. Their platform translates environmental and infrastructure data into clear, operational insight: where risk is forming, how it’s evolving, and what actions can be taken before a spark becomes something far more costly.

From the outside, it can look like a data problem. In reality, it’s a decision-making problem. When risk isn’t well understood, responses tend to be blunt—broad power shutoffs, reactive measures, and trade-offs that carry real consequences for communities. What Rhizome enables is something more precise. A way to act with context, not just caution.

When we first spent time with the team, what stood out wasn’t just the technical approach, but how grounded it was in the realities of the people using it. Utilities don’t need more dashboards—they need clarity in moments that matter. That focus on usability, on translating complexity into something actionable, felt both pragmatic and necessary.

It’s also where Mishal Thadani brings a clear point of view. He’s spoken about the growing concern around the loss of wildfire research infrastructure, particularly in high-risk regions like California and Oregon. The implication is straightforward: as foundational systems weaken, the burden shifts to new layers of intelligence to fill that gap. Not as a replacement, but as an adaptation to a changing reality.

That perspective shaped how Rhizome is being built. Not as a static model of risk, but as a living system—one that continuously ingests environmental signals and reflects how conditions are changing in real time. It’s less about predicting a single outcome, and more about helping operators stay aligned with a dynamic environment.

From an investment standpoint, this is the kind of work that tends to matter more over time. It sits at the intersection of climate, infrastructure, and decision-making—areas where the cost of inaction is rising, and where better tools can meaningfully shift outcomes.

What makes it compelling isn’t just the scale of the problem, but the specificity of the solution. Rhizome isn’t trying to solve everything. It’s focused on a critical layer—helping the systems we rely on every day become more aware, more responsive, and ultimately more resilient.

Looking ahead, it’s hard to imagine a future where infrastructure operates without this kind of intelligence embedded into it. As risks become more complex and less predictable, the ability to see clearly—and act early—becomes foundational.

Rhizome is building toward that future, one decision at a time.

Read the full article on Fast Company

Some of the most meaningful shifts in technology don’t arrive all at once. They show up in workflows—quietly changing how things get done until the old way no longer fits.

That’s what’s happening with AI-generated code.

Developers today are increasingly guiding systems that can write, test, and deploy code on their own. It’s a leap in speed and capability, but it also introduces a new kind of risk—one that’s harder to see. When code is created autonomously, understanding how it was generated, what it accessed, and where vulnerabilities may exist becomes far less straightforward.

This is where Operant AI is focused.

With its Endpoint Protector, the company is addressing the rise of “shadow AI” and autonomous coding agents operating beyond traditional oversight. Instead of relying on static defenses, Operant AI focuses on runtime behavior—observing how AI systems interact with code, data, and infrastructure in real time.

What stood out to us early was the clarity of that approach. As AI changes how software is built, security needs to evolve alongside it—not as a constraint, but as an embedded layer of understanding.

That perspective is shaped in part by Vrajesh Bhavsar, who has consistently pointed to the growing gap between how quickly AI is being adopted and how slowly traditional security models are adapting. It’s not a future problem—it’s already here.

From our lens, this is where new categories begin to form. When foundational workflows shift, the infrastructure around them has to be rebuilt. And the companies that matter most tend to be the ones that recognize that early.

Operant AI is building in that direction—toward a world where AI-driven development is not only faster, but also more visible, accountable, and secure.

Read the full article on CXOtoday.com

Mallaby opened with a question that’s been haunting the best minds in technology: what does it actually take to build something that changes everything? His book traces Demis Hassabis and DeepMind’s decades-long quest toward superintelligence. It’s a story about conviction held long before the world caught up and it felt like exactly the right place to start for the moment we’re all living through. Hassabis has famously said, “First you solve AI. Then you solve everything else.”

The story of DeepMind, and of AI, is the backdrop of today’s entrepreneurial world. At Everywhere Ventures we’re not focused on these obvious trends, but where the tailwinds are taking us. We’re looking at the second, and third-order effects, and where founders are expanding on this new premise. As such, it couldn’t have been more fitting to have Mallaby set the stage for our AGM.

Our Story

Jenny and Scott founded Everywhere Ventures eight years ago with a simple belief: the best founders don’t always come from the places most funds are looking. Instead they’re everywhere, building in places that feel early, uncomfortable, and not yet obvious to everyone else. Today, we’ve invested into hundreds of companies and have built a thriving community of founders. Trends evolve, and tailwinds shift, but what remains the same are founders building at the edge, innovating and pushing us forward in new ways. The premise of Everywhere is that it’s our community that keeps us on this frontier, even as the tides shift, and the world changes. The best founders perennially move to the edge of the technology map, and we trust in their stewardship to lead us toward the non-consensus frontiers the world hasn’t fully adopted.

How We Got Here

Venture capital is a high-dispersion asset class. The difference between a top-quartile manager and a median one isn’t incremental, it’s enormous. This means differentiation isn’t a nice-to-have; it’s the whole game. Our job is to find non-consensus ideas where price still lags inherent value.

Today, we see three second-order effects creating mis-priced, non-consensus opportunities.

The first is fintech infrastructure. The fintech boom of the past decade created a wave of consumer-facing products, and underneath them, a tangle of legacy plumbing that was never built for what it’s being asked to do. The obvious bet is on the apps. Our bet is two layers down: KYC and identity management, fraud and risk tooling, data layers, stablecoin rails. That’s where the durable margin is, and that’s where we’ve backed Complyance, founded by Richa Kaul. Complyance automates regulatory compliance for financial institutions, replacing manual, error-prone processes with an AI-powered platform built for the pace of modern regulation. That’s the infrastructure financial institutions actually need for the world they’re operating in now.

The second is AI infrastructure. Everyone is betting on the large models. We’re betting on what the models can’t run without. Security, energy, orchestration, the unsexy, load-bearing infrastructure that makes AI deployable at scale. Operant.ai, founded by Vrajesh Bhavsar, Ashley Roof, and Priyanka Tembey, sits squarely in this thesis. Operant provides runtime security and observability for AI systems, giving enterprises the visibility and control they need to deploy AI safely in production. When enterprises ask how they actually run AI reliably at scale, Operant is part of the answer.

The third is AI-native companies, but not just any AI wrapper. The ones that win will do three things: generate net new data that didn’t exist before, take action rather than just surfacing insight, and amplify humans rather than replace them. Those companies become genuinely impossible to displace. Keebler Health, founded by Isaac Park and Andrew Stickney, is a company we believe fits that profile. Keebler Health uses AI to streamline the prior authorization process, cutting the administrative burden that slows down patient care and burns out clinical teams. That’s AI building in a space where it can meaningfully change outcomes, not just automate busywork.

The One That Made Us Look Twice: Pair Eyewear

Speaking of non-consensus…we’re a fund focused primarily on b2b software opportunities, and yet we wrote a pre-seed check into a consumer eyewear company with co-ceos still in university. But when we met Nathan Kondamuri and Sophia Edelstein, we knew they were special, with a vision for launching an entirely new category of eyewear: weekly product drops, a subscription membership, on-demand manufacturing, and a lens lab with 90+ patents. They built an incredible brand with the average customer owning more than eight top frames. They’re now in more than 1200 retail stores and gaining strong traction. Sometimes the best bet is the one nobody expected you to make.

Hot takes, Macro trends

We closed the AGM with new General Partner Anna Barber and Fortune’s Allie Garfinkle in a wide-ranging conversation about the new world of venture. One that’s headed away from bloated firms, consensus trades, and old assumptions about how companies scale, and back toward something smaller and more human. Drawing on both venture history and today’s market, Allie argued that the firms built for true venture-scale returns will increasingly separate from the asset managers simply deploying ever-larger pools of capital. The middle is getting squeezed, high AI valuations are masking fragile companies, and secondaries may do more than the long-awaited IPO window to reshape liquidity. Her core point was that venture still matters because it remains one of the few financial structures designed to back ambitious people with unproven ideas. We agree. We think the future belongs to small, focused firms staying close to founders and true to the craft of early-stage investing.

Looking ahead

We left the AGM feeling what we always feel after spending time with this community: optimism and momentum. The future of venture capital belongs to firms that are genuinely differentiated: in their sourcing, their diligence, their networks, and their conviction. Not funds that follow the consensus, but firms that form their own view early and hold it. We’re glad you’re part of it.

Looking to explore more insights from our team? Dive into Jenny’s book, and follow Scott and Anna on Substack.

E-prescribing was meant to modernize how prescriptions move through the healthcare system. In practice, it often digitized the same fragmentation. Scripts are sent, but patients are left to navigate pricing, availability, and fulfillment on their own.

Photon is taking a different approach. Rather than treating prescribing as a handoff, the company is rebuilding it as an end-to-end experience that connects providers, pharmacies, and patients in a single flow.

At the center of this shift is Otto Sipe, who is focused on making prescriptions more transparent and actionable. The goal is not just to send a prescription electronically, but to give patients clarity and control at the moment decisions are made.

Photon’s platform allows providers to guide patients through key steps during the visit itself. Pricing options, pharmacy selection, and fulfillment paths are surfaced upfront, reducing the uncertainty that typically follows a prescription. Instead of a fragmented process, it becomes a coordinated one.

This reframing addresses a deeper issue. E-prescribing has long been optimized for transmission, not experience. By shifting the focus to outcomes and usability, Photon is redefining what that infrastructure layer can do.

The approach also aligns with broader expectations in healthcare. Patients increasingly expect visibility into cost and choice, while providers are looking for tools that reduce friction rather than add to it. Bridging that gap requires systems that work across stakeholders, not in silos.

Photon’s model suggests that the next phase of healthcare infrastructure will be less about digitization alone and more about integration. Connecting the full lifecycle of a prescription, from decision to fulfillment, creates a more reliable and patient-centered experience.

As platforms like Photon gain traction, the definition of e-prescribing itself begins to shift. What was once a simple electronic transfer is becoming a coordinated system that supports both clinical decisions and patient outcomes.

Read the full announcement on Hospitalogy

Homebuilding is a major pillar of the U.S. economy and is one of the largest markets still running on outdated infrastructure. The top 500 U.S. builders sell roughly $250–500 billion of new homes each year, which is about half the size of U.S. e-commerce. Yet most of the buyer experience still happens through phone calls, paper forms, and scattered legacy systems. Foundation is bringing buyers and teams online with the native customer experience suite for enterprise‑scale homebuilders. They provide white‑label mobile and web apps that give buyers a modern, branded journey, while sales and operations teams get powerful tools and workflows that increase speed and efficiency. They support single‑family, townhome, and multifamily builders, and part of their job has been to underwrite their decisions by proving the product works across all these builder types. The ultimate goal is to help builders sell more homes, make more profit, and give buyers a better experience from start to finish.

Co-founder and CEO Luke Groesbeck considers building Foundation a full-circle story. His co-founder and CCO Derek Schairer previously sold homes for Lennar, one of the leading homebuilders in the world, and Luke interned at Lennar in college before moving into tech. Luke and Derek met later at Opendoor, where Luke led product for field tools before moving to partnerships to help scale the company’s fastest-growing customer acquisition channels and Derek was the GM of homebuilder partnerships. Working closely together, they saw firsthand how large-scale homebuilding and transactions actually operate in the field, from construction workflows to customer experience. That shared context shaped their decision to start Foundation, where they now combine their backgrounds to build consumer-grade buyer experiences, operational tools for builder teams, and an API platform for integrations and automation.

Why Foundation and why now?

At Opendoor, we worked on the homebuilder partnerships channel, which turned out to be one of the company’s most efficient and scalable customer acquisition channels. As we worked with homebuilders, we kept hearing the same request: they wanted better technology to run their sales process and improve the buyer experience. Most builders were still relying on outdated, offline systems despite selling hundreds of billions of dollars of homes each year. That’s when we realized there was a massive opportunity to bring modern software to one of the largest industries that had been left behind.

What’s Foundation’s mission?

Our mission is to build Silicon Valley–grade technology that helps homebuilders sell more homes, more profitably, and deliver a better experience for buyers.

Tell us about some recent milestones that Foundation crushed.

• In 2025 we grew 5x.

• By the end of 2025, our builders collectively sold more than 10,000 homes a year through Foundation. That’s the equivalent scale of a top‑10 public U.S. homebuilder.

• We launched our first builder‑branded mobile apps less than 18 months ago, and in that time we’ve grown to power more than 40% of the homebuyer and homeowner apps offered by top‑200 builders.

How does Foundation inspire ”customer love”?

Foundation turns a fragmented and stressful homebuying process into a simple, modern experience. Buyers can track construction progress, manage checklists, and communicate with their builder in one place during the 6 to 12 months their home is being built. For builders, that means happier customers, fewer manual touchpoints, and teams that can focus on building and selling homes instead of constantly managing updates.

Why is Foundation going to win?

You have to have an ROI‑driven product, but beyond that, winning comes down to two things: who moves the fastest, and who’s closest to their customers. We’ve built Foundation around that intersection. We ship quickly and iterate constantly based on real feedback from builders. Most of our relationships are built on the ground and that proximity gives us a deep understanding of their problems. I believe that combination of speed and closeness is what will ultimately make us hard to beat.

Any favorite podcasts?

The only podcast I consistently listen to is Odd Lots, a Bloomberg show that’s generally business‑oriented but features fascinating guests across many industries. I like hearing from people who are deep experts in their own strange corners of the economy.

Fun fact:
I have two daughters under six and we’ve become the typical Boulder family: spending our time outdoors hiking, rock climbing, snowboarding, and skiing.

Listen to Alfie Pearce-Higgins with Jenny Fielding, on the Venture Everywhere podcast: Not My First Rodeo. Now on Apple & Spotify. Check out to all our past episodes here!

Healthcare is increasingly moving toward convenience, privacy, and accessibility. &you is part of that shift, offering a model that brings consultations, treatments, and ongoing care into a more seamless digital experience.

In a recent Tatler Asia feature, Emil Eriksen, founder and CEO of &you, shared how the company is addressing gaps in how people access healthcare. Many patients still face barriers such as stigma, limited availability of specialists, or the inconvenience of traditional clinical visits. These challenges are particularly visible in areas like sexual health, dermatology, and wellness, where discretion and ease of access matter.

&you’s approach is to remove those barriers by combining telehealth consultations with end-to-end treatment and delivery. Patients can consult with licensed doctors online, receive prescriptions when appropriate, and have treatments delivered directly to their homes. The experience is designed to feel simple, private, and continuous rather than fragmented.

Eriksen’s perspective is rooted in the idea that healthcare should fit into people’s lives, not the other way around. By focusing on accessibility and user experience, the platform is helping normalize conversations around conditions that are often under-addressed in traditional systems.

The company’s growth reflects a broader shift in consumer expectations. Patients are increasingly seeking healthcare that is not only clinically effective but also convenient and discreet. Digital platforms are becoming an important layer in meeting those expectations, particularly in regions where access to care can be uneven.

&you is positioning itself within that evolution by building a model that integrates consultation, treatment, and follow-up into a single experience. This reduces friction for patients while improving continuity of care.

As digital healthcare continues to expand, platforms that prioritize both access and experience are likely to play a larger role in shaping how care is delivered. Under Eriksen’s leadership, &you is building toward a future where healthcare is more approachable, responsive, and aligned with how people live.

Read the full article on Tatler Asia

Tinned fish is having an unexpected resurgence. What was once considered a pantry staple is now being reintroduced as a premium, design-forward product with growing cultural relevance. Fishwife is at the center of that shift.

In a recent Chowhound feature, the brand received a notable nod from Alton Brown, who highlighted Fishwife as a standout in the category. The recognition reflects a broader movement, where consumers are rediscovering tinned seafood not just for convenience, but for quality, sourcing, and experience.

Built alongside co-founder Becca Millstein, Fishwife has approached the category differently from the start. Rather than competing on price or shelf space alone, the company has focused on elevating both the product and the brand. From packaging to storytelling, Fishwife positions tinned fish as something to be enjoyed and shared, not just stored.

This approach aligns with changing consumer behavior. As food culture continues to evolve, there is growing interest in products that combine quality, sustainability, and aesthetic appeal. Tinned fish, once overlooked, is benefiting from that shift.

The mention from Alton Brown underscores how far the category has come. Influential voices in food are helping reframe perceptions, introducing tinned seafood to a new audience that values both taste and presentation.

For Fishwife, the opportunity goes beyond a single product category. By redefining how consumers engage with tinned fish, the company is contributing to a broader reimagination of pantry staples.

As demand continues to grow, brands that combine strong product quality with thoughtful positioning are likely to shape the next phase of the category. Fishwife’s rise reflects how even the most traditional products can find new life when approached with a fresh perspective.

Read the full article on Chowhound

In episode 116 of Venture Everywhere, Jenny Fielding, co-founder and general partner at Everywhere Ventures, talks with Alfie Pearce-Higgins, co-founder and CEO of Rodeo — an AI-powered careers platform helping people understand the job market, build personalized career plans, and find the right opportunities. Alfie shares how watching startups outperform years of institutional work in developing countries convinced him that the right people, tools, and capital could change everything — a conviction that led him back to the UK to fix one of the most broken markets he’d seen: the job search. He explains how the collapse of the traditional job application market, driven by zero-click application on one side and AI screening tools on the other, created a Akerlof-style market failure that Rodeo is purpose-built to solve.

In this episode, you will hear:

• How synthetic applicants and fake job posts are breaking the job application market.

• Rodeo’s voice-first onboarding approach to building rich, personalized career profiles.

• Combining AI efficiency with human advisors for the most powerful careers solution.

• How AI is making parental and school career advice dangerously out of date.

If you liked this episode, please give us a rating wherever you found us. To learn more about our work, visit Everywhere.vc and subscribe to our Founders Everywhere Substack. You can also follow us on YouTube, LinkedIn and Twitter for regular updates and news.

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Transcript:

00:00:04 VO: Everywhere Podcast Network.

00:00:14 Jenny Fielding: Hi, and welcome to the Everywhere Podcast. We’re a global community of founders and operators who’ve come together to support the next generation of builders. So the premise of the podcast is just that, founders interviewing other founders about the trials and tribulations of building a company. Hope you enjoy the episode.

00:00:32 Jenny: Hi, everyone. Welcome to Venture Everywhere. This week, we’re very excited to talk to Alfie Pearce-Higgins, who’s the co-founder and CEO of a London-based company called Rodeo, which you’re going to hear all about.

00:00:44 Jenny: Because it’s a startup building the tools that help job applicants plan their careers. This couldn’t be the most opportune moment to talk about this. So I’m really excited. Because everything that’s going on in the world of AI, when it comes to job opportunities and preparing professionals, it’s a very important time. Welcome to the show.

00:01:03 Alfie: Hi. Thank you very much. It’s great to be here.

00:01:06 Jenny: Awesome. I’d love to start a little bit at the beginning because you, like me, started in a very different place. I worked at J.P. Morgan, seems like you worked at Barclays. So I’d love to hear a little bit of the journey of going from finance and big company to starting your own startup.

00:01:23 Alfie: I’ve definitely had, what you’d call a nonlinear career, which is an interesting start for someone trying to build advice tools for other people. I studied economics and maths, and the natural place to go was going into the city and do some finance.

00:01:35 Alfie: I did a bit of M&A and then ended up trading credit default swaps back in the post-financial crisis days, which was quite fun. I think I’d do the same again.

00:01:55 Alfie: I always recommend to young people, big companies are great places to get training, to understand the world of work and to get that platform. But it didn’t take me that long to realize it wasn’t something I wanted to do for 30, 40 years.

00:01:53 Alfie: So I then left and went into international development and spent the next five years in Nigeria and then Nepal implementing economic reform projects. Trying to help privatize Nigerian electricity companies, trying to support investment into agriculture and tourism in Nepal after the earthquakes there.

00:02:11 Alfie: I enjoyed it, but I didn’t realize that… there were frustrations working for big donors. I love the work environment, but not necessarily the structure of the industry.

00:02:21 Jenny: Were you living in these places or you were just traveling?

00:02:24 Alfie: I was in Nigeria for two and a half years and then Nepal for about the same. And then lots of travel to various other parts of sub-Saharan Africa along the way.

00:02:31 Alfie: And I really enjoyed the environment. So I then very fortuitously got an opportunity to join SafeBoda, which was an early stage company in East Africa doing motorized taxis.

00:02:41 Alfie: Uber on two wheels is the easiest way for the European or American audience to understand it. And that was amazing. We were training drivers, we were giving them helmets, generally for the first time. And then we were helping to improve driving standards.

00:02:53 Jenny: And so interesting because you were working on the other side of that, which I imagine was slow and institutional. And then all of a sudden you’re like on the ground with the drivers. You’re actually seeing things happen from a consumer point of view every day, I imagine.

00:03:07 Alfie: There’s a marked contrast between what startups can achieve in developing countries versus some of the very slow institutional top-down structures. We’re all amazed and impressed by what startups can do in the UK or the US.

00:03:19 Alfie: That’s nothing in terms of the power that can be achieved by the right people with the right tools and a bit of capital in developing countries. Be that in healthcare, be that in education or consumer services.

00:03:28 Alfie: I was blown away by how powerful that can be. I certainly think that I achieved more with SafeBoda in three years than any of the development projects that I’d seen.

00:03:37 Jenny: Incredible. Incredible. So then what gives you the confidence or idea to start your own company?

00:03:42 Alfie: Well, lots of things that turned out not to be true. And we’ve come a long journey since then. So, the thesis with which one starts a company is often not the same thesis that one ends up building around.

00:03:52 Alfie: We started building tools for gig economy workers in the UK. Interesting. I saw how in developing countries, the economy can be very powerful because, in fact, you’re taking even more informal labor and adding more structure to it. Whereas in developed countries, it’s often perceived as taking structured jobs and removing security and making them less secure and more flexible.

00:04:11 Alfie: So I was amazed coming back to the UK by some of the challenges in the gig economy in the UK. I teamed up with an old friend who is ex-delivery. We started building tools for gig workers in the UK to understand and optimize their earnings.

00:04:24 Alfie: We had some success with that. We built a tool that took a lot of traction. There were real challenges around the data. To cut a long story short, the platforms didn’t see data the same way we did and were very aggressive about us helping drivers to understand their data.

00:04:38 Alfie: I think similar stories have played out in the US. It’s a longer story there. But we’ve then adapted that. But what we’ve stayed true on is that our aim is always to build tools that help workers navigate the world of work.

00:04:50 Alfie: What that’s evolved into is AI-powered careers advice to help people understand what the job market looks like, understand what their strengths are, how they can best fit into the job market and build a career plan.

00:005:01 Alfie: And then take it from there into finding live opportunities for training, for learning, for earning, for accommodation of all of that, And helping them secure those opportunities.

00:05:10 Alfie: Any of us who have had any engagement with the job market in the last couple of years will realize it’s changing extremely fast. What jobs are out there is changing. What skills are needed for them, what the jobs are called, how to apply for them.

00:05:21 Alfie: There is a speed of transition going on in the labor market that I think is unprecedented. And unfortunately, the people who are bearing the brunt of this are younger people entering the workforce for the first time. That’s very much part of our mission is to help younger people understand and enter the world of work.

00:05:37 Jenny: I would imagine also mid-career people that are getting caught in the transformation that’s happening. They grew up under one paradigm. And now, the way you get a job, the way that you approach a job search probably changed from when they enter the workforce. So I imagine that’s a big opportunity as well.

00:05:56 Alfie: Absolutely. I think looking for a job, planning a career, these have gone from occasional things that we will do to an ongoing, evolving question.

00:06:04 Alfie: We haven’t seen entire job categories destroyed or removed in the way some people have predicted. So I don’t think there are a whole bunch of mid-career people who suddenly wake up one morning and find that industry is no longer there.

00:06:14 Alfie: That may happen. Who knows? So I think a lot of people are seeing their jobs evolve. And the smart ones are realizing that if they want to progress or if they want to take their career forward, they’re going to adapt what their skill set is or how they’re going about it or where they’re specializing.

00:06:28 Alfie: This spans across the whole of careers, but I think it’s felt most acutely at the early stage of phase.

00:06:34 Jenny: So can you just talk more specifically what was broken about traditional job boards that you guys sought to transform and rodeo? As investors, we’ve seen so many iterations of this over the years. And so I’d love to kind of get your perspective on what you were really focused on.

00:06:52 Alfie: There are two things we should reference. One is job boards and the other is careers advice. Job boards are definitely broken. The model of a job board has always been someone turns up, searches for something, whether that used to be in the back of a newspaper or on a website or an app or with an agent. And then they try and identify the right roles.

00:07:10 Alfie: It’s broken a number of different ways. Job boards are typically optimized for volume. And that’s been same the metric. If you put up a job, you want to get enough applicants. And then you kind of assume that there are enough applicants, somewhere in there will be the right applicant. And we’ve seen that.

00:07:24 Alfie: The first job I applied for was, I think, in a supermarket stacking shelves. And at that point, it was a form. I had to stop in at the supermarket, pick up a form, fill it in and take it back in order to be able to spend my Saturdays stacking shelves.

00:07:36 Jenny: I did that as a waitress. I walked in and I was like, “Hey, are you guys hiring?” And they’re like, “Yeah.” And then they went behind the counter and they handed me a form. I mean, this was a long time ago, but that was wild.

00:07:46 Alfie: Exactly the same. And then the… next would be banking internships. And that was online. But I remember each of them taking about half a day because I had to write a creative essay about why J.P. Morgan was so close to my heart. You had to write out these answers and it took a while.

00:08:01 Alfie: And then you fast forward a bit and then you got one click, apply. Then you got your LinkedIn one click apply. Indeed did something similar. We’re now in a world where there’s zero click apply. You don’t even need to apply now. You can have an agent apply for all these jobs in your sleep.

00:08:13 Alfie: You can apply for a thousand jobs by next week without lifting a finger. Now, the reality is that in some ways, a rational approach on the applicant side, it’s a numbers game.

00:08:21 Alfie: What we’ve seen then on the business side is that they’ve deployed a whole bunch of AI screening tools because no one can conceivably sort through a thousand CVS between now and next week.

00:08:31 Alfie: It’s a really interesting case of market failure that both sides of the job application market have behaved rationally in adopting AI. And the combined result has been a complete breakdown of the actual mechanism.

00:08:42 Alfie: I remember studying Akerlof’s lemons and the second hand car markets and how asymmetric information causes market collapse in that sense. There’s lots of parallels with what’s happened on the job market.

00:08:53 Alfie: If someone applies, you’ve got no idea what they’re serious. You maybe don’t even know they’re a real person. And as an applicant, you don’t know whether it’s worth putting effort into applying for this job because you don’t know whether your CV is going to be read by a human being, how serious they are on their side.

00:09:08 Alfie: So I think there’s been a definite breakdown there. And ironically, to your point, we might be going back to a world where you stop in the cafe and ask for a job. I’ve heard people talking about going back to handwritten applications, because if someone sends you a handwritten application, you know that they’re real, they’re serious.

00:09:22 Alfie: We’ve seen a lot of job fairs because it’s much easier to do that early stage of applications if someone’s in front of you. That’s kind of on the job market side.

00:09:30 Jenny: It’s like I hadn’t really thought about that, but everyone’s creating synthetic users. So I imagine there’s synthetic applicants that these people have to deal with all over the place.

00:09:39 Alfie: Absolutely. And synthetic jobs. There’s a lot of fake jobs that are either swiping data or just collecting CVs. It’s a real mess. That’s connected to what we’re doing. We haven’t set up just to try and solve that problem. And there are bits that we can’t solve.

00:09:52 Alfie: In the other side is the careers advice. I don’t know who gave you careers advice, but this has always been a slightly messy market for how people get that advice. Parents still rank highest as a source of careers advice.

00:10:03 Alfie: That’s a challenge because… it’s always been out of date. What your teacher or your parents say to you is probably always got like a 20-year time lag. That’s always been an issue.

00:10:11 Alfie: Now it’s a serious issue because 10-year-old information about the job market is worse and useless when it comes to advising someone entering the job market today. So what the market people are advising on bears no resemblance to what their children are actually going to be entering into.

00:10:26 Alfie: People get careers advice from social media. That’s become a common source. It’s mostly about highly aspirational, get-rich-quick, not particularly reliable advice, but it can be quite useful in some cases.

00:10:36 Alfie: And then obviously people go to ChatGPT, off-the-shelf AI tools that we see in the data, are now cited as one of the most common sources for careers advice. And that can be really powerful.

00:10:46 Alfie: I’m sure that many of your listeners have probably experimented with giving Claude a copy of their CV. They are really powerful tools for brainstorming, for ingesting this information, but they come with quite big risks.

00:10:58 Alfie: One of the ones that we’ve seen mostly is sick fantasy. I’m sure that if I put my CV into ChatGPT, it’ll tell me that I should absolutely be a FTSE 100 CEO in five years and do I want him to make me a step-by-step route to getting there? Which is lovely to hear, but can be quite damaging.

00:11:14 Alfie: And those are not very good with structured data. Getting an AI tool to do a job search for you often will result in unreliable information and there’s no safeguarding, if you’re talking about young people who engage with this to make quite consequential decisions for their lives.

00:11:28 Alfie: So AI is already playing a big role in this. Our aim is to build the responsible tooling around that, which means being able to enable, to turn it into a properly useful tool for people to understand, build up a rich picture of who they are, what they need to do right now, what their current situation is, what their long-term ambitions are, how this could be mapped into their local job market – because a lot of this is contextual and local – and then turn it into an actual plan and help support them through putting that in place.

00:11:52 Jenny: I love the part about career advice. In my generation, the people that I asked were my friend’s parents. I was going to law school and my best friend’s father was a lawyer. My parents weren’t lawyers.

00:12:04 Jenny: And so I asked him. There we are in like, his Hampton’s house. He was like a partner at some big firm. I was like, Steve, what’s the advice you would give? Because like I think I was going into law school, so I was like 20.

00:12:14 Jenny: And he was like, well, Jenny, I think you should join a members club, a private club, and you’re going to get a lot of business there. And I was like… 20 years old. And I was a woman. I feel like a lot of those members clubs of that generation were these old guys and whatnot.

00:12:27 Jenny: I just like… remember looking at him and being like, “Wow, that seems a little out of touch.” But like that was literally the advice that I got.

00:12:35 Alfie: Absolutely. I’ve had so many experiences of going into a degree and then being like, how come this isn’t what I thought it was going to be? Because that’s what it was like 20 years ago while I was being told about that world. It’s always been a problem. But the speed of change of AI has just radically exacerbated that.

00:12:50 Jenny: So you mentioned this idea of agents. And just for anyone who’s not fully caught up, how do you see the agent on behalf of the person looking for a new career, looking for a new job?

00:13:02 Jenny: In the future, how do you see that actually playing out? I’m really interested. We obviously hear a lot of talk about it. There’ll be agents out there running around, looking for jobs. How do you actually see it benefiting individuals?

00:13:13 Alfie: In the first instance, it’s about helping people to understand themselves, the job market, the intersection of those two points and make a plan. In the longer term, there are a lot of open questions.

00:13:22 Alfie: It’s interesting to think about how human agents work in, say, the creative industries. If you’re an actor or musician, you will have an agent. Or maybe if you’re a writer.

00:13:30 Alfie: And that agent will know you quite well, go out into the market and source opportunities on your behalf and then bring them to you and say, Jenny, there’s a new film next year. Do you want to do four months’ work in Hawaii playing this? Here’s a script.

00:13:44 Alfie: So, you’ve got this idea of human agents in some sense. I think there’s a world in which we professionally have that type of structure. Whereby rather than us ourselves trawling through LinkedIn or Indeed or any of these job boards and trying to identify opportunities, we are represented by an agent who understands a great deal about us and we share information.

00:14:05 Alfie: We update that over and over and it iterates and it learns us better and better and then goes out and represents us. I think that’s particularly the case in areas of work which could become more freelance-dominated.

00:14:15 Alfie: One of the questions that we’re really interested in is what AI does to the employment versus self-employment balance. We’re already seeing some indications that that could be… there could be a bit of a tipping towards more self-employment because in some ways, AI makes it much easier to manage freelancers.

00:14:32 Alfie: In the UK, we’ve also got challenges around cost of employment going up and up, which has always been a sort of nudge towards the use of self-employed workers. So I think there… there are really open questions around what the medium to longer term structure of the labor market looks like.

00:14:47 Alfie: When people talk about labor market, they talk about both the market for work and the market for matching. On the matching side, I think, sourcing applicants is way more common than in the past now and there’s been a lot of tooling built around that.

00:14:59 Alfie: We don’t profess to have a clear view on what that’s going to look like in the longer term, but I think the idea of a personal creation that understands you and helps you frame your thoughts, understand your local job market, and identify the best opportunities for you to learn new skills, apply for work, and get the job and career that you want is very much in keeping with what people need right now.

00:15:21 Jenny: Let’s talk a little bit about that idea. You guys focus on personalization. You can’t train agents or think about getting great recommendations without it being personalized. So can you talk about some of the signals and architecture that you’re using to really understand the user and their context and really what they’re looking for?

00:15:39 Alfie: The personalization is all about understanding the user. And we’ve iterated a bit on how to do that. Most of us, if we’re asked a question, what do you want to do with your life and given a blank piece of paper, we struggle.

00:15:49 Alfie: And the reason why human beings are good at this stuff is because they’re good at encouraging us to open up. The prompts, the nudges, the encouragement to reveal more information and to share and to frame your thoughts.

00:16:00 Alfie: We found that voice calls are just by far the best way of getting the early information out of people and building the starting point of a profile. So our onboarding is quite heavily structured around voice calls. We still use CVs as well, because it’s useful to get that information early on for people. But voice calls are generally a very good way of understanding more about someone’s situation.

00:16:18 Alfie: And then after that, there’s always the opportunity to go back to that and add more context. But it’s also intuitive. A user will come to us, they’ll upload a CV if they have one. They’ll chat to their personal career agent in a voice call. They will then be presented with this starting point of a career plan.

00:16:35 Alfie: They will give feedback on things they like, things they don’t like. We’ll start sharing opportunities that we think might be a good match. They will give feedback on them and we’ll improve it over time.

00:16:45 Alfie: Their situation may change. They may suddenly decide they want to go in a different direction. And this can all be incorporated into it. And that’s what AI is really good at, is piecing together this information into where a human careers advisor often struggles. Because remembering what happened in the last meeting, keeping notes, updating it all when you’ve got 30 different people you’re speaking to today, it’s super tough.

00:17:05 Alfie: The personalization is a question of gathering early information in a way that makes the user comfortable and encourages them to open up and frame their thoughts, and then constantly improving and adding to that to build a rich picture of the person that you’re trying to help.

00:17:18 Jenny: Just out of curiosity, are candidates recording their calls and then feeding that information to their coaches? The best way to get feedback is to be on a call with someone. You can’t usually be on a call with someone who’s in an interview, but if that was recorded… Sometimes it’s recorded by the employer and shared, and sometimes you’re using your Granola or whatever. That could be interesting.

00:17:39 Alfie: At the moment, people are generally using us as an alternative to... They may be using a career service on the side. And then we’re hoping to work or we’re scoping out opportunities to work directly with career services.

00:17:49 Alfie: I can tell you more about where we see the hybrid approach. But no, we haven’t encountered people sort of recording one meeting and feeding that back in. But it’d be a great way of testing.

00:17:57 Jenny: Yeah! Again, everyone needs to opt in. But you know, everything is getting so transparent now. It’s like, everyone assumes in our meetings as VCs that that meeting is being recorded on both sides.

00:18:09 Jenny: And so oftentimes those meeting notes are just shared amongst the parties. And that a year ago, if you would have told people that that was happening, people would have freaked out and be like, no, I just want to have an intimate conversation. And now it’s just normal.

00:18:22 Alfie: Give it a year or two and AI-powered glasses will mean that every conversation you have in person is also recorded and filmed. Our goalposts definitely moved on that in terms of what people assume is recording.

00:18:33 Jenny: So do you think that the traditional CV and traditional way is just dying? Or this is a subset of people that are taking things to the next level? I mean, I notice when we’ve had job roles, people don’t even really offer to send a CV. They just like send their LinkedIn and maybe a little paragraph. And obviously, if you ask for it, they’ll do it. But I feel like it’s really changing.

00:18:54 Alfie: Yeah. It’ll vary a lot sector to sector. LinkedIn is great for some sectors. It doesn’t work for others. I think there’ll be more sector-specific tools for different areas of expertise.

00:19:05 Alfie: The truth is, I think, the CV is evolving. People are always lying on their CVs. They’ve always exaggerated stuff. I’m sure that’s happening in the US. But certainly, the UK has had its fair share of political scandals, where it turns out someone was quite liberal with the truth when it came to what their professional record says.

00:19:21 Alfie: So that’s nothing new. I think any good hiring process only ever seen a CV as a very small part of the bigger picture. Anyone who’s hired based purely on CVs is probably not going to get the results they wanted.

00:19:32 Alfie: The two major things that have changed is that it used to be that a well-written, tailored CV… a real sign of seriousness for an applicant. That’s no longer the case.

00:19:41 Alfie: The most beautiful CV that speaks directly to the job that you’ve advertised and looks lovely, and it could have been generated in two seconds passing. So that’s no longer a good signal of seriousness.

00:19:53 Alfie: And then the other side is that CVs have stopped being written for human beings. Applicants have had to adapt what they’re doing, because it may well be that these CVs are getting screened by some sort of AI feature, whether that’s in an ATS, whether that’s separately.

00:20:07 Jenny: Can you give us an example of that? What’s a tweak that a candidate would make to their CV because they realize a person isn’t looking at it?

00:20:15 Alfie: Reflecting back the exact language of the job description, which means it doesn’t always read that naturally. And I’m very skeptical about that. I think there are lots of people promising candidate screening tools. The risk with AI that we mistake precision and accuracy. You can put 100 CVs into your AI screening tool, and it will tell you that I’ve scored 64.2%.

00:20:34 Alfie: Now, that sounds really precise. Could just be very randomized. Put the same information again and see how much variance there is in results. And it may well be that you rank 10 candidates using your whatever AI screening tool you’ve been pitched by some company. And they come up with different ranks. That’s a pretty big drawback.

00:20:52 Alfie: The second is that they completely overlook non-traditional candidates, which I think is an issue in terms of bias, fairness, and also just not good sense. Mostly applied for jobs that I haven’t been properly qualified for, but have managed to, at some point, convince someone that the skills that I do have offset the things that I’m missing.

00:21:12 Alfie: They may well have regretted it. But the good hiring processes always have that subjective, qualitative approach and are willing to accept that maybe what they thought they were looking for isn’t actually what they need and be persuaded of that. If you automate a lot of the hiring process on the employer’s side, you lose that flexibility.

00:21:30 Jenny: So really, the way to stand out is not in that first round of screening. You want to basically mimic what the job description is. And then the place that you stand out is potentially in the interview or the next rounds.

00:21:42 Alfie: If there is that sort of objective hurdle that you need to get through early on, then yes. I think one of the things that worries me, and this applies less to the world we’re looking at, more to the sort of white-collar mid-career, is it feels to me like there’s been a bit of a return to nepotism.

00:21:56 Alfie: Because the application market is broken in many of the ways that we’ve discussed, the easiest result is, well, look, I know so-and-so who’s recommended so-and-so, or so-and-so has got a connection into the company, or whatever it happens to be.

00:22:09 Alfie: And LinkedIn kind of makes it a lot easier because you can look at connections. And you end up hiring people through warm introductions. That’s something like the VC world has always been, if I could say, a little bit guilty of. The way money is allocated, the way people raise money, the way people get injections, has always been quite warm introduction-driven.

00:22:27 Alfie: And there’s some logic to aspects of that. But I think it’ll be a sad sign if that becomes more widespread in the jobs market, because it’s not marriage granting. It’s about connection that favors some people over others.

00:23:38 Jenny: Right. So getting back to Rodeo, you guys have had some iterations, and I’d love to just hear a little bit more of where you are today and how you’re thinking about the future of the company.

00:22:48 Alfie: Today we’ve got a product and a whole bunch of users using it to do everything from plan their careers to find opportunities to apply for, to supporting them with their applications. It’s a free tool.

00:22:58 Alfie: It’s really important to us. We’re getting a lot of good validation. I love talking to people who are using the product. I love getting negative feedback. I love getting positive feedback. That’s working well.

00:23:06 Alfie: Ultimately, we see this ideally as working in partnership with traditional career services. There’s a risk of a sort of either or debate between human careers advisors and AI. We’re very strongly of the belief the most powerful solution to ensuring everyone gets the advice and support they need is for a hybrid approach.

00:23:27 Alfie: There’s some people who will prefer to lean more on an AI solution. And this is particularly true because setting up a careers appointment in two weeks time when you’re trying to juggle two jobs that you’re working and family commitments, everything, can be really difficult. AI is amazingly convenient, can enable people to fit it around their lives a lot more easily and has some super powerful aspects to it.

00:23:49 Alfie: But ideally, it can also be combined with human career support so that other people who need the human support most can get access to that. So that there could be safeguards in place, things can be escalated if they need to.

00:24:00 Alfie: Or the AI can be used for something AI’s really good like speedy onboarding via an agent voice call or skills mapping from CVs, job database searches. And then the humans can be used for other aspects that they’re really good at like some of the empathy and support and sense checking. I think there’s a lot of opportunity.

00:24:18 Alfie: So we’re talking to everything from the government through to career services, colleges and schools and further education colleges and charities that work in this area.

00:24:27 Alfie: So the whole ecosystem of traditional career support, which we think is and will remain super valuable. We hope that we can turbo charge it and augment its impact and scale because there is this massive problem that… unemployment is high in the UK and a lot of people have dropped out of the labour market.

00:24:43 Alfie: I’m ultimately an optimist. I think AI is a productivity enhancement. It should make society richer, but the transition is going to be highly disruptive and could be very painful. Giving people support and advice to help them through that transition is going to be super vital.

00:25:00 Jenny: I mean, you’ve mentioned the word impact a couple of times. So what does success look like for you and Rodeo?

00:25:06 Alfie: Our mission is to ensure that all young people have the advice and support they need to help them find the right career path. Now, there are a whole range of different metrics that. But that’s a mission and success will be judged by how many people we’re able to help through that process.

00:25:19 Jenny: Love it. All right. We’re going to just move to the speed round. So just quick, one sentence answers. Book, newsletter, podcast, some media that you’re enjoying right now?

00:25:28 Alfie: I’m currently reading Alison Gopnik’s The Gardener and the Carpenter. She is a professor of psychology and philosophy somewhere on the West Coast, Her work spans everything from childhood development through to AI and cognitive models.

00:25:41 Alfie: I just think that if we’re going to understand what artificial intelligence is, then the starting point and crucial bit needs to be to understand what human intelligence is. And the best place to start back is to understand how babies and children develop and like how we develop human intelligence.

00:25:54 Alfie: Probably also helps that I’ve got a small daughter and so I spend most of my time trying to like understand her. Try and be a vaguely good parent in terms of supporting her.

00:26:02 Alfie: And then I spend a lot of my work life trying to understand what AI is doing, how it works, how it’s going to change society. And so I think her work is this really great intersection of those two areas of understanding like what intelligence is and how it develops, both in a human and artificial sense.

00:26:18 Jenny: Love it. If you could live anywhere in the world for just one year, where would it be?

00:26:22 Alfie: I mean, I’ve been lucky enough to live in a few places. I really enjoyed my time in East Africa. I mean, I was in Uganda, but across Uganda, Kenya, Rwanda. And so given the chance, I would love to go back there at some point in my life, probably not just yet. But the scenery, the outdoors, exploration and activities were just amazing.

00:26:38 Jenny: I love it. Favorite productivity hack?

00:26:41 Alfie: I find handwritten to-do lists are just so much better than anything else. Having a notepad and writing down each morning what I’ve got to do, physically crossing stuff off.

00:26:50 Alfie: I’ve tried over the years any number of tracking tools or perhaps Linear tickets or any of those other tools. But I just want to be able to write everything down in bullet points in a… on pen and paper and then cross it off when I’ve done it.

00:27:03 Jenny: That’s great. And then where can listeners find you?

00:27:05 Alfie: We’ve talked about LinkedIn. It’s not my natural environment. I’m as critical as probably many other people are of that environment, but it’s functional. So either that or, I don’t know, Strava.

00:27:15 Jenny: LinkedIn or Strava? First time answer on that one.

00:27:18 Alfie: Depends what you want to ask me.

00:27:20 Jenny: There you go. All right. Well, this was a real pleasure, Alfie. Thanks so much for taking the time. And we are excited about Rodeo and all those lives that you’re going to be impacting. So thanks for joining.

00:27:30 Alfie: Great. Lovely to talk. Thanks, Jenny.

00:27:34 Scott Hartley: Thanks for joining us and hope you enjoyed today’s episode. For those of you listening, you might also be interested to learn more about Everywhere. We’re a first-check pre-seed fund that does exactly that, invests everywhere. We’re a community of 500 founders and operators, and we’ve invested in over 250 companies around the globe. Find us at our website, everywhere.vc, on LinkedIn, and through our regular founder spotlights on Substack. Be sure to subscribe and we’ll catch you on the next episode.

Read more from Alfie Pearce-Higgins in Founders Everywhere.

• Strong: Maintain inputs, generate greater output with AI amplification

• Skinny: Truncate inputs, generate the same output on AI efficiencies

Ten years after publishing my book, THE FUZZY AND THE TECHIE, I still get invited once a quarter to speak to corporate audiences about the evolution of technology, and importance of human skills, soft skills, in the stewardship of that technology toward the gravest problems. This story has evolved from one of spotting bias in big data, providing context to code, into one that revolves around human skills in the era of AI. Today AI proliferation has further deepened the leadership trade-offs around AI adoption for efficiency gains and cost cuts, weighed against AI adoption for growth and output expansion through human labor amplification.

In 2017 I gave the example of radiologists, whom many at the time claimed would be “out of a job,” to highlight where this “skinny” myopia overlooked the reality that AI would not obviate all radiology jobs, but bring costs down, expand the demand for MRIs, and fuel a massive boom in radiology where there might be net new demand. In those ten years since the book we’ve seen the rise of Prenuvo, Ezra, EverLab, Function Health, Axo Longevity, and a number of players bringing this reality to fruition. While many feared a “skinny-only” outcome, what prevailed was predominately strong. Inputs remained relatively fixed, meaning not all those radiologists became unemployed, output expanded, costs came down, and demand skyrocketed. Today there are more radiologists than there were ten years ago. Opposite of expectation.

As AI efficiencies brought radiology costs down, and expanded demand, radiologists were pushed farther out onto the frontier of the “map,” working on edge cases that AI could not fully automate. What was truncated were not radiology jobs, but the rote tasks within radiology that made the work inefficient. By streamlining these rote tasks with the aid of AI, the time required to perform and interpret a scan went down, and thus radiologists were able to perform more scans in the same unit of time. This expansion in output on the same inputs (Strong), drove costs down. Relatively price sensitive consumers were suddenly able to afford something previously inaccessible, and so demand expanded, yielding a much larger than expected consumer market.

In every organization there are many jobs. And Jobs are comprised of individual tasks. Those tasks break down along two vectors. Tasks can be manual or cognitive, and they can be routine or non-routine. For rote manual tasks, robotics can substitute for human labor. For rote cognitive tasks, artificial intelligence can enable significant gains. Within these subsets of tasks, like routine radiology, AI can yield efficiency gains that allow leaders to make a choice. If they opt for “Skinny” they could cut inputs, and perhaps still generate the same output. But in a competitive market where your competition may not do the same, “Strong” is often the preferred choice, where inputs are maintained or only marginally cut, and AI-enabled outputs drive growth. In the case of radiology, given the relative price sensitivity of consumers, this growth actually yielded expansion in market size, demand, and net new jobs were created.

In practice, there are adoption timelines for technological substitution, and even in a “Skinny” scenario where a company chooses to cut human labor for machine, there are, in nearly every case, situations in which a human steward is still required. Technological capability still belies the organizational complexity of adoption, so the headlines will always front-run the reality we experience as employees.

For most jobs, however, a different story exists. Most jobs contain a high proportion of non-routine tasks that require creativity, collaboration, communication, and these are jobs wherein AI adoption is not an ultimatum, but a serum for higher productivity. These are the domains of “Strong” AI adoption, where AI might rather be called IA, or intelligence amplification. Human skills are enhanced, not obviated, and AI pushes these employees out of the rote and routine tasks into more human capabilities.

Omar Haroun, CEO at Eudia, talks about a “Pareto Compression,” where the old adage of 20% of inputs driving 80% of outputs is being compressed. In this new world, expertise is rewarded exponentially, with perhaps 5% of inputs driving 95% of outputs. This doesn’t mean we’re all doomed. What it means that in every role, every job function, humans will be pushed into that zone of genius, that 5% where what they do truly counts. In the case of radiologists, it’s the interpretation of non-routine cases, more patient engagement, an expansion in the need for soft skills, coordination, and communication. Pareto Compression does not mean a compression in the number of people to do the job; it means a compression in the task-sets within every job, pushing every performer to higher, more satisfactory marginal work, on top of AI. Like the radiologists, it can expand demand, and push the role to the frontier of accretive human capability. In other cases, it doesn’t mean everyone must become an expert; on the contrary, it broadens the base of who can do the job, and opens doors.

In a competitive environment the choice of Strong vs Skinny is not an ultimatum, or a zero-sum choice; it is the requirement of every organization to dive deep into jobs, identify tasks, and choose where to apply “Skinny” and where to apply “Strong.” Most organizations will have to do both by trimming non-routine tasks with robotics and AI, and by amplifying non-routine tasks with these extraordinary new tools. The world of AI adoption is one of rote task elimination, a broadened base of who can do the work, and a Pareto Compression of expert work amplified significantly. The error is in thinking that AI adoption is only one zero-sum choice; in fact, it is many, and in most cases leaders will find needing to have a “Yes, And” conversation about cost cuts and Skinny, and growth drivers and Strong, a widened funnel of whom they can employ, and a premium paid for true domain expertise and depth of experience.

Special thanks to David Travers and the ZipRecruiter leadership team for inviting me to speak on these and other themes in April 2026, and further refine the ideas. For more about Everywhere Ventures, subscribe to our 40,000 person newsletter.