1. How do I remain disciplined to my thesis and entry price?

2. How do I not miss truly asymmetric venture-scale outliers?

I always say in the short run discipline matters, but in the long run your job as a venture capitalist is provide accretive capital to unseen opportunities that pre-date market consensus, and that in turn, will afford you outlier performance and make your limited partners money. Asymmetries in returns come from two things:

1. Finding consensus before the market

2. Harvesting under-priced assets, where price < intrinsic value

One empirical observation we’ve had at Everywhere Ventures over the last 8 years, and I saw during my prior 7 years investing at Mohr Davidow Ventures and founding Two Culture Capital, was that our biggest outliers were always “in the tails.” They were in the periphery of what we said that we did. “We don’t do consumer,” but we were the first check into Pair Eyewear, which has gone up 100x since we invested. “Markets like Algeria are too small,” but we returned 100x on Yassir when it became the Super App of Francophone Africa, and the Maghreb. “Hardware is hard,” but we saw Umbra build Synthetic Aperture Radar (SAR) satellites and catapult to a billion dollar valuation and massive profitability. “Data centers in space is a crazy idea,” but as cost per kilogram continues to fall with SpaceX, Benchmark validated our early bet by marking up that position 100x and turning Starcloud into a unicorn in 17 months, the fastest ever in Y-Combinator history. When we pre-seeded Headway in 2018 many presumed mental health wasn’t a big enough market. Andreessen led their last round at over $2.4 billion. My co-founder Jenny Fielding pre-seeded Chainalysis at Techstars when web3 wasn’t yet on the map. Today Accel and GIC value it at $8.6 billion.

Part of investing in the tails is that firms operate on conviction. Generally to do something truly out of the box, one partner on the team has to feel it viscerally, be unable to sleep, keep bringing up the idea at partner meetings until the rest of the team eye rolls and says “ok fine, if you love it so much you should just do it!”

Every deal requires a “burden of proof” to get done. Diligence. Calls with customers. Reference checks on the team, the cap table, the co-investors. But when one partner is going farther out into the tail, that burden of proof has to go up. You have to prove with unreasonable conviction that you want to make the investment anyway. Despite convention, despite disagreement, despite consensus opposition. Great deals are not lukewarm, they are polarizing. And if you’re an entrepreneur and some people don’t absolutely hate your idea, or think you’re crazy, you’re not swinging big enough.

The second truth today is that venture capital is no longer a cottage industry like it was when I was a kid in Palo Alto, when soccer dads played with Palm Pilots and talked about personal computing and the rise of DotCom opportunities. Back then there were 20 firms on the map, and there was not a glut of capital chasing the same opportunities. The frontier of the "tail” moves. The Internet was a tail bet in the mid 1990s. The social web was a tail bet in the mid aughts. SaaS was a tail bet in the early teens. Crypto was a tail bet in the late teens. Perhaps space is a tail bet today.

What’s clear is that frameworks lag reality, as I’ve written about before. Heuristics of what worked last year are most certainly NOT what will work this year. Part of this means that a glut of capital is chasing a lagging consensus, and therefore any potential alpha is “priced in.” When I called my friend Clay Bavor when he started Sierra AI, he said “sure write a check, but Sequoia’s doing it at a billion.” Clay and Bret Taylor are exceptional, and Sequoia was already pricing in value at a billion. There is still certainly alpha in that company, as it’s a rising AI juggernaut, but that’s not the game we play at Everywhere Ventures. We’re primarily investing outside the Valley, and looking for asymmetries in the tails, and mis-priced assets, where there are undiscovered, yet exceptional founders, who have a mismatch of price to value.

In a consensus deal value is priced in. In the example above, the price may be nine, but the value is ten. The value capture to the investor is one. Outside of key industries, in first-time rounders, and in remote geographies, the delta between price and value is higher. In non-consensus deals (for the above reasons) price may be four when value can still be ten. This delta of six is the potential non-consensus upside, the alpha.

At Everywhere Ventures we ourselves are a non-consensus firm. We’re bicoastal, we’re very early pre-seed, we primarily invest outside of the Bay Area gold rush. But what we believe is that our network of 1,000 founders as LPs and portfolio CEOs give us unfair access into these tails via compounding network effects in venture capital. And because we constantly evaluate “where the tail is,” whether sector, geography, or founder archetype, we’re focused on finding these mis-priced assets in the market. We write three times more checks than firms our size, and we return capital. We’ve charted to the top decile every fund, every cohort, every year for the last decade.

Venture capital is an evolving game, but the lessons above hold true. Always.

—

Scott Hartley is Co-Founder & Managing Partner of Everywhere Ventures. Special thanks to Michael Barone and Amelia Muro Garlot for support on this post.

Startups face immense pressure to quickly race through fundraising rounds, often seeking the next milestone as soon as possible. It’s believed that reaching the next milestone signals progress, validation, and perhaps, an increased likelihood of success. But this race to fundraising validation is both a red herring, and actually can hamstring a company by ratcheting up the post-money valuation (which I’d also consider a “hurdle rate” for where the company needs to raise going forward). We’ve all heard of the “down round,” and this happens when valuation outpaces traction.

“Down-rounds happen when valuation outpaces traction for too long, when emotional buy of ‘vision’ is eclipsed by the rational buy of ‘traction.’”

Of course there can exist a period of time where the Steve Jobs “reality distortion field” enables a startup to raise at valuations significantly higher than might be logically justified by key performance indicators (KPIs), or traction metrics, but eventually the music stops. The music stops fast, due to macro whims, changing investor opportunity cost, or even “animal spirits.” And when it does better have the metrics to support your valuation, or the dreaded “down round” can come down like an anvil on your cap table, creating pay to plays for investors, and founder dilution. As this outcome is unsavory for all existing participants in the cap table, seasoned investors should know this, and not push companies to raise too big, too fast.

I often tell founders of what I call the "Billy Madison approach" to startup fundraising. What I offer is an entirely different strategy—one that encourages startups to delay advancing to the next round until they are truly ready.

This approach draws inspiration from the movie character Billy Madison, who repeats a school grade to become the biggest and strongest in his class (and also of course, partly because he is aiming to prove to his father that he can graduate from high school, but we’ll leave that aside). Similarly, startups can benefit from staying in their current fundraising stage longer, allowing them to grow stronger ahead of the expectations that come with every new round, specifically at Series A, B, and C.

The Benefits of “Staying Back a Grade”

In the context of fundraising, the Billy Madison approach advises startups not to rush into becoming a Series A or Series B company until absolutely necessary. By remaining at the Pre-Seed or Seed longer, founders gain the flexibility to develop their business at a more deliberate pace. This strategy allows them to build a solid foundation, ensuring that their KPIs and metrics are consistently ahead of what is typically expected at their current stage. One example of this I often see is when a high velocity pre-seed company perhaps has the opportunity to raise $7M or $10M. While there are certainly advantages to taking an incremental $3M, the cost of that is the round shifting optically from a “Large Seed” round to a “Series A.” I’d currently demarcate that line between Seed and A at around $7-8M. Therefore the benefits of accepting the $7M round are “staying back a grade,” and therefore like Billy Madison, being the biggest kid in the grade, with the most money and metrics.

Optical Advantages

One of the main advantages of this approach is the ability to manage the optics of fundraising rounds. By not advancing too quickly, startups can create the perception of being more advanced relative to their peers in the same round cohort. In Billy Madison speak, you’re the biggest, strongest kid in the grade. You’re not smallest kid subject to bullying, and peer pressure, like can happen when you skip grades. This can be particularly beneficial when it comes to attracting investors, as it demonstrates that the company is not only meeting but exceeding the expectations associated with its current stage. This methodical progression can lead to more favorable terms and conditions when the time does come to move to the next round. Terms are ratchets, and if you get beyond plain vanilla into the Baskin Robbins 31 flavors of non-standard term sheets, in most cases, there’s no going back. You can’t undo that.

Strategic Growth

The Billy Madison approach encourages startups to focus on strategic, ideally product-led growth rather than rapid advancement at all costs. By taking the time to refine product, expand true, repeatable customer base, and tighten the business model with a deep understanding of unit economics, customer acquisition cost, churn, retention, how to grow ACVs, startups can ensure that they are truly ready for the challenges and opportunities of the next fundraising stage. This approach reduces the risk of overextending the company and allows for more sustainable growth.

Conclusion

In summary, the Billy Madison approach to startup fundraising advocates for a more measured and strategic progression through fundraising rounds without the ego-driven, alluring Sirens of big rounds and TechCrunch fame before your business is solid. By staying in their current stage longer, startups can build a stronger foundation, manage the optics of their growth, and ensure that their KPIs consistently exceed expectations. This strategy not only positions them for success in future fundraising rounds but also sets the stage for long-term growth and sustainability.

If in doubt, and you can call what you’re doing a Pre-Seed, do it. If you must call it a Seed, accept it. Don’t flip the switch to Series A or B until you absolutely have to, because the stakes are higher, expectations less forgiving, and board more developed. Capitalizing the business is important, but doing so in a measured way, always asking the question “how can I be the biggest, strongest kid in the grade?” because no one wants the pat on the back for being precocious, when it means you get beat up.

For more thoughts on fundraising and valuations, see my prior post on the Goldilocks Valuation Matrix, and the trade-offs of dilution, runway, and hurdle rates, and how every pitch, no matter what stage, requires selling both Vision and Traction.

—

Scott Hartley is Co-Founder and Managing Partner of Everywhere Ventures.

The promise of artificial intelligence is exponential. It accelerates productivity, compresses time, and allows us to navigate vast domains of information with unprecedented speed. But beneath that curve of acceleration lies a quieter dynamic: the substrate on which AI operates—human knowledge—is not expanding at the same rate. AI systems are trained on data, and data is, by definition, historical. It is what has been written down, recorded, and digitized. In that sense, AI does not engage directly with reality—it engages with a representation of reality, a subset, of a subset, of a subset of the lived human experience.

It operates on the map, not the territory.

And the map, however vast, is incomplete. Human knowledge has never been confined to what is recorded. It lives in experience, in intuition, in culture, in tacit understanding—what is practiced but not formalized, felt but not fully articulated. These are not edge cases; they are the frontier of human experience. When we equate the digital corpus with the totality of knowledge, we collapse the territory into the map. AI, for all its power, can only see what has already been captured.

This creates a paradox. As AI increases our ability to process, recombine, and optimize existing knowledge, it gives the appearance of accelerating discovery. But if the underlying domain of lived, human-generated knowledge is not expanding—if we are not continually pushing into new experiences, new observations, new forms of meaning—then we risk entering a recursive loop. AI generates outputs from the past; humans consume and refine those outputs; and those outputs become future training data. The system begins to feed on its own reflections. Progress becomes increasingly efficient navigation within the map, rather than exploration of the territory beyond it.

This tension is not new. As Tim Wu observed as far back as 2018, modern society has long been oriented toward efficiency—toward removing friction, compressing time, and optimizing the human experience. From industrial assembly lines to the imagined futures of The Jetsons, we have consistently equated progress with convenience. The moving sidewalk becomes a metaphor for a broader ambition: a life in which effort is minimized and everything flows seamlessly. AI represents the apex of this trajectory.

But to be human is not to be optimized. Efficiency is a property of systems; exploration is a property of people. The essence of human progress has never been the elimination of effort, but the expansion of the unknown. We are the species that leaves the map—that ventures into ambiguity, that generates new knowledge not by recombining what exists, but by encountering what does not yet have language or taxonomy.

Throughout history, progress has been driven by those willing to sail past the edges of the known world. Ferdinand Magellan did not optimize existing trade routes—he circumnavigated the globe. James Cook did not refine existing maps—he redrew them. Ibn Battuta did not summarize known cultures—he immersed himself in unfamiliar ones, denoting his 75,000 miles of travels in his epic work The Rihla. These were not acts of efficiency; they were acts of expansion.

The same is true of innovation. The most consequential builders do not simply stand on the shoulders of giants—they climb onto those shoulders and then step off into the unknown. Nikola Tesla imagined electrical systems that did not yet exist. Steve Jobs reframed the relationship between humans and machines. Elon Musk pushed into domains—electric vehicles, private spaceflight—that were widely considered impractical or premature. These are not incremental optimizations of the map; they are expansions of the territory. Today we see this in our own portfolio in companies like Starcloud who are pushing the frontier of what’s possible by moving data centers, and more notably inference, into space, helping NVIDIA pioneer new chipsets architected to optimize space-to-space workloads for off-Earth optimizations.

This is, fundamentally, the work of venture capital. At its best, investing is not the allocation of capital to known outcomes—it is the identification of individuals and ideas operating at the boundary of the map. Great investors are not simply pattern matchers; they are frontier detectors. They look for the non-consensus, the contrarian, the ideas that do not yet fit cleanly within existing frameworks precisely because those frameworks lag reality. The goal is not to fund what is already legible, but to recognize what will become legible. The goal is not to be contrarian for its own sake, but to back explorers pushing the frontiers of new territory, building the map of where humanity is perhaps already going, but more importantly, desires to go.

In an AI-driven world, this role becomes even more critical. If AI excels at navigating and optimizing within the known map, then the highest-leverage human activity shifts toward expanding that map. The entrepreneur becomes an explorer—someone who ventures into domains that are under-explored, misunderstood, or entirely new. And the investor becomes a backer of expeditions, placing bets not on certainty, but on the possibility that the territory itself is larger than we currently understand.

This reframes the relationship between humans and machines. AI can be an extraordinary engine for exploitation—for extracting value from what is already known. But exploration remains a deeply human endeavor. It requires curiosity, risk tolerance, imagination, and the willingness to be wrong. It requires stepping into spaces where data is sparse, where signals are weak, where there is no clear precedent to follow. And today exploration can also be done with the aid of AI, leveraging new technologies under human stewardship to push and define the edges of the map.

The risk is not that AI will outpace us, but that we will allow ourselves to become overly reliant on what it can already see. The opportunity is to use AI as a tool in service of exploration—to extend our reach, to test hypotheses more quickly, to navigate the known world more efficiently so that we can spend more time at its edges. To be human, in this context, is not to compete with the machine on efficiency, but to complement it through exploration.

We are not just explorers on the map. We are authors of the territory it defines, and as venture capitalists and entrepreneurs, we’d do service to ourselves to remember this orientation as explorers stewarding time and capital in the service of pushing the edges of the map, where new knowledge and value can be unlocked.

—

Thanks to Rishi Jaitly and the Virginia Tech Leadership in Technology program for providing the basis of discussion leading to this post, and to Amelia Muro Garlot for her excellence in graphical storytelling and design at Everywhere Ventures.

Convincing someone to adopt your technology, or buy your product is one of the hardest things. There are many “go-to-market” (GTM) strategies, and it always starts with solving a customer pain point, ideally that’s a “heart attack, not a headache.”

Paul Graham was famous for telling his founders at Y-Combinator to get out of the building. Go talk to customers. Until you’ve nailed your initial customer profile (ICP), and really honed in on the problem you’re solving, and the customer pain point, there’s not much a GTM strategy on its own can achieve. But you can sharpen your ICP according to a few vectors, or dimensions. What sector are you going after? What company size and segment are you going after (startup, small business, or enterprise)? What target buyer are you selling into (CEO, CTO, head of sales, etc). Does this target buyer control the budget? If so, how can you align incentives with them, primarily by helping them either expand revenue, or increase their cost savings. Once you’ve nailed down your ICP you’re one step closer to isolating your GTM motion.

The sharper your ICP, the more refined your GTM can become.

I often tell founders I work with about the early days of Google Docs. When Gmail launched in 2004, internally it was called Caribou. Soon acquisitions of companies like 2Web Technologies and Writely in 2005-2006 led to a consolidation, and Google’s initial offering of Google Docs. Back then at Google we had Docs and internal spreadsheets called “Trix,” after the acquisition of 2Web. 2Web had created a product called XL2Web that converted offline Excel files to web-based spreadsheets. I still recall the internal sheets having the name and icon “Trix” at the upper left, long before these tools were external and packaged into today’s Google Workspace, that now includes everything from Drive to Forms to Gmail to Docs to Sheets.

In those early days I recall Google’s go to market motion that was trying to supplant Microsoft 365. While the collaborative web-based alternatives like Docs and Sheets offered manifold benefits, I recall early Product Manager and Product Marketing Managers explain to me how their own GTM had suffered by over-selling the many ways in which Google was in fact superior to offline Microsoft Office. The problem was, there were so many differentiated features that it was hard for buyers to grok. People would hear 10 or 20 points of differentiation, and inevitably latch onto one thing they didn’t like and say, “well that’s why it’s really not for us.” It could be for one reason or another, but too many features becomes a distraction, and a reason for a no. Don’t oversell complexity to create a reason for someone to say no.

What came about is what I call “Plain Vanilla with a Twist.”

The key differentiator for Google Docs was collaboration. You could have multiple users working on a Doc or a Sheet at the same time. What early product and marketing leaders had to hone in on was that ONE salient differentiator, and then cut out all the noise. They had to be Plain Vanilla… but with one twist. Only one.

We sold Google Docs as “exactly like Microsoft Word, except you can have multiple people in the document at the same time.” This simplification cut out 20 other reasons it might have been better, but it simplified the story into something dead simple that slot into both a user buyer behavior with one key differentiation that was plausible, not scary, and moreover enabled that ICP or buyer to retell the story.

In other words you’re often not just selling, but you’re creating a game of telephone. You have to enable your buyer to be able to retell and resell what you’ve already sold them. They need to understand the narrative so that they can amplify the buy in. Adoption only comes when your narrative is dead simple, clear, and easy to explain. Ideas have to travel, and it’s up to you to package your product and narrative in a bite-size way where someone else can explain it in an elevator, or at a cocktail party. Product led growth (PLG), and true viral growth, happens when not only is the narrative dead simple, but moreover the embedded mechanics and incentive structures enable your users to share the story, and the product itself on their own.

What’s plain vanilla to your ICP? What’s their existing buying behavior? And how can you isolate one key point of differentiation that makes them say “aha.” For early Google Docs and Sheets it was a dead-simple narrative that isolated and explained one key point of differentiation versus Microsoft 365. Plain vanilla, with a twist.

In this episode, you will hear:

• Leveraging unused dark fiber to deliver modern internet without costly overbuilding.

• Bridging the digital divide where 85% of U.S. homes still lack fiber connectivity.

• Building intelligent network operations for the explosion of connected home devices.

• Securing government contracts to deploy fiber in underserved urban areas.

• Positioning as infrastructure for AI, robotics, and autonomous vehicle bandwidth demands.​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

Learn more about Prashanth Vijay | Flume

LinkedIn: https://www.linkedin.com/in/prashanthvijay 

Website: https://www.flumeinternet.com/ 

Learn more about Scott Hartley | Everywhere Ventures

Linkedin: https://www.linkedin.com/in/scotthartley 

Website: https://everywhere.vc

In this episode, you will hear:

• Building a universal health app patients can use to navigate their health. 

• Empowering patients to own their health data and receive personalized insights. 

• Raising from investors who understand the long-term vision. 

• Capturing patient-reported data that other companies don't have access to.

Learn more about Kate Lambridis | Human Health

LinkedIn: https://www.linkedin.com/in/katelambridis 

Website: https://www.human.health 

Learn more about Scott Hartley | Everywhere Ventures

Linkedin: https://www.linkedin.com/in/scotthartley 

Website: https://everywhere.vc

In Brooklyn, a block off the waterfront on Broadway, there’s a Williamsburg staple called Diner. One of its most endearing features is there are no QR code menus, no digital ordering. Analog only. Instead, the waitress usually slips into the booth with you, sharpie in hand, and jots down the options right onto the white butcher-paper tablecloth. Oysters, a market crudo, their famous burger, scribbled in shorthand.

Along with the waitress, I sat across from Matias Corea, a Catalan polymath designer who founded and sold Behance to Adobe for $150 million before diving headfirst into jazz and riding motorcycles across continents. He’s written the coffee table books about transcontinental adventures (and wipe outs) to show for it.

I’d come off a red eye from who-knows-where, and at 8pm Matias and I were just beginning our night. Three hours of philosophy, politics, design, jazz, motorcycles, and startups later, we had commandeered the sharpie and were trading ideas on the tablecloth, and we’d finally converged on tech. The food was gone, the Martini glasses had been pushed aside into a jumble of clinking glass, and ideas were flowing.

Matias frantically penned a series of short lines followed by long lines, or the outliers. “This is commercial” he said with conviction, staring at me over his black rimmed glasses, “and this is art,” he said pointing at the outlier lines protruding among the others like skyscrapers. “Great art, genius; it stands out.” As we riffed on this idea my mind went to venture capital, my work and lens on the world for the past 15 years. “What’s interesting I said, is a lot of those outlier lines know each other. Genius trades notes, and when you amplify that you get MASSIVE signal.” What we were thinking about was collaboration, great bands, great artists. But also, great entrepreneurs. Knowing what’s true signal is access, and it matters.

At Everywhere Ventures we’re a community of founders first, and a venture capital firm second. Our hundreds of limited partners, or investors across our funds, are themselves founders. They’re the spikes in the graph. They’re the ones who left a commercial life behind to build art, to solve a problem, to go insanely deep on an idea or a sector. Great art, like great tech, is somewhat anarchic. Iconoclastic.

Spikiness does not mean you went to Harvard, nor that you worked at McKinsey. These are not points of salience; these are points of mainstream adoption and are antithetical to what we’re talking about. Greatness in some area, or spikiness, isn’t’ about privilege. It’s about patina, a refinement, a rock in the tumbler until every edge is worn down and there’s an endurance, and a grittiness that shines through in specific areas. It could be Madonna or Andy Warhol or Elon Musk. It’s also about charisma and authenticity, being so true to your vision that you’re able to step away from the “commercial” world, and be anarchic. And the amplitude of these spikes are signals, and when they’re multiplied in collaboration, there are compound effects. Charisma, grit, patina, authenticity, conviction, these are compound signals.

As we scribbled and talked, I began to think about how any one of us can manufacture luck. Luck is a byproduct of listening to intuition, and intuition is pattern recognition of what stands out from the status quo melded with a viewpoint on the world. When those things stack up, and when we truly listen, there is some signal in the noise.

Pretending to do statistics, we started discussing Gaussian distributions, or bell curves. The median is the midpoint, and also the mean, or 50^th percentile. Multiplying two signals at the median is like multiplying a ½ perspective by a ½ perspective. There’s not much advantage or compounding effect. But when you start thinking about what it looks like to multiply two inputs that are one, two, or three standard deviations away from the mean, you start to see the impact of compound spikiness or signals in noise. You start to see that outlier signals really compound.

If we talk to a dozen companies in each problem area or sector, and we diligence those ideas by trusting signals we deem to be a few standard deviations out from the mean on the curve in that domain, we start to be able to amplify signals. We start to see things really standing out clearly as spikes within the graph, as extreme right points on the bell curve. I often say, “if you’re going to get heart surgery, you should ask a heart surgeon who would do their surgery.” That’s a compound signal.

Alfred Lin at Sequoia talks about how they talk to 1,000 entrepreneurs for every one check that they write. They look for exceptional founders or CEOs they deem to be not average, not good, not great, but outliers. These are 1/1000 builders at the forefront of the problem they’re solving, tech they’re building, or idea they’re chasing.

These are true “artists” as Matias put it. And yes, CEOs are also building companies to achieve sustainability, meaning they generate profits, value, and outsized wealth creation, so to call them “non-commercial” is disingenuous, but that’s the word we’re using. They’re non-commercial in the “play the game,” societal sense, of ticking boxes and status quo. These are game changers, category creators, dreamers and builders.

As an entrepreneur, as an artist, as a venture capitalist, how can you listen to the inputs and separate signal from noise? How can you refine your inputs such that everyone around you is a polished stone in their own zone of genius, and then how can you learn to categorize those inputs to know which to listen to when? If you can do that, I’d argue, you can start to refine signal from noise, and surround yourself with more amplitude around big ideas, and you’re going to create your own luck.

We didn’t, on the face of it, set up Everywhere Ventures for this purpose, but we did have a hypothesis that static partnerships don’t scale with dynamic innovation. Innovation is a frontier, a moving target, an organic set of ideas that changes with the tools of the day, the current zeitgeist, and so the only inputs that can keep pace are those that are dynamic and evolving with that frontier. We raise money from LPs who are founders because we think the value is in their voice, not their dollar. And we listen to them on inbound deals and in diligence, and in how we refine our own perspective on big opportunities and markets. Signals as building blocks.

If you’re a founder the best way you can pitch a VC is by getting an introduction from someone they deeply trust as an expert, and a signal, in that zone of genius. This isn’t about an insider’s game of access, nepotism, or where you went to school; it’s about putting yourself at the center of that spikiness where every signal is telling others to listen, to lean in, and maybe to write that 1,000 check. It doesn’t matter where you live on the map, or what background you come from. We can all build our own luck.

Build yourself around your areas of passion, and surround yourself with the highest value, most respected signals in that space, and good things will start to happen.

—

For more, check out our Founder Spotlight series at Everywhere Ventures. And special thanks to Michael Barone and Amelia Muro Garlot on the graphics.

In this episode, you will hear:

• Reinventing furniture through tech-enabled, vertically integrated operations

• Delivering sofas from order to home in days, not months

• Offering modular, customizable designs shaped by customer feedback

• Scaling multiple home brands on a single operational platform

• Reducing waste while improving quality, comfort, and value

Learn more about Ben Parsa | CABA Design

LinkedIn: https://www.linkedin.com/in/benparsa 

Website: https://cabadesign.co 

Learn more about Jenny Fielding | Everywhere Ventures

Linkedin: https://www.linkedin.com/in/jennyfielding 

Website: https://everywhere.vc 

In this episode, you will hear:

• Digitizing last-mile supply chains in emerging markets 

• Empowering small distributors and retailers through digital tools 

• Partnering with Unilever to modernize distribution networks 

• Scaling an asset-light model across Africa and beyond 

• Unlocking working capital through partnerships with Mastercard and local banks

Learn more about Mike Quinn | Boost Technology

LinkedIn: https://www.linkedin.com/in/mikep-quinn/ 

Website: https://www.withboost.co/ 

Learn more about Dr. Rio Xin Chen | Pandas

Linkedin: https://www.linkedin.com/in/dr-rio-xin/

In this episode, you will hear:

• How VetriFi serves the 80% of enlisted service members traditional banks overlook.

• Building financial fitness through non-patronizing guidance and earned wage access.

• The power of the veteran community as the most connected network in America.

• VetriFi’s mission-first culture and approach to customer acquisition and brand identity.

• Lessons from West Point on preparation, discipline, and leadership in building a startup.

Learn more about Tim Hsia | VetraFi

LinkedIn: https://www.linkedin.com/in/timhsia

Website: https://www.vetrafi.com/

Learn more about Scott Harley | Everywhere Ventures

Linkedin: https://www.linkedin.com/in/scotthartley/

Website: https://everywhere.vc/ | https://scotthartley.com/

In this episode, you will hear:

• Applying actuarial innovation and fertility data to build predictive models.

• Creating a new insurance category merging life, health, and P&C for reproductive care.

• Flora’s portable fertility benefits that lower employer costs and boost retention.

• Expanding access through voluntary programs like FSAs, HSAs, and ICHRAs.

• Developing a low-code/no-code platform to digitize underwriting and claims.

Learn more about Laura McDonald | Flora

LinkedIn: https://www.linkedin.com/in/laurajaynemcdonald/

Website: https://heyflora.com/

Learn more about Scott Harley | Everywhere Ventures

Linkedin: https://www.linkedin.com/in/scotthartley/

Website:  https://everywhere.vc/ | https://scotthartley.com/

In this episode, you will hear:

• Advancing infectious disease testing with genomics and AI

• Turning academic research into scalable healthtech innovation

• Building faster, more precise pathogen detection tools

• Combating antimicrobial resistance through smarter diagnostics

• Expanding into women’s health and clinical partnerships

Learn more about Niamh O’Hara | Biotia

LinkedIn: https://www.linkedin.com/in/niamh-b-o-hara 

Website: https://www.biotia.io 

Learn more about Cristian Ponce | Tetsuwan Scientific

Linkedin: https://www.linkedin.com/in/cristian-ponce5 

Website: https://tetsuwan.com

In this episode, you will hear:

• Kiki Milk's impact on families with dietary restrictions and allergy needs

• Building a mission-driven CPG brand rooted in integrity and sustainability

• Moving from kitchen formulation to scalable, co-manufactured production

• Expanding from D2C success into national retail distribution

• Upholding ingredient transparency and glyphosate-free sourcing standards

Learn more about Alex Abelin | Kiki Milk

LinkedIn: https://www.linkedin.com/in/alexabelin

Website: https://www.kikimilk.com 

Learn more about Emily Groden | Evergreen Waffles

Linkedin: https://www.linkedin.com/in/emily-cole-groden 

Website:https://eatevergreen.com  

In this episode, you will hear:

• How Switchboard connects retailers and suppliers through a seamless hub model

• Building the "pipes" that move data reliably between disparate enterprise systems

• Enabling real-time visibility and collaboration across global supply chains 

• Eliminating manual errors through automated order and invoice workflows

• Switchboard's vision for open-source supply chain data standards

Learn more about Hugh Dixson | Switchboard

LinkedIn: https://www.linkedin.com/in/hughdixson/ 

Website: https://www.switchboardcloud.com/ 

Learn more about Bobby Katoli | Ceres Technology

Linkedin: https://www.linkedin.com/in/bobbykatoli 

Website: https://www.cerestech.co/ 

In this episode, you will hear:

• Circle's role in enabling compliant collaboration between hospitals and insurers

• Navigating constant regulatory changes that can impact customer viability

• How Circle's AI integration reduces full-day workflows to 20-minute tasks

• Tracking consumers from the first touchpoint through their healthcare journey

• Maintaining compliance while removing jargon from healthcare communications

Learn more about Joseph Schneier | Circle

LinkedIn: https://www.linkedin.com/in/joschneier

Website: https://www.circleengage.ai/

Learn more about Jenny Fielding | Everywhere Ventures

Linkedin: https://www.linkedin.com/in/jennyfielding 

Website: https://everywhere.vc 

In this episode, you will hear:

• How DoWhatWorks uses every major brand’s experiments to enable their enterprise customers to get more impact from their UX. 

• How speed is only half the battle. Launching fast isn’t enough. 

• Turning testing patterns into actionable "BetScores" to know what to bet on before launching 

• What the industry gets wrong about experimentation 

• Industry-specific insights that distinguish what works in B2B vs. B2C

• Lessons in building two businesses where getting to critical mass is essential for winning

Learn more about Jenny Fielding | Everywhere Ventures

Linkedin: https://www.linkedin.com/in/jennyfielding 

Website: https://everywhere.vc

Learn more about Andres Glusman | Do What Works

LinkedIn: https://www.linkedin.com/in/glusman/

Website: https://www.dowhatworks.io/

In this episode, you will hear:

• VOW’s real-time seating, guest lists, and ticketing to sync across devices

• Balances human strategy with AI automation to power high-stakes live experiences

• Scales effortlessly from intimate gatherings to high-profile global events

• Empowers organizers, sponsors, and partners with shared visibility and control

• Delivering the first guest-facing event assistant in every attendee’s hand

Learn more about Jennifer Brisman | VOW

LinkedIn: https://www.linkedin.com/in/jenniferbrisman 

Website: https://www.vow.app 

Learn more about Scott Hartley | Everywhere VC

Linkedin: https://www.linkedin.com/in/scotthartley 

Website: https://everywhere.vc/

In this episode, you will hear:

• Vincent’s media-first strategy (newsletters, podcasts, investor events) to build trust and literacy.

• Operational hurdles of serving small-ticket investors due to compliance costs.

• Breaking down accreditation barriers and easing investor entry into alternative assets.

• Designing accessible, packaged investment options that fit into everyday portfolios.

• Exploring how AI can enhance both investment products and investor decision-making.

Learn more about Eric Cantor | Vincent

LinkedIn: https://www.linkedin.com/in/ecantor 

Website: https://www.withvincent.com 

Learn more about Jenny Fielding | Everywhere Ventures

Linkedin: https://www.linkedin.com/in/jennyfielding 

Website: https://everywhere.vc

In this episode, you will hear:

• LGND's use of transformer models to compress satellite imagery into geo embeddings.

• The shift toward AI-driven building blocks as the foundation of geospatial analysis.

• Building intuitive interfaces that let analysts fine-tune models through yes/no feedback.

• Integrating Earth observation with weather and infrastructure data.

• Exploring on-satellite processing to send embeddings instead of raw imagery.

Learn more about Nathaniel Manning | LGND

LinkedIn: https://www.linkedin.com/in/nathanielmanning/

Website: https://lgnd.io/

Learn more about Scott Harley | Everywhere Ventures

Linkedin: https://www.linkedin.com/in/scotthartley/

Website:  https://everywhere.vc/ | https://scotthartley.com/

In this episode, you will hear:

• Replacing static risk questionnaires with continuous, real-time monitoring

• Applying AI to bridge compliance frameworks and remove blind spots

• Driving transparency in regulated industries like finance, healthcare, and education

• Embedding empathy and relationships into risk management practices

Learn more about Daniel Faddoul | Auditive

LinkedIn: https://www.linkedin.com/in/danielfaddoul/ 

Website: https://auditive.io/ 

Learn more about Alberto Escarlate | Tough Day

Linkedin: https://www.linkedin.com/in/albertoescarlate/ 

Website;https://www.tough.day/