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.

At the 2026 Upfront Summit, Harley Finkelstein called agentic commerce the biggest transformation in Shopify’s history - and described the TAM expansion it will generate, far beyond today’s digital commerce.

It makes sense - if we free shopping from the click-through checkout process on a retailer’s website, then commerce suddenly appears in far more places where there is intent. Commerce shifts from destination behavior to ambient behavior.

But just a week later, OpenAI appeared to be pulling back from the idea that ChatGPT would directly own in-chat commerce, according to reporting from The Information.

This contradiction just demonstrates the reality of retail economics. While transactions may expand dramatically, the capture of profits will not.

Retail is not a high-margin industry. According to sector data compiled by Aswath Damodaran at New York University Stern School of Business, the median gross margin for general and specialty retail is 33-35% - not terrible, but half of what margins are for classic software businesses.

And those margins already support a crowded stack of intermediaries - payment processors, marketplaces, logistics providers, advertising platforms. To support a new agentic commerce player, someone else has to get bumped - there’s not more margin to go around.

If ChatGPT or Perplexity or Daydream is getting a cut, someone else probably isn’t - a Shopmy influencer? A marketplace referral? Even a retailer? To own the transaction and take that margin will require that it come from somewhere.

So who is positioned to be the big winner of all this TAM expansion? Shopify and the other infrastructure players of course. NOT the new demand surfaces.

Shopify’s view is that AI agents simply become another demand surface plugging into their backend. The company has been promoting a concept called the Universal Commerce Protocol, which allows agents to connect directly to merchant infrastructure for inventory, pricing, checkout, and fulfillment. In the same way Shopify supports social commerce, influencer commerce, live commerce - it can also support agentic commerce. So from Shopify’s perspective, the new world of agents isn’t really disruptive - it’s just a growth engine.

That may explain why the companies building the most powerful AI agents are not rushing to become commerce platforms. There are many other opportunities for those companies to capture much bigger margins - and operating a full commerce stack is more like running a global financial network than building a software interface.

So who else besides Shopify is positioned to win agentic commerce? Companies that already operate the messy, operational layer of commerce - inventory, payments, checkout, fulfillment. Companies like Max Retail (I’m an investor), founded by Melodie van der Baan - who has built an uparalleled supply network of in season and near season fashion inventory - and made it available to any sales channel, including your AI agent. Or Constructor - a company founded a decade ago but well positioned to support agentic commerce. Or a company like Rebuy - focused on repeat purchases, where an AI agent has a natural role to play. I also think the established marketplaces with unique supply will be winners - Etsy, eBay and of course our friends at Amazon.

In this environment, the companies most likely to win are not necessarily the ones creating the agents. They are the ones already running the rails underneath them. Side note - this is is part of our investment thesis at Everywhere Ventures - where we invest in second-order effects businesses. If the big, game-changing innovation is agentic commerce, we look for the second order effect - the infrastructure companies that will capture margin created by that new market. If you are early building in agentic commerce infrastructure - real time inventory management and data aggregation, order routing, fraud detection, agent payments - we’d love to chat.

Anna Barber is former Managing Director of TechStars, former partner at M13, and a General Partner at 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.

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.

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.

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Cybersecurity has become the backbone of our digital lives. Yet, for too long, security has been treated as a series of patches and walls, static defenses in a world of dynamic, ever-evolving threats. As new technologies emerge every day, new risks arise, demanding proactive strategies that connect the pieces rather than remedy them one by one. The reality is that security isn’t a single layer or a one-time fix, it’s a moving target. At Everywhere Ventures, we are backing founders who are building a new, more integrated future for security.

We believe the future of cybersecurity will be shaped by the seamless integration of its many parts. We are backing founders architecting the platforms and defenses to repair fractures in the system and strengthen digital trust:

1. Platform Agnostic <> Point of Access: Pairing universal foundations with secure, seamless authentication at the entry point.

2. Point of Access <> Targeted Security: Combining entry-point protections with tailored defenses for high-risk industries and geographies.

3. Targeted Security <> Digital Defense: Fusing industry-specific safeguards with adaptive, software-driven tools that detect and neutralize evolving threats.

4. Digital Defense <> Platform Agnostic: Harnessing intelligent software to fortify broad, cross-industry platforms that serve as the foundation of cybersecurity.

1. Platform Agnostic <> Point of Access: Universal Foundations, Stronger Entry Points

The front door, where users and systems prove their identity, remains the most exploited part of the digital world. We back companies that are making this universal entry point both fundamentally secure and seamless to use, because the strongest security is the one that actually gets used.

• Privacy-First Access: Dapple Security is rethinking authentication with a platform built on advanced biometrics that puts privacy at the center, ensuring access protects both identity and individual rights.

• Zero-Trust Authentication: Ideem’s Zero-Trust Security Module delivers seamless two-factor authentication by binding passkeys directly to a user’s device. It works across browsers and devices, giving quantum-safe protection without the hassle of one-time codes.

By strengthening access at its foundation, these companies are making identity verification more secure across the digital ecosystem.

2. Point of Access <> Targeted Security: Protecting the Point of Access in High-Risk Environments

Access isn’t just an entry point, it’s the frontline. For high-risk industries and geographies, strong authentication isn’t enough; defenses need to be precise, adaptive, and built for context. We’re investing in companies that make access both seamless and secure, while tailoring protection to the markets and environments where defenses are still emerging.

• Intelligent Surveillance: Physical spaces can be just as vulnerable as digital ones. Moksa.AI brings AI-powered video analytics to existing cameras, spotting suspicious behavior in real time and delivering actionable insights from loss prevention to employee safety.

• Securing Identity: High rates of identity fraud don’t just drain resources, they erode trust. Smile ID closes this gap with real-time verification and KYC compliance, giving businesses across Africa a secure way to confirm identities and protect the people they serve.

These companies are redefining access for industries and regions exposed to risk and are setting a new standard for trust at the front door.

3. Targeted Security <> Digital Defense: Scaling Protection for Tailored Threats

Some threats are universal, but others cut deep into specific industries. From fraud in financial networks to vulnerabilities in space operations, these gaps demand defenses that are both tailored and adaptive. We are backing platforms that combine contextual expertise with software-driven intelligence to protect at scale.

• Adaptive Fraud Defense: Fraud in emerging markets often outpaces regulation and overwhelms traditional tools. Orca Fraud provides Africa’s first fraud orchestration platform, giving businesses no-code, AI-powered tools to prevent and mitigate fraud. Cybera provides a scam reporting and recovery platform that weaves in advanced AML intelligence, helping organizations not just spot fraud but respond effectively by tracing transactions and recovering stolen assets across both crypto and fiat.

• Mission-Critical Cybersecurity: Rebel Space Technologies provides an AI-powered platform for comprehensive space system security, built to safeguard mission operations through continuous monitoring and rapid, adaptive response.

These companies exhibit how software-driven defenses can be tailored to the unique demands of targeted industries and locations.

4. Digital Defense <> Platform Agnostic: Smarter Tools, Stronger Foundations

Platforms are only as strong as the software intelligence that powers them. By weaving adaptive safeguards into the core layers of infrastructure and data, these companies are fortifying cybersecurity without adding complexity.

• Cloud-Native Defense at Scale: As applications move faster into the cloud, protection has to move with them. Operant AI embeds protection directly into infrastructure, APIs, and Kubernetes environments, while Pensar AI secures codebases by automatically detecting and patching vulnerabilities, especially those introduced by AI-generated code.

• Resilient Data & Vendor Trust: Shard Secure’s Microshard technology breaks data into fragments and distributes them so no single piece is usable on its own, ensuring privacy and resilience across hybrid and multi-cloud systems. Auditive complements this by automating third-party security reviews so organizations can trust vendors without slowing down business.

• Securing the AI Layer: Ciphero is an AI Verification Layer that allows enterprises to control and verify all AI interactions within the workspace. By monitoring and managing both internal and third-party models, it ensures compliance with company policies, prevents loss of proprietary or confidential data, and keeps sensitive information securely within the organization.

By strengthening both the systems at the core and the networks surrounding them, these companies are shaping how universal security is built into different industries.

The last decade has made clear that static defenses can’t keep pace with adversaries who move faster and strike smarter. Security today is not a single checkpoint but a living framework, stretching across access, industries, data, and platforms, and reshaping itself as the threat landscape shifts. By backing companies at these critical intersections, we aim to support the foundations of a more resilient and trustworthy digital ecosystem.

We’ll be sharing more insights soon. In the meantime, if you’re a founder working in this space, we’d love to hear from you. You can find our complete cyber security portfolio here.

Our healthcare investments are built on this core belief: true transformation lies in building healthcare’s connective tissue. We are backing founders who are creating the platforms and infrastructure to bridge the critical gaps, creating a more seamless, intelligent, and patient-centric ecosystem. This vision unfolds across four interconnected pillars, each reinforcing the next.

1. Patient Access <> Financial Automation: Reducing cost and friction in access through integrated financial pathways.

2. Financial Automation <> Clinical Operations: Aligning operations with revenue and cost insights for smarter delivery.

3. Clinical Operations <> Data Infrastructure: Driving coordinated, data‑informed operations across the care journey.

4. Data Infrastructure <> Patient Access: Empowering access with unified, real‑time health data.

1. Patient Access <> Financial Automation: Simplifying Access and Affordability with Financial Integration

The financial experience is often the most broken and cumbersome part of a patient's journey and a major source of administrative waste for providers. We invest in companies that automate this complexity, allowing providers to focus on care, and patients to concentrate on healing.

• Optimizing the Revenue Cycle: Disconnected payment rails often delay patient care and strain provider finances. Adonis brings AI to the revenue cycle, streamlining billing and cutting denials so claims pay faster and patients aren’t stuck in the waiting room.

• Coverage Clarity: When insurance coverage is uncertain, schedules get delayed and cash flow is disrupted. Nirvana Health replaces uncertainty with instant verification and runs out-of-network reimbursement, streamlining patient intake and providing immediate clarity on benefits.

• Precision Where It Matters: For providers managing Medicare and Medicaid populations, accurate coding is critical. Keebler Health provides an AI-powered risk adjustment platform to ensure billing accuracy and efficiency.

By automating these financial workflows, these companies create a more stable and predictable foundation for patient care delivery.

2. Financial Automation <> Clinical Operations: Building Resilient and Efficient Practices

A financially healthy practice must also be operationally sound. Provider burnout, staffing shortages, and administrative overload are existential threats. We are backing platforms that streamline the complex internal workings of healthcare organizations.

• Smarter Scheduling: Rigid staffing models can lead to burnout and an inefficient use of resources. M7 Health optimizes nurse scheduling by aligning shifts with individual preferences and professional goals, improving retention while creating a more predictable work environment for clinical staff.

• Automating Administrative Tasks: Repetitive administrative work consumes countless hours. Tabflows provides an AI copilot that automates tasks across multiple software systems from a single interface, while Phase Zero offers a no-code platform that allows non-technical users to quickly build and manage everything from patient intake to care coordination.

• Integrated Provider Finance: Disparate financial systems and complex multi-entity operations can hinder timely decision making and resource allocation. Flychain delivers a comprehensive financial platform that consolidates bookkeeping, reporting, and treasury management, simplifying the business of healthcare.

These tools empower providers to operate practices that are more efficient, sustainable, and ultimately more humane.

3. Clinical Operations <> Data Infrastructure: Creating a Single Source of Truth

For decades, valuable healthcare data has been trapped in silos. To optimize operations and improve outcomes, that data must be unlocked, standardized, and made securely accessible. This is the critical plumbing and intelligence layer of modern healthcare.

• Connected Patient Data: Particle Health aggregates and standardizes patient records from a vast network of providers via API, enabling real-time access for care teams to coordinate treatment and improve quality.

• Delivering Market Intelligence: Strategic decisions require accurate data. Health Data Atlas provides a proprietary market intelligence platform with detailed organizational hierarchies and provider profiles, while Handl Health gives consultants the data analytics tools to design better, more cost-effective health plans.

• Connecting the Ecosystem: Circle Engage serves as a data-driven hub, connecting benefit providers, health companies, and communities by integrating systems and workflows to optimize member experiences and uncover new opportunities.

These companies are laying the groundwork for a healthcare ecosystem where operational decisions are informed by comprehensive, connected data.

4. Data Infrastructure <> Patient Access: Empowering Access with Unified, Real-Time Health Data

When clean, connected datasets power eligibility checks, transparent benefits, and guided care journeys, patients navigate the healthcare system with greater confidence and efficiency. This intersection focuses on solutions that leverage data throughout the patient lifecycle to deliver clarity and smoother experiences.

• Navigating Complex Benefits: Understanding and using health benefits can be bewildering. Outfox Health is an AI-powered platform that gives health plan members instant answers and guides them to high-quality, low-cost care. Leeway Health simplifies benefits management for businesses with personalized plans and streamlined payments.

• Data-Powered Navigation: We’re backing platforms that make finding specialized care effortless by uniting patient and network data. Headway connects individuals with mental health providers who accept their insurance, streamlining search, booking, and billing through built-in verification.

• Supporting the Entire Care Journey: Effective care extends beyond the clinic. Nilo Saúde provides software that streamlines patient relationship management and communication, while RubyWell offers a comprehensive support and compensation system for family caregivers, helping them navigate the financial and logistical burdens of care.

By integrating data directly into the patient journey, these innovators create the accessible, human-centered experience everyone deserves.

Our Interconnected View:

Our investment thesis is not linear; it’s a self-reinforcing flywheel. For too long, healthcare has been defined by handoffs between providers, payers, systems, and patients, slowing care and adding complexity. By bridging disciplines, such as patient access, financial processes, clinical operations, and data infrastructure, our portfolio is building a healthcare system that is more transparent and patient-oriented. It’s not just about moving money or managing records. It’s about ensuring that every interaction in the care journey is faster and clearer for everyone involved.

Stay tuned for more insights and if you're a founder building in this space, we'd love to hear from you! Here is our comprehensive portfolio in healthcare payment infrastructure.

Listen to Eric Cantor with Jenny Fielding on the Venture Everywhere podcast: Alts with Vincent. Now on Apple & Spotify. Check out to all our past episodes here!

We see the future of supply chains not as a linear process, but as an interconnected ecosystem. For our initial focus, we're honing in on four critical areas where we believe transformative innovation is happening:

1. Global <> Manufacturing: Enhancing visibility and resilience from raw material to factory floor.

2. Manufacturing <> Local/Last Mile: Bridging the gap between production and agile, localized delivery.

3. Local/Last Mile <> Distribution: Optimizing the complex journey to the end consumer.

4. Distribution <> Global: Leveraging data to create smarter, more responsive global networks.

1. Global <> Manufacturing: Forging Resilient and Transparent Production Networks

The journey from raw materials to finished goods is often a complex, opaque, and global undertaking. We're backing companies that bring clarity, predictability, and efficiency to this foundational stage.

• Anticipating and Mitigating Risk: In a world of constant flux, understanding multi-tier supply chain risks is paramount. Ceres Technology is at the forefront, leveraging an AI-powered platform and over 25,000 real-time datasets to help businesses predict and preempt disruptions months in advance.

• Streamlining Procurement: The direct procurement of materials, especially in sectors like construction, can be fraught with inefficiencies. MatBook is changing this by enabling direct sourcing of construction materials from manufacturers, offering full visibility and quality control through its tech-enabled platform.

• Ensuring Compliance and Sustainability: As global regulations tighten and consumer demand for ethical sourcing grows, comprehensive supply chain mapping is essential. Sourcemap provides a vital platform for this, allowing companies to monitor and verify their supply chains from end-to-end, ensuring compliance, tracking sustainability, and assessing risks.

These companies exemplify the shift towards intelligent, transparent, and resilient global manufacturing networks.

2. Manufacturing <> Local/Last Mile: Enabling Agile Production and Market Access

Once goods are manufactured, the challenge shifts to getting them where they need to go, efficiently and flexibly. This is especially crucial for customized products or when serving dynamic local demands.

• Precision in Custom Manufacturing: For OEMs in North America, sourcing custom industrial components can be a fragmented process. Autana AI provides a digital platform that streamlines custom manufacturing, from die-casting to CNC machining, managing the entire process with software-driven precision and transparency.

• Building Brands with Integrated Logistics: Bringing innovative consumer products to market requires more than just a great idea; it demands robust product development, sourcing, and supply chain logistics. Caba Design excels here, creating and managing a portfolio of consumer brands by mastering these critical elements.

• Unlocking New Markets: For businesses in emerging economies, accessing global B2B trade can be transformative. Yumari is globalizing LatAm's B2B wholesale trade by streamlining the entire logistics process from supplier warehouse to buyer destination, delivering significant savings and opening up new avenues for growth.

This nexus is about creating more responsive and adaptable connections between where things are made and where they are ultimately needed.

3. Local/Last Mile <> Distribution: Revolutionizing the Final Frontier

The "last mile" is famously the most complex and expensive part of the delivery chain. Innovation here is critical for customer satisfaction, cost efficiency, and sustainability. We are particularly excited by companies tackling this challenge head-on.

• Empowering Local Economies: In Africa's vibrant convenience economy, Boost is a B2B commerce platform digitizing operations for manufacturers and distributors with tools like WhatsApp ordering, fulfillment apps, and vital stock financing.

• The Future of Delivery: IONA Drones is pioneering autonomous drone technology to optimize last-mile deliveries, promising lower costs, quicker routes, and a greener footprint for shipping companies. Similarly, Nearfleet leverages AI and a novel "asset-light hub-and-spoke" model to offer affordable, scalable same-day delivery for pharmacies and retailers.

• Distributed Fulfillment & Optimized Networks: Packsmith is building a network of local distribution centers, enabling businesses to store inventory closer to customers for faster delivery. For the crucial trucking sector, SmartHop offers a booking and fleet management platform that empowers carriers and dispatchers to maximize profits and efficiency.

• Tackling Reverse Logistics and Sustainability: E-commerce returns are a growing challenge. Sway simplifies this with an easy-to-use solution for managing and automating product returns. And, focusing on broader sustainability, WATS provides a digital platform for businesses to manage and reduce waste, automating carbon footprint reporting and improving landfill diversion.

These companies are redefining efficiency, speed, and sustainability at the crucial interface with the end customer.

4. Distribution <> Global: Powering Intelligent and Integrated Global Flows

Finally, tying everything together requires intelligent platforms that can manage inventory, forecast demand, and streamline operations across global distribution networks.

• Smarter Inventory Management: Bops AI is at the cutting edge, using its AI-driven platform to enhance Collaborative Planning, Forecasting, and Replenishment (CPFR) for consumer brands, reducing stockouts and excess inventory.

• Digitizing Maritime Operations: The maritime industry is a cornerstone of global trade. MagicPort connects maritime stakeholders through a digital collaboration platform, offering real-time intelligence and automation to streamline shipping and promote sustainability.

• Automating Freight Forwarding: Starboard provides AI-powered SaaS tools that automate and optimize freight forwarding operations, from quoting to invoice reconciliation, boosting productivity.

• Seamless Data Exchange: Efficient supply chains run on data. Switchboard is a supply chain integration platform that automates data exchange and transaction processes between trading partners via EDI and API integrations, reducing manual work and operational costs.

These innovators are building the data-driven infrastructure for more synchronized and responsive global distribution.

Connecting the Dots: Our Holistic View

This isn’t about investing in isolated siloes. We believe the greatest value creation lies at the intersection of these domains. Innovations in global manufacturing visibility directly impact how goods are distributed locally. Advancements in last-mile delivery create new possibilities for manufacturers. Data-driven global distribution platforms inform smarter sourcing and production.

The common threads running through our portfolio are the intelligent application of data, AI, automation, and platform models to solve entrenched problems and unlock new levels of efficiency, transparency, and sustainability.

The Journey Ahead

The transformation of global supply chains and logistics is a multi-decade endeavor, and we are just scratching the surface. At Everywhere Ventures, we are excited to partner with founders who share our vision for a more connected, intelligent, and resilient commercial world that is global, and integrated.

We believe that by focusing on these critical nexuses, we can help build the foundational companies that will define the future of how goods are made, moved, and managed across the globe.

Stay tuned for more insights and if you're a founder building in this space, we'd love to hear from you! Here is our comprehensive portfolio in supply chain.

Happy 4th of July! We hope you enjoy the holiday. As you soak up the festivities, tune in to the latest episodes of the Venture Everywhere Podcast.

One of the ways I describe venture capital as an asset class to those considering investing is that it’s like a very lumpy, long-term annuity. An annuity requires a lump sum up-front investment for guaranteed payments at a later date in time. It’s a phase shift, or a time dislocation, moving money from now to some time in the distant future. Whereas an annuity has next to no risk, but also next to no upside, consistent allocation into venture capital can provide a long run dislocation of capital, but with higher risk and also much higher potential future returns. It’s a “lumpy annuity.”

If you’re bearish on today, but bullish on tomorrow, you might want to shift some of your capital out into this future set of opportunities or outcomes. You might also want to consider the cost of holding onto your capital today, and the purchasing power erosion you might experience if your money is not growing with the economy. In other words, you might want to allocate into venture capital, because this is a bet on the future, and a time shift of smaller returns today for bigger returns tomorrow.

And if you’re a founder, you might want to allocate your time, your human capital, phase shifted out to a future you can see that’s necessary, and better than today. Entrepreneurs are optimists about the future, and so are venture capitalists.

One of the most common refrains one hears on panels today is that exit markets are bad, and if you look at the data this is empirically true. IPOs are well below the traditional steady state rate, even accounting for the 2020 and 2021 SPAC bonanza.

There is a lot of self loathing introspection about the prognosis of venture capital as an asset class based on a few years of aberrational data. Many wannabe Francis Fukuyamas (the political scientists who famously wrote on the “end of history”) herald the “end” of venture capital once a week. But like Mark Twain quipped, “the report of my death was an exaggeration.” Similarly, the death of venture capital is likely an overstatement, if for some period of time the exit hurdle has gone up.

What’s not an exaggeration is the blunting of the term venture capital to be far too inclusive of non-risk taking activities. Whereas a few years ago many hedge funds and PE firms ran “down market” into venture capital, today some of the largest “VC” funds have clearly just grown into PE. In Spanish venture capital is synonymous with “risk capital,” and is known as “capital de riesgo.” In English somehow we’ve lost that notion, with a parade of me-toos claiming to do venture capital, but only seeking relatively risk-free businesses with cookie cutter KPIs, like “$1-2 million in ARR.”

Venture capital is by definition the few percentage points of frontier capital that finances opportunities that cannot be supported elsewhere in the economy. Venture capital as mainstream is, I believe, an oxymoron. By definition, it is the highest risk-bearing capital. Here’s to the crazy ones, the Internet entrepreneurs, the web3 infrastructure builders, the creators of the space economy. Simply requiring every company get to “$1-2 million in ARR” is antithetical to frontier risk taking, and therefore lower market private equity lamenting the loss of VC returns, while having completely forgotten what it means to take venture scale risk.

As an asset class the very expansion and inclusiveness belies the definition of what the function of VC is; namely to take frontier technology bets that are not being financed by other market mechanisms. Inclusivity as far as who is performing this allocating is wonderful; inclusivity in the definition of VC is oxymoronic.

The other common refrain I hear existentially muttered from “expert panels,” is about what the death of unicorns means, and lack of public market liquidity. What many fail to realize, going back to the time dislocation of venture capital, is that for current cohorts of investments, all that matters is what the world looks like phase shifted out 5-7 years. Commentary on today only matters coming from those who were in the market 5-7 years ago. We think of these cycles at Everywhere Ventures, and we’re of the strong conviction that this cyclicality lends itself to two phases of opportunity.

1. When exits are bad, there is an “illiquidity discount”

   1. This means that current valuations deflate, and things are cheap

   2. This means that many entrepreneurs are fearful, so better ones stick around

   3. This means that under-convicted LPs leave the asset class

   4. This means that stalwart investors are buying in at a massive discount

   5. To us this is a “risk on” environment, because we’re focused out in 5-7 years

2. When exits are good, there is a “liquidity premium”

   1. This means that current valuations balloon, and everything gets overpriced

   2. This means that startup tourists flood the market chasing riches

   3. Bad founders abound and it’s hard to separate wheat from the chaff

   4. Non traditional LPs flood the market chasing returns

   5. Tier 2 funds that can’t win access to top deals pay earlier forward valuations

   6. To us this is a “risk off” environment in which to exercise caution

   7. This is a great time to harvest winners, and sell into the glut of demand

When you next hear a panelist describe the “death of venture capital,” or the “exit window being shut,” think to yourself about time dislocations in venture capital, and how indexing into this set of cohorts affords the best discounts for future wins. For those investing today with a heavy illiquidity discount, all the better for tomorrow. For those considering building a company, today is the best possible time to create the future you want to exist in 5-7 years, and venture capitalists will agree.

The definitions of venture capital have not changed, but the parade of wannabes has expanded, and the death of returns greatly exaggerated. Exits still exist for those investors boldly backing zero-to-one, risk taking companies, and if the market is tight today, that only means the illiquidity discount gives you an advantage for tomorrow. The Buffet adage holds true; be greedy when the world is fearful, and fearful when the world is greedy. Keep your own map of where you think we are in the cycle. If you’re a founder, lick your chops, because if you wade into this market, you’re not only bolder than most, but your very courage is a stamp of self-selecting validation that you’re hungry, you believe in tomorrow, and you’re a fighter. If that’s you, let’s chat.

Scott Hartley is Co-Founder and Managing Partner of Everywhere Ventures. You can also read his work Everywhere with Scott on LinkedIn or his Substack.

Listen to Samantha Coxe chat with Allison Stoloff on the Venture Everywhere Podcast: Flaus First. Now on Apple & Spotify and check out all our past episodes here.

Last week I had the pleasure of speaking in an online forum with a few thousand entrepreneurs, family office principals, and venture capitalists from around the world. On my panel an interesting opinion surfaced around the use of social media. Is there a strong rationale for backing, or avoiding, a founder with a high disposition for sharing, or as it’s now more popular to say, “building in public.” While some laud this behavior as core to building your business, or creating teachable moments, others see it as self-centered, or further fueling the cult of founder in an era of team.

I think, with many things, the lesson is two layers deep. It’s less about if and how a founder, or any individual for that matter, is vocal on social media, and more “why.” I learned a framework a while back known as R.I.C.E. For the injury prone athletes among us, I’m not referring to “Rest, Ice, Compression, Elevation,” but rather:

• R: REWARD

• I: IDEOLOGY

• C: COERCION

• E: EGO

Reward as a motivation speaks to what that individual is seeking. This outcome could be money, status, fame, or any external or internal reward for their actions. We can debate the value of working for such “reward” outputs, or if there are more “pure” motivations, but think of this as Jerry McGuire shouting, “Show me the money.”

Ideology is more about a world view, or a fundamental belief system. It could be perhaps framed as values. What does this founder see in the world that they want to will into being? At some point this is where job becomes career becomes mission. It’s perhaps less common that you hear about someone with a life-long passion and core ideology about building a CRM, but in some sectors like, say, climate, it’s prevalent. I would argue someone like Elon Musk is an ideological founder. He really doesn’t need more rewards. He’s building because of core convictions about humanity, risks technology can impose, and benefits it can engender if stewarded correctly.

Coercion is about trying to affect change, but perhaps more aggressively, without authentic openness to alternative viewpoints. The line between an ideological founder and a coercive founder is perhaps thin, but inheres in a founders genuine openness to logic or objectivity, and a humility about the validity of alternative viewpoints.

Ego is about how others perceive this person, how they want to be known. Ego invokes a narcissism, a self obsession, and a mirror back on oneself for why or how they might be acting. In truth, we all probably exhibit elements of all four letters from time to time. We work for reward, ideology, to coerce others, and because of ego. And of course we certainly self-identify in ways far more flattering than reality.

As a venture capitalist, namely as someone tasked with deploying money on behalf of limited partners into founders and CEOs whom we believe can impact the world, I would argue that I try to isolate and invest in those working in earnest toward Reward and Ideology. My belief is that founders with these two motivations are aligned with the outcomes that drive venture scale financial returns for my limited partners. As we live in a capitalistic society, I do fundamentally believe it’s ok for a founder to want to become a billionaire. That’s not mutually exclusive with creating an insanely positive company for the world and many in it. Therefore I seek out founders with a “chip on their shoulder,” and a drive to win, to chase “reward.” I also fundamentally believe this is an honest, if not purely “good” motivation. As stewards of capital, we as investors also maintain a huge responsibility to steer capital into ideas that are fundamentally “world positive,” or on a trajectory we believe to be net good. As such, there are many categories of business that we will never invest in, even if they’re moneymakers. The founder’s hunger or drive, insofar as they’re going the right direction, is just fuel.

Ideology is a motivation that can cut both ways. It can mean principled, but it can also mean stubborn, intransigent, fundamentalist and uncompromising. These are not good characteristics, and not ones that we seek in our portfolio CEOs. Great founders are coachable, adaptable, malleable, and flexible. They might have a strong world view, but they are not ideologues incapable of change. They are those who can listen, hear both sides of an argument, and make the best informed decision for the company, regardless of how it makes them look. The truly competent founder can therefore put Reward or outcome far above Ideology, Coercion, or Ego.

Ideology can also be a slightly less honest motivation, in my view. Most people say they want to make supply chain efficient, or reduce climate change, but in their own hierarchy of needs they probably first want to safeguard their own health, their family, their security. As such I’m wary of the overly idealogical founder who can’t also admit the type of outcome they’re seeking, because I don’t trust them completely. People often want to appear ecumenical, so might wear the mask of Ideology, but then this is just a betrayal of their own Ego, and the importance of how they’re being viewed. As such, I’d much rather a founder be dead honest and tell me what they want.

The Coercive founder is the one who’s quick to tug on the FOMO strings. “I got an extension to close for a few more days because I want to take in your check.” “I’ll let you in on the old SAFE valuation but I’m closing by Friday.” Or my absolutely favorite, “We’re already oversubscribed,” (as the email comes in the middle of the weekend). Coercion is a yellow flag, a manipulative tactic that, sure, might close you some sales leads, but is not a core characteristic or motivation that I seek in founders I back. I think authenticity, solving a huge problem, and building authentic brand have more impact on long-term sales success than any form of glib “hustling.”

And finally Ego is the red flag that makes me run for the hills. I think this may come across as contrarian to some, but I’d argue that the nuance here is the difference between Reward or Ideology, and pure Ego. Pure Ego is concerned with vanity, with narcissism, with the appearance of success rather than the attainment of it. Nothing screams Ego like the TechCrunch article that comes out about the acquisition that lost everyone money. Yes, saving face can be nice, but ultimately this is Ego. The company failed, and the “optical win” is dishonest pat on the back (and this can be motivated either by the founder wanting to project success, or the investors also wanting to mollify their limited partners, and project success. Ego is all around). While we’ve all seen these, and probably even helped write press releases, Ego is the characteristic that probably led to that failed outcome in the first place. Oh, the irony.

The problem with Ego is that it’s actually irrational when juxtaposed with Reward as a motivation. For example, we’ve all had the experience where the terms of a great deal change at the last minute. The egocentric founder walks away because they were slighted, bruised. Or they chalk it up to “principles” or “values,” hiding their own Ego behind the mask of being Ideological (but we know that’s a ruse). “That person shouldn’t have done that.” But if one fundamentally believes it’s still a good deal, even after the terms have changed, even after your pride is bruised, swallow your Ego, and make the deal happen. Make the rational decision as pertains to your job, which is to prioritize the success of your company, not to babysit your ego and pretend that you’re teaching someone a lesson. Don’t take two steps back to punish an enemy when you could take one step forward by being the better person, and swallowing your pride. The founder who can maintain an even keel and make the best business decision every time will win against the one who has to babysit their Ego behind an Ideology.

So in the case of a CEO who has a vocal social media presence, it’s complicated. I’d say that as a potential co-founder, as an employee, and as an investor, it’s good to take a look at why you think this person is sharing, and how they’re communicating. Is this all about their Ego, photos, only their own opinion? Or is this about teaching or a perspective on their industry? Is this about trying to move a company forward for the impact of their Ideology, or the seeking of their Reward, or is it about optics? Is it about keeping up with the Joneses and looking successful? As with all of us there’s no right answer, but maybe this helps you self-evaluate, and think about how you are motivated. Are you impelled by the R, the I, the C, or the E? And in those you most admire and respect, how do you think that they’re authentically motivated?

The purest among us are probably ideologically inclined, yet detached, though I’d argue that for most that’s probably a mask we all wear on top of Reward or Ego. To those overly ideologically inclined, the risk is the quick slip into Coercive behavior that becomes too attached to outcome. Ideological motivation with detachment probably supersedes Reward on the hierarchy of “good motivations,” but for my day job as an investor, rather than philosopher, I’ll back founders motivated by R or I.

And for those Coercive or Egocentric motivations… a la poubelle.

Scott Hartley is a Co-Founder and Managing Partner of Everywhere Ventures. Check out Everywhere with Scott on LinkedIn.

You can also read more straight from his Substack feed here.

Warren Buffett is famous for saying, “It's far better to buy a wonderful company at a fair price than a fair company at a wonderful price.” While we’re also value investors, at the earliest stage of venture capital what makes a bigger difference than price is backing a company with tremendous market or regulatory tailwinds, and an amazing founder at the helm. The Why Now and the Who. This isn’t to be conflated with Simon Sinek’s "Start With Why.” This isn’t about Purpose. This is about seeing where the world is already going, and capitalizing on those market tailwinds.

Venture capitalists fall into archetypes of decision making, namely those who:

1. Focus on the “What” (Product-Led VCs)

2. Focus on the “Who” (Founder-Led VCs)

3. Focus on the “Why Now” (Velocity-Led VCs)

The “Product VC” will go really deep on product and on technology. They’ll get in the weeds with you, do deep technical diligence, and make their decision on the basis of how good is the “What” that you as the founder are building. Series A, B, and growth stage investors tend to have to be excellent product VCs, because at that point there are many players, their investments are highly concentrated and large dollar amounts, and the depth of nuance in how the product or technology functions is really the delta between whose product wins, and who assumes the majority of the market.

The “Founder VC” will focus on you as the founder. Why are you the perfect person to be building this? Do you have the grit and perseverance to stick with it longer than anyone else, to not quit when the market gets bad, or competition attacks? Do you have a chip on your shoulder and a will to win more than others? Do you have the charisma to story-tell, and get others to follow you to the ends of the Earth, as you will need to to fundraise, hire, and sell the hell out of your product and idea? Many people wrongly think that VCs are looking for someone who went to Harvard or who worked at McKinsey. Quite the opposite. I tend to think of those as my initial yellow flags. Those are high-achievement, risk-averse logos that signal ego and inflexibility. What we are looking for in founders we back at Everywhere are three things:

1. High Communication (Open, and not conflict-avoidant)

2. High Coachability (Humble, able to integrate feedback)

3. High Cadence (High-energy, focused problem solvers)

These are what I call “The Three C’s.” Communication means that you’re transparent, you telegraph change, you get on the phone quickly to walk through scenarios that might not be easy to address, you can meet conflict head-on, with depth, reason, and calm. Coachable means that you’re humble in conflict, you can take feedback, you have a backbone but you also listen deeply, and think about how to integrate new ideas. Cadence refers to your pace of problem solving. It’s not running around chasing shiny objects, to reinvoke the metaphor of the “Rocking Horse” founder who is always in motion but never progressing. In contrast, high cadence refers to a founder who can prioritize rigorously, not waste effort or motion, and laser in on the most important pieces of the puzzle, knocking down outcomes quickly. When things don’t go as planned, they’re quick to reasses, cut losses, change direction decisively. A high cadence founder solves something in hours, not days, and not weeks. They need not always be right, but they’re directionally accurate, and iterating at a high pace.

The “Velocity VC” prioritizes the “Why Now” more than the “What” or the “Who.” Many people wrongly throw stones at Velocity VCs believing them to be FOMO-laden shiny object chasers. Some are, to be fair, this. But other Velocity VCs I’d argue have a strong opinion about the direction of the market, and they look to back companies already three sheets to the wind in a fast moving gale. They look to back companies who’ve already picked their spot in the wave. To consider the quote by Buffett, this is the argument for backing a fair company in a fast-moving market, rather than a wonderful company in a slow-moving market. In other words, I’d argue if your “What” and your “Who” are right in a fair market, you might do 11/10 things right and still lose. In contrast, if your “Why Now” is explosive, you can do 6/10 things right and still win. In other words you might be the wrong “Who” and not even the best “What,” and you’ll still end up succeeding because of the exogenous drivers of your business. (Of course, over time poor leadership and poor product will still destroy a good thing, but all things being equal these exogenous drivers of Why Now matter hugely).

Overall when I think through my own investing heuristics, “Why Now” is my most important initial lens. If I can’t understand why there isn’t an absolutely burning problem and a reason for this to happen this year, not next, not last, then it’s hard to get on board even if there’s an incredible “Who” or “What.” In pre-seed investing the second-most important criteria is certainly “Who.” You could argue that it’s all Who in the sense that a great founder will identify a big opportunity market and essential Why Now, but I still don’t think that’s always true. What’s clear though is the least important component of pre-seed investing is the “What.” Exactly what someone has built, or very early traction is to me a red herring, meaning it can be a distraction. If you’re getting early validation but in a market that truly has a small TAM, and does not have meaningful tailwinds for a red-hot, burning “Why Now,” I’d advise hitting pause and finding that gale force wind rather than chasing early traction. Many wrongly chase very small MRR in a small TAM-market thinking that investors want traction. This is the path to slogging into the slow-moving tides, where you have to do 11/10 things right to generate a mediocre outcome. In contrast, by thinking about the macro tailwinds, you might find a jet-stream elsewhere. Think about what investor archetype you’re pitching to, and stress-test their own advice. A Product VC will always push you for more traction, better metrics. A Velocity VC might ask you to take a week off and read the newspaper and think about where you think the world is truly going, and how can you capitalize on something no one else yet sees.

One of my favorite stories in my book is about Shoaib Makani and what was then called Keep Truckin’ now Motive. They saw coming regulatory change around electronic engine logging device (ELD) regulation for truck drivers, and built a company into this regulatory slipstream. Many companies in telemedicine, and other sectors that really identify core changes, can build into explosive markets. They need not have the perfect “What,” but merely pitch the potential of the “Why Now.”

In our portfolio we recently backed Operant AI. This was a prime example of backing first “Why Now,” then “Who,” then “What.” The Why Now was that every app is now built on and reliant upon dozens of infrastructure layer services and APIs, and this induces huge security risks. Operant was going to build a “Runtime Security” company focused on real-time cyber risk identification and mitigation. That was the Why Now. The Who was that the team came out of deep expertise at Apple, VMWare and Google. The What was conceptual, and unbuilt. Some investors would have waited, but to us, Felicis Ventures, and others, the Why Now was red-hot. As long as we knew the team would be highly iterative, and directionally correct, and that their product would be tech-enabled, high-margin, and have some returns to scale, we didn’t really need to know the exact What, and we didn’t need any early traction.

This is perhaps a controversial opinion, but start with “Why Now.” Ask yourself where you can most authentically attack an explosive market with high communication, coachability, and cadence. What’s more important than even size of market is the rate of change in that market. Then once you’ve identified these table stakes, then begin thinking about the “What.” What you build certainly becomes hugely important, but don’t get stonewalled by early Product VCs that over analyze what you’re building. Focus on People and Velocity VCs who recognize your talents, and help you find those markets with explosive rates of change, where you need not be excellent to win.

For more thoughts on this, read my piece on pitching Vision vs Traction.

Scott Hartley is a Co-Founder and Managing Partner of Everywhere Ventures. Check out Everywhere with Scott on LinkedIn.

You can also read more straight from his Substack feed here.

In 2018, surrounded by incredible animal motifs in the Michele-era Gucci offices near Firenze, I watched an older Italian man hammer small nails into the underside of a loafer as I walked the factory floor. I’d been living in Italy for the summer, teaching in a program on artisanal manufacturing and automation related to my book which lauds human skills in the era of AI (already obvious in 2017). When I asked the Gucci manager if they’d tried to automate certain processes he scoffed, talking about all of the inefficiencies it added with greater transaction costs in every hand-off. Whereas one human could perform many tasks fluidly, the machine was far more rigid. The gains in efficiency in one specific task were completely offset by its lack of flexibility.

We lose sight of the fact that technology does not substitute for jobs, but for tasks. And every job is comprised of hundreds of extremely diverse tasks. Human-machine substitution is easy click-bait, but activation costs are high, and efficiencies low.

Because of the inefficiencies caused by an attempt at full technology for labor substitution, in most cases companies will opt for what I call “Strong” not “Skinny,” generating far greater output thanks to AI. While much of the media attention is on job dislocations, we ought to focus more on to obviation of tasks, and rote task dislocations that only take away from us what was minimally human to begin with. What we’re left with is a far better world of greater output, and human work better allocated toward tasks requiring greater flexibility of mind and body.

A year ago I wrote a piece where I framed the future of work debate around AI as one in this parlance of, “Strong versus Skinny.” The basic idea is that new tools like AI enable companies to make efficiency trade-offs to do one of two things:

1. Generate the same output on vastly fewer inputs (Skinny)

2. Generate much more output on the same inputs (Strong)

The argument I made is that AI will be net positive for output, because many more companies will choose Strong over Skinny. In other words the impact on labor will be acute, while the benefits to output will be broad-based, generating net gain. This isn’t to minimize acute losses in certain rote and routine processes, and certainly some jobs that contain a high proportion of such tasks may fully disappear, but this is to say that on the whole, AI will create more output on the same labor and capital inputs.

As AI predominately optimizes or makes more efficient specific tasks within jobs, it’s difficult to eliminate full jobs if the benefits of technology are felt only in specific workflows. Let’s say that the benefits of AI could fully replace 90% of a given worker’s tasks, the company may still not be able to fully replace a human with a machine. This is because the gap in those 10% of tasks not being done would generate such transaction cost that it would, in addition to the activation costs of switching, create far greater burden than gain. Understanding that AI obviates tasks not jobs, is a vital distinction that that can help us think through the following hypothetical:

• If AI can do 90% of what a human can, and if I have 10 FTE, I can fire 9 and keep 1. This is the false belief in full substitution of technology for human because it takes into account the belief that AI doing 9/10 of tasks is the same as it doing 9/10 full human scale jobs, each job with its own subset of 10% complex tasks. This knee-jerk argument for Skinny, or cutting 9/10 of your workforce because of AI, is one that lacks nuance in understanding how AI will actually substitute for tasks, not jobs. This is the click-bait headline. If a company chooses this path they risk spiraling into downward productivity because of high activation costs, the fact that 9/10 tasks done does not equate to substituting 9/10 jobs done, not to mention cultural erosion or morale for the one employee who lost 9 colleagues.

• In actuality, AI obviates 90% of the tasks for all 10 people, making all 10 FTE able to dedicate 100% of their time to the 10% of the tasks that are complex, that are non-routine, that require collaboration or creativity. In other words, each employee just became 10x more efficient by able to spend 100% of their time on only 10% of the hardest tasks. So across your 10 FTE you have 100x productivity increase, meaning that you eliminate the competition who don’t follow suit. This is the non-obvious, and far more optimistic headline that everyone keeps their jobs, and the company just generated 100x the output on identical inputs.

AI will merely supplement those human workers in amplifying ways around specific tasks, making them more efficient, and better able to focus on non-routine, complex coordination, collaboration, and creativity. Because the benefits of AI won’t fully eclipse all functions a person can do, companies making choices around machine for human substitution will be faced with inefficiency if they choose this swap. As illustrated in the above example, if they choose to supplement humans with machines they will be amplifying their aggregate employee output by 100x.

I had the chance to speak on this for Huawei at the Drucker Forum in Vienna at the end of 2023. Our comparative advantage is to become more human, not less.

Scott Hartley is a Co-Founder and Managing Partner of Everywhere Ventures. Check out Everywhere with Scott on LinkedIn.

You can also read more straight from his Substack feed here.

In a world where all the information is universally available, as it already is, we are rate limited by our ability to ask and structure questions to retrieve answers. At one point we asked questions to Larry Page’s blinking cursor on Google; now we’re all prompt engineers, Socratic ping-pong players with Sam Altman’s ChatGPT. What’s marginally changed is the format of answers; what hasn’t changed is the premium we pay to smart questioners, structured thinkers, and those able to laser cut to the bone of signal in a world of endless disinformation and noise. Judge a person by their questions, not by their answers, Voltaire once advised (every hiring manager).

David Deming, a Harvard economist, published years ago about the importance of soft skills in business, but from an analytical and interesting angle. Generally large organizations solve complex problems. Complex problems require some compartmentalized knowledge, or specialization among workers, and so functional large organizations must “task trade” between individuals. You do one thing, and I’ll do another. In an organization requiring task trading, there are coordination costs, or friction points between individuals and teams. If an organization doesn’t place a value on interpersonal skills, there is a high degree of friction in task trading, so less cooperation leads to slower problem solving. Soft skills like communication actually materially impact the terms of trade between individuals, reducing friction, coordination costs, and thus enabling greater frequency of task trading to solve increasingly complex human capital challenges. So in a world where more rote and routine tasks are going to AI, and where complex tasks remain the bread and butter of what human workers do, it makes sense therefore that there are a premium for these soft skills that enable greater task trading, and reduce coordination costs. This was the thesis of my book, and why “the Liberal Arts will rule the digital world.”

In many domains, economy is a sign of mastery. Verbosity is the result of lack of command on language. Economy of language is the badge of honor for writers like Steinbeck and Hemingway, something we point at to define their greatness.

Hemingway’s The Old Man and the Sea, which was around 27,000 words, won him the Pulitzer Prize. Abraham Lincoln’s Gettysburg Address was 275 words, and is remembered 160 years later as one of the greatest speeches in American history. Similarly, Instagram was 13 employees when it sold to Facebook for $1 billion, and WhatsApp was 55 when it sold to Facebook for $19 billion. In a world where plenty solve the problem, or get it done, the greats get it done with the utmost efficiently. They’re the ones who quietly ace the test, not the ones who brag about studying hard.

With the rise of AI taking on more rote and routine tasks, more companies will become what I call “Thin Companies,” or companies where tremendous value will accrue to very few individuals who master this trifecta of accuracy, precision, and economy. Accuracy refers to nailing product market fit and customer demand. This is the question of can you find the bullseye of what people want, and where there’s willingness to pay. Precision refers to your ability as a team to execute against this vision (accuracy) repeatedly with a low error rate. Once you know where the bullseye is, how do you hire, scale, and maintain culture such that you can continue to hit this target (knowing that the target also moves depending on market dynamics, the competition, and even where you are in the product adoption lifecycle. To Geoff Moore’s great book Crossing the Chasm, selling to an early adopter is dramatically different than selling to the early or late majority or to a laggard). The reality is that the target moves, and as a leader you need to both maintain accuracy and precision. Moreover, as you scale beyond your ability to hire directly, your leadership manifests itself as culture when you’re not in the room, and culture is all that keeps precision functioning when you as the leader no longer have your hand in every decision. Many leaders discount culture, but culture is what’s in the room when the CEO leaves it. It’s the determinant of values, and how people act when you are no longer in the room.

1. Accuracy - Locating demand, and ever-changing product market fit

   1. Reliant on customer interviews, humility, listening to feedback

2. Precision - Laser focused execution, even at scale

   1. Reliant on leadership, hiring, and culture when you’re not in the room

3. Economy - Getting it done as efficiently as possible

   1. Reliant on leveraging new tools like AI, and prioritizing soft skills

Finally to Dan’s point, and to the above points of David Deming, and the examples of Hemingway, Steinbeck, Instagram and WhatsApp, economy matters. How well you can achieve accuracy, precision, and then economy will determine a lot. Economy allows you to, all things considered, stay alive longer on the same amount of capital. This means one of two things, 1) you can raise less money, thus preserving greater equity for the founders and employees, or 2) you can generally survive as others die, thus harnessing a market that materializes around you. You have more time to find the target (accuracy), and streamline the team and execution (precision), on less input of labor and capital (economy). Better founders won’t adopt AI because this is the latest buzzword, or en vogue thing to do, but because it is a tool that helps them zero in on these three levers of accuracy, precision and economy. AI may mean you hire fewer, more senior workers, highly specialized, and to Deming’s point, those with the soft skills that enable streamlined task trading for efficient complex problem solving.

Thin startups of tomorrow will ruthlessly prioritize accuracy, precision, and economy, meaning they will prioritize listening and humility to find product market fit, internal culture to continually refine precision and execution, and leverage new tools such as AI to trim and refine the inputs necessary to achieve more company on less.

Scott Hartley is a Co-Founder and Managing Partner of Everywhere Ventures. Check out Everywhere with Scott on LinkedIn.

You can also read more straight from his Substack feed here.

Listen to Arianna Armelli with Scott Hartley on the Venture Everywhere podcast: There’s No Place Like Home. Now on Apple & Spotify and check out to all our past episodes here!

The world is complex, so we all use heuristics, or short-cuts. We pattern match, and we put concepts into buckets, or frameworks. We derive frameworks from data we experience and collect. They are our rough-cut approximations to explain what we have seen. It is therefore difficult for frameworks to be predictive, or forward looking.

“To a man with a hammer, everything looks like a nail,” or so goes the well-trodden quote attributable to Samuel Clemens, otherwise known as Mark Twain. To the person with a framework, everything looks like it fits, or deviates, from expectation.

As frameworks seek to explain our world, but are derived from past data, they lag reality. Reality is dynamic, while frameworks are largely static. As such, in time frameworks stop explaining the current state of reality accurately. They get stale.

I’ve noticed that for a time mainstream opinions that may fit within a given framework can be both popular, and still accurate. But in time, while they remain popular, or at least putative, they can begin to lag reality, and become quite inaccurate. As such, there is a period of time where popular opinion inaccurately describes reality, or lags, because the late majority, or the laggards, in a given domain are using a formerly accepted framework to describe a world that no longer exists.

Let’s discuss what many call “Deep Tech.” “Deep Tech” as a framework might accurately be described as a true frontier technology that is heavily reliant on science, and may have a bimodal outcome of success or failure. Perhaps it’s so frontier that it’s reliant on certain unknowns becoming true, and if those things do not become true there is no possible way for the technology to come to fruition. Quantum computing, or certain methods of CRISPR gene targeting, perhaps fall into this bucket. But whereas “Deep Tech” is a framework that can accurately describe parts of the world, it’s often misattributed as a framework to describe things that were once science experiments that no longer are. I’d argue that many elements of technology as relates to drones, or perhaps Low Earth Orbit (LEO) satellites, were once deep tech, but no longer are. Components are a commodity, and organizations like SpaceX have reduced both the cost and risk of getting into orbit to be accessible, even pedestrian. Many continue to use this framework to describe a world, and a reality, where this descriptor is no longer accurate to the inherent risks in these companies.

It might therefore be “Contrarian” to invest in these companies, as many presume their risks to far outpace reality. In fact, on a risk adjusted basis perhaps the returns in these spaces is now better than it ever has been before. We recently backed a number of companies that are focused on drones and space and someone came to me and said, “I didn’t realize that you guys did Deep Tech at Everywhere Ventures.” My response was, “these aren’t Deep Tech companies, this is the Future of Work.” My beef isn’t with the nomenclature, but rather with the presumption of risk. Deep Tech implies frontier science, and bimodal risk; Future of Work implies fait accompli reality.

I have a broader contention with “venture capitalists” who seem to forget that our asset class is one of wide variance in outcomes. Wide variance implies that we must actually take broad risk, and actually take on risk that society writ large isn’t willing to. The very job of a venture capitalist isn’t to isolate and target low variance returns by backing only “SaaS businesses between $1M and $5M ARR;” it is to capitalize ideas and founders who seek to change the world, and build fundamental technology.

In developed markets, technologies largely dis-intermediate or disrupt incumbents; in developing markets, technologies seeks to organize or consolidate out of disorder.

The job of a venture capitalist is to spot asymmetries in how things are being done, to see when commonly accepted frameworks lag reality, and take a contrarian position.

Enterprise resource planning (ERP) for first responders is beginning to happen with drone overflights. This isn’t deep tech, but rather future of work ERP that better optimizes human resources in a constrained environment where human safety and response time are paramount. More and more data is being collected from space, and data download, in-orbit computing, and space-based cyber operations aren’t so much frontier as they are essential to how cloud computing is already beginning to take shape. The lenses and frameworks of “Deep Tech” lag reality, and there exists an opportunity that is to disagree, be contrarian, and invest despite the framework mismatch with how others judge your risk taking. As an investor I believe our job is to spot these asymmetries in how the world is acting, and where you disagree, and it’s to lean into environments where visionary founders aren’t just taking the bet, they’re wagering years and decades of their life that these opportunities are ripe.

Where are the frameworks lagging? What lens do you spot that mismatches reality, where you fundamentally disagree with a popular opinion? Chances are if you can be intellectually honest as to why you disagree, and it isn’t for the sake of Sophistry or being argumentative for its own sake, you’ve probably spotted opportunity. If you have that conviction and no one else is doing it, chances are it’s big. Own it.

Scott Hartley is a Co-Founder and Managing Partner of Everywhere Ventures. Check out Everywhere with Scott on LinkedIn.

You can also read more straight from his Substack feed here.

Listen to Philip Johnston with Scott Hartley on the Venture Everywhere podcast: Data Centers in Space. Now on Apple & Spotify and check out to all our past episodes here!

I was recently speaking to one of our portfolio founders, Pau Sabria, who’s building Remotely.Works. Remotely is, by most accounts, a services company that helps U.S. companies source and hire offshore talent in modular, scalable ways. Remotely helps assemble full-scale remote engineering teams, facilitating all of the complexity around remote hiring, human resources, compliance, and cross-border payment.

A few years ago investors would have shrugged and said, “nice lifestyle business.” In many ways, Pau and his co-founders, who had all sold OlaPic for $130 million in 2016, were actually looking to build a lifestyle business. They wanted to build a company that had some returns to scale, but more than going after huge venture rounds, and the frenetic chase of a billion dollar exit Pau and team wanted predictable cashflow. They wanted a “default-alive” business that wouldn’t take a ton of VC dollars to scale, and something that could afford them all a steady stream of profits over time.

But with AI, everything has changed.

In 1886, Scottish author Robert Louis Stevenson wrote the Gothic novella, The Strange Case of Dr. Jekyll and Mr. Hyde. The novella, which flew off shelves, was one of the late nineteenth century’s “Shilling Shockers” or “Penny Dreadfuls” as these cheap, quick, serial paperbacks were dubbed. The story was an account of a well heeled Dr. Henry Jekyll and his friendship with a London legal practitioner, Gabriel John Utterson, who was investigating the dark murders by a Mr. Edward Hyde. In time, it’s come to be known that Dr. Jekyll and Mr. Hyde are in fact the same man. Dr. Jekyll has been taking a serum he’s created to compartmentalize his own darkness in the form of Mr. Hyde, but eventually Mr. Hyde overpowers Jekyll, and ends up ruling his actions to the point that Jekyll is lost, and ultimately commits suicide.

While we won’t go so far as to draw the dark analogy all the way through, AI is providing service companies with somewhat of their own serum to draw out their more efficient qualities. Whereas a services business might once have been dubbed a relatively tame Dr. Jekyll, the tools of AI enable them to undergo a full transformation. For example, a traditional services business would scale linearly with per-employee efficiency remaining relatively constant. To secure growth, most services businesses need to add headcount, or FTE. More people means more output, and there are not necessarily a ton of scale efficiencies to getting bigger. As Pau states, however, today “AI is the transformer” or the “multiplier” that enables a few counterintuitive things to happen. For example, as Remotely gets a larger client base:

1. AI and automation allow for new efficiencies

2. Because of these efficiencies, Remotely is able to reduce, rather than expand, FTE

3. Constant growth % on an expanding client base means revenue acceleration

4. Revenue acceleration on reduced FTE means exponential efficiency growth

In other words, his services business has an expanding client base, a fixed growth rate, a reduction in the number of people necessary to perform tasks, and therefore an explosion in efficiency gain. This AI transformer is like the serum that allows for Dr. Jekyll-like formal services businesses to transform into Mr. Hyde efficiency monsters. The inputs are being reduced, and yet growth and outputs continue to expand.

In many ways this is a new revelation vis-a-vis a piece I wrote in August 2023 which highlighted the AI choice being one between “Strong” versus “Skinny.” There I made the point that on fixed inputs, AI enables an expansion of outputs, i.e. "Strong." Or AI enables a reduction in inputs, and a maintenance of the same output, i.e. "Skinny." Managers therefore had the choice to opt for either "Strong" or "Skinny" by optimizing around growth vs cost. But what is now becoming possible looks to be actual expansions in efficiency in this form of AI as transformer from Dr. Jekyll services businesses into Mr. Hyde AI companies, where both inputs go down, outputs go up, and efficiencies go through the roof. The AI Mr. Hyde serum is now enabling both Strong and Skinny, both expanded growth and reduced cost.

This is why we’re seeing, and will continue to see firms like Sequoia Capital and General Catalyst perform private equity-style roll-ups of traditional services businesses in areas like accounting or legal, and why at Everywhere Ventures, we will continue to invest in companies transforming traditional industries.

Scott Hartley is a Co-Founder and Managing Partner of Everywhere Ventures. Check out Everywhere with Scott on LinkedIn.

You can also read more straight from his Substack feed here.

Listen to Julie Kennedy with Scott Harley on the Venture Everywhere podcast: Getting Paid to Care. Now on Apple & Spotify and check out to all our past episodes here!

In HBO’s Silicon Valley the obnoxious investor Russ Hanneman talks about the “Three Comma Club” in reference to the number of commas in a billion. In a recent Category Design workshop we held with Peter Goldie and Deborah Kattler Kupetz for our portfolio companies, we were introduced to the concept of the zero comma club, or the “Zero-Billion Dollar Market,” a concept they’d explored over past years that was popularized by Steve Vassallo of Foundation Capital, as well as endorsed by leading investors like Ann-Miura Ko at Floodgate, and funds like Sequoia Capital.

The notion of a Zero-Billion Dollar Market is that it’s something that you can see as an entrepreneur, but no one else believes. As we’ve talked about before, being consensus and correct is wonderful in many parts of life, but not in entrepreneurship. If you’re trying to create something new and valuable then it’s generally down the less traveled path, against the grain, and certainly not wildly obvious to others. Value is located when you’re both non-consensus, or contrarian, and also correct.

My friend Tommy Dyer recently alerted me to the fact that Don Quixote has been taught at Stanford Graduate School of Business (GSB), with particular focus on his “Lessons for Leadership.” Among other lessons, Quixote creates his own reality through action. Though often considered mad, particularly as he begins to see the windmills around himself as monsters, it’s perhaps all only in his imagination, and as Tommy delves into in his recent writings, perhaps it’s even him living in his own “reality distortion field,” to use the Steve Jobs turn of phrase. Indeed entrepreneurs must be Quixotic, or exceedingly idealistic, even impractical or “crazy” to invent new categories. “Here’s to the crazy ones. The misfits. The Rebels...” This is the edge between vision and reality where contrarianism dwells.

This is the land of Zero-Billion Dollar Markets, where some people will call you crazy for “tilting at windmills,” or imaginary monsters. Andy Grove, former CEO of Intel, wrote the 1999 book entitled “Only the Paranoid Survive.” Only those crazy enough to be out there in the arena, or on the journey, and also paranoid enough to tilt at windmills, survive long enough to create their own reality and new market.

So what’s better than a hundred billion dollar TAM? A Zero-Billion Dollar Market.

This is the one you’re inventing from the ground up, like Apple debuting the iPad, or Chrysler creating a new category of car in the Minivan. Five Hour Energy decided not to play the game of battling for the beverage aisle and instead decided to sell at point of sale, with a straight-forward name, and new form factor. Other examples abound, but one of particular interest over the last decade was the emergence of Qualtrics as the clear winner over Survey Monkey. Whereas at one point these “online survey” tools were indistinguishable, and in many ways, Qualtrics, sitting in Utah rather than Silicon Valley, was at the disadvantage, until Qualtrics decided to invest in defining their own category. In order to do this they looked at “the gap” between what CEOs or employees thought their company or product did, and what their customers did.

This “gap” between perception and reality has to be closed by better understanding your customers. Of course these could be called surveys, but by reframing the conversation around a market opportunity that hadn’t been tapped, and inventing the category of “Customer Experience” or CX, they became the creators of a Zero Billion Dollar Market. They were no longer playing the game of competition. They were the monopoly player in their own new invented world of CX, and through the course of an acquisition and later IPO spin out, they grew to nearly 15x the market cap of Survey Monkey over only a few years. This clear eruption in growth came from one thing: Narrative. Category creation isn’t simple marketing or new words, it’s internalizing this Quixotic spirit of inventing the world around you through action, even when others only see Windmills and think you’ve gone crazy, until you’re right.

A few key steps to finding your Zero-Billion Dollar Market:

1. Where is there a gap in society between reality and perception? Where has the hype cycle overshot, or what’s the underbelly of a major trend?

2. What unique skills or lenses predispose you to see two markets converging where others do not think there is overlap or opportunity? (e.g. Ballet + Robots)

3. Play what I call the “Animal Flip Book Game” of trying to mix and match industries you might understand with business models (SaaS, marketplace, etc). Do this to come up with non-sensical combinations that might lead to identifying Zero-Billion Dollar categories everyone else thinks are too crazy to chase. There’s nothing like an Elephant head, Giraffe body, Duck foot company that will weird people out to the point that you just might be contrarian, and right.

At Everywhere Ventures, one of our portfolio companies, Radicle Science, did the same. They observed what they call a “proof gap” between what customers believe supplements like melatonin do, and what they actually scientifically do. Radicle Science now offers clinical trials, or “Proof as a Service,” for companies in the less regulated supplements industry to prove to consumers and retailers what they do. Radicle Science created the market of democratized clinical trials. My friend Catie Cuan did in creating the field of Choreorobotics, or the blending of dance and robotics. She noticed a “trust gap” between man and machine that could be filled by adding graceful movement into the orchestration of robotics, particularly in medical environments where the goal of machines is to improve patient wellness, not increase anxiety. For years as a professional dancer no one understood her interest in both ballet and mechanical engineering until she completed her PhD at Stanford, and invented an entire new field of study now offered at universities such as Brown. She created an entire new category for herself, and other artists in technology, and is evidence of the value of plurality of interests, and the categories in their overlap.

In Joseph Heller’s Catch-22 if you asked not to fly dangerous missions you were deemed sane, and therefore had to fly. And if you were insane you were happy to fly and never thought to abstain. So the only those who could get out of missions where those unfit to fly, but if you identified as unfit to fly you were deemed sane and therefore were required to fly. In entrepreneurship it’s not an entirely dissimilar logical loop. On some level you need to be wildly Quixotic. “Crazy” enough to try something, and make your investor believe you are “swinging for the fences.” But at the same time you have to be measured and polished in how you communicate this mild insanity. You have to be fit to fly, and assemble a team of passengers alongside you, knowing that only crazy ones will sign up for this decade-long mission.

So be the one just crazy enough to invent the Zero-Billion Dollar Market you see, and then just sane enough to dominate the category you create for yourself.

Check out Everywhere with Scott on LinkedIn.

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Scott Hartley is Co-Founder and Managing Partner of Everywhere Ventures. You can read more from him here.

Listen to Matt Biringer with Jenny Fielding on the Venture Everywhere podcast: Cloud Cover. Now on Apple & Spotify and check out all our past episodes here!

The backstory is that during the ZIRP years, the tech narrative was more about growth at all costs rather than about running a sustainable business, so the financial model was often pushed aside. More recently, with a return to sanity within the tech sector, concepts such a profitability, unit economics and sustainable burn are no longer taboo phrases and the upshot is that more investors and founders are paying attention to the financial model itself. 

Download the Financial Model

The Tweet and subsequent Linkedin post unleashed a heated debate among founders and funders centered on if an early stage company should even bother building out a financial model or wait for a later inflection point in the business. One school of thought is that early stage founders need to focus on building world class products and getting them into the hands of customers, not on building out complex financial models. With inevitable pivots and incorrect assumptions, what exactly is the point anyway? The other side of the debate is in favor of putting a model together, despite the fact that many of the assumptions will be incorrect. Whichever camp you are in, our perspective is that a financial model should not be scary, overcomplicated or intimidating to founders.

As a 2x founder who did not go to Business School and does not have exceptional Excel skills, Jenny is the first to acknowledge that building out a financial model is not her idea of a fun time. So to make this all a bit easier for founders, we teamed up with Eric Friedman and his team at Graph Advisors who put together a simple yet effective financial model for founders to use (thank you Eric!) 

To be clear, the goal of this financial model is not to predict the future - but rather to help any leader have a basic plan, understand the levers needed to scale and be one step ahead of key business decisions. 

An effective financial model can also help with fundraising by telling the story of the business in quantitative terms. Getting investors over the line typically requires a magic trifecta - the pitch, demo and model. While a pitch is focused on vision, founder DNA and articulation of a monster opportunity and the demo or prototype shows an ability to understand customers and build product, it’s the financial model that demonstrates the health of the business itself and how the concurrent growth trajectory and associated costs all fit together.

Do you absolutely need to build a financial model as a pre-seed stage company? 🤷🏻‍♀️ But the model is an empowering exercise that all founders can benefit from and hopefully, the template and explainer video will make it much easier to get started.   

Download the Financial Model

Check out Liz O’Sullivan with Noah Spirakus on the Venture Everywhere podcast: Vera-fy with AI, Listen now on Apple and Spotify and check out all our past episodes here!

For millennia we have created tools that stand outside of ourselves. Yuval Noah Harari, author of the widely acclaimed books Sapiens and Homo Deus, someone I had the pleasure of hearing speak at the 2018 Penguin Annual Lecture in New Delhi as my book was also published by Penguin, talks about how a distinct characteristic of human beings is our ability to imagineer things outside of ourselves. We imagined tools, and instruments, like the harp to harness music in the vibrating string. Technae. And since then we’ve imagineered a million other applications, taking technology to greater and greater abstractions away from nature, where we all came from.

He talks about how Peugeot is a figment of our imagination. It is a car company, but what is a company but a series of legal fictions to create an entity that is an “other,” not any one particular person, but a representation of people to cooperate, or perhaps disagree, outside of ourselves. In the creation of a company, an LLC, a trust, or any legal fiction, we instantiate into the world another incarnation into which we imbue properties. We ascribe characteristics to this entity, this other. We state what it shall do, where it shall sit, how it shall be organized, whom it shall represent.

Now imagine that this isn’t a harp, or a company, but a rather more “cutting edge” idea like an algorithm, or a sequence of code we deem to be “artificially intelligent.” (Isn’t calling technological intelligence “artificial” redundant, if we think about the etymological origins of the word “tech?” Anything not pure nature is by definition technology, and therefore artificial, but I digress. Let’s just keep calling it AI for the sake of being “with the times,” albeit redundant.) The same way that we codify into contract the characteristics of a company, we codify into code the characteristics of an AI. We instantiate this third-party representation of people and ideas and process into code, and those who write the code make the judgements as to how that code is sequenced, what it prioritizes, how it acts upon data, and how it “decides.”

Jonathan Haidt is a moral philosopher, and now also a professor at NYU Stern School of Business where he speaks broadly on issues of morality in business and technology. He has a great experiment he once ran that asked participants to answer a few very simple questions that would measure if, and how, people would act “morally” or not. These questions tested participants by asking them if they would perform tasks for $100, $10,000, or $1 million, or if there was no amount of money they could be paid to perform that particular task. Would you inject an unknown substance into the arm of a child? Would you state an unpopular political opinion? Would you crawl around naked acting as an animal? His Moral Foundations Test probes into the ways in which we prioritize Care/Harm, Fairness, Loyalty, Authority, and Purity.

Most children know that there are gradations in authority. For example, when children are asked if one of their peers could violate school dress code, even small children differentiate the importance of this as lesser than the importance of protecting another child from physical, or worse, harm. “Would it be ok to push your friend off the swing, even if the teacher told you to?” In this case perhaps we individually, largely, have an innate characteristic to prioritize Care over Authority, or at least the vast numbers of us in society are conditioned this way. We won’t get into nature versus nurture, but suffice it to say that individuals have a moral compass, and most of us wouldn’t inject the arm of a child, but we would state something politically unpopular, or defy authority if it meant causing another explicit harm.

These gradations also inform our duties, and what we deem just or fair. Imagine a bouncer at a hot night club telling you you can’t come in because there’s a fire hazard, and he doesn’t want people to get hurt (prioritizing care). Or he might say "this guy comes here all the time, so I’m letting him in first” (loyalty). Or he might say “you don’t belong here,” or “you’re not dressed well enough,” (in-group/belonging) just puff out his chest and say, “you can’t come in because I say so” (authority). In all of these cases your reaction will be vastly different. Your idea of justice will be different.

But going back to the legal fiction of companies, entities, algorithms and AIs, these versions of “technae” or the harnessing of nature, do not ipso facto have their own morality. There are mere reflections of the priorities we codify into code. They do not, of their own accord seek to do no harm, or prioritize Care over Authority.

We accept justice if we are values aligned, and we agree with the “sequence” of how those moral values are stacked in a hierarchy. The same might go for how we view Facebook News Feed. The extent to which we might view it as “just” depends on if we agree or disagree with how they sequence the hierarchy of moral foundations, and how they explain it. Much of corporate communications is a values explanation. So then who behind Facebook News Feed is making that determination for what is Harmful and Caring, and what is actually Authority dictating freedom of speech? Which images stay, and which go? It’s governed by an “algorithm,” but that’s again just a technological fiction, a separating of values into a seemingly more objective “other,” the same way we might install assets in a trust, or IP in a corporation. Facebook might purport to assert Care above all, but then Authority above In-Groups or Fairness. After all, it takes a stand on editorial license and its owners define Harm and Care insofar as how these values are inscribed into company culture and code.

Uber famously rewards drivers for logging in more often. While they might still prioritize good driving above all (Care), they then largely preference Loyalty. Drivers who do more for Uber have a better experience than those who split time with Lyft. Is this fair? This is the moral hierarchy of Uber, like it or not. The founders and creators of that company have made explicit choices around values, and hierarchy of morals, and they have instantiated those truths into their company and their code.

That’s a relatively harmless example, but a laser guided bomb also makes calculations based on data inputs as to what is a target, and what is a non-enemy combatant. Does this missile prioritize Care over Authority? Or is it programmed to do what it’s told, with Authority trumping any consideration of Care once it’s launched?

How we make moralistic determinations also might depend on a definition of comprehensiveness. Did we exhaust all the options before acting? If the missile did one million calculation before making a determination to kill, are its creators any less culpable, or any more moral? Is a more comprehensive missile more moral than one that is haphazard, or less discerning in its algorithms? Both create equal Harm.

Algorithms and AI are neither good nor bad. But they are also not agnostic. Code is not complicit; human beings are. So how do we build diverse teams who are asking the right questions as they build, as they collect data, as they create the priorities and the sequences? How do we consider these debates as we compose contracts and compile code? These are the questions not of tomorrow, but of today. Leadership institutes revolve around Management theories, when they need to reorient around Moral ones. These are the most pressing questions of today, and certainly tomorrow.

Despite my book, The Fuzzy and the Techie, being nearly seven years old this April 2024, these debates are only becoming more poignant, and more urgent. I’m thankful to Virginia Tech’s Institute for Leadership in Technology for modeling a program off of the themes of my book, and for allowing me to teach alongside my friend Professor Rishi Jaitly, former head of Twitter India and Senior Advisor to OpenAI.

Our Spring Module builds on this idea of Full Stack Human, a framing that came out of my conversations with Virginia Tech President Tim Sands in 2023. This framing treats the Liberal Arts as the “Infrastructure Layer” in a stack, on top of which these lenses and modalities can be applied to any number of modern questions.

Read more about why Leadership and the Humanities go together.

Thanks for reading Everywhere with Scott Hartley! Subscribe for free to receive new posts and support my work.

Also available via LinkedIn Newsletter.
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Scott Hartley is Co-Founder and Managing Partner of Everywhere.vc. You can read more from him here.

For any who may have recently watched Apple’s series, The New Look, or High & Low, the 2023 documentary about designer John Galiano, this piece’s analogy may resonate, as it takes a step off the runway, and a page out of haute couture.

A few years ago my friend Soren Larson and I were discussing the reduction of the modern world, and we boiled it down to two comparative advantages; vastly superior technology, and vastly superior brand. We called this concept Silicon and Gucci. What wins in today’s economy, and probably even more so in the future, is superior access to the raw inputs of better, faster, cheaper technology, what we call Silicon, or premium storytelling, and Quality (capital Q, in the Robert Pirsig sense), which we call Gucci. At the end of the day, startups and products win because they obsess over one or the other, or in extremely rare occasions, they obsess over both.

As a kid in Palo Alto I had the fortune of getting to do my math homework at my soccer teammate’s house across the street from Steve Jobs Ramona Street home. I used to purposefully park in front of his house to catch a glimpse of him washing his dishes through the kitchen window after completing calculus problem sets. I’d wedge my 1986 clunker between his fleet of Mercedes that flocked the front yard.

Jobs was obsessed with Mercedes, and saw Apple as a luxury products company, a brand, not just a technology. It was this obsession that probably explains why Apple is in the very rarified space of possessing both excellence in being Silicon and Gucci.

In every business there are commoditized elements, and then there are elements which truly differentiate. In 2017 I made the contrarian argument that technology, in most cases, is actually the commodity, and it’s the human breadth of experience, critical thinking, historical context, and broadly “Liberal Arts” backdrop that makes technology’s application actually useful, and drives the majority of value. There is of course core infrastructure tech that is singularly about base layer efficiencies. Examples of this might include new language models, or web3 rails. This is deeply valuable, and where we’d say that the comparative advantage is Silicon. But this is about the only world in which being Gucci, or being deeply focused on brand and story, might be discounted, and secondary to truly exceptional tech alone. The truth is, most companies parade as “technology” but this is a relatively commodity component, and they are in fact “tech enabled,” and predominately dependent on access to distribution, scale, creativity in storytelling, and brand. Aka Gucci.

So in all application layer features, companies, products, and platforms, Gucci matters. Story matters. Brand matters. Distribution matters. What we first wrote off as “influencer marketing” is now I’d argue actually a core driver of differentiation. This reversal is a consequence of the democratization of technology and production whereby many more people have the means to produce, but not always distribute.

We saw Jessica Alba launching The Honest Company, which at one point seemed to be the aberration, an exceptionally-driven “Hollywood outlier playing the game of startups.” But what’s emerged over the past years is a reality that if you can distribute something, that is differentiation; production is the commodity. Technology is in many cases the red herring distracting us from what really matters, which is 1) how can this founder create brand and story around these ideas, and 2) how can they get to scale and distribution faster than their competition? Who has the idea, and who can build it, is no longer the bottleneck; story and distribution are. Being a first mover helps, but for example in the case of Kin Euphorics, bringing on Bella Hadid as a co-founder instantly meant that 60 million people recognized the brand. I was on a phone call with a co-investor in Germany, and his 16-year-old daughter shouted in the background, “Dad, is that Bella Hadid’s new company?” Distribution matters.

Alex Rampell who co-founded Affirm, and is GP at Andreessen Horowitz wrote on this a decade ago in his piece on Distribution versus Innovation. Startups race to get distribution against incumbents who race to innovate. Incumbents are slow to innovate, but startups often fail to get distribution. The success or failure of a startup looking to unseat an incumbent comes down to “can you get to distribution faster than the incumbent can get to innovation?” Again, Gucci matters. Sometimes a company is purely building a 100x better product, and Silicon matters, but short of that 100x differential in tech, speed, or quality, scale and adoption matter more to get the startup to a place of sufficient traction to be able to capitalize the business.

The production of beauty products is commodity, but Rihanna’s distribution is differentiated, and she, not product, is why Fenty can win against incumbents. We’re likely to see more celebrity-led brands not just because this is a cute gimmick, but because people are realizing that distribution trumps production in a highly democratized world where the walled gardens of information asymmetry, raw material access, supply chain optimization, and on-demand production have withered, and anyone can find their way in. In a capital constrained world, distribution, or growth hacking to access scale for lower cost per acquired user, is what wins. Someone like Rihanna or Cristiano Ronaldo with CR7 have community baked in. In the nicest sense of the word, they are “cult leaders” with an ability to influence. It is their uniqueness, not technology or product, that drives success of their brands. Of course, just slapping someone’s name on a product isn’t sufficient. What I’m arguing here is that scale and access to genuine distribution matter as much as product.

At Everywhere Ventures I often think about this lens of Silicon or Gucci. I ask myself, does this startup, or founder, have a true comparative advantage when it comes to differentiated technology. Can they win at tech? Or if not, do they have an obsession with Gucci, a perfectionism around storytelling, vision, and identity. There is a rock and a hard place between those two extremes, so ask yourself, are you Silicon or are you Gucci, and depending on your answer own that identity and invest in it. Chances are, unless you’re building core frontier infrastructure, your visual identity, story, and softer factors around your brand matter a whole lot more than you think, as do the ways you might need to get creative on distribution if your name’s not Rihanna.

Think of how your own business needs to pull from these worlds of Silicon and Gucci, and don’t discount the importance of both poles as you build your company.

Thanks for reading Everywhere with Scott Hartley! Subscribe for free to receive new posts and support my work.

Also available via LinkedIn Newsletter.
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Scott Hartley is Co-Founder and Managing Partner of Everywhere.vc. You can read more from him here.