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

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

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

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

The 2026 hardware resurgence is being driven by a:

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

• Perceived defensibility of a product vs merely a software service

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

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

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

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

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

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

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

—

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

On Thursday April 30th we held our Everywhere Ventures AGM, and we were honored to have six-time author Sebastian Mallaby fly in from London to join us. Mallaby and I met in London’s National Gallery in 2016, when both of our books were up for a Financial Times prize. His book, THE MAN WHO KNEW, on Alan Greenspan, won the Financial Times Business Book of the Year. Mallaby went onto write one of the best books on venture capital, THE POWER LAW, in 2022.

Mallaby’s latest book, THE INFINITY MACHINE: Demis Hassabis, Deepmind, and the Quest for Superintelligence, published in March 2026 with Penguin Press, is not only a biopic of Hassabis, but a broad survey into the rise of artificial intelligence.

In unsurprisingly prescient fashion, Mallaby sought out Hassibis for interviews and a biography nearly a decade ago. Cornering him with logic, Mallaby pointed out that if Hassabis was the top of his field, and if AI would become as transformative as he argued, then Hassabis would himself, become undeniably important. No hiding would change that, so better to get ahead and control the narrative with a book.

Mallaby’s book, however, goes far beyond rote logic. He is a master storyteller, taking us from sun-speckled London cafes to Cambridge quads, chronicling the polymath upbringing of Hassabis. The son of immigrants, Hassabis grew up a chess prodigy and early gaming programmer who went onto become a neuroscientist and AI pioneer.

Hassabis attributes part of his focus on science and technology to a moment in childhood where a wrong-move on the chessboard brought a room of grown men to their feet in critique. Surely there was more to the world than a room full of people watching a child move rooks and pawns across a series of black and white squares.

But chess and designing video games taught him about bounded realities. Great players in chess don’t brute-force all possible moves, they recognize patterns. They compress information and prioritize higher-probability paths. This early observation paved the way to an idea that became reenforcment learning (RL). Similarly, as video game agents came to navigate and optimize actions in dynamic, and increasingly complex bounded worlds, this became a staging ground for early intelligence.

Hassabis is a deeply curious person. Books on philosophy helped him form an opinion on the nature of reality. He internalized the Kantian idea that our reality is an outward reflection of the mind, of human perception, rather than objective truth. That idea drove him inward to study the inner-workings of the brain, neuroscience.

While the early world of AI was deterministic, rules based, Hassibis depth of study of the mind brought him to a more fluid set of ideas around information, and the probabilistic nature of the mind and our perceptions of reality. This rooting in philosophy and neuroscience laid a less conventional path into artificial intelligence. He found like-minded co-creators Shane Legg and Mustafa Suleyman, and together they founded DeepMind in 2010 with a fundamentally different vision for AI.

DeepMind converged disparate academic AI silos. It pushed the idea that rules need not be hard coded in advance. Rather than pre-program intelligence, a machine could learn through repeat exposure, reward, and optimization of its own experience. This paved the way for representative learning and the early generative advances.

In 2016 DeepMind’s AlphaGo played Lee Sedol in the game of Go, a highly complex board game where brute force compute cannot solve for the total number of possible moves. AlphaGo’s Move 37 was a deviation from any human expectation. It was “a one in 10,000 move,” according to commentators. At first it was viewed as a hallucination, perhaps a machine error. But in time as AlphaGo beat the world champion it was heralded to have been a stroke of machine genius, a deviation from expectation or any prior training data, a probabilistic leap, intuition, a move in its own style. Through the course of a million self-plays, AlphaGo had developed its own pattern recognition of possibilities, choices, and style. I actually highlighted this seminal moment for generative AI in my own 2017 book, THE FUZZY AND THE TECHIE.

The rise of AI over the last five years is nothing short of extraordinary. Humans have long harnessed nature, transforming raw materials into new forms. The Greek Technae, the etymological root of technology, means craftsmanship. It means turning raw materials in nature into new forms. In Antiquity, the harp was an example of technae, transforming raw wood and string into beautiful sound. Today we’ve turned silicon and sand into NVIDIA chips powering ChatGPT. We’ve turned a material with low information density into one with high information output.

Mallaby’s book does probe the more dire questions of the day, drawing parallels to Oppenheimer and the Manhattan Project. The desire for technological progress never comes without conscious hesitation as to risks and responsibilities of stewardship. But I choose to lean into the more sanguine threads in this powerful book.

If Hassabis’ philosophical view is that the constituent unit of the universe is information, then perhaps in turning sand and silicon into semiconductors and AI we are in fact repackaging atoms into more information-dense forms. Long past the evolutionary stage of simply building tools, we’re now taking low information-density inputs, and transforming them into radically information-rich outputs. Therefore in our harnessing of nature, perhaps we truly are already expanding the universe.

—

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

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

In this episode, you will hear:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

—

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

In this episode, you will hear:

• Applying the human-in-the-loop thesis from autonomous vehicles to legal AI.

• Winning AmLaw 20 firms through zero behavior change integration.

• Taking financing workflows from days to minutes with email-native agents.

• Shifting corporate legal work from billable hours to fixed-fee engagements.

Learn more about Elliot Katz | mixus AI

LinkedIn: https://www.linkedin.com/in/elliot-katz-002b6684

Website: https://www.mixus.ai

Learn more about Scott Hartley | Everywhere VC

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

Website: https://everywhere.vc/

In episode 114 of Venture Everywhere, Scott Hartley, managing partner at Everywhere Ventures, talks with Dylan Lew, co-founder and CEO of Ecotone Renewables, a company replacing traditional waste hauling with on-site biodigesters that convert commercial food waste into a locally distributed organic fertilizer. Dylan shares how building and operating an early digester that broke down constantly pushed him to rethink the entire food waste system from the ground up. He discusses how Ecotone is rewriting the economics of food waste management, turning what businesses write off as a disposal cost into a revenue stream and a locally sourced fertilizer supply chain.

In this episode, you will hear:

• Using state food waste bans to landfill as a distribution accelerant.

• Building a decentralized grid of on-site digesters over centralized facilities.

• Upcycling commercial food waste into a locally distributed organic fertilizer.

• Expanding from digester hardware to AI-powered waste intelligence.

• Sequencing a multi-sided business from digester manufacturing to retail fertilizer.

Learn more about Dylan Lew | Ecotone Renewables

LinkedIn: https://www.linkedin.com/in/dylan-lew412

Website: https://www.ecotonerenewables.com

Learn more about Scott Hartley | Everywhere VC

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

Website: https://everywhere.vc/

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

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

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

1. Finding consensus before the market

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

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

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

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

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

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

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

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

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

—

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

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

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

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

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

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

The Benefits of “Staying Back a Grade”

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

Optical Advantages

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

Strategic Growth

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

Conclusion

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

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

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

—

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

In this episode, you will hear:

• Deploying universal screening to close the gap between first symptom and treatment.

• Building school-based infrastructure to bridge families, health plans, and providers.

• Navigating parent consent and data privacy in K-12 health adoption.

• Leveraging state screening mandates as a distribution accelerant.

• Expanding from screening tool to full-stack school and health system network.

Learn more about Kenzie Butera Davis | Maro

LinkedIn: https://www.linkedin.com/in/kenzie-butera-davis

Website: https://www.meetmaro.com/

Learn more about Jenny Fielding | Everywhere VC

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

Website: https://everywhere.vc/

In episode 112 of Venture Everywhere, Jenny Fielding, managing partner at Everywhere Ventures, talks with Allison Lee, co-founder and CEO of Revive — a turnkey virtual tailoring solution that helps fashion brands reduce returns and dead stock by refurbishing and reselling garments. Allison shares how a pandemic-forced pivot exposed a damages problem every brand was quietly absorbing but no one was solving. She discusses how Revive challenges the industry consensus that damaged inventory is a write-off, instead unifying item-level inspection, refurbishment, and resale into one system that recovers value brands didn't know they had.

In this episode, you will hear:

1. Replacing SKU-level inventory systems with item-level inspection and decisioning.

2. Bridging the gap between brand needs and 3PL capabilities.

3. Refurbishment as a higher-recovery alternative to donation and liquidation.

4. Live commerce as the resale channel for Gen Z shoppers.

5. Human-in-the-loop operations where AI augments rather than replaces physical inspection.

Learn more about Allison Lee | Revive

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

Website: https://www.byrevive.com/

Learn more about Jenny Fielding | Everywhere VC

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

Website: https://everywhere.vc/

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 episode 111 of Venture Everywhere, the host is Matthew Brimer, co-founder of Everywhere Ventures and co-founder of ZZ Driggs, a furniture and design company serving the real estate and hospitality industry. He talks with Helle Jeppsson, co-founder and CEO of SCAPE, a wellness tech platform delivering on-demand spa services across Latin America. Helle shares her path from opening physical spas in the U.S. and Mexico to building an alternative after COVID exposed how hard it is to make brick-and-mortar wellness scale. She discusses how SCAPE pivoted from a B2C consumer brand into a white-label spa product for hotels and businesses, converting a traditional loss-center amenity into a profit center for properties that can't justify the overhead of a full spa operation.

In this episode, you will hear:

1. Building on-demand wellness infrastructure across Latin America.

2. Pivoting from B2C to B2B after spotting organic demand from the hotel industry.

3. Using consumer brand recognition to break into corporate wellness.

4. Creating better work conditions and higher earnings for women therapists in Latin America.

Learn more about Helle Jeppsson | SCAPE

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

Website: https://scape.mx/

Learn more about Michael Barone | ZZ Driggs | Everywhere VC

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

Website: https://zzdriggs.com/

Website: https://everywhere.vc/

In episode 110 of Venture Everywhere, Michael Barone, analyst at Everywhere Ventures, talks with Chris Priebe, founder and CEO of Zelt — an all-in-one workforce management platform consolidating the fragmented tools companies rely on to manage their people. Chris shares how years in private equity watching acquired companies struggle to untangle messy, unintegrated employee systems revealed a gap no modern vendor was solving. He discusses how Zelt challenges the industry consensus that fragmented HR stacks are simply the cost of doing business, instead unifying employee data into one system where org chart logic, payroll, and expenses all speak the same language.

In this episode, you will hear:

1. Replacing fragmented HR, payroll, and IT stacks with a single workforce management platform.

2. Scaling go-to-market with a focused geo strategy after expanding to 40+ countries.

3. Balancing product depth and ease of use across growing markets and customer segments.

4. Embedding AI into Zelt to simplify access to a feature-rich platform.

5. Overcoming change management when introducing a new system to resistant teams.

Learn more about Chris Priebe | Zelt

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

Website: https://zelt.app/

Learn more about Michael Barone | Everywhere VC

Linkedin: https://www.linkedin.com/in/michael-barone-0a5375241/

Website: https://everywhere.vc/

In this episode, you will hear:

1. Building a tilt-rotor drone optimized for light cargo delivery in remote and underserved communities.

2. Delivering strong results with a lean funding base.

3. Creating a new logistics category for shipments of 20 parcels or fewer.

4. Leveraging energy efficiency as a core competitive advantage over traditional delivery vans.

5. Expanding into the US market with a proven drone platform and regulatory certifications already in hand.

Learn more about Etienne Louvet | Iona Drones

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

Website: https://ionadrones.com/

Learn more about Jenny Fielding | Everywhere VC

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

Website: https://everywhere.vc/

In this episode, you will hear:

1. Building the first online marketplace to compare and buy extended car warranties.

2. Offering dealership products at 40% lower prices through digital channels.

3. Replacing aggressive sales tactics with transparency and education.

4. Leveraging AI for customer support and AI search optimization.

5. Embedding warranty APIs for cross-selling through partner platforms.​​​​​​​​​​​​​​​​

Learn more about Reto Bolliger | Chaiz

LinkedIn: https://www.linkedin.com/in/reto-bolliger

Website: https://www.chaiz.com

Learn more about Harm-Julian Schumacher | OneLot

Linkedin: https://www.linkedin.com/in/harm-julian-schumacher

Website: https://www.onelot.ph

The host of episode 107 of Venture Everywhere is Hugh Dixson, CEO and founder of Switchboard, a platform that integrates enterprise IT systems in supply chains and automates manual processes. He talks with Ali Gara, founder and CEO of MagicPort, a software platform bringing intelligence and automation to shipping operations. Ali shares his journey from private equity in Asia to founding MagicPort in Singapore, where he saw an opportunity to modernize the inefficient, manual processes around port calls. He discusses how MagicPort provides maritime intelligence and an operating system for port call management, helping shipping companies leverage data for business development, optimize operations, and streamline collaboration across the fragmented industry.

In this episode, you will hear:

1. Aggregating data from sources for real-time vessel and port intelligence.

2. Replacing Excel and email with an integrated operating system for port calls.

3. Driving adoption in a change-resistant, fragmented shipping industry.

4. Leveraging AI and automation to deliver immediate customer value.

5. Building trusted networks on smart platforms for efficient global trade.

Learn more about Ali Gara | MagicPort

LinkedIn: https://www.linkedin.com/in/ali-gara-4034082/

Website: https://magicport.ai/

Learn more about Hugh Dixson | Switchboard

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

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

In this episode, you will hear:

1. Adapting startup playbooks to the physical fitness business model

2. Insights into the rise and fall of Connected Fitness, and lessons learned

3. The power of honest storytelling during a company’s challenging exit

4. The real challenges of entrepreneurship: navigating legal battles and team dynamics

5. Reimagining success and personal growth while transitioning between ventures

Learn more about Helaine Knapp | CITYROW

LinkedIn: https://www.linkedin.com/in/helaine-knapp

Website: www.helaineknapp.com

Learn more about Pau Sabria | Remotely.works

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

Website: https://www.remotely.works/

In this episode, you will hear:

1. Venture’s evolution from dot-com to mobile/cloud to AI-driven startups.

2. Why insight and relationships matter more than ever.

3. Shift to founder-led community platforms over traditional venture funds.

4. Human judgment and personal connections in identifying exceptional founders.

Learn more about Anna Barber | Everywhere Ventures

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

Website: https://everywhere.vc

Learn more about Scott Hartley | Everywhere Ventures

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

Website: https://everywhere.vc

Learn more about Jenny Fielding | Everywhere Ventures

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

Website: https://everywhere.vc

In this episode, you will hear:

1. Unlocking unrealized revenue potential in fragmented tech stacks.

2. Autonomous revenue generation beyond productivity tools.

3. Real-time commerce ontology that learns and acts.

4. Proving autonomous systems in high-churn e-commerce.

5. Democratizing enterprise capabilities for mid-size brands.

Learn more about Kat Garcia | Ground

LinkedIn: https://www.linkedin.com/in/kat-garcia-online/

Website: https://joinground.com/

Podcast: https://open.spotify.com/show/4zuhbqKwF34X0wqDXa1Lsx?si=91da32c586844f1a

Learn more about Scott Hartley | Everywhere Ventures

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

Website: https://everywhere.vc

In this episode, you will hear:

1. Bridging cashflow gaps of healthcare providers.

2. Addressing financial infrastructure crisis in healthcare practices.

3. Leveraging accurate EMR data for AI-driven decision-making.

4. Partnering with healthcare networks to reach underserved practices.

5. Positioning for value-based care and consolidation landscape shifts.

Learn more about Ethan Schwarzbach | Flychain

LinkedIn: https://www.linkedin.com/in/ethan-schwarzbach-baa09571/

Website: https://www.flychain.us/

Learn more about Jenny Fielding | Everywhere Ventures

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

Website: https://everywhere.vc