There is a scene early in Moneyball - the film, not the book - in which the Oakland A’s scouts sit around a long table. They debate prospects whose names are on the board for transfer. “Who do you want to talk about first?” asks Brad Pitt, playing Billy Beane. One player has “a good face” and a “good jaw.” Another has “a baseball body.” The men nod and murmur in agreement. In 2002, this was how America’s best baseball minds chose which players to recruit.

It was called the ‘eye test’.

The point of the scene is not that the scouts were stupid. The point is that intelligent humans, when asked to predict something they cannot quantify, default to pattern-matching against visible traits and call it judgment. In the movie - and the book - Billy Beane brings in a Yale economics graduate with a spreadsheet, on-base percentage replaces the look of a player’s jaw as a predictive variable. Within a decade, every team in the league will have a quant department.

As an emerging manager learning the ropes of investing, I think venture is in its ‘Moneyball moment’ today.

Two Minutes Nineteen Seconds

Let’s start with the fact that investors don’t actually spend very much time evaluating founders. Gompers, Gornall, Kaplan, and Strebulaev surveyed 885 institutional VCs across 681 firms and found that the median fund considers around 100 deals per investment. The median fund logs 118 hours of due diligence for each closed deal - 81 for early-stage, 184 for late-stage. That sounds quite substantial until you remember it is the total spread across weeks, associates, and the gaps between other deals. DocSend’s pitch deck benchmark, run across thousands of investor sessions, puts the first-look average at under 2 minutes and 18 seconds. Venture capitalists spend ten times less time on a founder’s deck than the typical person spends picking a Netflix show to watch. Most decisions are made in the spaces between other decisions, often with worse information than you’d have when deciding where to go on holiday.

On top of how little time investors spend evaluating founders, consider that we don’t have much to go on and can’t often define what we saw or didn’t see. Huang and Pearce’s 2015 paper on angel investors found that the decision was dominated by what investors themselves called ‘gut feel’ - a tacit combination of intuition and analysis in which, when the two disagreed, intuition won and the investor could not fully explain why. This sits atop nearly half a century of psychology, from Nisbett and Wilson (1977) onward, demonstrating that humans are systematically worse at explaining the determinants of their own judgements than they think - and that the confidence with which the explanation is offered is uncorrelated with whether it is right. Nearly the same proportion of venture capitalists indicate (when surveyed) that they are above average in their industry as the number of drivers who report that they are above average drivers when asked.

The Village Talks

Then consider the herd. Scharfstein and Stein’s 1990 model in the American Economic Review showed why following the consensus is rational behaviour rather than cowardice: if you back the herd and you’re wrong, you’re wrong with the room, and you keep your job; if you back the contrarian view and you’re wrong, you’re wrong alone, and your job is harder. When a company needs capital to get to scale, it normally raises it from multiple sources that support each other and step in and out between rounds. It takes a village, and the village talks. We’re all ‘contrarian investors’ - except that in order for the plates to keep spinning, we very much are not able to be.

Bikhchandani, Hirshleifer and Welch formalised the next step in 1992 - information cascades, where after a sufficient number of actors make the same call in sequence, the rational move for everyone downstream is to ignore their own information and copy the herd. “Who is your lead investor?” This is what the industry calls ‘signal.’ It is what statisticians call endogeneity.

Consider, then, what investors claim they do not look at but really do.

Brooks, Huang, Kearney and Murray, writing in 2014, ran identical pitches voiced by men and women in front of investors. 68.33% of participants chose to fund the male-voiced pitch versus 31.67% the female - same words, same numbers. In their field-data sample of three pitch competitions, male entrepreneurs were 60% more likely to win, and attractiveness produced a further 36% boost among the men; among women, attractiveness did nothing. Kanze and colleagues later found that VCs ask male founders promotion-framed questions (’how will you win this market?’) and female founders prevention-framed ones (’how will you defend against churn?’); the asymmetry alone predicts a roughly sevenfold funding gap.

Investment decisions are affected by weirder stuff too. How much a founder smiles moves the needle more than the things investors say they are looking for. Founder smile intensity correlates with Y Combinator admission. I’ll repeat that: the more you smile in your pitch, the more likely you are to get into YC - statistically speaking. My favourite weird and wonderful academic paper this month - Bahlmann’s 2024 Frontiers in Psychology study of 341 male VC-male founder dyads - found that the less attractive the male VC is (based on other people’s scores), the more capital he committed to relatively more attractive male founders (i.e., how attractive other people scored that founder to be).

We project at a deeply subconscious level that is quite challenging to regurgitate into our CRM. Ambady and Rosenthal’s thin-slice work - judgements made in under thirty seconds match judgements made over thirty minutes - explains why every effort to slow the process down tends to confirm the snap call rather than overturn it.

So we make instant decisions, are unable to define why we did, follow each other even as we advertise that we don’t, and are still fundamentally driven by the human forces that drive all of our decisions at a subconscious level.

Put it together, and you get an industry making capital allocation decisions in three to four minutes, copying the partner across the road, and rewarding founders whose jaw is, presumably, a good shape for baseball.

The question is whether any of this is fixable— and a growing number of funds think the answer is yes, if you’re willing to replace the eye test with something more rigorous.

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Enter the Quants

In the last few years, we’ve digitised more and more of the ‘signal’, the venture market has filled with more competition (for the best talent), and AI has unlocked the ability to make instant decisions more rigorously. From this context has emerged new funds that take a quantitative approach to selecting which founders to back.

Correlation Ventures has been running a co-investment model for over a decade - explicitly statistical, with a stated devotion to making investment decisions in under two weeks based on the model’s output. SignalFire built data infrastructure around employee relocation patterns to surface companies that their human partners would never have found through warm intros. EQT Ventures developed an internal system called Motherbrain that ranks opportunities ahead of any human seeing them. Quantum Light, founded by Nik Storonsky of Revolut, claims to be fully quantitative - no qualitative override. Moonfire - in the UK - is also leaning into the positioning of a ‘quant VC’ with more engineers than investor and a proprietary ML screening of ~50k companies per week.

What these funds have in common is a wager: that the parts of venture decision-making which are automatable have been left untouched not because they cannot be automated, but because the people doing the deciding had no incentive to find out. That the old way - which justifies their role - was the right way.

“You don’t put a team together for the computer, Billy...” Grady Fuson spits at Billy Bean. There were ‘intangibles’ - in baseball as in venture. “Major League Baseball thinks the way I think, you’re not going to win... you’re going to have to explain to your kid why you’re working at Dick’s Sporting Goods.”

The New Guard

I’ve really enjoyed learning how top funds are approaching the problem in the last year and the new guard leading the charge. Ashley, at NEA, has been someone really insightful in terms of how to think about streamlining outbound sourcing and the plumbing of venture capital. I’ve been impressed with Damian and Felix’s work at Rule30 - “an AI research lab building systematic strategies to identify and back outlier founders at scale”. It’s not just their work automating parts of the VC workflow, but also their open vision - thinking about networks, collaborating, and learning - which has impressed me and, frankly, impressed upon me the necessity of doing the same.

What is automatable, on the evidence so far, is uneven but apparent in each stage of venture capital. Take sourcing - GitHub commit patterns, LinkedIn delta, conference rosters, Twitter graph analysis. Take screening - career trajectories, prior exit history, time-to-first-hire, the speed of a founder’s previous zero-to-one, deck parsing, ARR sanity-checks, comparables. With large language models in the stack, the pitch transcript can inform sentiment, videos can betray biases (or ‘taste’) - and perhaps more. None of this is the whole job, but all of it is a part of the job which, until recently, was being done by an apprentice model of venture which is fundamentally changing.

Beating the Baseline

The objection, of course, is that the power law is by definition resistant to prediction. Babe Ruth was a slugger. The biggest outcomes are outliers, and outliers are not where statistical models live. True, but it may also be slightly beside the point.

The starting point is not a sophisticated process being marginally improved. The starting point is under 2 minutes and 19 seconds on a deck and a partner saying ‘I don’t see it.’ Almost any quantification, applied to almost any part of the funnel, has the opportunity to beat that baseline.

The Moneyball lesson was never that Peter Brand - played by Jonah Hill - could spot the next Babe Ruth. It was that Brand could identify the players the scouts had systematically underpriced - the ones the ‘good face’ filter had thrown out for reasons unrelated to performance on the field. The equivalent in venture is not predicting the next Stripe. It is identifying founders that the market was excluding for reasons unrelated to their potential performance.

The starting point is not a sophisticated process being marginally improved. The starting point is under 2 minutes 19 seconds on a deck and a partner saying ‘I don’t see it.’ Almost any quantification, applied to almost any part of the funnel, has the opportunity to beat that baseline.

There is so little data at early-stage startups that perhaps this is how human judgement earns its keep and gets on base. Surely picking out the moonshot needs a human in the loop - for the ‘intangibles’. How could a machine see what hasn’t happened before? You need to dream up the truly novel - see around walls, run through walls, throw your hat over them?

It may also be true we’re looking at venture through rose-tinted glasses of the past. Steve Jobs. Silicon Valley. Swinging for the fences. We’ve romanticised venture over generations into the hero’s story that it becomes almost impossible for us - as founders, investors, or readers - to believe that all of this can be boiled down to numbers.

Because venture is fundamentally romantic.

And remember, “it’s hard not to be romantic about baseball”.

Michael Hutson is a Partner at Oxford Seed Fund and an Associate Fellow at Oxford Saïd Entrepreneurship Centre. He is interested in exploring quantitative methods in venture capital and swapping notes with other investors innovating in this space.

Her parents, who immigrated from Lebanon and Malta, had a clear script: doctor, lawyer, or engineer. Something stable. Something that made money. Jas followed that script — Barclays Capital, then Deloitte, then an Oxford MBA, which she funded by continuing to consult on the side. Even the MBA wasn’t really a departure. “I always thought I’d go to a scaleup,” she tells me. “Or maybe explore the VC route. I never thought I’d find myself co-founding something.”

The thing that changed her mind was a question. During an MBA assignment — form a team, find a problem, pitch it — an Octopus Ventures investor on the panel looked at their idea, a health app for teenage girls, and asked who was going to do something with it once the programme ended. Jas’s co-founder, Jo, scheduled lunch with her shortly after. Jo wanted to build it, but Jas was not immediately convinced.

“Part of me felt envious,” she says. “Like, oh my God, she could go and do this and have this really successful company. But the other part of me just thought — how do we even start?”

She told Jo she’d consider it on one condition: they had to actually talk to the users first. But their product idea was built for teenagers, so they couldn’t do so during the MBA — both had signed a document prohibiting contact with under-18s for research purposes. So over the following six to eight weeks, they rang every teacher they knew and ran workshops in their old schools. The teenagers asked where they could download the app. Some asked about the co-founders’ work experience. “That was the final piece that pushed me to do this,” Jas says. “These potential users would actually really like to use this.”

From classroom to company

The original idea of luna (https://weareluna.app/) was a period tracker, with parents on the platform alongside their teens to help bridge communication gaps. The workshops dismantled both assumptions quickly. Teens said periods were only one part of a difficult adolescence — they wanted help with mental health, body image, acne, sleep, friendships, all of it in one place. They were equally unambiguous about the parent layer. They would never use the app if their parents were on it with them. Never.

luna listened, and they broadened the product and removed parents entirely.

Then the data said something the workshops hadn’t. Teens introduced to luna by a parent turned out to be four times more active than teens who found it themselves. Jas and her team went back and investigated why. The answer was almost mundane: the teens who’d discovered luna on their own were too shy to send their parents the link and ask them to pay. They sat on the free tier instead, with its more limited experience, and drifted. Having a parent introduce the app felt, as Jas puts it, like permission. “Like — my parent knows I’m using this, and they’re okay with it.”

The parent companion layer is now central to how luna monetises. Parents can't see what their teens are up to on the app, but receive general insights based on the wider teen cohort. luna's conversion rate from download to subscription — driven largely by parents paying — sits at around 40%.

“It went against what all these kids had told us," Jas says. The methods have had to scale with the product — the school workshops that produced that original insight have evolved into surveys, online sessions, and one-to-one user calls. But the instinct is the same: go back and ask. As she explains their journey to me, Jas sounds like someone who has made peace with the idea that users will surprise you, repeatedly, and that this is mostly a good thing.

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No cringe allowed

luna now has a network of nearly 250 teen brand ambassadors feeding real-time product feedback. Jas is candid about why: she and Jo are millennials building for Gen Z, and they know the gap that creates. “We’re not going to get this right on just our own.” when luna did its one significant branding exercise– the colour palette, the fonts, the tone of voice– teens were in the room. The goal, as Jas puts it, was that teens would be proud to have the app on their phone. Not just willing to use it, but proud.

That’s a harder brief than it sounds for a health product. Jas describes the aim as sitting at the crossroad of trustworthy and cool, something that doesn’t make a teenager cringe when a notification pops up in class. “We knew from the jump it had to be a cool brand.”

The channel strategy followed the same logic, though it came later, when monetisation forced them to think harder about parents. Before last year, luna only targeted teenagers. “It was only when we started to focus on monetisation that we realised parents do need to be in the loop — they’re the ones actually paying for it.”

Now luna’s TikTok is built entirely around teens. A scroll through @wearelunaapp makes the strategy immediately legible — it’s teen-led and community-focused, the kind of content that feels like it’s been made by the audience it’s for rather than a brand trying to speak to them.

The website and Instagram are aimed at parents, but with one constraint. “We try our best not to be cringy,” Jas says. Teens do end up on Instagram and the website. They Google “period tracker for teens” or “mental health app for teens” and land on the website. The parent-facing channels can’t be so calibrated towards mums and dads that they alienate the teenagers who stumble across them. It’s a balance luna admittedly hasn’t fully cracked.

Jas is also straightforward that luna’s Instagram isn’t right yet. “Our Instagram had been built on the premise of focusing on teenage girls– we had to completely retrain the algorithm now that we are communicating to parents too. There’s still a lot more work to be done,” when you’re a team of seven, she says, you pick your battles.

Building without a technical blueprint

Neither Jas nor Jo had a technical background when they started luna. The biggest sticking point with early investors was the absence of technical expertise on the team. “We had to prove to them, look, we might not be able to write code, but we can do stuff in no-code.” Jo built the first version of the app herself, got a hundred teenagers on it — drawn from the schools they'd visited — and put that data in the pitch deck.

What’s interesting in retrospect is how much that constraint may have shaped the product. Without an engineering instinct to reach for, every early decision had to be justified by what users actually wanted. The focus group in the founder’s old schools. The 250-person ambassador network. The branding workshop where teens picked the colours and fonts. The parent layer that got stripped out and quietly rebuilt when the data made the case. luna’s product history reads more like a long, ongoing conversation with its users– which may be exactly what a health app for teenagers needs to be, not just for this generation, but for future generations too. Technical founders build features. Jas and Jo kept asking questions.

From Oxford and beyond

luna was born out of a classroom at Oxford’s Said Business School, and the ecosystem gave them more than just the idea. Jas credits the MBA classmate network– people who had started businesses before and were generous with their time– as well as an Oxford advisor directory that connected them with VCs, exited founders, and consultants willing to take meetings in their first year. The Oxford Seed Fund invested in the second year of operations.

“I think we wouldn’t have had those connections if we didn’t choose to go to Oxford,” she says. But she’s clear-eyed about the limits of it. The MBA taught them some things, but building and running a company from scratch was something you can’t learn just from textbooks and class projects. The practical knowledge came from leaning on people, asking questions, and figuring it out as they went.

luna raised its pre-seed from Octupus and SyndicateRoom. When asked about the environment that makes the UK a suitable place to start a business, Jas admitted that the SEIS/EIS tax relief1 is a genuine structural advantage for early-stage fundraising– she’d known nothing about it until she started raising, and international investors consistently flag it as something they wish existed in their own markets. But valuations in the UK are lower than the US, risk appetite is lower, and the density of female angel investors doesn’t yet compare.

“I spent some time in the US last year, and I’m back and forth there often. There were a lot more female angel groups, successful women deploying into female-owned startups. There’s talk of that here, and some groups and initiatives forming, but it’s not on that level.”

On regulation – GDPR, and the UK’s Age Appropriate Design Code– she’s a lot more positive than most founders in her industry and position.

“I don’t really see it as a blocker because we’ve always had to deal with it. If anything, it gives us an advantage. Partners in other places in the world, she says, are excited about luna’s compliance rigour because European data standards are more stringent.

None of this — the regulatory navigation, the dual-audience marketing, the fundraising without a CTO — was what Jas had in mind when she enrolled at Saïd Business School. She wanted a stable next step. She got something harder and, by her own account, considerably better.

“I never really thought the entrepreneurial path was what I would go down,” she says. “But I love my job. It’s extremely hard. It’s ups and downs; you have to be really resilient. But I come to work happy every single day.”

She pauses. “I’m really excited for what the future holds.”

This article was written by Nessie Chu-Heng Lu, Strategic Marketing Lead @ Founders & Funders

Jas Schembri-Stothart is the Co-Founder of luna, a digital wellbeing companion helping teen girls navigate adolescence with confidence. Designed as a trusted “big sister in your pocket,” luna delivers medically vetted guidance across the physical, emotional and social challenges of growing up through expert content, moderated Q&A and safety-first design. As more teens turn to social media for answers about their health and wellbeing, luna provides a trusted alternative, delivering preventative health education at scale.

Jas is a former consultant turned entrepreneur and holds an MBA from Oxford Saïd Business School. She also sits on the governing board of her former high school.

I don’t remember much about the lunch conversations from those days. The sessions ran from early morning to late evening, and there was always something — a missing investor, a slide order wrong in one of the briefing rooms, a founder who couldn’t find their room. I wasn’t a founder, wasn’t pitching anything, but I felt it too — that low-level hum of everything mattering too much, all at once. But I remember talking to Susanna about Founder’s Syndrome. It was the first time I’d heard the term in two years of working in the startup world. She mentioned Elizabeth Holmes almost immediately, and I got it straight away. I’d already seen it in the wild. I just hadn’t known it had a name.

The other reason that conversation stayed with me is what happened that same afternoon, during the deliberation session. A female founder building an AI company was given a thumbs down by several mentors — all men — because of her demeanour. Her product was strong. Her confidence wasn’t, at least not in the way the room expected. Just as the discussion was moving on, a female investor intervened. She said she’d been working with the founder since the first session. She’d been turned down or ignored before, for reasons that had nothing to do with her product or her team. “She is super coachable,” the investor said. “Appearing confident in front of investors is something we can help her fix.”

She was right to intervene. I believed that then, and I still do. It’s one of the stories I return to whenever the conversation turns to gender representation in venture capital. Without that investor in the room, the founder would have been passed over for mentorship she’d already earned.

But the word that’s stayed with me– the one I keep turning over– is fix. Fix her for what, exactly? Fix her to fit a template that nobody ever formally agreed on, that most people in the room couldn’t describe if you asked them to, but that everyone seemed to recognise when it walked in?

That’s where our conversation starts this time. Susanna has spent years researching Founder’s Syndrome — the point at which a founder’s outsized influence stops driving a company forward and starts quietly dismantling it. Google it, and her work comes up in the first few results. But today we’re here to talk about something adjacent. What happened at CDL was about the template: the unwritten audition every founder has to pass before anyone takes them seriously. What I wanted to talk to Susanna about was what comes after — the pedestal that gets built around the founders who pass, and what it does to everyone in the room once it’s there.

We started, as many conversations about founder culture eventually do, with The Bear.

Everything Is on Fire

If you haven’t watched it: The Bear is an FX series set in a Chicago restaurant kitchen, built almost entirely out of controlled chaos. The head chef screams. The staff absorbs it. Everyone operates at the pitch of a near-emergency, all the time. I watched the first season and had to stop — it gave me genuine anxiety on top of an already anxious day job. Susanna laughed when I told her that. “It gives everyone anxiety,” she said. “That’s the point.”

What the show gets at, she argues, goes beyond the restaurant world. It’s the logic of urgency running through almost every lean, high-growth startup — the sense that everything is on fire, that the deadline was yesterday, that there’s an enemy closing in. That logic serves a function. “When we’re all in fight-or-flight, we hand over our decision-making to whoever’s at the top. We stop questioning. We stop reflecting.” The urgency doesn’t have to be manufactured consciously. In many cases, the founder genuinely believes it. The effect is the same either way.

The real-world parallel she points to is René Redzepi, the chef behind Noma (linked below)— for years ranked the best restaurant in the world, and recently the subject of a reckoning over the working conditions that produced that ranking. The question she keeps asking about Noma, and about the startups that run on the same logic, is a chicken-and-egg one: does that level of achievement actually require that environment? Or is the environment just what people tell themselves to justify it after the fact? “Nobody is going to die,” she says, “if the product ships three days later.”

Susanna Kislenko

The Fear that surrounds Founder’s Syndrome

Less than two weeks ago, René Redzepi, the founder and head chef of Danish restaurant Noma, the world’s #1 restaurant with three Michelin stars, resigned from his post following allegations of psychological and physical abuse from employees. Since then, a…

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3 months ago · 6 likes · Susanna Kislenko

How to Look Like a Founder

The founder template, as Susanna describes it, isn’t a formal document. But spend enough time in accelerator rooms, pitch competitions, or investor meetings, and you start to feel its edges. Confidence that reads as certainty. Urgency that reads as vision. A particular kind of presence — commanding, unflappable, slightly larger than the room — that makes people believe you’re the right person to bet on.

Elizabeth Holmes didn’t stumble onto that template. She studied it. Specifically, she studied Steve Jobs — the black turtleneck, the reality distortion field, the biographical myth of the singular genius who sees what everyone else can’t. Jobs is, Susanna notes, almost always the reference point. “It was a blueprint she saw, and other people had told her,” she says. “And it worked. For a decade, it worked.”

What interests Susanna is the earlier question: what made the template so easy to inhabit? Holmes was nineteen when she founded Theranos. Susanna doesn’t think she walked in with a fully formed plan to deceive anyone. She thinks the environment met certain traits Holmes already had and fed them — slowly and then all at once — until the performance became the person. “The environment can breed certain things out of a leader,” she says. “It feeds what’s already there. The question is whether the people around you are keeping you grounded — or just watching it happen.”

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The Pedestal — Nobody Can Tell You You're Wrong

That’s the point where the template and the pedestal become the same problem. The template gets you in the room. The pedestal makes it so nobody in the room can tell you you’re wrong. Susanna interviewed board members and founders for a study a couple of years ago, asking both sides what happened when they disagreed. “When a board member told me ‘oh, that never happens,’ it was very clear what was going on,” she says. The most telling signal wasn’t conflict. It was the absence of it.

Theranos had a board member who did push back — someone who had worked at Apple, who saw the problems early and said so. Holmes made him leave. That’s how the pedestal works: it doesn’t prevent dissent so much as it makes dissent unsustainable. You can say the thing once. You probably won’t be there to say it twice.

Different Casting, Same Script

Holmes is an extreme case, but an instructive one. She’s a woman who didn’t adapt the template — she disappeared into it. Susanna argues this is the trap that keeps recurring for female founders. The template doesn’t exclude them outright. It just has one way in.

She’s been watching this play out on screen recently. Serena Williams produced a reality series called The CEO Club, built around female founders. “I really wanted to see alternative models,” Susanna says. What she saw instead was a lot of girlbossing — female leaders performing toughness, performing authority, performing the unflappable certainty the template demands. Different casting, same script.

Susanna Kislenko

All glitz and no soul: why girlbossing is still bossing us

I generally get excited when there is a new show out about women, especially anything documentary-style, especially anything about women building their own thing, especially anything that involves Serena Williams (and, what’s more, is executive produced by her!). I get excited in spite of being disappointed so many times before; I continue to have hope…

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4 months ago · 4 likes · 2 comments · Susanna Kislenko

“Why are our idealised models of female leadership still so male?” The counter-example she reaches for is unexpected. Not a founder, not an investor — Amy Winehouse1. There’s an early talk show interview, Susanna says, where the host asks Winehouse whether the music industry tried to change her image when she first came up, during the peak Britney Spears era of manufactured pop. Winehouse’s answer: they couldn’t, because she already knew who she was. “She came in with her stuff figured out,” Susanna says. “She wasn’t a blank slate.” The template couldn’t find a way in. That kind of groundedness is, Susanna thinks, the strongest protection any founder has — regardless of gender or background. The problem is you can’t manufacture it. You either have it going in, or the environment gets there first.

Susanna has been running retreats (Queen Bee Retreats) for women in leadership for seven years, the whole premise of which is to create enough distance from the day-to-day that people can actually examine the models being handed to them. In those settings, she says, something different tends to emerge — more collaborative, more community-focused. “As long as people put their masks aside. As long as they’re not performing who they’re supposed to be in the outside world.”

The outside world, of course, is still largely run by the template. And the template, as Susanna is careful to point out, isn’t just a cultural habit– it’s structurally reinforced at the funding level. The richest people in the world right now are, with very few exceptions, white men who made their money in tech. They fund what they recognise. And they recognise what they are.

Who Rewrites the Rules

Changing that requires something more specific than putting more diverse voices in investor seats, though that matters too. It requires those people to already have enough of a track record within the existing system to be taken seriously when they push against it. Susanna points to Reese Witherspoon, who built a production company to fund complex female-led stories — from deep enough inside Hollywood that nobody could dismiss her. “She was successful in the system,” Susanna says. “And then she changed it.”

The question she’s sitting with now — the one she’s about to start a formal study on — is whether Founder’s Syndrome looks different in women than in men. Her instinct is yes. It’s not that women are less susceptible. It’s that their version of it may be harder to see. It doesn’t always look like dominance. Sometimes it looks like performance. Sometimes it looks like survival.

But the study she’s most animated about is the investor side. Who sits on the other side of the table, what they’re trained to see, and whether the template is being reproduced or quietly rewritten every time a different kind of person makes a funding decision. I think back to that room at CDL — the investor who intervened, who saw what the others had missed. One person, one moment. Susanna would probably say that’s where it starts.

The template doesn’t change in a press release. It changes when someone who survived the game decides to rewrite the rules.

This article was written by Nessie Chu-Heng Lu, Strategic Marketing Lead @ Founders & Funders

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About Dr. Susanna Kislenko

An internationally recognized expert on founder leadership, Dr. Susanna Kislenko created and directs The Founder Leadership Research Lab at Kellogg College at the University of Oxford. She is also an Adjunct Research Professor at Carleton University. As a social psychologist, Susanna’s research is focused on founder leadership beyond the founding (start-up) stage in all contexts. Using qualitative research methods, Susanna’s work pulls back the curtain on the challenges and potentially darker organizational consequences of long-term founder leadership, including Founder’s Syndrome. Having a long-term interest in how leadership impacts organizations and systems, Susanna holds an IBBA from the Schulich School of Business at York University, an MA in Political Science from McGill University and a PhD in Organizational Behaviour from IESE Business School.

Prior to entering academia, Susanna worked in the non-profit sector in Canada, holding a number of leadership roles in social service organizations. Susanna’s research is driven and motivated by her practical experience and she is always looking for ways to translate research into practice and large-scale systemic shifts.

$15,474. That’s what America spends per person on healthcare - more than double the UK’s ~$6,747 per capita (OECD Health at a Glance, 20251).

For HealthTech founders in the UK and beyond, the gap represents the largest commercial opportunity in global health. But here’s what the statistics don’t show: the vast majority of UK digital health companies fail to capture the opportunity. As an American healthcare executive now embedded in the UK innovation ecosystem, I’ve seen what separates the winners from the cautionary tales.

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The allure is obvious: beyond the spending gap, the US has a fast-paced innovation culture that rewards bold bets. Most digital health startups eventually set their sights on America. But entering the US market can be as challenging as it is attractive. In this article, I’ll share hard-won strategies and data on US market entry to help founders navigate the journey and minimise unwanted surprises.

Why UK HealthTech Founders Continue To Look West

The United States spends more on healthcare than any other nation – an estimated $5.6 trillion was spent in 2025, approximately 18% of Gross Domestic Product (Peterson-KFF Health System Tracker, 20253).

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For UK HealthTech founders, the timing has never been better, or more urgent. The US healthcare system is under unprecedented pressure to reduce costs while improving outcomes, creating openings for innovative solutions. The 2024 expansion of Medicare coverage for digital therapeutics has opened new reimbursement pathways (APA, 20255). Meanwhile, UK companies bring something American startups often lack: evidence of real-world effectiveness within a national health system. NHS validation can be a differentiator if you know how to translate it.

The US healthcare system’s scale (340 million people) and high per-capita spending mean a successful solution can access a much larger revenue pool than in smaller markets. The sheer size of the prize, in terms of funding, customers, and partners, makes the US incredibly attractive for global HealthTech innovators.

3 Key Challenges in the US Market: Culture, Regulation, and Payment

Entering the US healthcare market requires understanding how it fundamentally differs from the UK:

Cultural Nuances: While the US and the UK share a common language, the cultural gulf is wider than one might think. Complex decision-making units are an integral aspect of the sales process when seeking to win over employers, hospitals, and insurance carriers.

I was involved with the sale of a SaaS wellness solution to a leading North American insurance provider. The discussions took 6+ months and involved multiple site visits, including one full-day meeting at our HQ with 15+ executives flying in from 3 different areas of the business. Identifying true budget holders early is critical. Timely follow-ups, explicit asks, and visible momentum are cultural signs of seriousness. For solutions that have been built in the UK, US clinicians may also expect validation on American patient populations, who often behave like consumers, making decisions based on deductibles and co-pays.

The surge of direct-to-consumer healthcare ads during this year’s Super Bowl, from telehealth platforms to prescription weight-loss brands, underscores just how aggressively healthcare is marketed like any other consumer product in the US. Your value proposition must speak to that consumer mindset, not just the clinical efficacy of your solution.

Regulation Pathways: Food and Drug Administration (FDA) approval, or clearance, is often mandatory to commercialise in the US. The process differs from UKCA or CE marking, often requiring more extensive US-specific evidence, particularly for novel AI or digital therapeutics.

But, FDA clearance is only the first gate; it determines whether a product can be marketed, not whether it will be reimbursed. Reimbursement typically depends on coding decisions made by the American Medical Association (AMA), which issues CPT codes, and payment rates set by the Centers for Medicare & Medicaid Services (CMS). Among the 1,350+ AI-enabled medical devices with FDA clearance, very few of these tools are actively reimbursed. In January 2026, Medicare began paying doctors more than $1,000 for the use of AI that analyzes coronary plaque (Palmer, 20266). It’s one of only 3 AI tools that have received a Category I CPT code. UK entrepreneurs should frame their regulatory strategy not just around clearance, but around coding, coverage, post-market evidence generation, liability exposure, and alignment with provider incentives.

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Reimbursement: In the UK, the NHS is often the primary customer, but in the US, new market entrants face a complex payer landscape. Revenue might come from private insurance companies, Medicare / Medicaid (government programs), employer health plans, or patients directly – each with distinct adoption criteria.

Relative Value Units (RVUs) are often how physicians get paid under fee-for-service healthcare, and products that protect or increase RVU capture tend to gain faster adoption. The rapid uptake of ambient AI documentation tools like Nuance’s DAX and Abridge illustrates this: by automating note generation, they allow physicians to reduce admin time and see more patients, creating a clear financial return.

Strategies for Successful US Market Entry

Plan Early for FDA and Clinical Evidence: Map out your FDA regulatory pathway as early as possible. Engage via programs like Breakthrough Device Designation or informal pre-submission meetings to clarify requirements. Be ready to conduct US-based clinical trials or real-world studies. US regulators and customers will expect evidence that your solution works for American patients.

Cambridge-based CMR Surgical is a strong example. After receiving CE mark approval in 2019, they built substantial international evidence with 26,000+ procedures across Europe, Latin America, Asia, the Middle East, and Africa (CMR Surgical, 20248). In October 2024, CMR’s product, the Versius Surgical Robotic System, became the first multi-port soft tissue robotic device to gain FDA de novo clearance, followed by 510(k) clearance for the Versius Plus in December 2025 (Hale, 20259). The company had raised approximately $1 billion, including a $600 million round in 2021, to support this expansion.

Leverage Pilot Programs and Beachheads: Many UK startups enter the US by first securing a pilot contract with a single reputable US institution. For instance, you might join an American accelerator that gives you access to healthcare provider networks.

Programs like the ABHI US Accelerator offer mentorship and introductions, and diaspora networks (e.g., Americans in Oxford or UK founders who have expanded stateside) can be a valuable source of support. Kneu Health, an OSE portfolio company, found that participation in the Cedars-Sinai Accelerator provided direct access to clinicians, pilot opportunities, and guidance on market complexity. A strong beachhead market, or key partner, lets you refine your product-market fit and signals commitment to the market.

That said, beware of the “pilot purgatory” trap. Set clear success metrics upfront: including what triggers a commercial contract, the timeline for conversion, and who holds budget authority.

Understand the Payment Model & Your Value Proposition: Frame your solution around cost savings or revenue gains for providers.

If your product reduces hospital readmissions, quantify the cost savings under US reimbursement codes. If it improves outcomes, show how it helps a clinic perform under value-based care programs. Health economic analyses that translate UK success into US dollars saved can be persuasive.

Build Local Team and Partnerships: Hire a US-based lead or advisor who has navigated healthcare commercialisation before. Their network can open doors with health system executives or payer innovation teams.

You’ll also need practical infrastructure: a US entity (typically a Delaware C-corp for venture capital compatibility), and key hires will require appropriate visas, such as L-1 for intracompany transfers or O-1 for “extraordinary ability.” Establishing a local entity also simplifies equity grants for hiring senior executives, tax treatment, contracting with payers and providers, and investor due diligence. Many UK companies stumble by trying to operate from London for too long. Without decision-makers on the ground, it is difficult to build relationships, close enterprise deals, or recruit top-tier commercial talent.

Be Ready to Scale and Adapt: Initial growth in the US should be viewed from a hyper-localised perspective in terms of geography, patient populations, and the unique needs of providers and payers. If your US beachhead goes well, be prepared to scale up quickly. The US market can amplify demand overnight, so ensure you have the operational capacity to meet growing US demand.

Also, stay ready to iterate: integrating with Epic or Cerner Electronic Health Records is often critical for hospital-facing digital health tools, and the US sub-market can have distinct needs.

Final Thoughts

Breaking into the US healthcare market demands patience, investment, and adaptation. But, it can also serve as a launchpad to transform a UK HealthTech startup into a global leader. America’s healthcare system, for all its complexities, rewards innovation that can improve outcomes or reduce costs at scale, and UK founders bring world-class science and resourcefulness from operating under NHS constraints.

Treat US market entry not as an afterthought, but as a core part of your strategic bet: gather data, talk to experts, and perhaps most importantly, build relationships on the ground. The opportunity is real, but so are the challenges. Success comes to those who prepare thoroughly and adapt quickly.

This article was edited by Nessie Chu-Heng Lu, Strategic Marketing Lead @

About the Author - Benny Axt

Benny is Oxford Science Enterprise’s (OSE) first Entrepreneur-In-Residence (EIR) to join from the US, bringing more than 15 years of experience in healthcare strategy, operations, and international expansion. He has lived and worked in healthcare systems on 6 continents, supporting providers, payers, regulators, and patients to improve access, outcomes, and economics. As an EIR, he’s on a mission to identify and ultimately found a new high-impact venture that translates Oxford’s cutting-edge science into global healthcare impact.

Previously, Benny served as Vice President of Strategy at Dialogue Health Technologies Inc., where he supported US market entry and helped grow the company from a venture-backed startup to IPO and acquisition by Sun Life. He also held leadership roles at DaVita, a Fortune 500 healthcare provider, and has advised multiple early-stage digital health startups as a consultant and board advisor.

Benny holds a Master of Health Care Delivery Science from Dartmouth College, an MBA from Thunderbird School of Global Management, and a BA from the University of Wisconsin-Madison.

NVIDIA is committing £2 billion to AI startups from Oxford and across the UK1

When Jensen Huang, NVIDIA’s founder, stood alongside Prime Minister Keir Starmer in September 2025, he framed it as a historic moment:

“This is the age of AI — the big bang of a new industrial revolution.”2

Huang described “the largest AI infrastructure investment the UK has ever seen”, including new supercomputing capacity and expanded support for British startups. For Oxford, and the UK more broadly, this announcement was both a rallying cry and a reminder of how much is at stake.

And it’s not just NVIDIA. Larry Ellison, Oracle’s billionaire founder, is also betting big on Oxford3. Through the Ellison Institute of Technology (EIT), he has committed over £100 million to joint research, infrastructure, and scholarships, is building a billion-pound Oxford campus by 2027, and has already launched a £118 million AI-vaccine initiative with Oxford’s Vaccine Group. Ellison has even taken a significant stake in Oxford Nanopore, one of the university’s most high-profile spinouts. Moreover, the recent news of EIT’s planned £10bn investment over the next 10 years into Oxford was applauded by the Chancellor of the Exchequer, Rachel Reeves, and Oxford’s Vice-Chancellor, Professor Irene Tracey.

These headline-grabbing moves signal something larger: Oxford is no longer just a historic university town. It’s one of the front lines in the next industrial revolution.

Oxford’s Engine in Motion

Despite successes like DeepMind, Isomorphic Labs, and Helsing, scaling world-class AI companies in the UK has been notoriously hard — limited compute, patchy venture capital outside London, rising energy costs, and uneven support for founders spinning out of universities like Oxford.

Yet Oxford’s own numbers show its innovation engine is firing. According to Oxford University Innovation (OUI)4, between August 2023 and July 2024 the university raised £19.5m in seed funding and £872m in follow-on investment, launched 15 new companies (10 spinouts, two social ventures, three startups), filed 93 patents, and struck a record 1,239 licensing deals across sectors from HealthTech to clean energy.

The story doesn’t stop at Oxford’s gates. PitchBook ranked Oxford the #1 European university for raising VC among undergraduate alumni founders5 — think Monzo’s Tom Blomfield or Onfido’s Hussayn Kassai. Postgraduate alumni kept Oxford on top again, raising $29.9 billion in 2023, edging past Cambridge’s $29.3 billion. Globally, Oxford sits 8th for producing startup founders, just behind Cambridge (7th) — the only two non-U.S. universities in the top 10.

(Source: Sifted, “Which European universities boast the best-funded founders?”)

Patents, Spinouts, and Startups

The IP story is equally striking. A study by the German Economic Institute6 ranks Oxford the highest non-U.S. university (7th worldwide) for international patent filings. Licensing revenues tell a similar story: £145m in 2023, supercharged by the AstraZeneca COVID-19 vaccine license.

In deep tech, Oxford continues to dominate. According to the Royal Academy of Engineering’s Spotlight on Spinouts 20257, Oxford leads the UK with 225 spinouts as of January 2025, ahead of Cambridge (175) and Imperial (132). Together, the Golden Triangle (Oxford, Cambridge, London) accounts for 27.7% of all UK spinouts.

Startups show the same pattern. PitchBook reports that UK universities make up 80% of the top 10 institutions for undergraduate alumni founders in Europe8. Oxford undergraduates lead with 286 founders, while Cambridge edges ahead at the graduate level. Yet Oxford still claims a strong second, with 981 postgraduate alumni founders recorded through 2023.

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The Oxford Paradox

But here’s the twist.

Over coffee a few years back, a founder sighed: “Oxford is both the best place and the hardest place to build a company.”

Her frustration is backed by data. Research by Oxford academics9 (Hellmann, Mulla & Qian, 2025) shows the university has trimmed its equity stake in spinouts from ~47% in 2010 to ~17% in 2021 (close to the Spotlight Report’s 20.8% average). Yet it still takes nearly twice as much as Cambridge (10.8%).

That gap reflects deeper differences in IP policy. Oxford claims ownership of most staff and student IP under its Statutes. Cambridge allows researchers to opt out, and students generally keep their IP unless tied to employment or specific funding.

The implications for founders are twofold:

1. Dilution — A 20%+ university stake means less equity left for the people building the company.

2. Investor concerns — VCs worry about “founder incentive misalignment.” Every 1% increase in university stake reduces the chance of raising VC by 0.8–0.9%, and spinout formation by 0.6–0.8%.

On the one hand, Oxford’s brand is a magnet — investors trust the pipeline of ideas. On the other, the very structure of its deals risks slowing down company creation.

By contrast, Cambridge looks more like the big U.S. powerhouses. Universities like MIT (~5%) and Stanford (low single digits) take only a sliver, aligning incentives for both founders and investors. It’s hard not to wonder how many more companies might come out of Oxford if its equity model looked more like MIT’s.

(Recently, Equinox launched to build a more connected and equitable innovation ecosystem across Oxford and Cambridge. Source: Cherwell)

Capital Firepower

Oxford has leaned heavily on Oxford Science Enterprises (OSE), which helped lift spinout activity from just 4–5 a year to 15. But questions remain: does Oxford have enough firepower to compete globally?

Cambridge launched Cambridge Innovation Capital (CIC), a £50m fund designed to back IP and founders with a hybrid model that blends returns with ecosystem building. Oxford has no equivalent.

The need is pressing. In a submission to the UK Parliament’s Science and Technology Committee (Jan 2025), Oxford estimated its spinouts will require £2–5 billion over the next five years, particularly in capital-intensive fields like quantum, climate mitigation, and late-stage scale-ups.

AI as the New Industrial Revolution

But capital alone isn’t the story.

At the centre of this transition are the founders themselves — the scientists-turned-entrepreneurs, the postdocs coding AI models at 2 a.m., the clinicians pushing biotech out of labs and into trials.

Ever since the release of ChatGPT in late 2022, the innovation landscape has been rewritten. What was once a slow, technical march of AI research suddenly leapt into public consciousness. For universities, startups, and investors, it unlocked dizzying possibilities but also thorny challenges: new markets opening overnight, fierce competition, ethical dilemmas, and an arms race for talent and compute.

Whether we fancy it or not, AI now underpins everything from finance to health to national security. Europe excels in deep-tech research (2025 European Deep Tech Report10), but it lags the global AI race.

The U.S. and China lead with unified markets, capital, national AI strategies, and massive GPU clusters. AI has become the connective tissue across deep-tech — accelerating drug discovery, reshaping climate modelling, supercharging materials research. Europe’s limited access to infrastructure and capital only widens the perceived innovation gap.

Oxford sits right at this fault line. Its 225 spinouts are part of Europe’s attempt to anchor the deep-tech transition in an AI-driven era.

The Billion-Pound Question

Collaboration becomes ever more important in Oxford’s innovation ecosystem. Founders may carry the torch, but their success depends on a whole constellation of allies — mentors, investors, policymakers, and peers. The question isn’t just how many spinouts Oxford can produce, but whether the community and ecosystem around them can give founders the lift to compete globally.

This means tighter links between labs, capital, and market, but also bridges outward — to London VCs, European partners, U.S. and Asian markets, and policymakers shaping the rules of AI.

And this brings us back to NVIDIA and Larry Ellison. Their billions directly address Oxford’s biggest pain points: compute, infrastructure, and early-stage capital. Yet their investment doesn’t erase the deeper structural challenges — high equity stakes, fragmented markets, and the absence of scale-up funding.

If Oxford is to anchor Europe’s role in the new innovation era — an era reshaped daily by advances in AI, shifting geopolitics, and technological upheaval — it cannot go it alone.

(Source: Oxford University)