I asked a senior tech transfer officer at a leading research university what happens to the inventions that are useful but not fundable. The ones that are promising, sometimes even patented, but unlikely to become startups. Where do they end up?

He didn’t have to think about it. “There’s a huge number of our technologies that are seeking licensee,” he said, putting air quotes around that, “and they just kind of sit there.”

“Does that feel like a problem?”

“It’s definitely an elephant in the room.”

He had been thinking about it. Maybe you change the model. Package some of the unlicensed ones together. Give a large company rights to a group of things for relatively cheap, just to generate interest. “Historically, we’ve resisted that,” he said. “We don’t want it to be shelved. But if something’s sitting and not being licensed for twenty years, it is being shelved.”

It would be easy to assume these inventions are simply overlooked in the volume of university research. But that isn’t quite right. In reality, each one gets evaluated, protected to some extent, maybe lightly marketed, and occasionally, licensed. Tech transfer offices are reasonably good at the front end. What they don’t do, and aren’t organized to do, is take coordinated ownership of the ones that don’t become startups and move them forward.

A rapid point-of-care test for lupus, requiring no lab equipment, for a disease that takes an average of six years to diagnose. A portable biopsy device for settings without fully equipped hospitals; the kind of tool that could transform cancer detection in places that currently have none. A wearable device for continuous pain monitoring in patients who struggle to self-report, such as people with dementia, or intubated patients, or young children. A soft exoskeleton for the partial ankle paralysis that affects roughly two million Americans after stroke, for which the current standard of care is a rigid plastic brace that’s been on the market for decades. An ultrasound gel formulation that stays warm, because the current one doesn’t, and everyone who has ever had an ultrasound knows exactly what that means.

These inventions have almost nothing in common except that the current system has no idea what to do with them. Each lacks an owner, someone responsible for deciding what to do next and making it so. What evidence is sufficient, what vendors to hire, which clinical community to organize around, how much and what kind of capital belongs behind it, and whether it should be licensed, piloted, built directly, or sold.

The startup became the default response to this problem for a reason. It gives an invention a clear owner. Traditionally, that means handing a small group of people a bundle of capital, decision-making authority, risk and day-to-day responsibility, and telling them: Yours now. Move it forward. When the market’s large enough and the idea has an attractive enough return profile, that works exactly as intended.

But it’s a mistake to treat this useful organizational solution as a universal one. For inventions that can’t justify a venture-backed company around them, this ownership function never materializes. When the tech transfer office is done with its patenting, cataloging, and profiling in the university newsletter, no one is there to take it forward.

A startup is just one answer to the coordination problem surrounding an invention. There are others. Licensing arrangements built around active development rather than passive marketing. Product studios that take inventions through to commercialization. Disease foundations or professional societies sponsoring development of the products their members need. Funds capitalized through royalties rather than equity. None of these are exotic; they exist in adjacent sectors. But they rarely show up in healthtech, where the startup default has been so dominant that it has left them largely unexplored.

For most of these inventions, the ownership model I find most promising is a portfolio. A portfolio runs multiple programs through shared infrastructure, so what one figures out becomes available to the next.

It mixes programs that are close to market—with simpler regulatory paths, existing reimbursement, or buyers already identified—alongside others that are earlier, slower, and more uncertain. The faster ones generate revenue that subsidizes the longer ones. No single program has to carry itself, as long as the portfolio does. This is what allows the portfolio to work without the fiction that any one program has to be a billion-dollar opportunity. The ultrasound gel and the soft exoskeleton don’t survive in the venture market as standalone bets. They can exist, and productively, as part of a portfolio.

That mix extends beyond stage. The optimal portfolio wouldn’t be limited to medical devices, but would draw from adjacent categories such as lab tools, training devices, and consumer applications. Different timelines, regulatory burdens, and buyers mean progress in one area isn’t undercut by delays in another.

In this kind of organization, most programs are held and operated rather than sold. Some can be out-licensed or exited when the right fit and buyer turn up, but that’s not the organizing objective. Instead, what builds up over time is the operator’s ability to move products through the same predictable bottlenecks: prototyping, vendor selection, regulatory strategy, reimbursement judgment, buyer access. The next program starts cheaper and moves faster than the one before it, and that accumulated velocity is the asset.[1]

Within an organization like this, ownership doesn’t need to claim the life of the inventor or require recruiting a founder to build a company around a single asset. It can live with an operating entity whose job is to decide which inventions to adopt, which to move forward, which to stop, what infrastructure to share, what evidence is sufficient, and what buyers matter.

That opens up roles for faculty inventors that the current system doesn’t have. The startup-per-invention model effectively asks them to take on second jobs—careers, really—finding time they don’t have to acquire new skills, and spending nights and weekends on cap tables and pitch decks instead of the problems they actually understand. Many decline, and reasonably so. A portfolio offers something closer to what many of them want: a meaningful stake in the outcome, a part-time advisory role organized around their domain expertise and clinical network, and someone else doing the operational work. The scientific judgment that produced the invention stays in the program. The inventor stays in their lab, their clinic, their classroom.

A portfolio also creates a place for a kind of talent that healthtech underuses. Scientifically trained people who may not want academic careers but can learn commercialization by working across programs. Regulatory strategy, vendor management, and reimbursement are teachable. Scientific judgment, comfort with ambiguity, and curiosity about clinical problems are harder to acquire late. A portfolio gives junior experts a way to build those skills without pretending that each invention needs a founder-CEO.[2]

How the portfolio is organized matters as much as what it holds. Get the shape wrong and the overhead you meant to avoid reassembles itself one layer up. Too many buyer types, sales motions, and regulatory environments to manage at once.

Better to organize around a product category—diagnostics, surgical tools, training aids—where programs share buyers and commercial infrastructure across very different clinical applications. Or around a clinical pathway, where the same clinicians see the patient through every product and commercial relationships compound rather than being rebuilt each time. Or around a site of care. Infusion centers. Physical therapist offices. Pharmacies. Each a buyer community small enough to reach and large enough to matter.

No single university catalog is likely to contain enough mutually relevant inventions in a given domain to make the portfolio logic work. The portfolio has to draw from many. The right inventions from wherever they are. Which is partly why no university has built one.

A startup has it easier here. One product, one market, one organization that can orient around making them fit. A portfolio has neither luxury. It has to bundle products that share enough infrastructure to be worth holding together, and it has to do market-making at the level of the portfolio or the clinical community, because no single product has the gravity to pull a market on its own.

One way the portfolio can make progress on both is to work closely with professional societies. For the clinical-facing products that live inside medical workflows, society members encounter the relevant problems every day, write them up as unmet needs in journal editorials, and argue about them at conferences. The right partnership puts that knowledge to work on both sides of the effort, helping decide which inventions to take on and bringing them to the people who will eventually use them.[3]

A portfolio and a society can collaborate on a target product profile (TPP), a specification of what a product needs to do to be worth using (”a pediatric ventilator for low-resource settings needs to do X, Y, Z”). TPPs are a standard discipline in drug development, defined at the start of a program, tested against the market, and used to guide development. A society can do something similar, naming what a useful product would look like in a clinical area its members know well and letting the operating entity develop toward that spec. Any product that meets it benefits, without the society picking a winner. The TPP also makes it easier to distinguish between an improvement that matters clinically and one that just looks good on a slide.

Such a partnership can also anchor advance commitments from health systems or payers before a product is finished, turning “we think there’s a market” into “a market has already said yes.” It can pull users into design early enough that usability and workflow are worked out before launch rather than after. These partnerships take care to set up. A society endorsing the wrong product compromises both the society and the product, so the cleanest arrangements keep the society setting standards rather than picking products. Other configurations work too.

Some inventions in the catalogs are both too large for a portfolio and not good candidates for venture. Their science may be more ambitious, their timelines longer, or the capital required on a different level. A targeted antibody for a treatment-resistant infection, for example, or a diagnostic platform for neglected tropical diseases that requires multi-site validation across continents before anyone can know whether it works at scale. What exists for these kinds of projects?

Convergent Research, a nonprofit founded in 2021, builds what it calls Focused Research Organizations, time-bound teams assembled around specific scientific problems that are technically solvable and clearly important, but going nowhere. The obstacle is usually not the science but the lack of an institution organized to pursue it. Anastasia Gamick, one of Convergent’s founders, points to antibiotic resistance, which causes over a million deaths a year, as a health example that fits the pattern. The science continues to progress, but the development pipeline has narrowed for two decades. Today, almost no institution will absorb the coordination costs. No one has yet tried an FRO for it.

Renaissance Philanthropy designs and funds programs at the edge of what traditional philanthropy will touch, structuring returns through royalties and milestones rather than grants or equity. Its Buffalo Initiative focuses on ultra-rare diseases—conditions affecting only a few thousand patients—where families have often spent years organizing and funding research themselves. Here the problem is market failure rather than coordination. Opportunities too small for venture but too commercial for philanthropy. The Initiative matches capital to a ten-year development horizon and assembles a coordinating team to do the work the market has abandoned. As Sarah Constantin put it, “For a major new project to happen, it needs to be somebody’s job to make it happen. Several somebodies, really, at this scale.”[4] Healthtech has no equivalent organization that I know of. Without models like this, many university inventions that could help small but important patient populations, over long timelines, have nowhere obvious to go.

Most of what’s in tech transfer catalogs has the opposite problem. The inventions are individually too small for venture. That’s fine. The portfolio is built to hold programs that work, not to exit them, and the capital that fits it is designed to deliver debt-like returns rather than venture ones. Family offices with healthcare conviction. Foundation program-related investments, a mechanism that’s legally well-established and underused. Synthetic royalties, which have matured quickly in pharma and have no structural reason they couldn’t fund programs like these. Clinical communities organizing as their own funders, the CF Foundation being the clearest precedent. Vendors willing to take a share of future program revenue in exchange for deferred fees. None alone is sufficient. Combined, they can capitalize a portfolio at the pace its products require and generate returns that look attractive against the alternatives those investors have.

What matters as much as where the capital comes from is what it expects. The current system gives a program a year or two to show venture-scale traction, and when it can’t, it dies between seed and Series A regardless of whether the product was working. Fund a portfolio on the timeline its programs actually need, and more inventions survive.

The conditions for trying this are unusually favorable. Products that once required years and dedicated engineering teams to prototype can now be designed, improved, and tested in months. Contract manufacturers will run small batches that used to be uneconomical. Regulatory consultants who spent careers inside FDA have built practices around smaller, faster programs. The agencies are moving that way too.[5] The fixed costs that made building a full company the only viable path have come down far enough that it’s no longer the only option. Some of what sits in tech transfer catalogs was too expensive to attempt five years ago and isn’t now. No one has gone back to look.

Each invention in a university catalog is effectively a side quest. The physician who watched patients suffer through a diagnostic odyssey and spent nights designing a faster test. The engineer who sat in an ICU long enough to see the same preventable complication happen twice. The proof of concept may need another iteration, or the science more work, but the challenge is real. These are records of what someone close enough to feel it noticed and tried to fix, and they’ve already done more work than the system gives them credit for.

Most of that work now goes nowhere. The expert insight, the creative intelligence, the commitment to a possibility nobody asked them to chase. It gets disclosed, reviewed and patented, indexed and published, and quietly filed in catalogs almost no one browses. These aren’t backlogs of failed ideas. Most of what’s in them never had a real chance. They’re an ever-lengthening list of unowned opportunities, waiting for someone willing to take them on.

I’ve started building a version of this and will write here as it develops. If you’re a faculty inventor with something working (or nearly) and no interest in building a company around it, an investor interested in funding portfolios of smaller, real products, or an operator who wants to do this kind of work, I’d like to hear from you. A short note with context is perfect. Email me at dvansickle@gmail.com.

Footnotes

[1] Sometimes the right answer is even simpler. Some inventions just need someone to pick them up and build a small, profitable business around them. That mindset has mostly gone missing from the university commercialization conversation, which tends to treat anything smaller than a venture-scale company as a failure of ambition.

[2] The Rice University Innovation Fellows program has been running a version of this for five years, training PhD students and postdocs to lead commercialization of research from their own labs. Over half of their fellows are working on therapeutics, devices, or other health applications.

[3] In 2024, the American Thoracic Society surveyed 164 pulmonary researchers and found most reported significant gaps in business training, few had engaged effectively with their tech transfer offices, and many lacked access to commercialization resources. The working group called for structured external support organized around the investigator. See Vukmirovic M, Benam KH, Rose JJ, et al. “Challenges and Opportunities for Commercializing Technologies in the Pulmonary Arena: An Official American Thoracic Society Report.” Annals of the American Thoracic Society 2024;21(1):1–11. doi:10.1513/AnnalsATS.202310-872ST

[4] Sarah Constantin, “The Dream Machine,” Rough Diamonds (Substack), December 4, 2024.

[5] In April 2026, CMS and FDA announced the RAPID coverage pathway, designed to compress the gap between FDA authorization and Medicare national coverage from roughly a year to as few as two months. Eligibility is restricted to FDA-designated Breakthrough Devices, which excludes most of what's in tech transfer catalogs. But it's a meaningful signal that the reimbursement timeline may not be as fixed as it’s appeared. See "CMS and FDA Announce RAPID Coverage Pathway to Accelerate Patient Access to Life-Changing Medical Devices," FDA News Release, April 23, 2026.

One surprising thing about life sciences investing is how vividly it confronts you with the many ways the human body can fail. In practical terms, that means many of the companies you see are developing treatments for grave diseases that are far from household names.

The other surprising thing is how tiny the teams can be.

The small markets are not the mystery. Neither are the small teams. A drug for thirty thousand people can still command a price that makes the math work, through orphan-drug incentives, pricing power, and the rest of the apparatus behind it. And a five-person team can move a program forward by contracting with organizations running the same sequence of milestones for dozens of others at once. Humanity has, in effect, industrialized its response to some of those thousand doors. The machinery exists; you just have to run it. The real puzzle is how drug development became one of the few places where a clinically valuable asset can be advanced without building a full company around it.

Now compare that with companies building devices, diagnostics, decision-support software, or research equipment. The underlying situation is usually similar. There is a promising invention, a credible clinical case, and a real patient need. Some supporting pieces exist, including contract manufacturers, regulatory consultants, and the occasional specialized CRO. But the vendor system is thin and fragmented, organized around too many different development routes to generate the concentrated demand that makes shared infrastructure self-sustaining. Downstream, the pull is weaker too. There are fewer large acquirers with business-development functions built to value and absorb assets in development, and fewer investors who know how to price a milestone they have seen a hundred times before. Each team is advancing a program by hand that looks as if it should work, through infrastructure that almost exists, toward a market not quite ready to receive it. “Come back when you have FDA clearance,” say investors. “Come back when you have revenue,” acquirers add. It can be a grinding, undercapitalized march toward a finish line that keeps moving.

Assume drug developers are not smarter or more disciplined. What created the conditions for a different organizational form to emerge, and how did that arrangement become so self-reinforcing that it came to seem natural?

Nobody planned it. The FDA organized drug review a certain way, for its own reasons, and the result was that suddenly everyone needed the same kinds of work done at the same stages (ie, IND, Phase 1, Phase 2, Phase 3, NDA) across hundreds of simultaneous programs. Predictable demand at predictable stages is what makes shared infrastructure economically viable, and the FDA created it without intending to.

Contract research organizations grew up to run preclinical studies and clinical trials because enough drug programs needed those services at those stages. Contract development and manufacturing organizations grew up to handle process development and scale-up for the same reason. Regulatory consultants who had shepherded dozens of IND applications became available by the hour. The fixed costs of world-class science (eg, equipment, expertise, facilities) could be spread across enough clients that each paid only for the work actually done.

That infrastructure wasn’t designed to enable lean drug companies. It emerged because the demand was there, and then made them possible as a side effect. The virtual pharma model — in its purest form, a company with no employees that converts capital directly into development work — did not precede the infrastructure. Nobody sat down in 1985 and decided to build a system that would let a tiny company advance a billion-dollar program. That approach only followed once the supporting infrastructure existed.

Ryan Avent, speaking about his time as a senior editor at The Economist, described his firm as not so much a business but as “a way of doing things consisting of an enormous set of processes. You run that programme, and you get a weekly magazine at the end of it.”[1] Drug development works much the same way. Run the program, and, if the bets are right, you get a drug. The company is almost incidental. What matters is whether the process is managed well enough to carry the asset forward.

A virtual pharma company is a handful of people and a lot of vendor contracts. That can look like a cost-cutting measure. It is really an answer to a more specific organizational question about how little structure an asset actually requires. In this case, the asset is a drug program — defined by its IP, development data, and regulatory route — with value independent of the organization built around it. It can become valuable long before the company does. It can be sold at almost any stage (eg, post-preclinical, post-Phase 1, post-Phase 2) as it accumulates evidence and value. Those exits do not require a complete company, just a credible asset and a minimal coordination layer.

The work itself is narrower than you might think. It sets direction, especially around indication and endpoints. It manages milestones and makes go or no-go decisions as results arrive. It selects and manages vendors. It talks to potential acquirers early, sometimes very early, to understand what an eventual buyer will need to see. And it allocates capital with enough discipline to reach the next decision point without building organizational overhead that will outlive its usefulness. What it does not do is equally important: employ bench scientists, operate manufacturing facilities, own equipment, or run clinical trials in-house. A Phase 2 biotech might have a handful of people.

This works in practice because the people who run these programs share a professional culture as much as a skill set. There is a cadre of experienced drug developers — scientists and operators who have worked across multiple programs, in startups and in industry — who can assemble around a new asset and be productive almost immediately. Everyone knows their role and expects their colleagues to know theirs; they speak an occasionally arcane lingua franca. The playbook is embodied in the people, not just the infrastructure.[3]

At the other end, large pharmaceutical companies maintain business-development functions whose job is to value and absorb mid-development assets. They can look at a Phase 2 program, understand what it is worth, and move quickly. That acquisition readiness is part of what makes the early-exit model work. In medtech, the function is weaker and less systematic, which is one reason devices don’t have the equivalent early-exit window.

Virtual pharma shows that advancing a clinically valuable asset does not require building a full company in the traditional sense. What it requires is the right coordination layer and the shared infrastructure to support it. Healthtech has neither, in the concentrated, self-sustaining form that model depends on, and not by accident.

Medical devices don't move through a single development route. There is no device equivalent of Phase 1, 2, and 3. A PMA, reserved for novel high-risk devices, might seem closest to what drugs go through, but the FDA doesn’t impose a standardized sequence. Two PMA programs can look completely different from each other. Specialists exist (eg, device CROs, regulatory consultants, contract manufacturers) but they are fewer and less specialized than their drug development counterparts, organized around too many different configurations to generate the same concentrated demand. The consequence is that device companies have to internalize more of the work that drug developers can contract out. The company grows to fill the gap the infrastructure can't supply. In that sense, every device startup is partly a workaround.

Reimbursement makes the problem worse in a specific way. For drugs, the route from approval to patient access is frustrating but at least roughly linear. For devices, the commercial path is often a separate maze: bundled payments, facility fees, professional fees, distinct coding for the device and the procedure, Medicare coverage that varies by administrative contractor, payer-by-payer contracting with commercial insurers. A 2023 study found that among novel devices and diagnostics requiring a new Medicare reimbursement route, the median time to achieve at least nominal coverage was 5.7 years after FDA authorization, longer, on average, than it took to get FDA clearance in the first place. More than half were still waiting when the study was published. That delay keeps asset value low until very late in development, which closes the early-exit window that virtual pharma depends on.[4]

But the stranded innovations in university tech transfer offices are not just devices. Some are diagnostics, which may have access to contract labs but not to anything like the combined clinical, regulatory, and reimbursement support that drug developers can buy off the shelf. Some are lab equipment and research tools, which may be manufacturable on contract but still have to build quality systems, field service, and customer support for themselves. Some are research software and algorithms, for which there is barely a specialist ecosystem at all. Others are lower-risk products — training aids, monitoring tools, consumer health applications — that face no major regulatory gauntlet but still have to figure out commercialization on their own. The details differ, but the pattern is the same. There is too little volume in any one category to support shared infrastructure, and no organization willing to take responsibility across categories. So each new program ends up building its own solution, not because the people involved are unsophisticated, but because no one has made it their job to assemble what already exists.

Novonate began inside Stanford’s Biodesign Innovation program, where a team spent years making prototypes and gathering clinical feedback before spinning out. They had a specific and well-documented problem in sight. Umbilical catheters in neonatal intensive care units were secured with tape and adhesive, a method associated with high infection rates and “catheter migration” in one-third to one-half of cases. The solution was LifeBubble, a dome-like device that gave nurses a consistent, reliable way to secure the catheter. It went through dozens of design iterations before it was ready for market.[5]

NICUs are not a venture-scale market. A few hundred high-acuity units in the United States, a patient population most investors dismiss. But the concentrated customer base that looked like a liability turned out to be a feature; the entire market was reachable with a small commercial effort. And the team had asset discipline, letting what the product needed determine everything else, without accumulating organizational overhead the asset didn’t require. Small raises, surrogate endpoints instead of expensive pivotal trials, a path to commercial viability that stayed visible throughout. CEO Eric Chehab recalls: “We felt like we might have a shot to make the economics work.” Novonate was acquired by Laborie Medical Technologies in February 2023.

Not a unicorn. As James Wall, a Stanford pediatric surgeon who advised the company, put it: “No one’s retiring off this technology or off this company.” But it was a successful outcome that returned value to investors and put a product in the hands of nurses who needed it. What Novonate doesn’t tell us is what to do when the asset can’t reach a natural acquirer and the early-exit window never opens.

That was exactly the problem Duke Rohlen set out to solve. After a career of building and selling medtech companies at unusual speed, he concluded that the early-exit model that therapeutics relies on simply does not work in medtech. Rohlen explains:

“Single product companies are highly risky...Single product companies are inefficient...Single product companies are beholden to a buyer universe that has all the power.”

So he went in the other direction entirely.

Maverix Medical, formed with backing from KKR and Hologic, is building a portfolio of diagnostics and devices across the lung-cancer care pathway, from early detection through biopsy and interventional technology. It acquires assets where they exist and develops them where they don’t, using Hologic’s diagnostic capabilities and commercial infrastructure and KKR’s capital as supporting resources. The exit is not the standalone sale of any one product but the clinical and commercial value of an integrated lung-cancer platform.[6]

This is not virtual pharma. It is a different organizational answer to the same structural problem of how to advance assets when the environment is too harsh for them to survive on their own. Rohlen’s answer was to expand the unit of organization from the product to the clinical domain. Asset discipline — the same logic Novonate applied to a single product — now operates across a portfolio, with infrastructure, relationships, and institutional knowledge accumulating across programs rather than being rebuilt for each one. Rohlen had the track record and the capital relationships to organize at that scale. Not everyone does. Maverix demonstrates that the coordination logic is portable, even if the organizational form has to change entirely.

The Cystic Fibrosis Foundation didn’t have a commercial rationale or a strategic backer. It had a disease community watching the median survival age sit at 32, and an impatient president, Robert Beall, who believed that “we have a responsibility for our patients’ destiny.”[7] Here is Beall on why the Foundation had to act:

“We had the gene. We had some targets. We understood the basic underlying defect... All these things could come together in a test tube, but it was taking academic scientists too long to do it. We had to accelerate the pace and bring industry in to that process, which meant we had to de-risk it. We had to take the early risk to draw them in.”

Beall again: “I approached several groups. Not many people returned my phone call.”

So the Foundation supplied patient capital, took on the coordination role the market had abandoned, and organized the effort around a defined clinical need rather than market size. The result was the first genuinely effective CF treatments and, eventually, a royalty stream that returned $3.3 billion to the Foundation when it monetized its Vertex-related royalties in 2014, funding further research.

The CF Foundation model is not virtual pharma either. CF affects roughly forty thousand Americans, which meant the usual commercial model was not available: the patient population was too small, the commercial interest too weak. The coordinating role that shared infrastructure usually plays in drug development was not going to emerge on its own. The Foundation stepped in, bringing together the clinical knowledge, development resources, and capital the market would not have assembled by itself. Capital, development strategy, and execution all mattered, but they were downstream of a more basic decision. Someone had to decide it was their job to make this happen, whether or not the market agreed it should.

Three teams, working in unrelated clinical domains with resources that aren’t comparable, each found a different way to supply what the standard model could not. That work can be lean when asset discipline is in place and the market is contained enough to reach. It can expand to the level of the broader clinical problem when no single product can bear the economics alone. It can be supplied by a stakeholder organization willing to act as coordinator of last resort.

Novonate worked because a particular team stayed disciplined and found a natural home in Laborie’s portfolio, a company organized around urology and gastroenterology workflows and built to acquire and commercialize exactly this kind of asset. Maverix works because Rohlen had the track record and the capital relationships to focus on an entire clinical domain. The CF Foundation worked because a disease community had both the motivation and the organizational capacity to take on the role the market had walked away from. None of these examples produced a replicable system. Each depended on people who saw the gap and had the means to do something about it.

Virtual pharma is an accidental demonstration of what happens when transaction costs fall far enough: you stop needing discrete companies and start needing coordination that the market won’t supply on its own. Healthtech keeps building companies around inventions not because companies are the right answer, but because no other credible model exists. Similar, improvised solutions keep appearing elsewhere, but only as one-offs. A repeatable alternative only becomes possible when you stop organizing around the company and start organizing around the output.

For the inventions this series has been describing — too small for venture, too useful to abandon, too numerous to keep treating as exceptions — that’s the problem. The pieces exist in healthtech. What’s missing is an organization whose job is to assemble them.

Endnotes

[1] Ryan Avent, quoted in Tim O’Reilly, WTF? What’s the Future and Why It’s Up to Us (2017). Avent was describing The Economist’s print operation. The drug development parallel holds well, with one difference: Avent’s concern was about how embedded processes resist change.

[2] The term “virtual pharma” dates to the biotechnology boom of the 1970s and 1980s. Genentech operated as a virtual company from its inception in 1976 until one of its founders established an in-house lab two years later. The model is older than it looks and more standard than it sounds.

[3] The medtech equivalent of this cadre exists, but lightly. There are experienced operators who have built and sold device and diagnostic companies and could in principle bring that knowledge to bear on a portfolio of programs. The problem is throughput. Drug development runs enough simultaneous programs to generate a continuous pipeline of people who have done it before. Medtech doesn’t, at least not at the same density. You can’t build a professional culture around a model that hasn’t been practiced enough to have practitioners. The virtual pharma model also creates an unusual entry point for faculty inventors, who can take advisory or part-time roles in programs built around their work without leaving their academic positions. In short, a meaningful stake in the outcome without a full career transition. That flexibility is structurally absent in most healthtech domains, where commercialization has historically required someone to go all-in.

[4] Sexton ZA, Perl JR, Saul HR, et al. “Time From Authorization by the US Food and Drug Administration to Medicare Coverage for Novel Technologies.” JAMA Health Forum. 2023;4(8):e232260. The study examined 64 novel technologies authorized through premarket approval and de novo pathways between 2016 and 2019 for which no Medicare reimbursement route already existed. FDA clearance isn’t the finish line, just the start of another, different race.

[5] Stanford’s Biodesign program describes the Novonate team working through dozens of design iterations before spinning out. Eric Chehab and James Wall quotes are from “Innovating for Niche Populations,” Medtech Talk podcast, July 29, 2025. Wall served as advisor and board member to Novonate and is currently at Intuitive Surgical.

[6] Maverix Medical was announced November 30, 2023; see “KKR, Hologic and Ajax Health Create New Platform to Accelerate Medical Device Innovation,” Business Wire, November 30, 2023. Cirrus Bio acquisition announced March 29, 2024; see PR Newswire, “Maverix Medical Closes Acquisition of Cirrus Bio as Foundation for Diagnostics Platform in Lung Cancer.” For Rohlen’s account of the underlying model, see transcript of “The Middle Path to Innovation,” Duke Rohlen keynote with Mano Iyer, LSI Europe ‘24.

[7] Robert Beall quotes are from Nature Medicine, “Straight Talk with… Robert Beall,” March 2012, interview by Elie Dolgin.

Imagine a grad student who discovers a better way to detect sepsis from a drop of blood. She discloses it to her university’s tech transfer office, who reviews her filing and calls her up: “Great work. Really promising. Let’s turn this into a company. Who’s going to be CEO?”

The grad student, thinking of loans and promises to parents, and the post-docs she’s been interviewing for, protests: “I’m less than a year away from finishing my PhD. I already have plans to work in a lab. Can’t we just license it to a diagnostic company?”

TTO again: “We tried. They want to see more data and another validation study. You need to de-risk this first. We’re going to need to get a company together and raise a seed round to fund that. We can help. Meet us next week and we can get the process rolling.”

Some version of this conversation happens at research universities across the country day after day. It’s also the moment when a lot of promising inventions stall.

No matter how good the invention, the only way it’s allowed to grow up is to become something it may or may not be: the founding technology of a scalable, venture-backable company.

This post, this series even, isn’t an argument against startups. Some of these inventions absolutely warrant them, and the ones that do deserve a system that serves them well. It is an argument about what happens to the ones that don’t, and about why the system can’t tell the difference.

You build a company when organizing things internally is cheaper than coordinating through markets. Hire the expert rather than contracting with one, own the equipment rather than renting it. That’s economist Ronald Coase’s famous answer to why companies exist at all. And for a long time it was a pretty good description of why university spinouts made sense too.

For many early-stage university innovations, that logic has largely stopped applying. Specialized capabilities have never been more accessible in the market. What once required building a company (research, manufacturing, regulatory and reimbursement expertise, software engineering) can now be suitably contracted out as needed. And in many cases, the costs to develop healthtech (and other) products have fallen dramatically.[1] The economic case for company formation is weaker than it’s ever been.

So, while nobody makes a conscious decision to push every invention toward a startup, the machinery surrounding inventions on university campuses is built squarely around company creation. Tech transfer offices, accelerators, incubators, pitch competitions, venture funds. Each has its own processes, metrics, and incentives, and every one of them points toward a startup as the natural outcome.[2]

On the ground, even university programs purpose-built to foster entrepreneurship awkwardly manifest the same patterns. At UW, the “primary organization tasked with driving entrepreneurship on campus” is called Discovery to Product, or D2P for short. (Sounds like exactly what this series is arguing we need.) But D2P’s own description of its goals is to mature projects “so that they become competitive for the funding needed to form a company.” Since 2014, it has helped launch or grow more than 140 startups. What it hasn’t built is a route for inventions that shouldn’t be startups. Recent university efforts to strengthen campus entrepreneurship have made the move explicit, excluding licensing and industrial partnerships from their mandate, effectively drawing a circle around company formation and calling it entrepreneurship. The underlying assumption, that every invention should become a company, goes unexamined.[3]

This is the best example I’ve found that Professor Coase’s logic has inverted. The startup infrastructure has today become so dominant that the cost of doing anything other than forming a company is higher than the cost of doing it the familiar way. You could say that what was supposed to solve a market coordination problem has itself become one.

If company formation is the default, licensing should at least be the obvious fallback. In practice, it rarely is. (At least for things that aren’t drugs.) And how that usually fails, which you can hear in the tech transfer dialogue, is exactly what pushes offices toward company creation in the first place.

So, why doesn’t licensing work? First, established companies aren’t interested in early-stage university research. WARF’s public affairs director Kevin Walters explains the incumbent’s perspective plainly: large medtech and device companies “don’t often want to take risks on basic research patents, what we call ‘deep tech.’ To take the massive risks, you’ve got to create a new company.”[3] Most strategics are hunting incremental improvements to products they already sell. An early-stage university invention asks them to absorb a kind of risk they’re not built for.

Second, most of what arrives at a tech transfer office isn’t ready to license anyway. Greg Keenan, who runs WARF’s Accelerator and Ventures programs, describes most disclosures as “early-stage technologies, sometimes just lab bench experiments, that aren’t products in any meaningful commercial sense.”[3] A licensing deal requires a licensee who can see a plausible route from here to something they can sell. That’s a hard case to make for something that hasn’t left the lab.

Since licensing rarely works for non-pharma innovations, doing nothing (ie, not starting a company) means the invention sits in a catalog nobody browses. That’s part of why the institutional default is so hard to dislodge.

Underneath all of this is a simpler problem: where does the money come from? An inventor has a compelling idea, and to develop it, she needs funding. But the only financing available at meaningful scale is venture capital, and it requires a compelling story about market size, because venture returns depend on a small number of large outcomes. So the innovation needs not just a company, but a company with an addressable market big enough to support that return profile. Otherwise, the capital simply isn’t there, regardless of the clinical or commercial merits of the invention.

The human cost is most visible in pediatric medicine. “A lot of companies avoid making things for kids,” one ICU physician told me, “just because it isn’t worth their time.” After all, that’s a market with smaller patient populations, lower reimbursement, and higher regulatory complexity. As a result, clinicians routinely adapt adult devices for pediatric use, off-label and at some risk, because the right-sized product simply doesn’t exist.[5]

It isn’t only the smallest-market innovations that fall through, leaving real gaps in patient care. There’s a whole category of outcomes in the tens to hundreds of millions that are achievable, valuable, and still, eventually, orphaned. They’re objectively not failures; they’re the right size for what they are, even if venture needs them to be bigger and private equity wants them to be already profitable. The acquisition market that used to bridge that gap has mostly closed its doors. Large medtechs rarely acquire pre-commercial companies anymore, which means there’s no buyer waiting at the end of the runway for a mid-sized outcome either. A substantial portion of healthtech innovation quietly disappears between those positions.

The same logic distorts the route for innovations who have a clear path to profitability. Immuto Scientific, a WARF spinout from UW-Madison, developed a faster, cheaper method for determining protein structure. It had obvious value to pharmaceutical companies and a natural, straightforward path to profitability in a few years through recurring revenue from their per-protein analysis fees. “We knew right away that there was a service business here,” CEO Faraz Choudhury has said. “And we could make some money on services.” But the logic of the capital structure pointed them elsewhere; to justify venture investment, the company needed a different “North Star.” As Choudhury put it: “How do we make this into a high-growth business? We’ve got to make drugs of our own with this platform.” So the business pivoted toward making drugs and walked away from a profitable model to chase a venture-scale bet.[3]

Universities apply their own pressure. They behave as if they were licensing a de-risked drug through a well-understood regulatory pathway, when what they’re actually doing is asking someone to build a company from scratch to take an unproven product into an uncertain market. Calibrating their equity demands to the invention rather than to the full cost of building a company around it gets the pricing wrong.

Demands for substantial equity extract a disproportionate share of a venture that hasn’t been built yet. Past a certain point, the terms make the deal not worth doing for exactly the people you’d want to attract. There have been serious international efforts to reform and standardize academic licensing terms, but the conversation has stayed focused on making spinout deals faster and the equity curve fairer, rather than helping to define when a spinout is the right vehicle in the first place.[6] European universities on the wrong side of that curve restrict supply through onerous terms, then try to compensate by subsidizing demand through accelerators and innovation hubs, which is a somewhat expensive way to avoid asking whether the terms were right in the first place.

And then there are the companies that form anyway, on the strength of early enthusiasm, friends-and-family capital, maybe an angel round, with big promises about markets that don’t quite pencil out. Eventually the company hits the institutional funding market and stalls. The market isn’t large enough, the team doesn’t have the right track record, the capital needs are too high for what’s on offer. Many of these companies don’t fail cleanly. They drift, cycling through accelerators and pitch competitions, collecting small grants, accumulating just enough forward motion to stay alive. The inventor stays attached long after the point of rational exit, spending time on fundraising instead of research, patient care, or finding a buyer with the resources to take it forward. There is no defined endpoint, no moment at which the system declares the bet lost and lets everyone go home.

Think about what that grad student actually needed. The tech transfer office pushed her toward a startup because that was the available mechanism. What would have really helped her is someone with commercial instincts and domain knowledge who could take the invention, do the development work, find a licensing partner or an industry buyer, and shepherd the product to market without building a standalone company around it. It’s easy to imagine that person and yet that’s the one role with no institutional home in the current system. Tom Chapman, co-founder of a medical device commercialization company, describes the situation directly: “The gap isn’t ideas. It’s pathways for business-oriented operators to step in early, explore viability, and lead.”[7] It’s not just that capable operators are scarce or that incentives are misaligned but that we have no defined positions for them to fill.

This is a problem of selection, too. When Norway transferred IP ownership from faculty to universities, startup formation among science and engineering academics fell dramatically.[8] The reform didn’t change who was still trying, but faculty with the most valuable research had the most options and they exercised them.

The inventors I’ve spoken with in preparing this series confirm the picture from the other side. One physician-researcher described what most of his colleagues would actually want as “something like a side hustle” — passive income from an invention without having to become a founder. The financial appeal, he said, is real: most faculty “are taking a pretty substantial pay cut” to stay in academic medicine, and the idea that an invention could generate some additional return is genuinely attractive. What makes a startup unrealistic isn't interest; it’s “bandwidth.” The skill acquisition required, he said, “is rather steep. Networking, fundraising, pitching” are things that, to pursue seriously, would require “a leave of absence or a big percent reduction” from a traditional faculty role. That's not a realistic ask for someone trying to keep a lab running and a chair happy.

Another was more direct. “I am not interested personally in starting a company,” he said. “I don’t have time to be honest with you.” He actively wants his inventions at the bedside — “that’s the reason,” he said, “why I am looking into a solution like this.” But the route through company formation doesn’t fit his career or his life, and there is no other path on offer in which he can move the invention forward without a high-stakes career detour. Forcing him to retrain as a startup CEO is a rookie misallocation.

Every spinout builds from scratch the operational capability to function as an independent company. Sure, the specific questions differ: regulatory strategy for a diagnostic differs from regulatory strategy for a surgical device. But each founder-CEO figures out how to manage a cap table, negotiate a manufacturing contract, engage a regulatory consultant, build a board, while also trying to develop a product. The institutional knowledge accumulated by one spinout doesn’t flow to the next one down the hall. The overhead serves one program and disappears.

Economists have a name for what’s missing here: economies of scope. Economies of scale says you get more efficient as you do more of the same thing. Economies of scope says it’s cheaper to produce two different things through the same platform than to produce each one independently. The savings come not from volume but from variety. Shared infrastructure serving different programs simultaneously costs less than building separate infrastructure for each. This is, I should say, a pretty basic idea.

Procter & Gamble doesn’t build a separate company for every product in its portfolio. Tide, Crest, Gillette, and Pampers share legal, regulatory, manufacturing, and distribution infrastructure; the marginal cost of adding a new product to that platform is a fraction of what it would cost to stand it up independently. But the current startup-per-invention model does exactly the opposite. Every new spinout rebuilds operational capability from scratch, and when the program ends, what was learned evaporates with it.

But universities and accelerators are rewarded for creating new companies, not for efficiently developing realized inventions. Startup numbers feature in rankings, press releases, grant applications, and annual reports. And the incentive to keep doing what’s countable is more powerful than the incentive to explore alternative ways to get gadgets to market.

Universities also know how to get paid from the current model. Decades of deal-making have produced templates for how equity stakes and royalty streams flow back to the institution from a spinout or a license. The legal infrastructure exists, the precedents are clear, the finance office knows where to put the income. A product-not-company model has none of that. No established deal structure, no standard royalty framework, no clear accounting treatment for returns from a product commercialized through another mechanism. For a tech transfer office trying to justify its budget, the known deal is always easier than inventing a new one.

The venture studio, a professional company-creation platform that uses shared legal, regulatory, and operational infrastructure to systematically create startups, is the most direct response to the newco-for-every-invention overhead problem. A growing number of universities have attached these directly to their tech transfer efforts. That means having operators on staff so the inventor doesn’t have to become a CEO, and focusing on active development instead of passive licensing. Venture studio advocates report better financial returns and faster paths to subsequent funding rounds than traditional approaches.[9]

It solves the overhead problem completely, for companies. But each of those still needs to build a team, raise venture capital, and clear the market size hurdle. It’s a more efficient expression of the institutional default, not an alternative to it. The innovations that were never right for the company form don't get rescued by a more efficient company-creation machine. They get processed more quickly to the same outcome.

Anastasia Gamick, of Convergent Research, gets closer to the right diagnosis. There’s a class of companies that should get built but don’t, she argues, sitting in a valley between capital categories. The problems they address are “well-characterized, urgent, and solvable” — she specifically names diagnostics, antibiotics, and others with roots in healthtech — but the companies they require are “Too slow for venture. Too early for PE or growth equity. Too capital-intensive for bootstrapping. Too profitable for charity.” Her answer is more patient capital and better-structured funds. That will help. But it’s still a company-creation answer when the problem runs upstream of capital structure. This series is asking whether companies are the right unit in the first place.[10]

The system is very good at producing companies. What it cannot do is develop a product without building the typical startup around it. Post 3 is about different organizational logics that already exist in corners of the life sciences and elsewhere, which turn out to be much better at getting inventions to patients.

Endnotes

1. Getting a medical device through clinical validation and early commercialization remains expensive and is getting more so. Development costs are rising, particularly at the clinical validation stage. Pivotal trials for novel devices now routinely run into the tens of millions of dollars, IDE study requirements have grown more demanding, and post-market surveillance obligations have expanded significantly under updated regulatory frameworks on both sides of the Atlantic. Meanwhile, the commercialization gauntlet has lengthened. A 2023 study found that among 64 novel devices and diagnostics that required establishment of new Medicare coverage after FDA authorization, the median time to achieve at least nominal coverage was 5.7 years. That figure applies only to the 44 percent that achieved coverage at all. The majority were still waiting at the time of analysis, meaning the true average is likely considerably longer. (See Sexton ZA et al., "Time From Authorization by the US Food and Drug Administration to Medicare Coverage for Novel Technologies," JAMA Health Forum 4, no. 8 (2023): e232260. doi:10.1001/jamahealthforum.2023.2260). FDA clearance, long a viable exit milestone for acquirers, no longer reliably triggers reimbursement, leaving companies to navigate the full coding, coverage, and payment process on their own. This is one of the structural reasons the drug development analogy, while instructive, doesn’t port directly to healthtech. Post 3 is specific about where it holds and where it breaks down.

2. SBIR and STTR programs, the federal grants marketed as non-dilutive alternatives to venture capital, sound, on the surface, like an off-ramp from the company form, or at least a small-business alternative to the venture model. In practice they require a legal entity to receive the grant, reward the same milestones venture does, and run through the same tech transfer machinery. They reinforce company formation rather than offering a way around.

3. “Empowering the Wisconsin Idea: The Future of Entrepreneurship at the University of Wisconsin–Madison.” Report of the ad hoc working group commissioned by Chancellor Jennifer Mnookin, September 2024. The report is notable for what it excludes as much as what it recommends: the working group explicitly scoped its charge to entrepreneurship resulting in company formation, excluding industrial partnerships and licensing to established companies. That framing is the institutional default made visible.

4. John Allen, “WARF Seeks the UW’s Next Big Thing,” On Wisconsin, Fall 2025. The Walters and Choudhury quotes are drawn from this article. The Keenan quote is also sourced here.

5. Pathak et al., “High-risk Therapeutic Devices Approved by the US Food and Drug Administration for Use in Children and Adolescents From 2016 to 2021,” JAMA Pediatrics 177, no. 1 (2022). Of 124 high-risk therapeutic devices approved by the FDA during the study period, 80 percent were approved for adults only. Estimates of off-label device use in pediatric patients range from 60 to 75 percent. See also Sutherell et al., “Pediatric interventional cardiology in the United States is dependent on the off-label use of medical devices,” Congenital Heart Disease 5, no. 1 (2010): 2–7, cited in Haller et al., “Experience With Pediatric Medical Device Development,” Frontiers in Pediatrics (2020).

6. The USIT Guide was developed by TenU, an international collaboration of leading technology transfer offices including MIT, Stanford, Oxford, Cambridge, Imperial College, and others. Its focus on deal structure, equity terms, royalties, and licensing mechanics reflects the consensus view that the problem with university spinouts is how deals are constructed, not whether spinouts are the right vehicle. That’s meaningful reform, but not the reform this series is describing.

7. Tom Chapman’s LinkedIn post

8. Hvide, Hans K., and Benjamin F. Jones. “University Innovation and the Professor’s Privilege.” American Economic Review 108, no. 7 (2018): 1860–1898. The pattern is worth sitting with: the reform designed to increase commercialization activity reduced it most sharply among exactly the faculty most likely to produce commercially valuable work.

9. Matthew Burris, “The University Venture Studio: Unlocking the Innovation Potential of Higher Education,” Venture Studio Perspective (Substack), August 5, 2025, citing Global Startup Studio Network / Morrow & Co, “Disrupting the Venture Landscape.” The data comes from industry sources with an interest in the conclusion and has not been independently validated. The directional claim, that studios outperform traditional approaches, is widely accepted among practitioners even if the specific figures are contested.

10. Gamick’s piece is worth reading alongside this series. Post 4 returns to this distinction when examining what an alternative infrastructure might actually look like and how it might be capitalized.

    Anastasia Gamick

    Companies that should exist

    There is a class of companies that should exist but don’t because the economics don’t fit neatly into any existing capital structure…

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    2 months ago · 136 likes · 22 comments · Anastasia Gamick

Here is something that happens every day at research universities. A scientist, or an engineer, or a physician has an idea for a product. Could be modest: a small improvement to a piece of lab equipment, an update to a diagnostic algorithm. Could be significant: a device that makes a common surgery meaningfully safer, a test that catches a disease earlier, a piece of software that gives a clinician needed information at the right moment. The ideas range from mundane to transformative; most of them share one characteristic: they never go anywhere.

Not because they don't work. Not because there are no customers. Because they're misfits in an ecosystem designed for a very specific kind of product.

I’ve spent much of my career watching valuable innovations get quietly stranded in this way. This is the thing I want to explain. And, eventually, do something about.

The Three Inventions

Let me give you some examples, drawn from patterns I've seen repeat across universities and clinical settings. The details are composites, but the problem is not.

A biomedical engineer builds better equipment for tissue culture. Research labs will pay $50,000 per unit. You could sell five hundred units a year at a healthy profit. The product works, the customers exist, the economics are fine, and if you described this situation to a normal person they would say “Ok, so build the product and sell it to the labs.” But there’s no recurring revenue. No platform potential. No network effects. No odds to achieve the kind of returns that attract venture capital. So it sits.

A computer scientist develops clinical decision support software for a specific surgical procedure. Hospitals need it. It demonstrably improves outcomes. It could save millions of dollars and, more importantly, meaningfully improve patient care. But it isn’t an AI platform with applications across fifty procedures. The sales cycle into health systems is long and grinding. This is a good product for a real market, and a good product for a real market is, we’ll come back to this, not actually what the available funding mechanisms are designed to support.

A physician-scientist creates a better diagnostic test for a condition affecting a hundred thousand people a year. The existing commercial lab networks could pick it up and sell it profitably tomorrow. What they need is someone to develop it far enough that it’s a real commercial asset. But the venture investors who might fund that development want a platform for dozens of tests, not one really good one. (The one really good one is, financially speaking, not very interesting to them. More on why in a moment.)

Notice what these three examples have in common. They aren’t ideas that failed to find customers. They’re working ones that can’t find a route forward. They’re orphaned innovations. The product works. The market exists. Someone would pay for it. And yet.

How Venture Capital Works (And Why That’s A Problem)

To understand why this happens, you have to understand what venture capital is actually for.

Venture capital is not a general-purpose mechanism for funding good ideas with real customers. It’s a specific financial instrument with specific mathematical requirements. A venture fund raises money from limited partners (eg, endowments, pension funds, family offices) with the promise of outsized returns. “Outsized” means something specific: five to ten times the invested capital, in seven to ten years, across a portfolio where most investments fail. The structure demands that the winners be very large, because the losers are total losses, and the math only works if enough winners are large enough to return the whole fund.

Mike Partsch, who runs WARF Ventures, the venture fund of the University of Wisconsin’s tech transfer office, explains this directly. “If an entrepreneur said ‘If you invest in us and we’re wildly successful, you could get a 25 to 30 percent return,’ we’d be like, ‘No, not interested.’” He’s not being unreasonable. He’s describing the actual constraints of his fund’s structure. A 25 percent return is an extraordinary result in most investment contexts. For a venture fund, it’s disappointing. The math requires something different.

So when we say a company’s market is “too small for venture capital,” we don’t mean the market is small in any ordinary sense. A business that generates $30 million in annual revenue at healthy margins, helping a hundred thousand patients, is not a small business. It’s a good one. It’s just the wrong shape for the only growth-capital mechanism that touches early-stage health technology. The problem isn’t the size of the opportunity. It’s that the opportunity is the wrong size for the only financing model available.

(The alternative, licensing the technology to an existing company, works well in exactly one industry: pharmaceuticals, where large global businesses have dedicated teams actively scouting for new assets. In most of healthtech and medtech, the licensing market is thin, the buyers are fragmented, and the transition from university IP to commercial product is murky enough that it rarely happens in practice.)

How Universities Think About This

Here’s where it gets interesting. Universities know this is a problem. Or at least some of them do.

Tech transfer offices, and the accelerators and entrepreneurship programs that have grown up around them over the past two decades, are almost universally organized around a single output: new companies. This makes a certain kind of institutional sense. Startups are legible and easy to count. You can report them to your board, feature them in your alumni magazine, compare your number to peer institutions. When the University of Wisconsin’s working group on entrepreneurship released its 2024 report, Empowering the Wisconsin Idea, it noted that UW ranks eighth in the country in research expenditures but thirty-first in startup formation. The gap is framed as the problem to solve.

A better startup pipeline would be valuable. UW has since appointed Lewis Sheats as inaugural director of a new Wisconsin Entrepreneurship Hub, and the language around it is deliberately ambitious.

But here’s the thing: the same report explicitly sets aside other commercialization pathways (eg, licensing to existing companies, product development outside the startup model) as beyond its mandate. The most self-aware reform effort currently underway at one of the country’s leading research universities has defined success as producing more startups. The solution to the problem of innovations not reaching patients is, per the working group, more and better startups.

This would be fine, or at least less problematic, if most faculty innovations were good startup ideas. Many are not. Starting a company requires a particular kind of founder commitment. You are, typically, leaving or substantially reducing an academic career. And it requires a particular kind of commercial trajectory: A good product that could profitably serve a modest market is not, in most cases, a good startup idea. It doesn’t have the growth dynamics that justify bringing on investors, hiring aggressively, and building toward an exit. Pushing it through the startup model doesn’t make it a better startup. It just adds a layer of expensive institutional overhead to what might have been a straightforwardly buildable product.

The Norway Problem

For a long time, watching innovations stall at exactly these points (eg, promising product, no path, wrong shape for the only available capital) I assumed the explanation was local. Bad luck. Weak management. An inventor who didn’t work hard enough. Then I came across a natural experiment that suggested something more structural was going on.

In 2003, Norway changed its IP laws to transfer ownership of university research from individual professors to their institutions, basically adopting the American approach. The theory was that centralizing ownership would professionalize commercialization and increase spinout activity. Give institutions control over the IP, build out the infrastructure, produce more startups.

What actually happened: university startup formation fell fifty percent. Among science and engineering researchers, startup formation fell sixty-three percent on a per-worker basis. Patenting rates fell by a similar margin, and patent quality declined alongside.

The system designed to encourage commercialization suppressed the commercialization instinct instead. Not because Norwegian professors stopped having ideas, but because the institutional process replaced a decentralized, faculty-driven impulse with a centralized administrative one that most faculty found alienating and most innovations couldn’t survive. And the institutional machinery that replaced it was built, as ours is, around a single output: new companies. It didn’t just produce fewer startups. It substituted an organizational reflex for the real question: whether a startup is the right vehicle for a given innovation in the first place.

You can read this as a specifically Norwegian story about institutional culture. Or you can read it as a canary: a visible version of a dynamic that operates more slowly and less visibly in the American model, where it’s a little harder to observe because the change happened gradually rather than all at once.

I tend toward the second.

What Gets Left Behind

The University of Wisconsin receives roughly three hundred fifty to four hundred invention disclosures per year. About one per day, as Greg Keenan, a senior leader of WARF, describes it. They patent roughly half. A small fraction become companies. The rest find another path, or none at all.

The true cost of this isn’t visible in any annual report. It lives in the distance between what could have reached patients and what actually has. Universities measure patents filed, startups formed, venture capital raised. They don’t measure how many innovations actually reached patients, how many profitable products never got built because the market was deemed too small, or how many faculty inventors gave up after the tech transfer-to-startup process proved too slow or too discouraging to navigate. The metric and the thing you actually care about, clinical impact, have decoupled almost entirely.

Ben Reinhardt, who runs Speculative Technologies and thinks carefully about where innovation gets stuck, calls this the missing middle: the space between what philanthropy will fund and what venture capital finds interesting. Most promising faculty innovations fall into the gap between them. Too commercial for philanthropy, which wants pure research with no financial entanglement. Not commercial enough for venture capital, which needs billion-dollar exits to work. Too early for strategic acquirers. Too dependent on makeshift licensing channels not built for this category of innovation.

It’s not a theoretical construct. It’s an actually crowded space of working innovations with real clinical value and no institutional home or way forward. The device that helps tens of thousands of patients, profitably, at modest scale. The diagnostic test that works and has customers and needs someone to build the business-shaped wrapper around it, just not a venture-backed company. The software that hospitals need and will pay for, just not at the growth rates that justify a Series A.

These aren’t failed ideas. They’re working ones that we’ve forgotten, or never learned, how to handle. Once the Bayh-Dole Act established the university-to-startup pipeline in 1980, we lost the habit of asking what an innovation actually needs, rather than what the system is designed to produce.

In the next three posts in this series, I take up why the system defaults to companies when it should sometimes just make products, why drug development figured out a model for this that healthtech hasn't adopted, and what it might actually take to create something new. Nobody has built that machinery yet. That is what this series is about.

Part 2 is now up. Read it here.

Citations

Greg Keenan quotes and WARF disclosure figures: John Allen, “WARF Seeks the UW’s Next Big Thing,” On Wisconsin, Fall 2025; and Brittney Kenaston, “Taking Ideas to Market,” In Business Madison, August 2025.

Mike Partsch quote: John Allen, “WARF Seeks the UW’s Next Big Thing,” On Wisconsin, Fall 2025.

WARF Ventures and Accelerator timeline: Brittney Kenaston, “Taking Ideas to Market,” In Business Madison, August 2025.

UW rankings and report: Empowering the Wisconsin Idea: The Future of Entrepreneurship at the University of Wisconsin–Madison, UW–Madison Working Group Report, 2024.

Lewis Sheats appointment: Rodee Schneider, “Lewis Sheats named inaugural leader of UW’s Wisconsin Entrepreneurship Hub,” UW–Madison News, January 20, 2026.

Norwegian IP reform data: Hvide, Hans K., and Benjamin F. Jones. “University Innovation and the Professor’s Privilege.” American Economic Review 108, no. 7 (2018): 1860–1898.

Spectech Newsletter

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The National Science Foundation’s Directorate of Technology, Innovation, and Partnerships (NSF-TIP) recently issued a request for information on a new “Tech Labs” program. From the RFI…

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3 months ago · 11 likes · 2 comments · Ben Reinhardt

2. I read Wooded Shore, a great collection of short stories by Thomas McGuane. Full of unexpected plot twists and surprising writing - “I thought she was slapping herself in the face in the bar where we met, but it was how she eats peanuts.”

3. I am still reading Pacific Crucible. I had not appreciated that two months passed after Pearl Harbor before the US military launched its first offensive against Japanese positions. Hard to imagine such a timeline today. Meanwhile, my bedside book tower is piling up. I just received Halle Tecco’s new and acclaimed Massively Better Healthcare, which I’m looking forward to. Some fiction in the mix, too.

4. I recently learned about Holoclara, a clinical-stage company built from Caltech researcher Andrea Choe's insight that roundworms (helminths) evolved mechanisms to affect the human immune response so they could live inside us. She realized that meant there was probably something useful in their biology we could isolate and put to work. Holoclara is now developing novel therapeutics derived from those symbiotic organisms that modulate inflammation, metabolism, and tissue regeneration. With GLP-1s having emerged from gila monster saliva, who knows where roundworm spit will get us?

5. I built a small tool I’ve been wanting for a while: a digital version of those year-at-a-glance calendars that show all 12 months in one view. It started because managing multiple kids’ school breaks plus my own work and travel schedule was such a hassle. It’s called YearMap. Click and drag to mark date ranges, color-code by category, add notes, and export to your regular calendar. Free, runs entirely in your browser, no account required…If that sounds useful, I’d love your feedback. Just reply and I’ll send you the link.

6. My friend Laura Strong let me know you can order a free Lp(A) test in the mail from the Family Heart Foundation. Just got mine and am sending it away. I’ve been wondering for a while about Lp(A) - a type of LDL cholesterol that’s an inherited risk factor for heart disease - but my primary care doctor is either bored or befuddled by preventive medicine! So I've recently been on a bit of a solo march to get ahead of my heart health.

7. GitLab founder Sid Sijbrandij was diagnosed with a rare bone cancer in 2022, exhausted every standard treatment, and watched it come back. So he assembled a team and went after it with the full force of the latest genomics and experimental therapies from around the world, and put his cancer into remission. Elliot Hershberg's inspiring profile is a stunning look at what coordinated, first-principles cancer care can look like and accomplish.

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Welcome to The Century of Biology! This newsletter explores data, companies, and ideas from the frontier of biology. You can subscribe for free to have the next post delivered to your inbox…

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4 months ago · 79 likes · 11 comments · Elliot Hershberg

8. Daniel Moerman spent decades compiling Native American Ethnobotany, a 900-page reference cataloging how 306 tribes used over 42,000 plants for nearly 67,000 medical applications.1 After Moerman died earlier this year, developer Adam Fleury converted the entire work into a searchable database. You can explore by ailment, plant, or tribe. Want to know what the Cherokee used for headaches, or which remedies five or more tribes independently converged on? It's a remarkable collection of indigenous knowledge, now far more accessible than it's ever been thanks to Adam’s generous side project.

It’s been a grey and cloudy few days in Madison. Cold and snow lurk in the lineup next week, and I’ve put the ski racks on my car in anticipation. With any luck, and some round-the-clock snowmaking, we’ll be exercising outside again one day soon.

Last month, I wrote about Windblock, my script for wind sports enthusiasts. Today, it boasts a dedicated collection of 11 users, all of whom I guess discovered it organically via the Workspace Apps Marketplace, or whatever its awkward name is. Over the last month, I shifted to a more reliable weather API provider and solved a bunch of annoying problems.

I’ve also recently been finishing up another software thing I made. It’s called Yesterday’s Weather and it’s the goofiest weather app in the market today. All it does is send you an email each morning summarizing the weather from the day before for places you care about. I built it to keep track of what’s happening in towns where my kids go to school and where my siblings live. It still needs work, but give it a try and let me know what you think. The first people to find it have been using it to keep an eye on the weather where they have a second home.

I’d do that, too, but it’s not easy to get weather data from the Bahamas. I learned that a couple of weeks ago when I was there making repairs and fixing up our house when Hurricane Melissa roared by. It was windy and the seas were up!

Back home, I enjoyed catching up with my friend and health economist, John Mullahy, and talking about the entrepreneurship initiatives on campus. He steered me toward the concept of economies of scope. These are “efficiencies formed by variety, not volume,” and often missing in academic spinouts. My working theory is that absence limits the number of faculty medtech and healthtech inventions getting to market.

Relatedly, I enjoyed a recent piece by Sam Enright exploring how national-level differences in a professor’s rights to their inventions suggest ways to increase translation and commercialization.

“‘Professor’s privilege’ is the concept that academics should own the rights to any technologies they create or patents they file in the course of their employment. Today, Sweden is the only country in the world that has this policy. But several countries used to have it, and in a paper from 2018, Hans Hvide and Benjamin Jones exploit a fascinating natural experiment: the end of professor’s privilege in Norway in 2003.

Progress Ireland

Turning Academics into Entrepreneurs

If an academic invents something while working at a university, who should own the patent? Who should be entitled to the profits? And how does this affect the rate of entrepreneurship in an economy…

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6 months ago · 19 likes · Sam Enright

In fun reading this month, Raymond Chandler’s books, The Big Sleep and The Long Goodbye (my favorite of the two), accompanied me on the long flights to/from London, where I spent the better part of a week for LifeArc activities. I also managed to see a performance of Othello at the Theatre Royal Haymarket, with Toby Jones, David Harewood, and Caitlin FitzGerald. It runs through January 17th. Recommended.

Happy Thanksgiving week!

1. After a fun summer with everyone home, and some family travel, two of my boys are back to college, one finishing up in MN and the other starting his run in CA. For that reason, September was kind of a sigh. I also sat out much of the month after hand surgery and weeks without wind kept me off the lake.

2. I used the time to build a handful of small software utilities for myself. The first is Windblock, a little script that automatically monitors the wind forecast for a given zip code and then schedules time in Google Calendar when the wind meets your threshold (and removes the holds if the forecast changes). This keeps those hours free for the invaluable days when the wind does turn up :)

3. Listening-wise, I’m decades late but have become a big fan of Radio Paradise the past couple of months. It’s a free, non-commercial, listener-supported internet radio station run by a father-daughter team who curate mixes across a handful of channels. When my wife isn’t blasting Studio 54 🛼 radio, I’ve got it streaming in our main living area all the time. Great place to discover new music and unexpected old favorites.

4. I just finished my multi-year effort with the Liberation Trilogy, Rick Atkinson’s engrossing three-volume set on WWII in Europe. How challenging and unsettling it can be to read about the events of those years. “Thirty thousand Belgians bade them adieu from the Antwerp docks, while pledging to look after the 61,000 Americans who would remain in those ten European cemeteries, ‘as if,’ one man vowed, their tombs were our children’s.’” I hadn’t appreciated enough the American “prodigy for organization” nor the British “genius for cozenage.”1

5. My son and I recently watched the documentary Race To Alaska, which was an entertaining history of a little-known boat race 750+ miles up the coast from Port Townsend, WA to Ketchikan, AK. The only rules: no motor and no support. The film profiles a few unlikely contenders over the years - eg, a guy on a paddleboard, a Frenchman who rows the distance, and a team of brothers who learn to sail en route. It made we want to sign up for something adventurous and challenging!

6. Speaking of which…I’m getting close to what I want to do next. With some friends, I’ve been working on a new med/healthtech project to get more inventions by university faculty and staff into patients’ hands, faster. More on how we plan to do that soon.

7. Relatedly, I’ve recently been digging into how a few ambitious funding experiments are underwriting misfit research, making early bets on biotech tools, and figuring out new ways to get ideas off the ground. Renaissance Philanthropy is matching bold donors with unconventional bioscience and healthtech projects. Arcadia Science is building a company around open, curiosity-driven research. And Convergent Research is creating focused research organizations that look like startups + public labs. Together it feels like first moves toward a new model for discovery. Here’s an account from someone stepping into the thick of it:

   Rough Diamonds

   The Dream Machine

   I recently started working at Renaissance Philanthropy. It’s a new organization, and most people I’ve met haven’t heard of it. So I thought I’d explain, in my own words and speaking for myself rather than my employers, what we (and I) are trying to do here…

   Read more

   a year ago · 94 likes · 18 comments · Sarah Constantin

8. I’m still happily managing a portfolio of work with LifeArc, SHS, and board and advisor roles. Some recent company highlights: CLM launched Wedosify, a tool for personalizing the dosing of GLP-1s, and Ozlo announced a partnership with Calm.

9. My friend Amber Vodegel, previously the founder of Pregnancy+, is back at it building 28x, a privacy-safe period tracker that’s free-to-use. As you’d expect, she’s been deliberate about funding the business so she can prioritize trust and safety rather than advertising. It’s launching later this year, but you should sign up for the waiting list now.

10. Sven Dethlefs has been named CEO of Celea Therapeutics, a new PureTech company focused on transformative treatments for serious respiratory diseases. Celea is advancing deupirfenidone (LYT-100), a Phase 3-ready therapeutic candidate with the potential to set a new standard of care for idiopathic pulmonary fibrosis (IPF). Bringing his experience as former CEO of Teva North America, Sven says the company will “offer something truly differentiated for patients in a treatment landscape that hasn’t seen enough meaningful change.”

And, I’m heading back to London mid-November. Let me know if you’re around and want to meet up!

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At the top of the list—truly, it has a Guinness Record—is ISAAC, the International Study of Asthma and Allergies in Childhood, which launched in 1991, and by its conclusion in 2005, had involved more than 2M children from more than 100 countries.

Some of the most well-known advances in understanding asthma origins and prevalence, such as the hygiene hypothesis, emerged from its efforts. But we no longer explore cross-cultural variation in the origins of chronic disease with the same energy, and the world has also grown more interconnected in the last two decades. These studies and their warehouses of data capture important variability in exposure that often no longer exists.

ISAAC has a website that curates the history of the project, including interviews and personal reflections with its investigators, summaries of its methods and main results, and citations to its 500+ published papers.

Reading the history, I wondered what we might learn by re-examining the data collected by ISAAC using the new AI tools we now have at hand. Over the past week, I pointed o1 pro at the site and its accumulated publications and reports, and asked it to revisit ISAAC and explore the project for overlooked insights.

Granted ChatGPT could only access a fraction of the published papers, and none of the raw participant or center-level datasets or detailed statistical outputs. Nor did I give it an ability to explore other, relevant + complementary data that might exist elsewhere.

I asked the model to report its findings in the form of a research letter for a journal, limiting itself to 1,200 words on the most interesting observations. After a few rounds of feedback and editing, I had a workable manuscript, highlighting several surprising inconsistencies and mismatches, which I’ve submitted to a respiratory journal (with appropriate disclosures).

Before the datasets and project archives from ISAAC are lost to time, re-analyses using new analytical tools could be a worthwhile way to uncover productive new clues about asthma, especially for a team with access to underlying data.

Other landmark studies warrant similar attempts. For example, projects like MONICA, which explored cardiovascular disease in 10 million people from 20+ countries, and EPIC, which studied cancer and chronic diseases in relation to dietary, lifestyle, and environmental factors among >500k participants from 10 European countries. And unexpectedly, I’ve recently seen news that datasets like YRBSS and others are being taken offline and made inaccessible to the public. Those study databases and others like NHANES, may be worth exploring, too, before they fade or are pushed into obscurity.

It’s a fascinating puzzle that’s been known for decades, and observed in almost every population in which it’s been measured. But when I tried to ask an AI about the riddle of marijuana use and vital capacity, I got nothing…

It looked right past the unexpected narrative violation, and offered me the respiratory symptoms commonly resulting from marijuana smoking.

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We’re becoming collectively enthusiastic that AI will accelerate scientific discovery and deliver prodigious, world-changing insights. But so far its contributions feel like the results of a tireless laborer. As Benedict Evans puts it, it’s doing the work of “one intern with infinite patience.”

A more apt analogy is the painstaking work of the naturalists of the Enlightenment and Victorian eras, who made tens of thousands of meticulous observations of details of plant and animal life.

AI has already identified phenomena we’ve overlooked or could not see. For example, after studying 84,743 scans, a new machine learning model can tell male from female retinas. Previously, no one knew there was a difference.

As Raymond Joseph Teller, of the magician duo Penn & Teller, explains, "Sometimes magic is just someone spending more time on something than anyone else might reasonably expect."

I’m sure there are and will be many more such cases, in drug discovery and beyond. 

But the marijuana story reminds me that humans, too, have powerful abilities to recognize patterns. And we’re even more uniquely tuned to spot anomalies, and become inquisitive in moments when our expectations break. Don’t bet against us realizing when there’s something to learn by exploring the question, “What is going on here?” We’re especially good at noticing differences over longer periods of time in how things unfold compared to how we expected.

In respiratory alone, we’ve followed our observations to some remarkably productive theories. Evidence that people in Bavaria who, as children, lived in proximity to cows had lower rates of asthma gave us the hygiene hypothesis and opened new lines of work in immunology. Another theory about the origins of asthma arose after we realized that young migrants to the US were surprisingly protected from the disease. More generally, physician-epidemiologist David Barker noticed that cohorts of individuals who’d lived through collective tragedies (eg, famines, pandemics) decades earlier, developed higher rates of disease later in life. 

We already ask remarkable questions, and have for a long time. For many years, the Edge Foundation ran an annual project where leading thinkers responded to provocative questions, or shared the most important puzzles in their fields. Wikipedia has a page full of unanswered questions/unsolved problems across scientific domains. So many big conundrums, and more by the day. 

Just this week, I’ve run into reviews that highlight areas of curious uncertainty and significant potential impact in respiratory disease:

• A recent issue of the European Respiratory Journal reviewed the questions at the center of our early, emerging understanding of the airway epithelium, its role in respiratory health and disease, and its potential as a target of prevention and treatment. For example http://dx.doi.org/10.1183/13993003.01397-2023

• Somewhere between 70-90 percent of those with COPD have never been diagnosed, resulting in missed opportunities to start lung-preserving treatments. A new paper summarizes the challenging tradeoffs in screening and case finding what is already the world’s third biggest killer

• An American Thoracic Society expert panel review distressingly reported that after all the work that has gone into developing and testing interventions to reduce indoor air pollution in low- and middle-income countries, "it remains unclear which household energy interventions reduce exposure, improve health, can be scaled, and are sustainable." 

• According to data from the U.S. Centers for Disease Control and Prevention, American Indian and Alaska Native people continue to suffer a higher prevalence of asthma than any other specific racial or ethnic group in the United States. The Indian Health Service has announced a Strategic Initiative - Asthma Control in Tribal communities (ACT). We need better ideas for how to control asthma and reduce the burden of asthma-related illness and death among American Indian and Alaska Native people.

In each case, we’ve identified fundamental challenges or perplexing observations, synthesized from years of curated science, and need help comprehending the situation we find ourselves in.

Ahead lie startling insights, new avenues for further research, and provocative breakthroughs. The trick is in figuring out how to use the exhaustive naturalists we now have at hand to explore new unseen angles on these challenges.

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More, recent writing

• I wrote a post on LinkedIn about the current market’s benign neglect of ambitious medical device startups.

• And another one on LinkedIn about the startups working on diagnostic and therapeutics tool for earlier detection and better treatment of lung cancer.

Other stuff

• 💥Incredible hiring opportunity → One of my favorite former Propeller colleagues — our head of dev ops, a technical monster, and an ace leader and pillar of our team — is becoming available and looking for his next role. Please contact me for details. 

• I’m helping a founder with an ophthalmic drug delivery business, about which I know very little. I’d appreciate any suggestions/introductions to people with relevant expertise I might speak with to learn more

• I’ll be in London the week of May 20th and would love recommendations about companies I should meet, best places to eat, hang out, etc.

Thought I’d share this short note about two companies I found working on IPF, which warrants much more attention than it gets. Let me know what you think of the summary, and thanks for reading.

Most people have never heard of idiopathic pulmonary fibrosis (IPF), though it takes the lives of 40k people each year in the US. Roughly the same number as breast cancer, except no one talks about it.

As the name suggests, its causes are a mystery. Frustrating and disheartening to treat, it is, in the words of one physician, “a relentless disease that turns lungs to stone.”

Unfortunately, funding for research on IPF is poor. Drugs that can slow its progression have only recently arrived. Still, more than half die within four years of being diagnosed, a survival rate worse than most cancers.

Thankfully, my recent survey of the respiratory healthtech market turned up at least two companies working on IPF/PF.

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The first, IMVARIA, led by Joshua Reicher, M.D. and team, analyzes lung CT scans with AI to help physicians identify and diagnose IPF much earlier on.

That speed is important. On average, it currently takes doctors 2-3 years to diagnose IPF after someone develop symptoms. IMVARIA expects it can reduce this delay, enabling people to start lung-function preserving treatments at an earlier stage.

In January, Fibresolve, the company’s lead AI biomarker, received FDA marketing authorization and Breakthrough Device Designation. Shockingly, this is apparently the first-ever clearance of a diagnostic tool of any type in lung fibrosis.

The second product is Almee, which offers a 9-week program of digital cognitive behavioral therapy (CBT) specifically focused on the treatment of anxiety symptoms related to pulmonary fibrosis.

It’s the result of a collaboration between Alex Therapeutics and Vicore Pharma AB designed to target the significant psychological impact of the disease. No surprise that an unforeseen and deadly diagnosis, which progressively limits physical functioning, can also have a terrible mental burden.

Almee was also granted FDA Breakthrough Device Designation, after a recent clinical trial showed its effectiveness in reducing anxiety symptoms and improving health-related quality of life.

With more than 250 thousand Americans living with IPF/PF, and its incidence on the rise, I’m grateful that companies are exploring new options to help people get more timely care and life-improving treatment 🙏

You can download the list from here

This is a handcrafted collection of 95+ companies, with details including:

• company name, website and LinkedIn page

• a short product description

• Founder/CEO name, LinkedIn profile, and general location

For now, I've excluded: 

• Most pharmaceutical and drug development companies 

• Asthma self-management apps (endless numbers)

• Most surgical instrument makers

• Most air quality products, and

• Allergy-specific solutions

I've also excluded sleep, which was well-surveyed by Supermoon Capital in its reports on sleep insights and interventions.

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Undoubtedly, I've made some errors and have missed companies who should be included. Please let me know; I'll do a revision soon, and update occasionally. 

I'd also welcome your feedback. I’d like to make a more curated and annotated version of this list, together with a companion write-up analyzing patterns and opportunities in the market. But, before I do that, please take a minute to let me know what would be most valuable to you. 

To suggest a company, make a correction, or share a general comment/suggestion please go here.

Thanks!
David

It opens with a slightly patronizing preface from Prof Peter Calverley:

“Digital medicine is moving from being a technology in search of a problem, to providing novel solutions to the challenges of expanding healthcare need.”

It’s hard to imagine technology entrepreneurs have struggled finding problems to fix. Innumerable shortcomings in the care and treatment of chronic respiratory disease mean morbidity, mortality and costs remain at near-record highs.

The dismissive start perplexed me, but how the editors’ introduction took it from there is what got me to the keyboard this weekend.

In it, they assert that digital technology, left to its own, will increase healthcare inequalities, and therefore, that its development and implementation needs to be directed to achieve a goal of “fairness.”

The editors suggest the “social challenges” of respiratory health must come first. By which they mean that healthtech companies have a duty to “optimiz[e] digital interfaces for individuals….who, for whatever reason, have limited ability to access and benefit from digital healthcare.” And that they (ie, physicians and healthcare organizations) have a responsibility to ensure “the needs of remote and/or deprived communities of demographically disadvantaged groups are addressed as a prerequisite of implementing digital healthcare.” [emphasis mine]

In the business community, technology companies are taught to solve, maybe own is a better word, the specific problems of one person. Next, ten. Then a hundred, and so on. “The perfect target market for a startup,” says Peter Thiel, “is a small group of particular people concentrated together and served by few or no competitors.” Spend a lot of time on little things. Start out in a niche and serve them well. Then expand.

Rather than build what a group of users is pulling out of the company, the “equity prerequisite” positions entrepreneurs on systemic problems. Important as those are, it’s a path to generic, standardized experiences sanctioned by consensus and guided by compliance. And no one becomes wedded to a product designed for everyone. That’s how Apple’s Asthma Health App launched in 2015 with fanfare and 50k downloads, but ended up with just 175 active users six months later.

Start too broad and you'll inevitably have to crawl back into a niche.

Early on at Propeller, we built a version of our inhaler sensor for dumb phones. It was a complicated feat of Bluetooth engineering and a naive misuse of time and resources. Health systems had objected that our technology needed to work for every patient, not just those with smartphones. Though already clear there was no trend toward fewer smartphones, we gave in to those demands. By the time it was ready two years later, no one wanted it.

The unacknowledged reality of chronic respiratory disease for health tech entrepreneurs is that the labels of asthma and COPD encompass a lot of phenotypic variation that already make them complex product challenges.

Asthma contains multitudes. As The Lancet puts it:

“The notion of asthma as one unifying disease concept is disappearing further into the realm of historical oversimplification…Asthma is at best a syndrome with different risk factors, different prognoses, and different responses to treatment.”

Different demographics, too. Asthma in kids is often not the same as it is in adults. And each of these is substantially its own experience. Even medically, the disease has no home. You may see a pediatrician, allergist, pulmonologist, or an occupational medicine physician. COPD has similar diversity.

How does one engineer and commercialize a single solution to serve all these different personas and permutations? Can you create something for a child who wheezes with viral infections that an adult with work-related asthma will also find valuable?

Add the requirements that it must also address the social challenges of healthcare—it should work well for those with poor health literacy, for example, or those who lack internet access—and the project becomes semi-quixotic.

Creative technology works by variation and selection. Each product is a conjecture about what people want. Most are refuted. So far, only a tiny fraction of people with chronic respiratory disease have had any experience with digital health.

Thankfully, we’re only beginning to imagine what’s possible now that we could not do before. One day, some ideas will gain meaningful traction among people with respiratory disease. Until then, the most productive path forward is an emergent strategy of creative (maybe opinionated) solutions focused on the specific problems of a narrow set of users. That includes interventions designed for the unique difficulties faced by disadvantaged populations.

We’re better off scaling those targeted products, successful in their limited aims and audience, than chasing mythical universal experiences that are impossibly compelling, perfectly equitable and accessible to everyone.

Early Spring Hodgepodge

• Fellow anthropologist Susannah Fox has a new book out, Rebel Health, about peer-to-peer healthcare, or as the blurb tells it: “An action-oriented and radically hopeful field guide to the underground, patient-led revolution for better health and health care.” Please get yourself a copy and support her work.

  

• If your company is working on respiratory disease, heads up that the Respiratory Innovation Summit at ATS (San Diego, May 17th) is still accepting applications. Learn more and apply here

• Closed Loop Medicine, where I serve on the board, published promising results from a trial of personalized dosing of amlodipine in Journal of the American Heart Association.

• This week (March 7th), I’m joining Pete Kazanjy (author of Founding Sales) in a free online session for founders put on by Kentucky’s Launch Blue accelerator. More info / register

• I’ve moved my personal site to Substack, and started a “What I’m doing now” page. I’m lining up some fun stuff—eg, interviews, market overviews—for future newsletters. If you have suggestions, or feedback on the newsletter in general, please send me a note or leave a comment.

Thanks for reading!

DVS

• A single calendar page that prints every day of the year. Weirdly useful.

• I had all my boys home last week and I can tell you that Koala, a sound sampling app for your phone, is more fun than it should be.

• We have such an accumulated history of beautiful art now in the public domain. A lot of it is available at Artvee, which is amazing and will capture an afternoon, easy…but has also made me notice its absence in my daily life. There have to be more ways to keep these in circulation. Who’s working on this?

• Speaking of artists, check out Thomas Deininger on Instagram, and buy me one of his pieces if you’re feeling generous. Maybe this one

• Deep into Kolymsky Heights, a spy thriller set in Siberia…so not really on brand for this section, but very, very good reading

• I’ve recently been using Felt to make a map of travel recommendations for friends. You can annotate, draw routes, highlight neighborhoods, pin photos, and so much more. If this keeps getting better I might make travel guides in my retirement :)

• Eternity is the last album Alice Coltrane released before she became monastic. It is achingly good. Beautiful instrumentation around her, out front on electric piano. I’m now all but lost digging into her unusual life story and discography.

• The first tornado ever recorded in Wisconsin in February. It was a wild storm.

  

Otherwise, myself, two things sprang up this month that I would really love your help with:

1. Competitive Intelligence + Investigative Research

The media apocalypse is here. More than 20,000 people have been laid off in the last year. From Washington Post, to Conde Nast, to amazing journalists or entire teams at some publications.

To help, I'm assembling a bench of world-class freelancers and journalists who’ve been affected by these layoffs, and connecting them to digital health and healthtech startups and investors.

If your company/firm would benefit from competitive intelligence/research, or private, investigative reports [Note: if you don’t have someone on the team doing this, you definitely would] please message me or add your name to the list here.

The other thing is this:

2. An acquisition workbook

As you know, I've recently been helping a handful of founders who are exploring potential acquisitions of their companies. I really love this work, but boy, for the most part these projects are cold start.

I’ve decided to collect the most common + valuable lessons—from my own experience and others'—and make a workbook to help founders warm up, do some stretching, and prepare to navigate the process.

Still noodling on format but think it’s going to be an annotated set of 10-15 exercises/activities people can do on their own to spark, encourage and close a successful acquisition.

If you’re interested in getting a copy, or want to recommend topics I should cover, please add your name here.

Speaking of which, last weekend I received this message in an investor update from a company: “Acquisition offer went into exclusive LOI and acquirer dramatically changed the offer at the last minute.” Bummer. Unexpected? Nope.

Let this be your reminder that this happens all the time. No one tells you, but in most cases you should delay exclusivity as long as possible. It constrains your startup while doing nothing to bind your potential acquirer to the deal, or its provisional terms. That kind of stuff.

Have a good weekend!

Over the break, I ran across slides from a presentation I gave at Stanford Medicine X way back in September 2012, making the case that digital health could open a new front on prevention. I’ve been reflecting on how this never really took hold (with a few exceptions) and thought the topic and the work of Geoffrey Rose worth resurrecting. So I converted the deck into this post and added some asthma epidemiology to illustrate the opportunity.

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The investigative potential of digital health

Propeller (or Asthmapolis as it was called then) was put together to track asthma. We aimed to create technology that helped people and their families accomplish the work of illness. But we did so primarily because we thought collecting information that made visible its day-to-day burden would revealing something about the disease that could be used to prevent it in the first place. Our work with the city of Louisville was our best example of that effort.

This ambition remains an under-appreciated potential of digital health. In the accumulation of data from new sources and at previously impractical scales, it becomes possible to sneak up on prevalent chronic diseases, catch them in moments of weakness and odd behavior, and deliver insight that can help reduce their prevalence. 

It requires technology companies embrace a broader, comparative perspective on health. That means thinking beyond an individual and their physician and the current configuration of healthcare in the US. But it was well described nearly four decades ago, when in 1985, physician-epidemiologist Geoffrey Rose published a paper entitled Sick Individuals and Sick Populations. 

Rose's essay became a landmark for clearly demonstrating how the causes of disease in an individual could differ from those that determine the overall rate of disease in a population. He made the point with this famous figure illustrating the distribution of systolic blood pressure in populations of Kenyan nomads and London civil servants.

“Why is hypertension absent in Kenya and common in London?”

Rose explained that you could investigate in each population why some individuals had higher blood pressure than others and arrive at the same conclusion. You would probably settle on genetic variation, and find some contribution of environment and behavior. In other words, you could achieve a complete understanding of why individuals within each population varied, and yet miss the most important public health question: Why is hypertension nearly absent in Kenyans and common in Londoners?

The answer to that question, Rose argued, had nothing to do with the characteristics of individuals. Rather, it had to do with some kind of force acting on the population as a whole, shifting the distribution one direction or the other.  

His paper provoked epidemiologists to contemplate their dual-sided mission. Did they want to explain why one individual became ill (or stayed healthy), or might they use their techniques to understand and prevent disease in populations?  

Digital health has unwittingly labored with the same dilemma. Should the field be content to improve the day-to-day management and quality of life of individuals with a disease, or could we achieve something more significant and meaningful, potentially reducing the prevalence and incidence of diseases in populations?

There are puzzling data and questions in asthma epidemiology that illustrate the differences in these orientations and highlight how digital health can get us closer to those more ambitious goals. 

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Established risk factors for asthma don't explain prevalence patterns and time trends

For the past four or five decades, the prevalence of asthma has been rising inexorably, almost uniformly, and in nearly every population in the world where it’s been measured. But recently, changing and contrary trends in its epidemiology have challenged a lot of our understanding of the origins of the disease. 

Just when we thought we had the evidence and theoretical framework to account for the long rise in asthma cases, now we have to explain why in some populations we appear to be witnessing a decline, while in similar and neighboring ones, wholly different patterns exist. 

It’s not easy to untangle. For example, Statistics Canada recently reported that prevalence of asthma in Canadian children had fallen to its lowest level in ten years. But during the same period the United States saw a stabilization of prevalence and then a return to a significant increase. 

Official reports from Statistics Canada pointed to improvements in air quality and reductions in tobacco smoke exposure. But the situation is quite a bit more complex than that. Unfortunately, Stats Canada has it wrong, but at least they’re wrong in a way that productively highlights the distinction between the causes of disease in individuals and the causes in populations.  

The key difference hinges on the existence of two types of risk factors. Stats Canada, like much of public health, is naturally attracted to secondary risk factors, or things that determine who in a population will develop a disease. Rose called these the “causes of variants.” By contrast, primary risk factors — what Rose termed the “causes of incidence” — determine the overall level of a disease in a population. 

Tobacco smoke offers a good example of how easy it is to mix them up. We know that someone exposed to tobacco smoke during childhood faces significantly higher odds of developing asthma. But we also know that tobacco smoke exposure can’t be responsible for the epidemic of asthma. The increase in asthma occurred precisely during times when the population prevalence of smoking was falling significantly. Air pollution tells the same story. Air quality improved substantially across many countries during the same period when rates of asthma were increasing. Two figures from Our World in Data show the historical data.

Put another way, these are important risk factors that influence how likely a given individual is to develop the disease. But they cannot be causative factors in the epidemic of asthma overall in the population because they demonstrate counter trends at the population level.  

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Primary and secondary risk factors in digital health

It’s natural for patients, physicians, and public health to assume the salient exposures they observe causing disease in individuals should be relevant and important for the population. But their perspective almost guarantees they will pay attention to secondary risk factors. Rose predicted this. He warned that patients and physicians were neither motivated nor capable of detecting the underlying risk factors of disease in a population. In fact, he noted that, in most cases, primary risk factors are not even evident until summed up across the population.

With its focus on the individual and his or her clinical care and treatment, digital health has similarly emphasized the experience of patients and physicians. And, in the last decade, we’ve learned a lot from technology about the (secondary) risk factors popular in clinical reports and case studies, but the evidence for their role in primary causation is weak.

In short, the widespread adoption of digital health has not translated into a better understanding of the causes of incidence, or led us to new preventive strategies. For the most part, we’re stuck with temporary, palliative public health measures. We have to continually focus on identifying and protecting the most susceptible individuals in the population because they will always be there.

For us to make progress at the population level — to make asthma and other chronic diseases less common in the first place — we need to figure out how to use technology to reveal or identify the underlying risk factors of the disease. One of the most important ways we might do that is by collecting vastly more and different types of data than we have ever had before. 

Consider the traditional public health surveillance approach to asthma, designed largely to focus on analyzing information about the 30,000 or so sentinel events — hospitalizations and emergency room visits — that occur each week in the US. These are important outcomes but they represent a tiny fraction of the available information about asthma in the community. For example, we can assume there are between forty to fifty million uses of an albuterol inhaler every week across the country, all of them readily capturable and analyzable with distributed networks of sensors and connected medicines.

The bottom-up approaches of digital health bring the daily experiences of many more people—incl health, disease, environment, diet, social behavior—under statistical scrutiny. This means more opportunities to identify underlying primary risk factors, with other benefits such as greater timeliness, sample variability and geographic specificity, too. 

But we need more than numbers. Rose noted the hardest cause to identify is the one universally present, because then it has no influence on the distribution of cases. He provided the example of the relationship between cardiovascular mortality and the overall hardness of the public water supply. Rose showed that in Scotland where everyone’s water is soft (left of the vertical, dotted line in the figure below), there appears to be no discernible relationship. The possibly adverse effect only becomes evident when you extend the study to areas with greater variation in exposure.  

Rose pointed to tobacco use as another example. If everyone smoked twenty cigarettes, he explained, then every methodological tool we have — case control studies, clinical studies, cohort studies — would suggest that lung cancer was a genetic disease. And, in fact, they would be kind of right, for if the exposure to the necessary agent is uniform across the population then all that determines the distribution of cases is individual susceptibility.  

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Deliberately comparative research at scale

In the case of asthma and other chronic diseases, it’s probably too late to search for primary risk factors among the majority populations of high-income countries. In those settings, exposure to primary risk factors is likely already ubiquitous across the population, leaving us with just the effects of secondary risk factors. Fortunately this is one of the things that digital health is best suited to address.

With smartphones nearly universal, we can now involve many different types of people — populations that vary in prevalence, in exposures, and even those that abruptly change their lifestyle and environment. We can carry out international comparisons and cross-cultural experiments that have a greater probability of helping us understand primary risk factors underlying the cause of these diseases. Yet these efforts remain extraordinarily rare in digital health. 

The healthtech industry can and should focus on improving day-to-day management, raising quality of life, and preventing individuals from developing disease when possible. But we should also aim for more fundamental victories. By rekindling and investing in an all-but-forgotten hunt for primary risk factors, we increase the chances that one day we can control the determinants of incidence, lower the mean levels of risk factors, and finally start to shift populations in a favorable direction.

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Two things I’ve been enjoying this month:

Drumha.us - A classic drum machine in a web browser, with a friendly interface and fun collection of sample kits and instruments. Compact, open-source, and free to use for anyone interested in music production.

The Peregrine by JA Baker - Recommended by Werner Herzog, who lists it as required reading for his film students, citing its “intense, visionary observation.” Structured as a diary, I’m reading the entries day-by-day and reminded to observe the water birds and raptors around my own place more closely.

Ok, on to a quick post about departures when starting →

Digital health mafia departures

Vive Collective recently published a list of digital health mafias - the companies whose employees in turn founded the most new startups. Propeller tops the list (on a per employee basis) with five, though my memory of the number of people who left to start their own thing is twice that at least.

I’m proud of that record and those people, and one day soon it would be fun to check in with them and write about their companies and what they’re up to today. But thinking about them today also reminded me how challenging it was when people chose to leave the company, particularly early on. 

Startups are bands. As a founder, few moments are more meaningful than having exceptional people find enough inspiration in your emerging vision to sign up. It confirms and energizes your ambition, and those people define and frame vital parts of the company. Amidst that, it’s easy to convince yourself you’re assembling a permanent lineup, whose familiar faces will be there in ten years when you cross the finish line together. 

But when people start to peel off for new projects, doubts and confusion arise. We don’t talk much about how to make sense of that experience as a founder.

At Propeller, I often found it perplexing. I tried to cheer them on without celebrating so much that others got ideas. But privately I worried about who was going to do what they did, or if anyone could. I asked myself what it meant to lose that person. Was the appeal of the company diminishing? Had they foreseen some problem on the horizon? At times, I was even slightly envious they got a fresh start to tackle a new set of problems.

One trick for me was realizing that, just like many bands, startups don’t have a permanent cast. Instead, they’re a classic example of a Ship of Theseus, the thought experiment that poses the paradox: If every piece of a ship is gradually replaced does it still remain the same object? 

Building a startup is a similar puzzle. It continually challenges you to juxtapose the identity of the company with the transience of its constituent parts. Initially, the team is the company, but these things separate with time. After all, there are natural tours of duty - as Reid Hoffman, Ben Casnocha and Chris Yeh describe in their book, The Alliance. According to their experience, the majority of people join startups for two-to-three year stints that modestly transform their careers (and the company). Consider and understand these dynamics, they argue, and you can co-create successful and productive missions at the company.

As a founder, it feels weird to balance the continuity of a company identity with the reality of employees’ dynamic careers. But if you can appreciate that organic renewal is its natural state, you realize how it’s invigorated by evolution. The whole thing will go through periods and variations in styles, just like a band. 

If you’re lucky, you get to see all kinds of versions of your company, unique embodiments made up by the different mixes of people. And later, when your own foundational tour has come to an end, how rewarding it is to see the long arcs of people who joined and left to lead their own adventures, and watch their energy echo through a new generation of companies. 

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Heading back to the US this afternoon for Thanksgiving tomorrow with my family at Chief O’Neill’s in Chicago, and in the spirit of the week, I’ve been remembering and appreciating the people who, at a distance, influenced my career. Here’s a story about an important one of them. Happy Thanksgiving! →

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Startups are bands, yet we don’t often ask founders about their influences. I’ve been reflecting on mine recently and how they shaped me. One of the earliest and most important was artist and engineer Beatriz da Costa who indirectly nudged my professional life in a new direction many years ago. I wish she was still alive so I could thank her for that.

Da Costa is best known as the person behind an unlikely project in 2006 that outfitted a flock of homing pigeons with tiny backpacks, filled with small equipment that would measure and report air pollution as they flew home to their coops through miles of city. 

She called it PigeonBlog, as though the birds were posting about the air quality as they traveled. Inspired by a historical photograph of a pigeon with a camera around its neck, ready for wartime duty, she saw the potential for recruiting the birds a hundred years later as partners in understanding and calling attention to the environment. 

It was, in many ways, a ridiculous and laughable electronic art project. It was also a momentous breakthrough - proof, more than a year before the first iPhone was released, that cell phone components (eg, GPS/GSM modules) could be used to connect objects in the world to the network, and that the information from them could be made immediately available and useful on the internet. 

Her work created a powerful new vector in technology. These were the first signs of social, networked objects that would become ubiquitous years later.  

Her artwork declared we already had in hand the tools and materials needed to start building previously impossible things. We were only limited by our imagination. In mine, she illuminated a path to chasing asthma in the wild. I realized if it was possible to do this with pigeons, so too would it be with medicines. Soon after, I started adding electronics to inhalers and creating real-time maps of asthma symptoms in the community. Asthmap — later, Asthmapolis, and then still later, Propeller — was born. 

Da Costa and her peers were combining art and technology to make platforms for annotating the world. By inventing new ways to collect and convey information, they saw a way one could interest people again. Often they were just reintroducing us to ordinary things we had not noticed or had grown bored of.

Occasionally this took the form of grassroots citizen science. Da Costa used her art to spark imagination and interest in things that mattered, like poor air quality, but with which we had stopped bothering. 

In this case, she built PigeonBlog out of holes she found in science. Twenty years ago, one could assume that all the interesting questions about air quality had been asked and answered. Cities had scattered networks of fixed, costly, sophisticated monitoring stations. To get an estimate for somewhere in between, you used math and an interpellation model. 

Da Costa appreciated, when few others did, exactly how much this approach assumed. She realized that measurements by the birds had the potential of validating, or challenging, these dispersion models and might make us aware of other things we weren’t seeing. 

Hers was the first three-dimensional view of air quality, for example. Monitors on the ground couldn’t tell you what was happening hundreds of feet up, where the birds flew and traffic-related pollution was mixing and drifting. 

And while monitors were generally positioned in quiet, low-traffic areas rather than known pollution hotspots (to obtain more representative values), pigeons tend to fly by means of visual cues such as highways and landmarks. Measuring air quality along these common routes, could yield more accurate estimates of actual exposures to residents of the area.

The project made scientific sense, but she never really meant for it to be about that. Instead, she told the NY Times, it was “a way of generating attention, a way of disturbing things.” Or, as Alain de Botton recently put it, “We don’t need to make art in order to learn the most valuable lesson of artists, which is about noticing properly, living with our eyes open and thereby, along the way, savoring time.” 

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Da Costa’s work challenged and surprised, but it also divided the audience. The media wasn’t sure what to make of PigeonBlog, or of university art faculty doing research - or was it activism? She was uniquely qualified to capture our attention, but her scientific qualifications and credibility were challenged. PETA accused her of animal abuse. The Cornell Lab of Ornithology asked her to join a board of directors. She was approached by DARPA about converting the technology into a defense instrument. In her artist statement, she talks through the disorienting and unexpected support and criticism she received.

Thankfully she kept going, as though the only thing that mattered was how many people appreciated her work, not the number that didn’t. She knew you only needed a few people to start to believe to get something going. 

Da Costa was one of those creative people who work in the open with others so effectively they basically create a scene: she joined the Critical Art Ensemble, co-founded another group called Preemptive Media, and worked at Eyebeam Art and Technology Center.

She was, in fact, astonishingly prolific. It is difficult to summarize how much she produced. Like Werner Herzog, she just never stopped making stuff. On her own she created projects like Molecular Invasion, Free Range Grains, and GenTerra. With others, she made projects like Zapped, Swipe, and Air, a participatory air quality monitoring community that helped inspire the work Propeller would later do with the city of Louisville.

She always appeared to be working, but watch a few videos of her projects and you get the sense that it feels like play to her. That seems like a valuable secret to relearn from her. Aim to create and work on more projects in the spirit of fun.

She died just a few years after PigeonBlog, from a recurrence of cancer, for which she had been treated several times as a teenager and young adult. Right up until her death, she was making projects the drew us into the difficult aspects of life with cancer, and its research and treatment, like The Cost of Life, The Endangered Species Finder, Memorial for the Still Living, The Life Garden, Dying for the Other, The Delicious Apothecary and The Anti-Cancer Survival Kit.

There was always something urgent in her art and imagination. It showed us new ways to notice the world, to be curious and healthy in it, to be connected to each other and to our communities and places, and to live in that state of wellbeing as long as we can. Her projects lead us to the mystery and truths that come from the interaction of nature and art, of facts and imagination. And she taught us to surround ourselves with people who make and notice things with energy as part of their daily life, and to try to be one yourself. 🙏

• Recently we took our boys to see the movie Canary, about glaciologist Lonnie Thompson. We’ve been fortunate to know him ever since my wife and I joined an expedition to the Bona-Churchill ice field 20+ yrs ago for a profile - “The Vanishing World of Lonnie Thompson” - that Lolly wrote for National Geographic Adventure. While an inspiring and heroic character, the movie should have been better and shorter - as they all should be nowadays! Read Lolly’s profile or check out the great book about Lonnie by Mark Bowen, Thin Ice.

  

• After I saw this Pitchfork review of Jaimie Branch’s third and final album, I’ve been listening to her music for the first time. This record - Fly or Die Fly or Die Fly or Die ((world war)) - is “a heartbreaking glimpse of where she might have gone next” and full of joyful, “jubilant ensemble playing.”

• Such a boom of amazing writing these days, and yet never more difficult to discover new, relevant talents. My favorite solution: Every morning, The Sample sends me one article from a random newsletter that more or less lines up with my interests. When you get one you like, you can subscribe to the writer with one click. Sign up here.

Ok, onwards! Taking a different direction this week, with an overview of a reporting tool I found valuable that has all but vanished from startups today…

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The Power of the Waterfall Chart 

Nothing is forgotten faster than a missed forecast. Yet projecting results and comparing progress against past plans is one of the most effective ways to understand how a company works and improve its performance. 

Enter the waterfall chart - a simple but powerful framework you can use to make projections, and then compare your actual results over time against your initial plans and subsequent forecasts. 

We started using it in the early days at Propeller and it quickly became my favorite way to see how we were doing, where we might be getting off track, and what adjustments were needed. I still use waterfall charts for a variety of personal goals, and in director roles where I can build them from materials shared with the board.

Today, though, it’s rare to run across a company who’s even heard of a waterfall chart. I generally try to change that! It’s one of the first and most common recommendations I make to founders and their teams.

Here is a quick overview of the approach and some thoughts about its benefits:

A waterfall model is a sedimentary way of capturing projections over time. The top row represents the original Plan of Record for the goal. Actual results for the month of August are placed at the intersection of the August column and row. Subsequent months on that row display the new forecast you make at the end of each period. 

This arrangement forms a stair-step or waterfall shape, with actuals cascading diagonally. You can strengthen the visual message by shading the actuals surpassing the plan in green and those falling short in red. Cumulative totals and year-to-date figures can be added for clarity. 

The time period can vary with the chosen variable; just remember that you need results that are finalized within the prior period for the chart to work. 

Waterfall charts can be used to monitor a variety of goals. The target does not matter. It can be a financial metric like revenue or cash, or an operational one like headcount or enrolled patients. At various times, we used waterfall charts to track revenue, bookings, cash / burn, operating expenses, new customers, enrolled and retained patients, clinical outcomes, and upstream targets like marketing leads. You can build company-wide charts or specific ones for a given customer. 

Their visual nature makes things immediately evident. Few tools provide such a clear way to assess progress against goals. When a company starts to veer, it’s easy to spot in the chart and impossible not to acknowledge the drift. With a quick look you can answer key questions like: How is our performance against the original plan? What about against recent forecasts? Where is the probable outcome by fiscal year-end? Is our predictive accuracy improving? 

Waterfall charts create new views on your business. Nowhere else can you find an accumulated history of forecasts and your performance consolidated like this. They combine an aerial view of your performance with a dynamic map of your trajectory. That gives you nearly the animated perspective and insight one usually gets from a flip book, and makes it easier to envision the changes that lay ahead. 

Waterfall charts encourage intervention and build focus, energy and momentum around a plan. By continuously juxtaposing actuals against forecasts, they foster a culture of experimentation rather than analysis. The looming re-forecast provokes an invaluable shift in mindset. It motivates people to ditch the idea that they must first know the cause of something before they try to solve it. Instead they realize the need to launch a barrage of interventions against a problem to look for movement - to understand causes by testing solutions. 

Regular reviews improve your planning and forecasting. Each time you update the model you have an opportunity to re-calibrate based on what’s been learned. Those moments often prompt better questions about the business, which lead to sharper planning. Even with limited historical data, the regular, iterative refinement makes it possible to achieve increasingly accurate forecasts. You also get better at analyzing trends and foreseeing potential challenges and opportunities. And since plans are based on assumptions, waterfall models often highlight inaccurate ones. In our case, it challenged our unrealistic conclusions about the rate at which we’d be able to enroll patients from clinics. 

Waterfall charts are useful communication tools. They simplify complex data while adding historical context. Their combination of visual simplicity and depth of information means anyone can quickly grasp the company’s position relative to its goals, see whether your forecasts are becoming more precise, and understand where you’ll likely end up the year. The continuous updates create familiarity with the evolving picture of the business and reduce the likelihood of surprises. 

The time it takes to maintain the chart is its biggest drawback. Keeping it up requires a decent amount of effort, though that investment is more than returned by the number of unnecessary meetings and discussions it eliminates. We did occasionally have concerns about negative reactions to missed plans or forecasts, but timely updates help to minimize that risk and encourage rational and productive responses.

Waterfall charts are due for a comeback - go build one! Few things rival its ability to help a team track, forecast, and adjust its strategies. Perhaps Causal or another of the other emerging finance tools will introduce something like this. In the meantime, you can get started with a simple Excel version here.

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I’ve finally finished The Long Ships, a hefty Viking epic, full of adventure and fellowship, which I thoroughly enjoyed and recommend. “You are worth all your luck, and that is a very good deal.”

Speaking of which, I’m excited to announce that I’ve joined LifeArc Ventures as a member of their Early Ventures Investment Committee. LifeArc Ventures is the investment arm of LifeArc, a UK-based, self-funded medical research charity with more than 25 years of translating early science into health care treatments. Their collaborative model has created a strong portfolio of innovative companies across a range of therapeutic and digital health fields. I’m looking forward to working with Clare Terlouw, Deborah Harland and the rest of the committee to help great teams build better healthcare experiences through technology.

I’ve also been writing some short pieces about strategy recently, which I’ve not been sending out via Substack but have collected below. If we’re not already connected, please take a moment to find me on Twitter or LinkedIn, where I’ve been posting them.

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Finally, before we jump in, check out Tony Williams 🔥 drumming with Chick Corea and Stan Getz at Montreux from 1972 (thanks to Ulysses Owens Jr. for the clip)

Summer Posts

Here’s the rundown of recent short posts I’ve written from reflections on startup strategy and digital health:

One-Page Business Maps 🗺️ (Substack) (LinkedIn)

When to Drop Your Tools ⚒️ (Substack) (LinkedIn)

Prioritize Your Exceptional Capabilities 🌟 (Substack) (LinkedIn)

Imagine Integrations With Potential Partners ⚙️ (Substack) (LinkedIn)

On to my latest, which is about adding chaos to a situation where you’ve otherwise lost the advantage. I’ve observed this on a number of occasions when founders have needed to rebuild energy in a financing or commercial partnership / acquisition.

When Stalled, Consider Chaos

I spoke with two founders recently whose financings have stalled. Their weeks are filled with an exhausting drizzle of lukewarm interest in their rounds. 

In these situations, it sometimes seems the company’s story is working against it. Often these are teams starting to deliver on their promise and strategic narrative, so you’d expect emerging conviction about the business.

But instead, as the company develops it inadvertently reduces beneficial uncertainty about its possibilities. By growing and stabilizing their business, founders make its future easier to characterize, and increase the specificity about what it is not. 

Paradoxically, steady positive progress can bind with investor expectations and gum up their aspirations. You get drag instead of alacrity. And the more you reduce error in your trajectory the easier investors can plot the slope of your business and set the whole thing to the side.

One move I’ve seen work in these circumstances is for founders to add chaos to the system. That means introducing information and behavior that violates the storyline and conventions of the company narrative. 

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What do these countermeasures look like? Generally, fluid and fast-moving tactics that disrupt the thinking of potential investors. It can be as complex as a partnership deal that stakes out new positioning, or as simple as a provocative executive hire or clever new pricing. Sometimes, you’re better off with guerrilla wizardry.

Either way, time kills all deals. If things have stalled, consider what options you have to deploy uncertainty through assertive and unexpected maneuvers. These force people outside to discount their prior assessments and reevaluate your company, its position, and potential trajectory, and can help you recover your momentum and recapture the advantage.