Written by Sarah Rodriguez, PhD and Jennifer Kan, PhD

Twenty years ago, reading a human genome was a national undertaking that cost billions and took years; today it costs a few hundred dollars and takes about a day. That cost collapse reshaped biology, built one of the most durable franchises in the life sciences, and rewarded the investors who saw where the curve was heading. We think the next chapter is beginning, and this time the molecule is protein.

Genes are instructions, but proteins are what a cell actually builds and uses, the machinery behind nearly every biological function and nearly every disease. Most approved drugs act on proteins, not genes, which is why the genome describes only what could happen while the proteome describes what is happening in a given cell on a given day. Yet although we can read a genome from end to end for a few hundred dollars, reading a protein the same way, amino acid by amino acid, remains far harder and far less mature. The first single-molecule protein sequencers have only just reached the market, but they still identify only a subset of the twenty amino acids (a few specific technologies showcased here and here) and carry error rates DNA sequencing left behind years ago. None can yet read every amino acid in sequence the way we read DNA bases. Closing that gap, from first instruments to end-to-end sequencing, is the frontier this essay is about.

Why We Need Protein Sequencing

Despite decades of progress, the tools we use to study proteins still can’t read one directly. Proteomics, the study of the proteins in a sample, is largely performed by mass spectrometry, which identifies proteins by matching them to a reference database, closer to fingerprinting than to reading, and affinity assays, which find only the proteins a researcher already knows to look for. The shortcomings of mass spectrometry and affinity assays as tools boil down to bias.

For example, in blood plasma a handful of abundant proteins, such as albumin and immunoglobulins alone make up roughly 90% of total protein mass, dominate any bulk measurement and bury everything else. The proteins that matter most for disease are often the rarest: plasma protein concentrations span about ten orders of magnitude, and more than 90% of FDA-approved protein biomarkers sit in the lowest 1% of that mass, far below the abundant proteins that swamp the signal. A tool that recognizes only what it expects will miss what is unexpected: a sequence not in any reference, a disease-specific proteoform (a modified form of a known protein, e.g., altered by chemical changes such as phosphorylation or glycosylation), or a low-abundance signaling protein no panel was built to detect. These are what are measured poorly and often not measured at all by today’s tools. Protein sequencing, which reads the actual order of amino acids and modifications on each individual molecule the way a genome is read base by base, is the tool that is required to identify the low-abundance, previously uncharacterized variant.

Why Proteins Resist Being Read

Reading a protein is harder than reading DNA, and the difficulties compound. First, the alphabet is larger and messier. Twenty amino acids against DNA’s four, several so chemically alike they are hard to tell apart, and a residue can read differently depending on its neighbors. Worse, each amino acid can be “decorated” with chemical modifications such as sugars, phosphates, acetyl groups, multiplying what a reader must distinguish.

The molecule is also physically uncooperative. It folds into a rigid three-dimensional shape, and unlike DNA’s uniformly negative backbone, a protein’s charge varies residue by residue. This makes it difficult to adopt existing DNA sequencing technologies (such as nanopore) to proteins.

Finally, protein sequencing has to work one molecule at a time. Proteins can’t be amplified like DNA, so a faint signal can’t be copied louder; everything must come from the molecules in hand. Protein sequencing requires single-molecule reading to count discrete events so that we can understand nuances, such as whether two modifications sit on the same protein.

Why Now

Protein sequencing is a hard problem, but exciting and credible companies are forming now because several things have converged at once. Single-molecule detection matured on the back of semiconductor chips, engineered nanopores, and optics borrowed from the sequencing and chip industries. Machine learning and AI can decode the noisy readouts these methods produce, so that reading a molecule can now smooth a signal enough to identify an amino acid residue. Protein engineering supplied the recognizer proteins, pores, and enzymes that many of these emerging technologies depend on. And the genome boom left behind experienced operators who have built a measurement platform before and investors trained to recognize the pattern.

The Approaches in Competition

So far, no company has yet achieved from-scratch sequencing at scale, and the contenders differ chiefly in how directly they read. Reported efforts cluster into a few families: mass spectrometry, the incumbent of bulk proteomics that infers sequence from peptide fragment masses; Edman-style chemistry read out by fluorescence, or fluorosequencing; direct nanopore reading of an intact chain; and more indirect routes that reconstruct sequence from DNA-based molecular recording or single-molecule Raman spectroscopy, each trading directness of read against scale (dive in more here and here).

The most direct approaches read residues in order. Quantum-Si sells a single-molecule sequencer that uses enzymes to shave amino acids off a peptide’s end one at a time while fluorescent binding proteins identify each newly exposed residue, though its throughput remains far below whole-proteome depth. The fluorosequencing techniques pursued by Erisyon and Prisma labels a few reactive residue types and reads the pattern left behind as the peptide is stripped, enough to match a protein to a reference database. Nanopore sequencing, a strategy pursued by Oxford Nanopore, demonstrates the most faithful to reading an intact chain and the hardest, threads a protein through a pore and reads the current it disturbs, contending with the molecule’s uneven charge and rigid folds through unfolding, molecular motors that ratchet it along, and enzymes that cleave residues at the pore’s mouth. Others read the molecule’s own physics: Pumpkinseed uses a nanophotonic chip to capture each amino acid’s Raman vibrational signature, identifying residues by their intrinsic spectra rather than by tags or fragmentation.

Against all of these stand the proxies that true sequencing means to surpass: mass spectrometry, and the affinity fingerprinting of Nautilus, Olink, and SomaLogic, which establish which known proteins are present but never read an unknown sequence from scratch.

Engineering is only half the race. The differentiator is increasingly software: machine learning and AI turn noisy single-molecule readouts into accurate calls, compensating for what hardware and wet-lab chemistry can’t perfect. Models that denoise and decode these signals push accuracy and sensitivity past what the physics alone delivers, so the winning platform may be decided as much by its data engine as by its chemistry.

What remains unsettled is which approach will be broadly commercialized first, which will ultimately reach the throughput, parallelization, sensitivity, and cost to become the standard, and whether the winning product is full sequencing, partial fingerprinting, or something in between. No obvious winners have emerged, and exciting breakthrough platforms are emerging. In our opinion, this is a great time to be paying attention.

The Size of the Prize, and Who Won Last Time

The proteomics market is estimated at 42 billion dollars (2025), growing toward roughly one hundred billion by the middle of the next decade, and one analyst has sized the next-generation opportunity at about 75 billion. The more instructive question is whether any single company won DNA sequencing. One did. In 2007, Illumina acquired Solexa’s sequencing-by-synthesis chemistry for about six hundred million dollars in stock, and by 2014 it held roughly seventy percent of the market and had won the thousand-dollar genome. While the acquisition price isn’t eye-popping, it was the platform that helped carry Illumina to a peak of roughly seventy-five billion dollars in market value by 2021, more than a hundredfold above what it paid. It won because its chemistry was best on the metric that came to define the market with cost per accurate base at scale, and locked that lead in with a razor-and-blade model whose switching costs became a moat.

One Technology, Many Frontiers

The stakes of protein sequencing reach well beyond life sciences tools and medicine. In agriculture, it can reveal how crops respond to drought or disease and help breed hardier varieties. In food and nutrition, it can verify what’s actually in a product, spotting allergens, and confirming a product is what the label claims. In environmental monitoring, it can identify what living organisms are doing to our soil and water, offering a readout of ecosystem health that DNA alone can’t give. It matters for security and forensics, too, where a protein signature can flag a biological threat or place a person or species at a scene.

We are looking closely at the latest advances in protein sequencing, from detection chemistry and device physics to the software that turns a noisy signal into an answer. If you are working on reading proteins, we want to hear from you!

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Written by Michael Luciani and Jennifer Kan, PhD

The use of biological components, processes, and living organisms to create useful products has applications beyond medicine, but most bioethics frameworks are centered on patients and clinics. The companies we invest in often have neither. The ethical weight of our companies sits elsewhere: in environmental release and irreversibility, dual-use and biosecurity, ecosystem and food-system effects, as well as impact and consent at the population rather than patient level. This is still a bioethics conversation; it’s just different from one centered on human health.

Why this matters now

The capabilities underlying modern biology are becoming cheaper, faster, and more powerful each year. Work that once took an established lab years to complete now sits within reach of a seed-stage company. That shift is precisely what makes the field investable. It is also what makes ethical scrutiny urgent rather than optional.

Much of this comes down to timing. The standards that will govern bioindustrial work are still taking shape, and the companies being built today are setting precedents before any settled consensus exists. The decisions a founding team makes early, about containment, data, and how a technology is deployed, tend to become embedded in the company before their solutions reach the market. The most influential moment to ask these questions is therefore also the earliest one.

For early-stage investors, that represents both an opportunity and a responsibility. We are frequently the first institutional capital in the room, engaging with founders while a company’s direction is still wide open. Taking ethics seriously at that stage is not a constraint on building ambitious companies; it is part of building them well.

A foundation decades in the making

Over the past few decades, as biotechnology’s reach has widened, reputable institutions have built bioethics into a discipline, and its foundations are now well established. In 1979, the U.S. National Commission’s Belmont Report set out the enduring triad of respect for persons, beneficence, and justice. That same year, Tom Beauchamp and James Childress expanded it into the four-principle approach (autonomy, beneficence, non-maleficence, and justice) that still serves as the common vocabulary of the field.

The conversation grew as the science did. In 2010, after the J. Craig Venter Institute built the first self-replicating bacterial cell with a synthetic genome, President Obama called for a review of the ethics of synthetic biology, which produced the Presidential Commission’s “New Directions: The Ethics of Synthetic Biology and Emerging Technologies“ and its recommendations for capturing the field’s benefits within sensible ethical limits. Two years later, the UK’s Nuffield Council on Bioethics published “Emerging Biotechnologies: Technology, Choice and the Public Good,” which mapped the challenges common to emerging biotechnologies and offered a practical way to weigh them.

More recently, the scientific community has written frameworks for itself: the Engineering Biology Research Consortium’s Guiding Ethical Principles in Engineering Biology Research (2021) established principles, values, and ethical standards to which engineering biology should adhere.

As we build our own framework for investing, we also draw principles from the Cartagena Protocol on Biosafety, the international agreement governing the safe handling and use of living modified organisms, and the WHO’s global guidance framework for the responsible use of the life sciences.

Juniper’s ethical investing framework

Part 1: Categorical exclusions

We do not invest in companies whose primary business is, or materially depends on:

1. Biological weapons or offensive agents: any work whose foreseeable primary use is to cause mass harm.

2. Genetic-use-restriction (”terminator”) technologies.

3. Any work whose aim or foreseeable effect is to enhance the transmissibility or virulence of a potential pandemic pathogen.

4. Covert or non-consensual collection, use, or sale of human genomic or biometric data.

5. Biometric or genetic identification systems whose primary market is population-scale monitoring by states or employers.

6. Animal-derived inputs from species or systems with serious welfare concerns.

7. Nucleic acid synthesis or benchtop synthesis services that do not adhere to recognized sequence-of-concern and customer-verification standards.

8. Engineered organisms or gene drives for open environmental release without a containment strategy, reversibility plan, and ecological review.

Part 2: Six axes of ethical diligence

For every company that clears Part 1, we use these axes to structure conversations with founders. None are pass/fail; they’re forcing functions for honest discussion. Not every axis applies to every company; we use the ones that fit the technology and solution.

1. Benefit and public good. What concrete problem does this solve, for whom, and how large is the benefit relative to the status quo? Could the same benefit be achieved with materially less risk or irreversibility?

2. Containment and reversibility. Does the technology stay where it’s supposed to? If an engineered organism, material, or molecule escapes its intended context, what happens? Can deployment be unwound?

3. Distributional effects. Who wins and who loses if this company succeeds at scale? Bio-based technologies could displace petrochemicals, traditional agriculture, animal agriculture, and traditional mining practices. We don’t view incumbent disruption as inherently bad, but we want to understand how founders think their solutions might affect workers, farmers, communities, and other stakeholders.

4. Ecological footprint. How does the technology affect biodiversity, water, soil, and land-use as it scales? Does it trade a win for a loss elsewhere?

5. Dual-use and biosecurity. How could this be misused, and how hard would misuse be? For platform technologies, such as DNA synthesis, protein design, and organism engineering tools, we want to understand what screening protocols, customer vetting, and biosecurity policies are in place.

6. Animal welfare and sentience. Where relevant, such as cell-cultured foods, ingredients and materials replacing animal-derived inputs, does this product reduce net animal suffering relative to its conventional counterpart? And does that gain hold across the entire production chain, including upstream inputs (growth factors, scaffolding materials, cell-line derivation) and validation/testing that may still rely on animal use?

What we’re committing to

We apply the Part 1 exclusions in our diligence, and use the six axes to structure our conversations with founders at every company that touches them substantively. We keep updating this framework as our thinking matures, and we are candid about the cases where reasonable people might disagree with where we land.

And when a potential investment raises questions we cannot answer with the framework we have, we seek outside expertise. The version of ethics we believe in is the one that thinks carefully before investing, brings in people smarter than us when it needs to, writes down what it learns, and adapts as the technology moves.

If you’re thinking about the ethics of bioindustrials and other non-medical applications of biology, we’d love to learn with you.

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Over the past thirty years, roughly 6% of venture deals have generated about 60% of the asset class’s returns. This is the power law that defines venture capital, and the conversation around it is well-worn. What gets discussed less is what those 6% of deals tend to be.

Most of them are what we call category creators.

Anyone who has read Play Bigger has seen the underlying data: across a large sample set of venture-backed companies since 2000, the category leader, what the Play Bigger team calls the “category king”, captures around 76% of the total market capitalization of the category it defines. Everyone else splits what’s left.

Taken collectively, these two findings suggest that the investments that return venture funds are usually made in companies that define the categories in which they sit.

Pattern recognition

Look at the canonical examples of breakout companies from the last twenty-five years. None of them won on price. None of them won by being marginally better. Each one defined a new unit of work in its category at the moment the underlying technology made that unit economically viable.

• Salesforce defined SaaS when broadband and the browser made it credible to deliver enterprise software over the cloud instead of on a CD.

• Stripe defined modern payments infrastructure when accepting payments online became something a developer could turn on in a few lines of code, rather than a multi-month negotiation with a bank.

• Uber defined ride-sharing when GPS in every pocket made it possible to match a driver to a rider in real time.

• Illumina put sequencing on a flow cell and made access to genomic data fast and inexpensive. Scientists had previously deciphered one gene at a time.

• Twist put DNA synthesis on silicon chips and made it routine to design and order whole genes. Labs had previously stitched together short fragments by hand.

The pattern is consistent. As a cost curve bends, a new unit of work becomes economical, and whoever defines the category around that unit captures most of the eventual market cap.

Why biology is next

As the raw cost of engineering biology has dropped roughly five orders of magnitude over the past twenty years, four shifts are compounding:

Read, write, edit DNA. The cost of sequencing has fallen from ~$3 billion for the first human genome to a few hundred dollars today, and is still falling. The cost to synthesize and edit DNA has followed a similar trajectory.

Equipment as a service. Third-party providers have made fractional access to lab infrastructure routine. A pre-seed company can create minimum viable products (MVPs) without owning any equipment at all.

Robotics and automation. Lab robots are cheaper than the labor they replace. They are more precise, scalable, and run 24/7.

AI. Biological experiments used to happen at the bench: they are slow, manual, and expensive. Increasingly they happen in silico. AlphaFold collapsed protein structure prediction from a multi-year crystallography project to an afternoon of compute. Strain design teams that once cycled through hundreds of physical variants now screen millions in simulation and only build the top candidates. This is just the tip of the iceberg of what AI can do for biology.

As a small founding team raising under a million dollars today can do what required fifty million and five years a decade ago, many more ideas now justify a venture-backed company than did a decade ago.

It starts out looking like a toy

In 2010, Chris Dixon wrote a short essay called “The next big thing will start out looking like a toy.” It’s only a few hundred words long, and it has aged well.

Dixon’s argument, building on Clay Christensen, is that disruptive technologies get dismissed as toys because, at launch, they “undershoot” what users need. The first telephone could only carry voices a mile or two. Western Union passed on acquiring it because they couldn’t see how it would help the railroads, their primary customer. The same dismissal happened to the PC, to digital cameras, to Skype.

The non-obvious part of Dixon’s argument is why some toys become disruptive and others stay toys. His answer: it depends on whether the product is designed to ride an external cost curve. Microchips are getting cheaper. Bandwidth is becoming ubiquitous. Mobile devices are getting smarter. The toys that catch one of those curves get carried up by it. The ones that don’t, don’t.

That’s what we think is happening in biology right now. The cost curves underneath engineering biology have fallen far enough, and are still falling, that founders are now building what look, today, like toys. Microbes that grow textiles. Lettuce that produces GLP-1s. Dogs with sensors in their noses. Proteins that pick rare-earth metals out of e-waste. Trees engineered to grow faster and sequester more carbon. Bringing back a woolly mammoth.

Each of these is easy to dismiss in 2026. So was online payments in 2010.

What we’ve backed

At Juniper, our portfolio is built around this thesis. Among the first checks we’ve written:

• Colossal, the de-extinction company restoring the woolly mammoth and the thylacine using state-of-the-art biological tools.

• Cache DNA, storing DNA and other biomolecules at room temperature and removing the cold chain from genomics.

• Modern Synthesis, growing textiles from microbial cellulose for fashion and footwear.

• Alta Resource Technologies, using engineered proteins to selectively recover critical minerals from e-waste and ore.

• Evolv, engineering biology to manufacture high-value functional molecules, starting with GLP-1 oral peptides.

• General Sense, digitalizing the sense of smell for agriculture, healthcare, and everywhere else.

• Trilo.bio, self-driving labs turning ideas into breakthroughs at a pace the world has never seen before.

None of these companies are trying to be a marginally cheaper version of an incumbent. Each one is defining a category that didn’t exist before. Not all of them will get it right, but some will become the category king.

The thesis

We believe category creation is the most under-appreciated source of venture-scale return in biotech. The cost curves underneath engineering biology have fallen far enough that the experiments founders can credibly run have outpaced the categories that exist to absorb them. New categories will get defined. The companies that define them will capture the majority of the value.

Juniper writes the first checks into category-creating companies enabled by breakthroughs in biology, engineering, and computation. If you’re building one, especially if it currently looks like a toy with the potential to re-imagine an entire category, we’d like to hear from you.

Community Updates

Shout-Outs!

Over the last month, we’ve received 20 new founder referrals from 12 Scouts. Big shout out to Janina Motter and Erin Huiting as our top referrers of the month. Thank you also to Rachel Shapiro, Nelli Morgulchik, Alison Hirukawa, Tuzun Guvener, Benjamin Wong, Judy Su and David Kim and to our repeat referrers Sapyr Sebaoun, Stephen Sameroff and Barak Dror. We look forward to getting to know the founders and companies you shared with us.

Juniper at SynBioBeta in San Jose

We were out in force at SynBioBeta this month, co-hosting a bioindustrial happy hour with Forbion and the BioInnovation Institute, and meeting up with Juniper Scouts at a special hangout!

Juniper Sponsored JBIMS Panel at Berkeley

This month marked our first Scout-sponsored event: the JBIMS Microbiome Innovation Forum 2026 - AI × Microbiome Innovation at Berkeley, with 198 attendees from 10+ academic institutions and 65+ companies and non-profits. Thank you to our Scout Asa Conover for organizing this event on behalf of JBIMS!

We are pleased to have Srilekha on our team joining a panel alongside Cheri Ackerman Araromi (Concerto Biosciences), Jenny Yang (Outpost Bio), Adam Arkin (LBNL / UC Berkeley), and Jessica Green (ARPA-H) to discuss how microbiome technologies can achieve economic viability at scale.

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Part 1 of 2. Part 2 will examine how AI is enabling a new generation of category creators in the bioeconomy.

Written by Sarah Rodriguez, PhD, and Jennifer Kan, PhD

Engineering biology has the ability to produce nearly any molecule of commercial interest. However, the remaining challenge has been unit economics, where bioindustrial products compete with petrochemical-based manufacturing, whose costs have been optimized over the past century. Competing companies that couldn’t optimize fast enough have become case studies in cost curves that never bent fast enough.

We believe this has changed and is changing. We describe four structural shifts in commercializing bio-innovations, each driven by AI: increased speed, reduced tool costs, big data moats, and automation, that are converging on the variable that determines whether a bio-based chemical can compete with its petrochemical incumbent on unit economics.

The argument that follows is not that AI guarantees success in bio-industrials; plenty of failure modes remain. But rather, we argue that base rates for the category have shifted, and that the question of why now has an astonishingly credible answer.

Time compression

For fifty years, the central bottleneck in biology was speed. A single protein structure consumed months of crystallography. Moving from target identification to a commercial candidate took four to six years. That made bioindustrial venture economics nearly impossible to close.

That bottleneck is now vanishing.

AlphaFold now predicts protein structures to atomic accuracy in minutes, roughly three times more accurate than the next-best system. One enzyme researcher, after using it to design plastic-degrading proteins, put it plainly: “What took us months and years to do, AlphaFold was able to do in a weekend.” EvolutionaryScale’s ESM3, published in Science in January 2025, went further; when prompted to design a novel protein, it produced a result researchers estimate is equivalent to simulating over 500 million years of evolution in a single run.

When research timelines compress from years to days, the set of commercially viable products expands. Companies building on this infrastructure are hitting milestones in months that would have taken a decade.

Cost reduction

The cost of engineering biology has dropped roughly five orders of magnitude over twenty years. DNA sequencing has fallen from ~$3 billion for the first human genome to a few hundred dollars today. A pre-seed bioindustrial company that raised <$1M can now accomplish what required $50M and five years a decade ago. When protein design moves from six-figure budgets to near-zero marginal cost, the universe of commercially viable bio-based chemicals, materials, and fuels expands substantially.

Data moat

Foundation models like AlphaFold and ESM3 are trained on broad public datasets. They are useful starting points but cannot predict how a specific organism will behave on a specific feedstock in a specific fermenter. That prediction is system-specific, and the data required to make it does not exist outside the companies that generate it.

A company that has run hundreds of campaigns on its own system has trained yield-prediction models its competitors cannot replicate. Higher predictive accuracy expands the design space the company can effectively search, finding high-yielding scenarios that pure trial and error never reaches. We’d argue that two companies pursuing the same molecule with the same tools will not arrive at the same costs of goods sold (COGS). The one with more proprietary data on its own system will land at a lower number.

Autonomy

Three operating expenses (OpEx) dominate fermentation cost after feedstock: labor, batch failures, and utilities. AI-driven control addresses all three, though by different mechanisms. Real-time sensor models adjust temperature, pH, dissolved oxygen, and feed rates without human intervention, reducing both plant headcount and the R&D operator burden. Predictive and interactive models flag drift before it becomes a failure, addressing the ~4% of commercial batches lost to operator error, the single leading cause. While utility consumption is largely physics-bound, AI plays a role in compressing it per kilogram of product by shortening cycle times. Predictive and autonomous batch control get each batch to target titer faster, cutting the hours of steam, cooling, agitation, and aeration consumed per kilogram produced.

The unit economics layer

The four shifts above reshape how fast, cheap, unique and effective an AI enabled bio-industrial start up can be. But the compounding effect that matters most to bioindustrial economics happens after the strain leaves the lab, in the fermenter, every day, for years.

Feedstock often accounts for 40–70% of COGS in a commodity biochemical, so yield, grams of product per gram of feedstock, is a variable that is enormously impactful. Titer and cycle time matter too, but yield sets the floor on how low COGS can go.

AI tools can move and pull this lever quickly. Strain design models fine-tuned on proprietary fermentation data, redirect metabolic flux from byproducts toward the target molecule, and push real-world yields toward the theoretical maximum. Add real-time process control and three variables move together: yield, cycle time, and uptime act multiplicatively.

For example, take a 500 m³ commercial fermenter running at a 0.30 g/g yield on industrial glucose (~$400/tonne), with 72-hour batches and 80% uptime, producing 1,000 tonnes/yr. Total COGS land near $3/kg, with feedstock making up about $1.30 of that. Now, if we use AI to optimize for yield to 0.45 g/g, batches to 48 hours, and uptime to 95%. That same asset produces nearly 3x the output, and COGS drop to $1.70/kg.

To recap, the result is a drop from $3/kg to $1.70/kg. Obviously, these back-of-the-envelope calculations are oversimplified, but they are directional. Almost halving the cost structure dramatically closes the gap to the incumbent petrochemicals. And of course, the exact numbers depend on the molecule and plant size, but it is the shape of the curve that matters.

Venture outcomes in many bioindustrials are gated not by whether the molecule can be made, but by whether the unit economics can compete with incumbent petrochemicals. We have shown only a handful of ways that AI changes the slope of what’s achievable per dollar invested and per year of runway, giving founders tools to move yield, cost, and cycle time on timelines that match venture capital. Not all bioindustrial investments compete head-to-head with the petroleum industry, but we purposely chose to showcase it as a tough hurdle to clear and how AI is already playing a big role in doing so. If your company is clearing this hurdle, or playing a big role in helping the sector to do so, we’d love to hear from you!

Next month, in Part 2 of AI x Bioindustrials, we’ll share ways AI is enabling a whole new generation of category creators.

Community Updates

Shout-Outs!

The Juniper Scout community is hitting its stride - this month we’ve received 25 high quality referrals from 9 Scouts. A big shout out to Sapyr Sebaoun and Harshit Chellani for the highest number of referrals and thank you to Sara Anjum, Gia-Bao Dam, Stephen Sameroff, Leela Ghimire, Emma Watts, Barak Dror and Joe Buccina for sharing great founders and companies you came across with us. Keep them coming.

Juniper Happy Hour in San Francisco

Earlier this month, we hosted a Juniper Happy Hour during San Francisco Climate Week, in partnership with Citizens Private Bank, Goodwin and Pilot. Through this gathering of founders, investors, and Juniper Scouts, we got to spend time with new and old friends and hear what our community is researching, building, and investing in. For those who couldn’t make it, we’ll be back - and we’re already scoping the next city.

Upcoming Events

Juniper will be at the following events in the coming months. We look forward to seeing you in person soon.

• Bioindustrials Happy Hour - a small gathering of investors and corporates hosted by the BioInnovation Institute, Forbion, and Juniper during SynBioBeta. 📅 May 5 | 📍San Jose | RSVP here

• AI x Microbiome Innovation Forum - Srilekha will be speaking at this event, designed to connect microbiome-related researchers, startup founders, investors, policy-makers, and non-profit groups. 📅 May 8 | 📍Berkeley | RSVP here

• Alpha Summit by Allocator One - Jenny will be speaking at this invite-only gathering of GPs, LPs, family offices and founders. 📅 May 28 | 📍San Francisco

• Dinner with Juniper - ​Michael and Jenny are hosting an intimate, invitation-only dinner for LPs and friends of Juniper. 📅 May 29 | 📍San Francisco

• Drops of Juniper: Bio-Industrials Happy Hour - happy hour during New York Climate Week. More details to come soon. 📅 September | 📍New York

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Written by Sarah Rodriguez, PhD

The bigger picture

Methane is often described as the “low-hanging fruit” of climate mitigation. The greenhouse gas we could reduce fastest with the most immediate warming impact. But we think the better metaphor is that methane is a raw material hiding in plain sight: globally abundant, thermodynamically rich, and increasingly accessible to biological conversion. The companies that figure out how to use it, not just abate it, will build some of the most important industrial platforms of the next decade.

This distinction matters. The dominant framing of methane has been as a liability: something to capture, flare, plug, or regulate away. Bloomberg Philanthropies has committed $172 million to methane detection and monitoring since 2019, including a $100 million initiative announced at COP30 to scale satellite surveillance of super-emitters globally. The Global Methane Pledge, signed by over 150 countries, commits to at least a 30% reduction in anthropogenic methane emissions by 2030. These are important efforts. But detection and abatement are only half the equation. The real opportunity lies in manufacturing products from this gas rather than simply destroying it.

Biology’s methane engines

Over 570 million tonnes of methane are released into the atmosphere every year from landfills, livestock operations, oil and gas infrastructure, and wastewater treatment. Methane traps over 80 times more heat than CO₂ over a 20-year window, yet the Global Methane Hub estimates that only 2% of climate finance has targeted it. Most of it escapes because the sources are too dispersed, too contaminated, or too small-scale for conventional gas processing infrastructure.

Biology has the ability to change that.

Methanotrophs, bacteria that consume methane as their sole carbon and energy source, are emerging as a serious industrial platform. These organisms thrive naturally in wetlands, landfills, forest soils, and hot springs, using specialized enzymes to oxidize methane step-by-step under mild, low-energy conditions and naturally account for 43-80 MT of atmospherics methane removal per year. Scientists have studied them for decades. The difference now is our ability to engineer them.

Recent advances in genome editing, synthetic biology, and gas-phase bioreactor design are transforming methanotrophs from environmental curiosities into production hosts. Engineered methanotrophs can now produce single-cell protein (with an amino acid profile similar to fishmeal, already approved in EU salmon feed), biodegradable plastics (PHAs), methanol, and a growing list of specialty chemicals.

The economics

From an investment standpoint, methane-to-products has several structural advantages.

The feedstock is free, or nearly free. Methane from landfills, wastewater treatment, and oil and gas operations is currently vented or flared because existing infrastructure can’t economically process it. Companies that convert this stranded methane into products aren’t just reducing input costs, they’re getting paid to take the feedstock. This is the same economic logic that makes waste-feedstock approaches so powerful across bioindustrials.

The end markets are massive and established. Single-cell protein targets a $180B+ global animal feed market. Biobased fertilizers target a $190B+ market. Bio-based plastics target a $400B+ plastics market. Methanol is one of the most widely traded industrial chemicals on earth. These are not speculative markets waiting to be created, they are mature industries where bio-based alternatives can compete on cost and performance, with sustainability as a structural advantage.

The regulatory tailwind is real and growing. The Global Methane Pledge, EPA methane emissions rules, expanding satellite monitoring networks, and carbon credit markets all create increasing economic pressure to capture methane rather than release it. Companies that convert captured methane into saleable products stack product revenue on top of regulatory compliance value.

Why this matters even more now

The political and policy environment makes domestic biomanufacturing more urgent than ever.

In December 2025, the FY2026 National Defense Authorization Act included 17 provisions designed to elevate biotechnology across the defense and intelligence communities, including the creation of a Biotechnology Supply Chain Resiliency Program authorizing the DOD to accelerate domestic biomanufacturing solutions. A February 2026 GAO report found that DOD has invested $965 million in biomanufacturing since 2021, while acknowledging that the U.S. still lacks sufficient infrastructure to advance biotechnology projects from lab to commercial scale. The bipartisan Biomanufacturing Excellence Act of 2025 proposes a national center of excellence to strengthen domestic production capacity.

The supply-chain tailwind is growing as well. Supply-chain disruptions and geopolitical fragmentation have increased pressure for resilience-oriented reconfiguration, including diversification, near/friend-shoring, and selective domestic capacity-building in strategic sectors . Methane is well-positioned to support localized feedstock strategies because it can be sourced from natural gas and biogenic streams, including landfill-derived gas, and is increasingly being explored as an input for higher-value outputs.

Meanwhile, the NSCEB continues its “Biotech Across America Roadshow,” warning that the U.S. has a roughly three-year window to act before falling irreversibly behind China in biomanufacturing capacity. China has launched the world’s largest coordinated biomanufacturing buildout, 43 companies across 37 industry directions, and has positioned industrial biotech as a Politburo Standing Committee priority.

As we wrote in our December newsletter, this is a familiar pattern: a critical upstream technology gets cornered by whoever scales fastest and invests most deliberately. The difference is that in biomanufacturing, the U.S. still has a significant innovation advantage. Whether that innovation gets manufactured here, at scale, on competitive terms, or commercialized elsewhere, depends on the decisions made in the next few years.

For early-stage investors like us, this convergence of low-cost feedstock, massive markets, accelerating regulation, bipartisan policy support, and national security urgency creates a window that is opening now.

What we’re building toward
We’re tracking the entire methane-to-products ecosystem: companies converting methane into protein for aquaculture, into bioplastics, into drop-in fuels, and into chemical building blocks. With a biological emphasis, we’re interested in companies that have engineered the methanotroph for methane fixation in plants, bioreactors, soil, and even forest contexts.

More broadly, we’re interested in investing in use cases that take advantage of methane’s real thermodynamic edge. Methane is a single-carbon, hydrogen-rich feedstock, which makes it especially well suited to produce products like hydrogen, methanol, and the reducing gases used in lower-carbon steelmaking. In other words, some of the strongest demand tailwinds for methane are not in trying to force it into every downstream category, but in building around the products methane is naturally best positioned to make.

We’re also watching methane pyrolysis and methane-derived carbon materials as meaningful sources of optionality. Pyrolysis can co-produce hydrogen alongside solid carbons such as graphitic carbons and nanotubes, which could become more valuable as battery demand rises and graphite supply remains concentrated.

With biological emphasis, we’re interested in companies that have engineered the methanotroph in plants, bioreactors, soil, and even forest contexts.

As we think about the next generation of companies in this space, we believe the winners won’t just be the ones that can capture methane but will be the ones that can route it into the highest-value end markets, especially where biology can unlock feedstocks that conventional infrastructure still treats as too small, too dirty, or too distributed to matter. And the opportunity is broader than molecules alone: in a world increasingly shaped by energy security, affordability, and competitiveness, methane also has value as a localized industrial input. Industrial biology sits at the center of that shift.

The broader market signal is still hard to ignore, even if U.S. support for some clean energy categories has become less consistent. The IEA projects USD 3.3 trillion of global energy investment in 2025, including roughly USD 2.2 trillion for clean energy, suggesting that the structural drivers behind the transition, resilience, energy security, and industrial competitiveness, remain intact. We believe methane-to-products is especially relevant in that context because it is not just a decarbonization story; it is also a domestic feedstock, manufacturing, and resource-efficiency story.

If you’re building in this space, we want to hear from you!

Upcoming events

Juniper is co-hosting a couple of events with Lichen Ventures the week of April 20th in Washington, D.C., for DC Climate Week. We would love to have you join us at any of the following:

• April 21: Climate GP/LP Matching

• April 23rd: Grown, Not Drilled: Biomanufacturing and the Race to Remake Everything

• April 23rd: Your First Climate Investment: A Lunch for New Angels and Emerging LPs

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Written by Jennifer Kan, PhD

In software, speed is a feature. In biology, speed is often perceived as a constraint.

Fast-growing bacteria like E. coli can double in 20 minutes under optimal conditions. Yeast in 90 minutes. Mammalian cells? 12–24 hours. We can’t make products faster than cells divide and grow. We accept this as the fundamental limit of biology.

But is it?

We observe existing biological systems and assume they represent the limits of what’s possible, but we’re only seeing the solutions that survived. Perhaps faster biological systems existed but were selected against in the natural world because speed carried hidden costs—higher energy consumption, increased error rates, or reduced robustness.

Historically, biology has been a discipline to be studied. It’s observational, descriptive, and takes a long time to understand. Today, we’re entering an era where biology can be engineered, and is even programmable. This is an era where the speed of biology is defined not by survival of the fittest, but by the law of physics and human creativity.

The venture case of accelerating biology

In venture, time is money and speed compounds. At Juniper, we believe breaking the perceived speed limits of biology represents the rare opportunity to invest in technologies that don’t just create new products, but fundamentally restructure the economics of entire industries. The time savings enabled by faster biology could compress research and development timelines, mitigate risk through faster iteration, reduce time to market, and ultimately improve the capital efficiency of commercialization.

Speed advantages in biology are frequently highly defensible. Unlike software where competitors can often replicate features quickly, biological speed improvements require deep technical expertise. The first companies to achieve significant biological acceleration will likely maintain substantial competitive moats.

Importantly, companies that solve biological speed constraints don’t just create products, they become central nodes in our economy, capable of accelerating multiple markets and capturing value across entire ecosystems. A company with 100x faster protein production capabilities, for example, could address a trillion-dollar opportunity across pharmaceuticals, food, materials, and cosmetics markets. This platform approach creates the kind of massive addressable markets and multiple expansion paths that generate venture-scale returns.

Frontiers we’re tracking

Given these economic advantages, we are excited by technologies that defy biology’s perceived speed limits. We believe this requires us to think beyond the conventional boundaries of any single discipline. The most powerful solutions will likely emerge from the convergence of multiple technological frontiers. Here are opportunities we are paying attention to:

• Context Engineering

The speed of biology isn’t just determined by cells and molecules, it’s also shaped by the context in which biology operates. Could we better engineer localized environments or micro-compartments that alter the physical and chemical conditions biology operates in? How could we manipulate electromagnetic, acoustic, or gravitational fields to guide biological systems to our advantage? For example, synthetic organelles that concentrate substrates 1000x above normal cellular levels, or electromagnetic fields that guide molecular assembly in ways impossible in natural environments.

• Hybrid Systems

Biology can operate with greater speed and control when integrating with complementary technologies. Optogenetics already demonstrates this principle, enabling microsecond control of gene expression—1000-fold faster than chemical signaling—by coupling light with biological systems. Bio-electronic systems can sense biological signals, deliver electrical stimulation, or even perform computation faster than biology. Chemo-enzymatic systems can outperform either chemistry or biocatalysis alone in speed and selectivity in how we make molecules. These convergences are redefining what’s possible when biology is part of a larger, optimized system rather than operating in isolation.

• Molecular Engineering

At the foundation of biological speed lies the molecular machinery of life itself. Advanced molecular engineering lets us rebuild that machinery. Some engineered enzymes already exceed natural limits by orders of magnitude—artificial proteases that cut proteins 100x faster than their natural counterparts, or synthetic DNA polymerases that replicate at speeds approaching theoretical limits. With the rise of AI, genetic engineering tools, and automation we see unprecedented opportunities in accelerating gene expression, molecular synthesis, and cell growth. These advances enable biological systems to operate on timescales previously considered impossible.

The transformation ahead

These three technological frontiers converge on a single transformative capability: the compression of time. While technical risk remains substantial, the potential returns are asymmetric. Success doesn’t just create new products, it creates entirely new categories of what’s possible. Imagine when biological processes that normally take hours can be completed in minutes, and we can run millions of these processes simultaneously with state-of-the-art automation, what could we build with this capability?

If you’re building in this space, we want to hear from you!

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Boyer was busy; he had no interest in speaking with Swanson. But after their 10-minute meeting turned into hours, Genentech was born.

By 1978, Genentech’s small team had achieved something no one thought possible: they had engineered the bacteria E. coli to produce human insulin. A year later, they did the same for human growth hormone, a protein important for the growth of bone, muscle, and metabolic regulation.

In 1980, Genentech became the first biotech company that went public on NASDAQ. Shares quickly leapt to $88 at opening, more than double the offer price of $35, and closed at $71. Genentech was suddenly worth over $500M. The IPO was 20 times oversubscribed. The Wall Street Journal called it “one of the most spectacular market debuts in recent history.”

What made this IPO significant was that it essentially created the biotech investment category. Before Genentech, investors were skeptical about putting money into companies based purely on scientific potential rather than proven products and revenue streams. The success of Genentech’s IPO opened the floodgates for biotech investing and paved the way for hundreds of other biotech companies to go public in the following decades. It also validated the business model of university research spinning out into commercial ventures, which became a cornerstone of the modern biotech industry.

Genentech went on to develop and market several successful drugs, including Activase for heart attacks and later becoming a pioneer in cancer treatments. The company was eventually acquired by Roche in 2009 for $47 billion, representing one of the most successful returns on investment in biotech history.

Breaking down the anatomy of Genentech’s success:

Start with a visionary translator

Many breakthrough science startups start with a visionary translator, someone who sees the commercial potential before the field does and is unafraid of the challenges ahead. Swanson played that role at Genentech. His “beginner’s mind” allowed him to see commercial possibilities. He was unafraid of the technical and regulatory hurdles that Genentech needed to overcome. This, coupled with his exceptional sales and persuasion skills, were critical to recruiting early believers—talent, customers, investors—to fuel Genentech’s success.

We see this pattern in Juniper’s portfolio too, such as Ben Lamm’s role in Colossal. Lamm (CEO and co-founder of Colossal) is a serial entrepreneur, master at crafting narratives that inspire. He was instrumental in taking Colossal from a seed-stage company to becoming a decacorn in just four years.

Go infrastructure light

Genentech initially outsourced all of its lab work to academic collaborators, including UCSF and City of Hope. It’s possible to advance groundbreaking science without fully integrated infrastructure.

Today, many early-stage science companies can get to critical milestones using shared infrastructure such as R&D partners, contract research organizations (CRO), contract development and manufacturing organizations (CDMO), and public-private infrastructure. We encourage our portfolio companies to consider this strategic move before building their own infrastructure, to move fast and optimize for capital efficiency, especially in the early days.

Partner to scale

Genentech didn’t commercialize insulin alone, it licensed it to Eli Lilly. This provided Genentech steady revenue and credibility from an established pharma company, crucial for investor confidence. It also allowed the company to avoid costly infrastructure for manufacturing, distribution, and regulation.

Strategic partnerships can serve as early scale levers, especially in complex or regulated markets. Owning discovery and IP while outsourcing commercialization is a powerful capital-efficient move to go to market in the deep tech commercialization playbook.

Incremental validation

Rather than swinging for the fences immediately, Genentech chose to work on proteins that were already well-understood medically (like insulin and growth hormone.) This reduced both science and regulatory risks and built credibility before Genentech tackled more novel products.

Incremental validation builds credibility systematically and prioritizes learning velocity in addition to speed of execution. The best entrepreneurs we’ve worked with are all masters in the chess game of incremental validation.

Culture is a moat

Genentech succeeded by creating a hybrid culture that attracted top scientists who wanted more autonomy and faster decision-making than traditional pharma offered, while still maintaining commercial discipline. This cultural positioning became a key recruiting advantage.

Culture is often overlooked for founders building a science company. But the right ethos will become a talent magnet, and talent is one of the best cornered resources a company can own.

The biotech revolution that began in a university lab nearly five decades ago continues to accelerate at unprecedented speed. Today’s entrepreneurs have access to far better tools and deeper scientific understanding than Swanson and Boyer could have imagined. Yet the fundamental attributes of success remain remarkably consistent: find visionary translators who can bridge science and business, optimize for capital efficiency, validate incrementally to build systematic credibility, and create cultures that attract exceptional talent.

We believe the next Genentech is being built right now—perhaps in a garage, a university lab, or over coffee between a curious entrepreneur and a brilliant scientist. If you’re working on building an industry-defining company, we’d like to hear from you!

Written by Jennifer Kan, PhD

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Relinquishing leadership in biotech would have consequences far beyond economics. From energy and food systems to human health and defense readiness, engineered biology is increasingly determining national strength and security. If the U.S. intends to remain competitive and resilient in the coming decades, maintaining leadership in biotechnology is not optional, and early-stage investment will play a critical role.

Written by Mackenzie Scurka and edited by Jennifer Kan, PhD

This summer, China announced plans to support pilot biomanufacturing plants across 20 companies by 2027. By the fall, that plan had been expanded and formalized to include 43 companies spanning 37 industry directions and six major application areas, including raw material industries, equipment manufacturing, consumer goods industries, information technology, emerging and future industries, and common needs. The objective? To establish a national pilot production network within the next two years – likely the largest coordinated biomanufacturing scale up effort in the world.

While China continues to invest in biopharmaceutical innovation, momentum is increasingly shifting toward industrial biotechnology, or “bioindustrials”, where engineered biology fundamentally changes how ingredients, chemicals, materials, fuels, fertilizers, and other critical intermediates are produced at scale.

Heavy investment in industrial biotechnology follows a well-established national industrial strategy: concentrate capital and policy support in a critical upstream technology, scale faster than global competitors, and lock in cost and supply-chain advantages over time. China applied this strategy to rare earths in the 1990s by scaling extraction and processing capacity until it controlled the majority of global supply. In the 1970s-1980s, Japan dominated the DRAM semiconductor market by investing aggressively in scale, process improvement, and yield improvement, successfully driving out global competition with sustained cost pressures. South Korea reached peak dominance in shipbuilding in the 2000s by having the state provide long-term subsidized capital, tolerate years of below-cost pricing, and scale shipyard capacity faster than other countries until learning-curve and cost advantages became structural.

Bioindustrials now fit this same pattern. Biomanufacturing enables greater self-sufficiency by reducing reliance on imported oil, chemicals, and critical materials, allowing domestic production from biomass and waste streams. At scale, it creates durable cost advantages through improved fermentation efficiency, AI-enabled process optimization, and cumulative learning effects. Control over bio-based production of chemicals, fuels, and materials allows nations to dominate upstream inputs and shape downstream global supply chains – just as prior industrial leaders have done in energy, electronics, and infrastructure.

Biotechnology is often perceived as slow and risky as drugs take a long time to develop, the probability of success is low, and the upfront cost is high. Commercializing bioindustrials is different. Products such as enzymes, specialty materials, and industrial chemicals have short paths to market and face lower regulatory barriers. Companies can reach profitability quickly with a short payback period. At the same time, biomanufacturing supports energy security and climate objectives by converting low-cost feedstocks into valuable outputs. These capabilities have direct national security implications as well, enabling on-demand production of materials and chemicals critical to defense logistics and supply-chain resilience during periods of disruption. By establishing early leadership in biotechnology, countries can shape global technical standards and regulatory norms, translating into long-term structural advantages.

But crucially, the pace at which these advantages can be captured has changed.

The speed at which progress in biotechnology has accelerated over the past decade would not have been possible without advances in AI. AI has fundamentally changed biotech research: tasks that once required years of lab work can now be completed in weeks or even days, and open-source tools like DeepMind’s AlphaFold have significantly lowered barriers to entry. However, different political and economic systems are taking markedly different approaches to applying AI in this domain.

The United States’ approach has been largely compute-driven and private-sector-led, with an emphasis on general-purpose models intended to eventually solve complex biological problems. Significant investment has flowed into foundational bio-AI platforms, drug discovery models, and large-scale computational infrastructure. Another approach treats AI primarily as an industrial tool, explicitly directed toward improving manufacturing efficiency, reducing costs, and accelerating biomanufacturing scale-up. In China, this orientation has been formalized at the national level, with biomanufacturing-enhancing AI elevated as a priority in the 15th Five-Year Plan. Chinese biomanufacturers also benefit from lower energy costs, subsidized infrastructure, and government-supported projects.

The result is a growing asymmetry. While the United States leads in frontier AI and biological discovery, preserving bioindustrial leadership will require deeper coordination and sustained investment. Important efforts are underway across the federal government through agencies such as the Department of Defense (DoD), Department of Commerce (DOC), and the Department of Agriculture (USDA). In addition, initiatives like BioMADE strengthen domestic bioindustrial capacity and de-risk scale-up through the construction of hundreds of thousands of square feet of pilot plants, formalized workforce development plants, and the efficient deployment of millions of dollars in awards intended to bridge the gap between lab breakthroughs and commercial production.

That said, biomanufacturing is still in the process of developing the same level of sustained coordination and strategic alignment that has defined U.S. efforts in frontier AI. Today, responsibility is shared across multiple agencies, each advancing critical pieces of the ecosystem. There is an opportunity to build on this strong foundation by further strengthening coordination, connecting pilots to full-scale deployment, and aligning incentives across agencies to elevate industrial biomanufacturing as a core national priority.

China is not the only other major player. The EU is also moving deliberately to scale its bioeconomy through coordinated industrial policy. Under the EU Net-Zero Industry Act and in a Bioeconomy Strategy plan released recently by the European Commission, European policymakers are focused on accelerating deployment from lab to market, expanding pilot and demonstration-scale capacity, streamlining regulation, mobilizing blended public-private financing, and securing biomass and feedstock supply chains across Member States. Together, these efforts aim to translate Europe’s strong scientific base into industrial-scale bio-based production and reduce dependence on imported fossil inputs.

If leadership in biotechnology consolidates outside the United States, economic influence would shift, and the national security risks would be significant. Reliance on foreign bio-based supply chains would leave U.S. food systems, industrial inputs, and critical materials vulnerable to geopolitical disruption. At the same time, advanced biotech capabilities introduce difficult security challenges, from population-scale biological data collection, agriculture disruption, to the dual-use risks of engineered organisms. Maintaining domestic leadership, capacity, and control in these technologies is essential.

So… what can be done?

There is still time for the United States to respond, but the choices made now will be path-dependent. The National Security Commission on Emerging Biotechnology (NSCEB) has called for a minimum of $15 billion in federal investment over five years to catalyze private capital, alongside a coordinated national strategy spanning leadership, scale-up, defense integration, innovation protection, workforce development, and allied coordination. At a baseline, the U.S. must ensure that breakthroughs developed domestically can also be manufactured at home, at scale, on competitive economic terms, and with safeguards against IP theft and supply-chain dependence. Achieving this will require bringing government into the technology development process earlier and establishing transparent, continuous engagement with regulators to avoid surprise delays that stall commercialization.

Early-stage capital will be decisive in whether that ecosystem takes shape. The companies defining future biomanufacturing processes, fermentation economics, and production architectures are being built now, well before industrial pathways are locked in. To succeed, the U.S. biotech ecosystem must address several structural challenges: developing repeatable frameworks for academic–industry partnerships beyond biopharma; enabling secure, interoperable biological data sharing to support collaboration; and making substantial investments in domestic fermentation capacity to compete on both cost and scale.

As early-stage investors with a specialist focus on bioindustrials, we have been invited into conversations with DARPA and the NSCEB to share on-the-ground perspectives and help strengthen coordination between the public and private sectors. We’ve seen encouraging signals and had productive discussions, but momentum must translate into sustained action. The decisions we make in the coming years will shape the trajectory of biotechnology, and whether the United States continues to lead, or becomes dependent on those who do.

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Written by Mackenzie Scurka and edited by Jennifer Kan, PhD

Space expands humanity’s capacity to solve problems on Earth. Gravity, atmospheric pressure, radiation shielding, and resource availability all shape the scope of what life can and cannot do. By removing or altering these constraints, space introduces an entirely new environment for biological discovery. Weak gravity, vacuum conditions, extreme radiation, and the necessity of closed-loop resource cycles dramatically expand the total design space for life, giving us unprecedented ability to build solutions for some of humanity’s most urgent challenges.

How do these properties actually lead to biological innovation, and what kinds of solutions are we talking about here?

In space, microgravity profoundly alters how life behaves by eliminating sedimentation and buoyancy, the forces that cause heavier particles to settle and lighter ones to rise. Without these gravitational cues, cells, particles, and fluids remain uniformly suspended, enabling biological interactions that rarely occur on Earth. This environment can support the growth of complex 3D organoids that more closely mimic in vivo tissues for studying diseases and tissue regeneration, improve biomining by increasing contact time between rock particles and microbes, and enable innovations in climate-resilient agriculture through optimized plant architecture and hydroponic systems. Microgravity also allows for the production of higher-quality protein crystals for rational drug design and supports the formation of novel, highly uniform biomaterials.

Exposure to altered or extreme radiation in space introduces DNA damage, mutagenesis, and oxidative stress while degrading the stability of pharmaceuticals and biologics. These challenges have catalyzed advances in radiation-shielding strategies, such as self-replicating melanin-rich fungal shields, as well as on-demand bioproduction of protective reagents to safeguard astronauts and biological systems.

The isolation and resource scarcity of space require fully circular, highly efficient systems for food, water, air, and waste. These constraints drive innovation in sustainable manufacturing and life-support technologies, including algae-powered photobioreactors that simultaneously produce food and oxygen while supporting closed-loop waste recycling and bioremediation, and autonomous life-support systems for plant cultivation beyond Earth.

But surely the cost of such endeavors is prohibitively high, with launch access that’s prohibitively low?

While full-scale testing and validation ultimately require orbit, a surprising amount of early-stage space biotech R&D and de-risking can be achieved on Earth. Companies are utilizing microgravity simulators, high-radiation testing environments, vacuum chambers, and analog habitats to iterate quickly and cheaply before ever obtaining a launch slot.

These tools accelerate iteration and reduce cost, but they cannot perfectly mimic the true conditions in space. The good news is that access to orbit has never been more affordable, due in large part to private companies like SpaceX and Blue Origin. Compare SpaceX today vs NASA of the past: the cost of SpaceX’s Falcon 9 is around $2,700 per kilogram, while the cost for NASA’s Space Shuttle was approximately $54,500 per kilogram two decades ago. In addition, rocket reusability and the use of smaller satellites and rideshare missions are making previously uneconomical ventures, like satellite constellations, viable. As we build out lab space, greenhouses, and bioreactors in space, experiments will require less equipment to be transported, further reducing the cost curve.

At Juniper, we believe the most compelling space biotech companies today pursue a dual-track strategy: a near-term terrestrial business model capable of generating real revenue today, often in sectors like pharma, agriculture, or specialty chemicals to fund R&D, reduce grant dependency, and build commercial traction; and a long-term vision for applications in space when orbital infrastructure, launch capacity, and economics reach commercial maturity.

Interstellar Lab exemplifies this approach. Long term, their AI-driven modular greenhouses are designed to grow food for humans in space. Today, their largest partnerships are terrestrial, such as their collaboration with the Robertet Group to optimize plant growth and molecular composition, using their ‘biopods’ to sustainably produce key ingredients for the cosmetics and perfume industry.

We are at an exciting moment in time when scientists and entrepreneurs can push the boundaries of what’s possible with biology in space while providing immediate value on Earth. Designing closed-loop food systems for space helps us build agricultural systems that are more efficient and sustainable. Engineering radiation-resistant biology for astronauts helps us develop prevention and therapeutics for cancer. Creating space-grade biomanufacturing solutions improves supply-chain resilience and drives industrial transformations.

We believe space biotech will become a meaningful contributor to human progress. It expands our ability to design life and materials, moving beyond Earth’s constraints to find solutions to the most complex challenges facing our planet. For now, patience and selectivity are key. As launch costs continue their decline, private spaceflight capacity scales, and commercial orbital biomanufacturing platforms mature, the economic conditions will inevitably improve. When they do, we expect a wave of space biotech companies to emerge, leveraging the unique advantages of orbit to build tools, medicines, materials, and food systems that help us thrive on Earth, and maybe, one day, beyond.

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Earlier this month, Juniper hosted a panel on how Donor-Advised Funds (DAFs) can be used to invest in for-profit ventures (recording here!) The conversation attracted hundreds of founders, investors, and mission-driven donors who are eager to understand how philanthropic giving can play a more catalytic role in the deep tech venture ecosystem.

But DAFs are just the tip of the iceberg. There’s a rich landscape of alternative funding models that can help diversify a startup’s capital stack, reduce dilution, and align funding with mission-driven impact.

Here are some of the alternative funding models, tools, and infrastructure we’re exploring:

Donor-Advised Funds (DAFs)

DAFs have long been a vehicle for charitable giving, allowing donors to receive immediate tax benefits when they contribute money to funds designated for future philanthropic use. However, it’s a lesser-known fact that DAF capital can also be invested into for-profit, mission-driven technologies and even the venture funds that back them.

Examples of organizations and intermediaries facilitating DAF investing include:

• Neta

• CapShift

• Mission Investors Exchange

• Fidelity Charitable

Program-Related Investments (PRIs) & Mission-Related Investments (MRIs)

PRIs and MRIs allow philanthropic organizations to support commercially viable solutions while advancing their missions. MRIs are financial investments made from a foundation’s endowment that aim to generate both social impact and financial return, but they are not considered charitable activities and must meet standard prudent investment rules. In contrast, PRIs are treated like charitable grants that must primarily further the foundation’s exempt purposes, with profit not being a significant motive, and they receive special tax treatment.

Examples of active PRI/MRI funders and intermediaries:

• The Rockefeller Foundation

• The David and Lucile Packard Foundation

• Prime Coalition

Recoverable Grants

Recoverable grants are philanthropic funds that are repaid only if certain milestones are met, such as raising additional capital or generating revenue. They’re designed to recycle capital for further impact while offering early ventures flexible support.

Organizations using this model:

• CapShift

• ReFED Catalytic Grant Fund

• BioInnovation Institute

• Activate

Thesis-Driven Philanthropic Funds

Thesis-driven philanthropic funds are an emerging model of funding that applies a venture-style approach to philanthropy. Led by domain experts and focused on specific, time-bound missions, these funds pool capital from philanthropists to back high-risk, high-impact science that’s often too early for institutional or commercial investors.

Examples include:

• Homeworld Collective Garden Grants

• Sentinel Bio

• EQT Foundation Breakthrough Science Grants

• Founders Pledge

Traditional Grants from Private Foundations

Philanthropic and private foundations are increasingly funding early-stage deep tech innovation through non-dilutive, traditional grants. These funds don’t need to be repaid and often target work that’s too early for venture capital.

Active funders include:

• Grantham Foundation

• Schmidt Family Futures

• Breakthrough Energy

• Lemelson Foundation

Fiscal Sponsorship

Under a fiscal sponsorship arrangement, a 501(c)(3) non-profit can sponsor a for-profit startup whose mission aligns with its own, allowing the startup to receive tax-exempt donations via the sponsor. While this doesn’t guarantee funding, it opens doors for philanthropic contributions that may otherwise be off-limits.

Specialized fiscal sponsors include:

• Fiscal Sponsorship Allies

• New Venture Fund

• Tides Center

• Inspire Access

Strategic Capital

Not long ago, corporate partnerships were viewed with skepticism in the early stages of company-building. Today, the narrative has flipped. Corporate venture arms, innovation programs, and industry foundations are providing strategic capital that combines funding with market insight and commercialization pathways.

Active strategics in bioindustrials include:

L’Oréal, Johnson & Johnson, Microsoft, ADM, Shell, Chevron, H&M, Lululemon, Cisco, Amazon, BASF, and Toyota.

Decentralized Autonomous Organizations (DAOs)

DAOs represent a new frontier: community-governed, blockchain-native capital pools that allocate funding through transparent, democratic voting processes. They move fast, often with fewer constraints than traditional grant mechanisms.

Juniper’s portfolio company, ValleyDAO, is a DAO focused specifically on climate biotech. Other examples in deep tech include Molecule and VitaDAO.

Written by Mackenzie Scurka and edited by Jennifer Kan, PhD

Did we miss a model you’re excited about? Tell us in this short survey! We’re planning another panel on alternative funding in science, and we might feature your idea in our next expert-led discussion.

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Our investment philosophy stems from our own experience as scientists and founders. Having developed breakthrough technologies ourselves and navigated the challenges of building companies from the ground up to exit, we understand both the technical complexities and commercial nuances of building venture-scale companies.

Since 2021, we have evaluated thousands of bioindustrial startups at their earliest stages and invested in over 50 companies and counting. The best-performing company in our portfolio grew from seed to decacorn status in just four years. Our concentrated focus on bioindustrial companies gives us asymmetric advantages in identifying winners. We have a deep understanding of market timing and competitive positioning, and can quickly differentiate between technology ready for commercial value creation and science that is still too early.

Our investment beliefs center on six core pillars—our "6T" framework—that we believe are fundamental to building category-defining companies. The best companies we have seen leverage synergies across all six pillars to launch and scale in record time.

Team

Great founders operate with high agency and speed of execution. They are comfortable with uncertainty, bias toward action, and play offense. They don't wait for perfect conditions or exhaustive planning. They move quickly to test hypotheses, iterate on feedback, and capitalize on opportunities as they emerge.

They are also self-directed learning machines, eager to master whatever skills are needed to reach their destination. They don't wait for external permission or complete information. They understand that the ability to execute rapidly and iterate often matters more than having the perfect strategy.

We believe the following characteristics are table stakes in founding teams:

1. Unique insight that is often non-obvious or contrarian

2. Exceptional technical expertise, with skillsets that are hard to master or a combination of skillsets that are rare to find

3. Business savviness and an obsession in solving problems for their customers

4. Deep commitment to their mission, driven by a desire to create something far greater than themselves

5. Talent magnetism, the ability to inspire the best people to work with them

Ultimately, we believe entrepreneurs who are scrappy—those who can accomplish far more than their available resources would allow—will win.

Total Addressable Market (TAM)

Market size matters because companies need room to grow to drive venture-scale returns. We focus our investments on visionary category creators who can architect new markets worth billions of dollars in the near future, or companies that are solving significant problems in large and expanding markets.

These companies demonstrate a deep, nuanced understanding of their competitive landscape. They know their competitors' strengths, weaknesses, strategies, and blind spots better than those competitors understand themselves, giving them significant strategic advantages in positioning and execution.

We believe the most attractive markets are those where technical innovation can unlock multi-billion-dollar potential that would be impossible to achieve through other means. These are markets where breakthrough technology fundamentally transforms what's possible, creating entirely new value propositions and business models.

Timing

Great companies know "why now" is the optimal moment to build their solution. We specifically look for two types of timing advantages:

1. Tech timing: inflection points driven by technology that fundamentally redefine the cost, speed, and scope of what was previously possible, creating new opportunities for innovation and disruption. At Juniper, we are especially excited about inflection points that leverage breakthroughs in biology, engineering, and computation.

2. Market timing: specific opportunities that emerge from market shifts, supply chain constraints, regulatory changes, or other external factors that create windows of opportunity for prepared companies.

Great companies demonstrate sophisticated understanding of their "why now". They carefully position themselves to ride the right tailwinds, or use headwinds to their advantage, just like the best surfers can read the waves and position themselves to catch a wave early and generate speed and power.

Technology

The technologies we find most compelling are those that are inherently difficult to replicate, possess genuine competitive moats, and are sufficiently de-risked to enable rapid execution and scaling. We are particularly excited about technology that creates categories the world has never seen before, or technology that rewrites the cost structure of products the world cannot live without.

For companies creating physical products, we typically invest at technology readiness level (TRL) 3 or higher, where experimental proof of concept is demonstrated in the lab, to ensure the technology is sufficiently de-risked for commercialization. We also assess the technology’s differentiation, moat, technoeconomics and scalability. For software and AI-driven solutions, we evaluate value creation, utility, differentiation, stickiness, scalability, and data moat. We draw on our expertise and network to understand these attributes and how they may evolve over time. The most compelling technologies generate compounding commercial advantages as they mature and scale.

Traction

Great companies understand their customers and are obsessed with serving them. This manifests in deep market knowledge, rapid iteration based on customer feedback, and the ability to translate customer insights into product and business strategy decisions. We want to see founders who don't just understand their market academically, but who have developed genuine empathy for their customers' challenges and priorities, and use that knowledge to drive the development of their technology.

We look for early proof points that demonstrate both speed of execution and plausible paths to repeatable revenue generation. This includes ideal customer profiles that reflect deep market segmentation work, high-conviction testimonies and letters of intent from potential customers, and meaningful B2B partnerships and pilot programs that validate both the technology and the business model. These early indicators help us assess a company's ability to translate their vision into repeatable commercial success.

Terms

We seek round terms that reflect both the opportunity and risk profile of each company, while ensuring that the company retains sufficient room to scale and grow without being constrained by its capital structure. Our approach to terms prioritizes incentive alignment. We typically avoid terms that might limit the company's ability to attract follow-on investment or create complexity in governance or decision-making.

Our portfolio construction strategy requires terms that are compatible with our overall investment approach and return expectations. This means structuring partnerships that allow us to participate meaningfully in the company's success while providing founders with appropriate incentives and control.

Our 6T investment framework reflects our belief that exceptional bioindustrial companies are powered by compelling technology and built at the intersection of great people, large opportunities, and the right moment in time. While each pillar is important individually, we believe it's the combination and synergy of these elements that drives outsized outcomes in the bioindustrial revolution we're accelerating.