Democratizing Biotech: Kevin Chen's Journey and Synthetic Biology's Rise

In recent episode of talk is biotech! - host Guru Singh(CEO of Scispot), an experienced biotech entrepreneur and advocate for democratizing biotech innovation, engages in a compelling conversation with Kevin Chen, co-founder and CEO of Hyasinth Bio, a pioneering startup in synthetic biology focused on bioengineering medicinal compounds.

Synthetic biology is catalyzing a revolution in biotechnology, enabling an era where a majority of physical goods might eventually be produced through biological processes. This bold prediction, highlighted by a McKinsey analysis, underscores how synthetic biology, combining biology with engineering and advanced digital tools, is accelerating and democratizing innovation in the life sciences through talk is biotech!. Enablers like Scispot, known for providing an industry-leading AI stack to life science labs, a company delivering AI-driven lab operating systems to accelerate biotech research, are helping even small research teams leverage automation and data-driven insights. In this environment, entrepreneurs such as Kevin Chen are at the forefront of biotech's new wave. Chen is the co-founder and CEO of Hyasinth Bio, a synthetic biology startup that bioengineers medicinal compounds, in his case, producing cannabinoids using engineered yeast instead of farming cannabis plants. Hyasinth's approach marked a shift from an agricultural model to a synthetic biology solution that was "more efficient and more sustainable" than traditional cultivation. Chen's journey from disillusioned science student to biotech CEO exemplifies the broader trend of how synthetic biology, coupled with community and AI-driven tools, is transforming biotech innovation. Key themes emerging from his experience include young scientists launching companies earlier, new accelerators and communities lowering barriers to entry, synthetic biology's evolution into an engineering discipline, and the growing importance of mentorship and collaboration in biotechnology. What follows is a deep dive into these insights, through the lens of Kevin Chen's entrepreneurial journey and the rise of synthetic biology as a transformative force in biotech.

From Academia to Startup: A Scientist's New Path

Kevin Chen's story begins with a pivotal career choice during his undergraduate years. Like many science students, he initially saw two conventional paths: academia or industry. But the academic track appeared increasingly unappealing, "one in 10 PhD graduates will actually find a job in research" afterward, a statistic that left Chen questioning the odds of ever running his own lab or project. What he truly wanted was to "do his own research projects and invent cool solutions", such as the idea that would become Hyasinth Bio. Frustrated by the narrow prospects in academia, he looked to a third option that was virtually unheard of at the time: launching a biotech startup straight out of university. In 2014, this was a bold move. As Chen humorously noted, in those days "starting biotech companies was for old white people, basically", in other words, the domain of established professors or industry veterans, not 22-year-old recent grads. Yet a confluence of factors gave Chen the confidence to leap. He had spent a summer working at a friend's genetic engineering startup, which "gave him the seed of understanding what the startup journey was like". He also observed a nascent movement of young scientists pursuing synthetic biology ventures (often inspired by student competitions like iGEM). Sensing an "unknown" path opening up in biotech entrepreneurship, Chen decided to forgo a PhD program and co-found Hyasinth Bio immediately after college. This decision to "jump in" early, as opposed to the traditional route of years of doctoral and postdoctoral training, reflects a wider generational shift in biotech. Increasingly, scientifically minded innovators are choosing to start companies sooner, leveraging synthetic biology breakthroughs to address real-world problems without waiting for academic credentials. Chen's leap of faith would soon be validated by the ecosystem beginning to form around biotech startups.

Bootstrapping a Biotech Startup: Early Challenges and Accelerator Support

Embarking on a biotech startup in 2014, Chen and his co-founders faced an uphill battle to assemble the basic ingredients of a company: funding, laboratory space, equipment, and talent. Unlike software startups, which could be launched from a dorm room, a biotech company requires costly lab infrastructure and scientific know-how. "In undergrad, you don't have a decent lab or access to resources" by default, as Guru Singh remarked in their conversation. Chen's team had to be resourceful and "figure out a lot of that stuff on their own". They tapped every network and favor available, asking professors and universities for spare lab benches, scrounging for used equipment, and recruiting like-minded peers to join the mission. Crucially, Hyasinth Bio caught an early break by getting into IndieBio, the world's first startup accelerator dedicated to synthetic biology startups. "We did the IndieBio program and that gave us initial capital" to get started, Chen recalls. In fact, Chen's cohort was essentially a prototype, "the very first cohort" of IndieBio, which has since grown into one of the globe's top biotech accelerators. IndieBio provided a small stipend (just enough to pay rent in those days) and, more importantly, a crash course in entrepreneurship. Coming from "pure academics, we needed to learn what it's like to work with an investor, how to pitch, and build a company" in terms that outsiders could grasp. The program taught the young founders how to craft a business model and tell a compelling story, rather than a purely scientific proposal. With IndieBio's help, Chen and his team raised a seed round of about $500,000 USD immediately after the accelerator, funding that went into buying key lab equipment and hiring early team members. Still, building a laboratory operation from scratch on a shoestring required ingenuity. Each piece of the puzzle came from opportunistic finds. For example, "the equipment was leftover equipment from a pharma company that shut down" and could be acquired second-hand. The "lab space was just extra space that the university had in their basement" which the team managed to secure through academic contacts. Even Chen's co-founders were drawn from the emerging bioentrepreneurial community, he met them through "community work" and DIY biotech meetups that he and his friends were organizing, a grassroots "hacker space for biotechnology" enthusiasts. In essence, Hyasinth's founding was a case study in bootstrapping: using creativity, community, and early-stage support to overcome the high barriers to entry in biotech.

Early ingredients for a biotech startup (Hyasinth Bio's example):

• Lab space: borrowed a university's unused basement lab facilities

• Equipment: obtained second-hand instruments from a closed pharma company

• Team: found co-founders via community science networks and student competitions (e.g. iGEM)

• Capital: secured accelerator grants and a $500k seed round to cover R&D costs

• Know-how: learned business basics through programs like IndieBio (pitching, fundraising, business modeling)

  
  

Notably, Chen points out that launching a biotech startup is gradually becoming easier as the ecosystem matures. "It was a very different journey than it is now," he says, because a decade ago there were "no startup accelerators or incubators for biotechnology specifically". Today, universities are more willing to support student entrepreneurs, many have dedicated biotech incubator programs and will "share equipment" or facilities to help new companies get off the ground. Additionally, the rise of major startup networks like Y Combinator and IndieBio has "really shaped the future of biotech" by providing founder-friendly capital and advice. Modern biotech startups also benefit from specialized platforms designed for early-stage biotech companies that provide digital infrastructure from day one. In short, the scrappy improvisation that Hyasinth relied on is slowly giving way to a more structured support system for biotech innovators. But back in 2014, Chen's team had little to rely on beyond their own hustle, a visionary idea, and the timely boost from an accelerator willing to bet on biotech's next generation.

Synthetic Biology's New Paradigm: From Bio-Bricks to AI Design

At the core of Hyasinth Bio's venture, and indeed of modern biotech startups, is the emerging toolkit of synthetic biology. Synthetic biology represents a paradigm shift in how we approach biological engineering, making it more akin to software or hardware engineering than traditional lab science. Chen explains that when engineering a microorganism (like yeast) to produce a target compound, biologists now think in terms of standard components and systems. The host cell, such as a yeast or bacterial strain, is viewed as a "chassis", the foundational platform analogous to a car's chassis onto which new functions can be built. Into this chassis, engineers insert genetic modules. In the "iGEM world" (the community of the International Genetically Engineered Machine competition), these modular DNA sequences are often called "BioBricks". Each BioBrick is a standardized genetic part (a gene, promoter, enzyme, etc.) that performs a known function. By mixing and matching these "Lego pieces" of DNA, synthetic biologists can program cells to execute new tasks. In Hyasinth's case, Chen and his colleagues assembled a pathway of "five or six different genes from the cannabis plant or other organisms" and engineered yeast cells to carry this genetic circuit. The goal was to have the yeast chassis convert simple nutrients into cannabinoids (the active compounds normally found in cannabis). Essentially, they were retooling yeast, a microbe used for millennia to brew beer, into a microscopic factory for pharmaceuticals.

Achieving this was far from trivial; it involved extensive trial and error. Chen notes that you might "try that assembly a million times and it doesn't work, and then you find one that works", incrementally improving the strain's performance. But the process has dramatically accelerated in recent years thanks to advances in DNA synthesis and computational design. A decade ago, the dream in synthetic biology was to "just write DNA on your computer, order it, and have it be done for you". Today, that dream is much closer to reality. Writing and ordering custom DNA sequences has become routine, scientists can design a gene or an entire genome on a laptop and get synthetic DNA delivered in days, at a cost unimaginable in the early 2000s. This means startups can quickly test countless genetic designs without having to manually clone genes in the lab, vastly speeding up experimentation.

Moreover, the convergence of AI and bioinformatics is pushing the paradigm even further. Chen describes a cutting-edge example: a colleague used AI-based protein structure prediction tools to design a novel protein entirely in silico (on computer). They "ordered like two copies of that gene and they worked" as intended on the first try. In other words, machine learning models were able to predict a functional biomolecule without the need for laborious wet-lab screening of dozens of variants. This was practically science fiction a few years ago, and now it's becoming a laboratory reality.

The implications of AI-driven design and high-throughput DNA synthesis are profound. It heralds a future where much of biotech R&D can be done in a digital, automated fashion, minimizing the costly guesswork of traditional experiments. As Chen puts it, "we can finally just do this all digitally, letting the machine keep up" to identify working solutions faster. Such capabilities not only accelerate the pace of discovery but also lower the barrier to entry, you no longer need a fully equipped lab to prototype a biological solution. Researchers with a good idea can design DNA sequences on cloud software, outsource the wet lab steps, and obtain results quickly. This feeds into the democratization of biotech: more players, in more places, can now participate in bio-innovation because the tools are cheaper, faster, and increasingly automated. AI applications in biotech are becoming essential for modern biotechnology companies to remain competitive and efficient. Synthetic biology's paradigm, with its "BioBricks" and digital design ethos, is transforming biotech from an artisanal practice into a scalable engineering discipline. And it is this paradigm that underpins the new generation of biotech startups like Hyasinth Bio, which can leapfrog older methods (for example, farming plants) with clever genetic programming of microbes.

The Power of Community: Collaboration in the Bio-Innovation Ecosystem

One of the most striking insights from Kevin Chen's journey is the central role of community and collaboration in biotech innovation. Traditionally, biotechnology could be an insular field, academic labs worked in silos and companies guarded IP closely. But synthetic biology's rise has been accompanied by a more open, community-driven ethos. Chen himself benefited from early DIY bio meetups and citizen science groups, where "enthusiasts, regular people" interested in biotech could gather, much like a coding hacker-space but for biology. These grassroots communities fostered connections that proved vital, indeed, Chen met his Hyasinth co-founders through such networks.

As biotech has evolved, so too has the recognition that solving complex biological problems requires bringing diverse people together. Chen is now actively cultivating this spirit through SynBio Canada, a nonprofit community co-founded by Kevin Chen to unite synthetic biology enthusiasts and professionals across Canada. "The synthetic biology community was kind of fragmented around Canada," he observed, with isolated pockets of researchers who "don't necessarily know each other" despite working on similar challenges. SynBio Canada's mission is to bridge these gaps by creating a network where students, scientists, industry experts, and even hobbyists can share knowledge and resources.

Chen's vision for the community is very pragmatic: he wants to "make it easy for people to form meaningful connections and become mentors or mentees to one another", as well as to share physical resources like DNA samples, strains, and lab materials within a trusted network. In effect, he imagines something akin to an open-source ecosystem for biotech, where instead of code, people freely exchange biological parts, data, and expertise to help each other succeed. "Systems of collaboration", such as repositories or hubs for sharing genetic constructs, could dramatically "reduce costs" and duplication in research by pooling community assets. It's a bold contrast to the old model of each lab or startup working in isolation, and it aligns with the broader trend of open science in the life sciences.

Both Chen and Guru Singh note that this communal approach is not just nice to have, but likely the key to unlocking biotech's full potential. When asked to bet on one biotech trend for the future, Chen's answer was telling: "people are going to get better at coming together and creating solutions" in biotechnology. In his view, more collaboration, "more people, more collaborations, more sense of community", will yield a diversity of ideas and solutions that a siloed approach cannot match. This means inviting people of varied backgrounds to the table: not only biotechnologists, but also folks from the industries facing challenges, policy makers, and even traditionally underrepresented groups. The synthetic biology revolution, Chen believes, will be driven as much by social innovation as by technical innovation.

Guru Singh concurs that "we need more humans, more collab", since many technical hurdles are being solved and the human factor becomes the critical ingredient for progress. A clear example of community-driven progress is the movement towards democratized biotech or "DIY biotech". In their discussion, Guru outlines three pieces that must coalesce to realize the vision of widespread, democratized innovation in biotech: (1) making biotech tools and infrastructure more accessible and affordable (so that conducting experiments doesn't require millions of dollars in equipment), (2) leveraging digital platforms and AI to reduce the trial-and-error and complexity of biological research, and (3) engaging more curious and diverse people in the endeavor. "Once these three pieces come together," he argues, "we will see more democratized biotech" with even "citizen scientists" contributing to breakthroughs.

Chen emphatically agrees, underscoring that the people aspect, growing the community of biotech problem-solvers, is one of the most exciting and critical components of this evolution. His work with SynBio Canada is precisely aimed at bringing these pieces together: lowering barriers to entry (through shared resources and knowledge), updating everyone on the latest tools (from DNA synthesis services to AI predictors), and most importantly, building a "sense of community where people can ask each other for things" and team up to tackle problems.

The parallels to the software world are hard to ignore. Just as open-source code libraries and collaboration platforms (like GitHub) accelerated software innovation, the biotech sector is moving toward greater openness and sharing. Singh draws the analogy explicitly, envisioning a future where biologists might openly share DNA constructs or protocols much like coders share code. Chen notes a caveat that biology involves physical materials, so establishing sharing hubs (regional bioresource centers or cloud labs, perhaps) is needed to complement digital exchanges. Nonetheless, the trajectory is clear: community-driven collaboration is becoming a cornerstone of biotech's growth. Through communities like SynBio Canada (and similar groups globally), entrepreneurs and researchers can find mentors, partners, and even replacement lab parts with a simple message on Slack or LinkedIn. In Chen's case, this community factor was pivotal from the start, and he is paying it forward by nurturing the ecosystem that nurtured him.

From Platform to Problem: A New Startup Mindset

In the early days of synthetic biology entrepreneurship, many startups fell in love with their technology platform without a specific application in mind. Kevin Chen reflects on this, noting that when Hyasinth Bio started, "we had a lot of different ideas, we didn't really know how industry would work" with the solutions we were developing. A decade ago, it was common to pitch a broad technology, "a synthetic biology solution for all kinds of things", and only later figure out real use cases. The mindset was "technology-first", hoping a powerful platform (for example, a new method to engineer microbes) would find markets down the line. This approach, while fostering innovation, often struggled to gain traction because it lacked a clear problem-solution fit.

Today, there is a discernible shift toward a "problem-first" ethos among biotech startups. Chen observes that the field has learned the importance of focusing on a tangible problem and working backward to the technical solution, rather than the reverse. "Platforms need to have solutions they're developing and problems they're solving," he emphasizes. In the current thinking, a founder might start by identifying a pain point in a specific industry, say, a waste byproduct in cheese manufacturing or an environmental pollutant in textiles, and then figure out if synthetic biology can solve it. This often means engaging domain experts early. Chen gives the example: "Let's say you work at a cheese factory and have a lot of byproduct waste. Biotechnology might be something off the radar for you. But as the tools become easier, you can find a bit of research and that'll lead to a useful product". In other words, as synthetic bio becomes more user-friendly, people embedded in traditional industries (the "problem owners") can conceive biotech solutions in collaboration with technologists. The result is a startup that from day one has a clear customer and a clear problem to solve.

This shift is healthy for the industry. Chen notes that it brings "more awareness of what problems could exist" and ties the success of a biotech venture to delivering a concrete benefit for a user or customer. It also fosters interdisciplinary collaboration, "both the users of the solution and the developers of the product" need to be in dialogue. Guru Singh echoes this, saying he advises founders to focus on "the beneficiary, the problem they are solving, rather than tech", especially as technical challenges become less daunting with new tools. In Chen's experience, newer entrepreneurs he mentors already "have a much better connection with customers and the actual problems they're looking to solve" compared to a decade ago.

This trend of market-driven biotech is a sign of a maturing sector, one that seeks to avoid the "solution in search of a problem" trap. Hyasinth Bio's own journey reflects this learning curve. Initially, the company's cannabinoid-producing yeast was a platform that could, in theory, make many cannabinoids or derivatives. Over time, however, the team honed in on specific high-value cannabinoids of medical interest and the processes to scale them. After years of R&D, Chen's company reached the stage of translating their platform into a commercial product. He admits that "it took a lot more time and capital than expected" to get there, in part because they were doing pioneering science. But by 2022, Hyasinth was shifting "from R&D to thinking about commercial work", moving from proving the platform to solving a customer's supply chain problem (providing rare cannabinoids reliably and cheaply). For the synthetic biology field at large, this maturation from cool tech demos to real-world impact is essential. It is driving startups to be more disciplined in choosing projects that align with clear needs, whether in healthcare, agriculture, materials, or consumer goods. Kevin Chen's advice to the new generation is clear: identify an important problem first, then build the biotech solution for it, not the other way around.

Mentorship and Ecosystem Building: A Canadian Perspective

Behind every successful biotech innovator is often a network of mentors and an ecosystem that supports innovation. Kevin Chen is both a product of and a contributor to the evolving biotech ecosystem in Canada. He acknowledges that mentors played diverse roles throughout his journey, sometimes formal advisors, other times peers or even family who provided guidance. "Mentorship comes in a lot of different forms," Chen notes, and it serves to de-risk and instill confidence in founders who face steep learning curves. For scientists-turned-entrepreneurs, having experienced voices to consult (on everything from experimental design to fundraising to hiring) can be invaluable.

Chen now pays this forward by mentoring startups through programs like Ontario Genomics' BioCreate initiative. Such programs pair seasoned biotech founders with nascent companies to help navigate early challenges. "They've asked me to mentor a few companies as part of their BioCreate program," he says, adding that the value of mentorship in the community is "really important these days". Finding mentors, Chen suggests, starts with "putting yourself out there" and boldly sharing your passions and ideas. Whether through startup incubators, conferences, or online communities, young entrepreneurs benefit from engaging with the ecosystem openly, a lesson borne out in his own experience. In fact, one impetus for building SynBio Canada was to create a platform for mentorship and networking, so that a student in one city could easily connect with an industry expert or fellow founder in another. "The goal of the community is to make meaningful connections" among members, Chen emphasizes. By joining forums like SynBio Canada or even global networks, emerging innovators can find their "tribe" and advisors more readily than in the past. The old model of mentorship, a lone professor guiding a student in academia, is giving way to a richer tapestry of peer-to-peer and cross-sector mentorship, which is especially vital in a fast-changing field like biotech.

Canada's biotech ecosystem, in particular, is an interesting case study in how a country can foster innovation. On one hand, Canada boasts tremendous assets for biotechnology: strong university programs, abundant natural resources, and a pool of talented researchers. "The talent is for sure there, all the ingredients are there," Chen affirms. Yet historically, Canada has lagged in translating that potential into a vibrant biotech industry. Part of the issue, as Chen and Singh discuss, has been a fragmentation and lack of a central strategy. While Canada has multiple agencies involved in biotech governance, there isn't a single dedicated ministry focused solely on biotechnology, Singh points out, and government funding, while generous for basic research, often "goes to patent applications or IP" without ensuring those discoveries become real products. The result is what Chen calls a "pretty major gap" in getting innovations out of the lab and into the market. "That's the gap that I'm in now," he says, referring to the challenge of commercializing Hyasinth's technology and more broadly, the challenge many Canadian biotech startups face in scaling up.

However, there are concerted efforts to close this gap. Chen believes that better organization and alignment within the community can present a clearer picture of the solutions biotech can deliver for Canada's needs. By uniting researchers, industry partners, and government stakeholders "at the same table" from the start, Canada can more efficiently push biotech projects from concept to implementation. This means fostering public-private collaborations and keeping end-users involved early (echoing the problem-first approach). Singh notes that Canada has all the makings of a bioeconomy superpower, land, water, talent, and supportive policymakers, if only it prioritizes execution and impact over just intellectual property.

Encouragingly, the mindset is shifting. The government's focus on areas like AI and bio is growing, and initiatives like SynBio Canada can serve as the "big pot" to "mix the ingredients all together", a metaphor Chen uses for creating a cohesive national ecosystem. He envisions SynBio Canada helping to gather talent, share knowledge, and spur collaborations across the country, ultimately cooking up "something really nice" from Canada's raw ingredients. In sum, Kevin Chen's perspective highlights that science alone isn't enough; mentorship and ecosystem-building are critical to biotech success. By mentoring others and building communities, experienced founders can propagate a culture of innovation and practical problem-solving. In Canada, this translates into transforming a "scrappy" but promising scene into a powerhouse where homegrown breakthroughs not only emerge but also flourish commercially. The lesson extends globally: a thriving biotech sector requires networks of support, from mentors and accelerators to forward-thinking policies, all aligning to help startups cross the perilous bridge from lab to market.

The Road Ahead: Synthetic Biology and AI Shaping Global Biotech Innovation

Kevin Chen's entrepreneurial journey, set against the backdrop of synthetic biology's rise, offers a glimpse into the future of biotech. It is a future where biology and technology intermingle more deeply than ever, lowering barriers that once constrained who could innovate and what problems they could tackle. Synthetic biology has already demonstrated its power by enabling startups to do in a few years what older biotech firms took decades to achieve, whether it's producing complex drugs in microbes or designing organisms with novel functions.

Looking ahead, the pace of innovation is poised to accelerate even more. Analysts estimate that with current and emerging technologies, as much as 60-70% of the physical inputs of the global economy could be produced biologically in the coming decades. This bio-revolution will span materials, food, pharmaceuticals, energy, and beyond, fundamentally redefining industries.

A key driver of this future is the synergy of artificial intelligence with biotechnology. AI and machine learning are rapidly becoming indispensable in designing experiments, predicting biological outcomes, and optimizing processes, effectively acting as force-multipliers for human ingenuity. By integrating electronic lab notebooks, data lakes, and automation workflows, Scispot's platform (often described as an AI-powered Lab Operating System) allows researchers to manage experiments, analyze data, and even get AI insights in one place. Such tools mean a lone graduate student today has capabilities at their fingertips that might have required an entire department a generation ago.

As these AI-driven lab platforms and cloud laboratories proliferate, the cost and time required to go from hypothesis to validated result will drop dramatically. This opens the door to a truly democratized innovation landscape in biotech: a startup in a small city or a research team in a developing country can access computing power, robotic automation, and datasets on a global scale, erasing the traditional advantages of big, resource-rich institutions.

Moreover, the community-centric approach that pioneers like Chen advocate will ensure that this innovation is not happening in isolation. We can expect to see robust international networks of bio-foundries, shared genetic libraries, and open-source datasets that collectively push the field forward. Just as open-source software enabled the tech startup boom, open science and collaboration in synthetic biology could unleash a wave of biotech startups solving niche problems and grand challenges alike. Imagine a scenario where environmental activists, citizen scientists, and academic labs around the world coordinate to engineer microbes that degrade a new pollutant, and they do it via shared digital platforms and AI modeling, with a company like Scispot providing the backbone for their collaborative research. This is not far-fetched; it is the logical extension of trends already in motion.

In the coming years, synthetic biology, fortified by AI, automation, and global community collaboration, is set to become an even more transformative force in biotechnology. We will likely see faster development cycles for drugs and vaccines, bio-based alternatives replacing petrochemicals in consumer products, and localized manufacturing of food and materials through fermentation and cell culture. Kevin Chen's Hyasinth Bio is one case in point: a small team using yeast and genes can produce therapeutic compounds that traditionally required vast farms. Multiply that by hundreds of startups, and it's clear the bioeconomy will significantly expand. As Chen highlighted, it's not just about any single technology but about "people coming together and creating solutions" collectively. The future of biotech innovation will be written by those who can effectively bring biology, technology, and community together.

Conclusion

The biotechnology industry is entering a new chapter, one defined by democratization and acceleration. Synthetic biology provides the technical means to program life, AI provides the intelligence to design and optimize it, and a globally connected community provides the collaborative network to share and execute ideas. Entrepreneurs like Kevin Chen embody this intersection, having broken from the old mold to chart new territory in biotech. Their experiences show that with the right support systems (accelerators, mentorship, and AI-powered tools), even ambitious ideas can be realized by determined innovators outside the traditional elite circles.

The rise of synthetic biology is not just about new scientific breakthroughs; it's about a new culture of biotech, faster, more inclusive, and solution-oriented. If the trends outlined here continue, the coming decades will see biotechnology emerge as a central driver of economic and social progress, much as digital technology has been in recent decades. And this time, thanks to the democratizing forces at work, the revolution in biotech will have a broad base of contributors, from professors and CEOs to students and citizen biohackers, all working to harness the power of life for the betterment of our world.