Can Biotech Innovation Move at Software Speed?

In a recent episode of the talk is biotech! podcast, host Guru Singh sat down with Kevin Chen to explore a provocative question: Can biotech innovation move at software speed?

Guru Singh, the Founder and CEO of Scispot, brings deep expertise in biotech digitization through his company's AI-driven technology stack designed specifically for life science laboratories. Scispot has emerged as a leader in laboratory informatics software, helping biotech companies streamline their workflows and accelerate discovery through intelligent automation.

His guest, Kevin Chen, serves as Co-founder and CEO of Hyasynth Bio, a pioneering synthetic biology company that engineers yeast to produce cannabinoids through fermentation rather than traditional plant cultivation. Chen, who also serves as President of SynBio Canada, has been at the forefront of the synthetic biology revolution, advocating for open innovation and community-driven biotechnology development.

The conversation highlighted a dramatic trend: the time to bring biotech products to market is shrinking from decades to years, and some even envision a future of development cycles measured in months or hours instead of years. This article dives into that discussion, examines the data, trends, and real-world examples behind the idea that biology can advance as quickly as software development, and explores what this means for biotech investors and entrepreneurs.

From Decades to Years: A New Clock Speed for Biotech

Bringing a new biotech product, especially a therapeutic drug, to market has traditionally been a slow, expensive journey. On average, it takes 10 to 15 years to develop a new medicine from initial discovery through regulatory approval. This lengthy timeline stems from the painstaking cycles of lab research, clinical trials, and regulatory checks required to ensure safety and efficacy. Industry analyses show that even just the clinical trial phases alone typically span around 9 to 10 years in total.

In short, biotech innovation has historically moved in lab time, not real time. Yet recent successes have proven that those timelines can be dramatically compressed.

The COVID Vaccine Breakthrough

The most striking case was the development of mRNA COVID-19 vaccines in 2020. It took less than a year from the virus's genetic sequencing to an authorized vaccine, representing a timeline years faster than any prior vaccine development. Moderna's team designed a vaccine candidate in just two days after Chinese scientists published the SARS-CoV-2 genome. This unprecedented speed, enabled by mRNA technology and digital design, demonstrated that certain biotech products might indeed be developed on software-like time scales.

Accelerating Timelines Across Biotech

Kevin Chen and Guru Singh discussed how the biotech timeline is collapsing from decades to years, and potentially to months or even hours. While achieving development in hours remains aspirational, even reaching development timelines measured in months would be revolutionary.

Some synthetic biology startups already boast rapid design-build-test cycles measured in weeks. Chen's own company, Hyasynth Bio, moved from a wild idea of engineering yeast to produce cannabinoids to tangible products in just a few years. This process, when done via traditional agriculture, typically requires decades of plant breeding and farming optimization.

The key drivers of this acceleration are technological: automation, digitization, and the application of software development principles to biotechnology.

Biology Turning Digital: Coding DNA and AI-Driven Labs

Biology today is increasingly programmable and digitized, much like software. Guru Singh points out that the next era of biotech will see "citizen scientists, DIY biohackers, and solopreneurs who will code DNA as quickly as software," with AI-powered "self-driving" labs conducting experiments autonomously.

Lab work is no longer confined to slow manual processes. It's being transformed by robotics and artificial intelligence, meaning experiments can be executed faster and more reliably, even by smaller teams. In silico design (computer-simulated experiments) and automation allow for rapid iteration that parallels the agile cycles of software development.

AI Revolutionizing Protein Structure Prediction

A prime example is DeepMind's AlphaFold AI system, which solved a grand challenge in biology by predicting protein structures at unprecedented speed and scale. Determining a protein's 3D structure used to take scientists months or years of laboratory effort. AlphaFold can now predict complex protein structures within minutes of compute time, achieving atomic-level accuracy.

This breakthrough demonstrated how a problem thought to require painstaking experimentation can be solved with a computational approach, essentially moving a biological discovery into the realm of software speed. As noted by venture capital experts, AlphaFold's impact came not just from AI algorithms but from applying a rigorous engineering mindset with large teams and robust code to a problem that academics had tackled much more slowly.

Automated Biofoundries and High-Throughput Research

Beyond AI in the cloud, physical lab processes are also accelerating. Lab automation and "biofoundries" (highly automated laboratories) enable thousands of experiments to run in parallel with minimal human intervention.

Ginkgo Bioworks, a prominent synthetic biology platform company, built its business on this premise. Their automated foundry can run massive "design-build-test-learn" cycles where thousands of genetic engineering experiments can be executed in a fraction of the time that traditional manual methods would require. This high-throughput approach has been critical in fields such as vaccine development, where time and scalability are essential.

Ginkgo and similar platforms serve as the equivalent of cloud computing for wet labs, enabling rapid prototyping and scaling with far less friction. As a result, the timeline to discover and optimize a new biological product (whether an enzyme, strain, or drug precursor) can shrink from years to months.

Digital Infrastructure and Data Integration

This paradigm shift also relies on better data infrastructure. Modern biotech companies are adopting digital lab notebooks, cloud data platforms, and AI analytics to streamline research and development. Scispot's LabOS exemplifies this trend, integrating lab data and automating workflows with built-in AI analytics.

These tools help eliminate the inefficiencies of paper notebooks or ad-hoc spreadsheets, allowing experiments to be conceived, tracked, and analyzed much faster. Biotech is gaining an equivalent of the software development stack, complete with version control for genetic constructs, collaboration platforms, and continuous integration of experimental results.

It's no surprise that venture capital is investing heavily in startups offering "SaaS for labs" or AI-driven discovery platforms. The biotech industry's appetite for science-native software tools is accelerating, promising to modernize how new therapies and products are invented and tested.

Open-Source Mindset: Toward Biotech's GitHub Moment

One theme Guru Singh and Kevin Chen explored is the idea of biotech needing its own version of GitHub, a platform for open-source collaboration in biological innovation. In software, code sharing and open libraries have drastically reduced development time by allowing developers to build on each other's work.

Biotech, by contrast, has historically lacked such a culture of open sharing due to factors like intellectual property concerns, complex material transfers, and regulatory barriers. However, these barriers are starting to be addressed.

Emerging Open-Source Biology Initiatives

We're seeing early moves toward communal biotech knowledge, from academic labs sharing CRISPR tools openly to startups providing open-source protocols for experiments. Kevin Chen himself has been involved in community science initiatives through his role as President of SynBio Canada, which fosters an open synthetic biology community.

The vision is a future where biotech innovators can easily access standardized biological parts, datasets, and protocols, much like a developer pulls a JavaScript library, instead of reinventing the wheel for each experiment. If achieved, this would greatly accelerate innovation through reuse and collaboration.

For instance, an entrepreneur working on a new engineered microbe could draw on a public repository of DNA sequences or fermentation recipes, cutting down development time significantly.

Platforms Driving Collaboration

Some platforms are already moving in this direction. The iGEM Registry of Standard Biological Parts represents an early "GitHub for bio" concept, listing open-source DNA parts contributed by students and researchers. Cloud-based data repositories and experiment hubs are emerging to let scientists share results more freely.

The cultural shift toward preprints and open data in biotech also supports faster progress. Researchers don't have to wait for lengthy peer review to build on each other's findings.

Singh and Chen argue that an open-source ecosystem in biotech could be a game-changer. When information flows more freely, biological innovation can iterate and scale faster, much like open-source software projects that evolve at breakneck speed thanks to global collaboration.

Real-World Cases: When Biotech Moves Fast

Skeptics might wonder if "software speed" in biotech is just hype. However, beyond the COVID vaccine example, there are concrete cases demonstrating order-of-magnitude accelerations in biotech research and development.

AI-Designed Drugs in Record Time

In 2019, Insilico Medicine used a generative AI system to design a novel drug molecule in just 21 days, then synthesized and tested it in the lab within another 25 days. This 46-day total timeline from idea to preclinical drug candidate was 15 times faster than a typical pharmaceutical research cycle.

The AI not only proposed chemical structures quickly but also helped prioritize the best candidates, dramatically shortening the "design" phase. Although clinical trials still take time, AI is clearly compressing early-stage discovery from years to months.

Lightning-Fast Antibody Discovery

Vancouver-based AbCellera offers a platform that can screen millions of immune cells to discover therapeutic antibodies at unprecedented speed. Their technology was tested during the pandemic, where within only three days, AbCellera tested 5.5 million immune cells and identified about 500 unique human antibodies against the SARS-CoV-2 virus.

Using microfluidics and AI, they found a potent antibody (later developed with Eli Lilly as bamlanivimab) in a fraction of the time traditional methods would take. In their partnership with Lilly, they compressed steps that usually take years into just days and weeks. Nearly six million cells were screened in 3 days, gene sequences obtained in 2 days, and lead antibodies tested in only a week. This led to a Phase I trial starting just 90 days after the project began.

Synthetic Biology Platforms Accelerating Development

Companies like Ginkgo Bioworks and newer entrants like Synthace or Strateos have demonstrated that automating lab work can shrink development cycles for new biological products. High-throughput foundries allow rapid iteration, running thousands of experiments in parallel and in a fraction of the time of manual approaches.

For example, engineering a new microbial strain to produce a specialty chemical might have once taken a graduate student several years of trial-and-error. Automated platforms can often accomplish this in a few months by testing many genetic modifications simultaneously and using machine learning to identify successful variants.

This parallelization mirrors how cloud computing allows software developers to run many tests or versions quickly. It's essentially accelerating the biological trial-and-error process through brute force and intelligent automation.

AlphaFold's Ripple Effects

Beyond its initial breakthrough, AlphaFold has had cascading effects throughout biology. By releasing predicted structures of approximately 200 million proteins to the public, DeepMind and its partners provided biologists with a treasure trove of data that would have taken decades of experiments to produce otherwise.

Now a researcher can retrieve a likely structure for a human protein in seconds from a database, rather than spending months in a lab crystallizing it. This open AI-driven tool has accelerated research in everything from drug targeting to enzyme engineering, effectively removing a major bottleneck and enabling subsequent innovation to happen much faster.

Real-World Success: Hyasynth Bio's Innovation

Kevin Chen's own company offers another compelling example. Hyasynth Bio bioengineered yeast to produce cannabinoids like THC and CBD via fermentation. Traditional cultivation of cannabis plants to extract these compounds can take months to grow a crop, not to mention land and supply chain limitations.

Hyasynth's fermentation process can produce cannabinoids in days within bioreactors, with the potential for continuous production once a strain is optimized. This points to a future where factories for biological products run on timelines closer to brewing beer (weeks) than farming (seasons or years).

Kevin Chen's Vision: Implications for Investors and Entrepreneurs

Kevin Chen's vision, as discussed in the talk is biotech! podcast, is fundamentally optimistic. He imagines a future where biological innovation moves at "software speed," unleashing creativity and new business models in biotech.

Chen envisions that while today's biotech breakthroughs take years, tomorrow's could happen in months or even hours. This shift would dramatically change how investors view the industry.

Transforming Investment Dynamics

Investors would no longer have to assume a 10-year timeline and binary risk for every biotech investment. If product development cycles shorten, return on investment can be realized faster and capital can recycle more quickly, much as it does in the tech startup world.

This could attract more capital into biotech overall, as the sector starts to align with venture capital time horizons, which typically prefer exits within approximately 7 to 10 years or sooner.

Venture investors are already leaning into this trend. Despite recent challenges in biotech public markets, venture capital funding has remained robust for innovative "platform" biotech startups, especially those using computational and engineering approaches. Platform technologies, including AI-based drug discovery and cell and gene therapy platforms, have dominated these investments.

Strategic Implications for Biotech Companies

For biotech executives and entrepreneurs, the move toward software-like speed has several critical implications:

Embracing New Tools and Talent

The competitive edge will go to companies that harness AI, automation, and data infrastructure to accelerate their research and development. This means hiring computational biologists, data scientists, and engineers alongside traditional wet-lab scientists, and investing in digital infrastructure early.

Success increasingly comes from focusing on capabilities and technology rather than single therapeutic programs. Biotech leaders should think of their lab as a technology stack, asking how each step can be optimized or automated.

Adopting an Iterative, Agile Mindset

Traditional biotech planning followed a waterfall model with years of sequential preclinical work followed by clinical phases. But if cycles shorten, an agile approach becomes feasible: rapid prototyping of therapies or bio-products, quick go/no-go decisions, and even "pivoting" to new ideas based on data.

Kevin Chen's story exemplifies this agility. Starting Hyasynth in an accelerator with no lab, no equipment, and no formal playbook, just a bold idea, they improvised, used available resources and mentorship, and managed to develop a viable process quickly.

Future biotech founders might similarly start in "cloud labs" with rented wet-lab time, iterate on designs digitally, and only then commit to scaling successful prototypes. The barrier to entry is lowering, and as Singh noted, biotech is becoming more accessible and collaborative, even "as user-friendly as personal computing" with community-driven resources.

Strategic Regulatory and Production Planning

While regulation cannot move at pure software speed, there are improvements such as the FDA's accelerated pathways and adaptive trials with interim data analysis. Biotech executives should plan how to leverage any regulatory innovations that can save time.

On the production side, if your innovation is a product like a material or ingredient rather than a drug, the path to market can be measured in months. For instance, a synthetic biology startup making a new enzyme for industry can iterate and scale to pilot manufacturing in a year or two and start generating revenue, much closer to a software startup's timeline.

Competitive Landscape and Ecosystem Participation

If everyone can innovate faster, competition will intensify. The same technology that enables rapid development means rivals could outpace hesitant companies. This spurs more collaboration and partnerships earlier in the development process.

Being part of an ecosystem, whether using an open-source data platform or partnering with a cloud lab, could become essential. No single biotech company will have all the best tools in-house. Models like "foundry as a service" or shared bio accelerators are becoming commonplace, preventing startups from wasting time reinventing capabilities that can be accessed externally.

The Role of Mentorship and Culture

Kevin Chen's perspective emphasizes mentorship and culture as crucial factors in speeding up innovation. He recounted how in the early 2010s, biotech startups were rare and not considered "cool," but mentors and accelerator programs were crucial in getting his company off the ground.

Now that startup culture has permeated biotech, there's a whole generation of experienced founders, incubators, and mentors to guide new entrepreneurs. This ecosystem inherently helps them avoid mistakes and move quicker, lowering the risk profile for investors.

Conclusion

The convergence of biology and technology is fundamentally resetting biotech's pace of innovation. While biology will never be as simple as coding, and living cells and human patients aren't as predictable as software code, the processes to engineer biology are undeniably catching up to the speed of our silicon-based endeavors.

In Guru Singh's words, "biology is rapidly becoming programmable," and that programmability is enabling a shift from traditional lab bench timelines to computational timelines. Kevin Chen's vision of biotech moving at software speed is already reflected in multiple industry trends, from AI algorithms solving overnight problems to robots running continuous experiments.

The message for biotech executives and investors is clear: the clock speed of innovation is accelerating. Those who internalize this by adopting new technologies, fostering open collaboration, and rethinking development strategies stand to lead the next wave of biotech breakthroughs. Those who don't may find themselves disrupted by faster-moving competitors.

The coming years could very well represent a renaissance period where launching a biotech product becomes as iterative and dynamic as launching a software application. In this new era, the winners will be those who can blend the rigor of science with the agile mindset of technology.

As we've seen from companies like Hyasynth Bio, Scispot, and the broader synthetic biology ecosystem, when this combination is executed well, what used to take decades might indeed be accomplished in days. The future of biotech innovation is not just about moving faster; it's about moving smarter, more collaboratively, and with the power of digital tools amplifying human ingenuity.

The talk is biotech! podcast conversation between Guru Singh and Kevin Chen illuminates a path forward where biology and technology converge to create unprecedented opportunities for innovation, investment, and impact on human health and sustainability.