Biotechnology, Geopolitical Competition, and the Next Frontier of Innovation: An interview with Cameron Watson and Dirk van der Kley

You may listen to this interview from HERE. 

Cam is a deep tech strategist specialising in the commercialisation of technologies at the intersection of biotechnology, advanced materials, AI, and robotics. He previously led strategy at two UK–US biotech startups and has recently relocated to Shanghai to join a Sino-British university’s deep tech entrepreneurship campus. There, he advances engineering biology innovation and commercialisation by working with founders to shape new companies and investing in early-stage ventures through its venture studio incubator.

Dirk is a Technology Researcher at the Australian National University’s National Security College and leads the Genes and Geopolitics program. He specialises in technology competition and innovation between the US and China, with a particular interest in biological technologies. Prior to joining ANU, Dirk was the Program Director for Policy Research at China Matters. He previously worked at the Lowy Institute for International Affairs. Dirk has a BA from the University of Sydney, and a PhD from the ANU. He is fluent in Chinese, Russian and currently studying Korean.

Q1. Please introduce yourselves and tell us about your current interests.

Cam:

I’m Cam Watson, a Deep Tech Strategist currently based in Shanghai. I lead work at Xi’an Jiaotong–Liverpool University’s (XJTLU) Deep Tech Entrepreneurship Hub, where I’m expanding the university’s incubator from its existing focus on deep tech to also support biotech ventures. Alongside this, I consult for biotech companies at stages ranging from pre-seed through to scale-up, and advise on policy initiatives related to biotech innovation and commercialisation.

My background is in biotechnology — I hold a PhD in enzyme engineering — and I’ve worked across strategy and innovation in the life sciences. I began my career at L.E.K. Consulting as a Life Sciences Specialist, before joining LabGenius, a company using AI, automation, and synthetic biology to transform drug discovery, where I led strategy for two years. Most recently, before moving to Shanghai, I was Strategy Lead at Nuclera, a company developing technology to produce novel proteins in days rather than months.

Right now, my key interests lie in supporting early-stage biotech companies as they grow and in exploring new funding structures for non-therapeutic biotech. I’m also mapping the Asia-Pacific non-therapeutic biotech ecosystem to better understand emerging opportunities across the region.

Dirk:

I’m Dirk van der Kley, a tech policy researcher based in Canberra. I lead a team that does in-depth research on the interplay between geopolitics and biotechnology. We focus a lot on China because that is the fastest changing ecosystem in terms of cutting-edge products. My teams runs the Bio Brawl substack which focuses on these areas.

My background is in geopolitics and China-watching. I began my career as a translator in China. I got my first break back in Australia doing China research at the Lowy Institute and then did a PhD on Chinese foreign aid at the Australian National University. Even then I could see the requests of China’s aid recipients were all trending toward digital and manufacturing tech – and old questions of agricultural aid and infrastructure aid/loans (which were the rage at the time) were not going to be the mainstay of geopolitical tension.

My team has an eye to where US-China tensions are going to be in 2-3 years. Biotech has probably been the least studied of all the major emerging tech and will likely be a source of tension, regardless of who is in the White House.

Q2. As this is a very broad sector, it would be helpful to explain which are the main branches of biotechnology (pharmaceutical, agricultural, industrial, etc.). How does biomanufacturing fit within this landscape?

Cam:

The largest and most established area of biotechnology is the pharmaceutical sector. This is where biological products are used in place of chemicals to act as therapeutics — for example, monoclonal antibodies, cell and gene therapies, and other biologically derived medicines.

Closely related is the diagnostics sector, which uses biological agents that are sensitive to features on pathogens or diseased cells. For instance, antibodies can be engineered to recognise specific proteins on the surface of tumour cells. Together, therapeutics and diagnostics make up the major medical applications of biotechnology and account for the majority of the field today.

Beyond healthcare, there’s industrial biotechnology, which focuses on developing bio-based chemicals and materials such as bioethanol and bioplastics. A key goal of this field is to replace fossil-fuel-derived sources with renewable biological alternatives. Enzymes — or biocatalysts — are also used in industrial processes to make chemical manufacturing more efficient and sustainable, and similar approaches can be applied in waste management to convert waste into less harmful or more useful forms.

The agricultural biotechnology sector is another major branch. Most people associate it with GMOs — genetically modifying plants or animals for desirable traits such as higher yield, disease resistance, or drought tolerance. It’s essentially a faster and more precise version of what humans have been doing for millennia through artificial selection. In parallel, biotechnology can also be used to create biological alternatives to chemical pesticides, herbicides, and growth stimulants — for example, antibodies or proteins that selectively target harmful insect species while sparing beneficial ones such as honeybees. Similar principles as the diagnostics sector underpin the development of environmental biosensors used for monitoring and remediation.

The veterinary sector sits between the agricultural and pharmaceutical sectors wherein bio-based medicines are produced for animals instead of humans.

There’s also a growing consumer biotechnology segment, particularly in cosmetics, supplements, and food. In cosmetics, companies are looking for non-animal, non-fossil-fuel sources for ingredients — collagen, for example, can now be produced through fermentation rather than animal extraction. In food, biotechnology has long been present through fermentation processes like brewing, but new technologies now allow the cultivation of things like meat in bioreactor-style systems, providing animal-free sources of protein.

Finally, underpinning all of these sectors is the biotech tools and infrastructure industry, which can be grouped into three broad categories:

1. Equipment for research, analysis, and production — from lab-scale instruments to full-scale biomanufacturing systems such as fermenters and purification units.

2. Biochemical tools — including enzymes and reagents that enable genetic modification, molecule isolation, and other core processes.

3. Inputs and consumables, such as feedstocks (sugar sources, cell-culture media), engineered cell strains, and — critically — DNA itself.

Although many biotechnology products have been developed in laboratories for decades, only recently have we begun to see large numbers reach the market — largely thanks to advances in biomanufacturing, which is essentially the process of producing materials, chemicals, or therapeutics through biological means.

Broadly speaking, there are two main approaches. In one, we genetically modify cells — typically microbes, yeast, or mammalian cells — and cultivate them in large fermenters, where they act as miniature production factories. In the other, we extract biological catalysts, such as enzymes, from cells and use them to run processes that resemble traditional chemical synthesis pathways, but without the cells themselves. The choice between these approaches depends heavily on the type of product being made and the economics of production.

Given the breadth of biotechnology applications, the standards for what constitutes “successful” or commercially viable biomanufacturing vary enormously. At one end of the spectrum are biotherapeutics, where products are high-value and required in very small quantities per patient — meaning high production costs can be sustained by large margins. At the other end are industrial and agricultural biotechnology, where success depends on achieving high volumes at very low margins.

For many non-therapeutic applications, the main barrier has been the challenge of scaling biomanufacturing to a cost and efficiency level that can compete with traditional, fossil-fuel-based production methods. Overcoming this cost-performance gap is one of the defining challenges — and opportunities — for the next generation of biomanufacturing innovation.

Dirk:

Cam has given you the businessperson’s perspective on the market breakdown. If you are a policymaker or investor from outside the sector, there are a handful of new tools that make all these applications possible. That is the ability to read, write, edit and scale DNA. In practice, this is CRISPR-Cas9 gene editing; cheap DNA sequencing (reading DNA, allowing us to know what genes do in any organism); cheap DNA synthesis (writing DNA); AI biological design tools (to design organisms or parts of organisms); and massive databases of organisms and genes. Beyond these major breakthroughs, most subfields of biology are making rapid knowledge gains.

What we mean by *modern* biotechnology is the above tools being applied to health, agriculture and industry to create biological solutions for what was previously solved by chemistry or physics. Some examples: In health, using CAR T-cell therapy to reengineer a patient’s own T-cells to fight cancer as replacement or augmentation for traditional chemotherapy. In industry, this includes genetically engineered yeast fed by sugar or other biomass to grow chemicals, plastics, fibers, dyes, or basically anything (previously done via fossil fuel pathways). In agriculture, a focus is on creating biostimulants to replace traditional nitrogen fertilizers.

This is why the opportunity is enormous. If the technology succeeds anywhere near its potential, it will overturn current practices in almost every industry. That maximalist case may not arrive for decades or ever, but that potential is why China, among other countries, is investing huge sums across the range of potential applications.  

Q3. From a geographic perspective, which countries are well positioned in which areas (talent, tech, IP, funding, commercialization, etc.)? How do you think these advantages will manifest themselves in the years ahead?

Cam:

The United States remains the global leader in biotechnology. Its strength comes from the combination of world-class university and government research, a favourable policy environment, strong capital markets, and a deep bench of established companies across chemicals, agriculture, and pharmaceuticals. The U.S. also benefits from institutions like BioMADE, which play a critical role in bridging the gap between innovation and scale-up — an area where many regions still struggle.

In Western Europe, the most notable hubs for non-therapeutic biotech are the UK and Denmark. The UK boasts a vibrant early-stage ecosystem driven by outstanding universities, strong seed and venture capital availability, and progressive policy support — including a robust early-stage grant system. However, it remains constrained by a lack of domestic growth capital and relatively few established industrial players outside big pharma. As a result, non-therapeutic biotech companies often look abroad for collaboration and late-stage investment. Denmark shows similar dynamics, with the Novo Nordisk Foundation playing a particularly influential role in shaping the country’s innovation landscape. Other European centres — notably France, Switzerland, Germany, and the Netherlands — also contribute significantly, with Switzerland standing out for its biopharmaceutical strength.

Across Asia, China has become a biomanufacturing powerhouse, reportedly hosting more than a hundred profitable biomanufacturing companies, many producing bio-based chemicals. Interestingly, while early-stage funding remains relatively scarce, this has arguably strengthened the ecosystem: entrepreneurs must focus on profitability from the outset, and those who succeed are well-positioned to leverage China’s unparalleled manufacturing and scaling infrastructure. The government has also declared biomanufacturing a strategic priority, offering extensive incentives such as subsidies, housing allowances, and purpose-built facilities.

Singapore represents another standout in the region — a small but influential hub with progressive biotech policies, a strong early-stage ecosystem, and clear government commitment to building innovation capacity.

Japan and South Korea are both leaders in novel IP generation, with Japan in particular benefiting from a deep industrial base of chemical and fermentation companies now exploring ways to shift their portfolios toward bio-based production.

India is an emerging force, thanks to its vast talent pool and low-cost biomanufacturing potential. While government support is generally favourable, early-stage funding remains limited — particularly for idea-stage research. This highlights a broader point: the concept of funding high-risk ideas is relatively unique to the U.S. (and to a lesser extent, Europe); most other regions remain more risk averse.

Beyond these major centres, Southeast Asia and Latin America are increasingly interesting for cost-effective biomanufacturing, while Australia is developing a small but promising ecosystem grounded in strong research, supportive policy, and growing infrastructure for scale-up.

Ultimately, when comparing geographies for biomanufacturing, energy and manufacturing costs are the decisive factors. Wealthier countries tend to excel in research and innovation but often struggle to scale production profitably due to margin sensitivity in non-therapeutic markets. Conversely, emerging economies may lack early-stage funding and deep research capabilities, but they enjoy favourable economics for large-scale, cost-efficient production.

Broadly speaking, the U.S. is working to rebuild its manufacturing base, while China is investing heavily in research capacity. The interplay between those trajectories will likely define the global landscape for biomanufacturing over the next decade.

Dirk:

I break the ecosystem down into three parts: 1) The basic tools 2) The R&D of specific applications 3) The manufacturing. There is some blurring of the lines at the edges between the parts.

The US leads in the basic tools (editing, synthesis, biological design tools, etc.). These are akin to Electronic Design Automation (EDA) tools in semiconductors, except the barriers to entry are lower and the tools less mature. Both the Chinese government and Chinese firms are putting in serious effort to create alternatives to US tools. The result might be that tools become commoditized in many areas. This has already happened for DNA sequencing.

R&D is led by the US and China with a sprinkling of capacity elsewhere in the world. Biopharma is the most R&D intensive and thus led by US and China. I contend that China has almost caught the US in new drug development, although the market for drugs remains the US (44% of global pharma spend is there) and Chinese firms have to license their products to Western firms to sell in the US.

Outside of pharma, there are lot of companies globally doing R&D in industrial and agricultural applications. I still think US and China are leading but there are large pockets of capability all over the place.

On the manufacturing side, China and then Asia leads. There are certainly pockets of biopharma manufacturing in Europe and the US, and pockets of bioindustrial elsewhere. But it is led by China and then the rest of Asia. Going forward China has advantages – it has cheaper energy, transportation, infrastructure and skilled labor costs than Europe or North America. And legacy manufacturing in food additives, plastics or chemicals make the transition to bio-based production easier because it can be plugged into existing infrastructure. Plus, the Chinese government in highly committed to biomanufacturing as a technology.

A word of warning, manufacturing is often a mug’s game. It is hard to make money in modern manufacturing (even in China) because of Chinese overcapacity and fierce competition in many areas. My team is already starting to see bubbles in bio-based production of poly-lactic acid. We expect to see others.

Q4. Technology and innovation are part and parcel of the biotech story, and there have been 20 years of promises that haven’t necessarily been realized. What is happening now that makes this point in time different and which technologies or capabilities do you feel are most transformative?

Cam:

About twenty years ago, the hype around biotechnology centered on the idea of replacing environmentally harmful fossil-fuel–based processes with clean, carbon-neutral biological alternatives — materials and chemicals with natural degradation pathways. In many ways, biotechnology was originally seen as a form of clean tech or climate tech.

At the time, however, the infrastructure demands were enormous. Most companies had to invest tens of millions of dollars just to establish basic laboratory capacity, so they built large, platform-based organisations – demanding large VC rounds and sky-high valuations. While many succeeded in developing promising technologies, most failed to scale or commercialise, primarily because of unfavourable economics. Their products were simply more expensive than conventional alternatives, and adoption was limited as a result.

There was also a persistent product–market fit problem. Unlike therapeutics, where information about disease states and treatment paradigms is public and transparent, industrial and agricultural processes are typically opaque. Early synthetic biology companies were often spun out of academic labs rather than industry, meaning they had little insight into what target customers or markets actually needed.

Given the high upfront capital expenditure, scaling challenges, and narrow margins, most of these companies eventually pivoted toward drug discovery, where the economics looked more attractive: manufacturing volumes are small, margins are high, and the underlying data and market knowledge are much more accessible. However, even in therapeutics, the long and risky path through clinical trials meant many of these companies ultimately struggled as well.

Now, things look very different.

On the R&D side, the field has been transformed by the democratisation of research tools, along with the integration of AI and automation. These technologies are not only reducing failure rates but also expanding the scope of biological problems that can realistically be solved. Meanwhile, the rise of contract research organisations (CROs) means that individual innovators no longer need to build full in-house platforms — they can design biological products computationally and partner with CROs to produce and validate them. This has dramatically reduced the cost and complexity of entry. We’re even seeing companies succeed without traditional venture capital, giving them more flexibility in how they grow and scale.

On the biomanufacturing side, we’re witnessing major advances in process efficiency and scalability. Automation and AI-driven optimisation, both of biological strains and manufacturing processes, are improving yields and reducing costs. At the same time, government investment in infrastructure and the rise of contract development and manufacturing organisations (CDMOs) are making it possible to scale bioproducts “as a service.”

Together, these shifts are closing the gap between what is scientifically possible and what is commercially viable. This is turning decades of unrealised promise into genuine, scalable opportunity.

Dirk:

There has only been one global pathway for new manufacturing technology to scale in this century: a new fundamental breakthrough from the West (gene editing, synthesis and design tools in our case) and dedicated long-term manufacturing subsidization from the Chinese government.*

We have both the above elements in play now. There are already plenty of success stories in biopharma, even if these are fewer than expected, so let’s park that. In industrial biotech, there are success stories but most of those are on older technology. Very few companies have been able to dislodge incumbent fossil fuel-based products.

However, this reminds me of a lot of solar panels in the early 2000s. After a decade of perceived stagnation and failed take-up, VC and funders were writing off solar panels as unable to compete with a glut of cheap coal. Companies were looking at high-value, low-volume operations – military, space, remote and autonomous. Yet, behind the scenes, the underlying technology had improved. It just could not scale.

Then China did its thing. Within a decade, it was cheaper theoretically than coal. It took another decade to solve how to integrate broadly into grids, so it was practically cheaper. That is where I think we are now. But the use cases are so much broader. So, it is still probably further away than we think, but the underlying structures have subtly changed.

*There are two potential counterexamples: 1) Semiconductors. They were already scaled prior to 2000, and they still relied on large scale government subsidies in Taiwan, Japan and Korea (and now China). 2) LLMs. I do not consider them a pure manufacturing play.

Q5. Conversely, what are the key bottlenecks or barriers, both technical and otherwise, to progress?

Cam:

When thinking about barriers to progress in biotechnology, I tend to group them into three technical dimensions — design, prototyping, and scaling — alongside a cross-cutting issue around regulation and policy.

1. Design Complexity

Biology remains one of the most complex systems we’ve ever attempted to engineer. While AI is now giving us design capabilities we never thought possible, the models are still far from truly predictive. They can suggest promising hypotheses but often fail to anticipate real-world performance. As a result, we’re generating an explosion of design ideas that still need empirical validation.

I think we’re only at the beginning of this phase. We could see a wave of highly specialised and accessible AI models trained for specific biological design problems — from protein engineering to metabolic pathway optimisation. That level of granularity should eventually make design far more predictive, but for now it’s a major bottleneck.

2. Prototyping Efficiency

This leads directly into the next challenge: testing. No matter how powerful AI becomes, we still need to empirically test biological designs. While automation has made huge strides, prototyping remains the rate-limiting step in most R&D workflows. In practice, progress depends on how efficiently we can move through multiple design–build–test–learn (DBTL) cycles, using experimental feedback to refine both the biology and the AI models driving it.

The short-term goal is to automate the entire DBTL loop, as demonstrated recently in projects like BioMARS in China. The longer-term vision is an AI system capable of reasoning about biology in a way that dramatically reduces the need for wet-lab testing, though the computational demands of such a system would be enormous. Another persistent constraint is the need to create bespoke assays for every new biological product; designing, validating, and automating these tests can take months and often becomes the true bottleneck in the innovation pipeline.

3. Scaling Efficiency

Even when a biological process works, scaling it economically is an entirely different challenge. For most non-therapeutic applications, manufacturing cost is the key determinant of success. Petrochemical and industrial players are often only willing to switch to bio-based alternatives if they can achieve cost parity with incumbents.

In Western markets, the focus has largely been on strain optimisation — making microbes more efficient producers — whereas many Chinese companies have concentrated on manufacturing fundamentals such as energy inputs, capital efficiency, and operational costs. Combined with supportive government policies and subsidies, this has allowed China to make significant headway in profitable biomanufacturing. In contrast, high energy costs and limited infrastructure investment remain major barriers to cost-competitive production in Europe and the U.S.

4. Regulation and Policy

Finally, the regulatory environment remains a significant external bottleneck. While we’re starting to see modernisation in policy frameworks, particularly around genetic modification, Europe’s long-standing anti-GMO stance has held back both innovation and public acceptance. Decades of restrictive regulation have created a culture of caution that’s still being unwound. The situation is improving, but the regulatory pace continues to lag behind the technological one.

Dirk:

Everything Cam said above is true. Those need to be solved. But I think the bottleneck is more fundamental, and yes, I am repeating myself. The main global variable for scaling new manufacturing this century has been consistent Chinese government support. There has been no other pathway. Biomanufacturing has not been a high Chinese government priority until the last two years. That is the change which needed to happen.

Other pathways to global scale will probably occur in the next twenty years (Indian government support for example or embedded AI), but we have become addicted to the Chinese pathway.

Q6. Which end markets (enzymes, biomaterials, food, pharma, energy, etc.) are most relevant for biotechnology and what are the primary challenges for adoption?

Cam:

Biotechnology touches a wide range of end markets, from pharmaceuticals and food to enzymes, biomaterials, and energy, but each faces a very different set of economic and adoption challenges.

The pharmaceutical sector remains the most mature and profitable branch of biotechnology. Here, antibody-based treatments have already achieved widespread market adoption. The key challenges are less about acceptance and more about cost, speed, and accessibility. Biologics are expensive and difficult to manufacture, and regulatory processes are lengthy. Nevertheless, this market benefits from high margins that can sustain those costs.

Beyond healthcare, the near term frontier seems to be in industrial chemicals, enzymes, biomaterials, food and agriculture. These areas promise the greatest impact on sustainability but face the toughest path to adoption because of price sensitivity and scale requirements.

In industrial chemicals, enzymes and biomaterials, biotechnology offers clear technical advantages — greater specificity, milder processing conditions, and potential carbon neutrality — yet widespread adoption depends on reaching cost parity with petrochemical incumbents. Most chemical and materials companies will not switch to bio-based production unless economics are equal or better. Progress here hinges on improvements in biomanufacturing efficiency: strain optimisation, automation, and reduction of energy and capital costs.

In food and agriculture, adoption challenges are twofold. On one side, there’s public perception and regulatory hesitation around genetically modified or cultivated products; on the other, the economics of large-scale production are difficult. Cultivated meat and precision fermentation products are still expensive, though rapid progress in process design and feedstock utilisation is helping to close that gap.

Energy and biofuels remain technically feasible but economically constrained. Bioethanol and biodiesel production are established, yet scaling to compete with fossil fuels requires vast land, water, and feedstock resources, which introduces sustainability trade-offs.

Across all these sectors, a common thread runs through the challenge: manufacturing economics and infrastructure. In high-margin sectors like pharma, biology has already won. In low-margin markets like materials, food, and energy, biology’s success will depend on whether biomanufacturing can achieve competitive cost, reliability, and scale.

Dirk:

Agree. Price is the major challenge.

Q7. How do financing structures (VC/PE, corporate VC, government funding, etc.) shape innovation pathways, and how do investment horizons and risk appetites differ across regions such as China, Europe, and the US?

Cam:

Financing structures play a defining role in how biotechnology innovation unfolds, not just in what gets funded, but in how and where progress happens. The source of capital strongly shapes risk appetite, time horizon, and the type of innovation that emerges.

In the United States, venture capital remains the dominant force driving biotech innovation. The ecosystem is characterised by a high tolerance for risk and a willingness to fund ideas or moonshots well before commercial validation. This reflects the strength of the U.S. capital markets, a deep pool of specialist investors, and an exit environment that rewards early-stage innovation through IPOs and acquisitions. The result is a fast-paced, innovation-heavy ecosystem, but one that can be overly focused on short-term multiples, especially in therapeutic biotech, where the “one big win” model often dominates. There’s less incentive to pursue slower, lower-margin industrial or agricultural biotechnologies that require sustained investment and infrastructure before returns appear.

In Europe, funding structures are more conservative and fragmented. Public grants and government-backed initiatives play a larger role, particularly in the early stages. This creates a strong foundation for research translation and early-stage company formation. The UK, Denmark, and the Netherlands are good examples, but the region struggles with a shortage of growth capital. Most funds have shorter investment horizons and limited experience in scaling manufacturing-heavy businesses. As a result, promising technologies often stall after proof of concept, or companies are forced to seek late-stage capital and strategic partnerships abroad (typically the US). The European model excels at innovation quality but often lacks the commercial throughput to match it.

In China, the landscape looks very different again. Private venture capital exists but remains relatively small compared to the massive role of government-backed funding at both regional and national levels. Many local VCs now raise funds directly from state or municipal sources, which introduces shorter investment horizons — typically 5–7 years — and a stronger focus on profitability and tangible outcomes rather than speculative platform plays. This has created a highly disciplined environment: early-stage funding is scarce, but those companies that survive are forced to become commercially viable quickly. Once they do, they can scale rapidly through access to state-supported infrastructure, subsidies, and manufacturing incentives. It’s a system optimised for scaling and industrialisation, rather than for early-stage exploration.

Meanwhile, corporate venture capital (CVC) and private equity are playing increasingly strategic roles worldwide. In the U.S. and Europe, CVCs are bridging gaps between innovation and commercialisation and they play a critical role as an early customer voice as well as an investor Many international corporates remain hesitant to invest in China due to IP concerns while domestic corporates are far more integrated into the funding ecosystem than directly engaging with start-ups, often partnering with government or academia to accelerate industrial application.

Finally, government funding continues to be a powerful lever globally. In the West, it underpins early-stage science and risk-heavy innovation, and we are seeing more funding mechanisms to support scaling (BioMADE, SPRINGD). In China, it’s used more as a strategic tool for industrial capacity building.

Q8. What factors distinguish companies that reach commercial scale from those that remain at pilot stage? In your view, what models or case studies illustrate successful scale-up?

Cam:

The difference between companies that reach commercial scale and those that remain at pilot stage almost always comes down to a combination of economics, market integration, and execution discipline rather than purely technical capability. Many companies can produce a biological product at lab scale, but very few can do so reliably, cost-effectively, and at market-relevant volumes.

Broadly, I see four factors that distinguish those who succeed:

1. A Clear and Credible Market Thesis

The companies that scale successfully start with a precise understanding of their target market and value chain. They identify not only the technical gap but the economic and operational gaps that make their biological alternative competitive. Many early synthetic biology companies failed because they began with a platform and looked for a market later. The successful ones begin with a defined customer pain point and design their biomanufacturing process around it.

Checkerspot is a great example of this. Rather than trying to compete directly with commodity chemicals, they targeted high-value, design-driven markets such as performance materials and specialty coatings. By controlling both the biological production of their ingredients and the downstream formulation of final products (like skis and coatings), they had a highly product facing mentality that fed back to the development teams.

2. Process and Manufacturing Discipline

Companies that succeed at scale approach biomanufacturing as an industrial engineering problem, not just a biological one. This means optimizing every input — from feedstock sourcing and energy consumption to downstream processing and purification.

Huaheng, a leading Chinese biomanufacturer, exemplifies this mindset. The company has focused relentlessly on operational efficiency, applying automation, AI, process optimisation, and cost discipline to compete directly with petrochemical producers.

3. Access to Infrastructure for Scale-up

Companies that reach commercial scale are those able to secure fermentation capacity, downstream processing systems, and reliable logistics networks without overextending their balance sheet.

Some achieve this through partnerships with contract development and manufacturing organisations (CDMOs), while others leverage government-backed facilities or industrial joint ventures. The recent emergence of biomanufacturing clusters in China, the U.S., and parts of Europe has been critical in bridging the gap between pilot and commercial production.

Equally important is energy infrastructure. Biomanufacturing is deeply energy-intensive, from AI-driven design and automation systems to large-scale fermentation and purification equipment. Access to affordable, stable energy is now one of the most decisive factors in determining where biomanufacturing can scale competitively. In this regard, China currently holds a clear advantage over Western regions, combining relatively low energy costs with coordinated infrastructure planning to support industrial biotechnology at scale.

4. Financial and Organisational Patience

Finally, scaling biology takes time. It requires investors and leadership teams aligned on longer time horizons and realistic capital efficiency. Many early failures came from companies attempting to “grow like software,” while successful players recognise that industrial biology scales more like semiconductors. This is through cumulative learning, incremental cost reduction, and strong supply-chain integration. We are seeing this being acknowledged across the VC sector as investors shift their focus from traditional tech to deep tech where there is higher demand for specialist patient capital.

Q9. From a policy and regulation perspective, which regulatory frameworks and government incentive structures most strongly influence industrial biotechnology and biomanufacturing?

Cam:

From a policy standpoint, several regulatory frameworks and government incentive structures can profoundly accelerate industrial biotechnology and biomanufacturing. Many of these mechanisms are already well developed in China, and we’re now seeing Western policymakers begin to follow suit, recognising the strategic value of industrial bioeconomy development.

1. Infrastructure and Energy Policy Alignment

One of the most overlooked regulatory levers is energy policy. Biomanufacturing is fundamentally energy-intensive — spanning AI-driven design, automated labs, and large-scale fermentation systems. Regions that combine low-cost, stable energy with targeted infrastructure development will have a decisive competitive edge. China’s coordinated planning in this area is already visible; Western economies are only beginning to integrate energy strategy into their industrial bioeconomic planning.

2. Smarter Regulation of Biological Materials

Historically, biotechnology has been regulated at the organism level, which often creates unnecessary friction for industrial and non-therapeutic applications. For instance, a researcher might work with a protein derived from a crop parasite that is itself entirely harmless, yet because of its origin, the project must adhere to high-containment biosafety regulations, even if the protein can be produced synthetically without ever handling the parasite. This misalignment between regulatory framework and scientific reality often leads to projects being abandoned due to the added cost and complexity.

What’s needed is a more sophisticated, risk-based approach that differentiates between living systems, components, and derived materials. China has already begun implementing more pragmatic frameworks of this kind, while the EU, UK, and U.S. are starting to explore modernised GMO and bioengineering policies to reflect these nuances.

3. Biosecurity and Oversight in a Decentralised Landscape

As biological design tools and DNA synthesis become increasingly accessible, biosecurity and monitoring are growing policy concerns. Traditionally, oversight has relied on centralised DNA providers with the burden of compliance and screening falling largely on synthesis companies that verify orders against pathogen databases.

However, with the emergence of on-site DNA printers and distributed synthesis platforms, this model breaks down. Policymakers will need to design new, networked biosecurity systems — potentially using digital traceability or secure registries — that enable transparency without stifling innovation. Balancing biosecurity with accessibility will be one of the defining regulatory challenges of the next decade.

4. Supply Chain-Spanning Incentives

One of the clearest reasons China has surged ahead in biomanufacturing is the breadth of its incentive structure. Rather than subsidising only the producer, China often subsidises the entire supply chain — including upstream input providers and downstream customers — to actively stimulate adoption of biomanufactured products. This whole-of-chain approach de-risks early adoption and encourages market uptake, effectively ensuring that once a bioproduct is manufacturable, there is a viable commercial pathway for it.

By contrast, Western subsidy frameworks have historically focused on R&D grants or producer-side tax credits, which help innovation but do little to pull demand through the market. This is now beginning to change, with industrial policy frameworks in the U.S. (like the CHIPS and Science Act and BioMADE initiatives) and green industrial strategies in the UK and EU beginning to include downstream demand incentives.

Q10. How do you assess the US/China dynamic and what do you think both countries are getting wrong and right in their approaches? Do you see opportunities for cross-border collaboration that can transcend current geopolitical tensions? 

Cam:

The United States remains the global leader in biotechnology innovation. It continues to produce the largest number of new engineering-biology companies, supported by deep and specialised capital markets. There’s funding available for almost every kind of play, from mega-seed rounds in the hundreds of millions to small venture-studio models targeting early exits. Large chemical, pharma and agri-corporates are becoming more engaged, and organisations like BioMADE help bridge innovators to scale-up infrastructure. The US is also investing heavily in the enabling stack — AI, robotics and semiconductors — that underpins the next generation of biomanufacturing.

The country’s main weaknesses are structural. Energy cost and access remain the biggest constraint: a bioeconomy built on expensive or volatile power is unsustainable. The US continues to rely on fossil-fuel expansion as a short-term fix, creating future vulnerabilities as biomanufacturing, AI and automation compete for the same concentrated energy nodes. Feedstocks are another gap — limited progress on non-food sources could become problematic as the industry scales — and the venture model, optimised for fast multiples, often neglects service-based or infrastructure-heavy firms that quietly hold the ecosystem together.

China, by contrast, has almost the inverse profile. It combines decentralised, reliable energy and advanced logistics with one of the most coordinated industrial policies in the world. Once companies prove viable, the state moves quickly: regional governments compete to attract them with grants, subsidies, factories and workforce support. Crucially, China subsidises across the entire value chain, not only the producer but also suppliers and customers, creating strong network effects that lift whole ecosystems at once. It’s a model that stands in sharp contrast to the fragmented incentive structures of the US and Europe.

Its weaknesses lie earlier in the pipeline. The early-stage environment is fiercely competitive and relatively under-supported, which breeds disciplined founders but limits long-term exploration. Most capital ultimately traces back to the state, with five-to-seven-year time horizons that can be too short for deep-tech. Combined with lingering IP concerns, this makes Chinese founders cautious about deep partnerships. University tech-transfer systems and venture-studio models are only now starting to emerge.

Given the dual-use nature of biomanufacturing, further decoupling between the two seems likely. Many Western founders are already hedging by exploring neutral manufacturing hubs — particularly in Vietnam, Thailand, Malaysia, Chile, Argentina and Brazil — which could become the next beneficiaries of the global biomanufacturing shift. 

Dirk:

Cam has described the dynamic of US and China bio-industrial policy well. I think the medium-term challenges are different though.

China’s approach once scaled is not infallible. It will lead to big bio-based bubbles if it succeeds. Local governments have less financial firepower than before. They will be expected to pick up the bill for all that subsidization while many of them have stagnating – if not declining – tax bases. This level of subsidization is occurring while many Chinese people are getting poorer (notwithstanding 5% GDP growth, less for nominal growth).

That is different to all the other scaling we have seen in the last 25 years. In the years of solar, batteries, wind turbines and EVs getting subsidized, Chinese salaries were increasing and government revenues growing rapidly.

That is no longer true. Thus, the runway for long-term subsidization of the whole value chain looks harder for this technology than it has in the past (I acknowledge that Chinese macroeconomic fortunes may turn). Provided it remains a top 2 priority and the central government continues significant tax transfers, local government and companies can probably weather these challenges for some time. But the scale-up system is facing strain.

The US problem is cost. No country as expensive as the US has become the home of new large scale, low margin industrial manufacturing (at least since World War 2). The research system will still be amazing, regardless of political pressure.

It is possible (though I am skeptical) in the medium term that China and the US find a way to accommodate, since the advantages of collaboration are clear. Much more likely, the US will end up steering companies to produce in Southeast Asia or India where the costs might make sense. Biopharma’s margins make the US a possible manufacturing location.

Q11. In the face of US/China geopolitical tensions, how can important players like Europe, the UK, India, Korea, Singapore and Japan navigate these tensions to their own benefit? Are there going to be winners and losers if the US/China divide widens and countries find themselves under pressure to pick sides?

Cam:

As the United States and China move to onshore the full biotechnology value chain — from discovery to manufacturing — other regions are increasingly pursuing a “third path.” Rather than choosing sides, they’re defining complementary roles that reflect their own strengths, resources, and policy ambitions.

Smaller innovation hubs such as Denmark and Singapore are embracing their position as sources of early-stage innovation. They’ve built strong research and startup infrastructure and are now specialising within sub-sectors like industrial enzymes, synthetic biology tools, and alternative proteins. Their strategy is clear: focus on a defensible niche, build enabling infrastructure around it, and plug into global value chains as trusted nodes.

Across Southeast Asia, countries like Vietnam and Thailand are emerging as neutral, cost-effective manufacturing bases. They combine proximity to feedstocks, low energy and labor costs, geographic closeness to China, and improving regulatory clarity. By offering non-aligned, affordable capacity, they’re becoming attractive partners for companies seeking to scale outside the geopolitical fray.

In larger developed economies such as the UK, Japan, and across Europe, the approach is more hybrid. These countries recognise they can’t compete across the full biotech spectrum yet want to retain domestic manufacturing and scale their own companies. The pragmatic solution is to focus on high-margin, low-volume specialities — biocatalysts, biomaterials, advanced chemicals — while partnering with neutral allies for low-margin, high-volume production. Europe, in particular, has an opportunity to build specialised clusters of excellence that share infrastructure and align strengths across regions.

Finally, India may become one of the most important players in biomanufacturing. It offers low-cost scaling potential, vast agricultural feedstocks, and deep technical talent, but suffers from fragmentation and inconsistent coordination. If India can align its infrastructure, supply chains, and project finance, it could match China’s scalability, but without its geopolitical constraints.

In short, as the US and China double down on self-sufficiency, the rest of the world doesn’t need to pick sides; it needs to pick roles. The winners will be those that position themselves as innovation hubs, manufacturing nodes, or bridges linking the two.

Dirk:

We see now a real desire for every country to be a biomanufacturing power, as opposed to a biotech IP power. Japan, US, India, and China all have dedicated biomanufacturing plans. This is not unique to biotech. Everyone is obsessed with manufacturing.

Manufacturing writ large is such a mug’s game though. India and China competition will drive margins through the floor, and it will employ few people. The goal for expensive countries that have populations smaller than 300 million should not be to choose self-sufficiency, but to create prosperity through IP (roles, not sides as Cam suggests).

A model could be something like Samsara Eco in Australia. They produce plastic-eating enzymes. They have opened their first facility just outside Canberra (you can buy Lululemon clothes made from Samsara plastic. They’ve publicly stated their second manufacturing facility will be in Asia – one assumes for the cost benefits. The headquarters and research are in Australia. They have some early-stage capability here that allows for scale-up work. But the bulk of manufacturing remains elsewhere.

Q12. What are your thoughts on US tariffs and export controls, and more generally, the unpredictable nature of the current US administration? What are the second or third order effects of Trump’s policies and how might this impact consumers and the larger biotechnology space?

Cam:

In biotechnology, especially in tools, reagents, and biomanufacturing infrastructure, the tariffs create cost inflation, supply uncertainty, and restricted access to critical equipment and markets. The result is higher compliance costs and greater policy volatility, making long-term planning difficult even in otherwise stable parts of the value chain.

These pressures are accelerating a global bifurcation into parallel technology ecosystems: one aligned with the U.S. and its allies, the other centered on China and regional partners. While such a split may have been inevitable, recent policies have hastened it. Companies are now diversifying supply chains, duplicating production capacity in “trusted” jurisdictions, and prioritising resilience over efficiency. Capital, infrastructure investment, and talent are following these shifts, elevating neutral or low-risk hubs as new centres for advanced manufacturing and synthetic biology. Over time, this divide could harden into distinct standards, regulatory regimes, and IP frameworks.

For most companies, the major impact seems to be operational friction. Tool providers may face longer development cycles and fragmented market access; synthetic biology firms could encounter tighter controls on data sharing and manufacturing collaboration; and investors may apply higher risk premiums to cross-border ventures.

Dirk:

I agree with Cam on the uncertainty and difficulty in sourcing business components. No one knows what’s coming, especially in the long supply chains, which most biotech applications are a part of.

But the tariffs mask a bigger trend. Well before this round of tariffs, even before the tariffs on March 1, China was becoming less of an export market for rich liberal democracies’ firms – Australia was the only exception. Korea’s exports to China have been stagnant since 2013; EU exports to China are at the same level as 2019. Selling into China for manufacturers is increasingly difficult. So, what we really mean is that import dependency on China for the rest of the world has increased. Finding markets in China for manufacturers in any country remains difficult.

The tariff differential between China, India and Southeast is changing so frequently that it is hard to know where to do the duplication that Cam mentions.

Q13. Where do you see the role of machine learning, LLMs and related technologies in biotechnology? What is hype and what is real and how should investors view the impact of these technologies on the sector, and across what timeframe?

Cam:

Machine learning and large language models are becoming deeply embedded across the biotechnology value chain, from discovery to manufacturing. While the first wave of AI in biology focused primarily on in silico prediction, the real progress now comes from integrating those models with automated experimentation and production. These technologies are beginning to bridge the long-standing gap between digital insight and physical validation, turning biology into a faster, more iterative engineering discipline.

On the R&D side, the field has been transformed by the democratisation of research tools and the fusion of AI with automation. These systems are reducing failure rates and enabling scientists to explore biological questions once considered intractable. Crucially, the growth of contract research organisations means innovators no longer need to build full in-house capabilities. They can design biological systems computationally, test hypotheses in silico, and partner with CROs for validation. This combination of computational acceleration and outsourcing has lowered both the capital and operational barriers to entry, allowing more agile and capital-efficient companies to emerge.

At the same time, AI is reshaping biomanufacturing. Automation and data-driven optimisation are improving strain performance, fermentation efficiency, and overall process control. This translates directly into lower operating costs and shorter paths to profitability. As government-backed infrastructure expands and CDMOs mature, the ability to scale bioproducts is becoming a practical reality. The integration of AI across discovery and manufacturing is beginning to look less like a technology trend and more like a new operating model for industrial biotechnology.

In my opinion, investors can view this transformation through a layered lens. In the short term (1–3 years), the most credible opportunities lie in enabling technologies: automation hardware, multimodal data platforms, and orchestration software that connect AI models to wet-lab execution. In the medium term (3–7 years), returns will emerge from companies that demonstrate measurable performance advantages, such as higher yields, lower OPEX, and faster design-build-test cycles. Over the longer horizon, these dynamics may compress development timelines enough to change the sector’s capital structure, enabling biotech ventures to reach profitability without heavy dependence on venture capital.

There will likely be continuing hype around a Gen AI for biology – like DeepMind’s talk of a fully virtual cell, which will undoubtedly be an incredible scientific achievement but likely fairly impractical due to compute costs.

The hype lies in expecting AI models to “discover biology” independently. The reality is that value will come from integration, where AI augments, rather than replaces, experimental science. As those systems mature, we will see a new generation of biotech firms that are smaller, faster, and more computationally native, operating at the intersection of AI-driven automation and scalable biomanufacturing.

Dirk:

Not much to add. The only thing is that biological design tools (BDT) still require large scale automation for practical testing. BDTs might design hundreds or thousands of potential variants and those need to be tested with high-throughput automation. So, the AI-revolution only works for biology with automated throughput. 

Q14. While it is early days, what are the future implications as more chemicals can be synthesized through biological sources? What does this mean for the oil/gas sector and what is a realistic timeframe to see significant growth in biological sourcing?

Cam:

Conceptually, there’s no reason biologically sourced chemicals can’t ultimately replace petrochemical equivalents. Most molecular functionalities derived from oil and gas can be replicated, or improved, using biological processes. The real constraints are economic and infrastructural: cost parity, scalability, and the rate at which legacy assets can be adapted for bio-based processing. As AI, automation, and biomanufacturing converge, those barriers are steadily eroding.

If we follow the trajectory laid out in BCG’s Synthetic Biology Disruption Timeline back in 2022, the first wave of impact (0–5 years) has already taken hold in sectors such as pharma, beauty, and food ingredients, where biology enables product substitution with strong consumer pull and high margins. The next 5–10 years will see chemicals, textiles, and agriculture become central battlegrounds, driven by both economic pressure and policy incentives. Beyond the decade mark, fuels and base commodities are expected to follow, reflecting the higher capital intensity and tighter margins of those markets.

In practice, this means the petrochemical sector is unlikely to be displaced overnight but it does already seem to be changing. Several major oil and gas companies are actively exploring biological routes through academic collaborations, dedicated R&D teams, and corporate venture partnerships. They recognise that as fossil reserves tighten and carbon intensity becomes a regulated liability, bio-based production could extend the commercial life of existing infrastructure. The strategic question is not if but when the economics make sense. Conversations with industry leaders consistently return to one pivot point: widespread adoption will only occur when bio-based production achieves cost parity, or when government incentives or carbon pricing effectively enforce it, as is already happening in China.

Dirk:

In the lab this is now routine. The question is scale-up. As your readers know, there is an absolute glut of petrochemical production being driven by China. Now, my team is seeing a glut of bio-based chemical production and petrochemical production, but along their traditional paths. There have not been huge volumes of petrochemicals moving to a bio-based pathway yet. We will see subsidization of bio-based chemical purchasers, as it is the only way to counter the loss-making (yet-surviving) petrochemical plants.

Q15. Please share any favorite books, publications, blogs, podcasts or other resources that readers could use to improve their understanding of biotechnology, or other related topics.

Cam:

1. 1. 1. Decoding Bio – A weekly newsletter translating complex biotech and AI-bio developments into actionable insights for founders, scientists, and investors.
      2. Grow Everything – A podcast spotlighting innovators who use biology as a technology, from materials and food to cosmetics and sustainability.
      3. Drift Signal – A weekly newsletter by Nicolas Colin offering macro-finance and technology analysis through the lens of late-cycle investment theory and how shifting technological maturity reshapes markets, institutions and capital allocation.
      4. SynBioBeta – A global community and flagship conference bringing together entrepreneurs, engineers and investors driving the synthetic-biology transformation. Publishes a weekly newsletter.
      5. SynBioPunk / BioPunk Labs – Initiatives uniting a DIY-bio culture and community biotech labs to empower hands-on innovation, open-source biohacking and startup collaboration.
      6. Better BioEconomy A weekly newsletter covering deal-flow, strategy and commercial developments in the food- &-agricultural segment of the bio-economy.
      7. House of BioDesign – A newsletter bridging biological sciences and design, delivering stories and insights on how living systems and materials are reshaping creative and industrial practice.

Dirk:

1. 1. 1. Jamie Metzl, Hacking Darwin: Genetic Engineering and the Future of Humanity – A forward looking book on how humans might deal with genetic engineering (written by a non-scientist).
      2. Kyle Harper, Plagues upon the Earth – This is in the weeds and all you want to know about how humans suffered and then eventually overcame many contagious diseases.
      3. Jennifer Doudna, A Crack in Creation: Gene Editing and the Unthinkable Power to Control Evolution – this book is a play-by-play from a Nobel prize winner on how she discovered CRISPR.

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