Tech-Bio: The New Industrial Revolution Fueled by AI and Biology – A Vision by Dr. Vijay Pande of a16z

Over the past decade, numerous interdisciplinary technologies have emerged in the field of biotechnology. Driven by computational science, machine learning, and artificial intelligence, these advancements have transformed development patterns across many areas of biopharmaceuticals and healthcare, with biological progress beginning to exhibit a trend akin to Moore’s Law. In just ten years, scientists have not only achieved the ability to “read” genes but also, empowered by CRISPR technology, to “write” them. Furthermore, care delivery models, including those within hospitals, have undergone rapid transformation and fragmentation; telemedicine and community-based healthcare have broken through traditional barriers, shifting medical care toward a value- and outcome-oriented approach.

Driving this revolution is a new generation of startups and founders who are integrating biology with technology. This convergence extends beyond the narrow scope of biotechnology and digital technologies (often understood as digital health); it is fostering a broader, more disruptive technological paradigm. In short, technology-driven biology is impacting the entire world. It is not only transforming how we diagnose, treat, and manage diseases, but also how we access, pay for, and deliver healthcare services. Currently, technology-driven biology has also permeated manufacturing, food, and other industries, making biology an indispensable component of the industrial sector.

“AI + Biology,” this combination is driving a shift in empirical science toward more engineering-oriented research methodologies, which we refer to as the new era of biological industrialization. This era will constitute the next industrial revolution in human history, propelled forward by immense new driving forces.

We are accustomed to abbreviating legendary companies like “Google, Amazon, Facebook, Apple” as “GAFA.” We believe that a biological version of “GAFA” is on the horizon.

More than half a century ago, we witnessed the modern industrial revolution that unfolded over several decades in the postwar era. It began with medical technological advances that emerged during or shortly after the war, such as influenza vaccines, the commercial production of penicillin, and the widespread adoption of blood transfusions, as well as technological breakthroughs including jet engines, radar, microwaves, and electronic computing—most notably ENIAC, one of the world’s earliest general-purpose computers. Then, in the 1970s, innovations in mass manufacturing and automated production (of household goods, automobiles, and other products) sparked the most recent industrial revolution, accompanied by advances in semiconductors, electronics, computing, and artificial intelligence.

In recent years, the global pandemic has also served as a wake-up call. The massive casualties caused by the disease have spurred a new wave of innovation in the biopharmaceutical and healthcare sectors. This is evident from the development approach and speed of mRNA vaccines, which offer insights into the rationale and mechanisms underpinning the promises of healthcare engineering.

This is why I believe that the technological revolution in the post-pandemic era will give rise to an industrial-biological complex. The industrialization of biology will usher in a new industrial revolution, bringing with it novel therapies, healthier lifestyles, and advances in manufacturing and construction, among other developments. Thus, biology is becoming the new frontier of the manufacturing revolution—a direction worthy of attention, investment, and dedicated effort, whether for healthcare or industrial safety.

In this process, artificial intelligence (AI) will continue to play its transformative role.

As with previous revolutions, the shift in technological platforms will impact industries differently. According to international scholar Carlota Perez, the technologies driving the next industrial revolution are not necessarily an extension of those from the previous one; for instance, the technological revolution of the 1970s did not significantly influence the mass industrialization of consumer goods in the 1940s and 1950s. Similarly, artificial intelligence (AI) may not exert a profound impact on the current technological revolution. In domains where “computing” already dominates—sectors that have long adhered to Moore’s Law—the role of AI may be overstated. As Casado and Bernstein argue, AI does not necessarily make substantial contributions to all these business models. In other words, while it is tempting to label AI itself as an industrial revolution, Perez contends that technology alone does not constitute a revolution. Rather, an industrial revolution reflects the convergence of multiple transformative technologies with specific, vast potential markets.

In a biology and healthcare market predominantly driven by services, artificial intelligence (AI) has the potential to generate substantial returns, at least in part by transforming services into “computation.” AI is not always as useful in enterprise settings, as its development often lags behind realistic expectations and comes with high costs. However, in the biological sciences, AI plays a pivotal role: it helps convert what was previously expensive, labor-intensive, less efficient, and harder to access into lower-cost, higher-efficiency, and more effective “computation.”

Once this occurs, technology can penetrate industries that have previously failed to benefit from the IT revolution. Economists and entrepreneurs have long been puzzled by a question: why they have been unable to achieve the substantial gains seen in other sectors—such as healthcare—a phenomenon known as Baumol’s cost disease. This is because when technology successfully penetrates the healthcare industry, it transforms previously expensive services into more affordable commodities and frees up human resources to engage in more meaningful work. In other words, technology can make human healthcare more humane.

We are currently at a critical juncture. To date, the healthcare and biotechnology sectors have been dominated by services delivered by professionally trained scientists and physicians. Algorithms cannot replace these services, let alone generate greater value.

But now, we are at the dawn of a revolution. Artificial intelligence is industrializing biopharmaceuticals and healthcare, with applications spanning all areas from drug design and diagnostics to healthcare delivery and back-office functions. (Regarding the issues and challenges often involved in applying AI to the biological field, this piece addresses the “black box” problem of AI in healthcare and outlines the conditions required to achieve intelligent AI in the life sciences.)

Across all these domains (and others yet to be seen), the industrialization of biopharmaceuticals and healthcare can advance even further. For instance, rather than relying on apprenticeship-style training to impart skills to humans, machines can learn these capabilities instead. We can simply replicate them to scale expertise in the same way cloud-based services are deployed: rapidly, cost-effectively (compared to expert labor), and at massive scale, thereby achieving true reproducibility in biology in unprecedented ways and addressing one of the greatest weaknesses of “pre-industrial” biology. Moreover, this scalability is not limited to computing; automation and robotics can likewise be scaled up.

Drawing on the lessons learned from the tech industry over the past two decades, what can we expect from the “Tech-bio” sector in the next twenty years? What will happen as we increasingly shift toward engineered biology? What are the implications of the convergence of two major trends: the industrialization of biology and the post-pandemic landscape?

Carlota Perez has proposed that, like many technologies before it, mobile technology undergoes an “installation” phase prior to its “deployment” phase. Jerry Neumann later summarized this concept, noting that during the “installation” phase, capital flows toward building the infrastructure required for the technological revolution. This “installation” phase demands more push than pull. This is why marketing, partnerships, and other business development efforts are particularly crucial for biotech startups.

During the “deployment” phase, companies typically shift their focus from merely creating markets to expanding and consolidating existing ones. At this stage, competition intensifies in terms of cost, availability, and scale. For mobile devices, this transition has raised a new question: “What can we build by standing on the shoulders of giants?” (The giants in mobile communications include companies such as Apple and Google.)

We predict that “Tech-bio” will follow a similar development trajectory. Currently, we require capabilities in bioengineering as well as more comprehensive engineering capacities (i.e., we are still in the “installation” phase). In the technology sector, the internet industry gave rise to tech giants such as Amazon and Google during a comparable stage. Considering integrated trends, along with the substantial challenges and market size of the healthcare industry, it is anticipated that several companies with trillion-dollar market capitalizations will emerge in the healthcare sector—potentially rising to prominence akin to “GAFA.”

For “Tech-bio” founders, this means their ambitions can be greater than ever before—provided the aforementioned vision is realized. For the industry as a whole, it signifies a pathway to addressing the systemic and structural challenges within healthcare. These issues are currently fragmented or consolidated in misguided ways (technology can bypass those entrenched barriers and structures). Most importantly, for the general public, the industrial-biotech complex should be capable of improving the standard of care at low cost, delivering healthier, longer, and more fulfilling lives to the masses.

Although it is still too early, there are already some signs at this early stage indicating what future “Bio GAFA” companies will look like:

The rise of full-stack companies (those that build complete end-to-end products or services), if managed effectively, can bypass incumbent enterprises and other competitors. Drawing an analogy to ride-hailing, many companies previously attempted to develop and sell software directly to the taxi/limousine industry, but these incumbents lacked the systems to evaluate or adopt such software. It was only with the emergence of companies like Lyft and Uber, which built the industry from the ground up using technology, that ride-hailing services became a reality.

Similarly, incumbent players in the healthcare industry are not prepared to fully integrate advanced technologies; they merely layer artificial intelligence or other technologies onto their existing organizational structures, rather than redesigning the entire organization from the ground up to localize these technologies, as startups do.

There are many business models in the healthcare sector, but one framework for scalable business models used here is “Compete or Connect.” Bio companies that adopt the “Compete” strategy aim to become comprehensive rivals to existing players within the three major biotech categories—life sciences, suppliers, or purchasers—rather than selling to these industries (i.e., “connecting” customers through other companies). This “Compete or Connect” framework can also help founders and investors evaluate which bio startups among thousands are likely to generate the greatest impact and scale, and which business models will stand the test of time.

The Manhattan Project was initially established by the U.S. President and government before and during World War II, with the aim of integrating and coordinating various research and technological efforts to win the war. Vannevar Bush, who served as the Director of the Office of Scientific Research and Development for the Manhattan Project, later authored the renowned report “Science: The Endless Frontier,” which led to the establishment of the National Science Foundation. However, we have not seen a Manhattan Project-style initiative emerge in today’s U.S. political system for healthcare (or the environment), because the current system is not structurally designed to facilitate coordination and technological innovation on such a scale.

However, the government does play an extremely important role as a buyer. Particularly in the healthcare industry, the Centers for Medicare & Medicaid Services (CMS) has long been a central hub and driving force for healthcare innovation—whether through Medicare and Medicaid insurance programs or policies incentivizing data sharing, pricing, and transparency, CMS is often at the forefront of the payer market. Especially in terms of reimbursement, when they implement new reimbursement policies and payment models, commercial payers tend to follow suit.

Therefore, in the emerging industrial-biological complex, once population-level scale is achieved, winners will inevitably engage with federal and local government programs to leverage large-scale payment rails. Companies are already doing this by leveraging their platforms—which are lower-cost and more flexible—to serve as the technical “execution arm” for many of CMS’s new care models, with a focus on elderly and low-income patient populations. Such collaborations typically operate through advanced primary care models or full-stack technology-enabled carriers.

Although the ambitions of the next wave of “Bio GAFA” companies are immense and necessarily visionary, those that succeed will need to be more firmly grounded in reality. To achieve success, they must recognize the challenges inherent in applying biotechnology and other technologies. These challenges remain very much present in practice.

A pharmaceutical company that views biology as “simple” or as predictable as other technological fields will fail in its attempts to engineer biological systems, even when leveraging the most powerful artificial intelligence. Similarly, healthcare companies that underestimate the complexity and challenges of the healthcare system will also fail. Successful companies will not only deliver health benefits but also bring together experts from both the “Tech” and “Bio” domains to build their enterprises from the ground up.

Technology, biopharmaceuticals, and healthcare have traditionally been viewed as distinct sectors within the entrepreneurial landscape, each with its own investor base. However, these boundaries are now blurring: clinical medicine is assuming a greater role in healthcare delivery; pharmaceutical companies are engaging directly with payers; and diagnostic firms are seeking to transform the very nature of healthcare.

In the biopharmaceutical sector, we have already witnessed a shift toward platform companies focused on assets, as outlined by Jorge Conde. This transformation is driven by artificial intelligence, automation, and other engineering technologies that are turning customized processes into industrialized drug development. However, to go further, these technologies can also serve as distribution channels. This represents another potential indicator of the emergence of “Bio GAFA.”

Traditional biotechnology companies are typically so-called single-asset companies—developing, testing, and commercializing a single therapy derived from discovery. This represents, to some extent, a serendipitous search for drugs that prove effective within complex biological contexts.

Now, platform companies in the new era can design subsequent therapies from early-stage designs, building knowledge in an iterative manner. This is possible because they can leverage technology to derive and replicate solutions, often at great speed. Artificial intelligence in pharmaceutical companies can employ industrialized processes—automating biological and chemical experiments to feed data into advanced machine learning techniques—thereby transforming previously manual, low-hit-rate methods into more reproducible, predictable, and scalable industrial technologies. Even when errors occur, these technologies enable us to learn from failure and improve through continuous iteration. This has already been validated in CRISPR and CAR-T therapies.

As biological platforms become increasingly efficient—enabling a wide range of novel therapies—the industry’s bottleneck will shift from discovery to development. Consequently, resource-constrained early-stage startups will need to leverage their platforms to prioritize and schedule the diseases they can address, then advance promising drug candidates into clinical development (either internally or through partnerships). In this environment, production platforms that enable startups to independently resolve distribution challenges can now serve as channels for many other companies to bring multiple products to market—much like Amazon leveraged its marketplace and commercial infrastructure to expand from selling books to selling everything.

This is not limited to the biopharmaceutical sector. If technological means are employed to improve the medical insurance system—by bringing together patients, healthcare providers, and payers through value-based reimbursement pathways—it may be possible to more effectively assist patients by serving as a single direct point of contact, thereby reducing costs and improving patients’ quality of life.

Bioengineering holds immense market potential, extending beyond pharmaceuticals and healthcare to include manufacturing, construction, and durable goods. Consumer demand is continuously growing, yet the biosector remains far from meeting this demand to date.

Furthermore, applying these technologies to biological systems can bring greater changes to people’s lives. For example, materials science applications could replace the cold-chain logistics currently required for the distribution of fruits and vegetables.

Along these lines, another promising technology involves replacing petroleum-based plastic infrastructure with biomaterials—including those capable of sequestering carbon from the atmosphere—thereby driving commodity costs close to zero (or even negative, when cap-and-trade systems are taken into account). Although these advancements have not yet benefited consumers, the roadmap for achieving them is clear, and the power of consumers is evident: they are eager for change.

With the development of biotechnology, we are on the verge of an unprecedented era of innovation. These forces may ultimately lead us toward economic systems that are more bio-based rather than petroleum-based.

Even after the pandemic ends, it will leave a lasting impact. I believe this is the “battle” our generation has experienced, and there is no denying that it bears similarities to the technological wave during World War II. What will we usher in when this “battle” concludes? Perhaps we will enter an era of bio-based development, giving rise to large-scale “Bio GAFA” companies.