In 2002, researchers at Stony Brook University chemically synthesized poliovirus from scratch—assembling the complete viral genome from commercially available oligonucleotides, without using any natural template, and producing infectious virus in the lab. It took three years and a team of virologists with deep expertise. In 2025, a paper in Science documented that AI-powered protein design tools had advanced to the point where researchers had to develop new nucleic acid biosecurity screening protocols to prevent generative models from being used to design novel pathogens. A December 2025 review in Viruses proposed CRISPR-based countermeasure strategies specifically for AI-designed synthetic viruses—a sentence that would have read as science fiction a decade ago and now reads as a biosecurity planning document.
The synthetic biology market was valued at roughly $20 billion in 2025 and is projected to reach somewhere between $53 billion and $137 billion by the early 2030s, depending on which analyst you trust and how broadly they define the field. The U.S. government committed $15 billion to biomanufacturing capacity. Ginkgo Bioworks, the self-described “organism company,” operates AI-powered automated foundries that have compressed organism development from years to months. Healthcare accounted for over 53 percent of the market’s 2025 revenue—gene-therapy vectors, mRNA vaccines, antibody libraries, and engineered microorganisms designed to produce therapeutic proteins. Precision fermentation is scaling bioidentical egg-white protein as a hedge against avian-flu supply disruptions. Genomatica has engineered microorganisms that convert renewable plant sugars into nylon precursors. Fashion companies are using synthetic biology to grow leather and silk without animals.
This is a field that is simultaneously producing nylon from bacteria, vaccines from engineered cells, and the technical capability to synthesize poliovirus in a basement—and the governance framework for the first two applications is not keeping pace with the dual-use implications of the third.
What synthetic biology actually is
The field uses engineering principles—standardization, modularity, predictive design—to modify existing organisms or construct entirely new biological systems. The conceptual leap is treating biology like software: genetic sequences as code, organisms as hardware, and biological functions as programmable outputs. CRISPR-Cas9 gene editing, which enables precise cuts and modifications to DNA at specific locations, is the most well-known tool, but it’s part of a broader toolkit that includes DNA synthesis (writing genetic sequences from scratch), DNA assembly (combining fragments into larger constructs), metabolic engineering (rewiring an organism’s chemical pathways to produce desired molecules), and increasingly, AI-driven design that predicts which genetic modifications will produce which functional outcomes without requiring exhaustive trial-and-error.
The cost curve matters. In 2003, sequencing a human genome cost roughly $2.7 billion. Today it costs under $200. DNA synthesis costs have dropped from dollars per base pair to fractions of a cent. CRISPR kits are available online for $169. The combination of cheaper tools, more powerful computational design, and a growing open-source community of practitioners—including the iGEM competition, which brings thousands of students and amateurs into synthetic biology projects annually—means that the barrier to entry for biological engineering is lower than it has ever been and continues to fall.
What it can do right now
The commercial applications in 2026 are real and expanding. In medicine: engineered cell therapies, mRNA vaccines designed and manufactured at speeds that would have been impossible before synthetic biology platforms existed, and therapeutic microbiomes that are purposefully designed for specific patient populations. Two microbiome-based therapeutics have received FDA approval. In agriculture: engineered crops with enhanced pest resistance, nitrogen fixation, and climate resilience. Ginkgo Bioworks partnered with Bayer in May 2025 to develop microbial strains for sustainable agricultural inputs that reduce chemical fertilizer dependence. In industrial chemistry: microorganisms engineered to produce biofuels, biodegradable plastics, specialty chemicals, fragrances, and bio-based materials that replace petroleum-derived products. In environmental applications: engineered organisms for bioremediation—cleaning up pollution by metabolizing contaminants—and carbon capture through engineered algae and microbes.
DARPA is funding research into bio-fabricating structures in microgravity—the concept of growing satellite components in space using engineered organisms rather than launching manufactured parts from earth. DNA data storage, which encodes digital information in synthetic DNA molecules, has achieved a 3,200-fold improvement in write speed. These are not speculative applications. They’re funded, operational, or in advanced development at institutions with real budgets and real timelines.
The risks nobody wants to talk about
The National Academies of Sciences, Engineering, and Medicine, in a report commissioned by the Department of Defense, identified a dozen ways synthetic biology could be used to create biological weapons. Three were designated highest priority: recreating known pathogenic viruses such as Ebola, SARS, or smallpox from synthetic DNA; engineering existing bacteria to be more dangerous by inserting genes for antibiotic resistance or increased virulence; and using synthetic biology to produce toxic biochemicals through normally benign microorganisms that a target population wouldn’t think to defend against.
The committee noted that the capabilities for the first two categories “have been around for a long time.” What’s changed is accessibility. Synthetic biology has lowered the technical barriers, reduced costs, and—critically—enabled AI tools that can design genetic modifications without requiring deep wet-lab expertise. A 2025 paper in AI & Society identified the core security problem: AI increasingly enables biological engineering by lowering technical barriers and making biosecurity threats “more intangible, diffuse, and decentralized.” The convergence of AI and synthetic biology means that the knowledge required to engineer a dangerous organism is migrating from tacit (learned through years of lab work) to explicit (encoded in software that can guide a novice through the process).
Gene drives—engineered genetic systems that can rapidly propagate a specific set of genes through a wild population, overriding normal Mendelian inheritance—represent a different category of risk. A gene drive designed to suppress malaria-carrying mosquito populations is being developed with the best of intentions. The same technology, if released without adequate containment or applied to a different target species, could cause irreversible ecological damage. The technology doesn’t distinguish between beneficial and harmful applications. It propagates whatever genes it’s designed to propagate, and once released into a wild population, it can’t be recalled.
The DIY biology community—biohackers working outside traditional laboratory settings, sometimes in garages or community labs—is mostly benign. The vast majority of DIY projects involve basic gene editing, fermentation experiments, and educational activities. But as the Carnegie Endowment for International Peace noted, “How long before the teenager next door is in his basement experimenting with the next lethal pathogen like smallpox, using a DIY CRISPR gene-editing kit he got online for just $179?” The answer, thankfully, is that creating and deploying a lethal pathogen still requires specialized knowledge, access to specific genetic material, and high-end equipment that a $179 kit doesn’t provide. But the distance between “still requires specialized knowledge” and “can be guided through the process by an AI tool” is shrinking, and the governance frameworks designed for the previous era of biotechnology are not equipped for the current one.
The governance gap
In the United States, biotech oversight is split among the FDA, USDA, and EPA—agencies that focus on physical products rather than the intangible design tools and AI-generated code that increasingly drive the field. The National Science Advisory Board for Biosecurity addresses dual-use research but rarely covers AI-generated biological designs. The EU’s GMO regulations, designed for an earlier generation of biotechnology, don’t adequately address CRISPR-based or AI-enabled methods. The Biological Weapons Convention, the international treaty that prohibits development and use of biological weapons, has no verification mechanism—it relies entirely on self-reporting by signatory nations.
DNA synthesis screening—the process by which commercial gene synthesis companies check orders against databases of known pathogen sequences to prevent customers from ordering dangerous genetic material—is the primary biosecurity choke point, and it’s voluntary. The International Gene Synthesis Consortium and newer organizations like IBBIS have developed screening standards, but compliance is not mandatory in most jurisdictions, and the standards were designed for known pathogens, not for novel sequences that AI tools might generate. A 2025 paper in Science by researchers at multiple institutions, including Ginkgo Bioworks, proposed strengthening nucleic acid biosecurity screening specifically to address the threat from generative protein design tools—acknowledging that the existing screening infrastructure was not designed for and cannot adequately address AI-designed sequences.
The honest assessment: synthetic biology in 2026 is a field where the commercial applications are generating tens of billions of dollars in value, the scientific capabilities are advancing faster than any prior era of biotechnology, and the governance infrastructure is designed for a world that no longer exists. The tools that enable a Ginkgo Bioworks to engineer sustainable agricultural microbes are functionally the same tools that could enable the engineering of a novel pathogen. The difference between the two applications is intent and governance, and intent can’t be regulated while governance hasn’t caught up.
We cover synthetic biology alongside fusion energy, quantum computing, gene editing, and 21 other civilization-scale technology challenges across our Moonshot 2169 course—including why the most powerful engineering toolkit of the 21st century is also the one with the widest gap between what it can do and what we’ve agreed it should.