Ginkgo Bioworks Holdings (DNA) stock price is $10.06, down 2.00% on the day, as of Oct 19, 2026. It has a market cap of $625.60Mn, and is classified in the Contract Research industry (Healthcare sector).
Enterprise Value
Margins
P/FCF
Key Stats
Market Cap625.60 Mn
P/E-2.15
P/S5.59
Div. Yield0.00
Revenue Growth (1y) (Qtr)-48.49
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About
Ginkgo Bioworks Holdings, Inc. is a biotechnology company that provides cell engineering and biosecurity services through an integrated platform designed to make biology easier to engineer. The company’s mission is to enable customers to program biology for the development of new therapeutics vaccines agricultural inputs industrial chemicals and biosecurity solutions. Its platform combines laboratory automation hardware software data assets and scientific expertise to…
Ginkgo Bioworks Holdings, Inc. is a biotechnology company that provides cell engineering and biosecurity services through an integrated platform designed to make biology easier to engineer. The company’s mission is to enable customers to program biology for the development of new therapeutics vaccines agricultural inputs industrial chemicals and biosecurity solutions. Its platform combines laboratory automation hardware software data assets and scientific expertise to deliver R&D services tools and analytics that improve with each project. Ginkgo serves customers across medicine agriculture industrial biotech and government sectors by offering customizable solutions and standardized tools that reduce development time cost and risk.
Revenue is generated primarily from cell engineering solutions which include service fees for R&D projects fees for cell engineering tools such as RAC systems and Datapoints services and support fees for automation and data offerings. Fees for cell engineering programs are often structured as fixed payments for defined scope of work with additional milestone payments tied to technical achievements. The company also participates in value share arrangements receiving royalties milestone payments or equity interests that provide upside when customer products succeed. Biosecurity revenue comes from fees for Canopy biomonitoring programs Horizon analytics services and related support while the segment benefits from scale driven improvements in data collection and analysis.
The company operates through the following segments: Cell Engineering and Biosecurity. Each segment is defined by the type of service offered and the primary customer groups it serves.
Cell Engineering provides research and development services and tools that help customers discover optimize and manufacture biotechnology products across medicine agriculture industrial biotech and government applications. Services include the Autonomous Lab for high throughput data generation RAC systems for flexible automation Datapoints for specialized data sets AI models for predictive design and solution offerings such as protein engineering genetic medicines crop nutrition crop protection plant traits small molecules enzymes and fermentation scale up. Revenue is derived from service fees support fees and value share arrangements that align the company’s earnings with customer success.
Biosecurity offers two core offerings Canopy which generates high value genomic data from strategically positioned nodes such as airports and borders through end to end biomonitoring programs and Horizon which provides global data analytics and insights for detecting monitoring and predicting biological threats. Canopy involves program design sample collection sequencing bioinformatics and delivery of insights while Horizon integrates early detection genomic surveillance and digital biothreat intelligence into a unified analytics platform. The segment serves primarily government customers and earns revenue from program fees data services insights and support while benefiting from platform improvements that increase with scale.
Ginkgo Bioworks Holdings, Inc. occupies a distinctive position in the biotechnology industry by combining laboratory automation software data assets and scientific expertise into a single platform that improves with scale. The company competes against internal R&D departments contract research organizations life science tools companies and vertically focused platforms such as AbCellera Codexis Senti Bio and WuXi biologics. Competitive advantages include the ability to generate larger and higher quality data sets through the Autonomous Lab the reuse of data and host cell assets across projects and the integration of AI driven models that accelerate design cycles. In biosecurity Ginkgo leverages a global network of detection nodes and partnerships with governments and multilaterals to provide end to end surveillance that few competitors can match. These strengths allow Ginkgo to offer customers faster development timelines lower experimental costs and higher probability of success compared with traditional in house efforts.
Ginkgo Bioworks Holdings, Inc. serves a diverse customer base that includes government agencies pharmaceutical and biotechnology companies agricultural firms and industrial biotech enterprises. Specific customers mentioned in the filing include Bayer Crop Sciences for crop nutrition projects Virica Biotech for AAV manufacturing optimization and pharmaceutical leaders such as Biogen Novo Nordisk Vertex Regeneron and Bayer. The company also works with academic research institutions and various specialty chemical and specialty materials companies to develop enzymes small molecules and other biobased products. Government customers span federal state and local agencies in the United States and abroad that rely on Ginkgo for biosecurity monitoring pathogen detection and pandemic preparedness initiatives. Pharmaceutical and biotech clients use the platform to advance drug discovery optimize manufacturing processes and create novel biologics gene therapies and vaccines. Agricultural partners seek Ginkgo’s expertise to improve crop nutrition enhance soil health develop biological pesticides and create sustainable farming solutions. Industrial biotech firms engage the company to produce enzymes chemicals and specialty materials through engineered microbial processes.
Sector:HealthcareSector rationaleGinkgo Bioworks operates as a biotechnology company providing cell engineering and R&D services for therapeutics, vaccines, and biologics, which falls under the Healthcare sector's Biotechnology and Contract Research industries. While it serves agricultural and industrial clients, these are described as applications of its core cell engineering platform, and the profile explicitly identifies it as a biotechnology company competing with other life science tools and CROs.Industries:Contract ResearchContract ResearchPrimaryGinkgo Bioworks operates as a platform providing R&D services, cell engineering solutions, and high-throughput data generation for external sponsors. Its revenue model is based on service fees, milestone payments, and value-share arrangements for projects involving therapeutics, vaccines, and industrial chemicals for customers like Biogen and Novo Nordisk.Life Sciences ToolsLife Sciences ToolsSecondaryThe company sells standardized tools and data assets, such as RAC systems and Datapoints, which are used for life-sciences research and bioproduction.Diagnostic LabsDiagnostic LabsSecondaryThrough its Biosecurity segment, specifically the Canopy and Horizon offerings, the company provides genomic surveillance, pathogen detection, and biomonitoring services to government customers.Classified using BQ-MICSCIK: 0001830214
Bull & bear
Investment Thesis
▲ Bull case
The autonomous lab initiative, particularly the Nebula system in Boston, represents a fundamental shift in biological research methodology that the market is underestimating. By enabling 24/7 operation (168 hours per week versus the traditional 40-hour lab week) and achieving a threefold reduction in required laboratory space through dense equipment packing, Nebula drastically lowers the cost per experiment while simultaneously increasing throughput capacity. More critically, the system's unique ability to run over 100 distinct protocols in parallel via its sophisticated scheduler software - driven by AI coding tools that translate scientist intent into executable commands without requiring coding expertise - creates a platform where data generation can scale exponentially. This positions Ginkgo not merely as a lab equipment provider but as the enabler of an AI-driven science flywheel, where increased data from autonomous labs improves AI models, which in turn design better experiments, leading to higher-value discoveries and creating a defensible moat that traditional CROs cannot replicate.
The recent launch of ADME-One, co-developed with Tangible Scientific and Inductive Bio, is a hidden catalyst for Ginkgo's Datapoints business that was not emphasized in the May earnings call but could significantly accelerate revenue growth and margin expansion. This fully integrated ADME platform addresses a critical inefficiency in drug discovery by moving comprehensive absorption, distribution, metabolism, and excretion profiling from the expensive lead optimization phase to the much earlier hit identification stage, thereby reducing wasted synthesis cycles and increasing the likelihood of identifying clinically viable candidates. Priced several times below industry standard and built on a fully U.S.-based workflow that appeals to companies reshoring preclinical efforts due to the BIOSECURE Act and data sovereignty concerns, ADME-One offers a compelling value proposition that could rapidly capture share from offshore CROs. The strategic addition of Jonathan Grob, a veteran medicinal chemist from Novartis and Valo Health, as VP of Small Molecules further de-risks adoption by top pharmaceutical clients, suggesting that this platform could become a core driver of recurring, high-margin revenue from Ginkgo's existing top-10 pharma relationships in Datapoints.
Ginkgo's strategic integration of its autonomous lab infrastructure with its cloud lab and data points services creates a powerful, defensible business model that the market is failing to fully appreciate as a source of sustainable, recurring revenue. By operating Nebula not just as an internal R&D tool but as a live, accessible cloud lab service (via data points.ginkgo.bio), the company transforms what would be a capital-intensive hardware play into a service-based offering with high gross margins and predictable usage-based revenue. This approach locks in customers through seamless integration - where scientists can design experiments using AI tools, instantly receive pricing estimates, run them on the autonomous lab, and get data back without leaving their workflow - creating significant switching costs that pure-play lab automation vendors or traditional CROs cannot replicate. Furthermore, the data generated from these cloud lab runs continuously improves Ginkgo's internal AI models and dataset offerings, enhancing the value of its data points business over time and creating a virtuous cycle that strengthens customer retention and expands addressable markets beyond early adopters to the broader pharmaceutical and biotechnology industry.
The autonomous lab initiative, particularly the Nebula system in Boston, represents a fundamental shift in biological research methodology that the market is underestimating. By enabling 24/7 operation (168 hours per week versus the traditional 40-hour lab week) and achieving a threefold reduction in required laboratory space through dense equipment packing, Nebula drastically lowers the cost per experiment while simultaneously increasing throughput capacity. More critically, the system's unique ability to run over 100 distinct protocols in parallel via its sophisticated scheduler software - driven by AI coding tools that translate scientist intent into executable commands without requiring coding expertise - creates a platform where data generation can scale exponentially. This positions Ginkgo not merely as a lab equipment provider but as the enabler of an AI-driven science flywheel, where increased data from autonomous labs improves AI models, which in turn design better experiments, leading to higher-value discoveries and creating a defensible moat that traditional CROs cannot replicate.
The recent launch of ADME-One, co-developed with Tangible Scientific and Inductive Bio, is a hidden catalyst for Ginkgo's Datapoints business that was not emphasized in the May earnings call but could significantly accelerate revenue growth and margin expansion. This fully integrated ADME platform addresses a critical inefficiency in drug discovery by moving comprehensive absorption, distribution, metabolism, and excretion profiling from the expensive lead optimization phase to the much earlier hit identification stage, thereby reducing wasted synthesis cycles and increasing the likelihood of identifying clinically viable candidates. Priced several times below industry standard and built on a fully U.S.-based workflow that appeals to companies reshoring preclinical efforts due to the BIOSECURE Act and data sovereignty concerns, ADME-One offers a compelling value proposition that could rapidly capture share from offshore CROs. The strategic addition of Jonathan Grob, a veteran medicinal chemist from Novartis and Valo Health, as VP of Small Molecules further de-risks adoption by top pharmaceutical clients, suggesting that this platform could become a core driver of recurring, high-margin revenue from Ginkgo's existing top-10 pharma relationships in Datapoints.
Ginkgo's strategic integration of its autonomous lab infrastructure with its cloud lab and data points services creates a powerful, defensible business model that the market is failing to fully appreciate as a source of sustainable, recurring revenue. By operating Nebula not just as an internal R&D tool but as a live, accessible cloud lab service (via data points.ginkgo.bio), the company transforms what would be a capital-intensive hardware play into a service-based offering with high gross margins and predictable usage-based revenue. This approach locks in customers through seamless integration - where scientists can design experiments using AI tools, instantly receive pricing estimates, run them on the autonomous lab, and get data back without leaving their workflow - creating significant switching costs that pure-play lab automation vendors or traditional CROs cannot replicate. Furthermore, the data generated from these cloud lab runs continuously improves Ginkgo's internal AI models and dataset offerings, enhancing the value of its data points business over time and creating a virtuous cycle that strengthens customer retention and expands addressable markets beyond early adopters to the broader pharmaceutical and biotechnology industry.
Despite aggressive restructuring that has reduced cash burn, Ginkgo's core service revenue continues to deteriorate at an unsustainable pace, signaling that the transition to autonomous labs may not compensate for declining legacy businesses quickly enough. Revenue from continuing operations fell 37% year-over-year in Q1 2026 (excluding the noncash BiomEdit termination revenue), dropping to just $19 million, while cash burn remained substantial at $48 million for the quarter - implying a cash burn rate that exceeds revenue by more than 2.5x. The company's reliance on large-system sales, such as the $47 million deal with Pacific Northwest National Labs, introduces significant execution risk due to the long sales cycles and high capital expenditure requirements for customers, which could be further delayed by the current cautious environment in biotech funding. Without a near-term inflection point in service revenue growth, the path to profitability remains distant and contingent on uncertain adoption of autonomous lab systems by cost-conscious enterprises that may prefer incremental automation over full-system replacements.
The autonomous lab technology, while technologically impressive, faces substantial adoption and reliability risks that the market is overlooking, particularly regarding the system's complexity and the readiness of end-users to leverage its full potential. Management acknowledged during the Q&A that "things break as we are scaling this up," highlighting ongoing reliability challenges with the Nebula system as it expands beyond 100 racks, which could undermine customer confidence in using the platform for critical, time-sensitive experiments. Furthermore, the scheduler software ("catalyst") is described as a "very complicated scheduling problem" that is "really easy to mess up," suggesting that scaling the system to handle hundreds of concurrent protocols may introduce unacceptable delays or errors, negating the promised throughput advantages. Most critically, the company's dependence on AI coding tools to make the system accessible to non-programmer scientists may prove ineffective in practice, as biologists often lack the training to effectively frame experiments for AI interpretation, creating a persistent bottleneck that prevents the autonomous lab from achieving its envisioned utility and limits the addressable market to only the most technologically sophisticated research groups.
Ginkgo's reported financial improvements rely heavily on non-GAAP adjustments and accounting treatments that obscure persistent weaknesses in its core unit economics and raise questions about the sustainability of its cost structure, particularly regarding real estate obligations. While the company highlights reduced adjusted EBITDA losses and lower cash burn, these metrics exclude significant ongoing costs such as the $16 million in Q1 2026 for excess lease space - a direct cash operating expense tied to underutilized facilities that remains a drag on profitability and is not easily eliminated due to long-term lease commitments. The company's ability to terminate leases or find sub-tenants for this space is uncertain in a shifting commercial real estate market, and failure to do so would continue to weigh on adjusted EBITDA and cash flow regardless of operational improvements in other areas. Moreover, the continued use of pro forma financials that retroactively recast prior periods to exclude the biosecurity business presents a rosier picture of historical performance than what was actually reported under GAAP, making it difficult for investors to assess the true trajectory of the company's core operating performance and increasing the risk of negative surprises if cost-saving initiatives fall short of expectations.
Despite aggressive restructuring that has reduced cash burn, Ginkgo's core service revenue continues to deteriorate at an unsustainable pace, signaling that the transition to autonomous labs may not compensate for declining legacy businesses quickly enough. Revenue from continuing operations fell 37% year-over-year in Q1 2026 (excluding the noncash BiomEdit termination revenue), dropping to just $19 million, while cash burn remained substantial at $48 million for the quarter - implying a cash burn rate that exceeds revenue by more than 2.5x. The company's reliance on large-system sales, such as the $47 million deal with Pacific Northwest National Labs, introduces significant execution risk due to the long sales cycles and high capital expenditure requirements for customers, which could be further delayed by the current cautious environment in biotech funding. Without a near-term inflection point in service revenue growth, the path to profitability remains distant and contingent on uncertain adoption of autonomous lab systems by cost-conscious enterprises that may prefer incremental automation over full-system replacements.
The autonomous lab technology, while technologically impressive, faces substantial adoption and reliability risks that the market is overlooking, particularly regarding the system's complexity and the readiness of end-users to leverage its full potential. Management acknowledged during the Q&A that "things break as we are scaling this up," highlighting ongoing reliability challenges with the Nebula system as it expands beyond 100 racks, which could undermine customer confidence in using the platform for critical, time-sensitive experiments. Furthermore, the scheduler software ("catalyst") is described as a "very complicated scheduling problem" that is "really easy to mess up," suggesting that scaling the system to handle hundreds of concurrent protocols may introduce unacceptable delays or errors, negating the promised throughput advantages. Most critically, the company's dependence on AI coding tools to make the system accessible to non-programmer scientists may prove ineffective in practice, as biologists often lack the training to effectively frame experiments for AI interpretation, creating a persistent bottleneck that prevents the autonomous lab from achieving its envisioned utility and limits the addressable market to only the most technologically sophisticated research groups.
Ginkgo's reported financial improvements rely heavily on non-GAAP adjustments and accounting treatments that obscure persistent weaknesses in its core unit economics and raise questions about the sustainability of its cost structure, particularly regarding real estate obligations. While the company highlights reduced adjusted EBITDA losses and lower cash burn, these metrics exclude significant ongoing costs such as the $16 million in Q1 2026 for excess lease space - a direct cash operating expense tied to underutilized facilities that remains a drag on profitability and is not easily eliminated due to long-term lease commitments. The company's ability to terminate leases or find sub-tenants for this space is uncertain in a shifting commercial real estate market, and failure to do so would continue to weigh on adjusted EBITDA and cash flow regardless of operational improvements in other areas. Moreover, the continued use of pro forma financials that retroactively recast prior periods to exclude the biosecurity business presents a rosier picture of historical performance than what was actually reported under GAAP, making it difficult for investors to assess the true trajectory of the company's core operating performance and increasing the risk of negative surprises if cost-saving initiatives fall short of expectations.