AI Is Getting Easier to Build. AI-Biotech Startups Still Have a Data Problem

As AI models become increasingly accessible, the competitive advantage in biotechnology may shift toward proprietary patient cohorts, biological samples and clinical data. For South Korean startups, that could turn the country’s healthcare infrastructure into a strategic asset.

Artificial intelligence is becoming easier for startups to access, but that does not mean the hardest problems in AI are disappearing. For an increasing number of biotech startups, the difficult part may not be building an AI model. It may be assembling the biological data needed to make that model scientifically useful.

That distinction is becoming more important as South Korea accelerates its push into AI-driven biotechnology. The government approved a national AI-bio strategy in late 2025 covering drug discovery, brain and anti-aging research, medical devices, biomanufacturing and agri-food. It plans to develop AI-bio models, establish research hubs and build infrastructure connecting industry, academia, research institutions and hospitals. By 2030, the government aims to secure more than 7 million high-quality bio datasets, including data linked to 1 million Koreans.

The private sector is moving in the same direction. Korean pharmaceutical and biotech companies had around 160 partnerships or joint research projects with AI drug-development companies as of 2025, with roughly 450 AI drug-development pipelines, including preclinical programs, underway. AI is increasingly being applied beyond compound screening to preclinical research and clinical-trial design.

The numbers point to a rapidly developing market. But they also expose a problem for startups: as access to AI models, computing and algorithms improves, what remains difficult to replicate? For Jungwoo Lee, CEO of South Korean AI-biotech startup Biobytes, the answer lies in the data underneath the technology.

“AI models are becoming widely available. What is much harder to replicate is a carefully characterized patient cohort with long-term follow-up and biological samples,” Lee told KoreaTechToday.

His observation points to a broader shift in the AI-biotech startup economy. If models become increasingly accessible, proprietary biological data could become one of the most important sources of defensibility.

AI is becoming infrastructure, not necessarily a moat

The first generation of AI startups often built their differentiation around the technology itself. Access to advanced models, specialized algorithms and computing resources could create meaningful barriers to entry. That environment is changing. Foundation models are increasingly available through APIs, open-source releases and cloud platforms. Specialized AI infrastructure can be rented rather than built. The cost and time required to develop applications on top of these systems are also falling. Biotechnology does not escape this trend. AI can now be applied to target identification, molecular design, compound screening, protein analysis, biomarker discovery and clinical research without every startup having to develop its own foundational model.

Korean pharmaceutical companies are already moving beyond simple experimentation with external AI tools. SK Biopharmaceuticals, for example, used SK Telecom’s AI capabilities to identify an early lead compound for targeted cancer therapies, reducing an early research phase that normally takes one to two years to five months, according to Seoul Economic Daily. JW Pharmaceutical is upgrading its JWave AI drug-development platform, while Dong-A Socio Group has developed an AI drug-discovery platform combining AI with biological data. Pharos iBio has taken a candidate developed through its Chemiverse platform into clinical development.

The implication for startups is significant. A model can be improved. An algorithm can be copied. A third-party model can be licensed by a competitor. A deeply characterized patient cohort is different. Building one can require years of recruitment, clinical partnerships, biological sampling, follow-up and validation. The resulting dataset can also contain information that is difficult to reproduce elsewhere because it reflects a specific disease, population, treatment history or longitudinal outcome. That makes data less like an input and more like infrastructure.

For AI-biotech startups, however, the data advantage cannot be measured by volume alone. The most useful datasets may be those that connect different layers of biology and clinical information. A patient record can become considerably more valuable when it can be linked with imaging, genomic information, biomarkers, functional measurements, treatment history and outcomes. This is particularly important in diseases where biological changes unfold over time. Lee argues that Korea’s opportunity lies precisely in making those connections.

“Korea’s biggest opportunity lies in connecting AI with real patients and biology,” he said in a conversation with KoreaTechToday. “Korea has excellent hospitals, digital infrastructure, and clinical data, but the real value comes from combining longitudinal patient data with genomics, biomarkers, imaging, and functional measurements.”

That is a fundamentally different proposition from simply putting an AI model on top of an existing database. The startup has to build a structured biological asset. This is also where the economics of AI-biotech diverge from those of many software startups. A software company can potentially acquire millions of data points almost instantly. A biotech company may need to recruit patients, collect samples, perform laboratory analysis and wait for clinical outcomes before its dataset becomes substantially more valuable. The process is slower, but the resulting asset can be considerably harder for competitors to reproduce.

Korea is trying to build the data layer for AI-bio

South Korea’s current policy direction reflects an understanding of this bottleneck. Its AI-Bio National Strategy calls for a multimodal, multi-scale bio foundation model and AI-bio innovation hubs that bring together researchers, AI developers, data scientists, companies and hospitals. The government also plans secure computing environments for sensitive human-derived data and intends to connect data generated through national R&D projects to the National Bio Data Integration Platform.

The scale of the planned infrastructure matters because fragmented data is one of the biggest barriers to AI-biotech development. Medical data may sit across hospitals. Biological samples can be held by research institutions. Genomic datasets may follow different standards. Clinical information can be difficult to link because of privacy, consent and interoperability requirements.

South Korea’s new AI healthcare strategy is explicitly addressing some of these issues. The national AI healthcare subcommittee is working on linking the National Data Integration Platform with the Healthcare Big-Data Platform, alongside multi-institution medical-data linkage, standardization and safeguards for combining hospital, public and personally generated health data.

For startups, that infrastructure could lower some of the barriers to building data-intensive businesses. But it does not eliminate the underlying challenge. A national data platform can make information more accessible. It cannot instantly create the deeply characterized cohorts that a biotech company needs for a specific scientific question. That remains a startup opportunity.

Biotech startups are building specialized datasets, not generic AI

Biobytes offers a useful example of what this model looks like at the company level. The Korean startup is building a multi-center sarcopenia biobank around a specific disease area. In a recent update, Lee said the cohort had surpassed 800 participants and that the company had secured its first muscle-tissue sample, expanding the biobank beyond clinical and functional assessment data.

The significance is not simply that Biobytes has collected 800 participants. It is that the company is progressively adding layers of biological information to a defined patient population. That approach illustrates the distinction between an AI application and a data-centric AI-biotech company. The application may be replicated. A disease-specific cohort containing longitudinal clinical information and biological samples becomes more difficult to reproduce.

The broader commercial opportunity is to translate those datasets into biomarkers, therapeutic targets, diagnostics or partnerships with pharmaceutical companies. This is one reason AI-biotech startups may increasingly resemble data companies, research organizations and drug-development businesses at the same time. Their moat is not necessarily a single algorithm. It is the accumulation of scientific and clinical assets that become more valuable as they are validated.

Asia’s data gap could become an opportunity

There is another dimension to this opportunity that matters beyond Korea. Global biomedical research has historically been unevenly distributed across populations. Asian populations remain underrepresented in genomic research despite accounting for a large share of the world’s population. A 2026 study published in Nature Communications noted that more than 80% of participants in genomic studies as of 2019 were of predominantly European ancestry. The researchers also highlighted the limited representation of South Asian populations, which account for more than 1.9 billion people but remain poorly represented in curated genomic datasets.

Another 2026 analysis published in The Lancet Regional Health – Southeast Asia found that South Asia accounted for only 1.8% of electronic health-record publications between 2014 and 2024, while South Asians represented just 0.2% of participants in major genome-wide association studies despite comprising roughly 25% of the global population. That creates both a scientific problem and a potential startup opportunity. Lee believes Korean startups could play a role in closing that gap.

“Korean startups can also contribute data from Asian populations, which remain underrepresented globally,” he told KoreaTechToday. “This could lead to new biomarkers, drug targets, and more inclusive drug development. The opportunity is not to create another generic dataset labelled “Asian.”

The value lies in building high-quality cohorts that capture meaningful biological variation and can be linked to clinical outcomes. Projects such as GenomeAsia have already demonstrated the scientific value of expanding genomic reference datasets across Asian populations. Its pilot dataset included whole-genome sequencing from 1,739 individuals representing 219 population groups and 64 countries across Asia. More recent work on South and East Asian reference data has similarly shown how broader representation can improve the discovery and analysis of population-specific genetic variants.

For startups, this creates a potentially powerful proposition: data that is difficult to obtain globally can become a proprietary asset if it is collected, structured and validated locally.

The data thesis should not be mistaken for an easy startup strategy. Clinical and biological datasets are expensive to create. Patient recruitment takes time. Biological samples require infrastructure. Longitudinal studies require sustained funding. Data must be standardized and governed. Sensitive health information creates additional regulatory and privacy obligations.

South Korea’s AI Basic Act, which took effect in 2026, also places healthcare among the areas subject to requirements for high-impact AI, including human oversight and transparency obligations. The framework is intended to support trust in AI, but startups operating in healthcare still face additional compliance considerations compared with conventional software businesses.

This creates a paradox. The same characteristics that make proprietary biomedical data valuable also make it expensive to build. For early-stage startups, that can make partnerships essential. Hospitals can provide access to patients and clinical expertise. Universities can contribute research capabilities. Pharmaceutical companies can provide development and commercialization pathways. Government programs can help finance infrastructure. The winning model may therefore be less about owning every component of the stack and more about becoming the company capable of connecting them.

The next AI-bio startup moat may be a network

This is already beginning to appear in South Korea’s pharmaceutical ecosystem. The roughly 160 AI partnerships reported across Korean drugmakers and AI companies in 2025 indicate that the sector is moving toward collaboration rather than treating AI as a standalone technology. Companies are combining internal biological data with external AI capabilities, building proprietary platforms and working with specialized AI firms.

That model could become increasingly important for startups. A young company does not necessarily need to build a billion-parameter biological foundation model. It may instead create a specialized dataset, develop the scientific infrastructure to interpret it and establish partnerships that turn those insights into clinical or commercial products.

The defensibility then comes from the network around the data. A startup with access to a particular patient population, a trusted hospital network, validated biomarkers and longitudinal outcomes can be difficult to displace even if another company has access to the same underlying AI models. This could change how investors assess AI-biotech companies. The important questions may increasingly be less about whether a startup has incorporated generative AI and more about what it can uniquely access, validate and learn from.

What investors should look for beyond the AI label

The emerging AI-bio market could therefore produce a different definition of technological advantage. For investors evaluating startups, several questions become more important:

• What proprietary data does the company control or have durable access to?
• How difficult would it be for a competitor to reproduce that dataset?
• Does the data connect to real clinical outcomes?
• Can the startup continue expanding the dataset over time?
• Does the company have the clinical and scientific partnerships required to validate its models?
• Can the data produce more than one commercial application?

The distinction matters because an AI-bio startup with a sophisticated model but no differentiated data may have a weaker moat than a company with a less novel model and an exceptionally valuable dataset. The latter can continue improving its models as AI infrastructure becomes more accessible.

The AI-bio race is becoming a data race

South Korea’s investment in AI-bio reflects a broader global shift. The country is now treating advanced biotechnology as a strategic growth sector alongside areas such as semiconductors, quantum technology and space. Its latest national technology strategy targets an AI-bio infrastructure by 2030 and explicitly includes AI-driven drug discovery, autonomous laboratories and advanced gene and cell therapies.

But infrastructure alone will not determine which startups succeed. The more difficult question is what happens on top of that infrastructure. If AI models become widely available, the scarce resource may move closer to the source of the biological problem: the patient, the sample, the clinical record and the validated outcome. That is why Lee’s argument extends beyond Biobytes or even South Korea.

“AI models are becoming widely available,” he said. “What is much harder to replicate is a carefully characterized patient cohort with long-term follow-up and biological samples.”

For the next generation of AI-biotech startups, that could be the defining competitive equation. AI may increasingly become the tool that enables discovery. The data may be what makes the discovery defensible. And for South Korea and the wider Asian startup ecosystem, the opportunity may be to turn populations that have historically been underrepresented in global biomedical research into well-characterized datasets capable of generating new biomarkers, drug targets and more representative therapies. The next AI-bio winners may not necessarily be the companies that build the most impressive models. They could be the ones that have spent years building something an algorithm alone cannot reproduce.

Quick Takeaways

• AI models are becoming easier to access, making the model itself a weaker competitive moat for AI-biotech startups.
• Proprietary biological data could become the real differentiator, particularly deeply characterized patient cohorts with longitudinal clinical data and biological samples.
• South Korea has a potential advantage through its hospitals, digital healthcare infrastructure, clinical data and growing government-backed AI-bio infrastructure.
• Data quality matters more than data volume. Connecting clinical records with genomics, biomarkers, imaging and functional measurements can create more valuable datasets.
• Asian patient populations remain underrepresented in global biomedical datasets, creating an opportunity for startups to build valuable, region-specific biological datasets.
• Biobytes illustrates this data-first approach, building a sarcopenia cohort and expanding it with biological samples to support biomarker and drug-target discovery.
• Building the data moat is difficult and expensive, requiring patient recruitment, clinical partnerships, laboratory infrastructure, validation and regulatory safeguards.
• Partnerships may become critical for AI-bio startups, connecting startups with hospitals, universities, pharmaceutical companies and research institutions.
• For investors, the key question may shift from “How good is the AI?” to “What can this startup uniquely access and validate?”
• The emerging AI-bio race could ultimately become a race for proprietary, high-quality biological data.