The convergence of technology and biology is one of the most transformative trends in the life sciences. Over the past few years, the “TechBio” moniker has entered the biotech lexicon to demarcate companies that go beyond the standard technological tools of the industry by deeply embedding digital technology into biological research. While the term once highlighted innovation, today, TechBio is most often used as a pejorative rather than a plaudit.
This mischaracterization couldn’t be further from the truth. The companies driving this wave — at the frontier of biotech — are building the foundational technologies that will power the next generation of biotech breakthroughs. In reality, modern biotech companies are continuously taking the best of the past — innovative translational science — and layering in cutting-edge hardware, software, and AI to supercharge their scientists.
These innovations reshape how we discover, test, and deliver therapies. This work doesn’t just add incremental improvements; it fundamentally redefines what’s possible in the industry. As we enter the next hyped (with substance) era of AI-enabled biotech the need for a new narrative is clear. The label TechBio no longer serves us and should be retired.
How did we end up here?
The term TechBio may no longer serve us, but its rise — and the skepticism it has faced — mirrors a recurring pattern in biotech innovation. Historically, both enthusiasm and doubt have greeted every new wave of transformative technology. While these innovations often begin with hype, they eventually face the realities of drug development: slow clinical timelines, tough biological complexities, and the market’s demand for proof over promises.
The ‘80s/’90s had CADD: Computer-Assisted Drug Discovery. The 2000s brought the massive promise of genomics and systems biology to cure all diseases. Innovations like high-throughput screening, combi-chemistry, virtual screening and docking simulations, and even gene editing tools like ZFN, TALENS, and CRISPR burst onto the scene with massive promise, only to run into the hard reality of the drug development timeline. The hype around new technologies often exceeded their short-term impact.
But, perhaps a touch ironically, each of these hyped and “crashed” technologies has become an integral part of the standard tool kit for teams across biotech. Today, drug development would be unimaginable without NGS (next-gen DNA sequencing), using software to map drug hits within binding pockets, or a quick shipment of predicted hits from Enamine REAL. What was once cutting-edge is now standard.
TechBio is just Biotech — many in the field just don’t want to admit it.
Frontier Biotech Companies, AI, and Data
The label TechBio is doing more harm than good. It isolates companies pushing the boundaries of biotech, delegitimizing their scientific approaches as if a tech-forward approach is less than traditional drug development. These companies are not outliers — they are pioneers. Like their compatriots in the tech industry building massive multimodal AI models — frontier biotech companies are investing heavily in the platforms and technologies that will define the next decade of biotech innovation. They are crucial to the industry’s growth and to increase the success rate of future drug discovery efforts.
Frontier biotech spans a broad range of advancements, from high-profile innovations like AI-driven drug discovery to the less visible but equally critical infrastructure and compute layers that make cutting-edge tools broadly accessible to scientists. These technologies are already being adopted across the biopharma landscape and are vital to the industry’s future.
Central to the success of this tech-forward future of biotech is their sophisticated approach to utilizing data. This is a consistent theme across all AI-adjacent industries but is vital to biotech, where public datasets are often smaller in scale and lower in quality than the equivalent in other verticals. Differentiated data assets are the key pieces that every organization needs to make AI produce what it promises.
In the same way cloud computing redefined enterprise software, data is redefining how we understand biology, discover therapeutics, and deliver healthcare. This trend isn’t stepwise and incremental; it completely reshapes the approach. Key to this transformation is acknowledging that this era requires rethinking legacy practices.
AI: Fantastic, But Not Magic
Techbio is now synonymous with the hype around AI in biotech, and AI often grabs headlines as the secret sauce powering the next generation of biotech breakthroughs. But let’s get real — AI in biology isn’t new. Computational chemistry has leveraged advanced algorithms for decades, from predicting molecular structures to simulating complex chemical reactions. Beyond this, many of these “AI” are just rebranded ML or good old data science.
What is new is the ubiquity of AI tools across pharma and the availability of massive compute (thanks to AWS, Nvidia, Google, and Microsoft) to power these models. Every major pharmaceutical company is leveraging AI for some aspect of drug discovery, development, and commercial activities, whether optimizing clinical trials, predicting protein structures, or identifying KOLs. Bucketing these companies and advances into TechBio minimizes their importance for the fundamental future of biotech AI, which is obviously not a magical solution; it’s a tool. Like any tool, its effectiveness depends on how it’s wielded — and what it’s fed.
What’s the Killer App for AI in Biotech?
For AI to truly transform biotech, it must tackle problems no one else can solve — especially at scale. This is why the TechBio label doesn’t serve the industry – it distracts from the deeper questions we should be asking about how AI and data can fundamentally redefine biotech innovation to deliver better drugs to patients.
Increasingly, therapeutic modalities are becoming commoditized. We are seeing a flood of sprint-to-the-clinic antibodies licensed from China, leading to a rapidly crowding landscape of compelling therapeutic hypotheses. In 2024, 1/3 of pharma’s externally licensed molecules came from China. We believe the real value inflections will come to companies and researchers who focus on breakthroughs that are unattainable without their proprietary data, scale, and drug development approach. Companies that stand out are asking, “What is newly possible by combining biological insights with the massive advancements in AI and data infrastructure?”
For example, categories like AI-based protein design, where machine learning models now power a Cambrian explosion of de novo designed proteins and corresponding companies, are creating therapeutic opportunities that were previously unimaginable. Yet, to create real value, these breakthroughs need to solve currently intractable problems and be tested in the lab at scale before moving to human trials. That is not TechBio, it is biotech evolving for the 21st century.
Data: The Ultimate Differentiator
If AI is the engine driving innovation, data is the fuel — and also the Achilles’ heel of the TechBio narrative. The success of AI in biotech hinges on the quality of its input data. “Garbage in, garbage out” has never been more relevant in this era of AI-enabled biotech. Models trained only on “success” data won’t understand why a molecule might fail, creating blind spots that can derail an entire development pipeline. Increasingly, data needs to be generated with an eye toward what data is needed to create or improve internal AI models and the associated output molecules.
Publicly available data alone isn’t enough. The Nobel Winning work of AlphaFold and the Baker Lab was only possible because of the Protein Data Bank, which was built on the backs of tens of thousands of grad students and postdocs grinding away on protein crystal structures over 50 years. Framing this achievement as TechBio diminishes the painstaking work and innovation that made it happen – this is biotech, plain and simple.
To illustrate the importance of unique data, consider this thought experiment: Imagine if the Protein Data Bank was privately owned and accessible to only one company. That company would have a monopoly on the foundational dataset powering structure prediction. How valuable would that advantage be? The answer lies in the billion-dollar valuations we’re already seeing for proprietary data-driven biotech companies.
Don’t expect other drug design problems to easily replicate the success of proteins. The data sources just don’t exist on par with PDB. In places with tons of data, like RNA in Recount — the data doesn’t have the same ground truth as protein structure. At the same time, the metadata annotations are insufficient to power differentiated drug discovery efforts.
Proprietary, high-quality datasets are the ultimate competitive advantage. Companies that can close the loop between design, test, and build will lead the pack.
Building Biotech for the Future
Integrating data and AI into life sciences isn’t a trend; it’s a fundamental shift. The companies that succeed won’t just be those that use the latest tools (and especially those that just claim AI will solve drug development) — they’ll be the ones that rethink the entire discovery and development process. Success will come from those who understand innovation is not about hype but creating lasting impact through bold ideas and rigorous execution.
We don’t need a term like TechBio to describe this future. It implies a separation where none exists. This is biotech, built for the 21st century. It is powered by data, honed by AI, and driven by a relentless focus on solving biology’s hardest problems to exponentially improve human health.
At Madrona, we’re proud to partner with the entrepreneurs and researchers building this future — and to help create the platforms, tools, and datasets that will not only define the next decade of innovation but will also shape a world where the boundaries of biotech are pushed further than ever before.
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