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Health, Wellness & Biotech Startups Venture

The Case For Revenue-Focused Platforms In The Life Sciences

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Having invested in the Techbio space since before it had a name, I鈥檝e seen life sciences investors steer founders toward a therapeutics pipeline in hopes of securing the payout of a winning drug or medical device.

But the high reward comes with even higher risk: The failure rate of clinical development means that a single, highly likely failure can destroy the entire value of a company. Given the global pharma industry is worth , even modest improvements to the failure rate would generate enormous gains in economic productivity and patient impact.

Caleb Appleton, principal at Bison Ventures
Caleb Appleton, principal at Bison Ventures

There is no denying that therapeutics offer a desirable path for strong returns and enormous human impact, but we can鈥檛 afford to continue ignoring the alternative. The business models that are most reliable, yet untapped, are the tools and services powering this field.

, the longtime / CEO and founding partner of my prior firm, , famously remarked: 鈥淩evenue solves all known problems.鈥

Recurring revenue with high gross margins ensures companies can define their destiny, such as choosing when and if to build out riskier product lines like therapeutics. It also enables unit economics that command a high multiple, resulting in lower dilution to founding teams. And while a pre-clinical therapeutic business must convince specialty investors that their hypothesis will play out, platforms can iterate on their product quickly to show real product-market fit.

Here鈥檚 a framework Techbio founders can use to determine whether they should market their platform to others, or leverage it to develop therapeutic assets of their own:

Is the platform uniquely enabling or broadly useful?

Just as 鈥檚 launch capabilities enable the entire space tech industry, when started offering sequencing technology, the industry belief was that affordable, accurate sequencing could support nearly all biological R&D efforts. Had Illumina only used it to run internal GWAS studies and drug development, it鈥檚 unlikely they would have reached an $80B market cap.

Does the market see value in the tool today, or do you need to educate?

Without ubiquitous adoption, complicated platforms are better suited for internal drug discovery. , a unicorn I previously backed at seed, fits this bill 鈥 the technology to track single molecules is immensely useful for understanding protein kinetics but very challenging to interpret. Spatial biology is another emergent tool where the vastness of the data and the nascency of the field requires specialized teams, making it better to build on top of rather than sell.

Is there a business model to support the technology?

Investors worry about stacking technical and go-to-market risk. Pharma is not accustomed to large SaaS and contract spend, but they do expect services and partnership spend 鈥 winning companies will understand how to use these purchasing behaviors to their advantage. A recent success in building a derivative business model is , which provides nonexclusive biologics discovery services, capturing substantial payments and margins without getting involved in slow royalties and milestone negotiations.

What is your superpower?

As much as product-market fit is important, so is fit between founders and business models. It鈥檚 unfair to ask a consumer app founder to succeed in enterprise SaaS, just as it is to ask a technology builder to be good at drug discovery overnight. Founding teams should lean into their superpowers, and if that is building technology, it鈥檚 probably also selling technology.

While the life science industry has long favored high-stakes therapeutic ventures, it鈥檚 time to embrace resilient, revenue-generating models that improve therapeutics success rates.


is a principal at focused on investments in biotech and AI applications in the physical world. Previously, he was a frontier tech investor at focused on surgical robotics, drug discovery, next-gen hardware, and applied AI. He recently spent two years at a growth-stage portfolio company, as senior VP and GM and he began his career at .

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