Modelling the future of Biotech Operations
By Anders Månsson, Senior Advisor at Ventures Accelerated
July 7, 2026
Anders Månsson, Senior Advisor at Ventures Accelerated
Biotechnology has never really been one industry with one business logic. It is a heterogenousfield of business models, and the three most important are the asset model, the technological platform model, and the service model. Each has its own economic logic, risk profile, and role in the innovation ecosystem.
What is changing now is not just which model is “best,” but which model is most financeable, most scalable, and most likely to survive a tougher capital market. The answer appears - at least to me - to be: not one model alone, but hybrids that combine elements of all three. The future may lie in synthesis.
The Asset Model
The asset model is the classic biotech play. A company builds around one drug candidate, or a small number of closely related assets, and concentrates its resources on advancing them through preclinical and clinical development. This approach can be brutally efficient asmanagement focus is narrow, capital is directed at a single value-creating path, and successful clinical data can create enormous upside.
Its appeal is obvious to investors. A single successful asset can become a blockbuster, attract a premium acquisition offer, or support an IPO. The model is especially attractive when the science is highly differentiated, and the medical need is urgent. In those settings, “one big bet” can be rational because the pay-off may be transformational.
But the weaknesses are just as clear. The asset model is exposed to binary risk. If the drug fails in the clinic, the company may have little else of value. It is also highly vulnerable to delays, regulatory setbacks, safety signals, and competitive readouts. In a capital-rich market, investors may tolerate that concentration; in a tighter market, they often demand a stronger cushion of optionality. At the very least, the single-asset risk significantly affects asset valuation.
The deeper criticism of the asset model is that it can be too dependent on timing. A strong molecule launched into the wrong market cycle may struggle to raise the capital needed for late-stage development, even if the scientific basis is good. In that sense, the model is not just a scientific bet but a financing bet.
The Platform Model
The technological platform model is built differently. Instead of one lead program, the company creates a repeatable technological engine: a modality, discovery engine, delivery system, data platform, or development workflow that can generate multiple assets over time. Think of this as biotech’s version of infrastructure. The first product matters, but the real value lies in whether the system keeps producing.
The main advantage is diversification. A platform can spread risk across several shots on goal(sorry for the football analogy but it is the World Cup after alla), which makes it more resilient than a single-asset company. It can also create strategic leverage, because partnerships, licensing deals, and co-development agreements may be monetized before a product reaches the market. In theory, this makes the platform model both scientifically dynamic and financially flexible.
Yet platforms come with their own burden. They are often expensive to build, slow to prove, and sometimes even harder to explain. Public markets and many investors still prefer a concrete lead asset over an abstract promise of future discovery capacity. A platform can be scientifically impressive and commercially underappreciated at the same time.
There is also a hidden challenge in that platforms can become a story of perpetual promise. If a company never converts technology into clearly differentiated, clinically meaningful products, the platform may be valued as interesting research rather than a business. So, the model’s success depends on disciplined execution and a visible conversion of platform power into assets is key to success.
The Service Model
The service model is less glamorous but often more durable. Companies that engage in this model provide enabling functions to the biotech ecosystem: contract research, contract development and manufacturing, analytical services, bioinformatics, lab tools, regulatory support, or other specialized capabilities. They do not usually need to invent the next breakthrough therapy. Instead, they benefit from being useful to many innovators at once.
Its main strength is revenue stability. Compared with the asset model, the service model can generate earlier cash flow and lower scientific binary risk. Compared with the platform model, it often requires less narrative risk because the business model is easier to understand. Clients pay for capacity, expertise, speed, or quality. That can make it more defensible in downturns.
The downside is that the service model usually has less upside as the services provided are likely to be less unique, at least over time. Margins can be pressured by competition, pricing, and customer concentration. Growth may be steady but not explosive. A service company can become indispensable to the industry, yet still fail to capture the full value created by the therapies it helps enable.
Which Models will be Favored in the future
To my mind, the current direction does not point to one universal winner. Instead, it points to a hierarchy of credibility. Asset models are favored when the asset is clearly differentiatedand close to meaningful value inflection. Platform models are favored when they are visibly converting technology into products or partnerships. Service models are favored when they can show recurring revenue, strategic indispensability, and margin discipline.
It could be that the most likely winners are hybrid structures. A platform company that also sells services can fund itself while proving its technology. An asset company that uses platform methods can broaden its pipeline and reduce single-program risk. A service company that builds proprietary tools can move up the value chain and capture more upside.
As I see it, biotech companies benefit from increasingly blending these models to reduce risk and improve financing resilience. In that sense, the future may not belong to one model at all, but to companies intelligent enough to build a business structure that combines scientific ambition with commercial pragmatism. That is at least my model of the future…
Anders Månsson