Executives love a comforting formula: invest in advanced science, and innovation impact will follow. It is a neat story, especially in sustainability, where “more R&D” is often treated as the default answer to complex problems. Yet in practice, a dollar spent on science does not always travel the same distance. Two firms can invest the same amount in R&D, but one builds a scalable capability that others reuse while the other produces high-performance improvements that remain locked inside a narrow niche.
The difference is not always about talent, capital, or even time-to-market. It can be about scientific trajectory: the underlying research path a firm chooses, the causal mechanisms it believes matter, and the vocabulary it uses to search for solutions. In other words, science does not just supply ideas – it shapes the map of where innovation can go. And some parts of that map are better at generating sustainable breakthroughs than others. This dilemma appears across industries, from batteries and AI to climate tech and advanced materials – wherever firms must decide not just how much science to pursue, but which scientific paths to follow.
The protein sector is a useful mirror for this problem. The world needs to feed more people while reducing emissions, land use, and water use. Yet traditional protein production through livestock is among the most resource-intensive parts of the food system. This tension generates a convergence of societal urgency and business opportunity: firms that innovate successfully in sustainable protein technologies can address a global challenge while building competitive advantage. The opportunity is enormous, but so is the risk of investing in the wrong scientific direction.
When managers talk about science, they often treat it like a meter: more inputs (papers, partnerships, labs) should mean more outputs (patents, products, impact). But science can also be understood as a map, a framework that makes some problems easier to see and some solutions easier to reuse.
When scientific knowledge is highly codified – clear mechanisms, stable concepts, shared language – it becomes easier for others to replicate, recombine, and scale. When knowledge is more application-bound, embedded in specific formulations, processes, and tacit know-how, it can still be valuable, but it travels less. That “travelability” matters for sustainability, because sustainability gains usually require diffusion: capabilities spreading across firms, sectors, and supply chains.
To understand how protein innovation evolves, we analyzed patents in the USPTO’s A23J subclass (proteins for foodstuffs) filed between 1990 and 2015 and linked them to upstream scientific language from academic food-science publications. This allowed us to identify four recurring scientific trajectories:
- Trajectory A: Animal-based protein research (a broad, traditional stream).
- Trajectory B: Cellular mechanisms and bioprocessing.
- Trajectory C: Protein chemistry and bioactive compounds.
- Trajectory D: Structure and stability (emulsification, gels, texture, shelf-life).
At its core, the question is simple: which scientific paths generate knowledge that others reuse, especially in sustainability-related innovation?
Trajectories do not diffuse equally
The patterns are clear: not all science-based paths generate the same downstream influence.
Patents associated with Trajectory B – cellular mechanisms and bioprocessing – show higher diffusion, acting more like platforms that others build upon, particularly in sustainability-linked innovation. Knowledge in this trajectory is reusable, modular, and easier to recombine into new applications.
By contrast, Trajectory C – protein chemistry and bioactive compounds – shows lower diffusion. The science can be deep and valuable, but it tends to be more specialized and more tightly tied to specific products or processing conditions, which limits spillovers.
Trajectory A, animal-based protein research, does not increase overall diffusion and is negatively associated with sustainability-related citations, a signal of shifting industry and policy priorities.
Trajectory D, structure and stability, does not predict higher diffusion on its own. But it matters as a complement: when paired with Trajectory C, it helps “unlock” deployment of specialized chemical innovations at scale.
Codification beats sophistication (for diffusion)
These results do not say that bioprocessing is “better science” than protein chemistry. They suggest that the structure of the underlying knowledge affects how it moves.
Trajectory B is anchored in relatively general mechanisms and shared process logics. Insights about fermentation parameters, metabolic pathways, or cellular stress can often be repurposed across organisms and applications. The language is standardized, the causal mechanisms are explicit, and the tools are modular – making the knowledge easier to absorb and reuse.
Trajectory C sits closer to downstream value capture. It produces high-performance, differentiated ingredients and functions, often tied to specific formulations and proprietary know-how. That can be a great strategy for margins and product leadership, but it is a weaker engine for broad spillovers.
In sustainability, spillovers are not a nice-to-have. They are part of how entire sectors shift. When the same core capability gets reused in many places, learning curves improve, standards emerge, suppliers invest, and complementary innovations appear.
The strategic implication: treat scientific direction like a portfolio decision
Most companies already manage portfolios of products, projects, and markets. Fewer explicitly manage portfolios of scientific trajectories. Yet trajectory choice can quietly determine whether an R&D program builds a platform for future growth or optimizes within a narrow corner of the search space. In that sense, leaders need both a map and a compass. The map reflects the structure of scientific possibilities, while the compass reflects strategic intent, such as sustainability priorities and long-term value creation.
Here are four practical moves innovation and R&D leaders can make.
1) Separate foundational science from product-oriented science.
Foundational trajectories build generalizable knowledge and methods that can travel across domains. Product-oriented trajectories translate knowledge into performance and differentiation within specific applications. Both are essential, but they generate different kinds of impact.
2) Measure diffusion, not just output
Patents, publications, and pilot launches signal activity. What matters strategically, however, is reuse: whether capabilities travel across domains, teams, and sustainability challenges.
3) Build translation layers
Scientific advances often need complementary capabilities in order to scale: formulation know-how, manufacturing readiness, and regulatory pathways. Pairing trajectories intentionally can unlock broader impact.
4) Revisit trajectories over time
Scientific paths evolve. What looked promising (or safe) a decade ago may be misaligned with today’s sustainability constraints. Periodic “trajectory reviews” can help leaders reassess the maps guiding their innovation search.
A quick diagnostic: five questions to ask about your R&D bets
- Is the core knowledge highly mechanism-based and codified, or primarily application-bound?
- Can the capability be recombined across products, organisms, or processes without major reinvention?
- Does the trajectory attract (or repel) complementary innovators (suppliers, startups, universities) who can accelerate diffusion?
- Where does value capture sit: in a platform others will reuse, or in a proprietary niche you will defend?
- If sustainability constraints tighten (carbon, water, land, regulation), does this trajectory become more relevant or less?
The bigger lesson (beyond proteins)
The protein sector is just one arena where sustainability demands a step-change in how innovation scales. Similar dynamics show up in batteries, hydrogen, circular materials, and climate-tech more broadly. Leaders must choose not only technologies, but the scientific trajectories that make some technologies easier to search, develop, and diffuse – essentially shaping how innovation travels beyond its original use case.
That is why the assumption that “more science = more impact” can be risky. Science can open new paths for innovation, or it can deepen investment into a cul-de-sac. The strategic task is to understand which is which, and to build portfolios that balance platform trajectories for system-level impact with specialized trajectories for differentiation.
© IE Insights.
