Welcome to the 110th edition of Deep Tech Catalyst, the educational channel from The Scenarionist where science meets venture!
This week, we move into advanced materials and specialty chemicals, and look at the question that quietly determines whether a scientific breakthrough becomes a VC-backable company: what business model actually captures the value.
I sat down with Tony Sun, Director of Corporate Venture Capital at GC Ventures, to unpack how an investor inside a chemical incumbent thinks about market pull, pricing power, and the strategic trade-offs founders face as they move from lab-scale innovation to a scalable commercial reality.
Key takeaways from the episode:
🧪 Why Specialty Chemicals Feel Fundamentally Different from Commodities
Commodity chemicals remain cyclical and price-competitive, often with stable revenue but squeezed margins. Specialty chemicals, when truly differentiated, tend to be priced by value—shifting the TAM and the investability story.
🏗️ Business Models Start with a First-Principles Choice
The real fork is organic versus inorganic growth. Venture capital only makes sense when speed to market is critical, and the market is large enough to justify a risk-and-return profile built for rapid scaling.
🔗 Owning Product, Manufacturing, and the Customer Relationship
In long chemical supply chains, value leaks through intermediaries. The most defensible path often means owning the final product and the end-customer relationship—and, in many cases, controlling manufacturing.
⚖️ BOO vs Asset-Light Is → Risk vs Control
Building, owning, and operating concentrates capex and operational complexity, but tightens control over margin, quality, and customers. Asset-light models can work, especially for formulation-led plays, but only if the core know-how is truly protected.
📈 Scale-Up Has Two Distinct Meanings—and the Proof Points Change
Some businesses need 10× manufacturing steps toward commercial scale. Others can replicate small reactors regionally, where the real validation isn’t manufacturing scale-up at all, but customer pipeline repeatability and the ability to scale sales execution.
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BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE
Framing Advanced Materials From a Venture Capital Perspective
Advanced materials can be commercialized through many paths—licensing, ingredient supply, toll manufacturing, formulation plays, or fully integrated product businesses.
But not every path is equally investable from a venture-capital perspective.
In many cases, the economics are shaped less by the novelty of the science and more by who owns the customer relationship, where margin pools sit in the value chain, and how much capital and execution risk is required to reach scale.
That is why business model choice matters: it determines whether a materials breakthrough becomes a defensible, scalable company—or a great technology trapped inside someone else’s P&L.
Commodity vs Specialty Chemicals
A key distinction that influences these choices is whether a product behaves like a commodity chemical or a specialty chemical.
This is not a naming convention. It’s a difference in how pricing works and what kind of strategic control a company can realistically build over time.
The venture focus naturally narrows toward materials that are high value, tightly specified to an application, and closely tied to performance outcomes—areas where differentiation is more defensible, and margins can prove more resilient.
In that context, materials become the asset to access higher-value end markets.
Moreover, for founders, this framing matters because it connects a materials innovation story to a clearer market pull and a more concrete path to adoption.
For investors, it helps explain why certain advanced materials opportunities can justify serious attention even when the broader commodity chemical backdrop remains difficult.
Focus on higher-margin segments as a strategic hedge.
As discussed in our conversation, over the last one to two years, a clear pattern has emerged across many public companies in the sector: revenues can remain roughly flat—and in some cases even grow—while margins still get squeezed.
That’s a common dynamic in commodity chemicals, where supply–demand cycles, capacity expansions, and intense price competition can compress profitability even when volumes hold up.
And that shifts what “strategic” means.
So, if the commodity base isn’t delivering attractive margin performance, the incentive to accelerate into specialty and higher-margin segments becomes stronger—especially when there are plausible pathways (even if complex) into more value-based pricing dynamics.
In this framing, the move toward specialty isn’t just trend-following. It’s about identifying higher-margin businesses that make sense within an existing supply chain and can be built.
That’s why themes like energy-efficiency materials, bio-based chemicals, and other specialty materials remain active areas of focus. They tend to be high-value, application-specific opportunities that can help a company shift its economics versus pure commodity exposure.
In other words, when only a limited number of players can reliably “do the job” for a specific application, pricing becomes less about competing down to the lowest number and more about what the product is worth to the customer—what is referred to as being “priced by value.”
There is nuance here: “specialty” can mean different levels of scarcity and differentiation depending on the product and who else can produce it.
How Fast Do You Need to Scale?
Business model conversations in specialty chemicals can get overly tactical too early—licensing versus manufacturing, direct sales versus distribution, asset-light versus BOO—without first clarifying what the company is actually trying to do with the technology.
A powerful question to ask is:
If a team believes it has a genuinely strong use case, what does it need to turn that opportunity into a business, and what kind of growth path should it choose to pursue?
And once that choice is made, it shapes almost every downstream decision about capital, market entry, and business model design.
Organic vs. Inorganic Growth
Growth can be pursued organically or inorganically.
If you have great technology and you don’t have meaningful capital behind you, there is a natural tendency toward an organic path.
You do what you can with what you have.
You find early customers where possible, you grow steadily, and you finance the journey through patient capital, private funding routes, or whatever resources are available.
In chemicals and materials, that organic path can be very real.
A surprising amount of success in specialty materials comes from companies that begin as small operators—sometimes operating out of family-business roots—where growth is measured, cash flow matters, and the business scales as capability and demand prove themselves.
That is not a lesser route. It is simply a different route. It assumes time is available and that the market does not require a rapid land grab.
The inorganic route, by contrast, is fundamentally about speed.
It is the choice to use outside capital to move faster than an organic approach would allow.
If the technology has the potential to be large, and timing matters, the logic becomes: raise money, build capability quickly, enter the market faster, and position the company to own the opportunity before competitors do.
That is the path that brings venture capital into the conversation, because venture investors are designed for high risk and high return outcomes.
The core promise is that additional capital can accelerate the company into the market in a way that materially changes the outcome.
The Venture-Scale Logic: Speed + a Large TAM
This is where many founders underestimate what they are implicitly asking investors to underwrite.
Venture is not just “money that helps you build.” It is money that expects a specific kind of outcome—one that can justify the decision to pursue inorganic growth in the first place.
If a company is raising venture capital, the expectation is that it is aiming high and going big.
That means it needs to show a credible case that the market it is targeting is large enough to support venture-scale returns.
In other words, it needs a large total addressable market. Without that, the fundamental justification for taking on venture-style risk weakens.
This is not about producing a slide with a big number.
It is about demonstrating that the company has a real path into a market that is genuinely expansive, where accelerating entry and scaling faster than the organic baseline can create an advantage that matters.
That is why business model discussions are so closely tied to TAM.
If the opportunity is narrow, a different kind of capital structure may make more sense.
If the opportunity is large and the window is time-sensitive, the case for venture-backed, inorganic acceleration becomes more coherent.
Why Business Model Choices Are Inseparable From Market Timing
The trap is to treat business model selection as a static optimization problem, as if the “best” model exists independent of context. In reality, it is dynamic.
Entrepreneurs have to understand what is happening in the market and adjust accordingly.
The model that makes sense when time-to-market is not critical may be the wrong model when a market is opening quickly and speed matters.
This is why understanding the venture-scale lens helps.
The question becomes:
Do you have time, or don’t you?
Are you optimizing for lower risk and a measured build, or are you optimizing for being first to market and owning the space?
An organic approach can be sensible when the company can afford to move carefully, build proof gradually, and let commercial traction accumulate.
In that context, licensing or early partnerships might be part of how the company learns and generates initial validation. The trade-offs are different, and the growth expectations should be different.
If the market is moving quickly and timing is critical, the logic shifts.
The company may need to raise capital, build its own capabilities faster, and act with urgency. The objective becomes to establish a position early enough that the company can defend the space and build a durable relationship with the market.
In practice, both routes can work, and both routes can fail.
The point is not that one is universally superior. The point is that the business model has to be coherent with the growth path, and the growth path has to be coherent with market timing.
If those elements are misaligned, the company ends up caught between strategies—too slow to seize the opportunity, but too capital-intensive to survive as a measured, organic operator.
Owning the Value Chain That Matters: Product, Manufacturing, and the Customer
Once the growth path is clear, the business model question becomes more specific:
Where does the company capture value in a chemical and materials supply chain that is long, complex, and crowded with intermediaries?
In specialty chemicals, the structure of the chain often determines whether a startup can build venture-scale outcomes or ends up as a narrowly positioned supplier with limited leverage.
The practical insight is that “being in the supply chain” is not the same as “owning the economics.”
Many different players can touch the product before it reaches the end user, and value can be diluted at each step.
That is why the most consequential choices usually center on 2 things:
Whether you own the final product that the customer buys.
Whether you own the relationship with the end customer.
The Chemical Supply Chain Is Long, And It Leaks Value
In specialty chemicals and materials, it is common to see a sequence of roles between the origin of a technology and the final application.
Feedstock suppliers sit upstream.
Formulators transform inputs into usable products.
Application developers and OEMs define performance requirements.
Brokers and distributors can sit between producers and customers.
Each layer can be necessary, but each layer can also become a place where value is captured by someone else.
For a founder, this structure matters because it could create a temptation to build a business around the easiest entry point—selling into a layer that is accessible, even if that layer is not where the durable economics reside.
For an investor, it matters because it can cap the upside. If a company doesn’t control how its technology reaches the market, it can end up doing a lot of work while other parties control pricing, customer access, and long-term account ownership.
Why Owning The Final Product And The End-Customer Relationship Becomes A “Must”
The most direct way to avoid that leakage is to ensure that the company ultimately owns the product and owns the relationship with the end customer.
In this framing, it’s the condition for building a large enough opportunity.
Owning the final product matters because it keeps the company close to the value proposition. It allows pricing to be tied to the performance delivered, not merely to input costs or contract manufacturing terms. And it creates room to build a defensible position as the product becomes embedded in an application.
Owning the customer relationship matters because it determines who learns from the market and who can scale commercially. If the relationship sits elsewhere—through a partner that controls the account—then the startup’s future becomes dependent on external incentives, external priorities, and someone else’s willingness to continue pushing the product.
This is why, when the goal is a venture-scale outcome, the logic tends to converge on the same conclusion: own the product, own the customer.
When Manufacturing Is Not Optional
The next question is how much of the manufacturing must be owned in order to make that strategy real.
The instinct from venture is often to prefer asset-light models, because capex and operational complexity can become a heavy burden.
But specialty chemicals do not always allow the clean separation that software companies enjoy.
In many specialty chemical businesses, the core know-how and defensibility sit inside the manufacturing process itself.
It lives in how the product is made, how it is formulated, how yields are achieved, and how quality is controlled. That manufacturing process is often the heart of the IP—something the company wants to protect carefully.
When that is true, owning manufacturing “to some extent” becomes a strategic requirement, not just an operational choice.
If the process is central to what makes the product hard to replicate, outsourcing manufacturing without a clear protection strategy can weaken the company’s moat.
Even if a startup can technically outsource production early, the long-term path often pulls it back toward greater control over the manufacturing layer.
This is also where the logic runs backward from the endpoint.
If the long-term objective is to own the product and the customer relationship, the company has to work back through the chain and decide what it must control to protect quality, margins, and IP.
In some cases, outsourcing remains viable if the defensibility is elsewhere. In other cases, manufacturing control is inseparable from the business.
Starting Points May Differ, But The Destination Must Be Strategic
The important nuance is that this is not necessarily where companies start. Startups often enter through whatever path matches their capital, timing, and immediate constraints.
But even if the early path is indirect, the strategy should still be designed with the end state in mind. Because the supply chain is complex, there are many ways to participate.
But if the ambition is to maximize the total addressable market and build a company with meaningful leverage, the model tends to consolidate around a clear destination: a business that owns its product, owns its customer relationships, and controls enough of manufacturing to defend its differentiation and protect the core of its IP.
BOO Model, Asset-Light Paths, and the Risk–Control Trade
The business model conversation tends to get framed as a binary—either you Build, Own, and Operate (BOO), or you stay asset-light.
In reality, the more useful way to think about it is as a trade between risk and control.
The more control you want over the variables that determine long-term value, the more you tend to concentrate on capex and operational responsibility.
The more you try to reduce upfront exposure, the more you accept constraints on margins, execution, and sometimes even on what parts of the value chain you truly “own.”
BOO Model (Build-Own-Operate)
A BOO model concentrates both financial exposure and operational complexity. It brings heavier upfront capex, and it shifts what the business has to manage day to day.
Suddenly, the P&L is not just about the selling price and gross margin as an abstract spreadsheet concept. It becomes about utilization, yield, and working capital demands—variables that can quietly dominate outcomes in chemical manufacturing.
That concentration of risk is precisely why many investors instinctively prefer asset-light models, especially at early stages.
But the flip side is that BOO can also provide tighter control over the levers that ultimately determine whether a specialty chemical company captures the value it creates.
Pricing, margins, quality, and—most importantly—the customer relationship become far easier to manage when the company controls production and delivery rather than relying on someone else’s infrastructure.
So the question isn’t whether BOO is “good” or “bad.” It’s whether the company’s strategy requires the kind of control BOO provides, and whether the team is prepared for the financial and operational load that comes with it.
Building Around Where The Core Know-How Sits
The more practical anchor is not the label of the model, but where the core IP and know-how actually live.
In specialty chemicals, defensibility often sits in the manufacturing process and the formulation details that are difficult to replicate.
If the heart of the advantage is embedded in how something is made, then the business model has to be designed around protecting that heart.
That doesn’t automatically mean full vertical integration from day one.
It means being honest about what must be controlled tightly, and what can be delegated without losing the moat.
The “right” structure tends to be the one that centers on the company’s unique point—whether the uniqueness is primarily formulation, manufacturing, or the way the company manages the customer relationship.
When Asset-Light Can Work: Formulation-Led Companies And The Protection Problem
There are situations where an asset-light approach can work well, especially for formulation-driven businesses.
If a company can source existing ingredients, keep the critical formulation capabilities in-house, and demonstrate a clear value proposition to the customer, it may be possible to outsource manufacturing to a blender while still building a strong business.
But that approach only holds if the company can protect what makes it special. In this particular case, it’s mandatory to ensure the IP is not easily reverse-engineered and that the “copycat risk” is managed.
If the defensibility is porous—if someone else can reproduce the product simply by observing it—then outsourcing production can turn into a strategic vulnerability.
In that sense, “asset-light” is not a free win. It is a model that must be earned through the ability to defend the differentiator without relying on ownership of the plant.
When BOO Faces Commodity Dynamics
Bio-based chemicals offer a clear example of how a BOO-heavy strategy can become challenging when the market still behaves like a commodity market.
About fifteen years ago, many teams built around a seemingly straightforward thesis: develop a bio-based route, produce a drop-in replacement for a commodity chemical, raise capital, build a large factory, and compete head-on.
The challenge wasn’t that the technology was uninteresting.
The challenge was that the pricing environment remained highly competitive and highly sensitive to oil prices. When oil prices were high, the economics could look reasonable. When oil prices were low, that advantage could disappear.
A number of companies were burned by this dynamic.
What stands out is what the survivors did next. Rather than abandoning the broader ambition, they redesigned the business model and go-to-market strategy.
They started from a more niche specialty position in order to capture higher margins and build a more resilient economic base.
At the same time, they continued development with the intent to be ready when the cycle and market conditions were favorable again.
The strategy became less about placing the entire bet on one huge asset at the outset and more about building optionality and timing the bigger move.
From Single Application to Broader Applicability
One of the best-case scenarios in advanced materials is when a company gains traction in a specialty foothold and then expands into adjacent applications over time—building optionality and reducing dependence on a single commercialization bet.
In this way, the business can evolve toward becoming a broader platform in advanced materials—something that can support multiple products, partners, or application paths over time.
That platform orientation doesn’t remove the hard decisions around capex and operating complexity.
But it changes the narrative from a single, fragile commercialization bet to a more strategic progression: prove the value in a high-margin niche, build credibility and revenue pathways, and then expand into adjacent opportunities with more leverage and better timing.
In the end, BOO versus asset-light is not a universal choice.
It is a decision that should follow 2 realities: where the core defensibility lives, and what level of risk the company is willing to take in exchange for control over margins, quality, and the customer relationship.
When Is the Right Time to Scale?
So, when should you build a factory?
This question often shows up as if there is a hidden formula: a specific revenue level, a specific volume threshold, a clean milestone at seed or Series A that reliably tells you it’s timeIn practice, that cheat sheet doesn’t exist.
The more realistic approach is to accept that scaling in specialty chemicals and materials is inherently scenario-driven, and that “scale-up” itself can mean very different things depending on the technology and the market.
The key is to stop searching for a universal rule and instead build a decision framework that can survive uncertainty.
Build 3 Scenarios
A disciplined way to do that is to build at least 3 scenarios.
One is the base case: what you genuinely think will happen.
One is the best case: what happens if timing, execution, and market pull align unusually well.
And one is the worst case: what happens if key milestones slip, if adoption takes longer, or if market timing turns against you.
This is not a pessimistic exercise. It is a planning exercise.
The value is that it forces the founder to think through how the company would respond under different conditions, and it gives investors and shareholders a realistic view of what the journey might demand.
Transparency is part of the strategy here.
If the company only communicates the good numbers, the relationship with investors becomes fragile when reality becomes harder, which, in deep tech, is more common than anyone likes to admit.
Sharing the range early allows everyone to prepare and to align on what decisions get triggered under which conditions.
The “3x Time, 3x Money” Rule
A great anecdote from our conversation captures the reality of Deep Tech execution from a VC’s perspective: “Whatever the entrepreneur says, it takes three times the time and three times the money to do whatever he says…If we are lucky.”
That multiplier is not a precise forecast. It is a reminder of the execution friction that shows up in materials and manufacturing businesses.
The effect of that mindset is not to make planning vague. It is to make planning robust. It pushes teams to build strategies that still work when things take longer, cost more, or require more iterations than originally assumed.
2 Scale-Up Approaches in Specialty Chemicals
Usually, scale-up considerations fall into two different cases.
In the first case, volume is inherently critical.
Cost reduction depends on economies of scale, and eventually, you cannot avoid building meaningful manufacturing capacity.
For these technologies, progression is typically framed as a series of step changes—often roughly 10× increases in volume or production capability from one stage to the next on the path to commercial scale.
In extreme cases, teams attempt 100× jumps, but those tend to be rare because confidence in the results declines when the leap is too large.
In this “volume-driven” category, the scaling plan is largely about managing the cadence and risk of manufacturing expansion: how big each step should be, what it costs, and how long it takes.
The scenarios help the team prepare for delays and capital needs, but the underlying reality is that large-scale production is part of the destination.
The second case is fundamentally different.
Here, scale-up does not necessarily mean building a bigger and bigger plant. It can mean building one small reactor and replicating it. The business grows by creating regional production centers that serve local customers, then repeating the same playbook in other geographies.
This model can look simpler on paper because it avoids a single massive factory build. But the critical validation shifts away from manufacturing scale-up and toward customer scale-up.
In a replication model, the core question is not: Can the process scale to a huge plant? The core question is: Can the company repeatedly build demand and convert it?
In simple terms, the operational hypothesis becomes: With one small reactor, one business development person, and one salesperson, can the company generate a predictable amount of revenue?
If that unit works, scaling becomes replication.
You don’t necessarily scale one facility to ten times the size; you replicate the unit across regions. But that only works if customer acquisition, sales execution, and pipeline repeatability are real.
This is a different kind of scale-up, and it requires different proof points. It demands early validation that commercial capacity scales in a way that can be translated into numbers.
The manufacturing challenge is still present, but it is no longer the primary bottleneck.
The bottleneck becomes whether the company can consistently build and convert customer demand across markets.
And that is why this model, while attractive to many, is also difficult. Successfully building a business on that basis is rare.
It requires a level of commercial repeatability that not every technical team can achieve, and it forces founders to treat sales execution as the thing being scaled—not merely the product.
The Final Takeaway: Define What “Scale” Means In Your Case, Then Prove The Right Thing Early
The common failure mode is to plan for the wrong kind of scale-up—building a manufacturing roadmap when the business actually hinges on a repeatable customer pipeline, or assuming replication is easy when the real challenge is commercial execution.
The more investable approach is to clarify which camp the company is in, build scenarios that reflect that reality, and validate the correct scaling constraint early.
In some businesses, that constraint is manufacturing capacity and the economics of scale. In others, it is the ability to reproduce sales outcomes across regions with a small, repeatable operational unit.
Either way, the path forward becomes clearer when the company stops looking for a universal threshold and starts designing a scaling strategy that matches what “scale” actually means for its technology and market.
