Authors note: part of this article was adapted from the author’s 2022 article entitled Quantum computing startups are not like regular startups.
Communication for deep tech startups is tricky. The obvious challenge is the complexity of the technology. Quantum computers are harder to explain than food delivery apps, right? And so the standard advice is to abstract away the technical details and focus on customer problems. But in deep tech, that’s easier said than done. And even when it’s possible, it’s often not enough.
The complexity in deep tech doesn’t just make explanations harder. It brings in a wider range of stakeholders, each with their own language, incentives, priorities, and biases. It also stretches development timelines, creating a long gap between promising research and viable products. This gap sometimes goes by the memorable name the valley of death. In the valley of death, competition for resources is more fierce and you have to stretch further to reach what each group of stakeholders cares about, creating its own communication challenges.
To understand how the valley of death comes about, let’s look at what makes deep tech startups different from regular tech startups. But before we do that, we should know what makes startups different from small businesses.
So let’s start there.
The difference between a startup and a small business
Most people I talk to seem to think that any new company is a startup. But it's useful to make a distinction between companies that grow organically and those that grow inorganically. There was a great discussion of this in the first episode of the The Startup Podcast where they gave the following definitions:
A small business is designed to grow organically while a startup is designed to grow inorganically.
A small business is designed to earn revenue and spend it while a startup is designed to raise capital and burn it.
Another way to think about it is that for a small business, there is a fairly linear relationship between capital investment and profit, while for a startup, the relationship is highly nonlinear. If things go well, the nonlinearity contains an inflection point that yields a hockey stick curve.
This distinction is important because it affects how you should run your business. If you're a small business and you operate like a startup (or vice versa), things might not work out as planned.
Incidentally, Quantum Salon is a small business, not a startup.
Two ways to burn capital: regular tech vs. deep tech
So what does it mean to burn capital? Is it just a fancy way to say "lose money"? Not at all. Burning capital is a conscious decision to spend money on growing your business inorganically until it reaches the point where it can generate profit.
Here are two scenarios in which this makes sense.
The first is when your product is built using software and relies on a huge number of users to generate profit. With software, the more people you serve, the cheaper it is to serve each person and you're able to create outsized returns at scale. But while you grow your user base, you need to offer your product at a loss. So you have to burn capital until you reach scale...that is, until you have enough users to generate profit.
The second is when your product relies on technology that is extremely complex, and takes a long time and a lot of money to develop and commercialize, but will be very valuable once it exists. You might not need scale effects to generate profit, but you do need a product. So you have to burn capital while you develop and commercialize the underlying technology.
The first approach is how most regular (aka traditional, aka Silicon-Valley style) startups work. The second approach is how deep tech startups work.
So why does this distinction matter?
In the regular startup world, the recommended approach is to build your company around a problem that needs to be solved, not around a product or a technology. There are sound business reasons for taking this approach, and this approach is completely feasible when solutions use well-developed technology like software. Once you've identified a problem to solve, you have a plethora of software tools and experts to develop the solution in a reasonable amount of time.
This isn't the case for the kinds of technologies being developed by deep tech startups. The underlying technology is usually developed during one or multiple people's PhD degree/s, possibly based on years of prior work in the PI's lab, then developed even further outside of the lab on its way towards commercialization. It's usually not practical to start from a problem and then ask someone to spend 20 years developing a deep-tech solution to it. Instead, deep tech startups start with the technology, then commercialize it (i.e. develop the product and move it to market). The terminology for this is that deep-tech startups are technology first.
Technology first, however, doesn't mean technology only. To success, every deep tech startup must identify real problems their technology can solve and develop viable business models around those solutions. The difference is in the process: they start with the technology, then find problems it can address, rather than starting with a problem and developing a technology to solve it. These are drastically different approaches to product development, and you should know which camp you're in so you're taking the right approach.
The valley of death
The technology first scenario presents a unique challenge for deep tech founders. They often take their technology out of the university setting well before they even have a product, let alone product-market-fit (PMF). Most investors naturally don't want to invest in startups where they don't see a clear path towards a timely return on their investment. The gap between university resources and commercial resources is sometimes referred to as the deep tech valley of death.
While deep tech startups should be working towards a product and product-market-fit from the beginning, the reality is that it can take years to get there, and in the meantime, those companies need funding and support.
In the deep tech world, the investors who are willing to fill this gap as sometimes referred to as patient capital or patient investors. These are investors who understand the longer development timelines and are willing to wait for returns. This might include government grants, strategic corporate investors, or specialized deep tech VCs who have experience with these extended development cycles.
But unfortunately, there isn't nearly enough of this support out there, as indicated in the image above, which presents deep tech startups with some unique challenges.
Why this makes communication tricky
While a regular startup can focus almost entirely on communicating with customers and investors, a deep tech startup also has to consider scientists, hardware engineers, academic partners, corporate partners, governments, and the public all at once. Each group comes with its own language, incentives, priorities, and biases. What excites one group can bore or even alienate another.
If you’re far from product-market fit, the stretch to reach investors who are used to funding post-PMF companies can pull you away from your scientists. Meanwhile, the scientists that are an integral part of your R&D can be difficult to align with company objectives. And the patient investors, who are most aligned with your timeline and resource needs are few and far between, which makes reaching them a different kind of communication challenge.
Figuring out what users want is harder when the product is still years from being developed. Even with an existing product, talking to potential customers can require navigating a great deal of technical complexity, especially for B2B or B2B2C companies whose customers are also working in deep tech industries.
Bringing in external help with communication, marketing, or go-to-market adds the extra step of getting them up to speed on the technology if they don’t already specialize in your field. And the technical people in the company who could do that onboarding often find it difficult to translate their knowledge for non-experts.
Beyond all of this, the road is long, and startups also need to work on reducing the size of the valley of death to create future opportunities. That means educating post-PMF-minded stakeholders on the value of investing in pre-PMF technologies. To complicate it further, they need to know when it makes sense to educate stakeholders on what they should value, and when it’s better to meet them where they are and appeal to what they already value. Getting this wrong leaves you talking at cross purposes and undermines your goals.
What to do about it
So how do you deal with this complexity? You need to start by taking stock of where you are in relation to the valley of death. What is your plan for getting through it? Do you need to pivot, focus on reaching patient investors, or try something else? What are your short-term and long-term goals? Which stakeholders matter most for those goals, what do they care about, and what might alienate them? Decide whether this is the moment to educate them on what you think is important, or do you need to meet them where they are for now?
Don’t just focus on the technology, but think about the big picture. Define a vision that’s compelling but doesn’t slip too far into overhype. Tailor your language and distribution to each audience, but keep the core message consistent. And most importantly, make sure everyone inside your company understands and buys into that strategy, because without alignment internally, you risk losing the trust and commitment of your own team.
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Looking for a communication strategy that makes sense for you? Connect with us at Quantum Salon, where we help deep tech teams explain what they’re building and why it matters.