May 10, 2021

The five questions investors need to ask deeptech startups

Investing in deeptech is different. There may be no customer yet and rapid iteration of an MVP will not work.

Simon King

7 min read

Deeptech is high on the agenda of governments and investors as the cutting edge of tech moves away from consumer software to areas like materials science, spacetech and robotics. However, deeptech is different. Both for founders and investors, the parameters are unlike other startups, and so are the risks, which means a different approach is often required.

In the first of a three-part series, drawing on over a decade of deeptech investing at Octopus, we offer a VC’s perspective to give founders the best chance of success. Here we’ll start with the basics on why deeptech is different and what VCs are really looking for when they invest.

Why is deeptech different?

First, let’s map out the obvious, starting with the pre-revenue elephant in the room. It can take years before things are ready to leave the lab and start generating revenue. There’s also the risk that the technology or product just might not work.


Then there are the characteristics of the company and its people. Unlike regular startups, the founding team will often be purely technical, perhaps from an academic background, and sometimes with little or no commercial experience. Many will also have spun out of research developed within universities.

The prospective customer may not yet be identified, nor the specific problem the technology is going to solve. Some deeptech innovations will have a mindboggling choice of applications, each of which could take the business in very different directions. Take image analysis using AI, for example, which can be pointed at a myriad of problems, from cancer detection in pathology to theft at self serve checkouts.

The lean startup methodology does not apply very easily.

Once the target problem and intended customer are identified, there will likely be stringent technical evaluations and multiple layers of sign-off required before achieving your first sale. And even then, a first sale might be a one-off payment from an R&D department, which is usually still a long way from having your technology or product adopted by a customer. The lean startup methodology, where you build a minimum viable product and improve it via fast feedback loops does not apply very easily here, meaning nascent customer relationships have to be even more skilfully handled.

Finally, the pool of sector-specific talent a growing deeptech startup requires is often very small. The talent required to build a quantum computer, for example, can’t be found just anywhere, making the hiring process much more focused and the competition for talent fierce.

What questions will VCs ask you?

For all these reasons, the way VCs evaluate deeptech startups is also a little different, as are the questions you might be asked by them. So, what do we, as potential backers, expect answers or at least views on from founders?

We’ll skip over the basics which apply to every startup, but remain important for deeptech companies: Does the business model scale? Could it reach a $1bn+ valuation? At some point, can it scale quickly?

1. Technology-market timing

One question to expect upfront will be how and when the technology can be brought to market. New technologies and products rarely go from being ‘in development' to 'ready' in a single step.

We will want to know how much further the technology needs to be developed before it solves a problem for an early adopter, and how much further before it’s ready for a wider market. On top of this, just because a technology or product now exists doesn’t mean that people will want to use it, as many other factors play a part in the adoption. Predicting when this will happen is the hardest part of investing in deeptech companies.

New technologies rarely go from being ‘in development' to 'ready' in a single step.

It’s possible that you’ll be creating an entirely new market, or displacing existing technology that customers are familiar with, so your vision and conviction are important when answering this question, but it helps to have some data points to strengthen your case.

These may be hard to come by where data is sparse, but something is better than nothing, and a good alternative can be to look at parallel or adjacent markets. For example, if you were talking about drones, you could look at what happened in aviation as a guide to future trends. Or perhaps the evolution of the smartphone ecosystem might offer some clues as to the likely adoption of augmented reality devices and applications.

2. Understanding your customer

Deeptech startups are rarely able to point to their web traffic, Instagram followers, or customer acquisition cost as an indicator of market traction. Instead, investors will look for any level of validation from people who have really got up close and tested your product, ideally prospective customers.

Do you understand your customers' motive for saying 'Yes'?

If they’re already buying from you, or willing to work with you at this point, then great; but if you’re still a long way from generating revenues we’ll try to gauge your understanding of potential customers.

Do you understand their problems and their motive for saying 'Yes', whether that be as a revenue driver, a cost-saver, a road map shortcut, or a transformer of their business model? The deeper the knowledge the better. How this affects your go to market strategy will be relevant too. You might not have definitive answers but, by the time you come to raise your Series A, you will need a good sense.

3. Value proposition

How you summarise and communicate your value proposition when dealing with complex technology speaks volumes to investors. Can you succinctly explain what your product is and why it’s special? If you’re compelling when talking to us, we will have more confidence you can do that when talking to potential customers (some of whom will be non-technical), new hires, and future investors.

This ties back into the profile of the founding team. Knowledge and deep sector expertise are essential but to gain traction out there in the world, someone needs to be able to explain the idea in a simple way that will excite a variety of audiences.

4. Intellectual property ownership and protection

Is your product or technology protected? And, if so, is the IP owned by the company? If you’re using someone else’s IP or don’t have full ownership of the technology in your product it could make selling the company difficult in the future as an acquirer will want control of the technology in a company they are buying.

We’d expect the core tech to be protected coming up to the Series A.

There are various strategies and nuances of patents that we could talk about but, as a general rule, if your technology is protectable with patents we’d at least expect the core tech to be protected coming up to the Series A. This likely means a single digit numbers of patents, balancing protection and cost. Demonstrating a good knowledge of the patent landscape you’re working in, such as who else if filing, is also helpful.

5. Share capitalisation (‘cap’) table

This is something that people don’t always like to talk about, but it matters, especially as deeptech startups are often ‘spinouts’ with universities on their cap table. We want to see a cap table that demonstrates the management team is appropriately incentivised, now and into the future. We also need to know that as a shareholder, we are going to get the sort of stake that we can build on, and where there is room for more investors (and more dilution). Equally important is a model that can be hired into, in the near and longer term, with new hires able to be incentivised by equity where appropriate.

It is important that the university doesn’t dominate the cap table.

While cap tables come in all shapes and sizes, in the case of spinouts it is important that the university doesn’t dominate the cap table. At Series A we would normally expect to see the university hold roughly the same stake as the founders.

In summary, when it comes to early-stage deeptech investing, it can feel like a leap of faith. We know that commercialisation might take time and is fraught with risk, so we are looking for the strongest indicators that give us confidence.

In the second article we’ll be talking much more about the next stage of growth and how to manage those risks to give yourself the best chance of scaling successfully.