Invest in “Product-Ready” Technologies, Not Moonshots

How to avoid being a sucker in deep tech

At Creative Ventures, we often hear from our peers, “You seem really fearless about technical risk.” Ironically, this could not be further from the truth.

It’s understandable why people feel that way. We have invested in everything from next-generation LiDAR, to synthetic biology platforms, to quantum computing. From that perspective, it really does look like we invest in things from some crazy science-fiction future. It probably doesn’t help that most of our team have been scientists and engineers of some sort. 

And to be fair, it’s easy to fall in love with exciting visions of technological marvels that are so advanced that they seem like magic.

However, we explicitly do not invest in technologies at Creative Ventures. We are a deep tech fund out of necessity because if existing technologies could tackle the massive and worsening secular trends we invest in (e.g. healthcare costs from chronic disease, global impacts of climate change), they would have already been adopted.

For a technology to be investable, we require two things:

1. A real, genuine market. Not a pile of maybe markets. Not a “look at how many applications we have.” Not “we could be used here.” A real market where the technology can be used and useful today.

2. Engineering, not R&D. Related to the “today” part of 1. Not something that could still use some marinating in a research lab — something that is ready to go today.

The first requirement is more self-explanatory. The second probably deserves more explanation.

Engineering, not R&D

Technical due diligence is one of the shortest parts of our investment process. Every time I’ve told someone that, it tends to shock them, but it’s true.

It’s either possible today, or it’s not.1

We don’t invest in things that may be useful. We don’t invest in things that with just this one breakthrough will become economically viable. We don’t invest in things proven at lab scale, but have no currently known way to go production scale.

Although we have peers who do invest in those kinds of companies, these technologies are all what we would classify “R&D”—as in, technologies that probably should be funded by grants or in research universities because the time it will take until they are commercially viable is entirely uncertain. It could be one year. Or it could be ten. Or it might not happen at all.

Now, given we invest in seed-stage startups, we obviously can’t expect our companies to already be ready to massively scale tomorrow. What we do expect is that the path to getting there is clear and bounded. Basically, you can—with a straight face—describe how many engineers and how long it’ll take to be “product-ready.” We call this “engineering” stage, not R&D.

Ten years from now, ten years from now

Gene therapy and AI are great examples of why we take this approach. In both cases, we see a lot of excitement today but those with longer memories will recall that this is not the first time for both. In fact, both of these technologies have been theorized since the 1960s-1980s and were in recognizable in “modern” form since the 1980s.

We have been able to insert genes into mammalian cells using viruses since 1984. Most of the base toolkit for modern deep learning (multi-layer networks, back propagation) was developed between 1982 and 1986.

In both cases, there were high hopes that faded by the 1990s.

In the case of gene therapy, there were still serious questions in targeting, immune response, and durability. These came to a head and brought the first gene therapy boom to a screeching halt from the death of 18-year-old Jesse Gelsinger.

The collapse of AI was less dramatic. By the late 1980s and 1990s, the massive limitations of expert networks and other “practical” AI technology became clear and basically killed off the market (deep learning was infeasible with the hardware of the time). This has been called an “AI winter”, and it wasn’t the first, just the first with more modern techniques. One could argue that, we’ve been disappointed by our expectations of what AI would be able to do in 10 years, every 10 years.

Lisp machine for AI from the 1980s. Michael L. Umbricht and Carl R. Friend (Retro-Computing Society of RI) CC BY-SA 3.0.
Lisp machine for AI from the 1980s. Michael L. Umbricht and Carl R. Friend (Retro-Computing Society of RI) CC BY-SA 3.0.

We don’t predict the future, just the present

We like to say that we don’t try to predict the future. We think that it’s sufficient to have a very strong grasp of the present.

It is epistemologically impossible to know how long it will take for some technologies to become product-ready. We take enough risk investing in early stage, deep technology startups; we don’t have to make things harder by needing to be right about when inherently random breakthroughs occur.

We have written and will continue to write posts about why technologies like AI now fit our two criteria and are actionable today. Meanwhile, the history of past booms and busts of frontier technologies will continue to function as a cautionary tale on how projecting forward can go wrong.


[1]: To be fair, our team does have an advantage given our advanced technical degrees and STEM backgrounds. We aren’t starting from zero; however, we apply that knowledge to understand what is and is not possible — not to imagine endless unrealized possibilities.


(Feature Photo by tableatnyCC BY 2.0)

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