Bringing Deep Tech To Market
For deep tech founders, there’s a common dilemma:
You must survive to thrive.
Startups face an unproven market, extended period without positive cash flow and a path to exit that’s messy, unpredictable and often upwards of 10 years. Whilst it may become a long slog, follow-on financing plays a hugely pivotal role in overcoming these early headwinds of deep tech venture.
Funding Deep Tech Is A Game of Patience
In deep tech, the growth in follow-on financing over time is generally slower than traditional soft tech and it’s commonly caused by the slower resolution of technical risk over time.
Deep tech investors who come in at this early (prototyping) stage are taking a bet more on the defensibility of the product’s intellectual property (IP) and founders’ execution rather than the market need. In other words:
It’s the disruptive tech, not disruptive idea which drives their value proposition.
As a result, traditional key performance indicators for software startups often fail to capture the progress by deep tech startups. Founders are faced with the task of progressing through rounds and raising fresh capital from investors who are religious devotees to “market-based milestones” and atheistic of intensive hardware projects. This certainly becomes more difficult when the deep tech startup’s first round is a series A and have yet to lock down a revenue generating business model — what generalists call Product Market Fit.
This predicament is what leads deep tech founders to raising two or three rounds at the same stage from existing investors before progressing to the next. The trade-off is that whilst the probability of follow-ons are on average higher, these follow-ons are often not funding growth or expansion but instead sustaining operations until the startup is ready to attract new investors. The high frequency of follow-ons at the early-stage creates funding congestion and its why we don’t see such exponential growth in round sizes by the 5th/6th round.
Note: When I say rounds I’m referring to events, not stages.
To satisfy the empiricists, I decided to take a look at the deep tech startups we’ve partnered with at Stoic.
Stoic VC : How we’ve “followed-on” so far
Since inception we have made 19 investments, co-investing alongside our partner Uniseed and other early-stage investors such as Brandon Capital and Main Sequence Ventures.
1. Early-stage bottleneck
Characteristic of an early-stage fund, 90% of our first cheques have been written at either the Seed or Series A- cognisant that in deep tech funding stages can become ill-defined. In any case, what we see is that by the third follow-on (fourth funding event), most of the companies have progressed to their respective next stage.
Note : Funding events are not funding stages i.e Series A1, Series A2 are funding events.
But by the sixth investment, a bottleneck begins to appear at the Series A stage , even after a ninth investment. Why is that?
2. 50% of cheques have been written to Biotech companies
The answer is that different sub-verticals warrant different funding requirements — the pertinent example being biotech. Let’s take a look.
Some argue that bio-tech is in its own little world within deep tech, and it has got to do with the developmental life cycle; in particular the go-to-market phase (GTM).
Biotech investees Occurx and Kinoxis, which both develop novel therapeutic drugs, require regulatory approval from the FDA (Food and Drug Administration) to get to market in the US. As a result, the entire GTM strategy is hinged upon the statistical significance of these trials.
There are three stages involved: Phase 1 (safety of product, small cohort), Phase 2 ( safety of product, efficacy, optimum dose, larger cohort), Phase 3 (efficacy, largest size cohort, focus on representative data, generally the last stage). As advancing through these stages can take upwards of six years, investors often must have deep enough pockets and sustained conviction to support founders with frequent enough cash infusions to survive the burn. This is especially critical at the pre-clinical and phase 1 stages, when the likelihood (%) of progression is lowest. Recent biotech implosions such as Zymmergen continue to highlight the pitfalls of attempting short-cuts in the process and in turn, the importance of staying grounded in the underlying vision rather than an aggressive exit.
Whilst our fund is certainly not representative of the entire deep-tech ecosystem in Australia, what I’d like you to take from the analysis and discussion so far, is this:
Deep tech financing is about taking a bet on the tortoise, not the hare.
For a generalist investor, mediocre IRR’s over the first 5 years and multiple follow-on bridge rounds before cracking a large external round are certainly not attractive signals. But, if you’re willing to forego the myopia and appreciate the process of deep science, you'll appreciate the silver lining of follow-on finance — it may turnout to be gold…
I’ll be writing content on the Aussie deep tech scene every fortnight, so follow to keep up to date 🙂
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