{"id":1588,"date":"2020-03-06T09:29:05","date_gmt":"2020-03-06T09:29:05","guid":{"rendered":"https:\/\/crowdforangels.com\/blog\/?p=1588"},"modified":"2021-07-02T14:20:28","modified_gmt":"2021-07-02T13:20:28","slug":"101-ways-to-lose-money-in-deeptech-angel-investing","status":"publish","type":"post","link":"https:\/\/crowdforangels.com\/blog\/101-ways-to-lose-money-in-deeptech-angel-investing\/","title":{"rendered":"101 ways to lose money in deeptech angel investing"},"content":{"rendered":"\n
\"101<\/figure>\n\n\n\n

After 10 years of building and backing deeptech companies at one of Europe’s leading universities, in the City and our own fund Deep Science Ventures<\/a>, one thing is clear. The outcomes can be big and exceed tech outcomes in both scale and speed. But any clues on how to navigate this landscape are largely hidden in hearsay. <\/p>\n\n\n\n

Such phrases banded\nabout can include \u201cavoid clean-tech,\nterrible bust last time\u201d,\n\u201chardware\nis too hard\u201d,\n\u201csynthetic\nbiology is hot right now\u201d\nand \u201cventures\naround a certain biological target are raising a lot of money recently\u201d. In reality, these are broad terms and often not true. For\nexample, the term \u2018cleantech\u2019 represents a hugely\ndiverse set of approaches, there have been some enormous hardware exits and\nthere are more struggling synbio companies than mattress companies. <\/p>\n\n\n\n

So it\u2019s\ncritical to pull out the real quantifiable drivers<\/a>\nunderlying these heuristics to have any chance in deeptech. Moreover, we\u2019ve\nseen teams completely transform investor feedback just by changing the way they\nare framed from \u201cmicrobiome\u201d to \u201csynbio\u201d. This is a sure signal that\n\u2018pattern matching\u2019 is regularly failing to identify commercial or technical\nfundamentals.<\/p>\n\n\n\n

People invest in deeptech for one or both\nof the following reasons.\nFirstly, the potential to own a monopoly asset via\npatent protection – a\nworld-first\nthat gives some breathing room,\nfree from competition to build a uniquely capable company. Or secondly, from\nthe perspective of making a positive impact on the world beyond that which can\nbe achieved with off-the-shelf software alone. <\/p>\n\n\n\n

Sadly, as enticing as the\nnovelty is, world class, patented IP alone often adds very little advantage. In\nfact, if the IP is the principle focus, it can even be a disadvantage, locking the company into a given approach<\/a>\nwith a false sense of security and revealing your plans to the competition. The\nsecond heuristic is \u2018an experienced management team\u2019. No doubt having people\nthat know what they\u2019re doing and an existing network helps significantly, but\noften the world has changed and the same actions this time aren\u2019t as valuable\nas a first principles perspective on the problem in hand. Here we\u2019ll be\nexploring how you can think about some of the challenges ahead in tackling\nproblems that require interaction with the bits, atoms and molecules from a\nmarket.<\/p>\n\n\n\n

Market dynamics, the\npeople and the motivations<\/strong><\/h2>\n\n\n\n

Zero\nrisk, zero effort is key: people act on fear, not upside<\/strong><\/p>\n\n\n\n

A potentially sector shifting technology\nrarely easily fits in the market. A new technology will inevitably fail if it\nintroduces risk vs. existing solutions or makes someone’s life harder. Yet as something new and\ndifferent it almost certainly will. There are some precedent examples here. Qbotix for example raised $23 million from energy\ngiants to remove the need for expensive motors<\/a>\nin large solar panel installations. The solar farm owners didn’t think the\nchange worth the risk and the ultimate winner was the motor company with a $330\nmillion exit<\/a>. In healthtech, ReVision Optics raised\n$194 million\nto develop a far better solution for vision loss but failed principally because\nthe procedure doubled the workload for the clinicians<\/a>.\nThis is why virtually every primary care diagnostic eventually bites the dust\nas well. As does\na lot of \u2018AI for biotech\u2019 software. The user doesn\u2019t want software, they want\nassets and results. The problem is not primary care diagnostics: it is rather\nthe burden that these diagnostics place on clinicians. This burden on\nclinicians is quantifiable, as are the factors that more generally tend to lead\nto category-specific failures. <\/p>\n\n\n\n

Avoid\nunquantifiable binary risk<\/strong><\/p>\n\n\n\n

Another trap is where there is one key\nthing that needs to be unlocked to enter the market.  This is often the case in healthcare. For example, without a\nrebate code you can\u2019t charge for your service or device and there is very\nlittle data to suggest how or when a change might occur. This is what\nultimately killed Claritas genomics which had\na better solution for pediatric genetic testing. Binary regulatory risk is\nunavoidable in some sectors but more quantifiable in some than others. Will the\nEU ever allow genetic modification of food? Who knows. Will the FDA allow a\ndrug with a specific risk profile through? Well there\u2019s a lot of data on\nprevious outcomes to quantify that decision, which is why you will find a lot\nmore biotech investors than synbio agri investors.   <\/p>\n\n\n\n

Deeptech\noften has <\/strong>a <\/strong>better\nIRR and faster time to liquidity vs. tech, but you have to have visibility on\nthe funding journey. <\/strong><\/p>\n\n\n\n

Deeptech is often faster to liquidity and requires less funding<\/a> than tech, in contrast to hearsay. In biotech specifically, whilst biotech only represents 11% of VC financing it represents 54% of VC backed IPOs and outperforms IRR in tech 27% to 22% with a median exit time of 8 vs. 9 years. The problem is that without thousands of seed-funds as in tech, most companies die simply because they run out of money before they\u2019ve proven the tech and<\/em> market. Some sectors have active investing ecosystems with very well understood dynamics, such as biotech, whilst others have very few investors due to endless failures of the past such as med-tech, chemicals, materials.<\/p>\n\n\n\n

All are served by the major investors once\nthe company is a real company rather than technology development. Grants help\nhere, as do proof of concept projects, but often serve to trap companies in a\nloop of niche projects with less than ideal partners that is near impossible to\nescape from. This was the reason behind the demise of BetterHealth<\/a>,\nwhich was spread\ntoo thin and misaligned with their partners. The only way to off-set this is to\nbuild products, the simplest possible product to get sales. Unfortunately far too many\ndeep-tech starts are research in disguise (Ri<\/em>D?).<\/p>\n\n\n\n

Ride shifting market\ndynamics<\/strong><\/h2>\n\n\n\n

The\nthreats are rarely in competing technology but in the diversity of approaches<\/strong><\/p>\n\n\n\n

Look at the problem space rather than the\ntechnology space. It\u2019s so easy to be sucked in by something that looks game\nchanging that is often, not even consciously, hiding the real threats. These\nwon\u2019t appear in the company\u2019s 2×2 competitor grid. The ventures in that grid\nwill either live or die for the same reasons, just look at the 200+ amyloid\nbeta antibody trials that have failed so far. How many hours were spent\nagonising over the minute differences between those in pitch decks when in\nreality it\u2019s not a competing technology which will win but a completely\ndifferent approach (more on our approach in Alzhiemers here).  <\/p>\n\n\n\n

An example of missing the bigger picture\nis that of much heralded and very well-funded Airware<\/a>, a drone\nanalytics company. They had clearly made by far the best customer experience\nfor undertaking facility analysis in a very noisy space. But expansion of\nconsumer grade drones made buyers realise that at the top of the market they\ncould buy their own kit and train experts to do it themselves for less, and at the lower end people\nsimply used off the shelf kit, leaving no space for Airware or any of the\nsimilar companies.<\/p>\n\n\n\n

Trends\nand supporting technologies shift faster than companies are built<\/strong><\/p>\n\n\n\n

One of the hardest aspects of deeptech investing is marrying the long time horizons of technology development with often rapid shifts in the macro market. Just a year ago clean-tech was a dirty word, now it\u2019s overflowing with investors, from angels to those managing billions in assets, driven by global sentiment that carries real risks to later stage funds. Two years ago trapped ions was the leading option for quantum computing, now it looks almost cute compared to what can be achieved with photonics, driven by completely independent materials research breakthroughs. A startup can\u2019t keep flip flopping between technologies but it can be flexible enough to ride the underlying waves. <\/p>\n\n\n\n

Periods of hype and enormous capital\ninjections can cover a lot of mistakes, 23andme is still lauded as a genome\ndriven drug development poster child, yet after 14 years its deals still\nrepresent a tiny proportion of the venture funding it has consumed. Likewise\nseveral of the well funded west coast synbio companies should ring alarm bells\nin that they\u2019re starting accelerators and funds to stimulate the market for\ntheir services. All you can realistically do here is build as close to\napplication as possible (see scaling below), build for where the market will be\nwhen you are product ready, and with a clear funding route at the time. Play\nthe hype if it arrives and gather as much money as you can to live through the\ntroughs in between. <\/p>\n\n\n\n

Founder: \u201cAll we needed to do was scale\u201d.
Narrator: \u201cit wasn\u2019t\u201d.<\/em><\/h4>\n\n\n\n

There\u2019s a good reason few people invest\nearly in deeptech – even when all of the market aspects covered above are well\nconsidered, the translation between discovery and application is rarely an easy\npath. <\/p>\n\n\n\n

This is a particular risk in materials\n(including semiconductors), chemicals and therapeutic production. The entire\n\u2018cleantech\u2019 biofuels boom and bust was based on assumptions that the economics\nwould be cost comparable to oil at scale, when in reality hundreds of millions\nwas lost due to issues like algae sticking to pipes. Nearly every battery\nresponds very differently at scale. Envia systems quickly lost cycle life\nonce built at scale, whilst Satki3\u2019s deal with Dyson ultimately fell apart as\nthe results weren\u2019t reproducible. <\/p>\n\n\n\n

Meanwhile, many approaches simply can\u2019t be\nmanufactured in existing facilities, and whilst it might sound realistic to\nbuild a factory for a huge multi-billion pound opportunity there are few\ninvestors willing to step in to build factories for things that aren\u2019t yet\nproven. Ahead of long term deployment costs and risks are just guesses. Take\nsomething as fundamentally groundbreaking as immunotherapy which has just\ndemonstrated a step change in cancer survival rates and created enormous wealth\nfor early investors. It was invented over 30 years ago. Making a small number\nof cells in the lab is one thing, making them at factory scale, or rather\nconvincing anyone to take a bet on this, quite another. <\/p>\n\n\n\n

Quantify the risk at\nfirst principles and enjoy the ride<\/strong><\/h2>\n\n\n\n

Deeptech investing can be hugely rewarding: you can enable life saving therapies, prevent the demise of the planet, launch entirely new computational paradigms and certainly many people have made a lot of money. However, whilst we haven\u2019t listed all 101+ traps here, hopefully it\u2019s clear that there are major buckets of risk that if, paid attention to, can reduce risk, but certainly not all are entirely quantifiable. If you\u2019d like to learn more you\u2019re very welcome to join our deeptech investor community over at dsv.io\/investorcommunity <\/a><\/p>\n\n\n\n

Written by: Guest Author, Mark Hammond (Founding\nDirector at Deep Science Venture)<\/em><\/strong><\/p>\n\n\n\n

\"Guest<\/figure><\/div>\n\n\n\n

Mark started his career with a PhD in Neuropharmacology and Artificial Intelligence working on better models for the development of epilepsy and neurodegeneration therapeutics, he then worked in cleantech venture before going on to build out Imperial College\u2019s deep-tech start-up accelerator and academic spin-out ecosystem, facilitating several exits to the major tech companies before leaving to start DSV in 2016.<\/em><\/p>\n\n\n\n

Looking to invest? Or raise funds? Check out Crowd for Angels and explore all opportunities, w: https:\/\/crowdforangels.com\/<\/a><\/em><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

After 10 years of building and backing deeptech companies at one of Europe’s leading universities, in the City and our own fund Deep Science Ventures, one thing is clear. The outcomes can be big and exceed tech outcomes in both scale and speed. But any clues on how to navigate this landscape are largely hidden […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[402,403,393,98,389],"tags":[47,8,349,412,413],"yoast_head":"\n101 ways to lose money in deeptech angel investing - Crowd for Angels<\/title>\n<meta name=\"description\" content=\"101 ways to lose money in deeptech angel investing. 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