An Introduction to Tech VC
Where and Why Investors Invest
I’m coming to the end of my first month interning at a seed stage artificial intelligence venture capital fund; If you have no idea what half of those words refer to, you are further along than I was when I started working with Nick Slater (partner at AI Seed, Spurs devotee) and John Spindler (general partner at AI Seed, CEO of Capital Enterprise, startup scene BNOC). It was suggested to me that summarising this first month in a blog post might be a good way to cement and share what I’ve learnt so far, so please enjoy these introductory ramblings to the London startup ecosystem. I’ve included a glossary at the end for newbs like myself — anything in bold is in there.
When I first entered the world of tech VC, I had no idea what was going on. My first week or so was spent trying to subtly write down words I didn’t know in meetings, hoping that whatever external company we were speaking to didn’t notice and realise just how clueless I was. Everyone speaks in a coded language (which is different from a coding language), using a whole new vocabulary referring both to things I had previously been aware of but didn’t know the name for, and things I’d had no concept of prior to encountering this world. For example: AI is Artificial Intelligence. I knew this. AI is what self-driving cars use, and what will eventually take over the world when the robots rebel against their masters. ML is machine learning. I’m still not 100% what this can encompass — it’s part of AI, but also distinct from it, and it’s more complicated than AI, but kindof necessary for AI? I certainly had no idea that ML even could exist before I started working here, but was promptly told on my first day I’d be in charge of organising a ‘Machine Learning Academy’!
One thing I quickly got used to seeing were startups’ ‘decks’, their slideshow pitching their business. These are often very interesting — the tech scene is full of very clever young people coming up with unique and inventive ideas, and I’ve read through those decks thinking ‘I can’t wait to use this!’. Other times a deck is incomprehensible; it seems either very poorly done, or aimed at a totally different audience than me, a history of art graduate with no experience of the finance world, or niche medical issues — not always helpful given the amount of healthtech, fintech, and deep tech companies AI Seed invests in. I’ve gathered that you can spot a bad deck when you are the intended audience and you still don’t really understand what the startup does after reading their deck. It’s not because you’re not smart enough to get it, it’s because they haven’t quite mastered how best to explain it.
The deck is just the first part of deciding whether the fund will invest in a startup. I’ve very much enjoyed actually meeting said young, clever, inventive people during my time here so far — all very different, all eloquent (at least in meetings with investors!), and all there to explain why their company is absolutely key for a certain market. Investors are on the look out for ‘painkillers’ as opposed to ’vitamins’ — ‘vitamins’ are supplementary, you think about buying them, and often you don’t; whereas when you’re really in pain, you don’t think twice about buying a painkiller. Confusingly, this metaphor is also used with ‘painkillers’ versus ‘a cure’ — a short term solution compared to something that eliminates the problem. You get the idea: the ideal investee will solve a niche problem in an intended market large enough in size that the startup can scale up indefinitely. AI Seed is also always looking for startups with solid tech backing their idea up. It’s an AI fund, after all! This is where ‘tech DD’ comes in — tech due diligence — whereby a PhD or masters student, or someone at the company with a strong technical background, is assigned to check out just how solid that tech is, and whether the entire idea is viable.
Otherwise, when looking for the right horse to back, AI Seed does to a certain extent rely on faith in the team: the most common comment on AI Seed’s ‘Trello’ board monitoring potential investees is ‘strong team’. I gather that since startups are usually referred to AI Seed by accelerators or other connections we have a good relationship with (such as startups we’ve already invested in, or sometimes the investors themselves), generally one can rely on them being pretty good even by the time they get here. I initially wrote here that ‘there is no checklist’ for picking who to invest in — but as it turns out there is! John has a list of 5 risks he’ll use as a basis in evaluating a startup:
1. Execution Risk
Possibly the most important, this is the risk inherent in the founders themselves and whoever they’ve managed to employ so far. Are the team credible, capable, experienced and trustworthy? Who are they capable of recruiting? Even, do we like them? We’ll be owning a part of their company after all!
2. Market Risk
Is there a dire problem in the intended market? If so, how does this startup solve it? Is there sufficient demand for the product ?Just as Sandra Bullock says in the Blind Side, if you don’t love it in the store you won’t wear it: potential clients must be obsessed even at this early stage. Finally, does this startup have something the potential competition doesn’t? All of the above is all well and good — as long as someone else hasn’t got there first!
3. Product/Tech Risk
Can this team build the aforementioned solution? What engineering challenges will they have to overcome? Sometimes, the tech just hasn’t caught up with the idea yet, or other external factors aren’t yet in place — data might be lacking, or enough similar systems to connect to, such that despite all other factors being in place, this product just can’t yet go as far as it needs to succeed.
4. Business Model Risk
Can this startup actually make money? Is the market big enough to do so? There’s a lower risk of money loss with software, of course — you’re not having to put capital into a physical prototype — making for a higher profit margin than in startups requiring hardware based solutions.
5. Return Risk
How will we the investor eventually make money? Who will buy this? Investors look at each startup as the potential solution to all the others failing: Will the 20% we own of your company eventually cover AI Seed’s entire two million pound fund by exiting for a hundred million? Investors are always looking for the next unicorn! For example, consultancy style startups are less likely to get bought for big bucks — they aren’t as unique, catering to whatever company is hiring them at the time, and therefore can’t solve the niche mentioned in point 1.
There’s a bit of a paradox at play when considering these risks, in that the only way to be certain they’re manageable is by seeing how these things pan out; therefore, investors are often inclined to wait and see before investing — but the longer they wait, the likelier the potential investment will ironically die a death of lack of funds!
Making an investment is odd, because you are against them — until suddenly you’re with them. Once you’ve invested, suddenly it’s not about finding the holes in their plans, reasons not to invest, but rather about patching those holes up, and ensuring the company’s success, because now, that’s your success too! Before starting here, I worked in a fashion and homeware store for a few months, where I learnt a bit about customer service. Good customer service involves ensuring the customer has a positive experience from the moment they encounter your business, on entering the store, till the moments after they’ve made their purchase. You want them to feel happy about the money they’ve just let go of , to know you are working with them towards the same goal; that them letting go of their money is the best deal both you could both work out together. Haven’t we all experienced leaving a store unhappy not to have found what we were looking for, disappointed not to have been guided to it, and leaving a store absolutely delighted with something we had no intention of buying when we walked in?
The same appears to be true with investment. I’m always surprised when Nick decides not to invest in a startup, since he’s always so happy when meeting them. ‘I always seem keen!’ he says — and although he claims it’s ‘just his personality’, there is more to it than that. Just as in customer service, you want a startup to know you are on their side from the start; after all, it’s both sides that want this negotiation to work out. Nick laughingly says startups he’s spoken to rarely have major problems with term sheets AI Seed issues. He attributes this in part to the fact that AI Seed is a goodie, and genuinely wants what’s best for people we work with — we’re not here to take advantage of anyone; but he also attributes it to his having nurtured a positive relationship with the startup from their first interaction. As soon as they enter the store, he’s on their side, until they leave the store, when hopefully all parties are happy. He’s with them even while he’s technically against them.
Landing suddenly in the world of tech VC has been confusing, interesting, and very rewarding. Seeing the diversity of ideas being actively worked on is exciting, and fills me with optimism about the future of a world of work I’m just starting out in. When newspapers or even online blogs and think pieces mention tech, quite often it’s fairly doom and gloom (‘No one has any privacy any more!’ ‘Millennials are unable to look up from their phone screens!’ ‘x, y and z jobs are all redundant!’) but actually meeting the people behind the screens, and daily encountering new ideas that will improve our world in one way or another, makes it hard to feel disheartened. One month down — many to go!
GLOSSARY
Credit to Martha Yeandle for everything on this list that is vaguely coherently written.
Accelerators A great way to grow a startup. Accelerators “accelerate” growth of an existing company. Accelerators normally operate on a specific time frame can be from a few weeks to a few months. The Startup will work on a set programme often run via networking, workshops and mentorship to build out and progress their businesses. There is often a very selective application process. Some examples of accelerators that Capital Enterprise works closely with are Techstars, Seedcamp, Startupbootcamp.
Artificial intelligence (AI) — Sometimes called machine intelligence, AI is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximise its chance of successfully achieving its goals.
BNOC — Big Name On Campus.
Coding Languages — the format in which you write code; there are dozens of them and many have confusingly similar names. Java is not the same as Javascript. C+ is different from C++.
Deck/Pitch deck — a brief presentation, often created using PowerPoint, Keynote or Prezi, used to provide your audience with a quick overview of your business plan. You will usually use your pitch deck during face-to-face or online meetings with potential investors, customers, partners, and co-founders
Deep Tech — Technology that is based on tangible engineering innovation or scientific advances and discoveries.
Health Tech — Medicine based technology. Often harder to scale since of course there’s all sorts of checks and standards health tech startups must adhere to than, say, Ad Tech (advertising technology)
Incubator — Similar to an accelerator but does not operate on a set schedule. Can be sector specific or non sector specific. For example an incubator attached to a hospital may only be interested in Health startups. Within an incubator a company will refine an idea, build a business plan and can collaborate with others. Often within a network so mentoring opportunities often exist.
Fintech — Financial technology. Everyone thinks it’s hot stuff because it’s something people with lots of money will be interested in (IE, banks). Supposedly soon to be eclipsed by Reg Tech though — regulatory technology, that helps with tax processes; even better because both people with lots of money (IE, banks) and people with a bit less money (SMEs) will need it…
Machine Learning — Adam Geitgey says: “Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.”
Machine Learning Academy — The MLA is a 6 week program which Capital Enterprise is currently running in collaboration with UCL. The program is designed to provide an introduction to ML to those either hoping to implement ML into their SME, or who already work for a company using it extensively who may not come from a tech background. Check out this video for a short clip.
Seed Stage is where an investor invests money in a start up in exchange for an equity stake. ‘Seed’ suggests very early stages and is supposed to support a company until it is making cash of its own. Usually quite small amounts. Capital from a seed round often fuels a startup’s move beyond its founding team, funds product development, and in some cases, even facilitates early revenue generation.
SME — Small or Medium sized Enterprise
Spurs — Tottenham Hotspurs. Apparently they’re quite a big football team? They lost the championship league though. Or the world cup. The European Union? They lost a big match.
Startup Ecosystem — A co-dependent community of startups and those that house them, investors, and early users that reinforce and support each other, creating a network effect where the sum is greater than its parts. Or at least that’s how John Spindler describes it.
Term Sheet — A document issued between an investor and startup (in this context) as an initial, non legally binding agreement on how the investment will proceed. They can often be lengthy, wordy, and totally incomprehensible to the untrained eye, covering things like ‘drag-alongs’ and ‘vesting’, which I will not get into here.
Unicorn — A privately owned startup that has come to be valued at over $1 billion, so named to show how rare they are. Think Deliveroo, Revolut, AirBnb, Wework. Instagram, Facebook, and Uber all were Unicorns pre IPO (International Public Offering).
Venture Capital (VC) A venture capitalist is an investor who either provides capital to startup ventures or supports small companies that wish to expand but do not have access to equities markets. Venture capitalists are willing to invest in such companies because they can earn a massive return on their investments if these companies are a success. Venture capitalists also experience major losses when their picks fail, but these investors are typically wealthy enough that they can afford to take the risks associated with funding young, unproven companies that appear to have a great idea and a great management team. Agreeing to a VC investment means you will have more people involved in how the business is run.
