Raising capital is for many startups a necessary evil. Sometimes even a recurring one. It’s a lot of work and it takes your focus away from the actual task at hand – Building a successful business. Fundraising is difficult and even exhausting. If you are raising capital for an AI startup you might find the job even more difficult than usual.
I have been through this myself a few times now and even though the experience is never exactly the same there are some recurring themes and challenges that you have to be prepared for. If properly prepared you can get through the fundraising quicker and get back to your business.
Why are AI-startups different?
Like any other startup, you need to show traction, an experienced, strong, and motivated team, and a market with potential for growth. As if that isn’t hard enough you got some extra challenges in AI.
Crating AI products is usually a very experimental process. You cannot know from the beginning exactly how well the AI will work and what it will cost to make. As a result, the business case is more difficult to put together.
Data can also complicate your business. Contrary to traditional IT, data is an essential part of the product. You cannot have AI without data and data is difficult and costly to attain. Many underestimate the efforts that go into securing the right amount of the right quality of data.
As I’ll explain in a minute you also have the problem that very few will understand how your product actually works. AI has only recently gained traction as a mainstream technology making us all walking on the untreated territory.
The VC understanding of AI
You can’t rely on venture capitalists(VC’s) understanding AI like you do. When VC’s look into investing in a SaaS(Software as a Service) company, they are on home turf. They know all the ins and outs, the dangers, and the dynamics. When they look into AI-companies the understanding ranges from almost non-existent to deep experts. That’s a problem. First of all, you might think that you being the expert when the VC is not will give an upper hand. Unfortunately, that is not how things go. The dialogue is better and more productive when speaking expert to expert. When the VC’s have very little knowledge on the subject there will be more concept confusion. The confusion results in insecurity about the inner workings of your company and that do not make the VC more likely to invest. It’s more to the contrary.
Secondly, you have to speak to several levels of understanding at once. When preparing a pitch deck and you have to communicate to both the expert and the beginner it’s just not easy. The same goes for the actual pitches. In the pitches though, you usually have the opportunity to get a feeling for the level of understanding. Try to get that early on. Beware though. Questions like “Do you understand AI?” does not get you anywhere. People generally say yes to questions like that regardless of their understanding. So ask open-ended questions.
So the VC’s understanding varies and you have to keep that in mind all the time.
What you need to prepare for
Alright. AI is different and we need to make sure our pitch takes that into account. So how do we do that?
I’ve put together a list of subjects here that you have to be well prepared on.
Positioning
No matter how unique you think your company and product is, it probably isn’t. If there’s something VC’s are world champions in, it is to find companies that do what you do. Sometimes these will be competitors that you never heard of but are very similar and sometimes it’s not close at all but it will look like it to the VC. Nevertheless, the answer is always positioning.
You have to be extremely clear about how you are positioned in the market. Make sure that your product positioning comes off as a strategically thought-through choice. Be upfront about how your product choices put your company in a special spot. Maybe it’s the way you get data, maybe it’s how or where you deploy the product or maybe it’s that you chose speed over quality. It can be anything. Be clear and upfront about these choices and why they are better for your customers and your competitiveness.
Be immune to change. The world changes and the technology changes and it’s happening with more speed than ever before. As a result, you should be clear about how you are immune to this change. Maybe it’s you lock-in due to high stickiness, maybe the position in an ecosystem or maybe it’s the customer relationship that is your weapon against change. It doesn’t matter as long as you have solid reasoning.
Data vs algorithm
First of all, many startups that say that they use AI don’t according to a study by the investment firm MMC Ventures. Don’t try to pull a quick one here. VC’s will do due diligence and backtracking is never fun. You don’t need AI to raise capital. You need a good scalable business. You are also not an AI-company if you use some standard off-the-shelf AI from Google. Being a technology company or an automation product is not worse than being an AI-company. It might in fact save you trouble not to focus on AI if you are not really deep into it.
You can still market yourself as an AI-company in your marketing if you utilize off-the-shelf AI in some clever way. Just keep it in the marketing and not in the pitch deck.
The algorithm
You don’t have a genius algorithm. I’m really sorry, but you just don’t. Whatever you spend a whole year building, Google will do better tomorrow. That is just how it is. Your algorithm is probably not anything special.
So if Google will do it better what’s the point anyway?
It’s a tough one and you should be ready for any potential investor asking “what if Google or another big tech targets this sector tomorrow?”. A possible solution here is to dig deep into the verticals. Big techs are strong because they are big but as a trade-off, they can’t specialize in verticals and specific markets. This can be your edge. Look at their off-the-shelf AI. Their vision can recognize a chair but ask it what’s the local brand of the chair and it probably won’t know. So look for edges like that when preparing your pitch.
The data
Data is a true competitive advantage. Make sure you tell investors how your data is different from your competitors. Maybe you get data at a lower price, it could also be higher quality or a bigger volume.
Many AI startups are surprised by the cost of keeping the flow of data to the AI alive. It’s the oxygen for artificial intelligence so you can’t just shut it off. Make sure that you can show a high cash efficiency here. Your competitors can easily run out of money just by keeping their AI alive.
If you haven’t heard about it, active learning is a machine learning strategy that can help you here.
Scaling
A question that I have gotten numerous times is how to product scales when entering other markets. Do you need to make a lot of adjustments or get a lot of new data for the product to work in other countries or other verticals?
Being from Denmark I was a bit surprised with the question at first. When your company is born in a small market you naturally build products that work in more than just your home market. Otherwise all hopes of becoming the big company you dream about. If you are not from a small market you might not be prepared for this question since it is not an initial challenge for your company.
As a last note, don’t hold back when it comes to the potential sales figures. AI scales and the investors know. You can easily feel like you are overselling your product when you show the potential that your research shows you have. Don’t worry. The investors see pitches every day and understand potential is not a promised sales figure. They like huge potential. Who would invest in anything less anyway?
