I. Rationale for the post

The current startups landscape is incredibly messy, with all the venture capitalists, angels, incubators, accelerators, private equity funds, corporate venture capital, private companies, and research grants. There are plenty of ways to get funded to start your own company — but how many of them are not simply ‘dumb money’?

This problem is particularly relevant for emerging exponential technologies such as artificial intelligence, machine learning and robotics. For those specific fields, highly specialized investors/advisors are essential for the success of the venture.

This is the reason why I wrote a long post on AI investors some time ago and why I am following up now with accelerators, which can be a valid investment alternative and business opportunity that’s often fully understood.

But first, some fundamentals…

II. Who’s who in the funding game

It’s hard to find a commonly shared definition for accelerators and incubators. Hence, I will provide two different definitions: one a bit more from a practitioner’s point of view, the other slightly more academic.

The distinction between an accelerator and an incubator is related to the rationale for a company to join such a program. An incubator helps the entrepreneur in the development of her idea, while the accelerator focuses more on growing the business. The two programs have two different goals and should be joined at a different stage of the startup lifecycle.

III. Are they worth their value?

If you are an entrepreneur, having so many different choices complicates the decision of whether to join one. If you are an investor, you might wonder if those programs suffer from an adverse selection problem: good companies go ahead with their feet while ‘lemon’ companies that cannot get funded or get the ball rolling go into these programs.

Entrepreneur Perspective: to join or not to join?

Unless you are already an experienced entrepreneur, the short answer is yes, accelerators and incubators are worthy (Hallen et al., 2016). Starting and running a company is something no university can teach you (no matter how many innovation workshops you take or entrepreneurial courses you attend) but it is grounded in real life experience. In this respect, accelerator programs are a full-time educational bootcamp in which you rapidly learn what you need to at least survive the first year.

Academic research, even if not unanimous (check this beautiful work by Yu, 2016), seems to confirm with data the value of those programs. Studies prove that accelerated companies reach milestones faster (Hallen et al., 2014), have a higher probability of raising further funding with respect to angel-supported startups (Winston-Smith and Hannigan, 2015), and that have even spillover effects on the entire entrepreneurial ecosystem (Fehder and Hochberg, 2015).

A warning though: even if some of those findings are true from a statistical point of view, there is a huge difference between different accelerators, and the quality of the program drastically impacts the positive effects for the startup.

Accelerators Assessment Metrics: is the program any good?

Here is a non-inclusive list of metrics to consider when evaluating accelerator programs.

i) Alumni network: who are the alumni of the program? This base represents the ‘customer base’ of the accelerator, so check it out if includes big names. Do not be trapped by average valuations of the portfolio of the program: having one Dropbox and dozen of ‘John Doe startups’ does not make it a good accelerator, it simply makes it a lucky one (look at different stats, if you want to, e.g., median, variance, etc.)

ii) Fundraising rounds: even though raising funds is not always a proof of business success, it is very often a good proxy for it. The more companies raise a further fund after the program, the better the program is. The more companies that reach their funding goal, the better the program is. Be careful: evaluating an accelerator on the basis of the average amount of dollars raised is a huge mistake and only exacerbates the already existing hype on AI.

iii) Survival rates: the accelerators are set to provide entrepreneurs with tools and network to survive for at least 12 months (this is my view). The higher number of companies are still operating after one year, the better the accelerator.

iv) Exits: ceteris paribus, if companies coming out from programs are obtaining higher valuation than their competitors, shortening the time-to-exit, or simply increasing the probability of an exit, it means that the accelerator did the job it was supposed to.

However, this point is controversial for at least two reasons: first, it is statistically hard to understand how an accelerator affects a final exit. Life is much more complicated than linking straight accelerator → higher exit, but if all the companies coming out from a specific program obtain higher valuations with respect to their peers, we know for sure that there is some endogeneity there, even if we might not be able to identify the specific factors that make a business more successful.

Second, visionary entrepreneurs do not start a company to sell it — they start something as it should run forever. An exit is a defeat for some of them (there are exceptions, e.g., DeepMind), but the reality is that this class of entrepreneurs is disappearing. Many start businesses nowadays with the idea in mind to sell out in 5 years to a specific buyer, or to use the technology developed to increase the salary base from $150k (a normal salary in big tech companies in the US for an AI researcher) to $7M (average amount got from acqui-hire in AI and machine learning sector).

I am not saying this is wrong and this is certainly what an investor wants, but it can invalidate the ‘Exit’ metric as one variable to track for accelerators’ performance;

v) Wider network: a good accelerator has top-level mentors and knows how to engage them to be effective. It also has people behind who can really understand AI technologies and can help entrepreneurs with latest developments in research, or partners that can provide datasets for feeding neural nets.

IV. List of AI Accelerators and Incubators

Here is a list of 28 accelerators and incubators which focus on AI:

I think it would be worthy to mention two other accelerators that are not explicitly AI-centric, but focus on related hardware: Industrio (Italy), a pure hardware accelerator, and Buildit (Estonia), an ‘accelerator of Things’.

V. Final Food for Thought

I tried to list all the accelerators I could find working specifically on AI, and I hope you find this useful. It looks clear to me now that:

i) the on-going confusion between accelerators and incubators facilitated the creation of mixed structures which have characteristics of both the programs;

ii) quality matters (not all the accelerator are equals). You get different value from different ecosystems even if the offer is the same on paper. Joining an accelerator in this list is also not a guarantee of success, and of course, there are many other excellent programs worldwide that can maybe work much better than some of the ones I showed above.

The motif (and my personal belief at this stage of AI development), is that specialized investors and accelerators can do a much better job in understanding and helping companies leveraging these exponential technologies.

A final interesting thing I noticed is the new concept of ‘specialized co-working space’. An example of this is AI-focused RobotX Space which has locations in multiple cities (Silicon Valley and Asia). I have never been there but I think it makes a lot of sense to create technology hubs like this one. This model might, in the future, even undermine the business models of accelerators and incubators.

As I always say, this type of list is the result of an intensive research work on publicly available data, but it can be still prone to errors. So, if I misled something or forgot someone, get in touch and let me know!

This article by guest contributor Francesco Corea was originally published on Medium. Contributor opinions are their own and do not reflect those of TOPBOTS.