Unsupervised Investments: A Guide to AI Accelerators and Incubators

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:

• AI Nexus Lab (NY): an intensive program run by Future Labs (NYU) and ff Venture Capital. During the program, the startups can get access even to NYU AI faculty, which means for some lucky entrepreneurs to potentially have the chance to work along side with Yann LeCun. They have just announced their first cohort: Alpha Vertex, Behold.ai, Cambrian Intelligence, HelloVera, Klustera;
• Alexa Accelerator (Seattle): powered by Alexa Fund in collaboration with Techstars, this accelerator has the goal of advancing voice-powered technologies. As one of the Techstars programs, startups receive $100k of funding upon acceptance in convertible notes, as well as $20k in exchange for 6% of equity (with a ‘Equity Back Guarantee’ clause, which basically gives the founders that chance to lower up to zero Techstars’ equity position within three days from the end of the program).
• Bosch DNA (Berlin): the Indo-German accelerator targets startups in different areas which uses enabling technologies such as deep learning, analytics, AI and machine learning to go from “Lab to Market”. The Nurture program lasts for 18 weeks: the first 3 weeks are dedicated to idea validation, a short 10-days bootcamp, and mentors meeting. Phase II is about 10 weeks mainly running through customer validation, while finally phase III concerns pitching preparation for final demo days. Usually 5 Indian and 5 German startups are selected;
• Botcamp (NY): run by Betaworks’ team (very good media investors), it is a program specifically designed for conversational interfaces. A $200,000 uncapped, safe note with a 25% discount is offered to companies;
• Comet Labs (Bay area): I have already mentioned Comet Labs Research Team in a previous article on AI investors, but they are also product builders. They will run different ‘labs’ starting from this April. The first one just announced is the Transportation Lab, with two more to follow. The first cohort includes 7 (impressive to me) companies: Nomoko; AutoX; Oculii; Deep Vision; Minds.ai; Point One; Syntouch. They do not provide an investment by default but rather on a case by case basis (in the form of a warrant, a convertible note, or a discounted equity investment);
• Creative Destruction Lab (Toronto): this is a program longer than usual, but aimed to support entrepreneurs with an MVP with mentorship on how to raise a round, develop the go-to-market strategy and deal with legal, accounting, and other business processes;
• CyberLaunch (Atlanta): accelerator coming out from Georgia tech scene and with a focus on machine learning and information security. It is Chris Klaus’ second accelerator after Neurolaunch (focusing on neuroscience startups). They have incubated companies like C3Security, Chincapi, Cyberdot, Diascan, iTreatMD, Realfactor.io, Securolytics, Vyrilland Yaxa;
• Data Elite Ventures (Bay area): Tasso Argyros and Stamos Venios founded DEV in 2013 with the idea of accelerating and investing in big data companies. They look to be inactive for a while (or at least off the radar), despite having supported good companies (Unravel, Weft, 451 Degrees) and an exit done (Weft has been acquired by Genscape last year);
• Deep Science Ventures (London): DeepScienceVentures is not a focused AI oriented accelerator, but rather a deep tech lab where to incubate ideas. It targets people rather than companies, as you can notice from their cohort (very similar to what EF is doing). As a scientist, you join the DSV team for a 3-months internship and if you find the right idea and co-founders, you get access to the following 3-months of MVP prototyping;
• Element AI (Montreal): created by famous AI scientist Yoshua Bengio, JS Cournoyer, Jean-Francois Gagné, Nicolas Chapados this lab lies on the idea the Canadian AI ecosystem is still one of the strongest worldwide — and this is very true about talents as well as funding raised. It is a mixed between a pure research lab and an incubator, and it has been backed up by Real Ventures. Very recent news: they acquired the entire team at MLDB.ai, an open source machine learning database;
• Eonify (Los Angeles): they focus on the healthcare vertical, so they offer perks such as help for Protocol development, regulatory applications, clinical trial design, or grant writing.
• Founders Factory (London): the Factory is a much wider accelerator who happens to have though a specific track for AI companies. The idea seems to be co-creation/development of two-three AI businesses within the acceleration program every year, for five years. The first two companies, recently announced, are Iris.ai (science research assistant) and Illumr (organizational pattern detection);
• IBM Alphazone (Israel): IBM created this accelerator with the goal in mind of fostering long-term technology and business partnerships with smaller companies in the Cloud, Big Data & Analytics and IoT space. They have another partnership in place with Becton, Dickinson and Company to jointly select up to 3 startups in healthcare delivery and decision making. For those startups they offer extra professional mentorship and matter experts, as well as a grant of up to $25,000. They supported NeuroApplied, Magentiq Eye and Articoolo;
• Innovat8 Connect (Singapore): a program that brings startups to work along side with Singtel group to develop new solutions useful to the group itself. Singtel Innov8, the VC arm of group (fund size of $250M) follows up with investments where and if needed. A good example of the program output is Xjera;
• Kapsch Factory1 (Vienna): The Factory1 Kapsch TrafficCom Accelerator 2017 is an acceleration program with a focus on future intelligent mobility solutions (Connected & Autonomous Driving, Big Data Analytics & Deep Learning, Smart Mobility). The CEO and a second team member (preferably the CTO) will have to be present in Vienna for the Kick-Off Bootcamp, the three Acceleration Weeks in Vienna and Berlin and the Demo Day in Montréal (Canada). All travel and accommodation costs are covered;
• Merantix (Berlin): run by Rasmus Rothe (co-founder with Adrian Locher), Merantix is a venture builder specialized in AI and with a stronger focus on four specific verticals: Finance, Healthcare, Advertising and Automotive;
• Microsoft Accelerator (Bangalore): this accelerator program is within for a different reason. It has not been set up, to my knowledge, as an AI-accelerator, but though in the last cohort all the 14 companies accepted were doing some sort of AI/machine learning. In other words, this is the first ‘ex-post AI’ accelerator, because it has been changing its own nature by the companies it selected;
• NextAI (Toronto): a Canadian accelerator for startups with no previous funding. You can apply either as individual as well as a team (but first always apply as individual). It provides startups with a capital of 50k CAD with can be increased by a 30k as well as other 150k throughout the program for top performing teams incorporating a venture ($50,000 for a SAFE with a $2mm CAP and up to an additional $150,000 no CAP, 20% discount to next round);
• Nvidia Inception (Virtual): this is a virtual accelerator program that helps startups during product development, prototyping, and deployment. They can apply for GPU hardware grants and the NVIDIA Deep Learning Institute (DLI) will show the latest techniques in designing, training, and deploying neural network-powered machine learning in different applications. With respect to others, it looks like a soft program, but it directly makes startups to be considered for the GPU Ventures Program ($500K — $5M, and help in sales & marketing, joint development, and product distribution);
• Play Labs (Cambridge, MA): this is a brand new accelerator, apparently only for MIT students and alumni. They have a strong focus on gamification and ‘playful technologies’, and provide companies with $20k funding plus other $80k (typically in convertible notes) at the end if certain requirements are met;
• Rockstart AI Accelerator (Netherlands): usually these guys run 5–6 months accelerators in Netherlands. The new program in AI is starting accepting applications in May and it will cost 6% of equity to startups (but only after having raised a further round of funding);
• Startup Garage (Facebook) (Paris): another brand new accelerator sponsored by Facebook within the startup campus called Station F. Facebook will provide 80 desks and space for 10–15 data-driven startups fro 6 months at no cost (or obligations to use FB products), as well as operational mentoring (marketing, legal, etc.) and technical help (from FAIR — Facebook AI Research). This confirms Facebook’s strategy to have a stronger technical presence in Europe and the ability of France to potentially become one of the major AI hub worldwide. According to VentureBeat, they have already selected a few startups for the first incoming program (Chekk; Mapstr; The Fabulous; Onecub; Karos);
• TechCode Global AI+ (Bay area): TechCode is a global network of startup incubators and entrepreneur ecosystems which will especially help companies in approaching the Chinese and Asian markets. 10 startups out of the 50 they selected for the program will benefit from an initial investment of $50k. Originally, they would earn a ‘success fee and equity stake’ only if the startup raised funding within 12months from the end of the program. Not sure how this changed for the Global AI+ program;
• The Hive (Bay area): they define it as a ‘co-creation studio to build and launch startups’ in AI (subdivided in deep learning, blockchain, AR, ‘ambient intelligence’ and ‘context computing’). They built companies as Sensify, Snips and Skry with their $22M second fund. They also host a meetup called ‘The Hive Think Tank’. Their business model is a bit atypical but not completely new: simply speaking, they either incubate existing companies or they think the idea, create the MVP and recruit executives to run this new startup;
• Voicecamp (Betaworks) (NY): as Botcamp above, this is also run by Patrick Montague and the Betaworks’ team but focuses on early stage companies building voice-based products. $200k uncapped, SAFE note with a 25% discount is offered to all the startups accepted.
• Winton Labs (London): the famous hedge-fund is now presenting the second cohort of its data science accelerator. First of all, it is really interesting to me that an investment firm in London decides to start an accelerator program without asking for anything in return. But it is more interesting to see what areas they want startups to work on: machine intelligence, forecasting, innovative data, or wildcard (not clear projects). Startups also get direct exposure to Winton Ventures, of course;
• Y Combinator (Bay area): Y Combinator is known to be one of (if not the) best accelerators in the world. They didn’t have any specific focus on AI until now, but they just announced an experimental batch on artificial intelligence. They claim to be agnostic to the industry and would eventually like to fund an AI company in every vertical. A specific thing they are looking for though is Robot Factories, and teams that use deep (reinforcement) learning to help to fix it.
• Zeroth AI (Hong Kong): Zeroth.AI is run by tak_lo and his team in Hong Kong, and has a wide spectrum of AI advisors although its young age and 10 early stage AI startups in their first cohort (4 of which in the bots/assistant space). This is probably going to change, with up to 20 startups and optional $120k of funding. The relocation for the program is not mandatory for the entire time frame but highly recommended at the beginning and at the end of the program.

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.