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Unsupervised Investments (II): A Guide to AI Accelerators and Incubators


 
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A meticulously compiled list as extensive as possible of every accelerator, incubator or program the author has read or bumped into over the past months. It looks like there are at least 29 of them. An interesting read for a wide variety of potentially interested parties - far beyond only the investor.



IV. List of AI Accelerators and Incubators

 
I then compiled a list as extensive as possible of every accelerator, incubator or program I read or bumped into over the past months. It looks like there are at least 29 of them:

  • 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). Historically, it seems that Techstars companies go on to average more than $2M raised after 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 proper 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. It has been announced not more than a few months ago (although they got already funded by Microsoft Ventures), so there are no more precise information about how it will work in practice (except that they are already working on 10 different projects). Very recent news: they acquired the entire team at MLDB.ai, an open source machine learning database;
  • Eonify (Los Angeles): they focus on healthcare vertical, so they offer perks such as help for Protocol development, regulatory applications, clinical trial design, or grant writing. There is not much more info out there about their accelerator program unfortunately;
  • 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);
  • H2 Ventures (Sydney): H2 Ventures is an Australian venture capital specialized in fintech which will be running a first accelerator program for AI and data analytics companies starting next August. They have a few requirements (e.g., founding team no larger than 4 people) and they are likely the only Australian accelerator for AI startup. Applicants will need to demonstrate their ability to deliver an MVP within 6 months and the intention of raising a Series A round of capital within 6–12 months;
  • 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 Rother (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. Active since one year, they contributed to build companies like Blinq;
  • 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 acceptedwere 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 deploy 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). Apparently, the Inception program includes over 1,300 startups up to date. 14 of those companies have been recently asked to pitch in front of investors and 6 of them eventually got funded through the venture programs (Abeja; Datalogue; Optimus Ride; SoundHound; TempoQuest; Zebra Medical);
  • 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 focus on hardware but that, although not AI-focused, for the current historical moment we live in are incredibly close to the AI development: Industrio(Italy), a pure hardware accelerator, and Buildit (Estonia), an ‘accelerator of Things’.

Summary of all the information for the accelerators listed above (only for those ones I could find information about). If you are interested in knowing why some accelerators don’t disclose information, check the theoretical work of Kim and Wagman (2014). Please consider the value of the funding as expressed in accelerator’s local currency and the length of the programs expressed in months sometimes approximated if originally in weeks.

BONUS PARAGRAPH: 10 Main Research Institutes

This is not really related to AI accelerators but I think worth to mention it for people working in the space. Some of the following institutes gather the best minds working on AI problems, and it might be useful for research developments, talent pipeline, as well as potential partnerships to keep track of them. I will not include in the following list the pure academic research institutes (i.e., the ones strictly belonging to/located within universities) because the list would be too long otherwise, and I won’t consider big tech companies as DeepMind, Google (Google Brain), Facebook (FAIR), Baidu, IBM, Microsoft and Toyota (but for an interesting discussion on the topic check here), as well as private research companies (e.g., Numenta, GoodAI, Cogitai, etc.). In no particular order then:

V. Final Food for Thoughts

 
I tried to list all the accelerators I could find working specifically on AI, and I hope it will help someone out there. It looks clear to me now that

  1. the on-going confusion between accelerators and incubators facilitated the creation of mixed structures which have characteristics of both the programs;
  2. 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, though (and my personal believe 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.

There is also something else emerging from the list: there are really few AI accelerators/incubators in Silicon Valley proportionally speaking, although the common expectation would be to find most of them in the American entrepreneurial district.

My guess is that, in reality, from a pure cost-benefit perspective, the Bay Area is not the best place to start a company.

 

It is the best place though to expose the startup to a larger market, investors and public acknowledgement.

This does not imply that being in Silicon Valley makes no sense, but rather the opposite. I actually see shaping an emerging pattern in Silicon Valley, the same one that characterized in the past 30 years the pharmaceutical and movie industries. The pharma industry, for example, moved from being a large industry where the same company did the research (expensive), developed the molecules (expensive) and eventually commercialized the final product (cheap and with good margins), into a two-ways sector where biotech companies took the higher risk of developing experimental molecules while big pharma corporations were in charge of FDA regulation approval and market launch.

Of course, it is a bit more complicated than that, but the main message is that the sector self-specialized and assigned to each class of players what they knew how to do more efficiently (research for biotech and commercialization for pharma companies).

In the same way, it will make sense probably to develop companies in other countries (where the real cost of starting up is much lower) to eventually land in California only once ready to either scale, raise larger rounds of financing or massively go to market.

A final interesting thing I noticed, which might be useful to some entrepreneurs: it is coming out the new concept of ‘specialized co-working space’, and we have something focusing on AI called RobotX Space in multiple cities (Silicon Valley and Asia). I have never been there (but hopefully I will in the future) but I think that 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 or lacks. So, if I misled something or forgot someone, got in touch and let me know!

References
Cohen, S. (2013). “What Do Accelerators Do? Insights from Incubators and Angels”. Innovations, 8:3/4: 19–25.
Cohen, S., Hochberg, Y. V. (2014). “Accelerating Startups: The Seed Accelerator Phenomenon”. Working paper.
Fehder, D. C., Hochberg, Y. V. (2014). “Accelerators and the Regional Supply of Venture Capital Investment”. Working paper.
Hallen, B. L., Bingham, C., Cohen, S. (2014). “Do Accelerators Accelerate? A Study of Venture Accelerators as a Path to Success”. Academy of Management Annual Meeting Proceedings.
Hallen, B. L., Bingham, C., Cohen, S. (2016). “Do Accelerators Accelerate? The Role of Indirect Learning in New Venture Development”. Available at SSRN: https://ssrn.com/abstract=2719810
Hausberg, J. P., Korreck, S. (2017). “A Systematic Review and Research Agenda on Incubators and Accelerators”. Available at SSRN: https://ssrn.com/abstract=2919340.
Hochberg, Y. V. (2015), “Accelerating Entrepreneurs and Ecosystems: The Seed Accelerator Model,” in Innovation Policy and the Economy, Volume 16, Josh Lerner and Scott Stern editors, National Bureau of Economic Research.
Isabelle, D. A. (2013). “Key Factors Affecting a Technology Entrepreneur’s Choice of Incubator or Accelerator”. Technology Innovation Management Review: 16–22.
Kim, J. H., Wagman, L. (2014). “Portfolio size and information disclosure: An analysis of startup accelerators”. Journal of Corporate Finance, 29: 520–534.
Yu, S., (2016). “How Do Accelerators Impact the Performance of High-Technology Ventures?”. Available at SSRN: https://ssrn.com/abstract=2503510
Winston-Smith, S., Hannigan, T. J. (2015). “Swinging for the fences: How do top accelerators impact the trajectories of new ventures?”. Working paper.

Original. Reposted with permission.

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