A list of 34 accelerators and incubators for AI startups I. Rationale for the post Well, let’s be completely honest: the current startups landscape is incredibly messy. Venture capitalist, angels, incubators, accelerators, private equity funds, corporate venture capital, private companies, research grants. There are plenty of ways to get funded to start your own company — but how many of them are not simply ‘ ’? How many of them give you some additional value and really help you scale your business? 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 and why I am following up now with accelerators, which can be a valid investment alternative and business opportunity but that are commonly not fully understood. a long post on AI investors some time ago But first, some fundamentals… Image Credit: Jinning Li/Shutterstock II. Who’s who in the funding game Since the edges are blurring, it is 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. In the industry, the distinction between an accelerator and an incubator is simply related to the rationale for a company to join such a program. In other words, an incubator helps the entrepreneur in the , while the accelerator focuses more on . The two programs have therefore two different goals and should be joined at a different stage of the startup lifecycle (Isabelle, 2013). development of her idea growing the business If we look instead at a more rigorous detailed academic definition, it would be worth to have a look at and . They actually define a startup accelerator as Cohen (2013) Cohen and Hochberg (2014) “a fixed-term, cohort-based program, including mentorship and educational components, that culminates in a public pitch event or demo day.” From this definition is clear that the authors looked at different traits to characterize and distinguish different programs from each other. The key features can actually be summarized as follows. Even though this academic definition clearly indicates thresholds and binary variables to identify different programs, it looks to me that — at least in the AI space — things are more complicated and actually it is really hard to define who is who (for help, check the brilliant review by Hausberg and Korreck, 2017). Furthermore, the important question we should ask is not whether to call a program accelerator or incubator, but rather what is the real value brought to the entrepreneur. III. Are they worth their value? If you are an entrepreneur, having so many different choices might make you wonder whether it might make sense to join one of those programs or not. And if you are an investor, a company, or anyone else looking at the space, you might start wondering if those programs suffer from an adverse selection problem: go ahead with their feet while that cannot get funded or get the ball rolling go into these programs. good companies ‘ lemon’ companies Entrepreneur Perspective: to join or not to join Unless you are already an experienced entrepreneur, the short answer is ( ). 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 on real life experience. In this respect, accelerator programs are sort of full-time educational bootcamp in which you rapidly learn what you need to at least survive the first year. Whether then you are gonna make it or not depends on how you transform that knowledge into the right actions. yes, accelerators and incubators are worthy Hallen et al., 2016 to do an exam: in this case, the full book would take you years to be read, while the summary takes a few months and can help you passing the exam. However, final graduation is a completely different thing. Joining an accelerator is actually as reading a summary instead of the full book ‘Accelerators = Business synopsis’ Academic research, even if not unanimously (check this beautiful work by ), seems to confirm with data the value of those programs (Hochberg, 2015). Studies prove that accelerated companies reach milestones faster ( ), have a higher probability to raise further funding with respect to angel-supported startups ( , and that have even spillover effects on the entire entrepreneurial ecosystem ( . Yu, 2016 Hallen et al., 2014 Winston-Smith and Hannigan, 2015) Fehder and Hochberg, 2015) : even if some of those findings are true from a statistical point of view, there is a huge difference between different accelerators, and the of the program drastically impacts the positive effects for the startup. A warning though quality Investor Perspective: should I stay or should I go A good investor is basically the one who is able to: i) pick straight the winner and helping him become bigger and stronger; ii) pick a winner with the right things in place and helping him become successful. potential The first case requires a lot of ex-ante work (due diligence) but not much after you invest. You simply seat down, relax and wait (it is not that simple actually, but let me go with this narrative for a second). The problem here is that there are few companies with these traits and everyone wants to invest in them, which considerably reduces the risk-return tradeoff. The second case is instead more interesting and shows the real skills and contribution of the investor. It is also what it happens, most of the time and with exceptions, with companies coming out from accelerators and incubators program. These are companies that, for whatever reasons (lack of previous experience, no access to funding, etc.), might not have made it by themselves but are now in the game. Think of big success stories as Dropbox, for example. So the question is: as an investor, should I invest in companies coming out from accelerator programs? Or am I buying a lemon? The answer is ‘simple’, once again: . yes, but mainly in those ones coming from excellent successful programs The proliferation of accelerators and incubators program made really difficult for investors to find real value in companies, especially for AI-related technologies and businesses. Good companies join accelerators for learning, mentoring and to get more exposure, all things as an entrepreneur you want to get from the best ones out there. And if good companies join an accelerator, the accelerator becomes more successful and attract better and better companies and founders on the next batches. It is a virtuous circle, which is creating a clear polarization in the industry, a positive skew distribution where very few programs deliver excellent results while the majority of them do not add any value (and in some cases are even detrimental) to the participants. In other words, I think there is a in the accelerators/incubators space. accelerated strong adverse selection problem Of course, this is not a law of nature and does not imply that every company coming out from Techstars is going to become a unicorn (or the other way round). It is simply a rule of thumb to allocate a bit more efficiently your capital. If you are then able to spot out a potential winner in a low-level accelerator, , give yourself a pat on the shoulder because you did a very nice job. chapeau Accelerators Assessment Metrics: is the program any good? The common denominator of the two perspectives is that everything comes back to how good an acceleration program is. I have no particular experience in setting up or participating in an accelerator, so I do not know for sure the problems or the metrics on how to assess it. This is my interpretation (quite general with some sprinkle of AI somewhere), but feel free to comment below and tell me more about different metrics and aspects I should also consider: i) : who are the alumni of the program? This base represents the ‘ ’ of the accelerator, so check it out if includes big names. 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.); Alumni network customer base Do not be trapped by average valuations ii) : 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; Raising the next round iii) : same considerations as above, with the additional aspect that companies need to raise a specific amount of money. The more companies can reach their funding goal, the better the program is. Raising a good next round : evaluating an accelerator on the basis of the and only increments the already existing hype on AI; Be careful average amount of dollars raised is a huge mistake iv) : 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 was; Survival rate v) : , if companies coming out from programs are obtaining higher valuation than their competitors, shortening the or simply increasing the probability of an exit, it means that the accelerator did the job it was supposed to. Exit ceteris paribus time-to-exit, 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 there, even if we might not be able to identify the specific factors that make a business more successful. endogeneity Second, it depends on your view about business and what it means starting a company. Real visionary entrepreneurs do not start a company to sell it — they start something as it . An exit is somehow a defeat for some of them (there are exceptions, e.g., DeepMind), but the reality is that this class of entrepreneurs is disappearing. People start business 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 in AI and machine learning sector). should run forever acqui-hire I am not saying this is wrong and this is certainly what an investor wants, but it can invalidate the ‘ ’ metric as one variable to track for accelerators’ performance; Exit vi) : 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. Wider network Image Credit: APTX4869/Shutterstock 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 of them: at least 34 : lead by , it is an incubator (with a model similar to a startup studio) that builds data-driven platforms for governments, corporations and startups. They have 1-2 different (academic researchers and professors) to support each single platform they build, and their first two products are called , a mobile-based micro-insurance platform for agricultural farmers, and , which tackles down specific challenges for building chatbots in Asia; Addo AI (Singapore) Ayesha Khanna Associate Partners nikka fonetica : an intensive program run by (NYU) and . 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 . They have just announced their first cohort: , , , , ; AI Nexus Lab (NY) Future Labs ff Venture Capital Yann LeCun Alpha Vertex Behold.ai Cambrian Intelligence HelloVera Klustera : 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 ‘ ’ 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; Alexa Accelerator (Seattle) Equity Back Guarantee : the AI2 is expanding its incubator for AI companies to let in outside startups, after the first two spinouts — and . Companies will get up to $250,000, six months of free office space, help with sales and marketing and access to 70 AI researchers and PhDs; Allen Institute for Artificial Intelligence (Seattle) Kitt.ai Xnor.ai : 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 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; Bosch DNA (Berlin) Nurture : run by ’ 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; Botcamp (NY) Betaworks a new embryonic accelerator launching this year and focusing on AI and cognitive sciences. No public information available up to date, so stay tuned for more as soon as further public announcements will be released; Cog Labs (London): : I have already mentioned in a , but they are also product builders. They will run different ‘labs’ starting from this April. The first one just announced is the , with two more to follow. The first cohort includes 7 (impressive to me) companies: ; ; ; ; ; ; . 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); Comet Labs (Bay area) Comet Labs Research Team previous article on AI investors Transportation Lab Nomoko AutoX Oculii Deep Vision Minds.ai Point One Syntouch : 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. In addition to the ‘standard’ AI/ML track, they also recently launched the first in order to attract people who want to work at the intersection between AI and Quantum Computing. They will be supported by three machine learning focused VCs (Bloomberg Beta, Data Collective and Spectrum 28). Participants will need to be in Toronto for the technical training portion of the program (but full relocation is not required) and can opt in or out of the pre-seed investment (US$80k for 8% equity); Creative Destruction Lab (Toronto) Quantum machine learning program : accelerator coming out from Georgia tech scene and with a focus on machine learning and information security. It is Chris Klaus’ second accelerator after (focusing on neuroscience startups). They have incubated companies like , , , , , , , and ; CyberLaunch (Atlanta) Neurolaunch C3Security Chincapi Cyberdot Diascan iTreatMD Realfactor.io Securolytics Vyrill Yaxa : 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 ( , , ) and an exit done (Weft has been acquired by last year); Data Elite Ventures (Bay area) Tasso Argyros Unravel Weft 451 Degrees Genscape : 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 (very similar to what 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; Deep Science Ventures (London) DeepScienceVentures cohort EF : created by famous AI scientist , , , this lab lies on the idea the — and this is . It is a mixed between a pure research lab and an incubator, and it has been backed up by . 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 , an open source machine learning database; Element AI (Montreal) Yoshua Bengio JS Cournoyer Jean-Francois Gagné Nicolas Chapados Canadian AI ecosystem is still one of the strongest worldwide very true about talents as well as funding raised Real Ventures MLDB.ai : 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; Eonify (Los Angeles) : 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 (science research assistant) and (organizational pattern detection); Founders Factory (London) Iris.ai Illumr a full-service studio that provides tailored technical and product support to AI and ML startups. Based on their assessment, they provide a customized offering for the AI startup and invite the entrepreneurs to work with us at Launchpad Studio in San Francisco without asking for any equity; Google Developers Launchpad (San Francisco): : 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; H2 Ventures (Sydney) : 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 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 , and ; IBM Alphazone (Israel) Becton, Dickinson and Company NeuroApplied Magentiq Eye Articoolo : a program that brings startups to work along side with Singtel group to develop new solutions useful to the group itself. , 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 ; Innovat8 Connect (Singapore) Singtel Innov8 Xjera : 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; Kapsch Factory1 (Vienna) run by Rasmus Rother (co-founder with ), 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 ; Merantix (Berlin): Adrian Locher Blinq 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 . In other words, this is the first accelerator, because it has been changing its own nature by the companies it selected; Microsoft Accelerator (Bangalore): the 14 companies accepted were doing some sort of AI/machine learning ‘ex-post AI’ they just announced they will open an AI accelerator in partnership with within the Station F campus in Paris, the same place where Facebook opened its . The first startup incubated will be , but more information will come soon; Microsoft Accelerator (Paris): INRIA Startup Garage Recast.ai : 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). They also provide structured business and technical curriculum taught by successful entrepreneurs and award winning faculty from Rotman (University of Toronto), Harvard, MIT, NYU, and others; NextAI (Toronto) 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 ($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 ( ; ; ; ; ; ); Nvidia Inception (Virtual): GPU Ventures Program Abeja Datalogue Optimus Ride SoundHound TempoQuest Zebra Medical : 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; Play Labs (Cambridge, MA) : usually these guys run 5–6 months accelerators in Netherlands. The new program in AI is starting accepting applications in May and it will 6% of equity to startups (but only having raised a further round of funding); Rockstart AI Accelerator (Netherlands) cost after : another brand new accelerator sponsored by Facebook within the startup campus called 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 and the ability of France to potentially become one of the major AI hub worldwide. , they have already selected a few startups for the first incoming program ( ; ; ; ; ); Startup Garage (Facebook) (Paris) Station F. Facebook’s strategy to have a stronger technical presence in Europe According to VentureBeat Chekk Mapstr The Fabulous Onecub Karos 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 will benefit from an initial investment of $50k. Originally, they would earn a ‘ ’ only if the startup raised funding within 12months from the end of the program. Not sure how this changed for the Global AI+ program; TechCode Global AI+ (Bay area): 50 they selected for the program success fee and equity stake : 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 , and with their $22M second fund. They also host a meetup called . 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; The Hive (Bay area) Sensify Snips Skry ‘The Hive Think Tank’ : as Botcamp above, this is also run by and the ’ 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. Voicecamp (Betaworks) (NY) Patrick Montague Betaworks 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; Winton Labs (London): : 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 . They claim to be agnostic to the industry and would eventually like to fund A specific thing they are looking for though is and teams that use deep (reinforcement) learning to help to fix it. Y Combinator (Bay area) Y Combinator experimental batch on artificial intelligence an AI company in every vertical. Robot Factories, : is run by 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 $120k of funding. The relocation for the program is not mandatory for the entire time frame but highly recommended at the beginning and at the end of the program. Zeroth AI (Hong Kong) Zeroth.AI tak_lo I think it would be worthy to mention two other accelerators that focus on hardware but that, although not AI-focused, for the current historical moment we live in are incredibly close to the AI development: a pure hardware accelerator, and , an ‘ . Industrio (Italy), Buildit (Estonia) accelerator of Things’ Summary of all the information for the accelerators listed above (only for those ones I could find information about). If you are interested in knowing why some accelerators don’t disclose information, check the theoretical work of Kim and Wagman (2014). Please consider the value of the funding as expressed in accelerator’s local currency (except for Creative Destruction Lab which is in US$) 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 (but for an interesting discussion on the topic ), as well as private research companies (e.g., , , , etc.). In no particular order then: Toyota check here Numenta GoodAI Cogitai ; The Alan Turing Institute (London) ; The Allen Institute for AI (Seattle) ; AI Research Institute (Korea) ; Machine Intelligence Research Institute (Berkeley, CA) ; Dalle Molle Institute — Swiss AI Lab (Manno, Switzerland) ; Sino-Israeli Robotics Institute (Guangzhou, China) ; The Montreal Institute for Learning Algorithms (MILA) (Montreal) OpenAI (San Francisco); ; Vector Institute (Toronto) ; Fondazione Bruno Kessler (Trento, Italy) 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 i) the on-going confusion between accelerators and incubators facilitated the creation of which have characteristics of both the programs; mixed structures 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, though (and my personal believe at this stage of AI development), is that can do a much better job in understanding and helping companies leveraging these exponential technologies. specialized investors and accelerators 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 (research for biotech and commercialization for pharma companies). sector self-specialized and assigned to each class of players what they knew how to do more efficiently 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 ‘ ’, and we have something focusing on AI called 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. specialized co-working space RobotX Space 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). “ ”. , 8:3/4: 19–25. What Do Accelerators Do? Insights from Incubators and Angels Innovations Cohen, S., Hochberg, Y. V. (2014). “ ”. . Accelerating Startups: The Seed Accelerator Phenomenon Working paper Fehder, D. C., Hochberg, Y. V. (2014). “ ”. W_orking paper_. Accelerators and the Regional Supply of Venture Capital Investment 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). “ ”. Available at SSRN: Do Accelerators Accelerate? The Role of Indirect Learning in New Venture Development https://ssrn.com/abstract=2719810 Hausberg, J. P., Korreck, S. (2017). “ ”. Available at SSRN: . A Systematic Review and Research Agenda on Incubators and Accelerators https://ssrn.com/abstract=2919340 Hochberg, Y. V. (2015), “ ,” , National Bureau of Economic Research. Accelerating Entrepreneurs and Ecosystems: The Seed Accelerator Model in Innovation Policy and the Economy, Volume 16, Josh Lerner and Scott Stern editors Isabelle, D. A. (2013). “ ”. 16–22. Key Factors Affecting a Technology Entrepreneur’s Choice of Incubator or Accelerator Technology Innovation Management Review: Kim, J. H., Wagman, L. (2014). “ ”. , 29: 520–534. Portfolio size and information disclosure: An analysis of startup accelerators Journal of Corporate Finance 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 — — Follow me on Medium Look at my other articles on AI and Machine Learning: A Brief History of AI Open Source in Artificial Intelligence AI and Speech Recognition: A Primer for Chatbots
