Summary:\u00a0<\/em><\/strong> Getting an AI startup to scale for an IPO is currently elusive.\u00a0 Several different strategies are being discussed around the industry and here we talk about the horizontal strategy and the increasingly favored vertical strategy.<\/em><\/p>\n \u00a0<\/p>\n OK, in the 80s it took a long time for the \u2018computer age\u2019 to show up in the financial statistics and maybe we\u2019re at the same place.\u00a0 Still, a bunch of people, especially VCs are wondering how to grow an AI company all the way to IPO.\u00a0 This is just now beginning to lead to several different visions of what a successful AI strategy should look like.<\/p>\n Recently we wrote about our own favorite strategy, Data Dominance<\/u><\/em><\/a>.\u00a0 There are two or three others you should be aware of and we\u2019ll talk here about two leading strategies, horizontal and vertical.<\/p>\n \u00a0<\/p>\n How Do We Know There\u2019s a Problem?<\/strong><\/span><\/p>\n In 2017 CB Insights reports that of the 120 AI companies that exited the market, 115 did so by acquisition.\u00a0 And the majority of those acquisitions were made by just 9 companies.\u00a0 Guess who.<\/p>\n But thanks to the shortage of AI talent, most of these startups ended up being nothing more than acquihires.\u00a0 We hope that at least some made that life-changing money but all those guppies ended up swallowed by whales and are now just features or products, not world changing businesses.<\/p>\n Not all startups will make it.\u00a0 And it certainly requires some soul searching when Google or Amazon come calling.\u00a0 But there\u2019s an open question about whether the strategies of these startups was viable, and that\u2019s where our conversation begins.<\/p>\n \u00a0<\/p>\n Horizontal Strategy<\/strong><\/span><\/p>\n The horizontal strategy is where our industry started, almost by accident.\u00a0 The core concept is to make an AI product or platform that can be used by many industries to solve problems more efficiently than we could before AI.<\/p>\n Then came what VCs refer to as the monoliths: Google, Amazon, IBM, and Microsoft.\u00a0 As we now know, their dominance in their respective areas of data gave them an almost insurmountable lead in offering generalized image, video, speech, and text AI tools<\/u><\/em><\/a> using the familiar MLaaS model.<\/p>\n None of these companies started out to be AI-first companies.\u00a0 This grew out of the phenomenon that was once called the data-sidecar strategy.\u00a0 It was there, so why not add it to their offerings.<\/p>\n The monoliths have become so dominant that there\u2019s a widely held principle among VCs that startups should have a maximum distance from these core competencies in order to be defensible.<\/p>\n There\u2019s a second tranche of horizontal competitors below the monoliths including startups.\u00a0 These reach all the way down to some of the newer automated machine learning (AML) companies like OneClick.AI who integrates deep learning with the standard assortment of ML algos.<\/p>\n \u00a0<\/p>\n Horizontal Strategy is Out<\/strong><\/span><\/p>\n The bottom line is that the horizontal strategy is out for a variety of reasons.<\/p>\n In short, this is not where VCs are putting their money.<\/p>\n \u00a0<\/p>\n Vertical Strategy<\/strong><\/span><\/p>\n The vertical strategy isn\u2019t the only alternative but it\u2019s certainly the most widely talked about these days.\u00a0 It shares the concern over data dominance with that strategy but goes further in specifying other aspects required for success.<\/p>\n The most vocal advocate for vertical is Bradford Cross, a founding partner at Data Collective DCVC, self-described as the world’s leading machine learning and big data venture capital fund.\u00a0 I don\u2019t know if Bradford is the inventor of the vertical strategy but he can certainly claim to have published more in detail than others.<\/p>\n The vertical strategy has four primary principles:<\/p>\n If you\u2019d like to read more from Bradford on the Vertical Strategy try this excellent article<\/u><\/em><\/a>.<\/p>\n \u00a0<\/p>\n Is That All There Is to the Vertical Strategy?<\/strong><\/span><\/p>\n Well no.\u00a0 For starters, as we pointed out in our Data Dominance<\/u><\/em><\/a> article, picking the right industry with the right problems suitable for AI is no small challenge.<\/p>\n Although any industry that meets the criteria for the vertical strategy might be ripe for defensible exploitation, Bradford Cross adds some insight unique to the VC world.<\/p>\n First of all, Mr. Cross quotes that 90% of AI startups are focused on enterprise markets, not consumer apps that have been so common up to this point.<\/p>\n \u201cCompared with consumer startups since 1995, enterprise startups have returned 40% more capital overall. Enterprise and consumer startups have generated equivalent IPO value, but enterprise has generated 2.5X the M&A value.\u201d\u00a0 Second, market exits come in cohorts.\u00a0 Interest in ML and AI in the M&A market tends to occur in groupings around hot industries in which a number of viable startup targets arise together.\u00a0 Right now that tends to favor fintech and healthcare, followed perhaps by energy, utilities, basic industry, and transportation.\u00a0<\/p>\n The point is simply that there are going to be more buyers in the market if you are a member of a cohort focusing on a hot industry.<\/p>\n Does this mean you should skip opportunities in other industries?\u00a0 Not at all.\u00a0 Just be aware that VCs are more likely to be generous with funding in favored industries than in outliers.<\/p>\n \u00a0<\/p>\n An Observation about Full-Stack Solutions<\/strong><\/span><\/p>\n In general I agree with the desirability of a full-stack solution, one that provides an easy interface and solves a really important customer problem.\u00a0 It\u2019s worth backing up a minute however to consider how much full-stack is enough full-stack.<\/p>\n Using predictive maintenance in IoT as an example, a recent BCG report listed all of the following as part of the total solution for full-stack predictive maintenance solution.<\/p>\n As you can see, providing this full-stack solution is a tall order.\u00a0 Pretty much on the scale of inventing a SAP or PeopleSoft integrated ERP solution.\u00a0 Would this be defensible \u2013 you bet.\u00a0 Is this necessary in the original vision of your AI-first business solution \u2013 probably not.<\/p>\n As a longer-term goal this would be an ideal case but you can probably build a significant business on just 3 or 4 of these elements.<\/p>\n Looking back at the examples from our previous article<\/u><\/em><\/a>, Blue River Technology, Axon, and Stitch Fix, these three are well on their way to defensible businesses using data dominance as their core goal.\u00a0 Will they grow to full-stack at some point \u2013 perhaps, but the solutions they currently offer are very high value.<\/p>\n What they share with the vertical strategy beyond data dominance is subject matter expertise and AI-first value creation.\u00a0 Full stack, however you wish to interpret that can follow so long as the application solves the immediate customer problem easily, efficiently, and accurately.<\/p>\n \u00a0<\/p>\n Other articles on AI Strategy<\/span><\/p>\n What Makes a Successful AI Company<\/u><\/em><\/a><\/p>\n AI Strategies \u2013 Incremental and Fundamental Improvements<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n Other articles by Bill Vorhies.<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n About the author:\u00a0 Bill Vorhies is Editorial Director for Data Science Central and has practiced as a data scientist since 2001.\u00a0 He can be reached at:<\/p>\n Bill@Data-Magnum.com<\/a> or<\/span> Bill@DataScienceCentral.com<\/a><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"![](\"http:\/\/api.ning.com\/files\/3qihtuz2qlfot87oaO5tffRgpjtnluU0sO-qexR3UNygtUwQ6-rOUfeLe6UeEiQ6vyjqNnY*lRWPf1UOkpFZRjnz9FSoTssK\/aistrategy.png\")
<\/a>Looks like there\u2019s a problem brewing in AI startup land.\u00a0 While AI is most certainly destined to be the next great general purpose technology, on a par with the steam engine, the automobile, and electrification, there just aren\u2019t any examples of new AI-first companies that look like they\u2019ll grow that big.<\/p>\n<\/a>AI startups are only a few years old and it\u2019s fair to say that when those founders started out they hoped not only to change the world but to make some life-changing money.\u00a0 That traditionally means IPO.<\/p>\n\n
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