Software. SaaS. IoT.
Each technological innovation is characterized by the emergence, acceptance, and eventually ubiquity of buzzwords that define the industry they represent. To the point, where business plans and even business models re-engineer themselves to ensure they are not left out of the mainstream conversation and of broader technological developments.
After strong early adoption, these technologies reach tipping point, or to use a more colorful analogy from physics at the point of hitting 'escape velocity'. That is when the demand for such technologies and skills escapes the forces of gravity, growing at a fantastic exponential rate.
Artificial intelligence (AI) and Machine learning (ML) are about to hit this escape velocity. I will not bore you with definitions, attempts to simplify by explaining in 'English terms' or by memes or by practical examples. There is a gush of information online from Webster-style definitions, academic coursework, and explanatory videos on this.
What's important is that the forces underpinning the growth of these technologies – humongous datasets, technical skills both at academic institutions and corporations, case studies of successful implementations, tremendous increase in funding for this sector and more – all lead to a groundswell in support and overall ecosystem expansion.
Startups in the early 2000s stopped saying 'We are a SaaS company" and moved to statements like "Our cloud-based application is available on public and hybrid clouds...". Similarly, Investors are seeing a very high proportion of business plans that leverage AI/ML – to the point where many (and rightly so) do not point out that they use AI/ML; they simply refer to applications in slides towards the middle of the presentation. Something that is representative of the ubiquity of AI/ML and a trend that we are excitedly embracing.
Most investors actually get disillusioned when we find business plans that have "AI/ML" sprayed all over. It usually smacks of a me-too business plan. Refined business plans crisply articulate how the AI and ML modules are effectively used to disrupt traditional processes, unravel new opportunities or markets, catalyze new discovery, and in general unleash new avenues of wealth creation or productivity improvements.
Good business plans articulate well how they 'traverse the AI lifecycle' - how they identified a distinct pain point in their target market, how data collection techniques in this field have evolved, how this data is collected, parsed, normalized, stored and then effectively analyzed. A more thorough technical due diligence should reveal how this startup is using state-of-the-art algorithms, high-pedigreed talent, and suitably robust infrastructure to unravel patterns in the data – patterns that can result in instant step-up, improvements over the current areas they are addressing.
As an example, a truly compelling healthcare startup could discover a groundbreaking cure for cancer treatment through genomics, by using hitherto unrealized techniques to capture and analyze patterns in infected cells, and executing this through a team comprising of luminaries and experts from both academia and the healthcare industry.
Artificial intelligence, Machine learning and related technologies are here to stay and will form the crux of next waves of innovation. Their effects are not ripples anymore. Indeed, strong waves (Schumpeterian gales of destruction, as MBA programs call them) are being felt in many aspects of the global economy.
Startups that harness these trends will reap windfalls as will investors that pick the winners from the crowd. Larger corporations will imbibe these trends - either organically or inorganically - and if it's more of the latter, we will be seeing a lot of interesting activity in the startup ecosystem in the years to come.
I also feel that the academic world will embrace this wave very quickly (if not doing so already) - both by offering extensive coursework in data manipulation programming languages; as well as in 'conceptual stuff' - how to get a data set and prepare it for anomaly detection, clustering (finding common traits), and so on. Abundant coursework will emerge at the intersection of 'Major X' and computer science - for example at the intersection of cellular biology and computer science (with some early stage finance courses from the business school also thrown in) for students aspiring to be genomics entrepreneurs.
Lots of exciting stuff to anticipate.
Devang Mehta is Partner, Anthill Ventures
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