AArtificial intelligence is a buzzword these days. Everyone from students to politicians is talking about how artificial intelligence (AI) will shape our world in the coming days. With such hype around AI, there are many opportunities for new jobs and building new businesses from scratch. To understand the scale of the opportunity, we need to turn the clock back to the late 90s.
In the late 90s, the now ubiquitous Internet was a new one. Internet penetration was in its infancy—most of the world used dial-up connections. Entrepreneurs saw the opportunity and rushed to create and set up dot-com companies. This led to a market crash called the dot-com bust. This market crash wiped out companies with products that did not have a good value proposition or product-market fit. However, looking back at the events, now we can clearly accept the benefits of the experimentation done during the time. The great internet and ecommerce companies of our age emerged unscathed from the dot-com bust and went on to change the world. Another benefit to human society was the emergence of free web browsers.
Just like in the late 90s, when everyone was convinced that the Internet was going to change our lives, we in today’s world have a broad consensus that AI will do the same (hopefully for the better). Just as entrepreneurs rushed out in the late 90s to build dot-com companies, today’s entrepreneurs are just about to start pushing the boundaries of what’s possible with AI. Does this mean we are headed for a bubble? I certainly hope so. An AI bubble, like the dot-com bubble, will lead to a period of experimentation where entrepreneurs will have the resources to push the limits of possible applications. While some speculators and even genuine investors will bring the money, the result will create the next innovative company that will change the way we live. AI companies with the right value proposition are likely to rule and shape people’s lives in the coming millennia.
So where are we in this cycle as far as AI is concerned? I feel we are in a phase where only Big Tech companies are using AI. The rush to establish an AI-driven company has not yet come. Data is the heart of any artificial intelligence we can build now. We are now in an era where data is collected and controlled largely by Internet companies and some large financial services companies. The debate over whether or not the data is owned by the people who created the data or by the platform where the data was created has not been resolved. Regulations, such as the PSI directive in the European Union, are just beginning to loosen the grip on data. Whether this type of legislation becomes normal in the rest of the world remains to be seen.
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AI is also awaiting its first tangible mass-market application. A driverless car or AI-driven guard bot is likely to be the harbinger of the AI rush. For early-stage entrepreneurs, there are headwinds such as access to data and raising capital. I believe that these winds will weaken significantly in the coming years. AI is mostly applied in areas like computer vision and natural language processing which are quite impressive, but we don’t have a tangible mass market application yet. When a mass market application comes along and when a few companies’ grip on data loosens, we will see a rush to build the next multi-billion dollar company. For early-stage entrepreneurs, the best bet is to look for an application where AI has yet to be adopted. Just as internet entrepreneurs have redefined our shopping habits, AI entrepreneurs should look to redefine the old ways of doing things. For example, AI drones could be used to detect potholes or AI-driven guard robots could be used for security. The opportunity to use AI to dramatically improve what seems like a trivial task like guarding a perimeter has a lot of potential and uses. AI-assisted agriculture is another area where there remains a significant opportunity for experimentation. The best opportunity for AI application is in the real world instead of the virtual world or the digital world.
Early stage entrepreneurs should first educate themselves on the basics of AI. A basic understanding of the algorithms that drive AI will help entrepreneurs understand the nuances of gathering data to build an AI. It will also help them understand how AI applications are built and used. This basic knowledge will enable early stage entrepreneurs to find and hire the right talent to build their product. Also, professional investors often look for founders who have some background and understanding of the field.
Armed with just the basics, early-stage entrepreneurs can go out and look for real-world applications. Identifying these opportunities should be the first big step in the right direction. After that, the entrepreneur can start thinking about collecting the data that will be the heart of the startup. At least in the current phase of AI evolution driven by supervised models. With the right team and the right data set, an AI application in the cloud is within the reach of every entrepreneur. A minimum viable product (MVP) built in the cloud can be used to present your idea to professional investors. With some tailwind and a good idea coupled with a working MVP, raising money shouldn’t be the hardest part. Finally, with the funds raised, scaling your company and taking it to the next level will become a possibility.
While this sounds easy on paper, it is perhaps the hardest thing to do on this planet. However, if you want to try something difficult, always do it in a fast growing industry. The story of the growth of AI is just about to begin.
Dr. Aditya Narvekar, Assistant Professor and Deputy Director, Student Engagement and Enhancement – Bachelor of Data Science, SP Jain School of Global Management
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