AI is also waiting for its first tangible mass-market application
Artificial Intelligence is a buzzword these days. Everyone, from students to politicians, is talking about how Artificial Intelligence (AI) is going to shape our world in the coming days. With such hype around AI, there are a lot of opportunities for new jobs and to build new businesses from scratch. To understand the scale of the opportunity, we need to wind the clock to the late 90s.
In the late 90s, the now ubiquitous internet was a fledgling. Internet penetration was in its infancy—most of the world was using dial-up connections. Entrepreneurs saw the opportunity and rushed to create and set up dot-com firms. That led to a market crash called the dot-com bubble burst. This market crash wiped out firms and products which did not have a good value proposition or did not have a product-market fit. However, looking back at the events, we can now clearly accept the benefits of the experimentation done during the time. Large internet and ecommerce firms of our age emerged unscathed from the dot-com burst and went on to change the world. Another benefit to human society was the emergence of free internet browsers.
Just like in the late 90s, when everybody was convinced that the internet was going to change our lives, we in the present world have a broad consensus that AI will do the same (hopefully for the better). Just like entrepreneurs rushed out in the late 90s to build their dot-com firms, entrepreneurs today are just about starting the push the limits of what is possible with AI. Does that mean we are heading for a bubble? I certainly hope so. An AI bubble, just 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 bear the brunt, the outcome will create the next innovative firm which will change our way of life. AI firms with the right value proposition are likely to lead and shape human lives over the next millennia.
So how far along are we in this cycle as far as AI is concerned? I feel we are in a phase where only Big Tech firms are using AI. The rush to establish an AI-driven firm is yet to come. Data is the heart of any artificial intelligence we can build right now. We are currently in an age where data is collected and controlled largely by internet firms and some large financial service firms. The debate about whether data is owned by the individual who created the data or by the platform where the data was created is yet to be settled. Regulations, like the PSI directive in the EU, are only just starting to loosen the grip on data. Whether this kind of legislation becomes normal in the rest of the world remains to be seen.
AI is also waiting for 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 these headwinds will weaken significantly in the coming years. AI is primarily being applied in fields such as 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 and when the grip of a few firms on data loosens, we will see a rush to build the next multi-billion-dollar firm. For early-stage entrepreneurs, the best bet is to look for an application where AI is yet to be adopted. Just like internet entrepreneurs redefined our shopping habits, AI entrepreneurs should look to redefine the old way of doing things. For example, AI drones could be used for detecting potholes or AI-driven guard bots could be used for security. The opportunity to use AI to incrementally improve what seems to be a trivial task like guarding a perimeter has tremendous potential and use. AI-assisted farming is another area where there remains a significant opportunity to experiment. The best opportunities for the application of AI are in the real world instead of the virtual world or the digital world.
Early-stage entrepreneurs should first educate themselves about 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 seek and hire the right talent to build their products. Also, professional investors often look for founders with some background and understanding of the field.
Armed with just the basics, early-stage entrepreneurs can then go out and look for applications in the real world. Identifying these opportunities would be the first big step in the right direction. After this, the entrepreneur can start thinking of collecting the data which 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 right data building, an AI application in the cloud is within every entrepreneur’s grasp. A minimum viable product (MVP) built in the cloud can be used to pitch your idea to professional investors. With some tailwinds and a good idea coupled with a working MVP, raising funds should not be the most difficult part. Finally, with the funds raised, scaling your firm and taking it to the next level will become a possibility.
While this sounds easy on paper, it is perhaps the most difficult thing to do on this planet. However, if you want to attempt something difficult, always do it in a fast-growing industry. The AI growth story is just about getting started.
Dr Aditya Narvekar, Assistant Professor & Deputy Director, Students Engagement and Enhancement – Bachelor of Data Science, SP Jain School of Global Management