We use the same social messaging app, the same search engine, the same video-sharing platform and the same social network across the world. The best product has won and the second best has been edged out of the market. This is in contrast to earlier times when multiple products and services could co-exist in different geographical niches and customer segments.
In today’s flat world, a digital product can be replicated and sold across the globe at pretty much no incremental cost. A messaging app built in California can be seamlessly distributed in India, without any shipping, logistics cost or bureaucratic hurdles. All our apps from India need to compete with it for the interest of Indian users. The winning app needs to be the best, to be the first to have user-adoption and grow fast. It will be boosted by network effect – attracting more users by virtue of having a higher number of current users. Further, it needs to continue to be the best over time – not to be disrupted by, say, a product based on a new technological innovation in user interface, by the use of artificial intelligence or otherwise.
This is the new “winner-takes-all market”. How well prepared is India to compete in such a market? Are our companies up for this challenge? To start with, our companies are not the first-comers. We did not build the world’s first internet companies, mobile companies and app companies, and we will probably not build the world’s first artificial intelligence companies. It is not surprising that we do not have the largest companies in any of these areas either. All these revolutions were born out of government-funded scientific research. The internet came out of the Advanced Research Projects Agency Network or ARPANET project, funded by the United States Department of Defence. Mobile phones became possible through several years of university-driven research in chip design and communication technology. Specifically, the iPhone exploited the dual-touchscreen technology invented at the University of Delaware. It used GPS that came out of a United States military project and speech recognition technology that was the result of several years of research in universities such as Carnegie Melon University.
Research-startup ecosystem
Research and technology revolutions are scripted by researchers at universities and research laboratories. Entrepreneurs in proximity to the research become the “first-comers” to exploit this research to build new products and improve existing ones. An ecosystem of research universities and startups together builds the world’s largest and most innovative companies. One such ecosystem is the Silicon Valley that is located in the proximity of several research universities including Stanford University. The Stanford community generates 55% of the total revenue generated by the “Silicon Valley 150” companies.
Unfortunately, research at universities in India does not measure up to world standards. We do not have the critical mass of researchers to script technology revolutions or to exploit them fast. In my recent book Leading Science and Technology: India Next?, I find our productive researchers are 17 times fewer than in the United States and seven times fewer than in China. We rank 12th in research productivity, whereas America ranks first and China second. It is hard to point out a world-changing science/technology idea that has emerged from India in the last 60 years.
Without proximity to great research and the lack of a research-startup ecosystem, we lose out on becoming the first-comers. And even if we innovate once, we cannot be the best in a sustainable manner. Consider the example of artificial intelligence, or AI. Research led to the first disruption in the beginning of the 1990s and recently in 2006, deep learning came out of the University of Toronto. (Deep learning is an AI technique that uses advanced neural networks and has disrupted the fields of computer vision and speech recognition.) These techniques will completely change the industry. Facebook chief executive officer Mark Zuckerberg showed up at NIPS (Neural Information Processing Systems), one of the top AI academic conferences to attract researchers, in 2013. Google has an active research group in AI. This just shows the importance of AI and research in AI to businesses. And AI is going beyond “digital businesses” – call centres, banking, manufacturing, advertising, the list goes on. The new winners in these categories will be those who adopt artificial intelligence to continue to be the best.
We are not ready yet again. Where China and the United States are vying for the top spot in artificial intelligence research, our total AI research output in top conferences is less than that produced by a single Chinese university. We do not have the critical mass of trained AI personnel who can help our industry be competitive, let alone help it continue to be the best. Our companies face the grave risk of being edged out by the new winners – robotised manufacturing, automated call centres and servicing bots.
How to be a winner
The good news is that winners will emerge in narrow product and service segments. Today, businesses and continuous innovation in narrow segments has become viable as a result of global market access. A company offering just a surveying tool, a referral management system or a bulk emailing service can have revenues worth several millions. Even though the winner will edge everyone out in their area of specialisation, there will be a large number of such areas.
Great research in India is a necessary condition for our companies to be the winner in some areas. In addition, we need an ecosystem of startups vigorously collaborating with researchers and research institutions. We can build a real Silicon Valley if we have research universities of the calibre of Stanford University at the centre of such an ecosystem. Else, we will lose in the new innovation-driven world economy.
Varun Aggarwal is the author of Leading Science and Technology: India Next? (Sage Publishing) and co-founder of Aspiring Minds and www.ml-india.org. He has a Masters in Computer Science from MIT, USA.