Since the mid-80s, and the success of biotech, universities the world over have rushed to embrace “the third mission” of collaborating with industry to commercialise technologies created in university laboratories. Achieving success in this third mission, which stands separately from the first two missions of teaching and research, has become a mark of esteem which shows the social relevance of research done at universities, helping them to lose the tag of living in an ivory tower. We have seen this recently, in the case of Covid vaccines where a large number of candidate vaccines were in fact developed in university labs.
Drugs chemicals and biotech, are a somewhat specific group of technologies, that lend themselves easily to technology transfer via patents. This is because in these product groups, one molecule is in fact one product. Many other technologies of yore were used to manufacture one product (e.g. cameras, cars). For this reason, some scholars refer to these technologies as “discrete technologies”. Both ICT and more recently AI, belong to this latter group. ICT is often referred to as the general-purpose technology (GPT) while AI has more recently been referred to as an enabling technology (ET). In the general-purpose technology, the same principle can encompass multiple uses. For example algorithm written to classify subjects for libraries, can also be used for producing music libraries or video libraries. The reuse of these algorithms, does not in any way reduce the value of the original application. As Teece (2018) notes “The mass production, ongoing enhancement, and widespread use of digital logic circuits has led to revolutionary improvements in the technologies they enable, including computers, telecommunications gear, the Internet, and, most recently, wireless networks that can, for most practical purposes, rival wired networks for speed.” Industry 4.0 is based on such enabling technologies.
In commercialising GPT and ET, University scientists encounter two types of problems:
Furthermore, picking the most valuable application, for example through the setting up of a start-up company may not be straightforward s opinions on technology valuation can differ. University researchers may also collaborate with industrial partner to scope out new application areas or raise resources to set up research programs but the inherent tensions, described above, will need to be confronted in order to create win-win situations for both the scientists and the industrialists.
In the Indian context, further problems to University industry collaboration arise due to the very low R&D performed by Indian firms and the lack of an enabling environment for technology transfer. The low R&D of Indian firms is both a cause and consequence of their weak technological capabilities. The R&D of the firm not only contributes to the production of new goods and services but also builds the ability of the firm to search and find and adapt technologies appropriate for its use. Firms that have very low R&D will usually find it hard to absorb University technologies that are at a low level of technology readiness. They may also be unwilling to raise and invest the sums required for customising university science to the needs of their product market. In turn, this raises the prospect that public funding may be needed to support and subsidise such collaborative R&D. In order to address this issue, MEITY has in recent years, followed the universal public policy prescription of subsidising collaborative R&D
Much has also been done in recent years to create an enabling environment for technology transfer in India’s universities. Most institutes of technology today boast of IP policies comparable to the best universities, flexibility of work contracts and technology transfer offices. In recent published work, I show that these centrally determined enabling conditions may not be enough. Successful academic engagement is associated with departmental (not university) support. The industry experience of technology transfer offices officers is often limited but cooperation by academic peers in a department can help in many areas. These include picking out valuable application areas, making available research resources (such as PhD students and faculty research time) and sharing of industry networks.
One area specific to artificial intelligence, where India is particularly deficient is that there are no clear rules on data governance. As artificial intelligence is ruled by the data it is allowed to train on, it is imperative that rules surrounding the ownership, use and misuse of data are crystal clear and penalties strictly enforced. In the absence of capable domestic firms and an overall conducive environment for data regulation, the vast potential for knowledge transfer from Indian universities to industrial firms, who can create new digital products and services for an ever eager population, will sadly not be realised.
Author: Prof Suma Athreye