How to cite this paper

Kay, Michael. “Why are some technologies more successful than others? And why are my predictions usually wrong?” Presented at Balisage: The Markup Conference 2024, Washington, DC, July 29 - August 2, 2024. In Proceedings of Balisage: The Markup Conference 2024. Balisage Series on Markup Technologies, vol. 29 (2024). https://doi.org/10.4242/BalisageVol29.Kay01.

Balisage: The Markup Conference 2024
July 29 - August 2, 2024

Balisage Paper: Why are some technologies more successful than others? And why are my predictions usually wrong?

Michael Kay

Saxonica

Michael Kay’s 50 years in the IT business falls neatly into two halves. The second half, falling into the current century, will be familiar to many members of the markup community; since the dawn of XSLT in 1999 he has been leading standards efforts in W3C alongside the development of the Saxon software library. His previous role, in a previous century, was less visible and is less well documented: after a PhD in database research at the University of Cambridge in 1975, he joined the British mainframe manufacturer ICL (since absorbed into Fujistu) where he became responsible for mainframe database software products, subsequently being appointed an ICL Fellow, in which role he was often asked to give advice to the company’s management on competing demands for technology investment. This talk will therefore draw on a wide range of experience with a wide range of technologies, and on extensive experience of backing the wrong horse.

Abstract

If asked what made XML successful, I could come up with many answers, ranging from ease of implementation through availability of support tools to dumb luck and good timing. But if, back in the day, I had been asked will the World Wide Web take off, I would have given the wrong answer: I could see the positive things it shared with current succesful technologies, like SGML, but I couldn’t have predicted the rise of TCP/IP that made the Web feasible. It’s not too hard to recognize the importance of low cost or potential benefits of promising technologies but much more difficult to predict the effects of timing or the endorsement of trusted influencers. And it is most difficult for us to see our own strong inbuilt biases that make prediction risky.