Where do various biotechnologies exist on the hype cycle for academic research and industrial research /manufacturing?

To start with, the Hype cycle is an entertaining but inaccurate model so to accept it unquestioningly is itself a problem.

As far as excessive or overenthusiastic promotion of these various biotechnologies is concerned, to add to Ian Welland's summation, Immunotherapy, and specifically Cancer Immunotherapy, currently seem to be coming off of peak hype, given the back-to-back reports of clinical trial deaths from Juno Therapeutics and ZIOPHARM Oncology in early and mid-July 2016, respectively.

Exploring the question's assumptions is a more interesting exercise.

• First, this wording, 'What changes need to be made to reach the plateau of productivity?' suggests a problematic assumption, that not only might a generic tool-kit exist but much like a magic wand, could be easily deployed to any over-hyped technology and so doing, could in short order make the process of its widespread adoption submit to rationality and render it more efficient. Doesn’t such an excessively technocratic approach contradict the way human societies really operate, which on the contrary reveal themselves to be much too haphazard and unpredictable? After all, were the latter not the case, would hype even exist?
• Next, the unquestioning acceptance that the hype cycle 'model' accurately represents how societies adopt technological innovations is also problematic. Where's the proof the hype cycle's even accurate?
• The problem with the hype cycle is it combines two separate phenomena, one, the fallible human tendency to fetishize novelty and all that entails such as contagion, excessive enthusiasm, speculation, in short, hype, and two, the classic S-curve that describes the Diffusion of innovations.
• In a recent meta-analysis of empirical studies on the hype cycle (1), Dedhayir and Steinert emphasize that one of its key weaknesses is it melds these two disparate phenomena that measure different outputs, and artificially forces on them the same y-axis parameter, namely, visibility (or expectations).
• The human hype-centric cycle typically assesses enthusiasm for the new technology. How to measure enthusiasm? This remains unclear since the hype cycle measures it using two different y-axes interchangeably, namely, visibility and expectation, which have different operational definitions. While visibility is defined as 'technology presence rate on media channels, conversations as well as in interpersonal conversations', expectation is defined as 'expected future value of an innovation' (1).
• Meantime, the S-curve for assessing diffusion of innovations measures neither enthusiasm nor visibility nor expectations but rather the innovation's rate of adoption.
• No wonder then that the resulting model doesn't have a track model for successful predictions which is what this recent meta-analysis also suggests (see below from 1).

A Meta-Analysis Suggests The Hype Cycle's A Flawed Model Found In <50% Of Empirical Results

Bibliography

1. Dedehayir, Ozgur, and Martin Steinert. "The hype cycle model: A review and future directions." Technological Forecasting and Social Change 108 (2016): 28-41. https://www.researchgate.net/pro...

https://www.quora.com/Where-do-various-biotechnologies-exist-on-the-hype-cycle-for-academic-research-and-industrial-research-manufacturing/answer/Tirumalai-Kamala