Today’s post marks the 1000th entry made here on
Terahertz Technology. I started this
blog almost exactly four years ago, in April of 2009 to attempt to “spread the word” about this exciting,
emerging technology. Please note, I don’t have any delusions about the
significance of the fact of 1000 posts here, to anyone but myself. Nobody is here to read my comments,
or my thoughts. Those that do frequent this blog, know that I do attempt to
locate, and repost “cutting-edge” information about the development and
maturation of THz. There are very probably many tens or hundreds of thousands
of 3rd graders who could perform this function, at a higher level,
but I do my best.
Ultimately, this
blog, has been my effort at self-education, and frankly, I’ve learned more than
I would have ever dreamed possible. My greatest education has come in learning
that investing in emerging technology is very, very, very, risky, and is not
for the faint of heart. If you are a retail investor in terahertz, then may the
Gods smile upon you, for you are brave of heart, (or perhaps foolhearty).
I thought the two posts on this blog yesterday were apropos
to this milestone, as they each reflect how far both the technology and this
blog have advanced over the last four
years, and cause me to seriously wonder if this ‘virgin” technology is finally
moving from the Gartner cycles, trough of disillusionment, to the slope of
enlightenment which hopefully will now proceed robustly into the plateau of
productivity. At least we can dream,
this is so.
The news about Advanced Photonix, (API) “breaking” into baggage
inspection for the Chinese airport industry
was particularly newsworthy. The story not only revealed a fifth (perhaps
sixth) iteration, of the API “T-Ray” application, but also demonstrates that
API maintains a strong foot-hold, and sizeable lead, (in my opinion), in moving
THz from the laboratory to the factory floor.
API has sold applications for use, in a variety of areas in the industrial
market for inspection and quality control in the nuclear gauge industry for plastic,
paper, as well as quality control
of nutraceuticals, and now airport baggage inspection.
I also featured the very “generous” comments of Dr. Mona
Jarrahi, which reflects another reason people read this blog, which is because
I do try to find out information about THz companies, products and industrial
development. I learned a great deal from Dr. Jarrahi’s comments, and
particularly appreciated her “take” on how CMOS applications involving THz,
continue to be limited.
Of course, much of the investment communities experience of
the “trough of disillusionment” also springs directly from unrealistic expectations
and a tremendous lack of understanding back in 2007, regarding the maturity of the technology, as a
result of API’s early success in 2007, by selling the very first commercial
application of it’s T-Ray 2000, to NASA for inspection of the heat tiles on the
space shuttle. Many thought this would open the door to immediate acceptance of
the technology onto many factory floors and in diverse areas, most notably in pharmaceutical
inspection, and quality control.
It didn’t happen that way, and the maturation process was
not made any easier by the global financial and market collapse of 2008, which
continues to haunt this technology niche. The premise of this post, is that if
you were an early investor like me, you are either very brave, a little crazy
or perhaps both.
To those of you, cheers!
The technology adoption
lifecycle, and variations or refinements of it, appear to have sprung from work
relating to purchase patterns of hybrid seed corn.
The technology adoption
lifecycle is a sociological
model developed by Joe M. Bohlen, George M. Beal and Everett M. Rogers at Iowa State University,[1]
building on earlier research conducted there by Neal C. Gross and Bryce Ryan.[2][3][4]
Their original purpose was to track the purchase patterns of hybrid seed corn
by farmers.
Beal, Rogers and Bohlen together developed a
technology diffusion model[5]
and later Everett Rogers generalized the use of it in his
widely acclaimed book, Diffusion of Innovations[6]
(now in its fifth edition), describing how new ideas and technologies spread in
different cultures. Others have since used the model to describe how
innovations spread between states in the U.S. [7]
The technology adoption lifecycle model
describes the adoption or acceptance of a new product or innovation, according
to the demographic and psychological characteristics of defined adopter groups.
The process of adoption over time is typically illustrated as a classical normal distribution or "bell curve."
The model indicates that the first group of people to use a new product is
called "innovators," followed by "early adopters." Next
come the early and late majority, and the last group to eventually adopt a product
are called "laggards."
The report summarized the categories as:
- innovators – had larger farms, were more
educated, more prosperous and more risk-oriented
- early adopters – younger, more educated,
tended to be community leaders
- early majority – more conservative but
open to new ideas, active in community and influence to neighbours
- late majority – older, less educated,
fairly conservative and less socially active
- laggards – very conservative, had small
farms and capital, oldest and least educated
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