I came across some recent charts about the growth of these two unrelated sectors, one disrupting manufacturing, the other disrupting software of all types (click to enlarge). On one hand, each chart commits the common error of portraying smooth parabola growth, with no range of outcomes in the event of a recession (which will surely happen well within the 8-year timelines portrayed, most likely as soon as 2017). On the other hand, these charts provide reason to be excited about the speed of progress seen in these two highly disruptive technologies, which are core pillars of the ATOM.
This sort of growth rate across two quite unrelated sectors, while present in many prior disruptions, is often not noticed by most people, including those working in these particular fields. Remember, until recently, it took decades or even centuries to have disruptions of this scale, but now we see the same magnitude of transformation happen in mere years, and in many pockets of the economy. This supports the case that all technological disruptions are interconnected and the aggregate size of all disruptions can be calculated, which is a core tenet of the ATOM.
I have recently come into contact with a few professionals in transition, many from the now-shrinking big semiconductor companies. In speaking to them, one thing that stood out is how it takes them 9-12 months or more to secure a new position.
Why is this the case, in an age of accelerating technological progress, as per the ATOM? This is an instance of where culture has prevented the adoption of a solution that is technologically feasible.
Where Cultural Inertia Obstructs Technology : Before the Internet age, if you wanted to research a subject, you had to go to the library, spend hours there, check out some books, and go back home. Overall, this consumed half a day, and could only be conducted during the library's hours of operation. If the books did not have all the information you needed, you had to repeat this process. Even this was available only in the dozen or so countries that have good public libraries in the first place. But now, in the Internet age, the same research can be conducted in mere minutes, from any location. The precision of Google and other search engines continues to improve, and with deep learning, many improvements are self-propagating. There is a 10x to 30x increase in the productivity of searching for information.
If you feel that this example is imprecise, take the case of LinkedIn. It has enabled many aspects of career research and networking that were just not possible before. If a young person wishes to explore dozens of career paths and estimate common patterns, the utility of a certain degree, or the probability of reaching a certain title, LinkedIn has an endless supply of information and people you can identify and communicate with.
Yet despite all of this, job searches are just as lengthy as in the days before the Internet, LinkedIn, and other resources. If a candidate can match with three potential jobs in their search region at any given time, then the connection between employer and candidate should take mere weeks, not close to a year. There is no other widespread transaction within society that takes anywhere near as long. Despite new apps to organize the job search and new social media outlets that announce endless meetups and networking events, technology has clearly failed to generate any productivity gains in this process.
For one thing, the Internet has reduced the marginal cost of an application to so little that each position receives hundreds of candidates, unlike three or four back when paper resumes had to be sent via the US Postal Service. To cope with this, employers use software that searches resumes for keywords. This method selects for certain types of resumes, with keyword optimization superceding more descriptive elements of the resume, and filtering out many suitable candidates in favor of those who know how to game the keyword algorithm.
From this point, a desire to mitigate hiring risk combined with the lack of imagination inherent to most corporations defaults into a practice of increasing the number of interviewers that the candidate faces. Three rounds and a dozen interviews is not uncommon, but by most accounts, job interviews are nearly useless as predictors of performance. In reality, a candidate only needs to be interviewed by three people : the hiring manager, the manager above that, and one lateral peer. If these three people cannot make an accurate assessment, adding several other interviewers is not going to add additional value. Indeed, if the boss's boss cannot make accurate assessment of candidates, then they are failing at the primary skill that an executive is supposed to have. Reference checks are also a peculiar ritual, as a candidate will only submit favorably disposed references who have been contacted beforehand.
Modernizing Hiring For the Information Age : Matching openings with candidates should not be so tedious in this age of search engines, emailed resumes, and LinkedIn. Resistance to change and a miscalculation of risk and opportunity cost are the human obstacles standing athwart favorable evolution.
To correct this obsolete situation, consider the mismanagement that occurs at the source. Only after a hiring manager sees a persistent and pronounced need for additional personnel does the process of getting a requisition approved and advertised commence. Hence, the job begins to receive resumes only several months after the need for a new hire arose. After that point, the lengthy selection and interviewing process takes months more.
Instead, what if the Data Analytics of a corporate setting could be gathered, mined, and processed, so that the AI identifies a cluster of gaps within the existing team, and identifies suitable candidates from LinkedIn? Candidates with the correct skillset could be identified with a compatibility score such as '86% fit', '92% fit', and so on. The entire process from the starting point of where a team begins to find itself understaffed to when a candidate deemed to be an acceptable fit is hired, can compress from over a year to mere weeks. The hefty fees charged by recruiters vanish, and the shorter duration of unemployment reduces all the indirect costs of extended unemployment.
For this level of dynamic assessment of gaps and subsequent candidate mapping, the capability of search and data analytics within a corporation has to evolve to a far more advanced state than presently exists. Emails, performance reviews, and project schedules, etc. all have to be searchable across the same search and patterning capabilities. Then, this has to interface with LinkedIn, which itself has to become far more advanced with the capability for a candidate to continuously re-verify skills and prove certain competencies (through tests, certified courses, etc.. The platform has had no real improvement in capabilities in the last few years, and the obvious next step - generating a complex set of skill parameters for LinkedIn members, and matching that pattern to employers with similar needs, is quite overdue. If this seems like added work for candidates, remember that this effort is far less than the amount of time and hassle it will save in the job search process.
But eventually, with technology companies leading the way, the massive hidden cost of current hiring practices may come to light, and give way to a system that uses AI to find more precise matches with much greater speed.
Conclusion : We now possess the machine learning capabilities to dynamically detect gaps within corporate teams and organizational structures that may be large enough to warrant an increase in headcount. These gaps can be matched with parameters that can be mined from LinkedIn profiles, and provide candidates with an assessment of their approximate fit. A percentage score calculated for each candidate is not only a more accurate indicator than the very imprecise interview process, but is far quicker as well. It is high time that these tools were created by LinkedIn and others, and that corporate culture shifted towards their adoption.
This application of AI is the second most necessary technological disruption that AI can deliver to our civilization at present. For the first, check back for the next article.
I do not have the time to pursue a company built around this type of machine learning product, but if someone else is inspired to take up this challenge, I would certainly like to be on your board of directors.