A Significant Economic Trend
In the book titled, “The Second Machine Age”, Brynjolfsson and McAfee, citing the social development theory, posit that the digitization process will continue to have major economic impacts on productivity globally. They oppose the view held by most economists, namely, that economic growth has stalled in the digital era, arguing that digitization has led to a surge in productivity. They present a number of reasons to support their position.
In their view, through digitization, a broad range of new, inexpensive, and effective products are now available in the economy. In particular, free digital goods, such as texting/chat services (Apple’s iChat) and calling applications (Skype) have improved human wellbeing. However, the conventional methods used to calculate the gross domestic product (GDP) do not capture the benefits of free digital goods. Thus, GDP should capture aspects of digital data like volume, usefulness, and form, which improve human wellbeing.
Another argument fronted to support the economic impacts of digitization relates to the growing income inequalities. Income disparities between people are widening because of reallocation of resources occasioned by digitization. The authors point out that applications like Facebook and Instagram have far fewer employees than traditional firms like Kodak (Brynjolfsson and McAfee 65).
Thus, digitization has changed income and wealth acquisition dynamics whereby skilled individuals benefit from the innovations while those with lower skills do not. In their view, important assets in the digital era include training, intellectual property, and ICT skills (Brynjolfsson and McAfee 81).
The authors argue that technology is not meant to substitute workers, but to revolutionize work systems to increase efficiency. Thus, while the demand for a lower-skilled workforce has declined, it has increased for skilled workers. People who will benefit from this technological shift are those who can develop digital goods that can serve millions of users.
Data that Prove the Authors’ Position
The authors cite a variety of sources to support their claim that the digitization process has led to growth in economic productivity. They note that while the median income of an average US citizen fell by 11 percentage points, the income of billionaires in the Forbes list grew four times in the past decade (Brynjolfsson and McAfee 87).
This indicates that the technology shift has enabled entrepreneurs to rake in economic profits by creating on-demand digital goods. The authors cite statistics to indicate that life expectancy is low among people without a high school diploma because they earn low wages. These statistics affirm their view that the digitization process has led to a surge in productivity, which, however, benefits entrepreneurs with requisite skills.
The authors also provide a detailed history of technological unemployment that came with the industrial revolution. During this era, machines took over lower-skilled tasks, leading to a rise in unemployment among lower-tier employees. However, according to the authors, digital devices enhance workflow efficiency, which translates into improved productivity. They note that unemployment reflects our inability to develop products of economic value to the population.
They write that although the benefits of technology may not be felt due to rising unemployment rates, improvement in computing power will transform the global economy. According to the authors, in the next two years, computing power will rise a thousand times. This information indicates that, in the short-run, technological advancements will continue to bring economic benefits for entrepreneurs and investors.
One notable example that the authors cite to support their argument is the analog versus digital photography. According to Brynjolfsson and McAfee, digitization has changed the “economics of photography in terms of production and distribution” (64). Instagram, an app with only fifteen founders, attracted over 230 million users sharing over six billion photos before Facebook acquired it at a cost of a billion dollars (Brynjolfsson and McAfee 66).
Similarly, Facebook with only 4,600 staff members has over a billion users globally. In contrast, Kodak had an elaborate distribution channel with over 145,000 employees before it went bankrupt a few years ago. This example illustrates how digital machines have led to an upsurge in productivity and profitability for companies.
Interesting Aspects of Business Intelligence
Brynjolfsson and McAfee provide interesting insights into the power of ‘intelligent technologies’. The capabilities of intelligent machines are so impressive that they perform astonishing tasks. One aspect of intelligent machines that I found interesting is their real-world capability.
Technologies used in driverless cars, drones, and robots mimic human abilities. They use sensors and algorithms to enhance precision and efficiency. I find this feature interesting because with further refinement the machines will be able to do human tasks and thus, replace certain employee types. Automation of such jobs will have far-reaching effects on the labor market. Employees with lower-level skills will become redundant as employers acquire intelligent machines that are more efficient and faster than humans are.
Another interesting fact about business intelligence is its ability to predict through ideation. This extends the creativity of intelligent machines beyond that of humans. I find this interesting because intelligent machines will soon have abilities thought to be in the domain of science fiction. Another interesting technology is the Massive Open Online Courses (MOOCs). This correspondence program “provides an interactive forum between students, professors, and tutors” (Masters 133). MOOCs allow students to learn in a virtual classroom.
Business intelligence has also revolutionized telecommunications. Of interest to me are the networking applications, such as LinkedIn, that employers rely on to match job requirements with employee skills. Firms such as HireArt and Knack use matching algorithms with high precision capabilities to extract specific employee information from databases.
In my view, such programs offer immense opportunities for employees to seek positions that best match their skills and training. Additionally, employers can search for workers with certain specialized skills that they need.
Automation is another key aspect of business intelligence innovations. Robots like Roomba, which runs on artificial intelligence, can do automated tasks such as vacuuming a floor. Robots can perform human tasks that involve repetition efficiently, hence useful in assembly line systems. In the future, robots will become programmable, allowing them to do different tasks. In my view, this enhances engineering and manufacturing efficiency, leading to reduced lead times and better quality products.
Intelligent machines also have digital sensors, making them useful in treating medical conditions. An example is an Orcam system, which was developed to improve the vision of the visually impaired. It has sensor and programs that mimic human vision. A computer that is linked to the sensor processes the image observed and tells the individual its meaning.
I find this interesting because, with further refinement, visual and hearing aids for the blind and the deaf will become a reality. The innovations use sensors and machine learning algorithms trained to mimic the human senses.
Examples of “Crowdsourcing” or Collaboration in the Book
Crowdsourcing is a process of networking or recombining technologies to enhance computing power by involving many individuals or groups in solving a problem. An example given in the book is NASA’s solar flare prediction technology. To enhance the accuracy of solar flares or solar particle events (SPEs) prediction, NASA developed an open source technology called the ‘innocentive’ that allowed different people, irrespective of their expertise, to access and use its data to solve the problem (Brynjolfsson and McAfee 40). As a result, an engineer, who was not an employee of NASA, developed a highly accurate prediction method for SPEs.
Another example of crowdsourcing is Kaggle, an online recombinant innovation that focuses on data-intensive tasks (Brynjolfsson and McAfee 41). Kaggle’s crowd helps improve baseline datasets provided by organizations. It presents contests to professionals who develop accurate prediction methods based on baseline data. For instance, based on car characteristics, Kaggle users were able to develop a refined method for predicting the likelihood of a compensation claim being launched against an insurance firm. Kaggle’s crowd has also been able to predict hospital readmission rates.
Another open-source firm, Quirky, recruits people with different expertise to “generate ideas for new consumer products” (Brynjolfsson and McAfee 41). The crowd develops innovations based on research and recommends improvements, which Quirky implements to boost its sales. Another example of a crowd given in the book is Affinnova.
The firm relies on crowdsourcing and prediction algorithms to select the best combinations or designs from a broad range of candidates. It uses modeling to determine the optimal combination of options. Carlsberg breweries used the Affinnova crowd to determine the optimal combination of qualities for its beer products.
Overall, the book is detailed and elaborate. I would recommend the book to other students because it provides good insights into the digitization process. It highlights key developments in the ICT sector, hence a useful material for learning.
Brynjolfsson, Erik and Andrew McAfee. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W. W. Norton & Company, 2014. Print.
Masters, Ken. “A brief guide to understanding MOOC’s”. The Internet Journal of Medical Education 1.2 (2011), 132-141. Print.