The Top 3 Most Powerful Technologies Driving Economic Growth in the 4th Industrial Revolution, Part I
The past three industrial revolutions allowed the economy to take giant leapfrogs forward. Each revolution in turn generated economic impacts that were greater and faster.
Steam engine technology was the catalyst for the first revolution in the 18th to 19th century. The economic effects of this “tech enabler” rippled across 100 years. Together, the steam engine and the railroad enabled large-scale distribution. Using Bradford DeLong’s Gross World Product (GWP) methodology (see footnote), the first revolution multiplied economic value by 2.8x. In constant US dollars, $175 in the year 1750 increased to $360 in 1850.
The second revolution at the turn of the 20th century witnessed factory technology, with large-scale manufacturing and access to large-scale electricity, which enabled mass production. In only 45 years, economic value expanded from $569 of GWP in 1875 to $1,734 by 1920. In less than half the time it took for the first revolution, economic value for the second revolution grew by 3.0x.
The microprocessor or computer chip was the third industrial revolution’s catalyst. Now cast in a global economy, this tech enabled personal computers, the Internet, and social media. With 1970 as its starting point and 2005 as its apex, economic value of $12,100 GWP expanded to $43,000 in 35 years. Again, greater economic expansion is compressed into a shorter period of time.
In today’s fourth industrial revolution, driven in part by the mainstream adoption of the smartphone, there has been explosive proliferation of massive datasets or “Big Data”. In actuality, there are more than a handful of emerging technologies enabling the fourth revolution, all of which have advanced very quickly in only a few years. However, we will focus on the top three most powerful enablers: Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain. Interestingly, these leverage and capitalize on Big Data.
Artificial Intelligence (AI)
AI is the intelligence of machines vs. the natural intelligence of human beings. Of the top three power enablers of the fourth industrial revolution, AI has its roots furthest back in time. Conceived in the 1950s, and after a few booms and busts, AI finally began to show up in the mainstream with noticeable milestones. Examples include IBM’s Deep Blue beating chess master Garry Kasparov in 1997 and IBM Watson winning Jeopardy in 2011. Such moments began to make AI more of a household word and gave us a pathway to thinking about its potential.
There are a few different types of AI systems, which include machine learning, natural language processing, and robotics. All serve different purposes, but the common theme is that each AI system needs to ingest large amounts of data to provide meaningful feedback. This collective data, Big Data, is growing at an exponential pace. In terms of economic value, International Data Corporation (IDC) expects revenues from Big Data and Business Analytics to be $189.1 billion by the end of 2019 (12% growth in one year). By 2022, that revenue is forecasted to grow to $274.3 billion (45% growth in three years). In terms of data itself, in 2020 alone it’s projected to grow to 40 Zettabytes (40 trillion Gigabytes). This is over 5,000 Gigabytes (GB) of data per person globally. 1 GB is 1 billion bytes. On its own, revenues from AI are expected to be $35.8 billion in 2019 and increase to $79.2 billion in 2022 (by 121% growth in three years).
In our personal lives, AI has brought many conveniences. Amazon has made us very familiar with one of the most popular uses of machine learning, personalization & recommendation. Most of us have seen the message “you might be also interested in….” The system needs machine learning as a way to analyze people’s behaviors, both individual actions and general populations. Otherwise, it would be nearly impossible to make recommendations in real time. And, if the systems were to recommend something not of interest, people would stop paying attention. When machine learning works well, it can even make recommendations that shoppers did not realize they’d wanted. Beyond personalized advertising, we are so accustomed to this type of personalization & recommendation, it would be hard to imagine our world today without it.
Natural language processing (NLP) has several applications. We may not associate some of them with the idea of what we know as “AI”, but most people are already either a user or a benefactor of the technology on a day-to-day basis. Cloud email systems such as Gmail already determine which emails should be filtered without a user having to set any rules. People can travel abroad without knowing a word of the country they’re visiting thanks to instant translation services online. And email is also starting to suggest phrases and words to complete sentences based on how they begin. Transcription service is becoming a big business as well.
Lastly, the robotics aspect of AI is becoming more science fiction-like with the introduction of technologies like chatbots and autonomous driving vehicles. Google introduced its self-driving car project in 2008 and has since developed a car without a steering wheel, brake or accelerator pedals. In Tokyo, autonomous taxis have already been operating on a restricted basis since the summer of 2018, and anticipated to extend service in advance of the 2020 Olympics. This is a first in the world of smart-mobility, and will inevitably be followed suit by other countries. Another application is online help robots. And all robotics need access to Big Data, which is used to “train” or teach human behaviors. Subsequent applications will only become more complex as we go on.
Internet of Things (IoT)
IoT is a technology that allows us to collect data from a number of objects with an embedded Internet connection. Since this is used in industrial machines, devices and goods, and with a large variety of use cases becoming widely adopted, the market size potential of IoT is huge. Worldwide spending on IoT is forecasted to reach $745 billion in 2019 from $646 billion spent last year (an increase of 15.4% in one year). IDC expects worldwide IoT spending will maintain a double-digit annual growth rate throughout the 2017–2022 forecast period and surpass the $1 trillion mark in 2022. When we consider that global GDP in 2018 was $80.7 trillion, we begin to get a sense of the magnitude of this technology.
Examples of current industrial use case applications are predominantly in supply chain and manufacturing sectors, which have been early leaders in utilizing the technology. Considering IoT capabilities like remote monitoring and tracking, this makes a lot of sense. In the past, if a cargo sender wanted to know where their cargo was, tracking activities had to go backwards to the beginning of the chain of custody. And even then, it may not have been 100% accurate. But today, a shipment’s location can be tracked down in just a few minutes, maybe even in a few seconds. Or, in the assembly line, a known bottleneck point can be continuously monitored in real time, allowing for easy adjustments for an efficiency-optimized manufacturing process. Both of these are reducing “supervisory administration” responsibilities by automating some of the most time-consuming and labor-intensive work. This is amazing progress for a technology that wasn’t even recognized by most people until 1999. However, the significant components that make up IoT — machines talking to machines, and even the Internet itself — were in development for years, if not decades, before IoT emerged. Even more, data that is created by IoT also contributes to Big Data, which in turn feeds into AI for analysis as appropriate.
At this time, the consumer side of IoT isn’t as entrenched into day-to-day tasks as industrial uses. Since much of industrial use is behind the scenes for manufacturers to collect data valuable for their own use, there’s minimal apparent benefit to consumers. For example, today’s computer-assisted cars send hundreds of data points to manufacturers, but consumers don’t see any personal benefit from that data yet. Potentially, in the future, auto manufacturers could share this data with auto insurers to offer personalized insurance policies. Or, the data could be used to recreate a warranty for auto parts. There are glimmers of potential consumer benefits from the telematics field, where commercial transportation fleets already use remote sensors, maps and road routes for safety and compliance. With advanced telematics companies like CCC Information Systems using AI and IoT to bring together insurance, auto parts, and collision repair industries at the enterprise level, consumers could be next to benefit. In the future, this data could even be used to make personalized recommendations at the point of sale based on driving history.
Imagine if refrigerator manufacturers could warn owners a few weeks before their machines break down, this could save tons of food waste. But such use cases aren’t yet visible on the horizon. Since there are major manufacturers like Samsung, LG, and Whirlpool already making “smart home” refrigerators with embedded cameras and apps for remote access, this could change quickly as competition to offer better products heats up.
Looked at from a sustainability perspective, data collected from solar panels could be used to determine optimal energy use. And, thereby eventually allow humans to move away from fossil fuel energy as solar energy could be incorporated efficiently into our daily living. While this too feels like decades away, emerging companies like Span.io are focused on driving adoption of efficient energy systems via IoT’s distributed network of connected devices and managing software. Such players in the energy IoT sector could accelerate consumer level adoption of solar or other renewable energies.
Critically, all of these IoT developments are not in regulated fields like financial services or healthcare data and have considerably less data security built in. Accordingly, IoT devices do provide avenues for hackers to penetrate into our daily lives all the same. If we were compromised, our lives would be disrupted just as much as if our financial and other private data were jeopardized. We’ll address cybersecurity on a separate article.
Uses of Big Data will benefit all of us eventually, but we need to be mindful of the risks and benefits that new use cases will bring. Governance, therefore, is a critical piece in a product development.
Blockchain
Blockchain is even younger than IoT. It is a shared immutable ledger that facilitates the process of recording transactions and tracking assets, tangible and intangible, in a network. Blockchain has its roots in cryptography, a long-existing technology that constructs and analyzes protocols to prevent third parties or the public from being able to read private messages. But blockchain technology as we know it today was introduced to us first by Satoshi Nakamoto in 2008 in the form of Bitcoin. Considering it has existed a mere 11 years, it’s remarkable how much we hear about this almost daily. Bitcoin’s wild swings in value, at least partially, have contributed to this level of public attention. Bitcoin went up as high as $20,089 on December 18, 2017 and has since moved up and down by eyebrow-raising amounts.
Blockchain is still new and emerging. Its use cases are not as readily apparent nor commonly known, or even broadly accepted. Understanding there are still skeptics who might question the wide application of this technology, in part two of this paper, we will break down the value components of blockchain.
FOOTNOTE:
In 1998, economic historian J. Bradford DeLong published an estimated Gross World Product (GWP), which used 1990 constant value US Dollars.
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