While technological innovation and disruption is a constant theme, every few decades we witness changes brought by a technology disruption that sets the stage for massive new opportunities while upending market leaders. In the 1980s, the disruption came in the form of the PC, which transformed the computing industry. For $2,500—roughly 1% of the cost of a mainframe—companies could now put computing power directly in the hands of their employees. Over the next decade, costs continued to plummet, processing power soared, and the Internet emerged, laying the groundwork for another disruption, in the 2000s: the WiFi-enabled smartphone. The new technology, priced around $300 to $500, triggered an avalanche of applications and transformed the way we live, work, and play.

Now we’re on the brink of the next major disruption: artificial intelligence (AI). AI will drive the cost of computing down in a manner similar to how PCs brought the cost of computing down back in the ’80s. For semiconductor companies that are early to recognize the revolutionary power of AI, the opportunities will be enormous.

The Rise of Intelligent Machines

Artificial intelligence is a complex topic, but at its core, it’s about simulating intelligent behavior in machines of all kinds. AI adoption will give all devices some level of computing power. AI is not new. However, the historical forms of AI have been passive tools that required human intervention; spreadsheets brought into existence by PCs are a crude example. Significant manual intervention is associated with the passive AI that has been developed over the years.

Today’s AI is active intelligence. Leveraging sophisticated machine-learning algorithms, AI-enabled computers can capture, analyze, and act on data in real time, and learn from that data to become more intelligent—all without the need for human intervention.

The concept of AI has been discussed and researched since the 1950s, but now it’s poised to become much more dominant thanks to the bending of the technology cost curve and the rapid advancement of the underlying technologies of the AI ecosystem—mobile and cloud infrastructure, sensors, processing power, and storage. These components are driving the next wave of AI, the Internet of Things (IoT). Connectivity, sensor data, and robotics—core components of the IoT—over time will make “dumb” devices intelligent, from cars and clothing to factory equipment. The development of neuromorphic chips, which mimic the way the human brain processes information, will further accelerate advances in AI. Qualcomm has already demonstrated a humanlike robot driven by neuromorphic chips,[1] and Nvidia, Intel, IBM, and Alphabet are among the other companies focused on this emerging technology [2]


AI’s Broad Impact

The focus of the PC was narrow; the technology targeted only business and personal computing. By contrast, AI can be applied to a broad range of products, services, and systems across sectors, from the consumer products and automotive industries to financial services and healthcare.

For the semiconductor industry, the scope of AI’s potential can be seen in forecasts for the burgeoning IoT market. PwC’s analysis of a compilation of semiconductor industry forecasts suggests that early applications of AI, in the form of IoT-related opportunities, will generate roughly $33 billion by 2019—almost 34% of the projected increase in industry revenues across all applications between 2014 and 2019.[3]


“The IoT is expected to drive a massive increase in connected devices and revenue growth across multiple industries”

Use Cases

Among the AI work underway today, one use case that gets ample media attention is self-driving cars. Major players across industries are jockeying for a position in this emerging market, from GM and Tesla to Uber and Google.

Industrial manufacturing is another active application area. Increasingly, factories are becoming more intelligent, driven by networks of AI-enabled devices and equipment that can monitor and optimize temperature and lighting, predict equipment failure, minimize factory downtime, improve communication across sites, and more.

The smart home is another use case. AI and machine learning are key components in the next generation of smart home devices, which will manage themselves rather than requiring homeowners to deal with the complexity involved (a problem that has stalled the smart home concept).[4]

Experiments in the emerging smart city concept demonstrate the breadth and scale of AI’s potential. One example is the “Connected Boulevard” of Nice, France, an intelligent network designed to optimize city management, from street lighting, parking, and traffic to environmental quality.[5] Theoretically, AI could be used to connect, monitor, and manage all of a city’s institutions, transportation systems, parks, hospitals and more in a single network, with the help of sensors embedded throughout the community.

These current use cases, and the many sure to follow, will drive growing demand for semiconductor products.


Looking Ahead

In the 1980s, the PC generated tremendous demand for memory and microprocessors, and Intel was a clear leader in benefitting from it. The emergence of AI will bring similar opportunities. Which incumbent leaders will benefit the most, and what new players will emerge? Only time will tell.

Success or failure will depend on whether company leaders have the vision required to identify and seize the new opportunities that AI presents. With global markets bigger and more connected than ever, and trade barriers lower, those who are early to embrace AI could see their successes accelerate rapidly.

Of course, there’s a risk of bringing innovations to market too early (think Apple’s Newton). But while perfect timing can’t be known in advance, the history of technology suggests one piece of advice worth following: if you see a compelling AI technology in the marketplace, don’t ignore it because you didn’t invent it. When Apple introduced the first touch-enabled smartphone in the form of the iPhone, some top competitors dismissed the innovation, but Samsung embraced it and soon became more dominant than Apple in the smartphone space.slide4

The winners will be those who understand the dynamics of the emerging AI-driven world and develop products best suited for it, regardless of what drove historical success. In 1985, Intel faced growing competition for memory chips, the technology on which the company was based and which still supplied most of its revenue. Andy Grove, then president, met with chairman and CEO Gordon Moore and asked: “If we got kicked out and the board brought in a new CEO, what do you think he would do?” Moore replied, “He would get us out of memory.” Said Grove: “Why shouldn’t you and I walk out the door, come back in, and do it ourselves?”[6] With that conversation, Grove famously shifted the focus of Intel from memory to microprocessors, enabling the company to capitalize on the disruptive technology of PCs, and setting the stage for its future success.

A Team Sport with Agility

In the 1980s no one could have fully envisioned the broad changes that PCs would bring to our lives—both additional opportunities and challenges—today no one can fully envision the broad changes that AI will bring to our lives over the coming decades.

Because of the broad range of potential applications of AI technology, we’re likely to see competing technologies, platforms, and solutions for different market segments.

As a result, to succeed, semiconductor companies must not only develop the right products but also the right partnerships and alliances. And most importantly, they must be willing to change their strategies and relationships if needed as the outlines of the future become clearer. As we’ve already seen, acquisitions may be required to fill AI capabilities gaps.

For example, to compete in IoT markets, Qualcomm acquired Cambridge Silicon Radio and Microchip Technology acquired ISSC Tech Corp.[7] There’s a particularly high level of activity in the area of self-driving cars, as tech companies and automakers, from Apple to Volkswagen, forge alliances and acquire startups to stake their claims in this fast-emerging market.[8]

For semiconductor companies, partnerships are essential to understanding the products and environments in which their chips will be used. Many emerging IoT applications come with serious real-world risks that must be addressed. For instance, to design chips for self-driving cars, semiconductor companies must understand the strict safety standards that automakers must meet. They will have to work effectively with OEMs and Tier 1 suppliers to ensure high reliability and service levels, and to eliminate the risk of recalls resulting from defective chips, which could cripple an automaker and potentially doom the chip supplier’s existence.[9]

Complete Solutions, Maximum Flexibility

In the technology future, as in the past, those who develop more comprehensive solutions are more likely to succeed. With that in mind, semiconductor companies may want to reassess their product portfolios and determine whether they need to create more vertical solutions or add services, to increase the odds of winning and profiting on a larger scale.

Flexibility also will be critical to success. Semiconductor companies should revisit their strategies and be ready to pivot if needed as the unpredictable technology environment evolves. Companies with the greatest agility in their roadmap and product portfolio, and the ability to shift gears rapidly, will reap the biggest rewards in a rapidly developing AI-enabled world.


[1]  Robert D. Hof, “Neuromorphic Chips,” MIT Technology Review. https://www.technologyreview.com/s/526506/neuromorphic-chips/

[2]  David J. Hill, “7 Key Factors Driving the Artificial Intelligence Revolution,” SingularityHub, August 29, 2016.http://singularityhub.com/2016/08/29/7-factors-driving-the-artificial-intelligence-revolution/

[3]  Raman Chitkara et al, “The Internet of Things: The Next Growth Engine for the Semiconductor Industry,” PricewaterhouseCoopers, May 2015. https://www.pwc.com/gx/en/industries/technology/publications/internet-of-things.html

[4]   Harriet Taylor, “How Your Home Will Know What You Need Before You Do,” CNBC, January 6, 2016. http://www.cnbc.com/2016/01/06/ces-smart-homes-of-the-future.html

[5]   Dr. Angelo Corsaro, Chief Technology Officer, PrismTech, “Connected Boulevard — It’s What Makes Nice, France a Smart City,” Industrial Internet Consortium, September 29, 2014. http://blog.iiconsortium.org/2014/09/connected-boulevard-its-what-makes-nice-france-a-smart-city.html

[6]  Andrew S. Grove, “Only the Paranoid Survive,” New York: Currency Doubleday, 1996.

[7]  Chitkara et al, “The Internet of Things” (see footnote 3).

[8]   Erin Griffin, “Who Will Build the Next Great Car Company?” http://fortune.com/self-driving-cars-silicon-valley-detroit/. A version of this article appears in the July 1, 2016 issue of Fortune with the headline “Some Assembly Required.”

[9] Raman Chitkara et al, “Spotlight on Automotive: PwC Semiconductor Report,” September 2013. https://www.pwc.com/gx/en/industries/technology/publications/semiconductor-report-spotlight-on-automotive.html

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