To Seize the AI Opportunity, Microchips Are Key
June 6, 2018

Artificial intelligence (AI) algorithms drive innovation and change across the business landscape. AI helps doctors diagnose and treat cancer. It powers personal digital assistants like Alexa and Google Home. It can do everything from training airplane pilots and air traffic controllers to detecting credit-card fraud, and from handling customer-service interactions to managing investment portfolios.

As remarkable as all that is, consider this: We’re just at the beginning of the AI revolution.

Take the automobile industry as an example. Today, AI senses other vehicles to help drivers avoid collisions and parallel park. In a year or two, we’re going to see the mass production of autonomous cars, each of them using AI to identify routes, recognize objects (think stop signs, road markings, other cars, pedestrians, a soccer ball bouncing suddenly into the street), and make appropriate driving decisions based on environmental and traffic conditions. Beyond tomorrow’s mass-produced autonomous cars, the ways transportation will evolve are anyone’s guess—but evolve it will, as demand evolves and AI technology advances.

What AI means for business

AI gives businesses the ability to develop offerings that are adaptive and autonomous, from virtual reality and augmented reality applications to internet of things (IoT) end points. It’s driving innovation and value creation across industry—from transportation to pharmaceuticals to agriculture to entertainment.

Of course, AI presents challenges as well as opportunities. AI-enabled offerings can help companies expand their bottom line by moving beyond mature existing lines of business, tapping into new markets and adding new, high-growth revenue streams. But the effort to build new lines of business brings with it a host of potential problems—for instance, how to market and sell to new audiences against a new set of competitors.

A salient example is Under Armour (UA). Originally a maker of athletic apparel, in recent years the brand has leveraged AI in wearable offerings to enable users to track their workouts; map workout routes; and measure and analyze sleep, fitness, activity, and nutrition. In the process it has expanded its target market into the health and wellness category. In addition, embracing the idea that it is now as much a technology company as an athletic-apparel company, UA has launched a platform allowing developers to build custom applications atop its AI-enabled offerings—adding value to UA products and potentially creating the kind of vibrant ecosystem that can lead to network effects and developer lock-in.

But at the same time as the company has created new business opportunities by leveraging AI, it’s also taken on some serious business challenges. Now, in addition to making apparel, it makes technology products—requiring it to take a new approach to everything from strategy to operational issues like hiring to process management. In addition to competing with Nike, Adidas, and the rest of its traditional competitive set, it competes against makers of wearables including Fitbit, Garmin, and Apple as well as with traditional players in the space such as nutritionists, weight-loss clinics, gyms, and yoga and Pilates studios. Beyond that, UA’s app-development platform introduces additional new strategic and operational issues—and introduces a new set of potential competitors that includes every company that provides developers with a platform for building apps, from Google and Facebook to Salesforce and SAP.

What AI means for semiconductor manufacturers

In the old days, the semiconductor business was cyclical. Semiconductors were needed mainly for business and scientific applications, and demand peaked predictably whenever a new generation of chips went to market. Only occasionally would a new technology drive demand for a new kind of chip.

Today, the situation is completely different. Demand is driven by an ever-growing and much wider range of applications, thus flattening the demand curve for semiconductors. And no innovation is broadening the need for semiconductors more than AI.

This is, of course, good news for semiconductor companies of all sizes. But to win in the AI-enabled future, chipmakers must plot their course carefully. Case in point: Given the momentum of autonomous-car development, it may be tempting for a midsize chipmaker to develop a chip for an automotive AI application; however, because automotive design cycles are so long, and since the cost of selling too few chips would be high, that would be a risky play.

Rather, smart, midsize semiconductor companies will look to serve niche markets, providing specialized solutions for AI applications where demand is healthy but not as broad as it is for applications using chips from big players like Broadcom.

And because enterprise procurement professionals will always prefer procuring a single system over the option of procuring its component parts, smart semiconductor companies will shift from delivering chips to delivering more complete solutions. Promising AI startups like Horizon Robotics and krtkl—not to mention big companies including Intel, Microsoft, Apple, and Google—are already doing just that.

Partnering with Canyon Bridge to seize the AI opportunity

Canyon Bridge helps midsize, small, and early-stage technology companies identify opportunities and drive business in major global growth markets, including China and other Asian markets—markets where demand for AI applications is skyrocketing. Our portfolio companies benefit from our extensive relationships and deep knowledge of Chinese and other Asian technology and financial markets. Indeed, we have as many professionals on the ground in China as we do in the United States. Our relationships and expertise enable us to raise capital and open new business channels in markets that can otherwise be difficult for them to enter.

Learn more about how we invest.


Ray Bingham
Co-Founder and Partner
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