Machine learning and artificial intelligence (AI) can evoke reactions ranging from Elon Musk's "summoning the demon" claims to Bill Gates' likening it to the "holy grail" of computing.
Either way, machine learning and AI software represent one of the most impressive growth opportunities in tech today. Case in point: Researcher IDC sees AI and machine-learning software sales growing from a standing start into a $47 billion market by 2020.
Investors want to know how they can cash in on this big-ticket trend. So let's review why dividend stocks such as International Business Machines (NYSE:IBM), NVIDIA (NASDAQ:NVDA), and Microsoft (NASDAQ:MSFT) offer investors compelling opportunities to invest in the rise of machine learning.
International Business Machines
IBM launched its Cognitive Systems business lineup in 2011, the same year the company's Watson beat the all-time greats of the game show Jeopardy! Now the company's advanced learning platforms are becoming an increasingly important piece of Big Blue's business.
What most impresses me regarding products like Watson is their tremendous profitability. Cognitive Solutions enjoys by far the most lucrative top- and bottom-line margin structure of any of IBM's reporting segments. For comparison, IBM produced gross margin of 51% and pre-tax income margin of 24% in Q4 2016, which falls far short of the 82% gross profit margin and 38% pre-tax profit margin Cognitive Solutions generated.
IBM shares currently yield an impressive 3.2%, far higher than the S&P 500's current 1.9% payout. The company has earned its long-standing reputation for capital return excellence; it has spent $108 billion on share repurchases alone since the turn of the millennium. As such, investors today can get paid to wait as IBM continues to lead the machine-learning revolution.
Graphics processing unit maker NVIDIA is one of a handful of component makers that produce the hardware that helps support most machine-learning technologies. As the go-to standard for graphics processing chips, NVIDIA possesses a technological leadership in graphic computing that places it in an enviable position for long-term growth. Consider that a research note from Goldman Sachs estimated that NVIDIA's AI opportunity could eventually reach $10 billion. Investors need to understand, though, that capturing this potential will require years and that sales related to machine-learning revenue totaled only $840 million during the company's most recent fiscal year.
As with its revenue growth story, NVIDIA's dividend payments will take years to grow into substantial sources of income. In fact, NVIDIA shares currently yield just 0.6% today. The company only began paying its dividend in 2012, but it has increased its full-year dividends per share every year since then. Moreover, the company's squeaky-clean balance sheet -- to say nothing of its widely expected long-term growth story -- gives the company a stable platform to steadily increase its payouts in the years ahead.
In line with other major suppliers of cloud computing, Microsoft has moved quickly to integrate machine-learning capabilities into its Azure cloud platform. It remains unclear to what degree customers have used Azure's machine-learning services -- Microsoft didn't detail this figure in its most recent report -- but the momentum behind the company's overall cloud-computing efforts is obvious. Azure revenues more than doubled during Microsoft's most recent fiscal year, and Microsoft expects cloud-computing sales to reach an annualized run rate of $20 billion by the end of its fiscal 2018. (Microsoft's fiscal 2017 ends June 30.)
Microsoft is one of the more storied income-producing stocks in all of tech. The company began paying its dividend in 2003, and it's increased its payouts for 13 consecutive years. Better still, Microsoft carries roughly $37 billion in net cash, and its ongoing Windows and Office franchises provide it with billions of dollars in cash flows each quarter. That's why Microsoft deserves a spot as one of the more compelling income investments for anyone seeking exposure to the budding machine-learning space.