Big Tech is Making a Massive Bet on AI … Here’s How Investors Can, Too
AI

Big Tech is Making a Massive Bet on AI … Here’s How Investors Can, Too

Artificial intelligence is becoming the future of everything. Yet, only a few large companies have the talent and the technology to perfect it.

That’s the gist of New York Times story published late last week. Rising costs for AI research are locking out university researchers and garage entrepreneurs, two of the traditional — and historically best — founts of innovation.

But it’s not all bad news for investors.

In the past, software engineers used code to build platforms and new business models. A prime example is Netflix (NFLX).

Managers there transformed the mail-order DVD business into a digital media behemoth. They revolutionized how we view and interact with media. They also shook up traditional Hollywood studios by giving new and independent voices a huge platform.

Not bad for a business decision that began with a new AI algorithm.

AI is the beginning of a new era. Data is becoming the code. Business models will evolve as computer algorithms start to make connections where none seemed to previously exist.

In the process, the companies with the best algorithms will start to solve the medical, economic and social problems that have vexed researchers and scientists for decades.

Investors need to understand that winners and losers are being determined right now as the cost of AI research becomes prohibitive.

Think of the research process as a set of increasingly complex math problems. Researchers throw enormous amounts of data at custom algorithms that learn through trial and error. As the number of simulations mount, so do costs.

Big problems like self-driving cars or finding the cause of disease at the cellular level require immense amounts computing power.

An August research report from the Allen Institute for Artificial Intelligence determined that the number of calculations required to perform cutting-edge AI research soared 300,000x over the course of the past six years.

Only a handful of companies have the resources to compete at that level.

Long ago, executives at Amazon.com (AMZN), Microsoft (MSFT), Alphabet (GOOGL) and Facebook (FB) had the foresight to begin building massive cloud computing scale. Their data centers, many the size of football fields, are strewn all over the globe. Millions of servers, connected with undersea cables and fiber optic lines, have replaced the mainframes of old.

If you want to do great things in AI research, you’ll probably need to deal with at least one of these four big firms.

It’s a pinch being felt even by large technology companies …

Adobe (ADBE) and SAP (SAP) joined an open data alliance with Microsoft in September 2018. A day later, salesforce.com (CRM) hooked up with Amazon Web Services, Amazon’s cloud computer arm.

There has been some effort to break up the concentration of power. But critics are still mostly focused on the wrong things. In their view, data is the new oil, and it begs for regulation.

In the early 1900s, oil was the lifeblood of industry. It was central to the development of new game-changing chemicals. It powered the nascent automobile and steel complex.

The oil barons were the gatekeepers to innovation. In the process, they amassed fantastic wealth, as did many other industrialists. Income inequality soared.

Eventually, this led to calls for regulation, and trust-busters were brought in to break up (and control) the oil giants.

Related post: Big tech has sneaky agenda in quest to regulate privacy

The parallels to today are convenient, and lazy.

Writers at The Economist in 2017, painted a dystopian picture of our future — one where the tech giants remain unregulated. The influential finance magazine concluded antitrust regulators must step in to control the flow of data, just as they did with oil companies in the early 1900s.

However, data is not oil. It’s not dear. It’s abundant.

Thanks to inexpensive sensors and lightweight software, there is a gusher of digital information everywhere. It comes from our wrists, cars and television sets. Soon it will shoot out of traffic lights, buses and trains; mining pits, farm fields and factories.

The limited resource is computing power. Enterprises, governments and researchers will need to pay up if they want to turn their data into something of value.

McKinsey, a global research and consulting firm, argues unlocking data should be a strategic priority at every enterprise. Analysts predict data will change business models in every industry, every business going forward.

The most important takeaway is that all future key AI breakthroughs are likely to come out of the big four. They have the technological and financial resources to attract talent. They have the scale to push the envelope.

It’s not a surprise that Amazon is leading in advanced robotics and language processing, or that Alphabet started developing self-driving cars in 2009.

Microsoft is building the biggest connected car platform in the world: Its engineers in Redmond, Wash., imagine a world of vehicle synchronization and the end of traffic.

Across town, Facebook researchers are working on augmented reality and brain computer interfaces.

These are big ideas with huge potential payoffs.

Related post: 5G will change your life … and maybe even save it

Amazon, Microsoft, Alphabet and Facebook are as important today as Standard Oil, Royal Dutch Shell and British Petroleum were a century ago.

Their resource is not oil, or data for that matter. It’s computing power. They’re leveraging that position to dominate AI research, the most important technology of the future.

For their investors, this is a good thing.

Growth investors should consider buying the stocks into any significant weakness. The story of AI is only getting started.

Best wishes,

Jon D. Markman

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Comments 1

Uri Gronemann October 1, 2019

What exactly do you mean by “computer power”? Massively parallel super computers? Or a sheer mass of cloud servers? In the latter case one needs some sophisticated super software to coordinate them for any task.
Are these the capabilities you identify in the “big 4”?

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