This column originally appeared on May 18 on Real Money, our premium site for active traders. Click here to get great columns like this.

At this point, most of those following the product announcements and R&D efforts of consumer tech giants know that the subset of AI known as machine learning -- broadly defined as the use of algorithms that can learn on their own by taking in relevant data -- is a big deal for practically all of them. And that it's being used to do things like field voice commands, detect objects within photos and get cars to drive themselves.

But there are a couple of facets to this trend that aren't as well-appreciated:

  1. The usefulness of a machine learning algorithm for handling tasks normally done by humans doesn't necessarily improve at a linear pace, but can improve exponentially or close to it when a tipping point is reached due to all of the data that the algorithm has been run against.
  2. Machine learning R&D work can often be applied to many different tasks, including some that don't have much in common at first glance.

Both of these phenomena work very much in Alphabet/Google's (GOOGL) favor. Though (AMZN) , Microsoft (MSFT) and others have also made tremendous progress in delivering AI-powered products and services on a large scale, Google arguably remains a step ahead when it comes to many of the tasks that both Google and rivals are trying to address. And as the announcements made at this week's Google I/O developers conference show, a lot of these offerings seem to be hitting a tipping point in terms of what they can do.

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