Company name Alphabet Inc Class A
Stock ticker GOOGL
Live stock price [stckqut]GOOGL[/stckqut]
P/E compared to competitors Good

MANAGEMENT EXECUTION

Employee productivity Good
Sales growth Good
EPS growth Good
P/E growth Poor
EBIT growth Good

ANALYSIS

Confident Investor Rating Good
Target stock price (TWCA growth scenario) $1710.05
Target stock price (averages with growth) $1664.23
Target stock price (averages with no growth) $1664.23
Target stock price (manual assumptions) $1724.83

The following company description is from Reuters: https://www.reuters.com/finance/stocks/company-profile/googl

Alphabet Inc., incorporated on July 23, 2015, is a holding company. The Company’s businesses include Google Inc. (Google) and its Internet products, such as Access, Calico, CapitalG, GV, Nest, Verily, Waymo and X. The Company’s segments include Google and Other Bets. The Google segment includes its Internet products, such as Search, Ads, Commerce, Maps, YouTube, Google Cloud, Android, Chrome and Google Play, as well as its hardware initiatives. The Google segment is engaged in advertising, sales of digital content, applications and cloud offerings, and sales of hardware products. The Other Bets segment is engaged in the sales of Internet and television services through Google Fiber, sales of Nest products and services, and licensing and research and development (R&D) services through Verily.

Confident Investor comments: At this price and at this time, I think that a Confident Investor can confidently invest in Alphabet Inc Class A as long as the indicators that I describe in my book The Confident Investor are favorable.

If you would like to understand how to evaluate companies like I do on this site, please read my book, The Confident Investor. You can review the best companies that I have found (and I probably invest my own money in most of these companies) in my Watch List.

How was this analysis of Alphabet Inc Class A calculated?

For owners of my book, “The Confident Investor” I offer the following analysis (you must be logged in to this site as a book owner in order to see the following analysis). If you have registered and cannot see the balance of this article, make sure you are logged in and refresh your browser.
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In order to assist you in using the techniques of this book, the values that I used when calculating the Manual pricing above were:

  • Stock price at the time of the calculation: $1106.81
  • Growth: 0.2
  • Current EPS (TTM): $39.87
  • P/E: 28
  • Future EPS Calc: $99.2
  • Future Stock Price Calc: $2777.86
  • Target stock price: $1724.83

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I hope that this makes you a Confident Investor.

NVIDIA Corporation [stckqut]NVDA[/stckqut] is driven by “specialized computing,” that is, the transforming of specific software tasks into physical silicon chips instead of depending on an ever-faster do-it-all CPU, or central processing unit. It has existed in some form or another for decades, but it has lately become the driving force behind pretty much everything cool in technology, from artificial intelligence to self-driving cars. Why? Because those CPUs aren’t getting faster at the pace they once were. Moore’s Law is dying.

Moore’s Law is the notion that, every two years or so, the number of transistors in a chip doubles. Its popular conception is that computers keep getting faster, smaller and more power-efficient. That isn’t happening the way it used to. “It’s not like Moore’s Law is going to hit a brick wall — it’s going to kind of sputter to an end,” says Daniel Reed, chair of computational science and bioinformatics at the University of Iowa.

As Intel and the other chip foundries spend fortunes to keep the wheel turning, chip designers across the industry are finding creative ways to continue at the old pace of Moore’s Law, and in many cases increase device performance even more quickly.

“Most of the advances today come from [chip] design and software,” says Nvidia chief scientist William Dally. “For us it’s been a challenge because we feel under a lot of pressure to constantly deliver twice the performance per generation,” he adds. So far, Nvidia has accomplished that cadence even when the size of the elements on the chip doesn’t change, and the only thing that does is its design, or “architecture.”

Here’s a less-than-exhaustive list of all the applications to which the principle of specialized computing has been applied: Artificial intelligence, image recognition, self-driving cars, virtual reality, bitcoin mining, drones, data centers, even photography. Pretty much every technology company that makes hardware or supplies it — including Apple, Samsung, Amazon, Qualcomm, Nvidia, Broadcom, Intel, Huawei and Xiaomi — is exploiting this phenomenon. Even companies that only produce chips for their own use, including Microsoft, Google, and Facebook, are doing it.

Many years ago, almost all computing was done with the CPU, one thing after another in sequence, says Keith Kressin, a senior vice president at Qualcomm. Gradually, often-used but processor-intensive tasks were diverted to specialized chips. Those tasks were processed in parallel, while the CPU did only what was absolutely required.

These task-focused chips come in a wide variety, reflecting the breadth of their uses, and the lines between them can be blurry. One kind, the graphics processing unit — think Nvidia and gamers — found wider use for tasks to which it’s uniquely suited, including artificial intelligence. Later on, the rise of smartphones created a gigantic need for another type, digital signal processing chips, designed to enhance photography, for example.

Source: How Chip Designers Are Breaking Moore’s Law – WSJ

Some things, even huge piles of money can’t buy.

One of those things might be the ability to unseat Amazon.com Inc.’s [stckqut]AMZN[/stckqut] AWS as the king of the cloud computing market. Not that others haven’t made a game effort. The two largest challengers— Microsoft Corp.[stckqut]MSFT[/stckqut] and Google parent Alphabet Inc.[stckqut]GOOGL[/stckqut]—have dropped about $52 billion combined in capital expenditures over the past three years, much of which goes toward their massive networks of data centers and related equipment. That’s double what the two spent over the previous three-year period.

It’s not been without results. Microsoft’s Azure cloud service more than doubled its revenue in 2016 to about $2.7 billion, according to estimates from J.P. Morgan. Google’s Cloud Platform surpassed $1 billion in revenue in 2016, estimates Aaron Kessler of Raymond James.

The latter is particularly of note, given that it’s been barely a year since Google brought in former VMware chief Diane Greene to run the cloud division and focus on enterprise customers. It took AWS at least five years to hit the $1 billion mark, judging from Amazon’s limited disclosures at the time.

Source: Amazon Rivals Have Big Clouds to Fill – WSJ

Two years ago, Google spent over half a billion dollars for the tiny artificial intelligence startup DeepMind. Since then, the unit has walloped Atari video games and beaten an impossible board game.

Impressive stuff, that. But those AI demonstrations have yet to spell actual revenue. Until now — although the efforts are helping Google save money on its most expensive part.

DeepMind chief Demis Hassabis told Bloomberg that his unit recently began applying its advanced AI to Google’s data centers, finding ways to reduce the company’s sizable energy bill.

Google started using machine learning for its data centers two years ago, searching for ways to reduce costs for one of the company’s top expenses. A month ago, it aimed the more specialized AI tools from DeepMind at the problem of cooling these server farms. That cut the energy needed for cooling by 40 percent, the company said.

It didn’t offer a dollar figure for that, but it’s safe to assume that it means hundreds of millions in savings over the long haul.

Source: Google has found a business model for its most advanced artificial intelligence – Recode

A federal jury here ruled that Google’s use of Oracle Corp.’s Java software didn’t violate copyright law, the latest twist in a six-year legal battle between the two Silicon Valley titans.

Oracle sued Google, a unit of Alphabet Inc [stckqut]GOOGL[/stckqut] [stckqut]GOOG[/stckqut]., in 2010 for using parts of Java without permission in its Android smartphone software. A federal appeals court ruled in 2014 that Oracle [stckqut]ORCL[/stckqut] could copyright the Java parts, but Google argued in a new trial this month that its use of Java was limited and covered by rules permitting “fair use” of copyright material.

A 10-person jury on Thursday agreed.

Google acknowledged using 11,000 lines of Java software code. But it said that amounted to less than 0.1% of the 15 million lines of code in its Android mobile-operating system, which runs most of the world’s smartphones.

Source: Google Wins Java Copyright Case Against Oracle