Chart: Vetting Alphabet's $4 Billion in "Other Bets"
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Chart: Vetting Alphabet’s $4 Billion in “Other Bets”

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Chart: Vetting Alphabet's $4 Billion in Other Bets

Chart: Vetting Alphabet’s $4 Billion in “Other Bets”

The Chart of the Week is a weekly Visual Capitalist feature on Fridays.

Regardless of whether the policy remains in place today, one of Google’s most famous management philosophies has been the “20% rule”. Legend goes that especially in Google’s earlier days, employees were encouraged to spend 20% of their time working on new initiatives that could potentially benefit Google outside of their regular workflows, teams, and projects. This meant there was time to explore new ideas or to challenge existing status quos within the organization.

It’s a seemingly arbitrary and systematic way to spur innovation, but it has worked well over the years. The 20% rule has apparently led to new products such as Gmail, Google News, and AdSense, which are all important aspects of Google’s business today.

It should not be surprising then, that Google’s parent company Alphabet also takes a systematic approach to innovating outside of its core search business. By making ambitious “Other Bets” and keeping investors up-to-date with their own segment on the company’s financial statements, Alphabet shows both commitment and discipline in finding its next multi-billion dollar game-changer.

“Other Bets”

In 2016, Alphabet was a cash machine. The company raked in $19.5 billion of profit off of a whopping $87.4 billion of revenue.

The only challenge? If you do the math, 99% of that revenue comes from Google, which currently dominates digital advertising with a 41% share of the entire market.

Wisely, the company does not want to put all of its eggs in one basket – and it spends about 7% of its annual operating costs on facilitating “Other Bets”. Here is what else Alphabet is up to:

Google Fiber
Google Fiber aims to provide internet at super speeds across the United States. Rollout has been costly though and only nine locations have been launched since 2010.

Calico Labs
Calico Labs is made up of elite scientists with $1.5 billion worth of funding to research the causes of aging and how to expand the human life span.

Nest
Founded by two former Apple engineers, Nest produces smart-home technology such as sensor-driven, Wi-Fi-enabled thermostats.

Verily
Verily’s mission is to reinvent healthcare using groundbreaking technology and data, such as a glucose-sensing smart contact lens for diabetics.

GV
GV, formerly known as Google Ventures, is Alphabet’s venture capital investment arm, making strategic investments in startup companies in fields such as life sciences, agriculture, and robotics, to name a few.

CapitalG
CapitalG makes investments that are “return-driven”, focusing on growth stage companies, such as Stripe, Airbnb and SurveyMonkey.

X
X uses breakthrough technology as a radical solution to big problems. Its most famous projects are its self-driving car and Google Glass.

Which “Other Bets” are Paying Off?

“Other Bets” generated $809 million in revenue in 2016, which is a 82% increase over 2015. This revenue came mostly from Nest, Fiber, and Verily.

Nest, which aims to dominate the smart home of tomorrow, was acquired for $3.2 billion in 2014. And while it does generate revenue for Alphabet, it has been viewed mostly as a disappointment even from the company’s perspective.

Verily is Alphabet’s business in life sciences, and is apparently profitable already. The unit partners with pharmaceutical companies to make money, and it will also eventually move forward with human clinical trials on its smart contact lens product.

Lastly, Fiber has been rolled out in nine cities across the United States to provide ultra-fast broadband speeds for internet and television. In recent news, Fiber has laid off employees, while halting many further expansion plans.

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Infographic: Generative AI Explained by AI

What exactly is generative AI and how does it work? This infographic, created using generative AI tools such as Midjourney and ChatGPT, explains it all.

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Generative AI Explained by AI

After years of research, it appears that artificial intelligence (AI) is reaching a sort of tipping point, capturing the imaginations of everyone from students saving time on their essay writing to leaders at the world’s largest tech companies. Excitement is building around the possibilities that AI tools unlock, but what exactly these tools are capable of and how they work is still not widely understood.

We could write about this in detail, but given how advanced tools like ChatGPT have become, it only seems right to see what generative AI has to say about itself.

Everything in the infographic above – from illustrations and icons to the text descriptions⁠—was created using generative AI tools such as Midjourney. Everything that follows in this article was generated using ChatGPT based on specific prompts.

Without further ado, generative AI as explained by generative AI.

Generative AI: An Introduction

Generative AI refers to a category of artificial intelligence (AI) algorithms that generate new outputs based on the data they have been trained on. Unlike traditional AI systems that are designed to recognize patterns and make predictions, generative AI creates new content in the form of images, text, audio, and more.

Generative AI uses a type of deep learning called generative adversarial networks (GANs) to create new content. A GAN consists of two neural networks: a generator that creates new data and a discriminator that evaluates the data. The generator and discriminator work together, with the generator improving its outputs based on the feedback it receives from the discriminator until it generates content that is indistinguishable from real data.

Generative AI has a wide range of applications, including:

  • Images: Generative AI can create new images based on existing ones, such as creating a new portrait based on a person’s face or a new landscape based on existing scenery
  • Text: Generative AI can be used to write news articles, poetry, and even scripts. It can also be used to translate text from one language to another
  • Audio: Generative AI can generate new music tracks, sound effects, and even voice acting

Disrupting Industries

People have concerns that generative AI and automation will lead to job displacement and unemployment, as machines become capable of performing tasks that were previously done by humans. They worry that the increasing use of AI will lead to a shrinking job market, particularly in industries such as manufacturing, customer service, and data entry.

Generative AI has the potential to disrupt several industries, including:

  • Advertising: Generative AI can create new advertisements based on existing ones, making it easier for companies to reach new audiences
  • Art and Design: Generative AI can help artists and designers create new works by generating new ideas and concepts
  • Entertainment: Generative AI can create new video games, movies, and TV shows, making it easier for content creators to reach new audiences

Overall, while there are valid concerns about the impact of AI on the job market, there are also many potential benefits that could positively impact workers and the economy.

In the short term, generative AI tools can have positive impacts on the job market as well. For example, AI can automate repetitive and time-consuming tasks, and help humans make faster and more informed decisions by processing and analyzing large amounts of data. AI tools can free up time for humans to focus on more creative and value-adding work.

How This Article Was Created

This article was created using a language model AI trained by OpenAI. The AI was trained on a large dataset of text and was able to generate a new article based on the prompt given. In simple terms, the AI was fed information about what to write about and then generated the article based on that information.

In conclusion, generative AI is a powerful tool that has the potential to revolutionize several industries. With its ability to create new content based on existing data, generative AI has the potential to change the way we create and consume content in the future.

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