Swipe to Unlock by Neel Mehta et al

By: Neel Mehta, Parth Detroja, and Adi Agashe




Would you like to be “fluent” in the language of technology? Read this book summary to familiarize yourself with the terms and strategies used in the tech world and become confident in your ability to discuss them with others. Whether you are looking to switch careers into the tech space or merely looking to grow your knowledge and understanding of the industry, this book summary will give you the foundation and insights you need. Understand the “what” and the “how” of key technologies, like how computers teach themselves new things, how Google Drive is like Uber, or how self-driving cars work. And more importantly, learn why the tech giants have made some of the business decisions that they have. Why did Google make the Android operating system free? Why did Microsoft acquire LinkedIn? Why does Nordstrom have free WIFI in their stores? Get under the hood of these questions and many more in this book summary.



First, understand how some of today’s most innovative technology works: Learn how companies like Netflix and Spotify easily tailor recommendations for you based on past viewing history. Understand how apps continually improve, decide on new features, and edge out their competitors with A/B tests. Speak intelligently about the shift towards the cloud and how Google Drive is a lot like Uber. Decode acronyms like API, NFC, and GAN, and apply your understanding in analysis of key business decisions of the world’s largest tech companies like Apple, Google, Facebook, and Uber. Gain familiarity with ever-improving innovations like self-driving cars and personal assistants.

Then, understand why tech companies make the decisions that they do: Why did Google make their Android operating system free? What motivated Microsoft to acquire LinkedIn? How do apps manage to generate revenue (other than via ads) if they’re free to download? Why was it a smart business decision for Nordstrom to install free Wi-Fi in all their stores? And, what’s behind the “software as a service” trend and the net neutrality debate?

How does it work?

This section outlines the “how” and “what” behind today’s key technologies. The goal of this section is to provide a solid foundational working knowledge from which to analyze business decisions, and therefore talk about them intelligently.

How do companies like Netflix and Spotify make recommendations for you?

The “recommendation” technology is quite ubiquitous today. For example, Amazon uses it to offer new products or services to you, while Netflix uses it for TV shows and movies and Spotify for music. But how does this technology actually work? With so many customers, there’s no way that these companies could employ the number of employees needed to make personal recommendations to each customer. There are two main types of algorithms.

First is a function called “collaborative filtering.” As customers shop, listen to music, or watch Netflix, they are slowly adding to their personal data cache that that company owns. For example, Netflix could keep a list of all the TV episodes you’ve watched in the last month. Finding new TV show recommendations for you using collaborative filtering involves comparing your “list” to others’ lists. When Netflix finds another customer’s list that is, say, 80% similar to yours, it has found a good match for collaborative filtering. After concluding that your list is somewhat similar to the other customer’s, Netflix will make the assumption that your overall tastes in television shows might be similar. Then, it will take a closer look at your respective lists of TV watched in the last month. Netflix will recommend to you the 20% of shows on the other customer’s list that you’ve not yet watched, and likewise for the other customer with the shows you’ve watched but they haven’t. Essentially, collaborative filtering works by identifying other customers who may have similar tastes and preferences as you, and seeing what they’ve found and liked that you may not yet have.

The second main way that companies make recommendations for you is by creating a personalized “taste profile.” For example, each song in Spotify is classified within a genre and a sub-genre. As you listen, Spotify keeps a running tally of the number of songs you’ve listened to within each genre and subgenre. Once a certain genre or subgenre gets enough “hits,” it will be added to your taste profile as a preferred type of music. Then, Spotify will begin recommending more songs that you’ve not yet listened to within those genres.

While recommendation algorithms are certainly more complex than described above, “collaborative filtering” and using “taste profiles” are the two main ways that companies can make solid recommendations to you based on “past listening patterns.”

How do apps successfully improve?

Ever wonder why apps like Facebook or Snapchat often change their interface and functions? Why all the changes, and how do they know they’re headed in the right direction? The technology behind these decisions is rooted in “A/B tests.” In the tech world, an A/B test is a process that companies use to decide between two (and in some cases more) alternatives – “option A” and “option B.” Because of the quantity, ease of access, and constant updating of their customer information, companies can simultaneously introduce option A and option B and determine which performs better among customers by measuring “clicks” or other identifiers of positive reception.

For example, the Washington Post regularly issues two versions of the same headline on their website. After analyzing the data, “developers decide which version is better and show the winning version to everyone.” Companies like Tinder, Buzzfeed, Upworthy, Facebook, Snapchat, Amazon, and many, many more utilize A/B tests to make critical business decisions about everything from the wording of headlines to new app capabilities and the most effective ads.

How is Google Drive like Uber?

Regularly utilizing Uber can greatly decrease one’s overall transportation expenses, depending on one’s lifestyle. In an urban area, taking Ubers instead of owning and maintaining a car can be far less expensive, when factoring in the costs of expenses like parking, gas, maintenance, insurance, and licensing. This “outsourcing” of an everyday need and shedding of responsibility for ownership is in fact the Google Drive model, or rather the “cloud computing” model in general. With the cloud, you can outsource the costs and maintenance associated with storing files. Instead of being on your hard drive, your files are stored in “the cloud,” or really on someone else’s server. “Google Drive is like Uber for computers. Instead of owning your own car or computer, you can get your files or transportation on-demand from anywhere with an internet connection.”

How can Apple Pay process transactions so seamlessly?

Though Apple Pay may appear “magical,” it actually uses an extremely secure technology known as “Near-Field Communication,” or NFC. NFC operates over radio waves and, when two devices are in close proximity, enables the transfer of information. This technology is very safe and secure for both customers and vendors. Apply Pay uses an encryption technology whereby the credit card information is not able to be decoded or stolen by hackers.

NFC technology is being used elsewhere as well and is likely to become more ubiquitous over time. For example, Chicago’s public transportation “Ventra” card uses this technology. Companies could begin using NFC in their ads so that potential customers could tap their phones to get more information. Some French cities have NFC stickers available that provide area maps on demand. “NFC is helping blur the line between the physical and digital world, and we think the uses of NFC will get more and more exciting.”

What technologies do Uber, Yelp, and Pokemon Go all have in common?

All applications are made up of “code.” In the case of Google Maps, Google has invested large sums of time and money in canvassing the globe to create their digital maps. Clearly, these maps are important and convenient technologies that can be applicable in a variety of scenarios. Rather than require that each app repeat this process to build their own digital maps for use in their apps, Google makes their maps available through “application programming interface” technology or “APIs.” APIs are “snippets of code” that let apps “talk to each other” and are a common occurrence in the world of technology. They basically allow an app to “pull in” the functionality of another app. In this way, many popular apps actually integrate the insights generated by other companies. For example, Yelp displays a locale on a Google Map as part of the details, and Pokemon Go uses Google Maps to identify your location. Uber uses PayPal’s Braintree API to process payments. Venmo uses APIs to send emails or text messages. They really are everywhere and in some ways have enabled the proliferation of many quality apps.

Why doesn’t Netflix crash when there are spikes in viewership?

Netflix is in an incredible position to handle extreme one-time spikes in viewership as well as growth in viewers over time due to its setup on Amazon’s cloud, Amazon Web Services. In 2008, it began transitioning its content to the cloud. Those the process took over seven years, it is likely that Netflix isn’t regretting the move. Because its content isn’t on its own servers, Netflix has “elasticity” and “scalability.” Elasticity means that “Amazon Web Services will instantly grow or shrink the computing power given to your app as your app’s usage goes up or down.” This means that Netflix no longer has to worry about a spike in viewers due to a new popular show crashing its network. And, when there are lulls in viewership, they don’t have to maintain expensive servers sitting inactive. Scalability in this case means that the cloud allows growth without worry about network capacity. “The video viewed on Netflix has grown 1,000-fold from 2007 to 2015.” Over this time period, Netflix didn’t have to worry about growing their capacity network-wise because of Amazon Web Service’s ability to provide more space, with no headache on Netflix’s side. Elasticity and scalability are two benefits that companies can leverage when using the cloud.

How do personal assistants like Siri or Alexa work?

These personal assistants work by first sending your voice to their company’s server rather than burdening your phone with attempting to process the language. With Siri, Apple’s servers then break down your speech phonetically and compare the sounds against their gigantic database of how others pronounce certain words until it finds a match. In doing so, they create “text” from the “audio file” you’ve created with your voice. From there, there are two main routes Siri could follow. If your request is somewhat complex, Siri may just do a general internet search and share the key results back with you. But, if it is a request more simply handled through an app, Siri will access an app to answer your query. An example of this could be opening the weather app or a calculator to solve a math question. Digital personal assistants are essentially a simple two-step process. Transcribe your query, then find the answer through an app or internet search.

How do computers teach themselves new things?

Computers can learn and improve surprisingly well due to technology called “generative adversarial networks” or GANs. These are a type of artificial neural network. We’ll first outline how a neural network functions and then explain how the GAN works. Humans learn through neural networks – that is, as we receive feedback, we change behavior accordingly. For example, we touch a hot stove and our hand is burned, so in the future we avoid making contact with hot surfaces. Computers have been designed to be wired in a similar way, such that they can learn and make refinements based on feedback and patterns over time. Some examples of artificial neural networks include “text autocorrect” on your cell phone or your email catching spam by picking up on suspicious patterns in the email.

Generative adversarial networks take this process one step further. They are so powerful they’ve even been used to create fake audio and video. So how does this exactly work? Putting GANs into action requires two artificial neural networks – one serving as the “generator” and one as the “discriminator.” The generator’s job is to convince the discriminator that what they are producing is a real version of whatever it is setting out to create (in this case, a fake news video or audio). “The networks get into a sort of arms race, with the generator trying to make more convincing forgeries and the discriminator trying to get better at policing fakes. The networks learn from each other, constantly improving, until the generator is churning out incredibly convincing fake things.” GANs were used in 2017 to create audio of prominent politicians reading their tweets aloud. But, it was fake audio. The politicians never read their tweets aloud. Rather, the audio was created using a GAN. This powerful technology is something to be aware, and perhaps wary, of.

What is the technology behind self-driving cars?

Self-driving cars use a combination of several technologies to orient themselves to the area and their surroundings and make decisions about how to operate themselves on the roads. First, there is the “onboard GPS” to help it figure out its general location. To get the details, it then relies on an “inertial navigation system,” or a set of sensors and compasses that are attached to the car. “These sensors tell how fast the car is moving and in what direction.” Next, to orient itself to its surroundings like people, road signs, and other cars, the self-driving car uses “hyper-detailed maps of the area” that “aren’t your garden-variety Google maps.” The race to develop these incredibly sensitive maps (“precise to the inch”) has led to competition among entrenched car manufacturers to be the first to master or acquire this technology. These maps can only help the car understand fixed objects, however, like curbs and street signs. To detect non-permanent or moving objects like other cars, bikes, and people, the self-driving car turns to additional sensors and cameras. LIDAR is a “spinning laser mounted on the top of the car” that “helps the car build a 360-degree model of its surroundings.” This sensor helps the car gauge distance from objects that it detects.

To identify what those objects are, however, the car uses cameras. Based on a repertoire of images that the camera has stored, it can identify objects based on their “color” and “shape.” It can also predict where they might be moving next (e.g., towards the car, away from the car, and how quickly) based on how they’ve recently moved or behaved. Self-driving cars are also equipped with “machine learning” capabilities. That is, “ the art of making predictions based on observed patterns.”

Using the combination of the GPS technology, detailed maps, sensors, cameras, and machine learning, the self-driving car is constantly making decisions and sending commands to the functions like the “wheels, brake, and gas pedal.” It makes these decisions by coming up with a number of plans for how it could proceed forward. Then, it analyzes those plans and eliminates those that would take it too close to other objects or people. While it may seem complicated, all of the steps just mentioned take less than 50 milliseconds to compute. Many major companies like Google, Uber, Tesla, and Nissan see self-driving cars as the future. Familiarity with the key technologies that power them will likely prove useful.

Do Amazon’s prices really fluctuate every ten minutes?

Yes, the short answer is that for many products they do. So what factors does Amazon use to make these adjustments? It will come as no surprise that Amazon has reams of data on every product and every shopper. To set a price, or change a price, Amazon digs into this data and analyzes things like “customer’s shopping patterns, competitors’ prices, profit margins, inventory, and dizzying array of other factors.” The goal with this process, which happens every ten minutes, is to “ensure their prices are always competitive and squeeze out ever more profit.” In this way, Amazon can edge out a distinct advantage over less-nimble competitors whose prices don’t have this flexibility.

How did a single typo take down 20% of the internet?

A small typo made by an engineer at Amazon disabled 20% of the internet and caused S&P 500 companies to lose over $150 million. How did this happen? It’s first necessary to understand that Amazon operates as much more than an online retailer. Their cloud service, Amazon Web Services, is the host for dozens of major companies. The Amazon engineer intended to shut down a few minor servers that would have enabled a billing issue to be resolved. Instead, his typo disabled a server called “S3.” S3 is a massive server that app customers pay Amazon to use as storage for their media like photos and videos. S3 was down for four hours, impacting companies like “Medium, Quora, Netflix, Spotify, and Pinterest.” This translated into millions of dollars of lost revenue for these companies and many others. Since then, Amazon has implemented safety checks and standards to ensure a typo of this magnitude won’t take place again.


Why do tech companies make certain decisions?

This section outlines the “why” that has driven many tech companies to make decisions that, on their face, may appear irrational. Here we show why these actions actually make sense in the world of technology and are smart business decisions.

Why did Google make the Android operating system free?

The operating system behind Apple’s iPhone cannot be used on any non-Apple device. In contrast, Google’s operating system Android can not only be used by anyone, on any device – it is also completely free! The lost revenue potential at first seems staggering, when you consider the amount of people globally who use the Android operating system on their phones. But, let’s consider Google’s strategy. It begins with “getting as many people as possible to use Android.” With that concept in mind, it makes more sense that Google would hand out Android free to phone manufacturers. They know that it is costly and time-intensive to develop one’s own operating system, so if Android, a high-quality system backed by giant Google, is available, it’s likely that phone manufacturers would not hesitate to make use of it. Furthermore, they began this strategy in the early days of the smartphone market, when BlackBerry and iPhone were about the only smartphone competitors. Google guaranteed itself a sizable foothold in this market by encouraging third party smartphone manufacturers to enter the space by making Android available for their use. Even if Google didn’t have a hardware smartphone as a runaway success, it sure made its presence was known through having all those competitor smartphones run on Android.

And, Google still makes lots of money on Android. In fact, in 2016 Google earned an estimated $31 billion in revenue from Android. Android’s ubiquitous presence enables Google to “convert it to cash” in a number of ways. First, Google requires that any phone using Android also pre-install all of Google apps on the phone (e.g., YouTube, Google Maps). These apps equal dollars for Google because they support Google’s bread and butter revenue source – ads. The more people that use Google apps, the more data Google is able to put to use in creating targeted ads, which generate revenue. In addition, Google gains revenue from app purchases on their Google Play app store (also required on phones that use Android).

Why did Microsoft acquire LinkedIn?

In 2016, Microsoft bought LinkedIn for $26.2 billion. This was its largest acquisition to date, and quite a sum considering LinkedIn is primarily a social network site lacking hardware or software products. Again, consideration of Microsoft’s overall strategy here is key. If, as some claim, Microsoft’s “future (and present) is in enterprise software, or tools built for businesses,” then their purchase of LinkedIn makes perfect sense. The LinkedIn purchase shores up their position in the “enterprise space.”

LinkedIn places Microsoft at “the center of the businessperson’s world.” Just picture it – a businessperson already likely uses Microsoft products to get their work done, whether that’s Microsoft Word, Excel, Outlook, or PowerPoint. Then, when they visit LinkedIn to update their profile with new jobs, skills, or achievements, network with connections, or search for a new job, they are also working within the Microsoft space. Just as many users of Facebook or Instagram strive to paint a cohesive social and aesthetic picture of themselves on those platforms, Microsoft wants your LinkedIn profile to become your “professional source of truth.” In addition to this strategic view, there are several tangible benefits Microsoft received through the acquisition.

Much of the acquisition’s value lies in its database of 433 million LinkedIn users, a database that enables LinkedIn to conduct countless analyses to improve upon and integrate with their existing products. Envisioning some possibilities could lead to things like “your Outlook calendar could show the LinkedIn profile of the next person you’re meeting with.” Microsoft could also integrate users’ LinkedIn profiles in their customer relationship management tool “Dynamics CRM,” thereby distinguishing the tool from competitors like Salesforce and Google. Microsoft was very keen that competitors not get their hands on this data first. LinkedIn also garnered a profit of $71 million in 2015 and shows signs of healthy growth, all positive indicators for Microsoft. And lastly, Microsoft benefits from bringing some of the key LinkedIn leaders, like chairman Reid Hoffman, closer to their own operation. Hoffman is “one of the best-connected and most-liked people around Silicon Valley.” The hope is that he will enhance Microsoft’s standing among the power players there as a result of now joining Microsoft’s board.

How do apps make money?

How do apps generate revenue when most are free to download? We’ll assume you are familiar with the fact that many tech companies and apps make money through targeted ads or upgraded “premium” versions of the basic app. These revenue models are focused on other, lesser known, ways that apps make money.


Wanderu is a “travel-booking service” that connects users with transportation options and lets them book things like bus tickets from places like Greyhound or Megabus. Users can access this service for free, but the bus lines are charged a “small commission” from the app “for the privilege of sending customers their way.”

“Grow first, monetize later”

Some apps don’t make any money but are instead focused on growth and doing what they do best. One example of this is Venmo. Its long-term strategy is to be used as a payment in stores, like Apple Pay or credit cards, and then charge the retailer a fee for its use. Other apps don’t have a clear-cut future monetization strategy, but just hope to be acquired due to their value. Mailbox was an app that provided a free email service and was acquired by Dropbox for $100 million before making any money at all.


The app Robinhood lets you trade stocks and make money on those transactions for free. Of course, they have a premium model that unlocks additional features. But, the more interesting way they make money is through “earning interest from unused money sitting in users’ accounts.” This is just like how anyone earns interest on money sitting in a savings account. They make this steady source of income while neither charging users or using ads.

Why did BlackBerry fail?

At BlackBerry’s peak of success in 2009, it had a 20% market share in the cell phone space and was the most popular mobile phone among many of the world’s elite, including politicians, celebrities, and corporate bigwigs. Some of its distinguishing features included its security, ease of typing, and “always-on” email functionality. So, what led to its downfall? Apple’s iPhone and Google’s Android had a lot to do with it. But, in the end, it was a result of not thinking around the curve.

BlackBerry did not feel threatened when the iPhone was released because they saw themselves as playing in two different spaces. The iPhone, they assumed, was for a younger generation who wanted entertainment, games, and to play with their flashy iPhones. In contrast, they saw BlackBerry as having a stronghold in the enterprise space. The iPhone wasn’t a serious competitor to the efficient and addictive workhorse that the BlackBerry was. But, the tables turned against them when iPhone started entering the enterprise space, and people realized they could do everything they needed on their iPhone, just as securely and reliably. In addition, the rise of Android and its availability to different manufacturers led to a crowded and competitive smartphone market. BlackBerry began to struggle in this space.

Almost as important as BlackBerry’s slowly diminishing appeal was their inability to successfully create apps for their platform. Developers began creating two versions of apps – one that would work on iOS, and one that would work on Android. But, very few developers made apps that would work on BlackBerry’s platform. Because they were so focused on their enterprise market, BlackBerry “didn’t realize customers wanted to do more than just send emails on their phones: they wanted apps, games, and instant messaging.” Yes, it’s important to focus on your niche in the market. But, as we see from BlackBerry’s story, failing to embrace innovations or anticipate shifts in the market can be toxic.

Why does Nordstrom offer free Wi-Fi in its stores?

Did you know that your mobile phone has a unique code called a “MAC address” that is inherent in the phone? And, that whenever you connect to Wi-Fi, the owner of the Wi-Fi can locate and track you and your phone via the MAC address as long as you are connected to their internet? Well, retailers discovered this in 2012 and have since been offering free Wi-Fi and leveraging the data it yields about users and their MAC addresses. The routers can pinpoint your location with such precision that they can track your movements around a given store. That is, they can see the pattern you take throughout the store, how long you stay in different sections, and when you decide to head to the register. These insights can lead them to make changes to things like store layout, inventory, or staffing levels. In addition, they can require that you log in with your email or use video surveillance in the stores to get more information about your identity and sell you targeted ads. In this way, they can know more about customers than just their MAC numbers, and improve their sales on both a macro level (e.g., store layout, inventory) and micro level (e.g., targeted ads). This technology could be applied in a number of different retail scenarios to gain valuable insights in a variety of industries.

Why can you no longer own Photoshop?

Avid Adobe Photoshop users can no longer own the software on their desktops or laptops, once and for all. Instead, they must purchase subscriptions and continually renew them. This is similar to how Microsoft now offers their suite of office products in two versions – Microsoft 10 and Microsoft 365, where Microsoft 10 can be owned and 365 is a subscription.

The technology behind these changes is the shift towards the “software as a service” model, or “SaaS.” Companies have realized that there are several benefits in selling something this way. For one, they have a more consistent, steady revenue stream over time, rather than receiving lump sum payments up front. This is preferable for planning and budgeting purposes. Second, this model enables the company to “push” tech updates or fix bugs as new versions of the subscriptions are released. This helps users have the most up-to-date and functional technology. In addition, the software is more “accessible” – more users have access to it because an annual subscription ($240) is cheaper than the one-time purchase ($700).

SaaS is becoming more and more common in the tech world. Other examples of SaaS models include Dropbox, G Suite, and Google Sheets, among others.

Why do tech companies care about the net neutrality debate?

The basic functionality of many apps and tech companies depends on the outcome of the net neutrality debate. Proponents of “net neutrality” argue that internet providers like Comcast are “common carriers.” That is, a service that moves goods or people and should therefore be regulated by the federal government and prohibited from discriminating among customers. Today, companies like FedEx and public utilities are subject to these laws. They can’t charge a certain segment of their customers more than others, they can’t deliver a certain type of goods more slowly because they think they’re less important, and they can’t refuse customers service. The basis for these laws is the belief that “everyone should have equal access to public services.”

For a time, internet service providers (ISPs) were held to these standards as well. As “shippers” of “Tweets, videos, and other information,” they had to provide the same service for all types of websites and apps. They were prohibited from “blocking, throttling, or paid prioritization.” Blocking is outright refusing to let a certain website or app come through the internet. For example, AT&T in 2012 attempted to “ban FaceTime for customers who didn’t pay extra.” Throttling is slowing down a given website, like when Comcast and Verizon slowed down Netflix in 2013 and 2014. And, paid prioritization is allotting faster internet speed to certain sites and not others. In 2017, the new Federal Communications Commission chairman Ajit Pai ruled that internet service providers were not common carriers and therefore did not have to follow these rules. As a result, today blocking, throttling, and paid prioritization is allowed. While some proponents favor this deregulation, many tech companies and digital and social media sites are not in favor. Abolishing net neutrality could lead to monopolization of the internet by a few big companies. Large tech behemoths could choke out new competitors by paying ISPs to make their sites faster. Consumers might see their bills rise too, if the internet goes the way of cable television, and users have to pay higher rates for access to more websites. Understanding the basics of the net neutrality debate, especially as it relates to tech companies, is crucial to speaking intelligently on these topics.