Automate This: How Algorithms Came to Rule Our World

Wall Street discovered that computers could rapidly sniff out arbitrage opportunities to make "free money" by exploiting differences in buy and sell prices in financial instruments. Sometimes it was as simple as buying it in New York and selling in Chicago. However, as more players became involved, algorithms needed to be improved (eliminating even a few nanoseconds could save tons of money.) Even the current internet was not seen as fast enough. The expense of building a more direct fiber route between the exchanges would be worth it in the fractions of a second that could be saved. No expense would be spared in snapping up all the brightest scientists and engineers to make better algorithms. When things go right, companies can make fortunes. When things don't? Well, we can have micro-crashes and great meltdowns like the recent sub-prime crash.
Christopher Steiner originally set out to focus his book on algorithms' impact on the financial markets, but expanded it to cover other areas of our life that are being taken over by computers. Markets had for centuries relied on an open-outcry system where person-to-person contact was key to any trades. These traders were seen as essential to the well functioning system. The NASDAQ market started to sneak on to the turf with an electronic trading system. However, even it required traders to manually enter trades in a terminal. Alas, somebody eventually was able to hack the system by connecting the terminal to a computer that could analyze quotes and quickly enter optimal trades. Once the NASDAQ caught wind of this, they tried to stop it by mandating that orders must be entered on the keyboard. Not to be deterred, the company created a system of "key-pressers" to manually key in the orders (and a screen reader to read data from the screen.) The engineers had their toe-hold on wall street and would soon go to take over. This has lead to greater transparency and lower costs for retail traders. However, it has also contributed to wild price gyrations and increasing challenges for big purchasers (such as mutual funds).
Other parts of society have also seen computers make significant inroads. Even in something as seemingly lowly as customer service, companies such as e-loyalty use voice scanning algorithms to help provide a better matching customer service agent and faster resolution. Some of the examples have not aged very well since the books publication in 2012. (Gaming company Zynga's algorithms have not helped it's financial condition.) The spends some time looking at some of the key contributors in mathematics and science that have helped us reach our current state. However, the focus is on the future. The author sees a need for more scientists and engineers. Alas, due in part to the tough math requirements, many students end up in liberal arts majors. Improving high school math could help with this. (Unmentioned is the fact that much of the math work is not needed in the day to day work of many of the algorithm coders.) Medicine is seen as a field that is ripe for greater automation, but also one that will still need a human hand. Career prospects are meek for those who cannot code. Being a hacker (in the good sense) is seen as the only career path that will continue to be viable. The book doesn't explore the possibility that we can automate coding and thus spend our lives eating bon-bons while our computers serve us. That may bea good thing.