The rules of the game, known to economists as institutions and to managers as corporate culture, usually entail inoperable ideas. That is, any country or business has some rules, but these rules coincide neither with optimal rules nor with leadership vision. Maybe with an exception of the top decile of performers or something like this.
This inoperability isn’t surprising since the rules have obscure formulations. Douglass North and his devotees did best at narrowing what “good institutions” are, but with North’s bird-eye view, you also need an ant-eye view on how changes happen.
An insider perspective had been there all the time, of course. Organizational psychology and operations management organized many informalities happening in firms. In general, we do know something about what managers should and shouldn’t do. Still, many findings aren’t robust as we’d like them to be. There’s also a communication problem between researchers and practitioners, meaning neither of the two cares what the other is doing.
These three problems—formulation, coverage, and communication of effective rules—have an unexpected solution in software. How comes? Software defines the rules.
Perhaps Excel doesn’t create such an impression, but social networks illustrate this case best. After the 90s, software engineers and designers became more involved in the social aspects of their products. Twitter made public communications shorter and arguably more efficient. In contrast to anonymous communities of the early 2000s, Facebook insisted on real identities and secure environment. Instagram and Pinterest focused users on sharing images. All major social networks introduced upvotes and shares for content ranking.
Governance in online communities can explain success of StackExchange and Quora in the Q&A space, where Google and Amazon failed. Like Wikipedia, these services combined successful incentive mechanisms with community-led monitoring. This monitoring helped dealing with low-quality content that would dominate if these services simply grew the user base, as previous contenders tried.
Wikipedia has 120,000 active editors, which is about twice as many employees as Google has (or alternatively, twelve Facebooks). And the users under the jurisdiction of major social networks:
So software defines the rules that several billion people follow daily. But unlike soft institutions, the rules engraved in code are very precise. Much more so than institutional ratings for countries or corporate culture leaflets for employees. Code-based rules also imply enforcement (“fill in all fields marked with ‘*'”). Less another big issue.
Software captures the data related to the impact of rules on performance. For example, Khan Academy extensively uses performance tracking to design the exercises that students are more likely to complete — something that schools with all the experienced teachers do mostly through compulsion.
Finally, communication between researchers and practitioners becomes less relevant because critical decisions get made at the R&D stage. Researchers don’t have to annoy managers in trenches because software already contains the best practices. Like at Amazon.com that employed algorithms to grant its employees access privileges based on the past performance.
These advantages make effective reproducible institutions available to communities and businesses. That is, no more obscure books, reports, and blog posts about best practices and good institutions. Just a product that does specific things, backed by robust research.
What would that be? SaaI: software as an institution?
While the Uber story shows that a poorly regulated industry may be a good place to start a new company, Elon Musk suggests another opportunity borne by government:
But government is inherently inefficient. So it makes sense to minimize the role of government such that government does only what it has to do, and no more.
After this quote, some people cut their Social Security cards into pieces and run to a libertarian sea platform, away from government. This is, however, not what Musk means. Here’s some background.
It’s not a secret that, since 1958, NASA received $1 trillion dollars from federal budget to create the stack of technologies that SpaceX currently uses in its own commercial projects. SpaceX’s initial capital of $100 million makes 0.01% of this investment in space odysseys. The other 99.99% came from the government, which is presumably the necessary minimum mentioned by Musk.
And as Musk rightly reminds in the same interview:
But funded by the government just means funded by the people. Government, by the way, has no money. It only takes money from the people. [Laughter.]
Tesla Motors, another company founded by Musk, sells cars eligible for a $7,500-worth federal subsidy and numerous of state subsidies of a comparable amount. It’s about 20% off each car to help Tesla compete with fossil fuel vehicles.
His third company, Solar City, also advertises solar tax credits and rebates as its competitive advantage over traditional utilities. It promises that “some [state governments] are generous enough to cover up to 30% of your solar power system cost.”
The subsidies are, of course, not the point here. They are the second way toward clean and renewable energy, after complete pricing of fossil fuels (which is broadly supported by economists, see Pigou Club). In practice this transition will happen very much like what Tesla and Solar City do now.
But for anyone practically or intellectually interested in how this business works, executives happen to be a pretty misleading source. Even when these executives write long books about their companies or hire well-known economists without giving them complete data. Instead of the story how the company really works, the reader gets ideological cliches about business, management, and government. With teachers like them, it’s not a surprise that 9 out of 10 startups end up nowhere.
This happens mostly in hi-tech, with all this sudden success and media exposure. But the most competent executives manage to keep a low profile even here, because they know that they are best at running companies, not at teaching people how things work.
Alibaba is sort of doing fine after the IPO. But what does it do? It replaces the state.
Roughly, if a firm picks a supplier, it wants supplies to be fine and to arrive in time. The supplier, in turn, want to make sure that the client pays as agreed.
Now, there are two ways to provide it for sure. Option A is the threat of legal actions if things went terribly wrong. Option B is to avoid bad partners at all. The state offers both options. It has licensing and regulators to prevent very bad companies from operating in the market. And the state also has a more traditional function of bashing bad businesses for violating the law.
Obviously, Alibaba is not the British East India Company—it cannot apply violence freely. But it does offer an alternative to government regulations, especially in countries where governments are not trusted. The website routinely offers inspections and secure payments. It encourages buyers to leave feedbacks. As a matter of punishment, it can ban businesses from the marketplace.
Alibaba reduces the risk, which would otherwise require more resources to meet. Though private inspections, insurance, and feedbacks have been there for centuries, IT technologies made them extremely centralized and embedded in a single company. The state also implies a monopoly—and online marketplaces have it! Amazon, eBay, and Alibaba have no strong competitors in their respective markets.
Does this replacement for weak governance affect economic development? Possibly. Alibaba is an international trade hub. Normally, small and medium enterprises are reluctant to deal with international partners due to uncertainty. For example, the World Bank points at political risks:
Those investors who actually work in emerging markets estimate the risk as being three times lower than that by investors who don’t consider investing in emerging markets at all. Uninformed investors overstate risks and stay away from what can be a perfectly normal market.
Many of the B2B transactions mediated by Alibaba might not have happened at all without the relevant information. For one reason, the baseline risk is high as governments in emerging markets are reluctant to prosecute local crooks. For another, western mass media cover these markets biasedly. Someone who read the Financial Times throughout 2014 might have an impression that China is nothing more but corruption, political trials, empty infrastructure, ghost cities, and permanently slowing down economic growth. Even if these materials are not necessarily biased against one country (the media look for a drama everywhere, right?), the readers can’t simply go out in the streets and check how things really are, as for domestic coverage. Therefore, businesses need a middleman who is more motivated than the state and more systematic than the media in helping shoppers in emerging markets.
I learned [from Lee] this [economy of effort] the hard way. Once, in response to a question, I wrote him three paragraphs. I thought I was comprehensive. Instead, he said, “I only need a one sentence answer, why did you give me three paragraphs?” I reflected long and hard on this, and realised that that was how he cut through clutter. When he was the Prime Minister, it was critical to distinguish between the strategic and the peripheral issues.
And that’s what Twitter does. It teaches brevity to millions. Academics and other professionals who face tons of information daily must love it. First, because it saves their time. Second, it prioritizes small pieces of important information.
Emails and traditional media do this badly because people can’t resist the temptation to get into “important details.” But my details are important only after you asked for them. And Twitter restrains me from writing them in advance by leaving me only 140 characters (right now, I’m over 100 words already). So, it saves two people’s time. As Winston Churchill, himself a graphomaniac, said, “The short words are the best.”
Like many other good ideas, this wasn’t the thing founders initially had in mind. They had to cut all messages to 140 characters to make them compatible with SMS and, thus, mobile. Later on, web services, such as Imgur, borrowed this cutoff. This time not as technical restriction, but to improve user experience. That’s an easy part.
The second part is difficult. Twitter is bad at prioritizing information. Tags and authors remain the major elements of structure. Search delivers unpleasant experience (maybe this made Twitter cooperate with Google). If you missed something in the feed, it’s gone forever.
This weak structure is partly due to initial engineering decisions. However, structuring information without user cooperation is difficult everywhere. And users won’t comply as twits should be effortless by design. It means engineers have to do more of hard work. In turn, it costs money and time. There must be strong incentives to do this. The incentive is not there because Twitter lacks competition.
Would anyone step in and fix it? Suppose, you’re taking a cheap way and ask users to be more collaborative. You can make Twitter for academics with all the important categories, links, and whatever helps researchers communicate more efficiently. This alternative will likely—if it hadn’t yet—fail to gain a critical mass of users. Even in disciplined organizations, corporate social networks die due to low activity. Individually, employees remain with what others use. The others use what everyone uses, and everyone uses what he used before. You need something like a big push to jump from the old technology.
Big pushes away from Twitter is more like science fiction now. Whatever deficiencies it has, the loss-making company priced at $30 billion dollars wins over better-designed newcomers. In the end, its 280 million users are centrally planned by Twitter’s CEO. That’s about the population of the Soviet Union by 1991.
It’s not new that big companies lock users in their ecosystems. The difference is, sometimes it’s justified, other times it’s not. For Twitter, it’s difficult to imagine any other architecture because major social media services all impose a closed architecture with third-party developers joining it on slavery-like conditions. To take the richest segment, most of iOS developers don’t break even. So, apart from technical restrictions that Twitter API has, the company doesn’t offer attractive revenue sharing options to developers that contribute to its capacities and, thus, market capitalization. For example, to address the structural limitations mentioned before.
All in all, interesting experiments in making communications more efficient end very quickly as startups reach traction. After that moment, they become conservative, careful, and closed. And this is a step backward.
The IT industry has two types of products: those that save time (think of Google Search) and those that waste consume time (like Facebook). Though both are free, time spent on Facebook is sort of opportunity costs, typically equal to the user’s wage or whatever he does instead.
Even if the consumer formally pays nothing for either of the services, his behavior is not the same. That’s because of demand elasticities. One marginally relevant example:
These are demand curves. Percentage shows adoption rates. Nevermind the goods on the right. These are not IT and even not the developed world, but this is the most illustrative data of this kind around.
Most goods have elastic demand here. The blue curve also shows the striking difference in demand between zero and any positive price. This is very much like web products: the user base shrinks rapidly when the price becomes positive. For the freemium models, the premium user base is south of 5%. That’s why startups avoid pricing users at early stages.
Facebook also likes to pose itself as a free product. But it’s not really free. According to stats, an average user spends 40 minutes per day on Facebook. Though overstated, such usage is equivalent to $13 paid each day with the median US wage taken as opportunity cost.
Facebook, unlike Google, can set nominal access fees. Users already pay a lot for it, and equilibrium is around inelastic zone of the demand curve. Paywalled Facebook would make its shareholders happier because its current evaluation at $200 per user skyrockets with the enhanced cash flow. The current ad-based model is a dead end for Facebook because its ads target cold clients (compared to Google’s and Amazon’s visitors). While current earnings are very low for such a big company, Facebook’s P/E ratio of 75 is what investors are ready to pay knowing the forthcoming switch to a viable business model—and the paywall is one of them.
The logic of low elasticity under positive opportunity costs is relevant for other time-consuming services. Major newspapers had got paywalls long ago, but for other reasons: they have fewer users and high labor costs. Genuinely scalable web services are reluctant to experiment with payments and settle with nicely looking “premium” prices, like $5 or $10, which are loosely connected with costs and nearby offers, but never look like empirically grounded. Generally, these services prefer rules of thumb to experimentation. Maybe that’s a miss, since when the monthly fee is way below the hourly wage, demand is expected to be inelastic, so revenue opportunities must be around.
And yes, that’s possible because the IT industry is basically many monopolies complaining a lot about competition which isn’t there.
Over the years Google earned a reputation as a unique workplace endlessly generating great innovations. This image of an engineering wonderland missed many important aspects of the company’s inners. You could expect Google’s management to be a bit more critical about this. But as Eric Schmidt’s new book How Google Works shows, it’s not the case. The book reestablishes all the major stereotypes, while paying little attention to the things that made up 91% of Google’s success.
The 91% is the share of revenue Google generates from advertising sold at the famous auctions occurring each time when someone opens a webpage. While an auction is an efficient way of allocating limited resources such as ad space, these ad auctions squeeze advertisers’ pockets in favor of the seller, that is, Google and its affiliates.
In economic terms, auctions eliminate consumer surplus:
That’s a “normal” market, when advertisers pay the equilibrium price. Instead, Google takes the entire surplus by selling ads in individual units—each for the maximum price advertisers would pay. The blue supply curve is nearly flat in this case, and the prices go along the red demand curve. Technically, advertisers pay the second highest price—the mechanism chosen by Google for stability (see generalized second-price auction and Vickrey auction)—but in intensive competition the difference between the first and second prices is small.
How does it work in practice? Suppose you are looking for a bicycle and just google it. When your AdBlock is off, you see something like this:
Now, you click on “made-in-china.com,” buy whatever it sells, and have your bicycle delivered to you. Made-in-China.com pays about $2.72 to Google for you coming through this link (you can find prices for any search query in the Keyword Planner). This price is determined during the auction, when many bicycle sellers automatically submit their bids and ad texts attached to the bids.
The precise auction algorithm is more complex than just taking the highest bid, because the highest bid may include an ad that you won’t click on and the opportunity will be wasted. Also, since conversion rates are way below 100%, Made-in-China.com has to pay these $2.72 several times before a real buyer comes by. It increases the price of bicycles the website sells. Some insurance-related ads cost north of $50 each—all paid by insurance buyers in the end.
Though this mechanism would make no sense without users attracted by Google’s great search engine, the mechanism takes most out of customers—and transfers it to Google.
How does Google Search attract users? Well, first, by showing them relevant results. It sounds more trivial now than it was ten years ago. Now users expect Amazon.com to be the first link for almost any consumer good and Wikipedia for topics of general interest. These websites are considered the most relevant not because they’re the best in some objective sense, but again because of particular technologies that made Google so successful.
Larry Page and Sergey Brin’s key contribution to their startup was PageRank algorithm. PageRank is patented, but the underlying algorithms are easy to find in graph theory. The more links point to your website, the higher position your website gets in search results. When I google “PageRank,” I have Wikipedia’s article on the top. When I link to this article here, it becomes more likely that Wikipedia’s article will remain at the top. As a side effect, linking to the first page of Google results creates a serious competitive advantage for top websites. For Wikipedia, it may be a plus as more people concentrate on improving its pages. But strong positions in search results also secure Amazon.com’s monopoly in e-commerce.
Google’s search technologies are supported by its intensive marketing efforts in eliminating its competitors. Google paid Mozilla for keeping Google as its default search all along before Yahoo! outbid it in 2015. Four years ago, Eric Schmidt testified at Senate hearing about unfair competition practices by Google regarding search results allegedly biased in favor of Google services. The European Commission investigates Google’s practices in Europe. In mobile markets, Google demands from hardware manufacturers to install Google Mobile Services on all Android devices—so users go after their status quo bias and stay with Google everywhere.
There’re more fascinating examples of Google protecting its market share. They’re missing in Eric Schmidt’s book, which gives all credit to Google’s engineers and nothing to its lawyers and marketing people.
When a typical business creates something, managers carefully look after costs. They negotiate with suppliers, look for quality, build complex supply networks, balance payments, insure their company from price shocks. Google is the fifth largest company in the world, but it’s mostly free of these headaches. Unlike Walmart, ExxonMobil, or Berkshire Hathaway, Google employees make things out of thin air and outsource routines, like training its search engine, to third parties.
It ensures that even entry- and mid-level employees are extremely skillful. Not surprisingly, most Google legends concern its HR policies. These legends split into two categories: that make sense and that don’t.
The culture stuff is what makes no sense. It’s easy to see in non-policies like granting 20% of time to personal projects. This rule might mean something for car assembling jobs; but here it’s software development. An engineer’s personal projects may take 50% of the time if he’s done his daily job—or zero otherwise. It depends on his ability to deliver results expected from his salary. More importantly, his personal projects belong to Google, even if he delivers his daily projects in time but once edited his personal code at the campus.
The book also mentions the 70/20/10 rule: “70 percent of resources dedicated to the core business, 20 percent on emerging, and 10 percent on new.” Even if the authors could prove that the rule is optimal, most other companies are so limited in resources that they have to put 100 percent into the core business.
Neither real things make the Google culture different. Each employee must have a decent workplace, attention, and internal openness, but these things are not sufficient for a great company. We are not in Ancient Greece. Other companies also treat employees well: not much slavery around, meals are fine. Google just tends to be at the extreme.
Laszlo Bock, SVP of People Operations, tried to dissuade the public from thinking that good HR policies require Google’s profit margins. In his opinion, you can get much out of people with openness and good treatment alone. His examples include telling employees about sales figures. It’s sort of an alienated example. First, sales numbers aren’t always as optimistic as Google’s history. Ups and downs, you know. You have to learn how to communicate downs to employees and keep them optimistic.
Soberness appears in less fortunate startups. Evan Williams of Blogger had the moment when the money ran out and employees didn’t appreciate it: “Everybody left, and the next day, I was the only one who came in the office” (from Jessica Livingstone’s Founders at Work, a good, balanced account on early days at startups). It’s just one example that relationships with employees are not as trivial as Bock presents them.
If not culture, then what makes the difference? Quite trivially, the privileged access to job candidates. First, it’s not about money because Google easily outbids everyone else. Its entry-level wages surpass those of Wall Street firms, including major hedge funds, like Bridgewater Associates and Renaissance Technologies. Second, Google has the right of the first interview. That comes with exceptional reputation, low-stress jobs, secure employment, ambitious goals and resources to implement big ideas.
How Google Works understates the actual achievements of the company. The book is all about famous corporate rules making the business look simplistic. It’s not. The $360 bn business consists of hundreds of important details in each key operation, like hiring, marketing, and sales.
Keeping these things together is an achievement of Eric Schmidt, Laszlo Bock and other executives. However, Schmidt’s book should not mislead other entrepreneurs into thinking that the 20% rule creates great products and reporting sales numbers to employees increases sales better than ad auctions do. Google is a good role model for learning hardcore IT business, but readers will have to wait for some other book to learn from this company.