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by
Tim O'Reilly
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December 30, 2018 - January 13, 2019
Over time, as networks reach monopoly or near-monopoly status, they must wrestle with the issue of how to create more value than they capture—how much value to take out of the ecosystem, versus how much they must leave for other players in order for the marketplace to continue to thrive.
In one of the early classics of systems engineering, Systemantics, John Gall wrote, “A complex system that works is invariably found to have evolved from a simple system that worked. The inverse proposition also appears to be true. A complex system designed from scratch never works and cannot be made to work. You have to start over beginning with a working simple system.”
Simple, decentralized systems work better at generating new possibilities than centralized, complex systems because they are able to evolve more quickly. Each decentralized component within the overall framework of simple rules is able to seek out its own fitness function. Those components that work better reproduce and spread; those that don’t die off.
The coordination is all in the design of the system itself.
A PLATFORM BEATS AN APPLICATION EVERY TIME
This is important: Amazon Web Services was the answer to a problem in organizational design. Jeff understood, as every network-enabled business needs to understand in the twenty-first century, that, as HR consultant Josh Bersin once said to me, “Doing digital isn’t the same as being digital.” In the digital era, an online service and the organization that produces and manages it must become inseparable.
“Stevey’s Platform Rant.” In it, Yegge describes a memo that he claimed Jeff Bezos wrote “back around 2002 I think, plus or minus a year.” As Yegge described it: His Big Mandate went something along these lines: 1. All teams will henceforth expose their data and functionality through service interfaces. 2. Teams must communicate with each other through these interfaces. 3. There will be no other form of interprocess communication allowed: no direct linking, no direct reads of another team’s data store, no shared-memory model, no back-doors whatsoever. The only
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Promise theory, as Burgess outlines it, is a framework for understanding how independent actors make promises to each other—the essence of that highly structured communication. Those actors can be software modules promising to respond in a certain way to an API call, or small teams promising to deliver a particular result. Burgess writes: “Imagine a set of principles that could help you to understand how parts combine to become a whole, and how each part sees the whole from its own perspective. If such principles were any good, it shouldn’t matter whether we’re talking about humans in a team,
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Spotify plots organizational culture along two axes: alignment and autonomy.
A modern technology engineering organization (or an entire organization like Amazon or Spotify) seeks to have high alignment and high autonomy. Everyone knows what the goal is, but they are empowered to find their own way to do it.
“I tell people, ‘Don’t follow my orders. Follow the orders I would have given you if I were there and knew what you know.’” That is, understand our shared objective, and use your best judgment about how to achieve it.
This outcome-focused, outside-in approach means that, effectively, a team is promising a result, not how they will achieve it. As in Afghanistan, high autonomy is required by the rapidly changing conditions of a fast-growing Internet service.
High-autonomy technical cultures have developed a technique—the stand-up meeting—by which people and groups must work together toward a common goal and review the status of their promises to each other.
Today software has become a process of constant, more or less incremental improvements. From the point of view of the company offering an online service, software has gone from being a thing to a process, and ultimately, a series of business workflows. The design of those workflows has to be optimized not just for the creators of the software but for the people who will keep them running day-to-day.
Much of this work is completely automated. Hal Varian calls this “computer kaizen,” referring to the Japanese term for continuous improvement. “Just as mass production changed the way products were assembled and continuous improvement changed how manufacturing was done,” he writes, “continuous experimentation . . . improve[s] the way we optimize business processes in our organizations.”
The practices of DevOps have continued to evolve. Google calls its version of the discipline “Site Reliability Engineering” (SRE). As described by Benjamin Treynor Sloss, who coined the term, “SRE is fundamentally doing work that has historically been done by an operations team, but using engineers with software expertise, and banking on the fact that these engineers are inherently both predisposed to, and have the ability to, design and implement automation with software to replace human labor.”
Often, when new technology is first deployed, it amplifies the worst features of the old way of doing business. Only gradually do individuals and organizations realize, through a cascading network of innovations, how to put new technology properly to work.
Simply putting a digital front end on a broken bureaucratic system often only makes the problem worse, because the digital system replicates existing processes without rethinking them from the ground up.
The second realization was that understanding service delivery is the key to good policy making.
Design with data; Do the hard work to make it simple; Iterate. Then iterate again; Build digital services, not websites; Make things open.
We also learned that the practices that make good apps turn out to be very relevant for making good rules as well.
If, instead, you step back and view these companies with a twenty-first-century mindset, you realize that a large part of what they do—delivering search results, news and information, social network status updates, relevant products for purchase, and drivers on demand—is done by software programs and algorithms. These programs are workers, and the programmers who create them are their managers. Each day, these “managers” take in feedback about their workers’ performance, as measured in real-time data from the marketplace, and if necessary, they give feedback to the workers in the form of minor
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The tasks performed by these software workers reflect the operational workflow of the digital organization.
summed up the approach that has been essential to the success of Google’s core search service. Its insight, that “simple models and a lot of data trump more elaborate models based on less data,”
When you are drawing a map of new technologies, it’s essential to use the right starting point. Much analysis of the on-demand or “gig” economy has focused too narrowly on Silicon Valley without including the broader labor economy. Once you start drawing a map of “workers managed by algorithm” and “no guarantee of employment” you come up with a very different sense of the world.
The algorithm is the new shift boss. What regulators and politicians should be paying attention to is the fitness function driving the algorithm, and whether the resulting business rules increase or decrease the opportunities offered to workers, or whether they are simply designed to increase corporate profits.
remixed into a “meme”—which has now come to mean a graphic or video representation of a key moment or quote that is freed from its original context, designed to be shared, designed for impact rather than deeper dialogue or understanding.
Humans are living in the guts of an AI that is only now being born. Perhaps, like us, the global AI will not be an independent entity, but a symbiosis with the human consciousnesses living within it and alongside it.
THE DESIGN OF THE SYSTEM SETS ITS OUTCOMES
Yes, the markets have become a hybrid of human and machine intelligence. Yes, the speed of trading has increased, so that a human trader not paired with that machine has become prey, not predator. Yes, the market is increasingly made of complex financial derivatives that no human can truly understand. But the key lesson is one we have seen again and again. The design of a system determines its outcomes. The robots did not force a human-hostile future upon us; we chose it ourselves.
Stock prices are a map that should ideally describe the underlying prospects of companies; attempts to distort that map should be recognized for what they are. We need to add “fake growth” to “fake news” in our vocabulary to describe what is going on. Real growth improves people’s lives.
Mistaking what is good for financial markets for what is good for jobs, wages, and the lives of actual people is a fatal flaw in so many of the economic choices business leaders, policy makers, and politicians make.
It isn’t Wall Street per se that is becoming hostile to humanity. It is the master algorithm of shareholder capitalism, whose fitness function both motivates and coerces companies to pursue short-term profit above all else. What are humans in that system but a cost to be eliminated?
Right now we’re at an inflection point, where many rules are being profoundly rewritten.
The ratio between a company’s revenues, cash flow, or profits and its market capitalization is one of many imaginary numbers that make up the world of financial capital. In theory, the intrinsic value of owning a stock is based on the net present value of its expected future profits. In practice, it is that net present value times the expectations of millions of potential buyers and sellers.
If you have access to supermoney, you can operate for years at a loss. This is one reason—not just the superior customer benefits and economic efficiencies of their technology or business model—that Internet companies can disrupt older, less highly valued companies.
The chorus of doubt about the jobless future sounds remarkably similar to the one that warned of the death of the software industry due to open source software. Clayton Christensen’s Law of Conservation of Attractive Profits holds true here too. When one thing becomes commoditized, something else becomes valuable. We must ask ourselves what will become valuable as today’s tasks become commoditized.
The status quo isn’t worth protecting. It’s so easy to be in reaction, on the defensive, fighting for the world we had yesterday. Fight for something better, something we haven’t seen yet, something we have to invent.
It was language that was our greatest invention, the ability to pass fire from mind to mind. In periods where knowledge is embraced and widely shared, society advances and becomes richer. When knowledge is hoarded or disregarded, society becomes poorer.
Searching out the frontier for enhancing human value is the great challenge for the next generation of entrepreneurs, and for all of society.
LEARNING: THE MASTER AUGMENTATION One key to understanding the future is to realize that as prior knowledge is embedded into tools, a
different kind of knowledge is required to use it, and yet another to take it further. Learning is an essential next step with each leap forward in augmentation.
This design pattern, that the future is built before it can be bought, is an important one to recognize. The future is created by people who can make and invent things and those who can tinker and improve and put inventions into practice. These are people who learn by doing.
That is the essence of the Maker movement. Making for the joy of exploration. Making to learn.
There’s no joy in our current education system. It is full of canned solutions to be memorized when it needs to be a vast collection of problems to be solved. When you start with what you want to accomplish, knowledge becomes a tool. You seek it out, and when you get it, it is truly yours.
is the practice of investigating what we don’t know. Ignorance, not knowledge, drives science.
We have far too little fun in most formal learning, and people are hungry for it. If you can’t inspire curiosity, chances are you are on the wrong path.
That was certainly true of me. I studied Greek and Latin in college. Everything I learned about computers, I learned on the job. The knowledge I learned in college was useless to me. The habits of mind that I formed were what mattered, the foundational skills of study, and particularly the ability to recognize patterns. The struggle to parse complex Greek texts that were, quite frankly, beyond my skill in the language was great preparation when I took on the challenge of documenting programs written in programming languages that at first I barely understood.
Over the course of my career, learning itself has been the most important part of my ongoing work.
The skills needed to take advantage of new technology proliferate and are developed over time through communities of practice that share expertise with each other. Over time, the new skills are routinized and it becomes easier to train lots of people to exercise them. It is at that point that they begin to affect productivity and improve the wages and incomes of large numbers of people.
