The End of the Firm

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 “The official line is that we all have rights and live in a democracy. Other unfortunates who aren’t free like we are have to live in police states. These victims obey orders or else, no matter how arbitrary. The authorities keep them under regular surveillance. State bureaucrats control even the smallest details of everyday life. The officials who push them around are answerable only to higher-ups. Informers report regularly to the authorities. All this is supposed to be a very bad thing — and so it is, although it is nothing but a description of the modern workplace.”

Bob Black, The Abolition of Work and Other Essays (1985)

Peter Drucker’s famous dictum  “If you can’t measure it, you can’t manage it” established math and management as the indisputable co-sovereigns of the modern workplace. As it turns out, Drucker apparently never actually said that[1], but the concept has dominated the workplace since the advent of factories and railroads, telegraphs and electricity. Consider, for example, what it’s like to work at Amazon.

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But, while math and management prospered together under the Industrial Revolution’s mechanistic worldview, today’s digitally-driven marketplace demands a freshly-nuanced management style, or in some cases, no management at all. Either idea challenges an even more foundational historical assumption:  that commerce is best conducted by a firm that must be managed. Eliminate the firm and you eliminate the need to manage it. Get rid of both, and you have an unimaginably different “description of the modern workplace” than Bob Black wrote about 33 years ago.

Last time, we looked at an article by science writer and artificial intelligence engineer George Zarkadakis called “The Economy Is More A Messy, Fractal Living Thing Than A Machine.” In it, he says this about the firm:

Ever since the invention of the assembly line, corporations have been like medieval cities: building walls around themselves and then trading with other ‘cities’ and consumers. Companies exist because of the need to protect production from volatile market fluctuations, and because it’s generally more efficient to consolidate the costs of getting goods and services to market by putting them together under one roof.  So said the British economist Ronald Coase in his paper ‘The Nature of the Firm’ (1937).

“Why do firms exist?” asks Ryan Avent in his book The Wealth of Humans:  Work, Power, and Status in the Twenty-First Century (2016). He provides the same answer as Zarkadakis:

According to a 1937 paper by Nobel Prize- winning economist Ronald Coase, it’s to bring all the necessary people, processes, and information under one roof, instead of contracting it all out. In exchange for the convenience of one-stop shopping, one-size-fits-all,  employees trade their independence and the possibility of greater personal market returns for the firm’ management structure and financial capital, which — as long as they conform to the company culture –  the way we do things around here — promises to keep them on task and to deliver a paycheck in return.

Today, however, the new “gig economy” is fast making that unimaginable the new normal — and that’s only the beginning, says Zarkadakis:

Now, in an era of Ubers-for-everything, companies are changing into platforms that enable, rather than enact, core business processes. The cost of reaching customers has dropped dramatically thanks to the ubiquity of digital networks, and production is being pushed outside the company wall, on to freelancers and self-employed contractors. Market and price fluctuations have been defanged as machine learning and predictive analytics help companies manage such ructions, and on-demand services for labour, office space and infrastructure allow them to be more responsive to changing conditions. Coase’s theory is nearing its expiry date.

The so-called ‘gig economy’ is only the beginning of a profound economic, social and political transformation. For the moment, these new ways of working are still controlled by old-style businesses models – platforms that essentially sell ‘trust’ via reviews and verification, or by plugging into existing financial and legal systems. Airbnb, eBay and Uber succeed in making money out of other people’s work and assets because they provide guarantees for good seller-buyer behaviour, while connecting to the ‘old world’ of banks, courts and government. But this hybrid model of doing digital business is about to change.

Avent concurs, and describes two key dynamics of the new anti-firm business model:  operating culture and rent: — how a business gets things done, and whether it owns the kinds of assets it can let others use, for a price:

Current workplace trends are bidding fair to tear down the firm model of operating. If you take employees out from under the firm umbrella — make them mostly freelancers, outsource jobs to countries on the make — then what’s left of value is mostly the company’s way of getting things done and the assets for which it can charge rent, in the economic sense of billing a premium for scarce assets. How assets become scarce becomes an essential policy-making function. These become essential “intangible” or “social” capital, replacing “human” capital.]

We’ll be talking more about social capital, rent, and other changing dynamics of the workplace.

[1] According to the Drucker Institute, Drucker never actually said that. And see this Forbes article for a rousing condemnation of the idea.

Reframing “The Economy”

We’ve seen that conventional thinking about “the economy” struggles to accommodate technologies such as machine learning, robotics, and artificial intelligence–which means it’s ripe for a big dose of reframing. Reframing is a problem-solving strategy that flips our usual ways of thinking so that blind spots are revealed, conundrums resolved, polarities synthesized, and barriers transformed into logistics.

The Santa Fe Institute is on the reframing case:  Rolling Stone called the Institute “a sort of Justice League of renegade geeks, where teams of scientists from disparate fields study the Big Questions.” W. Brian Arthur is one of those geeks. He’s also onboard with PARC — a Xerox company in “the business of breakthroughs” — and has written two seminal books on complexity economics:  Complexity and the Economy (2014) and The Nature of Technology: What it Is and How it Evolves (2009). Here’s his pitch for reframing “the economy”:

The standard way to define the economy — whether in dictionaries or economics textbooks — is as a “system of production and distribution and consumption” of goods and services. And we picture this system, “the economy,” as something that exists in itself, as a backdrop to the events and adjustments that occur within it. Seen this way, the economy becomes something like a gigantic container…, a huge machine with many modules or parts.

I want to look at the economy in a different way. The shift in thinking I am putting forward here is ,,, like seeing the mind not as a container for its concepts and habitual thought processes but as something that emerges from these. Or seeing an ecology not as containing a collection of biological species, but as forming from its collection of species. So it is with the economy.

The economy is a set of activities and behaviors and flows of goods and services mediated by — draped over — its technologies:  the of arrangements and activities by which a society satisfies its needs. They include hospitals and surgical procedures. And markets and pricing systems. And trading arrangements, distribution systems, organizations, and businesses. And financial systems, banks, regulatory systems, and legal systems. All these are arrangements by which we fulfill our needs, all are means to fulfill human purposes.

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George Zarkadakis is another Big Questions geek. He’s an artificial intelligence Ph.D. and engineer, and the author of In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence  (2016). He describes his complexity economics reframe in a recent article “The Economy Is More A Messy, Fractal Living Thing Than A Machine”:

Mainstream economics is built on the premise that the economy is a machine-like system operating at equilibrium. According to this idea, individual actors – such as companies, government departments and consumers – behave in a rational way. The system might experience shocks, but the result of all these minute decisions is that the economy eventually works its way back to a stable state.

Unfortunately, this naive approach prevents us from coming to terms with the profound consequences of machine learning, robotics and artificial intelligence.

Both political camps accept a version of the elegant premise of economic equilibrium, which inclines them to a deterministic, linear way of thinking. But why not look at the economy in terms of the messy complexity of natural systems, such as the fractal growth of living organisms or the frantic jive of atoms?

These frameworks are bigger than the sum of their parts, in that you can’t predict the behaviour of the whole by studying the step-by-step movement of each individual bit. The underlying rules might be simple, but what emerges is inherently dynamic, chaotic and somehow self-organising.

Complexity economics takes its cue from these systems, and creates computational models of artificial worlds in which the actors display a more symbiotic and changeable relationship to their environments. Seen in this light, the economy becomes a pattern of continuous motion, emerging from numerous interactions. The shape of the pattern influences the behaviour of the agents within it, which in turn influences the shape of the pattern, and so on.

There’s a stark contrast between the classical notion of equilibrium and the complex-systems perspective. The former assumes rational agents with near-perfect knowledge, while the latter recognises that agents are limited in various ways, and that their behaviour is contingent on the outcomes of their previous actions. Most significantly, complexity economics recognises that the system itself constantly changes and evolves – including when new technologies upend the rules of the game.

That’s all pretty heady stuff, but what we’d really like to know is what complexity economics can tell us that conventional economics can’t. We’ll look at that next time.