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Recent advances in artificial intelligence (AI) and computer technology are causing us to think again about some really basic questions: what is a firm? What can firms do better than markets? And what are the distinctive qualities of firms in a world of smart contracts and AI?
While there has been a lot of discussion about “what’s left for humans?” as AI improves at exponential rates — the customary answer is that humans need to focus on the things they are uniquely good at, such as creativity, intuition, and personal empathy — I think we now have to ask, “what’s left for firms?”
In many ways this is an old question, because it takes us back to the arguments of Nobel Laureates Ronald Coase and Oliver Williamson that firms exist to coordinate complex forms of economic activity in an efficient way. If computer technology has the capacity to simplify and streamline transaction costs, more and more work can be done through these smart-contract arrangements, making traditional human-managed firms obsolete. For example, when you say to Alexa “order more dog food,” a chain of activities is initiated that leads to the delivery of a fresh supply of Kibble 24 hours later, with little or no human intervention. This work is coordinated by a single firm, Amazon, but it often involves third parties (makers of dog food, delivery companies) whose systems interact seamlessly with Amazon’s.
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But is this coordination logic, this ability to internalize transactions to make them more efficient, really the raison d’etre of firms? I would argue that it is just one among many reasons that firms exist. And as computer technology simplifies and reduces transaction costs further, it is these other things that firms do uniquely well that will come more to the forefront. Here are four areas where firms excel.
1. Firms create value by managing tensions between competing priorities.
In today’s parlance, firms have to exploit their established sources of advantage (to make profits today) while also exploring for new sources of advantage (to ensure their long-term viability). However, getting the right balance between these two sets of activities is tricky because each one is to a large degree self-reinforcing. Hence the notion of organizational ambidexterity — the capacity to balance exploitation and exploration.
Artificial intelligence is evidently helping many firms to exploit their existing sources of advantage — whether through process automation, improved problem-solving or quality assurance. Artificial intelligence can also be useful in exploring new sources of advantage: in the famous case of AlphaGo, the winning “strategy” was one that no human player had ever come up with; and computers are increasingly writing new musical scores and painting Picasso-like landscapes.
But AI is not helpful in managing the tension between these activities, i.e. knowing when to do more of one or the other. Such choices require careful judgment — weighing up qualitative and quantitative factors, being sensitive to context, or bringing emotional or intuitive factors into play. These are the capabilities that lie at the heart of organizational ambidexterity and I don’t believe AI can help us with them at all right now. IBM’s recently-announced Project Debater is a case in point: it showed just how far AI has come in terms of constructing and articulating a point of view, but equally how much better humans are at balancing different points of view.
2. Firms create value by taking a long-term perspective.
As a variant of the first point, firms don’t just manage trade-offs between exploitation and exploration on a day to day basis, they also manage trade-offs over time. My former colleagues Sumantra Ghoshal and Peter Moran wrote a landmark paper arguing that, unlike markets, firms deliberately take resources away from their short-term best use, in order to give themselves the chance to create even more value over the long term. This “one step back, two steps forward” logic manifests itself in many ways — risky R&D projects, pursuing sustainability goals, paying above-market wages to improve loyalty, and so on. We actually take it for granted that firms will do many of these things, but again they involve judgments that AI is ill equipped to help us with. AI can devise seemingly-cunning strategies that look prescient (remember AlphaGo) but only when the rules of the game are pre-determined and stable.
An example: the “Innovator’s Dilemma” is that by the time it’s clear an invasive technology is going to disrupt an incumbent firm’s business model, it’s too late to respond effectively. The incumbent therefore needs to invest in the invasive technology before it is definitively needed. Successful firms, in other words, need to be prepared to commit to new technologies in periods of ambiguity, and to have a “willingness to be misunderstood,” in Jeff Bezos’s terms. This isn’t an easy concept for AI to get used to.
3. Firms create value through purpose — a moral or spiritual call to action.
There is a second dimension to long-term thinking, and that is its impact on individual and team motivation. We typically use the term purpose here, to describe what Ratan Tata calls a “moral or spiritual call to action” that leads people to put in discretionary effort — to work long hours, and to bring their passion and creativity to the workplace.
This notion that a firm has a social quality — a purpose or identity — that goes beyond its economic raison d’etre is well established in the literature, from March and Simon through to Kogut and Zander. But it still arouses suspicion among those who think of the firm as a nexus of contracts, and who believe that people are motivated largely through extrinsic rewards.
My view is that you just need to look at charities, open source software movements, and many other not-for-profit organizations to realize that many people actually work harder when money is not involved. And it is the capacity of a leader to articulate a sense of purpose, in a way that creates emotional resonance with followers, that is uniquely human.
Successful firms, in other words, institutionalize a sense of identity and purpose that attracts employees and customers. Ironically, even though blockchain technology is — by definition — about building a system that cannot be hacked, or misused by a few opportunists, people still prefer to put their faith in other people.
4. Firms create value by nurturing “unreasonable” behavior.
There are many famous cases of mavericks who succeeded by challenging the rules, such as Steve Jobs, Elon Musk, and Richard Branson. With apologies to George Bernard Shaw, I think of these people as unreasonable — they seek to adapt the world to their view, rather than learn to fit in. And if we want to see progress, to move beyond what is already known and proven, we need more of these types of people in our firms.
Unreasonableness is antithetical to the world of AI. Computers work either through sophisticated algorithms or by inference from prior data, and in both cases the capacity to make an entirely out-of-the-box leap doesn’t exist. Consider the case of investment management, where robo advisors are not just making trades, they are also providing investment advice to investors, and at a fraction of the cost of human financial advisors. But as the Financial Times said last year, “when it comes to investing, human stupidity beats AI.” In other words, if you want to beat the market, you need to be a contrarian — you need to make investments that go against the perceived wisdom at the time, and you need to accept the risk that your judgment or your timing might be wrong. Both qualities that — at the moment — are distinctively human.
So one of the distinctive qualities of firms is that they nurture this type of unreasonable behavior. Of course, many firms do their best to drive out variance, by using tight control systems and punishing failure. My argument is that as AI becomes more influential, though the automation of basic activities and simple contracts, it becomes even more important for firms to push in the other direction — to nurture unorthodox thinking, encourage experimentation, and tolerate failure.
In a recent Fast Company article, Vitalik Buterin described how all the elements of Uber’s ride-sharing service could be provided through Ethereum-based applications that worked seamlessly with one another: “the whole process is basically as before, but without the middleman [Uber].” This is may be true, but it doesn’t necessarily follow that a computer-mediated service is the better option.
For example, in 2016 a distributed autonomous organization (DAO) was launched on Ethereum. This idea was that it would run without human intervention, using pre-established rules and blockchain technology to operate seamlessly. But it had a small technical flaw, which allowed an un-named user to siphon of $55 million of the money raised in a matter of days. Faced with the meltdown of their entire creation, the founding fathers of Ethereum intervened, creating a so-called hard fork in the blockchain that allowed investors to get their money back, and for the development of Ethereum applications to continue.
No matter how powerful the technology, sometimes a little human judgment is necessary to keep things moving in the right direction.