Of course Buffett is a practicing manager and less interested in academic talk and macro-generalities than in the specifics of the firms in which BHI invested – whose managers were surely measured on how they pushed their firm’s productivity ahead.  His letter was a superb lesson on ‘business models’, the topic of much sloppy academic talk.  But Daniel Gross, executive editor at strategy+business, seemed alone in noting the letter’s display of Buffett’s deeper grasp, his own ‘secret sauce’.  Buffett’s tale of what happened to BHI’s Dexter shoe-making operation differed from the usual story of overseas competition’s impact.  It suggested a more fundamental business model or ‘theory of the firm’ – that firms can sometimes evolve faster than the people they employ.

How can this happen?  As shareholder value overtook firm growth as the goal, Wall Street innovated with maneuvers that included liquidating the firm, laying off its people, and directing the funds released into different lines of business.  So long as relevant markets existed, tangible (tradable) assets can be reallocated almost instantly.  People, less tradable, get left behind.  Buffett was not sympathetic, writing “When Wall Street gets innovative, watch out!” and that BHI “only goes where it is welcome”, recognizing every firm is more than its tradable assets and has been created by a workforce and community whose future cannot be airbrushed out with simplistic finance talk.  Entrepreneurial managers have a double responsibility; to make the firm’s future, but also to deal with its past.

There has been plenty of discussion about the ‘structural unemployment’ resulting from investor-oriented strategizing.  There is also a huge literature on ‘change management’ – but little attention to firms changing faster than the people who bring them to life.  Firms are actually puzzles we do not understand well – as Nobel-winner Ronald Coase pointed out in 1937 when he asked why firms exist and are as they are.  The most familiar metaphors for firms are: (a) carefully designed and operated machines, (b) communities of motivated people, or (c) as Citizen’s United vs FEC suggested, economic actors or ‘persons’ themselves.  None tell us much about firms and people evolving at different rates.  What was Buffett thinking?  Gross explained people face barriers to change that firms do not and so have fewer options and ‘levers to pull’.  True, but there seems to be more to it and maybe Buffett intuited something about the nature of firms, to use Coase’s term, that is unlike the nature of either people or machines.  Perhaps, being so thoughtful about the nature and social place of private sector firms, as well as successful, he knows something about (c) that informs BHI’s investing.

The recent Citizen’s United vs FEC and McCutcheon vs FEC judgments have centuries of legal debate behind them showing corporate lawyers are far from agreed about what firms are – even if management academics have no such doubts.  On the one side are ‘corporate nominalists’ who see the firm as a bundle of contracts between individual shareholders governing their property and its application.  On the other, ‘corporate realists’ who see firms as distinct legal entities with a ‘personality’ and rights and duties in the socioeconomy.  The 1930s development of ‘managerialist’ ideas, recognizing how in large corporations managerial control overwhelmed the share-owners’ rights, led many to see victory for the ‘realists’.  In the 1970s the tide was reversed by neoliberal ‘agency theorists’, sometimes to the extent that, along with pillorying government, managers were characterized as ‘the problem’ for corporate governance rather than its solution.  Maximizing shareholder value (MSV) is nominalism’s battle-slogan, corporate social responsibility (CSR) is realism’s.  The call for ‘more ethical management’ is realism pushing back against nominalism’s recent dominance.  For the most part management academics pay no attention to corporate law debates, choosing whatever position best suits the theory they purvey – without bothering to justify their choice as lawyers must when arguing real cases.  In contrast, Buffett is a practicing manager, aware of the dichotomy and its implications.  But with what resolution?  What is his working ‘business model’?  His letter suggested a model that escapes both corporate lawyers and management academics.

The American educationalist and philosopher John Dewey added to the legal debate in a 1926 article, arguing that realists treating firms as ‘legal persons’ misunderstood the complexity of ‘personhood’ in a capitalist democracy.  The dichotomy should be dismissed as confusing, obscuring the nature of the firm and the roles of investor and manager alike.  But Dewey also noted the concept of the corporation, to use Drucker’s term, had a ‘chameleon-like’ ability to change with the times.  In a footnote he recalled the older view of the firm as a ‘legal fiction’ which, stripped of its legal baggage, presents the firm as an ‘idea’ – not captured by either nominalist or realist views – noting such ‘imaginary creatures’ are ‘notoriously nimble’.  Today most theorists are stuck on one or other horn of the dichotomy, from where firms seem inherently static and lifeless, given changing assets or people is a challenge.  Thus ‘entrepreneurship’ often gets confused with ‘change management’.

But some are not so stuck, and Buffett may be among them.  Katsuhito Iwai, probing the differences between American and Japanese firms, embraced the firm’s ‘dual nature’, both nominalist and realist.  Rather than being tied to or defined by its tangible assets or its people, each firm is the flexible imaginary creature Dewey sighted inhabiting the middle ground between nominalist and realist abstractions.  How can this be?

Intangible assets, such as ‘know how’, shed some light here.  Buffett’s letter included many comments on intangible assets, the huge part they play in BHI, and the difficulties they present accountants, managers, and investors.  Nominalist theorists presume the meaning of assets is self-evident and that the firm’s accounts show the tangible and real.  Against this Edith Penrose pointed out the ‘nominalist fallacy’, that in practice assets are only as valuable as management’s ideas about how they can be applied.  At the other horn, realists presume people calculate rationally and know their purposes and aims.  Dewey pointed to the ‘realist fallacy’ that people do not know their own minds fully, that their tacit understandings matter.  Imai argued, as most of us do, that tacit knowledge emerges in the middle ground as people with assets pursue a shared idea or purpose.  Crucially this imaginary substance is living but cannot survive the cessation of the firm’s practice, its liquidation.  So the shareholders cannot reallocate it.  A ‘going concern’ has imaginary content that cannot be sold, yet is fundamental to its ability to transform the imagining that holds the horns of the dichotomy together into economic value.

This is not news to experienced managers.  But it is not easy to operationalize or explain as part of a specific firm’s business model, as Buffett did in his 2016 letter.  His language was homespun rather than academic, thank goodness, but not lacking in precision or power.  He identified three notions of a firm – nominalist, realist, and imaginary – so as to explain managing their intersection.  With a real business to run his model did what those of economists or management academics failed to: help BHI’s managers (1) embrace their particular firm’s trinity, (2) synthesize it into value-creating practice, and (3) highlight the risks of dichotomies between their firm, its people, and its surroundings, risks that are the focus of business ethicists and principal-agent theorists.  Leveraging from the insight that the nominalist, realist, and imaginary can change at different rates, Buffett’s model outlined answers to Coase’s questions about the nature of the firm – practical answers that bring the BHI shareholders’ concerns about productivity, managerial ethics, and financial performance together.  A secret sauce indeed.

About the author:

JC Spender trained first as a nuclear engineer then in computing with IBM.  He moved into academe as a strategy theorist, opening up a subjectivist/creative approach that complements mainstream rational planning notions of strategizing.  This 40-year project was brought to completion in Business Strategy: Managing Uncertainty, Opportunity, and Enterprise (OUP 2014).

First, since the time of Cantillon (who used the term in its modern business-oriented sense in 1732) business has become vastly more important, pushing back against lineage, religion, and political maneuver as sources of social and economic power. Thus entrepreneurship is foundational to democratic capitalism, another ‘good’.

Second, and a topic in Drucker’s writings, entrepreneurs are a ‘good’ as they push back against large-scale corporatism, advancing capitalist democracy to its ‘next phase’. The general view, framed by 19^th century Anti-Trust activists, revelations about ‘robber barons’, and especially by Berle & Means in 1932, is that large corporations have become too influential to serve our socio-economy well, being run by un-elected self-interested managers who maximize shareholder wealth rather than social benefit.

Entrepreneurship suggests a different kind of socioeconomy with higher aims – Entrepreneurial Society and its ‘creatives’. These urge increased freedom for individual entrepreneurs and the development of ‘entrepreneurial culture’ in existing corporations; so advancing capitalist democracy towards greater growth and ‘lifting all boats’.

Familiarity covers up important details. Cantillon’s entrepreneur was an individual (like himself, a rich Parisian banker) who spots an opportunity for ‘arbitrage’ – buying low and selling high. This is about information and its availability. Long before the Internet and ‘high speed trading’ many bankers had ‘private’ sources about what might tilt the economy, a pigeon carrying news of Waterloo being a famous (and scurrilously Anti-Semitic) legend.

The point here is that arbitrage is not creative, it simply signals uneven information in an existing market. Cantillon’s notion was refreshed by Kirzner and lives on as ‘effectuation’. Several decades after Cantillon, Jean-Baptiste Say, a founder of Paris’s ESCP, the first modern business school, and a great proselytizer for Adam Smith, re-purposed the term entrepreneur as the creator of a new business. Smith, of course, wrote that the division of labor, the ultimate source of wealth, is ‘limited by the extent of the market’. One of history’s curiosities is that Smith paid little attention to entrepreneurs even as he argued for ‘free markets’. In contrast, Say’s idea of entrepreneurship led to ever-popular but still un-proven claims that ‘small business’ is the main source of economic growth, though entrepreneurs like Henry Ford, Jamsetji Tata, and the Michelin brothers clearly built huge corporations.

The contrast of Cantillon’s and Say’s ideas remains a source of confusion. Say’s view is broader for it does not presuppose an existing market to carry the arbitrage. Rather the economy is extended, the new firm implementing an ‘entrepreneurial idea’ that existing markets do not allow, implying firms arise in reaction to ‘market failures’ that may well include desired but un-provided products and services or competition to monopolists. Entrepreneurial society facilitates such extension.

But there are traps for the unwary. While Cantillon’s notion hinges on maldistributed knowledge about the world that exists, Say’s notion pushes out into the unknown, to a world re-made by entrepreneurial activity. Who knew the world hungered for TV dinners or messages of less than 140 characters? Say’s idea seems more interesting but there is not much that can be said about it beyond ‘go forth!’ It is not a testable theory. Rather it defines search that is ‘under-determined’, probing what is uncertain and not known.

Ultimately it takes us beyond rational analysis and into the realm of imagination, a place of some challenge. Only those profoundly ignorant or dismissive of human history would presume that acting on what we imagine leads necessarily to a ‘good’. Thus the distinction between Cantillon and Say contrasts re-making what we know into something better versus throwing it over with revolution.

Many pontificating about entrepreneurship seem blissfully unaware of these implications. For instance, only this month (February 3^rd) Stanford University President John Hennessy defined entrepreneurship as “transforming an idea into something real that can have a wide impact” – very laudable for those on the right side of history, but evils often result. The bankers and ‘rocket scientists’ who developed derivatives were very entrepreneurial.

The technical point here is that if entrepreneurship ever becomes a testable science, we can anticipate avoiding entrepreneurial tragedies like Thalidomide or the Dust Bowl or cigarettes; the science being supported by rational markets that reveal truth as they respond to valuation errors. Behavioral economics helps show the way here.

But this is not Say’s view. In which case entrepreneurship is an utterly different term for how we explore our socio-economy’s uncertainties – an inherently moral and ethical matter. While Schumpeter’s ‘creative destruction’ is much cited, it is not a testable theory that explains economic growth. Nor does it provide any guidance about ‘new combinations’. In fact, he anticipated the obsolescence of the entrepreneur as large corporations corralled the R&D that drove economic growth.

Thus the enthusiasm for entrepreneurship is a curious resurgence of old political debate about the role of wealth and government in our socioeconomy. It is new political language that prioritizes anti-statism and ‘freedom’ over structure and predictability, without justification. Even if this language seems adequate for policy decisions, entrepreneurship is enmeshed with deeper social questions about, for instance, rising inequality, declining educational access, structural unemployment, and defense spending. Stanford graduates’ talk about becoming entrepreneurs is trifling, for as members of the elite they have access to funds, connections, and new science, with plenty of Plans B should they come unstuck (as most entrepreneurs do).

But when these ideas infect government policies will they benefit more than the already privileged? Will they help those trying to create employment in Arkansas, Marseilles, or Basilicata? There is urgency here, for in our post-Keynesian age governments around the world, especially in the EU and US, have chosen hands-off encouragement of private sector entrepreneurs over policies that can be monitored directly. Indeed, there is reason to suspect governments are deploying entrepreneurial arguments in precisely the ways Berle & Means feared, to hide their responsibilities, especially when the consequences include growing corruption – albeit cloaked in widely varying national garments sewed entrepreneurially out of political opportunity.

About the author:

JC Spender trained first as a nuclear engineer then in computing with IBM.  He moved into academe as a strategy theorist, opening up a subjectivist/creative approach that complements mainstream rational planning notions of strategizing.  This 40-year project was brought to completion in Business Strategy: Managing Uncertainty, Opportunity, and Enterprise (OUP 2014).

We have various ideas about how humans and computers interact. ‘Soft AI’ sees computers as tools to aid us in our endeavors; tireless and precise as an electric drill or robotic medical sampler obeying our instructions – but never more. ‘Hard AI’ emerges as we imagine these tools becoming (a) more complex than we are, and (b) able to ‘learn’, improve their performance and so move beyond our limitations.

It is not easy to define the singularity. The movie “Imitation Game” drew attention to Turing’s Test. This challenges us to decide whether questions posed to a hidden respondent are being answered by a human or a computer; whether a computer can ‘fool’ us enough to throw the man-machine distinction into doubt. It upends the idea that our capabilities can be usefully compared against a computer’s; memory, speed, and so on. Instead Turing’s test challenges our sense of consciousness. It suggests a computer might seem ‘more human’ than us as our sense of it being a ‘machine’ is displaced by our amazement at its human-like responses. Siri and the movie “Her” help show how readily we fool ourselves as we submerge what we have evidence for beneath what we desire.

Which indicates three views of the singularity.  Firstly, and most naïve, is that as we create ever more complex systems there must be a point when they are ‘more complex’ than us.  Even then, amazed at the system’s capabilities, we know these have been ‘engineered in’. Calling such a system ‘intelligent’ abuses the meaning of the term for its intelligence is only that of its human makers. Of course, the system might well ’fool’ those who do not understand what is going on, just as people unfamiliar with modern medicine may be fooled by a doctor’s ‘magic’.

A second notion, proposed by the Americans Herbert Simon and Allen Newell, is of machine intelligence based on heuristics.  Their insight was that humans’ non-logical work-rules, such as kitchen recipes or finding dates, could be programmed into a machine that is entirely logical. Medical diagnoses were early examples. Expert systems are now common. They are ‘intelligent’ to the extent the programmer transports rules humans see as intelligent into the system. Comparing the result against the rules can lead to feedback, a form of ‘machine learning’ as its rules are adjusted by a ‘higher order’ but no less human-generated rule. Nevertheless, this learning must be programmed, so Simon & Newell reminded us that the system is displaying the programmer’s learning, not its own.

Turing offered a third argument or speculation that led to his test. He presumed humans thought with rules but being emotional and ethical as well as logical, less precisely or speedily than computers. His famous 1950 Mind article pointed out that given machines can already ‘imitate’ us we may eventually be unable to distinguish them from us. Lacking a distinction, their intelligence must be considered fundamentally similar. We need not understand our own thinking. The singularity follows. Note ‘machine consciousness’ is not at issue; the emphasis is on our inability to distinguish the machine’s consciousness from our own. We might assume we have capabilities machines lack, but cannot ever define or prove them. Turing simply proposed that machines could acquire the rules necessary to ‘imitate’ us and so become socially acceptable. What more is needed?

The three arguments’ differences are instructive and help us grasp big data’s potential. The important questions revolve around human imagination, our evident capacity to deal with our life’s uncertainties. There are three types of uncertainty; ignorance, indeterminacy, and incommensurability. Ignorance, the uncertainty that drives most research, is of our not knowing what can be known, the real. Indeterminacy, which underlies game theory, arises because our knowing is both limited and various. Unable to enter another’s mind we cannot be certain of the result of interacting with them. Incommensurability underpins our inner doubts in that our knowing is grounded on a variety of unprovable assumptions and so fragmented into parts. Contradictions and paradoxes are incommensurabilities our imaginations cannot resolve. Crucially, our imagination is shaped but never bounded or determined by our experiences as we inhabit our social space. Even though computers ‘inhabit’ their own space, not ours, and do not live as we do, Turing presumed machines might learn to deal with our questions and so display ‘human imagination’. Given the coming ‘Internet of things’ they may also get to share our panoply of sensory equipment and display an adaptive capability we cannot distinguish from our own acts of imagination.

These stories help illuminate Tom Davenport’s three modes of big data – descriptive, predictive, and prescriptive. The first simply collects ‘data’, what is sensed. The second has a model or rule programmed in to link the computation to some human intention, predicting whether a chosen goal will be reached. The third has the capability to explore alternative models programmed in to find a best fit between the data being gathered and the goal brought to the analysis. This technique has the potential to surprise us by surfacing unanticipated goal-relevant relationships – what we sometimes call pattern-recognition. In short, big data can help us overcome ignorance about how best to reach our goals.

The possibility of any machine system’s dealing with indeterminacy and incommensurability, life’s other uncertainties, hinges on its being able to discover and successfully inhabit the multiple universes we inhabit. For us, forever bounded and unable to understand our situation in its entirety, the resulting pluralism of our knowing the lived world is held together by a dynamic and pragmatic sense of Self, of being a single competent intelligence, not schizophrenic. This arises from our unique reflexive capacity to both think and observe our thinking, to be both within and without our consciousness. Ultimately, Kurzweil’s singularity is about machine consciousness and most agree this is far off, even if some form of reflexivity can be engineered. In which case a ‘reverse’ Turing test hovers in the background challenging us to seem truly computer-like. Yet can a machine observe and learn to know itself as we do, become ‘conscious’? Could we ever recognize such consciousness for, absent the machine-dominated dystopias of science fiction, we cannot ever enter the machine’s universe? Likewise, we cannot enter a zebra’s intelligence – it only matters when we train it to our purposes. There can be no singularity. Management’s task is always to bring the machine’s ‘consciousness’ to bear in our world and so answer, “What does it mean to us?”

About the author:

JC Spender trained first as a nuclear engineer then in computing with IBM.  He moved into academe as a strategy theorist, opening up a subjectivist/creative approach that complements mainstream rational planning notions of strategizing.  This 40-year project was brought to completion in Business Strategy: Managing Uncertainty, Opportunity, and Enterprise (OUP 2014).

Sometimes the urgency of a business’s responses to changes beyond drowns out what is going on within it.  How a business generates economic value remains a puzzle.  Customer satisfaction is one thing, profit quite another.  Despite Drucker’s many insights into the workings of the socio-economy he did not explain how economic value arises.  Management’s presumably central role remained – and remains – unclear.  Economists label this puzzle the ‘theory of the firm’.  Drucker went beyond pre-WW2 thinking based on military order, control, and efficiency.  He refreshed the path-breaking work, for instance, of Chester Barnard and Mary Parker Follett, arguing inspiring leadership lay at the core of managers’ practice and had psychological, ethical, and moral dimensions that demanded full attention.  Thus he helped propel today’s psychology-based nostrums, often delivered as lists of managers’ ‘secrets’ or ‘steps to winning’.

‘Big data’ is new on managers’ radar.  Its impact is direct, on the business’s processes rather than on the participants’ motivation.  Business strategy is based on the facts of the situation and big data provides managers with a stunning magic telescope to reveal facts and relations otherwise hidden in the ‘fog of commerce’.  Its power is so considerable many writers assert business survival depends on deploying big data correctly.  Wiser commentators push back against any naiveté, pointing to the central place of the ‘human element’.  Newer telescopes do indeed provide more detail, but it remains up to us to ‘connect the dots’ and make sense of what is seen, to address the most profound of a business’s questions: “What does it mean for us?”

At first sight Drucker’s contribution was to remind us that managing is not as mechanical as many suggest, rather it is a human practice embedded in the tangled weave of our ambitions, relations, and politics.  Many commentators write about making organizational life more sympathetic and here Drucker was clearly in the van.  Maybe closer attention to ethics, social responsibility, and ecology will improve us.  But his ‘knowledge worker’ moves us in a different direction that may prove even more important.  Rather than the interaction between hardness of goal-directed labor and the softness of our social, political, or psychological natures, he reminded us of the complexity of human knowing.  This is essential to gauging the impact of big data and its limits.  In spite of the flood of exciting news about the mind, computation, and artificial intelligence, Drucker’s notion of ‘knowledge’ was radical because it looked beyond computable facts.  What we mean by the human element is tied up with our sense that ‘the facts’ are never all we know about our world or ourselves; facts never fully determine our practice.  We have passions, culture, and a history.  Drucker’s knowledge worker presumed those other aspects of human knowing that are essential to understanding the human condition – and profit – as non-mechanical and, perhaps most significant of all, deeply shaped by our imagination.

As we know, we do not live by bread (facts) alone, but also by the word.  Organizations are contexts of managerially-generated ‘natural language’ or communication with words, signals, and symbols that excite and shape the imaginations of others.  Natural language goes way beyond logical ‘formal language’, proofs and formulae, to shape others’ non-factual ways of knowing.  A poem is not a rigorous equation; yet it has power to persuade precisely because we are creatures of emotion and imagination.  Ultimately leadership is persuading others to do things they would not do otherwise – through passion rather than proof.  Hence management’s most powerful tool is persuasive language rather than data.  A value-creating business is a poem in motion, an opera perhaps; not a well-structured data repository.

Our insatiable appetite for natural language-interaction drives our newest industries just as Adam Smith noted our tendency to ‘truck and barter’.  A Twitter message of 140 characters, surely the opposite of big data, is seldom a packet of dry data.  Perhaps it is a haiku or a sonnet.  It impacts someone’s living.  No question, data is often material but never all we need to determine practice in the lived world.  Real time big data may alert a retailer to a product flying off the shelves, but what does it mean?  Likewise data from a drone may suggest, perhaps, a rooftop sniper.  What is revealed turns on how we respond to the data.  Is the item underpriced?  Is the figure someone sleeping?  Do movements at a North Korean site reveal a nuclear test?  Obviously the boundaries around big data’s capabilities and practice are changing fast, with the ‘internet of things’ as much as with deeper mining of ever more voluminous data.  But the strategic meaning of what is revealed is never contained in the data gathered.  Facts never ‘speak for themselves’; their meaning is always embedded in how we live.  Management’s skill lies in layering the knowing they distill from their business’s living over the data they gather.  Big or small, the data question is always strategic: “What does it mean to us?”

About the author:

JC Spender trained first as a nuclear engineer then in computing with IBM.  He moved into academe as a strategy theorist, opening up a subjectivist/creative approach that complements mainstream rational planning notions of strategizing.  This 40-year project was brought to completion in Business Strategy: Managing Uncertainty, Opportunity, and Enterprise (OUP 2014).