A World Without Work
A World Without Work: Technology, Automation, and How We Should Respond by Daniel Susskind (2020) Get it here
Dr. Susskind, scion of the storied futurology dynasty, will doubtless find enough general counsel anxious to be seen at the technological vanguard, and suckers for sci-fi alternative histories like me, to recoup his advance, but A World Without Work will not signpost much less dent the immutable trajectory of modern employment.
To my mind Susskind mischaracterises what work is and how humans, organisations and economies organise themselves to do it, and overlooks — neigh, contradicts — the whole geological history of technology. Technology has never destroyed employment overall. Susskind thinks it will now — that homo sapiens has reached some kind of Kubrickian tipping point — but gives no good grounds I could see to support that belief.
All innovations create unexpected diversity or opportunity — that’s more or less the definition of “innovation” — and all deliver more subsidiary complexity & inefficiency as a by-product. Both — the opportunities and the inefficiencies — “need” human midwifery, to exploit them (for the former) and effectively manage them (for the latter).
Nothing that the information revolution has yet thrown up suggests any of that has changed. The more technology is deployed, the more the fog of confusion and complexity — as in complexity theory, and not just complicatedness — engulfs us.
An excellent counterpoint, though equally flawed in other ways, is the late David Graeber’s highly provocative Bullshit Jobs: A Theory, which has a far more realistic, if no less glum, prognosis: soul-destroying jobs aren’t going away: they are only going to get worse. And there will be more and more of them. This feels more plausible to me.
But chess-playing supercomputers -
Chess and Go are complicated, not complex, problems. Both are hermetically and — ahh — hermeneutically sealed zero-sum games on small, finite boards with simple sets of unvarying rules between two players sharing a common and static objective. Their risk payoff is normal, not exponential. They can, in theory, be “brute force” managed by skilled operation of an algorithm, and the consequences of success or failure are predictable and contained — you win some, you lose some.
Either way, gameplay is deterministic: at the limit, the player with the better number-crunching power must win. Even here, the natural imagination of a human player, otherwise at a colossal disadvantage to the raw rule-processing power of a difference engine, makes beating the meatware by algorithm surprisingly hard.
This ought to be the lesson: even for simplistic binary games, it takes a ton of dumb processing power to beat a puny imagineer. But somehow, Susskind reads it instead as a signpost to the Apocalypse.
Life is not a two-person board-game on a small-board with fixed rules and a static, common, zero-sum objective. Not even at university. Life is complex. Complex problems — those one finds at the frontier, when one has boldly gone somewhere no-one has gone before, in dynamic systems, where information is not perfect, where risk outcomes are convex — so-called “wicked environments” — are not like problems in Chess. Here, algorithms are no good. One needs experience, wisdom and judgment. Algorithms get in the way.
Computers can’t solve novel problems
By design, computers always, unfailingly, follow rules. A machine that could not process instructions with absolute fidelity would be a bad computer. Good computers cannot think, they cannot imagine, they cannot handle ambiguity — if they have a “mental life”, it exists in a flat space with no future or past. Computer language, by design, has no tense. It is not a symbolic structure, in that its vocabulary does not represent anything. Machines are linguistically, structurally incapable of interpreting, let alone coining metaphors, and they cannot reason by analogy or manage any of the innate ambiguities that comprise human decision-making.
Until they can do these things — and conceptually there is no reason a machine couldn’t, but that’s just not how, to date, computers have been designed — they can only aid, and in most circumstances, complicate, the already over-complicated networks we all inhabit.
But chess-playing supercomputers -
But, but, but — how can we explain this seemingly relentless encroachment of the dumb algorithm on the inviolable province of consciousness? What will be left for us to do? Well, there’s an alternative explanation, and it’s a bit more prosaic: it is not so much that AI is breaching the mystical ramparts of consciousness, but that much of what we thought required the ineffable, doesn’t. Much of what we thought was “human magic” turned out to be just, in Arthur C. Clarke’s worlds, “sufficiently advanced technology” that it seemed like magic.
This isn’t news: impish polymath Julian Jaynes laid it all out in some style in 1976. If you haven’t read The Origin of Consciousness in the Breakdown of the Bicameral Mind, do. It’s a fabulous book. In any case, a lot less of what we take to require conscious thought actually does require conscious thought. Like driving a car. Or playing the piano.
And even this is before considering the purblind, irrational sociology that propels all organisations, because it propels all individuals in those organisations. Like the academy in which Daniel Susskind’s millenarianism thrives, computers work best in a theoretical, Platonic universe, governed by unchanging and unambiguous physical rules, and populated by rational agents. In that world, Susskind might have a point, but even there, I doubt it.
But in the conflicted, dirty, unpredictable, complex universe we find ourselves in — out here in TV Land — there will continue to be plenty of work, as there always has been, administrating, governing, auditing, advising, rent-seeking, and amusing ourselves to death, at least as long as the computer-enhanced, tightly-coupled complexity of our networks doesn’t wipe us out before we get the chance to do it to ourselves.
Employment and Taylorism
Susskind’s conception of “work” as a succession of definable, atomisable, impliedly dull tasks — a framework, of course, which suits it perfectly to adaptation by machine — is a kind of Taylorism. It is a common view in management layers of the corporate world, of course — we might almost call it a dogma — but that hardly makes a case for it.
The better response is to recognise that “definable, atomisable and dull tasks” do not define what is employment, but what it should not be. The JC’s third law of worker entropy is exactly that: tedium is a sure sign of waste in an organisation. If your workers are bored, you have a problem. If they’re boring each other, then it’s an exponential problem.