In the century ahead, those skill-shifts may be about to go into reverse. To see why, we need to ask ourselves what sets of humans’ skills robots are likely to find it hardest to reproduce and replace in the period ahead. My reading of the runes is that there are three areas where humans are likely to preserve some comparative (if not always absolute) advantage over robots for the foreseeable future. The first is cognitive tasks requiring creativity and intuition (“heads”). These might be tasks or problems whose solutions require great logical leaps of imagination rather than step-by-step hill-climbing. Existing machine learning algorithms allow huge numbers of solution permutations to be tried quickly and costlessly. This allows these hill-climbing algorithms to scale local peaks in record time. Whether they scale global peaks is altogether another matter. It is harder for machines to solve problems where it is difficult to define the solution steps in a logical sequence in advance. Humans call solutions arrived at in this way “intuition”. Despite rapid progress in deep learning techniques, it remains far-harder to devise search algorithms which solve problems requiring a leap into the unknown. 58  Even in a world of super-intelligent machine learning, a demand is still likely to exist for people with the skills to programme, test and oversee these machines. Some human oversight and judgemental overlay of these automated processes is still likely to be needed. Machine design, improvement and oversight require, at least for the moment, humans and machines to operate in partnership.
History demonstrates that shifts in the quantity of money have often been driven – or at least accompanied – by shifts in the demand for deposits (i.e. what the private sector would like to hold for a given level of spending). Following the financial liberalisation earlier that decade, banks began during the 1980s to pay interest on standard deposit accounts. This increased their attractiveness, relative to other “stores of wealth” – they were not the precursor to higher spending and inflation – and broad growth remained stubbornly high even as inflation declined sharply (at least until the boom at the end of the decade). For similar reasons broad money growth was extremely strong throughout the first fifteen years of inflation targeting (1992-2007). Nor is there a tight correspondence between “narrow” and “broad” money. QE involves the creation of central bank reserves to buy financial assets (usually government debt). So in the decade or so that passed between the first use of the policy in 2009 and the onset of the pandemic, reserves grew extremely rapidly. Yet broad money growth was significantly slower than it had been before the crisis (Chart 1). And, in both periods, average inflation was close to 2% (Chart 2). Page 4  Chart 1: Reserve creation neither necessary nor sufficient for strong growth of broad money  Sources: ONS, Bank of England and Bank calculations. Unless stated otherwise “broad money” refers to M4 excluding intermediate other financial corporations (M4ex).