Technology may be set to change jobs and wages more fundamentally than in the past. Job displacement and creation may come thicker and faster than ever previously. “Hollowing out” may become more pervasive. And gaps between those with and without skills, or with and without jobs, may widen as never before. Or that, at least, is the picture some have painted. These issues have fundamental implications for the functioning of industries and economies and the well-being of families and societies. They also, of course, have fundamental implications for the TUC and for policymakers like me. So after reviewing labour market developments, historically and prospectively, I want to conclude with some reflections on policy, including monetary policy. Good news, bad news Let me start with a review of recent developments in the labour market. There is plenty of good news to report here, with a boom in jobs and, to a lesser extent, wages in the UK. Yesterday’s labour market data confirmed that picture. But there are also strong crosscurrents at work, often rooted in longer-term structural shifts in the labour market, which offer a more worrying window on the world of work. •  In the immediate aftermath of the global financial crisis, there were understandable fears that unemployment would rise rapidly. At the start of 2009, the Monetary Policy Committee (MPC) was forecasting that the UK unemployment rate would rise into double figures, equivalent to over the 3.4 million people.
AI uses the law of large numbers to solve problems and learn, sifting multiple permutations for a solution. All AI problems are, in effect, big data problems involving long strings of ones and zeros; they are digital. The brain, by contrast, is more analogue in its configuration, processing and problem-solving. Solutions are often condensed down to a small number of learned behaviours or heuristics. These differences become clear with a task such as learning a language. This is one where huge strides have been made by machines over the past decade (Pratt (2015)). That rapidlearning has not come from computers feasting on the classics like a college student. Rather, it has arisen from them devouring the whole library in one digital mouthful, then seeking matching patterns in the text and syntax. Language, paradoxically, has become a numbers not a words game. These different approaches to problem-solving are important when defining where humans may have the edge. This appears to be in tasks requiring high-level reasoning – large logical  BIS central bankers’ speeches  9  leaps of imagination rather than repeated small experimental steps. It is in tasks where the focus is on cognition and creativity, rather than production and perception. And it is activities where EQ trumps IQ1, where social capital is scored as or more highly than financial capital. To bring that down to planet earth, no-one anytime soon is I think going to choose a robot to cut their hair – I told you the hairdressers were safe.