Everything can change very fast when Generative AI is involved. Only a few weeks ago I was writing about how although a number of occupations had been hit hard by Gen AL, in general the jobs outlook was relatively good. If course the present USA economic policy (if it can be called that) does not help in trying to work out what is going on.

The latest alarm seems to have been kicked off by Anthropic CEO Dario Amodei who said that ordinary people are not ready for the changes AI is about to unleash on the world. In a widely circulated interview with Axios on May 28, Amodei warned we are on the brink of what his interviewers describe as a “job apocalypse” that will wipe out half of entry level jobs and cause the unemployment rate to rise up to 20%. People are unprepared, Amodei says. "Most of them are unaware that this is about to happen.” But before we know it, “cancer is cured, the economy grows at 10% a year, the budget is balanced—and 20% of people don't have jobs."

The reality seems to be that Generative AI applications, like Athropic's Claude Large Language Model, are increasingly being used to automate jobs. At particular risk are administrative posts, software developers and those in creative occupations. LLMs have already got pretty good at writing software applications and now the development of Agents threatens to increase their capability.

Up until recently it was questioned whether AI was really threatening jobs since data suggested little increase in productivity. But in the past it has taken time for increases in productivity due to automation to be reflected in official data. And tools like Generative AI require considerable reorganization in work flows and company employment structures before they begin to impact. What is apparent is that where they think it will lead to cost savings companies will deploy AI and will reduce employment be it through redundancies or what seems to be more common, an AI first strategy. This means if there is a need for more staff and especially more skilled staff, managers have to look first if a post cannot be filled through AI.

But its not just in tech companies or in Hollywood where this is going on. In the UK the size of the civil service is being reduced by tens of thousands of roles through voluntary redundancy and not replacing leavers. The Guardian newspaper reports that government officials would be tasked with figuring how they could use AI technology to streamline their own work wherever possible.

There are several worrying trends in recent employment data. One is reduced job openings for recently graduated students. Employment at the ground floor, often for lower skilled work, provided a training pathway for recent graduates. These jobs are disappearing as AI takes over. Not only will this lead to rising unemployment but it raises the question of where recent graduates will learn their occupation.

Jobs traditionally held by women are at greater risk of disruption from AI than those typically held by men, according to a recent report released by the United Nations’ International Labour Organization (ILO).

The report highlighted that 9.6% of jobs typically performed by women—particularly in administrative and clerical roles—are likely to be significantly transformed by AI, compared to just 3.5% of male-dominated jobs. This trend is particularly evident in high-income countries where women are disproportionately represented in tasks vulnerable to automation.

Traditional wisdom has been that although new technologies may destroy jobs, this will be compensated by the new jobs such technologies create. Yet there is little sign of those new jobs. Demand for prompt engineers is not going to compensate for the thousands of jobs disappearing in the information technology sector. In such a situation it might be hoped that governments would step in. But rather they are embracing the opportunities not to increase public services but rather to reduce the cost of the services they presently provide.

About the image

Data annotation is becoming a professional field of its own, with many annotators working on it as full-time employees, following complex guidelines, and using a variety of different tools and platforms. What used to be considered 'menial' and 'low-skilled' work, is today a nascent field with its own complexities and skills requirements, which involves extensive training and specializing in different modalities: video labeling, medical imagery labeling, 3D LiDar labeling, and so on. It is important to recognize the value that data annotators bring and their expertise, in order to uplift them from the 'ghost work' position in which they are now.