The US economy is growing - so where are all the jobs? When 42-year-old Jacob Trigg lost his job as a project manager in the tech industry he didn't think it would take too long to find a new one - he always had before. But more than 2,000 job applications later he is still hunting, trying to make ends meet with jobs in package delivery and landscaping. It's a huge surprise because I've always been able to get a job very easily, said Trigg, who lives in Texas. It wasn't even on my radar to be prepared for more than six months of unemployment.

Highly skilled professionals were forecast to be more in demand in contrast with other recent research. Highly skilled professionals were forecast to be more in demand in contrast with other recent research. Up to 3m low-skilled jobs could disappear in the UK by 2035 because of automation and AI, according to a report by a leading educational research charity. The jobs most at risk are those in occupations such as trades, machine operations and administrative roles, the National Foundation for Educational Research (NFER) said. Highly skilled professionals, on the other hand, were forecast to be more in demand as AI and technological advances increase workloads "at least in the short to medium term".

The limited ability to reason across occupational data from different sources is a long-standing bottleneck for data-driven labour market analytics. Previous research has relied on hand-crafted ontologies that allow such reasoning but are computationally expensive and require careful maintenance by human experts. The rise of language processing machine learning models offers a scalable alternative by learning shared semantic spaces that bridge diverse occupational vocabularies without extensive human curation. We present an embedding-based alignment process that links any free-form German job title to two established ontologies - the German Klassifikation der Berufe and the International Standard Classification of Education. Using publicly available data from the German Federal Employment Agency, we construct a dataset to fine-tune a Sentence-BERT model to learn the structure imposed by the ontologies. The enriched pairs (job title, embedding) define a similarity graph structure that we can use for efficient approximate nearest-neighbour search, allowing us to frame the classification process as a semantic search problem. This allows for greater flexibility, e.g., adding more classes. We discuss design decisions, open challenges, and outline ongoing work on extending the graph with other ontologies and multilingual titles.

Australia must "lean in hard" to the benefits of artificial intelligence or else risk ending up "on the end of somebody else's supply chain", according to the new industry and science minister, Tim Ayres, with the Labor government planning to further regulate the rapidly evolving technology. Ayres, a former official with the manufacturing union, acknowledged Australians remained sceptical about AI and stressed that employers and employees needed to have discussions about how automation could affect workplaces. The minister said Australia had "no alternative" but to embrace the new technology and seek to become a world leader in regulating and using AI. "It's the government's job to lean into the opportunity to outline that for businesses and for workers, but also to make sure that they are confident that we've got the capability to deal with the potential pitfalls," Ayres told Guardian Australia. "I think the Australian answer has got to be leaning in hard and focusing on strategy and regulation that is in the interest of Australians."

I recently found myself at a dinner in an upstairs room at a restaurant in San Francisco hosted by a venture capital firm. The after-dinner speaker was a tech veteran who, having sold his AI company for hundreds of millions of dollars, has now turned his hand to investing. He had a simple message for the assembled startup founders: the money you can make in AI isn't limited to the paltry market sizes of previous technology waves. You can replace the world's workers – which means you can capture their salaries. Replacing all human labour with AI sounds like the stuff of science fiction.

In a section on "labour market risks", the report warns that the impact on jobs will "likely be profound", particularly if AI agents – tools that can carry out tasks without human intervention – become highly capable. "General-purpose AI, especially if it continues to advance rapidly, has the potential to automate a very wide range of tasks, which could have a significant effect on the labour market. This means that many people could lose their current jobs," says the report. The report adds that many economists believe job losses could be offset by the creation of new jobs or demand from sectors not touched by automation. According to the International Monetary Fund, about 60% of jobs in advanced economies such as the US and UK are exposed to AI and half of these jobs may be negatively affected.

The question of whether AI substitutes or complements human work is central to debates on the future of work. This paper examines the impact of AI on skill demand and compensation in the U.S. economy, analysing 12 million online job vacancies from 2018 to 2023. It investigates internal effects (within-job substitution and complementation) and external effects (across occupations, industries, and regions). Our findings reveal a significant increase in demand for AI-complementary skills, such as digital literacy, teamwork, and resilience, alongside rising wage premiums for these skills in AI roles like Data Scientist. Conversely, substitute skills, including customer service and text review, have declined in both demand and value within AI-related positions. Examining external effects, we find a notable rise in demand for complementary skills in non-AI roles linked to the growth of AI-related jobs in specific industries or regions. At the same time, there is a moderate decline in non-AI roles requiring substitute skills. Overall, AI's complementary effect is up to 50% larger than its substitution effect, resulting in net positive demand for skills. These results, replicated for the UK and Australia, highlight AI's transformative impact on workforce skill requirements. They suggest reskilling efforts should prioritise not only technical AI skills but also complementary skills like ethics and digital literacy.

The integration of artificial intelligence (AI) into the workplace is advancing rapidly, necessitating robust metrics to evaluate its tangible impact on the labour market. Existing measures of AI occupational exposure largely focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility, providing limited insight into actual adoption and offering inadequate guidance for policymakers. To address this gap, we introduce the AI Startup Exposure (AISE) index-a novel metric based on occupational descriptions from O*NET and AI applications developed by startups funded by the Y Combinator accelerator. Our findings indicate that while high-skilled professions are theoretically highly exposed according to conventional metrics, they are heterogeneously targeted by startups. Roles involving routine organizational tasks-such as data analysis and office management-display significant exposure, while occupations involving tasks that are less amenable to AI automation due to ethical or high-stakes, more than feasibility, considerations -- such as judges or surgeons -- present lower AISE scores. By focusing on venture-backed AI applications, our approach offers a nuanced perspective on how AI is reshaping the labour market. It challenges the conventional assumption that high-skilled jobs uniformly face high AI risks, highlighting instead the role of today's AI players' societal desirability-driven and market-oriented choices as critical determinants of AI exposure. Contrary to fears of widespread job displacement, our findings suggest that AI adoption will be gradual and shaped by social factors as much as by the technical feasibility of AI applications. This framework provides a dynamic, forward-looking tool for policymakers and stakeholders to monitor AI's evolving impact and navigate the changing labour landscape.

This white paper is the output of a multidisciplinary workshop in Nairobi (Nov 2023). Led by a cross-organisational team including Microsoft Research, NEPAD, Lelapa AI, and University of Oxford. The workshop brought together diverse thought-leaders from various sectors and backgrounds to discuss the implications of Generative AI for the future of work in Africa. Discussions centred around four key themes: Macroeconomic Impacts; Jobs, Skills and Labour Markets; Workers' Perspectives and Africa-Centris AI Platforms. The white paper provides an overview of the current state and trends of generative AI and its applications in different domains, as well as the challenges and risks associated with its adoption and regulation. It represents a diverse set of perspectives to create a set of insights and recommendations which aim to encourage debate and collaborative action towards creating a dignified future of work for everyone across Africa.

Gender bias in machine translation (MT) systems poses significant challenges that often result in the reinforcement of harmful stereotypes. Especially in the labour domain where frequently occupations are inaccurately associated with specific genders, such biases perpetuate traditional gender stereotypes with a significant impact on society. Addressing these issues is crucial for ensuring equitable and accurate MT systems. This paper introduces a novel approach to studying occupation-related gender bias through the creation of the GOSt-MT (Gender and Occupation Statistics for Machine Translation) Knowledge Graph. GOSt-MT integrates comprehensive gender statistics from real-world labour data and textual corpora used in MT training. This Knowledge Graph allows for a detailed analysis of gender bias across English, French, and Greek, facilitating the identification of persistent stereotypes and areas requiring intervention. By providing a structured framework for understanding how occupations are gendered in both labour markets and MT systems, GOSt-MT contributes to efforts aimed at making MT systems more equitable and reducing gender biases in automated translations.