We provide measures of artificial intelligence technologies for the UK at the Travel-to-Work Area (TTWA) level. The data is derived from Lightcast Technologies (formerly known as Burning Glass) vacancy. A keyword-based algorithm is applied to the text of the vacancy data to characterise vacancies as being related to either cloud computing or machine leaning technologies (collectively grouped as AI).We map and track the state of technological change in the UK, understand its drivers, impacts and help to improve the UK's productivity record via our collaboration and engagement with industry and policymakers. We focus on the role of frontier or 'future' technologies, such as AI, robotics, clean tech, blockchain and quantum. The contribution of these technologies to UK productivity will depend on the twin channels of their production and use (diffusion), the determinants of which we will seek to model theoretically and empirically. We will build up quantitative evidence on economic activity in the UK's 'future-technology-producing' and 'future-technology-using' sectors. First, through description - which will include both sectoral and geographical elements; and second through building evidence on the extent to which financial or skills frictions constrain investment. We are therefore addressing two of the challenges set out by the PIN workshop: understanding and improving innovation diffusion; and understanding and improving regional and local productivities. An innovation in our approach is that we will use a range of emerging databases on technology-oriented firms linked to text-based information on their activities to build a comprehensive empirical picture of the future technology sectors.

For the AI measures, a text-based keyword algorithm was applied to Lightcast technologies vacancy data to flag the presence of cloud computing and / or machine learning technologies. Specifically, the keywords are derived from the overlap of technology-related terms in earnings call and patents data, as outlined in Kalyani, Bloom, Carvalho, Hassan, Lerner and Tahoun (2023) "The Diffusion of New Technologies" (mimeo). For the location variable in the Lightcast dataset, we use the ‘County/UA’ field in the vacancy-level records. It is worth noting that the information in this field sometimes differs from the names given in official geographical units, leading to the need for some adjustments. To delineate the Lightcast County/UA field, we match the location name in Lightcast with the geospatial dataset ‘Counties and Unitary Authorities (December 2018) Generalised Clipped Boundaries UK’ from the ONS. We choose the December 2018 version to minimise the need for location name harmonisation and to settle the few discrepancies between Lightcast with the ONS source. It is important to note that we need to pool all related Lightcat county/UA records for areas within London (e.g. City of London, Camden, Hack- ney) into a single ‘Greater London’ cell, since the location name ‘Greater London’ appears in many Lightcast records. Finally, we overlay the County/UA shapefile with the TTWA boundaries shapefile from ONS to assign LAD/County/UA to TTWA. When a County/UA is spread over several TTWAs, we partition it to each TTWA using the area ratio.