In its September meeting, the ISSB received an update on its human capital project.

The staff paper noted the following as an area where more research is required:

• the importance of emerging workforce-related risks and opportunities, particularly those related to AI— AI deployment has advanced significantly over the course of the project and our discussions with investors suggest their information needs have changed as a result. Reassessing investor interest in disclosure about workforce-related risks and opportunities associated with AI would help us better understand and address their current needs.

Indeed, around that same time, there was a flurry of stories on the risks of AI, many suggesting that the main “workforce-related risk” associated with the area is that a workforce, any workforce, might find itself completely wiped out. David Wallace-Wells provided a handy summary in The New York Times:

• Something really big happened last week in the world of artificial intelligence: Quite suddenly, after several years of relentless and disorienting momentum, the course of rapid A.I. progress ran headlong into a wave of social panic.
• It had already been a dizzying summer: OpenAI agents finding their way out of their sandbox and onto the internet to hack the coding database Hugging Face, the controversial announcement of an A.I. solution to a difficult open problem in mathematics, an Anthropic report on the misuse of its generic models for purposes of bio-research and asymmetric warfare. Then, last Tuesday, Jacob Coxon, a junior researcher at Anthropic, posted an alarm-raising resignation thread to X, which included a post bluntly stating that “the people building A.I. earnestly believe that it could kill us all by the end of the decade.” The first post in the thread was seen close to 200 million times, including by prominent A.I. leaders who agreed that, yes, there was at least a 10 percent chance A.I. would bring about human extinction within the decade. From there, the panic spread to Washington, with dozens of national political leaders giving voice to the anxiety and demanding some form of immediate policy response. Over the weekend, the heads of the four biggest American A.I. firms — Dario Amodei of Anthropic, Elon Musk of SpaceXAI, Sam Altman of OpenAI and Demis Hassabis of Google DeepMind — called for a collective slowdown of the development of frontier models and some new shared paradigm for A.I. governance.

Against that background, the ISSB staff paper should surely seem insufficiently urgent: AI can now be seen as a direct threat to our collective sustainability, as much as anything explicitly addressed in the issued standards, perhaps more so. But in one of his most recent speeches, ISSB Chair Emanuel Faber didn’t mention the issue at all, and more broadly, little comes up from searching for “artificial intelligence” on the IFRS Foundation website. The most extended reflection came from Andreas Barckow in his last speech as IFRS Chair, his main focus also being workforce-related:

• Technological change is reshaping how information and data is produced, analysed and used. It offers real opportunities, but it also raises important questions about judgement, evidence and trust. Virtually every week you can read about a new area of life where AI is being tested or implemented; and at the same time, you read about hallucinations, false connections and hard-to-believe errors in reports and publications.
• We have seen the opportunities of using AI in our own work at the IASB. Disclosure analysis is a good example. AI can scan large numbers of financial reports from around the world against specific disclosure requirements and tell us how those requirements are being applied in practice. Work that used to take a significant amount of time can now be completed in a fraction of it.
• Speed clearly has value, but in financial reporting, it is not enough. Information must be robust, reliable and trusted. People still have to exercise judgement over the information provided through AI. And that judgment depends on experience—experience gained through thorough analysis, through making and correcting mistakes, through challenging information presented, through repetition.
• …My concern is not that AI will make people less capable. My concern is that if we remove too much of the ground-up work, we may also remove the experiences through which judgement is formed and sharpened. In many areas and professions, AI is taking over the more mechanical parts of work that used to be carried out by the more junior staff. We read that this takeover does not render their work superfluous, but leaves people with more time for judgement, problem-solving and client advice.
• I do not think the useful question is whether AI will matter. It already does. The more important question is how we use AI while preserving the experience and judgement on which high-quality financial reporting depends. Have we thought carefully enough about what happens if we automate too much of the work through which junior professionals learn? How do we ensure that the next generation of accountants, auditors and standard-setters can still learn, practise and develop judgement? 
• I do not pretend to have the answers. But I do believe that these issues deserve our attention now. They go directly to how we train, supervise and develop people.

One hopes the IFRS Foundation will take up the issues he raises. But its engagement with AI clearly needs to go beyond that, and at a pace far exceeding that of a regular project…

The opinions expressed are solely those of the author.