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Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.
The writer is president of Queens’ College, Cambridge, and an adviser to Allianz and Gramercy
There is little doubt that generative AI is a massively disruptive innovation that will bring both job destruction and enhancement. The balance between the two is now a hot topic where asset management increasingly finds itself, albeit unwittingly, serving as a “natural experiment”.
How the generative AI revolution, or Gen AI, is being deployed in the industry shines a spotlight not only on the job debate but also on broader organisational and regulatory issues that will impact the rest of finance, health, and well beyond.
One of the most striking aspects of the Gen AI revolution, is that it is just getting started. Its main drivers — computing power, data, talent and funding — are compounding at a scale and speed that will accentuate its disruptive forces. No wonder it has risen to the top of the agenda of chief executives in an ever-increasing number of companies and sectors.
Asset management is one of the sectors where Gen AI offers great promise, pointing to a series of changes to how the industry operates and is organised. Already, it is being used by the most agile companies to improve operational efficiency, communicate better and better protect against cyber attacks. And this is just the start.
Both investment and client-facing teams can now prepare presentational power point with incredible ease to convey capabilities and justify new trade ideas. The communication of returns and performance attribution to clients, a critical and time-consuming obligation, is done more quickly and accurately. And the tech teams have more tools at their disposal to combat the growing number of hacking attempts.
In every one of these cases, Gen AI is labour-enhancing. It augments what employees can do, helping them move up the value-added curve. While there will be job losses among routine-based, low-skill tasks, the overall impact on labour is positive, especially as more engineers are hired. Knowing how to talk to AI engines becomes an essential skill for both new and much of the existing staff.
Now look forward. It is not difficult to envisage a world where Gen AI engines are an integral part of all the higher-skill tasks of asset allocation, model portfolios, security selection and risk mitigation. These engines will be trained on the enormous data sets that reside in the sector and, currently, are grossly under-exploited.
Given advances elsewhere in technology, it is also not difficult to imagine Gen AI tools helping to create and structure new asset classes, trained in this case by a combination of actual and virtual data. With time, the most dynamic and successful parts of the asset management will combine Gen AI-enabled tools with new capabilities that, importantly, are Gen AI native. With that comes the ability to personalise in a much more refined manner individual investment accounts to meet clients’ risk tolerance and behavioural inclinations.
Yet the road ahead will also be bumpy. Existing capabilities are far from flawless and talent is not evenly distributed. Their application is subject to biases. There are still no good answers to who will internally police the AI and what broader set of domestic and, potentially, international regulations will govern it. And the increasing fragmentation of the technology stack between China and the US, a phenomenon that will only deepen, is making those living in the in-between particularly uncomfortable.
This is also a road that will see major disruptions to the structure of the industry. Those lagging in understanding the disruptive power of AI and its potential applications — particularly on account of talent, management agility and data organisation — will find it increasingly hard to catch up. The gap will only grow if they fail to take advantage of the leapfrogging opportunities that are only likely to be available early on.
Put together, this dynamic will further push the industry trends towards a structure of a handful of very big firms and a larger number of much smaller niche players. Mid-sized managers, those with $100bn to $500bn of assets under management, and the Gen AI-lagging firms will be pressured to consolidate or simply atrophy. This is where the job destruction occurs.
What asset management faces will be repeated in different ways elsewhere, including the rest of finance and health. It is a phenomenon that firms can only ignore at their peril. It is also one that will pressure those regulators who, having focused too much on banks, are already behind in their understanding and supervision of non-banks.
