Gus Carlson is a U.S.-based columnist for The Globe and Mail.
For several years now, some of the best jobs in technology haven’t been in the technology sector at all. They’ve been in financial services.
Banks, investment houses, hedge funds and private equity firms have been recruiting tech talent like mad, eager to get a jump on employing fast-emerging technologies such as artificial intelligence. They have been dangling juicy Wall Street compensation packages and lifestyle perks to lure data scientists, software engineers, business systems analysts and others from the tech sector and capture big brains from the top tech schools like MIT, CalTech and the Ivies.
This week, the Street got a glimpse of the payoff. Bridgewater Associates, the big hedge fund based in Connecticut, launched a US$2-billion fund that uses machine learning for decision-making and employs models developed in partnership with AI leaders including OpenAI, Anthropic and Perplexity.
While Bridgewater says the fund will be overseen by humans, make no mistake, this marks a watershed moment. Technology, which has slowly but steadily been displacing human talent in the finance industry, has broken through. Think Terminator 2 meets The Wolf of Wall Street. The machines win.
The nagging question is this: Who cares, as long as the fund makes money? Well, for those employed in a wide range of investment industry roles, this is the bell that tolls for thee.
McKinsey’s prediction in 2022 that 30 per cent of the hours worked by humans in the financial services sector would be wiped out by 2030 now seems too modest. The reality is it could be pushed much higher if funds like Bridgewater’s deliver great performance and spawn more like it. It’s no wonder research shows that more than half of all finance workers believe technology is putting their jobs at risk.
Bridgewater, which has more than US$100-billion in assets under management, may be a blueprint for the future. Company leadership has already said its push into machine learning will probably mean a sharper emphasis on hiring people such as data scientists, which will further shift the balance of its work force toward technology expertise, not finance know-how.
Like so many financial services companies, Bridgewater has been driving its technology transformation internally for almost a decade. The firm made big hires from the tech world – a senior member of IBM’s Watson team – and from academia – a statistics professor from Yale.
The company also created an AI skunkworks to develop the proprietary systems that will underpin the new fund. To make sure the technology worked, it was tested inside an existing fund over the past year. While past performance is no indication of future success, it’s noteworthy that the fund with the machine learning embedded delivered double-digit returns after years of mediocre performance.
While there is certainly a wow factor to Bridgewater’s fund, this should come as no shock. It is really the natural evolution of the business, especially considering the investment in technology made by the major players. And, honestly, there is a perhaps no better sector than finance for AI to show its stuff.
Machine learning and AI can do amazing things, and quickly, in the business of money. It can identify and capitalize on trading patterns in the blink of an eye. It can see causal relationships between markets, and it can scan, assemble and collate company reports, filings and announcements, media headlines and analysts’ reports from around the world in a nanosecond.
To be sure, there are some things AI can’t do, and never will be able to do. Namely, it can’t capture the whispers that often mean the difference between getting into a market at the right time and, as important, getting out.
AI can’t sense the nervousness of a chief financial officer or chief executive officer on a quarterly earnings conference call or in an investor day presentation trying to explain eroding EPS or unexpected spending on certain initiatives. It’s not privy to those off-the-record, behind-the-scenes, deep-background insights from inside sources about the viability of a company’s strategy or the competence and focus of its management. These are the nuances that don’t manifest themselves in bits and bytes, but often give investment professionals and their clients a competitive edge.
Even Greg Jensen, Bridgewater’s co-chief investment officer who will oversee the new fund, told Bloomberg that large language models “have the problem of hallucination. They don’t know what greed is, what fear is, what the likely cause-and-effect relationships are.”
But the plain truth is if this new fund generates healthy returns, conventional qualitative and contextual insight probably won’t matter – or at least will become subordinate in value. Like people who don’t care whether their favourite Netflix shows were created by people or AI as long as it entertains, investors won’t care how the fund delivers results as long as it does.