MAS reports net profit of S$20 billion driven by strong investment gains

“While investment gains vary from year to year, this year’s upturn was similar to last year’s and above our 10-year historical average of S$18.3 billion,” said Mr Chia.

The central bank’s board approved a return of S$2.5 billion to the Singapore government. “The amount to be paid to the government reflects the offsetting of losses accumulated from previous years,” he said.

Before this year, the last time MAS contributed to the consolidated fund was in the financial year 2020/2021. Last year, MAS reported a net profit of S$19.7 billion and did not make a contribution to the consolidated fund.

UNCERTAINTY OF THE AI INVESTMENT BOOM

Despite this, Mr Chia characterised the ongoing artificial intelligence investment boom as a “major uncertainty to the benign picture thus far”.

While these AI investments continue to boost economic growth in the near term, the situation is less certain in the medium term, since large equity and debt financing will be needed in the years ahead, he said.

“In the race for model advantage and to scale adoption, projected investments by hyperscalers and model builders have expanded beyond cashflows and commercial revenues,” said the MAS managing director.

Global growth, investment and financial market performance have become highly dependent on projections of data centre and semiconductor investments continuing well into the future, said Mr Chia.

“The sustainability of AI investments is thus highly consequential for global growth and financial stability,” he said.

He then described two possible scenarios in the medium term which can have a sizeable impact on the global economy.

If revenue growth accelerates and productivity gains broaden, the investment boom could be an extended one, but there are also risks on the path of AI investment monetisation, he said.

Mr Chia highlighted the escalating costs of energy and chips, supply bottlenecks of raw materials, regulatory uncertainty and intense competition, including from lower-cost open-weight models as risks to monetisation.