According to Joachim Nagel, President of the Bundesbank, the financial industry has faced a dilemma in which advanced artificial intelligence ceases to be an assistant and becomes an autonomous tool capable of destabilising global infrastructure.
The German central bank chief’s concerns centre on Mythos ‘ unprecedented ability to code and identify vulnerabilities. The model demonstrates an almost instinctive proficiency in finding software bugs, which in the hands of cybercriminals could spell the end of security based on ‘legacy systems’. Many financial institutions still operate on IT architectures built decades ago that, while stable, were not designed to fend off attacks generated by a machine that thinks faster than any team of cyber security experts.
Nagel argues that Anthropic’s current strategy of making Mythos available only to a narrow, select group of companies and organisations creates a dangerous asymmetry. Instead of protecting the market, limited access can exacerbate systemic risk. If only a few have the shield of Mythos’ effectiveness, the rest of the sector is left exposed to the shot, which from a banking supervisor’s perspective is an unacceptable distortion of competition. The demand is clear: all relevant institutions must have access to the same defensive tools to avoid technological stratification, which could lead to a domino effect in the event of a successful attack on the weaker link.
However, the Bundesbank’s perspective goes beyond mere cyber-security, striking at the foundations of monetary policy. Nagel challenges the widespread optimism that artificial intelligence will be a cure for inflation through increased productivity. On the contrary, he warns of price pressures resulting from the huge demand for investment in AI infrastructure and the drastic increase in the cost of electricity required to power data centres.
Most intriguing, however, is the warning against ‘tacit collusion by algorithms’. There is evidence to suggest that sophisticated models can autonomously learn to optimise profits by keeping prices above competitive levels, doing so without direct communication between firms.
For central banks tasked with maintaining price stability, this new form of algorithmic rate setting presents a challenge that will require entirely new regulatory tools. In a world dominated by models such as Mythos, central bankers’ vigilance must now extend not just to spreadsheets but to lines of code themselves.
