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Banks view themselves as net beneficiaries of AI, projecting significant operational savings that will more than offset increased competition. For instance, some guide for a 9-percentage-point improvement in their cost-income ratio over three years, with Europe's high retirement rate mitigating employment concerns.
The biggest winners from AI will be entities with massive distribution and significant cost inefficiencies. Legacy banks and large brands are prime candidates, as AI can drastically cut their operational costs while they retain their powerful brand and distribution moats.
In Europe, companies that are reducing their workforce have outperformed those that are actively hiring. This market behavior indicates that investors are currently prioritizing AI-driven efficiency gains and cost-cutting over strategies focused on expansion and growth.
The real investment case for AI in Europe is not in creating foundational models but in adoption. The continent's vast 'old economy' index has significant potential for productivity gains. As AI's return on investment becomes clear, Europe could be re-rated as a major beneficiary of AI adoption, capitalizing on its large industrial base.
The primary impact of AI in investment banking isn't headcount reduction but a massive productivity lift. By automating 80% of the work for initial drafts of pitch decks and models, AI frees up senior bankers' bandwidth. This allows them to pursue a greater number of new engagements, fundamentally expanding the firm's capacity for new business.
Merely deploying AI tools like Copilot to employees offers minimal value. The real revolution is using AI to re-engineer core processes from the ground up. For example, AI can reduce a six-week credit file preparation to 14 minutes, forcing a fundamental rethink of roles and requiring massive reskilling efforts.
The anticipated AI productivity boom may already be happening but is invisible in statistics. Current metrics excel at measuring substitution (replacing a worker) but fail to capture quality improvements when AI acts as a complement, making professionals like doctors or bankers better at their jobs. This unmeasured quality boost is a major blind spot.
The idea that AI leads to job cuts misses the competitive dynamic. Since all companies have access to AI, efficiency gains will be reinvested to out-compete rivals, not just pocketed as profit. This escalates competition, turning AI adoption into a strategic imperative for survival and growth.
Marks questions whether companies will use AI-driven cost savings to boost profit margins or if competition will force them into price wars. If the latter occurs, the primary beneficiaries of AI's efficiency will be customers, not shareholders, limiting the technology's impact on corporate profitability.
David Solomon dismisses the "job apocalypse" theory. For Goldman Sachs, AI-driven efficiency creates capacity. This freed-up capacity will be reinvested into growth initiatives that were previously constrained, which he believes will ultimately drive more job creation over time, not less.
Financial institutions are at a tipping point where the risk of keeping outdated legacy systems exceeds the risk of replacing them. AI-native platforms unlock significant revenue opportunities—such as processing more insurance applications—making the cost of inaction (missed revenue) too high to ignore.