BNY skipped the ‘tokenmaxxing’ craze. Here’s what AI metrics it tracks instead

Good morning. Tokenmaxxing quickly became one of the buzziest metrics in enterprise AI.Fortune’s Jeremy Kahn reported that “tokenmaxxing” turned into a status symbol at some big tech companies, where engineers were urged to climb leaderboards by burning more AI tokens. Critics argue that the practice skewed incentives and exposed a broader gap between AI spending and actual productivity gains.I recently spoke with Dermot McDonogh, the CFO of BNY, which is making major strides with AI. While some companies track success by the volume of prompts, tokens, or agents deployed, McDonogh said that framing never took hold inside BNY.“It’s not something we spend any time talking about,” he told me, noting that token costs are “modest within modest” relative to the firm’s broader engineering budget. Even as the topic gained traction externally, the bank’s leadership prepared to address it—but ultimately viewed it as a distraction from more meaningful measures of value.McDonogh said that BNY had an early and deliberate AI strategy. Since the emergence of ChatGPT, the bank has spent several years building an internal, LLM-agnostic platform and forging partnerships across hyperscalers and model providers. Just as important, he said, has been CEO-level commitment and a focus on cultural adoption.“There’s been a demystification,” McDonogh said. “People don’t feel insecure about AI. That’s a really important cultural point.”That approach has allowed BNY to scale AI without fixating on cost per query. Internally, systems route tasks to the appropriate models, ensuring efficiency without requiring employees to optimize prompts manually. “I couldn’t tell you how many prompts we did last week,” he said. “I’m focused more on outcomes.”Those outcomes are........

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