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 increasingly measurable. In the first quarter of 2026, more than 40% of BNY’s code was authored by AI, rising to roughly 50% more recently. AI is also embedded across operations: about half of annual account plans are drafted with AI, 25% of client onboarding is AI-supported, and roughly 70% of restricted-party payment screening is reviewed by AI.

The impact is showing up in financial metrics. Revenue per employee rose from $338,000 in 2022 to $401,000 in 2025, while pre-tax income per employee increased from $99,000 to $143,000 over the same period.

McDonogh frames these gains less as cost savings and more as capacity creation. “We haven’t reduced the footprint, but it’s allowed us to do more with the footprint that we have,” he said.

To track progress, BNY measures AI impact across core workflows—including innovating, prospecting, onboarding, transacting, and streamlining—while continuously building out its internal “Eliza” platform. The system serves as a firm-wide context layer, improving over time as it ingests more data and use cases.

Employee adoption is also structured. Staff progress through three levels of AI proficiency, culminating in a “pioneer” designation that requires formal training and testing. Access to more advanced models is gated by expertise, reinforcing both quality and accountability.

Within finance specifically, AI is already reshaping core processes. McDonogh points to regulatory reporting, balance sheet analytics and predictive modeling as key use cases. The technology is also playing a growing role in earnings preparation, helping synthesize analyst expectations and anticipate investor questions.

For McDonogh, the takeaway is straightforward: AI productivity is not about how much you use, but how effectively it changes what an organization can do.