The New York Stock Exchange is using Anthropic's Claude generative and agentic AI extensively throughout the organization. Sridhar Masam, chief technology officer at the exchange, spoke at a virtual event Anthropic hosted. "We see AI as a tremendous accelerator in 2026 as adoption internally grows and as we move on from experimentation to production to scale," Masam said.
Masam described how the tool's role has changed. "Eighteen months ago, it was more of a chat interface, and people were used to using it for code completion," he said. "Now with its agent ticket reasoning capabilities, it's more independent. It's more of a collaborator than an assistant. That was a big fundamental shift in how people use AI." He was referring to Anthropic agents based on Claude 3.7 Sonnet and the Claude Agent SDK that use "chain-of-thought" reasoning to process complex, multistep tasks like customer service, IT support or software bug resolution.
The NYSE has been reengineering its development process and using Claude in coding, writing tests and documenting new code. Where software development used to be a matter of "buy what you can, build what you must," Masam said, it is now becoming a matter of combining multiple models, multiple vendors, platforms, data and internal capability. "Assembly is becoming a key aspect where you need to assemble all these things to have a solution," he said. His team also used Claude code to build a reference implementation for the blockchain-based settlement ledger it is developing for trading tokenized equities, which will allow instant settlement of trades of U.S. listed equities and ETFs around the clock. "We see AI being part of our journey to accelerate that implementation," he said.
Masam said Claude models are effective in processing large documents and applying rules. The NYSE has used the technology to build agents that review proxy filings, audit Securities and Exchange Commission filings and generate news classifications.
The NYSE processes more than a trillion messages on a peak trading day. "Even at that scale, system resilience, determinism are paramount," Masam said. Using AI to develop software brings more accountability, he said. "Traditionally, we are so used to building deterministic platforms," he said. "You write code requirements and build. Now, with AI being probabilistic, the accountability doesn't end when the project goes live, but on a daily basis, you have to monitor behavior and outcomes."