How Mastercard tapped into GenAI

Welcome to Runtime! Today: Mastercard's George Maddaloni on rolling out internal AI tools and building with AI agents, Nvidia gives OpenAI more money to buy Nvidia chips and Oracle servers, and the latest funding rounds in enterprise tech.

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Prompting the prompters

Some companies that believe generative AI applications will unlock a productivity surge have tried to mandate their use, with mixed results. Mastercard's George Maddaloni, however, doesn't think it makes sense to enforce a one-size-fits-all strategy across wildly different job functions without a little help.

"I think [generative AI app adoption] is largely about change management and adoption." Maddaloni said in a recent interview with Runtime for our How We Built It series. "I think if you just throw it out there and expect people are going to be really using it effectively and being productive with it right away, you might not get the juice out of the squeeze."

• "Change management" as defined here starts with mandatory training on the company's data policies, but around the end of last year Mastercard also conducted a review of the different workflows used by categories of employees across the 35,000-person payments giant.
• In some cases generative AI tools didn't really move the needle, but the company found that by helping other employees understand how to use the tools effectively as well as making sure those tools could access the data sources they needed, adoption grew.
• "People needed to have a base level of [data] knowledge before they were granted access to the tool, but I think the thing that really drove adoption was helping people in the flow of work understand what some examples were of how people asked prompts correctly," Maddaloni said. "It's a little bit of a different language and [there is] a learning curve."

Like a lot of financial companies, Mastercard was no stranger to using AI both internally and externally for critical applications such as its fraud-detection system and other safety tools over the years. Last year the company released a product for banks called Decision Intelligence Pro that was built around a large-language model trained in-house that Maddaloni said was 20% better at detecting fraud with an 80% lower false-positive rate than the previous generation of the tool.

• Internally, it has rolled out Microsoft's Office 365 Copilot technology to 16,000 employees, and Mastercard developers are also using AI coding tools such as GitHub Copilot to build software.
• Before rolling out access to several coding assistants, including GitHub Copilot, last year Maddaloni led a pilot project with two small groups of developers to gather anecdotal information about what they liked and didn't like in a coding assistant.
• Mastercard developers are currently using AI assistants for coding and writing tests, but the company has yet to embrace the full agentic coding trend along with early adopters like Steve Yegge over concerns about security and stability.

Inside other parts of Mastercard, however, the company has rolled out agents to help consulting teams quickly find information about various parts of the company and to help train the next generation of management leaders, Maddaloni said. And at a time when every enterprise software company is desperately trying to establish themselves as the control platform for their customers' AI agents, he said Mastercard has found a lot of success building internally used AI agents on its own.

• "I think MCP was a big unlock, opening up the door for us to have multiple capabilities that our teams could employ," Maddaloni said.
• Anthropic's MCP standard has been quickly embraced by AI developers who value its ability to quickly and easily link multiple data sources together to serve AI prompts, but well-founded concerns about security mean Mastercard is unlikely to use MCP for external, customer-facing agents any time soon.

Read the rest of the full story on Runtime here.

What's $100B between friends

The circular funding cycle of the generative AI boom rolled merrily along Monday, after Nvidia announced that it would be investing $100 billion into OpenAI to set the stage for what it called "the biggest AI infrastructure deployment in history." For its part, OpenAI pledged to build up to 10 gigawatts of data centers using Nvidia's technology, including its newest "Vera Rubin" combo GPU/CPU platform.

Nvidia plans to roll out that investment in $10 billion chunks over time, according to CNBC, and the first data center built as part of the new partnership isn't expected to come on line until the second half of next year. Separately on Tuesday, OpenAI announced a new deal with Project Stargate buddies Oracle and Softbank to build five new AI data centers that they said would account for $400 billion of the $500 billion they promised to spend earlier this year.

Needless to say, building that many data centers in a relatively short period of time is going to be an enormously difficult undertaking, and that's assuming the parties involved can come up with the funding, the electricity, and the building permits from increasingly skeptical locals. "Our vision is simple: we want to create a factory that can produce a gigawatt of new AI infrastructure every week," said OpenAI CEO Sam Altman in one of his usual gauzy blog posts full of magical thinking.

Enterprise funding

AppZen raised $180 million in Series D funding for its AI agent platform, which was designed to help corporate finance teams automate new parts of their workflows.

Distyl AI landed $175 million in Series B funding as it builds out a consulting practice that helps enterprise companies deploy AI applications and platforms.

Empower Semiconductor scored $140 million in Series D funding for its power-regulation chips, which help data-center operators manage the supply of electricity to their hungry components.

Omnea raised $50 million in Series B funding as it builds out a platform of AI agents designed to simplify procurement.

Rocket landed $15 million in seed funding for its vibe-coding platform, one of the first startups out of India to take on the fast-growing AI coding assistant market.

Mycroft launched out of stealth with $3.5 million in seed funding for its security and compliance AI agents.

The Runtime roundup

Several European airports were struggling to resume normal operations Tuesday after a cyberattack took out automated check-in systems over the weekend.

Several data companies including dbt Labs, Salesforce, and Snowflake introduced the Open Semantic Exchange, a new would-be industry standard that would "establish the first vendor-neutral specification for semantic metadata," according to VentureBeat.

Thanks for reading — see you Thursday!