Fintech’s Role in Shaping Open Source AI

According to an article in The Financial Revolutionist by Jonathan Bailey, Fintech’s rapid adoption of AI contrasts sharply with its limited involvement in developing open source AI foundations.

Bailey notes that other sectors are already shaping the infrastructure that guides AI development. Health care organizations have developed biomedical models that shape safety expectations, enterprise firms have released frameworks governing how businesses interact with agents, and defense entities have provided datasets that inform model behavior in sensitive environments.

AI now represents a dominant share of virtual card deal activity, but the article asserts that many fintech companies rely heavily on community-built tools without helping shape frameworks, datasets, or guardrails that will define AI’s long-term direction.

The article states that such contributions serve strategic goals and position industries to shape what becomes viable and compliant. It describes a perceived gap between fintech’s regulatory sensitivities and its limited participation in open source AI contributions. It attributes the hesitation to concerns about compliance, data protection, production-grade reliability, and public exposure of code.

Bailey also notes industry-wide inefficiencies, citing repeated reinvention of solutions for data cleanup, compliance-aware agents, and legacy-system integration.

Examples from companies such as Extend, Stripe, and Ramp demonstrate that selective contribution can coexist with proprietary advantage, enabling firms to influence standards without compromising their competitive edge.

Lawyers advising fintech companies should note that the article suggests a growing relevance of technical standards in shaping regulatory expectations, increased scrutiny of AI tools used in financial workflows, and potential competitive disadvantages for companies that avoid contributing to shared infrastructure.

Participation in open ecosystems may become important in regulatory interpretation, talent development, and ecosystem partnerships. This is particularly true as AI governance increasingly reflects the work of those building publicly visible tools.