Lia Müller Peña
Chief Strategy Officer and Founding Partner at BitGPT.network
Please introduce yourself and your background. What has brought you into fintech?
I’m Lia Müller Peña, Chief Strategy Officer and founding partner at BitGPT.network. My work sits at the intersection of AI and decentralized systems, with a focus on ethical design, digital sovereignty, and system transparency.
I originally came from a background in data architecture and AI ethics, and over time, that naturally led me to blockchain. The deeper I got into AI systems, especially how they reflect the values and limitations of their creators, the more I saw the need for decentralized alternatives. That’s what brought me into this space: building AI that is not only powerful but accountable, accessible, and inclusive by design.
It’s rare to see someone bring together such technical depth with philosophical clarity! What originally sparked your interest in merging AI and blockchain technologies?
That interest goes all the way back to how I was raised. My mother is a professor of feminism, and my father is a neuroscientist. Growing up on the campus of Cambridge University, I was constantly surrounded by questions about how we think and how those thought structures shape systems.
When I began working in AI, I applied that lens directly: Why does AI think this way? How does it reach this conclusion? I became increasingly concerned about algorithmic opacity, especially in high-stakes areas like healthcare, credit risk, and insurance.
Blockchain, for me, offered an architectural answer. If centralized AI is a black box, decentralization gives us a path toward transparency and auditability. That became the foundation for BitGPT: a belief that combining AI with Web3 isn’t just another technological upgrade but an ethical imperative.
You’ve spoken openly about algorithmic bias, and your personal experience highlights how deeply embedded it can be. Can you share more about how gender bias shows up in AI systems and why it matters?
Let me give you a simple but revealing example: not long ago, there was a trend where people asked ChatGPT to generate an image of them based on what it “knew.” In my case, it generated an image of an Indian man.
It was quite comical and a perfect illustration of how AI models reflect their training data and embedded assumptions. When entire identity categories are underrepresented, mislabeled, or considered irrelevant during training, the outputs become not just inaccurate but biased in ways that shape how the system “sees” us.
These are the same models being used in high-stakes environments: credit, healthcare, and hiring. When their biases go unchecked, they reinforce systemic exclusion. And the fact that centralized models are often closed-source and unauditable only compounds the problem. That’s why addressing algorithmic bias is both a technical and a societal issue.
That’s such a powerful reframing! Zooming out from the personal to the systemic: do you think the industry as a whole is moving in the right direction on gender inclusion? Or are there still major gaps?
For a long time, we’ve tended to equate professionalism with masculine-coded traits, like how we resolve conflict, lead teams, or make decisions while overlooking the feminine qualities all humans carry.
Through personal and professional exploration, I began to question what we valorize in the workplace and what traits, incentives, and modes of leadership we reward. This mirrors my AI work: how we weigh and prioritize certain inputs in a system ultimately determines the outputs.
I came to see that being a woman in these environments wasn’t a deficit, but part of a larger opportunity. Collaboration becomes most fruitful when we focus on how different perspectives complement one another, rather than what’s missing. Feminine approaches to leadership, like collaborative problem-solving, empathy-led communication, and encouragement-based management, shouldn’t be viewed as “soft skills.” They’re strategic strengths. And I think more men are starting to recognize and nurture those qualities within themselves, too.
In the long term, we need to expand the pipeline by supporting more girls and racially diverse talent to pursue STEM fields from an early age. But in the short term, we can reframe how leadership looks so that it values diversity of thought, not just conformity to old norms.
You're offering a much more constructive lens here. In your view, what kinds of initiatives or structural changes would actually help more women enter and stay in AI and blockchain?
There are some beautiful communities already — HER DAO, She-Fi, The Bigger Pie — that create safe spaces for women in Web3 and AI. But I think the next step is also supporting men in navigating how to work with and support women more effectively.
Much of what alienates women in the workplace stems from a simple lack of awareness. I often say: we conflate intention with impact. But if no one has ever explained how certain dynamics feel unsafe or exclusionary, how can we expect change?
So yes, we need more inspiration and access for women. We also need education on how to build collaborative, inclusive environments, ones that work for everyone, regardless of gender, race, or background. That’s especially important when we’re not just building apps but shaping the next layer of how society operates.
This mirrors the advocacy work I do to raise awareness about decentralized AI. I believe it’s the only viable path to architecting systems that through decentralization can attract diverse builders, preserve privacy and support digital sovereignty — agentically, socially, and technologically.
