AI in RealTech & Fintech: A Mirror or a Map to Something Better?

Every few years, a new wave of technology promises to “transform everything.” But generative AI feels different; not because of what it does faster, but because of what it enables us to imagine and build differently.

Over the last few months, I’ve been reflecting on how AI is quietly but meaningfully reshaping both RealTech and Fintech – two sectors I have been associated with and continue to stay close to. These are personal thoughts, not official positions, but they stem from what I’m seeing on the ground, and more importantly, from what I’m beginning to question.

In real estate, especially among smaller developers, AI is no longer a buzzword, it's starting to become a partner. What used to be complex, expensive design tasks are now being handled by generative algorithms that don’t just draft floor plans, they optimize for light, airflow, local climate, cost constraints, and zoning norms in real time.

It’s easy to miss how quietly revolutionary this is. A team of two can now do what once required a floor of architects, consultants, and months of approvals. More people can now play the game, and play it smarter.

In the world of capital and credit, the shifts are even more striking.

Solo entrepreneurs are using AI to run models and simulations that were once the domain of analysts. Underwriting is evolving, not just with financial ratios, but with behavioural patterns, social signals, and alternative datasets.

But here’s where I worry a bit

Most of these models are trained on what has already happened, and if we aren’t careful, they will just keep repeating that.

• Historical bias gets embedded into design and lending decisions.

• Personalized recommendations start to trap users in the comfort of sameness.

• Affordability, sustainability, and access remain afterthoughts, unless we explicitly program for them.

Without intervention, AI will become a mirror, reflecting and accelerating our existing biases and blind spots. What we need instead is a map, to different futures we might not have considered.

As we start using AI more deeply in our industries, a few practical questions come to mind:

• Can models go beyond just predicting likely outcomes, and also suggest alternative scenarios we might not have considered?

• Can we build in priorities like affordability, environmental impact, and access, right at the core of what the model is optimizing for?

• Can we make AI more transparent, so people and regulators can clearly understand why a certain recommendation or decision was made?

• And instead of only regulating results, can we also ask: why is this model being used, and what’s it trying to solve?

I’m optimistic about the potential of AI, but we need to guide it with intention. Let’s make sure we are not just making things faster or more efficient, but also more inclusive, thoughtful, and aligned with long-term goals. What’s your view?