Till Data Soil, Don’t Drill Data Oil

During a recent visit to Dharamsala in northern India, I came across a scene that has stayed with me ever since.

Tibetan merchants, who had long found the doors of formal financial services closed to them, can now — with a payment QR code printed from an e-wallet — plug their small shops into India’s Unified Payments Interface, or UPI. This is more than payment convenience; it is digital public infrastructure, or DPI, at work.

Around the world, a growing number of displaced people are kept outside formal financial systems; they have little choice but to eke out a living in the grey economy. In India, the state lowered the barriers to participation, allowing people to build on the trust and community ties they already held.

This is the reminder the AI era needs most: Meet people where they are, not where a platform wants them to be.

Many worry that AI is assuming the shape of a “digital coloniser.” A handful of tech giants control compute, models and platform rules, while the rest of us simply rent intelligence. Once a platform pushes an update or adjusts its pricing, even a business may find that its own workflow has been quietly “extracted.”

Underneath this entirely reasonable anxiety lies a deeper question about how we understand the nature of “data.”

Treating data as oil leads to one logic: extraction and concentration. I prefer a brighter picture: Data is not oil, it is soil. AI models are crops, with the key questions: Who tends the data ecology and who mends the system in the event of an error.

Taiwan’s richest soil lies on the shop floors and factory floors of every trade. The owners of many small and medium enterprises, or SMEs, carry irreplaceable tacit knowledge. When a senior technician states that a machine “sounds off,” that wisdom is usually understood only by those who work alongside the machine. That touch, that judgement, is the core capability Taiwan’s SMEs have built up over decades.

The problem is that much of this knowledge is lost as the masters retire.

If AI remains under the control of a few large platforms, SMEs are left buying solutions designed by others. But imagine public infrastructure for data innovation, model-training frameworks, interactive interfaces and evaluation benchmarks, which any workshop could use — the same way UPI is available to any merchant with an e-wallet.

The masters need not surrender their trade secrets. They can instead calibrate the model according to their own standards, turning it into a site-specific “machine apprentice.” For perhaps the first time, those judgements that are almost impossible to express in words can be fully passed on.

And this raises another important question: Who is given a voice in the Age of AI?

The people who actually make AI work in real settings are rarely the tech giants; they are the ones a community already trusts. An experienced nurse in long-term care can speak the language of a senior’s breathing, and understand instantly when something is amiss. That kind of judgement, formed deep inside a relationship of care itself, is invaluable and can only be learned over time.

For AI to truly take root in every industry and profession, what matters most is not just real-time interaction, but whether its introduction earns genuine trust. This is our opportunity: we can become pioneers of Civic AI.

The real challenge in the Age of AI is not about racing to refine the largest model, but working to best till the data soil — so that every hand can sow, steer and share in the harvest of inclusivity and prosperity.

(Interview and Compilation by Yu-Tang You. License: CC BY 4.0)