From the Ground Up: People's Platforms in Asia

Platform economies are built on a foundational assumption: that people do not trust each other.

Credit cards are held as collateral. Transactions are intermediated by corporations that take a percentage of every exchange. Workers, restaurants, and consumers are connected by infrastructure they do not own and cannot govern. The panel hosted by the Research Group in Innovation for Social Solidarity and Inclusive Economy (ISSIE) at Chulalongkorn University opened the AI & Equality Festival of Ideas with a different premise entirely: that trust already exists in communities, that it has always existed, and that the task is to build platforms worthy of it.

Moderated by Steph Wright, the session brought together Akkanut Wantanasombut, head of the ISSIE research group at Chulalongkorn University in Bangkok, and Sameer Bajaj, research analyst at the Aapti Institute in Bangalore. Between them they brought accounts of platform cooperativism in Thailand, data cooperatives for climate action in Southeast Asia and India, and the slow, irreplaceable work of building technology from community trust upward.

Akkanut’s work began when he watched platform workers protesting outside corporate headquarters in Bangkok during COVID-19. Delivery platforms had increased their commission rates from restaurants while simultaneously cutting worker pay. Workers were angry, restaurants were angry, consumers were angry — and the platforms did not respond. He began building TamSang TamSong, a decentralised, community-owned food delivery platform built on the principles of social and solidarity economy: democratic governance, ownership by users, benefit over profit, and collaboration over confrontation. Each community node sets its own delivery fees, its own contribution rates, its own rules. Nothing is centralised. The platform is a tool, not a business entity, which means the informal workers who use it remain informal, while gaining something they had never had before: a printable income summary bearing the university seal that banks accept as proof of earnings.

The trust story at the heart of TamSang TamSong is not sentimental. At the very first stakeholder meeting, a restaurant owner stood up and told Akkanut the project would fail because motorcycle taxi workers were unreliable. The workers heard this, acknowledged it, and turned to each other. They said: we know. And we know we are losing our customers to commercial platforms. So we are going to fix it. That moment of interstakeholder honesty, convened by a university holding social capital neither group fully trusted each other with, became the foundation on which three and a half years of reliable operation was built.

Sameer Bajaj brought a different geography and a different set of problems, but the same underlying argument. His work at Aapti Institute has focused on data cooperatives and community-driven data stewardship, and the project he described in detail examined four case studies at the intersection of gender, climate, and data sovereignty: the Kerala Food Platform built on the infrastructure of self-help groups with 46 million members; a fisheries conservation project in Indonesia formalising transactions for fishing communities; an indigenous water governance platform in the Mekong region; and a carbon credit scheme for community forests in Malaysia. What connected them was not a common model but a common question: how do communities that generate data actually benefit from it, and what governance structures make that possible?

The answer, in every case, came down to pre-existing trust. The Kerala Food Platform worked because communities already trusted the cooperative banks that had given them low-interest loans during COVID. The fisheries project in Indonesia worked because one trusted local municipal leader mediated the conversation, gave community members their first smartphones, and did the capacity building himself before any technology was deployed. The women’s water governance platform worked because the first stakeholder consultations were held with women only, and men were only invited in once the women decided they were ready to include them. In each case, the digital layer was built on top of social infrastructure that already existed. The technology did not create the trust. It depended on it.

The panel closed on a question about replicability that cut to the heart of how development and technology thinking usually works. Both speakers pushed back on the idea of scaling up as the primary goal. Akkanut offered a three-part alternative: scale up, scale out, and scale deep. Scale out means replication that adapts to local context rather than imposing a single model. Scale deep means investing in the relationships and community infrastructure that make any intervention sustainable. Sameer made a parallel argument: the frameworks matter less than the experiments, and the experiments matter less than what is learned from them. The goal is not to find the answer. It is to find better starting points.

The session was the opening panel of a full day of conversations across every time zone. Everything that followed built on the premise it established: that the future of AI is not decided, that communities already hold the values and the trust needed to build something different, and that the work is to create the structures worthy of both.