Artificial Intelligence in the Hands of the People: The Rongjiang Experiment

Building the Institutional and Material Conditions

The experiment was made possible by conditions built at multiple levels.

Cun Chao was no technology project but a feat of organisation, and the organisational structure was standing before the first ball was kicked. In October 2021, a year and a half before that match, the county set up a leading small group for new media and rural e-commerce, headed jointly by the county Party secretary and the county governor. In a Chinese county the Party committee leads and the government departments administer, and those departments — agriculture, commerce, culture and tourism — each holds responsibility for part of any new undertaking; a leading small group is how a Party committee concentrates the efforts of different departments on a common task. Here the two highest officials in the county took the headship themselves, one from the Party and one from the government, which put new media at the top of the agenda.

Beneath the group the county placed a working team at the county level, a new-media service centre in every township, and a new-media service station in every village, and it set up a new-media company besides, a county state enterprise holding shares with private partners. The result was a single organisational chain linking three different but complementary capacities: Party leadership, grassroots mobilisation, and market activity. The Party committee provided political leadership; grassroots cadres reached every household; private firms and traders connected production to the market. When Cun Chao began in May 2023, the county set a dedicated Cun Chao Office on top of that standing structure, to mobilise cadres, coordinate security, and keep the media channels fed across all three levels at once.

When the county later turned toward AI, the mechanism had already put thirty-five thousand person-times — a tally of training instances, not unique individuals — through new-media training and built more than two thousand local livestream teams and over twelve thousand new-media accounts.

Rongjiang calls the shift the ‘Three New Rurals’: let the phone become the new farm tool, data become the new farm input, and livestreaming become the new farm work. As Party secretary Xu Bo puts it, the aim is to turn Cun Chao into new quality productive forces for local industry. New technology only raises the productive forces once the relations of production change to accommodate it — technology alone doesn’t do it; the social arrangements have to shift first to let it work. Led by the Party committee and carried by the villagers themselves, the county has therefore pursued this transformation by reshaping who is trained to use the tools, who holds the account they feed, and who keeps what a sale earns.

The material foundations for all this had been laid at national level. Through the poverty alleviation campaign the state carried roads, power, and broadband into places the market had never reached, in order to connect them to it: by the end of 2020, every eligible township and administrative village in the poor counties had a paved road, a bus service, and a postal route; reliable electricity, fibre broadband, and 4G had been extended across the countryside, with fibre and 4G covering more than 98 per cent of poor villages; and e-commerce services reached every poverty county. Only once these material conditions existed could digital transformation become a realistic strategy for rural development, e-commerce first and AI after it. One of the instruments that carried this into counties like Rongjiang is paired assistance, which binds a coastal province or work unit to an inland county and moves people, money, and technology along the link. It dates from a central decision of 1979, at the start of reform and opening up, following Deng Xiaoping’s strategy of letting some regions grow rich first and then bring the rest along. Rongjiang later applied the same logic to AI training, pairing those with greater digital capacity with those who had less experience.

Once these material foundations had been laid, AI became the next stage of national development strategy. This transition built on an earlier digital rural agenda: the central Digital Village Development Outline (16 May 2019) had already named ‘the improvement of peasants’ modern information skills’ as a strategic direction. On 26 August 2025, the State Council issued its Opinions on Deeply Implementing the ‘AI Plus’ Action, proposing six lines of action across science, industry, consumption, people’s wellbeing, governance, and global cooperation, and undertaking to strengthen the unified planning of intelligent computing power. Relying on the variable of the people’s adoption of digital tools, rather than the capital or hardware more often treated as decisive, the county organised its people to take up that opening on its own terms. It translated these national priorities into a local mobilisation plan, set out in a mnemonic it calls the ‘1-2-3-4-5’ target system, which illustrates how a local state translates a national technology strategy into mass participation:

• One benchmark. By the end of 2027, to have built Rongjiang into a national benchmark county for the universal learning and application of AI.
• Two pillars of support. A corps of three hundred home-grown technical backbones and thirty thousand ‘application pacesetters’, cultivated across at least five concrete AI application scenarios. The three hundred are not an elite but come from across the county’s cadres, civil servants, teachers, enterprise staff, village cadres, and returning graduates. They are seeded rather than bought: each of the three hundred trains ten, each ten a hundred, until the thirty thousand are reached.
• Three full coverages. One hundred per cent training coverage of government offices; one hundred per cent basic-AI-literacy coverage of every administrative village; one hundred per cent scenario-application coverage across every sector.
• Four-tier linkage. A four-tier AI application and promotion network running from county to township to village to enterprise.
• Five categories of benefit. Measurable gains in the efficiency of government services, in tourist satisfaction, in the coverage of quality educational resources, in the accuracy of medical diagnosis, and in the reduction of enterprise costs, summed up in the slogan: every village has an AI hand; every trade has an AI exemplar; every task has an AI enablement.

AI on Four Fronts of Rural Revitalisation

The county puts AI at the service of its people, their work, and their days; it turns it to the plain business of rural revival on four fronts.

1. AI Lowers the Cost of Being Seen

Before AI and new media reached the countryside, a village’s produce rarely left the mountains, for the only real route to market was to carry the harvest down to the nearest town and sell it there. Farmers had neither the capital nor the skill to present their goods attractively or to advertise them further afield, and a competent poster or promotional video meant hiring a professional crew few could bear. To make AI accessible beyond specialists, the county first trained villagers in practical AI-based content production, inviting outside professionals to teach simple workflows using mainstream AI tools and organising county-wide competitions to encourage experimentation. AI lowers the cost of being seen. With little more than a smartphone, villagers can now generate and refine the promotional material themselves – a poster, a short video, an animation – and distribute it on the same platforms that carried the league. Making a short film to promote Cun Chao, until lately beyond an average villager’s means, is now affordable and easily learned – and that same footage now carries other villagers’ goods to market too.

Producing enough good video by hand, across thousands of accounts, turned out to be slow and costly, and the traffic it won came unevenly; so the county integrated AI editing and large-scale distribution into its new-media system, operated by the villagers it trains as the league’s new-media promoters, allowing them to produce and circulate promotional content at a speed and volume the old way could not match. When a match is on, they film it and upload the footage, and the system writes the captions and edits it into a finished clip, ready in under half an hour. Distribution is made as simple as production: a tap on the on-site posting device, or a scan of a QR code, puts the clip on Douyin, China’s TikTok, within seconds. Promotional videos now carry built-in distribution links to local products, so that the same circulation that spreads the county’s stories also generates sales and commissions for those who share them.

2. AI Eases the Grassroots Cadre’s Burden

The chronic burdens of the grassroots cadre – the drafting of reports and notices, the compiling of statistical tables, weak policy publicity, slow response to grievances, the sheer weight of administrative toil – are eased by pressing AI into the workflow itself. It drafts the reports and assembles the meeting records, and, more valuable still, it recasts the official register of policy into plain language villagers can follow, which makes policy far easier to communicate. The county reports that a cadre who once spent half a day on a draft now finishes in half an hour, left to localise and refine rather than to assemble from scratch. The figures from Zhongcheng Town are plain: in the first two and a half months of 2025 alone, its village-level monitoring network flagged disputes early enough for staff to resolve sixteen of them before they escalated, AI-assisted forecasting fed into plans for nineteen projects, and cadres saved roughly two working days each. The county audit office, too, runs its own learning routine on cheap, off-the-shelf domestic models.

3. AI Turns Local Talent Into a Reason to Return

Where rural policy typically laments the outflow of the young, the programme attempts to reverse the valuation, so that mastery of AI, and the short films and animations that draw hundreds of thousands of views, become an attractive and remunerative occupation worth returning home for and worth staying for, aimed at returning youth, the women who remain in the villages, and individual traders. Liu Qinlan is the county’s own example: a kindergarten teacher earning a little over 2,000 yuan a month, she and her husband gave up their city jobs and came home when Cun Chao went viral in May 2023. She started out trying to sell the county’s own produce — luohan fruit, green and white tea, pickled fish and meat — and learned filming, editing, and livestreaming from scratch through the county’s training. Known online as Cun Chao‘s ‘Miao sister Lan Lan’, she now runs a batik studio by the Cun Chao ground, drawing on the region’s indigenous textile traditions and giving more than 180 embroiderers and dyers work without leaving their villages. In 2025 her batik and embroidery lines sold more than 2 million yuan.

4. AI Carries Local Culture Out of the Mountains

Cun Chao draws on its own peasant, working-class, and ethnic cultures. Made up of vendors, farmers, tilers, butchers, and factory workers, its villages name their teams after what they grow or do – a Monk Fruit team, a Passionfruit team, a Bayberry team, a Homestay team, a Rafting team, a Rice-Noodle team, even one that calls itself simply ‘the commoners’. The side that wins takes home not cash but the county’s own produce: the 2026 champions carried off a Guanling cow, the runners-up a Guizhou sturgeon, third place a crate of local duck. More than eighty per cent of Rongjiang’s people belong to the ethnic minorities of Miao, Dong, Shui, and Yao, home to heritage forms such as the Grand Song of the Dong (choral singing), traditional indigo dyeing, and the drum-tower. But these traditions – peasant, working-class, ethnic minority, even the Cun Chao festival itself – survive only insofar as they are seen and used. As such, AI animation, short video, and digital-human generation are turned to the work of carrying all of it – produce, trade, and heritage alike – out of the mountains and into national circulation, and from there into the commercial logic of cultural-tourism branding. At the provincial level this is already a working layer: a Cun Chao digital-human and smart-companion platform provides AI tour-companion services keyed to the league and to the Grand Song of the Dong, while the county’s enterprise training uses the Cun Chao brand, special crops, and heritage as its worked teaching cases.

Tailored Mass Training

The Rongjiang experiment rests on its teaching, the means by which an unfamiliar tool reaches farmers, shopkeepers, and cadres with little formal training. Its logic holds throughout: teach each person only what their own work requires, keep every lesson pared down to what can actually be done, and let those who have learned teach those who have not.

Nothing is taught one-size-fits-all. The county sorts the people it trains into groups, each with its own syllabus: for cadres those of governance, for merchants those of income, for the young and the heritage-keepers those of cultural creation. The same care goes into matching the task to the person, so that no one, the county insists, is made to learn what they cannot use. The county teaches AI as a practical skill rather than a technical discipline. Algorithms, programming, and the inner workings of the models are left aside in favour of using domestic AI applications and writing simple prompts. A visiting expert may bring a thousand possible applications of AI, but the local trainer’s task is to reduce them to the two or three that match local needs and market demand. The county therefore standardises its training around three practical courses — in AI document drafting, AI short-video production, and AI visual creation. The same philosophy shaped one of the county’s most recent courses, on AI comic drama in June 2026, for sixty returning youth, heritage artisans, and small entrepreneurs: it was designed for complete beginners, emphasised hands-on production, and connected local cultural heritage directly to market-oriented digital content creation.

Learning is treated as a rhythm rather than an event. Every Friday evening a trainer takes a livestreamed shift, a class that may draw two or three hundred viewers or, some nights, only one. Even one is considered worthwhile, because one person helped is one more person empowered. Since 2025, the weekly stream has not lapsed.

The teaching applies the same logic of paired assistance: those who learn first bring the rest along, with young cadres matched to middle-aged and senior staff to close the digital gap. The records bear it out. Zhongcheng Town designated twelve technology-promotion officers and established forty-two mentoring pairs, and a single round of centralised training covered ninety-eight cadres. At Bakai Town more than sixty cadres trained on four working scenarios, smart document handling, population-data modelling, new-media outreach, and emergency-response dispatch, with those trained to teach the rest, so that village cadres could run the systems on their own.

If pairing carries the skill from one person to the next, a chain of four tiers carries the plan itself from the county down to each level, with county, township, village, and enterprise each holding a distinct job.

1. The county designs. It sets the plan, marshals the teachers, builds the demonstration cases, and drives the work through weekly scheduling meetings that hold the lower tiers to account.
2. The township executes. It runs the routine mass training and the cadre drills.
3. The village is the front line. It is where villagers are taught and the locally useful applications are found.
4. The enterprise turns capability into revenue. At the end of the chain, it welds AI to culture-tourism, agriculture, and handicraft.

It is this chain, as much as the curriculum, that turns a directive issued in the county seat into a lesson under way in a village.

All of this rides on a project discipline recognisably that of a campaigning state, set out as a five-step closed loop for landing the programme in any village or unit.

1. Survey and preparation. An inventory of local industry, governance pain points, population structure, and the digital baseline yields a bespoke plan, using the ‘brainstorming meeting’ — the county’s instrument, inherited from the league campaigns — to identify concerns and potential challenges before they become real obstacles to implementation.
2. Cultivation of the backbone. The core group of technical backbones, the same three hundred named in the ‘1-2-3-4-5’ plan, are trained first.
3. Universal training by the layered method. That core group then cascades the training outward, each backbone teaching the next tier down, until it reaches the wider population.
4. Scenario landing. Training and application proceed together, so that each lesson attaches to a real task rather than staying abstract.
5. Review and quality-raising. What worked and what did not is assessed, and the next round of the loop is adjusted accordingly.

The Long March of the AI Era

Much of the Global South watches the spread of generative AI with unease: that it will deepen the dependence of poorer countries on a handful of firms in the wealthy core, that it will drive up the carbon cost of computation, that it will hasten the displacement of labour. None of these worries is misplaced. But fear on its own settles nothing, and the harder question is what a place without capital, advanced hardware, or a model of its own can actually do with the technology as it stands.

Rongjiang’s answer begins with cost. The computation does not run on the user’s device. The heavy capital sits in the cloud, paid for by firms such as Doubao and DeepSeek that built the models and by the infrastructure of states far from Moon Mountain, and what the villager needs is only an ordinary smartphone and the knowledge of how to speak to the model.

That the state should carry the compute is a matter of policy, and the ‘AI Plus’ Action names ‘strengthening the unified planning of intelligent computing power’ among its basic supports. The base model, moreover, has become a commodity: each is substitutable for the next, and the rural user is bound to no single vendor, so that should one tool withdraw or deteriorate, another takes its place. Substitutability leaves the monopoly rent nowhere to lodge, and what remains is an ordinary competitive cost — the production diffusion that China’s own development path has set against the rent-extraction of technological monopoly.

Even a smartphone, though, cannot everywhere be taken for granted. Rongjiang has met that difficulty directly. When it first carried new media into the villages, many people’s handsets were too old to run the apps. Its answer was not to wait until everyone was equipped, but to begin with the willing minority who already were, let them earn a living from it, and let their example draw the rest in.

Returning youth who used AI and short video to sell heritage crafts that had never before found a buyer, some of them earning a considerable income, pulled in the older women who made the goods. As those women began to earn, they bought phones of their own and began to film. The circle widens with the income.

What, then, could travel? Not the machine, since Rongjiang owns none of it, but the method: a way of organising the people around new forces of production. The institutional vehicle already exists. On 10 November 2025, the county and East China Normal University opened the South School in Toutang village, in Guzhou town, as a platform for the international communication of poverty alleviation and rural revitalisation. It opened into a wider moment: the 2025 Global South Academic Forum (Shanghai, 13–14 November 2025), co-hosted by Tricontinental: Institute for Social Research, ran a panel on digital sovereignty and AI in the Global South, and drew extensive involvement from organisations such as Brazil’s Landless Workers’ Movement (MST). The method has begun to circulate where movements, and not only governments, can take it up.

The Global South’s fight for digital sovereignty is usually pictured as building its own stack – its own chips, its own models, its own data centres. For most of the periphery, that whole edifice is out of reach at the moment, and a sovereignty of that kind, if it comes at all, will be won collectively rather than by any poor country alone.

Rongjiang works a different layer of the same struggle. Ownership of the machinery is not something one county can secure on its own; what it can secure, at its own scale, is the power of its people to use the tool on their own terms and for their own ends, and to keep the value they create rather than let it drain upward to a handful of firms in the North. That – a people able to use the tool, and to hold on to what it earns – is a sovereignty a poor county can actually build.

Ninety years ago, the Chinese Red Army carried the Long March to victory, won not by superior arms but by organised people, their endurance, and the peasants it mobilised as it passed, rousing their energy, their initiative, their creativity. Mao Zedong called that march a seeding-machine, for the revolution it sowed along the way.

Rongjiang has set out on a long march of its own, one for the AI era, and it moves in the same spirit: carried by the people, drawing out that same creativity, sowing among them a new capability as that march sowed revolution, and letting the gains of the technology flow back to them, to the many and not the few. It is not a slogan handed down but the patient work of turning a new productive force into a capacity held by the people, in the tradition of mass work. The technology will keep changing, and fast. What does not change is the insight at the centre: for a place that cannot build the stack itself, the question that remains is who, among its people, has made the model their own.