Liu Yonghao, chairman of New Hope Group and a long-serving member of China’s top political advisory body, used a media briefing ahead of the annual Two Sessions to press Beijing to accelerate the integration of artificial intelligence into livestock farming. Framing the debate in commercial terms, he warned against “downward” competition by cutting prices and urged traditional sectors to “compete upwards” through technology, innovation and learning.
Liu’s proposals are practical and wide-ranging. He recommends adding AI-enabled livestock equipment to the agricultural machinery purchase-subsidy catalogue, folding related data platforms into digital agriculture pilot schemes, and providing coordinated financial support across hardware, compute, algorithms and data. He also called for incentives for universities to create cross-disciplinary “smart farming” programmes, tax breaks for companies that adopt AI breeding technologies, and a national R&D programme for AI-driven livestock production led by industry–research consortia.
The New Hope chairman illustrated the rationale with his company’s own experiments. Using big-data analytics and AI, New Hope has tightened cold-chain logistics for fluid-dairy products to deliver what Liu described as “24-hour fresh milk” from farm to shelf, and it has introduced robotic systems into meat cutting and packing to generate new revenue streams. He has mandated that researchers, sales and management staff learn to use large AI models and integrate those tools into everyday operations.
Liu also flagged structural barriers that could slow diffusion: fragmented technology and data, a shortage of composite technical talent, and weak industry–university–research links. He noted a distinctive pattern in China’s broader robot industry — hardware capabilities are strong, but the software “brains” and integration layers lag — and urged investment in embodied-intelligence data-collection centres, simulation engines and shared data-processing frameworks to close that gap.
His pitch arrives against a policy backdrop that already favours AI–agriculture convergence. The 2026 Central No.1 Document explicitly called for promoting artificial intelligence in agriculture, and proposals delivered at the Two Sessions often shape subsidy and regulatory priorities for the year ahead. For a sector where yields, traceability and logistics are still improving, AI offers an obvious productivity lever and a way to upgrade supply chains without competing solely on price.
The wider implications are geopolitical and commercial. Rapid adoption of AI in farming could bolster domestic food security, reduce waste across cold chains, and create exportable Chinese agri-tech solutions. But adoption will not be evenly distributed: tax incentives and R&D programmes will likely benefit large agri-enterprises with the capital to deploy and integrate complex systems, accelerating consolidation in the industry.
There are trade-offs to manage. Faster automation in rural industries raises social questions about job displacement and the need for reskilling; the aggregation of farm and livestock data concentrates new forms of commercial power and creates governance challenges around privacy, biosafety and algorithmic transparency. Thoughtful policy design will be required to balance incentives for innovation with safeguards for competition and public interest.
Liu frames the push for AI as a learning curve — an inevitable investment in capability that traditional firms must make to avoid obsolescence. Given his three-decade record in national advisory roles and New Hope’s visible pilots, his proposals are likely to shape discussions in Beijing about where to direct subsidies, tax relief and research funding this year. If adopted, they would accelerate a pragmatic, industry-led modernisation of Chinese animal husbandry with consequences beyond farm gates.