Baidu and Alibaba Cloud Use Same Electricity Analogy as AI Race Moves to Industrialization

The overlap reflected a shared judgment: models are no longer the main bottleneck. The next phase of AI industrialization will be decided by who first delivers intelligence into thousands of industries. A year earlier, attention was on leaderboards, new models and benchmark scores. By September, both conferences had moved from how smart models are to how intelligence becomes productivity.

Baidu has built a full stack over years. In 2015, the Baidu Highest Award-winning team Xiantong worked on chips. By 2018, Baidu had formed an AI full-stack layout spanning chips, deep learning frameworks, platforms and ecosystems, before the large-model wave. In 2020, Baidu AI Cloud proposed cloud-intelligence integration. In 2023, Li Yanhong defined the AI-era IT stack as chip, framework, model and application. In October 2023, Baidu AI Cloud's Super Factory covered full-stack computing, model and application services. In 2024, it became an AI+ enterprise service system. In 2026, as focus shifted to agents, Li proposed a chip-cloud-model-agent full-stack AI, and Baidu AI Cloud positioned itself as a new full-stack AI cloud for large-scale agents.

Alibaba is also building a full stack quickly. It places chips, cloud and models on equal footing and pushes them forward together while waiting for a new species to emerge.

Shen Dou described chips, cloud, models and agents as four layers that amplify each other, forming a growth flywheel for AI as infrastructure. Alibaba's chips, cloud and models interlock in a similar machine. But the flywheels start differently. In the electricity era, some built power stations and waited for users; others first found factories that had to use electricity and then configured stations around demand. Both approaches survived, in different postures.

Baidu starts from real business value. Industrial agents enter enterprise frontline operations to solve problems and create efficiency and growth. Real business data drives model iteration, which supports larger cloud and chip investment. As scale expands, costs fall, allowing more companies and scenarios to use the technology and release new demand. The ignition point is whether industrial agents land and deliver value, not parameters in a presentation. Shen Dou said industrial agents are driving AI to become a real economic force, treating AI as an economic variable rather than only a technology.

Alibaba's flywheel relies on supply. As computing power rises, the Qwen RSI model becomes stronger. Stronger capability waits for a representative product, and that product waits for demand to explode. The supply-side bet is that a representative product will appear and trigger large-scale demand. Building 20 GW of data centers by 2032 is the bet behind that judgment. The approach matches Alibaba's belief: build the station first, then wait for users.

Each flywheel has a weak point. Baidu's business flywheel fears agents failing to do real work, stalling later links. Alibaba's supply flywheel fears the representative product arriving late, with the station built and users absent. The two affect each other. Faster agents increase Baidu's appetite for computing power, raising demand for the supply side. When Alibaba's representative product appears, the first companies to scale will still choose a cloud that delivers value. The contest is not who turns first but who turns out scale first.

Pricing shows the split. Alibaba follows token economics, charging by volume of thinking; the more tokens consumed, the longer the bill. It resembles early electricity meters, charging by kilowatt-hours first and building scale later. At the Apsara Conference, Wu Yongming said future total machine thinking will exceed human thinking by more than 1,000 times. If thinking volume rises 1,000 times, token consumption rises 1,000 times. Customers buy the possibility of capability.

Baidu follows productivity economics, tying revenue to business value. Shen Dou gave a global measure: over the past three years model capability kept rising, but GDP did not change markedly; AI capital expenditure was only 0.4 percent of GDP, while the electricity system was close to 5 percent. He predicted that by 2030, 3 billion industrial agents will run continuously, bringing more than 100 GW of inference demand, with infrastructure investment backed by real scenarios.

Customer ledgers show the difference. Alt, a leading domestic vehicle R&D company, worked with Baidu on wind resistance design optimization using Baidu AI Cloud's self-evolving super agent Famou. It autonomously evolved more than 120 rounds, reducing a single CFD simulation from hours to seconds. China Southern Power Grid used Baidu's ontology construction agent Shengsuan in real grid scenarios. Taikang deployed an agent development platform in April and has launched more than 3,500 agents, consuming 20 billion tokens per day on average across healthcare, risk control and compliance, sales and operations and other core scenarios. The standard is not model scores but whether problems are solved, costs reduced and growth generated.

Alibaba customers buy possibility. If total machine thinking reaches 1,000 times human thinking and usage costs fall to the price of electricity, the economic benefits would be difficult to estimate, though when that day comes is unknown. Under the supply-first approach, customers and cloud vendors act more like partners waiting for a new species. Two pricing models can coexist, showing the market has not yet settled. For now, only two players can sit at both ends of the table.

In the electricity era, greatness lay not in Edison building power stations or factories replacing motors, but in stations and users achieving mutual success and pushing the world into electrification. Power stations waited for motors, and motors made power stations worthwhile. The AI electricity era may reach that stage. Agents that land first are realizing value in real business cycles; computing capacity spread first is waiting for its representative product. The starting points differ, but both are deepening the foundation of China's AI industrialization. The real winner will be decided in the next industrial wave, when one flywheel turns out scale first.