On July 29, 2026, the National Bureau of Statistics and the Ministry of Industry and Information Technology jointly released H1 AI industry investment data: fixed asset investment across the industry grew 45.3% YoY, with AI application investment in manufacturing surging 62.1%, becoming the core growth engine. This indicates AI has fully transitioned from the previous 'concept hype' stage to a substantive growth stage of 'deployment and revenue generation.'
1. Manufacturing Leads: Intelligent Transformation Demand Explodes
According to the report, AI funding in manufacturing exceeded 120 billion yuan in H1, accounting for 36% of total AI investment, up 12 percentage points from last year. Specifically, auto manufacturing, electronic equipment, and chemicals were the main investment sectors, absorbing 28%, 22%, and 15% of funds respectively.
'Demand for AI in manufacturing has shifted from 'icing on the cake' to 'fuel in the snow,' said the Director of the Department of Information Technology Development at the Ministry of Industry and Information Technology at a press conference today. 'Against the backdrop of fading demographic dividends and rising labor costs, enterprises urgently need AI to reduce costs and improve efficiency. Intelligent quality inspection alone can reduce inspection personnel by 60% and increase yield rate by 30%.'
Investment Hotspots: Industrial Vision and Smart Scheduling
Further breakdown shows industrial vision and smart scheduling systems as the two most capital-attracting sub-sectors. According to the latest IDC report, industrial vision system investment reached 38 billion yuan in H1, up 89% YoY, with many startups receiving over 100 million yuan in financing. Smart scheduling and dispatching systems investment reached 29 billion yuan, up 74% YoY.
Take a leading domestic auto parts manufacturer as an example: by introducing an AI vision inspection system, it improved defect recognition accuracy from 85% (manual) to 99.5%, reducing annual losses by about 230 million yuan. The company's head revealed: 'We plan to upgrade all production lines to AI-driven by end of 2027, with a payback period estimated at less than 18 months.'
2. Three Drivers: Policy, Technology, and Cost Synergy
1. Policy Support Intensifies
Since the beginning of the year, four national policies have been issued directly benefiting AI-enabled manufacturing. Notably, the 'Three-Year Action Plan for AI Empowering New Industrialization (2026-2028)' released in May requires AI penetration in key industries to exceed 70% by 2028, granting up to 30% tax deduction for smart manufacturing projects. At the local level, provinces and cities have set up AI industry funds, with Guangdong, Jiangsu, and Zhejiang alone investing a total of 50 billion yuan in H1.
2. Technological Breakthroughs Lower Barriers
Rapid iterations of large model technology have significantly lowered the barrier for AI application. In H1 2026, multiple vendors launched lightweight, privatizable industrial large models, enabling SMEs to use advanced AI at lower cost. For example, a tech company released a 'Lightweight Industrial Large Model' that runs on just one server, with annual service fees dropping from 2 million yuan last year to 500,000 yuan.
3. Hardware Costs Continue to Decline
The price war in AI chips intensifies. As domestic GPU manufacturers rise, mainstream training chip prices have dropped about 35% YoY, and inference chips dropped 40%. This makes AI computing power more affordable for manufacturing enterprises, accelerating smart transformation.
3. Investment Logic: From Concept to Performance Delivery
Notably, the current AI investment boom differs from previous ones. In earlier years, massive funds flowed to foundational large models and general AI platforms, but enterprise revenue growth was slow. In 2026, capital is more inclined toward 'vertical application' companies with clear customers and deployment cases. Analysis shows manufacturing AI application companies' average revenue growth reached 78% in H1, while pure foundation model companies only achieved 18%.
'Investment has entered the 'penetration rate' evaluation stage,' noted famous financial analyst Li Yue in a recent report. 'Investors no longer just look at technical parameters but focus on whether AI can truly penetrate production processes and translate into quantifiable benefits. Companies with massive industry data and the ability to solve real pain points will earn excess returns.'
4. H2 Outlook: Three Main Themes Worth Watching
- Theme 1: Industrial Vision and Quality Inspection — Expected to maintain over 80% growth in H2, especially AI-driven X-ray, ultrasonic, and other NDT equipment.
- Theme 2: AI Scheduling and Supply Chain Optimization — As export companies face a more complex international environment, smart scheduling becomes a must-have, and related solution firms may accelerate IPOs.
- Theme 3: Predictive Maintenance — Using AI to analyze equipment vibration, temperature, and other data to predict failures, reducing unplanned downtime losses. The market is expected to grow 70% for the full year.
In summary, the high growth of AI investment in H1 2026 is no accident. With policy, technology, and cost benefits overlapping, the integration of AI and manufacturing is moving from 'pilot' to 'scale replication.' For investors, rather than chasing illusory general AI stories, it is better to focus on intelligent upgrades of the real economy—where growth is certain and value quantifiable. As one AI investor said in an interview: 'To invest in AI now, you must invest in projects that can make factories smarter and more profitable.'
