Since the beginning of 2026, the domestic machine tool industry has experienced a significant recovery in demand: the order volume of many machine tool manufacturers has increased significantly year-on-year, and the production schedule of some companies has been postponed to two months later. Official statistics also show that the growth rate of machine tool procurement demand in fields such as new energy, aerospace, and high-end equipment manufacturing is particularly prominent. The industry as a whole seems to have emerged from the previous cold winter cycle.
However, in contrast to the hot orders, many heads of machine tool manufacturers do not feel relieved.
Before them are two difficult problems that are difficult to avoid: First, there is a huge shortage of high-end technical workers. It is difficult to recruit skilled five-axis programmers even if they are offered high salaries, and personnel mobility remains high; second, the needs of downstream customers have undergone fundamental changes. In the past, bidding only required machine tools to meet basic parameters such as rigidity, accuracy, and stability. Nowadays, the bidding documents of many projects have clearly required that equipment must have AI intelligent programming and adaptive processing functions. Products without AI capabilities are not even qualified to bid.
The industry’s recovery is not a universal rise that lifts all boats, but a differentiation caused by track switching. Recovery and elimination are happening at the same time.
In the past, the core competitiveness of the machine tool industry was concentrated on the level of hardware parameters. Indicators such as spindle speed, guide rail life, and equipment rigidity directly determined the market competitiveness of the product. These parameters are still the basic threshold of the product, but they are no longer the core competitive advantage.
The real watershed in the current industry is the “intelligence level” of machine tools: traditional machine tools are like horses that need to be controlled by experienced riders and require extremely high technical levels of operators, while AI smart machine tools are more like self-driving vehicles with their own navigation. Even inexperienced operators can get started quickly. This is not a simple performance upgrade, but an intergenerational iteration at the product species level.
The current pain points in the domestic machine tool industry are very concentrated: difficulty in recruitment is the primary problem faced by most manufacturers, especially the long-term talent gap for five-axis programming and high-end equipment operation; at the same time, the programming efficiency of traditional five-axis machine tools is extremely low, and a medium-complexity part must be converted from drawings into executable processing
The procedure often takes hours or even days. Problems such as temperature changes, tool wear, and material hardness fluctuations during the processing require experienced masters to adjust parameters at any time. If you are not careful, a high scrap rate will occur. In a cycle with sufficient orders, these problems will directly lead to the loss of corporate profits.
In response to these industry pain points, a group of domestic machine tool companies have begun to use AI technology to provide solutions. Although the technical routes of different companies are different, the core logic is highly consistent: convert the processing experience of senior technical workers into algorithm models, so that machine tools have the ability to “think” and “adjust” independently.
The AI intelligent machine tool products that have been implemented so far have achieved technological breakthroughs at multiple levels: first, voice intelligent programming. Operators only need to upload processing drawings and describe material, tolerance and other requirements in natural language, and the system can automatically generate complete processing programs and optimize tool paths. The programming work that originally required a lot of time is compressed to one-tenth or less of the original; second, adaptive processing compensation, the equipment monitors processing status and vibration data in real time through a dense sensor network.
The AI system can automatically adjust parameters such as feed speed and spindle speed to offset accuracy deviations caused by thermal deformation and tool wear. It can maintain stable accuracy even if it runs continuously for a long time, and the scrap rate can be reduced by more than 60%. In addition, it also has a full-link digital management and control function. The equipment’s operating status, processing progress, tool change reminders, and fault warnings can all be synchronized to the management background in real time, significantly reducing non-cutting time. A single operator can watch 3-5 pieces of equipment at the same time, significantly reducing labor costs.
Market selection has verified the development trend of AI intelligent machine tools: in developed manufacturing provinces such as Guangdong, Jiangsu, and Zhejiang, the market penetration rate of domestic five-axis machine tools with AI functions is rapidly increasing. Downstream customers no longer blindly believe in imported brands and are more willing to pay for domestic AI machine tools that are “easy to use, smart, and worry-free.”
The price signal of the second-hand market also confirms this trend: the second-hand price of traditional five-axis machine tools without AI capabilities continues to fall. It is not that the hardware of the equipment itself is damaged, but that the product has failed to keep up with new market demand, just like no one is willing to pay high prices for functional machines that cannot access the Internet.
Every generational change in technology will bring about a round of industry reshuffle. Today’s machine tool industry is standing at the crossroads of technological iteration. AI capabilities are no longer the icing on the cake, but a basic threshold for enterprise survival.
Machine tool products without AI capabilities are depreciating at an accelerated pace, no matter how good their market reputation was. Companies that are the first to deploy AI technology are quickly seizing the market with lower operating costs, higher processing efficiency, and less manual dependence.
For machine tool manufacturers and downstream manufacturing companies, the window period for technology iteration will not be too long. When AI machine tools become a common industry standard, the current first-mover dividend will gradually disappear. Only the companies that are the first to embrace AI technology will occupy an advantageous position in the subsequent market competition.
