Summary of Key Points
The robotics industry is currently in a phase that is both exciting and prone to pitfalls. Policy guidelines (such as those from the Ministry of Industry and Information Technology) have clarified the development goals for humanoid robots, and companies in the market have begun to implement them in real-world scenarios (for example, Figure robots entering BMW factories, and collaborations between Yubixiang and Airbus). However, for investment funds (governmental or institutional industry-specific funds), it's not enough to focus solely on whether there are “star companies.” More importantly, these funds need to ensure that the invested capital leads to the creation of products that are actually useful, appealing to customers, generating measurable data, and resulting in profitable investments. The key is to establish five critical “verification chains”: the scenario chain, product chain, data testing chain, customer chain, and capital discipline chain, to avoid mistaking short-term popularity for long-term value.
Detailed Analysis
1. Enhancing the Scenario Chain: Beyond “Dancing Robots” to Functioning Tools
Many people think that as long as a robot can move, it’s useful. In real industries, however, being able to move is far from enough; a robot must also be capable of performing specific tasks effectively. For instance, Figure AI’s robots in BMW factories are not there for showmanship but to perform practical tasks like installing car parts, completing the process in 84 seconds with high accuracy (over 99%) and with minimal human intervention. These criteria transform what were once mere decorations into tools that can keep up with the production line’s pace.
Robots displayed at exhibitions or in competitions are impressive, but they represent performance scenarios rather than evidence of industrial readiness. Investment funds should support practical and sustainable use cases such as factory operations, inspections, and logistics. Only through repeated trials, parameter adjustments, and data collection in these real-world contexts can robots develop practical capabilities.
2. Moving from Customized Projects to Standardized Products
Robotic companies often start with customized projects for individual clients, which are costly and difficult to replicate. The product chain aims to transform these customizations into standardized products that can be mass-produced. For example, if a company like Zhiyuan produces 5,000 humanoid robots in batches, it indicates that its supply chain, manufacturing, testing, and after-sales systems are reliable. Standardizing components (such as joints) can reduce costs and make products more stable for customers. Companies should also shift from focusing on engineering to business operations—producing quickly, ensuring quality, and providing affordable maintenance, as higher orders increase financial pressure.
3. Building the Data and Testing Chain: From Movement to Intelligence
Robots’ intelligence doesn’t come naturally; it is acquired through data collected in real scenarios. However, companies often face high costs when handling data collection and testing on their own. The data and testing chain involves establishing shared platforms, such as computing power resources, digital twin training environments (using virtual simulations), and pilot production facilities (for testing before mass production). These resources can help reduce the cost of trial and error for all participants.
4. Strengthening the Customer Chain: Beyond Order Numbers to Repeat Business
Companies often highlight the number of orders they receive, but these do not necessarily reflect actual demand. The customer chain requires examining four key aspects: whether customers transition from trials to actual purchases, whether they increase their usage after buying, and whether companies can reduce delivery and maintenance costs for repeat purchases. For instance, while receiving 1.4 billion orders is a positive sign for Yubixiang, a slowdown in growth suggests that if customers are just trying out the products without making repeated purchases, revenue growth may be unsustainable. Investment funds should focus on metrics like repurchase rates, per-unit usage costs, and maintenance response times to truly assess customer satisfaction.
5. Ensuring Capital Discipline: Preventing Unprofitable Investments
The robotics industry is capital-intensive and risky. Investment funds can support early-stage experimentation, but they must not invest recklessly. The capital discipline chain involves setting clear阶段性 goals: during the exploration phase, assess whether the technology direction is correct; during verification, check if there are real-world applications; during consolidation, evaluate whether orders can be reused and whether industry capital is following up; and during expansion, monitor financial indicators like gross margins and cash flow. If there is no customer feedback or no evidence of value creation over time, adjustments (such as selling shares or reducing investments) should be made promptly.
Conclusion
The robotics industry offers many opportunities, but it can also become overly speculative. By establishing these five chains, investment funds can direct capital towards projects that are viable, profitable, and sustainable, preventing hype from turning into bubbles. For the general public, this analysis highlights a crucial point: when evaluating robotic companies, focus not on how impressive they are, but on their practical capabilities, customer acceptance, and the value of the investments made.