Colleagues, I’ve been delving into the capabilities of new AI models, particularly those like Gemini, and I believe their potential in the hardware domain is truly transformative, and perhaps even underestimated at present.
Currently, most AI models have limitations in understanding circuit schematics. They tend to treat circuits as visual patterns rather than abstract representations based on electrical principles. This directly impacts their ability to perform circuit debugging (debug) based on real-world lab conditions (e.g., oscilloscope waveforms, images of actual circuits).
However, the new generation of models, exemplified by Gemini, exhibit capabilities approaching those of experienced hardware engineers: They can interpret schematics, component specifications, pin definitions, and other critical information, and then offer iterative optimization suggestions for both circuits and overall hardware designs based on experimental results.
I believe this capability is epoch-making; it will reshape the entire process from conceptual design to physical product realization.
Reconstructing the Hardware Industry Value Chain
As for Google’s investment department, I think it is time to start taking action, acquiring upstream and downstream manufacturers, reshaping the entire industrial chain, and transforming the structure and process of the entire industry.
Let’s imagine the future of hardware development:
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From Idea to Prototype: Traditional product research, user analysis, cost and manufacturing cycle evaluations can all be efficiently handled by AI models.
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Virtual and Physical Convergence: AI can drive CAD software to generate virtual 3D samples and rapidly manufacture physical prototypes using 3D printing (or 6-axis CNC machining centers to directly machine metal enclosures).
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Automated Production: Combining automated PCB printing, robotic assembly, and other technologies to quickly produce engineering samples.
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Intelligent Testing and Iteration: AI-powered testing platforms, automatically built based on physical and industry knowledge bases, can comprehensively test samples and feed the results back to the design phase for multiple rounds of iterative optimization.
Advantages of AI-Driven Hardware Development
Compared to traditional hardware R&D models, this new AI-driven approach offers significant advantages:
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Accelerated Iteration: No longer limited by human resources, multiple designs and tests can be conducted in parallel, significantly shortening development cycles.
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Adversarial Optimization: By setting constraints and reward mechanisms, AI agents can monitor the results of prototyping and engage in “adversarial competition,” leading to superior designs.
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Exhaustive Optimization: In specific areas, such as fluid dynamics design, AI can automatically exhaust all possibilities, quickly generating a large number of prototypes for testing (e.g., wind tunnel testing), without the need for designers to manually experiment.
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Multi-Scenario Reuse: By designing reusable hardware platforms, coupled with different software and enclosures, the needs of various application scenarios (e.g., home, supermarket, restaurant, hotel) can be met.
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Extreme Optimization: In large-scale production, AI can optimize key processes like mold manufacturing, improving production efficiency and product yield.
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Complex System Design: Problems that previously required complex modeling can now be directly handed over to AI, allowing it to autonomously learn and optimize within constraints. Even if the process is a “black box,” the results are usable.
Breaking Through Existing Bottlenecks
Of course, to realize this vision, several challenges need to be addressed:
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Supply Chain Integration: It’s necessary to connect upstream and downstream companies to achieve full-process automation and data sharing.
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Standardization of Production Processes: Currently, there are differences in equipment and processes between small-batch trial production and large-scale mass production, requiring further integration.
AI-assisted production lines would use the small-batch production to be used directly for large-batch production, reducing costs and time.
Beyond “Industry 4.0”
It’s crucial to emphasize that AI here is not merely a supporting tool for “Industry 4.0,” but rather a core driving force, akin to a human expert. It should not be limited to 3D modeling and optimization but should possess a deep understanding of the principles of each step in the entire production process, the relationship between sensor data and changes in the physical world.
This is like the future of image recognition: not just simple object detection (like YOLO), but achieving a true understanding of object properties (like distinguishing between strawberries and pears).
I believe that new-generation AI models like Gemini have the potential to completely reshape manufacturing, just as AI has already disrupted the field of drug discovery. This is not just an improvement in production efficiency, but a revolution in the entire industry model.
