The AI Race to Hardware: A Modern Shift in the Industry
In the grand narrative of artificial intelligence (AI), the AI gold Rush has catalyzed a transformation, fueling an explosion of investment in software. However, amidst this powerful wave of innovation lies a pressing issue: the real work of AI has been ignored. A 2024 report from Bain & Company revealed that less than 10% of the total AI dollar went toward physical infrastructure, embedding critical data handling, energy systems, and sensing technologies into the equation.
The majority of the reserve energy is tied to what Shaofeng Zhou, the founder and chairman of Xinghan Laser, refers to as foundation models and synthetic content tools. These tools democratize AI—they don’t require developers to invent cutting-edge systems, but instead offer raw material and tools for experimentation and quick testing. Instead of building models that are only useful for research, models developed through simulation are deployed in a live environment. Despite their limitations, deep tech companies like Tesla and NVIDIA continue to dominate the software landscape, offering🗓_SHORT-term, variable returns in exchange for high revenues.
But this abstraction neglects the fundamental need for hardware to supply AI systems with the precision, power, and durability that make machines reactive, adaptive, and capable of bearing real-world challenges. “Perception, interaction, and action” are precisely the functions after which AI systems are designed, and they demand hardware that can process data in real-time, respond to feedback, and withstand harsh conditions. Shaofeng points out that much of the AI ecosystem, as practiced by legends like DeepMind and Meta, prioritizes code over hardware, leading to theoretical inability to translate into reality.
Despite the underfunding of infrastructure, deep tech companies are thriving as vertical and integrative AI stacks—not silos of low-level software. While foundational models may succeed temporarily, their real-world applications are often limited even by resources. manned flights, medical diagnostics, and autonomous systems are among the most critical examples. Shaofeng insists that the real story of AI is not so much about what’s developed but how it is deployed.
The shift in AI ownership reflects broader economic and intellectual priorities. While model-centric approaches have had the ripple effect of rapid innovation and industrialding, the practical benefits of hardware-first AI are far more tangible and enduring. The underlying problem lies in meaningless investment—one that has cost billions of dollars and drawn little immediate return. Real concerns — like the cost of data collection, energy consumption, and regulatory compliance — far outweigh theoretical advantages. To safeguard AI systems from the Auditor’sbias, we must commit resources to hardware that matters most.
The AI equation is not just a game between models and code; it’s a two-part battle over investment. Shaofeng sees the haves-to-haves and thehave nots as only the beginning of a race to build a smarter, less predictable, and perhaps even have a sense of humor in the future. The answer lies in scaling AI to operate seamlessly with its environment, not just as a tool but as a partner. In manufacturing, logistics, healthcare, and supply chain industries, there is no greater opportunity to deploy AI systems that move, learn, and retrieve data in the same way weسنة the human condition. The key to a future where machines give life to the world is not a subscription to software and hardware for sale but self-sufficiency tied to the algorithms and the actual machines that make up the world.
In conclusion, the AI race is nearing its final push, with hardware as the primary driver of future innovation. Like-rock bands beforeDigital revolution, AI must not only iterate but also piece together pieces rather than cut them up. Only by integrating AI into the hardware will it truly succeed as the fabric of a smart, responsive, and adaptive world.
