Throughout history, technology has evolved in cycles, with distinct periods of dominance ultimately giving way to new paradigmsIn an era where Nvidia’s market valuation has soared beyond a staggering $3 trillion, there exists an undeniable charm surrounding Andrew Huang, who is frequently lauded as the "Taylor of the AI Age." However, this enthusiasm seems to overlook a historical reality: all hardware supremacy is inevitably undermined by a deeper technological revolutionThe transition from vacuum tubes to integrated circuits, or from mechanical hard drives to cloud storage, teaches us that hardware is destined to be replacedIn contrast, the evolution of software and algorithms stands as a constant acceleration of progressWe may currently be at a pivotal inflection point in the decline of Nvidia’s empire.
Every major shift in technology follows patterns observed in the past, particularly regarding the fall of previously untouchable hardware dominanceA prime example is Kodak, which at one point monopolized 70% of the global film marketIts conviction that "hardware forms the barrier" caused the company to miss the digital camera revolution entirelyInterestingly, the first digital camera was invented by Kodak itself, yet the management clung stubbornly to the belief that "film profits are irreplaceable." This led to its downfall, as algorithms, namely image sensors coupled with compression technology, rendered Kodak's hardware-based model obsoleteThe glossier the hardware became, the more it seemed to trap Kodak in a cycle of innovation stunted by its own success.
Following this thread of history, another significant example is the decline of Intel's dominance due to its x86 architectureThis company reigned supreme during the PC era, but the rise of the mobile internet and the ARM architecture’s approach to "decoupling hardware and software" proved to be its undoingARM has thrived not by manufacturing chips, but by licensing its instruction sets, allowing devices running on lower power chips to outperform traditional systems through software optimization
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As we shift toward a landscape dominated by software-defined hardware, closed hardware empires like Intel’s can only expect to diminish in the face of open ecosystems.
Additionally, the complex relationship between IBM and its giant mainframes provides insightful context regarding the fallibility of hardware-centric paradigmsIn the 1980s, IBM firmly believed that "bigger mainframes equate to greater computing power." Ironically, it was the rise of personal computers and distributed computing that disrupted this viewGoogle’s introduction of the MapReduce algorithm further underscored this point, demonstrating that refining algorithms not only surpassed the brute force of stacked hardware—cloud computing as we know it required affordable server clusters rather than overpriced supercomputers.
As we witness what some call Nvidia’s “dangerous prosperity,” cracks within its GPU fortress have begun to widen significantlyThe transformation brought on by algorithmic advancements is already palpableGoogle's recent innovations in AI are evidenced by its Pathways architecture, which drastically reduced AI training costs by 80%, showing that the effectiveness of model architecture transcends raw computational powerConcurrently, the Mixture-of-Experts (MoE) model sharpens focus on dynamic activation of parameters, resulting in a staggering decrease in computing demandsRemarkably, techniques such as those pioneered by DeepSeek indicate that cost-effective small models can achieve 90% of GPT-4's performance while consuming just one-thirtieth of the computational power.
As algorithms grow increasingly adept at extracting the maximum capabilities from existing hardware, the myth of wildly overpriced GPUs serving as the backbone of AI innovation may soon dissipateUndercurrents of a software ecosystem aiming to “de-Nvidia” practices are already shifting the landscapeWith OpenAI’s Triton compiler, developers can optimize GPU workloads without relying solely on CUDA
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PyTorch 2.0 now natively supports GPUs from AMD and Intel, which further pushes the envelope of GPU competitionMeanwhile, companies, such as Moore Threads in China, are working on CUDA-compatible MUSA frameworks to subtly intrude upon established marketsThe moment any obstacle to “CUDA taxes” is eliminated, Nvidia’s premium on hardware may vanish entirely.
As we look ahead, the ultimate threat lurks in the potential awakening of general computing architectures, ranging from quantum computing to photonic chips and neuromorphic chipsAlthough these technologies are still nascent, their development points towards a promising future where specialized acceleration chips like GPUs represent merely a fleeting chapter in the narrative of computing powerJust as CPUs could not stave off the emergence of GPUs, the same may be true for GPUs resisting the next wave of architectural evolution.
DeepSeek’s emergence serves as a bellwether: it is often not a direct competitor that topples market giants but rather unique innovations that redefine the industryA vivid illustration of this can be drawn from the story of Nokia, which faced an insurmountable threat not from Motorola, but from Apple and the Android platform’s software-driven phone modelsSimilarly, some might dismiss SSDs as merely quicker hard drives; however, it was, in fact, algorithmic advancements that rendered traditional hard disk drives obsolete by employing error-correcting methods to minimize flash wearIn the landscape of mainframes, it was the open-source platform Linux that relegated IBM’s colossal machines to historical footnotes.
Today, with innovations spearheading trends towards “low computational AI,” DeepSeek may very well serve as a harbinger for the industry's futureIts inherent value lies not in simply showcasing what could replace Nvidia, but rather in demonstrating how we can ultimately move beyond dependence on Nvidia altogetherAs large models transition into a “slimmed-down era," efforts aimed at model compression and quantization are expected to diminish the demand for brute computational power significantly
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