Upon wiping out a trillion dollars from the market capitalization of mighty US tech firms in a single day, further to a wake-up call, DeepSeek reveals deep secrets along with its bold arrival. Unlike traditional market entrants, DeepSeek did not keep its firepower secret—it made it public by making its source code Open Source.
Upon performing necessary analysis on its performance and Open Source code, analysts and US tech giants acknowledged that DeepSeek is a cheaper alternative—costing only $5.6 million to train, compared to $100 million, due to its smarter algorithm.
However, a crucial question arises: Is DeepSeek merely a one-time boom or does it reveal deeper secrets? By analyzing statements from industry leaders, academics, and politicians like Donald Trump, along with articles and news reports, it becomes evident that DeepSeek revealed Seven Deep Secrets.
These secrets are worth pondering, shedding light on what it takes to create success from technology possibilities. More importantly, they unveil the secret of China’s rise and the fall of the USA’s technology edge. DeepSeek’s seven deep secrets may serve as a foundation for long-term research, preventing costly repetition, protecting investors from ballooning valuations, and guiding policy makers toward the right focus.
First Secret—DeepSeek Challenges Compute-Hungry Monopoly
The artificial intelligence (AI) landscape has long been dominated by a training-centric mega model approach, with billions poured into infrastructure-heavy development. This has resulted in high barriers to entry and monopolization by firms like OpenAI, Google, and Anthropic. These firms have relied on the hypothesis that AI performance scales linearly with increased computing power and data availability. However, DeepSeek’s emergence has shattered this notion, proving that cheaper AI alternatives can challenge this monopolistic stronghold.
One of the biggest flaws in the compute-heavy AI paradigm is hallucination, an issue that persists despite enormous computational investment. A Nature article published on January 21, 2025, confirmed that hallucination cannot be entirely eliminated. This has driven major AI firms to pursue an infinite demand for computing capacity and data, leading to multi-billion-dollar GPU-powered data centers. With real-world data scarcity, these firms have resorted to synthetic data, reinforcing the impression of an ever-growing AI market monopoly while fueling Nvidia’s dominance in the GPU supply chain.
By promoting the illusion that AI scalability would eventually allow models to take over all cognitive jobs, these tech giants have justified massive investments and ballooning valuations. However, DeepSeek’s Open Source Breakthrough has dented this monopolization hypothesis. Unlike its predecessors, DeepSeek has demonstrated that smarter algorithms can significantly reduce the compute cost—from $100 million to just $5.6 million. This has effectively lowered barriers for smaller entrants, hollowing out the industry’s monopolistic structure.
Additionally, Nvidia’s grip on AI hardware is now under threat. DeepSeek has shown that smaller, application-specific models can operate efficiently with less powerful GPUs supplied by various competitors. As a result, the AI industry is witnessing a shift from compute-intensive monopolization to a more decentralized and accessible model. This paradigm shift may redefine AI investment strategies, challenge the dominance of entrenched players, and ultimately lead to a more diverse and competitive AI ecosystem.
Second Secret: The Capital Market’s AI Blind Spot—DeepSeek’s “Sputnik Moment”
The second deep secret about AI is that the capital market does not fully understand why it has been assigning such high valuations to AI-related stocks, particularly the Magnificent Seven companies. On January 29, 2025, Semafor published an article arguing that investors do not truly understand AI. If they did, why did DeepSeek’s arrival trigger a “Sputnik moment” for the US AI industry, wiping out a trillion dollars in a single day?
The underlying belief in the capital market has been that AI requires an infinite demand for computing power and data. Investors assumed that only companies capable of mobilizing billions of dollars and securing the most powerful GPUs would succeed in monopolizing a multitrillion-dollar market. The expectation was that AI would replace millions of knowledge-centric human jobs, making these AI giants the ultimate winners. Consequently, venture capital firms went all-in, heavily investing in foundational model companies and Nvidia, the leading GPU supplier. The result? The Magnificent Seven saw their stocks soar by $10 trillion in just two years.
Then came DeepSeek, shattering this belief. Its efficient, cost-effective AI approach demonstrated that cheaper alternatives exist, undermining the assumption that AI progress depends solely on expensive computing resources. This “Sputnik moment” exposed the market’s lack of critical questioning:
- Why didn’t investors challenge the sustainability of a training-centric, monopolistic approach?
- Why didn’t they consider the possibility of cheaper alternatives using smarter inference algorithms rather than brute-force computing power?
- Why didn’t they scrutinize whether the training-centric model could scale beyond human knowledge generation without being plagued by hallucinations?
Had these questions been asked earlier, optimism around monopolization through ever-growing computing power would have been far more tempered. The capital market’s failure to anticipate disruption resulted in shock and rapid devaluation, as DeepSeek’s emergence exposed the fragility of the AI industry’s dominant business model.
DeepSeek’s success serves as a lesson for investors, policymakers, and industry leaders—AI’s future may not be dictated by sheer computational force, but rather by algorithmic efficiency and decentralized Innovation. Without a more nuanced understanding of AI’s true trajectory, investors risk falling into the same hype cycle, only to experience another trillion-dollar correction in the future.
Third Deep Secret: Tech Leaders Are Bewildered With the Commoditization of AI On Stock Prices
The third deep secret about AI is that big technology companies are uncertain about how AI commoditization will affect their stock prices. This uncertainty is evident in the mixed reactions of industry leaders following DeepSeek’s disruptive entry.
For example, Intel’s board was dissatisfied with Pat Gelsinger’s leadership as the company’s stock plummeted over 60% during his tenure. In contrast, Alphabet (Google’s parent company) was among the Magnificent Seven, which collectively gained $10 trillion due to the AI hype following ChatGPT’s release. However, after the DeepSeek shock, AI-related major stocks lost over $1 trillion in market capitalization.
On January 28, 2025, YahooFinance reported that Gelsinger defended DeepSeek, arguing that the market was misinterpreting its impact. He tweeted that DeepSeek’s ability to create AI cheaply would drive chip demand, not reduce it. According to him, higher computing efficiency lowers costs, thereby expanding the AI market, making it illogical for Nvidia’s stock to suffer a $500 billion loss in a single day.
Many industry leaders echoed Gelsinger’s view. Wharton professor Ethan Mollick tweeted a similar perspective, reinforcing the belief that affordable AI will increase demand rather than shrink it.
On February 5, 2025, NikkeiAsia reported that Google CEO Sundar Pichai, alongside Apple, Microsoft, and Meta chiefs, praised DeepSeek’s breakthrough. Pichai stated that lowering AI costs would be beneficial for both Google and the overall AI industry.
Despite the initial stock shock, some experts believe that AI commoditization could broaden the market, creating new opportunities instead of eroding existing tech giants’ dominance. The true impact of this shift remains to be seen.
AI Commoditization: A Threat to Monopolistic market power?
The market values the potential to create a new industry and monopolize it, allowing for high margins and sustained revenue growth. In contrast, a large, competitive market with many players is seen as less valuable. This explains why Tesla’s market capitalization soared far beyond Toyota’s, despite selling fewer cars. Similarly, Nvidia’s stock surged due to its monopoly on high-end GPUs, which fueled the AI industry’s insatiable demand for computing power.
Nvidia’s unmatched efficiency in delivering more computing power with less energy reassured investors that both margin and volume would keep increasing indefinitely. The assumption was that AI training required infinite computing power, making Nvidia’s dominance seem unshakable. However, DeepSeek’s breakthrough has fractured this belief. By offering a cheaper AI model with higher computing efficiency, DeepSeek has demonstrated that many chipmakers can now supply GPUs for less compute-hungry AI approaches.
This shift means that both AI costs and monopolistic market power will decline. While lower costs will expand the market, the profit margins that once justified sky-high valuations will shrink. As a result, market capitalization will likely fall, even as AI adoption accelerates.
DeepSeek’s Open Source approach will reduce entry barriers, democratize AI innovation, and speed up diffusion. While this could generate additional AI demand, it also challenges the capital-intensive, monopolistic model of today’s tech giants.
So, are tech leaders genuinely praising DeepSeek, or are they strategically embracing the inevitable to prevent further stock declines? Are they acknowledging the smarter (inference-based) AI approach while attempting to convince investors that expanding AI’s reach will compensate for falling margins? DeepSeek’s impact on the AI industry’s future profitability remains a critical question.
Fourth Secret: Misinterpreting Disruptive innovation in AI
A recent Harvard Business Review (HBR) article co-authored by three business school faculty members described DeepSeek as a “classic disruptive innovation” in action. However, this classification misinterprets the fundamental concept of disruptive innovation, a term that Professor Clayton Christensen sought to define precisely.
Disruptive innovation emerges when a new technology core introduces an initially inferior alternative to existing solutions. A classic example is the digital camera, which was originally far inferior to film-based photography but eventually outscaled and replaced it. In true disruption, incumbents avoid the new technology, dismissing it as a low-end substitute, only to later struggle to adapt as it improves and takes over the market.
By this definition, DeepSeek does not qualify as a disruptive innovation. Unlike traditional disruptions that introduce an entirely new technology core, DeepSeek, OpenAI, and Anthropic all use the same underlying neural network-based learning approach. DeepSeek differentiates itself not by replacing the core technology, but by implementing smarter inference algorithms and breaking problems into application-specific subsets, making training far less costly.
Rather than being a disruptive innovation, DeepSeek is an Incremental innovation that enhances efficiency within the existing AI paradigm. It introduces a more application-specific, lower-cost alternative to generalized large AI models, but it does not create an entirely new market that incumbents struggle to enter. Moreover, its Open-Source nature removes the switching barriers, making it easier for competitors to adopt.
Labeling DeepSeek as a disruptive innovation overlooks the nuances of Christensen’s theory and creates confusion in management thinking. Instead, DeepSeek should be recognized as a superior performer in the incremental innovation race, demonstrating that efficiency and specialization can challenge monopolistic AI business models.
Fifth Secret: Scaling with Massive Subsidies Cannot Prevent Smarter Entrants
One of the deep secrets of technological evolution is that scaling up an initial breakthrough through massive subsidies does not create an insurmountable barrier for smarter new entrants. History has repeatedly shown that true success emerges from sustained incremental innovation, not from brute-force expansion.
Take electric vehicles (EVs) as an example. While EVs appear to be a better alternative to gasoline-powered cars, limited range has been a major challenge. The obvious solution was to increase battery size, but that was not what led to true progress. Instead, incremental improvements in energy density and smarter materials drove cost reductions and efficiency gains. This is why Tesla’s early market dominance is now being overtaken by BYD, which produces better-performing EVs, powered by more cost-effective battery packs.
A similar pattern occurred in the semiconductor industry. The invention of the integrated circuit opened new frontiers for chip innovation. Instead of merely increasing wafer sizes to pack more transistors, the real success came from miniaturizing transistors, improving their performance, and making smarter design choices. This fundamental lesson of technological progress has been seen in camera sensors, graphical user interfaces, and countless other industries.
However, U.S. AI companies failed to absorb this lesson. Instead of making AI smarter, they focused on scaling up by securing massive subsidies to build larger data centers and server parks. Venture capital firms poured billions into AI Startups, believing that monopolization through capital-intensive expansion would ensure global dominance. This scalability illusion was shattered by DeepSeek, which proved that smarter inference algorithms could dramatically lower costs while maintaining high performance.
Had investors and tech leaders studied the life cycle of great ideas, they would have anticipated the arrival of a more efficient alternative. Instead, they reacted with shock, wiping out over a trillion dollars in market capitalization.
DeepSeek’s success signals an even larger shift—the rise of many more new entrants who will develop smarter algorithms that undermine capital-intensive AI scaling. If history is any guide, DeepSeek is just the beginning of a wave of leaner, more efficient AI models that will reshape the industry, proving that scaling through infrastructure alone is not a lasting competitive advantage.
Sixth Secret: US Companies Cannot Win AI Supremacy Through Hype and Massive Investment
The ballooning valuations of US AI companies may create the illusion of AI supremacy, but the DeepSeek shock has exposed the hollowness of this narrative. The rapid rise of OpenAI’s valuation to $157 billion, with speculation of reaching $340 billion (as reported by CNBC on January 30, 2025), and the $10 trillion market cap surge of the Magnificent Seven after ChatGPT’s release, suggest that AI dominance is secured through financial firepower. However, the DeepSeek revelation proves otherwise.
The US AI strategy has relied on a scaling training based approach, which improves AI performance by increasing computing power and feeding more data—even synthetic data—into models. This strategy allowed them to sell the illusion of progress in Generative AI (GenAI), Artificial General Intelligence (AGI), and Super Intelligence. The belief they spread was simple: whoever controls the most GPUs, data centers, and power plants will dominate AI.
By convincing the capital market of this narrative, they kept attracting billions in risk capital, allowing them to pour funds into infrastructure expansion. They even managed to convince President Donald Trump to mobilize $500 billion in AI investments, believing that scaling compute resources would ensure US dominance. However, DeepSeek shattered this thesis, proving that smarter inference algorithms could achieve similar or better AI performance at a fraction of the cost.
In response, rather than learning from DeepSeek’s breakthrough, US firms like Alphabet and Meta announced $75 billion and $65 billion investments in data centers to double down on their past approach. Instead of adopting more efficient AI models, they are attempting to scale up their existing thesis, ignoring the reality that smarter, cheaper alternatives will keep emerging.
As the dust settles and smaller, cost-effective AI models gain traction, consumers will resist high AI service costs—like OpenAI’s $200 per month subscription. If users refuse to pay premium prices, the profitability of AI giants will be in question. Without sustained revenue, investors may lose appetite for funding AI firms that are burning cash without profit. This could trigger a collapse in AI valuations, similar to the dot-com bubble burst.
Yet, big tech firms cannot publicly admit this risk, as it would accelerate their stock crashes. Instead, they continue to insist that their AI is on the right trajectory, despite mounting evidence that their monopolistic strategy is unsustainable.
Seventh Secret: China’s Technology Edge Over the USA is Not Built on Stealing Ideas
Contrary to the widespread belief that China’s technology rise is based on stealing ideas and offering inferior, cheaper alternatives, the DeepSeek breakthrough reveals a different reality. By developing superior AI algorithms, China has outperformed the computing power-hungry, costly brute-force methods championed by the USA. This is not an isolated case—China has demonstrated similar technological leadership in electric vehicles (EVs), 5G mobile networks, and solar energy.
In EVs, instead of following Tesla’s hype-driven stock inflation strategy, China has quietly developed a proprietary edge in battery technology and material supply chains. The result? BYD has overtaken Tesla’s early lead and has become the global leader in EV production. Besides, Tesla dependence on CATL’s battery raises serious questions about its future. Unlike Western startups like Northvolt, which despite a $15 billion investment failed to make a dent in the EV battery market, China’s sustained focus on battery innovation has cemented its dominance.
DeepSeek’s AI breakthrough also challenges the American model of tech development, which relies heavily on subsidies given by venture capital funds and Stock Market-driven hype. In contrast, China has pursued systematic, long-term innovation by focusing on developing proprietary technological strengths rather than artificially inflating valuations. While US tech giants like OpenAI, Google, and Amazon spent billions scaling up energy-hungry AI models, DeepSeek’s smarter, more efficient algorithms proved that a better approach existed all along. This realization sent shockwaves through the US market, wiping out 15% of Nvidia’s stock value and causing the Nasdaq to drop 3.5%.
Beyond technology, America’s policy framework has also played a role in this disparity. As Corbin Trent argued in The Nation, the Chicago School of Economics-inspired free-market policies, championed by Friedrich von Hayek and Milton Friedman, have left the US tech ecosystem fragmented and unsynchronized. The American model relies on private companies chasing short-term gains and stock market valuations, whereas China has been building an integrated system that links research, manufacturing, and innovation.
This divergence in strategy and execution is now reshaping the global technology balance of power. While the US has focused on valuation-driven narratives, China has been steadily building real-world technological leadership. DeepSeek’s breakthrough exposes the limits of the US model, revealing that stock market bubbles in creating billionaires and world’s richest people cannot replace true technological superiority. With each new innovation, China is proving that its approach—rooted in proprietary technology development and strategic industrial planning—offers a sustainable alternative to the US valuation-centric tech industry.
