Quick Note On DeepSeek And Market Volatility
DeepSeek is rattling Wall Street and exposing how little they understand AI
The massive one day drop in Nvidia, Vertiv Holdings, GE Vernova, Marvell, and other critical companies in the AI Infrastructure play has been a gross misunderstanding of DeepSeek and AI CAPEX.
For those that don’t know, DeepSeek is a Chinese artificial intelligence company founded in 2023 by Liang Wenfeng, headquartered in Hangzhou, Zhejiang, and owned by a Chinese hedge fund High-Flyer (that’s kind of important to this story since DeepSeek’s unverified claim of training costs tanked AI competitors- which would make for a great short play). But let’s just assume that DeepSeek is telling the truth and isn’t shorting the market with unverified claims…DeepSeek specializes in developing open-source large language models (LLMs) and has released several notable models:
DeepSeek Coder: Introduced in November 2023, this model is tailored for code generation and assistance, supporting both researchers and commercial users.
DeepSeek LLM: Launched later in November 2023, this 67-billion-parameter model was designed to compete with other LLMs available at the time, with performance approaching that of GPT-4.
DeepSeek-V2: Released in May 2024, this model offered strong performance at a lower cost, contributing to a price competition in China's AI model market.
DeepSeek-V3: Debuted in December 2024, this model comprises 671 billion parameters and was trained in approximately 55 days at a cost of around $5.58 million, using significantly fewer resources compared to its peers.
DeepSeek-R1: Unveiled in January 2025, this model focuses on logical inference, mathematical reasoning, and real-time problem-solving, utilizing reinforcement learning techniques.
Deepseek released their “research” on DeepSeek-R1 last week. Oddly enough, the market didn’t react until this morning, with those aforementioned companies dropping 15-30% by mid-day. So I jotted down 7 points on DeepSeek and AI Infrastructure that seem to be flying under the radar in today’s commentary.
I’ll follow up more on DeepSeek as a state-actor in the AI Soverignty strategy in the Second Cold War.
1. DeepSeek's Transparency and Technical Claims (Research)
DeepSeek says it is improving open reasoning, but its models and code are not truly open-source. Its research focuses more on storytelling than providing the technical details needed for others to verify its work. Without access to the training code and data to support their claims on compute usage and costs, their claims of efficiency and innovation can't be confirmed. This lack of transparency makes it hard for companies and researchers outside of China to trust DeepSeek.
2. Catching Up Versus Leading (Innovation)
DeepSeek's advancements in efficiency highlight a critical dynamic in technology development: replicating and optimizing an existing innovation is significantly faster, cheaper, and less risky than creating it from scratch. This phenomenon underscores the economic and technical disparity between pioneers and fast followers in the technology lifecycle.
Lower R&D costs for followers: Innovators bear the substantial upfront costs of research, development, experimentation, and failure. These investments include training foundational models, assembling specialized research teams, and developing proprietary hardware optimizations. By contrast, followers like DeepSeek can leverage publicly available research, open datasets, and established methodologies to bypass much of the trial-and-error phase.
Efficiencies from reverse engineering and optimization: DeepSeek exemplifies how followers can reverse-engineer state-of-the-art technologies and optimize them for lower-cost deployment. For example, using techniques like parameter pruning, quantization, and efficient distributed training, DeepSeek was able to train its models more cheaply than its pioneering counterparts (again, if their claims on training costs and GPUs are true). However, these optimizations represent incremental improvements rather than foundational advancements.
Economic realities of innovation: The first major breakthroughs in reasoning models demanded unprecedented levels of investment in computational resources and talent. DeepSeek, as a follower, avoided these costs by standing on the shoulders of prior innovations. This is consistent with historical patterns in technology: the first industrial robot, first integrated circuit, or first supercomputer required immense capital and years of development, while subsequent iterations became faster and cheaper to produce.
Innovation still drives market leadership: While followers can rapidly close the gap by replicating and refining technologies, they rarely lead to the next wave of breakthroughs. Market leaders, such as those in the US and Europe, remain crucial for advancing the frontier. The creation of new architectures, the exploration of novel use cases, and the transformation of enterprises rely on the sustained capital expenditures and risk tolerance of these innovators. Copycat innovations, though valuable in broadening market access, do not change the fundamental economics of driving transformative advancements.
In essence, DeepSeek's efficiency gains demonstrate how technological laggards can close the gap quickly. However, the lack of groundbreaking contributions means that they do not drive the fundamental advances required for the next wave of innovation. This reinforces the importance of true market leaders in shaping the trajectory of transformative technologies.
3. Challenges of Embedded Chinese Censorship (Models)
DeepSeek's open-source model is influenced by Chinese censorship protocols, which poses a dual problem:
The censorship introduces biases that compromise the utility and ethics of the model.
Businesses outside of China, particularly in democracies, demand AI solutions that reflect their own governance standards. Embedded censorship creates an insurmountable barrier to adoption in global markets (more on this in the next point).
This limitation narrows DeepSeek’s market appeal and relegates it to controlled ecosystems where such biases are permissible. DeepSeek’s only true path to democratic penetration is in consumer applications, where user’s are oblivious or don’t care about Chinese abuses of power and limitation of free speech.
4. Further Barriers to US Market Penetration (Products)
DeepSeek faces significant obstacles in gaining traction in the US and other Western markets for its applications due to legal and liability concerns tied to data privacy and Chinese national security laws:
Data privacy and security risks: Sending data to Chinese servers for processing by a Chinese large language model (LLM) introduces significant legal exposure for US companies. Under China's National Intelligence Law and Cybersecurity Law, the Chinese government has broad authority to access data stored or processed on servers within its jurisdiction. This creates a fundamental risk for enterprises handling sensitive or proprietary information, as data shared with Chinese models will feed Chinese intelligence applications.
Regulatory non-compliance: Using Chinese LLMs that require cross-border data transfer may conflict with stringent US and European data privacy laws. Additionally, transferring data to jurisdictions lacking equivalent protections could violate data sovereignty requirements.
Liability concerns: US companies risk legal and financial liability if sensitive data sent to Chinese servers is accessed or mishandled, potentially leading to intellectual property theft, data breaches, or compliance violations. This exposure could result in reputational damage and regulatory penalties.
Contractual and insurance risks: Many enterprise contracts and cybersecurity insurance policies include clauses that restrict or prohibit the use of vendors operating in high-risk jurisdictions. Utilizing Chinese AI models may void these agreements, leaving companies without recourse in the event of a security incident.
Given these risks, US companies would be extremely reckless to adopt Chinese LLMs, as doing so would introduce unacceptable legal, regulatory, and financial vulnerabilities. This makes market penetration in the US a significant challenge for DeepSeek.
5. Constraints Imposed by the Chinese Government (Politics)
If US companies become too-big-too-fail, Chinese companies become too-big-too-succeed. DeepSeek’s growth potential is fundamentally constrained by China’s centralized governance model, where the CCP exerts significant control over enterprises to ensure they align with state interests. This creates structural barriers that limit the scalability and international competitiveness of even the most innovative Chinese companies.
State control over enterprises: The CCP’s governance approach prioritizes political stability and centralized authority over unfettered economic growth. Successful enterprises are closely monitored to prevent them from amassing influence that could challenge or overshadow the state. This control extends to limiting companies' autonomy in decision-making, capital allocation, and strategic international expansion.
Historical precedents of intervention: The experiences of high-profile entrepreneurs and companies illustrate this dynamic. Jack Ma, the founder of Alibaba, faced significant pushback from the CCP following public criticism of China’s financial regulatory system, leading to the shelving of Ant Group’s IPO and heightened government oversight. Similarly, other tech giants like Tencent and Didi have faced regulatory crackdowns that curtailed their international ambitions and stifled innovation.
Barriers to "escape velocity": Even if DeepSeek achieves technological breakthroughs, its ability to scale internationally and compete on equal footing with global peers is undermined by political constraints. For instance, international enterprises and investors may be hesitant to engage with DeepSeek due to concerns about state influence over its operations, data handling, and strategic priorities. This limits the company's potential to secure global partnerships or establish a foothold in key Western markets.
Impact on innovation and scaling: The CCP’s centralized governance model creates a paradox for Chinese enterprises like DeepSeek. While the state provides substantial resources and support for technological development, this support comes with strings attached, including compliance with state directives, restrictions on certain business practices, and limitations on profit reinvestment. These constraints hinder a company’s ability to reinvest in innovation, attract global talent, and compete effectively on an international scale.
Ultimately, DeepSeek’s growth trajectory is tied not only to its technological capabilities but also to the political realities of operating within China’s governance framework. While the CCP’s control ensures alignment with state objectives, it also curtails the company’s ability to scale internationally, innovate freely, and achieve the independence necessary for long-term global success.
6. Impact on Data Center and GPU Efficiency (Infrastructure)
DeepSeek’s claims of efficiency, even if valid, would not reduce the need for substantial investments in data centers, energy, and GPUs. Historically, breakthroughs in efficiency tend to amplify demand for infrastructure, driving adoption and usage to higher levels rather than curbing investment. If the market shifts to utilizing more efficient and smaller reasoning models, the infrastructure demand remains, if not grows.
Efficiency fuels adoption: Advancements in efficiency reduce the operational costs of AI deployment, making it more accessible to a broader range of users and industries. As costs drop, businesses that previously found AI adoption prohibitively expensive begin to incorporate it into their operations. This expanded usage increases the demand for infrastructure, including GPUs, specialized processors, and scalable cloud services.
Compounding workload growth: More efficient models often lead to the development of increasingly sophisticated applications. This generates more opportunity for automation of services that were below the cost of AI implmentation. If this fast-follower aporach to reasoning models is viable then the surface area of AI automation just exponentially grew.
Infrastructure capacity scales with efficiency gains: While efficiency lowers the per-unit computational cost of AI operations, the overall demand for computation typically grows. This dynamic is evident in industries like telecommunications, where advancements in spectrum efficiency led to the proliferation of bandwidth-intensive applications, driving exponential growth in network infrastructure. Similarly, AI efficiency breakthroughs are likely to sustain or increase the pace of GPU and data center investments.
AI proliferation and capex implications: The widespread adoption of efficient AI models is unlikely to reduce the capital expenditure (capex) required to sustain the underlying infrastructure. Instead, it shifts the focus toward scaling infrastructure to meet broader adoption. Efficient, open-source models can unlock new use cases in highly-regulated sectors and small-businesses, driving up demand for storage, inference compute, and energy. This expanded scope necessitates significant capex to accommodate increased computational workloads and geographic reach.
Rather than curtailing the need for data center and GPU investments, efficiency breakthroughs are more likely to reinforce the market’s growth trajectory. By accelerating adoption, scaling workloads, and expanding the AI ecosystem, such advancements sustain the demand for infrastructure and perpetuate the cycle of technological and capital-intensive growth.
7. Commoditization of AI Models (Operations)
AI models are increasingly becoming commoditized as multiple companies achieve comparable levels of performance. The competition for model performance in arbitrary benchmarks, while still relevant, is diminishing in importance compared to the broader AI ecosystem that underpins productization and deployment.
Commoditization and parity in performance: With many companies now capable of delivering models with near-parity in performance, the competitive focus has shifted. Incremental improvements in model performance no longer translate into significant advantages unless paired with broader capabilities. DeepSeek’s model is an example of how new entrants can quickly reach competitive benchmarks without introducing transformative innovation, further contributing to the commoditization trend.
Competitive edge lies beyond performance: The true differentiators in the AI market are increasingly found in operational reliability, seamless integrations with existing enterprise systems, and governance frameworks that ensure compliance, security, and ethical use. Enterprises prioritize stability, ease of deployment, and the ability to meet regulatory standards over marginal gains in raw model performance. Companies that excel in these areas are better positioned to capture market share, irrespective of whether their models are the most advanced.
DeepSeek’s contribution to commoditization: By delivering a model that matches industry standards without offering disruptive features or innovations, DeepSeek reinforces the trend of commoditization. While its efficiency gains and performance metrics may align with competitors, they do not reduce the demand for infrastructure. Instead, its presence underscores the increasing accessibility of advanced AI technologies and the diminishing importance of proprietary performance advantages.
Infrastructure trajectory remains unaltered: The commoditization of models creates more demand for the resources required to support widespread deployment grows. This includes investments in data centers, high-performance GPUs, and network optimizations, ensuring that infrastructure remains a central pillar of the AI ecosystem.
Conclusion
Investors are overreacting to a narrative that positions DeepSeek as a disruptor capable of undermining AI infrastructure demand.
DeepSeek’s latest model, if it’s claims in cost and infrastructure are true (doubtful), may demonstrate cost-effective training and incremental improvements, but it does not fundamentally alter the demand for AI infrastructure. On the contrary, efficiency breakthroughs typically expand adoption, leading to greater workloads, more sophisticated applications, and a deeper reliance on chips, data centers, and complementary technologies. The commoditization of AI models, as exemplified by DeepSeek, shifts focus from performance to the ecosystem that supports deployment, integration, and operational reliability—all of which depend heavily on sustained CAPEX in infrastructure.
Today’s selloff is not just premature but also shortsighted, overlooking the reality that true market leaders in infrastructure will remain indispensable as AI adoption accelerates.