Introduction

The theme of the Global Technology Summit held in April in New Delhi under the auspices of India’s Ministry of External Affairs^[1] was Sambhavna—a Hindi-language word meaning “possibility”. It sums up the progressive, albeit cautious, sentiment expressed by Foreign Minister S. Jaishankar at the event^[2]—that in a talent- and data-centric world, disruptions to the status quo can lead to opportunities for exponential leaps, given there is political will.

With this in mind, it is worth recalling Chinese President Xi Jinping’s claim sometime in March 2023 that the West, led by the United States (US), was suppressing China’s development and growth.^[3] Besides the realm of China’s foreign trade, Xi referred to US crackdowns on Chinese telecommunications giant Huawei and social media platform TikTok. By January 2025, however, then newly elected President Donald Trump’s “Xi or me” argument^[4] faced a more difficult challenge.

Shortly after Trump’s announcement of ‘the Stargate project’,^[5] a new US$500-billion public-private commitment to the US’s Artificial Intelligence (AI) future,^[a] China responded with DeepSeek.^[6] DeepSeek is a startup with a “hacker spirit” that has disrupted^[7] the most advanced practices in global AI’s approach to compute capacity^[b] and algorithmic output. Essentially, it promises a more efficient, affordable, and adaptable approach to future AI development, undercutting^[8] the US’s “moat” comprising export controls^[9] and the CHIPS and Science Act.^[10]

The rest of this report analyses the impact of this technological milestone across pillars of the global system. It ponders how a rising power like India can respond.

Geopolitics, Geoeconomics, and Geotechnology

Various experts have termed China’s launch of DeepSeek’s latest model R1^[11] as AI’s “Sputnik moment”,^[12] drawing a parallel to the Soviet Union launching the first artificial satellite from Earth in 1957, which stunned the US government at the time and pushed it to boost its own Apollo space programme. The launch of DeepSeek-R1 on the day of Trump’s inauguration has added to speculations that it is linked to the US-China technological competition.^[13] The launch led to a slide in US stock markets, directly impacting Big Tech companies such as Nvidia, OpenAI, Meta, Microsoft, and Google. The Nasdaq Composite index lost more than US$1 trillion in a single day, on 27 January 2025, a week after the announcement.^[14] DeepSeek’s cheaper but capable open-source developments promise efficiency and productivity gains,^[15] especially comparing OpenAI’s resource-heavy approach to scalability and its “closed” approach^[16] in achieving commercial goals, strategy, and dealings.^[c]

Early analysis suggests^[17] that DeepSeek is an expected outcome of the trade restrictions being imposed by the US on China^[18] (directly and through partner nations/networks) for Nvidia’s top-grade H100 chips.^[d] This move forced the Chinese startup to innovate to overcome constraints. While some reports claim that the founder and CEO of DeepSeek, Liang Wenfeng, had amassed a large stockpile (10,000-50,000) of the chips before the restrictions on acquiring them came into effect,^[19] other accounts suggest that DeepSeek acquired these chips through illegal channels via intermediary nations like Singapore and the United Arab Emirates (UAE), even using OpenAI’s proprietary technology to develop its models.^[20] Therefore, assessing this development as a “Sputnik moment” may be premature, if not altogether misleading.^[21]

The long-term sustainability of facilitating the above-mentioned computational infrastructure to develop these frontier AI (large-language) models is also a subject of global debate.^[22] Advanced data centres have a steep cost. The financial and infrastructure investments impact the environment (Figure 1), such as by distorting the power supply (Figure 2) and worsening air quality.

Figure 1: The Environmental Cost of AI

Source: Stanford AI Index Report 2025^[23]

Figure 2: The Environmental Cost of AI

Source: Energize Capital^[24]

The question that arises is whether China’s evolving worldview can be considered as more “open” than the US’s increasingly “closed”, America-first stance. As China’s Foreign Minister Wang Yi has said, open-source AI is an opportunity for China to support Global South countries in developing their own AI ecosystems.^[25]

According to a white paper^[26] by the China Academy of Information and Communications Technology, as of July 2024, the number of AI large language models (LLMs) worldwide was 1,328, with 36 percent originating in China. China is also the global leader in publishing research papers on AI (Figure 3).

Figure 3: U.S.-China Rivalry in AI Research

Source: Allen Institute for Artificial Intelligence^[27]

Alternative Perspectives: Minimising Politics, Maximising Potential

Less hawkish observers are of the view that DeepSeek is just a Chinese upstart with young Chinese talent harbouring “open source zeal”, coming at a global opportunity in their own culturally and organisationally unique manner.^[28] Their reputation precedes them, from years of being seen as followers of western technology trends (servicing them at best) incapable of path-breaking innovation in software engineering on account of poor organisational and incentive structures; seeking global validation and approval; landing a milestone moment forced by constraints (known historical trend in deep-tech research and development endeavors). The timing of the launch of DeepSeek’s first-generation reasoning model, R1, likely had less to do with Trump’s inauguration and more with wrapping up a work milestone before the slowdown leading up to the Chinese New Year. Rather than the alarmist “Sputnik moment” comparison, the DeepSeek launch is being likened to Google’s 2004 S-1 filing, which was a public acknowledgment and sharing of the leaps in search-engine technology research and development at the time.^[29]

​​DeepSeek R1, trained via large-scale reinforcement learning (RL), draws on American company Meta’s open-source LLM Llama.^[30] Training the model reportedly cost the startup less than US$6 million.^[31] In April 2023, OpenAI CEO Sam Altman claimed that they had spent over US$100 million just in training the GPT-4 model.^[32] Indeed, analysts surmise that DeepSeek’s low-cost claims do not consider factors such as training and research on the model;^[33] nevertheless, the ~30x difference in pricing^[34] (Figure 4) between OpenAI and DeepSeek—with OpenAI’s o1 model costing US$60 per million output tokens compared to DeepSeek-R1’s US$2.19—has drawn global attention.

Figure 4: DeepSeek’s Pricing Disruption

Source: Stanford AI Index Report 2025^[35]

Computer scientist and entrepreneur Andrew Ng, who has played a central role in both the American and Chinese deep-tech ecosystems, highlighted some key points about the development.^[36] These include the implications for the AI supply chain of China catching up to the US in generative AI; open-source AI models commodifying the foundation-model layer, creating opportunities for application builders; and reducing training costs due to algorithmic innovations, proving that scaling up processing power is not the only path to AI progress.

Economies of scale aside, from a purely technical perspective, DeepSeek has cut down the processing power required for LLM computation by scheduling and batching their foundational-model training tasks.^[37] The model does this by using a “mixture-of-experts” approach, which is similar to the human brain, where numerous smaller neural networks are used and activated independently for specialised tasks. This approach results in less memory being required to train the overall model. Additionally, DeepSeek switched from the standard, time-consuming, and expensive supervised-fine-tuning (SFT) technique for LLM training to a rules-based reward system for learning to reason, called reinforcement learning (RL), and then combined both techniques to remove difficult-to-read mixed language responses.^[38]

Ng also warns against US companies pushing for regulation to stifle open-source development by inflating hypothetical AI dangers such as superintelligence^[e] and human extinction.^[39] Open-source/weight models have become a key part of the global AI supply chain, and many companies will use them. If the US continues to stymie the development of open-source models, China will begin to dominate this segment, which might result in many businesses using models that reflect China’s values rather than the US’s. A 2025 Stanford University study positioned China as the top nation for AI patent applications and the second-largest contributor to global AI behind the US (see Figure 5).

Figure 5: China’s Global AI Leadership

Source: Stanford AI Index Report 2025^[40]

More Competition and Collaboration

Historically, as in the case of energy consumption via fossil fuels (e.g., industrial coal and transportation gas),^[41] increased energy efficiency results in lower fuel consumption, thereby reducing overall dependence on and demand for the fuel. This results in better resource planning and a more sustainable future. However, a paradoxical theory, known as Jevons Paradox,^[42] states that the “rebound effect” of this energy efficiency can lead to increased consumption of a technology. This theory suggests that wider adoption of the DeepSeek approach and model could democratise AI’s capability and efficiency, driving up the global demand for AI production and the financial and infrastructural investments required for it.

It is worth examining whether this global democratisation of AI (and the resulting increase in demand) could lead to a review of the US’s export control policy. Possibilities include additional restrictions geared towards self-interest or a relaxation to serve the increased global demand for the H100 Nvidia semiconductors to participate in and control valuable supply chains. Early signs suggest that the 2022 chips ban^[43] passed by the Biden administration might have backfired. While its intention was to delay China’s rapid progress in advanced AI using high-end US silicon chips, the policy might have spurred China to invest in chips research and development (R&D) of its own. In April 2025, the Trump administration extended the ban on US AI chip exports to China by including the Nvidia H20 processor (scaled down version of the already banned H100). In response, Huawei immediately announced^[44] its next-generation processor called the Ascend 920,^[f] expected to compare with the Nvidia H100’s performance. It is due to hit mass production later this year.

However, it is clear that DeepSeek is not an outcome of any major technological advance in the Chinese semiconductor value chain (see Figure 6), but rather the result of innovation driven by constraints in computational capacity and technical brilliance in AI model R&D techniques—both despite the lack of access to high-end US chips due to trade restrictions.

Figure 6: The Global Semiconductor Industry

Source: McKinsey & Company^[45]

Worryingly for the US, there is a potential scenario in which the universal success of DeepSeek causes a democratisation of AI across labs, but defies Jevon’s Paradox. This could cause a supply-demand imbalance within the US’s resource-heavy, Stargate-led ambitions. This imbalance can happen in two ways:

• Widespread access and use of a resource-light DeepSeek model will reduce the workload of cloud-computing providers^[46] like Microsoft, Amazon, and Google, thus reducing the demand for top-grade Nvidia-chip-dependent graphics processing units (GPU) and their computational power across Big Tech.
• Technology-wise, most consumers, especially in Global South countries like India, do not need^[47] a supercharged AI model (e.g., LLMs) for everyday needs. Kompact AI, a framework developed by Indian startup Ziroh Labs in collaboration with the Indian Institute of Technology, Madras, confirmed this recently.^[48] Smaller models emerging from the DeepSeek innovation, or post-training, inference-stage LLMs in Kompact AI’s case, can run on edge-computing devices with CPUs (like PCs, mobile phones, and smart wearables) without the need for the security, privacy, and latency of cloud-computing providers. This will further depreciate the demand and price of heavy-duty GPUs.

Since the start of 2025, researchers at UC Berkeley have trained “reasoning” models comparable to OpenAI’s o1 for just US$450;^[49] since the release of DeepSeek’s open-source model R1, this figure has dropped to US$50.^[50] As with supercomputers of the past, advanced research labs working on long-term goals and improving upon current advances will remain a notable market for current computing architecture and language models. For many, the biggest concern with the DeepSeek moment is a scenario^[51] where open-source models become too powerful in their functional capabilities, which could have implications in both commercial competition and national security.

In such a context, how would the Chinese government perceive its firms pushing the AI frontier? The Chinese Communist Party is almost certainly following DeepSeek and its technology more closely^[52] and is highly likely to have a say on its open- versus closed-source nature. In a few years, it might be possible to access sensitive information or perform cyberattacks by using the capabilities of the DeepSeek model. Once these open-source foundational models begin to allow anyone to modify, study, build on, and use them, offering advantages such as external oversight, accelerating progress, and decentralised control, there is likely to be increased discretion around what is and is not allowed for international release.

Open-sourcing basic software is more conducive to defensive activities that guard against misuse (e.g., transparency over biases, community auditing), than allowing offensive misuse by malicious actors. On the other hand, open-sourcing advanced LLMs shifts the balance in favour of offensive use cases via the potential disabling of safeguards, increased public knowledge of vulnerabilities, likely perpetuation of flaws, and safety issues.^[53] Countries like Italy, Taiwan, Australia, South Korea, the US, and India have already taken measures to restrict or prohibit the use of DeepSeek’s “application layer”,^[54] if not the actual AI model,^[g] especially for sensitive functions like government and security.^[h]

According to OpenAI’s former Head of Policy Research, Miles Brundage, the US benefits from having a stronger AI sector than China’s, including through direct military applications, economic growth, speed of innovation, and overall dynamism.^[55] However, the US should not be stuck between letting Nvidia sell their entire offering and completely cutting off China. Instead, its policy approach needs to be more nuanced than a zero-sum strategy and involve more fine-grained controls. Despite the US’s interest in slowing down Chinese AI development, the US needs to have a policy vision that allows China to grow its own economy and benefit from the use of AI.^[56] However, such choices will require proper staffing in the Department of Commerce—which has the mandate to implement detailed agreements around retaining certain technologies for civilian purposes and restricting others from military uses—which may be challenging under the Trump administration’s “lean government” approach.^[57]

Have we therefore fundamentally misunderstood what this “AI race” is about? According to Yann LeCun, Meta’s Chief AI Scientist, neither the US nor China is “winning” as both are constrained by the same conceptual framework regarding the foundations of modern-day AI.^[58] This framework prevents truly “intelligent” behaviour in AI models^[i] due to a lack of factors that are considered natural to human intelligence, like understanding of the physical world, persistent memory, reasoning, and complex planning capabilities. Like other leading experts backing the exponential power of AI to become the definitive technology of the 21^st century,^[59] LeCun is of the view that real breakthroughs will not come from marginally better language models but from entirely new paradigms of AI architecture.

In a potential “second DeepSeek moment”^[60] —the March 2025 launch of the world’s first fully autonomous AI-agent, Manus, from Shenzhen, China—has led to the view that the world is nearing truly “intelligent” AI. In revolutionary style, Manus can think, plan, and execute real-world tasks independently, without human intervention.

The Requirements for India: Strong Foundations and Partnerships

Experts believe that DeepSeek gives hope to middle powers like India and France.^[61],[62] Both countries emphasise “sovereign AI”^[63] and will need to develop strategies that ensure cooperation with the US and independent development of AI. Even if the AI world never becomes truly multipolar, there will be enough room for other players to conduct research, development, and innovation.

As of 2024, India had 863 million internet users, US$568 billion in monthly Unified Payment Interface (UPI) transactions, over 100 unicorns, and handled 49 percent of global real-time payments.^[64] The IndiaAI Mission, launched in March 2024,^[65] aims to build a comprehensive ecosystem for the growth of responsible and inclusive AI in India via seven pillars: computing infrastructure, R&D, data access, impactful application, skills enhancement, financing, and trust and safety. However, in order to realise these goals and catch up with global AI powerhouses like the US and China, India needs to address existing challenges via four main approaches.^[66]

Set Up Sufficient High-End Hardware to Enhance National Computational Capacity Infrastructure

Despite being a Quad partner, India was excluded from the list of 18 countries granted unchecked access to the US’s high-end GPU chips in the January 2025 ‘Framework for Artificial Intelligence Diffusion’^[67] under the Biden administration.^[j] The framework comes into effect on 15 May 2025.

In response, the Indian government accelerated its pursuit of developing an indigenous, affordable, safe, and secure AI model by announcing the empanelment of 18,693 GPUs, including ​​12,896 Nvidia H100 chips, under a Common Computing Facility^[k][68] at the end of January 2025.^[69] At the time of writing, with 14,000 of these GPUs already accessible, reports suggest^[70] that the Indian government is looking to double this capacity by acquiring another 15,000 chips.

Invest in Foundational R&D Across Academia and Industry, Promoting Long-Term Innovation Alongside Short-Term Optimisation

India’s gross domestic expenditure on R&D (GERD) is below 1 percent of Gross Domestic Product (GDP),^[71] which is far less than other major economies. China, despite a slowing economy, invested 2.68 percent^[72] of GDP to R&D in 2024. AI industry stakeholders in India have echoed these concerns and sought commitments similar to the Common Computing Facility in core AI R&D in universities and labs across the country.^[73] They also highlight the need for public-private support to PhD programmes and scientific research papers on foundational/sovereign AI models (see Figure 7).

However, compared to the Central Government’s efforts, India’s private sector is investing far less in R&D compared to its counterparts in global hubs like the US, China, Japan, and South Korea.^[74] Even governments of states like Tamil Nadu (0.01 percent) and Karnataka (0.07 percent), among the top 5 wealthiest states in India, are faring below the national average on this commitment, having allocated less than 0.1 percent of their Gross State Domestic Product (GSDP) to R&D.^[75] Given India's diverse and complex ecosystem, developing a one-size-fits-all national LLM need not be the only goal. Instead, building Small Language Models focused on specific languages, industry domains, and organisational needs may be more effective and, when layered on top of horizontal LLMs, could pose the ideal multi-model approach.^[76]

Figure 7: Global Leaders in Foundational/Sovereign AI

Source: Stanford AI Index 2024^[77]

Organise Fragmented, Unstructured Data

Despite the scale of India’s digital ecosystem and the country’s growing digital public infrastructure,^[78] AI researchers, innovators, and startups in the country struggle to access good-quality, India-specific data (public and proprietary) in the volumes necessary to train foundational LLMs.^[79] This is primarily because most of the digital consumption in India occurs on global platforms, where user data is locked in.^[80] Critical, anonymised digital data from Indian public services like the UPI and the Unique Identification Authority (Aadhaar) is unavailable or available in outdated, AI-unfriendly formats. This hinders the development of AI language models and applications for Indian public sectors like telecom, education, healthcare, finance, agriculture, and transport.^[81]

India needs a long-term approach to AI-friendly data generation, focusing on quality and multilingual/multi-modal (text, image, audio, video) formats.^[82] Access to globally available public and licensed data needs to be pursued via partnerships. In March 2025, the Indian government launched its IndiaAI Datasets Platform, AIKosha, to start addressing some of these needs. Housed under the IndiaAI Mission, the IndiaAI Datasets Platform currently hosts 315 datasets and 84 models from 12 organisations.^[83] It has also partnered with various central and state government ministries, including the Digital India Bhashini division, and offers models from private players like Sarvam AI and Ola Krutrim.

Eventually, to supercharge its AI industry, India must aim to truly open-source its national data on a large scale. Much in the manner that the world’s largest free, open repository of datasets from the global web—Common Crawl^[84]—does, but with an Indian perspective on choosing and defining these datasets.

Leverage Soft-Power-Led Strategic Partnerships

India must deepen its strategic partnerships, leveraging frameworks like TRUST (Transforming Relations Utilising Strategic Technologies)^[85] (formerly the Initiative on Critical and Emerging Technologies) and the Indo-Pacific Economic Framework^[86]—to co-develop cutting-edge technologies and scale its domestic capabilities. Most importantly, as the US’s strategic partner in the Indo-Pacific, India should look to renegotiate its position^[87] to secure a place among the 18 Tier-1 countries in the AI diffusion framework to avoid being subjected to restrictions or controls going forward. De-risking against any misalignments in US investments and operations in India—or potential knowledge transfer and technological innovation away from India—is imperative.^[88]

Conclusion

In 2024, AI and machine learning startups received one-third of total venture capital funding globally.^[89] This raised concerns that too much money is being pumped into foundational models and infrastructure while not enough is being done towards the real-world application of AI.^[90] Through DeepSeek, China claims to have achieved in weeks what Silicon Valley had assumed would take years. DeepSeek demonstrated its global competitiveness without endless warehouses of US$40,000 chips, achieving remarkable results with 1/50^th of the resources. Beyond clever engineering, this is a complete reimagining of what is possible.

It is time to stop treating the future of AI like a nuclear arms race from the 20^th-century, post-Second World War era and start seeing it as a global public good in the 21^st century.^[91] Many of the world’s nuclear weapons sit as inactive stockpiles, used as a deterrent to large-scale military action and for occasional sabre-rattling. AI, on the other hand, permeates our everyday lives and has the potential to impact radically the way societies and nations function. What is required is collective thinking and a new “technological quotient”^[92] that considers not only the technology but also the skills and processes to develop it. In this context, the future will belong not to whoever has the most physical infrastructure (e.g., chips and data centers) but to those who invest and build beyond.

Endnotes

^[a] The project will expand data centres and computational infrastructure in the US.

^[b] Compute capacity refers to the amount of computational power a system, server, or network can provide to perform tasks, process data, or run applications.

^[c] In February 2023, Elon Musk, one of the co-founders of OpenAI, said, “OpenAI was created as an open source (which is why I named it ‘Open’ AI,) non-profit company to serve as a counterweight to Google, but now it has become a closed source, maximum-profit company effectively controlled by Microsoft.” See: https://www.cnbc.com/2023/04/08/microsofts-complex-bet-on-openai-brings-potential-and-uncertainty.html

^[d] The Nvidia H100 is currently the most powerful graphics processing unit (GPU) chip on the market. Capable of processing large amounts of data, it is ideal for AI applications that run on large language models (LLMs). See: https://www.weforum.org/videos/what-is-h100-gpu-chip-ai-nvidia/

^[e] Artificial superintelligence (ASI) is a hypothetical entity or agent (AI) that possesses intelligence surpassing that of the human mind in some or all measures. See: https://www.ibm.com/think/topics/artificial-superintelligence

^[f] The Ascend 920 is an upgrade on Huawei’s most powerful current offering, the Ascend 910C—which seemingly delivers about 60 percent of the US-based global GPU market-leader Nvidia H100’s inference performance. See: https://www.tomshardware.com/pc-components/gpus/huawei-introduces-the-ascend-920-ai-chip-to-fill-the-void-left-by-nvidias-h20

^[g] “Application layer” refers to a front-end technology interface that is accessed by general consumers—e.g., the DeepSeek AI chatbot that is available on signing up—unlike the back-end technology software that is used to power the application—in this case, the DeepSeek LLMs. Any and all data collected from the usage of DeepSeek’s application layer is automatically stored on servers in China, whereas downloading the AI model locally to power applications developed in a certain geography would lead to the data collected from users in those applications being stored on servers in that geography.

^[h] According to AI security and compliance firm Enkrypt AI, “In our evaluations, the [DeepSeek] model was found to be highly biased as well as highly vulnerable to generate insecure code, toxic, harmful and CBRN (Chemical, Biological, Radiological, and Nuclear) content. We also compared its performance with gpt-4o, o1 and claude-3-opus.” See: https://www.globenewswire.com/news-release/2025/01/31/3018811/0/en/DeepSeek-R1-AI-Model-11x-More-Likely-to-Generate-Harmful-Content-Security-Research-Finds.html

^[i] AI, as a technology, translates “intelligent” behaviour to everyday applications serving societal needs via AI models like DeepSeek.

^[j] This exclusion created hurdles for India’s AI sector. According to Ashok Chandak, President of the India Electronics and Semiconductor Association, “Restricted access to advanced AI chips may slow innovation and development and scaling up of installations. Second, licensing requirements could raise costs and introduce delays due to authorisations. Indian companies may rely heavily on global corporations for AI infrastructure, such as data centres.” See:

https://yourstory.com/2025/01/can-india-complement-americas-500b-stargate-ai-ambitions

^[k] This facility was announced 10 months after the government committed INR 10,000 crore over a five-year period to the IndiaAI Mission and is expected to host the latest DeepSeek open-source model after security clearance. See: https://yourstory.com/2025/01/deepseek-to-be-hosted-on-indian-servers-soon-ashwini-vaishnaw

^[1] Rudra Chaudhari, “At the Global Technology Summit, Realism Meets Imagination—and India Makes it Possible,” The Print, April 7, 2025, https://theprint.in/opinion/global-technology-summit-2025-sambhavna-geopolitics-tech/2580575/

^[2] The Print, “LIVE: EAM S Jaishankar's Keynote Address at Global Technology Summit,” Youtube video, 59:25 min, April 11, 2025, https://www.youtube.com/watch?v=9OltfaiQn1A

^[3] “President Xi Jinping Accuses US of Leading Western Nations to Suppress China,” The Indian Express, March 7, 2023, https://indianexpress.com/article/world/president-xi-accuses-us-leading-western-nations-suppress-china-8484756/

^[4] Rahul Batra, “TikTok’s Geopolitical Dilemma: Which Values, Whose Interests?,” Observer Research Foundation, May 1, 2024, https://www.orfonline.org/expert-speak/tiktoks-geopolitical-dilemma-which-values-whose-interests

^[5] Karen Hao, “OpenAI Goes MAGA,” The Atlantic, January 23, 2025, https://www.theatlantic.com/technology/archive/2025/01/openai-stargate-maga/681421/

^[6] Ananya Bhattacharya, “Non-Western Founders Say DeepSeek is Proof That Innovation Need Not Cost Billions of Dollars,” Rest of World, January 30, 2025, https://restofworld.org/2025/deepseek-ai-model-openai-dominance-challenge/

^[7] Mathew S. Smith, “What DeepSeek Means for Open-Source AI,” IEEE Spectrum, January 31, 2025, https://spectrum.ieee.org/deepseek

^[8] Sameer Patil and Saurabdeep Bag, “DeepSeek and the Shifting Tides of the US-China AI race,” Observer Research Foundation, February 12, 2025, https://www.orfonline.org/expert-speak/deepseek-and-the-shifting-tides-of-the-us-china-ai-race

^[9] Rahul Rao, “The U.S.-China Chip Ban, Explained,” IEEE Spectrum, November 21, 2022, https://spectrum.ieee.org/chip-ban

^[10] McKinsey & Company, “The CHIPS and Science Act: Here’s What’s In It,” October 4, 2022, https://www.mckinsey.com/industries/public-sector/our-insights/the-chips-and-science-act-heres-whats-in-it

^[11] DeepSeek-AI, DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning, January 2025, https://arxiv.org/pdf/2501.12948

^[12] Dan Milmo et al., “‘Sputnik Moment’: $1tn Wiped Off US Stocks After Chinese Firm Unveils AI Chatbot,” The Guardian, January 28, 2025, https://www.theguardian.com/business/2025/jan/27/tech-shares-asia-europe-fall-china-ai-deepseek

^[13] Erin Watson, “Chips, Clouds, and Checkpoints: The New AI Export Battlefield Under Trump 2.0,” Observer Research Foundation, March 17, 2025, https://www.orfonline.org/expert-speak/chips-clouds-and-checkpoints-the-new-ai-export-battlefield-under-trump-2-0

^[14] Milmo et al., “‘Sputnik Moment’: $1tn Wiped Off US Stocks After Chinese Firm Unveils AI Chatbot”

^[15] Caiwei Chen, “How a Top Chinese AI Model Overcame US Sanctions,” MIT Technology Review, January 24, 2025, https://www.technologyreview.com/2025/01/24/1110526/china-deepseek-top-ai-despite-sanctions/

^[16] David Gray Widder et al., “Why ‘Open’ AI Systems Are Actually Closed, and Why This Matters,” Nature 635 (2024): 827–833, https://www.nature.com/articles/s41586-024-08141-1#citeas

^[17] Michael Froman, “DeepSeek: Making Sense of the Reaction—and Overreaction,” Council on Foreign Relations, January 31, 2025, https://www.cfr.org/article/deepseek-making-sense-reaction-and-overreaction

^[18] Rao, “The U.S.-China Chip Ban, Explained”

^[19] Dylan Patel et al., “DeepSeek Debates: Chinese Leadership on Cost, True Training Cost, Closed Model Margin Impacts,” SemiAnalysis, January 31, 2025, https://semianalysis.com/2025/01/31/deepseek-debates/

^[20] Swagath Bandhakavi, “DeepSeek Being Investigated for Allegedly Using Restricted US-Made AI Chips,” Tech Monitor, January 31, 2025, https://www.techmonitor.ai/hardware/silicon/deepseek-under-scanner-restricted-us-made-ai-chips?cf-view

^[21] Sebastien Laye, “No, DeepSeek Isn’t America’s AI Sputnik Moment,” The Hill, January 30, 2025, https://thehill.com/opinion/technology/5113635-deep-seek-ai-impact-us/

^[22] James O’Donnell, “AI’s Energy Obsession Just Got a Reality Check,” MIT Technology Review, January 28, 2025, https://www.technologyreview.com/2025/01/28/1110599/ais-energy-obsession-gets-a-reality-check/

^[23] Yolanda Gil and Raymond Perrault, The 2025 AI Index Report, Stanford HAI, April 2025, https://hai-production.s3.amazonaws.com/files/hai_ai_index_report_2025.pdf

^[24] Alexandra Lum, “Powering the Future of AI: The Data Center Energy Crunch,” Energize Capital, November 19, 2024, https://www.energizecap.com/news-insights/powering-the-future-of-ai-the-data-center-energy-crunch

^[25] Alex Colville, “AI for All,” China Media Project, December 20, 2024, https://chinamediaproject.org/2024/12/20/ai-for-all/

^[26] State Council Information Office, The People’s Republic of China, China Home to Over One-Third of World's AI Large Language Models, July 3, 2024, http://english.scio.gov.cn/chinavoices/2024-07/03/content_117288420.htm

^[27] Field Cady and Oren Etzioni, “China May Overtake US in AI Research,” Allen Institute for Artificial Intelligence, March 13, 2019, https://medium.com/ai2-blog/china-to-overtake-us-in-ai-research-8b6b1fe30595

^[28] Lily Ottinger and Jordan Schneider, “DeepSeek: What It Means and What Happens Next,” China Talk, February 1, 2025, https://www.chinatalk.media/p/deepseek-what-it-means-and-what-happens

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