2025-01-29: DeepSeek's Disruptive Emergence in the Global AI Landscape
Catch up rapidly on the DeepSeek Phenomenon
Today’s Cognitive Asia is a little different as we dive into a comprehensive briefing on China’s DeepSeek and its ramifications for the AI industry in America and Asia.
Thanks for reading,
Rodney J Johnson
Executive Summary
DeepSeek, a Chinese artificial intelligence (AI) startup founded in May 2023, has rapidly shaken global markets and challenged longstanding assumptions about cost, performance, and innovation in AI. In late January 2025, DeepSeek’s newest models and breakthroughs triggered a dramatic sell-off in U.S. tech stocks—particularly AI-related equities—while also inspiring optimism among some U.S. policymakers about potential efficiency gains in AI.
This briefing presents an overview of the key events, dates, announcements, and stakeholder reactions related to DeepSeek, culminating in a context-rich foundation for strategic business and risk assessments.
1. Background and Founding of DeepSeek
Company Formation and Founder
Founded: May 2023 in Hangzhou, China.
Founder/CEO: Liang Wenfeng (born 1985 in Guangdong Province).
Education: Majored in computer engineering at Zhejiang University, a leading engineering institution in China.
Prior Venture: Co-founded a hedge fund named High-Flyer (2015) with two college classmates.
High-Flyer managed up to \$8 billion in assets, using deep learning for quantitative trading.
Around 2019, High-Flyer began stockpiling NVIDIA chips (approximately 10,000 GPUs), anticipating AI developments.
By May 2023, Liang spun off the AI research lab from High-Flyer into a standalone startup called DeepSeek.
Culture and Team: Known for recruiting fresh graduates, emphasizing technical skill over prior work experience. Small but highly skilled group from top Chinese universities.
Initial AI Projects (2023–2024)
DeepSec Coder (Nov 2023): An open-source AI coding model, marking the firm’s first public offering.
DeepSec-V2 (May 2024): Enhanced the original open-source capabilities; gained some attention domestically in China.
During 2024, DeepSeek expanded chip clusters (mostly NVIDIA H800 GPUs, which have lower specs than H100 due to U.S. export restrictions).
2. Breakthrough Models and Performance
DeepSeek-V3 (December 2024)
Cost and Hardware
Trained on an NVIDIA H800-based cluster (a lower-spec variant designed to comply with U.S. export controls).
DeepSeek published a figure of \$5.576 million in training costs. Analysts note this figure likely excludes labor, R&D, overhead, or other expenses, but it remains far below the typical \$100+ million scale for U.S. frontier models.
Competitive Benchmarks
Claimed performance similar to or better than Google’s and Meta’s leading LLMs.
Surpassed GPT-4o, Anthropic’s Claude 3.5 Sonnet, and Meta’s Llama 3.1 in certain tasks (e.g., math problem-solving, code generation, and multilingual evaluations).
Key Innovation
Emphasized more efficient training algorithms and reinforcement-learning approaches rather than simply scaling up GPU usage.
DeepSeek-R1 (January 20, 2025)
Inference-Focused Model
Announced on January 20, 2025, specialized in “reasoning” tasks.
Achieved an accuracy score of 79.8% on the AIME 2024 benchmark, edging out OpenAI’s “o1” (79.2%).
“Deep Thinking” Mode
Generates step-by-step reasoning “chains of thought,” visible to the user.
Notable for spontaneously “re-evaluating” problem-solving steps during math or coding tasks (referred to internally as “aha moments”).
API Pricing
R1’s reported API costs are significantly below U.S. equivalents—some estimates show one-thirtieth the cost of OpenAI’s o1 model.
Open-Source Commitment
DeepSeek has open-sourced multiple major versions (with disclaimers around data sets) and allows free usage or “distillation” of its outputs.
This approach contrasts with “closed” or partially open models from U.S. companies like OpenAI, potentially accelerating global adoption and collaboration.
3. Market Shock and Global Reaction
Stock Market Sell-Off (January 27–29, 2025)
Nasdaq Plunge:
On January 27, Nasdaq futures dropped 5% pre-market upon reports of DeepSeek’s low-cost, high-performance AI.
By close on the 27th, the Nasdaq fell over 3%—the heaviest daily drop since late 2024.
Key Tech Stocks:
NVIDIA: Dropped 17% on January 27, shedding approximately \$589–\$600 billion in market cap (the largest single-day loss ever recorded by any U.S. stock).
Broadcom: Fell 17.4%, losing its \$1 trillion market cap status.
Microsoft: Down 2–4%, subject to scrutiny for billions spent on AI infrastructure.
Oracle: Down 8–14% in various sessions, after prior gains from the White House “Stargate” AI project announcement.
Meta, Alphabet, AMD, Tesla and other AI beneficiaries suffered multi-percentage declines.
Global Spillover
In Asia, SoftBank (a major AI investor) dropped more than 8% in Tokyo, erasing gains from the White House’s Stargate project deal.
In Europe, semiconductor equipment provider ASML slumped 7%, reflecting concerns over reduced demand for high-end GPUs if “cheap AI” goes mainstream.
Power and Utility Stocks: Constellation Energy and Vistra Corp. saw double-digit falls (some over 15–20%) as markets questioned whether huge data-center power demand would be as large as forecast, given DeepSeek’s efficiency.
Analyst Commentary
Many on Wall Street are rethinking the “scale-only” logic. Key investor sentiment:
JP Morgan: Warns that efficiency breakthroughs may reduce the need for lavish GPU spending and data-center expansions.
Bernstein: Points out that DeepSeek’s true total cost is unknown, but acknowledges the model’s strong performance.
Citi & Evercore: Question if large U.S. tech firms will pivot to smaller or more efficient training methods.
4. Political Context and Government Reactions
White House “Stargate” Project
Announced by President Donald Trump in mid-January 2025, aiming to allocate \$500 billion toward AI infrastructure, advanced chips, and data centers.
DeepSeek’s success—using significantly cheaper processes—raised doubts about the necessity of such high-scale spending. Some now argue this could spur a reevaluation of how and where to invest in AI.
Export Controls
U.S. restrictions since 2022 have limited shipments of top-tier Nvidia and AMD GPUs to China.
DeepSeek’s reliance on the H800 GPU (a lower-spec export-compliant version) has fueled debate over whether current controls are effectively preventing China from achieving AI parity.
DeepSeek’s achievements are cited in discussions about the evolving “chip blockade” and the possibility that sanctions inadvertently pushed Chinese engineers to pursue more creative, cost-efficient solutions.
Public Statements
President Trump (January 27): Called DeepSeek’s success a “wake-up call” for American industry, suggesting that cheaper AI development is “not necessarily bad” if U.S. firms also learn to reduce costs.
Chinese government representatives and local media have praised DeepSeek as proof of China’s growing AI capabilities, though official statements remain cautious about any potential regulatory responses.
5. Key Technical Insights
Cost vs. Performance
DeepSeek’s major claim is achieving comparable or superior performance with only \$5.6 million for training (on V3) and an unknown but presumably low total for the R1.
Critics note these figures exclude full labor costs, R&D overhead, and initial chip hoarding expenses.
Reinforcement Learning Instead of Supervised Fine-Tuning
DeepSeek’s biggest reported innovation is relying on reinforcement learning with simpler reward metrics—allowing the model to “self-reflect” and allocate more time to complex problems.
This leads to “thinking out loud” or “chain-of-thought” features, visible to the user.
Censorship and Limitations
Like other Chinese models, DeepSeek censors politically sensitive topics (e.g., Tiananmen, Xi Jinping). The model avoids direct responses on issues deemed sensitive in China.
Founder Liang Wenfeng voiced concerns to Chinese authorities about U.S. GPU export restrictions, highlighting potential supply constraints on future model developments.
6. Implications for Business Decision-Making
AI Infrastructure Investment
If DeepSeek’s “high efficiency, lower resource” paradigm proves durable, it challenges the massive capital expenditures planned by Microsoft, Google, Meta, and others.
Investors may question the “scale at all costs” approach, prompting reallocation toward algorithmic efficiency R&D rather than raw GPU stacking.
Supply Chain Reassessments
Semiconductor firms reliant on high-end chip sales (NVIDIA, AMD) could face margin pressure if efficient AI models reduce the demand for cutting-edge hardware.
Cloud providers and colocation data centers must prepare for potential shortfalls in anticipated GPU demand—and possibly repurpose surplus capacity.
Competitive Landscape
DeepSeek’s open-source approach could quickly spawn numerous applications and smaller AI products built on top of V3 and R1.
U.S. tech giants may accelerate or expand open-source initiatives to avoid falling behind in algorithmic innovation.
Regulatory & Geopolitical Dimensions
Ongoing U.S.–China tech tensions likely to intensify amid proof that Chinese AI can thrive despite export restrictions.
Potential for stricter U.S. sanctions, or conversely, new negotiations on chip exports, as American policymakers weigh competitiveness.
DeepSeek has likely already run afoul of EU data privacy rules and the US's Protecting Americans from Foreign Adversary Controlled Applications Act.
User Adoption
DeepSeek’s surge to top the U.S. Apple App Store suggests user appetite for alternatives to ChatGPT and the ease at which models can be switched.
The brand’s censorship constraints and control by Beijing could complicate adoption by U.S. enterprise customers with political or data-sensitivity concerns.
The open source nature of the models, however, would allow enterprises to control, fork, and remake their own in-house models, thereby shielding their data and controlling outputs.
7. Timeline of Key Events
May 2023
DeepSeek (then “DeepSec”) founded by Liang Wenfeng in Hangzhou.
November 2023
Release of DeepSeek’s first open-source model, “DeepSec Coder.”
May 2024
DeepSeek-V2 launched, initiates a price war in China’s AI market.
December 2024
DeepSeek-V3 introduced with a reported training cost of \$5.576 million.
January 20, 2025
DeepSeek announces R1, claims performance surpassing OpenAI’s o1 in certain math and reasoning benchmarks.
January 24, 2025
U.S. markets begin to react; Nvidia stock slides 3%.
January 27–29, 2025
Global AI sell-off intensifies. Nasdaq falls 3–5% from pre-market to close. Nvidia, Broadcom, Oracle, and AI suppliers plunge.
DeepSeek overtakes ChatGPT on Apple App Store U.S. download rankings.
President Trump refers to DeepSeek’s success as “a wake-up call.”
Conclusion
DeepSeek’s swift rise—from a little-known Chinese AI lab to the top of the U.S. Apple App Store—has reshaped discussions on AI cost structures, chip supply chains, and the global balance of technological power. While uncertainties remain (such as actual total R&D spending and the extent of Chinese government censorship), DeepSeek’s success underscores that algorithmic innovation can rival or even outperform raw GPU scale.
This development may compel some business leaders to reevaluate AI investment strategies, especially regarding cloud, chip, and infrastructure build-outs. It also spotlights a potential shift in the competitive environment: one where efficient solutions, open-source collaboration, and agile development may carry the day over colossal budgets and proprietary code. We are not yet at that day, but the potential for 'edge computing' to democratize AI has always been a targeted goal.