Asian technology stocks fell sharply Monday as Chinese AI startup DeepSeek sparked sector-wide concerns about artificial intelligence investment sustainability and pricing pressures, triggering selloffs in chip-related shares while boosting some Chinese tech giants.

Semiconductor stocks lead market declines

Japanese chip equipment manufacturers bore the brunt of selling, with Disco Corp dropping 2.6% and Advantest plunging 8.8%. China’s top chipmaker SMIC declined 2.9%, mirroring pre-market weakness in Nvidia shares after U.S. trading signals. The selloff followed DeepSeek’s launch of its R1 model last week—a ChatGPT competitor that venture capitalist Marc Andreessen called “AI’s Sputnik moment” on X, referencing the Soviet satellite that triggered the space race.

Tokyo Electron and Fujikura saw concentrated selling after months of AI-driven gains, with Furukawa Electric plummeting 11.3%—the worst performer in Japan’s Nikkei 225. The cable manufacturer had surged since November due to data center demand before Monday’s reversal. A Tokyo-based fund manager linked the tech rout to recalculated AI hardware expectations: “The market was readjusting to the idea that hardware spending on AI could be a lot lower than current estimates.”

How to setup DeepSeek-R1 easily for free (online and local)?

Banking sector diverges from tech slump

Japan’s Topix index rose 0.2% as investors reacted to the Bank of Japan’s 0.25% rate hike last week. Shares of Mitsubishi UFJ Financial Group, Sumitomo Mitsui Financial Group, and Mizuho Financial Group all climbed about 1% on expectations of improved lending margins. This contrasted with the tech-heavy Nikkei’s performance, highlighting sector-specific impacts from the AI news.

DeepSeek’s pricing upends AI economics

Bernstein analysts revealed Sunday that DeepSeek’s models undercut OpenAI by 20-40 times on pricing. The Chinese firm charges $0.55 per million tokens for its Reasoner model compared to OpenAI’s $15 for equivalent usage of its o1 model. Tokens—the basic units AI uses to process text, equivalent to about three-quarters of a word—have become a key cost metric in generative AI operations.

Open-source vs proprietary model debate

DeepSeek’s open-source approach contrasts sharply with OpenAI’s closed system, with Bernstein noting the development “brings up very interesting questions about the viability of proprietary versus open-source efforts.” The pricing gap coincides with DeepSeek topping U.S. App Store downloads ahead of ChatGPT, despite being a relatively unknown Chinese startup until recently.

Barclays’ Mitul Kotecha highlighted market surprise at China’s tech advancements: “The fact they’re able to achieve high-end tech has caught a lot of people by surprise… this seems to be what’s driving the shift in sentiment today.” The comments follow U.S. efforts to restrict Chinese access to advanced semiconductor technology through export controls.

Mixed regional market reactions

Hong Kong’s Hang Seng Index gained 0.9% led by Tencent (+1.2%) and Alibaba (+0.8%), while Chinese AI specialist iFlytek rose 1.75%. The divergence reflects both DeepSeek’s domestic success and lingering questions about global AI infrastructure needs. U.S.-listed Chinese tech stocks showed early strength in pre-market trading despite the semiconductor sector’s woes.

Uncertainty over selloff duration

A dealer at a major Japanese brokerage noted conflicting market signals: “It’s hard to say how long the pain will last… some clients are using the DeepSeek news as an excuse to lock in profits on stocks that had performed well since January.” Traders await U.S. market opens for clearer direction, with Nvidia’s performance seen as a key bellwether.

DeepSeek claims its competitive models were developed on a “bootstrapped budget,” challenging assumptions that AI leadership requires massive capital expenditures. This assertion comes as global tech firms have committed billions to AI chip clusters and data centers, with Nvidia’s market value surpassing $3 trillion earlier this month.

The startup’s rapid ascent—from obscurity to topping app charts and rattling global markets within days—has intensified debates about AI development costs. Bernstein’s analysis suggests DeepSeek’s pricing could force industry-wide margin compression, particularly for companies relying on proprietary model revenue.

Featured image credit: energepic.com/Pexels

DeepSeek-R1 is dominating tech discussions across Reddit, X, and developer forums, with users calling it the “people’s AI” for its uncanny ability to rival paid models like Google Gemini and OpenAI’s GPT-4o—all while costing nothing.

DeepSeek-R1, a free and open-source reasoning AI, offers a privacy-first alternative to OpenAI’s $200/month o1 model, with comparable performance in coding, math, and logical problem-solving. This guide provides step-by-step instructions for installing DeepSeek-R1 locally and integrating it into projects, potentially saving hundreds of dollars monthly.

Someone stop DeepSeek: Meet Janus-Pro-7B, another free AI model

Why DeepSeek-R1 is trending?

Unlike closed models that lock users into subscriptions and data-sharing agreements, DeepSeek-R1 operates entirely offline when deployed locally. Social media benchmarks show it solving LeetCode problems 12% faster than OpenAI’s o1 model while using just 30% of the system resources. A TikTok demo of it coding a Python-based expense tracker in 90 seconds has racked up 2.7 million views, with comments like “Gemini could never” flooding the thread. Its appeal? No API fees, no usage caps, and no mandatory internet connection.

What is DeepSeek-R1 and how does it compare to OpenAI-o1?

DeepSeek-R1 is a revolutionary reasoning AI that uses reinforcement learning (RL) instead of supervised fine-tuning, achieving a 79.8% pass@1 score on the AIME 2024 math benchmark. It outperforms OpenAI-o1 in cost efficiency, with API costs 96.4% cheaper ($0.55 vs. $15 per million input tokens) and the ability to run locally on consumer hardware. DeepSeek-R1 is open-source, offering six distilled models ranging from 1.5B to 671B parameters for diverse applications.

Step-by-step installation guide for DeepSeek-R1 (local)

To install DeepSeek-R1 locally using Ollama and Open Web UI, follow these steps:

1. Install Ollama via terminal (macOS/Linux):

curl -fsSL https://ollama.com/install.sh | sh ollama -v #check Ollama version

2. Download a DeepSeek-R1 distilled model via Ollama:

# Default 7B model (4.7GB - ideal for consumer GPUs)
ollama run deepseek-r1

# Larger 70B model (requires 24GB+ VRAM)
ollama run deepseek-r1:70b

# Full DeepSeek-R1 (requires 336GB+ VRAM for 4-bit quantization)
ollama run deepseek-r1:671b

3. Set up Open Web UI for a private interface:

docker run -d -p 3000:8080 \
 --add-host=host.docker.internal:host-gateway \
 -v open-webui:/app/backend/data \
 --name open-webui \
 --restart always \
 ghcr.io/open-webui/open-webui:main

Access the interface at http://localhost:3000 and select deepseek-r1:latest. All data remains on your machine, ensuring privacy.

How to integrate DeepSeek-R1 into your projects

DeepSeek-R1 can be integrated locally or via its cloud API:

1. Local deployment (privacy-first):

import openai
Connect to your local Ollama instance
client = openai.Client(
base_url="http://localhost:11434/v1",
api_key="ollama" # Authentication-free private access
)

response = client.chat.completions.create(
model="deepseek-r1:XXb ", # change the "XX" by the distilled model you choose
messages=[{"role": "user", "content": "Explain blockchain security"}],
temperature=0.7 # Controls creativity vs precision
)

2. Using the official DeepSeek-R1 cloud API:

import openai from dotenv import load_dotenv import os
load_dotenv()
client = openai.OpenAI(
base_url="https://api.deepseek.com/v1",
api_key=os.getenv("DEEPSEEK_API_KEY")
)

response = client.chat.completions.create(
model="deepseek-reasoner",
messages=[{"role": "user", "content": "Write web scraping code with error handling"}],
max_tokens=1000 # Limit costs for long responses
)

DeepSeek-R1 provides a cost-effective, privacy-focused alternative to OpenAI-o1, ideal for developers seeking to save money and maintain data security. For further assistance or to share experiences, users are encouraged to engage with the community.

Featured image credit: DeepSeek