韩国科技巨头承诺投资超5500亿美元缓解“内存末日”
评分
51精选理由
该投资规模巨大,直接影响AI硬件供应链稳定性,且韩国两大巨头占据全球DRAM市场绝对份额,具备极高行业影响力。
AI 点评
「这标志着全球AI基础设施投资从算力芯片向存储芯片的转折,内存产能将决定AI模型训练速度与成本。」
速览
韩国两大内存芯片巨头(三星、SK海力士)宣布将投资超过5500亿美元建设更多内存晶圆厂,以应对全球AI算力爆发带来的内存短缺危机。此举旨在巩固韩国作为AI技术强国的地位,并缓解因HBM等高带宽内存需求激增而导致的“内存末日”局面。
- 三星和SK海力士承诺投入超5500亿美元建设新内存晶圆厂
- 旨在缓解AI训练和推理所需的高带宽内存(HBM)短缺
- 韩国政府支持这一计划,目标成为全球AI硬件供应链中心
正文
The world’s two largest memory chip companies plan to invest $518 billion (~800 trillion won) to build four new memory fabs in southwestern South Korea, a region that has historically attracted little semiconductor investment.
The announcement is part of the country’s sweeping national investment plan spanning semiconductors, AI data centers, and physical AI, which was unveiled at a presidential briefing on Monday, with the chairmen of Samsung and SK Hynix in attendance. The plan breaks down into three buckets. In the memory chip bucket is $518 billion for four new memory fabs in the southwest, plus $52 billion for an HBM (high bandwidth memory) packaging hub in the central region. Then there’s another $356 billion (550 trillion won) for AI data centers to be built by Korean tech and energy behemoths such as SK, GS, and Naver through 2035.
All told, South Korean tech companies have committed to spend over $900 billion on AI and the demands for chips it is creating. With this, the nation hopes to catapult itself into becoming more of an AI power player than it already is. Currently, Samsung and SK Hynix (along with U.S. memory chip maker Micron) are all enjoying record demand from what’s been called RAMageddon, a worldwide shortage of memory chips caused by the AI buildout.
“Semiconductors, physical AI, and AI data centers are the triple axis for South Korea’s next industrial era,” President Jae Myung Lee said in a televised address Monday, calling 2026 the year South Korea must establish itself as an “irreplaceable” industrial power.
Lee said existing chip facilities in Yongin and Pyeongtaek, the heart of South Korea’s semiconductor belt just south of Seoul, have “already reached their limits,” and urged companies to accelerate investment in the southwest, hoping to spread the AI wealth beyond the nation’s capital. “We must secure overwhelming production capacity in advance,” he said.
Yet, Lee pushed back against media reports that the government had pressured companies into the investments, reportedly saying the decisions reflected the companies’ own judgment. “The government’s role is to invest its capabilities so that companies can invest without losses and with better prospects,” he was quoted as saying.
Samsung separately published a press release Monday, announcing plans to invest 2,655 trillion won (~$1.7 trillion) over the next decade, with 425 trillion won earmarked for the Honam region, the southwestern corner of the Korean peninsula. The company cited expected incentives around power, water, workforce, and living conditions as key factors in selecting Gwangju, roughly 300 kilometers south of Seoul, for a new semiconductor fab, alongside an AI data center in Haenam, at the southern tip of the peninsula.
That is not an outlandish sum compared to U.S. tech giants Alphabet, Amazon, Meta, and Microsoft, which will collectively spend $650 billion on AI infrastructure this year alone, according to Reuters.
Meanwhile, SK Group announced a 2,100 trillion won (~$1.4 trillion) medium- to long-term investment roadmap, 1,100 trillion won to expand semiconductor production capacity and 1,000 trillion won for AI data centers nationwide. SK Hynix, the group’s core semiconductor affiliate, is central to the chip expansion push, while SK Telecom will lead the buildout of 15 gigawatts of AI data center capacity across the country.
Whether the ambition translates into execution is another question. Deep tech industries like semiconductors and AI don’t move on political or even customer demand timelines. Fabs take years to build and the risk is that, by the time they are ready, the demand that caused them will have ebbed, leaving companies with oversupply and crashing prices. For now, the world’s AI chip supply chain, especially those hungry for all things memory, will be watching to see if South Korea can pull it off.
Topics
AI, Hardware, memory tech, RAMaggedon, Samsung, SK Hynix, South Korea, South Korea tech ecosystem, TCWhen you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
Kate Park
Reporter, Asia
Kate Park is a reporter at TechCrunch, with a focus on technology, startups and venture capital in Asia. She previously was a financial journalist at Mergermarket covering M&A, private equity and venture capital.
View Bio
November 4
Boston
Last chance to save up to $190 on TechCrunch Founder Summit. Join 1,000+ founders and VCs at all stages for real-world scaling insights and connections that move the needle.
Savings end June 26, 11:59 p.m. PT.
Most Popular
-
Chevy built an all-American EV truck — why is nobody buying it?
-
Mark Zuckerberg tells staff that AI agents haven’t progressed as quickly as he’d hoped
-
Jersey Mike’s IPO illustrates how bad the AI hype has become
-
After $18B IPO, Bending Spoons founder says success comes from minimizing luck
-
-
-
Flipper Device’s new Busy Bar is a customizable display for productivity
相关条目
89
精选OpenAI研究:AI正在重新定义工作边界
如果你关心AI对实际工作的影响,这篇研究不容错过。它不讨论理论,而是用数据展示ChatGPT用户如何使用AI扩展自己的任务范围,这对HR、管理者以及每个希望提升技能的职场人都有直接启发。
87
精选OpenAI CFO 分享构建 AI 原生财务职能的五大经验
作为编辑,我认为这篇文章的价值在于它来自 AI 领军企业的财务一把手,而非外部咨询机构,因此更具实践参考性。推荐给财务数字化负责人、CFO 以及对 AI 转型感兴趣的从业者阅读。
87
精选智能体AI时代下的科学计算:OpenAI发布新领域报告
这份报告是当前AI agent热潮中最务实的应用分析之一,尤其适合计算生物学和药物研发领域的从业者阅读。它回答了“AI agent除了写代码还能干什么”这个关键问题,直接展示了在复杂科学计算中的生产力提升。
87
精选Real World VoiceEQ:量化语音AI的人类自然度
值得关注的是,这个基准真正聚焦'人类自然度'这个软指标,而不是传统的语音识别率或MOS评分。对从事语音合成、语音克隆或语音交互的团队来说,这是一个非常实用的参考工具,可以快速定位模型在情感、韵律等层面上的不足。不过目前支持语言有限,期待后续扩展。
85
精选OpenAI 推出AI时代记分卡:衡量投资回报的新方法
这篇博文直击当前AI行业的软肋——如何量化投入产出。对于正在或计划大规模部署AI的企业决策者而言,它能帮助避免盲目跟风。我个人建议结合自身业务场景尝试定义“有用工作”,避免指标设计流于形式。
85
精选代理时代如何管理AI投资:聚焦每美元有用功与高价值工作流
这篇文章对正在大规模部署AI代理的企业尤为重要。它提醒我们:单纯盯着API价格下降会陷入误区,真正要管理的是每单位工作的投资回报率。建议CTO和CFO联合阅读,并在内部建立量化评估机制。
85
精选设计能跟上AI的组织:组织延迟成为最大障碍
这篇文章真正值得关注的是提出了一个被忽视的瓶颈:技术再强,组织跟不上也是白搭。对CTO、CIO和转型负责人来说,这是比技术选型更重要的思考框架。
84
精选OpenAI宣布与美国能源部和国家实验室合作,推动前沿AI加速科学发现
这不仅是技术合作,更是AI国家战略的里程碑。对关注AI政策与科学创新的读者,值得细读框架设计。