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Intel Appoints Lip-Bu Tan as New CEO Amid Transition

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Intel has appointed Lip-Bu Tan, a seasoned chip industry veteran, as its new CEO, effective March 18. The move marks a significant leadership change just three months after the company ousted its previous CEO, Pat Gelsinger, whose efforts to revitalize the company had faltered and eroded investor confidence.

Tan, who served on Intel’s board prior to his appointment, brings extensive experience in both chip design and technology investing, making him a strong contender for the role. His appointment follows discussions with Intel’s board in December, as previously reported by Reuters.

In a letter to Intel employees, Tan expressed his commitment to restoring Intel’s position as a “world-class products company” and emphasized the goal of establishing Intel as a “world-class foundry” to better serve its customers. His optimistic message signaled confidence in the company’s turnaround strategy.

Intel’s stock surged 12% in after-hours trading on the announcement, reflecting analyst optimism about the leadership change. The company’s stock had suffered a 60% drop in 2024, reflecting its struggles amid a challenging industry landscape.

The company is navigating a historic transition, including significant investment to become a contract manufacturer of chips for other companies. Despite challenges in capitalizing on the boom in advanced AI chips—which has boosted the fortunes of rivals like Nvidia—Intel is actively working to recover its market position.

Amid Intel’s ongoing struggles, rumors have circulated that competitors, including Broadcom and TSMC, were exploring options to acquire or manage parts of Intel’s business. Most notably, TSMC was reportedly considering a joint venture to operate Intel’s factories, after the Trump administration encouraged TSMC to help revitalize the company.

Jack E. Gold, an industry analyst, praised Tan’s appointment, noting his deep understanding of both chip product design and manufacturing. Analysts anticipate that under Tan’s leadership, Intel will focus on stabilizing its operations, although any transformation will likely take years.

Tan, 65, originally from Malaysia and raised in Singapore, holds degrees in physics, nuclear engineering, and business administration. He previously served as CEO of Cadence Design Systems, a key supplier for Intel, from 2009 to 2021, during which time the company’s revenue and stock performance saw significant growth.

Tan had stepped down from Intel’s board in 2023 over disagreements related to the company’s culture and strategy, particularly its approach to contract manufacturing and workforce size. However, he will rejoin the board in his new role as CEO.

Industry experts believe that Tan’s appointment brings much-needed stability to Intel, which has been under pressure in recent years. Tan is expected to oversee the continuation of Intel’s foundry business while managing the company’s transformation efforts.

TSMC Proposes Joint Venture with Intel’s Foundry Division to Nvidia, AMD, and Broadcom

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TSMC (2330.TW) has pitched the idea of a joint venture involving Intel’s (INTC.O) foundry division to major U.S. chip designers, including Nvidia (NVDA.O), Advanced Micro Devices (AMD.O), and Broadcom (AVGO.O), according to sources familiar with the discussions. Under the proposal, TSMC, the world’s leading contract chipmaker, would oversee Intel’s foundry operations, which focus on manufacturing chips tailored to customer needs, but TSMC would retain no more than 50% ownership.

The proposal has been discussed with several other firms as well, including Qualcomm (QCOM.O), as part of TSMC’s efforts to partner with chip designers. The discussions are still in their early stages, and any potential deal would require approval from the U.S. government, particularly under the administration of President Donald Trump, who has shown interest in helping Intel recover from its financial struggles. Trump is particularly invested in boosting American manufacturing and supporting companies like Intel in remaining U.S.-owned.

Intel, which reported an $18.8 billion net loss for 2024, has seen a drastic decline in its stock price over the past year. As of December 31, the book value of Intel’s foundry division’s property and plant equipment stood at $108 billion. The company’s recent struggles have pushed its board members to consider various strategic moves, including partnering with TSMC for its foundry operations.

Despite some internal opposition, Intel’s board members have expressed support for exploring a joint venture with TSMC, with Intel’s executives holding different views on the matter. Intel’s foundry division, once a crucial part of Intel’s strategy under former CEO Pat Gelsinger, is now central to the company’s efforts to return to profitability, even as Gelsinger was replaced by interim co-CEOs in December.

TSMC’s push for a joint venture is complicated by the significant differences in manufacturing processes and technologies between the two companies. Intel and TSMC currently employ distinct chipmaking methods, which could pose challenges in aligning operations. Intel has previously partnered with Taiwan’s UMC (2303.TW) and Israel’s Tower Semiconductor (TSEM.TA), offering some precedent for potential collaboration, but the specifics of how such a partnership could function remain uncertain, especially regarding the sharing of trade secrets.

While TSMC’s interest is to involve Intel’s advanced manufacturing customers in the venture, discussions have also centered around Intel’s 18A manufacturing process, a key area of contention in the negotiations. Intel executives have claimed that its 18A technology surpasses TSMC’s 2-nanometer process, with Nvidia and Broadcom already testing Intel’s manufacturing capabilities, alongside AMD exploring the potential of Intel’s processes for its chips.

Meta Tests Its First In-House AI Training Chip

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Meta, the parent company of Facebook, has initiated testing of its first in-house chip designed specifically for training artificial intelligence (AI) systems. This development marks a significant step in Meta’s plan to reduce its reliance on external chip suppliers like Nvidia and move toward producing its own custom silicon. Sources told Reuters that Meta has begun a small deployment of the chip and plans to expand production if the test proves successful.

Meta’s push to develop in-house chips is part of a broader strategy to reduce the high infrastructure costs associated with its AI projects. The company has forecast total 2025 expenses between $114 billion and $119 billion, including up to $65 billion in capital expenditure largely driven by investments in AI infrastructure.

The new chip is a dedicated accelerator, meaning it is built specifically for AI tasks, making it more power-efficient compared to graphics processing units (GPUs) typically used for AI workloads. Meta is collaborating with Taiwan-based TSMC to produce the chip. The initial design, known as the “tape-out,” has been completed, a crucial milestone in chip development. While tape-out is expensive, costing tens of millions of dollars, it is an essential part of the process to test the chip’s functionality.

Meta has experienced setbacks in its Meta Training and Inference Accelerator (MTIA) series in the past, even scrapping one chip after its initial tests failed. However, last year, Meta began using a MTIA inference chip for content recommendation systems on platforms like Facebook and Instagram. This progress has encouraged Meta to pursue further development of custom chips, aiming to use them for both training and inference of AI models, including generative AI products like Meta AI.

Meta plans to start using its own chips by 2026 for training purposes, aiming to reduce costs associated with AI model training. Chris Cox, Meta’s Chief Product Officer, discussed the company’s phased approach, noting that while progress has been slow, the success of the first-generation inference chip for recommendations has been a significant achievement. Despite the setbacks in developing custom chips, Meta continues to rely heavily on Nvidia’s GPUs for its AI needs, making it one of Nvidia’s largest customers.

The broader AI industry has raised questions about the effectiveness of scaling up large language models with ever more data and computing power. Chinese startup DeepSeek has introduced new, more efficient AI models that rely more heavily on inference rather than the computationally expensive training process. This has sparked concerns about the future value of GPUs like those from Nvidia, which have faced significant market volatility this year.