Nearly every major AI system, from research prototypes to widely deployed products, has depended on NVIDIA hardware. But somewhere beneath the surface of the AI boom, another story has been unfolding. It is slower, less visible, and far more structural.

Some of the biggest technology companies in the world are beginning to design their own AI chips. If the trend continues, the AI hardware ecosystem of the future may look far more fragmented and far more specialised than it does today.

A $100 Billion Signal

The conversation gained fresh momentum recently when Broadcom suggested that the market for AI chips could exceed $100 billion, driven largely by custom silicon designed for companies building large AI models, such as OpenAI and Anthropic.

That projection did not necessarily surprise industry observers. Demand for AI computing has already been surging at an extraordinary pace. But what caught attention was something else – the implication that some AI developers may want more control over the chips running their models.

“Broadcom’s projection signals that AI compute is becoming core infrastructure,” said Jeffrey Cooper in comments to International Finance. Cooper is an AI and semiconductor author who previously held leadership roles at ASML, ABB, and General Electric.

According to Cooper, the shift reflects a deeper strategic calculation inside the largest AI and cloud companies.

“Hyperscalers are beginning to design custom silicon to optimise performance, power efficiency, and cost at massive scale,” he said.

For companies operating data centres with hundreds of thousands of GPUs, even small efficiency improvements can translate into enormous savings.

Why Big Tech Wants Its Own Chips

The idea of designing custom chips is not entirely new. Large technology companies have experimented with specialised silicon for years.

Google introduced its Tensor Processing Unit (TPU) architecture nearly a decade ago. Amazon has developed its Trainium and Inferentia processors. Meta has been building AI accelerators for its recommendation systems and machine learning workloads.

What is different now is the scale. Artificial intelligence systems are becoming vastly more computationally demanding. Training large models requires immense clusters of GPUs running continuously for weeks or months. That kind of infrastructure is expensive.

“Controlling their own silicon allows hyperscalers to optimise chips for their specific AI models and datacenter architectures while reducing long-term dependence on external suppliers,” Cooper told International Finance.

There is also a basic economic incentive. NVIDIA’s GPUs, while powerful, come at a high price. The company’s margins reflect the extraordinary demand for its hardware.

Karl Freund, founder and Principal Analyst at Cambrian AI Research, pointed out that hyperscalers are keenly aware of that dynamic.

“They all have distinct workloads that can benefit from distinct hardware designs. Plus, they get these chips at manufacturing cost,” Freund told International Finance. And that difference matters.

“NVIDIA makes roughly 75% gross profit,” Freund added. “So, these companies hope to derive significant savings.”

In other words, designing chips internally could reduce long-term infrastructure costs, especially as AI deployments expand.

Broadcom’s Role In The New Ecosystem

In this emerging landscape, companies like Broadcom may play an important enabling role. Unlike NVIDIA, which mainly sells general-purpose AI GPUs, Broadcom often works directly with companies to design chips built for very specific AI workloads.

For AI developers, that can be appealing. Many have unique computing needs, but do not necessarily have the deep semiconductor expertise required to design complex chips entirely on their own.

“Firms like Broadcom are increasingly acting as key collaborators. They bring the chip design expertise and industry partnerships needed to turn hyperscalers’ architecture ideas into chips that can actually be manufactured at scale,” Jeffrey Cooper said.

The arrangement allows AI companies to focus on model design and infrastructure strategy while relying on experienced semiconductor partners for chip development. Such partnerships could gradually reshape how AI hardware is created.

Rather than purchasing standardised GPUs from a handful of suppliers, large technology firms may increasingly collaborate with chip designers to build processors optimised for their own models.

What Changes For NVIDIA

The rise of custom silicon inevitably raises another question: Does it threaten NVIDIA? For now, most analysts believe the answer is ‘not really’, at least in the short term.

NVIDIA’s dominance in AI computing is not based only on hardware. The company also controls one of the most powerful software ecosystems in computing: CUDA (Compute Unified Device Architecture), the programming platform used by researchers and developers to build AI applications. That ecosystem has been built over more than a decade.

“These developments do not meaningfully threaten NVIDIA in the near term. Its CUDA software ecosystem, developer base, and deployment scale remain extremely difficult to replicate,” Cooper said.

Freund agrees that NVIDIA’s position remains extremely strong, even if competitors gain ground.

“Broadcom only has a few very large customers. But, it is clearly looking to expand its revenue streams and bring in a wider range of customers,” Freund said.

Companies like Broadcom and Marvell Technology could carve out portions of the market that do not rely on NVIDIA GPUs.

But, Freund expects NVIDIA to retain a commanding position. “I would expect Broadcom and Marvell to carve out a significant portion of the it’s-not-NVIDIA market. But NVIDIA could retain at least 80% share,” he said.

In other words, the AI chip market may become larger and more diverse without fundamentally displacing the current leader.

Training Models Vs Running Them

Another key difference in the AI chip conversation comes down to how these chips are actually used in practice. AI computing typically involves two different tasks: training models and running them once they are deployed.

Training requires enormous computing clusters and extremely flexible hardware, making GPUs particularly well suited to the job. Inference, running the trained models, can often be optimised with specialised hardware. That is where custom silicon could become especially important.

“In theory, custom chips could compete with GPUs for training. In fact, Google’s TPU has been used to train many models, including Gemini,” Freund said.

But the economics often look different.

“The economics of an Application-Specific Integrated Circuit (ASIC) will look much better for inference,” he added.

In practice, that could mean a future where GPUs remain dominant in training large models while custom chips handle the massive number of inference requests generated by AI applications.

Will AI Chips Start Splitting Into Many Directions?

If large tech companies continue building their own chips, the AI hardware space could gradually start splitting into different directions. Instead of one dominant design, different companies may begin shaping chips around their own models, workloads, and data centre needs.

“The ecosystem is likely to become more diversified,” Cooper said, “with specialised chips optimised for different workloads, such as training, inference, networking, and memory-intensive AI tasks.”

Some startups are already pushing this idea to its extreme. Freund pointed to emerging companies designing highly specialised AI processors.

One example is Etched AI, which developed the ‘Sohu chip’, an application-specific processor designed specifically for transformer models.

“It strips out support for non-transformer workloads so it can pack in more transformer-optimised compute,” Freund said.

The result, at least in theory, is dramatically higher throughput and energy efficiency for models such as Llama-style or GPT-style large language models.

Another company, Taalas, is experimenting with an even more radical approach, designing chips tailored to individual models. These kinds of designs would have seemed impractical only a few years ago. But advances in semiconductor design tools, and the use of AI to assist chip development, may make them increasingly feasible.

“As the market expands and AI itself lowers the cost of silicon development, we will see some fragmentation,” Freund said.

How Custom Chips Could Reshape the Supply Chain

The growing push for hyperscaler-designed chips could also start changing how the broader semiconductor supply chain works. Designing advanced AI processors requires access to cutting-edge manufacturing technology, particularly the capabilities of foundries like TSMC.

Cooper believes this trend could deepen relationships across the semiconductor ecosystem.

“The rise of hyperscaler-designed chips will likely deepen relationships with advanced foundries while increasing demand across the semiconductor equipment and advanced packaging supply chain,” he told International Finance.

That includes not only chip manufacturers, but also the companies building the tools used to produce those chips.

The AI boom has already driven massive investment across the semiconductor industry, from lithography equipment to advanced packaging technologies needed to connect multiple chiplets in a single processor. Custom silicon could amplify that trend.

Why Only the Biggest Tech Companies Can Do This

One big reason custom chip programs are mostly limited to a few companies is scale. Designing advanced semiconductors takes massive resources, from large engineering teams to years of development, and huge financial investment. Historically, many companies that attempted custom chip programs struggled to sustain them. Two well-known initiatives are the Brainwave AI Chip by Microsoft, and Habana Gaudi by Intel.

“Successful in-house chip programs typically require massive scale, strong software integration, and long-term commitment,” Cooper said. That combination is typically available only to the world’s largest cloud companies.

As a result, the next generation of AI chips may be designed primarily by a small group of hyperscalers with global data centre networks.

The Next Five Years

Looking ahead, analysts expect the AI hardware market to not only grow dramatically, but also become more complex. The demand for computing power shows little sign of slowing. AI models continue to expand in size and capability, while new applications, from autonomous systems to generative tools, create additional workloads. In that environment, both general-purpose GPUs and specialised processors may co-exist.

“I see the market increasingly diversifying with specialised chips for specific companies and specific use cases,” Freund said.

But he also emphasised that GPUs will likely remain central to AI infrastructure.

“The versatility and broad usability of GPUs will likely keep them at the centre of AI computing,” he said.

Cooper offers a similar outlook.

“Five years from now, the market will likely include both dominant general-purpose GPU platforms and a growing layer of specialised silicon designed by hyperscalers,” he said.

A Subtle Shift

For now, the AI chip industry revolves around NVIDIA. But the emergence of custom silicon, designed by the very companies building AI models, suggests that the structure of the market could gradually evolve.

Hyperscalers want more control over their infrastructure. They want chips optimised for their own models. And increasingly, they have the resources to build them.

The result may not be the end of NVIDIA’s dominance. But it could be the beginning of a more diverse, and far more specialised AI hardware ecosystem. In the world of artificial intelligence, infrastructure shifts like that rarely happen overnight. They unfold slowly, almost quietly, until suddenly they are everywhere.

The post Big tech’s silicon shift: Designing its own AI chips appeared first on International Finance.

International Finance unpacks how Taiwan emerged as a chipmaking powerhouse, the US bid to reclaim manufacturing, and how Washington’s export bans and China’s countermoves are reshaping the global economy. Along the way, we bring insights from industry leaders and policy experts on the economic fallout and the future of the silicon struggle.

Global semiconductor supply

Just 100 miles from China’s coast, Taiwan’s TSMC (Taiwan Semiconductor Manufacturing Co.) produces roughly 90% of the world’s most advanced semiconductors. These chips power everything from Apple iPhones and Nvidia AI accelerators to critical infrastructure and defence systems.

Taiwan’s dominance, especially at the smallest transistor sizes, makes it the linchpin of the global tech supply chain: a phenomenon sometimes called the “silicon shield.” The logic is simple. Taiwan’s role in chip supply makes military conflict an economic catastrophe for everyone involved, acting as a deterrent to aggression.

TSMC’s ascent was decades in the making. Founded in 1987 with state support, TSMC pioneered the “pure-play” foundry model, producing chips designed by others and steadily outpacing global rivals.

Today, its technical know-how lets it pack billions of transistors onto fingernail-sized chips, years ahead of competitors. Until recently, nearly all these leading-edge chips were made in Taiwan, a concentration that inspires both awe and anxiety.

On one hand, TSMC is an economic and strategic bulwark for Taiwan, seen as a “sacred mountain protecting the country.” On the other hand, it creates a single point of failure: a natural disaster or geopolitical event could disrupt the world’s chip supply, with devastating consequences.

Policymakers worry about what will happen to TSMC’s “fabs” if China ever attacks or blocks Taiwan. The stakes are enormous: advanced chips are critical for civilian technology and national defence.

Even within Taiwan, there’s anxiety over how much chip technology should be shared abroad. Morris Chang, TSMC’s 91-year-old founder, has called out the dilemma, “The US Commerce Secretary said repeatedly that Taiwan is a very dangerous place [and] America cannot rely on Taiwan for chips… that, of course, is Taiwan’s dilemma.”

While TSMC is expanding overseas, Chang notes that “in the chip sector, globalisation is dead. Free trade is not quite that dead, but it’s in danger.” His warning is that higher costs and less ubiquity for advanced chips will result if the world splits into competing tech blocs.

Taiwan’s role as a global chip linchpin brings leverage and vulnerability, a reality now pushing others to develop their own advanced chipmaking muscle.

‘Make it in America’ chip push

The United States once led the world in chip design and manufacturing. As production shifted to Asia, America’s share of global chip fabrication capacity fell from 37% in 1990 to just 12% by 2020.

Former Commerce Secretary Gina Raimondo summed up the American predicament by saying that America had “dropped the ball,” allowing Asian rivals to surge ahead. Now, after pandemic-driven supply chain shocks, Washington is determined to “onshore” and “reshore” semiconductor production. But is it working?

A flurry of policies followed, from tariffs and trade pressure to hefty investment incentives. President Donald Trump threatened tariffs to push TSMC and others into building US plants. As a result, TSMC agreed to a $12 billion fab in Arizona, later expanding to a $40 billion project, which marked their first advanced facilities outside Taiwan. These fabs, when fully operational, will produce 4nm and 3nm chips, still trailing Taiwan’s 2nm technology but among the world’s most advanced.

President Joe Biden followed with the CHIPS and Science Act, a $52 billion package of subsidies, grants, and tax credits designed to “supercharge” US semiconductor manufacturing. This resulted in big investments from both American and foreign firms.

TSMC secured $6.6 billion in US grants for its Arizona plants, Samsung got $6 billion for a new Texas plant, and Micron announced a $100 billion New York megafab. The irony? Onshoring incentives are also benefiting foreign giants, whose rise was built on decades of government support in Asia.

Building a robust domestic chip industry is proving complex. Chip fabs are among the world’s most sophisticated factories, requiring immense precision and years to build. TSMC and Samsung have faced delays, cost overruns, and skilled labour shortages in the US.

Arizona’s TSMC site even had to “import” technicians from Taiwan, causing friction with US labour unions. Making advanced chips takes armies of PhD-level engineers, yet US immigration policies restrict high-skilled talent.

Analyst Marc Einstein notes, “You can’t just magic PhDs out of nowhere.”

Many experts argue that expanding high-skilled visa programmes is essential for the US chip renaissance.

Chip manufacturing is a global ecosystem. An advanced chip may be designed in California and fabricated in Taiwan using equipment from the Netherlands and materials from Japan and Germany.

“No single country can do everything,” says TSMC Arizona president Rosemary Castanares.

Even US fabs rely on $150 million ASML lithography machines from Europe. For now, US-based fabs remain smaller and a technological step behind Asia’s mega-fabs.

Historian Chris Miller calls TSMC’s Arizona plants “a generation behind the cutting edge in Taiwan,” and much lower in output.

TSMC itself is clear: its most advanced chips, and bleeding-edge R&D, will stay in Taiwan. Arizona’s fabs get slightly older, though still advanced tech.

The US officials might tout reshoring wins, but the centre of gravity remains in Asia. Restoring US chip leadership is a long-term effort, needing not just money and factories but also investment in education, workforce training, and immigration reform.

US-China crossfire in semiconductors

As Washington tried to onshore chipmaking, it also wielded trade weapons to slow China’s technological rise. The US-China trade war, which started with tariffs in 2018, has increasingly focused on semiconductors as a strategic chokepoint. The Trump and Biden administrations have sought to deny China advanced chips and manufacturing equipment to “protect national security.”

A pivotal moment occurred in October 2022 when Washington enacted stringent export controls. These regulations prevent global companies from selling high-performance chips or chip equipment that utilises US technology to China without obtaining a difficult-to-secure license.

If a chip was made with US software or machinery, as almost all advanced chips are, exporting it to China is restricted. The rules even bar US citizens from working for certain Chinese firms, choking off a “key pipeline of American talent.”

American officials argue this is essential to prevent “sensitive technologies” from fuelling China’s military modernisation, since advanced chips are dual-use, meaning they power both civilian and military AI.

Beijing calls this “technology terrorism” and has filed complaints at the World Trade Organisation, accusing Washington of abusing export controls. Chinese officials warn that these moves destabilise global supply chains. The impact is very much real. Huawei’s handset business collapsed after US sanctions cut it off from advanced chips.

Other Chinese firms, like memory giant YMTC, have been blacklisted. Even the United Kingdom-based ARM won’t license its latest designs to Chinese customers. Washington’s allies in the Netherlands and Japan have joined in, restricting exports of crucial lithography and chip equipment to China.

China’s initial response was cautious, but it has since weaponised its own dominance in key minerals. In 2023, Beijing restricted exports of gallium and germanium, both vital for chipmaking, and later banned exports of more minerals to the US.

These tit-for-tat moves signal China’s willingness to hit back with strategic materials. China also imposed its own limited bans, such as restricting US firm Micron’s chips from critical Chinese infrastructure.

At home, China has doubled down on self-reliance, pouring tens of billions into its chip sector through national funds and the “Made in China 2025” campaign.

President Xi Jinping calls on China to excel in key core technologies to ensure domestic innovation, thus preventing the country from being hindered by foreign sanctions.

The trade war has forced Chinese firms to seek new markets and supply chain arrangements, but often with slimmer profits.

Meanwhile, allied equipment makers like ASML now face the loss of lucrative Chinese customers, which raises concerns about lost innovation and revenue.

Nvidia CEO Jensen Huang recently blasted US export controls as “backfiring.”

He notes that Nvidia’s share of China’s AI chip market fell from 95% to 50%, with Chinese firms ramping up in-house alternatives. The bans, he says, “have pushed Chinese companies toward home-grown alternatives, spurring Chinese investment.”

Bill Gates similarly says US pressure has forced China to “go full speed ahead” on its own chips.

While some US officials argue these bans buy the West a crucial lead time in military AI, critics warn the strategy may accelerate China’s self-sufficiency and ultimately weaken the American industry.

The Chinese playbook

China has responded to the chip war with a multipronged strategy. At the core: building a self-sufficient semiconductor ecosystem. State-backed funds and national strategies aim to reduce dependence on foreign tech, especially in critical areas like manufacturing equipment and chip design.

Chinese firms have aggressively recruited global talent, including Taiwanese and American engineers, and have sometimes resorted to industrial espionage. The urgency to innovate has only intensified after US sanctions nearly crippled companies like ZTE and Huawei.

One breakthrough occurred in 2023 when Chinese chipmaker SMIC produced a 7nm chip, used in Huawei’s Mate 60 Pro, despite lacking access to the world’s most advanced lithography equipment.

US experts suspect SMIC adapted older machines with multiple patterning to achieve the feat. The phone’s teardown revealed memory chips from South Korea’s SK Hynix, showing that China can still source key components through unofficial channels or stockpiles.

While 7nm lags behind Apple’s 3nm chips, the achievement signals that China can adapt around sanctions, even though it comes at a high cost. Chinese companies are also developing workarounds, like using open-source chip architectures (RISC-V) and clustering less advanced chips to achieve AI tasks. Diplomatic efforts target partnerships with countries like Russia and some in Southeast Asia.

In parallel, China is building its own software and tooling ecosystem to reduce reliance on US and allied IP.

Still, China faces several pitfalls, including corruption in its state funds, persistent dependence on imported materials, and the technological gap in ultra-advanced manufacturing.

Globally, the chip war is creating a bifurcated tech order. There is a US-led bloc with strict controls and the most advanced chips, while a China-centric sphere relies on indigenous innovation and sometimes older technology.

Countries such as those in Europe, India, and Japan are now pursuing domestic manufacturing as a strategic objective. The aim is to avoid dependency on a single foreign supplier.

The global economy

The US-China semiconductor standoff has global ramifications. Allies, from Europe to Japan and India, are launching their own chip initiatives to bolster supply chain resilience. The European Union’s Chips Act aims to double Europe’s production share by 2030.

Japan is subsidising TSMC’s new Kumamoto plant; India, pitching its low-cost labour and market scale, is working to attract chipmakers despite obstacles like land acquisition and water supply. All these moves indicate a shift since countries want to reduce overreliance on any one supplier.

The balancing act is delicate for Asia’s chip powerhouses such as Taiwan, South Korea, and Japan. While they are US security partners, they rely heavily on China for a significant portion of their chip exports. With extensive operations in China, Korean giants Samsung and SK Hynix have even required waivers from US regulations.

TSMC, while benefiting from the US “friendshoring,” is careful not to sever links with Chinese customers. Diversification, by building plants in the United States and Japan, hedges bets against both geopolitics and American pressure.

A major unintended consequence is tech ecosystem fragmentation. If the world splits into separate tech stacks, innovation could slow (due to duplication and lost scale), but could also spark alternative breakthroughs. If denied access to leading-edge chips, the Chinese firms might focus on alternative architectures or software innovations.

The US and its allies, wary of supply chain risk, are building redundancy at a higher cost. A Boston Consulting Group study estimates that a full US-China semiconductor split could cost US companies $80 billion in lost revenues and $20 billion less R&D annually. Despite these costs, there is hope that competition will spur next-generation innovation, including quantum chips, new materials, and more resilient supply chains.

Governments everywhere are pouring money into chip R&D, education, and mature-node production for critical industries like autos and defence. Recent chip shortages made clear that even older chips are vital.

AI is front and centre in this fight. US restrictions aim to hold back China’s AI progress by limiting access to the most powerful GPUs. In the short term, it’s working, as Chinese companies are scrambling to adapt.

But software-side innovation and hardware workarounds are likely. If anything, scarcity may force efficiency and new approaches to AI development. In the long run, stifling hardware access may backfire by spurring domestic breakthroughs in China and elsewhere.

The chip war also raises fundamental questions about economic sovereignty. Governments now ask whether they can count on secure chip supplies in a crisis.

For many, the answer is “not yet,” driving a rush to build national capacity and regional redundancy, even if it means higher costs. Taiwan’s “silicon shield” still matters, but if TSMC globalises, no one country will wield absolute leverage for long.

Looking ahead, an “armed détente” is possible: both superpowers invest in reducing their vulnerabilities, and a new equilibrium emerges. North America might reach 20% of global chip output by 2030; China could attain partial self-sufficiency in 7nm or 5nm nodes. The world could then operate dual tech systems, trade some chips while restricting others for security reasons.

A total rupture, such as war over Taiwan, remains a nightmare. Disruption to Taiwan’s fabs would cripple the global electronics industry.

So far, fear of mutual destruction has preserved the status quo. As the US and others reduce reliance on Taiwan, that deterrence may weaken over time.

The world is realising, sometimes painfully, how critical and fragile the semiconductor supply chain is. Chips have become the new oil, with control over them reshaping the global balance of power in the 21st century.

Silicon geopolitics is here to stay, and every country will feel its impact.

The post Silicon supremacy: Economic impact of AI chip wars appeared first on International Finance.

The manufacturer’s suggested retail price for the graphics cards is USD 599 for the AMD Radeon 9070 XT and USD 549 for the Radeon 9070. However, as the products hit the Japanese market, their cost reached USD 859.

“During the presentation of the graphics cards, AMD promised wide availability of the new products, but high specifications and performance, combined with an attractive price, significantly increased demand. This led to extremely high demand for the graphics cards,” stated AMD Corporate Vice President David McAfee during a broadcast on HotHardware.

The senior official also added that AMD is doing everything it can to ensure that customers can purchase graphics cards at the recommended price, while noting the efforts of the company’s retailers and partners to ensure that the supply chain remains uninterrupted.

Now, talking about the USD 210 billion semiconductor company, has a demanding work culture that has been fostered by Lisa Su, its CEO. Dr. Lisa Su reportedly conducts meetings on the weekends and regularly communicates with staff members late at night, anticipating answers even after midnight, according to CNBC. She feels that her team is inspired to push boundaries by the company’s ambitious goals, which are the driving force behind this demanding schedule. Notably, Jensen Huang, the CEO of Nvidia, is a cousin of the 55-year-old.

Dr. Lisa Su’s leadership and revolutionary influence on AMD’s market success have earned her the title of Time’s CEO of the Year 2024. She took up the role in 2014, and since then, the company’s stock price has increased nearly 50 times. Under her leadership, AMD has become a powerful force in the market, securing its place next to industry titans Nvidia and Intel in terms of market capitalisation.

Patrick Moorhead, a tech industry analyst and former AMD executive, says Dr. Su’s leadership style is defined by a results-driven approach and high expectations. Su’s strict management style can be difficult for certain employees, according to Mr. Moorhead, who left AMD before Su’s arrival, and those who break their promises frequently find it difficult to succeed there.

When her staff members receive lengthy documents at midnight, the CEO frequently expects them to discuss their complexities during morning calls. As soon as the prototype chips are delivered from the factory, she personally inspects them, demonstrating her famous attention to detail.

More Details About Lisa Su

Lisa Su was born in Taiwan and moved to the United States with her parents. Her father studied mathematics for his graduate degree in New York. Her father’s intense dinner table quizzes exposed her to mathematics at a young age, setting the groundwork for her subsequent academic endeavours. Her original goals, though, were different. She eventually realised her talents were elsewhere, but as a teenager, she dreamt of becoming a concert pianist.

Dr. Su’s outstanding academic performance brought her to the esteemed Massachusetts Institute of Technology (MIT), where she graduated with electrical engineering bachelor’s, master’s, and doctoral degrees. With stints at Texas Instruments and IBM in the 1990s, her stellar educational background helped pave the way for a prosperous career in the tech sector.

While pursuing her PhD, Su became one of the first engineers to look into the SOI (Silicon on Insulator) technology to increase transistors’ efficiency by building them atop layers of insulating material.

At the beginning of her career, Su worked in the Semiconductor Process and Device Centre (SPDC) at Texas Instruments, before being hired by IBM as a research staff member specialising in device physics. As an active member of IBM’s R&D, Su worked on copper technology, which was later launched in 1998. This innovation increased the productivity of chips by up to 20%.

In 2001, Su was titled “Top Innovator Under 35” by MIT Technology Review for her work with Emerging Products. This included a microprocessor that improved battery life in phones and other handheld devices. IBM then collaborated with Sony and Toshiba for a nine-processor chip that eventually became a cell microprocessor used in PlayStation 3.

Dr. Su became the first female CEO of AMD since its founding in 1969, just two years after joining the company as a senior vice president and general manager in 2012. AMD’s recent success has been greatly aided by Dr. Su’s technical expertise, as she is one of the few Fortune 500 CEOs with a PhD. Her experience as an engineer has allowed her to lead creative initiatives, such as the creation of faster CPU chips for computers.

The AMD boss has also published more than 40 technical articles, apart from winning multiple honours for her semiconductor industry leadership. She was named one of the “50 Most Powerful Women in Technology” by the National Diversity Council in 2016 and 2017. Su ranked 49th in Forbes’ list of “World’s 100 Most Powerful Women,” followed by 12th on Fortune’s list of Most Powerful Women in 2023. She was also included in Time Magazine’s 2024 list of the “100 Most Influential People in AI.”

AMD To Go For AI Hardware Leadership

In October 2024, during a keynote at the company’s “Advancing AI” event in San Francisco, Su said AMD was focusing on offering powerful hardware and co-innovating with a variety of partners. In what looked like a sneak peek of the company’s roadmap to become an “end-to-end AI leader,” Su outlined AMD’s four core themes.

The first one talked about providing high-performance, energy-efficient compute engines capable of supporting a variety of AI training and inference workloads. Su told the media that there’s a “no one-size-fits-all” approach to computing and that providing CPUs, GPUs, and NPUs capable of providing top-level performance across the board enables AMD to keep up with the competition from rivals like Nvidia.

The second theme was the desire to create and support an open and developer-friendly ecosystem for AI development. The third talked about AMD’s approach to leadership in AI, which would facilitate co-innovation with industry partners.

The final theme was about AMD maintaining its leadership in the AI chip market as a complete solution provider, offering hardware from chip-level to rack, cluster, and data centres. Su further stated that the strategy would help AMD capture a significant share of the rapidly growing AI market, estimated by the CEO to reach USD 500 billion by 2028 for data centre AI accelerators alone.

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The company has demonstrated how its computational operation and the speed of its product design, using an industry-standard Verilog simulation of the actual Prodigy post layout hardware, is the superior solution to current competitive offerings. Not only does Prodigy execute instructions at very high speeds, but Tachyum now has an infrastructure implemented for automatically checking correct results from the Verilog RTL. These automated tests check Verilog output for correctness compared to Tachyum’s C-model.

“This latest hardware milestone is a testament to the diligent work of our engineering team and the vast human resources we have been able to assemble to complete a revolutionary solution never before seen,” said Dr. Radoslav Danilak, Tachyum founder and CEO.  “We set out to produce the highest performance, lowest energy and most cost- efficient processor for the hyperscale, HPC and AI marketplace and these milestones are proving that we are achieving those goals. With a product that is faster than the fastest Intel Xeon or NVIDA A100 Chips, Prodigy is nearing all of its stated objectives and remains on track to make its debut as planned next year.”

This verification milestone dramatically increases Tachyum’s productivity and its ability to test the Prodigy hardware design efficiently in order to find bugs and correct them prior to tape-out. With this latest accomplishment, Tachyum now has automated the constrained random test generation capability, which further adds to its productivity.

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The company’s new Intel Atom P5900 processor is its first System on a Chip (SoC) particularly designed for wireless base stations. According to experts, wireless base stations are vital to 5G deployers. For that reason, Intel is planning to capture the 5G market with Atom P5900, media reports said.

Intel plans to become a leader in the base station silicon market by 2022. The company’s new Atom P5900 will provide high bandwidth and low latency compared to the existing base station silicon market. Telefonaktiebolaget LM Ericsson and ZTE will deploy Atom P5900 this year. 

Intel has launched a new version of Intel Xeon Scalable processors in addition to Atom P5900. Corporate Vice President and General Manager of Intel’s Network Platforms Group Dan Rodriguez, told the media, “5G is a massive inflection point, driving data and new data-driven services.”

Intel will use the processor as a tool to capture the majority of market share in the base station silicon market. Until 2015, Intel did not have a significant market presence in wireless base stations, Rodriguez said.

According to Intel, 5G networks necessitate new technologies to meet latency requirements. The company is also expanding its Open Network Edge Services Software toolkit to deploy advanced applications and services. From now, it will support standalone 5GNR and Enhanced Platform Awareness designed to help customers seamlessly deploy microservices, media reports said. 

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The occurrence took place after Bloomberg reported Apple’s plan to use its own brand’s chips for Mac computers in two years, according to Daily Sabah.

Previously, Apple has used chips from other companies such as Qualcomm. However, its focus to bring order to all its hardware and software has encouraged the brand to design its own chips.

Last month, Chief Executive Brian Krzanich said the new chips would secure the products against Meltdown and Spectre, which makes the device vulnerable to malicious attacks.

Intel had fixed issues for devices, but experts still believe the flaws were complex as hardware was deeply involved.

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