So, what’s the big deal about E-Beam lithography? Imagine trying to draw incredibly complex patterns, but thousands of times smaller than a human hair. That’s essentially what this technology does, using a focused beam of tiny electrons.

Such precision is vital for designing the microscopic components inside the chips that run everything from our smartphones and gaming consoles to life-saving medical scanners and advanced defence systems.

Semiconductors are already big business for the UK, adding around £10 billion to its economy each year. And that figure is only expected to climb, potentially hitting £17 billion by the end of the decade.

Nurturing this sector is a major opportunity for the UK—not just for bragging rights in advanced manufacturing, but for creating high-value jobs and driving real economic growth.

Speaking at the launch of the facility in Southampton, Science Minister Lord Patrick Vallance said: “Britain is home to some of the most exciting semiconductor research anywhere in the world—and Southampton’s new E-Beam facility is a major boost to our national capabilities.

“By investing in both infrastructure and talent, we’re giving our researchers and innovators the support they need to develop next-generation chips right here in the UK.”

Lord Vallance’s visit wasn’t just a photo opportunity, though. It came alongside some sobering news: fresh research published today highlights that one of the biggest hurdles facing the UK’s growing chip industry is finding enough people with the right skills.

We’re talking about a serious talent crunch. When you consider that a single person working in semiconductors contributes an average of £460,000 to the economy each year, you can see why plugging this skills gap is so critical.

So, what’s the plan? The government isn’t just acknowledging the problem; they’re putting money where their mouth is with a £4.75 million semiconductor skills package. The idea is to build up that talent pipeline, making sure universities like Southampton – already powerhouses of chip innovation – have resources like the E-Beam lab and the students they need.

“Our £4.75 million skills package will support our Plan for Change by helping more young people into high-value semiconductors careers, closing skills gaps and backing growth in this critical sector,” Lord Vallance explained.

Here’s where that cash is going:

• Getting students hooked (£3 million): Fancy £5,000 towards your degree? 300 students starting Electronics and Electrical Engineering courses this year will get just that, along with specific learning modules to show them what a career in semiconductors actually involves, particularly in chip design and making the things.
• Practical chip skills (£1.2 million): It’s one thing learning the theory, another designing a real chip. This pot will fund new hands-on chip design courses for students (undergrad and postgrad) and even train up lecturers. They’re also looking into creating conversion courses to tempt talented people from other fields into the chip world.
• Inspiring the next generation (Nearly £550,000): To really build a long-term pipeline, you need to capture interest early. This funding aims to give 7,000 teenagers (15-18) and 450 teachers some real, hands-on experience with semiconductors, working with local companies in existing UK chip hotspots like Newport, Cambridge, and Glasgow. The goal is to show young people the cool career paths available right on their doorstep.

Ultimately, the hope is that this targeted support will give the UK semiconductor scene the skilled workforce it needs to thrive. It’s about encouraging more students to jump into these valuable careers, helping companies find the people they desperately need, and making sure the UK stays at the forefront of the technologies that will shape tomorrow’s economy.

Professor Graham Reed, who heads up the Optoelectronics Research Centre (ORC) at Southampton University, commented: “The introduction of the new E-Beam facility will reinforce our position of hosting the most advanced cleanroom in UK academia.

“It facilitates a vast array of innovative and industrially relevant research, and much needed semiconductor skills training.”

Putting world-class tools in the hands of researchers while simultaneously investing in the people who will use them will help to cement the UK’s leadership in semiconductors.

See also: AI in education: Balancing promises and pitfalls

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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“AI has been advancing at an incredible pace,” Huang said. “It started with perception AI — understanding images, words, and sounds. Then generative AI — creating text, images, and sound. Now, we’re entering the era of ‘physical AI,’ AI that can perceive, reason, plan, and act.”

With NVIDIA’s platforms and GPUs at the core, Huang explained how the company continues to fuel breakthroughs across multiple industries while unveiling innovations such as the Cosmos platform, next-gen GeForce RTX 50 Series GPUs, and compact AI supercomputer Project DIGITS. 

RTX 50 series: “The GPU is a beast”

One of the most significant announcements during CES 2025 was the introduction of the GeForce RTX 50 Series, powered by NVIDIA Blackwell architecture. Huang debuted the flagship RTX 5090 GPU, boasting 92 billion transistors and achieving an impressive 3,352 trillion AI operations per second (TOPS).

“GeForce enabled AI to reach the masses, and now AI is coming home to GeForce,” said Huang.

Holding the blacked-out GPU, Huang called it “a beast,” highlighting its advanced features, including dual cooling fans and its ability to leverage AI for revolutionary real-time graphics.

Set for a staggered release in early 2025, the RTX 50 Series includes the flagship RTX 5090 and RTX 5080 (available 30 January), followed by the RTX 5070 Ti and RTX 5070 (February). Laptop GPUs join the lineup in March.

In addition, NVIDIA introduced DLSS 4 – featuring ‘Multi-Frame Generation’ technology – which boosts gaming performance up to eightfold by generating three additional frames for every frame rendered.

Other advancements, such as RTX Neural Shaders and RTX Mega Geometry, promise heightened realism in video games, including precise face and hair rendering using generative AI.

Cosmos: Ushering in physical AI

NVIDIA took another step forward with the Cosmos platform at CES 2025, which Huang described as a “game-changer” for robotics, industrial AI, and AVs. Much like the impact of large language models on generative AI, Cosmos represents a new frontier for AI applications in robotics and autonomous systems.

“The ChatGPT moment for general robotics is just around the corner,” Huang declared.

Cosmos integrates generative models, tokenisers, and video processing frameworks to enable robots and vehicles to simulate potential outcomes and predict optimal actions. By ingesting text, image, and video prompts, Cosmos can generate “virtual world states,” tailored for complex robotics and AV use cases involving real-world environments and lighting.

Top robotics and automotive leaders – including XPENG, Hyundai Motor Group, and Uber – are among the first to adopt Cosmos, which is available on GitHub via an open licence.

Pras Velagapudi, CTO at Agility, comments: “Data scarcity and variability are key challenges to successful learning in robot environments. Cosmos’ text-, image- and video-to-world capabilities allow us to generate and augment photorealistic scenarios for a variety of tasks that we can use to train models without needing as much expensive, real-world data capture.”

Empowering developers with AI models

NVIDIA also unveiled new AI foundation models for RTX PCs, which aim to supercharge content creation, productivity, and enterprise applications. These models, presented as NVIDIA NIM (Neural Interaction Model) microservices, are designed to integrate with the RTX 50 Series hardware.

Huang emphasised the accessibility of these tools: “These AI models run in every single cloud because NVIDIA GPUs are now available in every cloud.”

NVIDIA is doubling down on its push to equip developers with advanced tools for building AI-driven solutions. The company introduced AI Blueprints: pre-configured tools for crafting agents tailored to specific enterprise needs, such as content generation, fraud detection, and video management.

“They are completely open source, so you could take it and modify the blueprints,” explains Huang.

Huang also announced the release of Llama Nemotron, designed for developers to build and deploy powerful AI agents.

Ahmad Al-Dahle, VP and Head of GenAI at Meta, said: “Agentic AI is the next frontier of AI development, and delivering on this opportunity requires full-stack optimisation across a system of LLMs to deliver efficient, accurate AI agents.

“Through our collaboration with NVIDIA and our shared commitment to open models, the NVIDIA Llama Nemotron family built on Llama can help enterprises quickly create their own custom AI agents.”

Philipp Herzig, Chief AI Officer at SAP, added: “AI agents that collaborate to solve complex tasks across multiple lines of the business will unlock a whole new level of enterprise productivity beyond today’s generative AI scenarios.

“Through SAP’s Joule, hundreds of millions of enterprise users will interact with these agents to accomplish their goals faster than ever before. NVIDIA’s new open Llama Nemotron model family will foster the development of multiple specialised AI agents to transform business processes.”

Safer and smarter autonomous vehicles

NVIDIA’s announcements extended to the automotive industry, where its DRIVE Hyperion AV platform is fostering a safer and smarter future for AVs. Built on the new NVIDIA AGX Thor system-on-a-chip (SoC), the platform allows vehicles to achieve next-level functional safety and autonomous capabilities using generative AI models.

“The autonomous vehicle revolution is here,” Huang said. “Building autonomous vehicles, like all robots, requires three computers: NVIDIA DGX to train AI models, Omniverse to test-drive and generate synthetic data, and DRIVE AGX, a supercomputer in the car.”

Huang explained that synthetic data is critical for AV development, as it dramatically enhances real-world datasets. NVIDIA’s AI data factories – powered by Omniverse and Cosmos platforms – generate synthetic driving scenarios, increasing the effectiveness of training data exponentially.

Toyota, the world’s largest automaker, is committed to using NVIDIA DRIVE AGX Orin and the safety-certified NVIDIA DriveOS to develop its next-generation vehicles. Heavyweights such as JLR, Mercedes-Benz, and Volvo Cars have also adopted DRIVE Hyperion.

Project DIGITS: Compact AI supercomputer

Huang concluded his NVIDIA keynote at CES 2025 with a final “one more thing” announcement: Project DIGITS, NVIDIA’s smallest yet most powerful AI supercomputer, powered by the cutting-edge GB10 Grace Blackwell Superchip.

“This is NVIDIA’s latest AI supercomputer,” Huang declared, revealing its compact size, claiming it’s portable enough to “practically fit in a pocket.”

Project DIGITS enables developers and engineers to train and deploy AI models directly from their desks, providing the full power of NVIDIA’s AI stack in a compact form.

Set to launch in May, Project DIGITS represents NVIDIA’s push to make AI supercomputing accessible to individuals as well as organisations.

Vision for tomorrow

Reflecting on NVIDIA’s journey since inventing the programmable GPU in 1999, Huang described the past 12 years of AI-driven change as transformative.

“Every single layer of the technology stack has been fundamentally transformed,” he said.

With advancements spanning gaming, AI-driven agents, robotics, and autonomous vehicles, Huang foresees an exciting future.

“All of the enabling technologies I’ve talked about today will lead to surprising breakthroughs in general robotics and AI over the coming years,” Huang concludes.

(Image Credit: NVIDIA)

See also: Sam Altman, OpenAI: ‘Lucky and humbling’ to work towards superintelligence

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During the regular trading session on Tuesday, NVIDIA’s share price experienced a near-10% drop. The fall wiped £212 billion from its market value, marking the largest single-day loss for a US company in history.

While the wider market experienced a sell-off fueled by concerns over weak US manufacturing data, NVIDIA was hit particularly hard after Bloomberg reported that the US Department of Justice issued subpoenas to NVIDIA and other tech firms. 

Officials are reportedly concerned that NVIDIA’s business practices may be hindering client flexibility in switching to alternative semiconductor suppliers. Additionally, there are concerns about potential penalties imposed on buyers who opt not to exclusively utilise NVIDIA’s AI chips. Such actions would represent an escalation of the ongoing US antitrust investigation, bringing the government a step closer to formally charging NVIDIA.

In response, NVIDIA asserted its belief that its success is based “on merit, as reflected in our benchmark results and value to customers, who can choose whatever solution is best for them.” 

This latest downturn adds to the recent volatility experienced by NVIDIA and other AI-related stocks, such as Google, Apple, and Amazon. Investors are grappling with uncertainty surrounding the timeline for tangible benefits and concrete returns from the much-touted AI revolution.

Analysts suggest that investors are seeking greater clarity on the trajectory of gross margins as production of NVIDIA’s new Blackwell chip increases. Furthermore, they are eager for more concrete evidence that AI is delivering tangible returns for customers.

After a 9.5% decline on Tuesday alone and a 14% drop since last week’s earnings report, NVIDIA’s stock has shown marginal signs of recovery in today’s trading session, registering a modest 0.64% increase at the time of writing.

Looking ahead, NVIDIA will need to convince investors of its growth potential not only for 2025 but also for 2026. While Wall Street currently focuses on Blackwell chip shipments, there is increasing interest in the company’s next-generation chip offering.

(Photo by Sebastian Molina)

See also: xAI breaks records with ‘Colossus’ AI training system

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They also enjoy playing a diverse range of games. Some of the world’s most competitive eSports focus on PC hits, from FPSs like Counter-Strike: Global Offensive to MOBAs like Dota 2. Though console gamers are also present, most gamers associate these titles with PCs.

Beyond the scope of hyper-visible eSports hits, PC gamers also have access to other titles. Casino games, for example, are often played via browser. Slots are a popular choice, offering dozens of formats and hundreds of themes for them to choose from. Straight from a laptop or PC, players can spin the reel.

The same is true for DCCGs. These types of card games have taken off over the last decade, including new hits like Hearthstone and Marvel Snap. Though traditionally played in person, PCs are now the preferred format for many DCCG competitors.

One of the most unique developments in the world of PC gaming is the rise of AI-driven laptops and PCs. These powerful devices are designed to handle even more complex processing challenges. Both their hardware and software are advanced, and designed to handle machine-learning tasks.

If you’ve been considering an AI-driven laptop to improve your gaming experience, here are some of the products you should keep an eye on.

The metrics

The best AI-driven laptops and PCs have a performance-focused processor. Some of the best for AI-driven tasks are Intel Core i7 and i9—but you’ll have other options, too. Along with the processor, focus on the device’s graphics card—the higher, the better.

But keep a look out for storage, as the same is true for RAM. Better storage relates to faster loading times and general responsiveness—which are hugely important for gamers who need to avoid even a millisecond of lag. The NVMe SSDs are considered the best in the industry in 2024.

Alienware Aurora R14

In terms of actual products, the Alienware Aurora R14 is one of the best choices for AI-driven gaming. That’s because it has all of the most advanced hardware features, including a graphics card from NVIDIA GeForce, an Intel Core i9 processor, and 32GB of RAM. That’s fast, accurate, and advanced gaming at its best.

MSI Trident X

This PC is a little bit different in that it focuses on being more compact. Oftentimes, the more advanced the PC, the bulkier the hardware becomes. That’s not the case with the MSI Trident X. Despite its compact size, it has the Intel Core i9 processor, NVIDIA GeForce Graphics Card, and 32GB of storage—all the same as the Alienware Aurora, just in a slightly more manageable package.

HP Omen Obelisk

With the HP Omen Obelisk, the focus is on customisation. The AI-driven PC includes all the same features as the other two products—even down to the brands used. However, it also offers tool-free access to the interior, which makes both maintenance and upgrading a breeze. Unsurprisingly, it also has a glass side window that makes observing the hardware enjoyable.

HP Spectre x360 14

If you need to get more out of your AI-driven PC than gaming sessions, then the HP Spectre is a great option. (Although it’s a laptop—not a PC.) Its robust features will take your gaming sessions to the next level while also handling other types of demanding tasks, from graphic design to video editing. The sheer range of options makes this one popular for anyone who also needs a professional device.

ASUS ROG Zephyrus G14

This option is slightly more affordable than others on the list—though you’ll notice the more limited features. Specifically, it has less storage than the other AI-driven PCs, along with a slightly less powerful processor. But it still packs a huge punch into its space-saving laptop hardware, which makes it a solid option for most gamers.

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This acquisition, reportedly valued at about $600 million, is not SoftBank’s first foray into the UK tech scene.

In 2016, SoftBank controversially acquired British chip designer Arm in a much larger deal. However, the Graphcore purchase comes at a lower valuation than the total funding the company is said to have raised, which was around $700 million.

Graphcore will continue to operate under its own name and maintain its headquarters in Bristol, UK. The company also retains its offices in Cambridge, London, Gdansk, and Hsinchu, signalling SoftBank’s commitment to preserving Graphcore’s established presence and operations.

Nigel Toon, co-founder and CEO of Graphcore, said: “This is a tremendous endorsement of our team and their ability to build truly transformative AI technologies at scale, as well as a great outcome for our company.”

Toon went on to emphasise the ongoing demand for AI compute and the work that remains to be done in improving efficiency, resilience, and computational power to fully realise AI’s potential.

Graphcore’s key offering is a range of “Intelligence Processing Units” – accelerators designed specifically for AI workloads – along with a software stack that allows developers to utilise its hardware effectively.

The company’s technology has often impressed. In 2020, a Graphcore device outperformed an Nvidia A100 GPU, and in another instance, its hardware halved the time required to handle a GPU-based drug discovery workload.

Despite these technological successes, Graphcore has struggled to generate significant revenue and achieve profitability. In 2022, the company reported revenue of just $2.7 million – a 46 percent year-on-year decrease – while operating expenses reached $206.8 million.

Vikas J. Parekh, Managing Partner at SoftBank Investment Advisers, commented: “Society is embracing the opportunities offered by foundation models, generative AI applications, and new approaches to scientific discovery.

“Next generation semiconductors and compute systems are essential in the AGI journey, we’re pleased to collaborate with Graphcore in this mission.”

The mention of AGI (Artificial General Intelligence) in Parekh’s statement suggests that SoftBank sees Graphcore’s technology as a key component in the pursuit of more advanced AI systems that can match or exceed human-level intelligence across a wide range of tasks.

Graphcore has built a reputation as a leading employer in the UK’s high-tech economy, and the company has committed to continuing its investment in creating high-skilled jobs across various disciplines.

The acquisition of Graphcore by SoftBank is likely to provide the AI chipmaker with significant resources and opportunities for expansion. It also reflects the increasing competition in the AI chip market, where companies like NVIDIA, Intel, and AMD have been vying for dominance.

As AI continues to permeate various sectors of the economy and society, the demand for specialised AI hardware is expected to grow. Graphcore’s integration into SoftBank’s portfolio positions both companies to capitalise on this trend.

See also: PC market finds new momentum amid AI interest

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Recently, Arm unveiled new chip blueprints and software tools aimed at enhancing smartphones’ ability to handle AI tasks more efficiently. But they didn’t stop there – Arm also implemented changes to how they deliver these blueprints, potentially accelerating adoption.

Arm is evolving its solution offerings to maximise the benefits of leading process nodes. They announced the Arm Compute Subsystems (CSS) for Client, their latest cutting-edge compute solution tailored for AI applications in smartphones and PCs.

This CSS for Client promises a significant performance leap – we’re talking over 30% increased compute and graphics performance, along with an impressive 59% faster AI inference for AI, machine learning, and computer vision workloads.

While Arm’s technology powered the smartphone revolution, it’s also gaining traction in PCs and data centres, where energy efficiency is prized. Though smartphones remain Arm’s biggest market, supplying IP to rivals like Apple, Qualcomm, and MediaTek, the company is expanding its offerings.

They’ve launched new CPU designs optimised for AI workloads and new GPUs, as well as software tools to ease the development of chatbots and other AI apps on Arm chips.

But the real gamechanger is how these products are delivered. Historically, Arm provided specs or abstract designs that chipmakers had to translate into physical blueprints – an immense challenge arranging billions of transistors.

For this latest offering, Arm collaborated with Samsung and TSMC to provide physical chip blueprints ready for manufacturing, which was a huge time saver.

Samsung’s Jongwook Kye praised the partnership, stating their 3nm process combined with Arm’s CPU solutions meets soaring demand for generative AI in mobiles through “early and tight collaboration” in the areas of DTCO and PPA maximisation for an on-time silicon delivery that hit performance and efficiency demands.

TSMC’s head of the ecosystem and alliance management division, Dan Kochpatcharin echoed this, calling the AI-optimised CSS “a prime example” of Arm-TSMC collaboration helping designers push semiconductor innovation’s boundaries for unmatched AI performance and efficiency.

“Together with Arm and our Open Innovation Platform® (OIP) ecosystem partners, we empower our customers to accelerate their AI innovation using the most advanced process technologies and design solutions,” Kochpatcharin emphasised.

Arm isn’t trying to compete with customers, but rather enable faster time-to-market by providing optimised designs for neural processors delivering cutting-edge AI performance.

As Arm’s Chris Bergey said, “We’re combining a platform where these accelerators can be very tightly coupled” to customer NPUs.

Essentially, Arm provides more refined, “baked” designs customers can integrate with their own accelerators to rapidly develop powerful AI-driven chips and devices.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The Blackwell platform promises up to 25 times lower cost and energy consumption compared to its predecessor: the Hopper architecture. Named after pioneering mathematician and statistician David Harold Blackwell, the new GPU architecture introduces six transformative technologies.

“Generative AI is the defining technology of our time. Blackwell is the engine to power this new industrial revolution,” said Jensen Huang, Founder and CEO of NVIDIA. “Working with the most dynamic companies in the world, we will realise the promise of AI for every industry.”

The key innovations in Blackwell include the world’s most powerful chip with 208 billion transistors, a second-generation Transformer Engine to support double the compute and model sizes, fifth-generation NVLink interconnect for high-speed multi-GPU communication, and advanced engines for reliability, security, and data decompression.

Central to Blackwell is the NVIDIA GB200 Grace Blackwell Superchip, which combines two B200 Tensor Core GPUs with a Grace CPU over an ultra-fast 900GB/s NVLink interconnect. Multiple GB200 Superchips can be combined into systems like the liquid-cooled GB200 NVL72 platform with up to 72 Blackwell GPUs and 36 Grace CPUs, offering 1.4 exaflops of AI performance.

NVIDIA has already secured support from major cloud providers like Amazon Web Services, Google Cloud, Microsoft Azure, and Oracle Cloud Infrastructure to offer Blackwell-powered instances. Other partners planning Blackwell products include Dell Technologies, Meta, Microsoft, OpenAI, Oracle, Tesla, and many others across hardware, software, and sovereign clouds.

Sundar Pichai, CEO of Alphabet and Google, said: “We are fortunate to have a longstanding partnership with NVIDIA, and look forward to bringing the breakthrough capabilities of the Blackwell GPU to our Cloud customers and teams across Google to accelerate future discoveries.”

The Blackwell architecture and supporting software stack will enable new breakthroughs across industries from engineering and chip design to scientific computing and generative AI.

Mark Zuckerberg, Founder and CEO of Meta, commented: “AI already powers everything from our large language models to our content recommendations, ads, and safety systems, and it’s only going to get more important in the future.

“We’re looking forward to using NVIDIA’s Blackwell to help train our open-source Llama models and build the next generation of Meta AI and consumer products.”

With its massive performance gains and efficiency, Blackwell could be the engine to finally make real-time trillion-parameter AI a reality for enterprises.

See also: Elon Musk’s xAI open-sources Grok

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The report states that MGX — a state-backed group in Abu Dhabi — is in discussions to support OpenAI’s venture to build AI chips in-house. This information comes from two individuals with knowledge of the discussions.

In order to achieve its goal of creating semiconductor chips internally, OpenAI is reportedly seeking investments worth trillions of dollars from investors worldwide. By manufacturing its own chips, the San Francisco-based company aims to reduce its reliance on Nvidia, the current global leader in semiconductor chip technology.

As part of its funding efforts, OpenAI struck a deal with Thrive Capital in February 2023, which reportedly increased the company’s valuation to more than $80 billion, marking an almost threefold increase in under 10 months.

This comes as the UK semiconductor sector gains enhanced access to research funding through the country’s participation in the EU’s ‘Chips Joint Undertaking’.

The UK’s participation in the Chips Joint Undertaking provides the British semiconductor sector with enhanced access to a €1.3 billion pot of funds set aside from Horizon Europe to support research in semiconductor technologies up to 2027. The move is backed by an initial £5 million from the UK government this year, with an additional £30 million due to support UK participation in further research between 2025 and 2027.

“Our membership of the Chips Joint Undertaking will boost Britain’s strengths in semiconductor science and research to secure our position in the global chip supply chain,” said Technology Minister Saqib Bhatti. “This underscores our unwavering commitment to pushing the boundaries of technology and cements our important role in shaping the future of semiconductor technologies around the world.”

Back in the UAE, MGX — the group behind the potential investment in OpenAI — is an AI-focused fund launched earlier this week and headed by the UAE’s national security adviser, Sheikh Tahnoon Bin Zayed al-Nahyan. The fund was established in collaboration with G42 and Mubadala, with G42 having already entered into a partnership with OpenAI in October 2023 as part of the company’s Middle East expansion.

During the G42 partnership deal, OpenAI CEO Sam Altman stated that they plan to bring AI solutions to the Middle East that “resonate with the nuances of the region.”

One of the sources briefed on the MGX fund emphasised, “They’re looking at creating a structure that will put Abu Dhabi at the centre of this AI strategy with global partners around the world.”

As the race to develop cutting-edge semiconductor technologies intensifies, both the UAE and the UK-EU are positioning themselves as key players.

(Photo by Wael Hneini on Unsplash)

See also: EU approves controversial AI Act to mixed reactions

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Sources familiar with the matter disclosed to Reuters that OpenAI is actively exploring options, including evaluating an undisclosed company for potential acquisition to bolster its AI chip-making ambitions.

The shortage of chips, a fundamental component in AI technology, has prompted OpenAI to consider various strategies. These options include internal chip production, forging closer ties with its primary chip supplier NVIDIA, and diversifying its chip providers.

Earlier this year, OpenAI CEO Sam Altman voiced his concerns about the chip scarcity—resulting in delays to the company’s projects.

In a since-deleted blog post by Humanloop CEO Raza Habib, the AI expert wrote about his experience sitting down with Altman:

“A common theme that came up throughout the discussion was that currently OpenAI is extremely GPU-limited and this is delaying a lot of their short-term plans. The biggest customer complaint was about the reliability and speed of the API.

Sam acknowledged their concern and explained that most of the issue was a result of GPU shortages.The longer 32k context can’t yet be rolled out to more people. OpenAI haven’t overcome the O(n^2) scaling of attention and so whilst it seemed plausible they would have 100k – 1M token context windows soon (this year) anything bigger would require a research breakthrough.

The finetuning API is also currently bottlenecked by GPU availability. They don’t yet use efficient finetuning methods like Adapters or LoRa and so finetuning is very compute-intensive to run and manage.

Better support for finetuning will come in the future. They may even host a marketplace of community contributed models. Dedicated capacity offering is limited by GPU availability.”

If OpenAI proceeds with its plan to manufacture its own chips, it will join the ranks of industry giants like Google and Amazon who have already transitioned to in-house chip production. This move could potentially alleviate OpenAI’s dependency on external suppliers, empowering the company to meet the escalating demand for specialised AI chips.

Since the public launch of ChatGPT in November last year, the demand for specialised AI chips has skyrocketed—causing a surge in NVIDIA’s share prices as companies rush to procure the desirable hardware.

OpenAI has not made a final decision regarding the acquisition or in-house chip production, and discussions are ongoing to address the pressing chip shortage and sustain the company’s AI initiatives.

(Photo by Andrew Neel on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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Omdia’s Top AI Hardware Startups Market Radar finds that over 100 venture capitalists invested over $6 billion into the top 25 AI chip startups since 2018. However, it seems the good times weren’t to last.

The global chip shortage is becoming an inventory crisis. Meanwhile, the economic downturn and difficult monetary policies have made it difficult to raise funding.

“The best-funded AI chip startups are under pressure to deliver the kind of software support developers are used to from the market leader, NVIDIA,” says Alexander Harrowell, Principal Analyst for Advanced Computing at Omdia.

“This is the key barrier to getting new AI chip technology into the market.”

Omdia predicts that at least one major startup will exit the market this year, likely through a sale to a major chipmaker or a hyperscale cloud provider.

“The most likely exit route is probably via trade sales to major vendors,” adds Harrowell.

“Apple has $23 billion in cash on its balance sheet and Amazon $35 billion, while Intel, NVIDIA, and AMD have some $10 billion between them. The hyperscalers have been very keen to adopt custom AI silicon and they can afford to maintain the skills involved.”

Over half of the $6 billion invested in AI chip startups have focused on large-die, CGRA  accelerators that are designed with the aim of loading entire AI models on-chip. That approach is now being questioned due to the continuing growth of AI models.

“In 2018 and 2019, the idea of bringing the entire model into on-chip memory made sense, as this approach offers extremely low latency and answers the input/output problems of large AI models,” explains Harrowell.

“However, the models have continued to grow dramatically ever since, making scalability a critical issue. More structured and internally complex models mean AI processors must offer more general-purpose programmability. As such, the future of AI processors may lie in a different direction.”

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In an SEC filing, NVIDIA says the US government has informed the chipmaker of a new license requirement that impacts two of its GPUs designed to speed up machine learning tasks: the current A100, and the upcoming H100.

“The license requirement also includes any future NVIDIA integrated circuit achieving both peak performance and chip-to-chip I/O performance equal to or greater than thresholds that are roughly equivalent to the A100, as well as any system that includes those circuits,” adds NVIDIA.

The US government has reportedly told NVIDIA that the new rules are geared at addressing the risk of the affected products being used for military purposes.

“While we are not in a position to outline specific policy changes at this time, we are taking a comprehensive approach to implement additional actions necessary related to technologies, end-uses, and end-users to protect US national security and foreign policy interests,” said a US Department of Commerce spokesperson.

China is a large market for NVIDIA and the new rules could affect around $400 million in quarterly sales.

AMD has also been told the new rules will impact its similar products, including the MI200.

As of writing, NVIDIA’s shares were down 11.45 percent from the market open. AMD’s shares are down 6.81 percent. However, it’s worth noting that it’s been another red day for the wider stock market.

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The Chinese venture of the British semiconductor icon began operating as an independent company and conducted its own in-house R&D to create new IP. Dylan Patel, Chief Analyst at SemiAnalysis, even penned a piece titled: ‘The Semiconductor Heist Of The Century – Arm China Has Gone Completely Rogue’.

Arm-owner SoftBank sold 51 percent of its stake in the Chinese venture, Arm Limited, to a consortium of Chinese investors for $775 million. With its remaining stake, SoftBank no longer had a majority to make any major decisions.

Arm China fired its CEO, Allen Wu, in June 2020 after he was accused of offering discounts to customers if they invested in his side hustle, Alphatecture. However, Wu refused to leave arguing that: “Arm China did not convene any valid board meeting”.

What followed was lawsuits to oust Wu from his post. In the meantime, Wu reportedly got rid of staff loyal to Arm from Arm China and even employed security guards in a bid to keep out unwanted guests to retain his position.

However, Nikkei and Reuters have reported that Wu has now been removed.

SoftBank will be pleased with the news as the certainty it provides will make it easier for the company to launch an IPO of Arm.

Arm is set to launch an IPO after the collapse of a $40 billion acquisition offer from Nvidia. The deal collapsed following scrutiny from numerous global regulators that were concerned Nvidia could limit rivals’ access to Arm’s chip designs or shift resources towards areas that benefit its new owner.

SoftBank considered and subsequently rejected the idea of pursuing an IPO (Initial Public Offering) of the company in 2019 and again in early 2020.

“We contemplated an IPO but determined that the pressure to deliver short-term revenue growth and profitability would suffocate our ability to invest, expand, move fast, and innovate,” explained Simon Segars, CEO of Arm, in January.

The company’s hand is now being somewhat forced through a lack of alternative options.

Arm has struggled from relatively flat revenues and rising costs despite the huge success of the company’s licensees such as Apple, Qualcomm, and Amazon. However, SoftBank has been keen to hype the company’s future prospects.

“Arm is becoming a centre of innovation not only in the mobile phone revolution, but also in cloud computing, automotive, the Internet of Things, and the metaverse, and has entered its second growth phase,” said Masayoshi Son, Representative Director, Corporate Officer, Chairman, and CEO of SoftBank Group.

In March, Arm announced that it was cutting up to 1,000 jobs from its global workforce. The move was seen as a bid to show potential investors that it’s running a leaner operation.

“To stay competitive, we need to remove duplication of work now that we are one Arm; stop work that is no longer critical to our future success; and think about how we get work done,” wrote Arm CEO Rene Haas in an email to staff.

Haas, the former head of Arm’s intellectual property unit, recently took over as the company’s chief executive as part of its internal strategy shakeup to help navigate it through these choppy waters.

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