That question helps explain why AstraZeneca is bringing Modella AI in-house. The company has agreed to acquire the Boston-based AI firm as it looks to deepen its use of AI across oncology research and clinical development. Financial terms were not disclosed.

Rather than treating AI as a supporting tool, AstraZeneca is pulling Modella’s models, data, and staff directly into its research organisation. The move reflects a broader shift in the drug industry, where partnerships are giving way to acquisitions as companies try to gain more control over how AI is built, tested, and used in regulated settings.

Why AI ownership is starting to matter in drug research

Modella AI focuses on using computers to analyse pathology data, such as biopsy images, and link those findings with clinical information. Its work centres on making pathology more quantitative, helping researchers spot patterns that may point to useful biomarkers or guide treatment choices.

In a statement, Modella said its foundation models and AI agents would be integrated into AstraZeneca’s oncology research and development work, with a focus on clinical development and biomarker discovery.

How AstraZeneca moved its AI partnership toward full integration

For AstraZeneca, the deal builds on a collaboration that began several years ago. That earlier partnership allowed both sides to test whether Modella’s tools could work within the drugmaker’s research environment. According to AstraZeneca executives, the experience made it clear that closer integration was needed.

Speaking at the J.P. Morgan Healthcare Conference, AstraZeneca Chief Financial Officer Aradhana Sarin described the acquisition as a way to bring more data and AI capability inside the company.

“Oncology drug development is becoming more complex, more data-rich and more time-sensitive,” said Gabi Raia, Modella AI’s chief commercial officer, adding that joining AstraZeneca would allow the company to deploy its tools across global trials and clinical settings.

Using AI to improve trial decisions

Sarin said the deal would “supercharge” AstraZeneca’s work in quantitative pathology and biomarker discovery by combining data, models, and teams under one roof. While such language reflects ambition, the practical goal is more grounded: shortening the time it takes to turn research data into decisions that affect trial design and patient selection.

One area where AstraZeneca expects AI to have an impact is in choosing patients for clinical trials. Better matching patients to studies could improve trial outcomes and reduce costs tied to delays or failed studies.

That kind of improvement depends less on complex algorithms and more on steady access to clean data and tools that fit into existing workflows.

Talent and tools move in-house

The acquisition also highlights a change in how large pharmaceutical firms think about AI talent. Rather than relying on outside vendors, companies are increasingly treating data scientists and machine learning experts as part of their core research teams. For AstraZeneca, bringing Modella’s staff in-house reduces dependence on external roadmaps and gives the company more say over how tools are adapted as research needs change.

AstraZeneca said this is the first time a major pharmaceutical company has acquired an AI firm outright, though collaborations between drugmakers and technology companies have become common.

AstraZeneca joins a crowded field of pharma–AI deals

At the same healthcare conference, several new partnerships were announced, including a $1 billion collaboration between Nvidia and Eli Lilly to build a new research lab using Nvidia’s latest AI chips.

Those deals point to growing interest in AI across the sector, but they also underline a key difference in strategy. Partnerships can speed up experimentation, while acquisitions suggest a longer-term bet on building internal capability. For companies operating under strict regulatory rules, that control can matter as much as raw computing power.

What AstraZeneca is betting on next

Sarin described the earlier AstraZeneca–Modella partnership as a “test drive,” saying the company ultimately wanted Modella’s data, models, and people inside the organisation. The aim, she said, is to support the development of “highly targeted biomarkers and then highly targeted therapeutics.”

Beyond the Modella deal, Sarin said 2026 is expected to be a busy year for AstraZeneca, with several late-stage trial results due across different therapy areas. The company is also working toward a target of $80 billion in annual revenue by 2030.

Whether acquisitions like this help meet those goals will depend on execution. Integrating AI into drug development is slow, expensive, and often messy. Still, AstraZeneca’s move signals a clear view of where it thinks the value lies: not in buying AI as a service, but in embedding it deeply into how medicines are discovered and tested.

(Photo by Mika Baumeister)

See also: Allister Frost: Tackling workforce anxiety for AI integration success

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Grab operates at a scale where small efficiency gains can have out-sized effects. Its platform supports millions of deliveries in Southeast Asia, many of them carried out by riders on scooters and bicycles in dense urban areas, producing complexity that limits how much automation could replace human labour. By acquiring a company focused on robots designed for unstructured settings, Grab sees physical-world AI as mature enough to use in cases outside pilot programmes.

Delivery automation close to core operations

Rather than relying on off-the-shelf systems, Grab is opting to internalise the development loop. Infermove’s technology is designed to learn from real-world movement data, including information generated by non-motorised delivery vehicles. In practical terms, that means robots trained on how people actually navigate pavements, crossings, and crowded drop-off points, rather than how those spaces appear in simulations.

For a delivery operator like Grab, that distinction matters. Simulated environments can support early development, but they often struggle with the edge cases that define real cities. Bringing that learning process in-house allows Grab to shape how automation behaves under its own operating constraints, rather than adapting its delivery network to fit a third-party system.

From an enterprise perspective, the strategic value lies in control. Owning the technology gives Grab more influence over deployment pace, operating scope, and cost trade-offs. It also reduces long-term dependence on vendors whose priorities may not match Grab’s regional footprint or economic realities.

Automation, however, is not positioned as a replacement for human riders. Even as robots take on parts of the workflow, people remain central to service delivery. Grab’s interest appears focused on selective use, like structured first-mile or last-mile segments where tasks are repetitive and distances are short. In these areas, robots may help smooth demand spikes, reduce delays during peak hours, and ease pressure during labour shortages.

Managing cost pressure without breaking service

During an internal meeting in December, Grab’s chief technology officer Suthen Thomas described Infermove’s progress as “impressive,” highlighting both the technology and its early commercial use. He also said the company would continue to operate independently, with its founder reporting directly to him. The structure suggests Grab is prioritising execution and continuity rather than rapid organisational integration.

The approach reflects a broader shift among large digital platforms. Instead of treating AI as a layer added on top of existing systems, companies are embedding it deeper into core operations. In delivery and logistics, that often means moving beyond optimisation software into physical automation, where the risks and costs are higher but the potential gains are more structural.

The timing is also telling. On-demand delivery volumes continue to grow, but margins remain under pressure. Customers expect faster service and lower fees, while operators face rising wages, fuel costs, and tighter regulation. In that environment, automation becomes less about novelty and more about sustaining service levels without eroding profitability.

Bringing robotics development closer to operations may also help align incentives around data use. Training physical AI systems requires large amounts of real-world data, which delivery platforms already generate at scale. Keeping that feedback loop internal can speed iteration and reduce the need to share sensitive operational data externally.

There are still limits. Robots designed for pavements and short routes are unlikely to replace human couriers in an entire network anytime soon. Weather, local rules, and customer acceptance will continue to shape where automation can realistically operate. Expanding in multiple countries adds further complexity, as infrastructure and regulations vary widely.

Industry forecasts suggest rapid growth in last-mile delivery robotics, but those figures offer limited guidance for operators. The more immediate question is whether automation can lower cost per delivery without introducing new failure points. That depends less on market size and more on performance in live environments.

Seen through an enterprise lens, the acquisition of Infermove is not a bet on robotics as a product category. It is a move to tighten the link between AI, data, and physical operations. For platform companies built on logistics and mobility, that integration may become a key factor in managing growth under sustained cost pressure.

(Photo by Afif Ramdhasuma)

See also: The Law Society: Current laws are fit for the AI era

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Bloomberg says Apple will run the custom model on its Private Cloud Compute servers, while still relying on its own systems for some parts of Siri. Gurman reports that the Gemini model uses 1.2 trillion parameters, far more than the 150 billion parameters behind the current cloud-based version of Apple Intelligence.

Apple is preparing to spend about $1 billion each year on a powerful Google-built artificial intelligence model with 1.2 trillion parameters, according to people familiar with the matter, as reported by Bloomberg. The system is expected to play a central role in a major update to Siri, a project the company has been working toward for years.

After months of testing, Apple and Google are close to a deal that would give Apple access to the technology. The people discussing the plans asked not to be named because the talks are private.

Apple is turning to Google to help rebuild Siri’s core technology, laying the groundwork for a broad refresh of features planned for next year. The size of Google’s model would far exceed the AI systems Apple uses today.

Apple tested other outside options — including Google’s Gemini, OpenAI’s ChatGPT, and Anthropic’s Claude — before deciding to move forward with Google earlier this year. The goal is to rely on Gemini as a temporary solution until Apple’s own work reaches the same level.

The updated Siri is planned for release next spring, Bloomberg reported. Because months remain before launch, parts of the plan could still change. Apple and Google declined to comment.

Shares of both companies briefly rose after the news surfaced Wednesday. Apple’s stock gained less than 1% to $271.70, while Alphabet climbed as much as 3.2% to $286.42.

The custom Gemini model would be a major jump from the 150 billion parameter system Apple currently uses in the cloud for Apple Intelligence. The move is meant to increase Siri’s ability to process complex tasks and understand context at a deeper level.

The work is known internally as Glenwood and is led by Vision Pro headset creator Mike Rockwell and software chief Craig Federighi. The refreshed voice assistant, set to appear in iOS 26.4, is code-named Linwood.

Under the deal, Google’s model will support Siri’s summarizer and planner features — the parts that help the assistant understand information and decide on action steps. Apple’s own models will still handle some tools and responses.

The model will run on Apple’s Private Cloud Compute servers, keeping user data isolated from Google’s systems. Apple already set aside server hardware for the effort to support Siri’s new features.

Although the partnership is large, Apple is not expected to promote it to consumers. Google will act as a quiet technology provider, unlike the visible search agreement inside Safari. Siri’s improvements will likely appear without Google branding.

This deal is separate from earlier talks about placing Gemini directly inside Siri as a chatbot. Those conversations nearly turned into a product in both 2024 and again earlier this year, but never moved forward. The new agreement also does not place Google AI search features inside Apple’s operating systems, leaving Siri’s search behavior unchanged.

During Apple’s most recent earnings call, Chief Executive Officer Tim Cook said Siri may add more chatbot options in the future, beyond the current ChatGPT choice. Apple continues to look for ways to expand Siri without relying on one provider.

Other companies are also adopting Gemini. Snap and several major firms are building products using Google’s Vertex AI platform. For Apple, the move reflects how far behind it has fallen in AI — and how willing the company is to use outside tools to improve Siri.

Even so, Apple does not plan to use Gemini forever. The company has lost AI engineers in recent years, including the head of its models team, but Apple’s leadership still wants to develop its own technology and eventually replace Google’s system inside Siri.

Apple’s internal team is building its own cloud-based model with up to 1 trillion parameters, which could be ready for consumer use as early as next year. That work is expected to support Siri’s growth in the long run.

Executives believe they can match Google’s quality over time. But Google continues to improve Gemini, making the gap harder to close. Its 2.5 Pro version ranks near the top of most large language model comparisons, which affects how Apple plans Siri’s updates.

Apple also wants to bring Apple Intelligence and the updated Siri to China. Because Google services are banned in the country, the system used there will not rely on Gemini.

Instead, Apple plans to use its own models along with a content filter built by Alibaba Group Holding Ltd. That tool would adjust responses to meet government requirements. Apple has also explored a partnership with Baidu Inc. for AI features in the Chinese market, Bloomberg reported earlier this year.

(Photo by omid armin)

See also: Inside Tim Cook’s push to get Apple back in the AI race

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By acquiring AOL from Yahoo, Bending Spoons moves to consolidate high-retention consumer technologies in its expanding digital portfolio. As companies turn increasingly to synthetic data to feed their AI’s learning corpus, the deal shows a different tactic, one of using established data assets and user bases to accelerate AI personalisation, advertising efficiency, and digital identity information gathering. It illustrates how older platforms – perhaps written off as legacy – can become profitable fuel for innovation when combined with cloud-native architectures and machine learning models.

Bending Spoons has financed its expansion strategy with a $2.8 billion debt package from major global banks that include J.P. Morgan, BNP Paribas, and HSBC. There’s clearly growing lender confidence in the long-term monetisation of data, unlike during the ‘dot.com’ boom and bust, where the emphasis and interest was in purely software products. The acquisition, expected to close by year-end, follows Bending Spoons’ planned purchase of Vimeo. The two deals, if they go through, position the company as a major consolidator of internet assets.

Implementation and operational challenges

Integrating decades-old infrastructure like AOL’s presents technical challenges. Data migration from legacy email systems in line with current-day security protocols and compliance requirements needs careful stewardship. There’s also the not-insignificant issue of retraining staff for AI data stewardship on data that comes with significant buy-in from trusting service users. As with any digital acquisition, therefore, Bending Spoons’ success will depend on managing the technical and cultural dimensions of integration. Without strong governance, promising legacy platforms risk becoming compliance liabilities.

Early in any acquisition cycle, there will have been preparatory work in mapping data lineage, running integration and interoperability audits, and significant governance discussions. It’s worth noting that many integration pilots stall without shared accountability between technology and business functions: It’s easier to covet data than to work out how it can be put to business use, especially when the best an acquirer can hope for are limited examples of what they might get, once the ink has dried on the cheque.

Vendor and ecosystem context

Although Bending Spoons operates independently of major enterprise AI ecosystems, the logic of its acquisition aligns with Microsoft’s integration of LinkedIn data into Azure AI Foundry, and IBM’s efforts to reinvigorate legacy data with watsonx. AOL’s customer base and behavioural data could feasibly hold value with cloud analytics, customer profiling, and identity management frameworks, on even off-the-shelf platforms like AWS Bedrock, Azure, or Google Vertex AI.

Executive takeaway

Legacy platforms are not obsolete but they are often underused and undervalued. The differentiator lies in how organisations integrate historical data into modern AI governance and value delivery. Executives may see the AOL acquisition as a nostalgia play, but it’s a more hard-nosed imagining of a pure data asset. Perhaps the next wave of competitive advantage may come not from building new systems, but from reinterpreting older software and information that’s sometimes disregarded, simply because it’s not the latest-and-greatest ‘thing.’

(Image source: “Spoon” by felixtsao is licensed under CC BY 2.0.)

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The two machines represent about $1 billion in public and private investment. Once complete, they will form part of a secure national computing network designed to support AI research using standards-based infrastructure built in the US. The project reflects how a coordinated AI strategy can align national goals in innovation, energy efficiency, and data governance.

Dr Lisa Su, AMD’s chair and CEO, said the company is “proud and honoured to partner with the Department of Energy and Oak Ridge National Laboratory to accelerate America’s foundation for science and innovation.” She added that the systems “will leverage AMD’s high-performance and AI computing technologies to advance the most critical US research priorities in science, energy, and medicine.”

Lux AI: Training the next wave of AI models

Set to go live in early 2026, Lux AI will be the country’s first “AI Factory” — a facility built to train and deploy advanced AI models for science, energy, and security. The system is being developed with ORNL, AMD, Oracle Cloud Infrastructure, and Hewlett Packard Enterprise.

Lux will use AMD Instinct MI355X GPUs, EPYC CPUs, and Pensando networking to handle data-heavy AI tasks. It’s designed to speed up research in areas such as energy systems, materials, and medicine. The system’s architecture allows multiple groups to work together while keeping data secure and separate, a model that mirrors how many large organisations are starting to manage sensitive AI workloads.

Discovery: Strengthening America’s AI and supercomputing strategy

The Discovery system will follow in 2028 and become the DOE’s next flagship supercomputer at Oak Ridge. It will use AMD’s upcoming “Venice” EPYC processors and MI430X GPUs, which are part of a new series built for AI and scientific computing.

Discovery’s “Bandwidth Everywhere” design increases memory and network performance without using more power. This means it can process more data and run complex models efficiently while maintaining energy costs — a challenge many large data centres also face today.

The system builds on lessons from Frontier, the world’s first exascale computer, ensuring that existing applications can move easily to the new platform.

U.S. Energy Secretary Chris Wright said, “Winning the AI race requires new and creative partnerships that will bring together the brightest minds and industries American technology and science has to offer.” He said the new systems show “a commonsense approach to computing partnerships” that strengthen the country through shared innovation.

ORNL Director Stephen Streiffer said Discovery will “drive scientific innovation faster and farther than ever before,” adding that combining high-performance computing and AI can shorten the time between research problems and real-world solutions.

Partnerships driving AI innovation and long-term strategy

AMD, HPE, and Oracle each play key roles in building and supporting the systems. Antonio Neri, HPE’s president and CEO, said the collaboration will help Oak Ridge reach “unprecedented productivity and scale.” Oracle’s executive vice president Mahesh Thiagarajan said the company is working with DOE to “deliver sovereign, high-performance AI infrastructure that will support the co-development of the Lux AI cluster.”

When operational, Lux and Discovery will help the DOE run large-scale AI models to improve understanding in energy, biology, materials science, and national defence. Discovery will also help design next-generation batteries, reactors, semiconductors, and critical materials.

What it means for enterprise leaders

For organisations, these systems highlight how AI strategy and HPC can deliver faster research, improved efficiency, and secure data management. They also show that performance gains don’t have to come at the cost of higher energy use.

The DOE’s partnerships with technology providers reflect a model that private enterprises may follow — combining expertise across sectors to develop shared infrastructure while maintaining data control. As AI workloads grow, both public and private organisations will need to build systems that balance power, performance, and governance.

The Lux and Discovery projects show how that balance might look in practice: open, collaborative, and built to support discovery at scale — a lesson in how a forward-thinking AI strategy can turn infrastructure into long-term competitive advantage.

(Photo by Syed Ali)

See also: How to fix the AI trust gap in your business

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 part of TechEx and is co-located with other leading technology events, click here for more information.

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To support the rollout, Nvidia intends to invest up to $100 billion in OpenAI as the systems are deployed. The first phase is scheduled to go live in the second half of 2026, powered by Nvidia’s upcoming Vera Rubin platform.

A deal with wide implications

The agreement shows how closely tied the largest AI players are becoming. Nvidia, the main supplier of AI chips, would gain a financial stake in OpenAI, one of its biggest customers. For OpenAI, the deal brings both funding and guaranteed access to Nvidia’s sought-after processors.

The move could unsettle rivals. Some may see it as reinforcing Nvidia’s dominance in chips and OpenAI’s lead in AI software, raising questions about fair competition.

A person familiar with the matter said the partnership involves two linked steps: Nvidia will buy non-voting shares in OpenAI, and OpenAI will then use that money to purchase Nvidia chips.

OpenAI on why compute drives AI growth

“Everything starts with compute,” OpenAI CEO Sam Altman said in a statement. “Compute infrastructure will be the basis for the economy of the future, and we will utilise what we’re building with Nvidia to both create new AI breakthroughs and empower people and businesses with them at scale.”

The companies said details of the partnership will be settled in the coming weeks. They also noted that 10 gigawatts of chips would consume as much power as more than 8 million US households.

Nvidia’s stock climbed as much as 4.4% to a record high after the news. Oracle, which is working with OpenAI, SoftBank, and Microsoft on a $500 billion global AI data centre project called Stargate, rose about 6%.

How the deal is structured

According to the person familiar with the talks, once a final agreement is reached, OpenAI will formally purchase Nvidia systems. Nvidia will then invest an initial $10 billion in OpenAI, which was last valued at $500 billion.

The first delivery of Nvidia hardware is expected in late 2026, with one gigawatt of computing power coming online in the second half of that year on the Vera Rubin platform.

Analysts welcomed the agreement but raised concerns about whether some of Nvidia’s investment could flow back to it through OpenAI’s chip purchases.

“On the one hand this helps OpenAI deliver on what are some very aspirational goals for compute infrastructure, and helps Nvidia ensure that that stuff gets built. On the other hand the ‘circular’ concerns have been raised in the past, and this will fuel them further,” said Stacy Rasgon, an analyst at Bernstein.

OpenAI’s other AI chip ambitions

OpenAI, like Google and Amazon, has been exploring its own custom chips to lower costs and reduce dependence on Nvidia. A person close to the company said this deal does not change its existing compute plans, including its collaboration with Microsoft.

Earlier this year, Reuters reported that OpenAI was working with Broadcom and Taiwan Semiconductor Manufacturing Co. to design chips. Following news of the Nvidia partnership, Broadcom shares slipped 0.8%.

OpenAI has grown to more than 700 million weekly active users, with adoption across businesses of all sizes and by developers worldwide. The Nvidia partnership is expected to help the company push forward on its goal of building artificial general intelligence.

Industry backdrop

The OpenAI-Nvidia pact adds to a growing list of alliances among tech giants. Microsoft has invested billions in OpenAI since 2019. Nvidia recently announced a chip collaboration with Intel and pledged $5 billion in funding. Nvidia also took part in OpenAI’s $6.6 billion round in October 2024.

The size of the new deal could draw antitrust attention. Last year, the Justice Department and Federal Trade Commission reached an agreement to allow closer scrutiny of Microsoft, OpenAI, and Nvidia’s roles in the AI sector. So far, the Trump administration has taken a lighter approach than the Biden administration on competition issues.

OpenAI and Microsoft also said earlier this month they had signed a non-binding agreement to restructure OpenAI into a for-profit company, signalling further governance changes.

Antitrust lawyer Andre Barlow from Doyle, Barlow & Mazard said the Nvidia deal may reinforce both companies’ positions in ways that limit rivals.

“The deal could change the economic incentives of Nvidia and OpenAI as it could potentially lock in Nvidia’s chip monopoly with OpenAI’s software lead. It could potentially make it more difficult for Nvidia competitors like AMD in chips or OpenAI’s competitors in models to scale,” Barlow said.

He added that the Trump administration has so far taken a pro-business approach to regulation, removing barriers that could slow AI growth.

(Image by Nvidia)

See also: Thinking Machines becomes OpenAI’s first services partner in APAC

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This strategic move aims to make generative AI accessible to organisations of all sizes, allowing them to develop, possess, and safeguard their own generative AI models using their own data. 

The acquisition, valued at ~$1.3 billion – inclusive of retention packages – showcases Databricks’ commitment to democratising AI and reinforcing the company’s Lakehouse platform as a leading environment for building generative AI and LLMs.

Naveen Rao, Co-Founder and CEO at MosaicML, said:

“At MosaicML, we believe in a world where everyone is empowered to build and train their own models, imbued with their own opinions and viewpoints — and joining forces with Databricks will help us make that belief a reality.

We started MosaicML to solve the hard engineering and research problems necessary to make large-scale training more accessible to everyone. With the recent generative AI wave, this mission has taken centre stage.

Together with Databricks, we will tip the scales in the favour of many — and we’ll do it as kindred spirits: researchers turned entrepreneurs sharing a similar mission. We look forward to continuing this journey together with the AI community.”

MosaicML has gained recognition for its cutting-edge MPT large language models, with millions of downloads for MPT-7B and the recent release of MPT-30B.

The platform has demonstrated how organisations can swiftly construct and train their own state-of-the-art models cost-effectively by utilising their own data. Esteemed customers like AI2, Generally Intelligent, Hippocratic AI, Replit, and Scatter Labs have leveraged MosaicML for a diverse range of generative AI applications.

The primary objective of this acquisition is to provide organisations with a simple and rapid method to develop, own, and secure their models. By combining the capabilities of Databricks’ Lakehouse Platform with MosaicML’s technology, customers can maintain control, security, and ownership of their valuable data without incurring exorbitant costs.

MosaicML’s automatic optimisation of model training enables 2x-7x faster training compared to standard approaches, and the near linear scaling of resources allows for the training of multi-billion-parameter models within hours. Consequently, Databricks and MosaicML aim to reduce the cost of training and utilising LLMs from millions to thousands of dollars.

The integration of Databricks’ unified Data and AI platform with MosaicML’s generative AI training capabilities will result in a robust and flexible platform capable of serving the largest organisations and addressing various AI use cases.

Upon the completion of the transaction, the entire MosaicML team – including its renowned research team – is expected to join Databricks.

MosaicML’s machine learning and neural networks experts are at the forefront of AI research, striving to enhance model training efficiency. They have contributed to popular open-source foundational models like MPT-30B, as well as the training algorithms powering MosaicML’s products.

The MosaicML platform will be progressively supported, scaled, and integrated to provide customers with a seamless unified platform where they can build, own, and secure their generative AI models. The partnership between Databricks and MosaicML empowers customers with the freedom to construct their own models, train them using their unique data, and develop differentiating intellectual property for their businesses.

The completion of the proposed acquisition is subject to customary closing conditions, including regulatory clearances.

(Photo by Glen Carrie on Unsplash)

See also: MosaicML’s latest models outperform GPT-3 with just 30B parameters

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The deal caught the attention of global regulators with anti-competition investigations launched in several jurisdictions including the UK, EU, and US.

In November 2021, UK Digital Secretary Nadine Dorries decided to block the merger pending the results of a 24-week ‘Phase 2’ investigation.

With the merger looking almost impossible to be approved by regulators, Nvidia has decided to throw in the towel.

Jensen Huang, Founder and CEO of Nvidia, said:

“Arm has a bright future, and we’ll continue to support them as a proud licensee for decades to come.

Arm is at the centre of the important dynamics in computing. Though we won’t be one company, we will partner closely with Arm.

The significant investments that Masa has made have positioned Arm to expand the reach of the Arm CPU beyond client computing to supercomputing, cloud, AI, and robotics.

I expect Arm to be the most important CPU architecture of the next decade.”

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.

SoftBank, Arm’s current owner, 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, last month.

Following the collapse of the Nvidia acquisition, Softbank will now have to reconsider an IPO for Arm.

Dr Lil Read, Analyst in the Thematic Research Team at GlobalData, commented:

“Softbank now needs to think of Arm’s future. An initial public offering (IPO) looks likely – the UK government would surely like to see the home-grown chip designer float in London, and potential IPO reforms could create the perfect environment for this. 

Otherwise, Arm may be ripe for a takeover by a private equity consortium backed by chip-friendly giants such as Apple, Qualcomm, and TSMC – Arm’s largest customers.”

Some of Nvidia’s rivals are said to have offered to invest in Arm if it helps the company to remain independent. A takeover from a private equity consortium looks to be Arm’s best option. If the company has to launch an IPO, it could struggle and will face some difficult choices.

Arm’s largest market, mobile, is saturated. The company will struggle to crack the datacentre and PC markets in the face of strong incumbents like Intel and AMD that have established ecosystem of developers, software, systems, and peripherals, and profits that enable them to make large R&D investments.

In an earlier response to the UK’s Competition and Markets Authority, aiming to quell the regulator’s fears about its acquisition of Arm, Nvidia wrote:

“Nvidia is particularly concerned that these pressures would drive Arm to deprioritize datacenter and PC and to instead focus on its core mobile and growing IoT businesses.

The result would be a concentrated CPU market largely controlled by Intel/AMD (x86).”

Capital markets would likely expect Arm to cut costs to maximise the company’s value. However, SoftBank sounds bullish on its 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.

Arm has announced a management shake-up in the wake of Nvidia’s exit from the deal.

Rene Haas, the former head of Arm’s intellectual property unit, will take over as the company’s chief executive and lead it during these challenging times. Haas previously worked at Nvidia for seven years.

With the Nvidia acquisition off the table, we can only hope that Haas finds a way to ensure Arm can continue to deliver the semiconductor innovation that it has for three decades.

(Photo by Dustin Tramel on Unsplash)

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Nvidia announced plans to acquire Cambridge-based Arm back in September 2020 in a deal worth $40 billion. As two of the biggest names in chip manufacturing, the deal naturally caught the attention of competition regulators around the world.

In November 2021, UK Digital Secretary Nadine Dorries decided to block the merger pending the results of a 24-week ‘Phase 2’ investigation.

Wouldn’t an IPO be an alternative to all of this?

Arm has been struggling from relatively flat revenues and rising costs despite the huge success of the company’s licensees such as Apple, Qualcomm, and Amazon. Arm’s current 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.

Simon Segars, CEO of Arm, explained: “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.”

In addition, Arm’s largest market, mobile, is saturated and it could be difficult for Arm to crack the datacentre and PC markets in the face of strong incumbents like Intel and AMD that have established ecosystem of developers, software, systems, and peripherals, and profits that enable them to make large R&D investments.

“These observations are not criticisms of Arm’s technology or engineering team. Arm has great engineering talent in the areas where it focuses. But as a standalone IP licensing business, and without access to further capital, Arm has inherent scale, scope, and economic limitations that would impact Arm’s future as a standalone licensing firm,” reads the response.

Addressing competition concerns

Nvidia believes that its acquisition would provide Arm with the resources to move forward in the face of limited options. Capital markets. meanwhile, would expect Arm to cut costs to maximise the company’s value.

“Nvidia is particularly concerned that these pressures would drive Arm to deprioritize datacenter and PC and to instead focus on its core mobile and growing IoT businesses. The result would be a concentrated CPU market largely controlled by Intel/AMD (x86),” the response continues.

The company goes on to say that soaring profits for Arm’s customers are not a win for the company or for competition. Nvidia argues the “industry titans will be powerful and competitive, no matter what path Arm takes” and “the question at hand is whether regulators will approve the transaction and allow Arm to take the steps needed to enable others to compete.”

Nvidia highlights that it was SoftBank that approached the company about the potential of acquiring Arm rather than the other way around. Nvidia says it will continue to support x86 as it has a vested interest – building platforms such as Omniverse on such systems – but thinks that it can help Arm build a viable alternative ecosystem that will increase options and encourage the x86 giants to innovate.

Arm’s future as a British tech icon

From the UK’s perspective, another major concern was the impact on jobs and even the company’s future in the country. Hermann Hauser, the founder of Arm, once suggested the acquisition would be “surrendering the UK’s most powerful trade weapon to the US”.

In a binding offer, Nvidia committed to expanding Arm’s engineering teams in the UK—including a pledge to create a new group dedicated to creating purpose-built CPU IP for datacentres and PCs.

Nvidia chose to build its Cambridge-1 supercomputer in the UK, which was seen by many as a bid to show its commitment to the country. The firm also opened a new AI centre in Cambridge—home to an increasing number of exciting startups in the field such as FiveAI, Prowler.io, Fetch.ai, and Darktrace.

“We will create an open centre of excellence in the area once home to giants like Isaac Newton and Alan Turing, for whom key Nvidia technologies are named,” said Nvidia CEO Jensen Huang at the time.

“Here, leading scientists, engineers and researchers from the UK and around the world will come to develop their ideas, collaborate and conduct their ground-breaking work in areas like healthcare, life sciences, self-driving cars, and other fields.”

No incentive to foreclose

The final major concern around the acquisition is that the merged entities will refuse or restrict the licensing of future IP to give Nvidia a competitive advantage.

Nvidia points to the emergence of RISC-V and other alternatives to Arm that could become preferable “if the Merged Entity were to refuse to license future datacenter IP as soon as it can”. However, it says that would be many years down the road as “customers already have the IP in their possession, and it will be many years before their contracts are up for renewal”.

Furthermore, Nvidia says that it would have no economic incentive to foreclose:

“NVIDIA knows that such a strategy would be self-defeating, and has no incentive to pursue it. The Decision does not explain why any downstream customer would embrace a sole-source ecosystem. Even x86 has always had two bona fide suppliers (Intel and AMD), and now, x86 is licensable to anyone.”

It also says that Arm’s customers are Nvidia’s customers and any attempt to foreclose would damage its own business and reputation.

While regulators have been mulling whether or not to approve the deal, Nvidia says that Arm’s competition has been exploiting the delay to continue expanding their offerings.

Nvidia makes some fairly solid arguments in its response, but whether they’re enough to woo regulators is another question. It’s not just the UK’s regulator examining the deal, but also respective agencies from the US, China, and EU.

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Nuance gained renown for originally creating the backend of that little old virtual assistant called Siri (you might have heard of it?)

The company has since continued to focus on building its speech recognition capabilities and has a number of solutions which span particular industries such as healthcare to general omni-channel customer experience services.

Earlier this year, Microsoft decided Nuance is worth coughing up $19.7 billion for.

As such large deals often do, the proposed acquisition caught the eyes of several global regulators. In the case of the EU, it was referred to the Commission’s regulators on 16 November.

The regulator said on Tuesday that the proposed acquisition “would raise no competition concerns” within the bloc and that “Microsoft and Nuance offer very different products” after looking at potential horizontal overlaps between the companies’ transcription solutions.

Vertical links in the healthcare space were also analysed but it was determined that “competing transcription service providers in healthcare do not depend on Microsoft for cloud computing services” and that “transcription service providers in the healthcare sector are not particularly important users of cloud computing services”.

Furthermore, the regulator concluded:

• Microsoft-Nuance will continue to face stiff competition from rivals in the future.
• There’d be no ability/incentive to foreclose existing market solutions.
• Nuance can only use the data it collects for its own services.
• The data will not provide Microsoft with an advantage to shut out competing software providers.

The EU’s decision mirrors that of regulators in the US and Australia. However, the UK’s Competition and Markets Authority (CMA) announced its own investigation earlier this month.

When it announced the deal, Microsoft said that it aims to complete its acquisition by the end of 2021. The CMA is accepting comments until 10 January 2022 so it seems that Microsoft may have to hold out a bit longer.

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Arm plays a critical role in the global technology supply chain with its designs used for edge AI chips and processors for smartphones, tablets, desktops, and servers.

It’s of little surprise that Nvidia wants to bring Arm under its wing and is willing to pay $40 billion (£29 billion) for it.

Global regulators, including in the UK and EU, have launched investigations into the deal due to the widespread implications.

Holly Vedova, Director of the Bureau of Competition at the FTC, said in a statement:

“The FTC is suing to block the largest semiconductor chip merger in history to prevent a chip conglomerate from stifling the innovation pipeline for next-generation technologies.

Tomorrow’s technologies depend on preserving today’s competitive, cutting-edge chip markets. This proposed deal would distort Arm’s incentives in chip markets and allow the combined firm to unfairly undermine Nvidia’s rivals.

The FTC’s lawsuit should send a strong signal that we will act aggressively to protect our critical infrastructure markets from illegal vertical mergers that have far-reaching and damaging effects on future innovations.”

The complaint highlights that Nvidia already uses Arm’s designs for areas including DPU SmartNICs, CPUs for cloud computing, and advanced driving systems. The FTC is concerned that Nvidia would have an incentive to use its acquisition of Arm to limit competitors’ access to new designs.

Some of Nvidia’s rivals have offered to invest in Arm if it helps the company to remain independent.

Dr Lil Read, Analyst at GlobalData, commented:

“The Nvidia-ARM deal is on its last legs. The regulatory environment is much tougher now since Qualcomm has formed a consortium to invest in ARM.

The FTC won’t let it be – nor will the UK CMA or the EU regulator. It’s likely that even if the deal managed to clear those hurdles, Chinese regulators would throw another spanner in the works.

Tying the acquisition up for another two years is not in anyone’s interest – not Nvidia’s, and certainly not ARM’s. There could be hope for ARM if a non-chip firm recognises this opportunity for vertical integration – a trend that we increasingly see with the likes of Tesla and Apple.”

Arm founder Hermann Hauser even suggested the merger would amount to “surrendering the UK’s most powerful trade weapon to the US”.

Last month, UK Digital Secretary Nadine Dorries ordered the CMA (Competition & Markets Authority) to launch a “Phase Two” probe into the proposed merger.

As part of its ‘Phase One’ report, the CMA determined the merger has the possibility of a “substantial lessening of competition across four key markets”. Those markets are data centres, the Internet of Things, automotive, and gaming.

The CMA now has 24 weeks to conduct Phase Two of its investigation.

Nvidia, for its part, has promised to work with UK regulators to alleviate concerns. The company has already pledged to keep Arm in the UK and hire more staff.

“Arm is an incredible company and it employs some of the greatest engineering minds in the world,” said Jensen Huang, CEO of Nvidia. “But we believe we can make Arm even more incredible and take it to even higher levels.”

Today’s decision by the FTC to launch a lawsuit makes the likelihood of the merger proceeding ever more remote.

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The CMA (Competition & Markets Authority) has been investigating whether the deal is anti-competitive. In August, it declared the deal does indeed raise “serious competition concerns”.

Arm founder Hermann Hauser went further and suggested the merger would amount to “surrendering the UK’s most powerful trade weapon to the US”.

Among the concerns are that Nvidia could limit competitors’ access to key technologies. The CMA claims to have received “a substantial number” of concerns from rivals and some have even offered to invest in Arm if it helps the company to remain independent.

Dorries has ‘quasi-judicial’ powers under the Enterprise Act 2002 to intervene in mergers on public interest grounds.

As part of its ‘Phase One’ report, the CMA determined the merger has the possibility of a “substantial lessening of competition across four key markets”. Those markets are data centres, Internet of Things, the automotive sector, and gaming.

Beyond the impact on competition, evidence provided from departments across government have also led the Secretary of State to deem that national security could be harmed from the merger and warrants further investigation.

“I have carefully considered the Competition and Market Authority’s ‘Phase One’ report into NVIDIA’s proposed takeover of Arm and have decided to ask them to undertake a further in-depth ‘Phase Two’ investigation,” commented Dorries.

“Arm has a unique place in the global technology supply chain and we must make sure the implications of this transaction are fully considered. The CMA will now report to me on competition and national security grounds and provide advice on the next steps.”

The CMA now has 24 weeks to conduct Phase Two of its investigation, although this could be extended by eight weeks if necessary. Upon receiving the report, the Digital Secretary could take action to remedy any adverse effects to the public interest or refer it back to the CMA.

David Bicknell, Principal Analyst, and Dr Lil Read, Analyst, on the Thematic Research Team at GlobalData, commented:

“This latest government probe is another nail in the coffin for the proposed merger between Nvidia and Arm. With proceedings likely to extend into late 2022 at the earliest, Nvidia should just abandon the deal and focus on its future away from Arm.

Ordering a security review signals that the UK government doesn’t want this bid to succeed. Couple this with an already underway EU investigation and the further prospect of a China referral, and Nvidia faces some tough questions. It is unlikely it will want the continued uncertainty of a bid that is going nowhere.

We think it is time for Nvidia to move on, and for Softbank to return Arm to where it found it—the stock market.

Nvidia’s future beyond Arm extends into the metaverse, a virtual world where users share experiences and interact in real-time within simulated scenarios. Nvidia is already targeting the metaverse as a key pillar of its future – a wise decision as GlobalData believes it will be the next big technology megatheme.”

Nvidia, for its part, has promised to work with UK regulators to alleviate concerns. The company has already pledged to keep Arm in the UK and hire more staff.

The company’s announcement of a new AI centre in Cambridge last year – which features an Arm/Nvidia-based supercomputer, set to be one of the most powerful in the world – was expected to be part of a bid to show UK regulators of the firm’s commitment to the country.

“Arm is an incredible company and it employs some of the greatest engineering minds in the world,” said Jensen Huang, CEO of Nvidia. “But we believe we can make Arm even more incredible and take it to even higher levels.”

“We want to propel it – and the UK – to global AI leadership.”

(Image Credit: UK Parliament under CC BY 3.0 license)

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