Over the past year, the tech giant says it has prevented $4 billion in fraud attempts, blocking approximately 1.6 million bot sign-up attempts every hour – showing the scale of this growing threat.

The ninth edition of Microsoft’s Cyber Signals report, titled “AI-powered deception: Emerging fraud threats and countermeasures,” reveals how artificial intelligence has lowered the technical barriers for cybercriminals, enabling even low-skilled actors to generate sophisticated scams with minimal effort.

What previously took scammers days or weeks to create can now be accomplished in minutes.

The democratisation of fraud capabilities represents a shift in the criminal landscape that affects consumers and businesses worldwide.

The evolution of AI-enhanced cyber scams

Microsoft’s report highlights how AI tools can now scan and scrape the web for company information, helping cybercriminals build detailed profiles of potential targets for highly-convincing social engineering attacks.

Bad actors can lure victims into complex fraud schemes using fake AI-enhanced product reviews and AI-generated storefronts, which come complete with fabricated business histories and customer testimonials.

According to Kelly Bissell, Corporate Vice President of Anti-Fraud and Product Abuse at Microsoft Security, the threat numbers continue to increase. “Cybercrime is a trillion-dollar problem, and it’s been going up every year for the past 30 years,” per the report.

“I think we have an opportunity today to adopt AI faster so we can detect and close the gap of exposure quickly. Now we have AI that can make a difference at scale and help us build security and fraud protections into our products much faster.”

The Microsoft anti-fraud team reports that AI-powered fraud attacks happen globally, with significant activity originating from China and Europe – particularly Germany, due to its status as one of the largest e-commerce markets in the European Union.

The report notes that the larger a digital marketplace is, the more likely a proportional degree of attempted fraud will occur.

E-commerce and employment scams leading

Two particularly concerning areas of AI-enhanced fraud include e-commerce and job recruitment scams.In the ecommerce space, fraudulent websites can now be created in minutes using AI tools with minimal technical knowledge.

Sites often mimic legitimate businesses, using AI-generated product descriptions, images, and customer reviews to fool consumers into believing they’re interacting with genuine merchants.

Adding another layer of deception, AI-powered customer service chatbots can interact convincingly with customers, delay chargebacks by stalling with scripted excuses, and manipulate complaints with AI-generated responses that make scam sites appear professional.

Job seekers are equally at risk. According to the report, generative AI has made it significantly easier for scammers to create fake listings on various employment platforms. Criminals generate fake profiles with stolen credentials, fake job postings with auto-generated descriptions, and AI-powered email campaigns to phish job seekers.

AI-powered interviews and automated emails enhance the credibility of these scams, making them harder to identify. “Fraudsters often ask for personal information, like resumes or even bank account details, under the guise of verifying the applicant’s information,” the report says.

Red flags include unsolicited job offers, requests for payment and communication through informal platforms like text messages or WhatsApp.

Microsoft’s countermeasures to AI fraud

To combat emerging threats, Microsoft says it has implemented a multi-pronged approach across its products and services. Microsoft Defender for Cloud provides threat protection for Azure resources, while Microsoft Edge, like many browsers, features website typo protection and domain impersonation protection. Edge is noted by the Microsoft report as using deep learning technology to help users avoid fraudulent websites.

The company has also enhanced Windows Quick Assist with warning messages to alert users about possible tech support scams before they grant access to someone claiming to be from IT support. Microsoft now blocks an average of 4,415 suspicious Quick Assist connection attempts daily.

Microsoft has also introduced a new fraud prevention policy as part of its Secure Future Initiative (SFI). As of January 2025, Microsoft product teams must perform fraud prevention assessments and implement fraud controls as part of their design process, ensuring products are “fraud-resistant by design.”

As AI-powered scams continue to evolve, consumer awareness remains important. Microsoft advises users to be cautious of urgency tactics, verify website legitimacy before making purchases, and never provide personal or financial information to unverified sources.

For enterprises, implementing multi-factor authentication and deploying deepfake-detection algorithms can help mitigate risk.

See also: Wozniak warns AI will power next-gen scams

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.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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In recent submissions to the US government, the companies warned that Chinese models, such as DeepSeek R1, are becoming more sophisticated and competitive. The submissions, filed in March 2025 in response to a request for input on an AI Action Plan, highlight the growing challenge from China in technological capability and price.

China’s growing AI presence

Chinese state-supported AI model DeepSeek R1 has piqued the interest of US developers. According to OpenAI, DeepSeek demonstrates that the technological gap between the US and China is narrowing. The company described DeepSeek as “state-subsidised, state-controlled, and freely available,” raises concerns about the model’s ability to influence global AI development.

OpenAI compared DeepSeek to Chinese telecommunications company Huawei, warning that Chinese regulations could allow the government to compel DeepSeek to compromise sensitive US systems or infrastructure. Concerns about data privacy were also raised, with OpenAI pointing out that Chinese rules could force DeepSeek to disclose user data to the government, and enhance China’s ability to develop more advanced AI systems.

The competition from China also includes Ernie X1 and Ernie 4.5, released by Baidu, which are designed to compete with Western systems.

According to Baidu, Ernie X1 “delivers performance on par with DeepSeek R1 at only half the price.” Meanwhile, Ernie 4.5 is priced at just 1% of OpenAI’s GPT-4.5 while outperforming it in multiple benchmarks.

DeepSeek’s aggressive pricing strategy is also raising concerns with the US companies. According to Bernstein Research, DeepSeek’s V3 and R1 models are priced “anywhere from 20-40x cheaper” than equivalent models from OpenAI. The pricing pressure could force US developers to adjust their business models to remain competitive.

Baidu’s strategy of open-sourcing its models is also gaining traction. “One thing we learned from DeepSeek is that open-sourcing the best models can greatly help adoption,” Baidu CEO Robin Li said in February. Baidu plans to open-source the Ernie 4.5 series starting June 30, which could accelerate adoption and further increase competitive pressure on US firms.

Cost aside, early user feedback on Baidu’s models has been positive. “[I’ve] been playing around with it for hours, impressive performance,” Alvin Foo, a venture partner at Zero2Launch, said in a post on social media, suggesting China’s AI models are becoming more affordable and effective.

US AI security and economic risks

The submissions also highlight what the US companies perceive as risks to security and the economy.

OpenAI warned that Chinese regulations could allow the government to compel DeepSeek to manipulate its models to compromise infrastructure or sensitive applications, creating vulnerabilities in important systems.

Anthropic’s concerns centred on biosecurity. It disclosed that its own Claude 3.7 Sonnet model demonstrated capabilities in biological weapon development, highlighting the dual-use nature of AI systems.

Anthropic also raised issues with US export controls on AI chips. While Nvidia’s H20 chips meet US export restrictions, they nonetheless perform well in text generation – a important feature for reinforcement learning. Anthropic called on the government to tighten controls to prevent China from gaining a technological edge using the chips.

Google took a more cautious approach, acknowledging security risks yet warned against over-regulation. The company argues that strict AI export rules could harm US competitiveness by limiting business opportunities for domestic cloud providers. Google recommended targeted export controls to protect national security but without disruption to its business operations.

Maintaining US AI competitiveness

All US three companies emphasised the need for better government oversight and infrastructure investment to maintain US AI leadership.

Anthropic warned that by 2027, training a single advanced AI model could require up to five gigawatts of power – enough to power a small city. The company proposed a national target to build 50 additional gigawatts of AI-dedicated power capacity by 2027 and to streamline regulations around power transmission infrastructure.

OpenAI positioned the competition between US and Chinese AI as a contest between democratic and authoritarian AI models. The company argued that promoting a free-market approach would drive better outcomes and maintain America’s technological edge.

Google focused on urging practical measures, including increased federal funding for AI research, improved access to government contracts, and streamlined export controls. The company also recommended more flexible procurement rules to accelerate AI adoption by federal agencies.

Regulatory strategies for US AI

The US companies called for a unified federal approach to AI regulation.

OpenAI proposed a regulatory framework managed by the Department of Commerce, warning that fragmented state-level regulations could drive AI development overseas. The company supported a tiered export control framework, allowing broader access to US-developed AI in democratic countries while restricting it in authoritarian states.

Anthropic called for stricter export controls on AI hardware and training data, warning that even minor improvements in model performance could give China a strategic advantage.

Google focused on copyright and intellectual property rights, stressing that its interpretation of ‘fair use’ is important for AI development. The company warned that overly restrictive copyright rules could disadvantage US AI firms compared to their Chinese competitors.

All three companies stressed the need for faster government adoption of AI. OpenAI recommended removing some existing testing and procurement barriers, while Anthropic supported streamlined procurement processes. Google emphasised the need for improved interoperability in government cloud infrastructure.

See also: The best AI prompt generator: Create perfect AI prompts

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 stakes are high, with collective capital expenditure jumping 30% up from 2024’s $246 billion investment. While investors may question the necessity of such aggressive spending, tech leaders are doubling down on their belief that AI represents a transformative opportunity worth every dollar.

Amazon stands at the forefront of this AI arms spend, according toa reportby Business Insider. Amazon is flexing its financial muscle with a planned $100 billion capital expenditure for 2025 – a dramatic leap from its $77 billion last year. AWS chief Andy Jassy isn’t mincing words, calling AI a “once-in-a-lifetime business opportunity” that demands aggressive investment.

Microsoft’s Satya Nadella also has a bullish stance with his own hard numbers. Having earmarked $80 billion for AI infrastructure in 2025, Microsoft’s existing AI ventures are already delivering; Nadella has spoken of $13 billion annual revenue from AI and 175% year-over-year growth.

His perspective draws from economic wisdom: citing the Jevons paradox, he argues that making AI more efficient and accessible will spark an unprecedented surge in demand.

Not to be outdone, Google parent Alphabet is pushing all its chips to the centre of the table, with a $75 billion infrastructure investment in 2025, dwarfing analysts’ expectations of $58 billion. Despite market jitters about cloud growth and AI strategy, CEO Sundar Pichai maintains Google’s product innovation engine is firing on all cylinders.

Meta’s approach is to pour $60-65 billion into capital spending in 2025 – up from $39 billion in 2024. The company is carving its own path by championing an “American standard” for open-source AI models, a strategy has caught investor attention, particularly given Meta’s proven track record in monetising AI through sophisticated ad targeting.

The emergence of DeepSeek’s efficient AI models has sparked some debate in investment circles. Investing.com’s Jesse Cohen voices growing demands for concrete returns on existing AI investments. Yet Wedbush’s Dan Ives dismisses such concerns, likening DeepSeek to “the Temu of AI” and insisting the revolution is just beginning.

The market’s response to these bold plans tells a mixed story. Meta’s strategy has won investor applause, while Amazon and Google face more sceptical reactions, with stock drops of 5% and 8% respectively following spending announcements in earnings calls. Yet tech leaders remain undeterred, viewing robust AI infrastructure as non-negotiable for future success.

The intensity of infrastructure investment suggests a reality: technological breakthroughs in AI efficiency aren’t slowing the race – they’re accelerating it. As big tech pours unprecedented resources into AI development, it’s betting that increased efficiency will expand rather than contract the market for AI services.

The high-stakes gamble on AI’s future reveals a shift in how big tech views investment. Rather than waiting to see how efficiency improvements might reduce costs, it’s are scaling up aggressively, convinced that tomorrow’s AI landscape will demand more infrastructure, not less. In this view, DeepSeek’s breakthroughs aren’t a threat to their strategy – they’re validation of AI’s expanding potential.

The message from Silicon Valley is that the AI revolution demands massive infrastructure investment, and the giants of tech are all in. The question isn’t whether to invest in AI infrastructure, but whether $320 billion will be enough to meet the coming surge in demand.

See also: DeepSeek ban? China data transfer boosts security concerns

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 post Big tech’s $320B AI spend defies efficiency race appeared first on AI News.

Historically, finding new materials relied on laborious and costly trial-and-error experiments. More recently, computational screening of vast materials databases helped to speed up the process, but it remained a time-intensive process.

Now, a powerful new generative AI tool from Microsoft could accelerate this process significantly. Dubbed MatterGen, the tool steps away from traditional screening methods and instead directly engineers novel materials based on design requirements, offering a potentially game-changing approach to materials discovery.

Published in a paper in Nature, Microsoft describes MatterGen as a diffusion model that operates within the 3D geometry of materials. Where an image diffusion model might generate images from text prompts by tweaking pixel colours, MatterGen generates material structures by altering elements, positions, and periodic lattices in randomised structures. This bespoke architecture is designed specifically to handle the unique demands of materials science, such as periodicity and 3D arrangements.  

“MatterGen enables a new paradigm of generative AI-assisted materials design that allows for efficient exploration of materials, going beyond the limited set of known ones,” explains Microsoft.

A leap beyond screening

Traditional computational methods involve screening enormous databases of potential materials to identify candidates with desired properties. Yet, even these methods are limited in their ability to explore the universe of unknown materials and require researchers to sift through millions of options before finding promising candidates.  

In contrast, MatterGen starts from scratch—generating materials based on specific prompts about chemistry, mechanical attributes, electronic properties, magnetic behaviour, or combinations of these constraints. The model was trained using over 608,000 stable materials compiled from the Materials Project and Alexandria databases.

In the comparison below, MatterGen significantly outperformed traditional screening methods in generating novel materials with specific properties—specifically a bulk modulus greater than 400 GPa, meaning they are hard to compress.

While screening exhibited diminishing returns over time as its pool of known candidates became exhausted, MatterGen continued generating increasingly novel results.

One common challenge encountered during materials synthesis is compositional disorder—the phenomenon where atoms randomly swap positions within a crystal lattice. Traditional algorithms often fail to distinguish between similar structures when deciding what counts as a “truly novel” material.  

To address this, Microsoft devised a new structure-matching algorithm that incorporates compositional disorder into its evaluations. The tool identifies whether two structures are merely ordered approximations of the same underlying disordered structure, enabling more robust definitions of novelty.

Proving MatterGen works for materials discovery

To prove MatterGen’s potential, Microsoft collaborated with researchers at Shenzhen Institutes of Advanced Technology (SIAT) – part of the Chinese Academy of Sciences – to experimentally synthesise a novel material designed by the AI.

The material, TaCr₂O₆, was generated by MatterGen to meet a bulk modulus target of 200 GPa. While the experimental result fell slightly short of the target, measuring a modulus of 169 GPa, the relative error was just 20%—a small discrepancy from an experimental perspective.

Interestingly, the final material exhibited compositional disorder between Ta and Cr atoms, but its structure aligned closely with the model’s prediction. If this level of predictive accuracy can be translated to other domains, MatterGen could have a profound impact on material designs for batteries, fuel cells, magnets, and more.

Microsoft positions MatterGen as a complementary tool to its previous AI model, MatterSim, which accelerates simulations of material properties. Together, the tools could serve as a technological “flywheel”, enhancing both the exploration of new materials and the simulation of their properties in iterative loops.

This approach aligns with what Microsoft refers to as the “fifth paradigm of scientific discovery,” in which AI moves beyond pattern recognition to actively guide experiments and simulations.  

Microsoft has released MatterGen’s source code under the MIT licence. Alongside the code, the team has made the model’s training and fine-tuning datasets available to support further research and encourage broader adoption of this technology.

Reflecting on generative AI’s broader scientific potential, Microsoft draws parallels to drug discovery, where such tools have already started transforming how researchers design and develop medicines. Similarly, MatterGen could reshape the way we approach materials design, particularly for critical domains such as renewable energy, electronics, and aerospace engineering. 

(Image credit: Microsoft)

See also: L’Oréal: Making cosmetics sustainable with generative AI

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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Unveiled in December 2024, Phi-4 has been drawing attention for its cutting-edge capabilities despite its compact size. Its release on Hugging Face opens the door for even wider adoption, highlighting that powerful models don’t always require massive infrastructure costs.

From Azure to open access

Although Microsoft initially announced Phi-4 last month, its availability was confined to Azure AI Foundry—the company’s development platform aimed at building AI-driven solutions. This exclusivity created a stir among the AI community, with many eager to get their hands on the model.

Microsoft’s AI Principal Research Engineer, Shital Shah, addressed the demand on X: “We have been completely amazed by the response to phi-4 release. A lot of folks had been asking us for weight release. Few even uploaded bootlegged phi-4 weights on Hugging Face. Well, wait no more. We are releasing today official phi-4 model on Hugging Face!”

The official release eliminates the need for unauthorised or “bootlegged” versions, providing a legitimate channel for developers keen to explore Phi-4’s potential.

Why Phi-4 matters

Phi-4 isn’t just another entry in Microsoft’s AI portfolio—it represents an evolution in the conversation about AI efficiency and accessibility.

At a time when colossal models like GPT-4 dominate discussions due to their expansive capabilities, Phi-4 offers something revolutionary: big performance in a small package.

Key benefits of Phi-4 include:

• Compact size and energy efficiency

Phi-4’s lightweight architecture allows it to operate effectively on consumer-grade hardware, eliminating the need for expensive server infrastructure. Its compact form also translates to significantly reduced energy usage, which aligns well with the tech industry’s growing emphasis on sustainability and green computing.

• Excels in advanced mathematical reasoning

Phi-4 shines in tasks demanding mathematical reasoning, a capability measured by its score of 80.4 on the challenging MATH benchmark. This performance outpaces many comparable and even larger models, positioning Phi-4 as a strong contender for industries such as finance, engineering, and data analytics.

• Specialised applications

Training on curated datasets has made Phi-4 highly accurate for domain-specific uses. From auto-filling forms to generating tailored content, it’s particularly valuable in industries like healthcare and customer service, where compliance, speed, and accuracy are critical.

• Enhanced safety features

By leveraging Azure AI’s Content Safety tools, Phi-4 incorporates mechanisms like prompt shields and protected material detection to mitigate risks associated with adversarial prompts, making it safer to deploy in live environments.

• Making AI accessible to mid-sized businesses

Sustainability and security are vital, but so is cost-effectiveness. Phi-4’s capability to deliver high performance without the need for large computational resources makes it a viable choice for mid-sized enterprises eager to adopt AI solutions. This could lower barriers for businesses seeking to automate operations or enhance productivity.

• Innovative training techniques

The model’s training process combines synthetic datasets and curated organic data, boosting Phi-4’s effectiveness while addressing common challenges with data availability. This methodology could set the stage for future advances in model development, balancing scalability with precision.

Model for the masses

Phi-4’s launch with an MIT licence signifies more than just access—it represents a shift in how AI technologies are developed and shared. The permissive nature of this licence allows developers to use, modify, and redistribute Phi-4 with few restrictions, fostering further innovation.

This move also reflects broader trends in the AI field: a deliberate effort to democratise access to powerful models, enabling smaller organisations and independent developers to benefit from advanced technologies that were previously the preserve of tech giants or highly funded research labs.

As AI adoption becomes increasingly central across sectors, the demand for efficient, adaptable, and affordable AI models continues to climb. Phi-4 is positioned for this next phase of AI proliferation by offering impressive performance at reduced costs. It could catalyse growth particularly in industries like healthcare, where streamlined and precise computational tools make life-changing benefits possible.  

At the same time, Phi-4 highlights the viability of a more sustainable AI future. By showing that smaller AI models can excel in practical applications while consuming fewer resources, Microsoft opens the door for environmentally-conscious advancements in machine learning.  

Smaller, more efficient models are proving that size isn’t everything in AI—and the era of resource-intensive giants dominating the field may be giving way to a more diverse, inclusive, and innovative ecosystem.

See also: NVIDIA advances AI frontiers with CES 2025 announcements

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.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Their combined spending on AI is projected to exceed a jaw-dropping $240 billion. Why? Because AI isn’t just the future—it’s the present, and the demand for AI-powered tools and infrastructure has never been higher. The companies aren’t just keeping up; they’re setting the pace for the industry.

The scale of their investment is hard to ignore. In the first half of 2023, tech giants poured $74 billion into capital expenditure. By Q3, that number had jumped to $109 billion. In mid-2024, spending reached $104 billion, a remarkable 47% rise over the same period a year earlier. By Q3, the total hit $171 billion.

If this pattern continues, Q4 might add another $70 billion, bringing the total to a truly staggering $240 billion for the year.

Why so much spending?

AI’s potential is immense, and companies are making sure they’re positioned to reap the rewards.

• A growing market: AI is projected to create $20 trillion in global economic impact by 2030. In countries like India, AI could contribute $500 billion to GDP by 2025. With stakes this high, big tech isn’t hesitating to invest heavily.
• Infrastructure demands: Training and running AI models require massive investment in infrastructure, from data centres to high-performance GPUs. Alphabet increased its capital expenditures by 62% last quarter compared to the previous year, even as it cut its workforce by 9,000 employees to manage costs.
• Revenue potential: AI is already proving its value. Microsoft’s AI products are expected to generate $10 billion annually—the fastest-growing segment in the company’s history. Alphabet, meanwhile, uses AI to write over 25% of its new code, streamlining operations.

Amazon is also ramping up, with plans to spend $75 billion on capital expenditure in 2024. Meta’s forecast is not far behind, with estimates between $38 and $40 billion. Across the board, organisations recognise that maintaining their edge in AI requires sustained and significant investment.

Supporting revenue streams

What keeps the massive investments keep on coming is the strength of big tech’s core businesses. Last quarter, Alphabet’s digital advertising machine, which is powered by Google’s search engine, generated $49.39 billion in ad revenue, a 12% year-over-year increase. This as a solid foundation that allows Alphabet to pour resources into building out its AI arsenal without destabilising the bottom line.

Microsoft’s diversified revenue streams are another example. While the company spent $20 billion on AI and cloud infrastructure last quarter, its productivity segment, which includes Office, grew by 12% to $28.3 billion, and its personal computing business, boosted by Xbox and the Activision Blizzard acquisition, grew 17% to $13.2 billion. These successes demonstrate how AI investments can support broader growth strategies.

The financial payoff

Big tech is already seeing the benefits of its heavy spending. Microsoft’s Azure platform has seen substantial growth, with its AI income approaching $6 billion. Amazon’s AI business is growing at triple-digit rates, and Alphabet reported a 34% jump in profits last quarter, with cloud revenue playing a major role.

Meta, while primarily focused on advertising, is leveraging AI to make its platforms more engaging. AI-driven tools, such as improved feeds and search features keep users on its platforms longer, resulting in new revenue growth.

AI spending shows no signs of slowing down. Tech leaders at Microsoft and Alphabet view AI as a long-term investment critical to their future success. And the results speak for themselves: Alphabet’s cloud revenue is up 35%, while Microsoft’s cloud business grew 20% last quarter.

For the time being, the focus is on scaling up infrastructure and meeting demand. However, the real transformation will come when big tech unlocks AI’s full potential, transforming industries and redefining how we work and live.

By investing in high-quality, centralised data strategies, businesses can ensure trustworthy and accurate AI implementations, and unlock AI’s full potential to drive innovation, improve decision-making, and gain competitive edge. AI’s revolutionary promise is within reach—but only for companies prepared to lay the groundwork for sustainable growth and long-term results.

(Photo by Unsplash)

See also: Microsoft tries to convert Google Chrome users

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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While these interconnections make Edge a viable choice, Microsoft’s methods for persuading consumers to choose it have been far from covert.

From default settings that prioritise Edge to persistent prompts at startup, Microsoft has made it clear they want Edge to be the go-to for Windows users. And lately, it’s upped the ante: now, Edge can launch automatically when your computer boots up, instantly nudging you to bring over your data from other browsers.

The most recent update includes an auto-checked option to import browsing data from Chrome, such as history, bookmarks, and open tabs, in the name of users leveraging the features of AI assistant, Copilot. Although AI features may be appealing to some, the aggressive approach has left many users feeling annoyed rather than tempted.

The Verge recently noticed that when you start up your PC, Edge might decide to open on its own, promptly displaying a pop-up for its AI assistant, Copilot. Right next to Copilot, there’s a conveniently checked box allowing Edge to import data from other browsers automatically. For some users, this seems like an overreach, raising doubts about how far Microsoft is ready to go to make Edge the browser of choice.

Microsoft has confirmed this setup and stated that customers have the option to opt-out. Still, with default settings that favour data imports and an eye-catching import button, it’s easy for users to unintentionally make the switch, especially if they’re not paying attention. For those who prefer sticking with their existing browsers without interruption, the approach can feel unwelcome.

But even if users dodge the pop-ups, Edge isn’t exactly shy. Uninstalling it is a complex process, and it often gets reinstalled by Windows updates, much to the frustration of users who would rather go without. For many, this persistence feels more like a forceful sales pitch rather than a friendly suggestion.

Interestingly, this isn’t the first time Microsoft has tried this type of strategy. A similar message appeared to users earlier this year but was pulled back after strong objections. Now, it’s back, with Microsoft’s Caitlin Roulston stating the notification is meant to “give users the choice to import data from other browsers.”

In fact, Microsoft’s bold tactics go back some years. In 2022, it introduced a feature that could automatically pull data from Chrome into Edge – although users had the option to decline. In 2021, the company made it practically impossible to set any browser other than Edge as the default, resulting in enough outcry for Microsoft to back down.

While Microsoft promotes its intrusive pop-ups as a way to give users more control, others who value choice without constant nudges. The relentless push for Edge usage could actually be detrimental, as the company’s persistence may drive users toward other browsers rather than away. To truly compete, Microsoft might benefit from letting Edge’s strengths speak for themselves rather than relying on aggressive prompts to change hearts and minds.

(Photo by Surface)

See also: EU probes Microsoft-OpenAI and Google-Samsung AI deals

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.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Microsoft tries to convert Google Chrome users appeared first on AI News.

In this article, we delve deeper into the key findings and implications—drawing on additional sources to enhance our understanding.

Thriving industry with significant growth

The study highlights the remarkable growth of the UK’s AI sector. With over 3,170 active AI companies, these firms have generated £10.6 billion in AI-related revenues and employed more than 50,000 people in AI-related roles. This significant contribution to GVA (Gross Value Added) underscores the sector’s transformative potential in driving the UK’s economic growth.

Mark Boost, CEO of Civo, said: “In a space that’s been dominated by US companies for too long, it’s promising to see the government now stepping up to help support the UK AI sector on the global stage.”

The study shows that AI activity is dispersed across various regions of the UK, with notable concentrations in London, the South East, and Scotland. This regional dispersion indicates a broad scope for the development of AI technology applications across different sectors and regions.

Investment and funding

Investment in the AI sector has been a key driver of growth. In 2022, £18.8 billion was secured in private investment since 2016, with investments made in 52 unique industry sectors compared to 35 sectors in 2016.

The government’s commitment to supporting AI is evident through significant investments. In 2022, the UK government unveiled a National AI Strategy and Action Plan—committing over £1.3 billion in support for the sector, complementing the £2.8 billion already invested.

However, as Boost cautions, “Major players like AWS are locking AI startups into their ecosystems with offerings like $500k cloud credits, ensuring that emerging companies start their journey reliant on their infrastructure. This not only hinders competition and promotes vendor lock-in but also risks stifling innovation across the broader UK AI ecosystem.”

Addressing bottlenecks

Despite the growth and investment, several bottlenecks must be addressed to fully harness the potential of AI:

• Infrastructure: The UK’s digital technology infrastructure is less advanced than many other countries. This bottleneck includes inadequate data centre infrastructure and a dependent supply of powerful GPU computer chips. Boost emphasises this concern, stating “It would be dangerous for the government to ignore the immense compute power that AI relies on. We need to consider where this power is coming from and the impact it’s having on both the already over-concentrated cloud market and the environment.”

• Commercial awareness: Many SMEs lack familiarity with digital technology. Almost a third (31%) of SMEs have yet to adopt the cloud, and nearly half (47%) do not currently use AI tools or applications.

• Skills shortage: Two-fifths of businesses struggle to find staff with good digital skills, including traditional digital roles like data analytics or IT. There is a rising need for workers with new AI-specific skills, such as prompt engineering, that will require retraining and upskilling opportunities.

To address these bottlenecks, the government has implemented several initiatives:

• Private sector investment: Microsoft has announced a £2.5 billion investment in AI skills, security, and data centre infrastructure, aiming to procure more than 20,000 of the most advanced GPUs by 2026.

• Government support: The government has invested £1.5 billion in computing capacity and committed to building three new supercomputers by 2025. This support aims to enhance the UK’s infrastructure to stay competitive in the AI market.

• Public sector integration: The UK Government Digital Service (GDS) is working to improve efficiency using predictive algorithms for future pension scheme behaviour. HMRC uses AI to help identify call centre priorities, demonstrating how AI solutions can address complex public sector challenges.

Future prospects and challenges

The future of the UK AI sector is both promising and challenging. While significant economic gains are predicted, including boosting GDP by £550 billion by 2035, delays in AI roll-out could cost the UK £150 billion over the same period. Ensuring a balanced approach between innovation and regulation will be crucial.

Boost emphasises the importance of data sovereignty and privacy: “Businesses have grown increasingly wary of how their data is collected, stored, and used by the likes of ChatGPT. The government has a real opportunity to enable the UK AI sector to offer viable alternatives.

“The forthcoming AI Action Plan will be another opportunity to identify how AI can drive economic growth and better support the UK tech sector.”

• AI Safety Summit: The AI Safety Summit at Bletchley Park highlighted the need for responsible AI development. The “Bletchley Declaration on AI Safety” emphasises the importance of ensuring AI tools are transparent, fair, and free from bias to maintain public trust and realise AI’s benefits in public services.

• Cybersecurity challenges: As AI systems handle sensitive or personal information, ensuring their security is paramount. This involves protecting against cyber threats, securing algorithms from manipulation, safeguarding data centres and hardware, and ensuring supply chain security.

The AI sector study underscores a thriving industry with significant growth potential. However, it also highlights several bottlenecks that must be addressed – infrastructure gaps, lack of commercial awareness, and skills shortages – to fully harness the sector’s potential.

(Photo by John Noonan)

See also: EU AI Act: Early prep could give businesses competitive edge

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This past summer, investors questioned whether top AI stocks could sustain their sky-high valuations, given the lack of returns on massive AI spending. As Autumn approaches, major AI sectors—such as chips, LLMs, and AI devices—received renewed confidence. Nonetheless, there are an increasing number of reasons to be cautious.

Cerebras: A chip contender with a major risk

Chip startup Cerebras is challenging Nvidia’s dominance by developing processors designed to power smarter LLMs. Nvidia, a major player in the AI boom, has seen its market cap skyrocket from $364 billion at the start of 2023 to over $3 trillion.

Cerebras, however, relies heavily on a single customer: the Abu Dhabi-based AI firm G42. In 2023, G42 accounted for 83% of Cerebras’ revenue, and in the first half of 2024, that figure increased to 87%. While G42 is backed by major players like Microsoft and Silver Lake, its dependency poses a risk. Even though Cerebras has signed a deal with Saudi Aramco, its reliance on one client may cause concerns as it seeks a $7-8 billion valuation for its IPO.

OpenAI’s record-breaking funding – but with strings attached

OpenAI made the news when it raised $6.6 billion at a $157 billion valuation, becoming the largest investment round in Silicon Valley history. However, the company has urged its investors not to back competitors such as Anthropic and Elon Musk’s xAI—an unusual request in the world of venture capital, where spread betting is common. Critics, including Gary Marcus, have described this approach as “running scared.”

OpenAI’s backers also include “bubble chasers” such as SoftBank and Tiger Global, firms known for investing in companies at their peak, which frequently results in huge losses. With top executives such as CTO Mira Murati departing and predicted losses of $5 billion this year despite rising revenues, OpenAI faces significant challenges.

Meta’s big bet on AI wearables

Meta entered the AI race by unveiling Orion, its augmented reality glasses. The wearables promise to integrate AI into daily life, with Nvidia’s CEO Jensen Huang endorsing the product. However, at a production cost of $10,000 per unit, the price is a major obstacle.

Meta will need to reduce costs and overcome consumer hesitation, as previous attempts at AI-powered wearables—such as Snapchat’s glasses, Google Glass, and the Humane AI pin—have struggled to gain traction.

The road ahead

What’s next for AI? OpenAI must prove it can justify a $157 billion valuation while operating at a loss. Cerebras needs to reassure investors that relying on one client isn’t a dealbreaker. And Meta must convince consumers to adopt a completely new way of interacting with AI.

If these companies succeed, this moment could mark a turning point in the AI revolution. However, as tech history shows, high-stakes markets are rarely easy to win.

(Photo by Growtika)

See also: Ethical, trust and skill barriers hold back generative AI progress in EMEA

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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This change was observed in the latest update to the Baidu Baike robots.txt file, which denies access to Googlebot and Bingbot crawlers.

According to the Wayback Machine, the change took place on August 8. Previously, Google and Bing search engines were allowed to index Baidu Baike’s central repository, which includes almost 30 million entries, although some target subdomains on the website were restricted.

This action by Baidu comes amid increasing demand for large datasets used in training artificial intelligence models and applications. It follows similar moves by other companies to protect their online content. In July, Reddit blocked various search engines, except Google, from indexing its posts and discussions. Google, like Reddit, has a financial agreement with Reddit for data access to train its AI services.

According to sources, in the past year, Microsoft considered restricting access to internet-search data for rival search engine operators; this was most relevant for those who used the data for chatbots and generative AI services.

Meanwhile, the Chinese Wikipedia, with its 1.43 million entries, remains available to search engine crawlers. A survey conducted by the South China Morning Post found that entries from Baidu Baike still appear on both Bing and Google searches. Perhaps the search engines continue to use older cached content.

Such a move is emerging against the background where developers of generative AI around the world are increasingly working with content publishers in a bid to access the highest-quality content for their projects. For instance, relatively recently, OpenAI signed an agreement with Time magazine to access the entire archive, dating back to the very first day of the magazine’s publication over a century ago. A similar partnership was inked with the Financial Times in April.

Baidu’s decision to restrict access to its Baidu Baike content for major search engines highlights the growing importance of data in the AI era. As companies invest heavily in AI development, the value of large, curated datasets has significantly increased. This has led to a shift in how online platforms manage access to their content, with many choosing to limit or monetise access to their data.

As the AI industry continues to evolve, it’s likely that more companies will reassess their data-sharing policies, potentially leading to further changes in how information is indexed and accessed across the internet.

(Photo by Kelli McClintock)

See also: Google advances mobile AI in Pixel 9 smartphones

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The Norges Bank Investment Fund, which holds considerable shares in almost 9,000 companies worldwide — accounting for 1.5% of all listed stocks — has become a trailblazer in environmental, social, and corporate governance issues. About a year ago, the fund also provided its invested companies with recommendations on integrating responsible AI to improve economic outcomes.

Several companies still have a lot of ground to cover. Specifically, when stating that “Overall, a lot of competence building needs to be done at the board level,” Smith Ihenacho clarified that this does not mean every board should have an AI specialist. Instead, boards need to collectively understand how AI matters in their business and have policies in place.

“They should know: ‘What’s our policy on AI? Are we high risk or low risk? Where does AI meet customers? Are we transparent around it?’ It’s a big-picture question they should be able to answer,” Smith Ihenacho added, highlighting the breadth of understanding required at the board level.

The fund has shared its perspective on AI with the boards of its 60 largest portfolio companies, as reported in its 2023 responsible investment report. It is particularly focused on AI use in the healthcare sector due to its substantial impact on consumers, and is closely monitoring Big Tech companies that develop AI-based products.

In its engagement with tech firms, the fund emphasises the importance of robust governance structures to manage AI-related risks. “We focus more on the governance structure,” Smith Ihenacho explained. “Is the board involved? Do you have a proper policy on AI?”

The fund’s emphasis on AI governance is particularly relevant, given that nine of the ten largest positions in its equity holdings are tech companies. Leading among them are names such as Microsoft, Apple, Amazon, and Meta Platforms. Investments in these companies contributed to a 12.5% growth in the fund’s stock portfolio in the first half of 2024. The overall exposure to the tech sector increased from 21% to 26% over the past year, now comprising a quarter of the stock portfolio. This underscores the significant role that technology and AI play in the world today.

Though the fund favours AI innovation for its potential to boost efficiency and productivity, Smith Ihenacho has emphasised the importance of responsible use. She is quoted as saying, “It is fantastic what AI may be able to do to support innovation, efficiency, and productivity… we support that.” However, she also stressed the need to be responsible in how we manage the risks.

The fund’s adoption of AI governance aligns with rising global concerns about the ethical implications and potential dangers of these technologies. AI is increasingly utilised across various sectors, from finance to healthcare, and the need for governance frameworks has never been greater. The Norwegian sovereign wealth fund maintains a standard that requires companies to develop comprehensive AI policies at the board level, fostering the adoption of responsible AI practices across its large portfolio.

This initiative by one of the world’s largest investors could have far-reaching implications for corporate governance practices globally. As companies seek to harness the power of AI while navigating its complexities, the guidance provided by influential investors like Norges Bank Investment Fund may serve as a blueprint for responsible AI implementation and governance in the corporate world.

See also: X agrees to halt use of certain EU data for AI chatbot training

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According to the recent announcement, Palantir is integrating Microsoft’s cutting-edge large language models via the Azure OpenAI Service into its AI platforms. The integration will occur within Microsoft’s government and classified cloud environments. As this collaboration is the first of its kind, this specific configuration has the potential to completely transform the use of AI in critical national security missions.

Palantir, whose name draws inspiration from the potentially misleading “seeing-stones” in J.R.R. Tolkien’s fictional works, specialises in processing and analysing vast quantities of data to assist governments and corporations with surveillance and decision-making tasks. While the precise nature of the services to be offered through this partnership remains somewhat ambiguous, it is clear that Palantir’s products will be integrated into Microsoft’s Azure cloud services. This development follows Azure’s previous incorporation of OpenAI’s GPT-4 technology into a “top secret” version of its software.

The company’s journey is notable. Co-founded by Peter Thiel and initially funded by In-Q-Tel, the CIA’s venture capital arm, Palantir has grown to serve a diverse clientele. Its roster includes government agencies such as Immigration and Customs Enforcement (ICE) and various police departments, as well as private sector giants like the pharmaceutical company Sanofi. Palantir has also become deeply involved in supporting Ukraine’s war efforts, with reports suggesting its software may be utilised in targeting decisions for military operations.

Even though Palantir has operated with a large customer base for years, it only reached its first annual profit in 2023. However, with the current surge of interest in AI, the company has been able to grow rapidly, particularly in the commercial sector. According to Bloomberg, Palantir’s CEO, Alex Karp, warned that Palantir’s “commercial business is exploding in a way we don’t know how to handle.”

Despite the urgency of this mission, the company’s annual filing clearly states that it neither does business with nor on behalf of the Chinese Communist Party, nor does it plan to do so. This indicates that Palantir is especially careful in developing its customer base, considering the geopolitical implications of its work.

The announcement of this partnership has been well-received by investors, with Palantir’s share price surging more than 75 per cent in 2024 as of the time of writing. This dramatic increase reflects the market’s optimism about the potential of AI in national security applications and Palantir’s position at the forefront of this field.

Still, the partnership between Palantir and Microsoft raises significant questions about the role of AI in national security and surveillance. This is no surprise, as these are particularly sensitive areas, and the development of new technologies could potentially transform the sector forever.

More discussions and investigations are needed to understand the ethical implications of implementing these innovative tools. All things considered, the Palantir and Microsoft partnership is a significant event that will likely shape the future use of AI technologies and cloud computing in areas such as intelligence and defence.

(Photo by Katie Moum)

See also: Paige and Microsoft unveil next-gen AI models for cancer diagnosis

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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