The price changes do not affect the iPhone, but they push the entry-level MacBook Neo to $699 from its launch price of $599. Apple said skyrocketing memory and storage chip costs forced the company to review prices across parts of its product range.

“We have never seen a component price increase this much, this quickly. We have shielded our customers from these increases so far, but we have now reached a point where we need to begin raising prices on a number of products, including today’s increases for iPad and Mac,” the company said in a statement.

The biggest increases affect some of Apple’s higher-spec devices. The MacBook Air with 512GB of storage now starts at $1,299, up from $1,099, while the 1TB MacBook Pro now costs $1,999 instead of $1,699.

Apple also raised the price of the 11-inch iPad Air with 128GB of storage to $749 from $599. The 11-inch iPad Pro with 256GB of storage now sells for $1,199, up from $999.

Beyond its computers and tablets, Apple also increased prices for the HomePod, HomePod Mini and Apple TV set-top box.

Following the announcement, Apple’s shares fell nearly 5%, while Dell dropped by more than 8% as analysts warned that other PC makers could soon face similar pressure.

Analysts say Apple’s strong relationships with suppliers have helped it delay price increases longer than many competitors. Even so, they believe other manufacturers could be forced to make even steeper adjustments.

Ben Bajarin, CEO of Creative Strategies, said: “The memory environment is tough and remains structurally tough for the foreseeable future.”

The pressure comes as memory manufacturers focus on supplying chips for AI infrastructure instead of consumer electronics.

Companies building AI data centres, including Nvidia and other technology firms, have signed long-term supply agreements worth billions of dollars to secure memory chips, leaving fewer components available for smartphones, tablets and personal computers.

Micron recently said it had secured $22 billion in long-term customer commitments for memory supply, reflecting the strong demand from the AI market.

Apple had already warned investors that higher memory costs would begin affecting its business this year. During an earnings call in April, Chief Executive Tim Cook said: “We expect significantly higher memory costs.”

He added: “Where we don’t give color beyond June, I can tell you that beyond the June quarter, we believe memory costs will drive an increasing impact on our business.”

Apple has not disclosed what other measures it is taking to manage high component costs. However, the company acknowledged that customers would not welcome the latest increases.

“We know this is not welcome news, and we are working tirelessly to find solutions,” Apple said.

Research firm IDC believes the latest increases could influence buying decisions, especially with the launch of new iPhones expected later this year.

Nabila Popal, Senior Research Director at IDC, said: “The iPhone isn’t spared, its hike is coming. It was incredibly strategic for Apple to make the price hike announcements prior to the iPhone fall launch, so the headlines at launch is not the price hikes but the value the new phones bring.”

Market tracker TrendForce reported that prices of dynamic random access memory (DRAM), used in almost every modern electronic device, rose by as much as 98% in the first quarter of 2026. It expects prices to increase by another 58 to 63% during the current quarter.

The surge has already changed the competitive landscape. At $699, the MacBook Neo no longer holds its $100 price advantage over Dell’s XPS 13. It is also now more expensive than some Chromebook models from Lenovo and Asus.

IDC forecasts global smartphone shipments will fall by almost 14% in 2026, while PC shipments are expected to decline by 11.3% as manufacturers pass high costs on to buyers.

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Microsoft has been working towards matching all of its hourly electricity use with renewable energy purchases within the next five years.

That goal, set in 2020, stood out at the time because it went beyond annual offsets and focused on real-time energy use. Now, people familiar with internal discussions say the company is weighing whether to delay or drop it.

The discussion comes as Microsoft spends heavily on infrastructure to support artificial intelligence. Like Amazon and Alphabet, it is building large data centres to run services such as Copilot and its Azure cloud platform.

These facilities require vast amounts of electricity, and demand is rising faster than earlier projections suggested.

Some of the newer sites under development are expected to draw several gigawatts of power. To put that in context, one gigawatt can supply roughly 750,000 homes in the United States. Meeting that level of demand with renewable sources alone is proving difficult, especially within tight timelines.

Energy supply is now a practical concern. Renewable projects take years to plan and build. By contrast, natural gas plants and nuclear facilities can be brought online more quickly, and companies are turning to them to avoid delays.

Microsoft has already taken steps in that direction. In 2024, it reached an agreement with Constellation Energy to help restart a unit at the Three Mile Island nuclear plant in Pennsylvania. The deal shows how the company is balancing its climate targets with immediate energy needs.

The pressure is not limited to Microsoft. Across the sector, emissions have continued to rise despite public commitments to cut them. That gap is drawing attention from regulators and investors, who are watching closely as companies expand their AI operations.

For now, Microsoft has not commented publicly on the reported review. What is clear is that the scale of AI is changing earlier assumptions. The company set one of the most ambitious clean energy goals in the industry. Whether it can still meet it on time is now uncertain.

The post AI Power Surge Forces Microsoft to Reconsider 2030 Clean Energy Goal appeared first on Tech | Business | Economy.

The proposed deal follows a $30 billion bond issuance last year, the largest in the company’s history. With this new development, Meta is again turning to debt markets rather than relying only on its cash reserves.

A day earlier, Meta Platforms increased its 2026 capital spending forecast by $10 billion. It now expects to spend between $125 billion and $145 billion this year. The capital will go into data centres, custom chips, and energy systems needed to support AI tools and large-scale model training.

Across the industry, spending is increasing fast, with Big Tech companies expected to invest more than $700 billion in AI infrastructure this year.

Meta’s new bond sale is expected to include up to six tranches. One portion could mature in 2066. Early pricing discussions suggest the longest-dated notes may offer a yield of up to 1.8% points above US Treasuries, based on people familiar with the matter.

The company has not responded to requests for comment.

S&P Global has rated the planned debt as investment-grade and kept a stable outlook on the company. Its analysts said they expect Meta’s leverage to remain “well below” the downgrade threshold for at least two years.

Still, they noted that the scale of AI spending is beginning to affect the company’s credit profile.

To support this change, Meta has reduced spending on its metaverse unit, which has recorded losses for several years. At the same time, the company is preparing for job cuts.

Reports reveal plans to reduce its workforce by 20% or more, with an initial round affecting about half of that set for May 20.

Meta is not alone in raising funds this way. Other technology firms have also issued large amounts of debt this year.

Amazon raised about $54 billion across US and European markets in March, Alphabet has issued roughly $32 billion in dollar and euro-denominated notes, while Oracle completed a $25 billion bond sale that drew strong demand.

The post Meta Plans $25 Billion Bond Sale to Fund AI Spending Surge appeared first on Tech | Business | Economy.

The company said on Tuesday it is projecting an operating profit of 57.2 trillion won ($38.2 billion) for the January to March period.

This is far ahead of expectations and more than eight times higher than the 6.69 trillion won it reported a year earlier. It also exceeds the company’s total profit for all of last year.

This would be Samsung’s strongest quarterly result on record. Its previous high stood at 20 trillion won, reached in the final quarter of 2025.

Demand from data centres has pushed prices higher. Companies building AI systems buy large volumes of memory chips, stretching supply. As a result, prices for DRAM chips rose sharply in the first quarter, with estimates pointing to increases of more than 50%.

“As customers anticipated further increases, actual contract prices came in higher, leading to the beat,” Kim Sunwoo, a senior analyst at Meritz Securities, said.

Samsung appears to be benefiting across most of its business. Analysts estimate its memory division generated about 54 trillion won in operating profit during the quarter.

Its mobile unit also held up, reporting around 4 trillion won in profit, though slightly lower than a year ago. However, its logic chip business is still under pressure and is expected to post a loss.

Currency movements have also helped. The South Korean won has fallen to a near 17-year low against the U.S. dollar, lifting the value of overseas earnings when converted back.

Even so, there are signs that the pace of growth may slow. The high cost of energy linked to the conflict in the Middle East has added pressure on production. At the same time, there are concerns that customers may begin to push back against high chip prices.

“There are growing concerns about a peak-out in memory price increases. It does appear that we are now past the initial upcycle phase and into a later stage,” said Ryu Young-ho, a senior analyst at NH Investment & Securities.

Recent data support that view. Spot prices for DRAM chips eased last week, noting that buyers are struggling to keep up with current price levels.

New technology could also affect demand. Google recently introduced a memory-saving system known as TurboQuant, which may reduce the amount of memory needed for AI workloads.

Samsung has also been working to strengthen its position in high-bandwidth memory chips, which are used in advanced AI processors.

The company began shipping its latest HBM4 chips to Nvidia in February, narrowing the gap with its main opponent, SK Hynix. Still, these advanced chips account for less than 10% of its DRAM revenue, meaning most of the profit is still coming from standard memory products.

In the market, Samsung’s shares rose 1.8% following the earnings outlook, outperforming the index. Shares in SK Hynix also increased.

Despite recent challenges, Samsung’s stock is still significantly higher this year, building on strong gains recorded in 2025.

The company is expected to release full details of its first-quarter results on April 30.

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The White House said the “Ratepayer Protection Pledge,” first announced in Trump’s State of the Union Address, will see tech firms commit to measures ensuring that growth in AI infrastructure does not increase utility expenses for households and small businesses.

Sources familiar with the plan said the pledge may include commitments from companies to pay for upgrades to power delivery systems and to negotiate special electricity rates with utilities.

These tech firms are investing billions in AI computing capacity, which consumes large amounts of electricity.

Trump has urged companies to build or secure dedicated power capacity instead of relying solely on regional grids. This is intended to balance technological competitiveness with concerns over energy costs.

Jon Gordon, director at Advanced Energy United, warned that the plan might not ease stress on electricity grids quickly. “The real problem is the inability to get generation online fast enough to meet the data centre demand,” he said. “Hyperscalers paying for the generation doesn’t get it online any faster.”

Lawmakers and consumer groups have called for stronger protections to prevent utility bill increases linked to data centre build-outs.

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The agreement, which runs through 2028, is designed to speed up how ChatGPT and other OpenAI products respond to users, as competition gets stronger among the world’s largest technology firms to control the infrastructure behind advanced models. 

Cerebras will provide the capacity through its own cloud services, powered by its wafer-scale chips, with new data centres to be built or leased specifically for the contract.

Seven hundred and fifty megawatts is not incremental capacity, it shows industrial-level demand. 

OpenAI is no longer thinking only about training models but about inference, the everyday work of answering questions, reasoning through problems and serving millions of users at once. 

“Integrating Cerebras into our mix of compute solutions is all about making our AI respond much faster,” OpenAI stated. 

Talks between the two companies began last August after Cerebras showed that OpenAI’s open-source models could run more efficiently on its chips than on standard graphics processors. 

Months of negotiations followed. The result is a structure where Cerebras owns and operates the hardware, while OpenAI pays to access it as a service. Capacity will be added in stages over the next few years.

Cerebras is preparing to return to public markets after withdrawing its previous listing attempt in late 2024. It now plans to re-file for an initial public offering in the second quarter of 2026. This OpenAI contract helps address a long-standing concern among investors: dependence on a single customer. 

In 2024, UAE-based technology group G42 accounted for nearly 87% of Cerebras’ revenue. A multi-billion-dollar US client changes that picture.

The deal also fits neatly into OpenAI’s bigger strategy. The company is laying the foundation for a potential initial public offering that could value it at around $1 trillion. 

Its chief executive, Sam Altman, has publicly committed $1.4 trillion to building 30 gigawatts of computing capacity, enough to power roughly 25 million homes in the United States. 

Competition is high. Nvidia still tops the market for training chips, while Google and Microsoft are pushing their own custom hardware through cloud platforms. 

By turning to Cerebras, OpenAI is clearly trying to reduce reliance on a single supplier and gain an edge in speed, particularly for reasoning-heavy models.

But then, the spending spree is unsettling some observers, with warnings that the surge in valuations and capital commitments across the sector shows the excesses of the dot-com era. 

The counter-argument is equally strong, demand for inference compute is exploding as artificial intelligence moves from labs into daily use. 

The post OpenAI to Buy 750MW of Compute From Nvidia Rival Cerebras in $10 Billion Deal appeared first on Tech | Business | Economy.

The tech giant, which owns Facebook, Instagram, and WhatsApp, reported revenue of $51.24 billion for the third quarter of 2025, a 26% increase from a year earlier and above Wall Street’s expectations. 

But costs rose faster, climbing 32%, and a $15.93 billion charge linked to U.S. President Donald Trump’s “Big Beautiful Bill” slashed net income to $2.71 billion. Without that charge, Meta’s adjusted earnings would have been $18.64 billion.

Despite the strong revenue growth, Meta’s shares dropped 8% in after-hours trading as investors reacted to CEO Mark Zuckerberg’s plans to scale up spending on AI infrastructure, a move that could pressure profit margins in the near term.

Zuckerberg told analysts that Meta intends to “aggressively front-load building capacity,” arguing that it’s the right strategy to be ready for faster-than-expected breakthroughs in AI.

“There’s a range of timelines for when people think that we’re going to get superintelligence,” he said. “I think that it’s the right strategy to aggressively front-load building capacity, so that way we’re prepared for the most optimistic cases.”

The company has launched an initiative called Meta Superintelligence Labs, formed in June, to drive its AI goals. Zuckerberg said the unit already has “the highest talent density in the industry,” and Meta is among the top buyers of Nvidia’s sought-after AI chips. “We’re also building what we expect to be an industry-leading amount of compute,” he added.

Meta company now expects to spend between $70 billion and $72 billion this year, up from an earlier estimate of $66 billion to $72 billion, and says next year’s capital expenditure will be “notably larger.” 

Much of this increase will go toward expanding data centre capacity and hiring AI specialists, according to CFO Susan Li, who confirmed employee compensation will be a key cost driver in 2026.

Meta’s aggressive approach comes as Big Tech firms like Microsoft, Alphabet, Amazon, and OpenAI are all scaling up their compute capabilities to support advanced AI models. 

OpenAI’s CEO, Sam Altman, said earlier this week that he hopes the company will one day be able to “add 1 gigawatt of compute every week,” and that could cost upwards of $40 billion per gigawatt.

This ballooning investment across the tech sector has led to talks of a possible “AI bubble,” with analysts warning that spending may outpace returns. “Meta’s earnings reveal the growing tension between the company’s massive AI infrastructure investments and investor expectations for near-term returns,” said Jesse Cohen, senior analyst at Investing.com.

Still, Meta’s core business stays strong. Its AI-driven advertising systems are performing well, with tools that automate campaigns, create persona-based images, and enhance video ad quality for over 3.5 billion daily users across its platforms. 

The company’s expanding ad offerings on WhatsApp and Threads, and sustained growth in Instagram Reels, have strengthened its competitiveness with the likes of TikTok, YouTube Shorts, and Elon Musk’s X.

“While everyone else is still pitching AI moonshots, Meta has quietly turned AI into margin,” said Jeremy Goldman, senior director at Emarketer. “Its ad tools are sharper, its targeting smarter, and its short-form video business is finally paying off.”

For the fourth quarter, Meta expects revenue between $56 billion and $59 billion, slightly above analysts’ projections.

The post Meta’s $70 Billion AI Spending Plan Rattles Investors as Profit Takes $16 Billion Hit appeared first on Tech | Business | Economy.

The new systems are already being adopted by hyperscale customers including Microsoft Azure and Alibaba Cloud.

The launch will enhance data centre connections and scale. With the growth of AI training models that span multiple regions, Cisco’s P200 and 8223 systems aim to efficiently link data centres located thousands of miles apart, acting as a unified, high-performance computing network.

Martin Lund, executive vice president of Cisco’s Common Hardware Group, explained the scale of change the company is addressing: “AI compute is outgrowing the capacity of even the largest data centre, driving the need for reliable, secure connection of data centres hundreds of miles apart. With the Cisco 8223, powered by the new Cisco Silicon One P200, we’re delivering the massive bandwidth, scale and security needed for distributed data centre architectures.”

The P200 chip, a deep-buffer routing silicon, replaces 92 separate chips with one. It delivers more than 65% energy savings compared to traditional routers, an important development as hyperscalers face power challenges. 

The chip also supports interconnect bandwidth of over 3 exabits per second, allowing AI workloads to run across multiple facilities without performance loss.

The 8223 routing system is built to complement this. It can process more than 20 billion packets per second, supporting 64 ports of 800G, the highest density of any fixed router available. Its deep buffering feature absorbs data surges generated during AI training, keeping systems stable and responsive.

The system’s design prioritises power efficiency and flexibility. It is the most power-efficient 3RU router in its category, offering switch-like energy performance while managing the intense traffic of AI-driven networks. The 8223 can adapt to changing network protocols through programmability, reducing the need for hardware upgrades and keeping operations agile.

Security is another focal point, as the 8223 integrates line-rate encryption and post-quantum resilient algorithms, backed by continuous monitoring features that provide visibility into performance and threats. This ensures that sensitive AI traffic remains protected as data moves between facilities.

Dave Maltz, corporate vice president of Azure Networking at Microsoft, said: “The increasing scale of the cloud and AI requires faster networks with more buffering to absorb bursts. We’re pleased to see the P200 providing innovation and more options in this space.”

Alibaba Cloud is also deploying the technology to enhance its eCore architecture. “We are pleased to see the launch of Cisco Silicon One P200, the industry’s first 51.2T routing ASIC that delivers high bandwidth, lower power consumption, and full P4 programmability. This breakthrough chip aligns perfectly with the evolution of Alibaba’s eCore architecture,” said Dennis Cai, vice president and head of Network Infrastructure at Alibaba Cloud.

The 8223 is initially being released for open-source SONiC deployments, with IOS XR support to follow. Cisco also confirmed that the P200 silicon will be available for modular and disaggregated platforms, allowing consistent integration across different network sizes.

With AI workloads increasing across industries, Cisco’s latest launch will boost its competitive edge with companies like Broadcom in the networking chip market. More importantly, it stresses how infrastructure innovation, not just software, will determine how effectively global AI systems evolve.

Patrick Moorhead, CEO and chief analyst at Moor Insights & Strategy, said, “As AI workloads rapidly outpace the capabilities of traditional data centres, the industry faces new challenges in bandwidth, reliability, and scale. Cisco’s 8223, powered by Silicon One P200, marks a significant step forward, delivering the industry’s first 51.2-terabit fixed Ethernet router purpose-built for secure, power-efficient scale-across networking.”

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The agreement will see Meta tap into Google Cloud’s servers, storage, and networking capacity as it works to expand its AI growth. 

Although both companies declined to comment publicly, sources close to the negotiations confirmed the scale and duration of the deal, which was first reported by The Information.

This development is interesting not only because Google and Meta have long been competitors in digital advertising, but also because Meta has historically leaned heavily on Amazon Web Services (AWS) and, to a lesser degree, Microsoft Azure. 

In diversifying its cloud dependencies, Meta is making it transparent that it cannot rely on a single provider at a time when demand for high-performance computing has exploded.

Meta’s aggressive drive into AI infrastructure has been emphasised by recent financial disclosures. In July, the company raised its 2025 capital expenditure forecast to between $66 billion and $72 billion, with overall expenses projected at $114–118 billion. 

Much of that money is earmarked for AI-focused data centres and talent acquisition. CEO Mark Zuckerberg has been straightforward about the company’s goal, noting that Meta is willing to spend “hundreds of billions of dollars” to build what he calls personal superintelligence.

The deal with Google Cloud is also notable for its timing. Alphabet’s cloud unit posted $13.6 billion in revenue in the second quarter of 2025, a 32% year-on-year increase that far outpaced the parent company’s overall growth of 13.8%. 

Landing Meta’s business further strengthens Google’s position as it competes against AWS and Azure, both of which continue to stay on top in the sector.

Meta, meanwhile, is trying to secure every scrap of computing power it can find. In addition to building its own massive server farms, it has begun offloading $2 billion worth of data centre assets to external partners to ease the financial burden of its AI expansion. 

The company is already working on its first multi-gigawatt “Prometheus” AI supercluster, expected to go live in 2026.

Yet the scale of Meta’s infrastructure plans has ignited controversy. To power some of its new sites, the company has received approval to construct three natural gas plants in Louisiana, together generating 2.25 gigawatts. 

Analysts say this damages Meta’s pledge to reach net-zero emissions by 2030, leading to queries about the environmental cost of large-scale AI adoption.

For Google, the win follows another deal earlier this year with OpenAI, which agreed to use its cloud services despite a deep, long-standing reliance on Microsoft’s Azure. 

Together, these partnerships reveal how competition in the cloud sector is becoming inseparable from the race to be the go-to company in the next generation of AI.

The post Google Lands $10 Billion Cloud Deal with Meta to Power AI Expansion appeared first on Tech | Business | Economy.

As a result, Gartner predicts 40% of existing AI data centers will be operationally constrained by power availability by 2027.

“The explosive growth of new hyperscale data centers to implement GenAI is creating an insatiable demand for power that will exceed the ability of utility providers to expand their capacity fast enough,” said Bob Johnson, VP Analyst at Gartner. “In turn, this threatens to disrupt energy availability and lead to shortages, which will limit the growth of new data centers for GenAI and other uses from 2026.”

Gartner estimates the power required for data centers to run incremental AI-optimized servers will reach 500 terawatt-hours (TWh) per year in 2027, which is 2.6 times the level in 2023 (see Figure 1).

Figure 1: Estimated Incremental Power Consumption of AI Data Centers, 2022-2027

“New larger data centers are being planned to handle the huge amounts of data needed to train and implement the rapidly expanding large language models (LLMs) that underpin GenAI applications,” said Johnson. “However, short-term power shortages are likely to continue for years as new power transmission, distribution and generation capacity could take years to come online and won’t alleviate current problems.”

In the near future, the number of new data centers and the growth of GenAI will be governed by the availability of power to run them. Gartner recommends organizations determine the risks potential power shortages will have on all products and services.

Electricity Prices Will Increase

The inevitable result of impending power shortages is an increase in the price of power, which will also increase the costs of operating LLMs, according to Gartner.

“Significant power users are working with major producers to secure long-term guaranteed sources of power independent of other grid demands,” said Johnson. “In the meantime, the cost of power to operate data centers will increase significantly as operators use economic leverage to secure needed power. These costs will be passed on to AI/GenAI product and service providers as well.”

Gartner recommends organizations evaluate future plans anticipating higher power costs and negotiate long-term contracts for data center services at reasonable rates for power.

Organizations should also factor significant cost increases when developing plans for new products and services, while also looking for alternative approaches that require less power.

Sustainability Goals Will Suffer

Zero-carbon sustainability goals will also be negatively affected by short-term solutions to provide more power, as surging demand is forcing suppliers to increase production by any means possible.

In some cases, this means keeping fossil fuel plants that had been scheduled for retirement in operation beyond their scheduled shutdown.

“The reality is that increased data center use will lead to increased CO2 emissions to generate the needed power in the short-term,” said Johnson. “This, in turn, will make it more difficult for data center operators and their customers to meet aggressive sustainability goals relating to CO2 emissions.”

Data centers require 24/7 power availability, which renewable power such as wind or solar cannot provide without some form of alternative supply during periods when not generating power, according to Gartner.

Ike Nnamani says Data Centres’ll Run into Trouble in Nigeria…if

Reliable 24/7 power can only be generated by either hydroelectric, fossil fuel or nuclear power plants. In the long-term, new technologies for improved battery storage (e.g sodium ion batteries) or clean power (e.g small nuclear reactors) will become available and help achieve sustainability goals.

Gartner recommends organizations re-evaluate sustainability goals relating to CO2 emissions in light of future data center requirements and power sources for the next few years.

When developing GenAI applications, they should focus on using a minimum amount of computing power and look at the viability of other options such as edge computing and smaller language models.

[Featured Image Credit]

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