October 2025 saw a notable upswing in global equity markets, with artificial intelligence (AI) emerging as a key driver of investor enthusiasm.
In the United States, major indices closed the month firmly in the green, buoyed by strong third-quarter earnings and renewed confidence in AI’s transformative potential.
Tech giants such as Nvidia, Amazon, and Palantir posted robust results, reinforcing the narrative that AI is not just hype—it’s reshaping business fundamentals.
Nvidia’s leadership in AI chips and Amazon’s expanding AI-driven logistics were particularly well received, while Palantir’s government contracts underscored AI’s strategic reach.
The Federal Reserve’s decision to cut interest rates by 0.25% added further momentum, making growth stocks more attractive and amplifying the rally in AI-heavy portfolios.
Analysts noted that investor sentiment was bolstered by easing trade tensions and a cooling inflation outlook, but it was AI’s ‘secular tailwind of extreme innovation’ that truly captured market imagination.
While some caution that valuations may be running hot, the October 2025 rally suggests that AI is now central to market dynamics. A pullback is likely soon.
As 2025 draws to a close, investors are watching closely to see whether the optimism translates into durable gains—or signals the start of an AI bubble.
Google’s nuclear pivot aligns with green energy goals—but contrasts sharply with Alaska’s oil expansion, which raises environmental concerns
Google’s move to restart the Duane Arnold nuclear plant in Iowa is part of a broader strategy to power its AI infrastructure with carbon-free energy.
Nuclear fission, while controversial, is considered a low-emissions source and offers round-the-clock reliability—something solar and wind can’t always guarantee.
By locking in a 25-year agreement with NextEra Energy, Google aims to meet its AI demands while staying on track for net-zero emissions by 2030.
Why Nuclear Fits the Green Energy Puzzle
Zero carbon emissions during operation make nuclear a strong contender for clean energy.
High energy density means a small footprint compared to solar or wind farms.
24/7 reliability is crucial for powering AI data centres, which can’t afford downtime.
Google’s plan reportedly includes exploring modular reactors and integrating nuclear into its broader clean energy mix.
However, nuclear isn’t without its critics.
Concerns include
Radioactive waste management and long-term storage.
High upfront costs and long construction timelines.
Public resistance due to safety fears and historical accidents.
Alaska’s Oil Recovery: A Different Direction
In stark contrast, the Trump administration has announced plans to open 82% of Alaska’s National Petroleum Reserve for oil and gas drilling.
This includes parts of the Arctic National Wildlife Refuge, home to polar bears, migratory birds, and Indigenous communities.
The move is framed as a push for energy independence and economic growth, but it’s drawing criticism for its environmental impact:
Habitat disruption for Arctic wildlife and fragile ecosystems.
Reversal of previous protections, sparking legal and activist backlash.
The Bigger Picture
Google’s nuclear strategy represents a tech-led green energy evolution, while Alaska’s oil expansion reflects a traditional fossil fuel revival.
The juxtaposition highlights a growing divide in U.S. energy policy: one path leans into innovation and sustainability, the other doubles down on extraction and short-term gains.
Nuclear power produces virtually no carbon emissions during operation, making it one of the cleanest sources of large-scale, continuous energy—though waste disposal and safety remain key challenges.
But…
Nuclear power is clean in terms of carbon emissions, but its waste remains a long-term challenge—requiring secure containment for thousands of years.
While nuclear energy produces virtually no greenhouse gases during operation, it generates radioactive waste that must be carefully managed.
Here’s how the waste issue fits into the broader energy conversation
What Is Nuclear Waste?
High-level waste: Spent fuel from reactors, highly radioactive and thermally hot. Requires cooling and shielding.
Intermediate and low-level waste: Contaminated materials like tools, clothing, and reactor components. Less dangerous but still regulated.
How Is It Managed?
Short-term: Stored on-site in cooling pools or dry casks.
Long-term: Plans for deep geological repositories—sealed underground vaults designed to isolate waste for 10,000+ years.
UK example: The Low Level Waste Repository in Cumbria is being capped with engineered barriers to prevent environmental leakage.
France: Reprocesses spent fuel to reduce volume and reuse materials, though still produces waste.
Japan: Actively searching for a permanent disposal site, with local politics shaping progress.
Innovations and Controversies
New reactor designs aim to produce less waste or use existing waste as fuel.
Deep Fission’s concept: Building reactors in mile-deep shafts that could be sealed permanently.
Public concern: Waste disposal remains a top reason for nuclear opposition, especially in regions like Taiwan
What about greenhouse gasses emitted building a plant and the operation?
Nuclear power emits very low greenhouse gases during operation, but construction and fuel processing do produce emissions—though still far less than fossil fuels over the plant’s lifetime.Dealing with the waste is the real issue.
Here’s a breakdown of the full lifecycle emissions:
Lifecycle Emissions of Nuclear Power
According to the World Nuclear Association and IEA
Construction phase: Building a nuclear plant involves concrete, steel, and heavy machinery—materials and processes that emit CO₂. This upfront carbon cost is significant but amortised over decades of clean operation.
Fuel cycle: Mining, enriching, and transporting uranium also produce emissions, though modern methods are improving efficiency. Operation phase: Once running, nuclear plants emit virtually no greenhouse gases. They don’t burn fuel, so there’s no CO₂ from combustion. Decommissioning: Dismantling old plants and managing waste adds a small carbon footprint, but it’s minor compared to fossil fuel alternatives.
How Nuclear Compares to Other Energy Sources
Energy Source
Lifecycle CO₂ Emissions (g/kWh)
Coal
820
Natural Gas
490
Solar PV
48
Wind
12
Nuclear
12
Sources: World Nuclear Association
Nuclear’s carbon profile is front-loaded: it costs carbon to build, but pays back in decades of clean power. Compared to fossil fuels, it’s a dramatic improvement.
And unlike solar or wind, it’s not weather-dependent—making it ideal for powering AI data centres that demand constant uptime.
Still, critics argue that the slow build time and high capital cost make nuclear less agile than renewables. Others point out that waste management and public trust remain unresolved.
In a bold move that signals the escalating energy demands of artificial intelligence, Google has announced plans to invest heavily in nuclear power to fuel its data centres.
As AI models grow more complex and compute-intensive, the tech giant is turning to atomic energy as a stable, carbon-free solution to meet its insatiable appetite for electricity.
The shift comes amid mounting scrutiny over the environmental impact of AI. Training large language models and running real-time inference across billions of queries requires vast amounts of energy—often sourced from fossil fuels.
Google’s pivot to nuclear is both a strategic and symbolic gesture: a commitment to sustainability, but also a recognition that the AI era demands a fundamentally different energy paradigm.
SMR’s
At the heart of this initiative is Google’s partnership with advanced nuclear startups exploring small modular reactors (SMRs) and next-generation fission technologies.
Unlike traditional nuclear plants, SMRs are designed to be safer, more scalable, and quicker to deploy—making them ideal for powering decentralised data infrastructure.
Google’s goal is to integrate these reactors directly into its cloud and AI campuses, creating a closed-loop ecosystem where clean energy powers the very machines shaping the future.
Critics, however, warn of the risks. Nuclear waste, regulatory hurdles, and public perception remain significant barriers.
Some environmentalists argue that the urgency of the climate crisis demands faster, more proven solutions like solar and wind. Yet others see nuclear as a necessary complement—especially as AI accelerates demand beyond what renewables alone can supply.
This isn’t Google’s first foray into atomic ambition. In 2022, it backed nuclear fusion research through its DeepMind subsidiary, applying AI to optimise plasma control.
Now, with fission in focus, the company appears determined to lead not just in AI innovation, but in the infrastructure that sustains it.
The implications are profound. If successful, Google’s nuclear strategy could set a precedent for the entire tech industry, reshaping how data is powered in the 21st century.
It also raises deeper questions: Can the tools of the future be truly sustainable? And what does it mean when the intelligence we build begins to reshape the energy systems that built us?
One thing is clear—AI isn’t just changing how we think. It’s changing what we power, and how we power it.
We’re already seeing multiple classic bubble indicators: extreme valuations (Buffett Indicator, Shiller CAPE), record retail participation, AI-driven hype, and surging margin debt—all pointing to elevated risk.
Key Bubble Indicators Already Present
📈 Buffett Indicator (Market Cap to GDP) This ratio is at historically high levels, suggesting stocks are significantly overvalued relative to the economy. Warren Buffett himself has warned investors may be “playing with fire”.
📊 Shiller CAPE Ratio Another respected valuation metric, the cyclically adjusted price-to-earnings ratio, is also elevated—indicating unsustainable earnings multiples and potential for correction.
🧠 AI-driven speculation The rally is heavily concentrated in AI and tech stocks, with some analysts calling it a “toxic calm” before a crash. Search volume for ‘AI bubble‘ is at record highs, and billionaire Paul Tudor Jones has issued warnings.
📉 Retail investor frenzy A record 62% of Americans now own stocks, with $51 trillion at stake. This surge in retail participation is reminiscent of past bubbles, where optimism outpaces caution.
📌 New market highs The Nasdaq, S&P 500, and Dow have hit dozens of new highs in recent months. While bullish on the surface, this pace of gains often precedes sharp reversals.
💸 Margin debt and risk appetite Risk-taking is accelerating, with margin debt climbing and speculative behavior increasing. Analysts note this as a historically bad sign when paired with euphoric sentiment.
What’s Not Yet Peaking (But Worth Watching)
IPO and SPAC volume: While not at 2021 levels, any surge here could signal speculative excess.
Corporate earnings vs. valuations: Some firms still show strong earnings, but the disconnect is widening.
Narrative dominance: AI optimism is strong, but hasn’t fully eclipsed fundamentals—yet.
Microsoft Azure experienced a widespread outage on 29th October, beginning around 16:00 UTC, which affected thousands of users and businesses globally.
The disruption stemmed from issues with Azure Front Door, Microsoft’s content delivery network, and cascaded into failures across Microsoft 365, Xbox, Minecraft, and numerous third-party services reliant on Azure infrastructure.
Major retailers such as Costco and Starbucks, as well as airlines including Alaska and Hawaiian, reported system failures that hindered customer access and internal operations.
Users struggled with authentication, hosting, and server connectivity, with DownDetector logging a surge in complaints from 15:45 GMT onwards.
Microsoft acknowledged the problem on its Azure status page, attributing the outage to a suspected configuration change.
Full service restoration was achieved by about 23:20 UTC, though the timing coincided awkwardly with Microsoft’s Q1 FY26 earnings report, where Azure was reportedly highlighted as its fastest-growing segment.
The incident underscores the critical dependence on cloud infrastructure and raises questions about resilience and contingency planning.
As businesses increasingly migrate to cloud platforms, the ripple effects of such outages become more pronounced, impacting not just productivity, but public trust in digital reliability.
In October 2025, Nvidia’s stock surged past $207 per share, lifting its market capitalisation to $5.06 trillion. Once a niche graphics chip maker, Nvidia now powers the backbone of artificial intelligence worldwide.
CEO Jensen Huang confirmed over $500 billion in chip orders and plans for seven U.S. supercomputers.
This milestone, reached just three months after crossing $4 trillion, places Nvidia ahead of Microsoft and Apple, cementing its dominance in the AI era and redefining the future of computing.
Nvidia one-year chart as of October 2025
Nvidia one-year chart as of October 2025 passes $5 trillion Market Cap
Amazon has reportedly announced its largest corporate restructuring to date, with plans to lay off up to30,000 white-collar employees.
This represents nearly 10% of its global office workforce—as it accelerates its transition toward artificial intelligence and automation-led operations.
The move, confirmed on 28th October 2025, marks a dramatic shift in the tech giant’s internal priorities.
CEO Andy Jassy has framed the layoffs as part of a broader effort to streamline management. The company appears to want to eliminate bureaucratic inefficiencies and reallocate resources toward AI infrastructure.
‘We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs’, Jassy is reported as saying.
Affected departments span human resources, logistics, customer service, and Amazon Web Services (AWS). Many roles are deemed redundant due to AI integration.
Heavy investment
The company has been investing heavily in machine learning systems. These are capable of handling tasks ranging from inventory forecasting to customer support. This approach has prompted the reevaluation of traditional staffing models.
While Amazon employs over 1.5 million people globally, the layoffs target its 350,000 corporate staff, signalling a significant recalibration of its white-collar operations.
It was reported that the job cuts were delivered via email, underscoring the impersonal nature of the transition.
The timing of the announcement—just ahead of the holiday season—has raised eyebrows across the industry.
Analysts suggest Amazon is betting on AI to offset seasonal labour demands and long-term cost pressures. However, this risks reputational fallout and internal morale issues.
Structural challenges
Critics argue that the scale of the layoffs reflects deeper structural challenges, including overhiring during the pandemic and a growing reliance on technology to solve human-centred problems.
Others see it as a bellwether for the wider tech sector, where AI is increasingly viewed as both a productivity boon and a disruptive force.
As Amazon reshapes its workforce for an AI-driven future, questions remain about the social and ethical implications of such rapid automation.
For now, the company appears resolute: leaner, faster, and more algorithmically efficient—even if it means leaving tens of thousands behind in the process.
But, AI is also creating job opportunities in other areas.
The Binance founder had served a four-month sentence after pleading guilty to violating U.S. anti-money laundering laws—a conviction that formed part of a $4.3 billion settlement with the Department of Justice.
CZ’s release marks a dramatic turning point in the U.S. government’s approach to cryptocurrency regulation. Once emblematic of the Biden administration’s crackdown on crypto platforms, CZ now reportedly finds himself at the centre of a political pivot.
Trump’s pardon, announced in October 2025, has been met with both celebration and condemnation. Critics, including Senator Thom Tillis, argue the move undermines efforts to regulate illicit finance, while supporters hail it as a step toward restoring innovation in the digital asset space.
Now based in Abu Dhabi, CZ has vowed to ‘help make America the Capital of Crypto‘. His post-release activities suggest a shift from direct exchange management to broader influence.
Such as, investing in educational initiatives like Giggle Academy, backing blockchain startups, and lobbying for friendlier crypto legislation.
Despite the pardon, expectations remain high. CZ is under intense scrutiny—not just from regulators, but from the crypto community itself.
Many expect him to champion transparency, rebuild Binance’s reputation, and avoid the shadowy practices that led to its U.S. ban in 2019. His future influence may hinge on whether he can balance ambition with accountability.
For now, CZ’s return is symbolic: a signal that the crypto world is once again in flux, with its most controversial figure back in play.
It was just one week ago on Monday 20th October 2025, Amazon Web Services (AWS) experienced a major outage that rippled across the digital world, disrupting operations for millions of users and businesses.
The incident, which originated in AWS’s US-East-1 region, was reportedly traced to DNS resolution failures affecting DynamoDB—one of AWS’s core database services.
This technical fault triggered cascading issues across EC2, network load balancers, and other critical infrastructure, leaving many services offline for hours.
The impact was immediate and widespread. Major consumer platforms such as Snapchat, Reddit, Disney+, Canva, and Ring doorbells went dark.
Financial services including Venmo and Robinhood faltered, while airline customers at United and Delta struggled to access bookings. Even British government portals like Gov.uk and HMRC were affected, underscoring the global reach of AWS’s infrastructure.
World leader
AWS is the world’s leading cloud provider, commanding roughly one-third of the global market—well ahead of Microsoft Azure and Google Cloud.
Millions of companies, from startups to multinational corporations, rely on AWS for everything from data storage and virtual servers to machine learning and content delivery.
Its services underpin critical operations in healthcare, education, retail, logistics, and media. When AWS stumbles, the internet itself feels the tremor.
20 Prominent Companies Affected by the AWS Outage (20th Oct 2025)
Sector
Company Name
Impact Summary
E-commerce
Amazon
Internal systems and Seller Central offline
Social Media
Snapchat
App outages and delays
Streaming
Disney+
Service interruptions
News
Reddit
Partial outages, scaling issues
Design Tools
Canva
High error rates, reduced functionality
Smart Home
Ring
Device connectivity issues
Finance
Venmo
Transaction delays
Finance
Robinhood
Trading disruptions
Airlines
United Airlines
Booking and check-in issues
Airlines
Delta Airlines
Reservation access problems
Telecom
T-Mobile
Indirect service disruptions
Government
Gov.uk
Portal access issues
Government
HMRC
Service delays
Banking
Lloyds Bank
Online banking affected
Productivity
Zoom
Meeting access issues
Productivity
Slack
Messaging delays
Education
Canvas
Assignment submissions disrupted
Crypto
Coinbase
User access failures
Gaming
Roblox
Server outages
Gaming
Fortnite
Gameplay interruptions
This outage wasn’t the result of a cyberattack, but rather a technical fault in one of Amazon’s main data centres. Yet the consequences were no less severe.
Amazon’s own operations were disrupted, with warehouse workers unable to access internal systems and third-party sellers locked out of Seller Central.
Canva reported ‘significantly increased error rates’. while Coinbase and Roblox cited cloud-related failures.
The incident serves as a stark reminder of the risks inherent in centralised cloud infrastructure. As digital life becomes increasingly dependent on a handful of providers, the potential for systemic disruption grows.
A single point of failure can cascade across industries, affecting everything from classroom assignments to emergency services.
AWS has since restored normal operations and promised a detailed post-event summary. But for many, the outage has reignited questions about resilience, redundancy, and the wisdom of placing so much trust in a single cloud giant.
In the age of digital interdependence, even a brief lapse can feel like a global blackout.
In recent months, the S&P 500 has shown signs of evolving from a broad economic barometer into something far more concentrated: a proxy for artificial intelligence optimism.
While traditionally viewed as a diversified snapshot of American corporate health, the index’s current composition and market behaviour suggest it’s increasingly tethered to the fortunes of a handful of AI-driven giants.
At the heart of this transformation is the dominance of mega-cap tech firms. Microsoft, Nvidia, Alphabet, Amazon, Meta, and Apple now account for a disproportionate share of the index’s total market capitalisation.
As of late 2025 that heady combination of AI led tech represents just over 30% of the S&P 500.
Six AI related companies represent 30% of the S&P 500
These companies aren’t merely adjacent to AI—they’re building its infrastructure, shaping its software ecosystems, and embedding it into consumer and enterprise products.
Nvidia, for instance, has become synonymous with AI hardware, its valuation soaring on the back of demand for high-performance chips powering generative models and data centres.
Recent analysis reveals that roughly 8% of the S&P 500’s weight is directly tied to AI-related revenue.
An additional 25 companies within the index are actively developing AI technologies, even if those efforts haven’t yet translated into standalone revenue streams. This includes sectors as varied as autonomous vehicles, quantum computing, and predictive analytics.
Investor behaviour has only amplified this shift. The index’s recent rally has been fuelled largely by enthusiasm for AI breakthroughs, with capital flowing into stocks perceived as future beneficiaries of machine learning and automation.
This momentum has led some analysts to warn of valuation bubbles, urging diversification away from AI-heavy names in case of a sector-wide correction.
Narrower narrative
Symbolically, the S&P 500’s identity is shifting. Once a mirror of industrial and consumer strength, it now reflects a narrower narrative—one of technological acceleration and speculative belief in artificial intelligence.
This raises philosophical questions about what the index truly represents: is it still a measure of economic breadth, or has it become a momentum gauge for a single transformative theme?
For editorial observers, this evolution offers fertile ground. The index’s transformation can be read not just as a financial trend, but as a cultural signal—suggesting that AI is no longer a niche innovation, but the dominant lens through which investors, executives, and policymakers interpret the future.
Whether this concentration proves visionary or vulnerable remains to be seen.
But one thing is clear: the S&P 500 is no longer just a mirror of the American economy—it’s increasingly a reflection of our collective bet on intelligent machines.
30% of S&P 500
As of 2025, Microsoft, Nvidia, Alphabet, Amazon, Meta, and Apple—often grouped as part of the ‘Magnificent Seven’—collectively represent approximately 30% of the S&P 500’s total market capitalisation.
That’s a staggering concentration for just six companies in an index meant to reflect the broader U.S. economy.
For context, their combined performance was responsible for roughly two-thirds of the S&P 500’s total gains in 2024—a clear signal that the index’s movement is increasingly tethered to the fortunes of a few dominant tech giants.
In a surreal twist that briefly defied economic logic, Paxos—the blockchain infrastructure firm behind PayPal’s PYUSD stablecoin—accidentally minted $300 trillion worth of digital dollars in a technical mishap.
The error, reportedly spotted on Ethereum’s public ledger Etherscan, triggered a wave of astonishment across crypto circles before Paxos swiftly burned the excess tokens and issued a statement clarifying the blunder.
Technical error?
‘This was an internal technical error. There is no security breach. Customer funds are safe’, Paxos assured, adding that the root cause had been addressed.
To put the scale of the error in perspective: $300 trillion is more than double the estimated total GDP of the entire planet. And we trust these people and systems?
It’s a sum that could theoretically buy every publicly traded company several times over—and still leave room for a few moon bases. Fortunately, the minting was part of an internal transfer and never entered circulation.
Who is in charge?
PYUSD is designed to be a dollar-pegged stablecoin, backed by U.S. dollar deposits and short-term treasuries. Its promise of 1:1 redemption relies not on algorithmic magic but on real-world reserves and third-party attestations.
The incident, while resolved in under 20 minutes, underscores the fragility of trust in digital finance—especially when automation meets scale.
The crypto community, already wary of stablecoin transparency, seized on the event as a cautionary tale.
While no funds were lost and no users affected, the episode raises questions about auditability, protocol safeguards, and the symbolic weight of ‘minting’ in a decentralised economy.
In an era where digital assets are increasingly mainstream, even a fleeting glitch can ripple through markets and headlines.
Thin air
Paxos may have burned the tokens, but the spectacle of $300 trillion conjured from code won’t be forgotten anytime soon.
After years in Silicon Valley’s policy sanctum, Nick Clegg has re-emerged on British soil with a warning: the AI sector is overheating.
The man who once fronted a coalition government, then pivoted to Meta’s global affairs desk, now cautions that the ‘absolute spasm’ of AI deal-making may be headed for a correction.
Is this his opinion or just borrowed from other commentators. I, for one, am not interested in what he has to say. I did once, but not anymore.
It’s a curious homecoming. Clegg left UK politics after his party was electorally eviscerated, only to rebrand himself as a transatlantic tech ‘diplomat’ or tech tourist.
Now, with the AI hype cycle in full swing, he returns not as a policymaker, but as a prophet of moderation—urging restraint in a sector he arguably helped legitimise from within.
His critique isn’t wrong. Valuations are frothy. Infrastructure costs are staggering. And the promise of artificial superintelligence remains more theological than technical. But Clegg’s timing invites scrutiny.
Is this a genuine call for realism, or a reputational hedge from someone who’s seen the inside of the machine?
There’s a deeper irony here: the same political class that once championed deregulation and digital optimism now warns of runaway tech. The same voices that embraced disruption now plead for caution.
It’s less a reversal than a ritual—an elite rite of return, where credibility is reasserted through critique.
Clegg’s message may be sound. But in a landscape saturated with recycled authority, the messenger matters.
And for many, his reappearance feels less like a reckoning and more like déjà vu in a different suit.
The world’s largest contract chipmaker reported net income of NT$452.3 billion (£11.4 billion), far exceeding analyst expectations and marking a new high for the company.
Revenue climbed 30.3% year-on-year to NT$989.92 billion, driven by insatiable demand for high-performance chips powering artificial intelligence applications.
Tech giants including Nvidia, OpenAI, and Oracle have ramped up orders for TSMC’s cutting-edge processors, fuelling the company’s meteoric rise.
TSMC’s CEO, C.C. Wei, reportedly attributed the growth to ‘unprecedented investment in AI infrastructure’, noting that the company’s advanced nodes are now central to training large language models and deploying generative AI tools.
Despite global economic headwinds and ongoing trade tensions, TSMC’s strategic expansion—including a $165 billion global buildout across Arizona, Europe, and Japan—is positioning it as the backbone of next-gen computing.
The results also reflect a broader shift in the semiconductor landscape. As traditional consumer electronics plateau, AI-driven demand is reshaping supply chains and investment priorities.
Analysts suggest that AI chip spending could surpass $1 trillion in the coming years, with TSMC poised to capture a significant share.
For investors and industry observers, the message is clear: AI isn’t just a trend—it’s a fundamental shift. And TSMC, with its unparalleled fabrication expertise and global influence, is quietly shaping the future.
As the AI arms race accelerates, TSMC’s performance offers a glimpse into the future of tech: one where silicon, not software, defines the frontier.
The company’s latest earnings are not just a financial milestone—they’re a signal of where innovation is headed next.
Oracle Bets Big on AMD AI Chips, Challenging Nvidia’s Dominance
Oracle Cloud Infrastructure has announced plans to deploy 50,000 AMD Instinct MI450 graphics processors starting in the second half of 2026, marking a bold strategic shift in the AI hardware landscape.
The move signals a direct challenge to Nvidia’s long-standing dominance in the data centre GPU market, where it currently commands over 90% market share.
AMD’s MI450 chips, unveiled earlier this year, are designed for high-performance AI workloads and can be assembled into rack-sized systems that allow 72 chips to function as a unified engine.
This architecture is tailored for inferencing tasks—an area Oracle believes AMD will excel in. ‘We feel like customers are going to take up AMD very, very well’, reportedly said Karan Batta, Oracle Cloud’s senior vice president.
The announcement comes amid a broader realignment in the AI ecosystem. OpenAI, historically reliant on Nvidia hardware, has recently inked a multi-year deal with AMD involving processors requiring up to 6 gigawatts of power.
If successful, OpenAI could acquire up to 10% of AMD’s shares, further cementing the chipmaker’s role in next-generation AI infrastructure.
Oracle’s pivot also reflects its ambition to compete with cloud giants like Microsoft, Amazon, and Google. With a reported five-year cloud deal with OpenAI potentially worth $300 billion, Oracle is positioning itself not just as a capacity provider but as a strategic AI enabler.
While Nvidia remains a formidable force, Oracle’s investment in AMD chips underscores a growing appetite for alternatives.
As AI demands scale, diversity in chip supply could become a competitive advantage—especially for enterprises seeking flexibility, cost efficiency, and innovation beyond the Nvidia ecosystem.
The AI arms race is far from over, but Oracle’s latest move suggests it’s no longer content to play catch-up. It’s aiming to redefine the rules.
U.S. stock markets are behaving like a mood ring in a thunderstorm—volatile, reactive, and oddly sentimental.
One moment, President Trump threatens a ‘massive increase’ in tariffs on Chinese imports, and nearly $2 trillion in market value evaporates.
The next, he posts that: ‘all will be fine‘, and futures rebound overnight. It’s not just policy—it’s theatre, and Wall Street is watching every act with bated breath.
This hypersensitivity isn’t new, but it’s been amplified by the precarious state of global trade and the towering expectations placed on artificial intelligence.
Trump’s recent comments about China’s rare earth export controls triggered a sell-off that saw the Nasdaq drop 3.6% and the S&P 500 fall 2.7%—the worst single-day performance since April.
Tech stocks, especially those reliant on semiconductors and AI infrastructure, were hit hardest. Nvidia alone lost nearly 5%.
Why so fickle? Because the market’s current rally is built on a foundation of hope and hype. AI has been the engine driving valuations to record highs, with companies like OpenAI and Anthropic reaching eye-watering valuations despite uncertain profitability.
The IMF and Bank of England have both warned that we may be in stage three of a classic bubble cycle6. Circular investment deals—where AI startups use funding to buy chips from their investors—have raised eyebrows and comparisons to the dot-com era.
Yet, the bubble hasn’t burst. Not yet. The ‘Buffett Indicator‘ sits at a historic 220%, and the S&P 500 trades at 188% of U.S. GDP. These are not numbers grounded in sober fundamentals—they’re fuelled by speculative fervour and a fear of missing out (FOMO).
But unlike the dot-com crash, today’s AI surge is backed by real infrastructure: data centres, chip fabrication, and enterprise adoption. Whether that’s enough to justify the valuations remains to be seen.
In the meantime, markets remain twitchy. Trump’s tariff threats are more than political posturing—they’re economic tremors that ripple through supply chains and investor sentiment.
And with AI valuations stretched to breaking point, even a modest correction could trigger a cascade.
So yes, the market is fickle. But it’s not irrational—it’s just balancing on a knife’s edge between technological optimism and geopolitical anxiety.
Influential figures and institutions are sounding the AI alarm—or at least raising eyebrows—about the frothy valuations and speculative fervour surrounding artificial intelligence.
Who’s Warning About the AI Bubble?
🏛️ Bank of England – Financial Policy Committee
View: Stark warning.
Quote: “The risk of a sharp market correction has increased.”
Why it matters: The BoE compares current AI stock valuations to the dotcom bubble, noting that the top five S&P 500 firms now command nearly 30% of market cap—the highest concentration in 50 years.
🏦 Jerome Powell – Chair, U.S. Federal Reserve
View: Cautiously sceptical.
Quote: Assets are “fairly highly valued.”
Why it matters: While not naming AI directly, Powell’s remarks echo broader concerns about tech valuations and investor exuberance.
🧮 Lisa Shalett – Chief Investment Officer, Morgan Stanley Wealth Management
View: Deeply concerned.
Quote: “This is not going to be pretty” if AI capital expenditure disappoints.
Why it matters: Shalett warns that 75% of S&P 500 returns are tied to AI hype, likening the moment to the “Cisco cliff” of the early 2000s.
🌍 Kristalina Georgieva – Managing Director, IMF
View: Watchful.
Quote: Financial conditions could “turn abruptly.”
Why it matters: Georgieva highlights the fragility of markets despite AI’s productivity promise, warning of sudden sentiment shifts.
🧨 Sam Altman – CEO, OpenAI
View: Self-aware caution.
Quote: “People will overinvest and lose money.”
Why it matters: Altman’s admission from inside the AI gold rush adds credibility to bubble concerns—even as his company fuels the hype.
📦 Jeff Bezos – Founder, Amazon
View: Bubble-aware.
Quote: Described the current environment as “kind of an industrial bubble.”
Why it matters: Bezos sees parallels with past tech manias, suggesting that infrastructure spending may be overextended.
🧠 Adam Slater – Lead Economist, Oxford Economics
View: Analytical.
Quote: “There are a few potential symptoms of a bubble.”
Why it matters: Slater points to stretched valuations and extreme optimism, noting that productivity projections vary wildly.
🏛️ Goldman Sachs – Investment Strategy Division
View: Cautiously optimistic.
Quote: “A bubble has not yet formed,” but investors should “diversify.”
Why it matters: Goldman acknowledges the risks while maintaining that fundamentals may still justify valuations—though they advise caution.
AI Bubble voices infographic October 2025
🧠 Julius Černiauskas and the Oxylabs AI/ML Advisory Board
🔍 View: The AI hype is nearing its peak—and may soon deflate.
Černiauskas warns that AI development is straining environmental resources and public trust. He’s pushing for responsible and sustainable AI practices, noting that transparency is lacking in how many models operate.
Ali Chaudhry, research fellow at UCL and founder of ResearchPal, adds that scaling laws are showing their limits. He predicts diminishing returns from simply making models bigger, and expects tightened regulations around generative AI in 2025.
Adi Andrei, cofounder of Technosophics, goes further: he believes the Gen AI bubble is on the verge of bursting, citing overinvestment and unmet expectations
🧠 Jamie Dimon on the AI Bubble
🔥 View: Sharply concerned—more than most as widely reported
Quote: “I’m far more worried than others about the prospects of a downturn.”
Context: Dimon believes AI stock valuations are “stretched” and compares the current surge to the dotcom bubble of the late 1990s.
📉 Key Warnings from Dimon
“Sharp correction” risk: He sees a real danger of a sudden market pullback, especially given how AI-related stocks have surged disproportionately—like AMD jumping 24% in a single day after an OpenAI deal.
“Most people involved won’t do well”: Dimon told the BBC that while AI will ultimately pay off—like cars and TVs did—many investors will lose money along the way.
“Governments are distracted”: He criticised policymakers for focusing on crypto and ignoring real security threats, saying: “We should be stockpiling bullets, guns and bombs”.
“AI will disrupt jobs and companies”: At a trade event in Dublin, he warned that AI’s ubiquity will shake up industries and employment across the board.
And so…
The AI boom of 2025 has ignited a speculative frenzy across global markets, with tech stocks soaring and investors piling into anything labelled “AI-adjacent.”
But beneath the euphoria, a chorus of high-profile warnings is growing louder. From the Bank of England and IMF to JPMorgan’s Jamie Dimon and OpenAI’s Sam Altman, concerns are mounting that valuations are dangerously stretched, capital is overconcentrated, and the narrative is outpacing reality.
Dimon likens the moment to the dotcom bubble, while Altman admits many will “lose money” chasing the hype. Analysts point to classic bubble signals: retail mania, corporate FOMO, and earnings divorced from fundamentals.
Even as AI’s long-term utility remains promising, the short-term exuberance may be setting the stage for a sharp correction.
Whether it’s a pullback or a full-blown crash, the mood is shifting—from uncritical optimism to wary anticipation.
The question now is not whether AI will change the world, but whether markets have priced in too much, too soon.
We have been warned!
The AI bubble will pop – it’s just a matter of when and not if.
In the world of stock markets, few phenomena are as captivating—or as perilous—as bull runs and speculative bubbles.
Though often conflated, these two forces represent distinct psychological and financial dynamics that shape investor behaviour and market outcomes.
Bull Markets: Confidence with Momentum
A bull market is defined by sustained price increases across major indices. Typically driven by strong economic fundamentals, corporate earnings growth, and investor optimism.
In the U.S., iconic bull runs include the post-World War II expansion. The 1980s Reagan-era boom, and the tech-fuelled rally of the 2010s. The Dot-Com bull run, and subsequesnt crash is probably the most famous.
Bull markets feed on confidence: low interest rates, rising employment, and technological innovation often act as catalysts. Investors pile in, believing the upward trajectory will continue—sometimes for years.
But even bulls can lose their footing. When valuations stretch beyond reasonable earnings expectations, the line between bullish enthusiasm and irrational exuberance begins to blur.
Bubbles: Euphoria Untethered from Reality
A bubble occurs when asset prices inflate far beyond their intrinsic value. This is fuelled not by fundamentals but by speculation and herd mentality.
The dot-com bubble of the late 1990s is a textbook example. Companies with no profits—or even products—saw their valuations soar simply for having ‘.com’ in their name.
Similarly, the U.S. housing bubble of the mid-2000s was driven by easy credit and the belief that property prices could only go up.
Bubbles often follow a predictable arc: stealth accumulation, media attention, public enthusiasm, and finally, a euphoric peak.
When reality sets in—be it through disappointing earnings, regulatory shifts, or macroeconomic shocks—the bubble bursts! Leaving behind financial wreckage and a trail of disillusioned investors.
Spotting the Difference
While bull markets can be healthy and sustainable, bubbles are inherently unstable. The key distinction lies in valuation discipline.
Bulls are supported by earnings and growth; bubbles are driven by hype and fear of missing out (FOMO).
Tools like the cyclically adjusted price-to-earnings (CAPE) ratio and historical trend analysis can help investors discern whether they’re riding a bull or inflating a bubble.
📉 The Aftermath and Opportunity Ironically, the collapse of a bubble often sows the seeds for the next bull market. As excesses are purged and valuations reset, long-term investors find opportunities in the rubble.
The challenge lies in resisting the emotional extremes—greed during the rise, panic during the fall—and maintaining a clear-eyed view of value.
In markets, as in life, not every rise is rational, and not every fall is fatal
As of October 2025, many analysts argue that the U.S. stock market is exhibiting classic signs of a bubble. Valuations stretched across major indices and speculative behaviour intensifying—particularly in mega-cap tech stocks and passive index funds.
The S&P 500 recently hit record highs despite a backdrop of political gridlock and a government shutdown. This suggests a disconnect between price momentum and underlying economic risks.
Indicators like Market Cap to Gross Value Added (GVA) and excessive investor sentiment point to a speculative mania. Some experts are calling it the largest asset bubble in U.S. history.
While a full-blown crash hasn’t materialised yet, the market’s frothy conditions and historical October volatility have many bracing for a potential correction.
Anthropic has unveiled Claude Sonnet 4.5, its most advanced AI model to date—described by the company as ‘the best coding model in the world’.
Released in September 2025, Sonnet 4.5 marks a significant evolution in agentic capability, safety alignment, and real-world task execution.
Designed to power Claude Code and enterprise-grade AI agents, Sonnet 4.5 excels in long-context coding, autonomous software development, and complex business workflows.
Benchmark
In benchmark trials, the model reportedly sustained 30+ hours of uninterrupted coding, outperforming its predecessor Opus 4.1 and rival systems like GPT-5 and Gemini 2.52.
Anthropic’s emphasis on safety is equally notable. Sonnet 4.5 underwent extensive alignment training to reduce sycophancy, deception, and prompt injection vulnerabilities.
It now operates under Anthropic’s AI Safety Level 3 framework, with filters guarding against misuse in sensitive domains such as chemical or biological research.
New features include ‘checkpoints’ for code rollback, file creation within chat (spreadsheets, slides, documents), and a refreshed terminal interface.
Developers can now build custom agents using the Claude Agent SDK, extending the model’s reach into autonomous task orchestration4.
Anthropic’s positioning is clear: Claude Sonnet 4.5 is not merely a chatbot—it’s a colleague. With pricing held at $3 per million input tokens and $15 per million output tokens, the model is accessible yet formidable.
Whether this heralds a renaissance or a reckoning remains to be seen—but for now, Anthropic’s latest release sets a new benchmark for intelligent autonomy.
Valuations detached from fundamentals When companies with minimal revenue or unclear business models are trading at sky-high valuations purely because they’re ‘AI-adjacent’, surely it’s time to take note.
Overconcentration in a few stocks If market gains are disproportionately driven by a handful of AI giants (think Nvidia, Microsoft and Amazon etc.), it suggests fragility. A stumble by one could ripple across the sector.
Narrative dominance over substance When investor excitement is driven more by buzzwords (‘transformational’, ‘disruptive’, ‘AGI’) than by actual product performance or adoption metrics, the hype may be outpacing reality. But there is real utility in AI if managed carefully.
Corporate FOMO and rushed adoption Companies scrambling to integrate AI without clear ROI or strategic fit—especially when they start cutting staff to “reskill for AI”—can signal unsustainable pressure.
Retail investor mania If you start seeing AI-themed ETFs, TikTok stock tips, and speculative day trading around obscure AI startups, it’s reminiscent of past bubbles like dot-com or crypto.
What to watch for next
Earnings vs. expectations: If AI leaders start missing earnings or issuing cautious guidance, sentiment could shift fast.
Regulatory headwinds: New rules around data, privacy, or model transparency could reshape the landscape.
Labour market impact: If AI adoption leads to widespread job displacement without productivity gains, the backlash could be swift.
The term ‘AI super cycle’ is gaining traction among top investors, and for good reason.
According to recent commentary from leading venture capitalists, we may be entering a prolonged period of exponential growth in artificial intelligence—one that could reshape industries, economies, and even the nature of work itself.
Unlike previous tech booms, this cycle isn’t driven by a single breakthrough. Instead, it’s the convergence of multiple forces: unprecedented computing power, vast datasets, and increasingly sophisticated models.
From generative AI tools that write code and craft marketing copy, to autonomous systems revolutionising logistics and healthcare, the pace of innovation is staggering.
What makes this cycle ‘super’ isn’t just the technology—it’s the scale of adoption. AI is no longer confined to Silicon Valley labs or niche enterprise solutions.
It’s being embedded into everyday workflows, consumer apps, and national infrastructure. Governments are racing to regulate it, while companies scramble to integrate it before competitors do.
Some analysts believe this cycle could last 20 years, echoing the longevity of the internet era. But unlike the dot-com bubble, AI’s utility is already tangible.
Productivity gains, cost reductions, and creative augmentation are being realised across sectors—from finance and pharmaceuticals to education and entertainment.
Still, the super cycle isn’t without risk. Ethical concerns, data privacy, and algorithmic bias remain unresolved. And as AI systems become more autonomous, questions of accountability and control grow sharper.
Some also suggest the market is ‘frothy’ (including the Fed) and is due a correction or at the very least a pullback.
Yet for now, the momentum is undeniable. Investors are pouring billions into AI startups, chipmakers are scaling up production, and global markets are recalibrating around this new frontier.
If this truly is a super cycle, it’s not just a moment—it’s a movement.
Many traditional CPU-centric server farms are being retrofitted to support GPU-heavy or heterogeneous architectures.
Some legacy racks are adapted for edge computing, non-AI workloads, or low-latency services that don’t require massive AI computing power.
🧹 Decommissioning and Disposal
Obsolete hardware—especially older CPUs and low-density racks—is being decommissioned.
Disposal is a growing concern: e-waste regulations are tightening, and sustainability targets mean companies must recycle or repurpose responsibly.
🏭 Secondary Markets and Resale
Some older servers are sold into secondary markets—used by smaller firms, educational institutions, or regions with less AI demand.
There’s also a niche for refurbished hardware, especially in countries where AI infrastructure is still nascent.
🧊 Cold Storage and Archival Use
Legacy systems are sometimes shifted to cold storage roles—archiving data that doesn’t require real-time access.
These setups are less power-intensive and can extend the life of older tech without compromising performance.
⚠️ Obsolescence Risk
The pace of AI innovation is so fast that even new data centres risk early obsolescence if they’re not designed with future workloads in mind.
Rack densities are climbing—from 36kW to 80kW+—and cooling systems are shifting from air to liquid, meaning older infrastructure simply can’t keep up.
🧭 A Symbolic Shift
This isn’t just about servers—it’s about sovereignty, sustainability, and the philosophy of obsolescence. The old tech isn’t just being replaced; it’s being relegated, repurposed, or ritually retired.
There’s a tech history lesson unfolding about digital mortality, and how each new AI cluster buries a generation of silicon ancestors.
Infographic: ‘New’ AI tech replacing ‘Old’ tech in data centres
🌍 The Green Cost of the AI Boom
⚡ Energy Consumption
AI data centres are power-hungry beasts. In 2023, they consumed around 2% of global electricity—a figure expected to rise by 80% by 2026.
Nvidia’s H100 GPUs, widely used for AI workloads, draw 700 watts each. With millions deployed, the cumulative demand is staggering.
💧 Water Usage
Cooling these high-density clusters often requires millions of litres of water annually. In drought-prone regions, this is sparking local backlash.
🧱 Material Extraction
AI infrastructure depends on critical minerals—lithium, cobalt, rare earths—often mined in ecologically fragile zones.
These supply chains are tied to geopolitical tensions and labour exploitation, especially in the Global South.
🗑️ E-Waste and Obsolescence
As new AI chips replace older hardware, legacy servers are decommissioned—but not always responsibly.
Without strict recycling protocols, this leads to mountains of e-waste, much of which ends up in landfills or exported to countries with lax regulations.
The Cloud Has a Shadow
This isn’t just about silicon—it’s about digital colonialism, resource extraction, and the invisible costs of intelligence. AI may promise smarter sustainability, but its infrastructure is anything but green unless radically reimagined.
⚡ The Energy Cost of Intelligence
🔋 Surging Power Demand
AI data centres are projected to drive a 165% increase in global electricity consumption by 2030, compared to 2023 levels.
In the U.S. alone, data centres could account for 11–12% of total power demand by 2030—up from 3–4% today.
A single hyperscale facility can draw 100 megawatts or more, equivalent to powering 350,000–400,000 electric vehicles annually.
AI and Energy supply
🧠 Why AI Is So Power-Hungry
Training large models like OpenAI Chat GPT or DeepSeek requires massive parallel processing, often using thousands of GPUs.
Each AI query can consume 10× the energy of a Google search, according to the International Energy Agency.
Power density is rising—from 162 kW per square foot today to 176 kW by 2027, meaning more heat, more cooling, and more infrastructure.
🌍 Environmental Fallout
Cooling systems often rely on millions of litres of water annually. For example, in Wisconsin, two AI data centres will consume 3.9 gigawatts of power, more than the state’s nuclear plant.
Without renewable energy sources, this surge risks locking regions into fossil fuel dependency, raising emissions and household energy costs. We are not ready for this massive increase in AI energy production.
This isn’t just about tech—it’s about who pays for progress. AI promises smarter cities, medicine, and governance, but its infrastructure demands a hidden tax: on grids, ecosystems, and communities.
AI is a hungry beast, and it needs feeding. The genie is out of the bottle!
On 31st August 2025, Jaguar Land Rover (JLR), one of Britain’s most iconic automotive manufacturers, was struck by a crippling cyber-attack that forced an immediate halt to production across its UK facilities.
The incident, described by MP Liam Byrne as a ‘digital siege’, has since spiralled into a full-blown supply chain crisis, threatening thousands of jobs and exposing vulnerabilities in the nation’s industrial backbone.
The attack, believed to be a coordinated effort by cybercrime groups Scattered Spider, Lapsus$, and ShinyHunters, targeted JLR’s production systems, rendering them inoperable.
By 1st September, operations were suspended, and by 22nd September 2025, the shutdown had extended to three weeks, with staff instructed to stay home.
A forensic investigation is ongoing, and JLR has delayed its restart timeline until 1st October 2025.
The toll
The financial toll is staggering. Estimates suggest the company is losing £50 million per week. With no cyber insurance in place, JLR has been left scrambling to stabilise its operations and reassure its extensive supplier network—comprising over 120,000 jobs, many in small and medium-sized enterprises.
In response, the UK government has stepped in with a £1.5 billion loan guarantee, backed by the Export Development Guarantee scheme.
This emergency support aims to shore up JLR’s cash reserves, protect skilled jobs in the West Midlands and Merseyside, and prevent collapse among its suppliers.
Business Secretary Peter Kyle and Chancellor Rachel Reeves have both emphasised the strategic importance of JLR to Britain’s economy, calling the intervention a ‘decisive action’ to safeguard the automotive sector.
The cyber attack has also prompted broader questions about industrial cybersecurity, insurance preparedness, and the resilience of supply chains in the face of digital threats.
Unions have urged the government to ensure the loan translates into job guarantees and fair pay, while cybersecurity experts have called the scale of disruption ‘unprecedented’ for a UK-based manufacturer.
🔐 Ten Major Cyber Attacks of 2025
#
Target
Date
Impact
1️⃣
UNFI (United Natural Foods Inc.)
June
Disrupted food supply chains across North America; automated ordering systems collapsed.
2️⃣
Bank Sepah (Iran)
March
42 million customer records stolen; hackers demanded $42M in Bitcoin ransom.
3️⃣
TeleMessage (US Gov Messaging App)
May
Metadata of officials exposed, including FEMA and CBP; triggered national security alerts.
4️⃣
Marks & Spencer (UK)
April–May
Ransomware attack led to 46-day online outage; £300M profit warning.
5️⃣
Co-op (UK)
May
In-store systems crashed; manual tills and supply chain breakdowns across 2,300 stores.
6️⃣
Mailchimp & HubSpot
April
Credential theft and phishing campaigns; fake invoices sent to thousands.
7️⃣
Hertz
April
Global breach with unclear UK impact; customer data compromised.
8️⃣
Anonymous Data Leak
January
18.8 million records exposed; no company claimed responsibility.
9️⃣
Microsoft SharePoint Servers
Ongoing
Exploited by China-linked threat actors; widespread “ToolShell” compromises.
🔟
Ingram Micro (IT Distributor)
July
Ransomware attack by SafePay group; disrupted global tech supply chains.
As JLR works with law enforcement and cybersecurity specialists to restore operations, the incident stands as a stark reminder: in the digital age, even the most storied brands are vulnerable to invisible adversaries.
Other prominent recent major cyber attacks
#
Attack Name
Target
Impact
1️⃣
Change Healthcare Ransomware
U.S. healthcare system
Disrupted nationwide medical services; $22M ransom paid3
2️⃣
Snowflake Data Breach
AT&T, Ticketmaster, Santander
630M+ records stolen; MFA failures exploited3
3️⃣
Salt Typhoon & Volt Typhoon
U.S. telecom & infrastructure
Espionage targeting political figures & critical systems3
4️⃣
CrowdStrike-Microsoft Outage
Global IT services
Massive disruption due to botched update
5️⃣
Synnovis-NHS Ransomware
UK healthcare labs
Halted blood testing across London hospitals
6️⃣
Ascension Ransomware Attack
U.S. hospital chain
Patient care delays; data exfiltration
7️⃣
MediSecure Breach
Australian e-prescription provider
Sensitive medical data leaked
8️⃣
Ivanti Zero-Day Exploits
Global VPN users
Nation-state actors exploited vulnerabilities
9️⃣
TfL Cyber Attack
Transport for London
Internal systems disrupted; public services affected
As artificial intelligence surges into every corner of modern life—from predictive finance to generative art—the question isn’t just what AI can do, but what it consumes to do it.
The energy appetite of large-scale AI models is no longer a footnote; it’s the headline.
Training a single frontier model can devour as much electricity as hundreds of UK homes use in a year. And once deployed, these systems don’t slim down—they scale up.
Every query, every image generation, every chatbot exchange draws from vast data centres, many powered by fossil fuels or water-intensive cooling systems.
The irony? AI is often pitched as a tool for climate modelling, yet its own carbon footprint is ballooning.
This isn’t just a technical dilemma—it’s a moral one. The race to build smarter, faster, more responsive AI has become a kind of energy arms race. Tech giants tout efficiency gains, but the underlying logic remains extractive: more data, more compute, more power.
Meanwhile, communities near data centres face water shortages, grid strain, and rising costs—all for services they may never use.
Future direction
Where is this heading? On one side, we’ll see ‘greenwashed’ AI—models marketed as sustainable thanks to token offsets or renewable pledges. On the other, a growing movement for ‘degrowth AI’: systems designed to be lean, local, and ethically constrained. Think smaller models trained on curated datasets, prioritising transparency over scale.
AI power – the energy hunger game! NASA’s ambition is to place nuclear power on the moon
Governments are waking up, too. The EU and UK are exploring energy disclosure mandates for AI firms, while some U.S. states are scrutinising water usage and land rights around data infrastructure. But regulation lags behind innovation—and behind marketing.
Ultimately, the energy hunger game isn’t just about watts and emissions. It’s about values. Do we want AI that mirrors our extractive habits, or one that challenges them? Can intelligence be decoupled from excess?
The next frontier isn’t smarter models—it’s wiser ones. And wisdom, unlike raw compute, doesn’t need a megawatt to shine.
Why Nuclear Is Back on the Table
Global Momentum: Thirty-one countries have pledged to triple nuclear capacity by 2050, framing it as a cornerstone of clean energy strategy.
AI’s Power Problem: With data centres projected to consume more energy than Japan by 2026, nuclear is being pitched as the only scalable, low-carbon solution that can deliver round-the-clock power.
Baseload Reliability: Unlike solar and wind, nuclear doesn’t flinch at nightfall or cloudy skies. That makes it ideal for powering critical infrastructure—especially AI, which can’t afford downtime.
🧪 Next-Gen Tech on the Horizon
Small Modular Reactors (SMRs): These compact units promise faster deployment, lower costs, and safer operation. China and Russia already have some online.
Fusion Dreams: Still experimental, but if cracked, fusion could offer near-limitless clean energy. It’s the holy grail—though still more sci-fi than supply chain.
⚖️ The Catch? Cost, Waste, and Public Trust
Nuclear remains expensive to build and politically fraught. Waste disposal and safety concerns haven’t vanished, and public opinion is split—especially in the UK.
Even with advanced designs, the spectres of Chernobyl and Fukushima linger in the cultural memory. That’s a narrative hurdle as much as a technical one.
🛰️ Moonshots and Geopolitics
NASA’s push to deploy a nuclear reactor on the moon by 2029 underscores how strategic this tech has become—not just for Earth, but for space dominance.
The U.S.–China race isn’t just about chips anymore. It’s about who controls the energy to power them.
Nuclear is staging a comeback—not as a relic of the past, but as a potential backbone of the future.
Whether it becomes the dominant force or a transitional ally depends on how fast we can build, how safely we can operate, and how wisely we choose to deploy.
🌍 How ‘clean’ is green?
According to MIT’s Climate Portal, no energy source is perfectly clean. Even solar panels, wind turbines, and nuclear plants come with embedded emissions—from mining rare metals to manufacturing components and transporting them.
So, while they don’t emit greenhouse gases during operation, their setup and maintenance do leave a footprint.
Huawei has unveiled a bold new AI chip cluster strategy aimed squarely at challenging Nvidia’s dominance in high-performance computing.
At its Connect 2025 conference in Shanghai, Huawei introduced the Atlas 950 and Atlas 960 SuperPoDs—massive AI infrastructure systems built around its in-house Ascend chips.
These clusters represent China’s most ambitious attempt yet to bypass Western semiconductor restrictions and assert technological independence.
The technical stuff
The Atlas 950 SuperPoD, launching in late 2026, will integrate 8,192 Ascend 950DT chips, delivering up to 8 EFLOPS of FP8 compute and 16 EFLOPS at FP4 precision. (Don’t ask me either – but that’s what the data sheet says).
It boasts a staggering 16.3 petabytes per second of interconnect bandwidth, enabled by Huawei’s proprietary UnifiedBus 2.0 optical protocol. It is reportedly claimed to be ten times faster than current internet backbone infrastructure.
This system is reportedly designed to outperform Nvidia’s NVL144 cluster, with Huawei asserting a 6.7× advantage in compute power and 15× in memory capacity.
In 2027, Huawei reportedly plans to release the Atlas 960 SuperPoD, doubling the specs with 15,488 Ascend 960 chips. This reportedly will give 30 EFLOPS FP8 compute, and 34 PB/s bandwidth.
These SuperPoDs will be linked into SuperClusters. The Atlas 960 SuperCluster is reportedly projected to reach 2 ZFLOPS of FP8 performance. This potentially rivals even Elon Musk’s xAI Colossus and Nvidia’s future NVL576 deployments.
Huawei’s roadmap includes annual chip upgrades: Ascend 950 in 2026, Ascend 960 in 2027, and Ascend 970 in 2028.
Each generation promises to double computing power. The chips will feature Huawei’s own high-bandwidth memory variants—HiBL 1.0 and HiZQ 2. These are designed to optimise inference and training workloads.
Strategy
This strategy reflects a shift in China’s AI hardware approach. Rather than competing on single-chip performance, Huawei is betting on scale and system integration.
By controlling the entire stack—from chip design to memory, networking, and interconnects—it aims to overcome fabrication constraints imposed by U.S. sanctions.
While Huawei’s software ecosystem still trails Nvidia’s CUDA, its CANN toolkit is gaining traction. Chinese regulators discourage purchases of Nvidia’s AI chips.
The timing of Huawei’s announcement coincides with increased scrutiny of Nvidia in China, suggesting a coordinated push for domestic alternatives.
In short, Huawei’s AI cluster strategy is not just a technical feat—it’s a geopolitical statement.
Whether it can match Nvidia’s real-world performance remains to be seen, but the ambition is unmistakable.
Oracle Corporation has just staged one of the most dramatic rallies in tech history—catapulting itself into the elite club of near-trillion-dollar companies and reshaping the billionaire leaderboard in the process.
Founded in 1977 by Larry Ellison, Oracle began as a modest database software firm. Its first major boom came in the late 1990s, riding the dot-com wave as enterprise software demand exploded.
By 2000, Oracle’s market cap had surged past $160 billion, making it one of the most valuable tech firms of the era.
A second wave of growth followed in the mid-2000s, fuelled by aggressive acquisitions like PeopleSoft and Sun Microsystems, which expanded Oracle’s footprint into enterprise applications and hardware.
Boom
But its most recent boom—triggered in 2025—is unlike anything before. Oracle’s pivot to cloud infrastructure and artificial intelligence has paid off spectacularly. In its fiscal Q1 2026 report, Oracle revealed $455 billion in remaining performance obligations (RPO), a staggering 359% increase year-over-year.
This backlog, driven by multi-billion-dollar contracts with AI giants like OpenAI, Meta, Nvidia, and xAI, sent shockwaves through Wall Street.
Despite missing revenue and earnings expectations slightly—$14.93 billion in revenue vs. $15.04 billion expected, and $1.47 EPS vs. $1.48 forecasted—the market responded with euphoria.
Oracle’s stock soared nearly 36% in a single day, adding $244 billion to its market cap and pushing it to approximately $922 billion. Analysts called it ‘absolutely staggering’ and ‘truly awesome’, with Deutsche Bank reportedly raising its price target to $335.
Oracle Infographic September 2025
This meteoric rise had personal consequences too. Larry Ellison, Oracle’s co-founder and current CTO, saw his net worth jump by over $100 billion in one day, briefly surpassing Elon Musk to become the world’s richest person.
His fortune reportedly peaked at around $397 billion, largely tied to his 41% stake in Oracle. Ellison’s journey—from college dropout to tech titan—is now punctuated by the largest single-day wealth gain ever recorded.
CEO Safra Catz also benefited, with her net worth rising by $412 million in just six hours of trading, bringing her total to $3.4 billion. Under her leadership, Oracle’s stock has risen over 800% since she became sole CEO in 2019.
Oracle’s forecast for its cloud infrastructure business is equally jaw-dropping: $18 billion in revenue for fiscal 2026, growing to $144 billion by 2030. If these projections hold, Oracle could soon join the trillion-dollar club alongside Microsoft, Apple, and Nvidia.
From database pioneer to AI infrastructure powerhouse, Oracle’s evolution is a masterclass in strategic reinvention.
Oracle one-year chart 10th September 2025
Oracle one-year chart 10th September 2025
And with Ellison now at the summit of global wealth, the company’s narrative is no longer just about software—it’s about legacy, dominance, and the future of intelligent computing.
Has BIG tech and AI stopped hiring? Not quite, though the hiring landscape has definitely shifted gears. Here’s the current take…
🧠 AI Hiring: Still Hot, Just More Focused
Private AI firms like OpenAI, Anthropic, and Perplexity are still hiring aggressively, especially for Machine Learning Engineers and Enterprise Sales roles. These two categories alone account for thousands of openings.
Even legacy tech giants like Salesforce are scaling up AI-focused sales teams—Marc Benioff announced 2,000 new hires just to sell AI solutions.
The demand for ML Engineers has splintered into niche specializations like LLM fine-tuning, inference optimisation, and RAG infrastructure, showing how deep the rabbit hole goes.
🖥️ Big Tech: Cooling, Not Collapsing
Across the U.S., software engineering roles dropped from 170,000 in March to under 150,000 by July.
AI job postings fell from 80,000 in February to just over 50,000 in June, though July showed a slight rebound.
Despite the slowdown, AI still makes up 11–15% of all software roles, suggesting it’s a strategic priority even as overall hiring cools.
🌍 Beyond Silicon Valley
States like South Dakota and Connecticut are seeing surprising growth in AI job postings—South Dakota reportedly jumped 164% last month.
The hiring boom is expanding into non-traditional industries, not just Big Tech. Think biotech, retail, and even energy sectors integrating AI.
So while the hiring frenzy of 2023 has mellowed, AI talent remains a hot commodity—just more targeted and strategic.
The general reporting across August 2025 paints a clear picture of slower, more cautious hiring, especially in tech and AI-adjacent roles.
🧊 Hiring Has Cooled—Especially for AI-Exposed Roles
In the UK, tech and finance job listings fell 38%, nearly double the broader market decline.
Entry-level roles and those involving repetitive tasks (like document review or meeting summarisation) are increasingly at risk of automation.
Even in sectors with strong business performance, such as IT and professional services, job opportunities have continued to shrink.
🧠 AI’s Paradox: High Usage, Low Maturity
McKinsey reportedly says that while 80% of large firms use AI, only 1% say their efforts are mature, and just 20% report enterprise-level earnings impact.
Most AI deployments are still horizontal (chatbots, copilots), while vertical use cases (full process automation) remain stuck in pilot mode.
Infographic of AI effect on jobs and hiring
📉 Broader Market Signals
Job adverts have dropped most for occupations most exposed to AI, especially among young graduates.
Despite a slight uptick in hiring intentions in June and July, the overall labour market shows a marked cooling.
So yes, the general tone is one of strategic hesitation—companies are integrating AI but not rushing to hire unless the role is future-proofed.
AI In, Jobs Out: The Great Hiring Slowdown
It’s official: the AI revolution has arrived—but the job listings didn’t get the memo.
Across the UK and U.S., tech hiring has slowed to a cautious crawl. Once-bustling boards now resemble digital ghost towns, especially for roles most exposed to automation.
Software engineering vacancies dropped by over 20% in just four months, while AI-related postings—once the darlings of 2023—have cooled from 80,000 to barely 50,000.
The irony? AI adoption is booming. Over 80% of large firms now deploy some form of artificial intelligence, from chatbots to copilots.
Yet only 1% claim their efforts are ‘mature’, and fewer still report meaningful earnings impact. It’s a paradox: widespread usage, minimal payoff, and a hiring freeze to match.
Even in sectors with strong performance—IT, finance, professional services—the job market is shrinking. Graduates face a particularly frosty reception, as entry-level roles vanish into the algorithmic ether.
Meanwhile, AI firms themselves are hiring with surgical precision: machine learning engineers and enterprise sales reps remain in demand, but the days of blanket recruitment are over.
Geographically, the hiring map is shifting too. South Dakota saw a 164% spike in AI job postings last month, while London and San Francisco quietly tightened their belts.
So, AI isn’t killing jobs—it’s reshaping them. The new roles demand fluency in automation, compliance, and creative problem-solving.
The rest? They’re being quietly retired.
For now, the job market belongs to the adaptable, the analytical, and the algorithmically literate.
Everyone else may need to reboot, eventually, but not quite just yet.
