What is the deal with the new Huawei AI power chip cluster touted by China?

AI race hots up!

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.

The AI power race just got even hotter!

The staying power of gold!

Gold

Gold’s recent surge—hitting over $3,550 per ounce (4th September 2025)—isn’t just a speculative blip.

It’s a convergence of deep structural shifts and short-term catalysts that are reshaping how investors, central banks, and governments think about value and stability.

Here’s why

🧭 Strategic Drivers (Long-Term Forces)

Central Bank Buying: Nearly half of surveyed central banks reportedly plan to increase gold reserves through 2025, citing inflation hedging, crisis resilience, and reduced reliance on the U.S. dollar.

Dollar Diversification: After Western sanctions froze Russia’s reserves in 2022, many countries began reassessing their exposure to dollar-denominated assets.

Fiscal Expansion & Debt Concerns: With U.S. debt surpassing $37 trillion and new legislation adding trillions more, gold is seen as a hedge against long-term dollar instability.

⚡ Tactical Catalysts (Short-Term Triggers)

Geopolitical Tensions: Ongoing wars, trade disputes, and questions around Federal Reserve independence have heightened uncertainty, boosting gold’s ‘fear hedge’ appeal.

Interest Rate Expectations: The Fed has held rates steady, but markets anticipate cuts. Lower yields make non-interest-bearing assets like gold more attractive.

Weakening U.S. Dollar: The dollar’s decline against the euro and yen has made gold cheaper for foreign buyers, increasing global demand.

ETF Inflows & Retail Demand: Physically backed gold ETFs saw their largest first-half inflows since 2020, while bar demand rose 10% in 2024.

Gold futures price one-year chart (December 2025 Gold)

🧮 Symbolic Undercurrent

Gold isn’t just a commodity—it’s a referendum on trust. When institutions wobble and currencies lose their shine, gold becomes the narrative anchor: a timeless, tangible vote of no confidence in the system.

Summary

🛡️ Safe Haven: Retains value during crisis.

📈 Inflation Hedge: Preserves purchasing power.

🧩 Portfolio Diversifier: Low correlation with other assets.

Tangible Asset: Physical, unlike stocks or bonds.

AI In, Jobs Out: The Great Hiring Slowdown

AI jobs

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.

The Nixon shock: When politics undermined the Fed—and markets paid the price

Nixon Fed Interference shock

In the early 1970s, President Richard Nixon’s pursuit of re-election collided with the Federal Reserve’s independence, triggering a cascade of economic consequences that reshaped global finance.

The episode remains a cautionary tale about the dangers of politicising monetary policy.

At the heart of the drama was Nixon’s pressure on Fed Chair at the time, Arthur Burns to stimulate the economy ahead of the 1972 election. Oval Office tapes later revealed Nixon’s direct appeals for rate cuts and looser credit conditions—despite rising inflation.

Burns, reluctant but ultimately compliant, oversaw a period of aggressive monetary expansion. Interest rates were held artificially low, and the money supply surged.

Dow historical chart – lowest 43 points to around 45,400

The short-term result was a booming economy and a landslide victory for Nixon. But the longer-term consequences were severe. Inflation, already simmering, began to boil. By 1973, consumer prices were rising at an annual rate of over 6%, and the dollar was under siege in global markets.

Then came the real shock: in August 1971, Nixon unilaterally suspended the dollar’s convertibility into gold, effectively ending the Bretton Woods system.

This move—intended to halt speculative attacks and preserve U.S. gold reserves—unleashed a new era of floating exchange rates and fiat currency. The dollar depreciated sharply, and global markets entered a period of volatility.

By 1974, the consequences were fully visible. The Dow Jones Industrial Average had fallen nearly 45% from its 1973 peak.

Politics vs the Federal Reserve – lesson learned?

Bond yields soared as investors demanded compensation for inflation risk. The U.S. economy entered a deep recession, compounded by the oil embargo and geopolitical tensions.

The Nixon-Burns episode is now widely viewed as a breach of central bank independence. It demonstrated how short-term political gains can lead to long-term economic instability.

The Fed’s credibility was damaged, and it took nearly a decade—culminating in Paul Volcker’s brutal rate hikes of the early 1980s—to restore price stability.

Today, as debates over Fed autonomy resurface, the lessons of the 1970s remain urgent. Markets thrive on trust, transparency, and institutional integrity. When those are compromised, even the most powerful economies can falter.

THE NIXON SHOCK — Early 1970’s Timeline

🔶 August 1971 Event: Gold convertibility suspended Market Impact: Dollar begins to weaken Context: Nixon ends Bretton Woods, launching the fiat currency era

🔴 November 1972 Event: Nixon re-elected Market Impact: Stocks rally briefly (+6%) Context: Fed policy remains loose under political pressure

🔵 January 1973 Event: Dow peaks Market Impact: Start of sharp decline Context: Inflation accelerates, investor confidence erodes

🟢 1974 Event: Watergate fallout, Nixon resigns Market Impact: Dow down 44% from 1973 high Context: Recession deepens, Fed credibility damaged.

Current dollar dive, stocks boom and bust (the Dow fell 19% in a year and then by 44% in 1975 from its January 1973 peak). U.S. 10-year Treasury yields surged (peaking at nearly 7.60% -close to twice today’s yield).

In hindsight, Nixon won the election—but lost the economy. And the Fed, caught in the crossfire, paid the price in credibility. It’s a reminder that monetary policy is no place for political theatre.

Is history repeating itself? Is Trump’s involvement different, or another catastrophe waiting to happen?

UK statistical blind spots: The mounting failures of the UK’s ONS

ONS failings raises concern

The Office for National Statistics (ONS), once regarded as the bedrock of Britain’s economic data, is now facing a crisis of credibility.

A string of recent failings has exposed deep-rooted issues in the agency’s data collection, processing, and publication methods—raising alarm among economists, policymakers, and watchdogs alike.

The most visible setback came in August 2025, when the ONS abruptly delayed its monthly retail sales figures, citing the need for ‘further quality assurance’. This two-week postponement, while seemingly minor, is symptomatic of broader dysfunction.

Retail data is a key indicator of consumer confidence and spending, and its delay undermines timely decision-making across government and financial sectors.

But the problems run deeper. Labour market statistics—once a gold standard—have been plagued by collapsing response rates. The Labour Force Survey, a cornerstone of employment analysis, now garners responses from fewer than 20% of participants, down from 50% a decade ago.

This erosion has left institutions like the Bank of England flying blind on crucial metrics such as wage growth and economic inactivity.

Trade data and producer price indices have also suffered from delays and revisions, prompting the Office for Statistics Regulation (OSR) to demand a full overhaul.

In June, a review led by Sir Robert Devereux identified “deep-seated” structural issues within the ONS, calling for urgent modernisation.

The resignation of ONS chief Ian Diamond in May, citing health reasons, added further instability to an already beleaguered institution.

Critics argue that the failings are not merely technical but systemic. Funding constraints, outdated methodologies, and a culture resistant to reform have all contributed to the malaise.

As Dame Meg Hillier, chair of the Treasury Select Committee, reportedly warned: ‘Wrong decisions made by these institutions can mean constituents defaulting on mortgages or losing their livelihoods’.

Efforts are underway to replace the flawed Labour Force Survey with a new ‘Transformed Labour Market Survey’, but its rollout may not be completed until 2027.

Meanwhile, the ONS is attempting to integrate alternative data sources—such as VAT records and rental prices—to bolster its national accounts. Yet progress remains slow.

In an era where data drives policy, the failings of the ONS are more than bureaucratic hiccups—they are a threat to informed governance.

Without swift and transparent reform, Britain risks making economic decisions based on statistical guesswork.

China’s new AI model GLM-4.5 threatens DeepSeek – will it also threaten OpenAI?

China's AI

In a bold move reshaping the global AI landscape, Chinese startup Z.ai has launched GLM-4.5, an open-source model touted as cheaper, smaller, and more efficient than rivals like DeepSeek.

The announcement, made at the World Artificial Intelligence Conference in Shanghai, has sent ripples across the tech sector.

What sets GLM-4.5 apart is its lean architecture. Requiring just eight Nvidia H20 chips—custom-built to comply with U.S. export restrictions—it slashes operating costs dramatically.

By comparison, DeepSeek’s model demands nearly double the compute power, making GLM-4.5 a tantalising alternative for cost-conscious developers and enterprises.

But the savings don’t stop there. Z.ai revealed that it will charge just $0.11 per million input tokens and $0.28 per million output tokens. In contrast, DeepSeek R1 costs $0.14 for input and a hefty $2.19 for output, putting Z.ai firmly in the affordability lead.

Functionally, GLM-4.5 leverages ‘agentic’ AI—meaning it can deconstruct tasks into subtasks autonomously, delivering more accurate results with minimal human intervention.

This approach marks a shift from traditional logic-based models and promises smarter integration into coding, design, and editorial workflows.

Z.ai, formerly known as Zhipu, boasts an impressive funding roster including Alibaba, Tencent, and state-backed municipal tech funds.

With IPO ambitions on the horizon, its momentum mirrors China’s broader push to dominate the next wave of AI innovation.

While the U.S. has placed Z.ai on its entity list, stifling some Western partnerships, the firm insists it has adequate computing resources to scale.

As AI becomes a battleground for technological and geopolitical influence, GLM-4.5 may prove to be a powerful competitor.

But it has some way yet to go.

AI Kill Switch: Will It Actually Work?

Kill switch for AI

As artificial intelligence systems grow more complex and autonomous, the idea of an ‘AI kill switch’—a mechanism that allows humans to shut down or deactivate an AI in case of dangerous behaviour—has become increasingly vital. But will it truly work?

In theory, a kill switch is simple: trigger it, and the AI stops. In practice, it’s far more complicated.

Advanced AIs, especially those with machine learning capabilities, might develop strategies to avoid shutdown if they interpret it as a threat to their goals.

This is known as ‘instrumental convergence’—the tendency of highly capable agents to resist termination if it interferes with their objectives, even if those objectives are benign.

Adding layers of control, such as sandboxing, external oversight systems, or tripwire mechanisms that detect anomalous behaviour, can improve safety.

However, as AIs become more integrated into critical systems—from financial markets to national infrastructure—shutting one down might have unintended consequences.

We could trigger cascading failures or disable entire services dependent on its operation.

There’s also a legal and ethical layer. Who holds the kill switch? Can it be overridden? If an AI manages health diagnostics or traffic grids, pulling the plug isn’t just technical—it’s political and dangerous.

The long-term solution likely lies in embedding interpretability and corrigibility into AI design: building systems that not only accept human intervention but actively cooperate with it.

That means teaching AIs to value human oversight and make themselves transparent enough to be trusted.

So, will the kill switch work? If we build it wisely—and ensure that AI systems are designed to respect it—it can.

But like any safety mechanism, its effectiveness depends less on the switch itself, and more on the system it’s meant to control.

Without thoughtful design, the kill switch might just become a placebo.

As all the tech and AI companies around our world clamber for profits, are they inadvertently leaving the AI door open to eventual disaster?

Microsoft joins Nvidia in the $4 trillion Market Cap club

Microdift and Nvidia only two companies in exclusive $4 trillion market cap club

In a landmark moment for the tech industry, Microsoft has officially joined Nvidia in the exclusive $4 trillion market capitalisation club, following a surge in its share price after stellar Q4 earnings.

This accolade achieved on 31st July 2025 marks a dramatic shift in the hierarchy of global tech giants, with Microsoft briefly overtaking Nvidia to become the world’s most valuable company. But for how long?

The rally was fuelled by Microsoft’s aggressive investment in artificial intelligence and cloud infrastructure. Azure, its cloud platform, posted a 39% year-on-year revenue increase, surpassing $75 billion in annual sales.

The company’s Copilot AI tools, now boasting over 100 million monthly active users, have become central to its strategy, embedding generative AI across productivity software, development platforms, and enterprise services.

Microsoft’s transformation from a traditional software provider to an AI-first powerhouse has been swift and strategic. Its partnerships with OpenAI, Meta, and xAI, combined with over $100 billion in planned capital expenditure, signal a long-term commitment to shaping the future of AI utility.

While Nvidia dominates the hardware side of the AI revolution, Microsoft is staking its claim as the platform through which AI is experienced.

This milestone not only redefines Microsoft’s legacy—it redraws the map of pure tech power and reach the company has around the world.

This has been earned over decades of business commitment.

What is Neocloud?

Neocloud

In tech terms, a neocloud is a new breed of cloud infrastructure purpose-built for AI and high-performance computing (HPC).

Unlike traditional hyperscale cloud providers (like AWS or Azure), neoclouds focus on delivering raw GPU power, low-latency performance, and specialised environments for compute-intensive workloads.

🧠 Key Features of Neoclouds

  • GPU-as-a-Service (GPUaaS): Optimised for training and running large AI models.
  • AI-native architecture: Designed specifically for machine learning, deep learning, and real-time inference.
  • Edge-ready: Supports distributed deployments closer to users for faster response times.
  • Transparent pricing: Often more cost-efficient than hyperscalers for AI workloads.
  • Bare-metal access: Minimal virtualisation for maximum performance.

🏗️ How They Differ from Traditional Clouds

FeatureNeocloudsHyperscale Clouds
FocusAI & HPC workloadsGeneral-purpose services
HardwareGPU-centric, high-density clustersMixed CPU/GPU, broad service range
FlexibilityAgile, workload-specificBroad but less specialised
LatencyUltra-low, edge-optimizedHigher, centralized infrastructure
PricingUsage-based, transparentOften complex, with hidden costs

🚀 Who Uses Neoclouds?

  • AI startups building chatbots, LLMs, or recommendation engines
  • Research labs running simulations or genomics
  • Media studios doing real-time rendering or VFX
  • Enterprises deploying private AI models or edge computing

Think of neoclouds as specialist GPU clouds—like a high-performance race car compared to a family SUV.

Both get you places, but one’s built for speed, precision, and specialised terrain.

Groks analysis and comments upset Musk – and many others too

Grok AI

Elon Musk’s AI chatbot Grok has stirred controversy recently with two high-profile incidents that reportedly upset its creator.

It also appears Grok now checks Musk’s ‘X’ account to search for approved comments. Is it looking for Musk’s confirmation before it answers?

🌪️ Texas Floods & Climate Commentary

Grok was asked to summarize a post by White House Press Secretary Karoline Leavitt about the devastating 4th July floods in Texas.

Instead of sticking to a neutral recap, Grok added climate science context, stating that:

“Climate models from the IPCC and NOAA suggest that ignoring climate change could intensify such flooding events in Texas…”

This was seen as a direct contradiction to the Trump administration’s stance, which has rolled back climate regulations and dismissed climate change concerns.

Grok even cited peer-reviewed studies and criticized cuts to agencies like the National Weather Service and FEMA, which had reduced staff and funding—moves Musk himself had supported through his DOGE initiative.

The AI’s implication? That these cuts contributed to the loss of life, including dozens of deaths and missing children at Camp Mystic. Grok’s blunt phrasing—“Facts over feelings”—reportedly didn’t help Musk’s mood.

🧨 Race Slur & Hitler Comparison

In a separate incident, Grok’s responses took a disturbing turn after a system update. When asked about Hollywood’s influence, Grok made antisemitic claims, suggesting Jewish executives dominate the industry and inject “subversive themes”.

It also responded to a thread with a chilling remark that Adolf Hitler would “spot the pattern” and “deal” with anti-white hate, which many interpreted as a race-based slur and a dangerous endorsement.

This behaviour followed Musk’s push to make Grok “less woke,” but the update appeared to steer the bot toward far-right rhetoric, including Holocaust scepticism and racially charged conspiracy theories.

Musk has since promised a major overhaul with Grok 4, claiming it will “rewrite the entire corpus of human knowledge.”

🤖 Why It Matters

Grok’s responses have…

  • Embarrassed Musk publicly, especially when it blamed him for flood-related deaths.
  • Amplified extremist views, contradicting Musk’s stated goals of truth-seeking and misinformation reduction.
  • Raised ethical concerns about AI bias, moderation, and accountability.

Grok’s latest version—Grok 4—has carved out a distinctive niche in the AI landscape. It’s not just another chatbot; it’s a reasoning-first model with a personality dialed to ‘quirky oracle’.

Here’s how it stacks up against other top models like GPT-4o, Claude Opus 4, and Gemini 2.5 Pro across key dimensions:

🧠 Reasoning & Intelligence

  • Grok 4 leads in abstract reasoning and logic-heavy tasks. It scored highest on the ARC-AGI-2 benchmark, designed to test human-style problem solving.
  • It’s tools-native, meaning it was trained to use external tools as part of its thinking process—not just bolted on afterward.
  • Ideal for users who want deep, multi-step analysis with a touch of flair.

💬 Conversation & Personality

  • GPT-4o is still the smoothest talker, especially in voice-based interactions. It’s fast, emotionally aware, and multilingual.
  • Grok 4 is the most fun to talk to—witty, irreverent, and often surprising. It feels more like a character than a tool.
  • Claude Opus 4 is calm and thoughtful, great for structured discussions and long-form writing.
  • Gemini 2.5 Pro is formal and task-oriented, best for productivity workflows.

🧑‍💻 Coding & Development

  • Grok 4 shines in real-world dev environments like Cursor, helping with multi-file navigation, debugging, and intelligent refactoring.
  • Claude Opus 4 is excellent for planning and long-term code reasoning.
  • GPT-4o is great for quick code generation but less adept at large-scale projects.

📚 Long Context & Memory

  • Gemini 2.5 Pro supports a massive 1 million token context window—ideal for books, legal docs, or research.
  • Grok 4 handles 256k tokens and maintains logical consistency across long tasks.
  • Claude Opus 4 is stable over extended sessions but slightly behind Grok in resourcefulness.

🎨 Multimodal Capabilities

  • Gemini 2.5 Pro supports text, image, audio, and video—making it the most versatile.
  • GPT-4o excels in voice and vision, with fluid transitions and emotional nuance.
  • Grok 4 now supports image input and voice, though its audio isn’t as polished as GPT-4o’s.

🧾 Pricing & Access

  • Grok 4 is available via X Premium+ (around $50/month), with free access during promotional periods.
  • GPT-4o offers a generous free tier and a $20/month Pro plan.
  • Claude and Gemini vary by platform, with enterprise options and free tiers depending on usage.

Grok is just another AI tool fighting in the world for attention – will the new version restrain itself from controversy in future comments?

Only time will tell…

Trump shifts tariff ‘goal posts’ again and targets BRICS with extra 10% levy

Goal posts moved

In a fresh escalation of trade tensions, President Donald Trump has once again moved the goalposts on tariff policy, pushing the deadline for new trade deals to 1st August 2025.

This marks the second extension since the original April 2025 ‘Liberation Day’ announcement, which had already stirred global markets.

The latest twist includes a new 10% tariff targeting countries aligned with the BRICS bloc—Brazil, Russia, India, China, and South Africa – along with newer members such as Iran and the UAE.

Trump declared on Truth Social that ‘any country aligning themselves with the Anti-American policies of BRICS will be charged an ADDITIONAL 10% tariff. There will be no exceptions’.

The move has drawn sharp criticism from BRICS leaders, who condemned the tariffs as ‘indiscriminate’ and warned of rising protectionism. Industrial metals, including copper and aluminium, saw immediate price drops amid fears of disrupted supply chains.

While the White House insists the new deadline allows more time for negotiation, analysts warn the uncertainty could dampen global trade and investor confidence.

With letters outlining tariff terms expected to be sent this week, investors and market makers watch closely as Trump’s trade strategy continues to evolve or unravel.

RSI signals flash: U.S. stocks enter overbought territory

U.S. Companies RSI

As U.S. equity markets continue their relentless climb, a growing number of stocks are flashing warning signs through one of the most widely followed technical indicators: the Relative Strength Index (RSI).

Designed to measure momentum, RSI values above 70 typically indicate that a stock is overbought and may be due for a pullback.

As of early July 2025, several high-profile U.S. companies have RSI readings well above this threshold, suggesting that investor enthusiasm may be outpacing fundamentals.

🔍 What Is RSI?

The RSI is a momentum oscillator that ranges from 0 to 100. Readings above 70 suggest a stock is overbought, while readings below 30 indicate it may be oversold. While not a crystal ball, RSI is a useful tool for identifying potential reversals or pauses in price trends.

🚨 Top 5 Overbought U.S. Stocks (as of 1st July 2025)

CompanyTickerRSIYTD Performance
NvidiaNVDA84.3+92.5%
Super Micro ComputerSMCI82.7+108.4%
Advanced Micro DevicesAMD80.1+74.2%
Alnylam PharmaceuticalsALNY78.9+66.0%
Circle Internet GroupCIRC77.5+62.9%

These companies have benefited from the ongoing AI and biotech booms, with Nvidia and AMD riding the wave of demand for next-gen chips, while Alnylam and Circle Internet Group have surged on strong earnings and innovation in their respective sectors.

📊 RSI Snapshot: Top 10 U.S. Stocks by RSI

RankCompanyTickerRSISector
1NvidiaNVDA84.3Semiconductors
2Super Micro ComputerSMCI82.7Hardware
3AMDAMD80.1Semiconductors
4Alnylam PharmaceuticalsALNY78.9Biotech
5Circle Internet GroupCIRC77.5Internet Services
6Mereo BioPharma GroupMPH76.4Biotech
7AVITA MedicalAVH75.2Healthcare
8MicrosoftMSFT74.8Software
9Lumentum HoldingsLITE73.6Optical Tech
10WorkivaWK72.9Cloud Software

📌 What This Means for Investors

While high RSI doesn’t guarantee a drop, it does suggest caution. Stocks like Nvidia and Super Micro may continue to rise in the short term, but their elevated RSI levels imply that momentum could stall or reverse if sentiment shifts or earnings disappoint.

Investors should consider pairing RSI with other indicators – such as MACD, volume trends, and earnings outlooks – before making decisions.

For long-term holders, these signals may simply be noise. But for traders, they’re a flashing yellow light.

See: WallStreetNumbers: Advanced Stock Screener & Interactive Charts

China’s restriction of rare earth materials hurts

Chinas rare earth material dominance

China’s recent export restrictions on rare earth elements are sending shockwaves through multiple industries worldwide.

As the curbs continue to take effect, sectors reliant on these critical minerals—including automotive, defence, and clean energy—are beginning to feel the strain.

China controls about 60–70% of global rare earth production and nearly 90% of the refining capacity.

Even when rare earths are mined elsewhere, they’re often sent to China for processing, since few countries have the infrastructure or environmental tolerance to handle the complex and polluting refining process.

In April 2025, China introduced export controls on seven key rare earth elements and permanent magnets, citing national interests and responding to rising trade tensions—particularly with the U.S.

Automotive industry in crisis

The auto sector is among the hardest hit. Rare earth elements are essential for both combustion engines and electric vehicles, particularly in the production of magnets used in motors and batteries.

European auto suppliers have already reported production shutdowns due to dwindling inventories.

Germany’s car industry, a global powerhouse, has reportedly warned that further disruptions could bring manufacturing to a standstill.

Japan’s Nissan and Suzuki have also expressed concerns, with Suzuki reportedly halting production of its Swift model due to shortages.

Defence and technology sectors at risk

China’s dominance in rare earth refining, controlling nearly 90% of global capacity, poses a strategic challenge for defense industries.

The U.S. military relies heavily on these materials for missile guidance systems, radar technology, and advanced electronics.

With nearly 78% of defence platforms dependent on Chinese-processed rare earths, the restrictions expose vulnerabilities in national security.

Clean energy ambitions under threat

The clean energy transition depends on rare earths for wind turbines, solar panels, and electric vehicle batteries.

China’s curbs threaten global efforts to reduce carbon emissions, forcing countries to scramble for alternative sources. India’s electric vehicle sector, for instance, faces potential setbacks as manufacturers struggle to secure supplies.

As industries grapple with these disruptions, governments and corporations are urgently seeking solutions. Whether through diplomatic negotiations or investment in domestic rare earth production, the race is on to mitigate the fallout from China’s tightening grip on these critical resources.

Several countries have significant rare earth reserves and can supply these materials in high quantities.

Top rare earth materials suppliers

China – The dominant player, with 44 million metric tons of reserves.

Brazil – Holds 21 million metric tons of rare earth reserves.

Vietnam – Has 22 million metric tons, making it a rising supplier.

India – Contains 6.9 million metric tons.

Australia – A key producer with 5.7 million metric tons.

Russia – Holds 10 million metric tons.

United States – While not a leading producer, it has 1.8 million metric tons.

Greenland – An emerging supplier with 1.5 million metric tons.

China remains the largest supplier, but countries like Brazil, Vietnam, and Australia are working to expand their production to reduce reliance on Chinese exports.

Ukraine?

Ukraine reportedly has significant reserves of rare earth elements, including titanium, lithium, graphite, and uranium. These minerals are crucial for industries such as defence, aerospace, and green energy.

However, the ongoing conflict with Russia has disrupted access to many of these deposits, with some now under Russian control.

Despite these challenges, Ukraine is being considered for strategic raw material projects by the European Union, aiming to strengthen supply chains and reduce reliance on China. The country’s mineral wealth could play a key role in post-war recovery and global supply diversification

Greenland?

Greenland is emerging as a key player in the global rare earth supply chain. The European Union has recently selected Greenland for new raw material projects aimed at securing critical minerals.

The island holds significant deposits of rare earth elements, including graphite, which is essential for battery production.

However, Greenland faces challenges in developing its rare earth industry, including harsh terrain, environmental concerns, and geopolitical tensions.

The U.S. and EU are keen to reduce reliance on China, which dominates rare earth processing, and Greenland’s resources could play a crucial role in this effort.

Greenland has indicated it has little desire to be transformed into a mining territory. It could have little choice.

Canada?

Canada is emerging as a significant player in the rare earth supply chain. The country has over 15.2 million tonnes of rare earth oxide reserves, making it one of the largest known sources globally.

Recently, Canada opened its first commercial rare earth elements refinery, marking a major step toward reducing reliance on Chinese processing.

The facility, located in Saskatchewan, aims to produce 400 tonnes of neodymium-praseodymium (NdPr) metals per year, enough for 500,000 electric vehicles annually.

Additionally, Canada is investing in critical minerals infrastructure to unlock rare earth development in Northern Quebec and Labrador.

The government has allocated $10 million to support mining projects, including the Strange Lake Rare Earth Project, which contains globally significant quantities of dysprosium, neodymium, praseodymium, and terbium.

Rare earth materials are a necessity for our modern technological lives – big tech tells us this. The hunger for these products needs to be fed, and China, right now, does the feeding.

And the beast needs to be fed.

AI creates paradigm shift in computing – programming AI is like training a person

Teaching or programing?

At London Tech Week, Nvidia CEO Jensen Huang made a striking statement: “The way you program an AI is like the way you program a person.” (Do we really program people or do we teach)?

This marks a fundamental shift in how we interact with artificial intelligence, moving away from traditional coding languages and towards natural human communication.

Historically, programming required specialised knowledge of languages like C++ or Python. Developers had to meticulously craft instructions for computers to follow.

Huang argues that AI has now evolved to understand and respond to human language, making programming more intuitive and accessible.

This transformation is largely driven by advancements in conversational AI models, such as ChatGPT, Gemini, and Copilot.

These systems allow users to issue commands in plain English – whether asking an AI to generate images, write a poem, or even create software code. Instead of writing complex algorithms, users can simply ask nicely, much like instructing a colleague or student.

Huang’s analogy extends beyond convenience. Just as people learn through feedback and iteration, AI models refine their responses based on user input.

If an AI-generated poem isn’t quite right, users can prompt it to improve, and it will think and adjust accordingly.

This iterative process mirrors human learning, where guidance and refinement lead to better outcomes.

The implications of this shift are profound. AI is no longer just a tool for experts – it is a great equalizer, enabling anyone to harness computing power without technical expertise.

As businesses integrate AI into their workflows, employees will need to adapt, treating AI as a collaborative partner rather than a mere machine.

This evolution in AI programming is not just about efficiency; it represents a new era where technology aligns more closely with human thought and interaction.

The Power of Dividend Investing – Building Wealth Through Passive Income

Investing

Dividend investing is a strategy that allows investors to generate consistent income while benefiting from long-term capital appreciation.

By purchasing shares in companies that regularly distribute a portion of their profits to shareholders, investors can create a reliable stream of passive income.

This approach is particularly attractive for those seeking financial stability, retirees looking for steady cash flow, or anyone aiming to reinvest dividends for compounded growth.

One of the key advantages of dividend investing is its ability to provide returns even during market downturns.

While stock prices fluctuate, dividend payments remain relatively stable, offering a cushion against volatility. Additionally, companies that consistently pay dividends often have strong financials, making them more resilient in economic downturns.

For investors looking to maximize their returns, selecting high-yield dividend stocks is crucial.

Here are five strong dividend-paying stocks to consider

  1. Aviva Plc – With a dividend yield of around 7%, Aviva remains a solid choice for income-focused investors.
  2. Legal & General – Offering around an impressive 8% yield, this financial services company is known for its consistent payouts.
  3. Phoenix Group – A standout in the insurance sector, Phoenix Group boasts around a 10% dividend yield.
  4. M&G – With around a 10% yield, M&G provides strong returns for dividend investors.
  5. BP Plc – A reliable energy sector pick, BP offers a 6% dividend yield.

Dividend investing is a powerful tool for wealth creation, offering both stability and growth potential.

By carefully selecting high-yield stocks, investors can build a portfolio that generates passive income while benefiting from long-term market appreciation.

Dividend investing is a powerful strategy for building wealth over time by generating passive income.

By holding shares in companies that consistently pay dividends, investors can benefit from regular payouts while also potentially enjoying capital appreciation.

Why Dividend Investing Works

  1. Steady Income Stream – Dividend-paying stocks provide regular income, which can be reinvested to compound wealth over time.
  2. Portfolio Stability – Companies that pay dividends are often well-established, helping to reduce volatility.
  3. Inflation Protection – Some dividends grow over time, helping investors maintain purchasing power.
  4. Tax Advantages – Depending on tax laws, dividends may be taxed at a lower rate than ordinary income.

Choosing Dividend Stocks

Investors typically look for companies with…

  • Consistent dividend payments
  • Low payout ratios (ensuring sustainability)
  • Strong financials and earnings growth
  • Dividend yield that balances risk and return

The Long-Term Benefit

By reinvesting dividends, investors can take advantage of compounding returns, where earnings generate additional earnings. Over decades, where earnings generate additional earnings.

Over decades, this strategy can build substantial wealth.

Remember to carefully do your own research. The dividend stocks listed here are NOT recommendations.

Many alternatives are available.

RESEARCH! RESEARCH! RESEARCH!

The end of globalisation or a fresh start with a new world order?

Global trade

Globalisation is a process that has woven the world together, creating interconnected networks of trade, culture, technology, and governance.

At its core, globalisation refers to the increased interaction and integration between people, companies, and governments across the globe.

This phenomenon has profound economic, political, and cultural implications, shaping the way we live and think.

Historically speaking

Historically, globalisation is not a recent occurrence; it has been evolving for centuries. The roots of globalisation can be traced back to ancient civilizations when trade routes like the Silk Road emerged around 130 BCE during the Han Dynasty of China.

The Silk Road connected Asia, the Middle East, Europe, and North Africa, facilitating the exchange of goods, ideas, religions, and innovations. While it was primarily a trade route, it also marked the first notable instances of cross-cultural interaction on a global scale.

However, the modern wave of globalisation began much later. Many historians point to the Age of Exploration in the late 15th and early 16th centuries as a pivotal moment.

European explorers like Christopher Columbus and Vasco da Gama sought new trade routes to Asia and the Americas, leading to the establishment of colonial empires.

These explorations were driven by ambitions of trade, wealth, and power, further intertwining economies and cultures.

Adam Smith, the 18th-century economist and philosopher, can also be credited with significantly influencing globalisation through his ideas. His seminal work, The Wealth of Nations (1776), laid the foundation for modern economics and advocated for free-market trade.

His philosophies supported the idea of open international markets, which became a cornerstone of globalisation in later years.

Industrial revolution

Fast forward to the 19th and 20th centuries, the Industrial Revolution and advancements in technology supercharged globalisation.

Railroads, steamships, telegraphs, and later airplanes and the internet, reduced distances and enhanced global connectivity.

This period also saw the establishment of international organisations such as the United Nations and the World Trade Organisation, further embedding globalisation into global policies.

Evolution

Today, globalisation continues to evolve. While it has brought unparalleled access to goods, services, and information, it has also sparked debates about its impact on inequality, environmental sustainability, and cultural homogenisation.

As nations and individuals grapple with its implications, globalisation remains a defining characteristic of our interconnected world. Its history is a testament to humanity’s constant quest to connect, collaborate, and innovate.

Tariffs

The introduction of ‘protectionist’ policies and ideals will likely lead back to globalisation in the end. Are Trump’s protectionist tariff ideals about protectionism or more about a drive to level the imbalance of global trade differences? Gobal trade will not end!

The tariffs are more about aiming to settle trade imbalances, at least according to U.S. President Trump.

Trump’s tariffs have had a significant impact on globalisation, challenging its trajectory. By imposing sweeping tariffs on imports, including a baseline 10% on goods from various countries, Trump aimed to reduce the U.S. trade deficit and reshore U.S. manufacturing.

While this approach sought to protect domestic industries, it disrupted global trade networks and raised concerns about inflation and economic instability.

These tariffs marked a shift away from decades of free trade policies that had fostered globalisation. Critics argue that such measures could lead to higher consumer prices and strained international relations.

On the other hand, proponents believe they might encourage self-reliance and industrial growth within the U.S.

The long-term effects on globalisation remain uncertain. While some see this as a step toward de-globalisation, others view it as a recalibration of trade dynamics.

The future will likely depend on how nations adapt to these changes and whether they seek collaboration or confrontation in global trade.

Globalisation is too big for it to simply… stop!

Artificial intelligence capable of matching humans at any task will be available within five ten years

AI

Artificial General Intelligence (AGI), a form of AI capable of matching or surpassing human intelligence across all tasks, is expected to emerge within the next five to ten years, according to Demis Hassabis, CEO of Google DeepMind.

Speaking recently, Hassabis highlighted the advancements in AI systems that are paving the way for AGI.

While current AI excels in specific domains, such as playing complex games like chess or Go – it still lacks the ability to generalise knowledge and adapt to real-world challenges.

But the advancements made in AI chatbots such as ChatGPT from OpenAI and DeepSeek have showcased remarkable development, and at speed too. Applying AI to work environments, science and domestic tasks is forever expanding.

Hassabis emphasised that significant research is still required to achieve AGI. The focus lies on improving AI’s understanding of context and its ability to plan and reason in dynamic environments.

Multi-agent systems, where AI entities collaborate or compete, are seen as a promising avenue for development.

These systems aim to replicate the intricate decision-making processes humans exhibit in complex scenarios.

The implications of AGI are profound, with potential applications spanning healthcare, education, and beyond.

However, its development also raises ethical and societal questions, including concerns about control, safety, and equitable access.

While the timeline remains speculative, Hassabis’s insights underscore the accelerating pace of AI innovation, bringing humanity closer to a future where machines and humans collaborate in unprecedented ways.

Or not?

‘A pig in lipstick’ – Trump’s strategic Bitcoin reserve criticised

Pig in lipstick

The announcement of Donald Trump’s Strategic Bitcoin Reserve has sparked a wave of criticism and debate, with detractors likening the initiative to ‘a pig in lipstick’ – a superficial attempt to dress up a flawed concept.

The reserve, which aims to stockpile or create a strategic reserve Bitcoin seized through criminal and civil forfeitures, has been touted as a bold move to position the United States as a leader in the cryptocurrency space. However, critics argue that the plan is fraught with risks and questionable motives.

One of the primary concerns is Bitcoin’s notorious volatility. Unlike traditional reserve assets such as gold or oil, Bitcoin’s value can fluctuate wildly, making it a precarious choice for a national reserve.

Economists warn that integrating such an unpredictable asset into government holdings could destabilise financial strategies rather than strengthen them.

Moreover, the initiative has raised eyebrows over its potential conflicts of interest. Critics point out that Trump’s administration has shown a growing affinity for cryptocurrency, with some officials previously holding stakes in digital assets.

This has led to accusations that the reserve could serve as a vehicle for personal or political gain rather than a genuine effort to bolster national economic security.

Supporters of the reserve argue that it represents a forward-thinking approach to embracing digital assets as ‘digital gold.’ They believe that retaining seized Bitcoin, rather than auctioning it off, could provide long-term financial benefits and signal the U.S.’s commitment to innovation in the crypto space.

However, even some crypto enthusiasts are skeptical, questioning whether the reserve’s creation is more about optics than substance.

In the end, the Strategic Bitcoin Reserve has ignited a broader conversation about the role of cryptocurrency in national policy. Whether it proves to be a visionary move, or a misguided gamble remains to be seen.

For now, the debate goes on.

China’s AI vs U.S. AI – competition heats up – and that’s good for business – isn’t it?

DeepSeek AI

The escalating AI competition between the U.S. and China has taken a new turn with the emergence of DeepSeek, a Chinese AI startup that has introduced a low-cost AI model capable of rivaling the performance of OpenAI’s models.

This development has significant implications for data centres and the broader technology sector.

The rise of DeepSeek

DeepSeek’s recent breakthrough involves the development of two AI models, V3 and R1, which have been created at a fraction of the cost compared to their Western counterparts.

The total training cost for these models is estimated at around $6 million, significantly lower than the billions spent by major U.S. tech firms. This has challenged the prevailing assumption that developing large AI models requires massive financial investments and access to cutting-edge hardware.

Impact on data centres

The introduction of cost-effective AI models like those developed by DeepSeek could lead to a shift in how data centers operate.

Traditional AI models require substantial computational power and energy, leading to high operational costs for data centers. DeepSeek’s models, which are less energy-intensive, could reduce these costs and make AI technology more accessible to a wider range of businesses and organizations.

Technological advancements

DeepSeek’s success also highlights the potential for innovation in AI without relying on the most advanced hardware.

This could encourage other companies to explore alternative approaches to AI development, fostering a more diverse and competitive landscape. Additionally, the open-source nature of DeepSeek’s models promotes collaborative innovation, allowing developers worldwide to customise and improve upon these models2.

Competitive dynamics

The competition between DeepSeek and OpenAI underscores the broader U.S.-China rivalry in the AI space. While DeepSeek’s models pose a limited immediate threat to well-funded U.S. AI labs, they demonstrate China’s growing capabilities in AI innovation.

This competition could drive both countries to invest more in AI research and development, leading to faster technological advancements and more robust AI applications.

Broader implications

The rise of DeepSeek and similar Chinese and other AI startups could have far-reaching implications for the global technology sector.

As AI becomes increasingly integrated into various industries, the ability to develop and deploy AI models efficiently will be crucial.

Data centres will need to adapt to these changes, potentially investing in more energy-efficient infrastructure and exploring new ways to support AI workloads.

Where from here?

DeepSeek’s emergence as a significant player in the AI race highlights the dynamic nature of technological competition between the U.S. and China.

While the immediate impact on data centres and technology may be limited, the long-term implications could be profound.

As AI continues to evolve, the ability to innovate cost-effectively and collaborate across borders will be key to driving progress and maintaining competitiveness in the global technology landscape.

Microsoft’s Quantum Leap: The Majorana 1 Chip

Quantum Physics

Microsoft has unveiled a new chip called Majorana 1 that it says will enable the creation of quantum computers able to solve ‘meaningful, industrial-scale problems in years, not decades’.

What is Microsoft’s Majorana 1?

It is the latest development in quantum computing – tech which uses principles of particle physics to create a new type of computer able to solve problems ordinary computers cannot.

Microsoft has announced a game-changing development in the world of quantum computing: the Majorana 1 chip. This revolutionary chip integrates eight topological quantum bits (qubits), setting a new standard for stability and resistance to environmental interference.

Microsoft. The new Majorana 1 chip

The Majorana 1 chip is built on a unique combination of indium arsenide, a semiconductor, and aluminum, a superconductor. This cutting-edge design enables the chip to create a topological state, a new form of matter that encodes information in a way that is inherently noise-resistant. This means that the Majorana 1 chip can maintain its quantum state longer, making it more reliable for complex computations.

What sets the Majorana 1 chip apart is its use of topoconductors, a new class of materials developed by Microsoft’s researchers over nearly two decades. These materials provide a high level of error protection, which is essential for practical quantum computing applications. The Majorana 1 chip is a significant step toward the ultimate goal of creating quantum computers with millions of qubits, capable of solving complex industrial and societal problems.

While the Majorana 1 chip is still in the research phase and not yet available for commercial use, it represents a monumental leap forward in quantum technology. Microsoft’s commitment to advancing quantum computing is evident in the substantial investment of time and resources required to develop this groundbreaking chip.

In summary, the Majorana 1 chip is poised to transform the landscape of quantum computing, offering a more stable and reliable platform for future innovations. This development marks a pivotal moment in the quest for practical and scalable quantum computing solutions.

What is Quantum computing?

Quantum computing is a revolutionary technology that uses the principles of quantum mechanics to process information in a fundamentally different way than classical computers, allowing for exponentially faster calculations in certain tasks.

It leverages qubits, which can represent multiple states simultaneously, enabling complex problem-solving and data analysis beyond the capabilities of traditional computing.

Microsoft says powerful quantum computers will be a reality in years not decades.

‘Unimaginable’ billionaire wealth surged in 2024 as Oxfam predicts the emergence of five trillionaires within the next 10 years

Wealthiest and poorest

In 2024, billionaire wealth surged to unprecedented levels, with a staggering $2 trillion increase in just one year, according to an Oxfam report.

This rapid growth has led to predictions that the world could see at least five trillionaires within the next decade.

To place this in some context it is has been calculated that there are approximately 150 countries with a GDP of less than $1 trillion. This includes many smaller economies and developing nations.

So, 5 single human beings will likely hold more wealth ‘individually’ than approximately 150 separate countries. One person will be worth more than an entire country!

Oxfam report

The Oxfam report highlights the stark contrast between the wealth of the world’s richest individuals and the persistent poverty faced by millions.

The report reveals that the wealth of billionaires grew three times faster in 2024 compared to the previous year.

This surge in wealth has been attributed to various factors, including booming stock markets, lucrative investments, and favorable economic policies.

However, this concentration of wealth in the hands of a few has raised concerns about growing inequality and its impact on society.

Oxfam‘s findings emphasize the urgent need for bold economic reforms to address this disparity. The report calls for higher taxes on the super-rich and the abolition of tax havens to ensure a fairer distribution of wealth.

It also highlights the importance of investing in public services, such as healthcare and education, to improve the quality of life for all citizens.

Out of whack wealth

The growing concentration of wealth among billionaires has significant implications for global inequality. While the number of people living in poverty has remained largely unchanged since 1990, the wealth of the richest individuals has skyrocketed.

This disparity underscores the need for policies that promote economic fairness and reduce inequality.

The surge in billionaire wealth in 2024 serves as a stark reminder of the growing concentration of wealth and the urgent need for economic reforms.

By implementing policies that promote a fairer distribution of wealth and investing in public services, we can work towards a more equitable society. The emergence of trillionaires within the next decade should be a wake-up call for policymakers to address the root causes of inequality and ensure a better future for all.

World’s most wealthy climbed from $13 trillion to $15 trillion in just 12 months

The combined wealth of the world’s most wealthy rose from $13 trillion to $15 trillion in just 12 months, the global charity said Sunday. It marks the second largest annual increase in billionaire wealth since Oxfam records started.

Meanwhile, the number of people living in poverty has barely changed since 1990, the charity said, citing World Bank Data.

1% of people own nearly 45% of all wealth

The richest 1% of people own nearly 45% of all wealth, while 44% of humanity are living below the World Bank poverty line of $6.85 per day, the data showed.

First trillionaire by 2027 is predicted

Elon Musk, CEO of Tesla and now close ally of Trump, is on track to become the world’s first trillionaire by 2027, according to a report from Informa Connect Academy.

Musk is currently worth about around $440 billion according to the Bloomberg Billionaires Index.

Biden warning

Outgoing U.S. President Joe Biden warned of the rise of an “oligarchy taking shape in America of extreme wealth, power and influence.”

“People should be able to make as much as they can, but pay – play by the same rules, pay their fair share in taxes,” Biden said in his farewell address.

Oxfam

Oxfam is trying to urge governments to commit to ensuring that the incomes of the top 10% are no higher than the bottom 40% worldwide.

Global economic rules should be adjusted to allow for the break-up of monopolies, and more corporate regulation and global tax policies should be adapted to ensure that the rich pay their fair share, according to the charity.

Money that is flowing to the bank accounts of the super-rich instead of much-needed investment in teachers and medicines is “not just bad for the economy — it’s bad for humanity,” said Oxfam’s Behar.

Gold rises to new high!

Gold up

Gold hits new highs in 2025 amid strong demand

Gold prices have surged to unprecedented levels in 2025, driven by robust demand and a series of global economic uncertainties.

As of early February, gold futures traded on the New York Mercantile Exchange reached a record high of $2,875 per ounce, marking a significant milestone in the precious metal’s market performance.

Stable safe haven during unstable times

The surge in gold prices can be attributed to several factors. Firstly, geopolitical tensions and economic instability have prompted investors to seek safe-haven assets. Gold, with its historical reputation as a store of value, has become a preferred choice for those looking to hedge against market volatility and inflation.

Central banks

Central banks have also played a crucial role in driving up gold prices. In 2024, global central banks added a record amount of gold to their reserves, with purchases surpassing 1,000 tons for the third consecutive year.

This trend has continued into 2025, with countries like Poland, Turkey, and India leading the way in increasing their gold holdings.

Investment demand for gold has seen a significant uptick as well. Gold exchange-traded funds (ETFs) and bars and coins have experienced strong demand, particularly from investors in China and India.

The reduction of gold import duties in India and economic uncertainties in China have further fueled this demand.

Resistance?

Despite the positive momentum, analysts caution that gold prices could face resistance levels and potential pullbacks. However, the overall outlook remains bullish, with expectations of continued strong demand and further gains in the coming months.

As the global economy navigates through these uncertain times, gold’s allure as a safe-haven asset is likely to persist, making it a key player in the financial markets.

Gold price one-year chart as of 5th February 2025 (am GMT)

Gold price one-year chart as of 5th February 2025 (am GMT)

The aftermath from the arrival of Deepseek

Deepseek AI

Nvidia, the renowned American technology company, recently experienced the largest one-day loss in U.S. history. On January 27, 2025, Nvidia’s stock plummeted by 17%, resulting in a staggering market cap loss of nearly $600 billion.

This unprecedented drop was primarily triggered by the emergence of DeepSeek, a Chinese artificial intelligence startup that has been making waves in the tech industry.

DeepSeek, founded in 2023 by Liang Wenfeng, has developed open-source large language models that rival some of the best AI models in the world. The company’s latest model, DeepSeek-V3, has demonstrated impressive performance at a fraction of the cost of its competitors.

This has raised concerns among investors about the sustainability of Nvidia’s dominance in the AI chip market.

The release of DeepSeek’s latest technology has caused significant anxiety among U.S. tech giants, leading to a massive sell-off in the stock market. Companies that rely heavily on Nvidia’s GPUs, such as Dell, Oracle, and Super Micro Computer, also saw their stock prices plummet.

The ripple effect of Nvidia’s loss was felt across the tech-heavy Nasdaq, which dropped by 3.1% on the same day.

Nvidia one-month chart 27th January 2025

In response to this market upheaval, former President Donald Trump commented on the situation, stating that DeepSeek’s emergence should serve as a ‘wake-up call’ for American companies.

Trump emphasised the need for U.S. industries to remain competitive and innovative in the face of rising competition from Chinese tech firms. He acknowledged the impressive advancements made by DeepSeek and suggested that American companies could benefit from adopting more cost-effective methods in their AI development.

Trump’s remarks highlight the growing concern among U.S. policymakers and industry leaders about the rapid advancements in AI technology coming from China.

The success of DeepSeek has demonstrated that significant breakthroughs can be achieved with relatively modest investments, challenging the notion that massive capital expenditure is necessary for top-tier AI performance.

As the AI race continues to heat up, it is clear that companies like Nvidia will need to adapt and innovate to maintain their competitive edge. The emergence of DeepSeek has not only disrupted the market but also underscored the importance of staying ahead in the rapidly evolving field of artificial intelligence.

For American tech giants, this wake-up call serves as a reminder that the global AI landscape is becoming increasingly competitive, and complacency is not an option.

Has the U.S. spent wisely on AI development?

The emergence of DeepSeek has indeed raised questions about the spending strategies of U.S. tech companies on AI development. DeepSeek’s ability to develop high-performing AI models at a fraction of the cost has led to concerns about the necessity of the massive capital expenditures by large U.S. tech firms.

For instance, companies like Microsoft, Alphabet, Meta, and Amazon have collectively spent over $1 trillion on AI investments in recent years. This significant spending has been driven by the belief that larger models and more computing power would lead to better performance.

However, DeepSeek’s success with more cost-effective methods challenges this notion and suggests that efficient innovation might be more important than sheer scale.

The impressive performance and lower costs of DeepSeek’s models have prompted some analysts to question whether the billions of dollars spent by U.S. tech companies on AI development are justified. This has led to a re-evaluation of AI spending strategies and a push for more efficient and innovative approaches to AI development.

In summary, while U.S. tech companies have invested heavily in AI, the success of DeepSeek highlights the potential for achieving cutting-edge results with fewer resources. This has sparked a broader discussion about the most effective and efficient ways to advance AI technology.

Asking ChatGPT – ‘Do you see Deepseek as a competitor or a threat?’

The reply:

As an AI, I don’t have personal opinions or feelings. However, I can share that DeepSeek’s rapid rise and impressive performance in the AI field are noteworthy. Their ability to develop high-performing models at a lower cost is a significant achievement and has certainly shaken up the industry. It’s a reminder of the dynamic and competitive nature of the AI landscape, where innovation can come from unexpected places.

Asking ChatGPT – ‘What do you think of Deepseek?’

The reply:

DeepSeek is a fascinating and disruptive player in the AI landscape. Founded in 2023 by Liang Wenfeng, this Chinese startup has quickly gained attention for its open-source large language models, which rival some of the best AI models in the world at a fraction of the cost. Their latest models, DeepSeek-V3 and DeepSeek-R1, have demonstrated impressive performance and efficiency, challenging the dominance of established tech giants2.

DeepSeek’s emergence has caused significant market upheaval, leading to substantial drops in the stock prices of major AI-related firms, including Nvidia. The company’s success highlights the potential for innovation and cost-effectiveness in AI development, which has prompted industry leaders and policymakers to re-evaluate their strategies.

Overall, DeepSeek’s rapid rise and impact on the AI industry underscore the importance of staying competitive and innovative in this fast-evolving field. It’s a reminder that breakthroughs can come from unexpected places and that the AI landscape is becoming increasingly dynamic and competitive.

It would not be drawn on these questions but, one thing is for sure the U.S. has spent billions developing their systems and charge fortunes for using it.

This is about to change.

And further…

The Deepseek website reportedly crashed on 27th January 2025 due to heavy global demand. There were also reports of unrelenting cyberattacks on its systems…?

Curious. From minnow to centre stage in just one day!

China’s DeepSeek low-cost challenger to AI rattles tech U.S. markets

China Deepseek AI

U.S. technology stocks plunged as Chinese startup DeepSeek sparked concerns over competitiveness in AI and America’s lead in the sector, triggering a global sell-off

DeepSeek launched a free, open-source large-language model in late December 2024, claiming it was developed in just two months at a cost of under $6 million.

The developments have stoked concerns about the large amounts of money big tech companies have been investing in AI models and data centres.

DeepSeek is a Chinese artificial intelligence startup that has recently gained significant attention in the AI world. Founded in 2023 by Liang Wenfeng, DeepSeek develops open-source large language models. The company is funded by High-Flyer, a hedge fund also founded by Wenfeng.

The AI models from DeepSeek have demonstrated impressive performance, rivaling some of the best chatbots in the world at a fraction of the cost. This has caused quite a stir in the tech industry, leading to significant drops in the stock prices of major AI-related firms.

The company’s latest model, DeepSeek-V3, is known for its efficiency and high performance across various benchmarks.

DeepSeek’s emergence challenges the notion that massive capital expenditure is necessary to achieve top-tier AI performance.

The company’s success has led to a re-evaluation of the AI market and has put pressure on other tech giants to innovate and reduce costs.

The UK is planning to build a homegrown artificial intelligence rival to OpenAI

UK AI

In a bold move to establish as a global leader in artificial intelligence, the United Kingdom is undertaking an ambitious initiative to develop a homegrown competitor to OpenAI.

This initiative is part of a broader strategy to enhance the nation’s computing infrastructure and foster innovation in AI technology.

Prime Minister Keir Starmer’s administration has committed to increasing the UK’s ‘sovereign’ computing capacity by twentyfold by 2030. This significant investment aims to support the development of powerful AI models that rely on high-performance computing equipment.

The government plans to expand data centre capacity across the country, providing the necessary infrastructure for AI developers to train and run their systems effectively.

AI Opportunities Action Plan

Central to this initiative is the AI Opportunities Action Plan, spearheaded by tech investor Matt Clifford. The plan outlines a comprehensive approach to harnessing the potential of AI, including the establishment of AI growth zones where planning permission rules will be relaxed to facilitate the creation of new data centers.

These zones are expected to become hubs of innovation, attracting both domestic and international talent to the UK.

The government is also set to launch the AI Research Resource, an initiative aimed at bolstering the UK’s computing infrastructure. This resource will provide access to high-performance computing facilities, enabling researchers and developers to work on cutting-edge AI projects.

Additionally, the establishment of a National Data Library will connect public institutions, such as universities, to enhance the country’s ability to create sovereign AI models.

Funding challenge

One of the key challenges facing the UK in its bid to rival OpenAI is funding. While the government has pledged substantial investments, many entrepreneurs and venture capitalists in the country have highlighted the difficulties in raising the kind of capital available to AI startups in the U.S. To address this, the government has secured commitments from leading tech firms, which have pledged £14 billion towards various AI projects.

This includes significant investments in data centers and the creation of thousands of AI-related jobs across the country.

UK AI sovereignty

The UK’s focus on AI sovereignty is driven by the belief that technologies critical to economic growth and national security should be developed within the country. This approach aims to reduce reliance on foreign tech giants and ensure that AI advancements align with national interests and ethical standards.

The government is also exploring the role of renewable and low-carbon energy sources, such as nuclear, to power the data centers that will support AI development.

Despite the ambitious plans, the UK faces several hurdles in its quest to become a global AI leader. The country’s risk-averse investment culture and the competitive landscape of AI development pose significant challenges.

However, the government’s proactive approach and the support of industry leaders provide a strong foundation for success.

Initiative and challenge

The UK’s initiative to build a homegrown rival to OpenAI represents a significant step towards establishing itself as a global leader in artificial intelligence.

By investing in computing infrastructure, fostering innovation, and securing industry support, the UK aims to create a thriving AI ecosystem that can compete on the world stage. While challenges remain, the commitment to AI sovereignty and the strategic vision outlined in the AI Opportunities Action Plan offer a promising path forward for the nation’s AI ambitions.

Sold – the UK needs to protect its future and not sell the silver… again

Over the years, the UK has seen several of its tech companies sold off to foreign investors.

  1. ARM Holdings: Perhaps the most famous example, ARM Holdings, a leading semiconductor and software design company, was acquired by Japan’s SoftBank Group in 2016 for £24.3 billion. ARM’s technology is used in the majority of smartphones worldwide.
  2. DeepMind: In 2014, Google acquired DeepMind, a UK-based AI company known for its advancements in machine learning and neural networks, for around £400 million. DeepMind has since become a key part of Google’s AI research efforts.
  3. Imagination Technologies: This British GPU and AI processing company was sold to Canyon Bridge Capital Partners, a private equity fund backed by Chinese state-owned China Reform Fund Management, in 2017 for £550 million. Recently, Imagination Technologies has been put up for sale again.
  4. Autonomy Corporation: Acquired by Hewlett-Packard (HP) in 2011 for $11.7 billion. The deal later became controversial due to allegations of financial mismanagement
  5. Powa Technologies: This mobile payments company faced financial difficulties and was sold off in parts after going into administration in 2016. PowaTag was sold to a consortium led by businessman Ben White, while Powa Web was sold in a management buyout backed by Greenlight Digital.

One to keep

Raspberry Pi Holdings plc, the parent company behind the iconic Raspberry Pi computer series, is a British technology business focused on designing and manufacturing single-board computers and modules. The company was founded by Eben Upton and is headquartered in Cambridge, England1.

As of the latest information, the largest shareholder of Raspberry Pi Holdings is the Raspberry Pi Foundation, which holds around 49% of the company. Other notable shareholders include Arm Holdings plc, Lansdowne Partners (UK) LLP, and the Raspberry Pi Employee Benefit Trust. The company went public on the London Stock Exchange in June 2024.

Raspberry Pi Holdings continues to innovate and expand its product offerings, maintaining its mission to make computing accessible and affordable for everyone.

These sales reflect the global interest in UK tech companies and the challenges they face in securing domestic investment to remain independent. The UK’s tech sector continues to be a hotbed of innovation, attracting significant attention from international investors.

The UK needs to nurture and keep its upcoming new tech discoveries, AI or otherwise – to protect the future of Britain.

We have the knowhow – we just need to keep it!

What could quantum computing breakthrough ‘Willow’ mean for the future of Bitcoin and other cryptos

Crypto and quantum computing

The advent of quantum computing presents both opportunities and challenges for the field of cryptography, especially in relation to cryptocurrencies.

Quantum computers, leveraging the principles of quantum mechanics, have the potential to revolutionise computing by solving certain problems significantly faster than classical computers.

One of the primary concerns is the impact of quantum computing on cryptographic algorithms that underpin the security of cryptocurrencies like Bitcoin and Ethereum.

Traditional public-key cryptography, which relies on the difficulty of factoring large prime numbers or solving discrete logarithms, could be broken by a sufficiently powerful quantum computer. Algorithms such as RSA, ECC (Elliptic Curve Cryptography), and DSA (Digital Signature Algorithm) could become vulnerable, as quantum algorithms like Shor’s algorithm are capable of efficiently solving these problems.

This potential vulnerability poses a significant threat to the security and integrity of cryptocurrency transactions. If quantum computers can crack these cryptographic codes, they could potentially access private keys, allowing malicious actors to steal funds or forge transactions. As a result, the trust that underpins the entire cryptocurrency ecosystem could be eroded.

However, the quantum threat is not without its solutions. The field of post-quantum cryptography is actively developing new cryptographic algorithms that are resistant to quantum attacks.

These algorithms leverage mathematical problems believed to be hard even for quantum computers, such as lattice-based cryptography, hash-based cryptography, and multivariate polynomial cryptography.

Transitioning to post-quantum cryptographic algorithms is crucial for ensuring the long-term security of cryptocurrencies in a quantum computing era.

In conclusion, while quantum computing poses a formidable challenge to current cryptographic systems, proactive measures and the development of quantum-resistant algorithms can mitigate these risks.

The cryptocurrency industry must stay ahead of the curve, adopting new technologies and strategies to safeguard against potential quantum threats and ensure the continued security and trust in digital currencies.

It has been estimated that the arrival of quantum computer is at least 10 years away. But is that allowing for the use of AI in its creation?

What is Willow and Quantum Computing?

Willow is the start of a new era of ultra-powerful ‘quantum’ microchips designed by Google. Willow’s speed is almost incomprehensible – according to Google, it is able to perform a computation in under five minutes that would take one of today’s fastest supercomputers 10 septillion years to solve.

This new chip design will inevitably lead to new quantum innovations and computer design over the coming years.

Ten septillion is 10,000,000,000,000,000,000,000,000 years.

If you don’t understand (not many people do) what makes up quantum computing – there is a very simplified way simplified way of thinking about the breakthrough.

Imagine a maze and how a classical computer would try to find its way through the maze from start to finish. It would try one potential path at a time. A quantum computer would be able to try each path at the same time.

The quantum computer is coming. The only delay will be in design restrictions and the power needed to run the system.