Google goes nuclear: part 2 Powering the AI revolution – the effects!

AI goes Nuclear

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.

Increased carbon emissions, undermining climate goals.

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 SourceLifecycle CO₂ Emissions (g/kWh)
Coal820
Natural Gas490
Solar PV48
Wind12
Nuclear12

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.

Which of the AI bubble indicators are we already seeing? Should we be concerned?

Bubble in AI

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.

How far away are we from the AI bubble popping?

Will it deflate slowly or burst?

Has the S&P 500 Become an AI Index?

S&P 500 becoming an AI index

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.

AI in 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.

Nick Clegg’s AI Correction Prophecy: The Return of the Technocratic Tourist

AI commentator?

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.

Please don’t open your case.

China’s rare Earth clampdown continues to send shockwaves through global markets

Rare Earth Materials

China’s latest tightening of rare earth exports has reignited global concerns over supply chain fragility and strategic resource dependence.

With Beijing now requiring special permits for the export of key rare earth elements—used in everything from electric vehicles to missile guidance systems—the move is widely seen as a geopolitical lever in an increasingly fractured global trade landscape.

Rare earths, despite their name, are not scarce—but China controls over 60% of global production and an even larger share of refining capacity. The new restrictions, framed as national security measures, have already begun to ripple through equity markets.

Shares of Western mining firms such as Albemarle and MP Materials surged on the news, as investors bet on alternative sources gaining traction. Meanwhile, defence and tech stocks in Europe dipped, reflecting fears of supply bottlenecks and rising input costs1.

This isn’t China’s first foray into rare earth brinkmanship. Similar curbs in 2010 triggered a scramble for diversification, but progress has been slow.

The current squeeze coincides with rising tensions over semiconductor access and military technology, suggesting a broader strategy of resource weaponisation.

For investors, the message is clear: rare earths are no longer just a niche commodity—they’re a geopolitical flashpoint. Expect increased volatility in sectors reliant on high-performance magnets, batteries, and advanced optics.

Countries like the US, Australia, and Canada are accelerating domestic mining initiatives, but scaling up remains a long-term play.

In the short term, China’s grip on rare earths is tightening—and markets are reacting accordingly.

As the global economy pivots toward electrification and AI-driven infrastructure, the battle over these elemental building blocks is only just beginning. The stocks may rise and fall, but the strategic stakes are climbing ever higher.

China’s sweeping export restrictions on rare earths have triggered a sharp rally in related stocks, especially among U.S.-based producers and processors.

The market is interpreting Beijing’s move as both a supply threat and a strategic opportunity for non-Chinese firms to gain ground.

📈 Some companies in the spotlight

  • USA Rare Earth surged nearly 15% in a single day and is up 94% over the past five weeks, buoyed by speculation of a potential U.S. government investment and its vertically integrated magnet production pipeline.
  • NioCorp Developments, Ramaco Resources, and Energy Fuels all posted gains of approximately between 9–12%.
  • MP Materials, the largest U.S. rare earth miner, rose over 6% following news of tighter Chinese controls. The company recently secured a strategic equity deal with the U.S. Department of Defence.
  • Albemarle, Lithium Americas, and Trilogy Metals also saw modest gains, reflecting broader investor interest in critical mineral plays.
Company / SectorStock MovementStrategic Note
MP Materials (US)↑ +6%DoD-backed, key US supplier
USA Rare Earth↑ +15%Magnet pipeline, gov’t investment buzz
NioCorp / Ramaco / Energy Fuels↑ +9–12%Domestic mining surge
European Defence Stocks↓ 2–4%Supply chain fears
Chinese Magnet Producers↔ / ↓Export permit uncertainty

China’s new rules, effective December 1st, require export licences for any product containing more than 0.1% rare earths or using Chinese refining or magnet recycling tech. This has intensified scrutiny on global supply chains and elevated the strategic value of domestic alternatives.

🧭 Investor sentiment is shifting toward companies that can offer secure, non-Chinese sources of rare earths—especially those with downstream capabilities like magnet manufacturing. The rally suggests markets are pricing in long-term geopolitical risk and potential government backing.

Weekend update

Is President Trump in control of the stock market? A comment on TruthSocial suggesting that more China tariffs might be introduced in response to China’s restrictions on rare earth materials reportedly wipes out around $2 trillion from U.S. stocks.

Then it reverses as Trump says, ‘All will be fine’. Stocks climb back up. What’s going on?

It’s just a game.

But who is the game master?

Is Bitcoin truly an asset class investment?

Is Bitcoin truly an asset class investment?

Bitcoin’s status as an asset class remains fiercely debated—its classification hinges on whether one prioritises institutional frameworks or symbolic disruption.

✅ Arguments for Bitcoin as an asset class

Many investors treat Bitcoin as a distinct asset class due to its unique characteristics:

  • Scarcity: With a capped supply of 21 million coins, Bitcoin mirrors commodities like gold.
  • Divisibility & Fungibility: It can be split into smaller units, and each unit is interchangeable.
  • Liquidity: It’s widely traded and easily converted into fiat currencies.
  • Store of Value: Often dubbed ‘digital gold’, especially in inflationary climates.
  • Low correlation: Bitcoin’s behaviour diverges from traditional assets, offering potential diversification.

❌ Arguments against Bitcoin as an asset class

Institutions such as Hargreaves Lansdown argue Bitcoin lacks the stability and intrinsic value required for formal classification:

  • Volatility: Its dramatic price swings make it riskier than equities or bonds.
  • No intrinsic value: Unlike shares or debt instruments, Bitcoin doesn’t generate income or cash flow.
  • Regulatory ambiguity: Its decentralised nature and evolving legal status complicate analysis.
  • Speculative behaviour: Critics liken it more to a speculative punt than a foundational portfolio component.

So…

Bitcoin’s contested identity as an asset class reflects a deeper philosophical divide: is it a legitimate store of value shaped by digital innovation, or a speculative mirage challenging financial orthodoxy?

While its traits—scarcity, liquidity, and decentralised architecture—suggest asset-like behaviour, its volatility and lack of intrinsic value leave its classification unresolved.

For investors and thinkers alike, Bitcoin invites not just financial analysis but existential reflection on what we trust, value, and rebel against in the evolving economy.

AI Crash! Correction or pullback? Something is coming…

AI Bubble concerns

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.

Go lock up your investments!

Is the resilient stock market keeping the U.S. economy out of a recession and if so – is that a bad thing?

U.S. recession looming?

The Resilient Stock Market: A Double-Edged Shield Against Recession

In a year marked by political volatility, Trumps tariff war, soft labour data, and persistent inflation anxieties, one pillar of the economy has stood tall: the stock market.

Defying expectations, major indices like the Nasdaq, Dow Jones and S&P 500 have surged, buoyed by AI-driven optimism and industrial strength. This resilience has helped stave off a technical recession—but not without raising deeper concerns about economic fragility and inequality.

At the heart of this phenomenon lies the ‘wealth effect’. As equity portfolios swell, high-net-worth households feel richer and spend more freely.

This consumer activity props up GDP figures and masks underlying weaknesses in wage growth, job creation, and productivity.

August’s economic data showed surprising strength in consumer spending and housing, despite lacklustre employment figures and fading stimulus support.

But here’s the rub: this buoyancy is not broadly shared. According to the University of Michigan’s sentiment index, confidence has declined sharply since January, especially among those without significant stock holdings.

Balance

The U.S. economy, in effect, is being held aloft by a narrow slice of the population—those with the means to benefit from rising asset prices. For everyone else, the recovery feels distant, even illusory.

This divergence creates a dangerous illusion of stability. Policymakers may hesitate to intervene—whether through fiscal support or monetary easing—because headline indicators look healthy. Yet beneath the surface, vulnerabilities abound.

If the market were to correct sharply, the spending it fuels could evaporate overnight, exposing the economy’s dependence on asset inflation.

Moreover, the market’s resilience may be distorting capital allocation. Companies flush with investor cash are prioritising stock buybacks and speculative ventures over wage growth or long-term investment. This can exacerbate inequality and erode the foundations of sustainable growth.

In short, while the stock market’s strength has delayed a recession, it has also deepened the disconnect between Wall Street and Main Street.

The danger lies not in the market’s success, but in mistaking it for economic health. A resilient market may be a shield—but it’s not a cure. And if that shield cracks, the consequences could be swift and severe.

The challenge now is to look beyond the indices and ask harder questions: Who is benefitting? What are we neglecting?

And how do we build an economy that’s resilient not just in numbers, but in substance, regardless of nation.

Bleak news from U.S. doesn’t seem that bad for stocks – what’s going on?

Bleak Headlines vs. Market Optimism

It’s one of those classic Wall Street paradoxes—where bad news somehow fuels bullish momentum. What’s going on?

News round-up

S&P 500 closes above 6,700 after rising 0.34%. Samsung and SK Hynix join OpenAI’s Stargate. Taiwan rejects U.S. proposal to split chip production. Trump-linked crypto firm plans expansion. Some stocks that doubled in the third quarter.

Bleak Headlines vs. Market Optimism

U.S. Government Shutdown: The federal government ground to a halt, but markets didn’t flinch. In fact, the S&P 500 rose 0.34% and closed above 6,700 for the first time.

ADP Jobs Miss: Private payrolls fell by 32,000 in September 2025, a sharp miss – at least compared to the expected 45,000 gain. Yet traders shrugged it off as other bad news is shrugged off too!

Fed Rate Cut Hopes: Weak data often fuels expectations that the Federal Reserve will cut interest rates. Traders are now betting on a possible cut in October 2025, which tends to boost equities.

Historical Pattern: According to Bank of America, the S&P 500 typically rises ~1% in the week before and after a government shutdown. So, this isn’t unprecedented—it’s almost ritualistic at this point.

Why the Market’s Mood Diverges

Animal Spirits: Investors often trade on sentiment and positioning, not just fundamentals. If they believe the Fed will ease policy, they’ll buy risk assets—even in the face of grim news.

Data Gaps: With the Bureau of Labor Statistics’ official jobs report delayed due to the shutdown, the ADP report gains more weight. But it’s historically less reliable, so traders may discount it.

Tech Tailwinds: AI stocks and semiconductor news (e.g., Samsung and SK Hynix joining OpenAI’s Stargate) are buoying sentiment, especially in Asia-Pacific markets.

U.S. Government Shutdown October 2025

Prediction

Traders in prediction markets are betting the shutdown will last around two weeks. Nothing too radical, since that’s the average length it takes for the government to reopen, based on data going back to 1990.

The government stoppage isn’t putting the brakes on the stock market momentum. Are investors getting too adventurous?

History shows the pattern is not new. The S&P 500 has risen an average of 1% the week before and after a shutdown, according to data from BofA.

Even the ADP jobs report, which missed expectations by a wide margin, did little to subdue the animal spirits.

Private payrolls declined by 32,000 in September 2025, according to ADP, compared with a 45,000 increase reportedly estimated by a survey of economists.

Payroll data

The Bureau of Labor Statistics’ (BLS) official nonfarm payrolls report is now stuck in bureaucratic purgatory and likely not being released on time.

The U.S. Federal Reserve might place additional weight on the ADP report — though it’s not always moved in sync with the BLS numbers. Traders expect weak data would prompt the Fed to cut interest rates in October 2025.

It’s a bit like watching a storm roll in while the crowd cheers for sunshine—markets are forward-looking, and sometimes they see silver linings where others see clouds.

Summary

EventDetail
🏛️ Government ShutdownBegan Oct 1, 2025. Traders expect ~2 weeks based on historical average
📉 ADP Jobs ReportPrivate payrolls fell by 32,000 vs. expected +45,000
📈 S&P 500 CloseRose 0.34% to close above 6,700 for the first time
💸 Fed Rate Cut ExpectationsTraders now pricing in a possible October cut

When will it be time to worry about the AI bubble?

AI bubble inflating

Key Signals of an AI Bubble

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.

Are We in an AI ‘Super Cycle’? Some investors say Yes—and it could last two decades?

AI

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.

And we’re only at the beginning of the curve

With all the new AI tech arriving in the new AI data centres – what is happening to the old tech it is presumably replacing?

AI - dirty little secret or clean?

🧠 What’s Happening to the Old Tech?

Shadow in the cloud

🔄 Repurposing and Retrofitting

  • 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.

Just how clean is green?

The Intelligence Tax

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!

Stock market pullback in 4th quarter… how likely is it?

Taking Stock

While many investors are hoping for a year-end rally, several analysts are warning that a fourth-quarter pullback remains a real possibility.

Valuation concerns: Large-cap stocks are trading at historically high valuations, reminiscent of the 2021 peak. That leaves little room for error if economic data disappoints.

Tariff aftershocks: April’s ‘Liberation Day’ tariffs triggered a sharp sell-off, and although markets rebounded, strategists at Stifel expect an ‘echo’ effect—potentially a 14% drop in the S&P 500 before year-end.

Economic slowdown: Consumer spending is showing signs of strain, and real wage growth may not keep pace with rising prices. That could dampen demand and corporate earnings.

Trade uncertainty: The 90-day tariff pause expired in July 2025 (with adjustments), leaving markets to navigate the fallout—valuation echoes, trade uncertainty, and investor psychology now collide in Q4’s shadow. This could lead to headline-driven volatility through Q4.

Mixed sentiment: Some strategists remain cautiously optimistic, citing resilient labour data and hopes for more Fed rate cuts. But others warn that investors may be wishful thinking!

A U.S. stock market pullback is likely due in Q4 2025

The fourth quarter (Q4) of the calendar year runs from 1st October to 31st December. In financial and editorial contexts, it often carries symbolic weight—year-end reckonings, holiday spending, and final earnings reports all converge here.

A pullback is due, but when?

AI power – the energy hunger game!

Powering AI will not be clean...?

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.

How CLEAN is GREEN? Explainers | MIT Climate Portal

⚖️ Lifecycle Emissions Comparison

Here’s how different sources stack up in terms of CO₂ emissions per kilowatt hour:

Energy SourceCO₂ Emissions (g/kWh)Notes
Coal~1,000Highest emissions, plus toxic byproducts
Natural Gas~500Cleaner than coal, but still fossil-based
Solar<50Mostly from manufacturing panels
Wind~10Lowest emissions, mostly from materials
Nuclear (SMR/SNR)~12–20Low emissions, but waste and safety debates linger

Source: MIT Climate Portal

Are we looking at an AI house of cards? Bubble worries emerge after Oracle blowout figures

AI Bubble?

There’s growing concern that parts of the AI boom—especially the infrastructure and monetisation frenzy—might be built on shaky foundations.

The term ‘AI house of cards’ is being used to describe deals like Oracle’s multiyear agreement with OpenAI, which has committed to buying $300 billion in computing power over five years starting in 2027.

That’s on top of OpenAI’s existing $100 billion in commitments, despite having only about $12 billion in annual recurring revenue. Analysts are questioning whether the math adds up, and whether Oracle’s backlog—up 359% year-over-year—is too dependent on a single customer.

Oracle’s stock surged 36%, then dropped 5% Friday as investors took profits and reassessed the risks.

Some analysts remain neutral, citing murky contract details and the possibility that OpenAI’s nonprofit status could limit its ability to absorb the $40 billion it raised earlier this year.

The broader picture? AI infrastructure spending is ballooning into the trillions, echoing the dot-com era’s early adoption frenzy. If demand doesn’t materialise fast enough, we could see a correction.

But others argue this is just the messy middle of a long-term transformation—where data centres become the new utilities

The AI infrastructure boom—especially the Oracle–OpenAI deal—is raising eyebrows because the financial and operational foundations look more speculative than solid.

Here’s why some analysts are calling it a potential house of cards

⚠️ 1. Mismatch Between Revenue and Commitments

  • OpenAI’s annual revenue is reportedly around $10–12 billion, but it’s committed to $300 billion in cloud spending with Oracle over five years.
  • That’s $60 billion per year, meaning OpenAI would need to grow revenue 5–6x just to break even on compute costs.
  • CEO Sam Altman projects $44 billion in losses before profitability in 2029.

🔌 2. Massive Energy Demands

  • The infrastructure needed to fulfill this contract requires electricity equivalent to two Hoover Dams.
  • That’s not just expensive—it’s logistically daunting. Data centres are planned across five U.S. states, but power sourcing and environmental impact remain unclear.
AI House of Cards Infographic

💸 3. Oracle’s Risk Exposure

  • Oracle’s debt-to-equity ratio is already 10x higher than Microsoft’s, and it may need to borrow more to meet OpenAI’s demands.
  • The deal accounts for most of Oracle’s $317 billion backlog, tying its future growth to a single customer.

🔄 4. Shifting Alliances and Uncertain Lock-In

  • OpenAI recently ended its exclusive cloud deal with Microsoft, freeing it to sign with Oracle—but also introducing risk if future models are restricted by AGI clauses.
  • Microsoft is now integrating Anthropic’s Claude into Office 365, signalling a diversification away from OpenAI.

🧮 5. Speculative Scaling Assumptions

  • The entire bet hinges on continued global adoption of OpenAI’s tech and exponential demand for inference at scale.
  • If adoption plateaus or competitors leapfrog, the infrastructure could become overbuilt—echoing the dot-com frenzy of the early 2000s.

Is this a moment for the AI frenzy to take a breather?

Negative news is not affecting the market as the Nasdaq hits a new high!

Nasdaq rockets to new high

The Nasdaq Composite closed at a record high of 21,798.70 on Monday, 8th September 2025. That 0.45% gain was driven largely by a rally in chip stocks—Broadcom surged 3.2%, and Nvidia added nearly 1%.

The broader market also joined the party:

  • S&P 500 rose 0.21% to 6,495.15
  • Dow Jones Industrial Average climbed 0.25% to 45,514.95

Investor optimism is swirling around potential Federal Reserve rate cuts, especially with inflation data due later this week. The market’s momentum seems to be riding a wave of AI infrastructure spending and tech sector strength.

Negative news is not affecting the market – but why?

  • The Nasdaq Composite closes at a record high on Monday 8th September 2025.
  • Refunds could hit $1 trillion if tariffs are deemed illegal.
  • China’s Xpeng eyes global launch of its Mona brand.
  • French Prime Minister Francois Bayrou loses no-confidence vote.
  • UK deputy PM resigns after tax scandal.

Stocks are rising despite August’s dismal jobs report because investors are interpreting the weak labor data as a signal that interest rate cuts may be on the horizon—and that’s bullish for equities.

📉 The contradiction at the heart of the market The U.S. economy showed signs of slowing, with job numbers actually declining in June and August’s report falling short of expectations.

Normally, that would spook investors—fewer jobs mean less consumer spending, which hurts corporate earnings and stock prices.

📈 But here’s the twist Instead of panicking, markets rallied. The Nasdaq Composite hit a record high, and the S&P 500 and Dow Jones also posted gains.

Why? Because a weaker jobs market increases the likelihood that the Federal Reserve will cut interest rates to stimulate growth. Lower rates make borrowing cheaper and boost valuations—especially for tech stocks.

🤖 AI’s role in the rally Tech firms, particularly those tied to artificial intelligence like Broadcom and Nvidia, led the charge.

The suggestion is that investors may be viewing job cuts as a sign that AI is ‘working as intended’—streamlining operations and improving margins. Salesforce and Klarna, for instance, have both reportedly cited AI as a reason for major workforce reductions.

Summary

IndicatorValue / ChangeInterpretation
Nasdaq Composite📈 21,798.70 (Record High)Tech led rally, 
investor optimism
S&P 500➕ 6,495.15Broad market strength
Dow Jones➕ 45,514.95Industrial resilience
August Jobs Report📉 Missed expectationsLabour market weakness
Job Growth (June & Aug)📉 NegativeEconomic slowdown
Investor Reaction🟢 Rate cuts expectedBullish for equities
AI Layoff Narrative🤖 ‘Efficiency gains’Tech streamlining 
Featured StocksBroadcom +3.2%, Nvidia +0.9%AI infrastructure driving
Infographic summary

So, while the jobs report paints a gloomy picture for workers, the market sees a silver lining: rate relief and tech-driven efficiency.

It’s a classic case of Wall Street optimism—where bad news for Main Street can be good news for stock prices.

The career ladder is broken—but the Nasdaq is building a rocket.

The Fed up next to move the market.

Japan’s yield curve bites back as it hits new highs!

Japan' Bond Yields

After decades of economic sedation, Japan’s long-term bond yields are rising with a vengeance.

The 30-year government bond has breached 3.286%—its highest level on record—while the 20-year yield has climbed to 2.695%, a peak not seen since 1999.

These aren’t just numbers; they’re seismic signals of a nation confronting its delayed past, now its deferred future.

Indicative Yield Curve for Japan

For years, Japan’s yield curve was a monument to inertia. Negative interest rates, yield curve control, and relentless bond-buying by the Bank of Japan created an artificial calm—a kind of economic Zen garden, raked smooth but eerily still.

That era is ending. Inflation has persisted above target for three years, and the BOJ’s retreat from monetary intervention has unleashed market forces long held at bay.

This steepening curve is more than financial recalibration—it’s a symbolic reckoning. Rising yields demand accountability: from policymakers who masked structural fragility, from investors who chased safety in stagnation, and from a society that postponed hard choices on demographics, debt, and productivity.

The bond market, once a passive witness, now acts as judge. Each basis point is a moral verdict on Japan’s economic past.

The shadows of the Lost Decades—deflation, aging populations, and overspending—are being dispelled not by command, but through the process of price discovery.

In this new era, Japan’s yield curve resembles a serpent uncoiling—no longer dormant but rising with intent.

The question isn’t whether the curve will flatten again, but whether Japan can meet the moment it has long delayed.

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.

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.

Trump and Musk feud – love to hate in 137 days – a billionaire brawl

Trump Musk Argue

It’s a worry – arguably the most powerful man in the world and the richest man in the world in a highly visible fallout.

Unrest and distrust at the top of U.S. government and the and in the corporate world – so what’s new?

Donald Trump and Elon Musk, once allies, have engaged in a heated public feud over a tax and spending bill. The conflict began when Musk criticised Trump’s “Big Beautiful Bill,” calling it a “disgusting abomination” and warning it would increase the budget deficit. Trump retaliated on Truth Social, calling Musk “CRAZY” and threatening to terminate billions of dollars in government contracts for his companies.

Musk fired back on X, claiming Trump would have lost the election without his support and accusing him of being named in the unreleased Epstein files.

The spat has had financial repercussions, with Tesla’s stock plummeting over 14%, wiping out $152 billion in market value. Investors fear the fallout could impact Tesla’s regulatory environment under Trump’s administration.

Tesla 5-day chart

Tesla 5-day chart – 14% fall

Political figures have weighed in, with billionaire Bill Ackman urging the two to reconcile, while Steve Bannon suggested Trump should seize SpaceX under the Defence Production Act. Musk also polled followers on whether to create a new political party, gaining support from Mark Cuban and Andrew Yang.

It got worse

Elon Musk escalated his feud with Donald Trump by making explosive claims that Trump appears in the Epstein files, suggesting that this is why they have not been made public. Musk posted on X, “Time to drop the really big bomb: Donald Trump is in the Epstein files. That is the real reason they have not been made public.

“Have a nice day, DJT!”. He later doubled down, telling followers to “mark this post for the future” and insisting that “the truth will come out”.

Trump has denied any wrongdoing and dismissed Musk’s claims as retaliation for his tax bill. The White House press secretary called Musk’s comments “an unfortunate episode” and insisted that Trump is focused on passing his legislation.

Musk also endorsed a call for Trump’s impeachment, agreeing with a post that suggested Vice President JD Vance should replace Trump. This marks a dramatic shift, as Musk was previously a close ally of Trump and even held a government advisory role.

The feud continues to escalate, with Musk calling for the bill’s rejection and Trump defending it as a historic tax cut.

The position and authority of U.S. President Trump have been challenged. How will tariff trade negotiations and his standing with other world leaders progress from here?

I do have a couple of questions: why did Musk back Trump in the first place and, at what point in the 137 ‘love in’ days did he know about the Epstein link (if indeed there is one)?

Or did he know before?

Who to trust?

Shock but no ‘awe’ in Trump’s first 100 days in office

Sledgehammer policies

U.S. President Donald Trump has definitely brought a lot of shock in the first 100 days of his presidency, smashing trade links, alliances, and even his own government, but it can hardly be said to have left anybody truly in ‘awe’.

Donald Trump’s first 100 days in office during his second term have been a whirlwind of activity, marked by bold moves and significant controversy.

His poll rating is the lowest of any President of recent times for the first 100 days. It currently sits at around 41% (a CNN poll result suggests).

How does it compare?

Harry S. Truman, hit a rock-bottom approval rating of 22% in 1952. Other presidents like Richard Nixon and George W. Bush also dipped below 25%. But these were during their terms and not in the first 100 days.

His administration has focused heavily on reshaping trade policies, imposing tariffs that have disrupted global markets and strained relationships with long-standing allies.

Despite his claims of progress, no major trade deals have been finalised, leaving many questioning the effectiveness of his approach.

Legal challenges

Domestically, Trump’s policies have faced significant legal challenges, with numerous lawsuits filed against his administration. His stance on immigration and energy has sparked heated debates, reflecting the polarising nature of his decisions.

Trump’s ‘drill-baby-drill’ mantra has not had the desire reaction – oil prices has fallen with U.S. oil below $65 a barrel.

The automotive industry, for instance, has grappled with regulatory uncertainty and additional costs due to his tariffs, prompting him to soften some measures in response to industry concerns.

Internationally, Trump’s actions have raised concerns about U.S. credibility and stability. His hostile stance toward traditional allies, such as Canada, the EU and NATO, has left multi-decade relationships in tatters.

Meanwhile, his administration’s handling of the ongoing war in Ukraine and trade negotiations with China has drawn criticism for its lack of tangible results.

Despite these challenges, Trump remains confident in his vision for America. He has claimed progress in tariff negotiations with India, suggesting that a trade deal may be on the horizon.

No deals… yet

There has not been a single trade deal concluded with Trump’s administration – despite him reportedly claiming to have done ‘200 deals’ with only 195 countries in the world.

China is still striking a defiant tone on trade, and the war in Ukraine rages on. The president has also been forced to walk back on his “reciprocal tariffs.” 

However, his administration’s approach has left many wondering whether his first 100 days will be remembered for their impact or their controversy.

As the dust settles, the world watches closely to see how Trump’s policies will shape the future of the United States and its role on the global stage.

Trump may have wanted his first 100 days to be historic, and they were – but for all the wrong reasons.  

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!

Has ‘Rachel from accounts’ messed up the UK economy?

UK budget

The pound has continued to fall after UK government borrowing costs rose and concerns grew about public finances

Sterling dropped as UK 10-year borrowing costs surged to their highest level since the 2008 financial crisis when bank borrowing virtually ground to a halt.

Economists have warned the rising costs could lead to further tax rises or cuts to spending plans as the government tries to meet its self-imposed borrowing target.

The UK government creates its own financial crisis as it messes up its ‘go for growth’ policy

The UK economy is currently grappling with a series of financial challenges that have led to a significant fall in the value of the pound, soaring treasury yields, and high borrowing costs.

These developments have been largely influenced by the recent budget announced by Chancellor Rachel Reeves, which has sparked concerns among investors and economists alike.

Downward trajectory

The pound has been on a downward trajectory, recently hitting its lowest level since November 2023. Traders are betting on further declines, with some predicting the pound could fall as low as $1.12

This decline is partly due to the rising cost of government borrowing, which has surged to levels not seen since the 2008 financial crisis. The yield on 10-year gilts has climbed to 4.8%, while the yield on 30-year gilts has reached 5.34%, the highest in 27 years.

Recent UK budget

The recent budget has played a crucial role in these developments. Announced in October 2024, the budget included significant tax hikes and increased spending, leading to a substantial rise in government borrowing.

The budget deficit is expected to reach 4.5% of GDP this fiscal year, pushing the overall government debt close to 100% of GDP. This increase in borrowing has led to a higher supply of government debt, which in turn has driven down the price of bonds and pushed up yields.

Higher yields

Higher yields mean that the government has to pay more to borrow money, which has significant implications for its fiscal policy. The rising cost of servicing government debt could force the government to either raise taxes further or cut spending to meet its fiscal rules.

This situation is reminiscent of the market turmoil following Liz Truss’s mini budget in 2022, which also led to a sharp rise in borrowing costs and a fall in the value of the pound.

The impact of these developments extends beyond the government. Higher borrowing costs are likely to affect households and businesses as well.

Economic growth at risk

Mortgage rates, which are influenced by government bond yields, are expected to remain high, putting additional pressure on homeowners. Businesses, on the other hand, may face higher costs of borrowing, which could lead to reduced investment and slower economic growth.

The UK is facing a challenging economic environment characterized by a falling pound, high treasury yields, and rising borrowing costs.

The recent budget has exacerbated these issues, leading to increased government borrowing and higher debt levels. As the government navigates these challenges, it will need to carefully balance its fiscal policies to avoid further economic instability and ensure sustainable growth and not more ‘unfunded’ debt.

Meta boss bows to Trump re-aligning with their ‘free speech’ mandate

AI

Mark Zuckerberg’s recent actions seem to be driven by a mix of strategic business decisions and political pragmatism.

As Trump prepares to retake the White House, Zuckerberg has made several changes at Meta, including scaling back content moderation and fact-checking, and moving safety teams to Texas. These moves appear to align with Trump’s stance on free expression and reducing censorship.

Additionally, Zuckerberg and other tech leaders are likely seeking to build a favorable relationship with the incoming administration to navigate potential regulatory challenges and maintain their business interests. It’s a complex dance of power and influence, with both sides looking to benefit from the alliance.

Recalibrating for Trump

Zuckerberg, who has been summoned to Washington eight times to testify before congressional committees during the last two administrations, wants to be perceived as someone who can work with Trump and the Republican Party, it would appear.

Though Meta’s content-policy updates caught many of its employees and fact-checking partners off-guard, a small group of executives were formulating the plans in the aftermath of the U.S. election results. By the New Year – managers had reportedly begun planning the public announcements of its policy change.

It has been noted that Meta typically undergoes major ‘recalibrations’ after power changes hand. Meta adjusts its policies to best suit its business model and reputational needs based on the political landscape.

Does the company remain true to its original founding principles, whatever they are – or does it ‘cozy up’ with power to re-position itself to benefit politically? Let’s put some more money in the Trump inauguration pot.

Nothing new here then – but go watch the video of Zuckerberg’s announcement.

Does it may you cringe – or is it just me?

How is AI regulation likely to affect stock markets in 2025?

AI regulation

As we head into 2025, the landscape of artificial intelligence (AI) regulation is poised to undergo significant changes, and these shifts are likely to have a profound impact on the stock markets.

The introduction of new regulations, particularly in regions like the European Union and the United States, will create both challenges and opportunities for investors.

One of the most anticipated regulatory developments is the European Union’s AI Act, which aims to set a global standard for AI regulation. This act is expected to impose stringent requirements on AI systems, particularly those used in high-risk sectors such as healthcare, finance, and law enforcement.

Companies operating in these sectors will need to invest heavily in compliance, which could lead to increased operational costs and potentially affect their profitability. As a result, stocks of companies heavily reliant on AI technologies may experience volatility as investors react to these new regulations.

In the United States, the political landscape is also shifting, with the incoming administration expected to take a more hands-on approach to AI regulation. President-elect Donald Trump has appointed Elon Musk to co-lead a new Department of Government EfficiencyDOGE‘, which will focus on nascent technologies like AI. Musk’s influence and experience in the AI field could lead to more favourable policies for AI development, but it could also result in increased scrutiny and regulation of AI applications. Musk’s AI vision differs to that of Mark Zuckerberg’s for instance.

This dual approach of promoting innovation while ensuring safety and ethical use of AI could create a dynamic and unpredictable market environment.

The impact of AI regulation on the stock markets will not be uniform across all sectors. While companies in high-risk sectors may face challenges, those in industries like healthcare and finance could benefit from AI’s transformative potential.

For example, AI-driven innovations in healthcare, such as predictive diagnostics and personalised treatment plans, have the potential to revolutionize patient care and reduce costs. Companies that successfully integrate AI into their operations and comply with regulatory requirements could see their stock prices rise as investors recognize the long-term value of these advancements.

However, the regulatory landscape is not without its risks. Companies that fail to adapt to new regulations or face compliance issues may see their stock prices suffer. Additionally, the rapid pace of technological change means that regulations may struggle to keep up, leading to potential legal and financial uncertainties for companies operating in the AI arena.

AI regulation in 2025 is likely to create a complex and dynamic environment for the stock markets. While new regulations will pose challenges for some companies, they will also open up opportunities for those that can navigate the regulatory landscape successfully.

Investors will need to stay informed and agile, as the impact of AI regulation on the stock markets will be both significant and multifaceted.

Why has Sumsung fallen behind in the AI boom?

A Cartoon AI chip

Samsung’s struggle in the AI race

Samsung, previously a powerhouse in the semiconductor industry, has encountered significant hurdles in the AI competition, leading to a notable decline in market value. The company’s faltering stance can be attributed to a variety of factors, such as strategic errors, fierce competition, and swift technological progress in the AI field.

Missteps

A key factor in Samsung’s downturn in the AI sector is its insufficient investment in high-bandwidth memory (HBM) technology, which is vital for AI applications due to its ability to expedite data processing and enhance performance.

Although Samsung was once at the forefront of memory technology, it did not leverage the increasing demand for HBM, thus ceding ground to competitors such as SK Hynix. SK Hynix made significant investments in HBM and forged a robust partnership with Nvidia, an influential entity in the AI domain.

Competition

The AI sector is fiercely competitive, featuring key companies such as Nvidia, Google, and Microsoft, which are making substantial advancements in AI technology. Nvidia has notably become a frontrunner with its GPUs, crucial for AI training. Samsung’s struggle to match these developments has resulted in a decline in both market share and revenue.

Rapid technological advancements

The swift advancement of technology in the AI sector has presented challenges for Samsung. The company’s emphasis on conventional memory technology did not fully prepare it for the transition to AI-centric applications. With the rise of AI applications such as OpenAI’s ChatGPT, the need for sophisticated memory solutions surged, highlighting Samsung’s insufficient investment in High-Bandwidth Memory (HBM) as a notable shortcoming.

Financial implications

Samsung’s difficulties in the AI sector have significantly affected its finances. The company has seen a reported loss of around $122 billion in market value since July 2024, marking the most substantial drop among global chipmakers. This decline is largely due to Samsung’s challenges in adapting to the evolving AI industry and competing with its rivals.

Prospects

Despite facing challenges, Samsung is actively striving to advance in the AI domain. The company has recently introduced its next-generation Bixby AI, which utilizes large language model technology, positioning it to better contend with competitors such as ChatGPT and Google Gemini.

Additionally, Samsung is cultivating its proprietary AI model, named Samsung Gauss, with the goal of augmenting device functionality and elevating the consumer experience.

Samsung’s lag in the AI sector is due to strategic errors, fierce competition, and swift technological progress. Despite considerable financial setbacks, the company is vigorously pursuing new AI initiatives and investments to recover its standing in the industry.

The path forward is fraught with challenges, yet Samsung’s commitment to innovation and adaptation could enable it to regain its status as a frontrunner in the AI domain.