The recent rebound in technology shares, led by Google’s surge in artificial intelligence optimism, offered a welcome lift to investors weary of recent market sluggishness.
Yet beneath the headlines lies a more troubling dynamic: the increasing reliance on a handful of mega‑capitalisation firms to sustain broader equity gains.
Breadth
Markets thrive on breadth. A healthy rally is one in which gains are distributed across sectors, signalling confidence in the wider economy. When only one or two companies shoulder the weight of investor sentiment, the picture becomes distorted.
Google’s AI announcements may well justify enthusiasm, but the fact that its performance alone can swing indices highlights a fragility in the current market structure.
This concentration risk is not new. In recent years, the so‑called ‘Magnificent Seven‘ technology giants have dominated returns, masking weakness in smaller firms and traditional industries.
While investors cheer the headline numbers, the underlying reality is that many sectors remain subdued. Manufacturing, retail, and even parts of the financial industry are not sharing equally in the rally.
Over Dependence
Over‑dependence on highflyers creates two problems. First, it exposes markets to sudden shocks: if sentiment turns against one of these giants, indices can tumble disproportionately.
Second, it discourages capital from flowing into diverse opportunities, stifling innovation outside the tech elite.
For long‑term stability, investors and policymakers alike should be wary of celebrating narrow gains. A resilient market requires participation from a broad base of companies, not just the fortunes of a few.
Google’s success in AI is impressive, but true economic strength will only be evident when growth spreads beyond the marquee names.
Until then, the market remains vulnerable, propped up by giants whose shoulders, however broad, cannot carry the entire economy indefinitely.
Nvidia’s Q3 results show strength, but the real risk of an AI bubble may lie in the debt-fuelled data centre boom and the circular crossover deals between tech giants.
Nvidia’s latest quarterly earnings were nothing short of spectacular. Revenue surged to $57 billion, up 62% year-on-year, with net income climbing to nearly $32 billion. The company’s data centre division alone contributed $51.2 billion, underscoring how central AI infrastructure has become to its growth.
These figures have reassured investors that Nvidia itself is not the weak link in the AI story. Yet, the question remains: if not Nvidia, where might the bubble be forming?
Data centre roll-out
The answer may lie in the debt-driven expansion of AI data centres. Building hyperscale facilities requires enormous capital outlays, not only for GPUs but also for power, cooling, and connectivity.
Many operators are financing this expansion through debt, betting that demand for AI services will continue to accelerate. While Nvidia’s chips are sold out and cloud providers are racing to secure supply, the sustainability of this debt-fuelled growth is less certain.
If AI adoption slows or monetisation lags, these projects could become overextended, leaving balance sheets strained.
Crossover deals
Another area of concern is the crossover deals between major technology companies. Nvidia’s Q3 was buoyed by agreements with Intel, OpenAI, Google Cloud, Microsoft, Meta, Oracle, and xAI.
These arrangements exemplify a circular investment pattern: companies simultaneously act as customers, suppliers, and investors in each other’s AI ventures.
While such deals create momentum and headline growth, they risk masking the true underlying demand.
If much of the revenue is generated by companies trading capacity and investment back and forth, the market could be inflating itself rather than reflecting genuine end-user adoption.
Bubble or not to bubble?
This dynamic is reminiscent of past bubbles, where infrastructure spending raced ahead of proven returns. The dot-com era saw fibre optic networks built faster than internet businesses could monetise them.
Today, AI data centres may be expanding faster than practical applications can justify. Nvidia’s results prove that demand for compute is real and immediate, but the broader ecosystem may be vulnerable if debt levels rise and crossover deals obscure the true picture of profitability.
In short, Nvidia’s strength does not eliminate bubble risk—it merely shifts the spotlight elsewhere. Investors and policymakers should scrutinise the sustainability of AI infrastructure financing and the circular nature of tech partnerships.
The AI revolution is undoubtedly transformative, but its foundations must rest on genuine demand rather than speculative debt and self-reinforcing deals.
Anthropic has reportedly struck major deals with Microsoft and Nvidia. On Tuesday 18th November 2025, Microsoft announced plans to invest up to $5 billion in the startup, while Nvidia will contribute as much as $10 billion. According to a reports, this brings Anthropic’s valuation to around $350 billion. Wow!
Google has unveiled its newest AI model, Gemini 3. According to Alphabet CEO Sundar Pichai, it will deliver desired answers with less prompting.
This update comes just eight months after the launch of Gemini 2.5 and is reported to be available in the coming weeks.
Money keeps flowing
Money keeps flowing into artificial intelligence companies but out of AI stocks
In what seems like yet another case of mutual ‘back-scratching’, Microsoft and Nvidia are set to invest a combined $15 billion in Anthropic, with the OpenAI rival agreeing to purchase computing power from its two newest backers.
Lately, a large chunk of AI news feels like it boils down to: ‘Company X invests in Company Y, and Company Y turns around and buys from Company X’.
That’s not entirely correct or fair. There are plenty of advancements in the AI world that focus on actual development rather than investments. Google recently introduced the third version of Gemini, its AI model.
Anthropic’s valuation has surged to around $350 billion, propelled by a landmark $15 billion investment from Microsoft and Nvidia.
Anthropic, the AI start-up founded in 2021 by former OpenAI employees, has rapidly ascended into the ranks of the world’s most valuable companies, more than doubling its worth from $183 billion just a few months earlier.
A valuation of $350 billion for a company only 4 years old is astounding!
The deal reportedly sees Microsoft commit up to $5 billion and Nvidia up to $10 billion. Anthropic has agreed to purchase an extraordinary $30 billion in Azure compute capacity and additional infrastructure from Nvidia.
This strategic alliance is not merely financial; it signals a deliberate diversification of Microsoft’s AI ecosystem beyond its reliance on OpenAI. And Nvidia strengthens its dominance in AI hardware.
Anthropic’s valuation has reached $350 billion, following the massive $15 billion investment from Microsoft and Nvidia, which positions the company among the most valuable in the world.
This astronomical figure reflects both the scale of its partnerships — including $30 billion in Azure compute commitments and Nvidia’s cutting-edge hardware.
The valuation underscores both the intensity of the global AI race and the confidence investors place in Anthropic’s safety-conscious approach to artificial intelligence.
Yet, it also raises questions about whether such astronomical figures reflect genuine long-term value. Or is it the froth of an overheated market.
Hyperscalers keep pumping the money into AI but are they getting the justified returns yet? Probably not yet – but it will come in the future.
But by then, it will be time to upgrade the system as it develops and so more money will be pumped in
For some time now, talk of an ‘AI bubble‘ has largely come from investors and financial analysts. Now, strikingly, some of the loudest warnings are coming from inside the industry itself.
At the Web Summit in Lisbon, senior executives from companies such as DeepL and Picsart reportedly admitted they were uneasy about the soaring valuations attached to artificial intelligence ventures. Sam Altman of OpenAI has also sounded warnings of AI overvaluation.
DeepL’s chief executive Jarek Kutylowski reportedly described current market conditions as ‘pretty exaggerated’ and suggested that signs of a bubble are already visible.
Picsart’s Hovhannes Avoyan reportedly echoed the sentiment, criticising the way start‑ups are being valued despite having little or no revenue. He reportedly coined the phrase ‘vibe revenue’ to describe firms being backed on hype rather than substance.
These remarks highlight a paradox. On one hand, demand for AI services remains strong, with enterprises expected to increase adoption in 2026.
On the other, the financial side of the sector looks overheated. Investors such as Michael Burry have accused major cloud providers of overstating profits, while banks including Goldman Sachs and Morgan Stanley have warned of potential corrections.
The tension reflects a broader question: can the industry sustain its rapid expansion without a painful reset?
Venture capital forecasts suggest trillions will be poured into AI data centres over the next five years, yet some insiders argue that the scale of spending is unnecessary.
Even optimists concede that businesses are struggling to integrate AI effectively, meaning the promised returns may take longer to materialise.
For now, the AI sector stands at a crossroads. The technology’s transformative potential is undeniable, but the financial exuberance surrounding it may prove unsustainable.
If the warnings from within the industry are correct, the next chapter of the AI story could be less about innovation and more about value correction.
Artificial Intelligence: The Hype, The Hangover, and What Comes Next…
For the past two years, artificial intelligence has dominated headlines, boardrooms, and investor portfolios.
From generative models that write poetry to chips that promise to revolutionise data processing, AI has been hailed as the engine of a new industrial age. But as 2025 unfolds, the sheen is beginning to dull.
Beneath the surface of record-breaking valuations and breathless media coverage, a more sobering narrative is taking shape: the AI boom may be running out of steam.
Slowing down
Recent market activity paints a cautionary tale. Despite strong earnings from AI stalwarts like Palantir and AMD, stock prices have faltered a little.
Palantir plunged nearly 8% after a blowout quarter, and even Nvidia—long considered the crown jewel of AI hardware—has seen pullbacks.
Analysts warn that Wall Street’s tunnel vision on AI is creating distortions, with capital flooding into a narrow set of companies while broader market fundamentals weaken.
One major concern is overcapacity in data centres. Billions have been poured into infrastructure to support AI workloads, but growth in consumer-facing applications—particularly chatbots and virtual assistants—appears to be plateauing.
Businesses are also grappling with the reality that integrating AI into operations is far more complex than anticipated. From regulatory hurdles to ethical dilemmas, the promise of seamless automation is proving elusive.
Bubble?
The spectre of an ‘AI bubble‘ looms large. Comparisons to the dot-com crash are no longer whispered—they’re openly debated by investors and tech executives alike.
While AI is undoubtedly transformative, the pace of investment may be outstripping the technology’s current utility. As OpenAI’s CEO Sam Altman noted, ‘When bubbles happen, smart people get overexcited about a kernel of truth’.
That kernel remains potent. AI will continue to reshape industries, but the narrative is shifting from euphoric disruption to measured integration. The mania is not over—but it’s maturing.
Investors, developers, and policymakers must now navigate a more nuanced landscape, where realism replaces hype, and long-term value trumps short-term spectacle.
In short, the AI revolution isn’t collapsing—it’s sobering up. And that may be the best thing for its future.
Oracle Bets Big on AMD AI Chips, Challenging Nvidia’s Dominance
Oracle Cloud Infrastructure has announced plans to deploy 50,000 AMD Instinct MI450 graphics processors starting in the second half of 2026, marking a bold strategic shift in the AI hardware landscape.
The move signals a direct challenge to Nvidia’s long-standing dominance in the data centre GPU market, where it currently commands over 90% market share.
AMD’s MI450 chips, unveiled earlier this year, are designed for high-performance AI workloads and can be assembled into rack-sized systems that allow 72 chips to function as a unified engine.
This architecture is tailored for inferencing tasks—an area Oracle believes AMD will excel in. ‘We feel like customers are going to take up AMD very, very well’, reportedly said Karan Batta, Oracle Cloud’s senior vice president.
The announcement comes amid a broader realignment in the AI ecosystem. OpenAI, historically reliant on Nvidia hardware, has recently inked a multi-year deal with AMD involving processors requiring up to 6 gigawatts of power.
If successful, OpenAI could acquire up to 10% of AMD’s shares, further cementing the chipmaker’s role in next-generation AI infrastructure.
Oracle’s pivot also reflects its ambition to compete with cloud giants like Microsoft, Amazon, and Google. With a reported five-year cloud deal with OpenAI potentially worth $300 billion, Oracle is positioning itself not just as a capacity provider but as a strategic AI enabler.
While Nvidia remains a formidable force, Oracle’s investment in AMD chips underscores a growing appetite for alternatives.
As AI demands scale, diversity in chip supply could become a competitive advantage—especially for enterprises seeking flexibility, cost efficiency, and innovation beyond the Nvidia ecosystem.
The AI arms race is far from over, but Oracle’s latest move suggests it’s no longer content to play catch-up. It’s aiming to redefine the rules.
U.S. stock markets are behaving like a mood ring in a thunderstorm—volatile, reactive, and oddly sentimental.
One moment, President Trump threatens a ‘massive increase’ in tariffs on Chinese imports, and nearly $2 trillion in market value evaporates.
The next, he posts that: ‘all will be fine‘, and futures rebound overnight. It’s not just policy—it’s theatre, and Wall Street is watching every act with bated breath.
This hypersensitivity isn’t new, but it’s been amplified by the precarious state of global trade and the towering expectations placed on artificial intelligence.
Trump’s recent comments about China’s rare earth export controls triggered a sell-off that saw the Nasdaq drop 3.6% and the S&P 500 fall 2.7%—the worst single-day performance since April.
Tech stocks, especially those reliant on semiconductors and AI infrastructure, were hit hardest. Nvidia alone lost nearly 5%.
Why so fickle? Because the market’s current rally is built on a foundation of hope and hype. AI has been the engine driving valuations to record highs, with companies like OpenAI and Anthropic reaching eye-watering valuations despite uncertain profitability.
The IMF and Bank of England have both warned that we may be in stage three of a classic bubble cycle6. Circular investment deals—where AI startups use funding to buy chips from their investors—have raised eyebrows and comparisons to the dot-com era.
Yet, the bubble hasn’t burst. Not yet. The ‘Buffett Indicator‘ sits at a historic 220%, and the S&P 500 trades at 188% of U.S. GDP. These are not numbers grounded in sober fundamentals—they’re fuelled by speculative fervour and a fear of missing out (FOMO).
But unlike the dot-com crash, today’s AI surge is backed by real infrastructure: data centres, chip fabrication, and enterprise adoption. Whether that’s enough to justify the valuations remains to be seen.
In the meantime, markets remain twitchy. Trump’s tariff threats are more than political posturing—they’re economic tremors that ripple through supply chains and investor sentiment.
And with AI valuations stretched to breaking point, even a modest correction could trigger a cascade.
So yes, the market is fickle. But it’s not irrational—it’s just balancing on a knife’s edge between technological optimism and geopolitical anxiety.
Influential figures and institutions are sounding the AI alarm—or at least raising eyebrows—about the frothy valuations and speculative fervour surrounding artificial intelligence.
Who’s Warning About the AI Bubble?
🏛️ Bank of England – Financial Policy Committee
View: Stark warning.
Quote: “The risk of a sharp market correction has increased.”
Why it matters: The BoE compares current AI stock valuations to the dotcom bubble, noting that the top five S&P 500 firms now command nearly 30% of market cap—the highest concentration in 50 years.
🏦 Jerome Powell – Chair, U.S. Federal Reserve
View: Cautiously sceptical.
Quote: Assets are “fairly highly valued.”
Why it matters: While not naming AI directly, Powell’s remarks echo broader concerns about tech valuations and investor exuberance.
🧮 Lisa Shalett – Chief Investment Officer, Morgan Stanley Wealth Management
View: Deeply concerned.
Quote: “This is not going to be pretty” if AI capital expenditure disappoints.
Why it matters: Shalett warns that 75% of S&P 500 returns are tied to AI hype, likening the moment to the “Cisco cliff” of the early 2000s.
🌍 Kristalina Georgieva – Managing Director, IMF
View: Watchful.
Quote: Financial conditions could “turn abruptly.”
Why it matters: Georgieva highlights the fragility of markets despite AI’s productivity promise, warning of sudden sentiment shifts.
🧨 Sam Altman – CEO, OpenAI
View: Self-aware caution.
Quote: “People will overinvest and lose money.”
Why it matters: Altman’s admission from inside the AI gold rush adds credibility to bubble concerns—even as his company fuels the hype.
📦 Jeff Bezos – Founder, Amazon
View: Bubble-aware.
Quote: Described the current environment as “kind of an industrial bubble.”
Why it matters: Bezos sees parallels with past tech manias, suggesting that infrastructure spending may be overextended.
🧠 Adam Slater – Lead Economist, Oxford Economics
View: Analytical.
Quote: “There are a few potential symptoms of a bubble.”
Why it matters: Slater points to stretched valuations and extreme optimism, noting that productivity projections vary wildly.
🏛️ Goldman Sachs – Investment Strategy Division
View: Cautiously optimistic.
Quote: “A bubble has not yet formed,” but investors should “diversify.”
Why it matters: Goldman acknowledges the risks while maintaining that fundamentals may still justify valuations—though they advise caution.
AI Bubble voices infographic October 2025
🧠 Julius Černiauskas and the Oxylabs AI/ML Advisory Board
🔍 View: The AI hype is nearing its peak—and may soon deflate.
Černiauskas warns that AI development is straining environmental resources and public trust. He’s pushing for responsible and sustainable AI practices, noting that transparency is lacking in how many models operate.
Ali Chaudhry, research fellow at UCL and founder of ResearchPal, adds that scaling laws are showing their limits. He predicts diminishing returns from simply making models bigger, and expects tightened regulations around generative AI in 2025.
Adi Andrei, cofounder of Technosophics, goes further: he believes the Gen AI bubble is on the verge of bursting, citing overinvestment and unmet expectations
🧠 Jamie Dimon on the AI Bubble
🔥 View: Sharply concerned—more than most as widely reported
Quote: “I’m far more worried than others about the prospects of a downturn.”
Context: Dimon believes AI stock valuations are “stretched” and compares the current surge to the dotcom bubble of the late 1990s.
📉 Key Warnings from Dimon
“Sharp correction” risk: He sees a real danger of a sudden market pullback, especially given how AI-related stocks have surged disproportionately—like AMD jumping 24% in a single day after an OpenAI deal.
“Most people involved won’t do well”: Dimon told the BBC that while AI will ultimately pay off—like cars and TVs did—many investors will lose money along the way.
“Governments are distracted”: He criticised policymakers for focusing on crypto and ignoring real security threats, saying: “We should be stockpiling bullets, guns and bombs”.
“AI will disrupt jobs and companies”: At a trade event in Dublin, he warned that AI’s ubiquity will shake up industries and employment across the board.
And so…
The AI boom of 2025 has ignited a speculative frenzy across global markets, with tech stocks soaring and investors piling into anything labelled “AI-adjacent.”
But beneath the euphoria, a chorus of high-profile warnings is growing louder. From the Bank of England and IMF to JPMorgan’s Jamie Dimon and OpenAI’s Sam Altman, concerns are mounting that valuations are dangerously stretched, capital is overconcentrated, and the narrative is outpacing reality.
Dimon likens the moment to the dotcom bubble, while Altman admits many will “lose money” chasing the hype. Analysts point to classic bubble signals: retail mania, corporate FOMO, and earnings divorced from fundamentals.
Even as AI’s long-term utility remains promising, the short-term exuberance may be setting the stage for a sharp correction.
Whether it’s a pullback or a full-blown crash, the mood is shifting—from uncritical optimism to wary anticipation.
The question now is not whether AI will change the world, but whether markets have priced in too much, too soon.
We have been warned!
The AI bubble will pop – it’s just a matter of when and not if.
Japan’s Nikkei 225 hit another record high on October 7th 2025 for the second consecutive session. Intraday trading saw the Nikkei rip through 40,500.
The rally was driven by a tech-fueled surge, especially after a landmark deal between OpenAI and AMD sent shockwaves through global markets.
Nikkei 225 one-day chart 7th October 2025
AMD’s stock soared nearly 24%, challenging Nvidia’s dominance and lifting chip-related stocks in Tokyo like Advantest, Tokyo Electron, and Renesas Electronics.
The backdrop’s fascinating too: this optimism comes amid political upheaval in Japan, with Sanae Takaichi’s recent rise to LDP leadership sparking hopes of fresh fiscal stimulus.
However, on a cautionary note: Japan’s bond market is flashing warning signs—yields are spiking to levels not seen since 2008
Anthropic has unveiled Claude Sonnet 4.5, its most advanced AI model to date—described by the company as ‘the best coding model in the world’.
Released in September 2025, Sonnet 4.5 marks a significant evolution in agentic capability, safety alignment, and real-world task execution.
Designed to power Claude Code and enterprise-grade AI agents, Sonnet 4.5 excels in long-context coding, autonomous software development, and complex business workflows.
Benchmark
In benchmark trials, the model reportedly sustained 30+ hours of uninterrupted coding, outperforming its predecessor Opus 4.1 and rival systems like GPT-5 and Gemini 2.52.
Anthropic’s emphasis on safety is equally notable. Sonnet 4.5 underwent extensive alignment training to reduce sycophancy, deception, and prompt injection vulnerabilities.
It now operates under Anthropic’s AI Safety Level 3 framework, with filters guarding against misuse in sensitive domains such as chemical or biological research.
New features include ‘checkpoints’ for code rollback, file creation within chat (spreadsheets, slides, documents), and a refreshed terminal interface.
Developers can now build custom agents using the Claude Agent SDK, extending the model’s reach into autonomous task orchestration4.
Anthropic’s positioning is clear: Claude Sonnet 4.5 is not merely a chatbot—it’s a colleague. With pricing held at $3 per million input tokens and $15 per million output tokens, the model is accessible yet formidable.
Whether this heralds a renaissance or a reckoning remains to be seen—but for now, Anthropic’s latest release sets a new benchmark for intelligent autonomy.
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?
Oracle Corporation has just staged one of the most dramatic rallies in tech history—catapulting itself into the elite club of near-trillion-dollar companies and reshaping the billionaire leaderboard in the process.
Founded in 1977 by Larry Ellison, Oracle began as a modest database software firm. Its first major boom came in the late 1990s, riding the dot-com wave as enterprise software demand exploded.
By 2000, Oracle’s market cap had surged past $160 billion, making it one of the most valuable tech firms of the era.
A second wave of growth followed in the mid-2000s, fuelled by aggressive acquisitions like PeopleSoft and Sun Microsystems, which expanded Oracle’s footprint into enterprise applications and hardware.
Boom
But its most recent boom—triggered in 2025—is unlike anything before. Oracle’s pivot to cloud infrastructure and artificial intelligence has paid off spectacularly. In its fiscal Q1 2026 report, Oracle revealed $455 billion in remaining performance obligations (RPO), a staggering 359% increase year-over-year.
This backlog, driven by multi-billion-dollar contracts with AI giants like OpenAI, Meta, Nvidia, and xAI, sent shockwaves through Wall Street.
Despite missing revenue and earnings expectations slightly—$14.93 billion in revenue vs. $15.04 billion expected, and $1.47 EPS vs. $1.48 forecasted—the market responded with euphoria.
Oracle’s stock soared nearly 36% in a single day, adding $244 billion to its market cap and pushing it to approximately $922 billion. Analysts called it ‘absolutely staggering’ and ‘truly awesome’, with Deutsche Bank reportedly raising its price target to $335.
Oracle Infographic September 2025
This meteoric rise had personal consequences too. Larry Ellison, Oracle’s co-founder and current CTO, saw his net worth jump by over $100 billion in one day, briefly surpassing Elon Musk to become the world’s richest person.
His fortune reportedly peaked at around $397 billion, largely tied to his 41% stake in Oracle. Ellison’s journey—from college dropout to tech titan—is now punctuated by the largest single-day wealth gain ever recorded.
CEO Safra Catz also benefited, with her net worth rising by $412 million in just six hours of trading, bringing her total to $3.4 billion. Under her leadership, Oracle’s stock has risen over 800% since she became sole CEO in 2019.
Oracle’s forecast for its cloud infrastructure business is equally jaw-dropping: $18 billion in revenue for fiscal 2026, growing to $144 billion by 2030. If these projections hold, Oracle could soon join the trillion-dollar club alongside Microsoft, Apple, and Nvidia.
From database pioneer to AI infrastructure powerhouse, Oracle’s evolution is a masterclass in strategic reinvention.
Oracle one-year chart 10th September 2025
Oracle one-year chart 10th September 2025
And with Ellison now at the summit of global wealth, the company’s narrative is no longer just about software—it’s about legacy, dominance, and the future of intelligent computing.
Sam Altman, CEO of OpenAI, has never been shy about bold predictions. But his latest remarks strike a curious chord reportedly saying: ‘Yes, we’re in an AI bubble’.
‘And yes, AI is the most important thing to happen in a very long time’. It’s a paradox that feels almost ‘Altmanesque’—equal parts caution and conviction, like a person warning of a storm while building a lighthouse.
Altman’s reported bubble talk isn’t just market-speak. It’s a philosophical hedge against the frothy exuberance that’s gripped Silicon Valley and Wall Street alike.
With AI valuations soaring past dot-com levels, and retail investors piling into AI-branded crypto tokens and meme stocks, the signs of speculative mania are hard to ignore.
Even ChatGPT, OpenAI’s flagship product, boasts 1.5 billion monthly users—but fewer than 1% pay for it. That’s not a business model—it’s a popularity contest.
Yet Altman isn’t calling for a crash. He’s calling for clarity. His point is that bubbles form around kernels of truth—and AI’s kernel is enormous.
From autonomous agents to enterprise integration in law, medicine, and finance, the technology is reshaping workflows faster than regulators can blink.
Microsoft and Nvidia are pouring billions into infrastructure, not because they’re chasing hype, but because they see utility. Real utility.
Still, Altman’s warning is timely. The AI gold rush has spawned a legion of startups with dazzling demos and dismal revenue. This is likely the Dotcom ‘Esque’ reality – many will fail.
Many are burning cash at unsustainable rates, betting on future breakthroughs that may never materialise. Investors, Altman suggests, need to recalibrate—not abandon ship, but stop treating every chatbot as the next Google.
What makes Altman’s stance compelling is its duality. He’s not a doomsayer, nor a blind optimist. He’s a realist who understands that transformative tech often arrives wrapped in irrational exuberance. The internet had its crash before it changed the world. AI may follow suit.
So, is this a bubble? Yes. But it’s a bubble with brains. And if Altman’s lighthouse holds, it might just guide us through the fog—not to safety, but to something truly revolutionary.
In the meantime, investors would do well to remember hype inflates, but only utility sustains.
And Altman, ever the ‘paradoxical prophet’, seems to be betting on both.
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.
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.
The S&P 500 closed above 6,300 for the first time in history on Monday 21st July 2025, while the Nasdaq Composite notched yet another record, finishing at 20,974.17.
Investor enthusiasm for upcoming tech earnings has eclipsed broader concerns over looming global tariffs, fuelling a rally in major indexes.
Despite marginal losses in the Dow Jones Industrial Average, the tech-heavy Nasdaq rose 0.38% while the S&P 500 climbed 0.14%, buoyed by gains in heavyweights like Meta Platforms, Alphabet, and Amazon.
With over 60 S&P 500 companies having reported so far this earnings season, more than 85% have exceeded expectations, according to FactSet.
S&P 500 and Nasdaq Comp at new record highs 21st July 2025
S&P 500 and Nasdaq Comp at new record highs 21st July 2025
Alphabet shares advanced over 2% ahead of Wednesday’s results, and Tesla headlines the ‘Magnificent Seven’ group expected to drive the bulk of earnings growth this quarter. And not necessarily for the right reason.
Analysts reportedly expect the group to deliver 14% growth year-on-year, far outpacing the remaining S&P constituents’ average of 3.4%.
S&P 500
Despite tariff tensions simmering — with the U.S. setting a 1st August deadline for levy enforcement — investor sentiment remains bullish.
Bank of America estimates Q2 earnings are tracking a 5% annual increase, suggesting resilience amid geopolitical headwinds.
Strategists warn of potential volatility, as earnings surprises or policy shifts could spark swift market reactions.
Still, some analysts see space for further upside, projecting a potential S&P climb to 6,600 before any meaningful pullback.
As the tech titans prepare to report, all eyes are on whether optimism can keep the rally alive — or if tariffs will return to centre stage.
From FANG stocks, MAG 7 stocks to AI – the tech titans just keep giving.
AMD has officially lifted the curtain on its next-generation AI chip, the Instinct MI400, marking a significant escalation in the battle for data centre dominance.
Set to launch in 2026, the MI400 is designed to power hyperscale AI workloads with unprecedented efficiency and performance.
Sam Altman and OpenAI have played a surprisingly hands-on role in AMD’s development of the Instinct MI400 series.
Altman appeared on stage with AMD CEO Lisa Su at the company’s ‘Advancing AI’ event, where he revealed that OpenAI had provided direct feedback during the chip’s design process.
Altman described his initial reaction to the MI400 specs as ‘totally crazy’ but expressed excitement at how close AMD has come to delivering on its ambitious goals.
He praised the MI400’s architecture – particularly its memory design – as being well-suited for both inference and training tasks.
OpenAI has already been using AMD’s MI300X chips for some workloads and is expected to adopt the MI400 series when it launches in 2026.
This collaboration is part of a broader trend: OpenAI, traditionally reliant on Nvidia GPUs via Microsoft Azure, is now diversifying its compute stack.
AMD’s open standards and cost-effective performance are clearly appealing, especially as OpenAI also explores its own chip development efforts with Broadcom.
AMD’s one-year chart snap-shot
One-year AMD chart snap-shot
So, while OpenAI isn’t ditching Nvidia entirely, its involvement with AMD signals a strategic shift—and a vote of confidence in AMD’s growing role in the AI hardware ecosystem.
At the heart of AMD’s strategy is the Helios rack-scale system, a unified architecture that allows thousands of MI400 chips to function as a single, massive compute engine.
This approach is tailored for the growing demands of large language models and generative AI, where inference speed and energy efficiency are paramount.
AMD technical power
The MI400 boasts a staggering 432GB of next-generation HBM4 memory and a bandwidth of 19.6TB/sec—more than double that of its predecessor.
With up to four Accelerated Compute Dies (XCDs) and enhanced interconnects, the chip delivers 40 PFLOPs of FP4 performance, positioning it as a formidable rival to Nvidia’s Rubin R100 GPU.
AMD’s open-source networking technology, UALink, replaces Nvidia’s proprietary NVLink, reinforcing the company’s commitment to open standards. This, combined with aggressive pricing and lower power consumption, gives AMD a compelling value proposition.
The company claims its chips can deliver 40% more AI tokens per dollar than Nvidia’s offerings.
Big tech follows AMD
OpenAI, Meta, Microsoft, and Oracle are among the major players already integrating AMD’s Instinct chips into their infrastructure. OpenAI CEO Sam Altman, speaking at the launch event reportedly praised the MI400’s capabilities, calling it ‘an amazing thing‘.
With the AI chip market projected to exceed $500 billion by 2028, AMD’s MI400 is more than just a product—it’s a statement of intent. As the race for AI supremacy intensifies, AMD is betting big on performance, openness, and affordability to carve out a larger share of the future.
It certainly looks like AMD is positioning the Instinct MI400 as a serious contender in the AI accelerator space – and Nvidia will be watching closely.
The MI400 doesn’t just aim to catch up; it’s designed to challenge Nvidia head-on with bold architectural shifts and aggressive performance-per-dollar metrics.
Nvidia has long held the upper hand with its CUDA software ecosystem and dominant market share, especially with the popularity of its H100 and the upcoming Rubin GPU. But AMD is playing the long game.
Nvidia 0ne-year chart snapshot
Nvidia 0ne-year chart snapshot
By offering open standards like UALink and boasting impressive specs like 432GB of HBM4 memory and 40 PFLOPs of FP4 performance, the MI400 is pushing into territory that was once Nvidia’s alone.
Whether it truly rivals Nvidia will depend on a few key factors: industry adoption, software compatibility, real-world performance under AI workloads, and AMD’s ability to scale production and support.
But with major players like OpenAI, Microsoft, and Meta already lining up to adopt the MI400.
Saudi Arabia is making bold moves in artificial intelligence with a major acquisition from Nvidia.
The tech giant will be sending more than 18,000 of its latest GB300 Blackwell AI chips to Saudi-based company Humain, in a deal that marks a significant step toward the nation’s ambitions to become a global AI powerhouse.
The announcement was made by Nvidia CEO Jensen Huang during the Saudi-U.S. Investment Forum in Riyadh, as part of a White House-led trip that included President Donald Trump and other top CEOs.
Humain, backed by Saudi Arabia’s Public Investment Fund, plans to use the chips to develop large-scale AI models and establish cutting-edge data centers.
The chips will be deployed in a 500-megawatt facility, making it one of the largest AI computing projects in the region. Nvidia’s Blackwell AI chips are among the most advanced in the industry, used in training sophisticated AI models and powering data-intensive applications.
Saudi Arabia’s investment in AI technology aligns with its long-term vision of transforming its economy beyond traditional industries. With plans to expand its data infrastructure and deploy several hundred thousand Nvidia GPUs in the future, the country is positioning itself as a major AI hub in the Middle East.
As AI continues to shape global industries, Saudi Arabia’s investment signals a broader shift in how nations are competing for dominance in the AI revolution.
Nvidia’s involvement underscores the strategic importance of AI chips, not just in business, but in international relations as well.
OpenAI on Monday 31st March 2025 announced it had closed its $40 billion funding round, the most ever raised by a private tech company.
The deal values OpenAI at $300 billion, including the new capital.
The round includes $30 billion from SoftBank and $10 billion from a syndicate of investors.
OpenAI is now more valuable than Chevron.
The generative AI market is projected to exceed $1 trillion in revenue within the next decade. Companies such as Google, Amazon, Anthropic, and Perplexity are rapidly unveiling new products and features as competition to develop ‘AI agents’ intensifies.
U.S. tech giants are making bold strides in the development of humanoid robots, signalling a transformative shift in the robotics industry
Companies like Tesla, Google, Microsoft, and Nvidia are investing heavily in this cutting-edge technology, aiming to create machines that mimic human movement and behaviour.
These humanoid robots are envisioned to revolutionise industries ranging from manufacturing to healthcare, offering solutions to labor shortages and enhancing productivity.
Tesla’s Optimus project is a prime example of this ambition. CEO Elon Musk has announced plans to produce thousands of these robots, designed to perform repetitive and physically demanding tasks.
Optimus robots are expected to integrate seamlessly into factory settings, reducing the need for human intervention in hazardous environments.
Similarly, Boston Dynamics, known for its agile robots, continues to push the boundaries of what humanoid machines can achieve, focusing on tasks that require precision and adaptability.
The integration of artificial intelligence (AI) is a driving force behind these advancements. AI enables robots to learn from their environments, adapt to new tasks, and interact with humans in more intuitive ways.
Companies like Nvidia are leveraging their expertise in AI and machine learning are helping to develop robots capable of complex decision-making and problem-solving.
However, challenges remain. High production costs, limited battery life, and safety concerns are significant hurdles that need to be addressed before humanoid robots can achieve widespread adoption.
Despite these obstacles, the potential benefits are immense. From assisting the elderly to performing intricate surgeries, humanoid robots could redefine the boundaries of human capability.
As U.S. tech giants continue to innovate, the race to dominate the humanoid robotics market intensifies.
Tesla Optimus Gen 2
With China and other nations also making significant investments, the competition is fierce. Analysts warn that U.S. firms could lose out to China, which aims to replicate its success with electric vehicles in the robotics space race.
The future of humanoid robots promises to be a fascinating blend of technology, creativity, and global collaboration
U.S. companies that may benefit from this AI humanoid tech advancement
Tesla: Known for its Optimus humanoid robot project, Tesla is pushing boundaries in robotics and AI.
Google (Alphabet): A leader in AI and robotics research, with projects aimed at enhancing humanoid capabilities.
Microsoft: Investing in AI technologies that support robotics and automation.
Nvidia: Provides advanced AI chips and systems crucial for humanoid robot development.
Boston Dynamics: Famous for its agile robots like Atlas, focusing on precision and adaptability.
Agility Robotics: Creator of Digit, a humanoid robot designed for logistics and manufacturing.
Meta (Facebook): Exploring humanoid robots for social and interactive applications.
Apple: Investing in robotics and AI for potential humanoid advancements.
Amazon: Developing robots like Astro for home monitoring and other tasks.
Figure AI: Innovating humanoid robots like Figure 02 for various industries.
Bill Gates on AI
Bill Gates has shared some fascinating insights about AI recently. He reportedly believes that within the next decade, AI will transform many industries, making specialised knowledge widely accessible.
For example, he predicts that AI could provide high-quality medical advice and tutoring, addressing global shortages of doctors and educators.
Gates has also described this shift as the ‘age of free intelligence,’ where AI becomes a commonplace tool integrated into everyday life. While he acknowledges the immense potential of AI to solve global challenges – like developing breakthrough treatments for diseases and innovative solutions for climate change – he also recognises the disruptive impact it could have on jobs and the workforce.
Despite these concerns, Gates remains optimistic about AI’s ability to drive innovation and improve lives.
He has emphasised that certain human activities, like playing sports or hosting talk shows, will likely remain uniquely human.
However, despite all these predictions from powerful tech leaders – it does beg the question, do these ultra rich CEOs predict the future, or simply make it?
What if Quantum Physics coincides and collides with the ‘full’ arrival of AI and humanoid robots
Quantum computing could enhance the capabilities of AI-powered robots by solving complex optimisation problems, improving machine learning algorithms, and enabling real-time decision-making.
For instance, robots equipped with quantum sensors could navigate intricate environments, detect subtle changes in their surroundings, and interact with humans in more intuitive ways.
This fusion could revolutionise industries such as healthcare, manufacturing, and space exploration. Imagine humanoid robots performing intricate surgeries with precision, managing large-scale logistics, or exploring distant planets with advanced problem-solving abilities.
However, this convergence also raises ethical and societal questions. The potential for such powerful technologies to disrupt industries, impact employment, and challenge privacy norms must be carefully managed.
Collaboration between scientists, policymakers, and ethicists will be crucial to ensure these advancements benefit humanity as a whole.
The intersection of quantum physics, AI, and humanoid robotics is not just a technological milestone – it’s a glimpse into a future where the boundaries of human capability and machine intelligence blur.
It’s an exciting, albeit complex future humans are creating.
But will AI surpass human intelligence – and if it does what then for the human civilisation?
China’s embrace of open-source artificial intelligence (AI) is revolutionising the global AI landscape, challenging traditional notions of innovation and competitiveness in this rapidly evolving field.
Traditionally, the AI sector has been dominated by proprietary models and closed-source systems, particularly in the U.S.
However, China has made a strategic pivot towards open-source initiatives, driven by trailblazers like the AI startup DeepSeek.
DeepSeek’s R1 model, released earlier this year, has become a symbol of China’s open-source movement. Distributed under the permissive MIT licence, the R1 model allows unrestricted use, modification and distribution.
This approach has disrupted traditional business models by democratising access to cutting-edge AI tools. Companies from tech giants like Baidu and Tencent to emerging players like ManusAI have followed suit, releasing their own open-source models and fostering a collaborative environment for AI innovation.
This shift is seen by some as China’s ‘Android moment’ in AI – a reference to the impact of Google’s open-source Android operating system on the mobile app ecosystem.
The move towards open-source has enabled rapid cost reductions, increased accessibility, and accelerated product development. Chinese firms have leveraged these advantages to narrow the perceived technological gap with the U.S., with some analysts suggesting that the disparity has shrunk from years to mere months.
Despite these advancements, the open-source approach also raises questions about intellectual property, security, and sustainable business models.
While it has catalysed innovation, it remains to be seen whether open-source strategies can sustain long-term competitiveness against well-funded proprietary systems.
China’s open-source embrace exemplifies a bold shift in AI strategy, emphasizing collaboration and accessibility over exclusivity.
This paradigm shift could redefine global dynamics in artificial intelligence, fostering a more inclusive and innovative future for the industry.
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.
Elon Musk’s AI company, xAI, has recently released its latest AI model, Grok 3.
This new AI model is designed to be significantly more powerful and capable than its predecessor, Grok 2.
Enhanced Capabilities: Grok 3 boasts 10 times more computing power than Grok 2 and has been trained on an expanded dataset, including court case filings.
Reasoning Models: Grok 3 includes reasoning models that can carefully analyze and fact-check information before providing responses. This helps in avoiding common pitfalls of AI models.
Benchmark Performance: Grok 3 has outperformed other leading AI models, including OpenAI’s GPT-4o and DeepSeek’s R1, on various benchmarks such as AIME (math questions) and GPQA (physics, biology, chemistry problems).
New Features: The Grok app now includes a ‘DeepSearch’ feature that scans the internet and xAI’s social network, X, to provide summarised responses to user queries.
Subscription Plans: xAI has introduced a new subscription plan called SuperGrok, which offers additional reasoning capabilities and unlimited image generation.
Grok 3 is being hailed as the ‘smartest AI on Earth’ by Musk, and it’s expected to have a significant impact on various industries.
Definition
Grok is a neologism (a newly coined word or expression), referenced by Robert A. Heinlein for his 1961 science fiction novel Stranger in a Strange Land. It means to understand something so deeply that you become one with it.
Grok is a term used in computer programming to mean to ‘profoundly understand something‘, such as a system, a language, or an algorithm.
It is supposed to have ‘a bit of wit, a rebellious streak’ and it should answer the ‘spicy questions’ that other AI might dodge, according to a statement from xAI.
China’s Baidu reportedly plans to release the next generation of its artificial intelligence model in the second half of this year, according to information recently reported.
The planned update comes as Chinese companies race to develop innovative AI models to compete with OpenAI and other U.S. based companies.
Baidu was the first major Chinese tech company to roll out a ChatGPT-like chatbot called Ernie in March 2023.
However, despite initial momentum, the product has since been eclipsed by other Chinese chatbots from large tech companies such as Alibaba and ByteDance, as well as startups.
Shares of BYD, the Chinese electric vehicle (EV) giant, surged to a record high on Tuesday 11th February 2025, following the announcement of its new driver assistance technology.
This move is expected to significantly enhance the driving experience and safety features of BYD’s vehicles.
BYD’s founder and chairman, Wang Chuanfu, announced the launch of the DiPilot system at a livestreamed event, emphasizing that advanced smart driving will become a standard safety feature, akin to seatbelts and airbags (time will tell on that statement).
The system includes features such as remote parking and autonomous highway navigation. These features reportedly are being integrated into over 20 models. Budget-friendly options priced below 70,000 yuan ($9,555) will also have the system.
DeepSeek AI integration
The integration of DeepSeek’s AI technology is a game-changer for BYD. DeepSeek, known for its innovative chatbot that rivals U.S. competitors such as OpenAI and others brings high-quality AI capabilities to BYD’s autonomous driving systems.
This partnership allows BYD to offer advanced intelligent features at a competitive price, putting it ahead of its rivals in the fiercely competitive Chinese EV market.
Analysts have praised BYD’s strategic shift from price-cutting to upgrading vehicle functions and have noted that BYD is now dictating the pace of technological features in the market.
The company’s stock rose by 4.5% to a record high in Hong Kong, reflecting investor optimism about the new technology.
BYD’s move to integrate advanced driver assistance systems into budget models is expected to intensify the EV price war. The company’s aggressive pricing strategy, combined with cutting-edge technology, positions it well to capture a larger market share.
With more than 20 models featuring the new driver assistance tech, BYD is set to lead the way in smart vehicle innovation.
As BYD continues to expand its presence globally, the integration of DeepSeek’s AI technology marks a significant milestone in the company’s journey towards becoming a leader in the EV industry.
The future looks promising for BYD as it continues to innovate and push the boundaries of automotive technology.
Nvidia’s share price has been on a rollercoaster ride recently. After experiencing a significant drop due to concerns over the Chinese startup DeepSeek’s AI models, Nvidia’s stock saw a sharp recovery.
However, there are mixed opinions about the potential for more downside. Some analysts believe that Nvidia’s stock still looks weak on the technical charts and may face further declines.
Some analysts suggest that Nvidia shares may trade in the range of $105 to $135 and recommend a ‘sell on rise’ strategy. Some also pointed out signs of technical deterioration, suggesting that Nvidia’s stock may be entering an intermediate-term corrective phase.
On the other hand, some investors are optimistic about Nvidia’s long-term growth prospects, especially with its strong fundamentals and continued advancements in AI technology.
The market remains dynamic, and the stock’s performance will likely depend on various factors, including broader market trends and developments in the AI industry.
Nvidia meteoric will likely change dramatically when face with an alternative AI chip manufacturer.
Doubt cast
DeepSeek, has made significant advancements in AI technology. There are claims and speculations that DeepSeek may have used some U.S. technology to enhance its capabilities.
For instance, it was reported that DeepSeek acquired a substantial number of Nvidia’s high-performance A100 graphics processor chips before the U.S. imposed restrictions on their sales to China. Additionally, there have been allegations that DeepSeek copied some technology developed by U.S. rival OpenAI.
However, these are unfounded claims and it’s important to point out that DeepSeek has also been praised for its innovation and efficiency, developing AI models at a fraction of the cost compared to leading U.S. tech companies.
This may even aid Nvidia as it could drive the cost of AI down bringing it to a wider audience more quickly thus enhancing Nvidia’s future sales.
