Microsoft Azure experienced a widespread outage on 29th October, beginning around 16:00 UTC, which affected thousands of users and businesses globally.
The disruption stemmed from issues with Azure Front Door, Microsoft’s content delivery network, and cascaded into failures across Microsoft 365, Xbox, Minecraft, and numerous third-party services reliant on Azure infrastructure.
Major retailers such as Costco and Starbucks, as well as airlines including Alaska and Hawaiian, reported system failures that hindered customer access and internal operations.
Users struggled with authentication, hosting, and server connectivity, with DownDetector logging a surge in complaints from 15:45 GMT onwards.
Microsoft acknowledged the problem on its Azure status page, attributing the outage to a suspected configuration change.
Full service restoration was achieved by about 23:20 UTC, though the timing coincided awkwardly with Microsoft’s Q1 FY26 earnings report, where Azure was reportedly highlighted as its fastest-growing segment.
The incident underscores the critical dependence on cloud infrastructure and raises questions about resilience and contingency planning.
As businesses increasingly migrate to cloud platforms, the ripple effects of such outages become more pronounced, impacting not just productivity, but public trust in digital reliability.
It was just one week ago on Monday 20th October 2025, Amazon Web Services (AWS) experienced a major outage that rippled across the digital world, disrupting operations for millions of users and businesses.
The incident, which originated in AWS’s US-East-1 region, was reportedly traced to DNS resolution failures affecting DynamoDB—one of AWS’s core database services.
This technical fault triggered cascading issues across EC2, network load balancers, and other critical infrastructure, leaving many services offline for hours.
The impact was immediate and widespread. Major consumer platforms such as Snapchat, Reddit, Disney+, Canva, and Ring doorbells went dark.
Financial services including Venmo and Robinhood faltered, while airline customers at United and Delta struggled to access bookings. Even British government portals like Gov.uk and HMRC were affected, underscoring the global reach of AWS’s infrastructure.
World leader
AWS is the world’s leading cloud provider, commanding roughly one-third of the global market—well ahead of Microsoft Azure and Google Cloud.
Millions of companies, from startups to multinational corporations, rely on AWS for everything from data storage and virtual servers to machine learning and content delivery.
Its services underpin critical operations in healthcare, education, retail, logistics, and media. When AWS stumbles, the internet itself feels the tremor.
20 Prominent Companies Affected by the AWS Outage (20th Oct 2025)
Sector
Company Name
Impact Summary
E-commerce
Amazon
Internal systems and Seller Central offline
Social Media
Snapchat
App outages and delays
Streaming
Disney+
Service interruptions
News
Reddit
Partial outages, scaling issues
Design Tools
Canva
High error rates, reduced functionality
Smart Home
Ring
Device connectivity issues
Finance
Venmo
Transaction delays
Finance
Robinhood
Trading disruptions
Airlines
United Airlines
Booking and check-in issues
Airlines
Delta Airlines
Reservation access problems
Telecom
T-Mobile
Indirect service disruptions
Government
Gov.uk
Portal access issues
Government
HMRC
Service delays
Banking
Lloyds Bank
Online banking affected
Productivity
Zoom
Meeting access issues
Productivity
Slack
Messaging delays
Education
Canvas
Assignment submissions disrupted
Crypto
Coinbase
User access failures
Gaming
Roblox
Server outages
Gaming
Fortnite
Gameplay interruptions
This outage wasn’t the result of a cyberattack, but rather a technical fault in one of Amazon’s main data centres. Yet the consequences were no less severe.
Amazon’s own operations were disrupted, with warehouse workers unable to access internal systems and third-party sellers locked out of Seller Central.
Canva reported ‘significantly increased error rates’. while Coinbase and Roblox cited cloud-related failures.
The incident serves as a stark reminder of the risks inherent in centralised cloud infrastructure. As digital life becomes increasingly dependent on a handful of providers, the potential for systemic disruption grows.
A single point of failure can cascade across industries, affecting everything from classroom assignments to emergency services.
AWS has since restored normal operations and promised a detailed post-event summary. But for many, the outage has reignited questions about resilience, redundancy, and the wisdom of placing so much trust in a single cloud giant.
In the age of digital interdependence, even a brief lapse can feel like a global blackout.
The world’s largest contract chipmaker reported net income of NT$452.3 billion (£11.4 billion), far exceeding analyst expectations and marking a new high for the company.
Revenue climbed 30.3% year-on-year to NT$989.92 billion, driven by insatiable demand for high-performance chips powering artificial intelligence applications.
Tech giants including Nvidia, OpenAI, and Oracle have ramped up orders for TSMC’s cutting-edge processors, fuelling the company’s meteoric rise.
TSMC’s CEO, C.C. Wei, reportedly attributed the growth to ‘unprecedented investment in AI infrastructure’, noting that the company’s advanced nodes are now central to training large language models and deploying generative AI tools.
Despite global economic headwinds and ongoing trade tensions, TSMC’s strategic expansion—including a $165 billion global buildout across Arizona, Europe, and Japan—is positioning it as the backbone of next-gen computing.
The results also reflect a broader shift in the semiconductor landscape. As traditional consumer electronics plateau, AI-driven demand is reshaping supply chains and investment priorities.
Analysts suggest that AI chip spending could surpass $1 trillion in the coming years, with TSMC poised to capture a significant share.
For investors and industry observers, the message is clear: AI isn’t just a trend—it’s a fundamental shift. And TSMC, with its unparalleled fabrication expertise and global influence, is quietly shaping the future.
As the AI arms race accelerates, TSMC’s performance offers a glimpse into the future of tech: one where silicon, not software, defines the frontier.
The company’s latest earnings are not just a financial milestone—they’re a signal of where innovation is headed next.
Oracle Bets Big on AMD AI Chips, Challenging Nvidia’s Dominance
Oracle Cloud Infrastructure has announced plans to deploy 50,000 AMD Instinct MI450 graphics processors starting in the second half of 2026, marking a bold strategic shift in the AI hardware landscape.
The move signals a direct challenge to Nvidia’s long-standing dominance in the data centre GPU market, where it currently commands over 90% market share.
AMD’s MI450 chips, unveiled earlier this year, are designed for high-performance AI workloads and can be assembled into rack-sized systems that allow 72 chips to function as a unified engine.
This architecture is tailored for inferencing tasks—an area Oracle believes AMD will excel in. ‘We feel like customers are going to take up AMD very, very well’, reportedly said Karan Batta, Oracle Cloud’s senior vice president.
The announcement comes amid a broader realignment in the AI ecosystem. OpenAI, historically reliant on Nvidia hardware, has recently inked a multi-year deal with AMD involving processors requiring up to 6 gigawatts of power.
If successful, OpenAI could acquire up to 10% of AMD’s shares, further cementing the chipmaker’s role in next-generation AI infrastructure.
Oracle’s pivot also reflects its ambition to compete with cloud giants like Microsoft, Amazon, and Google. With a reported five-year cloud deal with OpenAI potentially worth $300 billion, Oracle is positioning itself not just as a capacity provider but as a strategic AI enabler.
While Nvidia remains a formidable force, Oracle’s investment in AMD chips underscores a growing appetite for alternatives.
As AI demands scale, diversity in chip supply could become a competitive advantage—especially for enterprises seeking flexibility, cost efficiency, and innovation beyond the Nvidia ecosystem.
The AI arms race is far from over, but Oracle’s latest move suggests it’s no longer content to play catch-up. It’s aiming to redefine the rules.
U.S. stock markets are behaving like a mood ring in a thunderstorm—volatile, reactive, and oddly sentimental.
One moment, President Trump threatens a ‘massive increase’ in tariffs on Chinese imports, and nearly $2 trillion in market value evaporates.
The next, he posts that: ‘all will be fine‘, and futures rebound overnight. It’s not just policy—it’s theatre, and Wall Street is watching every act with bated breath.
This hypersensitivity isn’t new, but it’s been amplified by the precarious state of global trade and the towering expectations placed on artificial intelligence.
Trump’s recent comments about China’s rare earth export controls triggered a sell-off that saw the Nasdaq drop 3.6% and the S&P 500 fall 2.7%—the worst single-day performance since April.
Tech stocks, especially those reliant on semiconductors and AI infrastructure, were hit hardest. Nvidia alone lost nearly 5%.
Why so fickle? Because the market’s current rally is built on a foundation of hope and hype. AI has been the engine driving valuations to record highs, with companies like OpenAI and Anthropic reaching eye-watering valuations despite uncertain profitability.
The IMF and Bank of England have both warned that we may be in stage three of a classic bubble cycle6. Circular investment deals—where AI startups use funding to buy chips from their investors—have raised eyebrows and comparisons to the dot-com era.
Yet, the bubble hasn’t burst. Not yet. The ‘Buffett Indicator‘ sits at a historic 220%, and the S&P 500 trades at 188% of U.S. GDP. These are not numbers grounded in sober fundamentals—they’re fuelled by speculative fervour and a fear of missing out (FOMO).
But unlike the dot-com crash, today’s AI surge is backed by real infrastructure: data centres, chip fabrication, and enterprise adoption. Whether that’s enough to justify the valuations remains to be seen.
In the meantime, markets remain twitchy. Trump’s tariff threats are more than political posturing—they’re economic tremors that ripple through supply chains and investor sentiment.
And with AI valuations stretched to breaking point, even a modest correction could trigger a cascade.
So yes, the market is fickle. But it’s not irrational—it’s just balancing on a knife’s edge between technological optimism and geopolitical anxiety.
Influential figures and institutions are sounding the AI alarm—or at least raising eyebrows—about the frothy valuations and speculative fervour surrounding artificial intelligence.
Who’s Warning About the AI Bubble?
🏛️ Bank of England – Financial Policy Committee
View: Stark warning.
Quote: “The risk of a sharp market correction has increased.”
Why it matters: The BoE compares current AI stock valuations to the dotcom bubble, noting that the top five S&P 500 firms now command nearly 30% of market cap—the highest concentration in 50 years.
🏦 Jerome Powell – Chair, U.S. Federal Reserve
View: Cautiously sceptical.
Quote: Assets are “fairly highly valued.”
Why it matters: While not naming AI directly, Powell’s remarks echo broader concerns about tech valuations and investor exuberance.
🧮 Lisa Shalett – Chief Investment Officer, Morgan Stanley Wealth Management
View: Deeply concerned.
Quote: “This is not going to be pretty” if AI capital expenditure disappoints.
Why it matters: Shalett warns that 75% of S&P 500 returns are tied to AI hype, likening the moment to the “Cisco cliff” of the early 2000s.
🌍 Kristalina Georgieva – Managing Director, IMF
View: Watchful.
Quote: Financial conditions could “turn abruptly.”
Why it matters: Georgieva highlights the fragility of markets despite AI’s productivity promise, warning of sudden sentiment shifts.
🧨 Sam Altman – CEO, OpenAI
View: Self-aware caution.
Quote: “People will overinvest and lose money.”
Why it matters: Altman’s admission from inside the AI gold rush adds credibility to bubble concerns—even as his company fuels the hype.
📦 Jeff Bezos – Founder, Amazon
View: Bubble-aware.
Quote: Described the current environment as “kind of an industrial bubble.”
Why it matters: Bezos sees parallels with past tech manias, suggesting that infrastructure spending may be overextended.
🧠 Adam Slater – Lead Economist, Oxford Economics
View: Analytical.
Quote: “There are a few potential symptoms of a bubble.”
Why it matters: Slater points to stretched valuations and extreme optimism, noting that productivity projections vary wildly.
🏛️ Goldman Sachs – Investment Strategy Division
View: Cautiously optimistic.
Quote: “A bubble has not yet formed,” but investors should “diversify.”
Why it matters: Goldman acknowledges the risks while maintaining that fundamentals may still justify valuations—though they advise caution.
AI Bubble voices infographic October 2025
🧠 Julius Černiauskas and the Oxylabs AI/ML Advisory Board
🔍 View: The AI hype is nearing its peak—and may soon deflate.
Černiauskas warns that AI development is straining environmental resources and public trust. He’s pushing for responsible and sustainable AI practices, noting that transparency is lacking in how many models operate.
Ali Chaudhry, research fellow at UCL and founder of ResearchPal, adds that scaling laws are showing their limits. He predicts diminishing returns from simply making models bigger, and expects tightened regulations around generative AI in 2025.
Adi Andrei, cofounder of Technosophics, goes further: he believes the Gen AI bubble is on the verge of bursting, citing overinvestment and unmet expectations
🧠 Jamie Dimon on the AI Bubble
🔥 View: Sharply concerned—more than most as widely reported
Quote: “I’m far more worried than others about the prospects of a downturn.”
Context: Dimon believes AI stock valuations are “stretched” and compares the current surge to the dotcom bubble of the late 1990s.
📉 Key Warnings from Dimon
“Sharp correction” risk: He sees a real danger of a sudden market pullback, especially given how AI-related stocks have surged disproportionately—like AMD jumping 24% in a single day after an OpenAI deal.
“Most people involved won’t do well”: Dimon told the BBC that while AI will ultimately pay off—like cars and TVs did—many investors will lose money along the way.
“Governments are distracted”: He criticised policymakers for focusing on crypto and ignoring real security threats, saying: “We should be stockpiling bullets, guns and bombs”.
“AI will disrupt jobs and companies”: At a trade event in Dublin, he warned that AI’s ubiquity will shake up industries and employment across the board.
And so…
The AI boom of 2025 has ignited a speculative frenzy across global markets, with tech stocks soaring and investors piling into anything labelled “AI-adjacent.”
But beneath the euphoria, a chorus of high-profile warnings is growing louder. From the Bank of England and IMF to JPMorgan’s Jamie Dimon and OpenAI’s Sam Altman, concerns are mounting that valuations are dangerously stretched, capital is overconcentrated, and the narrative is outpacing reality.
Dimon likens the moment to the dotcom bubble, while Altman admits many will “lose money” chasing the hype. Analysts point to classic bubble signals: retail mania, corporate FOMO, and earnings divorced from fundamentals.
Even as AI’s long-term utility remains promising, the short-term exuberance may be setting the stage for a sharp correction.
Whether it’s a pullback or a full-blown crash, the mood is shifting—from uncritical optimism to wary anticipation.
The question now is not whether AI will change the world, but whether markets have priced in too much, too soon.
We have been warned!
The AI bubble will pop – it’s just a matter of when and not if.
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.
As BIG tech poaches top AI talent, these companies are stripped to the bone as the tech talent is being hollowed out!
In the race to dominate artificial intelligence, America’s tech giants are vacuuming up talent at an unprecedented pace.
But behind the headlines of billion-dollar acquisitions and flashy AI demos lies a quieter crisis. The creation of ‘zombie companies’ — startups left staggering and soulless after their brightest minds are poached by Big Tech.
These zombie firms aren’t dead, but they’re no longer truly alive either. They continue to operate, maintain websites, and pitch to investors, yet their core innovation engine has stalled. The problem isn’t just brain drain — it’s brain decapitation.
When a startup loses its founding engineers, lead researchers, or visionary product designers to the likes of Google, Meta, or Microsoft, what remains is often a shell with no clear path forward.
The allure is understandable. Big Tech offers salaries that dwarf startup equity, access to massive compute resources, and the prestige of working on frontier models. But the downstream effect is corrosive.
Startups, once the lifeblood of AI experimentation, are now struggling to retain talent long enough to reach product maturity. Some pivot to consultancy, others limp along with outsourced development, and many quietly fold — their IP absorbed, their vision diluted.
This phenomenon is particularly acute in the U.S., where venture capital encourages rapid scaling but rarely protects against talent attrition. The result is a growing class of companies that exist more for optics than output — kept alive by inertia, legacy funding, or the hope of acquisition.
They clutter the innovation landscape, making it harder for truly disruptive ideas to gain traction.
Ironically, Big Tech’s hunger for talent may be undermining the very ecosystem it depends on. By stripping startups of their creative lifeblood, it risks turning the AI sector into a monoculture. This culture is then dominated by a few players, with fewer voices and less diversity of thought.
The solution isn’t simple. It may require new funding models, stronger incentives for retention, or even regulatory scrutiny of talent acquisition practices.
But one thing is clear: if the U.S. wants to remain the global leader in AI, it must find a way to nurture its startups — not just harvest them.
Otherwise, the future of innovation may be haunted by the walking dead.
Where is the standard for the tariff line? Is this fair on the smaller businesses and the consumer? Money buys a solution without fixing the problem!
Nvidia and AMD have struck a deal with the U.S. government: they’ll pay 15% of their China chip sales revenues directly to Washington. This arrangement allows them to continue selling advanced chips to China despite looming export restrictions.
Apple, meanwhile, is going all-in on domestic investment. Tim Cook announced a $600 billion U.S. investment plan over four years, widely seen as a strategic move to dodge Trump’s proposed 100% tariffs on imported chips.
🧩 Strategic Motives
These deals are seen as tariff relief mechanisms, allowing companies to maintain access to key markets while appeasing the administration.
Analysts suggest Apple’s move could trigger a ‘domino effect’ across the tech sector, with other firms following suit to avoid punitive tariffs.
Tariff avoidance examples
⚖️ Legal & Investor Concerns
Some critics call the Nvidia/AMD deal a “shakedown” or even unconstitutional, likening it to a tax on exports.
Investors are wary of the arbitrary nature of these deals—questioning whether future administrations might play kingmaker with similar tactics.
Big Tech firms are striking strategic deals to sidestep escalating tariffs, with Apple pledging $600 billion in U.S. investments to avoid import duties, while Nvidia and AMD agree to pay 15% of their China chip revenues directly to Washington.
These moves are seen as calculated trade-offs—offering financial concessions or domestic reinvestment in exchange for continued market access. Critics argue such arrangements resemble export taxes or political bargaining, raising concerns about legality and precedent.
As tensions mount, these deals reflect a broader shift in how tech giants navigate geopolitical risk and regulatory pressure.
Meta is making a serious play to become the dominant force in AI-powered consumer devices, and it’s not just hype—it’s backed by aggressive strategy, talent acquisition, and a unique distribution advantage.
🧠 Meta’s Strategic Edge in AI Devices
1. Massive User Base
Meta has direct access to 3.48 billion daily active users across Facebook, Instagram, WhatsApp, and Messenger.
This gives it an unparalleled distribution channel for deploying AI features instantly across billions of devices.
2. Platform-Agnostic Approach
Unlike Apple and Google, which tightly integrate AI into their operating systems, Meta is bypassing OS gatekeepers by embedding AI into apps and wearables.
It’s partnering with chipmakers like Qualcomm and MediaTek to optimize AI performance on mobile hardware.
3. Talent Acquisition Blitz
Meta poached Ruoming Pang, Apple’s head of AI models, and Alexandr Wang, co-founder of ScaleAI, to lead its Superintelligence group.
This group aims to build AI that’s smarter than humans—an ambitious goal that’s drawing top-tier talent from rivals.
4. Proprietary Data Advantage
Meta’s access to real-time, personal communication and social media data is considered one of the most valuable datasets for training consumer-facing AI.
This gives it a leg up in personalization and contextual understanding.
🍏 Apple and Google: Still Strong, But Vulnerable
Apple
Struggled with its in-house AI models, reportedly considering outsourcing to OpenAI or Anthropic for Siri upgrades.
Losing this battle could signal deeper issues in Apple’s AI roadmap.
Google
Has robust AI infrastructure and Gemini models, but faces competition from Meta’s nimble, app-based deployment strategy.
🔮 Could Meta Win?
Meta’s approach is disruptive: it’s not trying to own the OS—it’s trying to own the AI interface. If it continues to scale its AI across apps, smart glasses (like Ray-Ban Meta), and future AR devices, it could redefine how users interact with AI daily.
That said, Apple and Google still control the hardware and OS ecosystems, which gives them deep integration advantages. Meta’s success will depend on whether users prefer AI embedded in apps and wearables over OS-level assistants.
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.
In tech terms, a neocloud is a new breed of cloud infrastructure purpose-built for AI and high-performance computing (HPC).
Unlike traditional hyperscale cloud providers (like AWS or Azure), neoclouds focus on delivering raw GPU power, low-latency performance, and specialised environments for compute-intensive workloads.
🧠 Key Features of Neoclouds
GPU-as-a-Service (GPUaaS): Optimised for training and running large AI models.
AI-native architecture: Designed specifically for machine learning, deep learning, and real-time inference.
Edge-ready: Supports distributed deployments closer to users for faster response times.
Transparent pricing: Often more cost-efficient than hyperscalers for AI workloads.
Bare-metal access: Minimal virtualisation for maximum performance.
🏗️ How They Differ from Traditional Clouds
Feature
Neoclouds
Hyperscale Clouds
Focus
AI & HPC workloads
General-purpose services
Hardware
GPU-centric, high-density clusters
Mixed CPU/GPU, broad service range
Flexibility
Agile, workload-specific
Broad but less specialised
Latency
Ultra-low, edge-optimized
Higher, centralized infrastructure
Pricing
Usage-based, transparent
Often complex, with hidden costs
🚀 Who Uses Neoclouds?
AI startups building chatbots, LLMs, or recommendation engines
Research labs running simulations or genomics
Media studios doing real-time rendering or VFX
Enterprises deploying private AI models or edge computing
Think of neoclouds as specialist GPU clouds—like a high-performance race car compared to a family SUV.
Both get you places, but one’s built for speed, precision, and specialised terrain.
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.
As U.S. equity markets continue their relentless climb, a growing number of stocks are flashing warning signs through one of the most widely followed technical indicators: the Relative Strength Index (RSI).
Designed to measure momentum, RSI values above 70 typically indicate that a stock is overbought and may be due for a pullback.
As of early July 2025, several high-profile U.S. companies have RSI readings well above this threshold, suggesting that investor enthusiasm may be outpacing fundamentals.
🔍 What Is RSI?
The RSI is a momentum oscillator that ranges from 0 to 100. Readings above 70 suggest a stock is overbought, while readings below 30 indicate it may be oversold. While not a crystal ball, RSI is a useful tool for identifying potential reversals or pauses in price trends.
🚨 Top 5 Overbought U.S. Stocks (as of 1st July 2025)
These companies have benefited from the ongoing AI and biotech booms, with Nvidia and AMD riding the wave of demand for next-gen chips, while Alnylam and Circle Internet Group have surged on strong earnings and innovation in their respective sectors.
📊 RSI Snapshot: Top 10 U.S. Stocks by RSI
Rank
Company
Ticker
RSI
Sector
1
Nvidia
NVDA
84.3
Semiconductors
2
Super Micro Computer
SMCI
82.7
Hardware
3
AMD
AMD
80.1
Semiconductors
4
Alnylam Pharmaceuticals
ALNY
78.9
Biotech
5
Circle Internet Group
CIRC
77.5
Internet Services
6
Mereo BioPharma Group
MPH
76.4
Biotech
7
AVITA Medical
AVH
75.2
Healthcare
8
Microsoft
MSFT
74.8
Software
9
Lumentum Holdings
LITE
73.6
Optical Tech
10
Workiva
WK
72.9
Cloud Software
📌 What This Means for Investors
While high RSI doesn’t guarantee a drop, it does suggest caution. Stocks like Nvidia and Super Micro may continue to rise in the short term, but their elevated RSI levels imply that momentum could stall or reverse if sentiment shifts or earnings disappoint.
Investors should consider pairing RSI with other indicators – such as MACD, volume trends, and earnings outlooks – before making decisions.
For long-term holders, these signals may simply be noise. But for traders, they’re a flashing yellow light.
At London Tech Week, Nvidia CEO Jensen Huang made a striking statement: “The way you program an AI is like the way you program a person.” (Do we really program people or do we teach)?
This marks a fundamental shift in how we interact with artificialintelligence, moving away from traditional coding languages and towards natural human communication.
Historically, programming required specialised knowledge of languages like C++ or Python. Developers had to meticulously craft instructions for computers to follow.
Huang argues that AI has now evolved to understand and respond to human language, making programming more intuitive and accessible.
This transformation is largely driven by advancements in conversational AI models, such as ChatGPT, Gemini, and Copilot.
These systems allow users to issue commands in plain English – whether asking an AI to generate images, write a poem, or even create software code. Instead of writing complex algorithms, users can simply ask nicely, much like instructing a colleague or student.
Huang’s analogy extends beyond convenience. Just as people learn through feedback and iteration, AI models refine their responses based on user input.
If an AI-generated poem isn’t quite right, users can prompt it to improve, and it will think and adjust accordingly.
This iterative process mirrors human learning, where guidance and refinement lead to better outcomes.
The implications of this shift are profound. AI is no longer just a tool for experts – it is a great equalizer, enabling anyone to harness computing power without technical expertise.
As businesses integrate AI into their workflows, employees will need to adapt, treating AI as a collaborative partner rather than a mere machine.
This evolution in AI programming is not just about efficiency; it represents a new era where technology aligns more closely with human thought and interaction.
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?
The escalating AI competition between the U.S. and China has taken a new turn with the emergence of DeepSeek, a Chinese AI startup that has introduced a low-cost AI model capable of rivaling the performance of OpenAI’s models.
This development has significant implications for data centres and the broader technology sector.
The rise of DeepSeek
DeepSeek’s recent breakthrough involves the development of two AI models, V3 and R1, which have been created at a fraction of the cost compared to their Western counterparts.
The total training cost for these models is estimated at around $6 million, significantly lower than the billions spent by major U.S. tech firms. This has challenged the prevailing assumption that developing large AI models requires massive financial investments and access to cutting-edge hardware.
Impact on data centres
The introduction of cost-effective AI models like those developed by DeepSeek could lead to a shift in how data centers operate.
Traditional AI models require substantial computational power and energy, leading to high operational costs for data centers. DeepSeek’s models, which are less energy-intensive, could reduce these costs and make AI technology more accessible to a wider range of businesses and organizations.
Technological advancements
DeepSeek’s success also highlights the potential for innovation in AI without relying on the most advanced hardware.
This could encourage other companies to explore alternative approaches to AI development, fostering a more diverse and competitive landscape. Additionally, the open-source nature of DeepSeek’s models promotes collaborative innovation, allowing developers worldwide to customise and improve upon these models2.
Competitive dynamics
The competition between DeepSeek and OpenAI underscores the broader U.S.-China rivalry in the AI space. While DeepSeek’s models pose a limited immediate threat to well-funded U.S. AI labs, they demonstrate China’s growing capabilities in AI innovation.
This competition could drive both countries to invest more in AI research and development, leading to faster technological advancements and more robust AI applications.
Broader implications
The rise of DeepSeek and similar Chinese and other AI startups could have far-reaching implications for the global technology sector.
As AI becomes increasingly integrated into various industries, the ability to develop and deploy AI models efficiently will be crucial.
Data centres will need to adapt to these changes, potentially investing in more energy-efficient infrastructure and exploring new ways to support AI workloads.
Where from here?
DeepSeek’s emergence as a significant player in the AI race highlights the dynamic nature of technological competition between the U.S. and China.
While the immediate impact on data centres and technology may be limited, the long-term implications could be profound.
As AI continues to evolve, the ability to innovate cost-effectively and collaborate across borders will be key to driving progress and maintaining competitiveness in the global technology landscape.
Microsoft has unveiled a new chip called Majorana 1 that it says will enable the creation of quantum computers able to solve ‘meaningful, industrial-scale problems in years, not decades’.
What is Microsoft’s Majorana 1?
It is the latest development in quantum computing – tech which uses principles of particle physics to create a new type of computer able to solve problems ordinary computers cannot.
Microsoft has announced a game-changing development in the world of quantum computing: the Majorana 1 chip. This revolutionary chip integrates eight topological quantum bits (qubits), setting a new standard for stability and resistance to environmental interference.
Microsoft. The new Majorana 1 chip
The Majorana 1 chip is built on a unique combination of indium arsenide, a semiconductor, and aluminum, a superconductor. This cutting-edge design enables the chip to create a topological state, a new form of matter that encodes information in a way that is inherently noise-resistant. This means that the Majorana 1 chip can maintain its quantum state longer, making it more reliable for complex computations.
What sets the Majorana 1 chip apart is its use of topoconductors, a new class of materials developed by Microsoft’s researchers over nearly two decades. These materials provide a high level of error protection, which is essential for practical quantum computing applications. The Majorana 1 chip is a significant step toward the ultimate goal of creating quantum computers with millions of qubits, capable of solving complex industrial and societal problems.
While the Majorana 1 chip is still in the research phase and not yet available for commercial use, it represents a monumental leap forward in quantum technology. Microsoft’s commitment to advancing quantum computing is evident in the substantial investment of time and resources required to develop this groundbreaking chip.
In summary, the Majorana 1 chip is poised to transform the landscape of quantum computing, offering a more stable and reliable platform for future innovations. This development marks a pivotal moment in the quest for practical and scalable quantum computing solutions.
What is Quantum computing?
Quantum computing is a revolutionary technology that uses the principles of quantum mechanics to process information in a fundamentally different way than classical computers, allowing for exponentially faster calculations in certain tasks.
It leverages qubits, which can represent multiple states simultaneously, enabling complex problem-solving and data analysis beyond the capabilities of traditional computing.
Microsoft says powerful quantum computers will be a reality in years not decades.
DeepSeek’s impact probably isn’t yet fully reflected in U.S. stocks
The ramifications of the Chinese startup DeepSeek, with its promise of delivering cheaper and more energy-efficient alternatives to harness artificial intelligence (AI), have yet to be fully reflected in U.S. equities.
If DeepSeek ends up delivering a less costly way forward – it will make it much easier and cheaper for smaller more typical companies to create AI ‘agents’ or AI opportunities for their businesses.
Under this scenario there will be ‘useful’ and meaningful benefits from DeepSeek that could bring huge earnings potential for a broader mix of companies beyond the current AI heavyweights through greater efficiencies and productivity from less-expensive AI solutions.
It also put huge sums being pledged for AI infrastructure by U.S. mega cap tech companies under a microscope. Rather than back down, the U.S. spending race has intensified.
Meta’s Chief Executive Mark Zuckerberg spoke a week ago of spending ‘hundreds of billions of dollars’ on AI infrastructure in the coming years, after pledging $60 billion to $65 billion on AI this year.
Alphabet announced AI investment for 2025, a bigger figure than Wall Street was anticipating.
Google forecast $75 billion in capital expenditures in 2025, a bigger figure than Wall Street was anticipating.
Microsoft reported its cloud and AI spending grew 95% in its fiscal second quarter to $22.6 billion.
Amazon has reported big AI investment too.
The spending frenzy on anything AI sends the market into a spin. How much more has to be spent before we see capital expenditures reduced or decrease is anyone’s guess right now – but current levels of AI expenditure are high, and returns will be expected.
“When is enough, enough?”
Or more to the point you might ask – when is ‘enough’ too much?
Fresh AI-spending commitments helped lift shares of Nvidia on while we saw a slump for Tesla shares in the week.
China this week saw the U.S. slap new 10% tariffs, while Canada and Mexico saw Trump threaten but delay 25% tariffs by 30 days. China retaliated in kind.
Catching up with the ‘Magnificent Seven’
Despite the high scrutiny on AI stocks, there is also much renewed focus from investors on other areas of the market.
There has been a bit of a rotation – while tech has been under pressure, defensive and rate-sensitive parts of the market have been gaining. This seems to be an emerging pattern.
But there should be reason for caution. For one thing, the growth rate of ‘Magnificent Seven’ earnings has been tailing off in recent quarters, especially since the group reached a 61% yearly rate in the fourth quarter of 2023 – the spend on AI investment has yet to fully appreciate the full return.
Forward analysts’ expectations have this percentage reportedly closer to 16% to 18% for the end of this year.
But that also would move the group closer to the roughly 12% to 13% yearly growth rate expected for the rest of the companies in the S&P 500 index, potentially making the high valuations of the ‘Magnificent Seven’ tougher to justify.
One of the most surprising things of the past couple of weeks, given the news around DeepSeek and shocks on the trade front, is the fact that stocks were still close to their all-time highs.
The market is pretty resilient right now, but tech stocks are sitting at a very high valuation – a pullback is due, even a correction (in my opinion).
The arrival of DeepSeek creates an alternative ‘cheaper’ AI option and that will unravel the status quo.
U.S. technology stocks plunged as Chinese startup DeepSeek sparked concerns over competitiveness in AI and America’s lead in the sector, triggering a global sell-off
DeepSeek launched a free, open-source large-language model in late December 2024, claiming it was developed in just two months at a cost of under $6 million.
The developments have stoked concerns about the large amounts of money big tech companies have been investing in AI models and data centres.
DeepSeek is a Chinese artificial intelligence startup that has recently gained significant attention in the AI world. Founded in 2023 by Liang Wenfeng, DeepSeek develops open-source large language models. The company is funded by High-Flyer, a hedge fund also founded by Wenfeng.
The AI models from DeepSeek have demonstrated impressive performance, rivaling some of the best chatbots in the world at a fraction of the cost. This has caused quite a stir in the tech industry, leading to significant drops in the stock prices of major AI-related firms.
The company’s latest model, DeepSeek-V3, is known for its efficiency and high performance across various benchmarks.
DeepSeek’s emergence challenges the notion that massive capital expenditure is necessary to achieve top-tier AI performance.
The company’s success has led to a re-evaluation of the AI market and has put pressure on other tech giants to innovate and reduce costs.
President Donald Trump announced a joint venture with OpenAI, Oracle and Softbank to invest billions of dollars in artificial intelligence infrastructure in the U.S.
The project, dubbed Stargate, was unveiled at the White House by Trump, Softbank CEO Masayoshi Son, OpenAI CEO Sam Altman and Oracle co-founder Larry Ellison.
The executives committed to invest an initial $100 billion and up to $500 billion over the next four years in the project, which will be set up as a separate company.
Softbank’s Son had reportedly already promised a four-year, $100-billion investment when he recently visited then-President-elect Trump at his Mar-a-Lago resort.
And this new AI investment is over and above the investments from the likes of Microsoft, Google, Apple, Anthropic and many others already in progress.
The UK is charting its own course when it comes to regulating artificial intelligence, signaling a potential divergence from the approaches taken by the United States and the European Union. This move is part of a broader strategy to establish the UK as a global leader in AI technology.
UK AI framework
Britain’s minister for AI and digital government, Feryal Clark, emphasised the importance of the UK developing its own regulatory framework for AI.
She highlighted the government’s strong relationships with AI companies like OpenAI and Google DeepMind, which have voluntarily opened their models for safety testing. Prime Minister Keir Starmer echoed these sentiments, stating that the UK now has the freedom to regulate AI in a way that best suits its national interests following Brexit.
Unlike the EU, which has introduced comprehensive, pan-European legislation aimed at harmonising
AI rules across the bloc, the UK has so far refrained from enacting formal laws to regulate AI.
Instead, it has deferred to individual regulatory bodies to enforce existing rules on businesses developing and using AI. This approach contrasts with the EU’s risk-based regulation and the U.S.’s patchwork of state and local frameworks.
Labour Party Plan
During the Labour Party’s election campaign, there was a commitment to introducing regulations focusing on ‘frontier’ AI models, such as large language models like OpenAI’s GPT. However, the UK government has yet to confirm the details of proposed AI safety legislation, opting instead to consult with the industry before formalising any rules.
The UK’s AI Opportunities Action Plan, endorsed by tech entrepreneur Matt Clifford, outlines a comprehensive strategy to harness AI for economic growth.
The plan includes recommendations for scaling up AI capabilities, establishing AI growth zones, and creating a National Data Library to support AI research and innovation. The government has committed to implementing these recommendations, aiming to build a robust AI infrastructure and foster a pro-innovation regulatory environment.
Despite the ambitious plans, some industry leaders have expressed concerns about the lack of clear rules. Sachin Dev Duggal, CEO of AI startup Builder.ai, reportedly warned that proceeding without clear regulations could be ‘borderline reckless’.
He reportedly highlighted the need for the UK to leverage its data to build sovereign AI capabilities and create British success stories.
The UK’s decision to ‘do its own thing’ on AI regulation reflects its desire to tailor its approach to national interests and foster innovation.
While this strategy offers flexibility, it also presents challenges in terms of providing clear guidance and ensuring regulatory certainty for businesses. As the UK continues to develop its AI regulatory framework, it will be crucial to balance innovation with safety and public trust
Google released the first version of its Gemini 2.0 family of artificial intelligence models in December 2024
Gemini 2.0 Flash, as the model is named is available in a chat version for users worldwide, while experimental multimodal version of the model, with text-to-speech image generation capabilities, available to developers.
‘If Gemini 1.0 was about organising and understanding information, Gemini 2.0 is about making it much more useful,’ Google CEO Sundar Pichai reportedly said in a statement.
Google’s latest large language model surpasses its predecessors in most user request areas, including code generation and the ability to provide factually accurate responses. However, it falls short compared to Gemini1.5 Pro when it comes evaluating longer contexts.
To access the chat-optimized version of the experimental Flash 2.0, Gemini users can select from the drop-down menu on both desktop and mobile web platforms. According to the company it will soon be available on the Gemini mobile app.
The multimodal version of Gemini Flash .0 will be accessible through Google’s AI Studio and Vertex AI developer platforms.
The general availability of Gemini 2.0 Flash’s multimodal version is scheduled for January, along with additional Gemini 2.0 model sizes, Google announced. The company also plans to expand Gemini 20 to more Google products in early 2025.
Gemini 2.0 signifies Google’s latest efforts in the increasingly competitive AI industry. Google is competing with major tech rivals such as Microsoft and Meta, as well as startups like OpenAI, the creator of ChatGPT, Perplexity, and Anthropic, which developed Claude.
In addition to new Flash, other research prototypes are aimed at developing more ‘agentic’ AI models and experiences. According to the company, agentic models ‘can understand more about the world around you, think multiple steps ahead, and take action on your behalf, with your supervision’.
Nvidia third-quarter earnings beat expectations, but shares dropped 2.5% in extended trading.
The company’s revenue surged 94% year on year to $35.08 billion in the quarter ended 27th October 2024.
Net income climbed 109% from a year ago to $19.3 billion. Sales of Nvidia’s next-generation chip Blackwell, will be limited by supply, not demand, the company reportedly said.
Nvidia didn’t disappoint in terms of third-quarter revenue and net income, but it wasn’t enough for Wall Street. The forecast for the fourth quarter indicates a year-over-year growth of approximately 70%, marking a deceleration from the 265% growth experienced in the corresponding period the previous year.
Nvidia has emerged as the main beneficiary of the current artificial intelligence surge. Its shares have almost tripled in 2024, positioning it as the most valuable publicly traded company.
Numerous end-customers of Nvidia, including Microsoft, Oracle, and OpenAI, have begun receiving the company’s latest AI chip, known as Blackwell.
Nvidia one-year share price chart as of 20th November 2024
Nvidia one-year share price chart
The share price decline appears to be due to reserved guidance for Q4, with Nvidia’s management anticipating supply challenges for its next-generation Blackwell GPU. Investors were hoping for a more optimistic forecast, but the cautious outlook was disappointing.
It’s interesting to see how even strong earnings can sometimes lead to a drop in share prices if the future outlook doesn’t meet investor expectations.
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.
