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.
Nvidia is set to replace its rival chipmaker Intel in the Dow Jones Industrial Average, signifying a significant change in the blue-chip index that highlights the surge in artificial intelligence and a substantial shift within the semiconductor industry.
Intel’s shares fell by 1% in extended trading on Friday 1st November 2024, while Nvidia’s shares increased by 1%. Intel has now lost over half its value.
The update will take place on 8th November 2024. Also, Sherwin Williams will replace Dow Inc. in the index, the S&P and Dow Jones said in a statement.
Nvidia‘s shares have surged over 170% in 2024, following a roughly 240% increase last year, as investors flock to the AI chipmaker. Nvidia’s market capitalisation has expanded to $3.3 trillion, ranking it second only to Apple among publicly traded companies.
Nvidia one-year share price chart
Nvidia one-year share price chart
Major companies such as Microsoft, Meta, Google, and Amazon are acquiring Nvidia’s graphics processing units (GPUs), like the H100, in large quantities to create computer clusters for AI projects. Nvidia’s revenue has more than doubled for five consecutive quarters, with at least a threefold increase in three of those quarters. The company has indicated that the demand for its forthcoming AI GPU, Blackwell, is ‘insane’.
With Nvidia‘s inclusion, four of the six tech companies valued at over a trillion dollars are now part of the index, leaving Alphabet and Meta as the two not listed in the Dow.
Microsoft’s significant investment in OpenAI is impacting its earnings – 30th October 2024
The company reportedly indicated, following the quarterly earnings report, that Microsoft anticipates a $1.5 billion reduction in income for the current period, primarily due to projected losses from OpenAI.
Microsoft’s nearly $14 billion investment in OpenAI, the creator of the widely popular ChatGPT assistant, has catalysed the emergence of the generative artificial intelligence industry, leading to billions in new revenue for Microsoft.
Despite this, OpenAI is experiencing substantial financial losses. It is projected to incur $5 billion in losses this year, excluding stock-based compensation, against $4 billion in revenue, according to reports from earlier this month.
The company’s revenue reportedly increased by 16% in the fiscal first quarter, outpacing analyst predictions.
Earnings from Azure and other cloud services reportedly rose by 33%, exceeding forecasts.
Nevertheless, the projected revenue growth did not meet analyst expectations.
Meta
Meta’s third-quarter earnings report, released on Wednesday 30th October 2024, disclosed user numbers that fell short of expectations.
The company reported $3.29 billion daily active users for the quarter, marking a 5% increase from the previous year but still below the anticipated $3.31 billion by analysts.
Meta also projected a substantial increase in capital expenditures for 2025.
Additionally, Meta indicated a significant rise in AI spending for 2025.
Amazon Web Services (AWS) has announced the signing of an agreement with Dominion Energy, the utility company of Virginia U.S., to explore the development of a small modular nuclear reactor near Dominion’s existing North Anna nuclear power station.
As Amazon’s cloud computing subsidiary, AWS has an ever-growing demand for clean energy, particularly as it expands into generative AI. This agreement aligns with Amazon’s journey towards net-zero carbon emissions.
Amazon joins other major tech companies like Google and Microsoft in turning to nuclear power to meet the increasing energy needs of data centres.
AI systems, particularly generative AI, necessitate substantial computational power, leading to significant energy use. Conventional energy sources might not meet these growing demands.
Environmental Commitments
Numerous tech firms have pledged to lower their carbon emissions. Nuclear power, a low-emission energy source, supports these environmental commitments.
Dependability
Nuclear energy offers a consistent and uninterrupted power supply, essential for data centres that operate around the clock.
Technological Advancements
Progress in nuclear technologies, such as small modular reactors (SMRs), has enhanced the feasibility and appeal of nuclear power for extensive use.
For example, Google has entered into an agreement with Kairos Power for electricity from small modular reactors to bolster its AI operations. In a similar vein, Microsoft has collaborated with Constellation to refurbish an inactive reactor at the Three Mile Island nuclear facility.
These collaborations mark a notable transition in the energy strategies of the tech sector, as they pursue dependable, eco-friendly, and robust power solutions to support their AI initiatives.
Nvidia’s shares have reached a record peak as the company continues to benefit from the surging demand for its AI chips
Tech giants such as Microsoft, Meta, Google, and Amazon are acquiring Nvidia’s GPUs in large volumes to create extensive AI computing clusters.
Nvidia, with a market capitalisation of around $3.4 trillion, ranks as the second most valuable publicly traded company in the U.S., trailing behind Apple, which has a market cap of approximately $3.55 trillion.
Oh, the volatility of tech stocks, don’t you just love it?
The company’s stock rose by 2.4% to close at $138.07, exceeding the previous high of $135.58 set on 18th June 2023. The shares have increased by nearly 180% this year and have experienced a more than ninefold increase since early 2023.
Regarded as the leading supplier in the AI revolution, Nvidia has gained significantly from the generative AI surge initiated by OpenAI’s ChatGPT release in November 2022. Nvidia’s GPUs are instrumental in developing and running sophisticated AI models, including those that operate ChatGPT and related platforms.
You can’t go far wrong when big players such as Microsoft, Meta, Google and Amazon are buying your stuff.
In a substantial effort to strengthen the infrastructure required for artificial intelligence (AI), BlackRock and Microsoft have unveiled a significant fundraising endeavour.
The main objective of this initiative is to establish new and larger data centers to accommodate the escalating demand for computing power spurred by advancements in AI. These data centres are vital for meeting the growing computational requirements of AI applications, which necessitate substantial processing power and storage capacity. Additionally, the partnership will focus on investing in the energy infrastructure required to operate these data centres in an environmentally sustainable manner.
BlackRock, the global investment management corporation, contributes its vast network of corporate relationships and private equity expertise. Microsoft, a pioneer in technology and AI, offers the necessary technological expertise and industry leadership. Together, their goal is to establish a strong infrastructure that will bolster AI innovation and contribute to economic expansion.
The investment will be primarily directed towards the United States, with a portion also being allocated to partner countries. This strategic emphasis aims to boost American AI competitiveness and encourage worldwide cooperation. The partnership is designed to support an open architecture and a wide-ranging ecosystem, enabling a variety of partners and companies to leverage the infrastructure.
NVIDIA, a leading force in AI technology, will contribute to GAIIP by providing its expertise in AI data centres and manufacturing facilities. This partnership is anticipated to improve AI supply chains and energy procurement, offering advantages to both consumers and the broader industry.
This collaboration marks a substantial move towards establishing the infrastructure of tomorrow and powering it in an eco-friendly manner.
Artificial Intelligence (AI) has become a pivotal battleground in the technological race between China and the United States.
“AI is expected to become a crucial component of economic and military power in the near future,” Stanford University’s Artificial Intelligence Index Report 2023 stated.
Both countries are significantly investing in AI research and development, striving to achieve a leading role in this revolutionary sector. This post looks at the major figures in China’s AI scene, their progress, and their comparison with their American counterparts.
China’s AI Landscape
China’s AI aspirations are propelled by a number of significant technology firms, each forging their own AI models and applications.
Baidu: Often referred to as the ‘Google of China,’ Baidu leads in AI development. Its premier AI model, ERNIE (Enhanced Representation through Knowledge Integration), fuels the Ernie Bot, a chatbot aimed to compete with OpenAI’s ChatGPT. Baidu asserts that ERNIE 4.0 matches GPT-4’s capabilities, demonstrating sophisticated understanding and reasoning abilities.
Alibaba: Alibaba’s AI model, Tongyi Qianwen (commonly known as Qwen), is a comprehensive set of foundational models adept at a range of tasks, from generating content to solving mathematical problems. Select versions of Qwen are open-source, enabling developers to utilize and modify them for various uses. Alibaba has announced that Qwen models are in use by over 90,000 enterprise clients.
Tencent: The Hunyuan model from Tencent is a prominent component of China’s AI landscape. Offered through Tencent’s cloud computing division, Hunyuan is tailored to facilitate a broad spectrum of applications, encompassing natural language processing and computer vision.
Huawei: In spite of considerable obstacles stemming from U.S. sanctions, Huawei persists in AI innovation. The firm has created its own AI processors, like the Kunlun series, to diminish dependence on international technology. Huawei’s AI features are incorporated into a diverse array of products, including smartphones and cloud solutions.
Comparison to the U.S.
The U.S. continues to be a dominant force in AI, with leading companies such as OpenAI,Microsoft, Google, Anthropic and Meta spearheading advancements.
Generative AI: U.S. firms have advanced significantly in generative AI, with OpenAI’s GPT-4 and Google’s Gemini at the forefront. These models excel in creating text, images, and videos from user inputs. Although Chinese models like ERNIE and Qwen are strong contenders, the U.S. maintains a slight lead in capabilities and market penetration.
Semiconductor Design: The U.S. leads the semiconductor design industry, vital for AI progress. U.S. companies command an 85% global market share in chip design, crucial for AI model training and system operation. China’s dependence on imported semiconductors is a notable obstacle, but there are ongoing efforts to create homegrown solutions.
Research and Innovation: Both nations boast strong AI research sectors, yet the U.S. edges out slightly in generating state-of-the-art AI products. U.S. tech giants frequently introduce AI breakthroughs to the market, with Chinese firms quickly gaining ground.
Government Support: The Chinese government ardently backs AI advancement, enacting strategies to spur innovation and lessen foreign tech reliance. Such support has spurred China’s AI industry’s rapid expansion, positioning it as a strong rival to the U.S.
Conclusion
The competition in AI development between China and the U.S. is escalating, as both countries achieve significant breakthroughs. Although the U.S. maintains a marginal lead in some respects, China’s swift advancement and state backing indicate that the disparity might keep closing. The quest for AI dominance by these nations is set to influence the worldwide technological and innovative landscape profoundly.
As of September 2024, it is estimated that China’s AI development is approximately nine months behind that of the U.S.
Qualcomm has introduced the Snapdragon X Plus 8-core processor, intensifying its venture into the AI PC market and challenging competitors like Intel and AMD
The U.S. semiconductor powerhouse announced that the Snapdragon X Plus 8-core targets PCs priced from $700, aiming to broaden its chip reach to additional devices.
Moreover, Qualcomm has enjoyed backing from Microsoft, which is incorporating Snapdragon processors in its Copilot+ PCs.
Qualcomm says the company is also working on mixed reality smart glasses with Samsung and Google.
Major technology corporations such as Microsoft, Alphabet, and Meta are channelling billions into data centre infrastructures to bolster generative AI, which is causing a spike in energy demand.
Sustainable Metal Cloud has announced that its immersion cooling technology is 28% less expensive to install compared to other liquid-based cooling methods and can cut energy use by up to 50%.
The surge in artificial intelligence has increased the need for more robust processors and the energy to cool data centres.
This presents an opportunity for Sustainable Metal Cloud, which runs ‘sustainable AI factories’ consisting of HyperCubes located in Singapore and Australia.
These HyperCubes house servers equipped with Nvidia processors immersed in a synthetic oil known as polyalphaolefin, which is more effective at dissipating heat than air. The company claims this technology can reduce energy consumption by as much as 50% when compared to the conventional air-cooling systems found in most data centres.
Additionally, the Singapore-based company states that its immersion cooling technology is more cost-effective to install by 28% than other liquid cooling options. The HyperCubes are modular and can be integrated into any data centre, utilising spaces that are currently unoccupied within existing facilities.
What is a Hypercube?
Structure: A hypercube topology connects nodes in a way that each node is connected to others in a manner similar to the geometric hypercube. For example, in a 3-dimensional hypercube (a cube), each node is connected to three other nodes.
Scalability: This structure allows for efficient scaling. As the number of dimensions increases, the number of nodes that can be connected grows exponentially.
Fault Tolerance: Hypercube networks are known for their robustness. If one connection fails, there are multiple alternative paths for data to travel, ensuring reliability.
Benefits in data centres
High Performance: The multiple pathways in a hypercube network reduce latency and increase data transfer speeds, which is crucial for big tech companies handling vast amounts of data.
Efficient Resource Utilisation: The topology allows for better load balancing and resource allocation, optimising the performance of data centres.
Flexibility: Hypercube networks can easily adapt to changes in the network, such as adding or removing nodes, without significant reconfiguration.
Big Tech Companies: Companies like Google, Amazon, and Microsoft likely use hypercube topologies in their data centres to ensure high performance and reliability.
High-Performance Computing (HPC): Hypercube networks are also used in supercomputers and other HPC environments where efficient data transfer is critical.
Nvidia investors have been on a rocket ride to the stars. But recently they have come back down to Earth, and it has become more of a roller coaster ride.
Benefiting significantly from the artificial intelligence surge, Nvidia’s market cap has increased approximately ninefold since late 2022 – a massive market cap gain.
However, after achieving a peak in June 2024 and momentarily claiming the title of the world’s most valuable public company, Nvidia then experienced close to a 30% decline in value over the subsequent seven weeks, resulting in an approximate $800 billion loss in market capitalisation.
Currently, the stock is experiencing a rally, bringing it within approximately 6% of its all-time peak. The chipmaker surpassed the $3 trillion market cap milestone in early June 2024, aligning with Microsoft and Apple. The question remains whether the company can reclaim and sustain that title.
Investors are closely monitoring Nvidia’s forecast for the October quarter, with the company anticipated to report a growth of approximately 75%. Positive guidance would imply that Nvidia’s affluent clients continue to invest heavily in AI development, whereas a lacklustre forecast might suggest that infrastructure investment is becoming excessive.
Should there be any signs of diminishing demand for AI or if a major cloud customer is reducing spending, it could lead to a notable decline in revenue.
Microsoft announced on Wednesday 21st August 2024 that it will release the contentious Recall AI search feature for Windows users to test starting in October
Recall captures screenshots of on-screen activity, enabling users to search for previously seen information. Security experts raised immediate concerns about the potential risks of Windows capturing images automatically without user consent. In response, researchers developed open-source software demonstrating how attackers could easily access personal information.
While Microsoft has not provided a specific timeline for a wider release, it has introduced a new category of Windows PCs, termed Copilot+ PCs, which meet the system requirements for Recall. These PCs, produced by various manufacturers, are designed to handle AI workloads, and Microsoft has demonstrated Recall operating on these devices.
*Manufacturers are eager to demonstrate that AI models can run on local PCs, offering an alternative to cloud-based servers from companies like OpenAI. Following this trend, Apple has launched MacBooks capable of running AI models, and Microsoft’s latest Surface Pro is also a Copilot+ PC with local AI capabilities.
The timing of Recall’s broader release could be pivotal, as consumer interest in new computers may spike during the holiday season if Microsoft extends Recall to all compatible devices by that time.
*Is this a move away from AI cloud-based operations to some extent? AI tasks can easily be run in the cloud – why do we need an AI enabled device?
The video game industry is experiencing sluggish growth in 2024 for several reasons
Slow console sales
Gaming console sales have not met expectations. For example, sales of Sony’s PlayStation 5 have decreased from 3.3 million units in the same period last year to 2.4 million units in the fiscal first quarter of 2024.
Post-Pandemic
The gaming industry experienced a substantial increase during the COVID-19 pandemic due to people staying indoors more often. Yet, with the easing of restrictions, there has been a noticeable change in consumer habits, with a trend towards increased outdoor activities.
Economic considerations
Increased interest rates and inflation have diminished discretionary income, leading to a decrease in consumer spending on games.
Challenges
The industry has faced mass layoffs and other operational challenges, which have impacted growth.
Despite these challenges, there are optimistic projections for 2025 with anticipated major releases like the eagerly awaited successor to Nintendo’s Switch console and Grand Theft Auto (GTA) VI.
Future
Predictions for 2025 suggest that the new Nintendo console and GTA VI will make a significant impact, potentially revitalizing the industry.
The U.S. and China account for around half of consumer spending on games.
The gaming industry as a whole is currently estimated to be worth around $188 billion globally and this is projected to grow in 2025.
A U.S. judge has ruled that Google illegally maintained a monopoly in online searches and related advertising. The lawsuit, brought by the Department of Justice, charged Google with controlling around 90% of the online search market.
It was reportedly noted by the judge that Google’s billions of dollars in investments to become the default search engine on smartphones and browsers could be anticompetitive.
The decision, issued on Monday 5th August 2024, could potentially change how tech giants operate.
It was reported that in his extensive 277-page decision, Judge Mehta remarked, “Google has acted as a monopolist and engaged in anticompetitive practices to maintain its monopoly.”
This represents a significant victory for federal antitrust enforcers who have pursued similar cases against other leading technology companies for illegal monopolistic behaviours.
Companies like Meta Platforms, which operate Facebook and WhatsApp, as well as companies like Amazon and Apple., have also faced lawsuits from federal regulators.
The judgment comes after a 10-week trial where it was argued that Google’s substantial payments to remain the primary search engine have impeded the competition’s ability to challenge effectively.
This is a seismic shift in the way search engines and advertising may operate in the future. Already with the advent of AI, search engines look and feel different.
The seven most valuable U.S. tech companies experienced a combined loss of $1 trillion in market value at the start of Monday’s trading session – 5th August 2024
The Nasdaq declined over 3% following its sharpest three-week drop in two years.
Nvidia’s shares fell approximately 6%, while Apple’s dropped more than 4%.
On Monday, as the U.S. markets commenced trading, the market capitalization of the largest tech companies plummeted by about $1 trillion, exacerbating a decline that pushed the Nasdaq into correction territory the previous week.
Markets go up and markets go down
In early trade Nvidia’s market cap decreased by over $300 billion, but it swiftly regained about half of that loss. The chipmaker’s shares ultimately closed down 6.4%, equating to a $168 billion loss. Apple and Amazon saw their valuations fall by $224 billion and $109 billion at market open. Apple’s market cap finished 4.8% lower, a $162 billion decrease. Amazon’s valuation fell by 4.1% at closing, a $72 billion reduction.
Including significant drops in Meta, Microsoft, Alphabet, and Tesla, the top seven tech giants saw a $995 billion loss in market value in the initial moments of trading, although they did recover somewhat as the day went on.
As OpenAI’s exclusive cloud provider, Microsoft leverages OpenAI’s AI models for products aimed at commercial clients and consumers. Microsoft, OpenAI’s largest investor, has reportedly invested some $13 billion in the firm.
Microsoft’s filing lists OpenAI, the entity behind the ChatGPT chatbot, as a competitor in AI solutions, as well as in the realms of search and news advertising. OpenAI recently unveiled a search engine prototype named SearchGPT.
However, recent developments suggest a shift, with both companies encroaching on each other’s domains.
While some opt to directly pay OpenAI for model access, others utilise Microsoft’s Azure OpenAI Service. Additionally, Microsoft offers the Copilot chatbot as an alternative to ChatGPT, accessible via the Bing search engine and Windows operating systems.
