Mandy Lender

HUANG LAW and SUMMARY OF HIS KEYNOTE ADDRESS

Posted on November 5, 2025 by Mandy Lender

At the NVIDIA GTC 2025 keynote, Jensen Huang outlined the company’s pivotal role in advancing America’s AI infrastructure, boasting $500 billion in orders for new platforms. Key innovations include Blackwell and Rubin systems, partnerships for AI supercomputers, and advancements in AI, 6G, and quantum computing, driving NVIDIA’s market valuation to $5 trillion.
Here are some detailed notes and conceptual analysis of Jensen Huang’s keynote address at the NVIDIA GTC in Washington, D.C., on October 28, 2025.

Oh, wait… GTC stands for: GPU Technology Conference in the next Industrial Revolution. Jensen Huang is the High Priest of this conference. The conference is a major platform for unveiling new Nvidia technologies and setting the direction for future advancements in AI and computing.

Detailed Summary of GTC Keynote (October 28, 2025)
The keynote, themed around “America’s AI Infrastructure” and the “Next Industrial Revolution.” It was a strategic presentation focused on NVIDIA’s role in building national AI capabilities.

The central business announcement, which subsequently propelled NVIDIA to a $5 trillion market capitalization, was Huang’s statement that the company now has “visibility into half a trillion dollars” ($500 billion) in cumulative orders for its Blackwell and upcoming Rubin platforms through 2026.

Here are the key points by category:

1. Next-Generation Platform Roadmap (The “One-Year Rhythm”)
Huang reaffirmed NVIDIA’s aggressive one-year release beat, moving beyond the chip to full-platform co-design.

Blackwell ( – Current Generation): The Grace Blackwell platform (e.g., GB200 NVL72) is in full production at facilities in the U.S. (Arizona), reinforcing the “Made in America” theme. The Blackwell Ultra chip will be released later in 2025. (Not much time left).

Vera Rubin ( – Next Generation): The next major platform, named after the astrophysicist Dr. Vera Rubin, is scheduled for 2026. This is not just a GPU but it is an entire system architecture.

(Vera Rubin was an American astronomer whose work provided convincing evidence for the existence of dark matter.)

The Full Roadmap In Short:
2025 (2H): Blackwell Ultra
2026 (2H): Vera Rubin (including the Vera Rubin Superchip, CPX Compute Tray, and BlueField-4 DPU)
2027 (2H): Rubin Ultra

2. U. S. National AI Infrastructure & Supercomputing

This is a core theme, positioning NVIDIA as a national strategic asset.

U.S. Department of Energy (DOE) Partnership: NVIDIA announced it is powering seven new AI supercomputers for the DOE.
“Solstice” Supercomputer: The largest of these, built in partnership with Oracle, will be one of the world’s most powerful AI systems, featuring 100,000 NVIDIA Blackwell GPUs to support national security, energy, and science applications.
Los Alamos National Lab (LANL): The LANL’s next-generation systems will be among the first to be built on the upcoming Vera Rubin platform.

3. New Frontiers: 6G and Quantum Computing
Huang detailed NVIDIA’s expansion into two new, highly complex compute domains.
6G Telecommunications:

Hello – Nokia Partnership: A $1 billion strategic partnership with Nokia to develop an “AI-native 6G” platform.
NVIDIA Arc Aerial RAN Computer: A new, 6G-ready computing platform designed to infuse AI services directly into the mobile network. (For me and you…)

“All-American AI-RAN Stack”: A collaboration with T-Mobile, Cisco, and MITRE to build a U.S.-based 6G development stack.

Quantum Computing:
NVIDIA NVQLink: A new interconnect architecture designed to bridge the gap between classical and quantum computing. It allows NVIDIA GPUs to be directly linked to Quantum Processing Units (QPUs), enabling a hybrid quantum-classical system for complex simulations.

4. Physical AI (Robotics & Autonomous Systems)
Huang declared “Physical AI” as the next major wave, where AI agents perceive and interact with the physical world.

NVIDIA “Groot N1” Foundation Model: A new, general-purpose foundation model for humanoid robots.
Newton Simulation Platform: A new high-fidelity physics engine (an evolution of Omniverse) designed to simulate robots and their environments for training.
“Project Blue”: A collaborative humanoid robot project demonstrated with partners Google DeepMind and Disney Research.

Autonomous Vehicles:
Uber Partnership: A major partnership to deploy 100,000 autonomous robotaxis, powered by NVIDIA DRIVE, starting in 2027.
DRIVE Hyperion Platform: Expanded adoption by automakers including GM, Stellantis, Lucid, and Mercedes-Benz.

5. Geopolitical and Market Context
Hey, there is money to be here…

$5 Trillion Valuation: The keynote’s $500B order visibility statement was the primary catalyst for NVIDIA’s stock surge, making it the first company to achieve this valuation.
China Market: NVIDIA’s market share in China has fallen from 95% to “effectively zero” due to U.S. export controls and Beijing’s policies.

“America First” Alignment! Huang explicitly praised the Trump administration’s “America First” policies for incentivizing and revitalizing U.S. manufacturing, which he stated enabled NVIDIA’s new U.S.-based production.

Here are PhD-Level Lecture Notes and Conceptual Analysis of All That Said
Below are the core theoretical theses presented by Huang, abstracted from the product announcements.

Thesis 1: The End of Classical Scaling Paradigms
Concept: The death of Moore’s Law and, more importantly, Dennard Scaling (which stated power density remains constant as transistors get smaller) is now an accepted industry fact.
Argument: Sequential processing (CPU-centric) can no longer deliver the performance gains required. The only path forward is Accelerated Computing, a hybrid model where parallel processors (GPUs) work in tandem with sequential processors (CPUs).
Evidence: The entire keynote was a demonstration of this thesis. The core software foundation, CUDA-X, is the “operating system” for this new computing model, and every new hardware platform is designed to accelerate this specific paradigm. (you need to follow the link if you want to get an idea what is CUDA)

Thesis 2: The New Computing Model: “Generative” vs. “Retrieval”
Concept: Huang articulated a fundamental shift in the purpose of computing.
Retrieval Computing ( – The Past): The old internet and all prior computing were based on retrieval. A user requests information, and the system fetches a pre-written, pre-stored piece of data (a webpage, a document, a video).
Generative Computing ( – The Future): The novel AI models do not retrieve. They receive a prompt, understand the context and meaning, and generate a novel, never-before-seen answer (a “token”). (Hello Agentic…)
Financial Implication: This new model is computationally far more expensive and requires a new infrastructure. The basic unit of this new infrastructure is the “AI Factory.”

Thesis 3: The New Scaling Law: “Extreme Co-Design”
A new Concept is born: If single-chip performance (Moore’s Law) is no longer the primary driver, gains must come from a new scaling law.

Huang’s Law is: “Extreme Co-Design.”
Argument: Performance “X-factors” (multiplicative gains) are now achieved by co-designing the entire stack as a single product. This includes:
Silicon: The GPU and CPU (Grace Blackwell).
Interconnects: High-speed chip-to-chip links (NVLink).
(NVLink means providing high speed connectivity between two GPUs to increase performance).
Networking: The data center fabric (Spectrum-X Ethernet).
Power & Cooling: Liquid-cooling and power delivery systems.
Software: Optimized libraries (CUDA) and inference engines (NVIDIA Dynamo).
Evidence: The Grace Blackwell NVL72 is the canonical example. It’s not sold as 72 separate GPUs but as a single, liquid-cooled “thinking machine,” a single computational unit. The Vera Rubin platform continues this by integrating the BlueField-4 DPU directly into the system design.

Thesis 4: The Next Wave: From Digital AI to “Agentic & Physical AI”
Concept: Huang defined the next evolution of AI.
Agentic AI: Ha… It’s the AI that possesses all that ai agency. Can do. BUT it can perceive its environment, understand the context, reason, create a plan, and act to accomplish a goal. You can find some more on Agentic here.
Physical AI: The application of Agentic AI to the physical world, which is robotics.
Argument: To create Physical AI, models must be trained to understand physics, 3D space, and cause-and-effect.
Evidence: This thesis justifies the new product stack:
Groot N1: The “brain” or foundation model for the robot.
Newton: The “gym” or virtual world where the brain is trained (via simulation and reinforcement learning) before being deployed in the physical world.

Thesis 5: The Software-Defined Physical Stack (6G & Quantum)
Concept: NVIDIA’s strategy is to turn specialized, hardware-defined industries into software-defined ones running on NVIDIA GPUs.
Argument (6G): A 5G/6G base station is currently a complex box of fixed-function hardware (ASICs, FPGAs). The NVIDIA Arc platform turns it into a software-defined radio (SDR) running on a GPU. This allows telcos to push AI services (like AI-RAN) to the network edge as a software update.
Argument (Quantum): Quantum computers (QPUs) are brilliant at certain problems but useless at others. The NVQLink interconnect treats the QPU as a “quantum accelerator” in the same way a GPU is a “parallel accelerator,” allowing developers to write hybrid algorithms (within CUDA) that pass workloads between the CPU, GPU, and QPU.

A video from NVIDIA’s YouTube channel provides the full keynote address from GTC in Washington, D.C., where these notes were made.

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My Take
If you understand Huang’s Law of “Extreme Co-Design,” and you understand where Agentic AI is going you get a sense on how close we are to AGI.

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Tags: #AI #GTC #NVIDIA #HuangLaw #extremecodesign #agentic

Posted in AI, Business, technology, Uncategorized | Tagged Agentic, AI, Artificial Intelligence, Extreme Co-design, GTC, Huang Law, news, NVIDIA, technology | Leave a comment

LIVING IN THE NOW: ZEN INSIGHT FROM ALAN WATTS

Posted on October 22, 2025 by Mandy Lender

Past and future are illusions.

They exist only in the present,

which is what there is

and all that there is.

~ Alan Watts

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Posted in Buddhism,, existentialism, HEALTH AND WELLNESS, Mind - Body, Uncategorized, Zen | Tagged Alan Watts, reality of present, Zen | Leave a comment

Is Grokipedia the Future of AI Knowledge? Key Insights and Skepticism

Posted on October 10, 2025 by Mandy Lender

Elon Musk’s ambitious AI-powered Wikipedia rival, Grokipedia, is reportedly on the edge of its initial release.

Following Musk’s announcement on October 5, 2025, that a “version 0.1 early beta” would be available in two weeks, the tech world is keenly awaiting the first look at the platform developed by his artificial intelligence company, xAI.

So far there have been no official announcements confirming the beta’s public availability. However, the initial timeframe provided by Musk has arrived, suggesting a release is imminent.

Why Does it Matter?

Grokipedia is positioned as a direct competitor to Wikipedia, with the primary goal of addressing what Musk perceives as inherent biases and inaccuracies on the crowd-sourced encyclopedia. The core of Grokipedia will be powered by xAI’s Grok, a conversational AI designed to analyze a vast array of information, including existing Wikipedia articles, to identify and rectify perceived falsehoods and omissions.

The stated aim is to create a more objective and truthful knowledge base. According to early descriptions, Grok will be tasked with discerning the veracity of information and rewriting content to present a more comprehensive and unbiased perspective.

The Doubters:

The prospect of an AI-driven encyclopedia was met with a degree of skepticism. Critics have raised concerns about the potential for algorithmic bias. They question if an AI developed under a specific ideological framework can truly be impartial. The success and impact of Grokipedia will depend on the transparency of its data sources, the sophistication of its AI in handling nuanced topics, and its ability to gain the trust of a broad user base.

At present, concrete details about the beta version, including its accessibility and features, remain limited. Will the initial release will be open to the public or limited to a select group of testers? Also unknown.

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Tags: #grokipedia #wikipedia #technology #encyclopedia #elonmusk

Posted in AI, Artificial Intelligence, Creativity, Encyclopedia, technology, Uncategorized | Tagged AI, Artificial Intelligence, ChatGPT, Elon Musk, Grokipedia, technology, Wikipedia | Leave a comment

Understanding Lender Combinations in Chess

Posted on October 8, 2025 by Mandy Lender

I asked Claude from https://claude.ai/new to tell me what is Lender Combinations and it didn’t know.

See the answer below.

A distressed young man with tears in his eyes, sitting at a desk surrounded by crumpled papers and a book, as a lamp illuminates the scene.

The correct answer is found at www.lendercombinations.com
Lender Combnations is a variant in chess problem creations.

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Tags: #LenderCombinations #BaruchLender #Claude #Sonnet4.5 #gemini #ai

Posted in AI, art, Chess Problems, Creativity, Uncategorized | Tagged Chess Problems, Claude, Claude Sonnet, Lender Combinations | Leave a comment

WIKIPEDIA & ITS BIASES

Posted on September 22, 2025 by Mandy Lender

A CIRCUMFERENTIAL ESSAY

Exploring Wikipedia’s Bias: The Tension Between Neutrality and Human Nature

Of course. I’ll start with the definition of ‘bias’ by Wikipedia…:

“Bias is a disproportionate weight in favor of or against an idea or thing, usually in a way that is inaccurate, closed-minded, prejudicial, or unfair. Biases can be innate or learned.

I was inclined into the topic by a recent article in the N.Y. Post titled: “Wikipedia bias influences how ones perception of reality is perceived.”

A disclaimer: I was so far, a charitable contributor to the Wikimedia Foundation. That is the not for profit organization owning Wikipedia. Thus, I realized some questions about the organization over time.

A portrait of a thoughtful person with glasses, resting their chin on their hand, with subtle blurred backgrounds.

Is Wikipedia biased?

The answer to Wikipedia biases question isn’t a simple “yes” or “no.” The core tension of Wikipedia is a battle between a neutral ideal and the messy reality of human nature.

Below is a tabulation of some evidence, gathered from policies, historical controversies, academic studies, and internal community discussions.

The Wikipedia Bias & Accuracy Ledger

Wikipedia is Never Biased (The Ideal & The Mechanisms)	Wikipedia is Sometimes Biased (The Reality & The Challenges)	The Pursuit of Accuracy (“Wikipedia is Always Right?”)
Core Policy: Neutral Point of View (NPOV)	Systemic Demographic Bias	Self-Correction is Extremely Rapid
The foundational principle. NPOV mandates that articles must represent “fairly, proportionately, and, as far as possible, without editorial bias, all of the Significant views that have been published by reliable sources.” It’s not about finding a middle ground; it’s about describing the full spectrum of sourced views and giving them due weight. For example, on the topic of the Earth’s shape, the scientific consensus is given overwhelming weight, while the flat-Earth view is presented as a fringe belief, which is a correct application of NPOV.	Studies consistently show the editor base is overwhelmingly male (around 85-90%), white, and from North America and Europe. This “systemic bias” results in predictable outcomes: • Coverage Gaps: Far more detailed articles on topics of interest to this demographic (e.g., military history, video games) than on topics like feminist art, African literature, or traditional crafts. • Subtle Framing: Biographies of women are more likely to mention their marital status or family than biographies of men.	A famous 2005 study by the journal Nature found that Wikipedia’s accuracy on scientific articles was “surprisingly good” and approached the level of like the Encyclopædia Britannica. While errors existed in both, Wikipedia’s power was in its ability to fix them. Vandalism and simple factual errors on popular pages are often corrected within minutes, sometimes seconds, by automated bots (like ClueBot NG) and vigilant human editors.
Policy: Verifiability, not Truth	Coverage Bias & Notability Standards	The Power of Citations
This is a crucial, often misunderstood, policy. Editors are forbidden from adding their own opinions or original research. Every substantive claim must be attributable to a published, reliable source. This acts as a powerful brake on individual bias. An editor cannot simply write “Politician X is corrupt.” They must write, “The New York Times reported that Politician X was under investigation for corruption,” and provide a citation. The bias is thus shifted from the editor to the source, which can then be evaluated.	The “notability” guidelines (what merits an article) often favor subjects well-covered in Western, English-language media. A groundbreaking scientist from a non-Western country whose work was published in non-English journals may fail the notability test, while a minor reality TV star with numerous articles in English-language tabloids gets a lengthy page. This isn’t malicious bias; it’s a structural bias baked into sourcing requirements.	The requirement for citations means an interested reader can always check the sources for themselves. This transparency is a key part of the “accuracy” model. A statement in a traditional encyclopedia must be taken on faith; a statement on Wikipedia can be traced back to its origin. This makes it a fantastic starting point for research, if not the endpoint.
Mechanism: Talk Pages & Consensus Building	Conflict of Interest (COI) & Paid Editing	Biographies of Living Persons (BLP) Scrutiny
Every article has a “Talk” page, a forum for editors to debate content, sources, and wording. Contentious edits are often discussed at length. The goal is to reach a consensus based on policy, not to win a vote. This process forces editors with opposing biases to find a neutral way to present information that all can agree on, or at least accept.	Despite policies against it, undisclosed paid editing is a persistent problem. PR firms, corporations, and political campaigns have been caught “scrubbing” articles of negative information or inserting promotional content. This is a direct injection of extreme bias. Wikipedia has volunteer groups and policies to combat this, but it’s an ongoing battle against well-funded actors.	Following the 2005 John Seigenthaler controversy (where a user falsely implicated him in the Kennedy assassinations), Wikipedia instituted extremely strict sourcing standards for information about living people. Un- or poorly-sourced contentious material in a BLP article is subject to immediate removal. This makes articles on living people some of the most scrutinized on the site.
Mechanism: Transparency & Edit History	Ideological Edit Wars	Errors are Inevitable, but Not Permanent
Every single change made to an article is publicly logged and attributable to a user (or an IP address). Anyone can view the entire history of a page, see who added what information, and when. This radical transparency creates accountability and makes it difficult for a single biased viewpoint to take hold secretly.	On highly contentious topics (e.g., Israel-Palestine conflict, U.S. politics, GMOs), articles can become battlegrounds. Groups of ideologically-motivated editors may try to “own” an article, systematically removing information that contradicts their worldview and emphasizing information that supports it. This leads to biased “forks” of in article or long-term stalemates where the page reflects the view of the more persistent editing faction, not a true neutral point of view.	No encyclopedia is perfect. The key difference is the speed of correction. A factual error printed in a book in 2020 will still be there in 2025. A factual error on a high-traffic Wikipedia page is unlikely to survive a day. However, errors on obscure, low-traffic pages can and do persist for years. Therefore, “accuracy” is highly variable depending on the article’s popularity.

Now then, I return from the review journey with this impression:

Is Wikipedia Never Biased? The answer is false.

Wikipedia is written by biased persons. The writers/authors/scribes use sources that are themselves biased, and are subject to the systemic biases of the society the sources emerge from. The very structure of what is considered “notable”, worthy of inclusion as an entry, or in the text, is a form of bias.

2. Is Wikipedia Sometimes Biased?

This is demonstrably true. Wikipedia is sometimes biased. The evidence of demographic, coverage, and conflict-of-interest bias is overwhelming and acknowledged by the Wikimedia Foundation itself, which works to combat it through initiatives like edit-a-thons focused on underrepresented topics.

3. Is Wikipedia “Always Right”?

This is false. Wikipedia is not a source of ultimate truth, and it contains errors. However, its model is built for the pursuit of accuracy. Its strength is not infallibility but correctability. The open model, of transparency, the dedication of its self-administered community, create a system trying to detect falsehoods and vandalism that sometimes fail.

Concluding Questions:

Back to my opening disclaimer being a financial donor to Wikimedia Foundation, I wonder If my name, and the many other donor names deserve mention as an entry somewhere in Wikipedia.

Moreover, who decides what is item in Wikipedia is “notable”? And what is not notable for Wikipedia?
Who decides or appoints the “notability decision officers” on Wikipedia?

Omission by Wikipedia is a form of bias in and by itself.

In the end, maybe that Wikipedia should be treated like an ongoing conversation.

Like in many other human conversations the louder and vocal speakers get noticed.

Can’t wait for Grokipedia launch.

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Tags: #Wikipedia #WikimediaFoundation #wikipediabias #NPOV

Posted in Business, Creativity, Grokipedia, Mandy Lender, technology, Truth, Uncategorized | Tagged AI, Artificial Intelligence, ChatGPT, Grokipedia, NPOV, technology, Truth, Wikimedia Foundation, Wikipedia, Wikipedia bias | Leave a comment

Tales of Two “Jimmy Who?” – From Peanut Farmer to Primetime TV Pundit

Posted on September 19, 2025 by Mandy Lender

In the modern American political landscape, the line between entertainer and political commentator has all but vanished. Few embody this shift more than Jimmy Kimmel, the late-night host whose nightly monologues often serve as impassioned editorials on the state of the nation. While he prides himself on being a comedian, (sarcastic at that), his growing political influence invites a curious comparison to another ‘Jimmy’ who ascended the national stage from relative obscurity: Jimmy Carter.

In 1976, Governor Carter of Georgia was famously dismissed by the establishment as “Jimmy Who?” Carter was an outsider, a peanut farmer and a man of deep, quiet faith who ran on a platform of integrity and competence in the wake of the Watergate scandal. Carter did not pretend to be an entertainer. Carter was a serious, policy-focused politician who, against all odds, became the 39th President of the United States and went on to become the longest-lived. His path to power was through the traditional grind of retail politics—a testament to a bygone era.

Jimmy Carter was elected because he had presidential gravitas.

Contrast that with the Jimmy of our time. An established progressive entertainer from ABC TV, Kimmel wields a different kind of influence. His power is not derived from a state governorship or a party nomination, but from the media ecosystem itself—ratings, viral quips, and the cultivation of a para-social relationship with millions of viewers. While he may not officially be partisan, his sarcastic wit is consistently aimed at specific political targets, and his emotional monologues on healthcare and gun control have effectively mobilized public opinion and shaped national debate.

The question: Is Kimmel merely a comedian with a conscience, or does he harbor deeper political aspirations? The path from entertainment to executive office is no longer unthinkable; figures from Ronald Reagan to Donald Trump, and internationally, Volodymyr Zelenskyy, have proven that TV-radio celebrity is a potent political currency. Kimmel’s platform gives him a direct line to the American public that most traditional politicians can only dream of.

The fundamental difference lies in their approach to power. Carter worked from the outside-in, leveraging his status as a non-Washington figure to conquer the political system. Kimmel works from the inside-out, leveraging his status as a media insider to influence that same system without ever having to run for office.

Whether Kimmel ever places his name on a ballot is secondary. His current role as a cultural arbiter and de facto political pundit already makes him a significant, unelected force.

Carter asked Americans for their vote based on his character, gravitas and his plans. Kimmel asks only for their viewership and secondarily sales promotions. Yet he wields a power that can sway minds and drive policy. He is a new archetype in the American experiment, challenging us to decide where the stage ends and the state begins. You be the judge.

Note: Definition of sarcasm: Sarcasm is the use of irony in order to mock or convey contempt toward a person or subject.

A man in a light gray suit and maroon tie smiles confidently at the camera, seated in a studio with blue and orange lighting in the background.

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Tags: #JimmyCarter #JimmyKimmel #presidentialgravitas #ABCTV #politicalsarcasm

Posted in Business, Mandy Lender, politics, Uncategorized | Tagged ABC TV, history, Jimmy Carter, Jimmy Kimmel, news, political sarcasm, politics, presidential gravitas | Leave a comment

AI and the Coming Economic Boom: Trillion-Dollar Insights

Posted on September 15, 2025 by Mandy Lender

We review here insights about a coming economic boom of multi-trillion dollars expected in the next five and ten years. Ten nations are expected to be the winners.

The world is on the verge of an unprecedented economic expansion driven by artificial intelligence, with projections indicating a potential increase of trillions of dollars to the global gross domestic product (GDP) over the next five to ten years.

Get this: by the time you finished reading this short paper it is out of date…

Precise figures vary among leading economic analyses, but a consensus emerges that AI will be a significant driver of productivity and growth. Estimates from major financial institutions and consulting firms suggest a potential annual increase in global GDP ranging from 1% to as high as 7% in the coming decade, with a substantial portion of this growth materializing within the next five years.

A conservative synthesis of forecasts from sources like Goldman Sachs, McKinsey, and PwC based on research suggests a prospective increase in the range of $2.6 to $4.4 trillion annually in the near term. McKinsey predicts that over 66% of developed economies already have national AI strategies, compared to just 30% in developing economies and 12% in least-developed ones. AI has emerged as the defining technology of the 21st century. According to the conclusions of PwC‘s “Sizing the Price” report, AI can contribute up to $15.7 trillion to global GDP by 2030

This figure is expected to grow as AI adoption matures. Goldman Sachs, for instance, has projected a very modest 7% increase in global GDP over a decade, which translates to a significant economic uplift in the initial five-year period.

Currently, the trend now is agentic AI, which has rapidly emerged as a major focus of interest and experimentation in business enterprises and consumer technology. Agentic AI combines the flexibility and generality of AI foundation models with the ability to act in the world by creating “virtual coworkers” that can autonomously plan and execute multistep workflows.

This economic surge is not evenly distributed. A handful of nations are poised to capture the lion’s share of the economic gains. These countries are characterized by strong technology sectors, significant investment in AI research and development, and supportive government policies.

The Top 10 Nations Leading the AI-Driven Economic Transformation

Ten countries are best positioned to contribute the most to the increase in global GDP driven by artificial intelligence over the next five years. The estimate is based on their current AI investments, adoption rates, and overall economic strength. Different researchers may argue for a different ranking list. Note that Russia is missing on this top-ten list. This is likely caused by lack of reliable accurate information from this country. The World Bank economic activity projections ignore the effects of AI R&D, (which is a highly secretive and competitive field), on national GDP and still discuss globalism, trade relations and tariffs.

United States: As the undisputed leader in AI investment and home to the world’s largest technology companies, it is projected to be the single largest contributor to AI-driven GDP growth. Its vibrant venture capital ecosystem and deep talent pool continue to fuel innovation and commercialization of AI technologies across all sectors.
China: With a national strategy focused on becoming a global AI leader by 2030, China is making massive investments in AI research and implementation. China is rich with natural resources and developed a widespread education system. Its large domestic market, military industry and rapid technological adoption will drive significant economic expansion powered by AI.
United Kingdom: The UK has established itself as a European hub for AI, boasting a strong research base and a thriving startup scene. Government support and a focus on AI in key sectors like finance and healthcare will be significant drivers of its economic growth. The British government gets full cooperation from its American counterpart in the area of AI.
Germany: As a global manufacturing powerhouse, Germany is poised to leverage AI to revolutionize its industrial sector and growing military re-armament. The integration of AI into its “Industrie 4.0” strategy will enhance productivity and competitiveness.
Japan: Facing demographic challenges, Japan is turning to AI and automation to boost productivity and address labor shortages. Its strengths in robotics and advanced manufacturing of automotive and electronic products provide a solid base for AI-driven growth.
India: A large and growing digital economy, coupled with an enormous pool of IT talent, positions India to be a major contributor to AI-driven growth. The country will see significant AI adoption in sectors like IT services, finance, and agriculture. Note that CEOs of Google (Sundar Pichai) and Microsoft (Satya Nadella), are originally alums of India’s undergraduate education system early in their careers..
Canada: Recognized for its pioneering research in deep learning, has a strong foundation in AI. Government initiatives and a collaborative ecosystem between academia and industry are fostering innovation and economic benefits.
France: With a growing number of AI startups and a government committed to fostering a strong AI ecosystem, France is emerging as a significant player in the European AI landscape.
South Korea: Is a recognized global leader in technology, automotive products and innovation. South Korea is heavily investing in AI and maintains its competitive edge in electronics, communications, automotive, and other key industries.
Israel: Known for its dynamic startup culture and expertise in cyber security and machine learning, Israel’s “Silicon Wadi” is a hotbed of AI innovation that will contribute significantly to its economic output.

This ranking is based on a synthesis of factors including private and public AI investments, the maturity of the technology sector, and the potential for AI to be integrated into key industries. The economic impact of AI is expected to be a defining feature of the global economy in the coming years, with these ten nations at the forefront of this transformative wave.

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Tags: #AI #agenticAI #GDP #worldGDP #McKinsey #GoldmanSachs #PwC #worldbank

Posted in AI, Artificial Intelligence, Business, Creativity, Mandy Lender, Uncategorized | Tagged agentic AI, AI, Artificial Intelligence, Business, GDP, Goldmant Sachs, McKinsey, news, PwC, technology, World Bank, World GDP | Leave a comment

Eric Schmidt on Energy Needs for AI Transformation

Posted on August 27, 2025 by Mandy Lender

Eric Schmidt highlights the urgent energy requirements for data centers and warns that the race for artificial general intelligence will lead America to critical challenges.

Words of wisdom by The Guru – Eric Schmidt:

https://www.facebook.com/reel/651612677976201

At the U. S. House Committee on Energy and Commerce Schmidt said that: “The scale of energy needed to power this transformation is staggering. Data centers alone require 29 gigawatts of additional power by 2027 and 67 gigawatts by 2030. For context, a single U.S. nuclear plant produces just 1 gigawatt.Schmidt warns that the race toward artificial general intelligence—and eventually superintelligence—may arrive within a decade. To compete globally, America must solve its energy and infrastructure challenges quickly or risk falling behind.”

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Tags: #energyshortfall #datacenters #AGI #superintelligence #ai