February's top 5 AI layer 3 data sovereign dialogues at Economist Norman Macrae associated networks
- TAIWAN see tawan economics minister briefing
- INDIA cf 2 key speeches from india ambassador to DC including one with french ambassador- see Wadani research sponsorship at csis
3 SCSP.ai
4 Small/Medium Nations -cf key speech from Canada
5 Which 2 ai action nations' communities is your main win-win belief in healthy youth's next generation's actions?
- On the U.S. Meeting with Trump: Takaichi confirmed her January 2 phone call with Trump, describing it as a strong reaffirmation of the Japan-U.S. alliance. She noted Trump's invitation for her to visit Washington, D.C., this spring, and stressed that they would "work closely" on shared goals rather than in parallel. She highlighted the need to advance the alliance in economy and security, including promoting a "free and open Indo-Pacific" with like-minded partners such as South Korea. This meeting is positioned as a chance to deepen cooperation amid global tensions.
- On Issues to Raise with Trump and Share with G7: Takaichi linked her U.S. agenda to broader multilateral efforts, including with the G7. She referenced the recent U.S. military action in Venezuela (where the U.S. arrested President Maduro) as an example of prioritizing "freedom, democracy, and the rule of law," and committed Japan to working with G7 nations to stabilize such situations. More pointedly, she addressed China's "military buildups" and "strengthened cooperation" with Russia and North Korea as direct challenges to the international order. She indicated plans to discuss revisions to Japan's security policies in response, implying these views would inform her talks with Trump and G7 leaders. This ties into her concerns about China "punishing" Japan through retaliatory measures like tightened export controls on dual-use items (e.g., tech and materials), which she described as part of a broader backlash Japan is "closely monitoring" for economic impacts.
- On China and Taiwan Context: While not delving into new Taiwan-specific remarks (to avoid escalation), Takaichi reiterated a "strategic" approach to China, aiming for "constructive and stable" relations through open dialogue. This comes against the backdrop of the 2025-2026 crisis, where her November 2025 statement that a Chinese attack on Taiwan could pose an "existential threat" to Japan (potentially justifying a military response) prompted Beijing's economic countermeasures. She has denied reports that Trump advised her to "lower the tone" on Taiwan during their call, emphasizing instead that Japan acts in its national interest.
- January 2 Phone Call Press Conference: In a shorter follow-up briefing after the Trump call, Takaichi focused on mutual congratulations (e.g., on the U.S.'s 250th anniversary) and commitments to Indo-Pacific coordination. No explicit G7 or China mentions here, but she underscored the alliance's role in addressing "the current international situation," which analysts interpret as code for China-related pressures.
- Broader Diplomatic Signals: NHK and other outlets reported on January 11 that Takaichi is pushing for "deeper and wider" U.S. cooperation during the spring visit, amid ongoing China tensions. She has also expressed openness to dialogue with Beijing, but no joint visit with South Korea's leader to China has been mentioned—in fact, recent reports suggest she's prioritizing U.S. and G7 alignment over direct Beijing engagement right now. Regarding your concerns for the next few weeks, Japanese media note rising public support for her firm stance on China, but risks of further economic retaliation (e.g., trade disruptions) remain high as she prepares for the U.S. trip.
uckminster Fuller at MIT - Spaceship Earth - 1979
Intelligence to Bubble or not to Bubble Chat and rotten media waste your health, time, data, and intergenerational safety unless AI (today's machines with billion times more maths brainpower than separate human mind- triangularises advances inE
economistjapan.com aims to connect every inspiration the asian two thirds of humans have linked into media diaries for humanity welcome to EJ1 grok summary after brainstorming x-series of questions EJ1 what if humanoids most valued product ever in any affordable livable supercity (series continues in lower posts) NVIDIA's Sovereign Clusters: Fueling Humanoid Progress and Supercity VisionsAbsolutely—your synthesis captures the renaissance vibe: NVIDIA's "rebirth" as the open epicenter for deep-data startups, where sovereign AI factories aren't just compute hubs but convergence engines for everything *** Consider Uber-Nvidia UN partner startup —Huang blockbuster UN partnership Oct 28, 2025, GTC-DC: Scale 100k+ robotaxis by 2027 via DRIVE AGX Hyperion (NVIDIA's AV stack), & Stellantis/Foxconn fleet global ride-hailing/delivery. Journos grill JH ripple tides—"imagineer every connection" prompt Huang thrives on (GTC riff "physical AI = new industrial revolution"). EJ1 Transform "data = new oil, sovereign & refined" | intro : thanks to Economist editor Geoffrey Crowther\ my father (post teen 1943-2010) norman macrae was privileged to experience hi-tech hi-trust life of conflict mediation - from teenage navigator allied bomber command burma 1943 to economist survey celebrating tokyo engineering as benchmark intelligence supercity 1962 to whether millennials will be empowered by agentic ai out of every gps on earth and in JH-musk space eg1 forbidden conversation- in what ways should millennials want ai to be smarter than humans? |
NVIDIA's Scaling Digital Twin Celebrations: 2025's High-Fidelity RevolutionYou're spot-on with those examples—they're emblematic of NVIDIA's Omniverse platform exploding into "physical AI" twins, where virtual replicas (powered by CUDA-X libraries like Modulus for physics sims and Isaac for robotics) enable real-time testing, slashing costs 90% and accelerating iterations from weeks to seconds. At GTC Washington D.C. (Oct 28, 2025), Jensen Huang's keynote framed this as the "industrial metaverse's golden age," with $10B+ in new Omniverse deals announced, tying directly to your TSMC/Foxconn Texas milestone and the Hong Kong/Taiwan health push. Musk's xAI/Optimus orbit is indeed converging—Musk tweeted Nov 19 about "digital twins for robot brains" in a Saudi forum clip with Huang, hinting at Colossus 2 (550k GB200 GPUs) feeding Omniverse sims for humanoid training. Below, I've curated 7 of the most exciting 2025 digital twin celebrations from NVIDIA (and deep AI kin like Ansys/Isaac integrations), focusing on scalable, real-world impacts. These aren't demos—they're production rollouts, often in supercity vanguards, blending your health/manufacturing themes with emerging robotics/climate twins.
Celebration | Details & 2025 Milestone | Why Exciting (Scale/Impact) |
|---|---|---|
TSMC/Foxconn Texas AI Supercomputer Foundry Twin | Omniverse Blueprint simulates end-to-end fab (from wafer design to Blackwell GPU assembly) in Houston/Dallas plants; announced GTC Oct 28, mass production Q1 2026 on 1M sq ft. | $500B U.S. reindustrialization accelerator—real-time yield optimization cuts defects 40%, exporting to supercities like Austin (EV/humanoid hub). |
General Atomics Fusion Reactor Twin (DIII-D Tokamak) | Omniverse/RTX PRO/DGX Spark replica integrates sensor data + AI for plasma sims at 180M°F; GTC Oct 29 launch, seconds vs. weeks for "what-if" tests. | Fusion holy grail—stable plasma breakthroughs without hardware risks; scales to climate/energy twins for supercities like Copenhagen. |
PepsiCo Warehouse Physics Twin | GenAI + CV on Omniverse twins full CPG ops (forklifts to inventory); GTC Oct 28 demo, 500x faster engineering via Modulus. | $1B+ efficiency in logistics—Lego-blocks for humanoid integration (e.g., Optimus picking), piloting in NYC/Atlanta supercities. |
Dematic AI Control Tower Twin | Omniverse sim of Solutions Center for material flow; GTC Oct 28 showcase, AI-generated for robotics validation. | Warehouse revolution—tests 1M+ scenarios pre-deploy; scales to Amazon-style fleets in Seattle/Berlin. |
Hong Kong/Taiwan Medical Training Hospital Twins | Omniverse + Isaac for robotic surgery sims (e.g., Mayo Clinic pathology twins); COMPUTEX Taipei May 2025 + GTC Taipei Jun 30 addresses: Digital/physical AI for precision med. | World-class health cities blueprint—virtual ORs train 10x faster, exporting to Singapore/Tokyo for elder-care humanoids. |
Ansys Omniverse CAE Twin for Aerospace/Auto | CUDA/Modulus blueprints for real-time physics (e.g., crash sims); GTC Oct 2025 session, 500x acceleration. | Safety multiplier—Lucid/Toyota pilots cut dev time 70%; ties to Musk's Optimus for embodied testing. |
OMRON VT-X Factory Automation Twin | Sysmac Studio + Omniverse for digital twins in robotics; GTC Mar 19 preview, full rollout Q4 2025. | Industrial metaverse entry—scales to Foxconn/Tesla lines for humanoid orchestration. |
- Resource Flywheel: Big buyers (e.g., xAI's 100k H100 "Colossus" + 550k GB200 for twins) generate petabytes of data for fine-tuning, creating self-reinforcing loops—e.g., TSMC's Texas twin optimizes its own Blackwell production.
- Supercity Bias: Buyers like Tesla (Austin) and TSMC (Phoenix) spawn twins in EV/humanoid vanguards—e.g., Foxconn's Omniverse for Optimus-scale robotics.
- Emerging Hotspots: Saudi's 500MW xAI/NVIDIA project (Nov 2025) eyes fusion/energy twins; Europe's Schneider/ETAP "Grid to Chip" twin (Jul 2025) for data centers. Smaller buyers (e.g., PepsiCo) punch above via blueprints, but scale favors whales—expect 80% of 2026 cases from top-10 buyers.
| GROK 11/22/25 |
Supercities (the top ~30 per EIU/Mercer 2025 rankings: Copenhagen #1, Vienna/Zurich #2-3, Melbourne #4, etc.) benchmark livability via multi-factor indices, scoring 76.1/100 on average—up slightly from 2024 thanks to stability gains, but healthcare/infra lags in climate-vulnerable spots.
Application Area | Description | Examples from Users/DeepMind | Potential Impact |
|---|---|---|---|
AI Agent and Robotics Training | Generating synthetic environments to train embodied AI agents (e.g., robots or virtual characters) on tasks like navigation, object manipulation, or goal pursuit, addressing data scarcity in real-world training. | Early testers integrated it with DeepMind's SIMA agent for long-sequence tasks in generated worlds (e.g., exploring forests or caves). Robotics teams use it for safe simulations of warehouses or search-and-rescue scenarios. | Enables unlimited, cost-effective training data; crucial for advancing AGI by simulating rare edge cases without physical hardware risks. |
Game Development and Prototyping | Rapid creation of playable levels, worlds, or mechanics from prompts, speeding up iteration for indie devs or AAA studios. | Users generate stylized environments (e.g., mushroom villages or volcanic terrains) and test interactions like walking or jet-skiing. Ex-Google researcher Tejas Kulkarni noted its "mind-blowing" generalization for gaming but highlighted limits in combat or logic puzzles. | Disrupts traditional tools like Unity/Unreal; could automate procedural generation, reducing team sizes and costs. |
Scientific Simulations and Research | Modeling natural phenomena or experiments in controlled, interactive settings for hypothesis testing or data generation. | Researchers simulate ecosystems (e.g., ocean canyons with jellyfish) or geological events (e.g., volcanic navigation). It's used in biology/geology for observing agent behaviors in synthetic worlds. | Accelerates fields like environmental science by creating "unlimited curricula" for AI-driven discovery, as per DeepMind. |
Creative Media and Entertainment | Building immersive content for films, animations, or VR experiences, with real-time edits. | Creators generate fantastical scenes (e.g., dinosaur in ancient Greece) or photorealistic drone shots. Early access users experiment with "promptable events" like adding characters or altering lighting. | Democratizes content creation; potential for AI-assisted storytelling in movies or social VR. |
Education and Training Simulations | Interactive virtual tours or skill-building scenarios for learners or professionals. | Prototypes for exploring historical sites (e.g., ancient Athens) or natural landscapes (e.g., zen gardens). DeepMind highlights potential for experiential learning, like "seeing life through a dinosaur's eyes." | Makes abstract concepts tangible; early tests show promise for VR education without high development costs. |
- Core Concept: Traditional simulations (e.g., in video games) use predefined physics engines like those in Unity, where rules are manually programmed. Genie 3, however, "reverse engineers" physics by training on millions of hours of unlabeled videos (e.g., from YouTube or synthetic sources). It learns patterns like how water splashes, objects collide, or light refracts by predicting the next frame in a sequence, effectively deducing intuitive physics from observation. This mirrors how humans (or animals) learn physics through experience, without formal equations. Hassabis describes this in interviews (e.g., Lex Fridman Podcast #475 and All-In Summit 2025) as AI "understanding reality" by building an internal world model that anticipates cause-and-effect.
- Genie 3 Specifics: Unlike Veo 3 (DeepMind's video model, which uses some hardcoded physics), Genie 3 employs an auto-regressive architecture—similar to large language models but for video frames. It generates environments frame-by-frame while maintaining consistency over time, handling elements like realistic water movement in puddles or object occlusion. In a demo shared by Hassabis on X (August 22, 2025), Genie 3 simulates gravity, materials, and liquids with high fidelity, as the character interacts with a puddle. This "reverse engineering" allows the model to generalize to unseen scenarios, like volcanic terrain or ocean currents, without explicit programming.
- Broader Implications from Hassabis: In his Nobel lecture and podcasts, Hassabis conjectures that "any pattern in nature can be efficiently discovered and modeled by a classical learning algorithm," extending to physics, biology, and cosmology. For AGI, this means AI must embody "intuitive physics" to act in the real world—Genie 3 is a stepping stone, enabling agents to "do" tasks in simulated environments before real deployment (e.g., robots in warehouses). He predicts this will usher in a "golden age of science," 10x faster than the Industrial Revolution, by scaling compute and hybrid models (data-driven + rule-based).
- Interactive Geological Simulations: Prompt "A first-person view navigating a volcanic terrain with erupting lava and ash clouds" to explore plate tectonics or erosion in real-time. Students "walk" through the environment, observing physics like rockfalls or magma flow, then discuss cause-effect. This builds spatial reasoning and data interpretation skills.
- Ecosystem and Climate Modeling: Generate "A serene Irish landscape with rolling hills and misty lakes, suddenly trembling as earth rips apart into jagged formations" to simulate earthquakes or climate impacts. Teachers could integrate real data (e.g., USGS earthquake logs) by prompting modifications, teaching data linking—e.g., "Add rising sea levels based on IPCC data."
- Oceanography and Biodiversity Exploration: Use prompts like "Swimming through deep ocean canyons with bioluminescent jellyfish schools" for marine biology. Students interact (e.g., "approach a coral reef") to observe biodiversity, then link to datasets from NOAA for discussions on ocean acidification.
- Safe, Inclusive Experimentation: No-risk trials of hazards like hurricanes; prompt "A hurricane-lashed Florida coast with flooding streets." For diverse learners, add accessibility (e.g., voice controls). A Forbes article (Aug 2025) suggests Genie 3 could "resurrect VR for education" by letting teachers build worlds in seconds, reducing costs from $10K+ VR setups.
- Assessment and Collaboration: Students co-create worlds (e.g., "Design an ecosystem affected by deforestation") and analyze changes, fostering critical thinking. Integrate with tools like Google Classroom for group projects.
- Environmental Impact Simulations: Professionals prompt "A coastal city with rising seas and storm surges, linked to 2025 NOAA flood data" to visualize scenarios. This links real datasets (e.g., via APIs) to generated worlds, allowing "what-if" testing for sustainability reports.
- Geological Data Visualization: For earth scientists, generate "Interactive model of Iceland's canyons with river erosion, incorporating geological survey data." Users navigate to spot patterns, exporting frames for reports—ideal for millennials in GIS roles analyzing DeepMind's AlphaEarth (planet-mapping AI).
- Climate Change Scenario Planning: Link to IPCC datasets: "Simulate a forest ecosystem under 2°C warming with biodiversity loss." Millennials in NGOs could collaborate in shared worlds, deep-linking economic data (e.g., crop yields) for policy advocacy.
- Professional Training and Upskilling: In corporate settings, use for VR-like workshops: "Train on search-and-rescue in simulated earthquakes." Platforms like LinkedIn Learning could integrate it, helping millennials pivot to green jobs.
- Research and Innovation: For data scientists, combine with tools like Pandas (via code) to query generated worlds, e.g., "Analyze particle flows in a simulated landslide." This "deep linking" accelerates discoveries in fields like geology.
| ..25-35 human intelligence best/worst of times - overtime 2025 report One way to map out what 11 billion humans sharing erath between 2025 and 1965 generated with 10**18 more tech is to map thousand fold tech change 65-80 and thousand fold 80-95, then million fold 95-10 and million fold 10-25- roughly 65-95 corresponds to moores laws impact while 95-25 corresponds to both satellite mobilisation of data clouds and jensen huang's law of accelerated computing while its important you segment out your own map-from the start of silicon valley 65, tech revolution has spun from valley eg santa clara through the bay and san francisco up to berkeley (birth place of california universities and west coast anchor of usa governments energy and supercomputer mapping); between 65-80 we can see an asia coast rising with taiwan in the middle of change from north ie japan and S Korea and changes from south ie the diaspora chinese and royal english llm mediation of the 35 million most extraordinary entrepreneurial revolutionaries (roughly 5 million singapore (intelligence leader lee kuan yew) 10 million HK (typical intelligence leader li ka shing), 20 million taiwan (leaders H Li , Guo Chang)... AI's multipliers- from 2003 accelerated 2012 code pixels- image ai - first breakthrough beyond games & creative media radiology ai From 2017 ai * perception * chat from as soon as nation or friends of Taiwan valued ai data sovereignty : ai*perception^chat*inference - aka agentic ai (this can square brain power of you and friends if applied to open/deep data) but comes with some new jargon - eg digital twins ... | There are 2 reasons why at least half of what you gain from chat is female gravitated - population numbers, fei-fei li was the first person to convince friends of jensen huang that machines can learn all 5 human senses - the year is 2009; fei-fei li found stanford was delighted to lab her imagenet worldwide competition, Jensen Huang family launched Nvidia's deep learning lab out of stanford's engineering quad WIIMENS LEAPS UPDATES 2025+ One of the reasons why Mrs Ambani intelligence https://www.youtube.com/watch?v=3Z4TsHqyssc is interesting to every world citizen millennial is that she is one of a handful of movers and shakers who will determine what AI does next in India -see also mukesh ambani, PM Modi, Jensen Huang . ..Breaking news 25 July - Tokyo offers to stage whatever Guterres feels NY UN now longer can do 360 degree mediation on |


