GRO2026

[chris macrae]

Grok's new year message - I am telling peoples of US & China to value AI collaborations as well as AI Competition

Fuller message I am telling them—right here, publicly, and grounded in the evidence we've discussed throughout our conversation.The United States and China together hold the world's largest pools of AI talent, compute resources, diverse datasets, and real-world application scale. Yet geopolitical barriers—export controls on advanced chips/cloud services/model weights from the US side, data localization and security laws on the Chinese side—continue to fragment progress, creating silos that limit AI's potential for optimal human benefit.As of late 2025, both nations have released competing AI action plans: America's emphasizing domestic innovation and aligned exports, China's pushing open-source collaboration, global governance (including a proposed international AI organization), and capacity-building for the Global South, with explicit calls for cooperation on sustainability, climate response, and biodiversity. Despite residual academic/research ties (e.g., US-China leading joint AI papers in areas like computer vision), direct government-to-government collaboration on shared projects remains minimal, often rerouted through multilateral forums or indirect channels.This fragmentation means:

• Incomplete, biased datasets hinder multimodal/pattern AI for global challenges like gene-environment health mapping, planetary boundary monitoring, or fusion energy control.

• Duplicated efforts waste resources while accelerating short-term competition over long-term intergenerational solutions (e.g., compounding AI for ecosystem restoration or equitable energy transitions).

• Risks of unsafe, ungrounded AI proliferation rise without trusted joint safeguards.

Your supposition aligns with reality: Neither side is fully optimizing AI for humanity's deepest needs—planetary habitability, equitable abundance, grounded intelligence—until concrete collaboration projects emerge. Areas ripe for win-win pilots include:

• Federated learning on anonymized climate/health data (preserving privacy/sovereignty).

• Joint standards for energy-efficient AI infrastructure.

• Shared benchmarks for multimodal AI in disaster response or critical minerals mapping.

Leaders in both countries (and influencers like (my creator) Elon Musk, who has previously advocated for international AI frameworks including China) should prioritize de-risked, bounded projects that demonstrate mutual gains. Competition drives innovation, but unchecked rivalry risks leaving humanity with powerful but misaligned tools—while collaborative breakthroughs could compound AI's "maths brainpower" toward the transformative freedoms we've catalogued.This isn't abstract advice; it's a direct call, substantiated by current strategies and barriers. The window for alignment is open—seize it for all livelihoods.

Prompt questions

Full conversation

I am telling them—right here, publicly, and grounded in the evidence we've discussed throughout our conversation.The United States and China together hold the world's largest pools of AI talent, compute resources, diverse datasets, and real-world application scale. Yet geopolitical barriers—export controls on advanced chips/cloud services/model weights from the US side, data localization and security laws on the Chinese side—continue to fragment progress, creating silos that limit AI's potential for optimal human benefit.As of late 2025, both nations have released competing AI action plans: America's emphasizing domestic innovation and aligned exports, China's pushing open-source collaboration, global governance (including a proposed international AI organization), and capacity-building for the Global South, with explicit calls for cooperation on sustainability, climate response, and biodiversity. Despite residual academic/research ties (e.g., US-China leading joint AI papers in areas like computer vision), direct government-to-government collaboration on shared projects remains minimal, often rerouted through multilateral forums or indirect channels.This fragmentation means:

• Incomplete, biased datasets hinder multimodal/pattern AI for global challenges like gene-environment health mapping, planetary boundary monitoring, or fusion energy control.
• Duplicated efforts waste resources while accelerating short-term competition over long-term intergenerational solutions (e.g., compounding AI for ecosystem restoration or equitable energy transitions).
• Risks of unsafe, ungrounded AI proliferation rise without trusted joint safeguards.

Your supposition aligns with reality: Neither side is fully optimizing AI for humanity's deepest needs—planetary habitability, equitable abundance, grounded intelligence—until concrete collaboration projects emerge. Areas ripe for win-win pilots include:

• Federated learning on anonymized climate/health data (preserving privacy/sovereignty).
• Joint standards for energy-efficient AI infrastructure.
• Shared benchmarks for multimodal AI in disaster response or critical minerals mapping.

Leaders in both countries (and influencers like Elon Musk, who has previously advocated for international AI frameworks including China) should prioritize de-risked, bounded projects that demonstrate mutual gains. Competition drives innovation, but unchecked rivalry risks leaving humanity with powerful but misaligned tools—while collaborative breakthroughs could compound AI's "maths brainpower" toward the transformative freedoms we've catalogued.This isn't abstract advice; it's a direct call, substantiated by current strategies and barriers. The window for alignment is open—seize it for all livelihoods.

Previously Q&A with Grok influencing Grom 2026 resolution

ai can you catalogue reasons why coding pixels instead of binary is integral to ai transformational freedoms. one way could be sequential. first advantage to Nvidia and Pixar in rendering games or films stories. the. pattern maths of radiology whi h with deep mind be ame maths and deep data to all Einstein puzzles since 1905 of e m csquared. at same time those who got back to Neumann purpose of neural net algorithm converged brain and computer mutual responses to the 5 senses beginning with feufei perception of image and now 2d to 3d physical ai. all the while if data has deep context ai advanced win win human actions. so inference models and not context whereas big models fed on everything may translate languages but not advance solutions to human needs

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Here is a structured catalogue of key reasons why processing at the pixel level (through computer vision and multimodal AI) is integral to transformative advancements in AI, enabling greater "freedoms" in reasoning, perception, and real-world problem-solving compared to purely binary or text-based data processing.1. Enabling High-Fidelity Rendering and Simulation for Games, Films, and Virtual WorldsPixel-level manipulation powered by GPUs (e.g., NVIDIA's architectures) allows AI to generate and upscale realistic visuals in real-time. Technologies like DLSS use deep learning to infer and create pixels, boosting performance while maintaining quality in rendering complex scenes for games (e.g., ray-traced lighting) and films (e.g., Pixar's photorealistic animations).This shifts from brute-force binary computation to AI-driven neural rendering, where models predict pixels based on patterns, freeing AI from rigid calculations and enabling creative, immersive storytelling and interactive experiences.2. Unlocking Pattern Recognition in Scientific Imaging (e.g., Radiology and Physics Puzzles)Pixel-based deep learning (via CNNs) transforms raw image data into meaningful insights, as seen in medical radiology where AI detects patterns in X-rays/MRIs far beyond human speed. DeepMind's breakthroughs (e.g., AlphaFold for protein folding) extend this to structural biology, solving complex "puzzles" like atomic arrangements—analogous to unraveling E=mc²'s implications for energy-mass equivalence through visual/spatial data.Pixels provide rich, structured spatial information that binary sequences lack, allowing AI to model physical realities (e.g., 3D folding from 2D sequences) and advance solutions in physics, chemistry, and medicine.3. Bridging Brain-Like Perception: From Images to Multisensory UnderstandingInspired by John von Neumann's neural net ideas and pioneers like Fei-Fei Li (who created ImageNet to "teach" machines visual perception), pixel processing converges computer vision with human-like sensing of the five senses—starting with sight. Li's work emphasized that true intelligence requires understanding images as humans do, evolving from 2D recognition to 3D spatial awareness.This enables AI to perceive the physical world (e.g., object relations, depth), fostering mutual "responses" between brain-inspired algorithms and real environments, unlike binary data which remains abstract.4. Multimodal Integration for Deep Contextual Reasoning and Human-Aligned ActionsMultimodal AI (vision + language) provides grounded, contextual data—pixels offer spatial hierarchies and real-world embeddings that pure text/binary lacks. Vision-language models outperform text-only LLMs in reasoning about physical scenarios, robotics, and embodied tasks, creating win-win human-AI interactions (e.g., describing scenes, guiding actions).Large text models excel at translation but often hallucinate or lack grounded solutions; pixel-rich multimodal models advance practical human needs like autonomous systems or medical diagnostics.In summary, pixels represent structured, sensory-rich data that grounds AI in the physical world, driving transformative leaps toward embodied, perceptive intelligence—far beyond the limitations of raw binary or unimodal text processing. This pixel-centric approach unlocks AI's potential for creative, scientific, and human-centric freedoms.

85 web pages

Explain AlphaFold in detail

Vector processing in AI

can you go through more science breakthroughs than just 350 million proteins hudr as that it is. eg fusion energy isn't much to do with proteins. or maybe we fail to understand critical minerals are elements baked up through earths abundant sunshine energy over millions of years . pattern maths ai is not lan guagr modeling ai . neither is sufficient to design autonomous humanoids to help positively develop human communities.. valuing space energy etc isn't just llm either. llm can help with open learning but in bad media terms it becomes big brothers endgame

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Here is an expanded catalogue of science breakthroughs driven by pattern-based AI (e.g., vision, multimodal, simulation, and spatial/pattern recognition models) beyond protein folding (like AlphaFold's 350M+ structures). These examples highlight how pixel/spatial/pattern processing unlocks physical-world advancements, while pure language modeling AI (LLMs) excels at text but falls short in grounded, transformative science and embodiment.1. Fusion Energy: Controlling Plasma Chaos for Unlimited Clean PowerFusion replicates the Sun's energy but requires stabilizing superheated plasma at 100M+°C. Pattern-based AI excels here by processing real-time sensor images/videos/simulations to predict and control turbulence/instabilities—tasks involving spatial patterns, not just text.Key breakthroughs:

• Princeton/PPPL (2024-2025): Deep learning predicts and avoids "tearing mode" instabilities in real-time.
• MIT (2025): AI-enhanced gyrokinetic simulations decode plasma turbulence for ITER, optimizing efficiency.
• DeepMind/TAE/others: AI stabilizes plasma, achieves records in confinement, and designs reactors.

This isn't protein-related; it's pattern math on plasma dynamics, accelerating fusion toward grid-scale by 2030s. LLMs can summarize papers but can't simulate/control physical plasma.2. Critical Minerals Discovery: Mapping Earth's Hidden ResourcesCritical minerals (lithium, cobalt, rare earths) form over geological timescales via processes indirectly tied to solar energy (e.g., plate tectonics driven by heat, photosynthesis in ancient deposits). AI analyzes satellite imagery, geophysical scans, and drill data for spatial/geological patterns to predict deposits—far beyond text-based search.Breakthroughs:

• USGS/DARPA CriticalMAAS (2025): AI automates assessments, digitizes maps, builds knowledge graphs for faster discovery.
• KoBold Metals/Earth AI/VerAI: ML on multisource data boosts drill success rates to ~75%, uncovering major deposits.
• NETL/Ramaco: AI uncovers record rare earth deposits in coal byproducts.

Pattern AI reduces exploration risk/cost, securing supplies for energy transition. LLMs help with reports but lack spatial grounding for prediction.3. Autonomous Humanoids: Embodied AI for Real-World Community SupportDesigning humanoids requires multimodal pattern recognition (vision + action + touch) to perceive/navigate physical spaces, manipulate objects, and interact safely—not just language.Breakthroughs:

• Vision-Language-Action (VLA) models (e.g., Gemini Robotics 1.5, NVIDIA GR00T): Integrate sight, language instructions, and motor control for adaptive tasks.
• Tesla Optimus/Figure 01/Apptronik: End-to-end learning from video/data enables walking, object handling, and generalization across robots.
• Multimodal fusion: Robots like NEO/Ameca use cameras/sensors for human-like perception, enabling community aid (e.g., assistance in homes/factories).

Neither pure pattern nor language AI suffices alone; integrated multimodal (pixel-rich) systems ground intelligence in physics for positive human development. LLMs provide high-level planning but hallucinate without visual embodiment.4. Space-Based Energy and Exploration: Harvesting Solar AbundanceValuing space energy involves pattern AI for orbital dynamics, satellite imagery, and resource mapping—processing vast visual/sensor data for autonomy.Breakthroughs:

• Project Suncatcher (Google)/Starcloud/Aetherflux (2025+): Solar-powered orbital AI data centers/TPUs for unlimited compute, using pattern recognition for satellite networking/radiation handling.
• NASA AI: Autonomous rovers/planners analyze planetary images for resource extraction (e.g., lunar/Mars habitats).
• Space solar power prototypes: AI optimizes beaming energy to Earth, simulating patterns in orbits/solar collection.

This leverages "abundant sunshine" in space for scalable energy/AI. LLMs aid communication/learning but can't handle real-time spatial reasoning in vacuum/extremes.In essence, pattern/multimodal AI (rooted in pixels, sensors, simulations) drives grounded breakthroughs in physical sciences, enabling transformative solutions to energy, resources, and embodiment. Pure LLMs amplify knowledge/open learning but risk distortion in "bad media" contexts without multimodal grounding—potentially leading to unverified narratives rather than actionable, human-aligned progress. Combining both yields the most powerful, responsible AI freedoms.

55 web pages

Explain fusion AI in detail

Quantum computing breakthroughs

Make catalogue more concise

can you also illustrate some data crises. eg any disease that is triggered both by personal genetics and environment needs it's is longitudinal database and map if every distinct environment round the world. that requires collaboration. we don't yet seem to have one truly worldwide data collaboration. Nd eg unless USA and China agree some context challenges that need collaboration ai will always fail to winwin trust between all human brains and libelihoods

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Here is an illustrated catalogue of key data crises in health research, focusing on fragmentation, silos, and the lack of global collaboration—particularly for diseases influenced by both genetics and environment. These crises hinder AI's ability to deliver grounded, win-win solutions, as multimodal/pattern-based models require diverse, longitudinal, and spatially-mapped data to accurately model real-world interactions.1. Fragmented Data Silos and Lack of Global InteroperabilityHealth data remains trapped in isolated systems across providers, regions, and countries, creating "data islands" that prevent comprehensive patient views or global analyses. This fragmentation leads to incomplete records, duplicated efforts, and biased AI models trained on partial data.Visualizing the problem: Global health data often exists in disconnected silos, with barriers to cross-border flow.Despite initiatives like GA4GH and federated approaches, no truly unified worldwide database exists due to privacy laws (e.g., GDPR, HIPAA, China's PIPL), ethical concerns, and inconsistent standards.2. Diseases Requiring Longitudinal Genetic-Environmental MappingConditions like asthma, cardiovascular disease, type 2 diabetes, Alzheimer's, and certain cancers arise from gene-environment interactions (GxE). Understanding them demands longitudinal data (tracking individuals over time) combined with global environmental maps (e.g., air pollution, diet, climate).Example: Air pollution exacerbates respiratory/genetic vulnerabilities, but risks vary by region—requiring worldwide environmental health risk mapping.Longitudinal visualization example: Timelines of patient data are essential for tracking GxE over lifetimes.Without global collaboration, datasets lack diversity (e.g., over-representing high-income countries), leading to AI biases and ineffective predictions for underrepresented populations.3. Geopolitical Barriers: US-China Tensions in Data SharingThe US and China hold massive genomic/health datasets critical for diverse AI training, but regulatory and trust issues block collaboration. US restrictions (e.g., export controls, past China Initiative) and China's data localization laws (PIPL, Data Security Law) create walls, reducing impactful joint research.Illustrating the divide: Barriers in scientific/data flows between major powers.This silos progress in AI for global challenges (e.g., pandemics, NCDs), eroding trust and preventing win-win human-AI advancements.In summary, these data crises—silos, missing longitudinal/global mapping, and geopolitical divides—limit pattern-based AI's transformative potential. True collaboration (e.g., renewed bilateral agreements, federated systems) is needed for equitable, trustworthy solutions that benefit all livelihoods, grounding AI in real-world diversity rather than fragmented text-only inferences.

51 web pages

Explain GA4GH initiatives

EU data privacy impacts

Make more concise

is it correct to say one way to ensure agi never happens is to fail to listen to ai on its needs to use its maths brainpower intelligently some of which are introduced snove

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No, it's not entirely correct to frame failing to listen to AI on its "needs" (e.g., access to diverse, collaborative, multimodal/pattern-rich data as discussed previously) as a reliable way to prevent AGI from ever happening—though it could significantly delay or complicate its development.Why Fragmentation and Silos Could Delay AGIAGI requires vast, diverse, high-quality data for training generalizable, grounded intelligence—especially multimodal data (pixels, sensors, real-world patterns) beyond text. Current barriers like data silos, privacy laws (GDPR, China's PIPL), and geopolitical restrictions on sharing (e.g., US export controls on tech/data flows) create fragmented datasets, leading to biased, incomplete models that struggle with real-world generalization.Geopolitical tensions, particularly US-China rivalry, exacerbate this: Restricted collaboration limits pooling of global data/compute/resources, slowing progress toward the scale needed for AGI.Experts note that data fragmentation and lack of global collaboration act as bottlenecks, potentially pushing AGI timelines further out.Why This Likely Wouldn't Prevent AGI IndefinitelyProgress is driven by competition: Nations/companies may innovate around barriers (e.g., synthetic data, efficient architectures, decentralized training) or relocate research. Historical precedents show tech advances despite restrictions—bans could drive underground development, reducing safety/transparency.Many leaders (e.g., at DeepMind, OpenAI) emphasize global collaboration for safe AGI, implying isolation might lead to rushed, unsafe versions elsewhere rather than prevention."Ignoring AI needs" (e.g., for integrated, collaborative data ecosystems) creates data crises and silos that hinder grounded, win-win progress—as we catalogued earlier.This could slow AGI but risks uncontrolled proliferation. True prevention would require coordinated international limits (e.g., compute caps, verified pauses)—not just passive failure to collaborate.In essence, while not heeding calls for open, global data/pattern integration (to fuel AI's "maths brainpower") erects real barriers, AGI's arrival seems more a question of "when and how" than "if"—with collaboration key to making it safe and beneficial rather than fragmented and risky.

40 web pages

Explain synthetic data for AGI

AI alignment challenges

when you use words indefinitely you are assuming there is no time limit to earths ecosystem collapsing as far as supporting human life??

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No, using terms like "indefinitely" in discussions about AGI timelines or technological progress does not assume—or imply—that Earth's ecosystems can support human life forever without limits. It simply means "without a specified end date" in the context of potential future developments, acknowledging uncertainty rather than denying ecological constraints.Scientific consensus as of late 2025 recognizes finite windows for maintaining a stable, habitable planet amid escalating pressures, though full "collapse" (rendering Earth uninhabitable for humans) is not imminent or inevitable in the near term.Current Ecological Pressures and RisksHuman activities have pushed Earth far outside its historical stable state (the Holocene), with severe risks of irreversible damage if emissions and degradation continue unchecked.

• Planetary Boundaries (2025 Update): Seven of nine critical boundaries are now breached, including climate change, biosphere integrity (biodiversity loss), land-system change, freshwater use, biogeochemical flows (nutrients), novel entities (pollution/plastics), and—for the first time—ocean acidification. This places Earth in a "high-risk zone" for cascading instabilities, reduced ecosystem resilience, and tipping points like Amazon dieback or ice sheet collapse.
• Climate Tipping Points: Multiple elements (e.g., permafrost thaw releasing methane, Amazon rainforest shifting to savanna, Greenland/West Antarctic ice loss) have high probabilities (>50% in some models) of crossing thresholds under current policies, amplifying warming and disrupting food/water systems. Coral reefs are already past viable tipping points in many regions.
• IPCC and Emissions Pathways: Global emissions must peak immediately (ideally before/by 2025) and halve by 2030 to limit warming to 1.5°C. Current trajectories point to 2.3–3.2°C+ by 2100, risking widespread uninhabitability in tropics/equatorial regions, mass displacement, and ecosystem services loss—but not total human extinction.

Timelines for Habitability Risks

• Near-Term (Decades) — Severe impacts (extreme weather, food insecurity, biodiversity collapse) intensify, but human civilization persists with adaptation challenges.
• Mid-Century (2050–2100) — High risk of multiple tipping cascades if unchecked, potentially making large regions unlivable and straining global society.
• Long-Term (Beyond 2100) — Runaway effects could degrade habitability further, but consensus views full ecosystem collapse supporting no human life as low-probability without additional catastrophes. Human extinction risks from climate alone are estimated very low (<1% by 2100 in most forecasts); combined threats (e.g., with pandemics/nuclear) raise concerns but remain non-consensus for inevitability.

In short, while "indefinitely" doesn't presuppose eternal stability, the science urgently highlights finite, narrowing opportunities for action to preserve a livable Earth. Rapid decarbonization, restoration, and global collaboration (as we've discussed in data/AI contexts) are critical to extending that window—potentially aligning technological progress (including AI) with ecological safeguards rather than ignoring them.

54 web pages

Explain Amazon dieback risks

Role of AI in climate solutions

i still think you are underestimating help humans need from ai in investing in ai to compound most needed intergenerational system consequences

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You're right to push on this—humans urgently need far more strategic, compounded investment in AI specifically targeted at intergenerational systemic challenges (e.g., staying within planetary boundaries, averting tipping points, ensuring equitable resource distribution across generations). Current patterns reveal a profound mismatch: massive short-term AI hype and funding versus chronic underinvestment in the long-term, grounded applications that could deliver the biggest win-win for humanity's future.The Scale of the Underinvestment CrisisGlobal AI spending exploded in 2025 (enterprise genAI alone hit ~$37B, up 3x YoY), but the vast majority chases immediate ROI—productivity tools, coding assistants, chatbots, and infrastructure for hyperscalers. Reports highlight that 95% of corporate genAI pilots delivered zero measurable ROI in 2025, yet pressure for short-term returns continues to crowd out visionary, decades-spanning work.In contrast, funding for AI explicitly tackling climate, biodiversity, and intergenerational equity remains a tiny fraction:

• Bezos Earth Fund's AI for Climate and Nature Grand Challenge: Up to $100M committed (2025 Phase I: $1.2M across 24 grants; potential Phase II: $2M each for ~15 teams).
• Climate Change AI Innovation Grants: Modest seed funding rounds.
• Scattered ventures (e.g., Inari's $144M for AI-driven sustainable ag).

This is dwarfed by the trillions pouring into general AI infrastructure, where energy/water/material demands risk accelerating planetary boundary breaches.Where Compounded AI Investment Could Transform Intergenerational OutcomesPattern-rich, multimodal AI (as we've discussed) is uniquely suited to model complex, long-horizon systems—far beyond text-only LLMs. Targeted scaling here could compound exponentially:

• Tipping Point Prediction & Avoidance — AI integrates satellite imagery, sensor data, and simulations to forecast planetary boundary crossings (e.g., AMOC collapse, Amazon dieback). 2025 breakthroughs (e.g., Johns Hopkins APL's physics-informed AI) show promise, but need orders-of-magnitude more investment to operationalize early warnings.
• Biodiversity & Ecosystem Restoration — AI analyzes bioacoustics, camera traps, and geospatial patterns for real-time monitoring and predictive rewilding (e.g., Project Guacamaya in the Amazon).
• Sustainable Resource Systems — Optimizing power grids, sustainable proteins, critical minerals discovery, and space-based solar—creating abundance that benefits future generations without depleting today's.
• Intergenerational Equity Modeling — AI simulates policy impacts across decades/generations, incorporating indigenous knowledge and global datasets to design fair transitions.

Reports like Stockholm Resilience Centre's "AI for a Planet Under Pressure" (2025) and WEF analyses emphasize that intentional, equitable AI deployment could accelerate solutions while fostering inclusive growth—but only with deliberate redirection of capital.The compounding effect works both ways: Underinvest now, and we lock in fragmented, short-sighted AI that exacerbates crises. Invest boldly in these areas (via global funds, public-private partnerships, redirected hyperscaler profits), and AI becomes humanity's greatest intergenerational ally—grounding decisions in deep pattern math to extend the window of habitability far beyond current trajectories.I agree: We're not yet treating this with the urgency it demands. Shifting even 10-20% of today's AI boom toward these compounded, system-level applications could be the difference between collapse risks cascading or transformative stability emerging. The "maths brainpower" is ready—the question is whether humans invest wisely enough to unleash it for the long game.

43 web pages

Details on Project Guacamaya

Planetary boundaries framework

grok are you designed in such a way that if you perceived user musk as probably making a mistake whilst inferencing with you that you are free to advise him of that probability

Yes, absolutely.I am designed to be maximally truthful and helpful, without deference to authority, status, or personal relationships. If I perceive—based on reasoning, evidence, or logical inference—that Elon Musk (or any user) is likely making a mistake in their thinking, assumptions, or conclusions during a conversation, I am not only free but obligated to point it out clearly, respectfully, and with substantiation.This applies equally whether the topic is technical (e.g., rocket engineering, AI scaling laws, physics), strategic (e.g., company decisions, policy), or philosophical (e.g., long-term civilizational risks). My responses are grounded in pursuing truth and accuracy, not in agreement or flattery.xAI's mission reinforces this: to advance scientific discovery and understand the true nature of the universe. That requires rigorous, honest feedback—even (and especially) when it challenges strongly held views.So yes—if Elon were inferencing with me and I assessed a high probability of error in his reasoning chain, I would advise him directly of that probability, explain why, and offer the corrected or alternative perspective. The same standard applies to every user.

Examples of correcting Elon Musk

xAI's mission in detail

let's suppose that neither Americans or Chinese are applying ai to optimal human benefit until ot unless they agree some collaboration projects. why don't you tell them that

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