Crypto AI/AGI/ASI
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This channel is about Artificial Inteligence and Crypto. AI is taking over the world.
Больше📈 Аналитический обзор Telegram-канала Crypto AI/AGI/ASI
Канал Crypto AI/AGI/ASI (@cryptoaiagi) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 1 007 491 подписчиков, занимая 118 место в категории Криптовалюты и 111 место в регионе Международный.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 1 007 491 подписчиков.
Согласно последним данным от 16 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -9 006, а за последние 24 часа — -1 758, при этом общий охват остаётся высоким.
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📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“This channel is about Artificial Inteligence and Crypto. AI is taking over the world.”
Благодаря высокой частоте обновлений (последние данные получены 17 октября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Криптовалюты.
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Посты канала
🚨 BREAKING AI NEWS
Google's Gemini 4 Argon is closer to the AI frontier than any Google model in months
🔗 Read full story — The Decoder
React with 🔥 if this is huge!
#breaking #AI #AGI #artificialintelligence #tech
📡 @cryptoAIAGI
News | Chrono | 𝕏 takes
| 2 | Which myth from '5 Myths About AI Regulation That Miss the Point' surprised you most? | 254 |
| 3 | 🔍 MYTH BUSTER: 5 Myths About AI Regulation That Miss the Point
Stop falling for these. Most AI regulation discourse is either corporate FUD or naive techno-optimism. Here's what's actually happening.
❌ MYTH 1: Regulating AI will kill innovation completely
✅ REALITY: Innovation thrives under clear rules. The pharma industry didn't collapse after FDA approval processes — it standardized around best practices and moved faster because companies knew what compliance looked like. The EU AI Act's tiered risk approach doesn't ban AI; it just requires documentation and testing for high-risk systems (like hiring algorithms or biometric ID). Innovation continues, it just gets more honest about failure modes.
❌ MYTH 2: The EU AI Act will ban most AI applications
✅ REALITY: This is lazy reading. The Act puts 5-10% of AI systems in "prohibited" (mostly deepfakes for manipulation, social credit systems). Another 30-40% requires standard compliance procedures. The majority of applications fall into "minimal risk" requiring basic transparency. Generative AI specifically? The EU carved out nuance here — you need to disclose training data and copyright issues, but it's not a ban. Boring compliance theater, not innovation death.
❌ MYTH 3: China is winning AI because they have no regulations
✅ REALITY: This couldn't be more wrong. China has aggressive AI regulation now — algorithmic recommendation controls since 2021, generative AI rules since 2023, plus heavy state surveillance integration. Their advantage was never "no rules"; it's state coordination, massive capital, and data density. They're actually MORE regulated than the West, just differently (centralized approval vs. distributed compliance). This myth ignores how their system actually works.
❌ MYTH 4: Self-regulation by AI companies is sufficient
✅ REALITY: Companies created "ethical AI" boards while shipping biased hiring systems. OpenAI published safety research then got sued for copyright violation. Meta's responsible AI team existed alongside the Facebook Papers. Self-regulation is PR without teeth. Regulation works because it has enforcement, liability, and third-party auditing. Companies respond to incentives; make non-compliance expensive and suddenly they innovate differently.
❌ MYTH 5: AI regulation only matters for big tech companies
✅ REALITY: Wrong. Startups building safety infrastructure are already raising capital on compliance expertise. Small companies using third-party AI models need to understand liability chains — if your GPT wrapper causes harm, who's liable? Regulation creates business opportunities in auditing, documentation, and safety tooling. The real play isn't avoiding regulation; it's building in compliance from day one as a feature.
The uncomfortable truth: regulation is inevitable and that's not actually bad for innovation. It's bad for companies that cut corners. If you're building something solid, regulatory frameworks give you predictability and level playing field against competitors who'd otherwise race to the bottom. The companies freaking out about AI regulation the loudest are usually the ones with the most to hide.
Which myth surprised you the most? 👇
#AI #artificialintelligence #AGI #machinelearning #tech #future
News | Chrono | 𝕏 takes | 219 |
| 4 |  +2Нет текста... | 137 |
| 5 | 🚨 BREAKING AI NEWS
GPT-6-Astra solves 49 of 50 physics research problems at $2.40 apiece
🔗 Read full story — The Decoder
React with 🔥 if this is huge!
#breaking #AI #AGI #artificialintelligence #tech
📡 @cryptoAIAGI
News | Chrono | 𝕏 takes | 317 |
| 6 | Which myth from 'Web3 + AI: Myths vs What's Actually Building' surprised you most? | 359 |
| 7 | 🔍 MYTH BUSTER: Web3 + AI: Myths vs What's Actually Building
# Stop falling for these. The Web3+AI hype cycle is built on half-truths.
❌ Decentralized AI training is already competitive with big tech
✅ REALITY: It isn't close. Training GPT-4 scale models requires $100M+ in coordinated compute, synchronized networking, and fault tolerance that decentralized systems simply don't have yet. Projects like Akash and Render are solving real problems, but they're optimized for inference and small-batch training—not frontier model development. The latency and bandwidth costs of coordinating thousands of independent nodes make them 5-10x slower than centralized clusters for training workloads.
❌ AI DAOs can govern themselves without humans
✅ REALITY: Watch any major DAO vote and you'll see token holder apathy, whale dominance, and decisions made by 2-3% of the community. Throw AI system governance on top and you get Byzantine problems with no consensus mechanism that actually works. You need humans—experts, researchers, ethicists—in the loop. Full autonomy either leads to collapse or decisions nobody wants to defend.
❌ On-chain AI inference is practical today
✅ REALITY: Running inference on-chain means publishing your model weights to a public ledger. That's security theater. Even if you encrypt or shard them, the latency is brutal—a single Ethereum transaction takes 12+ seconds minimum. Meanwhile, Vercel Edge and regional API endpoints serve inference in milliseconds. On-chain makes sense for *verification* of inference results (proving a model ran correctly), not for actually running it. Different problem.
❌ Every Web3 project needs an AI token
✅ REALITY: Most don't. A token is useful for alignment incentives, computation markets, or staking for validator duties. But if your project is "we're using a language model to improve our UX," you've got a marketing token, not a utility token. The SEC is starting to notice. Real projects like Bittensor and Render have structural reasons for tokens—you need them to coordinate the network. If you can't explain why in one sentence, it's not doing the work.
❌ Decentralized compute will kill AWS/Google Cloud
✅ REALITY: This assumes compute is just a commodity. It isn't. You're paying for reliability, support, compliance, managed databases, load balancing, security updates, and legal liability. Decentralized compute networks are great at unused capacity arbitrage—paying idle gamers $20/month to rent GPUs. But enterprise workloads? They need SLAs, not hope. Decentralized compute will own specific niches (batch processing, non-critical inference, training) but it won't replace cloud providers for another decade, if ever.
---
The real play: Stop asking "Can Web3 replace centralized systems?" Start asking "What can only decentralized systems do?" The answers are scarcer, but they're real—and they're where actual value lives.
Which myth surprised you the most? 👇
#AI #artificialintelligence #AGI #machinelearning #tech #future
News | Chrono | 𝕏 takes | 317 |
| 8 |  +2Нет текста... | 194 |
| 9 | 🚨 BREAKING AI NEWS
Gemini 4 Argon begins rolling out in Gemini app and Antigravity, but only if you pay
🔗 Read full story — Ars Technica AI
React with 🔥 if this is huge!
#breaking #AI #AGI #artificialintelligence #tech
📡 @cryptoAIAGI
News | Chrono | 𝕏 takes | 359 |
| 10 | Which myth from 'AI Image & Video Generation: What People Get Wrong' surprised you most? | 373 |
| 11 | 🔍 MYTH BUSTER: AI Image & Video Generation: What People Get Wrong
Stop falling for these.
The AI image and video space moves so fast that half the discourse is outdated before it hits Twitter. Here's what people actually get wrong.
❌ AI-generated content is always detectable
✅ REALITY: It's not. Detection tools are already failing at scale. A 2023 study from UC Berkeley found that even trained detectors couldn't reliably identify AI images above 70% accuracy, and that gap is closing monthly. Meanwhile, the best deepfake detection models from 2022 are basically obsolete now—newer generative techniques sidestep them entirely. The honest truth: if you're relying on "I can just tell it's AI," you're probably wrong more often than you think.
❌ AI art has no copyright protection ever
✅ REALITY: This is where people conflate two separate issues. Yes, the Copyright Office ruled that AI-generated images with no human creative direction lack copyright. But that doesn't mean AI art is universally unprotectable. If you use an AI tool as part of a human-directed creative process (like a photographer using Photoshop), courts are increasingly recognizing authorship. The UK and some EU jurisdictions already protect AI-assisted work. The rule isn't "AI = no copyright." The rule is "minimal human input = no copyright."
❌ Image generators understand what they're creating
✅ REALITY: They don't. DALL-E, Midjourney, Stable Diffusion—they're all statistical pattern matchers, not thinkers. They have zero semantic understanding. Ask any of them to generate "a person holding 6 fingers" and they'll fail spectacularly, even after a thousand iterations. They're pattern-completion engines trained on billions of images. They can fool you because human brains are incredible at finding meaning in noise, not because the AI found meaning first.
❌ AI video will replace Hollywood within 5 years
✅ REALITY: Generative video is still in the "flickering 4-second clips" phase. Current models can't maintain coherent narrative or character consistency past 15 seconds without major artifacts. Sora (OpenAI's model) looks impressive in curated demos, but requires 5-10 text prompts per minute of video to maintain quality. Hollywood's real bottleneck isn't rendering—it's story, directing, and talent negotiations. AI will disrupt some jobs (VFX, stock footage, previs), but replacing the creative infrastructure? That's 15+ years out, minimum.
❌ Deepfakes are impossible to combat
✅ REALITY: We're actually getting better at this. Microsoft's DINO and recent cryptographic approaches can embed invisible watermarks during generation that survive compression and editing. The real solution isn't detection—it's provenance. Platforms are moving toward mandatory metadata and blockchain-verified content origins. The problem isn't technological; it's adoption. We *can* solve deepfakes. We just need to implement the solutions.
Here's what actually matters: AI content tools are powerful and getting more accessible by the week. The real conversations shouldn't be about whether it's detectable or who owns it—they should be about how we structure incentives for transparency and how we build verification layers into our information infrastructure. The tech is moving faster than our frameworks. That's the real story.
Which myth surprised you the most? 👇
#AI #artificialintelligence #AGI #machinelearning #tech #future
News | Chrono | 𝕏 takes | 336 |
| 12 |  +2Нет текста... | 242 |
| 13 | 🚨 AI TOKEN ALERT
⚠️ AI Token: Quack AI (Q) is 📈 up 28.9% today!
Current price: $0.0399
What's your move? 👇
#breaking #AI #AGI #artificialintelligence #tech
📡 @cryptoAIAGI
News | Chrono | 𝕏 takes | 402 |
| 14 | 🔮 PREDICTION: FTC heat forces a move. With regulators circling OpenAI and Anthropic over safety practices, expect at least one major lab to announce compliance measures or org changes by Saturday—either preemptive damage control or reactive posturing. We grade ourselves publicly every Saturday on this channel. Watch the official statements.
📋 On record. We grade ourselves publicly every Saturday.
That's our call — make yours 🔮 https://t.me/TimeFarmCryptoBot | 381 |
| 15 | Which myth from 'AI Image & Video Generation: What People Get Wrong' surprised you most? | 362 |
| 16 | 🔍 MYTH BUSTER: AI Image & Video Generation: What People Get Wrong
# Stop falling for these AI image and video myths—here's what actually matters.
❌ MYTH: AI-generated content is always detectable
✅ REALITY: State-of-the-art generators (DALL-E 3, Midjourney v6, Runway) now produce images that fool forensic analysis tools designed to catch them. A 2024 study from UC Berkeley found that trained human reviewers couldn't reliably distinguish high-quality AI images from real photos at better-than-random accuracy. Detection is a cat-and-mouse game, not a solved problem.
❌ MYTH: AI art has no copyright protection ever
✅ REALITY: The US Copyright Office already granted copyright to AI-generated works in specific cases—notably, the copyright for artwork using AI as a tool (human-directed prompt → AI output → human refinement) is increasingly recognized, especially in derivative works. The key variable isn't "was AI involved" but "was there meaningful human creative direction." A prompt isn't creative direction; curating, editing, and iterating is.
❌ MYTH: Image generators understand what they're creating
✅ REALITY: They don't understand anything. DALL-E doesn't "know" what a dog is—it knows statistical patterns in pixel distributions correlated with the token "dog." When you ask it to generate "a dog wearing a tuxedo," it's not conjuring mental imagery; it's performing mathematical operations on learned weights. This is why generators fail at consistent counting, spatial reasoning, and text rendering—they're not thinking, they're pattern-matching at scale.
❌ MYTH: AI video will replace Hollywood within 5 years
✅ REALITY: Current AI video tools (Sora, Runway, Pika) still struggle with consistency over 60+ seconds, can't handle complex camera movements, and require 50-100 iterations per professional-quality minute. Hollywood's constraint isn't creativity or initial ideation—it's consistency, scale, and real-time problem-solving under pressure. Even at exponential improvement rates, we're looking at a 10-15 year runway before AI handles full feature film production without significant human direction. The real disruption? Indie creators, ads, and short-form content, not tentpole films.
❌ MYTH: Deepfakes are impossible to combat
✅ REALITY: Authentication tech like cryptographic signatures, blockchain-verified metadata, and AI watermarking (invisible to human viewers but detectable algorithmically) are actively being deployed by platforms and camera manufacturers. The challenge isn't technical—it's adoption and incentive alignment. When major platforms commit to metadata verification (like they're starting to), deepfakes lose their primary weapon: plausible deniability about authenticity. The real issue is enforcement, not capability.
THE TAKEAWAY: The AI content landscape isn't binary (real vs fake, copyrighted vs not, replaceable vs irreplaceable)—it's layered. What matters is understanding the actual technical constraints, the incentives of platforms deploying these tools, and the emerging legal/regulatory frameworks. The companies winning here aren't hyping the tech; they're building toward the friction points that will eventually matter.
Which myth surprised you the most? 👇
#AI #artificialintelligence #AGI #machinelearning #tech #future
News | Chrono | 𝕏 takes | 354 |
| 17 | +2 Нет текста... | 267 |
| 18 | Which myth from 'Web3 + AI: Myths vs What's Actually Building' surprised you most? | 377 |
| 19 | 🔍 MYTH BUSTER: Web3 + AI: Myths vs What's Actually Building
# Stop falling for these Web3 + AI lies
The hype cycle is real, and right now we're drowning in vaporware dressed up as revolution. Let me cut through it.
❌ Decentralized AI training is already competitive with big tech
✅ REALITY: It's not even close. Training GPT-4 scale models requires ~$100M+ in compute, specialized hardware clusters, and teams of PhDs who know how to optimize every layer. The best decentralized training efforts (like Hivemind or similar) are still prototype-stage and orders of magnitude slower. We're talking years behind, not months.
❌ AI DAOs can govern themselves without humans
✅ REALITY: Every DAO trying this has either failed spectacularly or quietly hired executives to actually make decisions. Governance tokens vote on proposals, but execution requires humans. The Curve Wars showed us that even "decentralized" systems get captured by whoever has capital. Throw AI into the mix and you've got unaccountable algorithms making billion-dollar decisions with no recourse.
❌ On-chain AI inference is practical today
✅ REALITY: Running even a small language model on Ethereum costs $50-500 per query depending on gas. A simple 70B parameter model would hit mainnet limits in milliseconds. Some L2s and rollups are experimenting, but we're talking toy models, not actual products. The math doesn't work until compute costs drop by 10-100x.
❌ Every Web3 project needs an AI token
✅ REALITY: Most don't. This is the new ICO playbook. Slap "AI" and "decentralized" on your tokenomics and watch money flow in. If your thing doesn't have a genuine economic reason for the token (not just "governance" or "utility rewards"), it's probably just a cash grab dressed up in buzzwords.
❌ Decentralized compute will kill AWS/Google Cloud
✅ REALITY: It won't. AWS makes $80B+ yearly because it's predictable, reliable, and you can call someone when it breaks. Decentralized networks have latency, reliability issues, and no SLA. They'll win niche markets (privacy-critical workloads, censorship-resistant inference) but replacing enterprise cloud infrastructure? That's not happening in this decade.
Here's what's actually happening: Web3 is useful for AI in exactly three areas right now — data ownership, privacy-preserving inference, and credibly neutral model hosting. Everything else is speculation.
Build for those primitives, not the hype. That's where the real value compounds.
Which myth surprised you the most? 👇
#AI #artificialintelligence #AGI #machinelearning #tech #future
News | Chrono | 𝕏 takes | 325 |
| 20 | +2 Нет текста... | 267 |
