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Crypto AI/AGI/ASI: post #2852 — TG.ME

Crypto AI/AGI/ASI@cryptoAIAGI · 1.1M subscribers Open in TelegramOpen

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t.me/cryptoAIAGI/2852

August 30, 2026 313

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[media post] 230·Aug 31 🔍 MYTH BUSTER: 5 Things People Get Wrong About GPT & LLMs # STOP FALLING FOR THESE MYTHS ABOUT GPTs AND LLMs Your timeline is full of AI takes that sound smart but crumble under scrutiny. Here's what you actually need to know. ❌ MYTH: LLMs are just fancy autocomplete with no real use ✅ REALITY: This talking point died around 2022. LLMs are being deployed for protein folding (AlphaFold 2), legal document review, medical diagnostics, and billion-dollar code generation at enterprise scale. OpenAI's o1 can solve IMO-level math problems that require actual reasoning, not pattern matching. If it were "just autocomplete," companies wouldn't be spending millions integrating them into production systems. ❌ MYTH: ChatGPT and Claude have real-time internet access ✅ REALITY: They don't. ChatGPT's knowledge cutoff is April 2024 (GPT-4o), Claude 3.5 is April 2024. Neither model can browse the web by default—they can only work with information in your prompt or via plugins you explicitly enable. When they cite recent events, they're hallucinating or you're feeding them the data. This matters: if you're relying on these for live market analysis without providing feeds, you're flying blind. ❌ MYTH: AI remembers every conversation you ever had ✅ REALITY: Each conversation starts from zero. Your chat history exists on OpenAI/Anthropic's servers for safety/improvement, but the model itself has zero memory between sessions. This is actually a feature for privacy, but it also means you need to re-context everything if you want continuity. Long-context models (Claude 200k tokens) help, but they're not true memory—they're just bigger windows. ❌ MYTH: Open-source models are always worse than closed ones ✅ REALITY: Llama 3.1 405B matches or beats GPT-4 on many benchmarks. Mistral's models are punching above their weight class. The gap exists in certain domains (reasoning, instruction-following at scale), but saying "open = worse" ignores that you can fine-tune, run locally, and own your data with open models—advantages that matter for crypto and finance applications where privacy and control are non-negotiable. ❌ MYTH: LLMs will hit a plateau and stop improving dramatically ✅ REALITY: We said this about neural networks in 2016. We were wrong. Scaling laws still hold (more compute + data = better performance). Newer architectures (mixture of experts, test-time compute) are opening new frontiers. The bottleneck isn't the paradigm—it's energy, hardware, and quality training data. Until one of those hard stops, expect continued acceleration. Here's the meta-lesson: Most LLM myths exist because people extrapolate from their first interaction with ChatGPT. These models are advancing faster than takes about them. Stay skeptical of both hype and doom—the truth is usually in the details. Which myth surprised you the most? 👇 #AI #artificialintelligence #AGI #machinelearning #tech #future 205·Aug 31

[media post] 136·Aug 31 🔍 MYTH BUSTER: 5 AI Myths Killing Your Portfolio # 5 AI Myths Killing Your Portfolio Stop falling for these. Your portfolio isn't bleeding because you lack conviction—it's bleeding because you're betting on narratives instead of technology. Here's what's actually real. ❌ Every AI token is backed by real AI technology ✅ REALITY: Most "AI tokens" are just governance tokens for platforms that slapped machine learning into their whitepaper. We're talking companies with no moat, no users, and no actual AI—just APIs bolted onto Ethereum. Check the repos. Most haven't shipped meaningful code in months. If you can't find a GitHub with real contributions or a technical breakdown that makes sense to someone who actually codes, you're holding a ticker symbol, not technology. ❌ AI will make all crypto trading profitable ✅ REALITY: AI trading bots have been around since 2017. Most underperform buy-and-hold on crypto's extreme volatility because they get whipsawed by flash crashes and manipulation. A bot is only as good as its training data and market conditions—feed it 2021 bull run data and watch it blow up in the next bear market. Profitable AI trading exists, but it's not democratized in these tokens; it's locked behind institutional funds with real quant teams. ❌ More AI partnerships = higher token price ✅ REALITY: Partnership announcements are marketing theater. Look at the actual deals: most involve zero equity change, zero revenue sharing, and zero lock-in. A major enterprise announcing an "AI integration" doesn't guarantee adoption or volume. See: dozens of blockchain partnerships announced by Fortune 500s that generated exactly zero meaningful business. Track deployment metrics and user growth, not press releases. ❌ AI tokens follow AI company stock performance ✅ REALITY: They don't. NVIDIA up 200%? Doesn't mean your AI token follows. NVIDIA has earnings, recurring revenue, and hardware moats. AI tokens are speculative bets on unproven ecosystems with zero cash flow. They're driven by hype cycles and retail FOMO, not fundamental correlation to the AI industry. When the market rotates, they often move in the opposite direction because they lack the gravity of real business performance. ❌ Open-source AI means the token is worthless ✅ REALITY: This one trips people up. Open-source code doesn't kill value—controlled data, network effects, and infrastructure do. Linux is open-source and worth billions in enterprise deployments because it's embedded in mission-critical systems. A real AI token needs to own something defensible: proprietary datasets, network effects, or infrastructure lock-in. Open-source code alone? Worthless. Open-source code powering an actual ecosystem? Different story. The real lesson: AI hype is killing your returns faster than any bear market. Separate signal from noise by asking one question: If the token didn't exist, would the AI still work and generate value? If the answer is no, you're not investing in AI—you're speculating on sentiment. Do better. Which myth surprised you the most? 👇 #AI #artificialintelligence #AGI #machinelearning #tech #future 234·Aug 30

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