At the substrate, an invention-automation machine is coming online — one in which intelligence compounds by turning each invention into leverage for the next. #AGIALPHA
→ View original post on X — @ceobillionaire, 2026-04-11 03:20 UTC
AGI
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Once You See AGI You Cannot Unsee It
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this is a key statement “My personal takeaway from the last several years, and take on why there has been so much Shakespearean drama between the companies in our field, comes down to this: “Once you see AGI you can’t unsee it.” It has a real "ring of power” dynamic to it, and
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AI System Implements Advanced Features Autonomously Overnight
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It is nuts the features it came up with and implemented overnight
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Sub-agents expected to become major technology trend
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Agree! Such sub agents are going to be big
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LLMs Cannot Match Human Chess Expertise Without Specialized Training
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Garry @Kasparov63 retired from competitive chess over twenty years ago, and most or all of his tournament games are presumably publicly available – and yet I bet he still could crush any LLM that didn’t have chess-specific tools or special-purpose training. Gary Marcus (@GaryMarcus) Yet another illustration of why LLMs aren’t even close to being AGI. — https://nitter.net/GaryMarcus/status/2042701528352591972#m
→ View original post on X — @garymarcus, 2026-04-10 21:31 UTC
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Data Centre Bans Threaten Democratic Sovereignty in AI Age
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The greatest risk of our age is that democracies fail to reimagine sovereignty and prosperity in the age of AI.
— Nina Schick (@NinaDSchick) 10 avril 2026
A data centre ban is exactly that risk. https://t.co/VEUQzK2QEI pic.twitter.com/AlDSwY0WxAThe greatest risk of our age is that democracies fail to reimagine sovereignty and prosperity in the age of AI. A data centre ban is exactly that risk.
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LLMs Fail at Poker, Nowhere Near AGI
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Yet another illustration of why LLMs aren’t even close to being AGI. Tombos21 (@tombos21) The world’s best LLMs are still terrible at poker. We put each model into a 200bb heads-up NLHE match against GTO Wizard AI. The best one lost 16 bb/100. For context, a strong human pro only loses about ~4 bb/100. The benchmark is public, so anyone can test their own model. — https://nitter.net/tombos21/status/2042290717499015253#m
→ View original post on X — @garymarcus, 2026-04-10 20:29 UTC
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Gary Marcus agrees: LLMs need honest narrative without hype
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Indeed, if the LLM crew would just stick to this narrative, I would have a *lot* less to say 🤷♂️ Viktor (@wickviktorwick) All we want is truth. Instead of overhyping LLMs, they should keep narative: -LLMs are useful in certain domains (math, coding, language) -they have very little common sense -don't trust it blindly -don't expect AGI based on this architecture — https://nitter.net/wickviktorwick/status/2042677519418048574#m
→ View original post on X — @garymarcus, 2026-04-10 19:17 UTC
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OpenAI’s GPT Evolution: From Recognition to AGI Scaling
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Without diminishing your work and insight: OpenAI recognized the nascent intelligence in GPT-2, decided to scale it up to AGI, and told the world that GPT-3 was probably already too dangerous to be released. Many people got AGI pilled with GPT-3
