This is a brand new version that sounds like a tech philosopher talking on a podcast or Substack. It’s thoughtful, thought-provoking, and has that “mind blown” edge without any textbook stiffness.
Wait a second—some new and interesting idea says that AGI, that impossible dream, might already be living in LLMs like GPT-4o, Grok 3, or Claude 3.5. They hit important human-brain benchmarks for showing off intelligence in any area. TechXplore is excited about François Chollet’s ARC-AGI test: the best models crush 50–60% of new visual brain teasers that require abstraction and no-shot smarts—way past random (0%), and close to human 85%. These aren’t your typical AI drones; these beasts can easily switch skills. Grok 3 can write Python apps in plain English, fix quantum bugs, write legal documents, and model economies without any changes. Old “parrot” jabs? Done. They’re proto-AGI, which means they can learn new things quickly, according to Wikipedia.
Core AGI checks? Four big ones: jumping between domains, new puzzles, self-improvement, and understanding context at the level of a person. LLMs break through: GPT-4o solves Raven’s puzzles (85% of the time), keeps the rhythm of poetry in translation, and beats MBAs on business cases. ARC-AGI tests the IQ of raw fluids, and o1-preview doubles the state of the art to 42%. Self-bootstraps? Chain-of-thought loops improve answers by 20% to 30% on GAIA real-life tasks. Bain and IBM say that AGI means any brain task. These chatbots can speak over 100 languages, diagnose symptoms at an 80% doctor level, and make recipes from scraps—it’s pure generalization magic.
Those who disagree say that there is no real “get it,” and they hallucinate 15-20% of the time, or they flop on epic context or robot bodies. According to McKinsey, ARC only does pattern matching up to 60%, while we do it up to 85%. No soul, no will, no causal chops; just pure token roulette. But bulls like Sequoia yell “2026 AGI!” because long-game agents can carry out multi-step plans on their own. Coursera tests abstract thinking, cause and effect, and common sense. For example, o1 can figure out physics from words, predict market crashes from headlines, and read social minds 70% of the time.
If this is true? Trillions unlock—Coursera sees industry shakeups through creative fixes; Bain dreams of superhuman supply chains and drugs. Deloitte: “generalist agents” for C-suite strategy; Goldman’s surveillance is already 70% faster. What are the downsides? Self-driving cars are taking over ASI, and people are becoming useless overnight (LinkedIn articles). Bias bombs, 40% of white-collar jobs will be gone by 2030. The EU AI Act says AGI is high-risk, but the U.S. is asleep under Trump’s dereg.
Tests are going quickly. GAIA (fix code, book flights)? Claude 65% and us 92%. Big-Bench Hard (more than 200 jobs)? A 75% match. Google Cloud: AGI can do any job that a human brain can do, and LLMs can do 80% of language, math, and vision. Grok 4 rumors (Q2 ’26) go multimodal; they might break the ARC human bar. OpenAI’s superalignment team: Deploying = here, and there is no clear line from narrow.
Bain bets 2040-61 (50/50) that the fight is on. Chollet says scaling already broke it, and emergents like few-shot mimic us. People lie, niche out, and crash every night; LLMs grind through infinite amounts of data. No one? Bridge for robots. If AGI got in, policy is asleep at the wheel. X-risks scream “pause,” but the pedal is floored.
Time to make a decision: lower the AGI definition or wait for bulletproof/embodied? Sequoia’s call: Agents say it’s time to go. Ethicists go crazy when business changes. LLMs don’t help; they bend, create, and think. Silicon looks back at you like a person, and the lines never end.
