In one sentence
AGI (Artificial General Intelligence) is the focus of all fears and fantasies in 2026: Sam Altman says "in a few years", Yann LeCun says "20 years minimum", Yoshua Bengio says "5-10 years is plausible". The truth? Nobody knows, and the very definition of AGI is not consensus. But we can look at the facts, benchmarks, and serious predictions to form an informed opinion without falling into hype or denial.
🤖 Before AGI, here's what GPT-5 can already do
Our complete guide to GPT-5: strengths, limitations, pricing.
The timeline of AGI predictions: who says what?
But what really is "AGI"?
Benchmarks: where are we in 2026?
The 4 MAJOR obstacles to AGI
📚Why it's so hard to go from GPT-5 to AGI
1. 🌍 No model of the physical world
Ask GPT-5: "I'm holding a glass of water. If I turn it upside down, what happens?" → it answers well (text). Ask it to mentally simulate the trajectory of the water and predict where it will land → fails.
GPT-5 learned from text about the world, not from the world itself. Yann LeCun calls this the inverted "Moravec paradox": what's easy for a 3-year-old child (physical intuition) remains inaccessible to the best LLMs.
Potential solution: world models (DeepMind Genie 3, World Labs Marble) that learn from video/simulation. Still experimental.
2. 🔄 No continual learning
You ask GPT-5 a complex question. It makes a mistake. You explain the error. It learns it for this conversation (in-context learning), but FORGETS EVERYTHING in the next conversation.
A human who makes a mistake learns permanently. LLMs, no. Everything must go through costly re-training (months, millions of dollars).
Potential solution: neuromorphic computing, online learning. No major breakthrough in 2026.
3. 🧩 Causal reasoning flaw
GPT-5 is very good at correlations (X often occurs with Y). But mediocre at causation (X causes Y).
Example: "There's a correlation between ice cream sales and drownings. Conclusion?" → GPT-5 can say "common cause (summer)". But on new real cases, it invents causal links that are plausible but false.
This is what Judea Pearl (Turing 2011) calls the "ladder of causation": seeing > intervening > imagining. LLMs remain stuck at the first level.
4. 🎯 ARC-AGI-2: the test no LLM passes
Invented by François Chollet (creator of Keras), ARC-AGI measures the ability to solve NEW visual puzzles with very few examples, exactly what a 5-year-old human does easily.
| Model | ARC-AGI-1 | ARC-AGI-2 | |--------|-----------|-----------| | Average human | ~80% | ~80% | | Expert human | ~98% | ~85% | | GPT-4 (2023) | 9% | <1% | | GPT-5 (2025) | 75% | 5% | | o3-preview | 87% | 12% |
→ On ARC-AGI-2, the best LLMs are still under 15%. This is the wall that nobody knows how to cross.
Why it matters: if a system can't solve puzzles that children do, how can it claim to be AGI?
The 3 credible scenarios 2026-2050
3 scenarios for AGI arrival
| 🔮Scenario | 📊Probability (estimate) | |
|---|---|---|
| 🚀 FAST SCENARIO (2030-2035) | AI breakthrough enables crossing the wall of causal reasoning and continual learning. Combination of LLM + world models + RL. 'Competent' level AGI achieved. | 10-20% |
| 🚶 MODERATE SCENARIO (2040-2050) | Incremental progress. No single breakthrough but accumulation. LLM + agents + multimodal + memory become robust enough for 'Competent AGI'. Not truly 'general' but hard to distinguish. | 40-50% |
| 🐢 SLOW SCENARIO (2060+) | LLM plateau reached. Need a new paradigm (neuromorphic, quantum, other). Several decades of fundamental research needed. | 20-30% |
| ❌ 'NEVER' SCENARIO | AGI is impossible with current architecture, and no new paradigm works. Minority hypothesis but seriously defended (Yann LeCun, Gary Marcus). | 10-20% |
My analysis of the analysis:
- The moderate scenario (2040-2050) is most probable according to the majority of academic experts
- CEOs (Altman, Hassabis) lean towards fast (commercial interest in hype)
- Nobody serious says "never before 2100"
- Nobody serious says "in 2 years" without marketing quotes
What should YOU do in 2026?
The question of danger: should you be afraid?
The metaphor that sums it all up
Absolutely remember
- ✅ AGI = no consensus definition, so debate sometimes confused
- ✅ GPT-5 / Claude / Gemini beat humans on certain specific tasks (chess, code, math), not on everything
- ✅ 4 major obstacles: physical world, continual learning, causal reasoning, generalization (ARC-AGI-2)
- ✅ Academic expert median: AGI around 2040-2047 (50% chance)
- ✅ Sam Altman says "a few years" but it's marketing for investors
- ✅ Yann LeCun says "20 years min" because LLMs can't reach AGI according to him
- ✅ No-regret strategy: learn AI + level up + build your "moats" + invest (real estate/stocks)
- ✅ Serious risks: employment, disinformation, concentration of power, not Skynet
Nobody knows when AGI will arrive, or even if it will arrive. What's certain: current progress is already enough to transform the world. Focus on how to adapt today, not on tomorrow's fantasies.
What is the median AGI prediction according to the AI Impacts survey of 2,778 AI researchers?
To go further
- 🤖 GPT-5: everything you need to know, where we are today
- 💼 AI and employment: jobs in 2026, the concrete impact
- 🤖 AI agents: the next revolution, the step before AGI