#ai **Source:** [Sequoia Capital - 2026: This is AGI](https://sequoiacap.com/article/2026-this-is-agi/) ## Functional Definition of AGI Rather than pursuing abstract technical definitions, Sequoia defines AGI pragmatically - **the ability to figure things out**. This requires three components working in concert: 1. **Baseline Knowledge** (pre-training) - Large models trained on vast data 2. **Reasoning Ability** (inference-time compute) - Deep thinking capability (e.g., o1 reasoning models) 3. **Iterative Problem-Solving** (long-horizon agents) - Autonomous iteration over extended timeframes All three are necessary. Knowledge without reasoning is pattern matching. Reasoning without agency is still just a chatbot. Agency without knowledge fails immediately. The convergence of all three marks the moment we've crossed into functional AGI. ## The Three Technologies That Converged - **ChatGPT (2022)** provided the knowledge layer - **Reasoning models like o1 (late 2024)** added sophisticated thinking at inference time - **Recent coding and task agents** added the capacity for autonomous, multi-step iteration ## Shift from "Talkers" to "Doers" The substantive change is moving from interactive tools (ask a question, get an answer) to systems that work **autonomously for hours or days**. You can now run multiple agent instances in parallel on different subtasks. This reframes the productivity bottleneck: it's no longer "per-interaction speed" but rather "how many things can I delegate continuously?" ## Exponential Scaling Trajectory METR's research shows agent capability roughly doubles every 7 months: - **By 2028:** agents handling full-day expert tasks (what a specialist completes in 8 hours) - **By 2034:** agents capable of year-long projects This isn't linear improvement—it's exponential capability growth. ## The Strategic Question for Business **What's the work requiring sustained, persistent attention where the main bottleneck is "nobody can afford to have someone working on it continuously"?** That's where agents create immediate value. Not in replacing experts, but in tackling problems that were economically infeasible before because continuous human effort was required. Examples: specialized medicine, law, cybersecurity, recruiting, design work.