# [TECH] AI & Large Language Models
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**Artificial Intelligence** and **Large Language Models (LLMs)** are the current frontier of AI: neural networks trained on internet-scale data that can converse, reason, write code, generate images, and assist with scientific discovery.
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## Overview
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GPT-3 (OpenAI, 2020, 175B parameters) demonstrated that scaling language models produces qualitatively new capabilities. GPT-4 (2023), Claude (Anthropic, 2023), and Gemini (Google DeepMind, 2023) perform at human expert level across many domains. AlphaFold (2020) solved protein structure prediction; AlphaCode (2022) competes with professional programmers; AlphaGeometry (2024) solves olympiad geometry problems. AI is now being deployed in drug discovery, materials design, climate modelling, scientific literature synthesis, and industrial automation.
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## Key Actors
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- **Companies**: OpenAI (2015), Anthropic (2021), Google DeepMind (2023 merger), Meta AI, Mistral, Cohere, xAI
- **Investors**: Microsoft ($13B in OpenAI), Google ($400M in Anthropic), Amazon ($4B in Anthropic)
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## Key Technologies
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- Transformer architecture (Vaswani et al., 2017)
- Reinforcement learning from human feedback (RLHF)
- Constitutional AI (Anthropic)
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## Economic Value
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AI market: **$200 billion/year** (2023, Grand View Research). Goldman Sachs (2023) projects AI could add **$7 trillion/year** to global GDP within 10 years. McKinsey estimates $4.4T/year in value from generative AI alone by 2030.
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## Notes
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Goldman Sachs *The Potentially Large Effects of Artificial Intelligence on Economic Growth* (2023). McKinsey *The Economic Potential of Generative AI* (2023). Grand View Research *AI Market* 2023.
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# Parents
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* [TECH] Cloud Computing & Big Data⏎