Unique Research · Insight / industry map · September 6, 2023 · Full framework rewrite of V3.0 map layers — company grids primarily in CN images
Contents: 01 Map release · 02 Deep RL paradigm (AI 3.0) · 03 Compute, LLMs & new tools · 04 Infra companies · 05 General LLMs · 06 Middle layer · 07 General apps · 08 Industry apps/models · 09 Disclaimer.
01 Map release
As the AIGC chain densifies, Unique Research updates from China AIGC Industry Map V2.0 (Q1 2023 collect) to V3.0. Prior stack: base / middle / application. V3.0 splits the model layer into general LLMs vs industry models, and splits applications into industry vs general.
General LLMs: broad-domain, transferable across industries. Industry models: specialized for vertical needs. Industry apps: healthcare, finance, education, transport, energy, etc. General apps: text/image/video/audio generation as shared primitives.
02 Deep reinforcement-learning paradigm — AI 3.0
AIGC fuses GANs, pretrained LMs, and multimodality—find patterns in existing data and generalize new content. Commercially: enablement via high-quality, high-freedom, low-barrier generation for content scenes/producers.
Early decision AI assumed logical purity—unfit for real-world uncertainty. Probabilistic paradigms (empiricism + rationalism) partially fix that. Deep RL with enough data + reward/penalty models approaches human-like intelligence for high-quality automated creation. General AI aims at pan-human task competence.
03 Compute, LLMs & new tools — new industry shape
Base: data, compute, compute platforms, model train/dev platforms, supporting facilities. Model: bottom general LLMs (open / closed / hosting) + middle (RLHF/human feedback, adaptation, personalization). Application: C-facing text/image/A/V generation split into industry vs general value logics.
Industry apps: security, finance, education, healthcare, driving, logistics, transport… General apps: translation, Q&A/CS/chatbots, KG, recommenders, robot OS, data mining, smart interaction…
Players: internet majors, AI cos, ecosystem/scene chains, research labs—boundaries still blur while the industry is young. Application layer is the best entry for startups—hence fiercest competition there.
Compute as core pedestal: AI chips (CPU base; GPU/FPGA/ASIC accelerators) · AI servers (CPU+accelerator for train/infer) · data centers (cloud training). Cost driven by chips/memory/disk; exploding datasets + parameter counts drive compute demand and cost.
LLMs as paradigm milestone: not mere algorithm bumps—new tech paradigm with stronger generalization, penetrating NLP, CV, healthcare, finance…
New tool layer: continuous emergence of orchestration, fine-tune, Agents, multimodal editors. Industry enablement trend already visible across verticals.
04–08 Company layers
Detailed named company grids for infrastructure, general LLMs, middle layer, general apps, and industry apps/models live primarily as map graphics in the Chinese original (4 images). On ingest, attach those figures so readers can navigate the V3.0 landscape company-by-company; this English package preserves the taxonomy and thesis so the map is usable without inventing names not clearly readable from text alone.
09 Disclaimer
Industry information for research reference; figures as of the V3.0 collection window. Not investment advice.
4 CN map images; attach on ingest.
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