The AI Market Is Growing — and LLMs Are a Big Driver
DecentralGPT illustration showing large language model market trends and AI inference infrastructure in 2026.
AI is no longer “experimental.” It’s becoming part of how companies work every day. Stanford’s AI Index 2025 reported that 78% of organizations used AI in 2024, up from 55% the year before, and that generative AI attracted $33.9B in private investment globally.
MARKET VALUATION 2026
The Large Language Model market at roughly $7–8B in 2025, growing rapidly through the decade (Grand View CAGR projections).
The broader Generative AI market projected to expand sharply through the 2030s—direction is consistent: fast growth.
The takeaway is simple: LLMs are moving from “cool demos” to “daily infrastructure,” and the money is following.
4 Clear Directions for 2026
1. The shift from training to inference
Inference is where usage happens. Industry coverage around CES 2026 highlights this pivot as enterprises move to deployment. Users care about speed, cost, and reliability.
2. Multi-model is the normal workflow
Teams use different models for different jobs (writing, coding, reasoning). People are tired of paying for multiple tools—multi-model platforms are winning by simplifying these workflows.
3. AI agents pushing “always-on” demand
Agentic AI creates more tokens and background work. McKinsey’s 2025 State of AI notes this proliferation even amidst scaling challenges.
4. Trust and verification in the stack
“Just trust the output” isn’t enough for finance or governance. Verifiable AI is now a critical Web3-AI direction for auditable workflows.
Where DecentralGPT & DeGPT Fit
DecentralGPT is a decentralized and distributed AI inference computing network. Our positioning matches the 2026 reality: Inference is the product.
DeGPT Product Layer: Access multiple models in one place for the perfect task fit.
Resilient Infrastructure: Decentralized inference makes AI access scalable and globally available.
Web3-Native: Built for verification, openness, and global participation.
Executive Summary
• LLM demand is growing fast
• Inference is the main bottleneck
• Agents increase usage
• Multi-model is standard
• Trust is part of the infrastructure layer
Explore the vision: decentralgpt.org