AI: post transformers
AI: post transformers

OpenRouter 2025 Report: Analysis of Global LLM Usage Patterns

19 January 2026 14:04 mcgrof

Listen to episode

About this episode

We review two research papers, one from January 15, 2026 by OpenRouter Inc and a16z (Andreessen Horowitz) and another from April 2025 by Andrey Fradkin (Boston University and MIT IDE) which provide uses of OpenRouter, they analyze the evolving market dynamics and user behaviors within the Large Language Model (LLM) ecosystem, primarily using data from the **OpenRouter marketplace**. The studies document that new AI models see **rapid adoption** upon release, with demand patterns suggesting that models are **horizontally and vertically differentiated** rather than being simple commodities. Researchers found that while some releases **expand the overall market**, others primarily trigger **substitution** within specific model families. Furthermore, the data reveals significant **multi-homing**, where a single application utilizes a diverse mix of models to meet different functional needs. Later analysis introduces the **"Cinderella Glass Slipper"** framework, suggesting that models achieve long-term defensibility by perfectly fitting high-value, previously unsolved workloads. This research also tracks the **rising integration of tool-calling** and reasoning-based architectures, emphasizing that **user retention** is becoming the primary metric of success in a competitive landscape.


Sources:

January 15, 2026

State of AI:

An Empirical 100 Trillion Token Study with OpenRouter

OpenRouter Inc and a16z (Andreessen Horowitz)

https://arxiv.org/pdf/2601.10088


April 2025: Demand for LLMs: Descriptive Evidence on

Substitution, Market Expansion, and Multi-Homing

Andrey Fradkin

https://arxiv.org/pdf/2504.15440

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 AI: post transformers. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.