What 375 AI Builders Actually Ship
70% of production AI teams use open source models. 72.5% connect agents to databases, not chat interfaces. This is what 375 technical builders actually ship - & it looks nothing like Twitter AI.
70% of teams use open source models in some capacity. 48% describe their strategy as mostly open. 22% commit to only open. Just 11% stay purely proprietary.
Agents in the field are systems operators, not chat interfaces. We thought agents would mostly call APIs. Instead, 72.5% connect to databases. 61% to web search. 56% to memory systems & file systems. 47% to code interpreters.
The center of gravity is data & execution, not conversation. Sophisticated teams build MCPs to access their own internal systems (58%) & external APIs (54%).
Synthetic data powers evaluation more than training. 65% use synthetic data for eval generation versus 24% for fine-tuning. This points to a near-term surge in eval-data marketplaces, scenario libraries, & failure-mode corpora before synthetic training data scales up.
The timing reveals where the stack is heading. Teams need to verify correctness before they can scale production.
88% use automated methods for improving context. Yet it remains the #1 pain point in deploying AI products. This gap between tooling adoption & problem resolution points to a fundamental challenge.
The tools exist. The problem is harder than better retrieval or smarter chunking can solve.
Teams need systems that verify correctness before they can scale production. The tools exist. The problem is harder than better retrieval can solve.
Context remains the true challenge & the biggest opportunity for the next generation of AI infrastructure.
Interesting to see the prevalence of database connections over chat interfaces in production AI. The reliance on open source models also stands out.
Seeing those numbers reminds us how much real world AI work happens behind the scenes, with open source models and database linked agents driving true value for businesses.
Interesting to see the focus on database connections over chat interfaces for production AI. It reflects a pragmatic, data-driven approach to real-world applications.
This is embarrassing. Literally 9/10 of the comments on this post use the same prompt to generate a reply. 👇 1. Interesting to see what's really happening in production AI... 2. Interesting to see real-world AI deployments leaning so heavily on open source 3. Interesting to see open source and direct database connections 4. Interesting to see the database connection preference among production teams. 5. Interesting to see data that shows real-world AI usage differs so much from public perception. Great post btw, thanks for sharing!
Seeing what teams actually build versus the hype is always interesting. Focus on practical applications makes AI much more valuable. Good to see solid data backing that up.