Tomer Tunguz 解析 375 个 AI 构建者团队的生产环境调研结果
What 375 AI Builders Actually Ship
Tomer Tunguz 基于 375 个技术构建者团队的调研指出，生产环境 AI 比预期更成熟也更混合：70% 的团队在不同程度上使用开源模型，仅 11% 纯用闭源。
基于 375 个生产环境 AI 团队的调研数据，读者可以对照自身技术栈，了解开源采用、Agent 连接方式与上下文痛点的真实分布。
In short : Production AI is more mature & hybrid than expected : 70% use open source, agents connect to databases over chat, RLFT delivers 16-30% lift, & context remains the #1 unsolved problem.
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.
Explore the full interactive dataset here or read Lauren’s complete analysis.
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GP at Theory Ventures. Former Google PM. Sharing data-driven insights on AI, web3, & venture capital.
来源：Tomer Tunguz 博客（VC 分析） · tomtunguz.com