I’m fully forward deployed engineering pilled specifically because AI simply is not the same as software. In software, you deliver a stable piece of technology to a customer and they adopt it and that’s that (extreme over simplification). In AI, you’re delivering something that is constantly evolving both due to the nature of the new capabilities and best practices that emerge, but also because the underlying models change so much that they can meaningfully change the workflow as a result of their upgrades. For this reason it’s far more logical that one vendor can share best practices across thousands of companies more efficiently than every single company can learn and manage these best practices themselves. Further, the learnings from those customers should go right back into the core product as a result. As we go from chat systems to anyone can relatively easily adopt to agentic systems that require more meaningful efforts to manage and update, the FDE model (or equivalent) essentially becomes a core competency for anyone deploying AI at scale.
我之所以完全相信 forward deployed engineering，特别是因为 AI 确实不同于传统 software。在 software 领域，你把一项相对稳定的技术交付给客户，客户采用之后，事情基本也就到此为止了（这是极度简化的说法）。而在 AI 领域，你交付的是一种持续演进的东西：一方面，新能力和最佳实践（best practices）会不断出现；另一方面，底层模型变化非常大，以至于它们的升级会实质性地改变工作流（workflow）。正因如此，由一个 vendor 在成千上万家公司之间共享最佳实践，显然比让每一家公司都自己去学习和管理这些最佳实践要高效得多。进一步说，从这些客户那里得到的经验，也应该因此直接回流到核心产品中。随着我们从任何人都相对容易采用的 chat systems，走向需要投入更多实质性精力去管理和更新的 agentic systems，FDE model（或同类模式）本质上会成为任何大规模部署 AI 的组织的一项核心能力。
Headless software is the future
Headless software 是未来