AI

Originally published on Latent Space FDEs have the hottest job in AI. Labs, startups and PE firms are all hiring engineers to sit inside their customers’ operations and solve their problems. Almost none of them agree on what those engineers are supposed to accomplish, or what the strategy underneath the hiring actually is. I’m Vinoo, CEO of Kepler, the deterministic infrastructure for AI. I’ve built pieces of the forward deployed function three times, at three different institutions, over the course of over a decade. Here’s what I’ve seen work, what I’ve seen fail, and where I think this goes. ...

I spoke with Henry Chandonnet at Business Insider about Project Frontline and what companies are getting wrong about forward deployed engineering. The full as-told-to is there. A short version of what I told them. What Project Frontline Actually Was It was a rotation, not a hiring funnel. We took software engineers and put them in front of customers with real support underneath them — dual mentorship, a way back out, and people whose job was to make sure the rotation worked. Around 250 engineers went through it. A lot of them run forward deployed teams now at OpenAI, Anthropic, xAI, and Anduril. ...

Presented at AI Engineer. Finance used to be bottlenecked on producing content. It is now bottlenecked on verifying it. An analyst who once spent the day building a model now spends it checking whether a machine’s output can be defended to a client, a committee, or a regulator. ...

Presented at AI Engineer. Most companies hire forward deployed engineers as an extension of go-to-market: a technical body to keep a large account happy. That’s the wrong frame. FDE is a product strategy. The job is to find the real problem, ship the smallest thing that solves it, and turn what you learned into leverage for the product. ...

Most people now use ChatGPT, Claude, or Gemini every week without a clear mental model of what’s happening underneath. That’s fine right up until the model is confidently wrong and you have no way to tell. AI Model Basics is a new 20-minute LinkedIn Learning course aimed at exactly that gap. What the Course Covers How large language models actually work — enough mechanism to reason about them, without the math What prompting does and why it changes the output as much as it does Why models produce wrong answers, and what kinds of wrong to expect Personalization — what these tools can and can’t retain about you How to evaluate and improve what you get back instead of accepting the first response Who This Is For Anyone using AI tools at work who wants to understand them rather than just operate them. It’s beginner-level and assumes no technical background — no coding, no prior AI experience. ...