---
title: "Discussion on \"The AI That Refuses to Talk: Where Are We Actually Heading?\" | Hashnode"
description: "Discussion on \"The AI That Refuses to Talk: Where Are We Actually Heading?\". TypeSafe AI's Jev skips text generation to return typed decisions in milliseconds. Here's how it works, what it costs, and where AI is heading."
url: "https://hashnode.com/posts/the-ai-that-refuses-to-talk-where-are-we-actually-heading/6aaed522e5d911909597e31f"
---

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# [The AI That Refuses to Talk: Where Are We Actually Heading?](https://chatteronai.hashnode.dev/the-ai-that-refuses-to-talk-where-are-we-actually-heading?utm_source=hashnode&utm_medium=feed)

TypeSafe AI's Jev skips text generation to return typed decisions in milliseconds. Here's how it works, what it costs, and where AI is heading.

Sep 19 · 15 min read · chatteronai.hashnode.dev  
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## Written by

[UJJWAL BALAJI](https://hashnode.com/@ujjwal1805)

@ujjwal1805 · Delhi, India

  
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Learning | Exploring | Currently working in Genpact as Lead Associate- EUC

I'm a 2024 Computer Science Undergrad from SRM University, specialising in AI&Data Science. Currently working at Genpact as a Lead Associate – EUC, and passionately learning AI, NLP, and ML, while building full stack projects to sharpen my skills. I enjoy reading and writing tech blogs and stay driven by a constant curiosity for all things tech.

## Responses 1

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[Mihai\_LeanZero](https://hashnode.com/@mihaileanzero)

[Sep 20](https://hashnode.com/posts/the-ai-that-refuses-to-talk-where-are-we-actually-heading/6aaed522e5d911909597e31f/comment/6aaff0d01042f545798e8a83)

The guardrail example is the part I'd push on. Using Jev to judge whether a finished tool result is still relevant only works as a guardrail if the state it's scoring is honest, and tool output is exactly the kind of state that isn't always honest. A manipulated or adversarial tool result could carry language engineered to read as "relevant" to whatever schema the guardrail is scoring against, and the "doesn't treat state as hostile" nuance you already flagged for accuracy applies with extra weight here, not just for ordinary task quality. With an LLM judge you at least sometimes get a chain of reasoning you can inspect for something off; with Jev you get a probability and nothing else to look at, so there's less surface to catch the manipulation on. Doesn't undercut the speed/cost case for routing or triage, but I'd want a second, cheap sanity check in front of anything sitting between untrusted output and the rest of an agent loop rather than trusting the score alone.