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Core Concepts

Machine Native Intelligence

TypeSafe's name for AI with software-like properties: structure, reliability, observability, testability, speed, consistency, and low cost.

Category
Core Concepts
Also known as
—
Related terms
4
Directory entries
2
Docs
docs.typesafe.ai
Added
2026-09-24

Definition

The term comes from the TypeSafe AI primer. The bet behind it is that large-scale automation will be dominated by AI-to-AI and AI-to-software interactions, so the machine interface matters more than the chat interface.

Machine Native Intelligence shifts the design target from responses that read well to outputs that behave predictably inside software. Jev's typed answers and calibrated probabilities are the concrete form of that idea.

Tagsarchitecturecore

From the directory

Why TypeSafe trains decision models with RLCD instead of RLHF: calibrated probabilities where 0.2 outcomes happen about 20% of the time, and the case for machine-to-machine automation.
Sites & GuidesDocs#official#docs#evaluation
Official
TypeSafe's case for machine-native composable AI: intelligence as a dependable primitive that software branches on, rather than an assistant that keeps humans in the loop.
Sites & GuidesArticle#official#article#architecture
Official

From the community

Posts from builders shipping with Jev right now.

Follow @typesafeai

A real-time ad blocker that classifies every DOM element

I build an undetectable realtime adblocker extension with typesafe It checks every dom element and classifies as ad/non-ad and removes it if true Extremely fun to work with, expecting an incredible shift in how AI is being used in the future

Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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Jev plus Astra beats the Ender Dragon for under a dollar

Tool calling as classify plus action, back to twelve-factor agents

jev is the best excuse you could possibly have to go re-read 12 factor agents. Tool calling itself can be decomposed into classify+action, if you learn to design ai programs as pipelines that switch breathlessly between classification, structuring data, deterministic code, AND Show more

Dillon Mulroy
Dillon Mulroy
Cloudflare
@dillon_mulroy

i think jev is resonating with devs so well b/c it unlocks so many opportunities for composing ai into systems and products rather than ai _becoming_ the product/system really does feel like it was a missing primitive

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Why Jev encodes a state and outputs distributions instead

Jev is one of the more interesting model launches I have seen recently because it asks a very simple question: Why are we using autoregressive LLMs as insanely expensive if statements inside software? The easiest way to think about it is: LLM: text -> generate tokens Show more

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Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

Reply

How the agent stack moved in Jev's first three days

Jev plays Doom at ~10 calls/sec for ~$7/hour