Diego Almeida, a former OpenAI researcher who helped invent the reinforcement learning techniques behind today’s most popular chatbots, recently made a surprising observation: despite their massive success, language models have failed to deliver true software automation. Chatbots are excellent at talking to humans, but computer programs speak an entirely different language.

When developers try to insert standard AI-generated text into computer code, they run the risk of formatting errors, unexpected responses, or “hallucinations” that can break entire systems. To solve this, Almeida left OpenAI, raised $40 million, and founded TypeSafe AAI. After two years of stealth development, the company recently launched “Jev”—a revolutionary new class of AI designed specifically for machines, not humans.

Machine-Native AI and “Type Safety”

Jev is fundamentally different from a traditional Large Language Model (LLM). It does not output conversational text or paragraphs. Instead, it takes unstructured information and outputs precise, structured data and probabilities (calibrated decisions) that software can read instantly.

For example, if you ask Jev which department should handle a specific customer service email, it won’t write a sentence. It will return exact data: Billing (8%), Technical (85%), and Sales (7%). While this format is useless for a person trying to have a chat, it is absolutely perfect for computer code.

Crucially, Jev guarantees “type safety,” meaning it is mathematically restricted to returning only the pre-defined data structures you ask for. It cannot hallucinate a fake response or break the code, making it an incredibly reliable engine for background automation. TypeSafe achieved this by shifting away from standard AI training methods, instead utilizing a novel approach called Reinforcement Learning for Calibrated Decisions (RLCD).

Unprecedented Speed and Minimal Cost

Because Jev generates its decisions all at once in a single parallel pass—rather than generating text word-by-word like standard AI—its speed is staggering. While top-tier LLMs take anywhere from 3 to 329 seconds to process complex queries, Jev completes its tasks in just 70 to 500 milliseconds. This makes it roughly 40 to 200 times faster than its conversational counterparts.

The pricing model is equally disruptive. Standard AI models can charge between $10 and $20 per million tokens. Jev, however, costs a mere $0.042 per million input tokens (which translates to just $42 per billion tokens), and its output is completely free.

The Future: The Jevons Paradox

The model is appropriately named after William Stanley Jevons, a 19th-century economist who observed that as a resource becomes more efficient and cheaper to use, the demand for it actually skyrockets rather than decreases.

TypeSafe AAI is betting heavily on this paradox. By dropping the cost of digital intelligence by orders of magnitude and eliminating the latency and unreliability of language models, they anticipate an explosion of “smart software”. Developers are already successfully testing Jev to instantly route data, automate complex workflows, and even play fast-paced video games in real-time.

Jev isn’t an AI you will ever have a conversation with, but it is poised to become the invisible, lightning-fast brain powering the next generation of the internet.