The 7.5 Billion AI Without Chat

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SPEAKER_4 (1:26)

TypeSafeAI just raised $870 million at a $7.5 billion valuation for an artificial intelligence model that specifically avoids talking to human beings.

SPEAKER_5 (1:38)

Yeah, the model is called Jev. It was launched literally weeks ago by a team of heavyweights.

And somehow, almost 30% of the Fortune 500 is already using it to run their backend software.

SPEAKER_4 (1:49)

Which brings up the central question for you as you look at this sector. If the biggest tech companies in the world are already fast following with exact copies of this architecture, does simply being the first to cut out the chat interface entirely justify a $7.5 billion price tag?

SPEAKER_5 (2:06)

Well, you look at the core metric they released to justify that.

They stated they accidentally served trillions of tokens a day within those first few weeks of launch. That volume of processing is the mechanism they used to capture 29.4% of the Fortune 500

SPEAKER_4 (2:22)

We should probably look at the people driving this to understand where that volume came from. The founders are Diogo Almeida, Sasha Sheng and Eric Spock Gaffney.

Almeida came out of OpenAI. He's one of the co-inventors of Chat GPT, GPT-4, RLHF and Instruct GPT.

SPEAKER_5 (2:38)

Right, so you have the exact engineering team that built the conversational paradigm we currently live in. They built the systems that essentially taught artificial intelligence how to speak to us.

SPEAKER_4 (2:48)

RLHF being reinforcement learning from human feedback, the process that actually made chat bots polite and helpful.

SPEAKER_5 (2:56)

Exactly. Because before RLHF, a language model was essentially just an aggressive autocomplete engine.

If you gave it half a sentence, it would just guess the next word based on internet data. Which meant it could easily start spitting out random Wikipedia articles or highly toxic forum posts.

SPEAKER_4 (3:15)

Right. It didn't know how to behave.

SPEAKER_5 (3:17)

Yeah. So RLHF changed that. They hired humans to sit there and grade the model's responses. They gave it a thumbs up when it acted like a helpful assistant and a thumbs down when it went off the rails.

That process trained the model to prioritize a very specific conversational tone.

SPEAKER_4 (3:34)

And InstructGPT was the architecture that layered on top of that, allowing the model to follow a user's instruction step by step, rather than just predicting text. So these three founders literally wrote the playbook on making AI chatty.

SPEAKER_5 (3:50)

And their new company is built entirely on the premise that making AI chatty is a severe limitation for enterprise software. They're building the next iteration by actively removing the conversational layer they invented.

SPEAKER_4 (4:03)

I do want to push back on the word accidentally when we talk about their launch volume. They claim they accidentally serve trillions of tokens a day.

If you work in enterprise software, you know that capturing a third of the Fortune 500 is a grueling process.

SPEAKER_5 (4:19)

Oh, absolutely. It requires endless sales meetings, security reviews, compliance checks. You do not accidentally integrate your software into the core backend of 30% of the largest companies on earth in a matter of weeks.