It's weird because two days after Jev was released there were a dozen decision models, a week later there are several dozen, mostly open source, OpenAI's own Decisions API [1] beats it, and you can easily finetune your own [2]. But as others have pointed out, this doesn't matter.

EDIT: As I wrote this Microsoft just released their own Decision-1 model [3].

[1] https://developers.openai.com/api/docs/guides/decisions

[2] https://unsloth.ai/docs/basics/train-your-own-decision-model...

[3] https://commandline.microsoft.com/microsoft-decision-1-model...

Everyone appears surprised by this news. It’s clear that they don’t have a product with some incredible moat. But they clearly have good engineering and product people that came up with a product people wanted. On top of that they have very strong marketing muscle that took the AI world by storm. And as far as I’ve seen, they still lead in some part of the latency-quality (-cost) curve?

They may well be a good team to throw money behind if you are hoping to bet on a new AI lab.

I really don't understand how this can be. I have sat in fund raising meetings with VCs in toronto and my experience is that there is shit ton of due diligence at the tech level. a product which has no moat, was already available, was duplicated within a couple of days is valued at 7B - i thought we were past the peak of the hype cycle.

Is Jev being astroturfed on HN? It certainly feels like it. It's a middling product with virtually no moat (but great marketing).

Jev does seem to have become the Kleenex of decision models. Is brand recognition worth $7.5B? There are lots of other decision models out there that perform at or near jev-level (laya, gliner 2.5 decide, even embedding gemma 2) that you can also run locally, and honestly I think this kind of model makes the most sense running locally as well. Maybe if TypeSafe can ship fast they can stay the default. Guess we'll find out.

Has anyone actually eval'd the other open source options against Jev on real world tasks rather than looking at benchmarks?

I see a lot of people parroting the quick open source alternatives as being better on the benchmarks, but it's such a new category that I'm not convinced we have solid benchmarks.

I'm hoping a company releases an internal eval benchmark for these options. I'm sure some of the open source ones are solid in some cases, but would love to see more reliable data.

I think this is a hedge against major AI regulation.

Invariably near-AGI systems created by OpenAI/Anthropic will be very destabalizing. In the end the world will probably regulate AI capable of [any] <-> [any] input/output types. Models will need to be limited on their outputs by law so they cannot have unbounded, unpredictable outcomes. Jev is the ideal version of "benefits of AI without making humans obsolete" that might be the consensus once the track superhuman AI and its consequences are clear.

Whether they can compete on decision models or not, TypeSafe showed that a lot of the market had missed something important. With this much money, they have a lot more chances to discover other important things that are missing.

Remember when reaching $1B valuation made you an exotic "unicorn"?

That press release made me cringe a bit. Maybe corporate speak wasn't so bad after all.

What edge do they have over the market to justify such evaluation

Every time I see these headlines I wonder why the Scala company is back in the news

Their headline says "TypeSafe A raises series AI". Is that AI slope?

What’s interesting about the Jev moment isn’t just Jev, it’s the unleashing of distillation / fine tuning outside the frontier-adjacent labs. It’s the sudden explosion of a million Jevs.

If being an “AI Researcher” is a ticket to multimillion dollar salary, AI training talent cannot be contained to a handful of companies. It’ll become more common and diffuse. The old advice of not fine tuning, because it’s hard, goes out the window as that knowledge diffuses through the industry.

A similar thing is happening in search. For a long time labs have trained tailored embedding models. And now companies like SID training their own agentic models that are smaller and faster at search than GPT-5.

Can someone who actually knows these things share how might a company like this spend $870M over the years?

I wonder how much of it has been earmarked for astroturfing on HN and X? :D

They already got Sherlocked by OpenAI:

https://developers.openai.com/api/docs/guides/decisions

interestingly , it destroy the landscape of Chinese models.

Unless china takes leadership in frontier space the picture is next :

1. cheap workhorses for classification, routing, other scenarios : Jev 2. coding agents with less erros : Anthropic/Openai, etc. 3. Science /Legal/Medical : A mixture of Jev+Anthropic scenarios

A few year ago you could IPO at this valuation.

This looks like the top of the dot-com bubble...

~7B for a thing that we already have open-source?

Do they have patents over jev related tech or something valuable to justify this?

got the recruiter call only to essentially be summarily rejected because my pedigree is wack. looks like i would've gotten hosed on valuation anyways.

I’ll just use open source, thanks

So... are they worth more, or less than 0xide

Only just now I realized that "TypeSafe AI" are the people behind Jev, and "System One" isn't the company name, as I assumed, but a larger project label.

Good job typesafe.

You won the competition with VCs

Is there a second wave of AI bubble happening? How can an AI classifier company be worth of $7.5B

Another data point confirming that AI is a bubble.

Disclosure: I work at H2O.ai.

We released an Apache-2.0, open-weight 4B decision model that scores above Jev 1.13 on JevBench's composite score (72.5 vs 71.5) and is currently the top open model there: https://benchmarkheaven.com/jev-models . Newer models coming even larger than beat Jev in intelligence as well.

- Same contract as Jev: state + typed questions in, calibrated probabilities out, one forward pass, no generated tokens. - Your data never leaves your environment, and there's no per-call fee.

Weights, card and run instructions: https://huggingface.co/h2oai/h2o-lightning-4b

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