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.
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.
But Jev established the branding and investors are betting that Jev will be acquired by one of the big labs soon - and if they aren't, the money itself can create a positive outcome by allowing Jev to hire incredible talent and scale the company rapidly.
Most of them appear to be small LLM’s fine tuned for the role.
That’s a different set of properties in terms of size, cost, and latency. Jev (apparently, not like I’ve seen its insides) is extremely cheap, extremely fast, doesn’t cost any output tokens as it speaks the output natively, can’t get the output wrong because it speaks the format natively, and (presumably based on the docs), the context is separate from the question, meaning it should be immune (or at least highly resistant) to prompt injection attacks.
It’s not just about the accuracy of the result, it’s a collection of all the properties that make Jev interesting.
Jev took years to develop, I strongly doubt that a copycat that was put together within days after Jev’s release will be able to match it on a sun of its properties. Even if fine tuned LLMs can outperform it on raw accuracy.
Have you heard that from a different source than OpenAI? From what I'd heard other models haven't gotten close, and the open source ones are like running gemma4 E2B against Opus 5.5- sure, the API calls go in and are returned the same but the quality isn't close.
You are assuming that the VCs have done their due diligence. For a "hot" company like Typesafe AI, most likely little due diligence was done. That's the way it's played.
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.
There is a belief that there is going to be at least one more breakout success in startup AI labs - rather than OpenAI and Anthropic being the final word - and so investors want to own a part of whichever companies seem most likely to be that success. If you start from that premise and stack rank what company that might be, you could quite reasonably put TypeSafe toward the top of that list right now, based on the people at the company and the ability they've demonstrated to ship stuff that people care about and cut through the noise in a crowded space.
Also the situation isn't static. Investors know that the act of writing them a $870M check itself increases the chance that they'll be one of the winners, because that will attract more talent, customers, and funding to the company in a self-reinforcing cycle. And investors know that other investors know that, and that someone is going to write them that $870M check, so to some extent they're forced to think of the company as having already been successful at the fundraising and already having that momentum boost.
Only a small number of investors in the world can play the game at this level, because you have to smart enough to be right (often enough), and you have to be established enough to see the deals (be on every CEO's short list - because CEOs are only going to seriously pitch 5-10 VCs on a hot deal, if that). Otherwise you can't pull it off. Martin Casado and his team are among the few that can and I think their results reflect that.
The lack of a “moat” is mostly irrelevant because success is not decided by who can or can’t be cloned. TypeSafe invented[1] a new approach that became wildly popular almost immediately, if they can do that once, they can probably do it again. Venture capital is big bets, of course TypeSafe is going to fail, that’s inevitable, but if it has even a 10% chance of capturing 1/10th the market cap of OpenAI then it is a great investment! Plus, money means nothing any more, they’ve raised less at a lower valuation than Instinct, a personal assistant.
[1] not really but they did some innovative things and popularized a concept
This is the difference between a "hot" company in a good ecosystem like SF. Yeah, the funds can take 2-3 months to do their due diligence. By the time it's done, the round has closed, and then what good is the due diligence?
Probably the “nobody ever got fired for buying IBM” effect. If there’s a use case for the tech, buying the most well known implementation of it will always be useful for people who want credit without the threat of blame. This funding is based entirely on the hype and a bet that TypeSafe will have name recognition.
Isn't this the exact opposite? When people were saying that saying, the connotation was that IBM was an old, stodgy company that had been around forever. (These days I often think "No one got fired for choosing AWS"). Typesafe is a hot new startup that could, to my eyes, easily burst into flame or die in the next year.
> there is shit ton of due diligence at the tech level
Maybe in some cases. But counterexample, courtesy of The Information:
"It took just 15 minutes for Blue Owl executives to agree to invest up to $10 billion in future projects alongside real estate firm Primary Digital Infrastructure during their first in-person meeting two years ago, said Primary chief investment officer Bill Stein."
You are used to dealing with companies where the money bags hold the power.
When you are in the middle of a boom cycle, it's the hottest company that has the advantage. Investing in them is a matter of privilege and they get to pick and choose.
Also, Canadian VCs are bottom of the barrel as far as VCs go.
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.
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
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.
I just started experimenting with the decision models. I spun up Laya on a VM with a couple of vCPU and 6GB of RAM. I get the results in about half a second. No need for GPUs or tons of memory.
I am integrating it into the product I am building and to me it doesn't seem like there is much need to go with a SaaS for this since the requirements are so light. I just can run it in Cloud Run and get all of the scale I'll ever need, and I get to tell my customers their data never leaves my environment.
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.
They don't lead on latency nor quality; but their execution was superb
Who they trailing on quality?
I'm not the person you're asking, but:
https://benchmarkheaven.com/jev-models
According to this benchmark, Jev is currently trailing Quyet-1.0-Large and a few other hastily put-together LLM-based decision API-like setups.
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.
[1] https://developers.openai.com/api/docs/guides/decisions
[2] https://unsloth.ai/docs/basics/train-your-own-decision-model...
But Jev established the branding and investors are betting that Jev will be acquired by one of the big labs soon - and if they aren't, the money itself can create a positive outcome by allowing Jev to hire incredible talent and scale the company rapidly.
Most of them appear to be small LLM’s fine tuned for the role.
That’s a different set of properties in terms of size, cost, and latency. Jev (apparently, not like I’ve seen its insides) is extremely cheap, extremely fast, doesn’t cost any output tokens as it speaks the output natively, can’t get the output wrong because it speaks the format natively, and (presumably based on the docs), the context is separate from the question, meaning it should be immune (or at least highly resistant) to prompt injection attacks.
It’s not just about the accuracy of the result, it’s a collection of all the properties that make Jev interesting.
Jev took years to develop, I strongly doubt that a copycat that was put together within days after Jev’s release will be able to match it on a sun of its properties. Even if fine tuned LLMs can outperform it on raw accuracy.
Jev did not take years to develop. What it does was published in arxiv back in 2025. TypeSafe just marketed it.
What specific arxiv paper are you referencing here?
> you can easily finetune your own
no, you can't, and it's unclear why you would think this.
"It doesn't matter"
By all means, become an A16Z LP.
OpenAI's "Decisions" library has this in requirements:
To run the SDK examples below, use these OpenAI SDK versions or later: Python 3.26.0,
I thought Pythin 3.15.0 just came out, 3.26.0 must be really far off?
That is their Python SDK version, not Python version
> OpenAI's own Decisions API [1] beats it
Have you heard that from a different source than OpenAI? From what I'd heard other models haven't gotten close, and the open source ones are like running gemma4 E2B against Opus 5.5- sure, the API calls go in and are returned the same but the quality isn't close.
You are assuming that the VCs have done their due diligence. For a "hot" company like Typesafe AI, most likely little due diligence was done. That's the way it's played.
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.
There is a belief that there is going to be at least one more breakout success in startup AI labs - rather than OpenAI and Anthropic being the final word - and so investors want to own a part of whichever companies seem most likely to be that success. If you start from that premise and stack rank what company that might be, you could quite reasonably put TypeSafe toward the top of that list right now, based on the people at the company and the ability they've demonstrated to ship stuff that people care about and cut through the noise in a crowded space.
Also the situation isn't static. Investors know that the act of writing them a $870M check itself increases the chance that they'll be one of the winners, because that will attract more talent, customers, and funding to the company in a self-reinforcing cycle. And investors know that other investors know that, and that someone is going to write them that $870M check, so to some extent they're forced to think of the company as having already been successful at the fundraising and already having that momentum boost.
Only a small number of investors in the world can play the game at this level, because you have to smart enough to be right (often enough), and you have to be established enough to see the deals (be on every CEO's short list - because CEOs are only going to seriously pitch 5-10 VCs on a hot deal, if that). Otherwise you can't pull it off. Martin Casado and his team are among the few that can and I think their results reflect that.
The lack of a “moat” is mostly irrelevant because success is not decided by who can or can’t be cloned. TypeSafe invented[1] a new approach that became wildly popular almost immediately, if they can do that once, they can probably do it again. Venture capital is big bets, of course TypeSafe is going to fail, that’s inevitable, but if it has even a 10% chance of capturing 1/10th the market cap of OpenAI then it is a great investment! Plus, money means nothing any more, they’ve raised less at a lower valuation than Instinct, a personal assistant.
[1] not really but they did some innovative things and popularized a concept
I'd guess they justified the funding by revealing some grand scheme for a new product that they just need more runway to produce.
What was the moat of Dropbox? Of Instagram? Or of countless other very successful startups when they started?
Anyone know when this "have no moat" meme appeared? Even 5 years ago I don't remember seeing it on every post.
> was already available
no, it was not
> was duplicated within a couple of days
was it already available or did it become available in a couple of days? it cant be both (neither is true, actually)
This is the difference between a "hot" company in a good ecosystem like SF. Yeah, the funds can take 2-3 months to do their due diligence. By the time it's done, the round has closed, and then what good is the due diligence?
They got money and it needs to be put to work!
The API was duplicated, the results were not.
Probably the “nobody ever got fired for buying IBM” effect. If there’s a use case for the tech, buying the most well known implementation of it will always be useful for people who want credit without the threat of blame. This funding is based entirely on the hype and a bet that TypeSafe will have name recognition.
Isn't this the exact opposite? When people were saying that saying, the connotation was that IBM was an old, stodgy company that had been around forever. (These days I often think "No one got fired for choosing AWS"). Typesafe is a hot new startup that could, to my eyes, easily burst into flame or die in the next year.
I’m saying the bet is in them becoming THE System One Model company.
> there is shit ton of due diligence at the tech level
Maybe in some cases. But counterexample, courtesy of The Information:
"It took just 15 minutes for Blue Owl executives to agree to invest up to $10 billion in future projects alongside real estate firm Primary Digital Infrastructure during their first in-person meeting two years ago, said Primary chief investment officer Bill Stein."
https://www.theinformation.com/articles/blue-owl-eyes-new-de...
AI seems to make some people lose their damned minds.
It might be the people that are being acquired too, at huge inflated ai researchers salaries.
Acquired in the vc sense… not literal exit.
You are used to dealing with companies where the money bags hold the power.
When you are in the middle of a boom cycle, it's the hottest company that has the advantage. Investing in them is a matter of privilege and they get to pick and choose.
Also, Canadian VCs are bottom of the barrel as far as VCs go.
That is not how it works in the US.
brb gonna wrap claude with a new form of prompting and raise 500 mil
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.
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
Is Jev being astroturfed on HN? It certainly feels like it. It's a middling product with virtually no moat (but great marketing).
Their headline says "TypeSafe A raises series AI". Is that AI slope?
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.
I just started experimenting with the decision models. I spun up Laya on a VM with a couple of vCPU and 6GB of RAM. I get the results in about half a second. No need for GPUs or tons of memory.
I am integrating it into the product I am building and to me it doesn't seem like there is much need to go with a SaaS for this since the requirements are so light. I just can run it in Cloud Run and get all of the scale I'll ever need, and I get to tell my customers their data never leaves my environment.
Every time I see these headlines I wonder why the Scala company is back in the news
What edge do they have over the market to justify such evaluation
Probably their team. Ex-OpenAI people who have already proven they can ship and get buzz for what they're doing.
Upvotes and Twitter hype
100mm arr
Do they have patents over jev related tech or something valuable to justify this?
I’ll just use open source, thanks