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].
Decision models have the potential to have an even larger impact on the Real World than LLMs have to this point (which is obviously quite large). But the model itself matters less than the product experiences you build around the model, and its very likely that the incumbent labs are treating the area as something more like "oh yeah I guess we can ship that and then forget about it" rather than investing in what building business processes on decision models looks like. Unlike full language models, I don't think the primary business of Typesafe will be serving Jev at API pricing; it'll look a lot more like putting Jev at the center of a much more expensive suite of software.
There's the potential for an inverse LLM play. In contrast with LLMs, all that seems to matter is the model, and the products the labs build around the models are all really samey and boring; the same left panel list of agents, main view agent conversation, right hand extra context, and we're now in the era of everyone creating the same cutesey furry friend on top of all this tech.
pantelisk 1 hours ago [-]
Yes, the best way to think of a general classifier like this is like a smart switch statement. Essentially a "JEV" like thing becomes a sort of programming primitive. Once you see it, it's hard to not get excited.
But even if others surpass them and make better solutions, the fact that nobody was able to see it before typesafe is a testament of what they might be able to come up with next.
I sound like a fanboy but I swear I 'm unaffiliated with typesafe. I was building my own version of this way before they announced JEV (mine was ALE and it was mentioned here on HN for a bit), in use for VR gaming (so one can give commands to NPCs with voice and supports multiple commands in sequence in a single pass), but I missed the "killer usecase" of being a new primitive, like everyone else.
TLDR: There's a lot of value in thinking ahead and seeing the future. The clones are nice and exciting but they give me "I could have built this first, yes but you didn't" vibes. I hope they manage to keep it up and push the space forward again
overfeed 14 minutes ago [-]
> [...]the fact that nobody was able to see it before typesafe is a testament of what they might be able to come up with next.
Counterpoint: there are a lot of one-hit wonders, and they vastly outnumber the idea-factory people. This is not to minimize those people, a single idea can be very successful (see Zuckerberg), but it doesn't mean your subsequent ideas will also be great (see Zuckerberg)
aranelsurion 1 hours ago [-]
I remember your blog post! Thanks for writing it, was pretty cool and a practical application.
> Unlike full language models, I don't think the primary business of Typesafe will be serving Jev at API pricing; it'll look a lot more like putting Jev at the center of a much more expensive suite of software.
Precisely this. Should be top comment.
Also, the model moat is understated as training data for these purposes also accrues to the winner, which due to the first mover advantage as well as the distribution advantage you speak of, is typesafe. In contrast to relatively open coding data. Openai anthropic also have that, but like you say its a different business.
nlpnerd 2 hours ago [-]
Yeah, agreed. A model or primitive on its own has no moat and frankly limited value. The paradigm behind "System One" models on the other hand is potentially huge.
Skimming the post, it seems to argue for reconstructing the very rigidity that LLMs let us escape, and that very aspect of LLMs is what made them useful and explode in popularity so much.
ttul 2 hours ago [-]
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.
Oras 2 hours ago [-]
Rapidly? Their 2 years of stealth was replicated in 2 weeks.
I give it to them for creating the hype (good marketing), and for making a useful classifier. Not sure what they would scale rapidly though.
rsalus 2 hours ago [-]
I feel like half the game now is marketing though, so I can see why they'd be attractive to an investor. Maybe if they scale they can come up with something.
tiborsaas 32 minutes ago [-]
You can replicate any fitness app in no time and you will make close to $0. Brand recognition matters a lot.
throwaway7783 29 minutes ago [-]
100%. Product finesse + Marketing is now the "moat".
ModernMech 2 hours ago [-]
It’s the classic SV flip. You scale your investors, your executive team, your sales people, hire a bunch of engineering you don’t need, then sell the company. The company’s product doesn’t matter, the company is the product.
brink 2 hours ago [-]
Either the investors know something we don't, or the market is irrational.
2 hours ago [-]
mococa 1 hours ago [-]
> Their 2 years of stealth was replicated in 2 weeks.
Actually they stolen the idea from a paper.
shdh 38 minutes ago [-]
So did Oracle with relational databases by that logic
qsod 2 hours ago [-]
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dkersten 2 hours ago [-]
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.
devin 2 hours ago [-]
Jev did not take years to develop. What it does was published in arxiv back in 2025. TypeSafe just marketed it.
baobabKoodaa 2 hours ago [-]
What specific arxiv paper are you referencing here?
"SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization"
Jev is a general-purpose thing. That is a specific-purpose thing. General-purpose thing is not the same as specific-purpose thing. What makes people think these are the same thing? I don't get it.
devin 2 hours ago [-]
What do you mean? Jev is trivially different from what is described in this paper.
baobabKoodaa 1 hours ago [-]
Bullshit. Below is copypaste from the paper in the section that outlines the "key contributions" of the paper. As you can see, it is focused on one specific problem: predicting sales conversions. So if you were to take this system and use it for some other task ("evaluate customer mood" for example), it would not work. Because, again, it is not describing a general purpose solution. It is describing a solution that is specific to one problem: sales conversions.
Copypasta:
• A reinforcement learning architecture specifically designed for sales conversation analysis and conversion
prediction
• A synthetic data generation pipeline leveraging GPT-4O to create diverse and realistic sales conversations
• Novel state representation techniques using Azure OpenAI embeddings (3072 dimensions) with sales-specific
features
• A meta-learning approach enabling the system to express confidence in its predictions based on conversation similarity to training data
• Integration mechanisms providing real-time guidance within existing sales platforms
This is a fine-tuned model. The author even states that the model is competitive with Jev only if fine-tuned on the evaluation at hand.
Literally misses the point of Jev, which you don't need to fine-tune to get accuracy nor - and no other model has this - some sort of out of sample calibration
hbrn 2 hours ago [-]
> 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
But why? If the simplest way to achieve Jev's capabilities (accuracy, cost, latency) is by fine tuning a small model, what makes you think that this isn't exactly what Typesafe did?
And even if they did something different - what makes you think it was a good idea in the first place, given how easy their results were replicated without any "secret sauce"?
dkersten 51 minutes ago [-]
My point is that it’s not replicated. You replicate the accuracy, but not the other properties. The Jev-competitors only proved that you can get or beat the accuracy, nothing about the other properties. Especially the “zero hallucination” output and the (if it works how the documentation make it sound) prompt injection resistant architecture. You can’t get that with a fine tuned LLM.
hbrn 25 minutes ago [-]
> zero hallucination
Plenty has been said about this claim. If you're still falling for this, I feel sorry for you.
If you remove wheels from your car, your car will get a "no speeding ticket" property, and yet there's nothing exciting about it.
> You can’t get that with a fine tuned LLM
Of course you can. All these claims are nothing but marketing.
TeMPOraL 49 minutes ago [-]
Jev is something your favorite LLM could zero-shot months ago, if you pointed it to the right arXiv paper (some of which are linked in this thread).
girvo 35 minutes ago [-]
Counterpoint: my work has already allowed us to call and test Jev. Those others? Who knows when, if ever.
baobabKoodaa 2 hours ago [-]
> you can easily finetune your own
no, you can't, and it's unclear why you would think this.
ricericerice 2 hours ago [-]
you can easily finetune your own*
*if you have a sufficiently sized and quality dataset for the specific classifications you're targeting
baobabKoodaa 2 hours ago [-]
And even if you do have that, you haven't made your own Jev, because Jev is a general-purpose thing, whereas what you have built is a specific-purpose thing.
scottyah 2 hours ago [-]
> 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.
jgilias 53 minutes ago [-]
Isn’t the OpenAI decisions API basically just Luna cosplaying a decisions model and pretending the confidence score isn’t just a hallucination?
hbrn 12 minutes ago [-]
And what do you think Jev confidence score is?
Here's a hint: confidence is not generated by a model.
phalangion 50 minutes ago [-]
What’s the difference?
throwaw12 1 hours ago [-]
You are right in terms of how fast competition created alternatives.
But, for OpenAI this is not a primary business, for open source models as well, so they will not be chasing the market and customers to buy their product and promise them to maintain it.
TypeSafe will do all this, they will try to understand your use cases and then solve your pain point, while others are providing raw material.
bushbaba 1 hours ago [-]
A major VC could type safe ai money, then head to a larger AI company looking to raise their series E+ and demand they acquire typesafe as part of their funding allotment.
such an arrangement can end up beneficial to the VC firm
mlmonkey 2 hours ago [-]
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?
bayesianbot 2 hours ago [-]
That is their Python SDK version, not Python version
2 hours ago [-]
2 hours ago [-]
user3939382 30 minutes ago [-]
Investments aren’t made because the product is amazing, they’re made because there’s a compelling exit scenario. Engineers don’t want to hear this but more generally, the critical success factors for a business aren’t product or engineering they’re relationships i.e. sales and team dynamics. If technical excellence dictated business outcomes in tech Salesforce wouldn’t exist for example.
hkalbasi 2 hours ago [-]
> OpenAI's own Decisions API [1] beats it
Jev is 42$/B but OpenAI is 100$/B token.
amelius 2 hours ago [-]
I mean I'm already ditching my Apple stocks because soon AI will be able to replicate iOS and MacOS.
doctorpangloss 2 hours ago [-]
"It doesn't matter"
By all means, become an A16Z LP.
moralestapia 2 hours ago [-]
Nothing beats nepo, brother.
nlpnerd 2 hours ago [-]
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.
christina97 2 hours ago [-]
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.
binlog 11 minutes ago [-]
The model itself is a negligible part of the valuation. The company is priced as an acquisition target.
redanddead 5 minutes ago [-]
Every startup is priced as an acq target
soleveloper 2 hours ago [-]
They don't lead on latency nor quality; but their execution was superb
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.
tpetry 38 minutes ago [-]
And the 'better' ones are slower and cost more for a tiny bit more accuracy. Its hard to sell that as being better when speed and price have been JEVs main selling points.
baobabKoodaa 11 minutes ago [-]
The top alternative right now lists speed as faster than Jev?
nwhnwh 1 hours ago [-]
What even is this? What does it do?
rvz 2 hours ago [-]
This is all due to 40% marketing, 50% execution and 10% credentials (with the founders being associated with creating ChatGPT).
If anyone else came up with the same concept on a Reddit thread (they have) it no-one would care without those characteristics even if you are "first".
Rebranding, execution, marketing, ex-<big_name_company> and mostly importantly, hype is what gets the investors scrambling into throwing money at you.
verdverm 2 hours ago [-]
Wonder if they can get coin flips and dice rolls to make sense with this fresh funding, or if it even matters to people.
I have no faith in the technique if it cannot do the basics (i.e. not real probabilities, the confidence for coin flip outcomes)
the "not real probability" disclaimer only appears after you get a result
prometheus1992 3 hours ago [-]
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.
fidotron 1 hours ago [-]
> sat in fund raising meetings with VCs in toronto
There's your problem. The single biggest thing every Canadian VC is trying to figure out is "why are these people asking us for money when if they were any good they'd be in the US" so by simply asking them you're already signalling something bad. A lot of their enthusiasm for process is based on this suspicion and also that the entire industry is just a way for various professional services to extract most of the investment money, since that's the game they're so used to playing with the government.
There are some Canadian VCs earnestly trying to improve but they are overwhelmingly hilariously conservative and focused on unimportant signals over reality. This is one (but not all) of the major factors that drive basically every remotely ambitious Canadian company to run a corp in Delaware and go for funding from the US. The tax situation is the other major contributor.
redanddead 3 minutes ago [-]
Canada doesn’t have throwing around money like in the US. We have resource extraction -> export money that’s it
geoffschmidt 2 hours ago [-]
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.
ejeq 1 hours ago [-]
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reticulates 3 hours ago [-]
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
nateb2022 2 hours ago [-]
I'd guess they justified the funding by revealing some grand scheme for a new product that they just need more runway to produce.
binlog 9 minutes ago [-]
Because every VC knows that one of OpenAI/Anthropic/Nvidia/Microsoft/Google/Meta/SpaceXAI/AMD/Stripe... will acquire them within the next year for talent alone.
havercosine 1 hours ago [-]
Its not normal time in SF/Bay Area. For better or worse, VCs in this city/region are thinking very differently on AI bets.
I think the key differentiator was that a team found a whitespace in what ChatGPT was doing, main comes from the same pedigree and team is as conscious of marketing as their product. SF VCs love these out of the box challengers, and people are claiming to replicate doesn't seem to matter.
The amount raised feels surprising but again entire SF/US AI scene is primarily "add moar layers and GPU" one trick ponies at this point.
baobabKoodaa 2 hours ago [-]
> 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)
hirako2000 1 hours ago [-]
The tech behind it existed. They made it a specific product, and got replicated in days.
baobabKoodaa 1 hours ago [-]
Unclear what you're referring to. Please stop making vague claims and be specific.
prometheus1992 28 minutes ago [-]
maybe you entered the AI space during the vibecoding era but there had been ton of useful models before that. especially zero shot models- both for text and images.
baobabKoodaa 15 minutes ago [-]
Everything you said here is false. No, I didn't enter the AI space during the vibe coding era. I was training custom ML models back in 2017. And no, there haven't been models comparable to Jev before Jev was published.
Jev is:
- accurate
- general purpose
- fast and cheap
Models we had before Jev had at most 2/3 of above qualities, but none of them were 3/3.
euleriancon 54 minutes ago [-]
I think it is clear he is referring to zero shot classifiers with an LLM backbone. That tech has existed for a long time.
nlpnerd 2 hours ago [-]
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?
besterman23 3 hours ago [-]
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.
johnfn 3 hours ago [-]
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.
besterman23 2 hours ago [-]
I’m saying the bet is in them becoming THE System One Model company.
HarHarVeryFunny 1 hours ago [-]
A very major part of Jev is the cost and speed. Yes, classification is/will be a commodity business, just like LLMs are, and similarly there is no moat only production cost and pricing.
Yes, anyone can wrap a decisions API around an LLM, but so what? If you want to compete then you need to compete on price, and it's not clear if OpenAI and/or Anthropic are able or willing to do that without building a custom architecture, and even then if a race to the bottom on pricing is what they really want to pursue.
I'm not sure if OpenAI have announced pricing for their Decisions API, but they have said it's based on Luna which costs $0.10/M input, not even remotely competitive with Jev's $0.04/M input, which I'd expect has some headroom built into it.
Assuming that the architecture behind Jev is not just an LLM, and gives them some inherent efficiency/cost and speed advantage, then the question is whether OpenAI and Anthropic really want to duplicate this and have a race to the bottom on pricing for what may be a large part of the business automation market they are addressing. Is that what they want as their IPO pitch - we're selling potatoes, and think can grow them cheaper than Typesafe ?
bix6 2 hours ago [-]
They got money and it needs to be put to work!
kingcauchy 3 hours ago [-]
It might be the people that are being acquired too, at huge inflated ai researchers salaries.
Acquired in the vc sense… not literal exit.
InsideOutSanta 2 hours ago [-]
> 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."
AI seems to make some people lose their damned minds.
Onavo 3 hours ago [-]
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.
cmrdporcupine 2 hours ago [-]
Yeah your problem and my problem and others around here is the word you just said there... "Toronto." Canadian investors are risk averse as hell. And cheap. They can make more money helping sell bitumen or real estate, why bother with arcane tech?
And if you could put the words "Bay Area" or "Stanford" or "San Francisco" next to your name... different story.
The VCs are not buying the idea or the tech, they're investing in the people. And they invest in a formula that has already worked for them before to make big coin. Prop somebody up, let them hire like crazy, and then get them get acquired, and then cash out. They don't care if it fails if they can make it succeed 1/200 times.
Canadian investors want you to have already succeeded before they help you succeed a tiny bit more.
phren0logy 3 hours ago [-]
The API was duplicated, the results were not.
dvrp 3 hours ago [-]
That is not how it works in the US.
dist-epoch 2 hours ago [-]
What was the moat of Dropbox? Of Instagram? Of GitHub? 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.
hirako2000 1 hours ago [-]
Whether a product has network effects makes a difference.
I would argue Dropbox did have a moat. It didn't merely store your data. It made it possible to make backup efficiently when bandwidth wasn't all that good.
Reading "no moat" so often is also tied to the fact those companies happen to be getting surreal valuations, at a quite early stage, showing no profit, building a tech that doesn't seem difficult to reproduce.
IshKebab 58 minutes ago [-]
Dropbox had a super smooth UX that somehow nobody else replicated (seriously Google wtf). Instagram and Github won on network effects.
slopinthebag 3 hours ago [-]
brb gonna wrap claude with a new form of prompting and raise 500 mil
dvt 2 hours ago [-]
Is Jev being astroturfed on HN? It certainly feels like it. It's a middling product with virtually no moat (but great marketing).
denverllc 37 minutes ago [-]
Yes, it's astroturfed everywhere (like X and reddit).
Jev is used as an example of a successful marketing launch where they worked with many X "creators" prior to its release, so that all the creators would repost to put it to the top of everyone's feed. Then, over the following days they'd repost so it maintained momentum.
See: doomers.ai, clickstrike, growth matrix, etc. They use coordinated engagement, paid influencer networks, customized messaging, etc.
Jev isn't a terrible product, but it's way overhyped.
minimaxir 2 hours ago [-]
There have been a lot of posts/comments claiming "Jev-like models" but that's more of an shorthand for decision models, not astroturfing.
maherbeg 1 hours ago [-]
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.
This is awesome! Please submit updates when a new model comes out that people are saying are better than Jev!
dovin 2 hours ago [-]
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.
pickle-wizard 2 hours ago [-]
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.
jacobgold 2 hours ago [-]
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.
jghn 3 hours ago [-]
Every time I see these headlines I wonder why the Scala company is back in the news
stickfigure 2 hours ago [-]
Remember when reaching $1B valuation made you an exotic "unicorn"?
esafak 33 minutes ago [-]
The needle has moved. This is a wonderful thing.
redoxate 3 hours ago [-]
What edge do they have over the market to justify such evaluation
simonw 3 hours ago [-]
Probably their team. Ex-OpenAI people who have already proven they can ship and get buzz for what they're doing.
miguelacevedo 2 hours ago [-]
Same thing was said about Character AI, Cohere, etc. Companies started by the authors of the "Attention is all you need" paper. Look how that went... the team gets thrown around a lot as if it's the magic bullet. There is no magic bullet.
simonw 2 hours ago [-]
The ex-OpenAI people who started Anthropic are doing pretty great right now.
VC is a hits business. Just one hit pays for 9 that didn't work out.
denverllc 35 minutes ago [-]
Anthropic's Series A was $124 million; Typesafe's is 7x that. If Jev becomes a $2bn company it will be a failure to investors.
simonw 12 minutes ago [-]
Anthropic raised their Series A a year and a half before ChatGPT had been released, when LLMs were still unproven technology and most VCs weren't paying attention to the field (they were mostly still chasing crypto).
gavinray 3 hours ago [-]
Upvotes and Twitter hype
babelfish 3 hours ago [-]
100mm arr
rokhayakebe 59 minutes ago [-]
Can someone who actually knows these things share how might a company like this spend $870M over the years?
jypepin 2 hours ago [-]
Their headline says "TypeSafe A raises series AI". Is that AI slope?
throw03172019 2 hours ago [-]
They also say they are a fun team. Maybe it’s a pun.
We've tested the decisions API against Jev at work and it's worse in various dimensions. Costs more, higher error rates, slower, and the answers are worse.
A lot of people are shouting about how Jev hasn't actually differentiated itself, but I question how much folks are actually experimenting with what's out there before coming up with an opinion.
For us, it's cleae that OpenAI rushed this out to meet the hype in the market right now without having a product that actually meets the bar Jev has set.
denverllc 33 minutes ago [-]
> but I question how much folks are actually experimenting with what's out there
I did. Originally I had a project that I had been wanting to do and thought to use a decision model for it. Jev, OpenAI, etc. are all within percentage points of each other.
Then I used traditional ML and found a small classifier (gemma 4) with traditional embeddings worked 2x as well.
Jev is the general purpose ML pipeline for when you want average results. Nearly every application has a "better" option available with a small amount of work.
benatkin 2 hours ago [-]
They weren't running on OpenAI, so nope.
kevinkatzke 45 minutes ago [-]
A few year ago you could IPO at this valuation.
maxdo 2 hours ago [-]
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
kylehotchkiss 38 seconds ago [-]
See clef
mococa 1 hours ago [-]
~7B for a thing that we already have open-source?
enahs-sf 2 hours ago [-]
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.
bel8 3 hours ago [-]
Do they have patents over jev related tech or something valuable to justify this?
axus 47 minutes ago [-]
So... are they worth more, or less than 0xide
TeMPOraL 45 minutes ago [-]
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.
sajithdilshan 31 minutes ago [-]
Is there a second wave of AI bubble happening? How can an AI classifier company be worth of $7.5B
collimarco 48 minutes ago [-]
This looks like the top of the dot-com bubble...
robotswantdata 3 hours ago [-]
I’ll just use open source, thanks
Thorentis 40 minutes ago [-]
Another data point confirming that AI is a bubble.
pseudotensor 54 minutes ago [-]
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.
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...
There's the potential for an inverse LLM play. In contrast with LLMs, all that seems to matter is the model, and the products the labs build around the models are all really samey and boring; the same left panel list of agents, main view agent conversation, right hand extra context, and we're now in the era of everyone creating the same cutesey furry friend on top of all this tech.
But even if others surpass them and make better solutions, the fact that nobody was able to see it before typesafe is a testament of what they might be able to come up with next.
I sound like a fanboy but I swear I 'm unaffiliated with typesafe. I was building my own version of this way before they announced JEV (mine was ALE and it was mentioned here on HN for a bit), in use for VR gaming (so one can give commands to NPCs with voice and supports multiple commands in sequence in a single pass), but I missed the "killer usecase" of being a new primitive, like everyone else.
TLDR: There's a lot of value in thinking ahead and seeing the future. The clones are nice and exciting but they give me "I could have built this first, yes but you didn't" vibes. I hope they manage to keep it up and push the space forward again
Counterpoint: there are a lot of one-hit wonders, and they vastly outnumber the idea-factory people. This is not to minimize those people, a single idea can be very successful (see Zuckerberg), but it doesn't mean your subsequent ideas will also be great (see Zuckerberg)
Here if anyone is interested: https://pantel.is/projects/ai-gaming-companion/
Precisely this. Should be top comment.
Also, the model moat is understated as training data for these purposes also accrues to the winner, which due to the first mover advantage as well as the distribution advantage you speak of, is typesafe. In contrast to relatively open coding data. Openai anthropic also have that, but like you say its a different business.
https://seldon-ai.com/blog/fronter-llms-are-semantic-interpr...
I give it to them for creating the hype (good marketing), and for making a useful classifier. Not sure what they would scale rapidly though.
Actually they stolen the idea from a paper.
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.
and https://arxiv.org/abs/2510.01237
"SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization"
Jev is a general-purpose thing. That is a specific-purpose thing. General-purpose thing is not the same as specific-purpose thing. What makes people think these are the same thing? I don't get it.
Copypasta:
• A reinforcement learning architecture specifically designed for sales conversation analysis and conversion prediction
• A synthetic data generation pipeline leveraging GPT-4O to create diverse and realistic sales conversations
• Novel state representation techniques using Azure OpenAI embeddings (3072 dimensions) with sales-specific features
• A meta-learning approach enabling the system to express confidence in its predictions based on conversation similarity to training data
• Integration mechanisms providing real-time guidance within existing sales platforms
• Extensive comparative evaluation demonstrating significant performance improvements over LLM-based approaches
Literally misses the point of Jev, which you don't need to fine-tune to get accuracy nor - and no other model has this - some sort of out of sample calibration
But why? If the simplest way to achieve Jev's capabilities (accuracy, cost, latency) is by fine tuning a small model, what makes you think that this isn't exactly what Typesafe did?
And even if they did something different - what makes you think it was a good idea in the first place, given how easy their results were replicated without any "secret sauce"?
Plenty has been said about this claim. If you're still falling for this, I feel sorry for you.
If you remove wheels from your car, your car will get a "no speeding ticket" property, and yet there's nothing exciting about it.
> You can’t get that with a fine tuned LLM
Of course you can. All these claims are nothing but marketing.
no, you can't, and it's unclear why you would think this.
*if you have a sufficiently sized and quality dataset for the specific classifications you're targeting
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.
Here's a hint: confidence is not generated by a model.
But, for OpenAI this is not a primary business, for open source models as well, so they will not be chasing the market and customers to buy their product and promise them to maintain it.
TypeSafe will do all this, they will try to understand your use cases and then solve your pain point, while others are providing raw material.
such an arrangement can end up beneficial to the VC firm
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?
Jev is 42$/B but OpenAI is 100$/B token.
By all means, become an A16Z LP.
They may well be a good team to throw money behind if you are hoping to bet on a new AI lab.
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.
If anyone else came up with the same concept on a Reddit thread (they have) it no-one would care without those characteristics even if you are "first".
Rebranding, execution, marketing, ex-<big_name_company> and mostly importantly, hype is what gets the investors scrambling into throwing money at you.
I have no faith in the technique if it cannot do the basics (i.e. not real probabilities, the confidence for coin flip outcomes)
tried it a couple of days ago here: https://jevplayground.com
the "not real probability" disclaimer only appears after you get a result
There's your problem. The single biggest thing every Canadian VC is trying to figure out is "why are these people asking us for money when if they were any good they'd be in the US" so by simply asking them you're already signalling something bad. A lot of their enthusiasm for process is based on this suspicion and also that the entire industry is just a way for various professional services to extract most of the investment money, since that's the game they're so used to playing with the government.
There are some Canadian VCs earnestly trying to improve but they are overwhelmingly hilariously conservative and focused on unimportant signals over reality. This is one (but not all) of the major factors that drive basically every remotely ambitious Canadian company to run a corp in Delaware and go for funding from the US. The tax situation is the other major contributor.
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.
[1] not really but they did some innovative things and popularized a concept
I think the key differentiator was that a team found a whitespace in what ChatGPT was doing, main comes from the same pedigree and team is as conscious of marketing as their product. SF VCs love these out of the box challengers, and people are claiming to replicate doesn't seem to matter.
The amount raised feels surprising but again entire SF/US AI scene is primarily "add moar layers and GPU" one trick ponies at this point.
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)
Jev is:
- accurate
- general purpose
- fast and cheap
Models we had before Jev had at most 2/3 of above qualities, but none of them were 3/3.
Yes, anyone can wrap a decisions API around an LLM, but so what? If you want to compete then you need to compete on price, and it's not clear if OpenAI and/or Anthropic are able or willing to do that without building a custom architecture, and even then if a race to the bottom on pricing is what they really want to pursue.
I'm not sure if OpenAI have announced pricing for their Decisions API, but they have said it's based on Luna which costs $0.10/M input, not even remotely competitive with Jev's $0.04/M input, which I'd expect has some headroom built into it.
Assuming that the architecture behind Jev is not just an LLM, and gives them some inherent efficiency/cost and speed advantage, then the question is whether OpenAI and Anthropic really want to duplicate this and have a race to the bottom on pricing for what may be a large part of the business automation market they are addressing. Is that what they want as their IPO pitch - we're selling potatoes, and think can grow them cheaper than Typesafe ?
Acquired in the vc sense… not literal exit.
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.
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.
And if you could put the words "Bay Area" or "Stanford" or "San Francisco" next to your name... different story.
The VCs are not buying the idea or the tech, they're investing in the people. And they invest in a formula that has already worked for them before to make big coin. Prop somebody up, let them hire like crazy, and then get them get acquired, and then cash out. They don't care if it fails if they can make it succeed 1/200 times.
Canadian investors want you to have already succeeded before they help you succeed a tiny bit more.
Anyone know when this "have no moat" meme appeared? Even 5 years ago I don't remember seeing it on every post.
I would argue Dropbox did have a moat. It didn't merely store your data. It made it possible to make backup efficiently when bandwidth wasn't all that good.
Reading "no moat" so often is also tied to the fact those companies happen to be getting surreal valuations, at a quite early stage, showing no profit, building a tech that doesn't seem difficult to reproduce.
https://www.youtube.com/watch?v=xNgQtzEl4lY
Jev is used as an example of a successful marketing launch where they worked with many X "creators" prior to its release, so that all the creators would repost to put it to the top of everyone's feed. Then, over the following days they'd repost so it maintained momentum.
See: doomers.ai, clickstrike, growth matrix, etc. They use coordinated engagement, paid influencer networks, customized messaging, etc.
Jev isn't a terrible product, but it's way overhyped.
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.
[1]: https://tn1ck.com/blog/jevdit
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.
VC is a hits business. Just one hit pays for 9 that didn't work out.
I actually resized my browser thinking maybe something weird was going on with flex-wrap or overflow or whatever it is.
https://developers.openai.com/api/docs/guides/decisions
A lot of people are shouting about how Jev hasn't actually differentiated itself, but I question how much folks are actually experimenting with what's out there before coming up with an opinion.
For us, it's cleae that OpenAI rushed this out to meet the hype in the market right now without having a product that actually meets the bar Jev has set.
I did. Originally I had a project that I had been wanting to do and thought to use a decision model for it. Jev, OpenAI, etc. are all within percentage points of each other.
Then I used traditional ML and found a small classifier (gemma 4) with traditional embeddings worked 2x as well.
Jev is the general purpose ML pipeline for when you want average results. Nearly every application has a "better" option available with a small amount of work.
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
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