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.
> But the model itself matters less than the product experiences you build around the model
This is a very important insight. And it applies to LLMs as well. Very few people were impressed with the capabilities of GPT 3, it was mostly a techie novelty
But then when they added chat on top of gpt 3.5, all of a sudden it was a huge hit. Sure there were improvements in the model from 3 to 3.5, but the biggest impact was from the chat experience
Conversely, when they created Eliza, a basic chatbot more than 50 years ago, people even got addicted to it, despite it’s ai model being something super rudimentary and basic compared to what we have now. The model capabilities didn’t matter as much as the experience the chat created
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
> [...]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)
>> 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
This all sounds intelligent and likely, and yet we can come up with countless counter examples where the first mover is not the big winner, and nobody cares about who did it first. There is typically way more value in nailing the execution of a big idea someone else came up with, rather than "seeing the future".
> 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.
At least in theory (TypeSafe has been pretty close-lipped about the details so this might just be hot air, and I think the evidence is a bit spotty) this is false, since they use a different reinforcement training method.
If the word “calibration” in probability doesn’t mean anything to you, the difference isn’t very apparent, but that doesn’t mean it doesn’t exist.
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.
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.
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.
Yeah but there are also network effects of using fitness apps. Every engineer I know switches easily between Codex and Claude Code when one gets better than the other.
Jev has been around for a couple weeks. Cost and performance matter more than anything. Staying with an existing provider (the # of people choosing Jev without already having a frontier API key is probably zero?) is way easier than this.
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.
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.
That probably explains why there were so many competitors around withing days of the Jev announcement. They are not starting with a moat, and there doesn't seem to be any moat in sight. Just buzzword recognition because everything is comparing to "jev".
"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.
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
> 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"?
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.
I understand most major model providers support passing a JSON schema that is strictly followed in the output, accomplishing the same 'zero hallucination' and prompt injection resistance.
The only difference I am aware of is that probabilities are better calibrated with these decision models compared to regular LLMs which can output hallucinated numbers where your schema allows a number.
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.
Yup, I also released an open source classifiers tool, Jeffy. It comes with 68 pre trained classifiers which run and train on CPU alone. They run locally and are faster than Jev/Laya/Decisions. And they can do things like label email, all the way to even playing Doom
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.
I honestly was worried for them. Now, even with all the clones, they generally still come up on top in price and latency, but "good enough" is often sufficient.
Maybe I'm missing something, but why couldn't it be generated by the model? In older classification tasks with transformers like BERT, you could absolutely obtain a confidence score.
The dice roll prediction is about the way the prompt is setup misunderstanding how Jev works (they treat the confidence score as a probability score, which it isn't).
If you instead give it a list of probability for each number and ask it whats the probability of each number, the result will be accurate.
In my experience with OpenAI's decisions endpoint, it tends to return either 0 or 1 and doesn't return middle confidence levels very much at all. Would be interested to hear if others have experienced the same.
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.
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
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.
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.
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.
> people that came up with a product people wanted
We've yet to see whether this is true, or is it just manufactured demand. There are dozens of Jev demos, but pretty much all of them are either cool but useless, or simply fake (i.e. harness doing 99% of the work).
Measuring ARR based on 7 days of data is beyond stupid.
You can create a company with 2B shares and sell one share to your friend for $1000. Lo and behold, you own $2 trillion dollar company, leaving Elon behind.
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.
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.
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.
> 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.
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
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.
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.
> Models we had before Jev had at most 2/3 of above qualities, but none of them were 3/3.
You're the one being deceptive here. Jev is trading accuracy, speed, and cost for generality. It's less accurate, slower and more expensive than trained classifiers. So it's still 2 out of 3, but with decimals. Maybe 2.2 out of 3 if I'm being charitable.
And the reason we didn't have that before is because nobody thought it's a good tradeoff.
When you say "trained classifiers", you are referring to models which are trained (or fine tuned) to work on one specific problem, right? That is the opposite of "general purpose".
Would Jev be more accurate in a specific task if it had been developed only for that task, as opposed to general purpose? Of course it would. So, sure, Jev is trading accuracy for generality. According to you "nobody thought it's a good tradeoff", which again is false, there was huge demand for a cheap and accurate general purpose classifier.
> work on one specific problem, right? That is the opposite of "general purpose".
A business doesn't need Jev for the sake of Jev. Most business are solving specific problems.
And fine-tuning got a lot cheaper these days - I've seen claims here on HN that ~500 examples is enough to beat Jev.
> "nobody thought it's a good tradeoff", which again is false, there was huge demand for a cheap and accurate general purpose classifier
There wasn't. The hope is that there was a latent demand, but we've yet to see if it's truly latent or just manufactured.
Noone is saying "hell yeah, finally we got a general purpose classifier, my business needed it so much". The typical message is "this seems cool, let me see where I can apply it".
The fact that name itself is a play on Jevons Paradox illustrates that there was no demand until Jev was released.
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.
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?
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.
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.
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 is a race to the bottom on pricing really what they 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 ?
> 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.
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.
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.
No one finds it strange that every defector from Opensi gets half a billion dollars in funding and already has a third of Fortune 500 companies paying for their product in a few weeks with a product that's already been replicated 10 times very easily?
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.
Why is it a middling product? Most clones don’t approach its performance, and it solves a specific problem well in a way that was awkward and ignored by most frontier labs.
Because Jev is basically a "generalized classifier" which.. doesn't quite make much sense. Training a classifier is pretty easy and has been done routinely for like two decades now. Classifiers also tend to be very localized; for example, I've worked on classifiers that would bucket web traffic into "potential buyer" or "potential seller"—but this was very specific to the use case (vehicles, in our case).
If I seriously needed a classifier, I would just train my own and it would run on an iPhone. Any CTO worth their salt would suggest the same, because it's not even remotely comparable to training a large language model (w.r.t. compute or training data required).
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.
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.
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.
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).
With everything you must have going on right now, I love that you’ve come on to explain your headline is an intentional joke. All sorts being thrown around in this thread, but this was definitely the important thing to clarify.
For what it’s worth, however it works out, my guess is that the primitive Jev provides is likely to be considered essential in the future development of software.
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.
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.
> 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.
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
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.
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.
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...
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.
> Decision models have the potential to have an even larger impact on the Real World than LLMs have to this point
Why?
> But the model itself matters less than the product experiences you build around the model
This is a very important insight. And it applies to LLMs as well. Very few people were impressed with the capabilities of GPT 3, it was mostly a techie novelty
But then when they added chat on top of gpt 3.5, all of a sudden it was a huge hit. Sure there were improvements in the model from 3 to 3.5, but the biggest impact was from the chat experience
Conversely, when they created Eliza, a basic chatbot more than 50 years ago, people even got addicted to it, despite it’s ai model being something super rudimentary and basic compared to what we have now. The model capabilities didn’t matter as much as the experience the chat created
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
> [...]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)
I remember your blog post! Thanks for writing it, was pretty cool and a practical application.
Here if anyone is interested: https://pantel.is/projects/ai-gaming-companion/
Honestly, I don't feel the least bit of excitement here and I'm normally enthusiastic about AI.
Yes, this generates probabilities over a set of given options instead of the whole token vocabulary.
I don't see what's so exciting about it, compared to regular LLMs which support structured output options in the API.
>> 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
This all sounds intelligent and likely, and yet we can come up with countless counter examples where the first mover is not the big winner, and nobody cares about who did it first. There is typically way more value in nailing the execution of a big idea someone else came up with, rather than "seeing the future".
> 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.
The are still just 1tok output of pretty standard llms just along with the logprobs converted to some json
At least in theory (TypeSafe has been pretty close-lipped about the details so this might just be hot air, and I think the evidence is a bit spotty) this is false, since they use a different reinforcement training method.
If the word “calibration” in probability doesn’t mean anything to you, the difference isn’t very apparent, but that doesn’t mean it doesn’t exist.
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.
https://seldon-ai.com/blog/fronter-llms-are-semantic-interpr...
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.
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.
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.
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.
You can replicate any fitness app in no time and you will make close to $0. Brand recognition matters a lot.
Yeah but there are also network effects of using fitness apps. Every engineer I know switches easily between Codex and Claude Code when one gets better than the other.
Jev has been around for a couple weeks. Cost and performance matter more than anything. Staying with an existing provider (the # of people choosing Jev without already having a frontier API key is probably zero?) is way easier than this.
What the hell are we even talking about.
100%. Product finesse + Marketing is now the "moat".
It’s like openclaw. There was a bunch of technically better ones that came along afterwards, but nobody remembers what any of them were called.
You say this, while Hermes Agent has been at top of the openrouter.ai leaderboard for several months and currently has 3X the token usage of OpenClaw.
What? OpenClaw has been completely replaced by Hermes and others in the discourse
And Muse is what normal people use instead
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.
> Their 2 years of stealth was replicated in 2 weeks.
Actually they stolen the idea from a paper.
So did Oracle with relational databases by that logic
Either the investors know something we don't, or the market is irrational.
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 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).
That probably explains why there were so many competitors around withing days of the Jev announcement. They are not starting with a moat, and there doesn't seem to be any moat in sight. Just buzzword recognition because everything is comparing to "jev".
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?
https://arxiv.org/abs/2503.23303
and https://arxiv.org/abs/2510.01237
Yeah, I figured it was gonna be this one.
"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.
What do you mean? Jev is trivially different from what is described in this paper.
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
• Extensive comparative evaluation demonstrating significant performance improvements over LLM-based approaches
> Novel state representation techniques using Azure OpenAI embeddings (3072 dimensions) with sales-specific features
Jev is basically the embeddings side of an LLM. Yes, it's a good idea, but the moat is non-existent.
No. It can't simultaneously be both general purpose and having task-specific embeddings.
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
> 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"?
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.
> 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.
I understand most major model providers support passing a JSON schema that is strictly followed in the output, accomplishing the same 'zero hallucination' and prompt injection resistance.
The only difference I am aware of is that probabilities are better calibrated with these decision models compared to regular LLMs which can output hallucinated numbers where your schema allows a number.
> 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.
Yup, I also released an open source classifiers tool, Jeffy. It comes with 68 pre trained classifiers which run and train on CPU alone. They run locally and are faster than Jev/Laya/Decisions. And they can do things like label email, all the way to even playing Doom
* https://jeffyclassify.com/
* https://playground.jeffyclassify.com/#doom
* https://github.com/nicobrenner/jeffy
> you can easily finetune your own
no, you can't, and it's unclear why you would think this.
you can easily finetune your own*
*if you have a sufficiently sized and quality dataset for the specific classifications you're targeting
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.
I honestly was worried for them. Now, even with all the clones, they generally still come up on top in price and latency, but "good enough" is often sufficient.
Guess the (investment) market has spoken.
Hm? Jev's actual (network) latency is not that great and even small local models are doing far better latency-wise.
The Microsoft article GP linked even shows the MS model having 95ms latency.
Even on price Jev being matched (the same MS model is "Input tokens cost $0.042 USD per million tokens. Output tokens are free.", same as Jev).
Cloudflare's Clef-flash model is actually slightly cheaper: "$0.038 in / $0 out per 1M" too.
Jev is being matched or exceeded in performance and price within a month of them going public.
Isn’t the OpenAI decisions API basically just Luna cosplaying a decisions model and pretending the confidence score isn’t just a hallucination?
And what do you think Jev confidence score is?
Here's a hint: confidence is not generated by a model.
Maybe I'm missing something, but why couldn't it be generated by the model? In older classification tasks with transformers like BERT, you could absolutely obtain a confidence score.
Jev API returns both confidence and probabilities.
But confidence value is just a function applied to probabilities. It is not coming from the model, and it carries no additional information.
It is documented btw, and yet you will see plenty of claims that Jev is better than LLM because it returns both.
Thanks, fixed my understanding!
Do you think though that Luna being a model post-trained for chat produces over-confidence in logprobs?
Yeah, but I wouldn't be surprised OpenAI's decision API is a post-trained Luna with confidence calibration.
Typesafe claims that Jev is calibrated, but there are plenty of examples where it completely fails (predicting die roll being the most obvious one).
Unfortunately calibration is hard to benchmark.
The dice roll prediction is about the way the prompt is setup misunderstanding how Jev works (they treat the confidence score as a probability score, which it isn't).
If you instead give it a list of probability for each number and ask it whats the probability of each number, the result will be accurate.
What’s the difference?
Counterpoint: my work has already allowed us to call and test Jev. Those others? Who knows when, if ever.
In my experience with OpenAI's decisions endpoint, it tends to return either 0 or 1 and doesn't return middle confidence levels very much at all. Would be interested to hear if others have experienced the same.
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.
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
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
Jev is 42$/B but OpenAI is 100$/B token.
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.
I mean I'm already ditching my Apple stocks because soon AI will be able to replicate iOS and MacOS.
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.
"It doesn't matter"
By all means, become an A16Z LP.
Nothing beats nepo, brother.
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.
> people that came up with a product people wanted
We've yet to see whether this is true, or is it just manufactured demand. There are dozens of Jev demos, but pretty much all of them are either cool but useless, or simply fake (i.e. harness doing 99% of the work).
since their release they surpassed 100M arr and 1/3 of the F500
its clear they own the mindshare around this type of primitive which is a massive premium
Measuring ARR based on 7 days of data is beyond stupid.
You can create a company with 2B shares and sell one share to your friend for $1000. Lo and behold, you own $2 trillion dollar company, leaving Elon behind.
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.
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.
The top alternative right now lists speed as faster than Jev?
Here's another leaderboard: https://huggingface.co/spaces/multimodalart/jev-decision-ind...
What even is this? What does it do?
The model itself is a negligible part of the valuation. The company is priced as an acquisition target.
Every startup is priced as an acq target
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.
More like 10% 10% 80%.
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)
tried it a couple of days ago here: https://jevplayground.com
the "not real probability" disclaimer only appears after you get a result
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.
> 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.
Canada doesn’t have throwing around money like in the US. We have resource extraction -> export money that’s it
There are boatloads of tax breaks and incentives that mitigate all the financial stuff and make running a startup here just fine honestly.
But that does nothing to make up for the terrible investment community. Getting started here requires already being started.
When I briefly worked for a Toronto startup, it was like all of them went to the same private boy's schools together as kids. It was a status club.
I jumped ship to an American startup and made almost double the money dealt with 0% of the bullshit and they were bought by Google the next year.
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.
> 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)
The tech behind it existed. They made it a specific product, and got replicated in days.
Unclear what you're referring to. Please stop making vague claims and be specific.
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.
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.
> Models we had before Jev had at most 2/3 of above qualities, but none of them were 3/3.
You're the one being deceptive here. Jev is trading accuracy, speed, and cost for generality. It's less accurate, slower and more expensive than trained classifiers. So it's still 2 out of 3, but with decimals. Maybe 2.2 out of 3 if I'm being charitable.
And the reason we didn't have that before is because nobody thought it's a good tradeoff.
When you say "trained classifiers", you are referring to models which are trained (or fine tuned) to work on one specific problem, right? That is the opposite of "general purpose".
Would Jev be more accurate in a specific task if it had been developed only for that task, as opposed to general purpose? Of course it would. So, sure, Jev is trading accuracy for generality. According to you "nobody thought it's a good tradeoff", which again is false, there was huge demand for a cheap and accurate general purpose classifier.
> work on one specific problem, right? That is the opposite of "general purpose".
A business doesn't need Jev for the sake of Jev. Most business are solving specific problems.
And fine-tuning got a lot cheaper these days - I've seen claims here on HN that ~500 examples is enough to beat Jev.
> "nobody thought it's a good tradeoff", which again is false, there was huge demand for a cheap and accurate general purpose classifier
There wasn't. The hope is that there was a latent demand, but we've yet to see if it's truly latent or just manufactured.
Noone is saying "hell yeah, finally we got a general purpose classifier, my business needed it so much". The typical message is "this seems cool, let me see where I can apply it".
The fact that name itself is a play on Jevons Paradox illustrates that there was no demand until Jev was released.
>>I was training custom ML models back in 2017
but you truly do sound like an angry 19 year old from your arguments.
- accurate - on what? on trust me bro benchmarks?
- zero-shot model are fundamentally general purpose.
- fast and cheap ; models on hf are FREE and fast enough.
I think it is clear he is referring to zero shot classifiers with an LLM backbone. That tech has existed for a long time.
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.
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?
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.
They got money and it needs to be put to work!
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.
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 is a race to the bottom on pricing really what they 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 ?
It might be the people that are being acquired too, at huge inflated ai researchers salaries.
Acquired in the vc sense… not literal exit.
> 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.
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.
The API was duplicated, the results were not.
When you frame it as if we're in the Pets.com era of AI the continued gold rush makes sense.
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.
That is not how it works in the US.
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.
Instagram clearly did have one - Zuck had to acquire after internal efforts failed.
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.
Dropbox had a super smooth UX that somehow nobody else replicated (seriously Google wtf). Instagram and Github won on network effects.
brb gonna wrap claude with a new form of prompting and raise 500 mil
No one finds it strange that every defector from Opensi gets half a billion dollars in funding and already has a third of Fortune 500 companies paying for their product in a few weeks with a product that's already been replicated 10 times very easily?
OK, I'm jealous... Lol
Is Jev being astroturfed on HN? It certainly feels like it. It's a middling product with virtually no moat (but great marketing).
Yes, it's astroturfed everywhere (like X and reddit).
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.
Why is it a middling product? Most clones don’t approach its performance, and it solves a specific problem well in a way that was awkward and ignored by most frontier labs.
Because Jev is basically a "generalized classifier" which.. doesn't quite make much sense. Training a classifier is pretty easy and has been done routinely for like two decades now. Classifiers also tend to be very localized; for example, I've worked on classifiers that would bucket web traffic into "potential buyer" or "potential seller"—but this was very specific to the use case (vehicles, in our case).
If I seriously needed a classifier, I would just train my own and it would run on an iPhone. Any CTO worth their salt would suggest the same, because it's not even remotely comparable to training a large language model (w.r.t. compute or training data required).
There have been a lot of posts/comments claiming "Jev-like models" but that's more of an shorthand for decision models, not astroturfing.
distribution is a moat
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.
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 found this benchmark helpful: https://benchmarkheaven.com/jev-models
I also benchmarked them here [1] for content moderation. Jev is better than any of them.
[1]: https://tn1ck.com/blog/jevdit
This is awesome! Please submit updates when a new model comes out that people are saying are better than Jev!
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"?
The needle has moved. This is a wonderful thing.
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.
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.
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.
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.
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).
Upvotes and Twitter hype
100mm arr
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?
They also say they are a fun team. Maybe it’s a pun.
Jokes all around: https://jobs.ashbyhq.com/typesafe-ai/9a94651c-5d63-4e82-8854...
intentional to make it fun (:
With everything you must have going on right now, I love that you’ve come on to explain your headline is an intentional joke. All sorts being thrown around in this thread, but this was definitely the important thing to clarify.
For what it’s worth, however it works out, my guess is that the primitive Jev provides is likely to be considered essential in the future development of software.
Is it a pun or something? I have been called dense.
I actually resized my browser thinking maybe something weird was going on with flex-wrap or overflow or whatever it is.
That press release made me cringe a bit. Maybe corporate speak wasn't so bad after all.
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
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.
> 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.
They weren't running on OpenAI, so nope.
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
See clef
A few year ago you could IPO at this valuation.
This looks like the top of the dot-com bubble...
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.
~7B for a thing that we already have open-source?
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