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.<p>EDIT: As I wrote this Microsoft just released their own Decision-1 model [3].<p>[1] <a href="https://developers.openai.com/api/docs/guides/decisions" rel="nofollow">https://developers.openai.com/api/docs/guides/decisions</a><p>[2] <a href="https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unsloth">https://unsloth.ai/docs/basics/train-your-own-decision-model...</a><p>[3] <a href="https://commandline.microsoft.com/microsoft-decision-1-model-foundry/" rel="nofollow">https://commandline.microsoft.com/microsoft-decision-1-model...</a>
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.<p>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<p>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<p>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<p>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
The instruction tuning between 3 and 3.5 was a big deal. If you prompted raw GPT 3 "Tell me a story about a mouse", it was as likely to continue "2) Tell me a story about a cow. 3) Tell me a story about a rabbit" as it was to tell you a story. I tried to use GPT 3 for work and found its only utility was to get over blank page fear by writing something so bad I would fix it in anger.
It was impressive, but not that much. If you had a basic prompt prefix like:<p>“The following is a conversation between a human and a helpful AI assistant.<p>Human: [prompt]\n
AI:”<p>You would get consistent conversational chat, with the main limit being the model’s limited context window, and obviously frontier intelligence at the time.<p>I played around quite a bit with davinci-003 and conversational systems before ChatGPT. I kept using this format and API for a while, but at least during the ‘free research preview’ era, found it generally smarter than chatgpt. 3.5-turbo was probably the watershed moment where I moved away from completions on base models; to chat completions.<p>If you’d like to emulate this experience, spin up a base (not instruction tuned) checkpoint of Llama 1; or a more recent base model. You might underestimate how much ‘intelligence’ you can get :)<p>Back then the API exposed a lot of controls from sampling to logits, so you could specify a custom stop token (some rarely used Unicode); then switch to near-greedy sampling for more reliable “function calls”, etc; and then switch back to sampling for chat.<p>In the early days, before releasing something with the API, you had to get your use case approved by OpenAI, like the App Store review process.
i read that quote as "how it looks is more important than <i>if</i> it works"
> There's the potential for an inverse LLM play.<p>Exactly.<p>If JEV is here to stay, we can have another split along the "one LLM rule them all" regime<p>name it worker or implementator or something<p>- mostly for agent with with spec (not directly for human, as we tend be handwave with vague intent)
- follows the instruction verbatim
- lives in sandbox by default
- basically sol6; but WITHOUT magical situational awareness, lets work in group BS
> Decision models have the potential to have an even larger impact on the Real World than LLMs have to this point<p>Why?
A major limitation of directly using "System 1 decision models" (a.k.a., logistic regression, and related uncalibrated classifiers over the output/logit space) in enterprise settings (or other high-stakes settings) is that such estimators are not reliable estimators of the predictive uncertainty in the presence of covariate shifts, and such estimators also lack a means of instance-wise data attribution (i.e., interpretability-by-exemplar), so they're not the ideal estimator for verification, routing, uncertainty over retrieval and tool-calls, etc.<p>For decision-making with neural networks, we instead need the older idea of estimators of the predictive uncertainty with constraints in the feature-representation space (over training/support of the estimator), as with Similarity-Distance-Magnitude estimators: <a href="https://pypi.org/project/reexpress-sdm/" rel="nofollow">https://pypi.org/project/reexpress-sdm/</a>
Is it me or so far all the current HN replies to this particular “why” ask seem not even remotely answering it?
I just wanted to point out that I have started to note this "phenomenon", seemingly globally, on any topic.<p>The sum of people not even close to the topic has risen, according to my feels at least.<p>Has this rang true for anyone else?
it's not just you.<p>the claim is extraordinary, yet the answers cover only the mundane.
Because answering the why reduces investor hype train
If youre the one or have spoken to someone implementing "AI solutions" inside large companies recently, a decent chunk of it is soft policy enforcement with very basic context. They moved from gemini 2.5 flash lite type models to jev. Which is also why I found the price comparisons to "GPT Astra" on Twitter rather funny.
Right, example being expense pre-approvals (for low dollar value purchases like office equipment). General purpose LLM not required, and the product experience surrounding it does matter (reporting, auditing, fine tuning future responses, etc.).
I think you aptly describe the benefits this approach can offer.<p>What I haven’t seen answered is why it isn’t reproducible within very short amount of time and low effort.<p>Sure, the large labs are the lazy incumbents at this stage. But any other startups would be able to copy the experience. What’s the barrier of entry that I am missing?
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.<p>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.<p>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.<p>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.<p>Counterpoint: there are a <i>lot</i> 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.<p>Here if anyone is interested: <a href="https://pantel.is/projects/ai-gaming-companion/" rel="nofollow">https://pantel.is/projects/ai-gaming-companion/</a>
Honestly, I don't feel the least bit of excitement here and I'm normally enthusiastic about AI.<p>Yes, this generates probabilities over a set of given options instead of the whole token vocabulary.<p>I don't see what's so exciting about it, compared to regular LLMs which support structured output options in the API.
LLMs have higher accuracy and are more versatile, but that comes with a cost (slower and more expensive to run). On the other end you can have your own mini classifier something built on top of ModernBert or DIET or even a hash classifier (these are narrow).<p>Models like JEV fall in the golden middle. Somewhat cheap, somewhat fast, somewhat general.<p>Many people have replied saying that narrow classifiers (eg bert) are better, and they are better in many ways (essentially free and faster). But... the real world is messy, in my experience it's actually very hard to make a really good classifier that will work across a specific niche domain especially when there is very fuzzy input. And the most interesting real world scenarios are nuanced and overtime there will be edge cases discovered where it fails and that will require retraining and re-evaluation etc etc. It can becomes a full project on its own that eventually collapses into a game of whackamole (improved in some direction but regressed elsewhere).
Things like JEV (or similar models) provide ease of mind, just off load the complexity to it and move on to the next challenge type of thing.
You’re correct, it’s a great solution for a very narrow set of high volume classification needs that require very low latency. But that’s it.<p>I keep evaluating Jev for my product because of the hype, but the reality is that for my tasks, Luna is more accurate, only a little more expensive, and the latency doesn’t matter. I’d rather spend the extra $50 / month or whatever than have to shoehorn in another API and provider, and also lose the ability to change reasoning level and get reasoning summaries for eval purposes.
>> 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<p>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.<p>Precisely this. Should be top comment.<p>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.
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.<p><a href="https://seldon-ai.com/blog/fronter-llms-are-semantic-interpreters" rel="nofollow">https://seldon-ai.com/blog/fronter-llms-are-semantic-interpr...</a>
Because the American stock market is all overvalued stuff with monopoly money.
Marx called this <i>fictitious capital</i>.<p>> Fictitious capital could be defined as a capitalisation on property ownership. Such ownership is real and legally enforced, as are the profits made from it, but the capital involved is fictitious; it is "money that is thrown into circulation as capital without any material basis in commodities or productive activivity".<p><a href="https://en.wikipedia.org/wiki/Fictitious_capital" rel="nofollow">https://en.wikipedia.org/wiki/Fictitious_capital</a>
But Jev established the branding and investors are betting that Jev will be acquired by one of the big labs soon - and if they aren't, the money itself can create a positive outcome by allowing Jev to hire incredible talent and scale the company rapidly.
Most of them appear to be small LLM’s fine tuned for the role.<p>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.<p>It’s not just about the accuracy of the result, it’s a collection of all the properties that make Jev interesting.<p>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).
Jev did not take years to develop. What it does was published in arxiv back in 2025. TypeSafe just marketed it.
> 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<p>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?<p>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<p>Plenty has been said about this claim. If you're still falling for this, I feel sorry for you.<p>If you remove wheels from your car, your car will get a "no speeding ticket" property, and yet there's nothing exciting about it.<p>> You can’t get that with a fine tuned LLM<p>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.<p>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.
I’m not claiming you can’t get some of the properties in some ways with LLMs, just that Jev is the sum of its parts, not just one or two properties.<p>LLMs can produce structured output by limiting the next token based on a grammar, that ensures correctly formed output.<p>But (presumably, again I haven’t seen Jev’s insides) Jev doesn’t need to do that, it just has to output probabilities for each answer, and can do it natively without artificially limiting output tokens.<p>So jev is cheap, fast, never produces malformed output, its output always carries correct semantic meaning (but this doesn’t mean it always answers correctly), and it separates correct from prompt (which if it truly does that is the most exciting part).
Took me about 20 seconds to prompt inject the demo instance.... y'all would be great scammer targets
Jev is almost certainly not immune to prompt injection
It separates context/state from prompt/questions. That’s a key ingredient for prompt injection hardening. If it is designed and trained to never treat state as instruction, and you only ever put untrusted user input into state, and never the questions, then why wouldn’t it be?<p>The jev documentation says this is what you should do. It doesn’t mean that it’s actually designed for prompt injection resistance, but it could be. We won’t know for sure until they either release more details or someone proves otherwise.<p>But the split is one that doesn’t exist in normal LLMs and it’s the exact split that is needed for immunity or resistance to prompt injection.
Jev outperforms clef and OpenAI decisions on most of my tasks, outside of when I need multi modal image going into it. Jev is also cheaper but costs are minimal overall.
I just dont understand why anyone needs a general purpose classifier. Just build a classifier for your specific use cases.<p>Fortunately Jev is cheap, so I dont think it matters too much, but I think its robbing people of the opportunity to learn and implement this themselves.<p>Also, I dont really want 3 companies responsible for censorship/classification.
That's why it's useful, it's for when you don't know what needs to be classified or if you want to offer this classification models to your users.
Because training your own model requires substantial effort around data annotations, and then dealing with data drift etc in production.
Hm I had built out a system that tested a few hundred decisions and clef beat Jev pretty handily, I think there was actually 0 instances where clef regressed and it improved about 20% of the false decisions.
> OpenAI's own Decisions API [1] beats it<p>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.
I have ran a bunch of evals on production use cases where we are using Jev and OpenAI's model isn't close, but I'm hopeful about some of the OSS ones. IDK it's very cheap so I don't really feel like I need to look for an alternative. Why root for OpenAI over Typesafe? Isn't more diversity in the market good?
Isn’t the OpenAI decisions API basically just Luna cosplaying a decisions model and pretending the confidence score isn’t just a hallucination?
> you can easily finetune your own<p>no, you can't, and it's unclear why you would think this.
I see Typeface AI have 30-40 people working for them on LinkedIn (some are VC advisors / board members), a lot of them being engineers. Their openings suggest a strong developer market focus (to begin with). I don't necessarily see a lot of people at the company with enterprise sales channel experience, but they already claim to have several Fortune 500 companies as clients (maybe the VCs are helping there or there is natural dev-driven traction).<p>So they clearly have a product, a strategy around it and perhaps the compliance scaffolding (SOC2 Type II etc) that may be needed before actually being able to charge money for it. They also have the right brand names associated with the founding team. Execution, so far, seems good enough to create a splash, at least.<p>As an investor, the question(s) to ask is (in my view): "How do they make money? Will that way to make money survive?". The answer to the first: selling input tokens and perhaps subscriptions/credits eventually.The answer to the second: "Yes, but with the risk of unit revenues declining faster than their unit costs". How can they mitigate this problem: by being big (scale / mindshare etc) so that their unit costs (including for customer acq) fall faster than their unit revenues will - I believe that is the question most AI companies are trying to tackle these days. Any new competitor will have to tackle basic fixed costs (of getting started) first before even getting to the stage of having the luxury of worrying about unit-economics.<p>So yes, they might eventually be competed away but whoever is in their team is trying hard to make a useful product/ecosystem and that should be applauded, not ridiculed with "it's all marketing". This is way more than a simple github/huggingface-based open-source replica solution can hope to achieve without institutional backing (either big-tech or system-integrators).<p>What should rightly be questioned, of course, are the valuations the VCs are providing to them in hopes of passing this hot potato to a willing buyer (say a hardware maker like NVidia) - the incentives there are very well defined and depend very much on perceived TAM (which lately is on very shaky ground given how far token pricing has fallen causing, among other things, OpenAI to "miss" on the market's expectations for annualized revenues, even before they're listed!) [1]<p>[1]: <a href="https://www.ft.com/content/b66a9858-f8fb-46cb-b506-44bfe26fca2a?syn-25a6b1a6=1" rel="nofollow">https://www.ft.com/content/b66a9858-f8fb-46cb-b506-44bfe26fc...</a>
Currently you can’t even add a VAT ID to your account, leading to invalid invoices for all EU customers.<p>So yeah, not too much enterprise sales experience there for sure.
An Enterprise client at that size can just be someone at an f500 put a credit card in
It's possible that Jev has more in the pipeline. I mean, for shipping something so paradigm-shifting, this would be a bet on the founder/team continuing to do things faster or better.
Paradigm-shifting? A classifier? It's great and it's a cool use of Transformers, but AI really didn't only start with LLMs. We used to have an own term for AI models that can handle text instead of just numbers, now a model that outputs numbers instead of text is considered paradigm-shifting.
Testing I have seen from internal teams have shown it's still the best and cheapest model so far for pure classification work.
Did you actually try them, I tried a sample they were rubbish compared to Jev - I'm not saying they will survive, but the competition CURRENTLY sucks - and also they aren't actually cheaper if you need them at scale. OpenAI is coming in twice the cost and so is Cloudflare. Ad yes you can finetune, and yes I do, but it also SUCKS. Fine-tuning and managing your own datasets is yet another timesink. So we'll see, but it's not quite black and white.
Yeah, tried a couple open source ones, same experience. Seems to me a ton of people are in a mad dash to create one of these and most seem to have over fit on the Jev benchmark to get some claim to fame of beating them or replicating their couple years of work in two days or whatever.<p>Then everyone breathlessly repeats this story about how Jev is useless because open source models they never tried claim to do the same thing and better.
Counterpoint: my work has already allowed us to call and test Jev. Those others? Who knows when, if ever.
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<p>* <a href="https://jeffyclassify.com/" rel="nofollow">https://jeffyclassify.com/</a><p>* <a href="https://playground.jeffyclassify.com/#doom" rel="nofollow">https://playground.jeffyclassify.com/#doom</a><p>* <a href="https://github.com/nicobrenner/jeffy" rel="nofollow">https://github.com/nicobrenner/jeffy</a>
because all that is needed is to delete the outer while loop. it's an llm that is only run once and generates just one token. But it outputs confidence levels! Yeah confidence levels are the <i>only</i> output from an llm
And there are dozens of open-weight LLMs, but OpenAI and Anthropic are both immensely valuable because their models are actually intelligent.
You are right in terms of how fast competition created alternatives.<p>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.<p>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.
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.
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.<p>such an arrangement can end up beneficial to the VC firm
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.<p>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.<p>The Microsoft article GP linked even shows the MS model having 95ms latency.<p>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).<p>Cloudflare's Clef-flash model is actually slightly cheaper: "$0.038 in / $0 out per 1M" too.<p>Jev is being matched or exceeded in performance and price within a month of them going public.
> OpenAI's own Decisions API [1] beats it<p>Jev is 42$/B but OpenAI is 100$/B token.
I mean I'm already ditching my Apple stocks because soon AI will be able to replicate iOS and MacOS.
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.
"It doesn't matter"<p>By all means, become an A16Z LP.
Nothing beats nepo, brother.
OpenAI's "Decisions" library has this in requirements:<p>To run the SDK examples below, use these OpenAI SDK versions or later: Python 3.26.0,<p>I thought Pythin 3.15.0 just came out, 3.26.0 must be really far off?
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?<p>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<p>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).
> On top of that they have very strong marketing muscle that took the AI world by storm.<p>Not them per se, but doomers.ai [0] which is a uh... "launch virality agency".<p>So less that Typesafe has a strong marketing muscle, and more that they paid at least $100K [1] for "organic" buzz.<p>[0] - <a href="https://doomers.ai/work/typesafe-ai-case-study" rel="nofollow">https://doomers.ai/work/typesafe-ai-case-study</a><p>[1] - <a href="https://doomers.ai/guides/ai-marketing-agency" rel="nofollow">https://doomers.ai/guides/ai-marketing-agency</a>
They don't lead on latency nor quality; but their execution was superb
A great team is now worth $7.5B?<p>They can be both great and over-valued at the same time.
This is all due to 40% marketing, 50% execution and 10% credentials (with the founders being associated with creating ChatGPT).<p>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".<p>Rebranding, execution, marketing, ex-<big_name_company> and mostly importantly, hype is what gets the investors scrambling into throwing money at you.
The model itself is a negligible part of the valuation. The company is priced as an acquisition target.
Wonder if they can get coin flips and dice rolls to make sense with this fresh funding, or if it even matters to people.<p>I have no faith in the technique if it cannot do the basics (i.e. not real probabilities, the confidence for coin flip outcomes)<p>tried it a couple of days ago here: <a href="https://jevplayground.com" rel="nofollow">https://jevplayground.com</a><p>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<p>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.<p>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.<p>But that does nothing to make up for the terrible investment community. Getting started here requires already being started.<p>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.<p>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.<p>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 <i>someone</i> 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.<p>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.<p>[1] not really but they did some innovative things and popularized a concept
Its not normal time in SF/Bay Area. For better or worse, VCs in this city/region are thinking very differently on AI bets.<p>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.<p>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.
> Its not normal time in SF/Bay Area. For better or worse, VCs in this city/region are thinking very differently on AI bets.<p>"Throw a bunch of money at copycats in the trend of the day" is very normal in SF/SV for the last several decades, going back to the dotcom boom.<p>The definition of "a bunch" has changed but so many "crypto for X" or "recommendations for X" or "uber for X" or "social for X", etc, things raised amounts that seemed wildly divorced from their market position.
> was already available<p>no, it was not<p>> was duplicated within a couple of days<p>was it already available or did it become available in a couple of days? it cant be both (neither is true, actually)
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.
This is the difference between a "hot" company in a good ecosystem like SF. Yeah, the funds can take 2-3 months to do their due diligence. By the time it's done, the round has closed, and then what good is the due diligence?
They got money and it needs to be put to work!
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.
You are used to dealing with companies where the money bags hold the power.<p>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.<p>Also, Canadian VCs are bottom of the barrel as far as VCs go.
It might be the people that are being acquired too, at huge inflated ai researchers salaries.<p>Acquired in the vc sense… not literal exit.
<i>> there is shit ton of due diligence at the tech level</i><p>Maybe in some cases. But counterexample, courtesy of The Information:<p><i>"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."</i><p><a href="https://www.theinformation.com/articles/blue-owl-eyes-new-deals-pushes-deeper-ai-boom" rel="nofollow">https://www.theinformation.com/articles/blue-owl-eyes-new-de...</a><p>AI seems to make some people lose their damned minds.
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.<p>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?<p>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.<p>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 ?
The API was duplicated, the results were not.
I mean, a large majority of "pro jev" posts I see purely focus on the speed/cost and take their word on the accuracy. The entire product is "we guarantee the response will fit this schema, and will do it fast and cheap". Personally, I dont see how anything in this class can be "production ready" as a general classifier without the ability to fine tune on top.
That is not how it works in the US.
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?<p>And if you could put the words "Bay Area" or "Stanford" or "San Francisco" next to your name... different story.<p>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.<p>Canadian investors want you to have already succeeded before they help you succeed a tiny bit more.
What was the moat of Dropbox? Of Instagram? Of GitHub? Or of countless other very successful startups when they started?<p>Anyone know when this "have no moat" meme appeared? Even 5 years ago I don't remember seeing it on every post.
Whether a product has network effects makes a difference.<p>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.<p>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: is I have to move my entire collection and setup the app on all my devices<p>Instagram: I have to move all my posts and also convince all my network to move over<p>Github: not a terrible example<p>If I decided to move off of jev tomorrow it would be an api key and a base api path update. Maybe 30 seconds of work.
I meant more what was their moat when they launched, compared to their clones.<p>You can say the same thing about OpenAI/Anthropic, just an endpoint update, maybe a harness change if you use the CLI.<p>People have in fact been saying "what is the moat of OpenAI/Anthropic", yet here we are, trillion dollars valuation.
Instagram clearly did have one - Zuck had to acquire after internal efforts failed.
Dropbox had a super smooth UX that somehow nobody else replicated (seriously Google wtf). Instagram and Github won on network effects.
When you frame it as if we're in the Pets.com era of AI the continued gold rush makes sense.
brb gonna wrap claude with a new form of prompting and raise 500 mil
Is Jev being astroturfed on HN? It certainly feels like it. It's a middling product with virtually no moat (but <i>great</i> marketing).
Yes, it's astroturfed everywhere (like X and reddit).<p><a href="https://www.youtube.com/watch?v=xNgQtzEl4lY" rel="nofollow">https://www.youtube.com/watch?v=xNgQtzEl4lY</a><p>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.<p>See: doomers.ai, clickstrike, growth matrix, etc. They use coordinated engagement, paid influencer networks, customized messaging, etc.<p>Jev isn't a terrible product, but it's way overhyped.
Just found out from a previous comment: <a href="https://doomers.ai/work/typesafe-ai-case-study" rel="nofollow">https://doomers.ai/work/typesafe-ai-case-study</a><p>Obscene marketing is all you need it seems.
Cool, yeah my spidey sense was definitely going off. It’s a half-decent idea, but it does appear that marketing is all you need.<p>Kind of sad seeing so many “tech people” (including here on HN) falling for it hook, line, and sinker.
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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).<p>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 <i>or</i> 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
Oracle needs to hurry up and get downgraded to junk status at which point it will raise shit and at which point it will finally send off the avalanche of normalization across this industry.<p>There needs to be cleansing with fire. Weeds need to die. Trees need their branches cut. The sooner the better.<p>A shitty ass random "ai" startup built entirely on hype and astroturfing should not be raising anywhere near this amount of money.
If Typesafe really wants to do some good in the world, it'll raise money for those in need.
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.<p>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.
Their job opening as Safety Javeroni was a good laugh too [0]<p>[0] <a href="https://jobs.ashbyhq.com/typesafe-ai/9a94651c-5d63-4e82-8854-d1177b22e87a" rel="nofollow">https://jobs.ashbyhq.com/typesafe-ai/9a94651c-5d63-4e82-8854...</a>
Has anyone actually eval'd the other open source options against Jev on real world tasks rather than looking at benchmarks?<p>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.<p>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.
There is significant congregation of investment towards companies like this whereas many other with meaningful innovation are struggling to survive. The model is not a revolution like I must say how llm did at the face value. Yes, transformers were there for long but the use case etc. If you do cost comparison using regular model and setting it with prompt and structured output you can get result very close. This is what most open Jev and other are doing with tuning llm.
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.
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?<p>OK, I'm jealous... Lol
Hopefully some of the Typesafe hype spills over this way to Jeffy[0]<p>Open source[1]. It comes with 68 pre trained classifiers which run and train on CPU alone. They run locally, are faster than Jev/Laya/Decisions, and they can perform many different tasks; from labeling email, all the way to playing Doom[2]<p>[0] <a href="https://jeffyclassify.com/" rel="nofollow">https://jeffyclassify.com/</a><p>[1] <a href="https://github.com/nicobrenner/jeffy" rel="nofollow">https://github.com/nicobrenner/jeffy</a><p>[2] <a href="https://playground.jeffyclassify.com/#doom" rel="nofollow">https://playground.jeffyclassify.com/#doom</a>
What edge do they have over the market to justify such evaluation
Every time I see these headlines I wonder why the Scala company is back in the news
Remember when reaching $1B valuation made you an exotic "unicorn"?
I think this is a hedge against major AI regulation.<p>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.
I wonder how much of it has been earmarked for astroturfing on HN and X? :D
Their headline says "TypeSafe A raises series AI". Is that AI slope?
I guess they are investing in the team rather than on their demo product
Tbf this is THE way to get cost-effective AI into video games (think enemy AIs and NPC AIs that are much better than current ones and runnable on consumer GPUs), i'm really looking forward for decision models maturing.
Can someone who actually knows these things share how might a company like this spend $870M over the years?
They already got Sherlocked by OpenAI:<p><a href="https://developers.openai.com/api/docs/guides/decisions" rel="nofollow">https://developers.openai.com/api/docs/guides/decisions</a>
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.<p>A lot of people are shouting about how Jev hasn't actually differentiated itself, but I question how much folks are <i>actually</i> experimenting with what's out there before coming up with an opinion.<p>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<p>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.<p>Then I used traditional ML and found a small classifier (gemma 4) with traditional embeddings worked 2x as well.<p>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.
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.<p>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.<p>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.
The amount of investment being thrown at this when we have serious existential problems as a species is how I know the system is definitively broken. Capitalism got us this far, but it's time for something new.
That press release made me cringe a bit. Maybe corporate speak wasn't so bad after all.
interestingly , it destroy the landscape of Chinese models.<p>Unless china takes leadership in frontier space the picture is next :<p>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
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.
From Andreessen the Trump donor. Sad, will switch to something else now.
A few year ago you could IPO at this valuation.
I only use Jev at the tool-calling layer to keep tool costs low, but I don't really see what the big advantage is.
~7B for a thing that we already have open-source?
I’ll just use open source, thanks
I built JEV-search and JEV-chat and have been really happy with the results…<p>Curious to see how all of these can work together, and giving LLMs the ability to deploy their own workflows and built type 1 systems as they go has also been fun and useful.
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.
Is there a second wave of AI bubble happening? How can an AI classifier company be worth of $7.5B
Lol
Good job typesafe.<p>You won the competition with VCs
Another data point confirming that AI is a bubble.
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Disclosure: I work at H2O.ai.<p>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: <a href="https://benchmarkheaven.com/jev-models" rel="nofollow">https://benchmarkheaven.com/jev-models</a> . Newer models coming even larger than beat Jev in intelligence as well.<p>- 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.<p>Weights, card and run instructions: <a href="https://huggingface.co/h2oai/h2o-lightning-4b" rel="nofollow">https://huggingface.co/h2oai/h2o-lightning-4b</a>
Is it possible to quantize the model? Don't think I want to run 8 gigs on my local just for decisions but curious about it for sure
Update: Microsoft just announced Microsoft-Decision-1 (a post-trained Qwen3.5-9B) and benchmarked it against H2O-Lightning-4B, among others: <a href="https://commandline.microsoft.com/microsoft-decision-1-model-foundry/" rel="nofollow">https://commandline.microsoft.com/microsoft-decision-1-model...</a><p>One note on their speed comparison: the JevBench board shows self-hosted models with an adjusted latency of "2x + 0.15 s (assumption, not measured)". Our measured p50 is 29 ms; the adjusted figure is 0.21 s. They report 85 ms for theirs.
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