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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 Jev established the branding and investors are betting that Jev will be acquired by one of the big labs soon - and if they aren't, the money itself can create a positive outcome by allowing Jev to hire incredible talent and scale the company rapidly.

Most of them appear to be small LLM’s fine tuned for the role.

That’s a different set of properties in terms of size, cost, and latency. Jev (apparently, not like I’ve seen its insides) is extremely cheap, extremely fast, doesn’t cost any output tokens as it speaks the output natively, can’t get the output wrong because it speaks the format natively, and (presumably based on the docs), the context is separate from the question, meaning it should be immune (or at least highly resistant) to prompt injection attacks.

It’s not just about the accuracy of the result, it’s a collection of all the properties that make Jev interesting.

Jev took years to develop, I strongly doubt that a copycat that was put together within days after Jev’s release will be able to match it on a sun of its properties. Even if fine tuned LLMs can outperform it on raw accuracy.

> you can easily finetune your own

no, you can't, and it's unclear why you would think this.

Counterpoint: my work has already allowed us to call and test Jev. Those others? Who knows when, if ever.

Isn’t the OpenAI decisions API basically just Luna cosplaying a decisions model and pretending the confidence score isn’t just a hallucination?

> OpenAI's own Decisions API [1] beats it

Have you heard that from a different source than OpenAI? From what I'd heard other models haven't gotten close, and the open source ones are like running gemma4 E2B against Opus 5.5- sure, the API calls go in and are returned the same but the quality isn't close.

You are 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?

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.

> OpenAI's own Decisions API [1] beats it

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.

"It doesn't matter"

By all means, become an A16Z LP.

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.

Nothing beats nepo, brother.

> 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).

They don't lead on latency nor quality; but their execution was superb

The model itself is a negligible part of the valuation. The company is priced as an acquisition 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.

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

> 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

> 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)

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.

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?

When you frame it as if we're in the Pets.com era of AI the continued gold rush makes sense.

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.

A very major part of Jev is the cost and speed. Yes, classification is/will be a commodity business, just like LLMs are, and similarly there is no moat only production cost and pricing.

Yes, anyone can wrap a decisions API around an LLM, but so what? If you want to compete then you need to compete on price, and it's not clear if OpenAI and/or Anthropic are able or willing to do that without building a custom architecture, and even then if a race to the bottom on pricing is what they really want to pursue.

I'm not sure if OpenAI have announced pricing for their Decisions API, but they have said it's based on Luna which costs $0.10/M input, not even remotely competitive with Jev's $0.04/M input, which I'd expect has some headroom built into it.

Assuming that the architecture behind Jev is not just an LLM, and gives them some inherent efficiency/cost and speed advantage, then the question is whether OpenAI and Anthropic really want to duplicate this and have a race to the bottom on pricing for what may be a large part of the business automation market they are addressing. Is that what they want as their IPO pitch - we're selling potatoes, and think can grow them cheaper than Typesafe ?

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.

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.

The API was duplicated, the results were not.

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.

brb gonna wrap claude with a new form of prompting and raise 500 mil

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.

There have been a lot of posts/comments claiming "Jev-like models" but that's more of an shorthand for decision models, not astroturfing.

I found this benchmark helpful: https://benchmarkheaven.com/jev-models

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.

The needle has moved. This is a wonderful thing.

Probably their team. Ex-OpenAI people who have already proven they can ship and get buzz for what they're doing.

Upvotes and Twitter hype

100mm arr

They also say they are a fun team. Maybe it’s a pun.

intentional to make it fun (:

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.

They weren't running on OpenAI, so nope.

See clef