Frontier price wars, Decision models, & Bonsais

TLDR;

Welcome back! This week is packed with model releases, including two from OpenAI and Anthropic that dropped today. This past week also saw the launch of the first decision model, Jev, plus new Neuralink trials, market data, and products, so there’s a lot to talk about.

Must-read, in my opinion.

Enjoy!

RESEARCH

Meet Jev, the first decision model

An alternative to LLMs has arrived… although not quite, as you’re about to see. In reality, it provides a solution to a problem, automation, that LLMs have been consistently used for and never should have.

I wrote a detailed piece on Medium if you want to read it for free.

This concept is being defined as we speak, but I bet they will be called decision models, because that’s literally all they do: you give them a problem that requires a decision, and they make it.

Classifying emails, routing support tickets (as in the thumbnail), filtering tables… that’s the kind of work decision models will do.

But don’t LLMs already solve this? Well, not quite.

LLMs are too slow, expensive, non-deterministic, and non-calibrated (they consistently give overconfident responses), making them quite suboptimal solutions for automation.

Jev doesn’t generate text, images, or control humanoid minds. But it makes decisions correctly, fast, and consistently.

The price is also incredibly competitive: $0.042 per million input tokens, with output tokens literally free because there are so few that it’s too cheap to meter. This means this solution is hundreds of times cheaper than frontier models.

And did I say it’s fast? Because it’s really fast. It can output tens of decisions per second in the same period an LLM makes one. Most interestingly, decision models can be used in truly unexpected ways.

Just to show a few:

Playing Doom in real time: about 10 decisions a second from a structured game state, ~$7/hour. The clip is the actual control loop, not a write-up.

Jev playing Doom. Source: TypeSafe

An emoji searcher that shows you which emojis match your search. Paired with embeddings, this could build real recommendation systems that are fast enough and smarter than the average reranker. In fact, I believe we’ll have decision models in any product that requires search or filtering in no time.

Source

Finding Zürich → London flights on Google Flights in 7.1 seconds for $0.0039. The video is at 1× speed. You can watch the agent click, type, and land on results at incredible speed.

Classifying 500 emails in a few seconds for 3.5 cents — labels appear on screen as it runs. A longer run does 1,700 emails for 18 cents.

Playing Super Mario Bros. from an emulator state. The post is the gameplay, not the source tree.

Labeling 1,018 AI papers for $0.08 at 256 ms each, then browsing the map at 1kpapers.com.

Tearing down 724 live ads from 37 brands in 40 seconds for 9 cents — hook, format, offer, CTA, awareness stage, landing-page mismatch.

Putting jev() inside Postgres: WHERE jev(people, 'could work from home'). You…

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