Jev is (likely) a diffusion model. It seems to match other diffusion approaches in speed and performance (@InceptionLabs, @googlegemma, @adityagrover_) I built Jiffy to test whether an open diffusion model could reproduce similar decision performance. It got surprisingly close without task-specific retraining. On 231 public JevBench tasks: • Jiffy: 84.4% • Jev’s published results: 86.6% • Jiffy latency: 259 ms median on an H100 (so likely can be substantially improved just with better hardware). After days of distillation experiments, my strongest accuracy-latency result was essentially a thin wrapper around frozen DiffusionGemma. Open source, including Doom and Tetris demos: github.com/kidzik/jiffy

I did my PhD in mathematics. Today, work similar to what took me three years can be done in a week with a few prompts. I'm not particularly bothered by that. What I'm more anxious about is how this changes who gets to contribute. Mathematics felt meritocratic: with a paper, a pencil, and access to a library, you could take a serious shot at an important open problem. As research becomes something we can purchase as computation, access to capital will increasingly determine who can compete. How do we even know whether an open problem requires a new idea or simply a larger token budget? Many other disciplines will likely follow. More money buys more computational capacity. That capacity produces discoveries. Those discoveries can generate more money, which buys still more capacity. One could say it's always been like that, but as we replace humans with compute, this loop will accelerate, and inequality will widen.

Will you be loyal after this one more token top-up?

So where is the approved AI drug? I asked Claude to answer with a song, and here is what it made. @InSilicoMeds is getting close!

Arrow's information paradox: revealing information lets buyers assess its value, but can also give away what you hoped to sell. Jev shows a version of this in AI. TypeSafe's launch demonstrated the opportunity to customers and competitors alike. Open implementations are already appearing: Jev is a small model, so it's relatively cheap to build by distilling a large open-source one. When software is cheap to reproduce, part of the investment goes into figuring out what's worth building. Launching helps you earn that investment back and gives everyone else a clearer target reducing their discovery costs.

Jev made AI exciting again. Everyone is so tired of frontier labs and their giant models; fast intelligence which you can (almost) reproduce on a single GPU, play with, and experiment with feels like fresh air for hackers.

Why would arguably the most technologically advanced companies on Earth advocate a slowdown? Because staying at the frontier can become a financial liability. You have to keep spending to defend your position, even when staying ahead no longer justifies the cost. Cutting back alone risks handing the market to your competitor. The numbers in the image are hypothetical, but the incentives are straightforward. Both companies land in the red because losing the race costs more than continuing to spend. They land in a so-called Nash equilibrium, even though both would earn more in the green square. Regulation could move them there by imposing a slowdown.

It’s exciting to live in times when you can solve literally any open problem if only you have access to anonymized notes of a researcher working on that problem and $22M worth of tokens 🫢

Hyperrealistic visualization of vibe coding.

The rate of progress of AI in healthcare is so fast that I now have to update this jpeg annually.

🎯 𝗜𝗻 𝟮𝟬𝟬𝟲, 𝗚𝗟𝗣-𝟭𝗥 𝘄𝗮𝘀 𝗯𝗮𝗿𝗲𝗹𝘆 𝗼𝗻 𝘁𝗵𝗲 𝗿𝗮𝗱𝗮𝗿. Fast forward to 2025: GLP-1R has more new trials than any other target. The targets getting quiet investment today will define the next decade.