chronicles

It’s Monday, and so it’s time for the next issue of Chronicles. Last week was very significant because it represented a tectonic shift from using expensive, efficient models to cheap and still very efficient models. In other words, AI is getting democratized.

Let’s start with the hardware. This week we’ve seen interesting news from OpenAI, which published the first benchmarks for their Jalapeño chip, a custom inference chip designed with Broadcom. It’s pretty impressive. They published over 700 tokens per second per user on DeepSeek R1 at concurrency of one, and about 1400 on Kimi K2.5 and GPT-OSS. The variety here matters because the chip has been benchmarked not only on OpenAI’s own models but on open-weight models from other vendors as well. The chip is critical in the ongoing price war with the Chinese providers. Remember that last week we had data on cutting prices on their Sol model by 20% on input and 33% on output, which is a promotional window that expires on 21 November, and a week before that they cut the price of Luna by a whopping 80%.

Last week was all about the harness. Gone are the times when the models were all things-in-themselves. Now, as with early Homo erectus, the tools are the factor of survival. If we look at the types of harness-related posts and announcements, we will see three trends.

Harness products

Several companies have released either agents or infra for agents at once.

OpenAI has released the execution framework that their Codex (the CLI one) is based on. Unsurprisingly called Harness, it is their answer to the Claude Agent SDK and the GitHub Copilot SDK. The Harness provides the execution loop, memory, tools, and other necessities of agentic life. You can use it in three different ways. First, you can just use codex exec to run non-interactive jobs. Second, you can use the Codex SDK for building workflows. And last, you can use app-server to build apps that require conversation handling. One point worthy of attention is that they claim that the Harness improved the performance of the GPT-5.6 Sol model from 13.3% to 38.3% on ARC-AGI-3. It’s unclear what harness (not capitalized) was used as a baseline, though.

Over the last week, we’ve seen some announcements from both the highest-end and lowest-end sides of open-weight models. At the same time, labs are starting to think that maaaybe, just maybe, we move too fast and we need to stop and think. So, in their free time, they are starting price wars.

Models, hi

Last week we saw a bunch of releases of very large open-weight models. Z.ai shipped GLM-5.3, which is relatively small, just 743B, DeepSeek published their 1.6T V4-Pro weights under the MIT license, and Alibaba surprised with a whopping 2.4-trillion-parameter Qwen3.8. Although they are open, doing anything meaningful with models of this size requires hardware not a lot of individuals have, which defeats their openness a little. On the other hand, they open possibilities for 3rd-party hosting and incentivize the price wars I will talk about below.

This article opens a series (I expect it to become one) of posts in which I review the AI-related events of the past week and try to figure out how they fit into the bigger picture.

The main highlights of the past week are:

1. We continue to see real-world security breaches caused by AI system evaluations, and it looks like another race.

2. Major regulations came into force.

3. Data centres face increasing opposition from local communities. Companies seek workarounds.