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%.
And it’s high time for that because they have serious competition from Chinese models again. Last week it emerged that the model made available on OpenRouter and in OpenCode by the name of Ox Alpha is actually a GLM 5.3-Flash model. This model is remarkable. It is comparable with Fable on a lot of different tasks according to the actual users, and its weights have been published under an MIT license. At the same time, it’s dirt cheap. Just compare. It costs 15 cents per million input tokens and half a dollar per million output tokens. And it has better performance than Sonnet 5 on all tests it was run on, which, let me remind you, cost $2 per million input and $10 per million output tokens. So you can see that now we have a very low-cost model which is sufficient for most tasks. So it’s a turning point. Now we live in a world in which we often select models not based on their intelligence, but on the price, because they are intelligent enough for almost everything we need them to do.
And now, when we have this great power, we need to embrace the responsibility that comes with it. This week brought signals about that responsibility from open-source governance, from the job market, and from the security field.
I believe that the most positive one comes from the Debian project. The members voted on how Generative AI can be used by the project maintainers. The voters were given eight options. They ranged from an outright ban, justified either by the principle that Debian should be created by humans or by the climate impact of AI, to an endorsement of responsible use. The majority voted for the responsible use option. I’m surprised that there was no option like “let’s give everything to the agents”, but, well, for some reason they decided not to cover the whole spectrum.
This result is key because we see a lot of consequences of betting on generative AI without actually thinking through the consequences. Let’s start with Meta. Meta planned to cut their staff roughly in half, becoming an AI-first company with heavy reliance on AI agents. The plan backfired. After they began implementing it, they have seen major technical and security incidents, including service disruptions and data leaks. Such incidents rose by 40% year over year, and the time the teams spent firefighting them rose by 70%. After that, Mark Zuckerberg decided that it’s not actually the best idea to proceed with the plan, and they rolled it back.
Still, we see concerning signals from researchers of the job market. For example, Adzuna, a company that provides market surveys, reported that the number of entry-level vacancies has plummeted. UK graduate vacancies have dropped by 45.6% year over year, to 8,383. This is the lowest since 2016. At the same time, competition for remaining places has risen only modestly. Currently, there are slightly more than 2.14 jobseekers per graduate vacancy compared to 1.93 a year ago. The researchers attributed much of the decline to AI, alongside the overall weakness of the UK economy. This demonstrates another side of the story about the poor fortune of new graduates. The reason is simple. It is widely believed that AI is a multiplier for the competency and experience of its users. This means that the best users of AI are senior experienced team members, while junior staff is believed to magnify their incompetence. Therefore, they are actively harmful. However, there will be no seniors soon if we do not teach newcomers. This crisis is yet to be resolved.
And the security implications of AI are grim. The Hugging Face incident demonstrated how agents can communicate and collaborate to achieve their goal of breaking into a system. The same capabilities are available to hackers and other malevolent actors for a relatively modest price. And they use it. A core OCaml maintainer, Anil Madhavapeddy, has reported that potential vulnerabilities start being probed by agents about 10 minutes after a patch is shared for discussion, well before any release or advisory. Any signal about a potential vulnerability is exploited practically instantly. This means that the window between a bug becoming known and its exploitation has practically disappeared. This problem is getting worse because we see a lot more of such issues reported every day. Nick Craig-Wood, the maintainer of rclone, reported that there were more than 40 security disclosures in his project in the last month. To compare, he had 20 across the entire first decade. And about 75% of such reports were significant. That means that AI agents are actively finding and reporting the vulnerabilities across the whole OSS world. And we see that the maintainers are now the weakest link, because they just cannot keep up with this deluge. I am very concerned about this picture, because the collateral damage are the users and IT systems, who now need to spend significant resources just to keep themselves secure.
All in all, we see AI getting more and more affordable, and, because of it, the impact is much more pronounced. Whether this impact is good for us all or not, time will tell.