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:
We continue to see real-world security breaches caused by AI system evaluations, and it looks like another race.
Major regulations came into force.
Data centres face increasing opposition from local communities. Companies seek workarounds.
Memory hierarchy for AI gets a redesign.
Rogue agents on the loose
You probably remember the story of a security agent evaluation that led to the hacking of Hugging Face. Last week we saw a similar story from Anthropic, and now Meta and the UK AI Security Institute (AISI) join them. What makes the AISI incident different is that they didn’t even try to restrict internet access in their evaluations. A little bit of a reckless move, from my perspective. There is another common thread: an evaluation partner of all three AI giants, Irregular, also reported such incidents in the evaluation of their models.
As if in response, Washington has finalized the Cyber Frontier Framework, which structures voluntary 30-day pre-release government access. Later, it exempted open-weight models from this review. So, unlike closed-model providers, who are not forced to go through the review, open-weight models are not forced to go through the review… Wait, what. Anyway, there is a commonly held belief that open-weight models lag behind the best closed ones by about 7 months. So in early 2027 we can expect them to have capabilities similar to Mythos. I expect that this exemption will not hold for long.
EU AI Act and California AI Transparency Act
Speaking of regulations, the 2nd of August marked a milestone for them. Several articles of the EU AI Act went into effect. For now we are mainly talking about transparency of AI systems (although some other articles, such as Art. 4 on AI literacy, are now actually enforceable). California synchronized its efforts with the EU and adopted an amended AI Transparency Act, AB 853.
In general, those acts require providers of any generative AI systems capable of generating text (EU only), images, video or audio to watermark the generated content, and to provide tools that a) can reliably detect those watermarks and b) are interoperable with other systems.
Deployers (read: users) have a different set of responsibilities. They have to annotate any published AI-generated
text on matters of public interest (whatever that means)
audio or visual materials that can reasonably be mistaken for reality (deepfakes)
with a human-readable marking indicating AI provenance. There is a carve-out for text that underwent significant editorial review and for which someone takes responsibility.
I greatly simplified the requirements to fit them in a couple of paragraphs, and they actually differ in several specifics, but the gist is there. If you develop or provide such systems to your employees, you will need a deeper analysis.
Nobody wants a data centre near their home
We’ve seen societal pushback against data centres, as they generally cause electricity prices to skyrocket and compete for drinking water. Now we see actions from governments themselves. The governor of Texas ordered an audit of all data centres going through the interconnection queue. No project is allowed to proceed until the audit finishes. For reference, there are more than 1,800 projects that would consume 474 gigawatts in total. That’s about five times the record peak demand of the grid.
Nashville went further. The Metro Council voted to seize the land where DC Blox planned to build a 10-megawatt data centre near the zoo. Ten megawatts is measly by current standards, but it sets a precedent.
If you cannot build a data centre, make it mobile, thought Runware, and announced the Sonic Inference Pod, a shipping container stuffed with 1,200 GPUs and a megawatt of compute power. The container can be shipped to one of 160 locations, with the list growing. There are limitations, as the pod can only serve a model that fits into a single node, and tensor parallelism is limited. Nevertheless, it is a step up from pure edge model serving.
Memory hierarchy for inference is getting redesigned
This week brought several news items addressing the pressing need to serve models from somewhere. First, SanDisk and SK hynix published the High Bandwidth Flash specification, which, basically, can sit beside High Bandwidth Memory (HBM), using NAND-based flash chips at a much lower cost per gigabyte. Having 256GB or 512GB of storage for local inference would be a dream come true for consumer hardware, but it is still a long way off.
Second, AMD agreed to acquire Taalas, a company that etches model weights straight into the die. It allows dropping HBM entirely, but there is a downside: model weights spoil like milk. There are some use cases, like speech recognition, that may benefit from this approach, but for bleeding-edge models the die would end up in the garbage after several months.
Let’s wrap up
I’d really like to continue and talk about the open-task research evaluation showing that even frontier models cannot reliably operate when there is no feedback they can use, a handful of harness engineering announcements, and other topics, but this post is getting too long. We’ll see what the next week brings.