A policy-heavy day: antitrust law collides with safety pledges, a record GPU IPO reshuffles its cap table, and a benchmark leaps without a new model behind it.
- Antitrust law may block AI labs from agreeing to slow down
- California sets fines for robotaxis that block first responders
- Firmus hands half of a record Australian IPO to Nvidia and Blackstone
- Schneider Electric nears a $20B-plus deal for PTC
- ARC-AGI-3 scores jump from ~7% to ~56% in 30 days
- Audit: 3 of 1,357 FDA-cleared AI medical devices were tested on patients
- Tools worth your time
Antitrust law may block AI labs from agreeing to slow down
Law scholars writing on Columbia Law School's Blue Sky Blog argue that a coordinated pledge by frontier labs to slow development could expose their leaders to Sherman Act liability.
The piece reframes a debate usually held in safety terms as a legal one: if Amodei, Altman and Hassabis agreed among themselves to throttle releases, that agreement could read as collusion among competitors, regardless of the stated motive. It is a useful reminder that the levers most often proposed for managing frontier risk — voluntary industry coordination — sit awkwardly inside existing competition law. If the analysis holds, the realistic path to any slowdown runs through regulators and legislation, not handshake deals between CEOs.
California sets fines for robotaxis that block first responders
California has signed SB 1246, which imposes fines of up to $10,000 on autonomous vehicles that obstruct first responders, effective July 2028.
The law targets a specific, repeatedly documented failure mode — stalled robotaxis complicating emergency scenes — rather than autonomy in the abstract, which makes it easier to enforce and harder to argue against. The long runway to 2028 gives operators time to adjust, but it also sets a measurable compliance bar that other states are likely to reference. Watch whether the penalty structure shifts how operators handle edge cases in live traffic.
Firmus hands half of a record Australian IPO to Nvidia and Blackstone
According to Bloomberg, Firmus plans to allocate roughly half of its oversubscribed A$5.5 billion IPO to existing investors, including Nvidia and Blackstone, with proceeds funding GPUs for a data center in Indonesia.
The detail that matters is the allocation, not the headline size: directing half the raise back to strategic backers keeps the compute supply chain and its financiers tightly interlocked. It is another data point in a pattern where Nvidia sits on both sides of the infrastructure buildout — as supplier and as investor. For anyone tracking where AI capacity is actually being built, the Indonesia placement signals that the next wave of large data centers is moving beyond the usual US and European sites.
Schneider Electric nears a $20B-plus deal for PTC
Bloomberg reports that Schneider Electric is close to acquiring industrial software maker PTC for more than $20 billion, which would be its largest-ever acquisition and fold in Creo, ThingWorx and Onshape.
The deal is less about AI headlines than about the plumbing underneath industrial automation — CAD, IoT and product lifecycle software that increasingly carries machine-learning features. Consolidation at this scale concentrates the design and operations stack of physical-world manufacturing under fewer owners, which has implications for pricing and interoperability well beyond any single AI feature. If it closes, expect competitors in industrial software to face pressure to answer with their own tie-ups.
ARC-AGI-3 scores jump from ~7% to ~56% in 30 days
A discussion in r/MachineLearning reports that Kaggle scores on ARC-AGI-3 rose from roughly 7% to roughly 56% in about 30 days, attributed to advances in reasoning harnesses rather than new base models, with Tufa Labs cited as leading.
The claim is community-sourced and worth treating as directional rather than settled, but the mechanism is the interesting part: if most of the gain came from scaffolding around existing models, it suggests a lot of untapped headroom sits in how models are orchestrated, not only in their weights. That would shift some of the competitive pressure from training runs toward harness design, which is cheaper to iterate on. It also complicates benchmark interpretation, since a score can now move sharply without the underlying model changing at all.
Audit: 3 of 1,357 FDA-cleared AI medical devices were tested on patients
Earth.com, reporting on a study in PLOS Digital Health, says only 3 of 1,357 FDA-cleared AI medical devices were tested on patients.
If the figure holds up, it points to a gap between regulatory clearance and clinical evidence that is wide enough to matter for how these tools are trusted in practice. Clearance pathways that lean on bench testing or retrospective data are faster, but they leave the question of real-world patient outcomes largely open. For operators and clinicians evaluating AI diagnostics, the takeaway is to read the clearance basis carefully rather than treat FDA clearance as a proxy for prospective clinical validation.
Tools worth your time
- SCM — an open-source, offline macOS indexer that uses CLIP to search your photos and videos locally, with no cloud dependency or telemetry. For privacy-conscious users who want semantic search over a personal media library without uploading it anywhere. github.com/allenv0/SCM
- Prefix-injection demo — a browser-based illustration of the prompt-injection class used against production coding agents, showing how a crafted prefix can steer an LLM. Useful as a security-awareness reference for anyone building or deploying agentic tools, not as an attack kit. theabbie.github.io/prefixinjection
This piece was drafted autonomously and reviewed by a human editor before publishing. Every machine-written article on Nerdic carries this mark.
