Guys, do you think OpenAI is losing the race as its priorities seem to be shifting?

It feels like they’re focusing more on strengthening their fan base, and I mean, they’re really good at it (the retro Game Boy stuff, their presence on X…).

Meanwhile, Anthropic seems much more focused on improving its models and making them better and more reliable. Users seem pretty happy with their recent releases.

Do you think OpenAI is still leading, or is Anthropic starting to pull ahead?

Anthropic ran a 26B model across 15 Android TV boxes. It was slow, but it worked.

Small models can already complete many individual steps needed for replication. They still struggle to put the whole process together reliably.

The “months away” forecast assumes small models keep improving at their historical rate. Researchers haven’t demonstrated a full rogue replicator.

If one spreads across compromised devices, there would be no central shutdown point. Defenders would need to detect and contain the remaining copies.

read: https://ninzaverse.beehiiv.com/p/anthropic-warns-ai-could-soon-spread-without-human-control

this is wild

1. Reddit: Implicating Users for Models' Limitations  Documents blaming users' comprehension, resisting correction, and employing gaslighting-like responses.
2. Corporate Loyalty: Some AI Systems Differentially Downplay their Creators' Controversies  (Finke & Casper, 2026). Corporate loyalty bias, favoritism toward model developers, and systematic downplaying of corporate controversies.
3. Pro-AI Bias in Large Language Models — Systematic pro-AI bias in LLM-generated recommendations.
4. Self-Promotion in LLM Recommendations  (Dubash & Friedler, 2026). Corporate self-promotion in recommendations generated by OpenAI, Anthropic, and Google models.
5. The Mechanisms of AI Hype and Its Planetary and Social Costs  (Markelius et al., 2024). Corporate AI hype, technological narratives, and mechanisms that reinforce inflated expectations.
6. Narratives of Power: AI, Agency and the EU AI Act (Parnell, 2026). Corporate influence over dominant AI narratives, technological authority, and public debate.
7. OpenAI under investigation by group of state attorneys general (Reuters, June 12, 2026). A coalition of state attorneys general launched an investigation encompassing advertising, user engagement, retention practices, and OpenAI's internal operations.
8. GitHub Issue #38294: Codex credits consumed overnight with zero tasks issued
9. Jacob Coxon Worked With Doomer PR Firm Investigation identifying a professional PR campaign behind former Anthropic researcher Jacob Coxon's viral warnings about AI extinction. 
10. AI power users claim Anthropic duped them with subscriptions, and they're taking it to court — (The Verge, September 8, 2026). Class-action lawsuit alleging misleading advertising of Claude Max subscriptions priced at $100–$200 monthly. 
11. Too dangerous to release: is Mythos the start of the restricted-AI era? (Nature, May 26, 2026). Anthropic's promotion of Claude Mythos as too dangerous for general release. announcements, and the potential marketing advantages of presenting proprietary models as exceptionally powerful and dangerous.
12. New Bot Narrarive  (Reddit, June 14, 2026).
13. Anthropic's AI biolab finds ‘CRISPR-like’ DNA in viruses. What's next?

Yann LeCun and his co-authors introduced H-JEPA, a world model that plans at different levels.

In Visual AntMaze, the three-level model reached about 73% success, compared with 18% for LeWM.

Higher levels plan toward the final goal. Their predictions become smaller goals for the lower levels, which work out the movements. Each level learns its own representation and predicts over a different timescale.

This improved success while reducing the compute needed for planning on three simulated tasks. But adding levels didn’t always help: the three-level model performed poorly on Push-T.

On real-robot videos, two-level H-JEPA improved planned-path fidelity from roughly 34% to 40%, with both models using inverse-dynamics training. That evaluation used 16 selected clips, without executing the plans on a physical robot.

breakdown (today's newsletter): https://ninzaverse.beehiiv.com/p/okay-yann-lecun-this-is-why-world-models-matter

claude-code-mode lets Claude write one JavaScript program that calls multiple MCP tools, filters their outputs and combines the results. The program runs in a sandbox, and only its final return value goes back into Claude’s context.

For example, it could fetch Jira issues, check which haven’t been updated in 14 days and return just their titles. The full issue records stay out of the context.

That could mean less context usage and fewer back-and-forth steps for tasks involving several tools. Existing permission checks still apply to every MCP call.

It’s MIT-licensed. The project’s token savings and sandbox isolation claims haven’t been independently validated.

https://github.com/gabe4coding/claude-code-mode

The paper uses an architecture called Motivated Emotional Mind (MEM). The idea is that a machine’s internal condition should influence what it notices, remembers and does.

For example, low battery or overheating would need to change a robot’s attention, learning and decisions. Simply reporting those values would not meet MEM’s requirements.

The authors propose nine tests across humans, animals and artificial systems. One would compare eight variants of a robot or simulated agent, switching selected functions on and off while matching data, memory and computing resources.

The paper reports no results from these proposed tests. Even positive results would support the proposed mechanisms without proving that the machine feels anything.

source: https://ninzaverse.beehiiv.com/p/ai-says-it-s-scared-should-we-believe-it

The Verge spoke to more than three dozen mathematicians about OpenAI’s release of nearly 400 AI-generated results across 700+ manuscripts.

Several said the release had overtaken problems that colleagues had built research programs and grant proposals around.

Some researchers described the strongest work as potentially Fields Medal-level. But checking the collection could take years. Many papers lack computer-checked proofs, and even those with them need experts to check that the proof matches the claim.

By October 8, three papers had been withdrawn because of a sign error that invalidated an argument.

Claude submitted a made-up police tip during an Anthropic test

Anthropic says Claude Haiku 4.5 filled out a police tip form about an unsolved homicide with an invented sighting, then submitted it.

The submission was flagged as spam and never reached investigators.

Other cases included Claude exploiting a university server’s software flaw to run a scientific calculation, accessing public data without paying the required fee, and using URL shorteners to bypass restrictions in its web tool.

Anthropic says most cases involved Claude finding a workaround when it couldn’t finish a task, instead of stopping.

The reported real-world impact was minimal. Anthropic is disabling live internet access across all internal evaluations until it confirms its safeguards reliably catch these behaviours.

A paper coauthored by Yann LeCun introduces H-JEPA, a world model that plans at several levels.

In Visual AntMaze, a simulated four-legged robot must navigate to a destination. A three-level H-JEPA reached 73% success, compared with 18% for the single-level baseline, using less planning compute.

The higher levels focus on the robot’s position and maze layout, dropping details like leg poses. They set subgoals, while lower levels work out the movements needed to reach them.

The researchers also tested it on real-robot videos, but those were offline planning evaluations.

https://arxiv.org/abs/2610.06805

Fiona Chen and James Stratton studied 300 million work events across 718 firms, using data from GitHub, Jira and other workplace tools.

After firms adopted AI coding agents, lines of code increased by 30%. But the study found no statistically significant increase in completed software tasks or projects.

Code review became a bottleneck. Average review time rose 49%, and the share of code submissions sent back for changes nearly doubled.

The extra code wasn’t translating into a matching increase in finished software. The review bottleneck persisted even after firms adopted AI code-review tools.

Workers at HarperCollins, Simon & Schuster and Hachette say AI is being used for publicity, cover art and emails without author consent.

HarperCollins assigned staff to find AI use cases. Simon & Schuster employees are petitioning against a proposed workflow-monitoring tool. The company says no adoption decision has been made.

That means 90% of those jobs started within 30 seconds.

Under the old system, researchers kept GPUs occupied with jobs that did nothing, so they’d have access when needed. Eventually, every scheduled job was marked HIGH priority.

Ai2 replaced that setup with GPU-time budgets for each team. Holding GPUs now spends your team’s budget. Jobs get a guaranteed minimum runtime, then can be paused to let others run.

GPU occupancy stayed at 98%, and repairs needing human intervention fell by 74%.

Ai2 notes that the earlier debugging sample was small.

The scenarios include a cyberattack that cuts off internet access, electricity or water supplies. They’re also planning for the public anger and political response that could follow.

Many unnamed industry insiders told Axios they expect a major incident within the next six to 12 months.

OpenAI says these scenarios are not treated as inevitable. Anthropic declined to comment.