Two 2025 publications, "AI 2027" (Kokotajlo et al., 2025) and "If Anyone Builds It, Everyone Dies" (Yudkowsky & Soares, 2025), assert that superintelligent artificial intelligence will almost certainly destroy or render humanity obsolete within the next decade. Both rest on the classic chain formulated by Good (1965) and Bostrom (2014): intelligence explosion, superintelligence, lethal misalignment. This article subjects each link to the empirical record of 2023-2025. Sixty years after Good's speculation, none of the required phenomena (sustained recursive self-improvement, autonomous strategic awareness, or intractable lethal misalignment) have been observed. Current generative models remain narrow, statistically trained artefacts: powerful, opaque, and imperfect, but devoid of the properties that would make the catastrophic scenarios plausible. Following Whittaker (2025a, 2025b, 2025c) and Zuboff (2019, 2025), we argue that the existential-risk thesis functions primarily as an ideological distraction from the ongoing consolidation of surveillance capitalism and extreme concentration of computational power. The thesis is further inflated by the 2025 AI speculative bubble, where trillions in investments in rapidly depreciating "digital lettuce" hardware (McWilliams, 2025) mask lagging revenues and jobless growth rather than heralding superintelligence. The thesis remains, in November 2025, a speculative hypothesis amplified by a speculative financial bubble rather than a demonstrated probability.

The paper presents a feature-wise partitioning approach for distributed sparse regression. Unfortunately, the results are rather incremental for the level of NIPS, as the result only holds for random design matrices, and the paper in its current form lacks discussion of several lines of related work and experimental baselines. While I do definitely like the conceptual idea of the partitioning followed by de-correlation, the presented theory falls short of expectations as it only holds for random design matrices. The paper however does not clearly explain the novelty and differences over [9]. Also, in addition to [9], relations to related work [B,C] are not sufficiently discussed in the current version.

The most commonly studied and utilised version considers only a diagonal matrix proximal term.

Click here to read the full article. You could call it a contradiction in terms, or even a paradoxical blending of opposites--the U.S. Army is now designing a lightweight, highly lethal future armored tank that is easy to maneuver while being able to withstand dangerous enemy attacks and offering protection equal to or better than a 70-ton Abrams tank. This goal lies at the center of the Army's Next-Generation Combat Vehicle (NGCV), a family of new combat platforms now being developed by the service for future warfare. Its key goals are expeditionary, lightweight, fast, using artificial intelligence, potentially unmanned, extremely lethal and perpetually upgradeable. The effort involves building new infantry carriers, tanks and robotic vehicles networked together as part of an integrated tactical maneuver strategy.

M1A2 Abrams Tanks from A Company, 2-116th Cavalry Brigade Combat Team (CBCT), Idaho Army National Guard run through field exercises on Orchard Combat Training Center - file photo. Should U.S. forces be facing a massive armored enemy ground vehicle assault, they would need their own heavily armored vehicles -- such as infantry carriers, ground forces, unmanned attack vehicles and, perhaps of greatest significance, Abrams tanks. Large numbers of heavily armed, integrated and ready Abrams tanks would be needed for any kind of major ground offensive and "ready for war." Achieving this is not always as easy as it may sound; Abrams tanks are complex war machines that rely upon a wide range of properly functioning systems and technologies, including ammunition, mounted weapons, armor, sensors and electronics. Abrams parts often need to be repaired, upgraded and effectively maintained.

We propose Loco, an algorithm for large-scale ridge regression which distributes the features across workers on a cluster. Important dependencies between variables are preserved using structured random projections which are cheap to compute and must only be communicated once. We show that Loco obtains a solution which is close to the exact ridge regression solution in the fixed design setting. We verify this experimentally in a simulation study as well as an application to climate prediction. Furthermore, we show that Loco achieves significant speedups compared with a state-of-the-art distributed algorithm on a large-scale regression problem.