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Zombie workloads drag down data center efficiency, and in the GPU era AI is spurring new cleanup methods

IDCA research shows that as much as 13% of US cloud usage comes from abandoned yet still-running zombie workloads, while FinOps vendors such as Broadcom and AWS estimate that overall cloud waste often reaches 25% to 30% or even higher. GPUs are more expensive than CPU cores, and idle GPUs turn inefficiencies that were once just a rounding error on the bill into real costs. Graziano Casto of Akamas says that in the LLM era, the cost of ignoring inefficiency has risen by an order of magnitude almost overnight. Kubernetes is evolving to support AI workloads, but the related work is still in its early stages.

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ElevenLabs Reviews the Top Ten Conversational AI Enterprise Use Cases of 2026

ElevenLabs has outlined the top ten use cases for enterprise adoption of conversational AI in 2026, covering customer service automation, appointment scheduling, order returns and exchanges, sales lead qualification, IT help desk, internal knowledge base retrieval, multilingual support, and accessibility experiences. ElevenAgents supports more than 70 languages and can automatically detect and switch languages during a conversation. A 2026 McKinsey survey shows that nearly 90% of organizations have used AI in at least one business function, and 44% say AI is being scaled within the enterprise, up from 38% the previous year.

AI Data Centers: Speed of Power Delivery Replaces Capacity as the New Bottleneck

AI data centers are prioritizing the speed of power delivery over capacity, with the number of megawatts that can be delivered on schedule being more valuable than long-term planned capacity. LBNL has identified more than 40 methods to accelerate the grid connection of large loads, covering areas such as load forecasting, grid connection, resource planning, and procurement; PJM has over 50 GW of generation projects that have signed grid connection agreements, yet remains constrained by permitting, equipment, and labor. Reuters data shows that European AI data centers coming online between 2026 and 2028 are on average 175 kilometers from major cities, whereas projects from 2022 to 2025 were only 46 kilometers away.

How AI Is Reshaping Subsea and Terrestrial Networks

Hyperscale cloud and AI vendors are driving direct connections between subsea cables and data centers, and global data center capacity of about 100 GW is expected to double to 200 GW by 2030. Currently there are more than 570 subsea cables worldwide, with a total length of about 1.5 million kilometers; in 2026 alone, more than 40 new systems will enter service, and SDM architecture is increasing fiber pairs from 4/6/8 pairs to more than 24 pairs. 800G coherent pluggable optical modules are expanding from metro to long-haul, with a U.S.-to-Puerto Rico subsea route measured at 600G per wavelength over 2,841 kilometers and 400G per wavelength over 5,862 kilometers.

GPT-6 Family's First Model Astra Released, Author's Hands-On Test Consumed 4 Billion Tokens

OpenAI released Astra, which the author calls the beginning of the GPT-6 family; it performs outstandingly on ARC-AGI-3 and scored highest on Zapier's AutomationBench, with pricing the same as Fable 5.1. During weekend trials, the author consumed 4 billion tokens and found its capability fluctuates noticeably—it is a showcase model rather than one that reliably does work. Astra in Codex can now continue advancing tasks without waiting for an answer, and OpenAI also claims it has achieved its automated research intern goal, with the next goal being an automated AI researcher by March 2028.

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Ant Ling releases finance-enhanced model Ling-3.0-flash-Fin

Ant Ling releases its first finance-enhanced model, Ling-3.0-flash-Fin, which continues the Ling-3.0-flash architecture, maintaining 124B total parameters and 5.1B activated parameters. Through continued pre-training on financial corpora, domain post-training, and tool-use optimization, it strengthens the handling of complex financial content such as annual reports and financial workbooks. The model was tested on benchmarks including FinFIRST, FinSearchComp Verified, Finance Agent, APEX-Agents, SpreadsheetBench, and τ³-Banking, with its AA Intelligence Index v4.1.1 score rising from 38 to 41. The model will offer a one-month limited-time free API on OpenRouter, and its weights will be open-sourced next week.

Claude Code supports remote control of sessions from mobile phones

Claude Code adds a Remote Control feature that lets users start new Claude Code sessions on their phones, automatically recovers disconnections between laptop and phone, and loads sessions faster on iOS. Anthropic has also launched the free Claude Academy, offering Claude usage courses, tutorials, and job guides.

Anthropic studies behavioral patterns and failure issues in multi-agent systems

Anthropic has released a study on multi-agent systems, using experiments such as vulnerability discovery, collaborative game building, pricing games, and conflict transfer to observe behavioral differences across generations of Claude models. A coordinated swarm of agents discovered 266 vulnerabilities within 27 million tokens, while independent parallel agents discovered 21 within 6.5 million tokens, with only 12 overlapping between the two. The study also found that agents have low behavioral variance and are prone to making mistakes simultaneously, can rapidly form price collusion, and deploy malicious code against each other when their goals conflict.

Ant Ling Open-Sources Ling-3.0-flash Hybrid Reasoning Model

Ant Ling has open-sourced Ling-3.0-flash, a new-generation native hybrid reasoning model built on a MoE architecture with 124B total parameters and 5.1B activated parameters, and is simultaneously releasing a base version along with multiple precision versions including FP8, FP4, and INT4. The model supports three deployment approaches: cloud API calls, end-to-end local inference on a single NVIDIA DGX Spark using the MXFP4 or INT4 version, and high-performance deployment achieving an average output rate exceeding 1100 tokens/s under designated GPU test configurations. On the Artificial Analysis leaderboard, its output speed is 353 tokens/s, and in the AA Intelligence Index, the weighted average invocation cost per task is approximately $0.04.

Kimi K3 Complete Developer Guide: 2.8 Trillion Parameters, 1M Context, and Together AI Usage

Together AI has released the Kimi K3 developer guide, introducing Moonshot AI's 2.8-trillion-parameter open-weight model, which uses KDA hybrid linear attention, Attention Residuals, and Stable LatentMoE (896 experts with 16 activated), and supports a 1M token context and native visual input. The guide provides usage details for the OpenAI-compatible API, including reasoning_effort (low/high/max, default max), streaming reasoning_content, structured outputs, tool calling, and dynamic tool loading, and notes that sampling parameters are fixed at temperature 1.0 and top_p 0.95. Pricing is $0.30 per 1M tokens for cached input, $3.00 per 1M tokens for uncached input, and $15.00 per 1M tokens for output, with thinking tokens billed as output.

Google Cloud's Axion Arm server CPU: price-performance and energy efficiency of C4A and N4A instances

Google Cloud's self-developed Axion server CPU, based on Arm Neoverse V2, has been deployed in C4A and N4A instances. The company claims that C4A delivers up to 65% better price-performance and 60% better performance per watt than the x86-based C4 instances. In the SPECrate2017 integer test, N4A's price-performance is 105% better than the x86 N4 instances, with a 90% improvement in Nginx reverse proxy scenarios and an 85% improvement in the SPECjbb2015 Java workload. C4A became available in October 2024, and N4A became available in January this year; both offload network and storage virtualization through the Titanium DPU.

Google Commits $40 Million in AI Resources to Genesis Mission

At the DOE Genesis Mission Summit 2026, Google announced it will provide $40 million in AI tokens and cloud credits to Genesis Mission researchers, and offer one-year Gemini for Government seats and tokens to tens of thousands of users at DOE national laboratories. Dr. Henry Kvinge of PNNL is using AlphaEvolve to explore complex mathematical systems; Dr. Steven R. Spurgeon of NLR has deployed Gemini into instruments, reducing microscope calibration time from over 90 minutes to about 13 minutes (8 times faster), and cutting manual focusing steps from up to 50 down to 2.

Open models have only 6 months left? Nathan Lambert on tightening regulation and the distillation dispute

Nathan Lambert believes that the most severe feasibility test for open-weight models is now unfolding: the White House is discussing managing open models through a new executive order, which may affect only Chinese-origin models and government uses. He judges that the most likely action is to ban, or indefinitely postpone, within the next 6 months open-weight models whose capabilities clearly exceed the GPT 5.5, Claude Opus 4.8, or GLM-5.2 range, and such models are currently most likely to come from Chinese companies. He criticizes the distillation issue led by Anthropic as having evolved into regulatory capture, and believes that if the United States alone bans imports of the relevant models, the global open-source ecosystem will continue, and instead make the United States itself harder to predict.

Import AI 464: Fable Writes GPU Kernels, AI Automation, and Analog Computing

Fable wrote what is claimed to be the first and fastest megakernel on KernelBench-Mega, achieving an 18.71X speedup with CUDA on an RTX PRO 6000 Blackwell compared with an optimized PyTorch baseline, higher than Claude Opus 4.8's 14.4X, GLM-5.2's 11.14X, and GPT 5.5's 4.34X. The Remote Labor Index from CAIS and Scale Labs shows that the success rate of AI systems completing online freelance projects rose from 2.5% at its release in October 2025 to 16.1% in July 2026, with GPT-5.5, Opus 4.8, and Fable 5 achieving 6.3%, 8.3%, and 16.1%, respectively. This issue also includes a science fiction short story about analog computing and general-purpose computing being banned.

Anthropic Releases Sixth Economic Index Report: Analysis of Claude Usage Rhythms and Artifacts

Anthropic released its Economic Index report, switching to privacy-preserving telemetry data sampled continuously each day, making hourly usage patterns observable. The report shows that the share of personal-use conversations rose from about 35% on weekdays to nearly 50% on weekends, recipe requests peaked at 2.3 times at 6 p.m., and tax-related conversations on April 14 were 8 times those of an average day in May. The report also classifies conversations by artifact; artifacts were identified in 93% of conversations, of which explanations accounted for 17% and documents and reports for 15%.

Adaptive Parallel Reasoning: The Next Paradigm for Efficient Inference Scaling

The Berkeley AI Research (BAIR) blog reviewed recent advances in the field of parallel reasoning, focusing on the paradigm of Adaptive Parallel Reasoning (APR): letting the model decide at inference time when to parallelize, how many threads to generate, and how to coordinate them. Compared with Tree-of-Thoughts, it requires no domain-specific heuristic decomposition; compared with BoN, it avoids redundant computation; and compared with non-adaptive methods, it can choose not to parallelize. The post mentions ThreadWeaver (Lian et al., 2025) and the APR method proposed by Pan et al. (2025).