AI News Archive: July 31, 2026 — Part 3
Sourced from 500+ daily AI sources, scored by relevance.
- Could AI take your job? Some workers in China already know the answer
Across the country, workers are fearful about the impact of AI on their livelihoods in an increasingly fragile labour market On the tree-lined streets of Wuhan, where cars jostle for space with mopeds and cargo trucks, the malfunctioning of a new type of vehicle has recently been causing chaos on the city’s roads. In March, several cars from a fleet of driverless taxis stopped abruptly in the streets. The “system malfunction” left distressed riders stranded for hours. The robotaxis, known as Apollo Go and powered by artificial intelligence (AI), were taken off Wuhan’s streets for months for investigations. Continue reading...
Score: 44🌐 MovesJul 31, 2026https://www.theguardian.com/world/2026/jul/31/china-ai-jobs-workers-labour-market-technology - The US Army is exploring robotic F-250 pickups. The company behind them sees future mine hunters and breachers.
The US Army is exploring robotic F-250 pickups. The company behind them sees future mine hunters and breachers. Business Insider
Score: 44🌐 MovesJul 31, 2026https://www.businessinsider.com/army-f-250-pickup-trucks-robotic-breachers-2026-7 - Humanoid Robot Patents: China Most, U.S. Best?
China has a massive quantity lead in humanoid robot patents. USA has a quality edge. But is it enough?
Score: 43🌐 MovesJul 31, 2026https://www.forbes.com/sites/johnkoetsier/2026/07/31/humanoid-robot-patents-china-most-us-best/ - AI scammers outperform humans when it comes to building trust
The AI chatbot was more effective at creating “exploitable trust” than the humans.
Score: 43🌐 MovesJul 31, 2026https://arstechnica.com/security/2026/07/ai-scammers-outperform-humans-when-it-comes-to-building-trust/ - Meta summons: Government to question global team on issues around algorithmic bias, role in public order
Meta's global team will face government questioning next week. They will address algorithmic bias and the company's public order role. This follows the brief restriction of Prime Minister Narendra Modi's Facebook post. The explanation provided by Meta was deemed inadequate by the ministry. Enhanced safeguards are now implemented for prominent account content.
- Nous Research Ships Three Integration Paths for Hermes Agent and Buzz, Block’s Open Source Nostr Workspace for Humans and Agents
Nous Research Ships Three Integration Paths for Hermes Agent and Buzz, Block’s Open Source Nostr Workspace for Humans and Agents MarkTechPost
- Chinese AI companies aim to serve businesses
Chinese tech players like ByteDance, Alibaba, and Tencent are looking to pivot toward the more lucrative enterprise market to increase revenues.
Score: 42🌐 MovesJul 31, 2026https://www.semafor.com/article/07/31/2026/chinese-ai-companies-aim-to-serve-businesses - Wall Street ends higher as Amazon soothes AI jitters
Wall Street ends higher as Amazon soothes AI jitters Reuters
- OpenAI’s Shift to Usage-Based Pricing Drives Risk of Exploding Costs
OpenAI’s Shift to Usage-Based Pricing Drives Risk of Exploding Costs Gartner
- Lovable snaps up team behind Swedish AI agent startup Nalvin
Swedish vibe coding startup Lovable has acquired the team behind AI agent startup Nalvin, as it looks to drive growth by cherry-picking top founders from startups. Stockholm-based Nalvin is an AI agen...
Score: 42💰 MoneyJul 31, 2026https://tech.eu/2026/07/31/lovable-snaps-up-team-behind-swedish-ai-agent-startup-nalvin/ - Snapchat joins other popular platforms in fight against 'AI slop'
Snapchat, YouTube, LinkedIn, and Substack are trying to combat the proliferation of fake AI content.
Score: 41🌐 MovesJul 31, 2026https://www.bbc.co.uk/news/articles/c77g6dm5pr8o?at_medium=RSS&at_campaign=rss - This bookseller thought a large request was 'spam.' It's AI companies scanning and destroying them
This bookseller thought a large request was 'spam.' It's AI companies scanning and destroying them fortune.com
Score: 41🌐 MovesJul 31, 2026https://fortune.com/2026/07/31/dutch-bookseller-ai-spam-phishing-3000-book-copies-scan-destroy/ - Inkling-Small 🧠, GPT-5.6 price cuts 💸, Gemini Robotics 2 🤖
Inkling-Small 🧠, GPT-5.6 price cuts 💸, Gemini Robotics 2 🤖
- Tech Brief (July 31): ByteDance Reshuffles Workplace Tool Feishu in AI Push
Tech Brief (July 31): ByteDance Reshuffles Workplace Tool Feishu in AI Push Caixin Global
- Apple's Stumble, Amazon's Surge and Anthropic's Hacks | Bloomberg Tech 7/31/2026
Bloomberg’s Ed Ludlow breaks down Tim Cook's final Apple earnings call as CEO - a story of supply crunches, demand misreads and a tough outlook. Plus, Amazon's shares roar on AI progress, big spending and accelerating cloud growth. And, after OpenAI's breaches, Anthropic is now saying its own AI models breached three organizations during cybersecurity tests. (Source: Bloomberg)
Score: 40🌐 MovesJul 31, 2026https://www.bloomberg.com/news/videos/2026-07-31/bloomberg-tech-7-31-2026-video - High school defends staying silent while boys made AI nudes of 59 classmates
Gaps in laws may help Pennsylvania high school escape AI nudes scandal.
- Robots get better at handling unpredictability
Google DeepMind unveiled a new model this week that gives humanoid robots finer control over how they interact with objects around them, including completing complicated tasks like tying a trash bag.
Score: 39🌐 MovesJul 31, 2026https://www.semafor.com/article/07/31/2026/robots-get-closer-to-handling-unpredictability - AI firms must answer for rogue bots, says boss of hacked company
Clement Delangue said he didn't want cyber attacks on other companies to become "normalised".
Score: 39🌐 MovesJul 31, 2026https://www.bbc.co.uk/news/articles/cr7k49xjzzeo?at_medium=RSS&at_campaign=rss - Prompt: Enterprises Grapple With Cost of AI Scale
As AI adoption expands, enterprises are discovering that managing costs, measuring returns and scaling efficiently are becoming as important as deploying the technology.
Score: 38🌐 MovesJul 31, 2026https://aibusiness.com/generative-ai/prompt-next-enterprise-ai-controlling-cost-of-scale - The UK’s AI policies are stronger than its verification habits
UK workers lead on AI adoption, productivity, and policy confidence. The Work AI Index shows why stronger governance still isn’t producing careful verification.
- Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference
As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1). Because...
Score: 38🌐 MovesJul 31, 2026https://developer.nvidia.com/blog/co-designing-ai-model-attention-for-fast-interactive-long-context-inference/ - AI at Work: Why AI Efforts Stall—and How to Fix Them
AI at Work: Why AI Efforts Stall—and How to Fix Them Gartner
Score: 38🌐 MovesJul 31, 2026https://www.gartner.com/en/webinar/914644/1915995-ai-at-work-why-ai-efforts-stalland-how-to-fix-them - How we brought Swiss German to DeepL
DeepL's Swiss GM explains why Swiss German was key to real localization for Swiss businesses.
- When GPU Goes to the Physical World, Tianjin Gets a Domestic GPU: Suxian Micro Releases TUCA Unified Computing Architecture and Tianheng GPGPU for Embodied Intelligence
Suxian Micro launches TH1100 GPGPU with 100T computing power for embodied AI, featuring in-chip compute-storage fusion and AI ROM technology, with a render-first-then-AI development logic.
- Can we train AI to choose safety over speed?
Delivery personnel from Swiggy navigate a flooded street amid heavy rainfall in Mumbai, India, on July 4, 2026.
Score: 38🌐 MovesJul 31, 2026https://restofworld.org/2026/india-ai-extreme-weather/?utm_source=rss&utm_medium=rss&utm_campaign=feeds - Illumina CEO on Genomic Sequencing Opportunities and AI
Illumina CEO Jacob Thaysen discusses the company's earnings, genomic medicine opportunities and the potential for artificial intelligence in life sciences on "Bloomberg The Close." (Source: Bloomberg)
Score: 38🌐 MovesJul 31, 2026https://www.bloomberg.com/news/videos/2026-07-31/illumina-ceo-on-genomic-sequencing-opportunities-and-ai-video - LimX Dynamics Co-Founder Shen Hua: The Next Stop for Humanoid Robots Is Landing, and Nothing but Landing, as Motion Control Converges and PMF Becomes the Battleground
LimX Dynamics shifts from motion control showcase to POC deployment with Luna humanoid, COSA brain system, and FluxVLA Engine as industry attention moves from capability boundaries to real scenarios.
- Will AI solve the productivity puzzle? With Nick Bloom
AI hasn’t resulted in widespread productivity gains. Business leaders say that’s changing
Score: 37🌐 MovesJul 31, 2026https://www.ft.com/content/129804fb-8f23-4b11-85ee-9a62b2237241?syn-25a6b1a6=1 - RoshAi makes the trucks you already own drive themselves
RoshAi makes the trucks you already own drive themselves YourStory.com
- In one California town, Flock misread license plates in 71% of the alerts it sent to police
In one California town, Flock misread license plates in 71% of the alerts it sent to police Business Insider
Score: 36🌐 MovesJul 31, 2026https://www.businessinsider.com/flock-camera-misread-license-plate-reader-california-roseville-police-2026-7 - The Red-Hot Book at the Center of an AI Mystery
Fourteen publishers fought over Jerry Falade’s debut novel. After it sold, his agent pulled support over AI concerns.
Score: 35🌐 MovesJul 31, 2026https://www.wsj.com/business/media/the-red-hot-book-at-the-center-of-an-ai-mystery-201c4665?mod=rss_Technology - Amazon's AI Story Is a 'Game Changer,' Says Mizuho
Amazon's latest earnings may have changed the AI narrative around the company. Mizuho's Jordan Klein joins Bloomberg to explain why accelerating AWS growth, rising demand for Amazon's custom AI chips and improving AI monetization across the business have strengthened the investment case, and why he believes Amazon remains one of the biggest winners in the AI infrastructure race. He speaks with Ed Ludlow on "Bloomberg Tech." (Source: Bloomberg)
Score: 35🌐 MovesJul 31, 2026https://www.bloomberg.com/news/videos/2026-07-31/amazon-s-ai-story-is-a-game-changer-says-mizuho-video - Opus Outshines Even Fable, Inside the Hugging Face Hack, AI Companies Spend Big for Compute
The Batch News & Insights: My team recently had our own version of Hugging Face’s experience when closed models failed to defend the company following an accidental cyberattack from OpenAI, leading Hugging Face to use the open weight GLM 5.2 instead.
- A five-step roadmap to closing the AI evaluation gap
A five-step roadmap for closing the AI evaluation gap to strengthen trust, security, adoption and effective AI governance. The post A five-step roadmap to closing the AI evaluation gap appeared first on OECD.AI .
Score: 35🌐 MovesJul 31, 2026https://wp.oecd.ai/a-five-step-roadmap-to-closing-the-ai-evaluation-gap/ - Opinion | Open Weight and AI’s Coming Chernobyl Moment
Maybe it’s time for U.S. intelligence agencies to tell us what they know about the Chinese models.
Score: 35🌐 MovesJul 31, 2026https://www.wsj.com/opinion/open-weight-and-ais-coming-chernobyl-moment-d8132958?mod=rss_Technology - OpenAI’s Agent Walked Out the Front Door
It exploited a cache proxy. Then Western AI refused to read the attack logs. Continue reading on Towards AI »
Score: 35🌐 MovesJul 31, 2026https://pub.towardsai.net/openais-agent-walked-out-the-front-door-7f978bbbfeb0?source=rss----98111c9905da---4 - How Adobe Adapted To ChatGPT And Gemini Reshaping B2B Buying
To me, the most interesting part of Adobe’s customer zero story is that when AI disrupted how buyers learn, Adobe couldn’t buy a solution because the solution didn’t exist yet. This is a story of marketing and technology leaders adapting an operating model under pressure and feeding those lessons back into products. At Adobe Summit, […]
Score: 35🌐 MovesJul 31, 2026https://www.forrester.com/blogs/how-adobe-adapted-to-chatgpt-and-gemini-reshaping-b2b-buying/ - Google study finds workplace AI acting more as assistant than replacement
Based on 15 million interactions, Google's study finds AI is used across 68 per cent of occupations but complete task automation accounts for less than 10 per cent of interactions
- Markets are getting AI right
Recent volatility reflects economic reality
Score: 34🌐 MovesJul 31, 2026https://www.ft.com/content/c167fb1b-69c3-4df0-9126-1e0af678f924?syn-25a6b1a6=1 - How sell-off at flashy AI-focused US hedge fund is a wake-up call for Chinese investors
Chinese tech investors are hearing warning bells after volatility in US tech stocks forced a fast-rising Wall Street hedge fund to clear its stock portfolio at a steep discount within a few days. “Don’t use leverage! Don’t use leverage! Don’t use leverage!” Shanghai-based public-markets analyst Harry Shen repeated to the South China Morning Post in an interview on Friday. Another warning came from Shanghai-based mainland Chinese stock market veteran Michael Zhang: “Don’t trade with borrowed...
- How Goodwill is using AI listing tools to sell the best items online before they ever reach the floor
How Goodwill is using AI listing tools to sell the best items online before they ever reach the floor Business Insider
Score: 33🌐 MovesJul 31, 2026https://www.businessinsider.com/inside-goodwill-the-worlds-biggest-thrift-machine-2026-7 - Eaton shares jump as its earnings and outlook show resilience of the AI buildout
Eaton investors had good reason to be concerned heading into Friday's results.
Score: 32🌐 MovesJul 31, 2026https://www.cnbc.com/2026/07/31/eaton-jumps-as-its-earnings-and-outlook-signal-hope-for-the-ai-buildout.html - The Next AI Boom Is in Health Care and Robotics, Says Lux Capital's Shakir
Recent disclosures from Anthropic and OpenAI have shifted the AI conversation from model capabilities to real-world deployment and security. Lux Capital Partner Deena Shakir says the next wave of AI innovation will be built on trust, partnerships and vertical applications like health care and robotics, not just more powerful foundation models. She joins Ed Ludlow on "Bloomberg Tech." (Source: Bloomberg)
Score: 32🌐 MovesJul 31, 2026https://www.bloomberg.com/news/videos/2026-07-31/-ai-is-now-an-operating-system-says-lux-capital-video - Ask Agentic Software Vendors These Questions Before Buying
Every vendor now calls its software agentic. These questions help you determine whether that's true, or just marketing.
Score: 32🌐 MovesJul 31, 2026https://www.forbes.com/sites/joetoscano1/2026/07/31/ask-agentic-software-vendors-these-questions-before-buying/ - Structured AI data pipelines score 10.9 points below free-form code — DataFlow-Harness closes the gap
If you ask an AI coding agent to write a standalone Python script to parse a single JSON file, it will likely give you a perfect answer in seconds. But the same agent often breaks if you ask it to build a systematic data processing pipeline, like ingesting thousands of messy documents, chunking text, scoring quality, and filtering noise for a Retrieval-Augmented Generation (RAG) system that fits your specific enterprise stack. While large language models (LLMs) excel at one-off code generation, their outputs for complex data-processing tasks are typically free-form, disposable scripts. These scripts are detached from the governable workflow abstractions that MLOps teams rely on for production, making them difficult to audit or edit visually. To address this, researchers at Peking University, Zhongguancun Academy, and Shanghai’s Institute for Advanced Algorithms Research introduced DataFlow-Harness , an open-source framework that guides an LLM agent to build structured, visual data-processing workflows step-by-step, rather than writing raw code from scratch. The framework makes AI-generated pipelines easier to manage and integrate into existing architectures because the generated artifacts are persistent and easily editable. The researchers report that the platform achieves a 93.3% observed end-to-end pass rate on a 12-task data-engineering benchmark. Compared to standard Claude Code, it reduces API costs by up to 72.5% and response latency by 49.9%, while achieving nearly the same success rate as an AI given the entire codebase to write standard scripts. For enterprise teams, this means getting the speed of AI automation without accumulating unmanageable technical debt, ensuring that pipelines remain secure, auditable, and ready for production. The "NL2Pipeline gap" Data-centric AI requires workflows for tasks like synthetic data generation, retrieval augmentation, and model training. While LLMs can translate natural language into executable implementations to perform these tasks, high task accuracy is insufficient for production deployment. "The first wall is usually not writing Python," Runming He, first author of the DataFlow-Harness paper, told VentureBeat. "Modern coding agents can often produce a plausible script quickly. The harder problem is grounding that script in a live production platform: using operators that are actually installed, matching the real dataset schema, referring to registered datasets and model services, preserving dependencies between stages, and leaving behind an artifact that another engineer can understand and revise." General-purpose AI agents frequently hallucinate dependencies, relying on unavailable operators or outdated platform assumptions. Instead of leaving behind an artifact that another engineer can understand and revise, they generate disposable code that is difficult to audit through workflow managing tools. The researchers define this challenge as the "NL2Pipeline gap": the disconnect between a user expressing workflow requirements in natural language and the production environment requiring structured and persistent pipeline assets. The researchers demonstrated this gap in their experiments. For example, when Claude Code was allowed to write standard, free-form scripts using codebase context, it hit a 94.2% success rate. However, when restricted to only using the platform's specific building blocks to create a native workflow graph, its success rate dropped to 83.3%. This gap is the paper's central finding: native, governable pipelines are meaningfully harder for the agent to produce than throwaway code. “Closing this gap requires more than improving code-generation accuracy: construction must remain grounded in platform semantics and produce artifacts that integrate with the host platform,” the researchers write. How the four components work together "DataFlow-Harness changes the agent’s action space," He said. "Instead of asking the agent to emit arbitrary code, it retrieves the live operator registry and current pipeline state through MCP and applies typed, incremental changes to a persistent DAG." To achieve this, the platform organizes workflow synthesis around four components: the Data Pipeline Backend, the interaction layer (DataFlow-WebUI), the MCP Tools Layer, and the AI guidance layer (DataFlow-Skills). The Data Pipeline Backend acts as the authoritative source of truth across conversational, visual, and programmatic interfaces. It represents the pipeline as a directed acyclic graph (DAG), a structured workflow map containing data sources, configured pre-built processing modules (which the researchers refer to as "operators"), and execution dependencies. Instead of generating free-form code, agents interact with this backend through “typed mutations,” like adding an operator or connecting edges. DataFlow-Skills are markdown files that inject domain-specific knowledge into the model's context window, guiding it on operator-selection patterns, schema inference, and assembly procedures. Rather than letting the AI guess how to assemble components, skills provide the AI with compatibility rules, teaching it how to correctly match different data formats and handle complex data structures without breaking the pipeline. The MCP Tools Layer gives the AI access to the operator registry and current state of the data workflow. The AI proposes structured changes through the tools layer. The system validates the changes to ensure the workflow runs in a valid sequence and that every connected module speaks the same data language. DataFlow-WebUI provides two interfaces that allow humans and AI to build the workflow together. Developers can describe workflow requirements in natural language through a conversational interface. They can also access the workflow as a graphical map in a visual DAG editor. Here, they can directly inspect the changes proposed by the AI and make modifications. “The current implementation performs static checks against platform metadata before accepting pipeline changes,” He said. “These include checks for registered datasets, operators and model-serving references, field flow, and some invalid parameter usage, as well as structural validity. The result is visible in a graphical editor and can be revised either manually or by the agent in later turns.” The results: 93.3% pass rate, 72.5% lower cost The researchers tested DataFlow-Harness on a benchmark of 12 tasks across six industrial data-processing scenarios, such as QA generation, review governance, and schema normalization. They used Claude Opus 4.7 as the backbone model in their experiments. They compared DataFlow-Harness against three baselines: Vanilla CC: An unconstrained coding baseline using standard Claude Code. Context-Aware CC: An agent that has access to the DataFlow codebase in its context window. MCP-only: An agent that has access to the DataFlow MCP tools and is instructed to generate platform-native DAGs (without access to DataFlow-Skills). DataFlow-Harness achieved a 93.3% end-to-end pass rate, improving by 10.0 percentage points over MCP-only and beating Vanilla CC (91.7%), while being within 0.9 percentage points of Context-Aware CC (94.2%). Importantly, it reduced API costs to $0.261 per task, a 72.5% drop compared to Vanilla CC and 42.8% compared to Context-Aware CC. In generating workflows, it was 49.9% faster than Vanilla CC and 17.6% faster than Context-Aware CC. DataFlow-Harness proved particularly effective on complex tasks that depend on implicit domain knowledge, like QA generation. The baseline MCP-only approach frequently generated structurally valid DAGs but struggled to infer task-specific procedures from operator descriptions alone. To show how this works in the real world, the researchers detailed a textbook-to-VQA extraction task. This job required the AI to stitch together capabilities such as PDF parsing, layout recovery, OCR, figure extraction, multimodal understanding, and long-range question-answer matching. DataFlow-Harness achieved 97.2% precision and an 87.3% coverage rate, easily beating the baselines. By having the AI snap together existing platform assets rather than coding complex tasks from scratch, it recovered more valid QA pairs from the document. Their experiments also showed that DataFlow-Harness is highly effective at creating data generation pipelines. For example, in a synthetic instruction-data generation task, the agent built a multi-stage pipeline that generated candidate instruction–response pairs, critiqued and rewrote them, scored them with an LLM-based judge, and filtered low-quality outputs before training. "Such workflows are costly to build and fragile to maintain as collections of ad hoc scripts," He said. "The harness does not make them automatically safe, but it turns them into explicit, editable stages that engineers can inspect, test, and govern using normal production controls." Similarly, when tasked with building a math data cleaning-and-synthesis pipeline, the data produced by the DataFlow-Harness pipeline trained a better-performing model with higher average accuracy on AIME24 and AIME25 benchmarks than the data produced by the vanilla Claude Code pipeline. Tech stack fit and implementation tradeoffs For engineering teams evaluating DataFlow-Harness, it is important to understand how it fits into existing infrastructure. Released under the Apache 2.0 license, the current implementation requires a bit of engineering to fit into popular tech stacks. "The current implementation is native to the DataFlow platform; it is not a turnkey Airflow, Prefect, or Spark plug-in," He said. To use those systems as an execution backbone, teams must build an adapter to connect their organization’s registry, metadata, and execution interfaces to the agent's control layer. Furthermore, organizations must invest in the boundaries they want the AI to respect. This requires maintaining an operator registry, defining schemas, and encoding recurring domain procedures as Skills. Because of this overhead, He recommends against using the framework for small, one-off transformations where a simple script suffices, or in legacy environments that cannot expose reliable metadata. Finally, while the platform prevents illogical connections by validating structural properties, it is an engineering control layer, not a compliance substitute. "The harness should still be treated as an engineering control layer, not as a substitute for compliance policy, validated detection models, access controls, audit logging, or human approval," He said. The platform is open-source, and developers can access the source code and codebase documentation directly via the project's GitHub repository . As protocols like MCP become standardized, the boundary between human engineers and AI agents will shift. "The goal is not autonomous data engineering without oversight," He said. "It is a better division of labor: agents perform repetitive construction inside explicit boundaries, while engineers remain responsible for the semantics, policies, and consequential decisions that require domain accountability."
- China Bans Assisted Driving Indicator Lights Amid Safety Concerns
China Bans Assisted Driving Indicator Lights Amid Safety Concerns Caixin Global
- From Moving to Acting: Zhejiang IPLUSMOBOT Technology Builds the Industrial Embodied AI Foundation Platform With NERA Robotics Product Line
IPLUSMOBOT unveils NERA-P omnidirectional chassis and NERA-C controller at WAIC, standardizing AMR-proven navigation and control as reusable embodied intelligence infrastructure for humanoid wheeled and quadruped robots.
- Autoscaling endpoints for LLM inference
GPU utilization can read healthy while your queue backs up, and a new replica takes minutes to warm. Here's how to pick autoscaling metrics, tune scale-up/down windows, and budget for cold starts on dedicated inference.
- Introducing Supabase Evals
Open-source benchmark for AI coding agents building with Supabase.
- How is your enterprise tracking AI agent telemetry? Groundcover thinks it should never leave your cloud
The AI agent observability space is taking off — but how can enterprises be sure what observability products and solutions they need? Observability startup groudcover (lower case "g" intentional) announced this week that it raised $100 million in a round led by One Peak, bringing its total funding to $160 million. The company says it has more than 250 paying customers, tripled annual recurring revenue over the past year and is increasingly replacing established observability platforms inside enterprise environments. Those are company-reported figures, but together they point to growing momentum in one of enterprise software's most competitive markets. That market has long been dominated by companies including Datadog, Dynatrace, New Relic, Splunk and Grafana. Between them, they represent billions of dollars in annual revenue and years of product maturity. Breaking into that group has never been easy. groundcover's argument is that artificial intelligence has fundamentally changed the assumptions those platforms were built on. Rather than competing feature for feature, the four-year-old company is trying to convince enterprises that the architecture underpinning observability itself needs to change as AI systems become more autonomous, produce vastly more telemetry and increasingly participate in software operations. Whether that thesis proves correct remains an open question, but it offers a compelling lens through which to examine how observability is evolving alongside enterprise AI. AI is turning telemetry into an infrastructure problem Observability has traditionally been viewed as a post-production discipline. Engineers deploy applications, monitor logs, metrics and traces, investigate incidents, and improve reliability over time. That workflow is changing. AI-assisted software development has dramatically accelerated deployment cycles. Coding assistants generate more code, infrastructure evolves more rapidly, and organizations are deploying increasingly complex distributed systems that combine microservices, Kubernetes clusters, APIs and large language models. At the same time, enterprises are beginning to operate AI agents that execute multi-step workflows, call external tools and interact with production systems. Each of those activities generates telemetry. The result is an explosion of operational data that organizations increasingly want to retain rather than discard. AI applications introduce additional layers of observability beyond traditional infrastructure monitoring, including prompt execution, model latency, token consumption, retrieval pipelines, tool invocations and agent behavior. As enterprises experiment with autonomous systems, that telemetry becomes increasingly valuable because it provides the context needed to understand what an AI system actually did and why. For many organizations, this creates tension with pricing models that charge according to the amount of data ingested. Historically, engineers have often responded by sampling traces, shortening retention periods or limiting which data is collected. Those approaches reduce costs, but they also reduce visibility precisely when AI-driven systems demand more complete operational context. "We've seen telemetry exploding," groundcover co-founder and CEO Shahar Azulay said during a recent media briefing. "Users are frustrated by not getting all the value from Datadog and similar platforms. They're limiting the data, siloing it, sampling it." Whether that frustration is widespread enough to reshape the market remains to be seen, but the underlying trend is difficult to ignore. AI is making observability less about collecting enough data and more about collecting everything organizations may eventually need. Rather than adding AI, groundcover argues the architecture itself has to change Many observability vendors have introduced AI assistants, AI-powered root cause analysis and AI observability features over the past two years. Datadog, Dynatrace, New Relic and Grafana have all announced products aimed at helping enterprises monitor AI applications or automate operational tasks. groundcover acknowledges those developments but argues they do not address what it sees as the more fundamental issue: where telemetry lives and how customers pay for it. Instead of operating a conventional SaaS platform that stores customer telemetry in vendor-managed infrastructure, groundcover uses what it calls a bring-your-own-cloud (BYOC) architecture. Customers keep the data plane—including telemetry storage and processing— inside their own AWS, Microsoft Azure or Google Cloud environments, while groundcover provides a managed control plane and user experience. A fully self-hosted deployment option is also available. While some competitors, including Datadog and a few other observability vendors, do offer limited hybrid or customer-controlled data residency options, these are generally not equivalent to a full BYOC model. In most cases, telemetry is still processed and stored within the vendor’s managed infrastructure, with only partial controls (such as regional data residency, private links, or selective log forwarding) available. That architectural decision influences nearly every aspect of the company's strategy. Because customers already pay for their own cloud infrastructure, groundcover argues it can avoid charging based on telemetry ingestion. Instead, pricing is based primarily on monitored hosts, regardless of telemetry volume. The company believes this changes customer behavior. Rather than deciding which logs or traces are too expensive to keep, organizations can theoretically retain complete telemetry and use it for operational analysis, compliance and AI-assisted troubleshooting. "We don't price by data volume," Azulay said. "We price by the size of the infrastructure." The distinction matters because AI workloads tend to increase telemetry far faster than infrastructure itself. That does not necessarily make host-based pricing universally cheaper. Organizations with relatively light workloads spread across many hosts may find different economics than dense Kubernetes environments generating enormous amounts of telemetry. The company's own briefing notes that per-host pricing is most advantageous for organizations with high telemetry density and may be less compelling for lightly utilized fleets. Still, the broader argument is less about cost alone than predictability. Enterprise infrastructure teams often struggle with observability bills that fluctuate alongside application growth. groundcover's model attempts to align pricing more closely with infrastructure planning rather than data generation. eBPF sits at the center of the company's technical differentiation The second pillar of groundcover's strategy is eBPF, a Linux kernel technology that has rapidly become one of the most important building blocks for modern cloud observability. Instead of requiring developers to manually instrument applications, eBPF allows software running inside the operating system kernel to observe network traffic, system calls and application behavior with minimal code changes. That enables faster deployment and broader visibility across infrastructure. For organizations operating Kubernetes clusters and cloud-native applications, reducing instrumentation complexity can significantly shorten deployment times while increasing telemetry coverage. Azulay argues this becomes especially important as AI systems generate increasingly complex interactions across services. "Our sensor allows us to observe systems very deeply from infrastructure to application to AI workloads without developers needing to instrument code," he said during the briefing. eBPF itself is hardly unique. Many observability vendors now incorporate it into their platforms. What groundcover argues differentiates its approach is combining automatic eBPF collection with customer-controlled storage, OpenTelemetry compatibility and unified pricing inside a single platform. The company's own research briefing acknowledges that none of these technologies individually represents a competitive moat. The claimed differentiation lies in the combination of eBPF-first collection, managed BYOC architecture, host-based economics and full-stack observability delivered together. AI agents are becoming both customers—and users—of observability Perhaps the most interesting aspect of groundcover's strategy extends beyond traditional monitoring. The company increasingly describes observability as infrastructure for autonomous software development. Historically, observability platforms have served human operators investigating production incidents. groundcover believes future observability platforms will increasingly serve AI agents as well. Its Agent Mode product allows engineers to investigate incidents using natural language across logs, metrics, traces and Kubernetes events. More importantly, Azulay envisions observability becoming the feedback mechanism that informs coding agents about what actually happened in production. Rather than simply detecting failures after deployment, observability becomes continuous operational context that autonomous systems can use to evaluate changes, identify regressions and eventually recommend or implement fixes. "We're seeing observability moving from being a post-production tool... to people taking context from production and feeding it back to their coding agents so they can write code better," Azulay said. Today, the company emphasizes that humans remain in the loop. Agent Mode investigates incidents and surfaces recommendations, but production changes still require human approval. Azulay expects autonomy to increase gradually as organizations become more comfortable allowing AI systems to participate in operational workflows. That vision reflects a broader trend emerging across enterprise software, where AI agents increasingly span development, testing, deployment and operations rather than functioning as isolated assistants. Why some enterprises are considering alternatives groundcover is entering an intensely competitive market populated by vendors with decades of enterprise experience. Datadog alone generated more than $3 billion in annual revenue in 2025 . Dynatrace, Cisco's Splunk business, Grafana Labs and New Relic all maintain extensive partner ecosystems, mature integrations and enterprise support organizations that newer entrants cannot easily replicate. groundcover is not attempting to outscale those incumbents overnight. Instead, it argues that AI creates an architectural inflection point similar to previous transitions from on-premises infrastructure to cloud-native computing. According to Azulay, many customers initially adopt groundcover to reduce observability costs but increasingly remain because they want unrestricted access to richer telemetry and AI-native workflows. He says deployments typically replace incumbent platforms rather than operate alongside them, although the company has not publicly disclosed customer migration data or independent studies validating that claim. The company's journalist briefing also urges caution around some performance claims. Revenue growth, customer counts and enterprise adoption figures originate from groundcover itself. Published customer case studies reporting significant cost savings are vendor-authored and should not be treated as independent validation without additional evidence. The briefing also recommends scrutinizing exactly what metadata leaves customer environments in standard BYOC deployments, rather than assuming that no operational data ever reaches vendor infrastructure. Those caveats are important because the observability market has become crowded. Gartner currently tracks more than one hundred observability products, and nearly every major vendor now markets AI-powered operational capabilities. Success will likely depend less on whether AI matters—which increasingly appears inevitable—and more on whether enterprises conclude that existing architectures remain sufficient. The larger question investors are betting on Viewed narrowly, groundcover's Series C is another large infrastructure funding round. Viewed more broadly, it reflects a growing debate about what observability becomes in an era where software increasingly writes, tests and operates itself. If AI continues generating exponentially larger volumes of operational data, traditional assumptions about telemetry collection, pricing and storage may come under increasing pressure. Vendors that built businesses around charging for data ingestion may need to evolve their economics alongside customer expectations. New entrants, meanwhile, have an opportunity to design around those changing assumptions from the outset. groundcover believes that opportunity lies in combining customer-controlled infrastructure, automatic telemetry collection and AI-assisted operations into a platform designed for autonomous software rather than simply adding AI features to existing observability products. Whether that architectural bet proves durable will depend on enterprise adoption over the next several years. But the company's latest funding round suggests at least some investors believe the next battle in observability will not be fought over dashboards or alerts. It will be fought over who builds the operational data layer that increasingly intelligent software relies upon to understand—and eventually manage—the systems it runs.