<a href="https://chronosphere.io/">Chronosphere</a>, a New York-based observability startup <a href="https://chronosphere.io/news/chronosphere-marks-a-year-of-new-capabilities/">valued at $1.6 billion</a>, announced Monday it will launch <a href="https://chronosphere.io/ai-guided-troubleshooting/">AI-Guided Troubleshooting</a> capabilities designed to help engineers diagnose and fix production software failures — a problem that has intensified as artificial intelligence tools accelerate code creation while making systems harder to debug.The new features combine AI-driven analysis with what Chronosphere calls a <a href="https://chronosphere.io/learn/how-chronosphere-built-a-deployment-system-with-temporal/">Temporal Knowledge Graph</a>, a continuously updated map of an organization&#x27;s services, infrastructure dependencies, and system changes over time. The technology aims to address a mounting challenge in enterprise software: developers are writing code faster than ever with AI assistance, but troubleshooting remains largely manual, creating bottlenecks when applications fail.&quot;For AI to be effective in observability, it needs more than pattern recognition and summarization,&quot; said Martin Mao, Chronosphere&#x27;s CEO and co-founder, in an exclusive interview with VentureBeat. &quot;Chronosphere has spent years building the data foundation and analytical depth needed for AI to actually help engineers. With our Temporal Knowledge Graph and advanced analytics capabilities, we&#x27;re giving AI the understanding it needs to make observability truly intelligent — and giving engineers the confidence to trust its guidance.&quot;The announcement comes as the observability market — software that monitors complex cloud applications— faces mounting pressure to justify escalating costs. Enterprise log data volumes have grown <a href="https://chronosphere.io/learn/observability-log-data-trends/">250% year-over-year</a>, according to Chronosphere&#x27;s own research, while a study from MIT and the University of Pennsylvania found that generative <a href="https://economics.mit.edu/sites/default/files/inline-files/draft_copilot_experiments.pdf">AI has spurred a 13.5% increase in weekly code commits</a>, signifying faster development velocity but also greater system complexity.<h2>AI writes code 13% faster, but debugging stays stubbornly manual</h2>Despite advances in automated code generation, debugging production failures remains stubbornly manual. When a major e-commerce site slows during checkout or a banking app fails to process transactions, engineers must sift through millions of data points — server logs, application traces, infrastructure metrics, recent code deployments — to identify root causes.Chronosphere&#x27;s answer is what it calls <a href="https://chronosphere.io/ai-guided-troubleshooting/">AI-Guided Troubleshooting</a>, built on four core capabilities: automated &quot;Suggestions&quot; that propose investigation paths backed by data; the Temporal Knowledge Graph that maps system relationships and changes; Investigation Notebooks that document each troubleshooting step for future reference; and natural language query building.Mao explained the <a href="https://chronosphere.io/learn/how-chronosphere-built-a-deployment-system-with-temporal/">Temporal Knowledge Graph</a> in practical terms: &quot;It&#x27;s a living, time-aware model of your system. It stitches together telemetry—metrics, traces, logs—infrastructure context, change events like deploys and feature flags, and even human input like notes and runbooks into a single, queryable map that updates as your system evolves.&quot;This differs fundamentally from the service dependency maps offered by competitors like <a href="https://www.datadoghq.com/dg/monitor/free-trial/?utm_source=google&amp;utm_medium=paid-search&amp;utm_campaign=dg-brand-ww&amp;utm_keyword=datadog&amp;utm_matchtype=b&amp;igaag=95325237782&amp;igaat=&amp;igacm=9551169254&amp;igacr=673270769690&amp;igakw=datadog&amp;igamt=b&amp;igant=g&amp;utm_campaignid=9551169254&amp;utm_adgroupid=95325237782&amp;gad_source=1&amp;gad_campaignid=9551169254&amp;gbraid=0AAAAADFY9NlOpf6xdtUzDLzD2BUR67UTl&amp;gclid=CjwKCAiAt8bIBhBpEiwAzH1w6T3Vk9lkeno-VLHlyRmDR0PFF8gTUGxe72EBr8QGbSpTNY5qtp63eRoC25kQAvD_BwE">Datadog</a>, <a href="https://www.dynatrace.com/">Dynatrace</a>, and <a href="https://www.splunk.com/">Splunk</a>, Mao argued. &quot;It adds time, not just topology,&quot; he said. &quot;It tracks how services and dependencies change over time and connects those changes to incidents—what changed and why. Many tools rely on standardized integrations; our graph goes a step further to normalize custom, non-standard telemetry so application-specific signals aren&#x27;t a blind spot.&quot;<h2>Why Chronosphere shows its work instead of making automatic decisions</h2>Unlike purely automated systems, <a href="https://chronosphere.io/">Chronosphere</a> designed its AI features to keep engineers in the driver&#x27;s seat—a deliberate choice meant to address what Mao calls the &quot;confident-but-wrong guidance&quot; problem plaguing early AI observability tools.&quot;&#x27;Keeping engineers in control&#x27; means the AI shows its work, proposes next steps, and lets engineers verify or override — never auto-deciding behind the scenes,&quot; Mao explained. &quot;Every Suggestion includes the evidence—timing, dependencies, error patterns — and a &#x27;Why was this suggested?&#x27; view, so they can inspect what was checked and ruled out before acting.&quot;He walked through a concrete example: &quot;An SLO [service level objective] alert fires on Checkout. Chronosphere immediately surfaces a ranked Suggestion: errors appear to have started in the dependent Payment service. An engineer can click Investigate to see the charts and reasoning and, if it holds up, choose to dig deeper. As they steer into Payment, the system adapts with new Suggestions scoped to that service—all from one view, no tab-hopping.&quot;In this scenario, the engineer asks &quot;what changed?&quot; and the system pulls in change events. &quot;Our Notebook capability makes the causal chain plain: a feature-flag update preceded pod memory exhaustion in Payment; Checkout&#x27;s spike is a downstream symptom,&quot; Mao said. &quot;They can decide to roll back the flag. That whole path — suggestions followed, evidence viewed, conclusions—is captured automatically in an Investigation Notebook, and the outcome feeds the Temporal Knowledge Graph so similar future incidents are faster to resolve.&quot;<h2>How a $1.6 billion startup takes on Datadog, Dynatrace, and Splunk</h2>Chronosphere enters an increasingly crowded field. <a href="https://www.datadoghq.com/">Datadog</a>, the publicly traded observability leader valued at over $40 billion, has introduced its own AI-powered troubleshooting features. So have Dynatrace and Splunk. All three offer comprehensive &quot;all-in-one&quot; platforms that promise single-pane-of-glass visibility.Mao distinguished Chronosphere&#x27;s approach on technical grounds. &quot;Early &#x27;AI for observability&#x27; leaned heavily on pattern-spotting and summarization, which tends to break down during real incidents,&quot; he said. &quot;These approaches often stop at correlating anomalies or producing fluent explanations without the deeper analysis and causal reasoning observability leaders need. They can feel impressive in demos but disappoint in production—they summarize signals rather than explain cause and effect.&quot;A specific technical gap, he argued, involves custom application telemetry. &quot;Most platforms reason over standardized integrations—Kubernetes, common cloud services, popular databases—ignoring the most telling clues that live in custom app telemetry,&quot; Mao said. &quot;With an incomplete picture, large language models will &#x27;fill in the gaps,&#x27; producing confident-but-wrong guidance that sends teams down dead ends.&quot;Chronosphere&#x27;s competitive positioning received validation in July when Gartner named it a Leader in the <a href="https://www.gartner.com/en/documents/6688834">2025 Magic Quadrant for Observability Platforms</a> for the second consecutive year. The firm was recognized based on both &quot;Completeness of Vision&quot; and &quot;Ability to Execute.&quot; In December 2024, Chronosphere also tied for the highest overall rating among recognized vendors in Gartner Peer Insights&#x27; &quot;Voice of the Customer&quot; report, scoring 4.7 out of 5 based on 70 reviews.Yet the company faces intensifying competition for high-profile customers. UBS analysts noted in July that OpenAI now runs both Datadog and Chronosphere side-by-side to monitor GPU workloads, suggesting the AI leader is evaluating alternatives. While UBS maintained its buy rating on Datadog, the analysts warned that growing Chronosphere usage could pressure Datadog&#x27;s pricing power.<h2>Inside the 84% cost reduction claims—and what CIOs should actually measure</h2>Beyond technical capabilities, Chronosphere has built its market position on cost control — a critical factor as observability spending spirals. The company claims its platform reduces data volumes and associated costs by 84% on average while cutting critical incidents by up to 75%.When pressed for specific customer examples with real numbers, Mao pointed to several case studies. &quot;Robinhood has seen a 5x improvement in reliability and a 4x improvement in Mean Time to Detection,&quot; he said. &quot;DoorDash used Chronosphere to improve governance and standardize monitoring practices. Astronomer achieved over 85% cost reduction by shaping data on ingest, and Affirm scaled their load 10x during a Black Friday event with no issues, highlighting the platform&#x27;s reliability under extreme conditions.&quot;The cost argument matters because, as <a href="https://chronosphere.io/news/chronosphere-logs-raises-bar-in-observability/">Paul Nashawaty</a>, principal analyst at CUBE Research, noted when Chronosphere launched its Logs 2.0 product in June: &quot;Organizations are drowning in telemetry data, with over 70% of observability spend going toward storing logs that are never queried.&quot;For CIOs fatigued by &quot;AI-powered&quot; announcements, Mao acknowledged skepticism is warranted. &quot;The way to cut through it is to test whether the AI shortens incidents, reduces toil, and builds reusable knowledge in your own environment, not in a demo,&quot; he advised. He recommended CIOs evaluate three factors: transparency and control (does the system show its reasoning?), coverage of custom telemetry (can it handle non-standardized data?), and manual toil avoided (how many ad-hoc queries and tool-switches are eliminated?).<h2>Why Chronosphere partners with five vendors instead of building everything itself</h2>Alongside the AI troubleshooting announcement, Chronosphere revealed a new <a href="https://chronosphere.io/partners/">Partner Program</a> integrating five specialized vendors to fill gaps in its platform: Arize for large language model monitoring, Embrace for real user monitoring, Polar Signals for continuous profiling, Checkly for synthetic monitoring, and Rootly for incident management.The strategy represents a deliberate bet against the all-in-one platforms dominating the market. &quot;While an all-in-one platform may be sufficient for smaller organizations, global enterprises demand best-in-class depth across each domain,&quot; Mao said. &quot;This is what drove us to build our Partner Program and invest in seamless integrations with leading providers—so our customers can operate with confidence and clarity at every layer of observability.&quot;Noah Smolen, head of partnerships at Arize, said the collaboration addresses a specific enterprise need. &quot;With a wide array of Fortune 500 customers, we understand the high bar needed to ensure AI agent systems are ready to deploy and stay incident-free, especially given the pace of AI adoption in the enterprise,&quot; Smolen said. &quot;Our partnership with Chronosphere comes at a time when an integrated purpose-built cloud-native and AI-observability suite solves a huge pain point for forward-thinking C-suite leaders who demand the very best across their entire observability stack.&quot;Similarly, JJ Tang, CEO and founder of Rootly, emphasized the incident resolution benefits. &quot;Incidents hinder innovation and revenue, and the challenge lies in sifting through vast amounts of observability data, mobilizing teams, and resolving issues quickly,&quot; Tang said. &quot;Integrating Chronosphere with Rootly allows engineers to collaborate with context and resolve issues faster within their existing communication channels, drastically reducing time to resolution and ultimately improving reliability—78% plus decreases in repeat Sev0 and Sev1 incidents.&quot;When asked how total costs compare when customers use multiple partner contracts versus a single platform, Mao acknowledged the current complexity. &quot;At present, mutual customers typically maintain separate contracts unless they engage through a services partner or system integrator,&quot; he said. However, he argued the economics still favor the composable approach: &quot;Our combined technologies deliver exceptional value—in most circumstances at just a fraction of the price of a single-platform solution. Beyond the savings, customers gain a richer, more unified observability experience that unlocks deeper insights and greater efficiency, especially for large-scale environments.&quot;The company plans to streamline this over time. &quot;As the ISV program matures, we&#x27;re focused on delivering a more streamlined experience by transitioning to a single, unified contract that simplifies procurement and accelerates time to value,&quot; Mao said.<h2>How two Uber engineers turned Halloween outages into a billion-dollar startup</h2>Chronosphere&#x27;s origins trace to 2019, when Mao and co-founder Rob Skillington left Uber after building the ride-hailing giant&#x27;s internal observability platform. At Uber, Mao&#x27;s team had faced a crisis: the company&#x27;s in-house tools would fail on its two busiest nights — Halloween and New Year&#x27;s Eve — cutting off visibility into whether customers could request rides or drivers could locate passengers.The solution they built at Uber used open-source software and ultimately allowed the company to operate without outages, even during high-volume events. But the broader market insight came at an industry conference in December 2018, when major cloud providers threw their weight behind Kubernetes, Google&#x27;s container orchestration technology.&quot;This meant that most technology architectures were eventually going to look like Uber&#x27;s,&quot; Mao recalled in an <a href="https://greylock.com/greymatter/chronosphere-is-making-the-observability-platform-built-for-control/">August 2024 profile by Greylock Partners</a>, Chronosphere&#x27;s lead investor. &quot;And that meant every company, not just a few big tech companies and the Walmarts of the world, would have the exact same problem we had solved at Uber.&quot;Chronosphere has since raised more than <a href="https://chronosphere.io/news/chronosphere-marks-a-year-of-new-capabilities/">$343 million in funding</a> across multiple rounds led by Greylock, Lux Capital, General Atlantic, Addition, and Founders Fund. The company operates as a remote-first organization with offices in New York, Austin, Boston, San Francisco, and Seattle, employing approximately 299 people according to LinkedIn data.The company&#x27;s customer base includes <a href="https://www.doordash.com/">DoorDash</a>, <a href="https://www.zillow.com">Zillow</a>, <a href="https://www.snap.com/">Snap</a>, <a href="https://robinhood.com/us/en/">Robinhood</a>, and <a href="https://www.affirm.com/">Affirm</a> — predominantly high-growth technology companies operating cloud-native, Kubernetes-based infrastructures at massive scale.<h2>What&#x27;s available now—and what enterprises can expect in 2026</h2>Chronosphere&#x27;s <a href="https://chronosphere.io/news/ai-guided-troubleshooting-redefines-observability/">AI-Guided Troubleshooting</a> capabilities, including Suggestions and Investigation Notebooks, entered limited availability Monday with select customers. The company plans full general availability in 2026. The <a href="https://docs.chronosphere.io/integrate/mcp-server">Model Context Protocol (MCP) Server</a>, which enables engineers to integrate Chronosphere directly into internal AI workflows and query observability data through AI-enabled development environments, is available immediately for all Chronosphere customers.The phased rollout reflects the company&#x27;s cautious approach to deploying AI in production environments where mistakes carry real costs. By gathering feedback from early adopters before broad release, Chronosphere aims to refine its guidance algorithms and validate that its suggestions genuinely accelerate troubleshooting rather than simply generating impressive demonstrations.The longer game, however, extends beyond individual product features. Chronosphere&#x27;s dual bet — on transparent AI that shows its reasoning and on a partner ecosystem rather than all-in-one integration — amounts to a fundamental thesis about how enterprise observability will evolve as systems grow more complex.If that thesis proves correct, the company that solves observability for the AI age won&#x27;t be the one with the most automated black box. It will be the one that earns engineers&#x27; trust by explaining what it knows, admitting what it doesn&#x27;t, and letting humans make the final call. In an industry drowning in data and promised silver bullets, Chronosphere is wagering that showing your work still matters — even when AI is doing the math.

Chronosphere, a New York-based observability startup valued at $1.6 billion, has launched AI-Guided Troubleshooting capabilities to help engineers diagnose software failures more effectively. This innovation addresses the growing challenge of troubleshooting in an era where AI accelerates code creation, leading to manual bottlenecks during application failures. The technology combines AI analysis with a Temporal Knowledge Graph, mapping an organization’s services and infrastructure changes over time.

Chronosphere takes on Datadog with AI that explains itself, not just outages

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<h1>
 
 
 AI Amnesia: Erasing Knowledge Without a Trace
</h1>

Imagine your AI model accidentally learned something it shouldn't have – sensitive customer data, for example. Current methods for deleting this information often require retraining the entire model, an expensive and time-consuming process. What if we could surgically remove that knowledge without starting from scratch?

The key lies in a novel approach: creating artificial "forgetting cues." We're teaching the model to unlearn specific data patterns by exposing it to carefully crafted synthetic examples. These examples are designed to strongly contradict the information we want the model to forget, effectively overwriting the problematic associations in its memory. This works even if you don’t have access to the original data you need to erase.

Think of it like this: you're trying to forget a bad song stuck in your head. Instead of trying to actively suppress it (which rarely works), you blast an even more catchy song. The new song overwrites the old one, effectively erasing it from your mental playlist.

Benefits of Selective Forgetting:

<ul>
<li> Enhanced Data Privacy: Remove sensitive data without compromising the overall model's performance.</li>
<li> Reduced Retraining Costs: Avoid full model retraining, saving significant time and resources.</li>
<li> Improved Model Security: Eliminate vulnerabilities introduced by unintentionally learned patterns.</li>
<li> Adaptable Learning: Enables continuous refinement of AI models based on evolving data landscapes.</li>
<li> Compliance Ready: Supports compliance with data privacy regulations like GDPR.</li>
<li> Scalable Solutions: Works efficiently even with limited access to training data.</li>
</ul>

Practical Tip: One challenge is ensuring the synthetic data accurately targets the information you want to remove without negatively impacting the model's ability to generalize. Rigorous testing and validation with holdout datasets are crucial.

The promise of AI that can truly 'forget' opens exciting possibilities for responsible AI development. By enabling precise data deletion, we pave the way for more secure, compliant, and adaptable machine learning systems. Imagine AI models that can adapt to changing ethical guidelines or quickly unlearn incorrect information, all without massive retraining efforts. This is a crucial step towards trustworthy and responsible AI that respects data privacy and aligns with societal values. Future exploration could include extending this to different data modalities and model architectures.

Related Keywords: machine unlearning, data privacy, few-shot learning, zero-shot learning, synthetic data, model editing, catastrophic forgetting, incremental learning, continual learning, deep learning, neural networks, data security, algorithmic fairness, responsible ai, ethical ai, federated unlearning, privacy-preserving ai, model retraining, AI governance, data deletion, GDPR compliance

يتناول المقال نهجًا جديدًا لمعالجة مشكلة نسيان الذكاء الاصطناعي، حيث قد تحتفظ نماذج الذكاء الاصطناعي عن غير قصد بمعلومات حساسة. تتطلب الطرق التقليدية لحذف هذه البيانات غالبًا إعادة تدريب كاملة للنموذج، وهو ما يعد مكلفًا ويستغرق وقتًا. تتضمن التقنية الجديدة إنشاء 'إشارات نسيان' اصطناعية تساعد النموذج على نسيان أنماط بيانات معينة من خلال تعريضه لأمثلة صناعية تتناقض مع المعلومات غير المرغوب فيها، مما يسمح بإزالة المعرفة المستهدفة دون الحاجة للوصول إلى البيانات الأصلية.

El artículo aborda un nuevo enfoque para tratar el problema de la amnesia de la IA, donde los modelos de IA pueden retener inadvertidamente información sensible. Los métodos tradicionales para eliminar estos datos a menudo requieren un reentrenamiento completo del modelo, lo que es costoso y lleva tiempo. La nueva técnica implica crear 'señales de olvido' artificiales que ayudan al modelo a desaprender patrones de datos específicos al exponerlo a ejemplos sintéticos que contradicen la información no deseada, permitiendo así la eliminación selectiva de conocimientos sin necesidad de acceder a l…

L'article traite d'une nouvelle approche pour résoudre le problème de l'amnésie de l'IA, où les modèles d'IA peuvent involontairement conserver des informations sensibles. Les méthodes traditionnelles pour supprimer ces données nécessitent souvent un réentraînement complet du modèle, ce qui est coûteux et long. La nouvelle technique consiste à créer des 'cues d'oubli' artificiels qui aident le modèle à désapprendre des schémas de données spécifiques en lui présentant des exemples synthétiques qui contredisent les informations indésirables, permettant ainsi une suppression ciblée des connaissan…

The article discusses a novel approach to address the issue of AI amnesia, where AI models may inadvertently retain sensitive information. Traditional methods for deleting such data often require complete retraining of the model, which is costly and time-consuming. The new technique involves creating artificial 'forgetting cues' that help the model unlearn specific data patterns by presenting it with synthetic examples that contradict the unwanted information, allowing for targeted knowledge removal without needing access to the original data.

AI Amnesia: Erasing Knowledge Without a Trace

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Turning materials like wood chips, crop residues and municipal solid waste into fuels and chemicals is important for our country's energy independence.

طور الباحثون نماذج حاسوبية متقدمة تهدف إلى تحسين التنبؤات لعمليات طحن الكتلة الحيوية. تركز هذه الابتكارات على تحويل مواد مثل رقائق الخشب، بقايا المحاصيل، والنفايات الصلبة البلدية إلى وقود ومواد كيميائية قيمة، وهو أمر مهم لتعزيز استقلالية الطاقة في البلاد. من المتوقع أن تعمل النماذج على تحسين كفاءة معالجة الكتلة الحيوية، مما يساهم في حلول الطاقة المستدامة.

Investigadores han desarrollado modelos informáticos avanzados destinados a mejorar las predicciones para los procesos de molienda de biomasa. Esta innovación se centra en convertir materiales como astillas de madera, residuos de cultivos y residuos sólidos municipales en combustibles y productos químicos valiosos, lo que es crucial para mejorar la independencia energética del país. Se espera que los modelos optimicen la eficiencia del procesamiento de biomasa, contribuyendo así a soluciones energéticas sostenibles.

Des chercheurs ont développé des modèles informatiques avancés visant à améliorer les prévisions pour les processus de broyage de la biomasse. Cette innovation se concentre sur la conversion de matériaux tels que les copeaux de bois, les résidus de culture et les déchets solides municipaux en combustibles et produits chimiques précieux, ce qui est crucial pour renforcer l'indépendance énergétique du pays. Les modèles devraient optimiser l'efficacité du traitement de la biomasse, contribuant ainsi à des solutions énergétiques durables.

Researchers have developed advanced computer models aimed at improving predictions for biomass milling processes. This innovation focuses on converting materials such as wood chips, crop residues, and municipal solid waste into valuable fuels and chemicals, which is crucial for enhancing energy independence in the country. The models are expected to optimize the efficiency of biomass processing, thereby contributing to sustainable energy solutions.

Researchers develop computer models for better biomass milling predictions

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In what appeared to be a bid to soak up some of Google&#x27;s limelight prior to the <a href="https://venturebeat.com/ai/google-unveils-gemini-3-claiming-the-lead-in-math-science-multimodal-and">launch of its new Gemini 3 flagship AI model </a>— now recorded as the most powerful LLM in the world by multiple independent evaluators — Elon Musk&#x27;s rival AI startup xAI last night unveiled its newest large language model, <a href="https://x.ai/news/grok-4-1">Grok 4.1.</a>The model is now live for consumer use on Grok.com, social network X (formerly Twitter), and the company’s iOS and Android mobile apps, and it arrives with major architectural and usability enhancements, among them: faster reasoning, improved emotional intelligence, and significantly reduced hallucination rates. xAI also commendably published a white paper on its evaluations and including a small bit on training process <a href="https://data.x.ai/2025-11-17-grok-4-1-model-card.pdf">here</a>. Across public benchmarks, Grok 4.1 has vaulted to the top of the leaderboard, outperforming rival models from Anthropic, OpenAI, and Google — at least, Google&#x27;s pre-Gemini 3 model (Gemini 2.5 Pro). It builds upon the success of xAI&#x27;s Grok-4 Fast, which <a href="https://venturebeat.com/ai/what-to-know-about-grok-4-fast-for-enterprise-use-cases">VentureBeat covered favorably</a> shortly following its release back in September 2025.However, enterprise developers looking to integrate the new and improved model Grok 4.1 into production environments will find one major constraint: it&#x27;s not yet available through <a href="https://docs.x.ai/docs/models?cluster=us-east-1#detailed-pricing-for-all-grok-models">xAI’s public API</a>. Despite its high benchmarks, Grok 4.1 remains confined to xAI’s consumer-facing interfaces, with no announced timeline for API exposure. At present, only older models—including Grok 4 Fast (reasoning and non-reasoning variants), Grok 4 0709, and legacy models such as Grok 3, Grok 3 Mini, and Grok 2 Vision—are available for programmatic use via the xAI developer API. These support up to 2 million tokens of context, with token pricing ranging from $0.20 to $3.00 per million depending on the configuration.For now, this limits Grok 4.1’s utility in enterprise workflows that rely on backend integration, fine-tuned agentic pipelines, or scalable internal tooling. While the consumer rollout positions Grok 4.1 as the most capable LLM in xAI’s portfolio, production deployments in enterprise environments remain on hold.<h3>Model Design and Deployment Strategy</h3>Grok 4.1 arrives in two configurations: a fast-response, low-latency mode for immediate replies, and a “thinking” mode that engages in multi-step reasoning before producing output. Both versions are live for end users and are selectable via the model picker in xAI’s apps.The two configurations differ not just in latency but also in how deeply the model processes prompts. Grok 4.1 Thinking leverages internal planning and deliberation mechanisms, while the standard version prioritizes speed. Despite the difference in architecture, both scored higher than any competing models in blind preference and benchmark testing.<h3>Leading the Field in Human and Expert Evaluation</h3>On the <a href="https://lmarena.ai/leaderboard/text">LMArena Text Arena leaderboard</a>, Grok 4.1 Thinking briefly held the top position with a normalized Elo score of 1483 — then was dethroned a few hours later with <a href="https://venturebeat.com/ai/google-unveils-gemini-3-claiming-the-lead-in-math-science-multimodal-and">Google&#x27;s release of Gemini 3 </a>and its incredible 1501 Elo score. The non-thinking version of Grok 4.1 also fares well on the index, however, at 1465. These scores place Grok 4.1 above Google’s Gemini 2.5 Pro, Anthropic’s Claude 4.5 series, and OpenAI’s GPT-4.5 preview.In creative writing, Grok 4.1 ranks second only to Polaris Alpha (an early GPT-5.1 variant), with the “thinking” model earning a score of 1721.9 on the Creative Writing v3 benchmark. This marks a roughly 600-point improvement over previous Grok iterations. Similarly, in the Arena Expert leaderboard, which aggregates feedback from professional reviewers, Grok 4.1 Thinking again leads the field with a score of 1510.The gains are especially notable given that Grok 4.1 was released only two months after Grok 4 Fast, highlighting the accelerated development pace at xAI.<h3>Core Improvements Over Previous Generations</h3>Technically, Grok 4.1 represents a significant leap in real-world usability. Visual capabilities—previously limited in Grok 4—have been upgraded to enable robust image and video understanding, including chart analysis and OCR-level text extraction. Multimodal reliability was a pain point in prior versions and has now been addressed.Token-level latency has been reduced by approximately 28 percent while preserving reasoning depth. In long-context tasks, Grok 4.1 maintains coherent output up to 1 million tokens, improving on Grok 4’s tendency to degrade past the 300,000 token mark.xAI has also improved the model&#x27;s tool orchestration capabilities. Grok 4.1 can now plan and execute multiple external tools in parallel, reducing the number of interaction cycles required to complete multi-step queries. According to internal test logs, some research tasks that previously required four steps can now be completed in one or two.Other alignment improvements include better truth calibration—reducing the tendency to hedge or soften politically sensitive outputs—and more natural, human-like prosody in voice mode, with support for different speaking styles and accents.<h3>Safety and Adversarial Robustness</h3>As part of its risk management framework, xAI evaluated Grok 4.1 for refusal behavior, hallucination resistance, sycophancy, and dual-use safety.The hallucination rate in non-reasoning mode has dropped from 12.09 percent in Grok 4 Fast to just 4.22 percent — a roughly 65% improvement.The model also scored 2.97 percent on FActScore, a factual QA benchmark, down from 9.89 percent in earlier versions.In the domain of adversarial robustness, Grok 4.1 has been tested with prompt injection attacks, jailbreak prompts, and sensitive chemistry and biology queries. Safety filters showed low false negative rates, especially for restricted chemical knowledge (0.00 percent) and restricted biological queries (0.03 percent). The model’s ability to resist manipulation in persuasion benchmarks, such as MakeMeSay, also appears strong—it registered a 0 percent success rate as an attacker.<h3>Limited Enterprise Access via API</h3>Despite these gains, Grok 4.1 remains unavailable to enterprise users through xAI’s API. According to the company’s <a href="https://docs.x.ai/docs/models">public documentation</a>, the latest available models for developers are Grok 4 Fast (both reasoning and non-reasoning variants), each supporting up to 2 million tokens of context at pricing tiers ranging from $0.20 to $0.50 per million tokens. These are backed by a 4M tokens-per-minute throughput limit and 480 requests per minute (RPM) rate cap.By contrast, Grok 4.1 is accessible only through xAI’s consumer-facing properties—X, Grok.com, and the mobile apps. This means organizations cannot yet deploy Grok 4.1 via fine-tuned internal workflows, multi-agent chains, or real-time product integrations.<h3>Industry Reception and Next Steps</h3>The release has been met with strong public and industry feedback. Elon Musk, founder of xAI, posted a brief endorsement, calling it “a great model” and congratulating the team. AI benchmark platforms have praised the leap in usability and linguistic nuance.For enterprise customers, however, the picture is more mixed. Grok 4.1’s performance represents a breakthrough for general-purpose and creative tasks, but until API access is enabled, it will remain a consumer-first product with limited enterprise applicability.As competitive models from OpenAI, Google, and Anthropic continue to evolve, xAI’s next strategic move may hinge on when—and how—it opens Grok 4.1 to external developers.

أطلقت شركة xAI التابعة لإيلون ماسك Grok 4.1، وهو نموذج لغوي كبير مصمم لتحسين تجربة المستخدم على Grok.com والشبكة الاجتماعية X. يتميز هذا النموذج الجديد بتحسينات مثل التفكير الأسرع، والذكاء العاطفي الأفضل، ومعدل هلاوس منخفض بشكل كبير. حاليًا، لا يوجد وصول لواجهة برمجة التطبيقات متاح للمطورين. يأتي هذا الإطلاق في الوقت الذي تستعد فيه جوجل للكشف عن نموذجها Gemini 3، الذي تم التعرف عليه كأقوى نموذج لغوي كبير من قبل مقيمين مستقلين.

La startup de Elon Musk, xAI, ha lanzado Grok 4.1, un modelo de lenguaje diseñado para mejorar la experiencia del usuario en Grok.com y en la red social X. Este nuevo modelo presenta mejoras como un razonamiento más rápido, mejor inteligencia emocional y una tasa de alucinaciones significativamente reducida. Actualmente, no hay acceso a API disponible para desarrolladores. Este lanzamiento se produce mientras Google se prepara para presentar su modelo Gemini 3, reconocido como el modelo de lenguaje más potente por evaluadores independientes.

La startup d'Elon Musk, xAI, a lancé Grok 4.1, un modèle de langage conçu pour améliorer l'expérience utilisateur sur Grok.com et le réseau social X. Ce nouveau modèle présente des améliorations telles qu'un raisonnement plus rapide, une meilleure intelligence émotionnelle et un taux de hallucination considérablement réduit. Actuellement, aucun accès API n'est disponible pour les développeurs. Ce lancement intervient alors que Google se prépare à dévoiler son modèle Gemini 3, reconnu comme le modèle de langage le plus puissant par des évaluateurs indépendants.

Elon Musk's AI startup xAI has launched Grok 4.1, a large language model designed to enhance user experience on Grok.com and the social network X. This new model features improvements such as faster reasoning, better emotional intelligence, and a significantly reduced hallucination rate. Currently, there is no API access available for developers. The launch comes as Google prepares to unveil its Gemini 3 model, which has been recognized as the most powerful large language model by independent evaluators.

Musk's xAI launches Grok 4.1 with lower hallucination rate on the web and apps — no API access (for now)

<a href="https://writer.com/">Writer</a>, a San Francisco-based artificial intelligence startup, is launching a unified AI agent platform designed to let any employee automate complex business workflows without writing code — a capability the company says distinguishes it from consumer-oriented tools like <a href="https://copilot.microsoft.com/">Microsoft Copilot</a> and <a href="https://chatgpt.com/">ChatGPT</a>.The platform, called <a href="https://writer.com/agents/">Writer Agent</a>, combines chat-based assistance with autonomous task execution in a single interface. Starting Tuesday, enterprise customers can use natural language to instruct the AI to create presentations, analyze financial data, generate marketing campaigns, or coordinate across multiple business systems like Salesforce, Slack, and Google Workspace—then save those workflows as reusable &quot;Playbooks&quot; that run automatically on schedules.The announcement comes as enterprises struggle to move AI initiatives beyond pilot programs into production at scale. Writer CEO May Habib has been outspoken about this challenge, recently revealing that 42% of Fortune 500 executives surveyed by her company said AI is &quot;<a href="https://venturebeat.com/ai/ai-is-tearing-companies-apart-writer-ai-ceo-slams-fortune-500-leaders-for">tearing their company apart</a>&quot; due to coordination failures between departments.&quot;We&#x27;re delivering an agent interface that is both incredibly powerful and radically simple to transform individual productivity into organizational impact,&quot; Habib said in a statement. &quot;Writer Agent is the difference between a single sales rep asking a chatbot to write an outreach email and an enterprise ensuring that 1,000 reps are all sending on-brand, compliant, and contextually-aware messages to target accounts.&quot;<h2>How Writer is putting workflow automation in the hands of non-technical workers</h2>The platform&#x27;s core innovation centers on making workflow automation accessible to non-technical employees—what Writer executives call &quot;democratizing who gets to be a builder.&quot;In an exclusive interview with VentureBeat, Doris Jwo, Writer&#x27;s director of product management, demonstrated how the system works: A user types a request in plain English — for example, &quot;Create a two-page partnership proposal between [Company A] and [Company B], make it a branded deck, include impact metrics and partnership tiers.&quot;The AI agent then breaks down that request into discrete steps, conducts web research, generates graphics and charts on the fly, creates individual slides with sourced information, and assembles a complete presentation. The entire process, which might take an employee hours or days, can be completed in 10-12 minutes.&quot;The agent basically looks at the request, breaks it down, does research, understands what pieces it needs, creates a detailed plan at a step-by-step level,&quot; Jwo explained during a product demonstration. &quot;It might say, &#x27;I need to do web research,&#x27; or &#x27;This user needs information from Gong or Slack,&#x27; and it reaches out to those connectors, grabs the data, and executes the plan.&quot;Crucially, users can save these multi-step processes as Playbooks—reusable templates that colleagues can deploy with a single click. Routines allow those Playbooks to run automatically at scheduled intervals, essentially putting knowledge work &quot;on autopilot.&quot;<h2>Security and compliance controls: Writer&#x27;s answer to enterprise IT concerns</h2>Writer positions these enterprise-focused controls as a key differentiator from competitors. While <a href="https://microsoft.com/">Microsoft</a>, <a href="https://openai.com/">OpenAI</a>, and <a href="https://www.anthropic.com/">Anthropic</a> offer powerful AI capabilities, Writer&#x27;s executives argue those tools weren&#x27;t designed from the ground up for the security, compliance, and governance requirements of large regulated organizations.&quot;All of the products you mentioned are great products, but even Copilot is very much focused on personal productivity—summarizing email, for example, which is important, but that&#x27;s not the component we&#x27;re focusing on,&quot; said Matan-Paul Shetrit, Writer&#x27;s director of product management, in an exclusive interview with VentureBeat.Shetrit emphasized Writer&#x27;s &quot;trust, security, and interoperability&quot; approach. IT administrators can granularly control what the AI can access — for instance, preventing market research agents from mentioning competitors, or restricting which employees can use web search capabilities. All activity is logged with detailed audit trails showing exactly what data the agent touched and what actions it took.&quot;These fine-grained controls are what make products enterprise-ready,&quot; Shetrit said. &quot;We can deploy to tens of thousands or hundreds of thousands of employees while maintaining the security and guardrails you need for that scale.&quot;This architecture reflects <a href="https://writer.com/company/about/">Writer&#x27;s origin story</a>. Unlike OpenAI or Anthropic, which started as research labs and later added enterprise offerings, Writer has targeted Fortune 500 companies since its 2020 founding. &quot;We&#x27;re not a research lab that went to consumer and is dabbling in enterprise,&quot; Shetrit said. &quot;We are first and foremost targeting the Global 2000 and Fortune 500, and our research is in service of these customers&#x27; needs.&quot;<h2>Inside Writer&#x27;s strategy to connect AI agents across enterprise software systems</h2>A critical technical component is Writer&#x27;s approach to system integrations. The platform includes pre-built connectors to more than a dozen enterprise applications—<a href="https://workspace.google.com/lp/business/?utm_source=google&amp;utm_medium=cpc&amp;utm_campaign=1710046-Workspace-DR-NA-US-en-Google-BKWS-EXA-na&amp;utm_content=c-Hybrid+%7C+BKWS+-+EXA+%7C+Txt-Google+Workspace-Core-346911454270&amp;utm_term=google%20workspace&amp;gclsrc=aw.ds&amp;gad_source=1&amp;gad_campaignid=20159848966&amp;gclid=CjwKCAiAz_DIBhBJEiwAVH2XwDbhidrT22spa5fU8uNyySir49xeTOAji42tQEy7zvzTZpUAL4TFgRoCLz8QAvD_BwE">Google Workspace</a>, <a href="https://www.microsoft.com/en-us/microsoft-365">Microsoft 365</a>, <a href="https://www.snowflake.com/en/">Snowflake</a>, <a href="https://asana.com/">Asana</a>, <a href="https://slack.com/">Slack</a>, <a href="https://www.gong.io/">Gong</a>, <a href="https://www.hubspot.com">HubSpot</a>, <a href="https://www.atlassian.com/">Atlassian</a>, <a href="https://www.databricks.com/">Databricks</a>, <a href="https://pitchbook.com/">PitchBook</a>, and <a href="https://www.factset.com/">FactSet</a>—allowing the AI to retrieve information and take actions across those systems.Writer built these connectors using the <a href="https://www.anthropic.com/news/model-context-protocol">Model Context Protocol (MCP)</a>, an emerging standard for AI system integrations, but added what Shetrit described as an &quot;enterprise-ready&quot; layer on top.&quot;We took a first-principle approach of: You have this MCP connector infrastructure—how do you build it in a way that&#x27;s enterprise-ready?&quot; Shetrit explained. &quot;What we have today in the industry is definitely not it.&quot;The system can write and execute code on the fly to handle unexpected scenarios. If a user uploads an unfamiliar file format, for instance, the agent will generate code to extract and process the text without requiring a human to intervene.Jwo demonstrated this capability with a daily workflow she runs: Every morning at 10 a.m., a Routine automatically summarizes her Google Calendar meetings, identifies external participants, finds their LinkedIn profiles, and sends the summary to her via Slack — all without her involvement.&quot;This was pretty simple, but you can imagine for a salesperson it might say, &#x27;At the end of the day, wrap up a summary of all the calls I had, send me action items, post it to the account-specific Slack channel, and tag these folks so they can accomplish those workflows,&#x27;&quot; Jwo said. &quot;That can run continuously each day, each week, or on demand.&quot;<h2>From mortgage lenders to CPG brands: Real-world AI agent use cases across industries</h2>The platform is attracting customers across multiple industries. <a href="https://www.newamericanfunding.com/">New American Funding</a>, a mortgage lender, uses Writer Agent to automate marketing workflows. Senior Content Marketing Manager Karen Rodriguez uploads Asana project tickets with creative briefs, and the AI executes tasks like updating email campaigns or transforming articles into social media carousels, video scripts, and captions.Other use cases span financial services teams creating investment dashboards with <a href="https://pitchbook.com/">PitchBook</a> and <a href="https://www.factset.com/">FactSet</a> data, consumer packaged goods companies brainstorming new product lines based on social media trends, and marketing teams generating partnership presentations with branded assets.Writer has added customers including <a href="https://www.tiktok.com/en/">TikTok</a>, <a href="https://www.cmcsa.com/">Comcast</a>, <a href="https://www.keurigdrpepper.com/">Keurig Dr Pepper</a>, <a href="https://www.caa.com/">CAA</a>, and <a href="https://www.aptitudehealth.com/">Aptitude Health</a>, joining an existing base that includes <a href="https://www.accenture.com/">Accenture</a>, <a href="https://www.qualcomm.com/">Qualcomm</a>, <a href="https://www.uber.com/">Uber</a>, <a href="https://investor.vanguard.com/corporate-portal">Vanguard</a>, and <a href="https://www.marriott.com/default.mi">Marriott</a>. The company now serves more than 300 enterprises and has secured over $50 million in signed contracts, with projections to double that to $100 million this year.The startup&#x27;s net retention rate — a measure of how much existing customers expand their usage — stands at 160%, meaning customers on average increase their spending by 60% after initial contracts. Twenty customers who started with $200,000-$300,000 contracts now spend about $1 million annually, according to company data.<h2>&#x27;Vibe working&#x27;: Writer&#x27;s vision for AI-powered productivity beyond coding</h2>Writer executives frame the platform as enabling what they call &quot;vibe working&quot; — a playful reference to the popular term &quot;vibe coding,&quot; which describes AI tools like Cursor that dramatically accelerate software development.&quot;We used to call it transformation when we took 12 steps and made them nine. That&#x27;s optimizing the world as it is,&quot; Habib said at Writer&#x27;s AI Leaders Forum earlier this month, according to <a href="https://www.forbes.com/sites/stevenwolfepereira/2025/11/08/what-writers-ai-leaders-forum-revealed-about-enterprise-ai/">Forbes</a>. &quot;We can now create a new world. That is the greenfield mindset.&quot;Shetrit echoed this framing: &quot;Vibe coding is the theme of 2025. Our view is that ‘vibe working’ is the theme of 2026. How do you bring the same productivity gains you&#x27;ve seen with coding agents into the workspace in a way that non-technical users can maximize them?&quot;The platform is powered by <a href="https://writer.com/engineering/long-context-palmyra-x5/">Palmyra X5</a>, Writer&#x27;s proprietary large language model featuring a one-million-token context window — among the largest commercially available. Writer trained the model for approximately $700,000, a fraction of the estimated $100 million OpenAI spent on GPT-4, by using synthetic data and techniques that halt training when returns diminish.The model can process one million tokens in about 22 seconds and costs 60 cents per million input tokens and $6 per million output tokens — significantly cheaper than comparable offerings, according to company specifications.<h2>Making AI Decisions Visible: Writer&#x27;s Approach to Trust and Transparency</h2>A distinctive aspect of Writer&#x27;s approach is transparency into the AI&#x27;s decision-making process. The interface displays the agent&#x27;s step-by-step reasoning, showing which data sources it accessed, what code it generated, and how it arrived at outputs.&quot;There&#x27;s a very clear exhibition of how the agent is thinking, what it&#x27;s doing, what it&#x27;s touching,&quot; Shetrit said. &quot;This is important for the end user to trust it, but also important for the IT person or security professional to see what&#x27;s going on.&quot;This &quot;supervision&quot; model goes beyond simple observability of API calls to encompass what Shetrit described as &quot;a superset of observability&quot; — giving organizations the ability to not just monitor but control AI behavior through policies and permissions.Session logs capture all agent activity when enabled by administrators, and users can submit feedback on every output to help improve system performance. The platform also emphasizes providing sources and citations for generated content, allowing users to verify information.&quot;With any sort of chat assistant, agentic or not, trust but verify is really important,&quot; Jwo said. &quot;That&#x27;s part of the pillars of us building this and making it enterprise-grade.&quot;<h2>What Writer Agent Costs—and Why It&#x27;s Included in the Base Platform</h2>Writer is including all the new capabilities—Playbooks, Routines, Connectors, and Personality customization—as part of its core platform without additional charges, according to Jwo.&quot;This is fully included as part of the Writer platform,&quot; she said. &quot;We&#x27;re not charging additional for using Writer Agent.&quot;The &quot;Personality&quot; feature allows individual users, teams, or entire organizations to customize the AI&#x27;s communication style, ensuring generated content matches brand voice and tone guidelines. This works alongside company-level controls that enforce terminology and style requirements.For highly structured, repetitive tasks, Writer also offers a library of more than 100 pre-built agents and an AI Studio for building custom multi-agent systems aligned with specific business use cases.<h2>The Race to Define Enterprise AI: Can Purpose-Built Platforms Beat Tech Giants?</h2>The launch crystallizes a fundamental tension in how enterprises will adopt AI at scale. While consumer-facing AI tools emphasize individual productivity gains, companies need systems that work reliably across thousands of employees, integrate with existing software infrastructure, maintain regulatory compliance, and deliver measurable business impact.Writer&#x27;s wager is that these requirements demand purpose-built enterprise platforms rather than consumer tools adapted for business use. The company&#x27;s <a href="https://techcrunch.com/2024/11/12/generative-ai-startup-writer-raises-200m-at-a-1-9b-valuation/">$1.9 billion valuation</a> — achieved in a November 2024 funding round that raised $200 million — suggests investors see merit in this thesis. Backers include <a href="https://www.premjiinvest.com/">Premji Invest</a>, <a href="https://radical.vc/">Radical Ventures</a>, <a href="https://www.iconiqcapital.com/growth">ICONIQ Growth</a>, <a href="https://salesforceventures.com/">Salesforce Ventures</a>, and <a href="https://www.adobe.com/ventures.html">Adobe Ventures</a>.Yet the competitive landscape remains formidable. Microsoft and Google command enormous distribution advantages through their existing enterprise software relationships. OpenAI and Anthropic possess research capabilities that have produced breakthrough models. Whether Writer can maintain its differentiation as these giants expand their enterprise offerings will test the startup&#x27;s core premise: that serving Fortune 500 companies from day one creates advantages that research labs turned enterprise vendors cannot easily replicate.&quot;We&#x27;re entering an era where if you can describe a better way to work, you can build it,&quot; Jwo said. &quot;The new Writer Agent democratizes who gets to be a builder, empowering the operational experts and creative problem-solvers in every department to become the architects of their own transformation. That&#x27;s how you unlock innovation that competitors can&#x27;t replicate.&quot;The promise is alluring — AI capabilities powerful enough to transform how work gets done, accessible enough for any employee to use, and controlled enough for enterprises to deploy safely at scale. Whether Writer can deliver on that promise at the speed and scale required will determine if its vision of &quot;vibe working&quot; becomes the 2026 theme Shetrit predicts, or just another ambitious attempt to solve enterprise AI&#x27;s execution problem.But one thing is certain: In a market where 85% of AI initiatives fail to escape pilot purgatory, Writer is betting that the winners won&#x27;t be the companies with the most powerful models—they&#x27;ll be the ones that make those models actually work inside the enterprise.

تقوم شركة Writer، وهي شركة ناشئة في مجال الذكاء الاصطناعي مقرها في سان فرانسيسكو، بإطلاق منصة موحدة لوكلاء الذكاء الاصطناعي تهدف إلى تمكين الموظفين من أتمتة سير العمل التجاري المعقد دون الحاجة إلى كتابة كود. تُسمى المنصة Writer Agent، وتجمع بين المساعدة القائمة على الدردشة وتنفيذ المهام بشكل مستقل. اعتبارًا من يوم الثلاثاء، يمكن للعملاء من الشركات استخدام اللغة الطبيعية لتوجيه الذكاء الاصطناعي لإنشاء العروض التقديمية وتحليل البيانات المالية، وحفظ هذه العمليات كـ 'Playbooks' قابلة لإعادة الاستخدام تعمل تلقائيًا.

Writer, una startup de inteligencia artificial con sede en San Francisco, está lanzando una plataforma unificada de agentes de IA que permite a los empleados automatizar flujos de trabajo comerciales complejos sin necesidad de programar. La plataforma, llamada Writer Agent, combina asistencia basada en chat con ejecución autónoma de tareas. A partir del martes, los clientes empresariales pueden usar lenguaje natural para instruir a la IA en tareas como crear presentaciones y analizar datos financieros, guardando estos flujos de trabajo como 'Playbooks' reutilizables que se ejecutan automáticam…

Writer, une startup d'IA basée à San Francisco, lance une plateforme d'agents IA unifiée permettant aux employés d'automatiser des flux de travail commerciaux complexes sans coder. La plateforme, nommée Writer Agent, intègre une assistance par chat avec une exécution autonome des tâches. À partir de mardi, les clients d'entreprise peuvent utiliser un langage naturel pour demander à l'IA de créer des présentations et d'analyser des données financières, en sauvegardant ces flux de travail sous forme de 'Playbooks' réutilisables qui s'exécutent automatiquement.

Writer, a San Francisco-based AI startup, is launching a unified AI agent platform that allows employees to automate complex business workflows without coding. The platform, named Writer Agent, integrates chat-based assistance with autonomous task execution. Starting Tuesday, enterprise customers can use natural language to instruct the AI for tasks such as creating presentations and analyzing financial data, saving these workflows as reusable 'Playbooks' that run automatically.

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