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Writer unveils ‘AI HQ’ platform, betting on agents to transform enterprise work

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Enterprise AI company Writer unveiled a new platform today that it claims will help businesses finally bridge the gap between AI’s theoretical potential and real-world results. The product, called “AI HQ,” represents a significant shift toward […]

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Enterprise AI company Writer unveiled a new platform today that it claims will help businesses finally bridge the gap between AI’s theoretical potential and real-world results. The product, called “AI HQ,” represents a significant shift toward autonomous AI systems that can execute complex workflows across organizations.

“This is not another hype train, but a massive change coming to enterprise software,” said May Habib, Writer’s CEO and co-founder, at a press conference announcing the product. “The vast majority of the enterprise has not gotten meaningful results from generative AI, and it’s been two years. There has never before been such a gap between what the tech is capable of and what the enterprise results have been.”

AI HQ is Writer’s answer to this problem—a platform for building, activating, and supervising AI “agents” that can perform sequences of tasks traditionally requiring human intervention. These agents can make decisions, reason through problems, and take actions across different systems with little human oversight.

How Writer’s AI agents move beyond chatbots to deliver real business value

The announcement comes as many enterprises reevaluate their AI strategies. According to Habib, most AI implementations have failed to deliver substantial value, with businesses struggling to move beyond basic generative AI use cases.

“Process mapping is the new prompt engineering,” Habib said, highlighting how the company’s approach has evolved beyond simply crafting the right text prompts to designing entire workflows for AI systems.

AI HQ consists of three main components: a development environment called Agent Builder where IT and business teams collaboratively create agents; Writer Home, which provides access to over 100 pre-built agents for specific industries and functions; and observability tools for monitoring and governing agent behavior at scale.

During a product demonstration, Writer executives showed how customers are already using these technologies. In one example, an investment management firm uses Writer’s agents to automatically generate fund reports and personalized market commentary by pulling data from Snowflake, SEC filings, and real-time web searches.

Another demonstration showed a marketing workflow where an agent could analyze a strategy brief, create a project in Adobe Workfront, generate content, find or create supporting images, and prepare the material for legal review.

Enterprise AI that actually works: How Writer’s autonomous agents tackle complex business workflows

Writer’s pivot to agent-based AI reflects broader market trends. While many companies initially focused on using large language models for text generation and chat functions, businesses are increasingly exploring how AI can automate complex processes.

“Ten percent of the headcount is going to be enough,” Habib told Forbes in a recent interview about the potential workforce impact of agent technologies. This dramatic assertion underscores the transformative potential—and potential disruption—these technologies may bring to knowledge work.

Anna Griffin, Chief Marketing Officer at cybersecurity firm Commvault and an early adopter of Writer’s agent technology, spoke during the press conference about the value of connecting previously siloed systems.

“What if I could connect our Salesforce, Gainsite, Optimizely? What if I could pull together enough of the insights across these systems that we could actually work to create an experience for our customer that is seamless?” Griffin said. Her advice for others: “Think about the hardest, gnarliest problem your industry has, and start thinking about how agentic AI is going to solve that.”

The future of AI learning: Writer’s self-evolving models remember mistakes and learn without retraining

The event also featured a presentation from Waseem AlShikh, Writer’s co-founder and CTO, who unveiled research into “self-evolving models” — AI systems that can learn from their mistakes over time without additional training.

“If we expect AI to behave more like a human, we need it to learn more like a human,” AlShikh explained. He demonstrated how traditional AI models repeatedly make the same errors when faced with a maze challenge, while self-evolving models remember past failures and find better solutions.

“This unique architecture means that over time, as the model is used, it gains knowledge — a model that gets smarter the more you engage with it,” AlShikh said. Writer expects to have self-evolving models in pilot by the end of the year.

Inside Writer’s $1.9 billion valuation: How enterprise AI adoption is driving explosive growth

Writer’s aggressive expansion comes after raising $200 million in Series C funding last November, which valued the company at $1.9 billion. The funding round was co-led by Premji Invest, Radical Ventures, and ICONIQ Growth, with participation from major enterprise players including Salesforce Ventures, Adobe Ventures, and IBM Ventures.

The company has witnessed impressive growth, with a reported 160% net retention rate, meaning customers typically expand their contracts by 60% on average after initial adoption. According to a Forbes report published today, some clients have grown from initial contracts of $200,000-$300,000 to spending approximately $1 million each.

Writer’s approach differs from competitors like OpenAI and Anthropic, which have raised billions but focus more on developing general-purpose AI models. Instead, Writer has developed its own models — named Palmyra—specifically designed for enterprise use cases.

“We trained our own models even though everyone advised against it,” AlShikh told Forbes. This strategy has allowed Writer to create AI that’s more secure for enterprise deployment, as client data is retrieved from dedicated servers and isn’t used to train models, mitigating concerns about sensitive information leaks.

Navigating the $114 billion enterprise AI market: Opportunities and obstacles ahead

Writer’s ambitions face obstacles in a competitive landscape. The enterprise AI software market — projected to grow from $58 billion to $114 billion by 2027 — is attracting intense competition from established tech giants and well-funded startups alike.

Paul Dyrwal, VP of Generative AI at Marriott who appeared at Writer’s press conference, shared advice for enterprises navigating this rapidly evolving field: “Focus on fewer, higher-value opportunities rather than chasing every possibility.”

The announcement also comes amid growing concerns about AI’s impact on jobs. While Habib acknowledged that AI will change work dramatically, she painted an optimistic picture of the transition.

“Your people are instrumental to redesigning your processes to be AI-native and shaping what the future of work looks like,” she said. “We think that very soon, on a horizon of five to 10 years, we won’t be doing work as much as we will be building AI that does the work. This will create exciting new roles, new AI-related jobs that are interesting and rewarding.”

From software vendor to innovation partner: Writer’s vision for AI-native enterprise transformation

As Writer positions itself at the forefront of enterprise AI, Habib emphasized that the company sees itself as more than just a software vendor.

“We’re not a software vendor here. We see ourselves as more than that. We’re your innovation partners,” she said. “If you want to rebuild your company to be AI-native, if you want to be part of the most important enterprise transformation maybe ever, go sign up to be in the Writer agent beta right now. Together, we can dream big and build fast.”

The Agent Builder and observability tools are currently in beta, with general availability expected later this spring, while the Writer Home and library of ready-to-use agents are available to all customers starting today.

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Chevron Confirms Job Cuts to Rigzone

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IBM Cloud stumbles again: second major outage in two weeks

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OPEC Raises Oil Output as Group Begins Bumper Set of Increases

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Oil Extends Gains as Canada Wildfires Offset OPEC+ Output Hike

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Anti-renewables bills die in Texas House, but may reemerge in 2027

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Petronas Is Said to Explore Sale of $7B Canadian Business

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Amid rising energy prices, senators call for EPA to maintain Energy Star

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White House names energy attorney Swett to replace FERC’s Christie

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Enterprises face data center power design challenges

” Now, with AI, GPUs need data to do a lot of compute and send that back to another GPU. That connection needs to be close together, and that is what’s pushing the density, the chips are more powerful and so on, but the necessity of everything being close together is what’s driving this big revolution,” he said. That revolution in new architecture is new data center designs. Cordovil said that instead of putting the power shelves within the rack, system administrators are putting a sidecar next to those racks and loading the sidecar with the power system, which serves two to four racks. This allows for more compute per rack and lower latency since the data doesn’t have to travel as far. The problem is that 1 mW racks are uncharted territory and no one knows how to manage the power, which is considerable now. ”There’s no user manual that says, hey, just follow this and everything’s going to be all right. You really need to push the boundaries of understanding how to work. You need to start designing something somehow, so that is a challenge to data center designers,” he said. And this brings up another issue: many corporate data centers have power plugs that are like the ones that you have at home, more or less, so they didn’t need to have an advanced electrician certification. “We’re not playing with that power anymore. You need to be very aware of how to connect something. Some of the technicians are going to need to be certified electricians, which is a skills gap in the market that we see in most markets out there,” said Cordovil. A CompTIA A+ certification will teach you the basics of power, but not the advanced skills needed for these increasingly dense racks. Cordovil

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HPE Nonstop servers target data center, high-throughput applications

HPE has bumped up the size and speed of its fault-tolerant Nonstop Compute servers. There are two new servers – the 8TB, Intel Xeon-based Nonstop Compute NS9 X5 and Nonstop Compute NS5 X5 – aimed at enterprise customers looking to upgrade their transaction processing network infrastructure or support larger application workloads. Like other HPE Nonstop systems, the two new boxes include compute, software, storage, networking and database resources as well as full-system clustering and HPE’s specialized Nonstop operating system. The flagship NS9 X5 features support for dual-fabric HDR200 InfiniBand interconnect, which effectively doubles the interconnect bandwidth between it and other servers compared to the current NS8 X4, according to an HPE blog detailing the new servers. It supports up to 270 networking ports per NS9 X system, can be clustered with up to 16 other NS9 X5s, and can support 25 GbE network connectivity for modern data center integration and high-throughput applications, according to HPE.

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AI boom exposes infrastructure gaps: APAC’s data center demand to outstrip supply by 42%

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Cisco bolsters DNS security package

The software can block domains associated with phishing, malware, botnets, and other high-risk categories such as cryptomining or new domains that haven’t been reported previously. It can also create custom block and allow lists and offers the ability to pinpoint compromised systems using real-time security activity reports, Brunetto wrote. According to Cisco, many organizations leave DNS resolution to their ISP. “But the growth of direct enterprise internet connections and remote work make DNS optimization for threat defense, privacy, compliance, and performance ever more important,” Cisco stated. “Along with core security hygiene, like a patching program, strong DNS-layer security is the leading cost-effective way to improve security posture. It blocks threats before they even reach your firewall, dramatically reducing the alert pressure your security team manages.” “Unlike other Secure Service Edge (SSE) solutions that have added basic DNS security in a ‘checkbox’ attempt to meet market demand, Cisco Secure Access – DNS Defense embeds strong security into its global network of 50+ DNS data centers,” Brunetto wrote. “Among all SSE solutions, only Cisco’s features a recursive DNS architecture that ensures low-latency, fast DNS resolution, and seamless failover.”

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HPE Aruba unveils raft of new switches for data center, campus modernization

And in large-scale enterprise environments embracing collapsed-core designs, the switch acts as a high-performance aggregation layer. It consolidates services, simplifies network architecture, and enforces security policies natively, reducing complexity and operational cost, Gray said. In addition, the switch offers the agility and security required at colocation facilities and edge sites. Its integrated Layer 4 stateful security and automation-ready platform enable rapid deployment while maintaining robust control and visibility over distributed infrastructure, Gray said. The CX 10040 significantly expands the capacity it can provide and the roles it can serve for enterprise customers, according to one industry analyst. “From the enterprise side, this expands on the feature set and capabilities of the original 10000, giving customers the ability to run additional services directly in the network,” said Alan Weckel, co-founder and analyst with The 650 Group. “It helps drive a lower TCO and provide a more secure network.”  Aimed as a VMware alternative Gray noted that HPE Aruba is combining its recently announced Morpheus VM Essentials plug-in package, which offers a hypervisor-based package aimed at hybrid cloud virtualization environments, with the CX 10040 to deliver a meaningful alternative to Broadcom’s VMware package. “If customers want to get out of the business of having to buy VM cloud or Cloud Foundation stuff and all of that, they can replace the distributed firewall, microsegmentation and lots of the capabilities found in the old VMware NSX [networking software] and the CX 10k, and Morpheus can easily replace that functionality [such as VM orchestration, automation and policy management],” Gray said. The 650 Group’s Weckel weighed in on the idea of the CX 10040 as a VMware alternative:

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Indian startup Refroid launches India’s first data center CDUs

They use heat exchangers and pumps to regulate the flow and temperature of fluid delivered to equipment for cooling, while isolating the technology cooling system loop from facility systems. The technology addresses limitations of traditional air cooling, which industry experts say cannot adequately handle the heat generated by modern AI processors and high-density computing applications. Strategic significance for India Industry analysts view the development as a critical milestone for India’s data center ecosystem. “India generates 20% of global data, yet contributes only 3% to global data center capacity. This imbalance is not merely spatial — it’s systemic,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “The emergence of indigenously developed CDUs signals a strategic pivot. Domestic CDU innovation is a defining moment in India’s transition from data centre host to technology co-creator.” Neil Shah, VP for research and partner at Counterpoint Research, noted that major international players like Schneider, Vertiv, Asetek, Liquidstack, and Zutacore have been driving most CDU deployments in Indian enterprises and data centers. “Having a local indigenous CDU tech and supplier designed with Indian weather, infrastructure and costs in mind expands options for domestic data center demand,” he said. AI driving data center cooling revolution India’s data center capacity reached approximately 1,255 MW between January and September 2024 and was projected to expand to around 1,600 MW by the end of 2024, according to CBRE India’s 2024 Data Center Market Update. Multiple market research firms have projected the India data center market to grow from about $5.7 billion in 2024 to $12 billion by 2030. Bhavaraju cited aggressive projections for the sector’s expansion, with AI workloads expected to account for 30% of total workloads by 2030. “All of them need liquid cooling,” he said, noting that “today’s latest GPU servers – GB200 from Nvidia

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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