Large language models are now being used for evaluation and judgment tasks, extending beyond their traditional role of text generation. This has led to “LLM-as-a-Judge,” where models assess outputs from other language models. Such evaluations are essential in reinforcement learning pipelines, benchmark testing, and system alignment. These judge models rely on internal chain-of-thought reasoning, mirroring human judgment processes. Unlike conventional reward models that provide direct scores, these models simulate thoughtful evaluation, making them better suited for complex tasks such as math problem-solving, ethical reasoning, and user intent interpretation. Their ability to interpret and validate responses across languages and domains enhances automation and scalability in language model development. However, current AI judgment systems face issues with inconsistency and shallow reasoning. Many rely on basic metrics or static annotations, which are inadequate for evaluating subjective or open-ended prompts. A common problem is position bias, where the order of answers affects the final decision, compromising fairness. Also, collecting human-annotated data at scale is costly and time-consuming, limiting the generalizability of these models. Several existing approaches have addressed these challenges, but with limited success. Systems like EvalPlanner and DeepSeek-GRM rely on human-labeled data or rigid training schemes, which limit adaptability across task types. Others, like DeepSeek-R1, depend on distillation from large models but perform poorly on ambiguous prompts. Static datasets and offline tuning strategies hinder dynamic reasoning, while newer methods using score formatting or structured prompts have shown minimal accuracy improvements. Despite larger datasets and models, performance gains in traditional systems have stalled. Researchers from Meta’s GenAI and FAIR teams introduced J1 to address the above limitations. J1 trains judgment models through a reinforcement learning-based framework, making them capable of learning through verifiable reward signals. The team used synthetic data to create high-quality and low-quality responses to a prompt, transforming subjective tasks into verifiable pairwise judgments. This synthetic dataset included 22,000 preference pairs, split between 17,000 prompts from the WildChat corpus and 5,000 mathematical queries. These were used to train two versions of J1: J1-Llama-8B and J1-Llama-70B, initialized from the Llama-3.1-8B-Instruct and Llama-3.3-70B-Instruct base models, respectively. The models were trained using Group Relative Policy Optimization (GRPO), a reinforcement algorithm that eliminates the need for critic models and accelerates convergence. At the training strategy’s core is position-agnostic learning, where both (x, a, b) and (x, b, a) input formats are used in training to prevent position bias. Also, consistency-based rewards are applied only when the model delivers correct verdicts across both answer orderings. This structure allows the judge to be fair and reliable regardless of prompt or answer order. The training framework supports multiple variations: models can output final verdicts, numeric scores for each answer, or both. A pointwise judging variant is included, which evaluates single responses using scores from 0 to 10. These formats make J1 a versatile and generalizable system capable of judging various tasks. The results obtained using the J1 models reveal substantial performance improvements over existing systems. On the widely used Preference Proxy Evaluations (PPE) benchmark, J1-Llama-70B achieved an overall accuracy of 69.6%, outperforming models trained with over ten times more data. In contrast, models like DeepSeek-GRM-27B and EvalPlanner-Llama-70B scored 67.2% and 65.6%, respectively. Even the smaller J1-Llama-8B model exceeded baseline systems like EvalPlanner-Llama-8B, scoring 62.2% versus 55.5%. J1 also showed top-tier performance on other critical benchmarks such as RewardBench, RM-Bench, JudgeBench, and FollowBenchEval, demonstrating robust generalization across verifiable and subjective tasks. These improvements are not just marginal but significant, considering the limited training data used in J1 compared to the expansive datasets in other models. Several Key Takeaways from the Research on J1: J1 is trained using 22,000 synthetic preference pairs, including 17K from WildChat and 5K from MATH tasks. The training uses GRPO, which streamlines RL by avoiding the need for separate critic models. It introduces position-agnostic learning, reducing position bias through consistency-based rewards. Two main model variants, J1-Llama-8B and J1-Llama-70B, were trained on modest data but outperformed large-scale models. J1-Llama-70B scored 69.6% on PPE, exceeding DeepSeek-GRM-27B (67.2%) and EvalPlanner-Llama-70B (65.6%). Supports multiple judgment formats: pairwise with verdicts, pairwise with scores, and pointwise scores. Surpasses models distilled from DeepSeek-R1 and OpenAI’s o1-mini on several tasks. Demonstrates that reasoning quality, not just dataset size, is critical for accurate judgments. J1’s framework makes it a generalist judge applicable to verifiable and non-verifiable tasks. In conclusion, the J1 approach fundamentally redefines how judgment models are trained and evaluated. Synthetic data and reinforcement learning bypass the traditional need for costly annotations while promoting fair, logical, and consistent evaluations. This work illustrates that reasoning-driven judging can outperform larger models that rely heavily on data volume and static alignment techniques. It also validates the notion that judgment models should be thinkers first, and scorers second. With performance that rivals and often surpasses state-of-the-art systems, J1 sets a new benchmark in training LLM-as-a-Judge systems. Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Asif RazzaqWebsite |  + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/Sampling Without Data is Now Scalable: Meta AI Releases Adjoint Sampling for Reward-Driven Generative ModelingAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Google AI Releases MedGemma: An Open Suite of Models Trained for Performance on Medical Text and Image ComprehensionAsif Razzaqhttps://www.marktechpost.com/author/6flvq/NVIDIA Releases Cosmos-Reason1: A Suite of AI Models Advancing Physical Common Sense and Embodied Reasoning in Real-World EnvironmentsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Coding Guide to Efficiently Fine-Tune Qwen3-14B Using Unsloth AI on Google Colab with Mixed Datasets and LoRA Optimization

Recorded on May 21, 2025 Big Tech’s appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does even a single AI query use? And what does it mean for the climate? Hear from MIT Technology Review editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O’Donnell as they explore AI’s energy demands now and in the future. Speakers: Mat Honan, editor in chief, Casey Crownhart, climate reporter, and James O’Donnell, AI reporter. Related Coverage: Power Hungry: AI and our energy future We did the math on AI’s energy footprint. Here’s the story you haven’t heard. Everything you need to know about estimating AI’s energy and emissions burden These four charts sum up the state of AI and energy

Recorded on May 21, 2025 Big Tech’s appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does even a single AI query use? And what does it mean for the climate? Hear from MIT Technology Review editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O’Donnell as they explore AI’s energy demands now and in the future. Speakers: Mat Honan, editor in chief, Casey Crownhart, climate reporter, and James O’Donnell, AI reporter. Related Coverage: Power Hungry: AI and our energy future We did the math on AI’s energy footprint. Here’s the story you haven’t heard. Everything you need to know about estimating AI’s energy and emissions burden These four charts sum up the state of AI and energy

Big Tech’s appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does even a single AI query use? And what does it mean for the climate? Hear from MIT Technology Review editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O’Donnell as they explore AI’s energy demands now and in the future. Speakers: Mat Honan, editor in chief, Casey Crownhart, climate reporter, and James O’Donnell, AI reporter. Related Coverage: Power Hungry: AI and our energy future We did the math on AI’s energy footprint. Here’s the story you haven’t heard. Everything you need to know about estimating AI’s energy and emissions burden These four charts sum up the state of AI and energy

Continuous Integration and Continuous Delivery/Deployment (CI/CD) refers to practices that automate how code is developed and released to different environments. CI/CD pipelines are fundamental in modern software development, ensuring code is consistently tested, built, and deployed quickly and efficiently. While CI/CD automation accelerates software delivery, it can also introduce security risks. Without proper security measures, CI/CD workflows can be vulnerable to supply chain attacks, insecure dependencies, and insider threats. To mitigate these risks, organizations must integrate measures for continuous monitoring and enforcing security best practices at every pipeline stage. Securing CI/CD workflows preserves the software delivery process's confidentiality, integrity, and availability. Security challenges and risks in CI/CD workflows While CI/CD workflows offer benefits in terms of automation and speed, they also bring unique security challenges that must be addressed to maintain the integrity of the development process. Some common challenges and risks include: Lack of visibility and inadequate security monitoring: CI/CD workflows involve multiple tools and stages, which make it challenging to maintain security visibility into potential threats. Vulnerabilities, especially in third-party libraries or containerized applications, can introduce security risks that go undetected if not correctly managed. Without centralized monitoring, real-time threat detection and response become difficult. Manual, reactive incident response increases the risk of exploitation. Compliance requirements: Meeting regulatory standards such as GDPR or HIPAA while maintaining fast deployment cycles can be challenging. Organizations must balance enforcing security policies, data protection, and compliance requirements without slowing down their CI/CD workflows.Code and dependency vulnerabilities: Unpatched or outdated dependencies in the workflow can introduce significant security risks. Third-party libraries or outdated packages can become attack vectors if not regularly updated and monitored for vulnerabilities. These risks are increased by the fast pace of CI/CD, where vulnerabilities may go untreated.Container vulnerabilities and image security: While containers are mainly used in CI/CD workflows, they are not safe from security risks. Vulnerabilities in container images, such as outdated software versions, misconfigurations, or insecure base images, present a risk in CI/CD workflows and can be exploited by attackers. Without proper scanning and validation, these weaknesses can propagate through the pipeline.Misconfiguration of CI/CD tools: Improper configuration of CI/CD tools can leave the workflow open to unauthorized access or unintentionally expose sensitive code. Misconfigurations in access control settings can increase the likelihood of privilege escalation or code exposure. Additionally, hardcoded credentials or mismanaged environment variables introduce a risk of being extracted by attackers, which could lead to data breaches.Supply chain attacks: Compromised third-party dependencies can introduce malicious packages or vulnerabilities into the workflow. These vulnerabilities can spread throughout the entire pipeline and infect production environments, primarily when third-party tools or libraries are not sufficiently validated.Insider threats: Insider threats in CI/CD workflows involve authorized users such as developers, DevOps engineers, system administrators, or third-party contractors, who may intentionally or unintentionally compromise the pipeline. Weak authentication mechanisms, inadequate access controls, and a lack of monitoring can increase the risk of unauthorized changes, credential theft, or the introduction of malicious code into the workflow.Enhancing CI/CD workflow security with Wazuh Wazuh is an open source security platform that offers unified XDR and SIEM capabilities for on-premises, containerized, virtualized, and cloud-based environments. Wazuh provides flexibility in threat detection, compliance, incident handling, and third-party integration. Organizations can implement Wazuh to address the challenges and mitigate the risks associated with CI/CD workflow security. Below are some ways Wazuh helps improve security in CI/CD workflows. Log collection and system monitoring Wazuh provides log collection and analysis capabilities to ensure the components of your CI/CD environment are continuously monitored for security threats. It collects and analyzes logs from various CI/CD pipeline components, including servers, containerization and orchestration tools such as Docker and Kubernetes, and version control systems like GitHub. This allows security teams to monitor for unusual activities, unauthorized access, or security breaches across the CI/CD environment. Additionally, the Wazuh File Integrity Monitoring (FIM) capability can detect unauthorized changes in code or configuration files. By monitoring files in real time or on a schedule, Wazuh generates alerts for security teams about file activities like creation, deletion, or modification. Figure 1: Wazuh dashboard showing File Integrity Monitoring (FIM) alerts. Custom rules and streamlined security monitoring Wazuh allows users to create custom rules and alerts that align with a pipeline's security requirements. Organizations can create custom rules matching their specific security needs, such as monitoring code changes, server configurations, or container images. This flexibility allows organizations to enforce granular security controls tailored to their CI/CD workflow. For instance, the Center for Internet Security (CIS) Docker Benchmark provides guidelines for securing Docker environments. Organizations can automate the compliance checks against CIS Docker Benchmark v1.7.0 using the Wazuh Security Configuration Assessment (SCA) capability. Figure 2: Wazuh dashboard showing Wazuh Security configuration assessment (SCA) results. Integration with third-party security tools Wazuh can integrate with various security tools and platforms, including container vulnerability scanners and CI/CD orchestration systems. This is particularly important in CI/CD workflows, where multiple tools may be used to manage the development lifecycle. Wazuh can pull in data from various sources, which helps to provide a centralized view of security across the pipeline. For instance, Wazuh integrates with container vulnerability scanning tools Trivy and Grype, which are commonly used to scan container images for vulnerabilities, insecure base images, or outdated software versions. By scanning container images before they are deployed into production, organizations can ensure that only secure, up-to-date images are used in the deployment processes. You can configure the Wazuh Command module to run a Trivy scan on an endpoint hosting container images and display any detected vulnerabilities in the Wazuh dashboard. This helps to ensure that insecure images are identified and prevented from being pushed into production. Figure 3: Wazuh dashboard displaying vulnerabilities discovered on container images from a Trivy scan. Automated incident response The speed of CI/CD workflows means that threats must be detected and mitigated quickly to minimize the risk of breaches or downtime. Wazuh provides incident response capabilities that help organizations respond to security incidents as soon as they occur. The Wazuh Active Response module can automatically take action when a security threat is detected. For example, suppose a malicious IP address is detected trying to access a system that runs CI/CD processes. In that case, Wazuh can automatically block the IP address and trigger predefined remediation actions. This automation ensures fast response, reduces manual intervention, and prevents potential threats from escalating. Conclusion Securing CI/CD workflows is important for maintaining a reliable and safe software development process. By using Wazuh, organizations can detect vulnerabilities early, monitor for anomalies, enforce compliance, and automate security responses while maintaining the speed and efficiency of CI/CD workflows. Integrating Wazuh into your CI/CD workflow ensures that security keeps pace with development speed. Found this article interesting? This article is a contributed piece from one of our valued partners. Follow us on Twitter  and LinkedIn to read more exclusive content we post.

This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. By putting AI into everything, Google wants to make it invisible If you want to know where AI is headed, this year’s Google I/O has you covered. The company’s annual showcase of next-gen products, which kicked off yesterday, has all of the pomp and pizzazz, the sizzle reels and celebrity walk-ons, that you’d expect from a multimillion dollar marketing event. But it also shows us just how fast this still-experimental technology is being subsumed into a line-up designed to sell phones and subscription tiers. Never before have I seen this thing we call artificial intelligence appear so normal. Read the full story.—Will Douglas Heaven AI could keep us dependent on natural gas for decades to come Last December, Meta announced plans to build a massive $10 billion data center for training its artificial intelligence models in rural northeast Louisiana. Stretching for more than a mile, it will be Meta’s largest in the world, and it will have an enormous appetite for electricity. To power the data center, a Meta contractor called Entergy will build three large natural-gas power plants with a total capacity of 2.3 gigawatts. It’ll also upgrade the grid to accommodate the huge jump in anticipated demand.The choice of natural gas as the go-to solution to meet the growing demand for power from AI is not unique to Louisiana. The fossil fuel is already the country’s chief source of electricity generation, and large natural-gas plants are being built around the country to feed electricity to new and planned AI data centers. That’s all but wiping out any prospect that the US will wean itself off natural gas anytime soon. Read the full story.—David Rotman This story is part of Power Hungry: AI and our energy future—our new series shining a light on AI’s energy usage. Check out the rest of the package here. Take a new look at AI’s energy use Big Tech's appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does a single AI query use? And what does it mean for the climate?  Join editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O'Donnell at 1.30pm ET today for a subscriber-only Roundtables conversation digging into our new package of stories about AI's energy demands now and in the future. Register here.The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Democrats are on the hunt for a digital thought leader They’re (finally) realizing how far they’re lagging behind their opponents’ online efforts these days. (NYT $) + AI’s impact on elections is being overblown. (MIT Technology Review)2 At least two newspapers printed an AI-generated summer reading list 📰 The only problem is, some of the books don’t actually exist. (404 Media)+ It’s a useful reminder to never take anything chatbots produce as fact. (Axios)+ Even regional newspapers aren’t safe from AI slop. (The Atlantic $)+ Why AI hallucinates, and why we can’t stop it. (MIT Technology Review)3 The Earth may already be too hot to maintain polar ice sheetsEven if it stays at current temperature levels. (WP $)+ Why climate researchers are taking the temperature of mountain snow. (MIT Technology Review)4 How New York City’s child abuse algorithm flags families for investigation Critics believe it’s open to racial bias. (The Markup) 5 Here’s what it’s like to interview for a job at DOGE   The hiring process is remarkably fast, for a government entity. (Wired $)+ The department reportedly tried to enter the US government’s publishing operation. (Politico)+ DOGE’s tech takeover threatens the safety and stability of our critical data. (MIT Technology Review)6 Fortnite has finally returned to Apple’s App StoreAfter five years and a lengthy legal battle. (NYT $) + The recent ruling has major implications for the iOS economy. (Reuters)7 Most chatbots can be tricked into dispensing dangerous informationFrom hacking advice, to describing how to make drugs. (The Guardian) + Anthropic has a new way to protect large language models against jailbreaks. (MIT Technology Review)8 Young Indonesians are being trafficked to scam farmsFraudulent job ads on Telegram and Facebook lure them into a life of crime. (Rest of World) + Inside a romance scam compound—and how people get tricked into being there. (MIT Technology Review) 9  Inside the building in China where stolen western iPhones are stripped and sold You’ll find a buyer for every single component inside the Feiyang Times. (FT $)10 Amazon has started randomly refunding customers for old purchasesSome orders were placed as far back as 2018. (Bloomberg $) Quote of the day “Anybody who’s a computer scientist should not be retired right now. They should be working on AI.” —Google cofounder Sergey Brin says people with the right technical skills should copy him and quit being retired, TechCrunch reports. One more thing This fuel plant will use agricultural waste to combat climate change A startup called Mote plans to build a new type of fuel-producing plant in California’s fertile Central Valley that would, if it works as hoped, continually capture and bury carbon dioxide.It’s among a growing number of efforts to commercialize a concept first proposed two decades ago as a means of combating climate change, known as bioenergy with carbon capture and sequestration, or BECCS.It’s an ambitious plan. However, there are serious challenges to doing BECCS affordably and in ways that reliably suck down significant levels of carbon dioxide. Read the full story.—James Temple We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet 'em at me.) + These creepy little Labubu toys are everywhere. But why?+ Happy 25th birthday to one of London’s finest institutions, the Tate Modern gallery.+ Why the Mission Impossible film franchise just won’t die.+ Hummingbirds can fly backwards!? Wow.

This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. By putting AI into everything, Google wants to make it invisible If you want to know where AI is headed, this year’s Google I/O has you covered. The company’s annual showcase of next-gen products, which kicked off yesterday, has all of the pomp and pizzazz, the sizzle reels and celebrity walk-ons, that you’d expect from a multimillion dollar marketing event. But it also shows us just how fast this still-experimental technology is being subsumed into a line-up designed to sell phones and subscription tiers. Never before have I seen this thing we call artificial intelligence appear so normal. Read the full story.—Will Douglas Heaven AI could keep us dependent on natural gas for decades to come Last December, Meta announced plans to build a massive $10 billion data center for training its artificial intelligence models in rural northeast Louisiana. Stretching for more than a mile, it will be Meta’s largest in the world, and it will have an enormous appetite for electricity. To power the data center, a Meta contractor called Entergy will build three large natural-gas power plants with a total capacity of 2.3 gigawatts. It’ll also upgrade the grid to accommodate the huge jump in anticipated demand.The choice of natural gas as the go-to solution to meet the growing demand for power from AI is not unique to Louisiana. The fossil fuel is already the country’s chief source of electricity generation, and large natural-gas plants are being built around the country to feed electricity to new and planned AI data centers. That’s all but wiping out any prospect that the US will wean itself off natural gas anytime soon. Read the full story.—David Rotman This story is part of Power Hungry: AI and our energy future—our new series shining a light on AI’s energy usage. Check out the rest of the package here. Take a new look at AI’s energy use Big Tech's appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does a single AI query use? And what does it mean for the climate?  Join editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O'Donnell at 1.30pm ET today for a subscriber-only Roundtables conversation digging into our new package of stories about AI's energy demands now and in the future. Register here.The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Democrats are on the hunt for a digital thought leader They’re (finally) realizing how far they’re lagging behind their opponents’ online efforts these days. (NYT $) + AI’s impact on elections is being overblown. (MIT Technology Review)2 At least two newspapers printed an AI-generated summer reading list 📰 The only problem is, some of the books don’t actually exist. (404 Media)+ It’s a useful reminder to never take anything chatbots produce as fact. (Axios)+ Even regional newspapers aren’t safe from AI slop. (The Atlantic $)+ Why AI hallucinates, and why we can’t stop it. (MIT Technology Review)3 The Earth may already be too hot to maintain polar ice sheetsEven if it stays at current temperature levels. (WP $)+ Why climate researchers are taking the temperature of mountain snow. (MIT Technology Review)4 How New York City’s child abuse algorithm flags families for investigation Critics believe it’s open to racial bias. (The Markup) 5 Here’s what it’s like to interview for a job at DOGE   The hiring process is remarkably fast, for a government entity. (Wired $)+ The department reportedly tried to enter the US government’s publishing operation. (Politico)+ DOGE’s tech takeover threatens the safety and stability of our critical data. (MIT Technology Review)6 Fortnite has finally returned to Apple’s App StoreAfter five years and a lengthy legal battle. (NYT $) + The recent ruling has major implications for the iOS economy. (Reuters)7 Most chatbots can be tricked into dispensing dangerous informationFrom hacking advice, to describing how to make drugs. (The Guardian) + Anthropic has a new way to protect large language models against jailbreaks. (MIT Technology Review)8 Young Indonesians are being trafficked to scam farmsFraudulent job ads on Telegram and Facebook lure them into a life of crime. (Rest of World) + Inside a romance scam compound—and how people get tricked into being there. (MIT Technology Review) 9  Inside the building in China where stolen western iPhones are stripped and sold You’ll find a buyer for every single component inside the Feiyang Times. (FT $)10 Amazon has started randomly refunding customers for old purchasesSome orders were placed as far back as 2018. (Bloomberg $) Quote of the day “Anybody who’s a computer scientist should not be retired right now. They should be working on AI.” —Google cofounder Sergey Brin says people with the right technical skills should copy him and quit being retired, TechCrunch reports. One more thing This fuel plant will use agricultural waste to combat climate change A startup called Mote plans to build a new type of fuel-producing plant in California’s fertile Central Valley that would, if it works as hoped, continually capture and bury carbon dioxide.It’s among a growing number of efforts to commercialize a concept first proposed two decades ago as a means of combating climate change, known as bioenergy with carbon capture and sequestration, or BECCS.It’s an ambitious plan. However, there are serious challenges to doing BECCS affordably and in ways that reliably suck down significant levels of carbon dioxide. Read the full story.—James Temple We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet 'em at me.) + These creepy little Labubu toys are everywhere. But why?+ Happy 25th birthday to one of London’s finest institutions, the Tate Modern gallery.+ Why the Mission Impossible film franchise just won’t die.+ Hummingbirds can fly backwards!? Wow.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. By putting AI into everything, Google wants to make it invisible If you want to know where AI is headed, this year’s Google I/O has you covered. The company’s annual showcase of next-gen products, which kicked off yesterday, has all of the pomp and pizzazz, the sizzle reels and celebrity walk-ons, that you’d expect from a multimillion dollar marketing event.But it also shows us just how fast this still-experimental technology is being subsumed into a line-up designed to sell phones and subscription tiers. Never before have I seen this thing we call artificial intelligence appear so normal. Read the full story. —Will Douglas Heaven AI could keep us dependent on natural gas for decades to come Last December, Meta announced plans to build a massive $10 billion data center for training its artificial intelligence models in rural northeast Louisiana. Stretching for more than a mile, it will be Meta’s largest in the world, and it will have an enormous appetite for electricity.To power the data center, a Meta contractor called Entergy will build three large natural-gas power plants with a total capacity of 2.3 gigawatts. It’ll also upgrade the grid to accommodate the huge jump in anticipated demand.The choice of natural gas as the go-to solution to meet the growing demand for power from AI is not unique to Louisiana. The fossil fuel is already the country’s chief source of electricity generation, and large natural-gas plants are being built around the country to feed electricity to new and planned AI data centers. That’s all but wiping out any prospect that the US will wean itself off natural gas anytime soon. Read the full story. —David Rotman This story is part of Power Hungry: AI and our energy future—our new series shining a light on AI’s energy usage. Check out the rest of the package here. Take a new look at AI’s energy use Big Tech’s appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does a single AI query use? And what does it mean for the climate? Join editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O’Donnell at 1.30pm ET today for a subscriber-only Roundtables conversation digging into our new package of stories about AI’s energy demands now and in the future. Register here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Democrats are on the hunt for a digital thought leaderThey’re (finally) realizing how far they’re lagging behind their opponents’ online efforts these days. (NYT $) + AI’s impact on elections is being overblown. (MIT Technology Review) 2 At least two newspapers printed an AI-generated summer reading list The only problem is, some of the books don’t actually exist. (404 Media)+ It’s a useful reminder to never take anything chatbots produce as fact. (Axios)+ Even regional newspapers aren’t safe from AI slop. (The Atlantic $)+ Why AI hallucinates, and why we can’t stop it. (MIT Technology Review)3 The Earth may already be too hot to maintain polar ice sheetsEven if it stays at current temperature levels. (WP $)+ Why climate researchers are taking the temperature of mountain snow. (MIT Technology Review) 4 How New York City’s child abuse algorithm flags families for investigationCritics believe it’s open to racial bias. (The Markup) 5 Here’s what it’s like to interview for a job at DOGE  The hiring process is remarkably fast, for a government entity. (Wired $)+ The department reportedly tried to enter the US government’s publishing operation. (Politico)+ DOGE’s tech takeover threatens the safety and stability of our critical data. (MIT Technology Review) 6 Fortnite has finally returned to Apple’s App StoreAfter five years and a lengthy legal battle. (NYT $)+ The recent ruling has major implications for the iOS economy. (Reuters) 7 Most chatbots can be tricked into dispensing dangerous informationFrom hacking advice, to describing how to make drugs. (The Guardian)+ Anthropic has a new way to protect large language models against jailbreaks. (MIT Technology Review) 8 Young Indonesians are being trafficked to scam farmsFraudulent job ads on Telegram and Facebook lure them into a life of crime. (Rest of World)+ Inside a romance scam compound—and how people get tricked into being there. (MIT Technology Review) 9  Inside the building in China where stolen western iPhones are stripped and soldYou’ll find a buyer for every single component inside the Feiyang Times. (FT $) 10 Amazon has started randomly refunding customers for old purchasesSome orders were placed as far back as 2018. (Bloomberg $) Quote of the day “Anybody who’s a computer scientist should not be retired right now. They should be working on AI.” —Google cofounder Sergey Brin says people with the right technical skills should copy him and quit being retired, TechCrunch reports. One more thing This fuel plant will use agricultural waste to combat climate changeA startup called Mote plans to build a new type of fuel-producing plant in California’s fertile Central Valley that would, if it works as hoped, continually capture and bury carbon dioxide.It’s among a growing number of efforts to commercialize a concept first proposed two decades ago as a means of combating climate change, known as bioenergy with carbon capture and sequestration, or BECCS.It’s an ambitious plan. However, there are serious challenges to doing BECCS affordably and in ways that reliably suck down significant levels of carbon dioxide. Read the full story. —James Temple We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + These creepy little Labubu toys are everywhere. But why?+ Happy 25th birthday to one of London’s finest institutions, the Tate Modern gallery.+ Why the Mission Impossible film franchise just won’t die.+ Hummingbirds can fly backwards!? Wow.

Reading Time: 7 minutes The way brands look at data has changed over the past few years. While in the last decade, brands may have focused on data collection (the more data, the merrier), over the passage of time, marketers are dawning upon the realization that unless data is actionable, it’s not really usable. Marketers have struggled for years to draw actionable insight from fragmented customer data that exists in silos across countless tools.  Rather than adding yet another tool to their tech stack to mitigate this silo problem, marketers are re-evaluating their entire tech stack. As a result, forward-thinking brands realize that to unlock truly personalized, real-time customer experiences requires tapping into the single source of truth: their data warehouse. Let’s explore why this shift is happening and how a CDEP like MoEngage is the perfect partner in this new paradigm. From Passive to Active: The Evolution of The Modern Data Warehouses Data warehouses aren’t new. Traditionally, many businesses relied on on-premise warehouses, which were powerful but often rigid, expensive to scale, and sometimes siloed.  The game-changer has been the advent of cloud data warehouses (such as Snowflake, Google BigQuery, Databricks, Amazon Redshift, and Azure Synapse). Cloud data warehouses offer consumer brands the following advantages: Scalability: Easily handle vast and growing datasets without massive upfront infrastructure investment. Flexibility: Integrate structured and semi-structured data from teams like sales, customer support, finance, marketing, etc. Performance: Process complex queries rapidly, enabling faster insights. Accessibility: Democratize data access for various teams (with appropriate governance). Gone are the days when data warehouses were merely passive repositories of data. Today, they often contain the most complete, up-to-date, and trustworthy business view. Benefits of the Warehouse-First Approach for Campaigns Real-time data: Data warehouses often provide real-time data, enabling marketers to understand the pulse of the customer and make decisions based on timely and accurate information rather than the slightly-dated information that ends up in a CDP (customer data platform). Real-time analytics: A warehouse-first approach helps marketers to understand the current state of the business (as in ‘what is happening right now’. This real-time analytics also helps them spot deviations in key performance indicators (KPIs), detect problems proactively, and make faster decisions. Real-time segmentation: Traditional segmentation relies on slightly stale data (hours or days old). By automatically moving customers into or out of segments based on their latest actions/changes in attributes, marketers can personalize the customer experience in real time, thereby becoming more relevant and timely. Data consistency and quality: Loading data into a warehouse involves data cleansing, validation, standardization, and transformation, which improves the quality of data in the warehouse. Speed, scalability, and cost-effectiveness: Data warehouses offer scalable storage and processing power, allowing businesses to adapt to their evolving needs rapidly. Compared to storing and processing data within multiple tools, data warehouses are a more cost-effective way for businesses to manage massive amounts of diverse data. Faster time-to-value (Potentially): Once set up, activating new data points or segments can be quicker if the data already resides in the warehouse. This also means that campaigns can be launched faster vs. when data needs to be copied into a CDP before being sent to an email platform or an SMS automation tool. Improved data governance: By consolidating all data into a central repository, data warehouses ensure that data governance policies, standards, and procedures are standardized across the board.   Benefits of a Warehouse-First Approach For B2C Consumer Brands CDEP and the Warehouse-First Approach Forward-thinking consumer brands have realized that customer data management and customer engagement cannot exist in silos.  This is how a data-driven marketer is thinking in 2025: I have all the information about my customer consolidated in a data warehouse. The data combines customers’ past purchase history, product interactions, customer support history, marketing-related data, and many other pivotal data points. I want to use this data warehouse information directly to drive contextual, relevant, and personalized experiences through my campaigns in real-time. I don’t want to spend time copying the data into another tool and then into a marketing automation or a customer engagement platform. Any data latency will prevent me from providing real-time personalized experiences, and will negatively impact my engagement, retention, and monetization campaigns.   For consumer brands that need to respond to the pulse of the customer in real-time, the martech stack needs to be warehouse-native.  This is where a CDEP (Customer Data and Engagement Platform) fits in like a glove. What is a CDEP? A customer data and engagement platform (CDEP) is an integrated, all-in-one platform that combines robust data management capabilities with best-in-class engagement, AI, and analytical capabilities.  A CDEP can: Unify data to build a single customer profile with real-time data ingestion Segment your customers based on behavior, demographics, or propensity Hyper-personalize communications in real-time, at scale Automate campaigns via advanced AI functionalities Analyze campaign performance and customer behavior to unlock deep customer insights What Makes MoEngage the Perfect Warehouse-Native CDEP? A CDEP helps consumer brands leverage warehouse as the central system without creating new data silos.  A warehouse-native CDEP like MoEngage minimizes data duplication and data movement, leverages the warehouse’s processing power, enhances security, and can lower costs in the long run. To evaluate why a CDEP (Customer Data and Engagement Platform) like MoEngage shines in a warehouse-first marketing approach, we need to understand these three pillars: 1) Capabilities of a CDEP Data Ingestion, Unification, and Management Capabilities Instead of extensive data storage, a warehouse-native CDEP connects directly to your data warehouse. It allows marketers to build segments using rich data in situ within the warehouse environment, leveraging the warehouse’s processing power and data freshness. Side note: A CDEP can also double down as a data management platform if you do not have a data warehouse or do not wish to connect it with your warehouse.  Data Ingestion: A CDEP comes equipped with flexible and robust data management capabilities that can capture a complete view of customer interactions. Sources of data ingestion include event streaming, batch data uploads, platform integrations (via connectors and APIs), and data warehouse integrations (Snowflake, BigQuery, RedShift, etc). Data Storage: A CDEP would leverage your existing data warehouse as the primary, persistent storage location for comprehensive customer data. Data Unification: CDEPs can unify data from various sources into a 360-degree customer view for each individual, resolving identity across devices and channels. CDEPs do this by ingesting and recognizing identifiers (email, phone, user ID, device ID etc) and applying deterministic matching rules to stitch identifiers and associated data together. Data Governance, Privacy & Security: CDEPs have best-in-class data governance, privacy and security features like Data Access Management, Data Encryption, PII Masking and Tokenization, and Data Retention Policies. These measures ensure that data remains protected at all costs. Integrated Engagement Capabilities A CDEP seamlessly combines data processing with built-in omnichannel campaign orchestration and delivery across multiple channels. These best-in-class engagement capabilities are further augmented with AI capabilities that can help consumer brands improve the customer experience. Omnichannel Messaging Capabilities With over 11 channels, including email, SMS, push notification, WhatsApp, etc., a CDEP like MoEngage can help your brand build and optimize personalized customer journeys across channels and devices. AI Capabilities Generative and predictive AI capabilities offered by a CDEP can easily learn from the warehouse data and use AI agents to suggest the ideal customer journeys, decisions that help brands optimize campaigns, and generate segments. Segmentational Capabilities A CDEP can generate actionable segments in real time based on demographics and attributes, behavioral and transactional data, and propensity. A CDEP uses fresh and comprehensive data in the warehouse, allowing marketers to build customer segments based on demographics, attributes, behavior, transactions, or propensity scores. Analytical Capabilities After unifying data from different sources, a CDEP can help marketers make sense of the data through the power of its analytical capabilities. Be it identifying trends and patterns in customer behavior, segmenting the audience, predicting the likelihood of certain actions in the future, or informing future strategy, CDEP’s robust analytical capabilities can help marketers with actionable insights in real time. Reporting and Dashboards: Built-in data visualization capabilities can help marketing, growth, and product teams across consumer brands track key business metrics like campaign engagement rates, conversion rates, churn rates, DAUs, and MAUs. Behavioral Analytics: Features like funnel, RFM, and cohort analysis can help consumer brands analyze behavioral trends of customers. 2) Scalability, Reliability, and Elasticity Reliability offered by a CDEP means that the platform is consistently available without any downtimes or disruptions. A reliable CDEP ensures high availability, fast response times, and stable performance for brands like yours. A reliable CDEP is built on elastic infrastructure, which means that the platform’s capabilities will be available to you even during periods of extensive and high usage by other customers. 3) Customer Support  If you’re planning to migrate to a warehouse-native CDEP, you might have some concerns about the amount of effort, developer bandwidth, friction, and migration costs that you could incur while making the switch. MoUpgrade program by MoEngage accelerates migration timelines and delivers faster time-to-value (TTV) in weeks instead of months or years.  The program allows for a smooth migration to a warehouse-native CDEP without any disruptions to ongoing campaigns, data collection cadences, or your brand’s existing data pipelines. Conclusion: This shift towards warehouse-first marketing marks a pivotal change, moving beyond fragmented data collection to leveraging the data warehouse as the single source of truth. A warehouse-native CDEP like MoEngage emerges as the ideal solution. By integrating data management, engagement, and analytics directly with the warehouse, CDEPs empower brands to deliver truly contextual, real-time experiences, driving better results and faster time-to-value in 2025 and beyond. Talk to our team to understand how a CDEP can help your brand better harness the power of your data warehouse.   The post Warehouse-First Marketing: Why Brands in 2025 Are Choosing a CDEP appeared first on MoEngage.

The server maker’s CEO said it will consider expanding production in states such as Mississippi and Texas.