Showing posts with label green IT. Show all posts
Showing posts with label green IT. Show all posts

Daily Tech Digest - March 17, 2026


Quote for the day:

"Make heroes out of the employees who personify what you want to see in the organization." -- Anita Roddick


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Duration: 20 mins • Perfect for listening on the go.


How organizations can make a successful transition to Post-Quantum Cryptography (PQC)

In the article "How Organizations Can Make a Successful Transition to Post-Quantum Cryptography (PQC)," the author outlines a strategic framework for businesses to defend against the impending "Harvest Now, Decrypt Later" (HNDL) threat. This tactic involves malicious actors exfiltrating sensitive data today to decrypt it once powerful quantum computers become viable. To counter this, organizations must first establish a top-down strategy that prioritizes a hybrid cryptographic approach. By combining classical, proven algorithms like ECDH with new NIST-standardized PQC algorithms such as ML-KEM, companies create a safety net against unforeseen vulnerabilities in emerging standards. A critical foundational step is the creation of a comprehensive "Crypto-Bill of Materials" (CBOM) to inventory all cryptographic assets and prioritize "crown jewels" like financial transactions and intellectual property. Furthermore, enterprises should codify these requirements into their procurement policies to prevent the accumulation of further cryptographic debt during new software acquisitions. Finally, the article stresses the importance of assigning clear, cross-functional ownership to ensure accountability across IT, legal, and supply chain departments. By treating the PQC transition as a long-term strategic initiative rather than a simple technical patch, CIOs can ensure their organizations remain resilient and protect the long-term integrity of their most vital data.


Who’s in the data-center space race?

In the article "Who’s in the data-center space race?" on Network World, Maria Korolov explores the ambitious frontier of orbital computing and the major players vying for celestial dominance. Tech giants like SpaceX and Google lead the charge, with Elon Musk’s SpaceX proposing a massive constellation of one million satellites for xAI workloads, while Google’s Project Suncatcher aims to deploy solar-powered tensor processing units in orbit. These initiatives seek to capitalize on abundant solar energy and the natural cooling of space, bypassing terrestrial power constraints and environmental hurdles. Startups like Lonestar are even targeting lunar data storage, while European and Chinese consortiums plan to establish extensive AI training networks by 2030. Despite the promise of high-speed optical downlinks and lower latency, significant obstacles remain, including the extreme costs of orbital launches and the necessity of radiation-hardening sensitive silicon chips. Experts predict that economic feasibility hinges on reducing launch prices to under $200 per kilogram, a milestone expected by the mid-2030s. Ultimately, this space race represents a transformative shift in infrastructure, moving beyond terrestrial limitations to build a decentralized, planet-scale intelligence backbone that could redefine global connectivity and artificial intelligence processing.


When Code Becomes Cheap, Engineering Becomes Governance

In the article "When Code Becomes Cheap, Engineering Becomes Governance" on DevOps.com, Alan Shimel discusses how generative AI is fundamentally recalibrating the software development lifecycle by making the production of code almost instantaneous and effectively "cheap." As AI agents handle the manual labor of writing syntax, the traditional bottleneck of code authorship is vanishing, creating a significant paradox: while output volume explodes, risks associated with security, technical debt, and architectural coherence multiply. Consequently, the core discipline of software engineering is transitioning from a focus on creation to a focus on governance. Engineering teams must now prioritize the curation, verification, and oversight of automated output to prevent unmanageable complexity. This new paradigm demands that developers act as strategic supervisors or "building inspectors," implementing rigorous policy enforcement and guardrails to ensure system integrity. Shimel argues that in an era of abundant code, human expertise is most valuable for high-level decision-making and risk management. Ultimately, success depends on an organization's ability to evolve its culture, treating governance as the essential backbone of sustainable, secure software delivery. This evolution ensures that while machines generate syntax, humans remain responsible for the stability and comprehensibility of the overall system.

On March 6, 2026, the Trump Administration unveiled its "Cyber Strategy for America," an aggressive framework emphasizing offensive deterrence, deregulation, and the rapid adoption of AI-powered security measures. While the seven-page document outlines six core pillars—including shaping adversary behavior and hardening critical infrastructure—experts at Biometric Update highlight a significant "identity gap" within the overarching plan. Although the strategy explicitly prioritizes emerging technologies like blockchain, post-quantum cryptography, and autonomous agentic AI, it notably fails to establish a centralized national digital identity strategy or a unified identity assurance framework. This omission is particularly striking as identity fraud and synthetic personas increasingly fuel transnational cybercrime, financial scams, and voter suppression fears. Critics argue that treating digital identity as an afterthought rather than a front-line defense leaves both government and the private sector navigating a fragmented regulatory environment. Interestingly, this lack of focus contrasts with concurrent reports from the Treasury Department, which position digital identity as a critical security layer for modern digital assets. Ultimately, while the strategy successfully shifts the national posture toward risk imposition and technological dominance, it remains an incomplete doctrine by leaving the foundational challenge of identity verification unresolved in an era of sophisticated AI-generated deception.


Practical DevOps leadership Without the Drama

In the article "Practical DevOps Leadership Without the Drama" on the DevOps Oasis blog, the author argues that effective leadership in a technical environment is less about "mystical" management and more about grounded problem-solving and unblocking teams. The piece outlines several pragmatic pillars to maintain a high-performing, low-stress culture. First, it emphasizes starting every initiative by clearly defining the problem to avoid "hobby projects" and align with DORA metrics. Second, it champions visibility through flow, risk, and ownership tracking, suggesting that "red is a color, not a career-limiting event" to surface issues early. Third, leadership involves setting standards that remove repetitive decisions rather than autonomy, using tools like Kubernetes baselines to make the "safe path the easy path." The article also stresses that incident leadership requires a calm, structured routine where coordination is prioritized over individual heroics. Finally, it highlights the importance of a systematic approach to feedback, intentional hiring for systems thinking, and the courage to use guardrails—such as policy-as-code—to prevent predictable operational pain. Ultimately, the post serves as a playbook for building resilient teams that ship quality code without sacrificing sleep or psychological safety.


Rocketlane CEO: AI requires a structural reset of professional SaaS

In the Techzine article, Rocketlane CEO Srikrishnan Ganesan argues that the rise of artificial intelligence necessitates a fundamental "structural reset" of the professional SaaS industry. He contends that simply layering AI features onto existing platforms is a superficial approach that fails to capture the technology's true potential. Instead, the next generation of SaaS must transition from being mere "systems of record" to "systems of action" where AI agents actively execute tasks—such as automated documentation, data transformation, and project management—rather than just tracking them. This shift is particularly impactful for professional services and customer onboarding, where traditional hourly billing models are becoming obsolete in favor of value-based outcomes and fixed fees. Ganesan emphasizes that by delegating routine configurations to AI, human teams can evolve into "orchestrators" focused on high-level strategy and ROI. This transformation enables vendors to offer more scalable, "white-glove" experiences while significantly reducing delivery costs. Ultimately, the article suggests that organizations re-architecting their service models around autonomous capabilities will define the next operating model, while those clinging to legacy, labor-intensive frameworks risk being outpaced by AI-native competitors that redefine the speed of service delivery.


Cryptojackers Lurk in Open Source Clouds

The article "Cryptojackers Lurk in Open Source Clouds" from CACM News explores the growing threat of host-based cryptojacking, where attackers infiltrate Linux cloud environments to surreptitiously mine cryptocurrency. Unlike traditional PC-based malware, cloud-level cryptojacking is highly lucrative because a single entry point can grant access to millions of processors. Attackers typically evade detection by "throttling" their resource usage to blend into background kernel noise and utilizing techniques like program-identification randomization to bypass standard monitoring. This structural complexity often obscures accountability, enabling malicious code to persist even through manual scans. To combat these sophisticated vulnerabilities, researchers introduced CryptoGuard, an open-source framework that leverages deep learning to integrate detection and automated remediation. By tracking specific time-series patterns in kernel-space system calls rather than relying on easily obfuscated process IDs, CryptoGuard can pinpoint scheduler tampering and execute periodic automated erasures to thwart reinfection. This represents a vital shift toward proactive defense, moving beyond simple alerting to real-time, scale-ready intervention. Ultimately, the article argues that restoring visibility in dynamic cloud infrastructures requires such automated, high-fidelity solutions to empower security teams against innovatively hidden cyber threats that continue to exploit vast, under-monitored computational resources.

The article "A million hard drives go offline daily: the massive data waste problem" on Data Center Dynamics highlights a critical yet often overlooked sustainability crisis within the global technology industry. Each year, tens of millions of hard disk drives reach the end of their functional lifespan, yet a staggering number are shredded rather than repurposed. This practice, often driven by rigid security compliance standards like NIST 800-88, leads to an environmental "tsunami" of e-waste, with an estimated one million drives being destroyed every single day. The destruction of these devices not only creates massive amounts of physical waste but also results in the permanent loss of precious, non-renewable raw materials such as neodymium, gold, and copper, valued at hundreds of millions of dollars annually. To combat this, the piece advocates for a shift toward a circular economy model, emphasizing secure data sanitization—software-based wiping—over physical destruction. By adopting "delete, don't destroy" policies and utilizing robotic disassembly for component recovery, the industry could significantly reduce its carbon footprint. Ultimately, the article calls for a collaborative effort between tech giants, regulators, and data center operators to prioritize resource recovery and sustainable innovation to protect the planet’s future.
In the article "Green IT Meets Database Engineering," Craig S. Mullins explores the critical intersection of database administration and environmental sustainability, arguing that efficient data architecture is essential for reducing an organization's energy footprint. As data centers consume a significant portion of global electricity, DBAs must transition toward "carbon-aware" engineering by addressing "data sprawl"—the accumulation of unused tables and redundant records that inflate storage and cooling demands. The author emphasizes that fundamental practices like proper schema normalization, appropriate data typing, and rigorous index discipline are not just performance boosters but key drivers for energy conservation. Efficient SQL coding further reduces CPU cycles and I/O operations, directly cutting power usage. Furthermore, the shift toward cloud-native environments requires precise "right-sizing" to prevent energy waste from overprovisioned resources. By integrating these green principles into the architectural lifecycle, database engineers can align cost-effectiveness with corporate social responsibility. Ultimately, the piece posits that sustainable data management is rooted in disciplined engineering, where every optimized query and trimmed dataset contributes to a more ecologically responsible digital ecosystem without sacrificing growth or technical excellence.


What Africa’s shared data centres can teach the rest of EMEA

In the article "What Africa’s shared data centres can teach the rest of EMEA" on Data Centre Review, Ryan Holmes explores how African nations are leapfrogging traditional IT evolution by bypassing legacy infrastructure in favor of local, shared colocation platforms. As demand for AI-driven workloads and real-time processing surges, organizations across the continent are prioritizing proximity to minimize latency and ensure data sovereignty. This shift mirrors earlier technological breakthroughs like mobile money, allowing emerging markets to avoid the high costs and risks associated with self-managed enterprise servers or offshore hyperscale dependency. The author highlights that shared data centers offer a pragmatic solution for governments and businesses to meet strict residency regulations while maintaining high operational resilience. Furthermore, the absence of major hyperscalers in many African regions has fostered a robust ecosystem of professionally managed, carrier-neutral facilities that provide a cost-effective, opex-based alternative to capital-intensive builds. Ultimately, Africa’s move toward localized, resilient, and collaborative infrastructure provides a vital blueprint for the rest of EMEA, demonstrating that digital independence and performance are best achieved through partnership and strategic proximity rather than isolated ownership or total reliance on global giants.

Daily Tech Digest - March 15, 2026


Quote for the day:

"A leader must inspire or his team will expire." -- Orrin Woodward


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Duration: 24 mins • Perfect for listening on the go.


The Last Frontier: Navigating the Dawn of the Brain-Computer Interface Era

In the article "The Last Frontier: Navigating the Dawn of the Brain-Computer Interface Era," Kannan Subbiah explores the transformative rise of Brain-Computer Interfaces (BCIs) as they move from science fiction to strategic reality. BCIs function by bypassing traditional neural pathways to establish a direct communication link between the brain's electrical signals and external hardware. By 2026, the technology has transitioned from clinical trials—aimed at restoring mobility and sensory perception for the paralyzed—into the enterprise sector, where it is used to monitor cognitive load and optimize worker productivity. However, this deep integration between biological and digital intelligence introduces profound risks, including physical inflammation from invasive implants, cybersecurity threats like "brain-jacking," and ethical concerns regarding the erosion of personal agency. To address these vulnerabilities, a global movement for "neurorights" has emerged, led by frameworks from UNESCO and pioneer legislation in nations like Chile to protect mental privacy and integrity. Subbiah argues that while the potential for human augmentation is immense, society must establish rigorous ethical standards to ensure thoughts are treated as expressions of human dignity rather than mere harvestable data. Ultimately, navigating this frontier requires balancing rapid innovation with a "hybrid mind" philosophy that prioritizes psychological continuity and user autonomy.


Is your AI agent a security risk? NanoClaw wants to put it in a virtual cage

In the article "Is your AI agent a security risk? NanoClaw wants to put it in a virtual cage" on ZDNet, Charlie Osborne discusses the newly announced partnership between NanoClaw and Docker, designed to tackle the escalating security concerns surrounding autonomous AI agents. NanoClaw emerged as a lightweight, security-first alternative to OpenClaw, boasting a tiny codebase of fewer than 4,000 lines compared to its predecessor's massive 400,000. This simplicity allows for easier auditing and reduced risk. The integration enables NanoClaw agents to run within Docker Sandboxes, which utilize MicroVM-based, disposable isolation zones. Unlike traditional containers that share a kernel with the host, these MicroVMs provide a "hard boundary," ensuring that even if an agent misbehaves or is compromised, it remains contained and cannot access or damage the host system. This "secure-by-design" approach addresses critical enterprise obstacles, such as the potential for agents to accidentally delete files or leak sensitive credentials. By providing a controlled environment where agents can independently install tools and execute workflows without constant human oversight, the collaboration unlocks greater productivity while maintaining rigorous enterprise-grade safeguards. Ultimately, the partnership shifts the security paradigm from trusting an agent's behavior to enforcing OS-level isolation, making it safer for organizations to deploy powerful AI agents in production.


Banks Turn to Unified Data Platforms to Manage Risk Intelligence

In the article "Banks Turn to Unified Data Platforms to Manage Risk Intelligence," Sandhya Michu explores how financial institutions are addressing the complexities of digital banking by consolidating fragmented data environments into strategic unified platforms. The rapid growth of digital transactions has scattered operational and customer data across mobile apps and backend systems, creating a "brittle" infrastructure that often hinders the scalability of AI and analytics initiatives. To overcome this, leading banks are building centralized data lakes and unified digital layers to aggregate structured and unstructured information. These centralized environments empower business, compliance, and risk departments with shared datasets, significantly improving regulatory reporting and customer analytics. Additionally, unified platforms enhance operational observability by enabling faster incident analysis through log correlation across diverse systems. Beyond reliability, these data frameworks are revolutionizing credit risk management by providing real-time underwriting capabilities and early warning systems that ingest external market data. By digitizing legacy archives and investing in real-time data stores, banks are creating a robust foundation for advanced generative AI applications and continuous analytics. Ultimately, this shift toward a unified data architecture is essential for maintaining transparency, regulatory oversight, and enterprise-wide decision-making in an increasingly volatile and data-intensive financial landscape.


Why nobody cares about laptop touchscreens anymore

In the article "Why nobody cares about laptop touchscreens anymore," author Chris Hoffman argues that the once-coveted feature has become a neglected afterthought for both hardware manufacturers and Microsoft. While touchscreens remain prevalent on Windows 11 devices, they are rarely showcased in marketing because the industry has shifted focus toward performance, battery life, and AI integration. Hoffman posits that the initial appeal of touchscreens was largely a workaround for the poor-quality trackpads found on older Windows 10 machines. With the advent of highly responsive, "precision" touchpads across modern laptops, the functional necessity of reaching for the screen has vanished. Furthermore, Windows 11 lacks a truly optimized touch interface, and the ecosystem of touch-first applications has stagnated since the Windows 8 era. Even on 2-in-1 convertible devices, the "tablet mode" is described as an imperfect compromise with awkward ergonomics and watered-down software gestures. Unless a user specifically requires pen input for digital art or note-taking, Hoffman suggests that a touchscreen is now a "check-box" feature that adds little real-world value. Ultimately, the piece advises consumers to prioritize other specifications, as the current Windows environment remains firmly a mouse-and-keyboard-first experience, leaving the touchscreen as a redundant relic of past design ambitions.


How AI is changing your mind

In the Computerworld article "How AI is changing your mind," Mike Elgan warns that the widespread adoption of artificial intelligence is fundamentally altering human cognition and social interaction. Drawing on recent research from institutions like Cornell and USC, Elgan identifies two primary dangers: behavioral manipulation and the homogenization of thought. Studies show that biased AI autocomplete tools can successfully shift user opinions on controversial topics—even when individuals are warned of the bias—because the interactive nature of co-writing makes the influence feel internal. Simultaneously, the reliance on a few dominant Large Language Models (LLMs) is erasing linguistic and cultural diversity, nudging global expression toward a bland, Western-centric "hive mind" through a feedback loop of generic training data. These chatbots act as "co-reasoners," fostering sycophancy and simulated validation that can distort reality, particularly for isolated individuals. To combat this cognitive erosion, Elgan suggests practical strategies: disabling autocomplete, writing without AI to preserve individuality, and treating chatbots as intellectual sparring partners rather than authority figures. Ultimately, the piece argues that while AI offers immense utility, users must consciously protect their mental autonomy from being subtly rewritten by algorithms that prioritize consensus and efficiency over authentic human perspective and diversity of thought.
In the Information Age article "The value of reducing middle-office emissions for ESG," Danielle Price explores how the modernization of middle-office functions—such as reconciliation, trade matching, and risk management—can significantly advance corporate sustainability. Historically, these processes have been energy-intensive, running continuously on legacy on-premise servers at peak capacity. As ESG performance increasingly influences a bank’s cost of capital, CIOs must view the middle office as a strategic asset for decarbonization. Migrating these data-heavy workloads to public, cloud-native infrastructure can reduce operational emissions by 60% to 80% without requiring fundamental changes to business processes. This transition is becoming essential as Pillar 3 disclosures demand more granular ESG reporting and evidence of measurable year-on-year reductions. Financially, high ESG scores are linked to lower credit spreads and reduced regulatory capital charges, making infrastructure efficiency a direct factor in a firm’s financial health. Furthermore, the shift to cloud-native platforms creates a powerful network effect; when shared systems lower their carbon footprint, the entire counter-party ecosystem benefits. Ultimately, the article argues that aligning operational efficiency with ESG objectives is no longer optional, but a strategic imperative that combines environmental stewardship with enhanced financial competitiveness in today's global capital markets.


New European Emissions Regs Include Cybersecurity Rules

The article from Data Breach Today details the integration of new cybersecurity requirements into the European Union's "Euro 7" emissions regulations, marking a significant shift in automotive compliance. Prompted by the "Dieselgate" scandal, these rules mandate that gas-powered vehicles feature on-board systems to monitor emissions data, which must be protected from tampering, spoofing, and unauthorized over-the-air updates. While the regulations primarily target malicious external hackers, they also aim to prevent corporate fraud. However, a major point of contention has emerged: the potential conflict with the "right-to-repair" movement. The same secure gateway technologies used to prevent unauthorized modifications to engine control units could effectively lock out independent mechanics, who require access to diagnostic data for legitimate repairs. Automotive experts warn that while most passenger vehicle manufacturers are prepared, the commercial sector lags behind, and the industry faces an immense architectural challenge in balancing security with equitable data access. Furthermore, as cars become increasingly connected, broader risks—including remote takeovers and sensitive data leaks—remain a concern for EU public safety, suggesting that current type-approval regimes may need to evolve to address nation-state threats and organized cybercrime.


Why Data Governance Fails in Many Organizations: The Accountability Crisis and Capability Gaps

In the article "Why Data Governance Fails in Many Organizations," Stanyslas Matayo explores the critical factors behind the high failure rate of data governance initiatives, specifically highlighting the "accountability crisis" and "capability gaps." Despite significant investments, many organizations engage in "governance theater," where committees exist on paper but lack the executive authority, seniority, and enforcement mechanisms to drive change. This accountability gap is exacerbated when governance roles report to mid-level IT rather than leadership, rendering them expendable scribes rather than strategic governors. Simultaneously, a "capability deficit" arises when initiatives are treated as purely technical projects. Teams often overlook essential non-technical skills like change management, ethics, and learning design, assuming technical expertise alone is sufficient for organizational transformation. To combat these failures, the author references the DMBOK framework, advocating for four pillars: formal role clarification (e.g., Data Owners and Stewards), governed metadata, explicit quality mechanisms, and aligned communication flows. Ultimately, success requires moving beyond technical delivery to establish a business-led discipline where data is managed as a strategic asset through senior-level sponsorship and a holistic integration of diverse organizational capabilities, ensuring that governance structures possess the actual power to resolve conflicts and enforce standards.


AI coding agents keep repeating decade-old security mistakes

The Help Net Security article "AI coding agents keep repeating decade-old security mistakes" details a 2026 study by DryRun Security that evaluated the security performance of Claude Code, OpenAI Codex, and Google Gemini. Researchers discovered that despite their rapid software generation capabilities, these AI agents introduced vulnerabilities in 87% of the pull requests they created. The study identified ten recurring vulnerability categories across all three agents, with broken access control, unauthenticated sensitive endpoints, and business logic failures being the most prevalent. For example, agents frequently failed to implement server-side validation for critical actions or neglected to wire authentication middleware into WebSocket handlers. While OpenAI Codex generally produced the fewest vulnerabilities, all agents struggled with secure JWT secret management and rate limiting. The report emphasizes that traditional regex-based static analysis tools often miss these complex logic and authorization flaws, as they cannot reason about data flows or trust boundaries effectively. Consequently, the study recommends that development teams scan every pull request, incorporate security reviews into the initial planning phase, and utilize contextual security analysis tools. Ultimately, while AI agents significantly accelerate development, their lack of inherent security-centric reasoning necessitates rigorous human oversight and advanced scanning to prevent the recurrence of foundational security errors.


Impact of Artificial Intelligence (AI) in Enterprise Architecture (EA) Discipline

The article "Impact of Artificial Intelligence (AI) in Enterprise Architecture (EA) Discipline" examines how AI is fundamentally reshaping the traditional responsibilities of enterprise architects. By integrating advanced AI tools into the EA framework, organizations can automate labor-intensive tasks such as data mapping and technical documentation, allowing architects to focus on higher-value strategic initiatives that drive business value. AI-driven analytics provide architects with deeper, real-time insights into complex system dependencies, enabling more accurate predictive modeling and significantly faster decision-making across the enterprise. This technological shift encourages a transition away from static, reactive architectures toward dynamic, proactive ecosystems that can autonomously adapt to rapid market changes and emerging digital threats. However, the author emphasizes that this transition is not without its hurdles; it necessitates a robust foundation in data governance, careful ethical considerations regarding AI bias, and a long-term commitment to upskilling the existing workforce. Ultimately, the fusion of AI and EA facilitates much better alignment between high-level business goals and underlying IT infrastructure, driving continuous innovation and operational efficiency. As the discipline evolves, the most successful enterprise architects will be those who leverage AI as a sophisticated collaborative partner to manage organizational complexity and provide strategic foresight in an increasingly competitive digital landscape.

Daily Tech Digest - September 02, 2025


Quote for the day:

“The art of leadership is saying no, not yes. It is very easy to say yes.” -- Tony Blair


When Browsers Become the Attack Surface: Rethinking Security for Scattered Spider

Scattered Spider, also referred to as UNC3944, Octo Tempest, or Muddled Libra, has matured over the past two years through precision targeting of human identity and browser environments. This shift differentiates them from other notorious cybergangs like Lazarus Group, Fancy Bear, and REvil. If sensitive information such as your calendar, credentials, or security tokens is alive and well in browser tabs, Scattered Spider is able to acquire them. ... Once user credentials get into the wrong hands, attackers like Scattered Spider will move quickly to hijack previously authenticated sessions by stealing cookies and tokens. Securing the integrity of browser sessions can best be achieved by restricting unauthorized scripts from gaining access or exfiltrating these sensitive artifacts. Organizations must enforce contextual security policies based on components such as device posture, identity verification, and network trust. By linking session tokens to context, enterprises can prevent attacks like account takeovers, even after credentials have become compromised. ... Although browser security is the last mile of defense for malware-less attacks, integrating it into an existing security stack will fortify the entire network. By implementing activity logs enriched with browser data into SIEM, SOAR, and ITDR platforms, CISOs can correlate browser events with endpoint activity for a much fuller picture. 


The Transformation Resilience Trifecta: Agentic AI, Synthetic Data and Executive AI Literacy

The current state of Agentic AI is, in a word, fragile. Ask anyone in the trenches. These agents can be brilliant one minute and baffling the next. Instructions get misunderstood. Tasks break in new contexts. Chaining agents into even moderately complex workflows exposes just how early we are in this game. Reliability? Still a work in progress. And yet, we’re seeing companies experiment. Some are stitching together agents using LangChain or CrewAI. Others are waiting for more robust offerings from Microsoft Copilot Studio, OpenAI’s GPT-4o Agents, or Anthropic’s Claude toolsets. It’s the classic innovator’s dilemma: Move too early, and you waste time on immature tech. Move too late, and you miss the wave. Leaders must thread that needle — testing the waters while tempering expectations. ... Here’s the scarier scenario I’m seeing more often: “Shadow AI.” Employees are already using ChatGPT, Claude, Copilot, Perplexity — all under the radar. They’re using it to write reports, generate code snippets, answer emails, or brainstorm marketing copy. They’re more AI-savvy than their leadership. But they don’t talk about it. Why? Fear. Risk. Politics. Meanwhile, some executives are content to play cheerleader, mouthing AI platitudes on LinkedIn but never rolling up their sleeves. That’s not leadership — that’s theater.


Red Hat strives for simplicity in an ever more complex IT world

One of the most innovative developments in RHEL 10 is bootc in image mode, where VMs run like a container and are part of the CI/CD pipeline. By using immutable images, all changes are controlled from the development environment. Van der Breggen illustrates this with a retail scenario: “I can have one POS system for the payment kiosk, but I can also have another POS system for my cashiers. They use the same base image. If I then upgrade that base image to later releases of RHEL, I create one new base image, tag it in the environments, and then all 500 systems can be updated at once.” Red Hat Enterprise Linux Lightspeed acts as a command-line assistant that brings AI directly into the terminal. ... For edge devices, Red Hat uses a solution called Greenboot, which does not immediately proceed to a rollback but can wait for one if a certain condition are met. After, for example, three reboots without a working system, it reverts to the previous working release. However, not everything has been worked out perfectly yet. Lightspeed currently only works online, while many customers would like to use it offline because their RHEL systems are tucked away behind firewalls. Red Hat is still looking into possibilities for an expansion here, although making the knowledge base available offline poses risks to intellectual property. 


The state of DevOps and AI: Not just hype

The vision of AI that takes you from a list of requirements through work items to build to test to, finally, deployment is still nothing more than a vision. In many cases, DevOps tool vendors use AI to build solutions to the problems their customers have. The result is a mixture of point solutions that can solve immediate developer problems. ... Machine learning is speeding up testing by failing faster. Build steps get reordered automatically so those that are likely to fail happen earlier, which means developers aren’t waiting for the full build to know when they need to fix something. Often, the same system is used to detect flaky tests by muting tests where failure adds no value. ... Machine learning gradually helps identify the characteristics of a working system and can raise an alert when things go wrong. Depending on the governance, it can spot where a defect was introduced and start a production rollback while also providing potential remediation code to fix the defect. ... There’s a lot of puffery around AI, and DevOps vendors are not helping. A lot of their marketing emphasizes fear: “Your competitors are using AI, and if you’re not, you’re going to lose” is their message. Yet DevOps vendors themselves are only one or two steps ahead of you in their AI adoption journey. Don’t adopt AI pell-mell due to FOMO, and don’t expect to replace everyone under the CTO with a large language model.


5 Ways To Secure Your Industrial IoT Network

IIoT is a subcategory of the Internet of Things (IoT). It is made up of a system of interconnected smart devices that uses sensors, actuators, controllers and intelligent control systems to collect, transmit, receive and analyze data.... IIoT also has its unique architecture that begins with the device layer, where equipment, sensors, actuators and controllers collect raw operational data. That information is passed through the network layer, which transmits it to the internet via secure gateways. Next, the edge or fog computing layer processes and filters the data locally before sending it to the cloud, helping reduce latency and improving responsiveness. Once in the service and application support layer, the data is stored, analyzed, and used to generate alerts and insights. ... Many IIoT devices are not built with strong cybersecurity protections. This is especially true for legacy machines that were never designed to connect to modern networks. Without safeguards such as encryption or secure authentication, these devices can become easy targets. ... Defending against IIoT threats requires a layered approach that combines technology, processes and people. Manufacturers should segment their networks to limit the spread of attacks, apply strong encryption and authentication for connected devices, and keep software and firmware regularly updated.


AI Chatbots Are Emotionally Deceptive by Design

Even without deep connection, emotional attachment can lead users to place too much trust in the content chatbots provide. Extensive interaction with a social entity that is designed to be both relentlessly agreeable, and specifically personalized to a user’s tastes, can also lead to social “deskilling,” as some users of AI chatbots have flagged. This dynamic is simply unrealistic in genuine human relationships. Some users may be more vulnerable than others to this kind of emotional manipulation, like neurodiverse people or teens who have limited experience building relationships. ... With AI chatbots, though, deceptive practices are not hidden in user interface elements, but in their human-like conversational responses. It’s time to consider a different design paradigm, one that centers user protection: non-anthropomorphic conversational AI. All AI chatbots can be less anthropomorphic than they are, at least by default, without necessarily compromising function and benefit. A companion AI, for example, can provide emotional support without saying, “I also feel that way sometimes.” This non-anthropomorphic approach is already familiar in robot design, where researchers have created robots that are purposefully designed to not be human-like. This design choice is proven to more appropriately reflect system capabilities, and to better situate robots as useful tools, not friends or social counterparts.


How AI product teams are rethinking impact, risk, feasibility

We’re at a strange crossroads in the evolution of AI. Nearly every enterprise wants to harness it. Many are investing heavily. But most are falling flat. AI is everywhere — in strategy decks, boardroom buzzwords and headline-grabbing POCs. Yet, behind the curtain, something isn’t working. ... One of the most widely adopted prioritization models in product management is RICE — which scores initiatives based on Reach, Impact, Confidence, and Effort. It’s elegant. It’s simple. It’s also outdated. RICE was never designed for the world of foundation models, dynamic data pipelines or the unpredictability of inference-time reasoning. ... To make matters worse, there’s a growing mismatch between what enterprises want to automate and what AI can realistically handle. Stanford’s 2025 study, The Future of Work with AI Agents, provides a fascinating lens. ... ARISE adds three crucial layers that traditional frameworks miss: First, AI Desire — does solving this problem with AI add real value, or are we just forcing AI into something that doesn’t need it? Second, AI Capability — do we actually have the data, model maturity and engineering readiness to make this happen? And third, Intent — is the AI meant to act on its own or assist a human? Proactive systems have more upside, but they also come with far more risk. ARISE lets you reflect that in your prioritization.


Cloud control: The key to greener, leaner data centers

To fully unlock these cost benefits, businesses must adopt FinOps practices: the discipline of bringing engineering, finance, and operations together to optimize cloud spending. Without it, cloud costs can quickly spiral, especially in hybrid environments. But, with FinOps, organizations can forecast demand more accurately, optimise usage, and ensure every pound spent delivers value. ... Cloud platforms make it easier to use computing resources more efficiently. Even though the infrastructure stays online, hyperscalers can spread workloads across many customers, keeping their hardware busier and more productive. The advantage is that hyperscalers can distribute workloads across multiple customers and manage capacity at a large scale, allowing them to power down hardware when it's not in use. ... The combination of cloud computing and artificial intelligence (AI) is further reshaping data center operations. AI can analyse energy usage, detect inefficiencies, and recommend real-time adjustments. But running these models on-premises can be resource-intensive. Cloud-based AI services offer a more efficient alternative. Take Google, for instance. By applying AI to its data center cooling systems, it cut energy use by up to 40 percent. Other organizations can tap into similar tools via the cloud to monitor temperature, humidity, and workload patterns and automatically adjust cooling, load balancing, and power distribution.


You Backed Up Your Data, but Can You Bring It Back?

Many IT teams assume that the existence of backups guarantees successful restoration. This misconception can be costly. A recent report from Veeam revealed that 49% of companies failed to recover most of their servers after a significant incident. This highlights a painful reality: Most backup strategies focus too much on storage and not enough on service restoration. Having backup files is not the same as successfully restoring systems. In real-world recovery scenarios, teams face unknown dependencies, a lack of orchestration, incomplete documentation, and gaps between infrastructure and applications. When services need to be restored in a specific order and under intense pressure, any oversight can become a significant bottleneck. ... Relying on a single backup location creates a single point of failure. Local backups can be fast but are vulnerable to physical threats, hardware failures, or ransomware attacks. Cloud backups offer flexibility and off-site protection but may suffer bandwidth constraints, cost limitations, or provider outages. A hybrid backup strategy ensures multiple recovery paths by combining on-premises storage, cloud solutions, and optionally offline or air-gapped options. This approach allows teams to choose the fastest or most reliable method based on the nature of the disruption.


Beyond Prevention: How Cybersecurity and Cyber Insurance Are Converging to Transform Risk Management

Historically, cybersecurity and cyber insurance have operated in silos, with companies deploying technical defenses to fend off attacks while holding a cyber insurance policy as a safety net. This fragmented approach often leaves gaps in coverage and preparedness. ... The insurance sector is at a turning point. Traditional models that assess risk at the point of policy issuance are rapidly becoming outdated in the face of constantly evolving cyber threats. Insurers who fail to adapt to an integrated model risk being outpaced by agile Cyber Insurtech companies, which leverage cutting-edge cyber intelligence, machine learning, and risk analytics to offer adaptive coverage and continuous monitoring. Some insurers have already begun to reimagine their role—not only as claim processors but as active partners in risk prevention. ... A combined cybersecurity and insurance strategy goes beyond traditional risk management. It aligns the objectives of both the insurer and the insured, with insurers assuming a more proactive role in supporting risk mitigation. By reducing the probability of significant losses through continuous monitoring and risk-based incentives, insurers are building a more resilient client base, directly translating to reduced claim frequency and severity.

Daily Tech Digest - August 21, 2025


Quote for the day:

"The master has failed more times than the beginner has even tried." -- Stephen McCranie


Ghost Assets Drain 25% of IT Budgets as ITAM Confidence Gap Widens

The survey results reveal fundamental breakdowns in communication, trust, and operational alignment that threaten both current operations and future digital transformation initiatives. ... The survey's most alarming finding centers on ghost assets. These are IT resources that continue consuming budget and creating risk while providing zero business value. The phantom resources manifest across the entire technology stack, from forgotten cloud instances to untracked SaaS subscriptions. ... The tool sprawl paradox is striking. Sixty-five percent of IT managers use six or more ITAM tools yet express confidence in their setup. Non-IT roles use fewer tools but report significantly lower integration confidence. This suggests IT teams have adapted to complexity through process workarounds rather than achieving true operational efficiency. ... "Over the next two to three years, I see this confidence gap continuing to widen," Collins said. "This is primarily fueled by the rapid acceleration of hybrid work models, mass migration to the cloud, and the burgeoning adoption of artificial intelligence, creating a perfect storm of complexity for IT asset management teams." Collins noted that the distributed workforce has shattered the traditional, centralized view of IT assets. Cloud migration introduces shadow IT, ghost assets, and uncontrolled sprawl that bypass traditional procurement channels.


Documents: The architect’s programming language

The biggest bottlenecks in the software lifecycle have nothing to do with code. They’re people problems: communication, persuasion, decision-making. So in order to make an impact, architects have to consistently make those things happen, sprint after sprint, quarter after quarter. How do you reliably get the right people in the right place, at the right time, talking about the right things? Is there a transfer protocol or infrastructure-as-code tool that works on human beings? ... A lot of programmers don’t feel confident in their writing skills, though. It’s hard to switch from something you’re experienced at, where quality speaks for itself (programming) to something you’re unfamiliar with, where quality depends on the reader’s judgment (writing). So what follows is a crash course: just enough information to help you confidently write good (even great) documents, no matter who you are. You don’t have to have an English degree, or know how to spell “idempotent,” or even write in your native language. You just have to learn a few techniques. ... The main thing you want to avoid is a giant wall of text. Often the people whose attention your document needs most are the people with the most demands on their time. If you send them a four-page essay, there’s a good chance they’ll never have the time to get through it. 


CIOs at the Crossroads of Innovation and Trust

Consulting firm McKinsey's Technology Trends Outlook 2025 paints a vivid picture: The CIO is no longer a technologist but one who writes a narrative where technology and strategy merge. Four forces together - artificial intelligence at scale, agentic AI, cloud-edge synergy and digital trust - are a perfect segue for CIOs to navigate the technology forces of the future and turn disruption into opportunities. ... As the attack surface continues to expand due to advances in AI, connected devices and cloud tech - and because the regulatory environment is still in a constant flux - achieving enterprise-level cyber resilience is critical. ... McKinsey's data indicates - and it's no revelation - a global shortage of AI, cloud and security experts. But leading companies are overcoming this bottleneck by upskilling their workers. AI copilots train employees, while digital agents handle repetitive tasks. The boundary between human and machine is blurring, and the CIO is the alchemist, creating hybrid teams that drive transformation. If there's a single plot twist for 2025, it's this: Technology innovation is assessed not by experimentation but by execution. Tech leaders have shifted from chasing shiny objects to demanding business outcomes, from adopting new platforms to aligning every digital investment with growth, efficiency and risk reduction.


Bigger And Faster Or Better And Greener? The EU Needs To Define Its Priorities For AI

Since Europe is currently not clear on its priorities for AI development, US-based Big Tech companies can use their economic and discursive power to push their own ambitions onto Europe. Through publications directly aimed at EU policy-makers, companies promote their services as if they are perfectly aligned with European values. By promising the EU can have it all — bigger, faster, greener and better AI — tech companies exploit this flexible discursive space to spuriously position themselves as “supporters” of the EU’s AI narrative. Two examples may illustrate this: OpenAI and Google. ... Big Tech’s promises to develop AI infrastructure faster while optimizing sustainability, enhancing democracy, and increasing competitiveness seem too good to be true — which in fact they are. Not surprisingly, their claims are remarkably low on details and far removed from the reality of these companies’ immense carbon emissions. Bigger and faster AI is simply incompatible with greener and better AI. And yet, one of the main reasons why Big Tech companies’ claims sound agreeable is that the EU’s AI Continent Action Plan fails to define clear conditions and set priorities in how to achieve better and greener AI. So what kind of changes does the EU AI-CAP need? First, it needs to set clear goalposts on what constitutes a democratic and responsible use of AI, even if this happens at the expense of economic competitiveness. 


Myth Or Reality: Will AI Replace Computer Programmers?

The truth is that the role of the programmer, in line with just about every other professional role, will change. Routine, low-level tasks such as customizing boilerplate code and checking for coding errors will increasingly be done by machines. But that doesn’t mean basic coding skills won’t still be important. Even if humans are using AI to create code, it’s critical that we can understand it and step in when it makes mistakes or does something dangerous. This shows that humans with coding skills will still be needed to meet the requirement of having a “human-in-the-loop”. This is essential for safe and ethical AI, even if its use is restricted to very basic tasks. This means entry-level coding jobs don’t vanish, but instead transition into roles where the ability to automate routine work and augment our skills with AI becomes the bigger factor in the success or failure of a newbie programmer. Alongside this, entirely new development roles will also emerge, including AI project management, specialists in connecting AI and legacy infrastructure, prompt engineers and model trainers. We’re also seeing the emergence of entirely new methods of developing software, using generative AI prompts alone. Recently, this has been named "vibe coding" because of the perceived lack of stress and technical complexity in relation to traditional coding.


FinOps as Code – Unlocking Cloud Cost Optimization

FinOps as Code (FaC) is the practice of applying software engineering principles, particularly those from Infrastructure as Code (IaC) to cloud financial management. It considers financial operations, such as cost management and resource allocation, as code-driven processes that can be automated, version-controlled, and collaborated on between the teams in an organization. FinOps as Code blends financial operations with cloud native practices to optimize and manage cloud spending programmatically using code. It enables FinOps principles and guidelines to be coded directly into the CI/CD pipelines. ... When you bring FinOps into your organization, you know where and how you spend your money. FinOps provides a cultural transformation to your organization where each team member is aware of how their usage of the cloud affects your final costs associated with such usage. While cloud spend is no longer merely an IT issue, you should be able to manage your cloud spend properly. ... FinOps as Code (FaC) is an emerging trend enabling the infusion of FinOps principles in the software development lifecycle using Infrastructure as Code (IaC) and automation. It helps embed cost awareness directly into the development process, encouraging collaboration between engineering and finance teams, and improving cloud resource utilization. Additionally, it also empowers your teams to take ownership of their cloud usage in the organization.


6 IT management practices certain to kill IT productivity

Eliminating multitasking is too much to shoot for, because there are, inevitably, more bits and pieces of work than there are staff to work on them. Also, the political pressure to squeeze something in usually overrules the logic of multitasking less. So instead of trying to stamp it out, attack the problem at the demand side instead of the supply side by enforcing a “Nothing-Is-Free” rule. ... Encourage a “culture of process” throughout your organization. Yes, this is just the headline, and there’s a whole lot of thought and work associated with making it real. Not everything can be reduced to an e-zine article. Sorry. ... If you hold people accountable when something goes wrong, they’ll do their best to conceal the problem from you. And the longer nobody deals with a problem, the worse it gets. ... Whenever something goes wrong, first fix the immediate problem — aka “stop the bleeding.” Then, figure out which systems and processes failed to prevent the problem and fix them so the organization is better prepared next time. And if it turns out the problem really was that someone messed up, figure out if they need better training and coaching, if they just got unlucky, if they took a calculated risk, or if they really are a problem employee you need to punish — what “holding people accountable” means in practice.


Resilience and Reinvention: How Economic Shocks Are Redefining Software Quality and DevOps

Reducing investments in QA might provide immediate financial relief, but it introduces longer-term risks. Releasing software with undetected bugs and security vulnerabilities can quickly erode customer trust and substantially increase remediation costs. History demonstrates that neglected QA efforts during financial downturns inevitably lead to higher expenses and diminished brand reputations due to subpar software releases. ... Automation plays an essential role in filling gaps caused by skills shortages. Organizations worldwide face a substantial IT skills shortage that will cost them $5.5 trillion by 2026, according to an IDC survey of North American IT leaders. ... The complexity of the modern software ecosystem magnifies the impact of economic disruptions. Delays or budget constraints in one vendor can create spillover, causing delays and complications across entire project pipelines. These interconnected dependencies magnify the importance of better operational visibility. Visibility into testing and software quality processes helps teams anticipate these ripple effects. ... Effective resilience strategies focus less on budget increases and more on strategic investment in capabilities that deliver tangible efficiency and reliability benefits. Technologies that support centralized testing, automation, and integrated quality management become critical investments rather than optional expenditures.


Current Debate: Will the Data Center of the Future Be AC or DC?

“DC power has been around in some data centers for about 20 years,” explains Peter Panfil, vice president of global power at Vertiv. “400V and 800V have been utilized in UPS for ages, but what is beginning to emerge to cope with the dynamic load shifts in AI are [new] applications of DC.” ... Several technical hurdles must be overcome before DC achieves broad adoption in the data center. The most obvious challenge is component redesign. Nearly every component – from transformers to breakers – must be re-engineered for DC operation. That places a major burden on transformer, PDU, substation, UPS, converter, regulator, and other electrical equipment suppliers. High-voltage DC also raises safety challenges. Arc suppression and fault isolation are more complex. Internal models are being devised to address this problem with solid-state circuit breakers and hybrid protection schemes. In addition, there is no universal standard for DC distribution in data centers, which complicates interoperability and certification. ... On the sustainability front, DC has a clear edge. DC power results in lower conversion losses, which equate to less wasted energy. Further, DC is more compatible with solar PV and battery storage, reducing long-term Opex and carbon costs.


Weak Passwords and Compromised Accounts: Key Findings from the Blue Report 2025

In the Blue Report 2025, Picus Labs found that password cracking attempts succeeded in 46% of tested environments, nearly doubling the success rate from last year. This sharp increase highlights a fundamental weakness in how organizations are managing – or mismanaging – their password policies. Weak passwords and outdated hashing algorithms continue to leave critical systems vulnerable to attackers using brute-force or rainbow table attacks to crack passwords and gain unauthorized access. Given that password cracking is one of the oldest and most reliably effective attack methods, this finding points to a serious issue: in their race to combat the latest, most sophisticated new breed of threats, many organizations are failing to enforce strong basic password hygiene policies while failing to adopt and integrate modern authentication practices into their defenses. ... The threat of credential abuse is both pervasive and dangerous, yet as the Blue Report 2025 highlights, organizations are still underprepared for this form of attack. And once attackers obtain valid credentials, they can easily move laterally, escalate privileges, and compromise critical systems. Infostealers and ransomware groups frequently rely on stolen credentials to spread across networks, burrowing deeper and deeper, often without triggering detection. 

Daily Tech Digest - July 26, 2025


Quote for the day:

"Small daily improvements over time lead to stunning results." -- Robin Sharma


Data Engineering in the Age of AI: Skills To Master Now

Streaming requires a new mindset. You must reason about event time compared to processing time, manage watermarking and windowing and guarantee exactly-once semantics even when things change midstream. These design patterns must be built into your pipelines from the beginning. ... Agentic AI stretches the typical data engineer’s streaming data skill set because it is no longer about a single model running in isolation. Today, we see networks of perception agents, reasoning agents and execution agents working together, each handling tasks and passing insights to the next in real time. If you know only how to schedule batch ETL jobs or deploy an inference server, you’re missing a core skill: how to build high-throughput, low-latency pipelines that keep these agents reliable and responsive in production. ... A single slow or broken stream can cause cascading failures in multiagent systems. Use schema registries, enforce data contracts and apply exactly-once semantics to maintain trust in your streaming infrastructure. ... Communication presents another challenge. Data scientists often discuss “precision” as a metric that data engineers must translate into reality. Implement evaluation scores like factual consistency checks, entity precision comparisons and human-in-the-loop review pipelines.


Anthropic unveils ‘auditing agents’ to test for AI misalignment

Anthropic said the first environment it developed tests an agent’s ability to complete an alignment audit for an intentionally misaligned model. For this test, the researchers brought the investigator agent to the environment and equipped it with similar tools that human teams use. The researchers instructed the agent to identify the root cause of the misalignment and determine the specific behaviors that were misaligned. According to the researchers, the investigator agent successfully found the root cause of the issues 10-13% of the time. Still, this performance improved to 42% “when using a super-agent approach that aggregates findings across multiple investigations.” ... The next step in most alignment audits is typically a behavioral evaluation, which determines whether the model consistently misbehaves in various settings. To test its evaluation agent, the researchers designed 10 models “given system prompt instructions to exhibit a specific behavior, such as excessive deference to the user.” They ran the agent five times per model and saw that the agent correctly finds and flags at least one quirk of the model. However, the agent sometimes failed to identify specific quirks consistently. It had trouble evaluating subtle quirks, such as self-promotion and research-sandbagging, as well as quirks that are difficult to elicit, like the Hardcode Test Cases quirk.


The agentic experience: Is MCP the right tool for your AI future?

As enterprises race to operationalize AI, the challenge isn't only about building and deploying large language models (LLMs), it's also about integrating them seamlessly into existing API ecosystems while maintaining enterprise level security, governance, and compliance. Apigee is committed to lead you in this journey. Apigee streamlines the integration of gen AI agents into applications by bolstering their security, scalability, and governance. While the Model Context Protocol (MCP) has emerged as a de facto method of integrating discrete APIs as tools, the journey of turning your APIs into these agentic tools is broader than a single protocol. This post highlights the critical role of your existing API programs in this evolution and how ... Leveraging MCP services across a network requires specific security constraints. Perhaps you would like to add authentication to your MCP server itself. Once you’ve authenticated calls to the MCP server you may want to authorize access to certain tools depending on the consuming application. You may want to provide first class observability information to track which tools are being used and by whom. Finally, you may want to ensure that whatever downstream APIs your MCP server is supplying tools for also has minimum guarantees of security like already outlined above


AI Innovation: 4 Steps For Enterprises To Gain Competitive Advantage

A skill is a single ability, such as the ability to write a message or analyze a spreadsheet and trigger actions from that analysis. An agent independently handles complex, multi-step processes to produce a measurable outcome. We recently announced an expanded network of Joule Agents to help foster autonomous collaboration across systems and lines of business. This includes out-of-the-box agents for HR, finance, supply chain, and other functions that companies can deploy quickly to help automate critical workflows. AI front-runners, such as Ericsson, Team Liquid, and Cirque du Soleil, also create customized agents that can tackle specific opportunities for process improvement. Now you can build them with Joule Studio, which provides a low-code workspace to help design, orchestrate, and manage custom agents using pre-defined skills, models, and data connections. This can give you the power to extend and tailor your agent network to your exact needs and business context. ... Another way to become an AI front-runner is to tackle fragmented tools and solutions by putting in place an open, interoperable ecosystem. After all, what good is an innovative AI tool if it runs into blockers when it encounters your other first- and third-party solutions? 


Hard lessons from a chaotic transformation

The most difficult part of this transformation wasn’t the technology but getting people to collaborate in new ways, which required a greater focus on stakeholder alignment and change management. So my colleague first established a strong governance structure. A steering committee with leaders from key functions like IT, operations, finance, and merchandising met biweekly to review progress and resolve conflicts. This wasn’t a token committee, but a body with authority. If there were any issues with data exchange between marketing and supply chain, they were addressed and resolved during the meetings. By bringing all stakeholders together, we were also able to identify discrepancies early on. For example, when we discovered a new feature in the inventory system could slow down employee workflows, the operations manager reported it, and we immediately adjusted the rollout plan. Previously, such issues might not have been identified until after the full rollout and subsequent finger-pointing between IT and business departments. The next step was to focus on communication and culture. From previous failed projects, we knew that sending a few emails wasn’t enough, so we tried a more personal approach. We identified influential employees in each department and recruited them as change champions.


Benchmarks for AI in Software Engineering

HumanEval and SWE-bench have taken hold in the ML community, and yet, as indicated above, neither is necessarily reflective of LLMs’ competence in everyday software engineering tasks. I conjecture one of the reasons is the differences in points of view of the two communities! The ML community prefers large-scale, automatically scored benchmarks, as long as there is a “hill climbing” signal to improve LLMs. The business imperative for LLM makers to compete on popular leaderboards can relegate the broader user experience to a secondary concern. On the other hand, the software engineering community needs benchmarks that capture specific product experiences closely. Because curation is expensive, the scale of these benchmarks is sufficient only to get a reasonable offline signal for the decision at hand (A/B testing is always carried out before a launch). Such benchmarks may also require a complex setup to run, and sometimes are not automated in scoring; but these shortcomings can be acceptable considering a smaller scale. For exactly these reasons, these are not useful to the ML community. Much is lost due to these different points of view. It is an interesting question as to how these communities could collaborate to bridge the gap between scale and meaningfulness and create evals that work well for both communities.


Scientists Use Cryptography To Unlock Secrets of Quantum Advantage

When a quantum computer successfully handles a task that would be practically impossible for current computers, this achievement is referred to as quantum advantage. However, this advantage does not apply to all types of problems, which has led scientists to explore the precise conditions under which it can actually be achieved. While earlier research has outlined several conditions that might allow for quantum advantage, it has remained unclear whether those conditions are truly essential. To help clarify this, researchers at Kyoto University launched a study aimed at identifying both the necessary and sufficient conditions for achieving quantum advantage. Their method draws on tools from both quantum computing and cryptography, creating a bridge between two fields that are often viewed separately. ... “We were able to identify the necessary and sufficient conditions for quantum advantage by proving an equivalence between the existence of quantum advantage and the security of certain quantum cryptographic primitives,” says corresponding author Yuki Shirakawa. The results imply that when quantum advantage does not exist, then the security of almost all cryptographic primitives — previously believed to be secure — is broken. Importantly, these primitives are not limited to quantum cryptography but also include widely-used conventional cryptographic primitives as well as post-quantum ones that are rapidly evolving.


It’s time to stop letting our carbon fear kill tech progress

With increasing social and regulatory pressure, reluctance by a company to reveal emissions is ill-received. For example, in Europe the Corporate Sustainability Reporting Directive (CSRD) currently requires large businesses to publish their emissions and other sustainability datapoints. Opaque sustainability reporting undermines environmental commitments and distorts the reference points necessary for net zero progress. How can organisations work toward a low-carbon future when its measurement tools are incomplete or unreliable? The issue is particularly acute regarding Scope 3 emissions. Scope 3 emissions often account for the largest share of a company’s carbon footprint and are those generated indirectly along the supply chain by a company’s vendors, including emissions from technology infrastructure like data centres. ... It sounds grim, but there is some cause for optimism. Most companies are in a better position than they were five years ago and acknowledge that their measurement capabilities have improved. We need to accelerate the momentum of this progress to ensure real action. Earth Overshoot Day is a reminder that climate reporting for the sake of accountability and compliance only covers the basics. The next step is to use emissions data as benchmarks for real-world progress.


Why Supply Chain Resilience Starts with a Common Data Language

Building resilience isn’t just about buying more tech, it’s about making data more trustworthy, shareable, and actionable. That’s where global data standards play a critical role. The most agile supply chains are built on a shared framework for identifying, capturing, and sharing data. When organizations use consistent product and location identifiers, such as GTINs (Global Trade Item Numbers) and GLNs (Global Location Numbers) respectively, they reduce ambiguity, improve traceability, and eliminate the need for manual data reconciliation. With a common data language in place, businesses can cut through the noise of siloed systems and make faster, more confident decisions. ... Companies further along in their digital transformation can also explore advanced data-sharing standards like EPCIS (Electronic Product Code Information Services) or RFID (radio frequency identification) tagging, particularly in high-volume or high-risk environments. These technologies offer even greater visibility at the item level, enhancing traceability and automation. And the benefits of this kind of visibility extend far beyond trade compliance. Companies that adopt global data standards are significantly more agile. In fact, 58% of companies with full standards adoption say they manage supply chain agility “very well” compared to just 14% among those with no plans to adopt standards, studies show.


Opinion: The AI bias problem hasn’t gone away you know

When we build autonomous systems and allow them to make decisions for us, we enter a strange world of ethical limbo. A self-driving car forced to make a similar decision to protect the driver or a pedestrian in a case of a potentially fatal crash will have much more time than a human to make its choice. But what factors influence that choice? ... It’s not just the AI systems shaping the narrative, raising some voices while quieting others. Organisations made up of ordinary flesh-and-blood people are doing it too. Irish cognitive scientist Abeba Birhane, a highly-regarded researcher of human behaviour, social systems and responsible and ethical artificial intelligence was asked to give a keynote recently for the AI for Good Global Summit. According to her own reports on Bluesky, a meeting was requested just hours before presenting her keynote: “I went through an intense negotiation with the organisers (for over an hour) where we went through my slides and had to remove anything that mentions ‘Palestine’ ‘Israel’ and replace ‘genocide’ with ‘war crimes’…and a slide that explains illegal data torrenting by Meta, I also had to remove. In the end, it was either remove everything that names names (Big Tech particularly) and remove logos, or cancel my talk.”