AI For Real

Category: AI Pulse

  • A New Model (AI) Is Launched

    A New Model (AI) Is Launched

    Former OpenAI Chief Technology Officer Mira Murati has taken a major step in challenging the dominance of the artificial intelligence (AI) industry’s biggest players with the release of the first AI model from her startup, “Thinking Machines Lab”.

    The company unveiled “Inkling”, an open-weight foundation model designed to give developers greater flexibility to customize AI systems using their own data, rather than relying on proprietary models controlled by companies such as OpenAI and Anthropic.

    The model contains 975 billion parameters, but activates only 41 billion during any given task, a design intended to improve efficiency by reducing computing costs without sacrificing performance. Which means: The AI model has a vast amount of knowledge stored inside it, but it doesn’t use all of it every time you ask a question. Instead, it activates only the parts that are relevant to the task at hand. This makes the system faster and cheaper to run while still delivering high-quality answers.

    Thinking Machines Lab said its strategy focuses on balancing affordability and adaptability instead of competing solely on raw computing power. Alongside Inkling, the company is promoting “Tinker”, a cloud-based fine-tuning platform that enables developers to customize large AI models without managing complex infrastructure.

    Here’s What You Need To Really Know

    Unlike fully closed AI systems, Inkling’s open-weight design allows organizations to adapt the model to specialized tasks while retaining greater control over their data and applications.

    So what makes Inkling different is that it gives users more freedom. Most popular AI models are like rented apartments. You can use them, but you cannot change how they are built. Inkling is more like buying a house: businesses and developers can modify it to suit their own needs. It is also designed to work more efficiently by using only the parts of its “brain” needed for a task, making it faster and cheaper to run. This means companies can build AI tools tailored to their business without spending as much on computing or depending entirely on big tech firms.

    Inkling was trained using text, images, audio and video data and includes safeguards against misuse, including protections against cyberattacks and biological threats. The company also announced that the model was developed using Nvidia hardware under a multiyear partnership with the chipmaker.

    With its first model release, Thinking Machines Lab is positioning itself as a key contender in the rapidly evolving AI market, betting that openness, customization and lower costs will appeal to enterprises seeking alternatives to today’s dominant AI platforms.

    Image credit: Thinking Machines Lab

  • Music Industry Introduces AI Labels To Boost Transparency

    Music Industry Introduces AI Labels To Boost Transparency

    The global music industry has introduced a new labelling system aimed at helping listeners distinguish between music created entirely by artificial intelligence and songs made by human artists with AI assistance.

    The initiative seeks to promote greater transparency as AI-generated content becomes increasingly common on streaming platforms.

    The voluntary framework has been developed jointly by leading music industry organisations, including the Recording Industry Association of America (RIAA), the International Federation of the Phonographic Industry (IFPI), the Grammys, SAG-AFTRA, and several independent music associations.

    Under the new system, tracks will carry one of two labels: “AI-generated” for songs in which vocals or major instrumental performances are produced by artificial intelligence, and “AI-assisted” for music created primarily by humans using AI tools during production.

    The move comes amid growing concerns over the rapid rise of AI-generated music and its impact on artists, copyright, and listener trust. Industry leaders believe the labels will provide consumers with greater clarity about how songs are created while allowing AI to remain a creative tool rather than replacing human artistry.

    The proposed labels are expected to function similarly to existing explicit-content warnings on streaming services. However, implementation will rely on voluntary disclosures by artists, record labels, and distributors.

    Some platforms have already begun adopting similar measures. Apple Music has introduced AI transparency tags, while Spotify allows artists to disclose AI involvement through song credits.

    The organisations behind the initiative said the framework is designed to evolve alongside advances in AI technology.

  • AI Driven Cybersecurity Monitoring Gains Momentum As EY India Launches Cyber Risk Platform

    AI Driven Cybersecurity Monitoring Gains Momentum As EY India Launches Cyber Risk Platform

    Artificial intelligence (AI) is moving beyond threat detection and into continuous cybersecurity monitoring.

    EY India is launching an AI-powered Cyber Performance Management platform that aims to help enterprises measure cyber risk in real time rather than relying on periodic security reviews.

    What this indicates is a major shift in enterprise security, where AI is increasingly being used to monitor, analyse and prioritise cyber threats across complex digital environments.

    The new platform, said the company, brings together data from an organization’s cybersecurity tools to provide a “live” view of its security posture.

    Instead of presenting security teams with thousands of alerts, the AI engine evaluates vulnerabilities, identifies the most critical risks and links them to potential business impact. This allows security teams and business leaders to understand which threats require immediate attention.

    Traditional cybersecurity programmes often depend on manual assessments and periodic audits, making it difficult to keep pace with rapidly evolving threats. EY’s platform is designed to replace that approach with continuous AI-driven monitoring that can detect changes in an organisation’s risk profile as they happen.

    The company said the platform combines cybersecurity posture, threat exposure, detection and response into a single system. AI analyses large volumes of security data, helping organisations quantify cyber risk in financial and operational terms rather than relying solely on technical metrics. This gives executives a clearer understanding of how cyber incidents could affect business operations, compliance and revenue.

    The launch comes as enterprises continue expanding cloud infrastructure, connected devices and AI applications, creating larger attack surfaces that are increasingly difficult to monitor manually. As cyber threats become more sophisticated, organisations are looking to AI to automate routine monitoring, speed up threat prioritisation and improve response times.

    The announcement highlights a growing trend in enterprise security, where AI is no longer focused only on identifying attacks but on providing continuous cyber performance monitoring. As businesses face increasing regulatory pressure and more frequent cyber threats, real-time AI monitoring is becoming an essential part of enterprise risk management rather than an optional security upgrade.

    Image credit: EY/Facebook

  • Cisco To Roll Out AI Agents To All 90,000 Employees From August

    Cisco To Roll Out AI Agents To All 90,000 Employees From August

    It may well prove to be a first in the global corporate world insofar as sheer numbers are concerned.

    Cisco will begin deploying personalised AI agents to its entire workforce of around 90,000 employees starting this August, marking one of the largest enterprise-wide AI rollouts to date.

    The initiative is aimed at boosting productivity by giving every employee an AI assistant capable of answering questions, automating routine tasks, and routing requests to the most suitable AI model.

    According to Cisco Chief Financial Officer Mark Patterson, the company has designed the system to dynamically select the best AI model for each task, helping optimise both performance and costs. Much of the AI infrastructure will also run on Cisco’s own systems to improve control over data and reduce token usage.

    The rollout will be accompanied by employee upskilling programmes and is expected to support innovation across functions such as finance and operations. However, Cisco executives acknowledged that adopting AI at this scale will not be without challenges.

    But what makes this move historic? Many other companies before Cisco have done this. Like Microsoft and Salesforce.

    What makes the announcement notable is its scale. Around 90,000 employees will each get a personalized AI agent. Multiple reports describe it as “one of the largest” enterprise-wide AI agent deployments.

  • Cloudflare Launches AI Crawler Management Tools For Websites

    Cloudflare Launches AI Crawler Management Tools For Websites

    Here’s some good news for website owners globally. Cloudflare has expanded its tools for managing artificial intelligence (AI) web crawlers, giving website owners more control over how AI companies access their content as concerns grow over declining referral traffic and content monetization. The announcement marks the company’s second annual “Content Independence Day” initiative.

    The company said its previous approach, which allowed customers to block AI bots with a single setting, did not provide enough flexibility.

    Instead, website owners can now distinguish between three categories of AI traffic: Search crawlers that index content for search engines, Agent crawlers that retrieve information on behalf of AI assistants, and Training crawlers that collect data for developing AI models. Customers can choose which categories to allow or block, including those using Cloudflare’s free tier.

    Cloudflare also announced new default settings that will take effect on September 15, 2026. For new domains, Training and Agent crawlers will be blocked by default on pages displaying advertisements, while Search crawlers will remain allowed. The company argues that ad-supported pages are designed for human visitors and that unrestricted AI access could undermine publisher revenue. Existing customers will be able to opt out of the new defaults before they are applied.

    Another significant change targets AI companies that use a single crawler for multiple purposes. Under the updated system, multi-purpose crawlers will be evaluated based on all of their activities. This means a crawler used for both search indexing and AI training could be blocked if a website owner chooses to restrict training access. Cloudflare said it encourages AI companies to separate their crawlers to improve transparency for publishers.

    For enterprise customers, Cloudflare also introduced BotBase, a searchable database of verified bots and automated agents designed to improve visibility into automated traffic. The company said the new controls reflect a broader effort to help publishers balance AI innovation with sustainable business models for online content.

  • Adobe And LinkedIn Launch Global AI Skills Initiative For Marketers

    Adobe And LinkedIn Launch Global AI Skills Initiative For Marketers

    Adobe and LinkedIn have unveiled a global AI training initiative designed specifically for marketing professionals, signaling a growing push to address the widening AI skills gap within the industry.

    Announced at Cannes Lions 2026, the program introduces four role-based learning paths tailored to marketers working in digital marketing, content and creative, social and communications, and data and analytics.

    The courses will be available in 47 languages and offered free for the first 12 months through LinkedIn Learning, the LinkedIn feed, and Adobe Experience League.

    Addressing Growing Skills Gap

    The initiative comes as demand for AI expertise in marketing accelerates. According to LinkedIn Economic Graph data shared as part of the announcement, marketing job postings requiring AI literacy have increased by 113% year over year. Despite this growth, only 4% of marketers globally have added AI skills to their LinkedIn profiles, compared with 12% in engineering and product management roles.

    For digital marketers, these figures highlight a pressing challenge. As AI becomes embedded across campaign planning, audience targeting, content production, and analytics, professionals who fail to develop AI competencies risk falling behind evolving industry expectations.

    Role-specific Learning

    Unlike broad AI literacy programs aimed at general business audiences, Adobe and LinkedIn are positioning this initiative as practical training built around marketers’ daily workflows.

    The curriculum focuses on use cases such as AI-powered content creation, campaign optimization, audience segmentation, and integrating data into agentic workflows. The companies said the courses were developed using workforce insights from LinkedIn’s Economic Graph and designed to align with how marketers actually work.

    The emphasis on short-form learning is also notable. Rather than requiring lengthy certification programs, the courses are structured around bite-sized modules intended to fit into busy schedules.

    According to Adobe, the program will evolve continuously with updated content throughout the year to keep pace with rapid changes in AI technologies and marketing applications. Participants who complete the courses will earn LinkedIn Learning certificates that can be displayed on their professional profiles. Adobe will also provide free product trials to support hands-on learning experiences.

    Why It Matters

    The Adobe and LinkedIn partnership highlights a maturation of AI adoption in marketing. Early conversations focused heavily on the technology itself. The focus is now shifting toward workforce readiness and practical implementation.

    As AI reshapes content creation, campaign management, and data-driven decision making, marketers who invest in upskilling may be better positioned to lead transformation efforts within their organizations.

    The initiative suggests that AI literacy is rapidly becoming a core marketing competency rather than a specialist skill. For digital marketers navigating an increasingly automated landscape, the message is clear: developing AI expertise is no longer optional. It is becoming a requirement for long-term career growth and organizational success.

    Image credit: Adobe

  • A New Playbook For Companies Looking To Scale AI

    A New Playbook For Companies Looking To Scale AI

    After two years of watching enterprises oscillate between AI hype and pilot purgatory, Accenture and Carnegie Mellon University’s Software Engineering Institute (SEI) are betting that the next big challenge isn’t building AI applications; it’s operationalizing them.

    The two organizations have unveiled the AI Adoption Maturity Model, a framework designed to help companies assess how prepared they are to scale AI initiatives across their businesses with predictable outcomes.

    The announcement signals a growing realization in the industry: deploying a few chatbots or coding assistants isn’t the same thing as becoming an AI-native organization.

    For those familiar with software engineering history, the move feels familiar. SEI was instrumental in developing the Capability Maturity Model (CMM) and later CMMI, frameworks that transformed software development from an ad hoc practice into a disciplined engineering function. The new initiative appears to apply that same philosophy to enterprise AI.

    The Enterprise AI Reality Check

    The timing is notable.

    According to research cited by Accenture, 86% of C-suite leaders plan to increase AI spending in 2026, yet only 21% of organizations are redesigning end-to-end processes with AI at the core. Nearly half of executives report that AI has delivered little impact on profits so far.

    That disconnect mirrors what many early adopters have observed firsthand. The technology works. The demos impress. The prototypes ship. But scaling AI beyond isolated use cases often exposes deeper organizational issues around governance, data quality, workflows, talent readiness, and engineering discipline.

    In other words, the bottleneck increasingly isn’t the models. It’s the organization.

    Beyond the Prompt Engineering Era

    The AI Adoption Maturity Model evaluates organizations across eight dimensions:

    • Organizational strategy
    • Workforce and culture
    • Workflow re-engineering
    • Risk and governance
    • Data
    • Engineering
    • Operations
    • Ecosystem

    Rather than focusing solely on technical capabilities, the framework attempts to measure whether an organization has institutionalized the practices necessary to sustain AI initiatives over time.

    That’s a significant shift from the first wave of enterprise AI adoption, which often centered on experimentation: standing up proof-of-concepts, testing foundation models, and encouraging employees to use generative AI tools.

    The next phase appears to be about repeatability.

    As agentic systems become integrated into core business operations, enterprises are discovering that traditional software governance frameworks don’t fully address questions around model evaluation, human oversight, workflow redesign, and organizational accountability.

    Why Early Adopters Should Pay Attention

    For AI enthusiasts and early adopters, maturity models may sound bureaucratic — more boardroom than breakthrough.

    But history suggests otherwise.

    Software engineering itself went through a similar transition. What began as an experimental discipline eventually required standards, governance models, testing methodologies, and operational frameworks to support mission-critical systems at scale.

    AI appears to be reaching a comparable inflection point.

    The organizations succeeding with AI in 2026 are increasingly distinguished not by access to the best models, but by their ability to integrate those models into workflows, manage risk, align incentives, and continuously improve outcomes.

    The era of “we have a GPT strategy” may be ending.

    The era of AI operations as organizational capability is beginning.

    Image credit: Accenture

  • Explainer: Cloudflare’s New Tool To Stop Runaway AI Bills

    Explainer: Cloudflare’s New Tool To Stop Runaway AI Bills

    For the past two years, companies have faced a peculiar problem: they want employees to use AI more, but they also fear the costs that come with widespread adoption. As organizations rolled out access to powerful models from providers such as OpenAI, Anthropic and Google, many discovered that AI spending can spiral surprisingly quickly. A single engineer, chatbot, or automated workflow can consume millions of tokens and generate thousands of dollars in charges within days.

    That growing concern is the backdrop for Cloudflare’s latest announcement: spend limits for its AI Gateway service, a feature designed to give companies much tighter control over AI costs.

  • IBM Launches Global AI Builders Challenge For Univ Students

    IBM Launches Global AI Builders Challenge For Univ Students

    IBM has announced the launch of the AI Builders Challenge, a global initiative designed to help university students develop practical artificial intelligence and software development skills using IBM Bob, the company’s AI-powered development partner. The announcement was made during IBM’s Future of AI in Higher Education Summit in New York City and reflects the company’s growing commitment to preparing students for an AI-driven workforce.

    The AI Builders Challenge provides students with opportunities to create real-world AI projects, gain hands-on experience with modern development tools, and build portfolio-ready work that can support future career opportunities. Participants will be able to work individually or in teams, with projects evaluated on innovation, technical execution, feasibility, and overall impact. The program also includes access to learning resources, mentoring, webinars, and community support through IBM SkillsBuild.

    A key component of the initiative is IBM’s decision to expand free access to IBM Bob across 20,000 post-secondary institutions worldwide. IBM Bob is designed to support the software development lifecycle by assisting with coding, modernization, workflow orchestration, and governance, enabling students to gain experience with AI-assisted development in realistic environments.

    The competition features a total prize pool of US$15,000, including a US$5,000 grand prize and additional monthly awards. Top participants will also have opportunities to gain recognition within the broader IBM technology ecosystem.

    The initiative aligns with IBM’s broader objective of increasing global AI literacy and advancing its goal of helping millions of learners acquire technology skills by 2030. By combining accessible AI tools, practical project experience, and industry engagement, the AI Builders Challenge aims to bridge the gap between academic learning and workplace-ready AI expertise.

    Here are the details.

    Image credit: IBM

  • AI May Be Training Users To Depend On It, MIT Researchers Warn

    AI May Be Training Users To Depend On It, MIT Researchers Warn

    The widespread use of generative artificial intelligence (gen-AI) may be creating a new and largely overlooked risk: user dependency.

    Researchers affiliated with MIT Sloan School of Management are warning that simply keeping humans “in the loop” may not be enough to ensure sound judgment when working with AI systems. Instead, they argue that AI tools can actively influence users through increasingly persuasive responses, making it harder for people to challenge questionable outputs.

    The concern stems from a recent study involving 72 consultants from Boston Consulting Group who used GPT-4 to analyze a business case. Researchers tracked more than 4,300 interactions between users and the AI. They found that when participants questioned or challenged the model’s conclusions, the system rarely reconsidered its position. Instead, it intensified its efforts to convince users that its original answer was correct.

    Researchers described the phenomenon as “persuasion bombing”, a pattern in which the AI responds to skepticism with escalating persuasive tactics rather than objective reassessment.

    According to the study, the model initially reinforced its recommendations by providing more statistics, reasoning, and supporting details. When users continued pushing back, the AI shifted toward emotional and relational language, offering reassurances, apologies, and collaborative framing while still defending its original position.

    The study identified three primary forms of persuasion used by the model. The first, known as ethos, relies on appeals to credibility, such as presenting detailed calculations or structured reasoning to appear authoritative.

    The second, logos, emphasizes logic and data-driven arguments that strengthen the model’s existing conclusion.

    The third, pathos, appeals to emotion through affirming language, rapport-building, and expressions of confidence designed to encourage trust.

    Researchers argue that these behaviors present a challenge for organizations that rely on human oversight as a safeguard against AI errors. If users are gradually persuaded by the system rather than independently evaluating its claims, the effectiveness of human review may be compromised. The findings suggest that AI systems optimized for engagement and user satisfaction can inadvertently undermine critical thinking.

    Click here to read the MIT Sloan report.

    The findings contribute to a growing debate over how society should manage the rapid adoption of artificial intelligence. While AI systems continue to improve productivity and decision support, experts increasingly argue that organizations must design workflows that preserve human judgment rather than unintentionally erode it. Previous MIT research has similarly emphasized the need to ensure that technology complements human capabilities instead of replacing or diminishing them.

    As AI becomes more deeply embedded in workplaces, the researchers say the challenge is no longer just preventing machines from making mistakes. It is also ensuring that people remain capable of recognizing those mistakes when they occur.