.jpg)
We curate a lot of information daily. Our ongoing collections hold 100 article links, which amounts to about a week’s worth.
Every week, we select some of the articles to share in more depth.
ZDNet - What is Model Context Protocol? The emerging standard bridging AI and data, explained
MCP is rapidly emerging as a foundational standard for the next generation of AI-powered applications. Developed as an open standard by Anthropic in late 2024, MCP is designed to solve a core problem in the AI ecosystem: How to seamlessly and securely connect large language models (LLMs) and AI agents to the vast, ever-changing landscape of real-world data, tools, and services.
TechCrunch - OpenAI wants its ‘open’ AI model to call models in the cloud for help
For the first time in roughly five years, OpenAI is gearing up to release an AI system that’s truly “open,” meaning it’ll be available for download at no cost and not gated behind an API. TechCrunch reported on Wednesday that OpenAI is aiming for an early summer launch, and targeting performance superior to open models from Meta and DeepSeek.
SiliconANGLE - Why AI gateways are emerging as cloud-native’s next battleground
There’s a new front opening in the race to build intelligent applications: the intersection of artificial intelligence, application programming interfaces and infrastructure. At KubeCon + CloudNativeCon Europe 2025 in London, the surge of interest in AI wasn’t just about model training or graphics processing unit availability. It was about what happens after you’ve integrated AI, after you’ve deployed the model, and after inferencing becomes your new bottleneck, sparking demand for a new control point: AI gateways.
VentureBeat - Amazon’s SWE-PolyBench just exposed the dirty secret about your AI coding assistant
“Now they have a benchmark that they can evaluate on to assess whether the coding agents are able to solve complex programming tasks,” said Anoop Deoras, Director of Applied Sciences for Generative AI Applications and Developer Experiences at AWS, in an interview with VentureBeat. “The real world offers you more complex tasks. In order to fix a bug or do feature building, you need to touch multiple files, as opposed to a single file.”
The AI Agent Architect Substack - How to Architect an Enterprise-Grade AI Agent Securely
If your agent needs to access a database, it typically uses service credentials with admin-level privileges. Any third-party library in that process can potentially access those same credentials. As Durai aptly puts it, this creates a "zero trust issue" that makes the entire environment fundamentally insecure.
VentureBeat - Ethically trained AI startup Pleias releases new small reasoning models optimized for RAG with built-in citations
French AI startup Pleias made waves late last year with the launch of its ethically trained Pleias 1.0 family of small language models — among the first and only to date to be built entirely on scraping “open” data, that is, data explicitly labeled as public domain, open source, or unlicensed and not copyrighted.
Buy the Rumor; Sell the News Substack - OpenAI Isn't Building Models. It's Building Infrastructure
While Twitter debates fine-tuning methods and speculative emergent abilities, Altman is orchestrating Stargate: a $500 billion physical infrastructure project intended to dominate the future of cognition. The sheer scale of the investment implies a different thesis: that building AGI is less about coding superintelligence than about laying the groundwork for it in steel, silicon, and kilowatts.
VentureBeat - Ethically trained AI startup Pleias releases new small reasoning models optimized for RAG with built-in citations
They are all based on Pleias 1.0, and can be used independently or in conjunction with other LLMs that the organization may already or plan to deploy. All appear to be available under a permissive Apache 2.0 open source license, meaning they are eligible for organizations to take, modify and deploy for commercial use cases.
Chief Therapy Officer Substack - The new upgrades that make AI useful
The capability and extent of AI tooling we have today is impressive, but those models have no idea about how your specific CI pipeline works, what standards your company expects, or even what “prod-east-legacy” means in your org.
AI Adoption and Usage
InfoWorld - Why enterprise investment in AI agents hasn’t yielded results
Enterprises have rushed to capitalize on the transformative potential of AI agents, but a stark reality is emerging. Our recent survey of more than 1,000 enterprise technology leaders revealed that more than half of organizations (68%) have budgeted over $500,000 annually for AI initiatives, yet nearly all (86%) lack the foundational infrastructure needed to deploy them. This gap between ambition and execution capability isn’t merely technical—it represents a strategic challenge that threatens to undermine AI investment returns.
ZDNet - The 4 types of people interested in AI agents - and what businesses can learn from them
Today's consumers expect more than functionality -- they expect experiences that feel tailored, intuitive, and emotionally intelligent. While businesses are looking to AI for efficiency, 65% of consumers are looking to AI agents to help them make better decisions and make their lives easier.
CIO Influence - Cyberhaven Report: Majority of Corporate AI Tools Present Critical Data Security Risks
71.7% of AI tools are high or critical risk, with 39.5% of AI tools inadvertently exposing user interaction/training data and 34.4% exposing user data.
83.8% of enterprise data going to AI is going to risky AI tools, instead of enterprise-ready tools (low and very low risk).
Forbes - Strategically Implementing AI: A Guide For Businesses
Once you've decided to implement AI, it's important to understand that it requires access to high-quality, structured and accurate data to function effectively. The system “learns” from this information and makes decisions accordingly. Data accuracy directly impacts technology performance—if the input data is incomplete or incorrect, the system may generate flawed results, leading to confusion and errors. Therefore, a crucial pre-implementation step is assessing data quality, consistency and accessibility, followed by optimization if needed.
Information Week - Edge AI: Is it Right for Your Business?
"For example, in retail, one needs to analyze visual data using computer vision for restocking, theft detection, and checkout optimization, he says in an online interview. KPIs could include increased revenue due to restocking (quicker restocking leads to more revenue and reduced cart abandonment), and theft detection. The next step, Dutta says, should be choosing the appropriate AI models and workflows, ensuring they meet each use case's needs.
Marketing Ethics Digest Substack - Climbing the Generative AI Pyramid
Once curiosity is piqued, safety becomes essential. Policies, privacy protocols, and ethical guardrails come into play at this level. Organizations must address data usage, hallucinations, and intellectual property to foster confident, responsible experimentation.
Diginomica - How Freshfields is building a multi-agent AI strategy for legal workflows with Google Cloud
If you look right now at the legal industry specifically, it lacks optionality. It's a Microsoft dominated field. All of the ecosystems, everything, everybody is basically Microsoft. And now there's beginning to be a move over to Azure and a move to AI. I think that it is important to decouple cloud from AI.
SiliconANGLE - The long road to agentic AI – hype vs. enterprise reality
In contrast, traditional enterprises are taking a more cautious stance. While cloud giants’ spending on AI infrastructure is exploding, most enterprises lag in adopting these capabilities, constrained by practical realities of budgets, skills and legacy systems. In short, there’s a widening gap between the hype of what AI agents promise and the reality of what enterprises can feasibly implement today.
AiThority - How to close the 1,600% enterprise application visibility gap – and what it could mean for productivity
Despite the integration of AI tools designed to boost productivity and streamline workflows, digital inefficiencies are costing companies a lot more than one might think. WalkMe’s recently released 2025 State of Digital Adoption Report: Special AI Edition revealed that enterprises wasted an average of $104 million on underused technology in the last year alone. The report offers a comprehensive look at how enterprises are navigating digital adoption and Gen AI integration – and where critical gaps remain.
Information Week - Nailing the Initiative: LexisNexis Leverages Agentic AI
Quick to find uses for AI after the rise of ChatGPT, Jeff Reihl, executive vice president and CTO for the legal and professional side of LexisNexis, says his company shifted gears on certain projects. In some instances, it even meant putting some efforts on hold indefinitely in favor of a new, AI-driven path.
We update our ongoing collections starting at 8:00 am MST.
Here are the direct links: