IBM Granite 3.2 Ups the Ante on Open, Efficient, and Trusted Enterprise AI
On February 26th, IBM announced Granite 3.2, the latest iteration of its Granite 3.0 family of models. This release underscores IBM’s continued focus on smaller, more efficient large language models without sacrificing enterprise-grade performance. In our view, the key enhancements center on three pillars: reasoning (with a new toggle parameter), vision capabilities (for advanced document understanding), and updates to Guardian safety models—IBM’s companion system designed to help detect harm and inaccuracy.
We believe IBM’s decision to build these models in-house is a strategic move designed to give enterprises deeper trust and control over generative AI workloads. By owning the entire process—from data curation to model architecture—IBM aims to address compliance, data provenance, and scaling challenges more effectively than if it relied solely on third-party offerings. In our research, this approach stands out for prioritizing transparency, efficiency, and governance, ensuring that businesses can deploy AI solutions with confidence and manage operational risk more effectively.
Making AI Decisions Explainable
In this episode of the Next Frontiers of AI Podcast, I am joined by Marc Le Maitre, the CTO of Scanbuy, to discuss how his organization achieved a 10x ROI in digital advertising campaigns by creating fully explainable and transparent AI models through the emerging innovation of causal AI. Listen in and discover why Scanbuy is “flying their flag on Causal AI” to transform the world of programmatic advertising. You’ll also learn why causal AI will become a critical component in future Agentic AI systems and is rapidly being democratized for the masses to achieve similar business outcomes.
266 | Breaking Analysis | Why Intel Must Stay Independent, Spin Out Foundry, and Rebuild Its Iconic Brand
Few brands in technology carry the storied pedigree of Intel. For decades, “Intel Inside” was synonymous with progress in computing—an American icon that repeatedly raised the bar for processor performance and innovation. Today, Intel finds itself in a precarious position. Its once-vaunted manufacturing machine has fallen behind TSMC, which dominates advanced process nodes. Meanwhile, Intel’s core CPU design business—a substantial, if bruised, revenue engine—remains overshadowed by the mounting losses and capital burn in its foundry operations. Rumors swirl that Broadcom might step in and scoop up Intel’s design arm. But for those who believe in competition, national security, and the long-term integrity of an American technology brand, that outcome would be deeply misguided.
The Ladder to Agentic AI
The AI landscape is evolving fast, and 2025 is shaping up to be the year of Agentic AI—where AI moves beyond task automation and prediction to goal-driven decision-making. But here’s the challenge: 90% of enterprises want to embark on this journey, yet only one in three know where to start.
On the latest Next Frontiers of AI podcast, we broke down the roadmap to Agentic AI adoption with a simple framework—the Ladder to Agentic AI
265 | Breaking Analysis | Investors Cool on Cloud but CEOs Double Down
Earnings reports from the big three cloud players disappointed investors this week. All three US hyperscalers fell a bit short of consensus for their cloud revenues in the December quarter and investor reaction has been negative. But squinting through the data, there’s a lot to like about the position of AWS, Google Cloud Platform and Microsoft Azure. In particular, the IaaS and PaaS revenue alone for the big 3 approached $200B in 2024 and grew 25%. All three cited capacity constraints and, along with Meta, are committing more than $300B in CAPEX spend this year, most of it to support current and future AI demand. As is often the case, when a new wave hits it tends to be overhyped at the beginning of the cycle and underestimated when it hits a steady state. And in between points A and B we often see blips and bumps that represent opportunities for long term investors.
Riverbed Smart OTel to Deliver Right Data at the Right Time
Riverbed announced the general availability of Riverbed Smart OTel, delivering its approach to OpenTelemetry (OTel) that promises to deliver the right data at the right time for faster insights and decision-making. This launch takes advantage of the OpenTelemetry (OTel)open-source framework that was designed for collecting, processing, and exporting telemetry data such as traces, metrics, and […]
264 | Breaking Analysis | theCUBE Research Predictions 2025
Make no mistake. We are entering a technology cycle that is completely new. Massively parallel computing and the Gen AI awakening is creating an entirely different industry focus that has altered customer spending patterns. Moreover, this new computing paradigm has changed competitive dynamics almost overnight. Decisions whether to spend tens of billions or hundreds of billions on CAPEX are being challenged by novel approaches to deploying AI. Geopolitical tensions are higher than at any time in the history of tech. While the pace of change appears to be accelerating, causing consternation and confusion, the reality is that broad technology adoption evolves over long periods of time, creating opportunities, risks, and tectonic shifts in industry structures.
#1 – Predictions for AI in 2025
In this first episode of The Next Frontiers of AI Podcast, I am joined by my industry colleague Tim Sanders, VP of Research Insights at G2, to debate theCUBE Research’s predictions for AI in 2025. We begin 2025 grounded in the consensus that will see the rise of agentic AI. In our predictions, we won’t restate that widely held […]
Six Predictions for AI in 2025
We begin 2025 grounded in the consensus that this year will mark the rise of agentic AI. In our predictions, we won’t reiterate that shared belief; instead, we will focus on the underlying changes that will drive the value of agentic AI. We foresee a year of advancements that address real-world barriers to AI adoption and enable higher-ROI use cases.