As up to 10 million jobs disappear and quality content moves behind paywalls, the question isn’t if AI will reshape society. It’s whether 2026 is the year we’ll control the burn or watch it spread.

What modern storage really means, how on-prem arrays compare to first-party cloud services, and a clear checklist to pick the right fit for cost, control, scalability, and resilience.

Gina Rosenthal discusses how AI is transforming everything from cybercrime and fraud detection to government operations this year, revealing both breakthrough innovations and costly failures.

From self-organizing drones to software managing supply chains, agentic AI is creating systems that are reshaping industries. We break down the latest developments and what you can do to prepare.

Product marketers have long relied on NIST for clarity and consistency — but with new frameworks emerging for AI, it's time to ask whether these guidelines go far enough in prioritizing fairness, safety, and accuracy.

Generative AI is stealing the spotlight, but machine learning remains the backbone of AI innovation. This blog unpacks their key differences and how to choose the right approach for real-world impact.

DeepSeek claims its open-source AI model rivals top LLMs at a fraction of the cost—but is the hype justified? Here’s what you need to know about its tech, impact, and potential risks.

In her latest blog, Gina Rosenthal explores how AI is driving economics, politics, and computing, while demystifying key concepts to help navigate AI’s growing impact on society and technology.

Gina Rosenthal explores the promise and perils of AI solutions, with new tools turning data into actionable insights while also increasing the risks of AI-washing and cybersecurity breaches.

Former VMware Product Marketing Manager Gina Rosenthal examines Broadcom's $69B VMware acquisition, highlighting drastic pricing changes, SKU cuts, workforce reductions, and new opportunities for competitors in virtualization.

AI is transforming industries, but it also raises ethical challenges. This blog explores five key ethical considerations, from training data biases and social inequality to the environmental impact of AI models. Understanding these issues is vital for responsible AI deployment.