Stay Ahead in AI

Agentic AI might have the possibility to transform how enterprises innovate with data, but connecting disparate data sources with LLMs is a roadblock to innovation. Model context protocol (MCP) has the potential to change that.

We spent the past weeks deep diving into some R&D, and I’m genuinely surprised at how we’re still seeing Remote Code Execution (#RCE) vulnerabilities—particularly from command injection—emerge in 2025. It feels like we’re facing a regression in security, with these fundamental vulnerabilities resurfacing in modern technologies.

The first time I ran an AI pilot, it looked like a sure win in digital transformation.

The forecasts were precise, and the model was sophisticated. We scaled up production—only to watch demand tank. The AI crunched the numbers but missed the bigger picture.

That’s when I learned the hard way: Not all AI pilots succeed. Some drive massive ROI.

There's no such thing as an "infrastructure phase." Our data on MCP shows there is lots of building for founders to do — up and down the stack

Large language models are increasingly used to solve math problems that mimic real-world reasoning tasks. These models are tested for their ability to answer factual queries and how well they can handle multi-step logical processes. Mathematical problem-solving offers a reliable way to examine whether models can extract the necessary information, navigate complex statements, and compute answers correctly. 

When most people interact with AI, whether they’re typing a prompt or generating images, they assume a certain level of privacy. It feels like a conversation between you and the AI only. However, a recent report from Wired should make everyone think twice.

Anyone can learn to code, but coding is hard. Thanks to the power of AI, you can just get a chatbot to write the code for you, but is that a good idea?
Welcome to the world of "vibe coding," where anyone can make software, and it doesn't matter if you don't actually understand the code itself. Is that awesome, or is it actually a huge problem?

To successfully contend with AI’s expansion and acceleration, CIOs need to foster a culture of responsible innovation across the enterprise.
Whether you’re in an SMB or a large enterprise, as a CIO you’ve likely been inundated with AI apps, tools, agents, platforms, and frameworks from all angles. 

Enterprises increasingly rely on large language models (LLMs) to deliver advanced services, but struggle to handle the computational costs of running models. A new framework, chain-of-experts (CoE), aims to make LLMs more resource-efficient while increasing their accuracy on reasoning tasks. 

As more U.S. companies incorporate generative AI tools into the workplace, job posts related to the technology are increasing, including a new type of job title: generative AI management consultant, according to a Feb. 27 report by Indeed’s Hiring Lab.

Have you been taking your FOBO pills?  Because without a healthy Fear Of Becoming Obsolete, you will likely end up in a dark place, desperately searching for someone to buy what you’re selling.

Why and how Services-as-Software will rewrite the enterprise tech playbook

After researching 24 sources in seven minutes, ChatGPT came up with the top jobs that might be on the chopping block.