AI made it easy to produce content at scale. It also made it easy to produce content nobody wants to read. The companies winning in search and AI-driven discovery are the ones publishing content with real perspective, not the ones generating hundreds of keyword-stuffed articles a month.
Most AI training ends with excited teams and productivity gains. But a few weeks in, people are spending their AI time re-explaining context. Skills — portable, shareable instructions that teach AI how to handle specific work — change that pattern and help training compound across an organization.
This is a guide for the people who get a little nervous when they need to open Terminal (or don't know what it is). I'll try to break down the complexity, simplify the buzzwords, and allow you to build something quite incredible within the next hour.
AI runs my week. I started this as an experiment to test the capabilities of the latest models over a year ago. Since then, it has become a core part of my daily workflow. This post outlines that system, how it works, how it helps me, and where it still fails to do the innately human stuff.
Why AI pilots stall in 'pilot purgatory' and why failure is often the signal that teams are learning fast enough to make the next model jump count.
A practical look at the 30-day Implementation Launchpad approach for mapping workflows, shipping focused pilots, and building AI agents that actually work.
At CES, it was obvious how wide the gap still is between knowing about AI and actually running it inside everyday work. Even advanced audiences can describe the tools but very few are operating with autonomous agents or the workflows that make them worth it.
The companies pulling ahead are the ones that keep failing fast, redesigning team structures, and shrinking the human layer to make space for always-on execution partners. That shift is already here, and most organizations are not ready for it.
Anthropic built an AI-powered interview tool and used it to talk to 1,250 professionals about how they actually feel about AI at work. They spoke with the general workforce, scientists, and creatives.
The research validates a lot of what we're seeing. People tend to like AI, they use it frequently, and they're still uneasy about it.
With the launch of this new website, we're bringing our three divisions—Mostly Serious, Habitat, and MSAI—into one space. It's important to understand how this all came together.
Fifteen years ago, Mostly Serious started in a spare bedroom building websites. Along the way, we built a team and culture that actually works—and that foundation led to Habitat and MSAI. Three divisions, one team, solving connected problems.
As AI tools become part of everyday work, the model you choose—4o, o3, 4.5, o3-pro, or whatever comes next—has a huge impact on quality, speed, and how “smart” the assistant feels.
This article shares how we teach teams to think about model choice in AI Quest Foundations, including simple personas for today’s ChatGPT models and why learning to pick the right one is a skill that will only matter more over time.
Most companies try to bolt generative AI onto existing workflows instead of reimagining how work should run when AI is in the loop—and that choice quietly determines who gets real ROI from these tools.
Drawing on data from Wharton’s 2025 AI Adoption Report and lessons from more than 50 organizations in our AI Quest program, this piece explains why smaller, more flexible teams are seeing outsized gains and how any company can start redesigning work for generative AI.