Elevating your AI game (with a day job)

I was thinking recently about how to best integrate new AI tools into our lives as cloud security professionals.

And I concluded: all hours of the day aren’t created equal for this. Let me explain.

As working professionals, many of us operate within the structure and scale of large enterprise environments.

These organizations are complex, often carrying the weight of legacy systems, strict regulatory requirements, and deeply embedded processes.

While these constraints are necessary for stability and compliance, they also mean that the adoption of new AI technologies tends to lag behind what is available in the broader tech ecosystem.

This isn't a critique of enterprises. The slow pace of change is often a byproduct of due diligence, risk management, and the need to ensure interoperability across vast, distributed systems.

But the reality is this: there's a growing disconnect between the tools and workflows available to us in our professional roles and those we can access outside of work.

When we step outside the enterprise perimeter—whether at home or in a personal project—we often find ourselves in what feels like an entirely different reality.

Here, new AI-powered productivity tools are just a click away. The friction is minimal and the experimentation is boundless.

Given this contrast, one useful mental model is to categorize our career activities into two buckets:

1. "Day Job" Career Activities: These are tasks performed within the enterprise environment, on corporate-managed devices, governed by internal policies and external compliance requirements. Innovation here tends to be incremental, and tool adoption is slower due to necessary vetting and integration processes.

2. "Outside of Day Job" Career Activities: These are self-directed initiatives we pursue in our own time and space. Think: studying the intricacies of Azure networking, experimenting with AI coding agents, writing technical blog posts, or engaging with the community on social platforms.

Both categories offer valuable growth opportunities, but it's the second category that currently presents the richest soil for experimentation and rapid skill development.

The lack of constraints allows us to test new tools, explore novel workflows, and stay ahead of the curve—sometimes by a year or more.

In a time where AI is reshaping the landscape faster than ever, leaning into these "outside the day job" activities isn’t just a nice-to-do.

It's an essential strategy for staying relevant, sharpening our skills, and ultimately bringing fresh value back into our organizations when the time is right.

Or maybe starting your own lil’ thing one day 😉 

So embrace the dual reality. Use your evenings, weekends, and passion projects as a sandbox to play with the future—because by the time it reaches your day job, you’ll already know how to make it work.

Things I've Been Enjoying

GPT4o's new image generation mode

This has been SO much fun. Revolutionary stuff. Like the rest of the internet, I've become addicted to Studio Ghibli-fying images:

This one deserves it's own post - added to my backlog.

Gemini 2.5 Pro Experimental

New state-of-the-art model across most benchmarks (topping LMArena leaderboard). This model really shines with long context uses cases (can handle 1M+ tokens!!).

Another one that deserves it's own post - I'd love to once I've played around with it more.

A16z's Top 100 GenAI Consumer Apps (4th Edition)

This is a list of highest traffic / most used AI consumer apps by venture capital firm Andreessen Horowitz.

Fascinating to see what makes the list and how it has changed since previous iterations. Few funny ones in there, including the "LooksMax AI" app which has been downloaded over 5 million times:

Let me know if you want me to try this one for the good of science.

Inside Bolt: From near-death to ~$40m ARR in 5 months (Lenny’s Podcast)

Very enjoyable episode covering the founding story behind Bolt.new.

The main takeaway for me was how critical their "WebContainer" technology was to the success of their platform. So really, it was an "overnight success 7 years in the making".

Quote of the day

 “The future is already here – it's just not evenly distributed.”
— William Gibson

Hope you got something from this braindump.

Till next time,

Nelson