Hey {{first_name | default: there}},

Every few months someone sends me an article about how AI is going to replace cloud engineers, IT professionals, or tech workers broadly.

I read them. I think about them. Then I go back to work.

Not because I think the threat should be ignored. AI is already changing how technical work gets done, and some tasks that used to require hours of manual effort can now be accelerated dramatically.

But I think a lot of people are asking the wrong question.

Instead of asking, "Will AI replace me?" I think the better question is:

Am I learning how to use AI to become more valuable at what I already do?

I do not think anyone can confidently predict exactly which technology jobs AI will eliminate or create over the next decade.

What we can see right now is that AI is changing the baseline for what one skilled professional can accomplish.

Tasks that once required hours of searching documentation, writing scripts, troubleshooting syntax, or building configurations can increasingly be accelerated with AI assistance.

That changes the value equation.

The advantage will not simply belong to the person who knows the most commands or can write everything manually.

It will increasingly belong to the person who understands the technology deeply enough to know what should be built, why it should be built, and whether the AI-generated answer is actually correct.

That last part matters.

Infrastructure as Code is an obvious example.

AI tools can already help generate and refine Bicep and Terraform configurations, write Azure CLI and PowerShell scripts, create deployment pipelines, and troubleshoot infrastructure code. Microsoft now has training specifically dedicated to using GitHub Copilot for these kinds of cloud operations.

That does not mean I would let AI generate infrastructure and deploy it blindly.

Quite the opposite.

Someone still needs to understand networking, identity, security, architecture, cost, governance, and the business requirements behind the environment.

AI can generate the code. You still need to know whether the code makes sense.

Cost optimization is another example.

Azure's AI tooling can assist with analyzing costs and surfacing optimization opportunities, while Microsoft's FinOps tooling is increasingly integrating AI into cost analysis workflows.

The same pattern is appearing in troubleshooting, monitoring, documentation, security, and automation.

AI handles more of the mechanical work.

That gives the professional more time for judgment.

And judgment is where experience becomes valuable.

A lot of the AI conversation is defensive.

How do I protect my job?

I think there is another way to look at it.

What can I build now that would have required a team five years ago?

That question gets much more interesting.

If AI allows you to research faster, automate repetitive work, create documentation faster, write scripts faster, analyze environments faster, and prototype solutions faster, the economics of building something on your own begin to change.

That matters for consulting.

It matters for digital products.

It matters for small software businesses.

And it matters for professionals trying to build income outside their paycheck.

This is where AI connects directly to what we have been building toward in this newsletter.

For Caribbean and African diaspora professionals trying to create more independence, technology has already given us geographic reach.

AI can potentially give us something else.

Scale.

Imagine a cloud consultant who used to have enough time to serve three clients effectively. If AI and automation reduce the administrative and repetitive parts of the work, that consultant may be able to deliver more without immediately hiring a large team.

That does not mean AI automatically creates a profitable consulting business.

You still need expertise.

You still need clients.

You still need trust.

You still need to deliver.

But the cost of turning expertise into output is changing.

For someone trying to build a lean business around knowledge they already have, that is worth paying attention to.

Do not try to learn every AI tool released this month.

Pick one that fits work you already do.

Use it repeatedly.

Learn where it saves you time. Learn where it gives you bad answers. Learn what context produces better results. Most importantly, learn where human judgment still matters.

Then add another tool when there is a reason to.

The goal is not to become someone who uses AI for everything.

The goal is to become someone who knows when AI makes the work better and when it does not.

That is a much harder skill to replace.

If you work with code, scripts, automation, or Infrastructure as Code, GitHub Copilot is a practical place to start.

It integrates into development environments such as Visual Studio Code and can assist with code completion, chat, code review, agents, and other development workflows.

There is currently a free tier that includes limited usage, so you can experiment without immediately paying for a subscription. GitHub Copilot Pro is currently $10 per month and adds unlimited code completions and additional capabilities and usage.

For cloud professionals, I would start small.

Take a PowerShell script you understand.

Ask Copilot to explain it.

Then ask it to improve the error handling.

Give it a simple Bicep template and ask what could be improved.

Ask it to help you write a KQL query for a problem you already know how to investigate.

Do not just measure whether it produces an answer.

Measure whether it saves you time without lowering the quality of your work.

That is the test that matters.

Pick one repetitive task in your current tech workflow.

Something you already understand but spend too much time doing manually.

Give yourself thirty minutes and test whether an AI tool can help you do it faster.

Then ask yourself two questions:

Did it save me time?

Did I trust the result?

If the answer to both is yes, you may have found a workflow worth keeping.

You are not trying to replace yourself.

You are trying to increase what one version of you can accomplish.

One workflow.

This week.

See you next Tuesday,

Migrate to Millions

P.S. Hit reply and tell me: Are you using AI in your work right now? If so, which tool is actually saving you time? I want to know what is working for people in our community, not just what is getting the most hype.

Disclaimer: This newsletter is for educational purposes only and is not financial or career advice. Technology, employment, and AI tools are changing rapidly. Evaluate tools and career decisions based on your own circumstances.