I have received this question more times than I can count. Are jobs gone? Is AI taking over everything? Why are internships not coming through? What do I do with my degree or my skill?
Here is my honest answer — not a motivational one.
AI has made things easier, and harder at the same time. Because AI can now do multiple things, the person who knows how to get the right things done from AI is preferred over the one who does not. That is the new baseline. Learn the basics of AI regardless of which path you choose — not to become an AI engineer, but to use it well in whatever you are doing.
Beyond that, I see three paths. Each one has its own direction. The mistake most people make is not choosing one and going deep on it.
Path 1: Build a Personal Product
This is the path I am on. I will be honest about where my experience ends.
Start by finding a problem — ideally one you personally face. That is your built-in validation. If you have the problem, others likely do too. Finalise the idea, confirm the problem is real, then learn the skill you need to build the solution. Do not learn everything first. Learn what you need, build, then learn more.
Once the product is built, the work shifts to marketing. This is where most builders stop — because it feels like a different skill entirely. It is. But it is learnable the same way development was. Start with early users, gather feedback, improve, then do more marketing.
I am at the marketing stage myself. I cannot tell you what comes after from personal experience — only that the direction is clear and the work continues.
How to share: Talk about the problems your customer faces. Occasionally present your product as one example of the solution — not the pitch, just the example. If you run ads, use them to push that same message to more people. Not to convince anyone to buy. Just to reach more of the right people with something honest and useful.
Path 2: Get a Job
The approach here is the same one that got someone I spoke to recently his first client — focus on one thing.
Learn the basics of your field first. In AI for example: machine learning, deep learning, RAG, automation. Get a working understanding of each. Then pick one and go deep. Not because the others do not matter, but because depth in one thing is what makes you visible and hireable. A RAG engineer who knows RAG well is more useful to an employer than someone who knows five things at a surface level.
The projects you build matter too. Do not build dummy projects. Build things that solve real problems — the kind of problems an employer is already solving for his clients. That is what shows you can do the actual work.
How to share: Document what you are learning. Share projects as examples of solutions to real problems — not as a showcase of your effort. The employer coming across your work should see someone who thinks in terms of problems and solutions, not someone listing what they built. I have not done this myself so I cannot speak from experience — but the direction is clear.
Path 3: Freelancing
The learning direction is similar to the job path. Pick a niche, go deep, build relevant projects. The difference is in how you show up publicly.
As a freelancer you are not trying to impress an employer — you are trying to attract a client. That client is not looking for someone learning. They are looking for someone who can solve their problem right now. So the sharing shifts: less about what you are learning, more about what you are doing, what you are working on, how it helps, why it matters.
Same angle as everything else — lead with the problem, present your work as one of the solutions. The client who reaches out already understands what you do and why it is relevant to them.
The thing all three paths share
In every path, the way to get clients or employers is the same: inbound, not outreach.
Outreach costs time and energy. Cold messages ask a stranger to trust you before they have any reason to. Inbound builds that trust before the conversation starts — through consistent, specific, honest work shared publicly over time.
Answer the questions your audience is already asking. Talk about what they are thinking. Share your work in a way that shows you understand their problem. The right people find you.
This is the core of They Ask You Answer and Show Your Work — two books worth reading if you want to understand the implementation deeply, not just the idea.
A few things that apply regardless of path
Keep learning continuously. The field moves and stopping is not an option.
Set a clear goal. Pick a path and know what you are working toward. Vague direction produces vague results.
Stay consistent when the phase changes. Development to marketing. Learning to sharing. Building to selling. Each transition feels like starting over. It is not. It is just the next stage of the same work. Most people quit here. Do not.
And learn AI basics regardless of your path. Not to become an AI specialist — to use it well in whatever you are already doing. The person using AI effectively will always have an edge over the one who is not.
Jobs have not disappeared. The bar has shifted. The people getting hired and getting clients are the ones who picked a direction, went deep, and made theirselves visible to the right people, the right way.
This is the whole answer.
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