Because what it is today is something entirely different.

Modern SEO is rooted in empathy, creativity, technology, and adaptability. It’s not just about rankings anymore. It’s about understanding how humans search for and engage with information.

SEO Is Misunderstood

When people think of marketing, most people think ads, influencers sponsorships, social media, or maybe even some fancy billboard. SEO is a bit different. It’s quieter. Less glamorous. Often misunderstood even by so-called “experts” on social media.

It’s a fast-evolving field full of nuance. Full of “it depends.” It’s constantly changing, and anyone in the field knows that certainty is rare and predictability is basically nonexistent.

And yet, I believe it’s one of the best ways to develop modern skills, especially in this AI-driven future we’re heading towards.

Why SEO Is a Crash Course in Modern Skills

Mastering User Psychology

Search intent is healivy based on consumer paychology. Understanding what people really want when they type something into a search bar is what we’re all about. And few other marketing fields incentivize you to map human needs so directly to useful content.

But a lot of SEOs still don’t get this. I see it all over social media with people clinging to keywords and outdated link-building techniques. In reality, keywords are dead. And this will become even more appareant as people increasingly do their searching with prompts instead of keywords.

Backlinks are still very useful, but not the edge they once were.

What matters now is this: understanding what information people need and actually delivering on that. It sounds idealistic, but it aligns with the incentives of the search engines themselves. Google and ChatGPT succeed only if users consistently find what they’re looking for on their platforms, maintaining returning users. That means these platforms are designed to promote helpful content, resources, and tools. The better you understand user needs and how to satisfy it, the better your content performs.

Understanding How AI Thinks

Even before ChatGPT, AI was baked into search engines like Google, and therefore, something SEOs needed to learn about. Google’s BERT and RankBrain were early efforts to decode how humans speak, think, and search so machines could better surface results. The Knowledge Graph was built to create relationships between entities, making AI better able to understand the relationships between different people, places and things.

Now we’ve got GEO, AEO, LLMO (whatever name the community settles on). They’re all still SEO, but in the age of modern AI. And if you’re in SEO, you’re already working within, and feeding , these systems. That puts SEO professionals in a uniquely advantageous position. It’s our job to understand AI.

Standing Out In The Age Of AI

SEO is becoming more and more about originality. That’s what Google will be doubling down on — and it makes total sense.

AI learns from the web. But if all the content out there is recycled or AI-written itself, there’s nothing new to feed it. That’s where Information Gain comes in.

Information Gain means adding something new to a topic — not just rewording what’s already ranking.

This does two major things:

1. It counters the sameness of AI-generated content, which can’t create truly novel ideas or perspectives by itself.

2. It signals to search engines that your site is offering something of value, not just noise.

It’s an opportunity to think outside the box and ask yourself “What can I add to this discussion?”, “How can I take my content a step further?” or “How can I make this extra useful to my users?”

But this isn’t just about search engines. It’s about art, uniqueness and what we are contributing to the world. As AI raises the bar for what qualifies as “good enough,” our challenge becomes: How do we make content that goes beyond that?

The Other Skills SEO Teaches You

Beyond human psychology and AI, SEO also trains you in skills like:

Web Development: Tech SEOs know this well. To operate at a high level, you need at least a working knowledge of HTML, CSS, JS, hosting structures, and how websites actually function. SEO and web development overlap more than people realize.

Data Analysis: SEO without data is like flying blind. Data is the bedrock of good strategy.

Writing: Writing with purpose is core to SEO. Even if you don’t do the writing yourself, good SEO learns to recognize how good writing helps users.

UX Design: You learn to see content through the eyes of the user. What makes a page feel good? What makes it confusing?

Adaptability: Google changes constantly. Strategies evolve. You learn to let go, pivot, and rebuild.

Cross-Functional Collaboration: You don’t do SEO alone. You work with developers, writers, designers and more, learning how to communicate confusing SEO concepts in their languages.

Comfort with Uncertainty: This used to bother me, but now I love it. SEO is full of ambiguity. There’s a lot that’s left to interpretation, and there’s lots of assumptions that need to be tested. It’s half art, half science

Hopefully by now you understand how SEO is truly a one-stop-shop for a lot of incredibly valuable skills that can serve you far beyond an SEO career.

SEO is messy. It’s uncertain, and I don’t really know where it’s going. But I’m grateful I get to do it now — while it’s still this weird mix of everything.

“I just wrote a children’s book in under two minutes using AI, and here’s how you can do it too,” boasts a woman in a YouTube ad Google thought I’d like. (Google really lets people advertise anything these days, huh?) I find these ads upsetting. Just today, as I’m writing this, I saw another ad for a company supposedly selling little puppy robots fueled by AI. The video was clearly just real puppies and Sora-generated videos of puppies, but I guess some people will think that robot technology is so advanced that we can essentially create robot pups indistinguishable from the real thing? Humanity doesn’t really have the best track record when faced with new technology, and generative AI seems easy to exploit — from get-rich-quick schemes to passing AI-generated content off as human creativity. Regardless, AI is here to stay, so we need to learn to live with it. This begs the question: what’s the best way to use generative AI?

Whether at work or across the internet, I kept hearing people discuss how those who learn to use AI will thrive compared to those who do not. I never truly understood this, nor did I know how to apply the idea. Are we all expected to become “prompt engineers,” whatever that entails?

Don’t get me wrong — I actually really like Artificial Intelligence. As a child, I always wanted my own personal version of Jarvis, Iron Man’s AI-powered super assistant (just without it getting a body and leaving me for Elizabeth Olsen). With each major advancement in AI technology, I feel as though we are getting closer and closer to that dream. But until then, what am I supposed to do with AI right now?

As someone who has always enjoyed various creative arts, I don’t love the use of AI for artistic creation. Yes, for assistance — but not for creation. I find AI images to look weird many times, though this has been improving a lot recently, especially after the controversial AI generative Studio Ghibli art trend. Writing is dull when AI has full reign. Of course, if AI is trained on pre-existing data, and creativity is often the pursuit of something new, then AI is both inefficient and barely a real threat against any real artistic pursuit (or at least that’s what I hope). Yet, AI “art” is how I find most people on the internet use the technology.

So, if creative expression isn’t the right use for AI, then what is? For me, the answer came through my work as an SEO Analyst.

How I Learned To Use AI

My relationship with generative AI began as I started working as an SEO Analyst. AI has made SEO a pain, but my encounters with AI were in an entirely different context. I found myself consistently confronted with AI as a search engine. With tools like Search GPT, Perplexity, and AI Overviews, AI is now essential in the search for information. This began my inevitable use of AI.

AI Search

I type a Google query and often find myself satisfied with the AI-generated answer — especially when the query is not too important that I need to verify the factuality deeply. For more complex queries, I turn to SearchGPT and Perplexity. For example, I might type “What is information gain?” (SEO stuff, please bear with me) into a standard search engine like Google if I want a basic definition. However, if I need a more in-depth explanation of something very specific — such as “What is the relationship between information gain and the Google Knowledge Graph?” — then I might use SearchGPT or Perplexity.

DeepResearch features take things to a whole new level in terms of automating research. I especially like ChatGPT’s version of it. DeepResearch involves AI taking a longer amount of time to analyze the steps it needs to take to answer your question. It’ll go deeper into the internet to find specific information across webpages to relate them to each other. The response takes around 9 minutes, depending on what you’re looking for. My longest response was 11 minutes, when I asked it to search for houses, based on a very specific set of criteria. To my surprise, it worked (kind of)! Two of the houses were no longer available and the 3rd was ugly, but I was surprise with how far it had gotten.

I always advise everybody fact-check the most important pieces of information if warranted. However, for most use cases, this is good enough to get the ball rolling on finding ideas.

AI-Powered Knowledge Base

Another AI tool I began using was Notebook LM, which quickly became my favorite AI tool. It works by allowing the user to create a notebook into which they can upload documents, webpages, YouTube video links, and more. The AI is then “grounded” in that information, which greatly reduces one of AI’s biggest flaws: hallucinations. For me, this makes Notebook LM feel like the more trustworthy AI tool for specific things I’m looking to learn about. Therefore, it is my go-to for learning.

Notebook LM is more than a standard text-based chatbot, though. A key feature of Notebook LM is called “Audio Overview,” a podcast-style conversation between two AI people discussing the sources you uploaded. You can either customize the conversation they have or join it yourself to ask questions about specific topics within your sources.

Notebook LM makes learning much easier. I often upload sources I have read but may forget details about, allowing me to revisit them and relate different sources to one another to understand how multiple concepts work together. My first real use case of it was to understand the role that artificial intelligence has within Google Search, in preparation for a presentation. And I wouldn’t have learned nearly as much about such a complex topic if it wasn’t for Notebook LM (Google’s AI, helping me learn more about Google’s AI).

ChatGPT, Perplexity, and Claude all share a feature known as “Projects” (or “Spaces” in Perplexity). These are similar to Notebook LM in that they function as folders where you can upload information and documents to create a centralized knowledge base. Despite the similarities, I believe their use cases are very different. While Notebook LM is ideal as a study tool, I find that folders in other AI tools are more practical for tasks rather than pure learning.

Here is an example of what I mean: You can create a folder in ChatGPT and upload a PDF listing your family’s food preferences. Then, you can brainstorm with SearchGPT to come up with new recipes your family may enjoy, or even upload links to recipe pages and the AI will analyze if it’s a good choice. These chats are saved, so ChatGPT remembers previous interactions, making these folders particularly useful for ongoing tasks where a certain set of guidelines is needed. Notebook LM could be used similarly, but its chats are erased, so there’s no context of previous conversation nor ongoing work.

AI Is Here, So Let’s Use It Well

Humanity, by nature, advances technology without stopping. AI is just the latest massive leap that results from this. There are understandable concerns over what AI can do to job availability for humans, and I feel that. Until there’s some legislation to stop any major downside that may come from this, the best option is to learn how to use AI to know how to not get replaced by it. Maybe we can even use it to become more efficient in our work. The most ethical way forward, I believe, involves honest use of AI tools.

My current favorite use of AI is to facilitate knowledge search and knowledge management, but this is just the beginning.