Today, September 8, is International Literacy Day.

For many of us reading this, literacy is so embedded in everyday life that we rarely think about it. We read messages, search for information, fill out forms, sign contracts, learn new skills, apply for jobs, and communicate with people across the world. Reading and writing became part of how we navigate life so long ago that it’s difficult to imagine moving through the world without them.

Yet literacy is still far from universal. According to UNESCO, at least 739 million youth and adults worldwide still lack basic literacy skills, and almost two-thirds of them are women.

That number is big, yes, but for me, it’s also personal.

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My father’s mother never learned to read or write.

She was a poor woman living in a world where the opportunities available to her were extremely limited. As an orphan when she was seven, education wasn’t something she had chosen to leave behind. For a woman in her circumstances, there weren’t many options to begin with.

But when she had children of her own, she was adamant that they would receive an education.

People around her disagreed. Her boys didn’t need school, they told her. They needed to learn how to work because if they wanted to eat, they had to work. Education could seem unnecessary when survival itself demanded so much.

She saw something different.

She couldn’t read a book herself, but she understood that education could change lives. She didn’t have studies about social mobility or access to research explaining the economic impact of education. She couldn’t search for statistics or listen to experts discussing how to give children better opportunities.

She just knew she wanted something different for hers.

In her own way, she was a visionary. She saw what nobody around her could see or explain.

And generations later, here I am: her granddaughter, someone who has spent much of her professional life writing. I’ve written articles, emails, websites, technical content, marketing content, and more things than I can probably remember. Words became not only something I learned to use, but part of how I make a living.

I’ve studied at universities. I’ve built a career working with people and companies in different countries. Today, I live in another country by myself.

And I’m writing this article in my second language.

There is a line between her insistence and my life today. It isn’t perfectly straight. There are generations, sacrifices, decisions, privileges, circumstances, and many other people along that line. But it is there.

She couldn’t read. Her granddaughter makes a living partly because she can write.

And this is something I will never take for granted.

Now, we’re learning another kind of literacy

The meaning of literacy has expanded as the world around us has changed. Reading and writing remain foundational, especially when hundreds of millions of people are still excluded from those skills. But technological shifts also require us to develop new ways of understanding and navigating the world.

Right now, many of us are learning another language: AI literacy.

And I don’t think AI literacy means only mastering the perfect prompt or becoming an expert in every new tool. ChatGPT, Claude, Gemini, and whatever comes next will continue changing. Interfaces will change. Models will improve. What feels advanced today may feel ordinary a few years from now.

AI literacy has to go deeper than knowing which buttons to press.

It means understanding what these systems can and cannot do. It means knowing when AI can accelerate our work and when human judgment needs to take over. It means questioning an answer rather than assuming confidence equals accuracy, and thinking seriously about privacy, bias, attribution, and responsibility.

Most importantly, it means learning how to use AI without handing our thinking over to it.

That’s what makes this particular kind of literacy both fascinating and difficult: we’re learning the language while we’re also writing some of its rules.

There is no finished handbook waiting for us. Companies are developing policies. Governments are working through regulation. Schools and universities are deciding what responsible use looks like. Professionals are experimenting with where AI belongs in their work, while creators are navigating questions around originality, ownership, and creative responsibility.

We’re not entering a world where everyone else already knows what they’re doing. We’re learning together while the technology itself keeps changing.

Literacy gives us agency

Maybe that’s the connection I keep coming back to.

The power of literacy has never been limited to recognizing words on a page. Literacy gives people greater agency in the world. It allows us to access information, question it, communicate our ideas, learn independently, and participate in systems and conversations that might otherwise exclude us.

AI literacy can offer a similar kind of agency.

The goal isn’t to turn everyone into an AI expert, just as learning to read doesn’t require everyone to become a novelist. It’s to understand enough to participate: enough to ask better questions, recognize opportunities and risks, challenge what we’re given, and make informed choices about how we want these technologies to fit into our lives and work.

That’s also why AI literacy matters beyond productivity. Yes, AI can help us create faster, research more efficiently, automate parts of our work, and do things that once required skills we didn’t have. But the bigger question is who gets to understand and use these capabilities, and who risks being left outside another major shift in how the world works.

What one generation makes possible for the next

My grandmother couldn’t have imagined the world her granddaughter would eventually live in. She couldn’t have imagined me working through a computer with people thousands of kilometers away, building a career across countries, living independently in another part of the world, and publishing words written in a language that wasn’t the one I grew up speaking.

She certainly couldn’t have imagined me sitting here learning how to work with artificial intelligence.

But I think she would have understood the instinct behind it.

She knew that when the world gives you an opportunity to learn something that can expand your possibilities, you reach for it. And if you have the chance to make that opportunity available to the people who come after you, you fight for that too.

Today, on International Literacy Day, I’m thinking about the woman who never learned to write her own name but refused to let that define what was possible for her children.

She couldn’t cross that particular threshold herself, so she made sure the next generation could.

And decades later, her granddaughter is still walking through doors she helped open.

Now there is another language to learn, another threshold to cross, and another question worth asking: what doors can we help open for the people who come next?

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For a long time, I could describe what I was good at relatively easily.

Writing. Research. Content strategy. Taking complicated information and turning it into something people could understand. Coming up with ideas. Organizing information. Looking at a business, an audience, or a problem and figuring out what to communicate and how.

These were skills I had built over years of working in communications and marketing. They were part of how I understood myself professionally.

Then AI came along and started doing a surprising number of them.

It can draft an article in seconds. It can research a topic, summarize a long document, generate campaign ideas, analyze information, reorganize messy notes, rewrite an email, create an outline, and identify patterns in data.

And the better these tools get, the stranger one question becomes:

If AI can do many of the things I thought I was good at, what exactly am I good at now?

When your skills stop feeling as special

I don’t think this question is unique to writers or marketers.

Many of us built professional identities around things that required time, practice, and expertise: writing clearly, researching efficiently, analyzing information, brainstorming ideas, organizing projects, producing reports.

AI doesn’t eliminate the need for these skills. But it changes their scarcity.

Producing a competent first draft feels different when almost anyone can produce one. Summarizing information becomes less distinctive when a tool can do it instantly. Brainstorming twenty ideas isn’t particularly impressive when you can generate fifty in thirty seconds.

But as I’ve used AI more, I’ve noticed something interesting. Some of the abilities I used to consider my main professional strengths are becoming less visible, while others I barely thought of as “skills” are becoming harder to ignore.

The skills hiding underneath the skills

When I ask AI to write something, the important part isn’t necessarily that I could write the first draft myself.

It’s that I can read what it produced and know that something is off.

The argument doesn’t make sense. That sentence sounds impressive but says nothing. We’re answering the wrong question. We’re missing the customer’s actual problem. This research is relevant, but it doesn’t support what we’re trying to say.

Sometimes I can’t articulate the problem immediately. I just know the output isn’t right. Then I have to figure out why.

That ability didn’t appear because of AI. It came from years of writing, editing, communicating, watching campaigns succeed and fail, working across different industries, making mistakes, and learning what good work looks like.

AI only made that layer of the work easier to see.

Pattern recognition. Context. Curiosity. Knowing which question to ask next. Recognizing when something is incomplete. Understanding which details matter and which are noise. Connecting information that initially seems unrelated.

And, increasingly, deciding what is worth doing at all.

AI can give me ten articles, fifty ideas, another email sequence, landing page, report, or workflow. That doesn’t mean any of them should exist.

Someone still has to decide what matters, and what we should prioritize right now.

I’m also discovering a different side of myself

There’s another realization happening alongside this one.

I’ve always had an operational side. I like organizing things, figuring out how the pieces fit together, spotting gaps, solving problems, and improving the way work gets done.

But because I’ve worked in marketing for years, I’ve mostly described myself through marketing disciplines.

The more I experiment with AI, the more I notice that I’m just as interested in the system behind the work as I am in the final output.

Now, when I see a specific gap in a process, I find myself asking: could I build something to help with this?

Maybe it’s an assistant or skill or artifact that helps create product pages more consistently. Maybe it’s a tool supporting a repetitive content workflow. Maybe AI can analyze information that would otherwise take someone hours to review.

I’m becoming interested in identifying a very specific problem, understanding the process around it, and figuring out how AI can fill that gap.

And I don’t think this is an entirely new side of me.

Looking back, I can see it in other parts of my career: organizing information, creating processes, coordinating projects, finding more efficient ways to get things done. AI has given me a new way to act on that instinct.

The output isn’t the whole skill

This has changed the way I think about my career.

I’ve spent years describing myself through disciplines: communications, content marketing, email marketing, strategy. Those descriptions aren’t wrong, but lately they feel incomplete.

When I look across the different things I’ve done, the patterns become clearer.

I like taking messy information and creating structure around it. I like spotting connections. I like figuring out why a process isn’t working and imagining how it could work better. I like translating between different worlds: technical and non-technical, business and audience, ideas and execution.

And more recently, as I’ve started building AI assistants in ChatGPT and Skills in Claude Cowork and experimenting with tools I never imagined myself creating, another pattern has emerged:

I really like building things that solve problems.

For years, I might have described myself primarily as someone who writes, communicates, or creates strategy. AI hasn’t taken those abilities away from me. I use them constantly when working with it.

But it has separated the output from the thinking behind the output.

And once those two things are separated, you start noticing which part you actually enjoy.

What did doing the work teach you to see?

Maybe that’s a more useful question as AI becomes part of more professions.

We’ve been trained to describe ourselves by what we do: writer, designer, analyst, developer, researcher. Increasingly, some of the tasks associated with those identities can be shared with machines.

So I’m becoming more interested in looking underneath the task.

What did years of doing that work teach you to notice? What can you recognize that someone without your experience might miss? What questions do you know to ask? What connections do you make? What do you know not to trust?

When do you recognize that an answer is technically correct but practically useless?

And when AI gives you almost unlimited possibilities, how do you decide which ones are worth pursuing?

I don’t have a clear new professional identity to replace the old one. That’s partly why I’m writing The AI Work Journal. Using AI every day keeps exposing questions I didn’t know I had about work, expertise, creativity, and what I actually want to spend my time doing.

But AI is helping me separate two things I once treated as the same: what I know how to do and what makes me good at what I do.

The first will keep changing as the technology changes.

The second has been accumulating quietly for years.

And maybe one of AI’s unexpected effects is helping us see more clearly what we’ve been bringing to the work, and the way we work, all along.

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There’s a strange assumption attached to AI-generated work: if AI helped you do it faster, then somehow you didn’t really work for it.

You see it especially around creative and knowledge work. Write a blog post with AI? You took a shortcut. Generate an image? Too easy. Build a simple automation without learning how to code it from scratch? You barely built anything. There’s an almost automatic connection between speed and effortlessness, as though the amount of time spent producing something is what determines the value of the thinking behind it.

And yes, AI can make things effortless.

You can ask Claude or ChatGPT to write a blog post, copy whatever appears on the screen, publish it, and move on. You can generate an image and use the first result without questioning whether it communicates what you intended. You can ask AI to create an email sequence without thinking about the audience, the customer journey, the offer, or what should happen after someone clicks.

AI has made it extraordinarily easy to produce something.

But producing something and producing something well have never been the same thing.

The work AI doesn’t see

I use AI to help me write or structure my ideas. I use it for long-form content, emails, landing pages, brainstorming, research, analysis, and plenty of other things that would have taken me much longer a few years ago.

And I absolutely appreciate the speed.

But when I look at something AI has written, I’m not looking at it as someone encountering that kind of work for the first time. I’m bringing years of experience into that review, where I decide what stays and what goes, where I scratch words, sentences, even entire paragraphs and sections.

It may be well-written, but it’s me who decides what I say and how I want to say it.

A-L-W-A-Y-S :)

I can tell when an introduction takes too long to get to the point. I can notice when an email sounds polished but not compelling. I know when a CTA feels disconnected from everything that came before it. I can recognize when a paragraph technically makes sense but doesn’t sound human, when the structure is repetitive, when an argument needs more evidence, when the language doesn’t fit the audience, or when something isn’t good enough to put in front of a client.

AI didn’t give me that judgment.

Years of writing did.

So did years of editing. Reading. Learning about marketing. Creating campaigns. Seeing what performed and what didn’t. Working with clients in different industries. Getting feedback. Making mistakes. Writing things I would probably cringe at today. Testing ideas that went nowhere. Learning what people respond to. Learning when the problem is the copy and when the problem is somewhere else entirely.

All of that is sitting behind the seemingly simple act of looking at an AI-generated draft and saying, No. This isn’t there yet.

Speed compresses execution, not experience

This is where I think the conversation about AI and effort gets interesting.

We tend to see the final interaction with the tool. Someone types a prompt. Something appears. They edit it. They publish it.

What we don’t see is everything that person brought to that interaction.

A photographer using AI image tools brings an understanding of composition, lighting, visual storytelling, and aesthetics. A marketer building an AI-assisted campaign still has to understand positioning, audiences, customer behavior, offers, channels, and conversion. Someone creating a basic automation still needs to understand the process they’re trying to automate, what should trigger what, where things can go wrong, and whether automating that particular process even makes sense.

Of course, expertise isn’t automatically present just because someone uses AI. That’s exactly the point.

AI gives both the experienced person and the complete beginner access to powerful production capabilities. What it doesn’t automatically give them is the same ability to evaluate what comes out.

The gap increasingly lives in judgment.

And judgment is difficult to see because it often looks like a tiny decision.

Delete this paragraph.

Change the structure.

Don’t use that claim.

This isn’t the right image.

We need more context here.

That automation shouldn’t trigger yet.

This sounds impressive but in the end doesn’t mean anything.

Start over.

Each decision might take seconds. But the ability to make it confidently may have taken time to develop.

“I made this in an hour” doesn’t tell you much

Before AI, we often used time as a proxy for effort.

Something that took ten hours presumably required more work than something that took one. In many cases, that was true simply because execution itself consumed so much time. Researching, drafting, formatting, creating variations, editing, designing, organizing information, and completing repetitive technical steps could swallow entire days.

AI is changing that equation.

If someone with ten years of experience can now produce excellent work in two hours instead of eight, those six hours haven’t somehow contained the person’s expertise.

The expertise was already there.

The tool compressed parts of the execution.

In fact, I think this is one of the things we’re going to have to learn to value differently as AI becomes embedded in more professions. Time spent and value created are becoming even less connected than they already were.

The person who recognizes the right answer in five minutes may be able to do so precisely because they’ve spent fifteen years learning what the wrong answers look like.

The temptation to skip the thinking

There is, however, another side to this.

AI makes it incredibly tempting to confuse output with completion.

That’s where “effortless” AI becomes a genuine problem.

You can generate the article and never interrogate the argument. You can produce the strategy without understanding the business. You can create twenty social posts without asking whether anyone needs to read them. You can build an automation without thinking through the exceptions. You can generate something that looks professional enough and assume that means it is professional.

The barrier to mediocre work has become veeryy low.

But so has the barrier to experimenting.

That’s what makes this moment so interesting to me.

People who previously couldn’t build certain things can now try. Someone who doesn’t know how to code can create a basic tool. Someone without design skills can explore visual concepts. Someone who struggles with a blank page can get a starting point in seconds.

The question becomes what happens after the generation.

Do you accept what the machine gives you because it looks finished?

Or do you have enough understanding of what you’re trying to accomplish to question it, reshape it, test it, discard parts of it, and sometimes throw the whole thing away?

That second process doesn’t always look like hard work from the outside.

But it is work.

Maybe effort is becoming less visible

I suspect we’re entering a period where some of the most valuable work will become increasingly invisible.

The visible part might be a prompt and a result.

Behind it sits the person deciding what problem is worth solving, what information matters, what the output should accomplish, what needs changing, what shouldn’t be trusted, what feels off, and when something is finally ready.

AI can help me write faster today because I already know a lot about writing.

It can help me create things I couldn’t have created on my own a few years ago because I can bring knowledge from other areas and use AI to bridge some of the technical gaps.

Neither of those things makes the work meaningless.

If anything, using these tools has made me more aware of how much knowledge we carry without noticing it.

The years behind our work don’t disappear because the final execution gets faster. The failed experiments don’t disappear. The courses, books, projects, client conversations, feedback, mistakes, successes, and thousands of small decisions that shaped our judgment are still there.

We just don’t have to manually perform every step anymore.

And perhaps that’s one of the shifts we need to navigate in the age of AI: learning to stop measuring work by how difficult it looked to produce.

Faster isn’t necessarily effortless.

AI can compress the work into minutes. It cannot compress the years that taught you what’s worth creating.

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For the past few years, a lot of the conversation around AI and work has focused on learning the tools. Learn ChatGPT. Prompting, automation, agents… Learn whatever new platform appeared this week before another one arrives next Tuesday.

I understand the impulse. I use AI every day, and experimenting with these tools has changed the way I work. I’ve built GPT assistants, I’ve been creating Skills with Claude Cowork, and I use AI for research, writing, analysis, brainstorming, and increasingly for building things that I would have assumed required technical skills not very long ago.

But the more I use AI, the more I wonder whether knowing how to use it will be the differentiator we think it is. Eventually, almost everyone will know how to use AI. Maybe not at the same level or in the same ways, but opening an AI assistant, explaining what you need, uploading documents, or asking it to analyze something will probably become as ordinary as searching Google or creating a spreadsheet.

If that’s where we’re headed, then “I know how to use AI” won’t be much of a career advantage.

The more interesting question becomes: What can you do with it that someone else can’t?

The tool is becoming the easy part

When ChatGPT first appeared, knowing how to get useful results from it felt almost like a specialized skill. Prompt engineering became a term people suddenly added to their profiles. Courses appeared. People collected prompt libraries and formulas designed to produce better results.

Some of that knowledge was genuinely useful. But the tools themselves have also become much better.

You don’t necessarily need the perfectly engineered prompt you might have needed a few years ago. You can explain what you’re trying to accomplish conversationally, provide examples, upload documents, ask the AI to interview you before beginning, and correct it as you go. Increasingly, AI systems can work across multiple steps rather than just answering one isolated prompt at a time.

As that friction disappears, I think the value begins to move somewhere else. Knowing which button to press matters less when everyone can press it. Knowing why you’re pressing it, what you’re trying to accomplish, and whether the result is useful becomes much more important.

Your experience becomes part of the prompt

One thing I’ve noticed while building AI assistants is that the AI itself is only part of what makes them useful.

I can ask an AI to create a marketing campaign. So can thousands of other marketers. But I can also give it what I’ve learned from years of working in marketing and communications: how I think about audiences, what I look for in a customer journey, how sales and marketing should support each other, what makes an email sequence useful rather than simply automated, and what information I need before creating content.

Suddenly, I’m not just asking AI to “do marketing.” I’m giving it a way of thinking about marketing.

I’ve noticed this particularly while creating GPT assistants and Claude Skills. A surprising amount of the work involves taking something that previously existed mostly in my head and translating it into instructions, questions, criteria, examples, and processes that another system can follow.

And that has made me look at professional experience differently.

Years of experience aren’t just a list of jobs you’ve had. They’re also thousands of tiny judgments you’ve accumulated. You notice something that doesn’t look right. You know which question to ask next. You recognize when a technically correct answer doesn’t make sense for this particular situation. You know when something sounds impressive but probably won’t work.

AI can help us execute much faster. But we still have to bring something to the table.

Judgment might become more valuable, not less

There is an understandable fear that AI makes expertise less valuable because people can suddenly produce things that previously required specialized knowledge.

I’m experiencing a version of this myself. I’ve never been a programmer or developer, yet I’m now creating tools with AI. Someone who isn’t a designer can create surprisingly good visuals. Someone who isn’t a professional writer can produce a decent first draft. Barriers that used to be quite high are getting lower very quickly.

That’s exciting. It also creates an interesting problem.

When producing things becomes easier, we produce a lot more things.

There are more articles, posts, products, apps, automations, strategies, ideas, and experiments competing for our attention. Being able to make something is becoming less unusual. Being able to decide whether it’s worth making becomes much more important.

AI can generate twenty ideas in seconds. Which one is worth pursuing? It can draft a strategy. Does the strategy make sense? It can create a workflow. Should that workflow exist? It can write an email. Does the email understand the person receiving it?

It can build something.

Does anyone need it?

Those questions require something beyond knowing how to operate an AI tool. They require context, experience, curiosity, and judgment.

The advantage might be in the combination

I’ve spent much of my career as a generalist, moving between communications, content, email, CRM, strategy, project coordination, marketing, even accounting and admin support. For a long time, being a generalist could feel uncomfortable in a professional world that constantly encouraged people to specialize: pick your niche, become known for one thing, make your career easy to explain in a sentence.

AI has made me reconsider some of that.

When execution becomes easier, being able to connect different areas may become more useful. A marketer who understands sales can use AI differently from someone who only understands content. A writer who understands customer behavior can ask different questions. Someone who has worked across several disciplines can bring those perspectives together when solving a problem.

The AI might be the same. The person using it isn’t.

All of us bring different careers, industries, mistakes, successes, instincts, curiosities, and ways of seeing problems into the interaction. Perhaps that’s where part of our career advantage will come from: not from competing with AI on how quickly we can produce something, but from bringing everything we’ve learned into the way we direct it.

Knowing how to ask is only the beginning

We’ve talked a lot about prompting over the past few years, and asking good questions matters. But there’s another layer beyond prompting: you have to know what deserves a question in the first place.

You need enough understanding of a problem to recognize what’s missing. Enough curiosity to challenge the first answer. Enough experience to notice when something doesn’t quite fit. And enough confidence to tell the AI that its well-structured response is useless and start again.

This is one of the things I enjoy most about working with AI. The best sessions don’t feel like asking a machine for an answer and accepting whatever comes back. They feel more like working through something. I push, it responds, I disagree, we explore another direction, I provide more context, and occasionally something emerges that I hadn’t considered before.

The quality of that process depends partly on the AI, but it also depends on what I’m bringing to the conversation.

So what should we be learning?

There’s a big pressure right now to keep up. Every week brings another model, feature, platform, workflow, or prediction about which jobs are disappearing next. It can make professional development feel like an endless race.

I don’t think ignoring AI is a good strategy. Learning how to work with it is increasingly becoming part of knowing how to work. But I’m also becoming less convinced that we need to become experts in every tool that appears.

The tools will change. They will become easier, more capable, and more integrated into the software we already use. Some of the AI skills that seem sophisticated today will probably feel completely ordinary a few years from now.

So yes, learn AI. Experiment with it. Build things. Try the new tools. Figure out what they can do for your work.

But while doing that, keep developing everything you bring to them: your expertise, your judgment, your curiosity, your understanding of people, your ability to connect ideas, and your willingness to question an answer instead of accepting it because a machine delivered it confidently.

The career advantage in the age of AI may not belong to the person who knows the most AI tools.

It may belong to the person who knows how to make those tools more useful because of everything they already know.

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I have never been a programmer.

I have never been a developer. I don’t have a technical background, and for most of my career, I assumed there was an entire category of things I couldn’t build.

Apps. Software. Automated tools. Anything that required code.

Those belonged to another world, inhabited by people who understood programming languages, terminals, APIs, repositories, and all the other words I vaguely recognized but didn’t really know what to do with.

I could have ideas for those things. I could explain what I wanted them to accomplish. I could probably write the copy, think through the user experience, or identify the problem they should solve.

But actually build them?

That was where my involvement ended.

At least, that’s what I thought.

I’ve always considered myself a creator

The funny thing is that I’ve never had trouble thinking of myself as a creative person.

The Creator is actually one of the brand archetypes I’ve always resonated with most. Creating has been part of my identity for as long as I can remember.

I write. I paint. I come up with ideas and turn them into things that didn’t exist before. I’ve built content strategies, newsletters, campaigns, frameworks, workshops, and countless other things throughout my career.

But somehow, I had drawn an invisible line around technology.

Creativity lived on my side of that line. Technology lived on the other.

I could use technology to create, of course. I’ve been doing that for years. But I didn’t think I could create technology itself.

Over the past few years, and particularly as I’ve started experimenting more deeply with ChatGPT assistants and Claude Cowork, that distinction has become much less clear.

Because now I can sit down with an idea and say:

“This is what I want to build.”

And instead of the conversation ending there because I don’t know how to build it, that’s where the conversation begins.

From “I don’t know how” to “how could we do this?”

This has probably been one of the most exciting changes AI has brought into my work.

I can describe the problem I’m trying to solve. I can explain what I want a tool to do, who will use it, what information it needs, what the output should look like, and how I want the process to work.

Then I can ask AI to help me figure out how to make it happen.

What should I build first? What instructions does the tool need? What am I missing? Is there a simpler way to accomplish this? Why isn’t this working? How could I improve it? What should happen if the user gives it this kind of information instead of that kind?

It becomes an iterative process.

I ask. It proposes. I test. Something doesn’t work. I go back. We troubleshoot. I change the instructions. I test again. I realize there’s another possibility I hadn’t considered. I add it. Eventually, something that started as an idea begins to actually do something.

I’ve built custom GPTs. I’ve been experimenting with Claude Skills and Cowork. I’ve created tools around marketing workflows and tasks I understand because I’ve spent years doing them manually.

And every time one of these experiments actually works, there’s still a tiny part of me thinking:

Wait. I made this?

Not alone, obviously. AI is doing things I couldn’t do myself. That’s precisely what makes this moment so interesting to me.

I’m bringing the domain knowledge, the problem, the judgment, the desired outcome, the testing and the decisions. The AI brings capabilities I don’t have and helps translate what I’m trying to accomplish into something functional.

I’m learning to co-create with it.

AI has expanded what “creative” means to me

This experience has made me rethink something much bigger than whether I can build a GPT or a Claude Skill.

It’s changing my understanding of creativity itself.

For a long time, I associated my Creator side with writing, painting and other obviously creative endeavors. Creativity meant starting with a blank page or canvas and turning an idea into something tangible.

But isn’t that also what I’m doing when I build one of these tools?

There’s still a blank page.

There’s still an idea.

There’s still that messy middle where the thing in your head doesn’t quite match the thing in front of you.

There’s experimentation, frustration, iteration and the occasional moment when something suddenly clicks.

The medium is different.

Instead of paint, words or images, sometimes I’m working with instructions, workflows, knowledge bases and AI capabilities I barely knew existed a few years ago.

And I find that incredibly exciting.

Technology no longer feels quite as much like something happening somewhere else, built exclusively by people with skills I don’t have.

There’s a small corner of it where I can make things too.

Of course, this doesn’t suddenly make me a developer

There are limits to this, and I think they’re important to acknowledge.

Being able to build something with the help of AI doesn’t give me the expertise of someone who has spent years studying computer science, software engineering, security, architecture or any number of technical disciplines.

There are things I don’t know. There are things I don’t even know that I don’t know.

But that’s true of every profession.

I wouldn’t expect someone to become a marketing strategist because they can ask ChatGPT to create a marketing plan. I wouldn’t expect someone to become a designer because an AI image generator can produce an illustration for them. Access to a capability and deep professional expertise aren’t the same thing.

What has changed is the entry point.

I don’t need to become a developer before I’m allowed to experiment.

And that distinction matters.

Because for someone like me, the biggest barrier was often not a lack of ideas. It was the assumption that implementing those ideas required skills so far outside my world that there wasn’t much point in trying.

AI has lowered that barrier enough for me to step over it.

Work keeps changing underneath us

There is another side to all of this that I don’t want to romanticize.

As a non-technical professional, I sometimes struggle with just how quickly work is changing.

Actually, I think a lot of us do.

There’s always another model, another tool, another feature, another workflow we’re supposedly falling behind on. Something you learned six months ago has changed. Something that seemed impossible last year is now a button inside an app.

And this isn’t confined to people working in technology.

Marketing is changing. Writing is changing. Design is changing. Customer service is changing. Education is changing. Research is changing. Operations are changing. Management is changing.

Whatever you do for work, AI has probably touched some part of it already.

Keeping up can be exhausting.

I don’t think the answer is to chase every new tool or force ourselves to become experts in everything AI can do. I’m increasingly interested in a different question: What becomes possible for me now that wasn’t possible before?

That’s a much more exciting question to explore.

I’m touching things I wouldn’t have touched before

A few years ago, if you had shown me something that looked remotely technical, my instinct probably would have been to assume it wasn’t for me.

Now I’m much more likely to poke at it.

I open things I don’t completely understand. I ask questions that might be incredibly basic. I experiment. I break things. I ask AI why I broke them. I try again.

Sometimes I discover that what I wanted to do is more complicated than I expected.

Sometimes I discover that I can actually do it.

And sometimes the experiment itself gives me another idea.

That might be the part of this shift I value most.

AI hasn’t only given me new tools. It has made me more willing to enter rooms I previously assumed were closed to me.

I’m still a non-technical person. I’m still a writer, a marketer, a strategist and someone who would rather start with the problem than with the technology.

But now I’m also building things.

Small things, imperfect things, useful things, experimental things.

Things I wouldn’t have attempted a few years ago because I had already decided they were outside the boundaries of what someone like me could create.

Those boundaries are moving.

And amid all the uncertainty about what AI will mean for our jobs, our careers and the way we work, I’m glad some of those boundaries are moving too.

Because perhaps one of the most interesting things about this moment isn’t only what AI can create.

It’s who gets to create now.

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When ChatGPT was released, I was one of the first people I knew to start experimenting with it. Curiosity was enough. Since then, AI has become part of my everyday work. I use ChatGPT and Claude daily to write, think, research, solve problems, and build.

What surprised me wasn’t how quickly the tools improved. It was what happened to the questions I was asking.

Every experiment led to another. Every new capability raised a bigger question—not just about AI itself, but about the world of human work. What does expertise look like when knowledge is instantly accessible? Which skills become more valuable? How do we build meaningful careers when the way we work is changing so quickly?

Those questions are why this journal exists.

The Conversation I Couldn’t Find

Over the past couple of years, I’ve consumed more AI-related content than I can count. Newsletters. Podcasts. YouTube videos. Product launches. Prompt libraries. Threads explaining why one model is better than another.

I’ve learned a lot from all of them.

But eventually I realised I was looking for a different kind of conversation.

Most of what I found focused on the technology: what changed, what launched, what became possible. Those conversations matter because they help us keep up with a field that seems to reinvent itself every week (or day!)

Yet every time I closed another article or another video, I found myself thinking about something else entirely.

Not What can this model do?

But What does this mean for us?

What does it mean for someone who has spent years building expertise in writing, design, marketing, software, education, consulting, or leadership? What happens when the tasks we’ve mastered become easier, faster, or partially automated? What skills become more important, and which ones quietly fade into the background?

Those questions felt bigger than any product announcement.

They felt personal, and that changed everything.

AI Changed More Than My Workflow

Like many professionals, I initially saw AI as a productivity tool.

Something that could help me write faster.

Summarise documents.

Generate ideas.

Improve drafts.

It certainly does all of those things, but somewhere along the way, I realised the real transformation wasn’t happening on my screen.

It was happening in the way I thought about my work.

Every new capability forced me to re-evaluate something I’d taken for granted. What clients actually value. What expertise looks like when information is abundant. Whether experience becomes less important—or perhaps more important—in a world where anyone can generate a competent first draft in seconds.

I still don’t have definitive answers. In fact, the opposite has happened.

The more I learn about AI, the more questions I seem to have.

One thing that’s worth mentioning is that I didn’t come into this world as an engineer or a developer. My background is in marketing, communications, and writing. I don’t build foundation models, and I can’t explain every technical breakthrough happening behind the scenes.

Yet over the past couple of years, curiosity has gradually turned me from a user into a builder. I now spend part of my days creating AI assistants and workflows with Claude and ChatGPT that solve real problems, all because the tools made building accessible in ways I hadn’t imagined.

That’s the perspective I bring to this journal: someone exploring what becomes possible when curious professionals begin building with AI, and what that shift means for the future of work.

And I’ve come to think that’s a good sign, because curiosity has always been one of the most valuable professional skills we have.

Why I Started This Journal

This publication probably won’t help you become an AI expert, even if from time to time I’ll be sharing my own AI experiments, and my experience in the process.

This journal explores what it means to navigate work while AI reshapes it in real time.

I don’t expect every reader to use the same tools I do. Some of you may rely on ChatGPT every day. Others might be experimenting with Claude, Gemini, Copilot, or tools that haven’t even been released yet.

The tools will change, that’s for sure.

The questions won’t.

How do we continue learning without feeling permanently behind?

How do we stay valuable when knowledge is increasingly available to everyone?

How do we build trust when AI can produce content in seconds?

How do we balance efficiency with originality?

How do we protect space for deep thinking when speed is becoming the default?

Those are the conversations I want this publication to explore because I suspect many of us are asking the same questions in different ways.

A Journal, Not a Playbook

The name The AI Work Journal was intentional.

A journal isn’t a manual because it’s not here to pretend to have everything figured out.

It’s a record of observations, experiments, successes, mistakes, conversations, and ideas that evolve over time.

That’s exactly what I want this publication to become.

Some essays will explore how AI is reshaping work and professional identity.

Some will document experiments I’m running with new tools and workflows.

Others will reflect on creativity, careers, writing, leadership, and the skills that may become even more valuable as AI becomes increasingly capable.

If there’s a common thread, it’s this:

I’m interested in exploring what AI reveals about the way we work.

Technology is changing quickly but the more interesting story is how people respond to that change.

How they adapt.

How they resist.

How they reinvent themselves.

And perhaps most importantly, how they find meaning in their work when the rules are being rewritten in real time.

An Invitation

I don’t know exactly where AI will take us over the next five or ten years.

Anyone who says they do is probably more confident than they should be.

What I do know is that we’re living through one of the most significant shifts in modern professional life, so I’d rather explore it more with curiosity than certainty.

That’s what you’ll find here.

If you’re navigating this new world too—whether you’re excited, overwhelmed, skeptical, optimistic, or simply curious—I hope you’ll find something that resonates.

Welcome to The AI Work Journal.

I think we’re going to have some conversations worth having in a world that’s changing at a pace it’s hard to keep up with.

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