Is AI Ruining Words?

Is AI ruining words? It’s a big question, and probably an uncomfortable one, because we’re all starting to rely on AI more and more. It might also feel like a bit of a dramatic question for some. But hey, language is constantly changing – with culture, technology, and time. That is nothing new. What’s new about it is the role AI is starting to play. Machines are now producing writing at scale, affecting how we communicate every day. And that is making many of us wonder: is AI changing words in a helpful way, or is it ruining their meaning altogether?

This question matters because it’s happening right now. The debates, the controversies, and the conversations about AI and language are everywhere. You see them online, in workplaces, in the content we consume daily, at the dinner table (especially if you’re in our house). People are asking what AI means for B2B communications and marketing and how it’s shaping the messages we put out into the world. It also matters because we already feel the effects of overused, jargon-heavy content in marketing materials, on landing pages, social posts, and blogs.

There is a growing sense that connecting with the content we read is getting harder. We want it to feel authentic and real, like it was written purposefully and matters to someone. We really want to trust that what we’re reading is honest and meaningful.

So, how is AI changing how we write and communicate in B2B marketing, especially in a world where content is produced faster and en masse? Is it making some words feel tired and hollow, or is it simply reflecting patterns we’ve been following all along?

Maybe the bigger question isn’t whether this needs addressing in the first place, but how on earth would we even begin to fix it? Do we rely on more AI to help us write “better,” or is the answer a more deliberate, human approach to editing? If words feel overused and meaningless, is that the fault of the technology or how we’re using it? Should every app and every part of business keep adding more AI? Is the solution really more AI and fewer humans? It feels like that’s the direction we’re heading. But again, I ask (because I’m genuinely, honestly wondering about all this) is that really the answer?

These are big questions, and they feel bigger than just whether AI is “owning” certain words. Many of the words tied to these conversations, like empower, optimize, redefine, leverage, elevate, and foster, were already overused and a quite cringe-worthy long before AI came along. They’d started to lose their impact, becoming the language we skim past without much thought. What AI has done is turn up the volume, flooding us with these words at a scale we’ve not seen before. AI did not create the problem, but it’s made us hyperaware of it.  

How AI Shapes the Words We Use

AI isn’t thinking, and it isn’t clever. It’s a machine. It processes huge amounts of data, spots patterns, and predicts what might come next. As linguist Emily M. Bender puts it, large language models are “stochastic parrots” — they generate text that sounds right, but they don’t understand what they’re saying.

The tech behind tools like ChatGPT is based on massive datasets — everything from books and blogs to research papers and random corners of the internet, most of it scraped without consent — stolen, you might say. One estimate from BBC Science Focus put the early training data at around 570 GB, covering roughly 300 billion words. That’s how these models learn what language “looks like” — not by understanding it, but by recognizing recurring structures and sequences.

They’re great at producing language that’s fluent and convincing. But that’s the key word: convincing. It feels plausible, not personal. It’s all driven by math — probability, prediction, pattern recognition. AI doesn’t know what a sentence means; it’s just trying to guess what comes next. This is also why AI-generated content can often feel flat, repetitive, or strangely hollow. It’s not thinking. It’s mirroring.

AI will absolutely become more sophisticated — but right now, it’s still essentially a very advanced probability engine. A polished output experiment. A costly, power-hungry tool born from Silicon Valley’s obsession with scale — and one we’ve all ended up using, often without really questioning who it was built for, or why.

And here’s the part that really matters for marketers, especially in B2B: AI wasn’t built for us. It wasn’t trained to write strong positioning statements, compelling value props, or smart, context-aware messaging. It wasn’t designed with a content strategy in mind. It doesn’t care about your customer journey or your brand tone of voice. It’s just repeating what it’s seen, and what it’s seen is — frankly — a lot of generic, jargon-heavy, mediocre content.

But because the output feels polished and professional, it’s easy to believe that these tools were made for marketing. That they “get” your brand or your audience. They don’t. And they weren’t.

AI is trained on what’s already out there — and what’s out there is a sea of safe, vague, overused language. That’s why we keep seeing the same words pop up: leverage, empower, elevate, transform. They’re not bad words, but they’ve been used so often and so loosely that they’ve lost their meaning.

The result is lots of sameness. More middle-of-the-road copy. More content that fills space but says nothing. In B2B, where trust and clarity matter most, that’s a real problem. Because when your messaging sounds like everyone else’s, you don’t stand out — you blur into the ether.

So yes, AI can help you draft faster. But it won’t help you say what matters. It can’t make creative leaps. It can’t understand nuance or relevance. And it absolutely cannot replace your judgment, your voice, or your thinking. That’s the human part. And it’s the part that actually makes your marketing work.

Why These Words?

Certain words show up repeatedly in AI-generated content, such as “leverage,” “delve,” and “redefine.” They’re common even in Reddit threads about making writing sound less AI-like. It makes you wonder if it wouldn’t be easier to edit in a more authentic way. These words are popular because they’re versatile enough to fit almost any context while still sounding important. That’s why both AI and humans rely on them so often, they create the appearance of sophistication without much effort to explain or clarify.

“Delve” is meant to convey depth and exploration, the act of digging deeper into a subject or uncovering something meaningful. But it’s been used so generically that it’s lost its impact. You’ll find phrases like “delve into the details” or “delve deeper into insights” everywhere, especially in SaaS marketing. At first, it sounds thoughtful, but with constant repetition, it feels more like a shoe skimming gravel than a drill breaking through magma. It’s too easy to say “delve into powerful analytics” or “delve into smarter workflows,” but these phrases don’t explain anything. They trend, but they don’t clarify anything.

Then there’s “redefine”, a word designed to suggest grand transformation. But when everyone claims to “redefine collaboration” or “redefine the future of work,” it quickly loses its oomph. It creates a fleeting sense of importance, but when the moment passes, the reader is left asking: What’s actually being redefined? Why does it matter? It’s a promise without proof.

Then we have “leverage”. Borrowed from science and transplanted into marketing, it’s a hard word to avoid. “Leverage your data,” “Leverage powerful tools,” “Leverage this opportunity.” But when words like this are overused, they start to feel forced. They try too hard to sound impressive.

But the problem isn’t just the overuse of words like these — it’s what they reveal about our reliance on meaningless language. AI doesn’t exist in a vacuum. It reflects what it’s trained on: us. Years of mediocre copy and convoluted language brought us here. Blaming the tech bros and saying, “Well, they did this,” feels a bit childish. We’re all part of this, turning a handful of buzzwords into a monotonous vernacular and creating a cycle where garbage trains more garbage, slop builds on top of slop.

So, Is AI Ruining Words?

Yes… and no. It’s picking up on our worst habits — the hyperbole, the filler, the copy that sounds important but says very little — and helping them stick. But it’s not exactly ruining language. It’s just holding up a mirror to the habits we’ve already built.

In other fields like journalism, essays, novels, or screenplays, many of these words can still be beautiful, descriptive, and meaningful. When used thoughtfully, they add depth and value. But in B2B marketing, they often land like greige or duck egg paint: vague, soft, and oddly forgettable, when your audience is really craving something bold and clear.

The focus in B2B should always be on context and clarity. What does your product, service, or solution actually do? Be honest about that. Don’t let AI convince you that “leverage” sounds smarter than “use.” Don’t let the robots nudge you toward the kind of language that feels satisfying to write but leaves your reader blinking at the screen, unsure what you just said.

If we don’t want AI to flatten our words, we have to be the ones to push back. That means noticing the patterns — and choosing something better. Slower, maybe. But more deliberate. More human. Take the time to think critically about your language and make intentional choices.

After all, that’s still one thing AI hasn’t mastered: meaning it.