You Ask, I Answer: Can AI Write as Well as a Human?

Deborah asks, “Can AI write as well as a human?”

The answer depends on what the human is writing. If it’s complex, original works like novels, the answer is no. If it’s marketing swill, the answer is yes. If we examine some common readability metrics – which are decent proxies for content quality – we see that marketing-centric content tends to be junk writing.

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In today’s episode Deborah asks Can AI right as well as a human? Well? The answer is it depends on what the human is writing language generation models, natural language generation of models, like open a eyes GPT series of models, or the T five Transformers from Google can write reasonably well, you know, in a very general sense, but the more specific the task, the better they get, the more guidance you give them and more guardrails you put on the better they get something like a complex original work like say a novel answer’s no machines cannot write as well as human not without an extraordinary amount of training and resources.

To the point where it’s not cost efficient, you’d be better off hiring novelist to write a novel, then you would be to try and train a machine.

Even if the machine could generate, you know, 1000 pulp novels.

The amount of editing time and QA that would have to go into it would effectively negate any gains you got out of it.

Now, if it’s marketing swill, the answer is yes.

And the reason for that is, a lot of what we write in marketing is junk, right? It’s not very good quality content.

It’s actually it’s pretty appallingly bad content.

And it’s easy for machines to replicate crap.

Think about a bell curve, right? On one end, you have low quality content on another and you have high quality content in the middle is a bunch of mediocre stuff.

As machines evolve, they go from, they can replicate total garbage, right, because that’s pretty easy.

And then they start to iterate and get slightly better and now they’re kind of at the meeting.

Yoker phase, right? They can write mediocre marketing as well, can they write good quality copy or great quality copy, not really not without, again, a lot of training to the point where it is not cost efficient to do that unless you’re a company that is dedicated to doing that.

But if you or the look at the metrics, in fact, let’s do this.

Let’s bring this up here.

What we’re looking at here is four sets of readability scores for a lot of marketing copy.

So this is articles, blog posts, press releases, essentially it is content marketing stuff, right and not very good.

And this is 130,000 of these that we have stored in the TrustInsights.ai database.

I have four quadrants, we have the jargon score, which is the smog score, simple measure of gobbledygook.

We have the flesh Kincaid readability index.

We have the Coleman layout index.

And the flesh Kincaid grade level.

What you notice here is that there is a definite skew towards the right side for three graphs and sort of a bell curve in the middle.

So let’s walk through these briefly.

The jargon score, lower is better, right? So you want that content be easier to read than harder to read.

And in this case, there’s a very definite kurtosis or lean towards the harder to read side means that our content is full of jargon, and a lot of it on readability, the readability score, 100 is great.

Zero is bad.

And we see a bell curve there in the middle, you know, 5055 60 is where most continents so it’s, again, it’s mediocre content, right? There’s very, very little on the far side here of the readability index, it says, Yeah, you got great content, very thin.

On Coleman Liao.

Again, this is similar to grade level, you can see there’s a tremendous amount of very difficult to Read content on the far right hand side and then I’ll hold opposite field.

And then on the flesh Kincaid grade level, we see that marketing content is around nine ninth to 11th grade content because of the jargon because of the amount of stuff that we put in our copy that is difficult to read.

That is extensively polysyllabic, which means that we use real big words.

You know, think about the, the list of corporate buzzwords that we love to use.

And you can see that reflected here in this data that this data very clearly shows we love our our fancy, complicated language.

When you have language like that, when you have, you know, buzzword bingo, you have templates, an awful lot of things like you know, basic blog posts are very templated when you have press releases, announcements, They all follow a copy that is so formulaic, that is very easy for machines to replicate and probably do a better job than the majority of humans.

Because when these natural language generation models are trained on language, they’re not trained on just one specific domain.

They’re trained on as much language as possible.

The most recent version of GPG three was trained on something like 170 5 billion parameters, which is a massive, massive, massive amount of texts, basically, the bulk of the readable text in English online.

That means that when these machines go to generate language, they will naturally use more vocabulary a little bit more.

lexical diversity is the technical term than a human would because in a lot of cases, humans will just copy and paste the last thing they did.

I used to work at a public relations agency and I would literally watch you know, Junior associates Just copy and paste from one press release to the next change the name of the company in the name of the CEO.

But effectively, they all say the same thing.

You know, the chief executive says, you know, we’re so excited or proud or pleased or release our new version, whatever, whatever, whatever.

And we’re flexible, scalable, industry leading agile, you know, can a machine replicate that hundred percent, hundred percent a machine can replicate that and do better than, than the humans do? So can AI right as well as the human? It depends on the context.

But for sure.

machines can now right at, I would say the mediocre level, right? The they could they’ve got bad down.

They’ve got a mat down.

They’ve got they’re getting mediocre down.

Now.

As each model improves, as the technology improves, they will eventually get down good.

Good writing.

Right.

And for those companies that have the strongest infrastructure and the greatest level of resources, some will get great writing down.

What does that mean for you as a as a marketing practitioner, it means that you’ve got to be improving your skills.

If you are on the bad side of writing, you probably need to stop writing for marketing and look at a different profession.

Because the machines can already crank out swell better than you can.

If you’re in the mat mediocre, you better uplevel those skills, take some courses, do some writing workshops, do anything that whatever you can do, that will improve your skills and get them to good write.

If you’re a good writer, you’ll keep working to become a great writer.

But whatever you do, you cannot stay static.

You cannot just rest on your laurels at wherever you are this continuum.

Because the machines are advancing faster than then we are collectively.

Will there come a day when you push a button and the machine spits out a novel probably We are already seeing a prototype examples of this with GPT three open AI model.

Is it good yet? No.

But it definitely shows what is possible.

What is what is theoretically possible.

And what is possible today is easily achievable in five to 10 years, maybe less depending on how fast compute power goes up.

So that’s the answer to this question.

AI can write as well as humans who are bad at their job.

AI can write as well as humans who were mediocre at their job.

And AI will eventually write as well as humans who are good at their job.

So your job as a human is to become great at writing so that you stay ahead of the machines.

If you have follow up questions, leave in the comments box below.

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