How to Future-Proof Your Marketing for an AI-Centric Search World?

By admin

Today
DATE

,

Google
ORG

’s search results still look (mostly) like lists of snippets and links, e.g.

But, plenty of search marketers and industry observers suspect we’re in

the closing days
DATE

of this format, and the future holds something closer to how AI-tools like ChatGPT and

Bard
PERSON

answer user-inputted prompts, e.g.

If that’s the case, what can/should marketers do to give themselves a fighting chance at continuing to earn, if not traffic, at least potential references and nudges from

Google
ORG

(or whatever replaces it)?


This week
DATE

’s

5-minute
TIME

whiteboard takes a look at how those answers are calculated, and what it means for how marketers can potentially impact the results.

Transcript:


Howdy SparkToro
PERSON

fans and welcome to another edition of

5-minute
TIME

whiteboard.


This week
DATE

, we’re chatting about how to future proof your marketing for a potential world in which AI and large language models are the core of how search and discoverability works on the web.

So, let’s imagine that you think in

2026
DATE


Google
ORG

’s interface functions much more like this rather than saying, best dishwasher and getting a list of results, you instead type in what’s the best dishwasher brand for fast drying. And

Google
ORG

gives you an answer that is similar to their generative search experience that they that they’ve demoed

today
DATE

, and that is essentially a paragraph or a couple of paragraphs, maybe a list of text rather than a bunch of links that you can go visit on the web and maybe some images and, information.

So

Google
ORG

might say something like the best brands for drying dishes fast are the (not sure how to pronounce it; I think it’s)

Bosch
ORG

.


Bosch 900
ORG

and

the KitchenAid SuperDry
ORG

. Fantastic. Great. How did

Google
ORG

come up with those? Well, what we know for certain is how large language models work.

They essentially predict tokens based on their input. So what has happened is this information comes from the token prediction system of a large language model. When you go to ChatGPT or you go to

Bard
PERSON

or you go to

Google
ORG

’s generative, search experience, what you’re getting is a crawl of massive amounts of data. Similar to what

Google
ORG

crawls on the web

today
DATE

.

And then that crawl has been tokenized into words and phrases and combinations of words and phrases and numbers and all that kind of stuff. Then it’s trained by machines, right, in a in a classic machine learning style and also with lots of human input. You can see lots of stories about how, individual human beings all over the planet, especially in developing economies are very poorly paid to help train, classify, categorize, and improve large language models for use in services like

OpenAI
ORG

’s ChatGPT. These are then aggregated and the system is designed to predict the common responses.

Right? So it’s trying show you the kinds of things that you would get if a human being who had written on the web had written that content. So in this case, right, If the large language model,

Google
ORG

’s, you know, index of content of the web shows that when people on the web have written about and talked about fast drying dishwashers, and those sources often mention

Bosch
ORG

and

KitchenAid
ORG

, then those are the ones most likely to show up in these generated answers.

Via How to Get Better Outputs from Your Large Language Model on NVidia

This is the core of how AI answers work. It it’s not surprising to a lot of folks; plenty of people professionally in the search world and the

AI
ORG

world have talked about this before. What hasn’t been talked about too much, unfortunately, is what do we do if we’re a marketer?

Well, I think what’s great about this explanation is it actually makes the marketing very obvious. What you need to do as a marketer is make sure that if your brand is tied to words and phrases that people might enter into search experiences now or in the future, you are mentioned. Your brand is mentioned hopefully in positive ways in all of the places where a large language model might derive their generated answers from.

That’s the core of marketing in an AI centered world. Now what I wanna be clear on is we don’t know what that AI centric world will look like in the future. And I believe it’s honestly a little premature to say, “Oh, I’m gonna try and make sure that my brand is present in the places that I predict in the future,

Google
ORG

’s gonna use, you know, in the in the large language model training set.”

You don’t know for sure.

But here’s the thing. What you know for sure is that in

the present day today
DATE

, there are lots of sources where people go to get information about your brand:

What are the best brands? Where should I get software? Where should I get which video game should I buy? Which dishwasher should I buy? What brand of shirts are really good for, you know, men with tiny shoulders like mine?

The reported training set corpus for an early version of

Google
ORG

’s

Bard LLM
PRODUCT

And you can be present in those places right now. You can through content marketing and through PR and through outreach and sources-of-influence style marketing you can be present in those places. If you think

Reddit
PRODUCT

is a likely source where lots of people are discussing your topic and

Google
ORG

’s almost certainly training on it and

Reddit
ORG

’s gonna sell them that data, fantastic.

How do you make sure that editors or people who want to talk about your brand in connection to these words and phrases that might be used the future? That’s something you can do

today
DATE

that will also for future proof your marketing.

Alright. Look forward to seeing you

next week
DATE

on another edition of

five minute
TIME

whiteboard. Take care!