London is best described as lots of little towns that grew into one larger city, over many years. This is also a good way to think about where we are now with AI tools like ChatGPT.
But first, let’s take a tour of the “little towns” that helped build this larger city.
First, we had hyper-targeted Social Media ads and email-flows. These were based on “what do people want” and used many of the psychological levers highlighted by Cialdini in his book Influence: The Power of Persuasion. But they weren’t human-focused. They were profit-focused: how can we get as many people as possible, who are interested in widgets, to buy my widget?
Then we had “Big Data”, where we took ALL the information we had collected from these targeted ads and CRM platforms and organised it into databases to tell us “if these people like X, then they’ll also probably enjoy Y”.
Again, it wasn’t human-focused. It was business-knowledge-focused: how can we get as many people in these cohorts, who show behaviours that suggest they might like widgets, to buy my widget?
Then we had automated chatbots – simplistic automatons useful for helping inform call-center staff, when they did eventually speak to a customer 1-on-1. Other than that, they were a cost-saving exercise by upper-management and a huge frustration for human customers.
And once again, they weren’t human-focused. They were business-protection focused: how can we get as many customers as possible, to not complain about our widgets and/or buy more widgets?
The common theme here is that all these technologies were coming from an out-dated broadcast media mindset of one-way-traffic: we say, you listen (and then buy).
The other issue with all of these technologies is that they were also based on large amounts of averages: whichever thread is the most common is the one we will follow.
We still see this today in PMax campaigns. After a certain number of creative optimisation rounds, the images end up all looking like the same generic image, albeit with a few slight differences because that’s what the machine told us worked best, on average. But humans are all unique, so after a while performance plateaus and we have to start the process all over again.
Which is what makes the current AI models so radical: they’ve learned how to talk like a human, but think like a machine. This means it can give us highly accurate information, tailored to our own individual quirks.
Essentially, AI models, like ChatGPT, are now merging all the above “little towns” into one central, intelligent, *human-focused* capital city. And that’s the key: it’s human-focused. It’s based on the formula: humans + machines > humans alone.
This is why ChatGPT has exploded in popularity.
It takes people as who they are. It listens to them and adapts to each one’s own communication style. It’s more like a really-helpful, very patient friend, than an obvious robot.
AI’s true intelligence was being able to learn from all these separate intelligences, bring all their knowledge together into one space and understand how to communicate it as a human.
Dive deeper into the AI revolution! Still curious about how the world of AI and human interaction is evolving? Tune in our Paris Talks Marketing podcast episode offering a glimpse into the future of AI assistants and interactive Web3 with Alexander De Ridder!
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