Natalija Pavic

Billions in car incentives made sales spike. The company may still have lost money.

But what would frequently happen is the sellers at the dealer would just really pull ahead. They would pull ahead the deal in order to get the benefit of the promotion in month as opposed to closing it next month. And so it looked like they would have a spike, you know, a spike of sales. But actually, overall, they may have been losing money.

SEO had its era. Now brands have to worry about being recommended by GPT.

The concept of optimizing your products for generative is relatively new. In the past, we used to worry about SEO, but now we have to worry about GPT and making sure that your products are discoverable with GPT.

She has to tell image generators to include diverse people. That says something about the training data.

Generating images needs to be a lot better. It's okay, but it's still very, very creepy. And I think that there's also, like, I don't know what data set we're training it on, but there's, like, definitely a lack of diversity. Like, if you ask it to generate people, there's definitely, like, a lack of diversity in the people that it generates. Like, a lot of them end up being white.

Technology does not happen to us. We build it, and we are responsible for it.

I think somewhere along the line, we lost the thinking. Like, we, in this era, you know, beginning of the Industrial Revolution, we thought of us as being people that pushed technology forward. And there's this narrative, like, technology happens to us, which doesn't make any sense to me. Like, we create the technology, we moderate and we regulate how it's used.

"Democratizing data" can be a polite way of saying "giving up privacy."

So I don't think necessarily, I think the word democratization is sometimes a euphemism of lack of privacy. So I don't necessarily think that data should be completely democratized. I think that if you're going to have a data strategy, you need to have a privacy policy in place.

Your hiring AI can decide you need more Mark Smiths.

Like, you know, a great example would be, let's say you launch AI to do hiring and you happen to have a lot of Mark Smiths at your company. And then so your algorithm decides that you need more Mark Smiths. Those are the ones that are going to do well. And like that name has nothing to do with whether or not that person is qualified to work at your company.

Generative AI is still at the bulletin board stage of the internet.

I think we're at the like bulletin board stage of Generative where we, you know, we've had this new technology. It's been released kind of like the beginning of the internet, but we haven't really defined the use cases very well.

Chess was supposed to die when computers got good. People still don't want to watch robots play.

When Deep Blue came out, there was like a whole scare of like, oh no, now it's like chess is not, you know, chess is going to be replaced by robots. But like nobody wants to watch robots play chess with each other. So that wasn't the thing.

Coding was the skill to learn. Now it's writing the right prompt.

prompt engineering is now the new skill. Right before it was like, oh, coding. Now it's like, actually, how do I create the prompt that's going to give me the result that I'm interested in? It's actually a new skill that we now have to learn in addition to it helping us.

If you can just ask for a web page, do you still need the wizard UI?

So now that we can generate code, the question is, how important is the wizard? If we can just ask it to generate a web page for us, do we really need the UI to walk us through that? And so potentially we have a future in which we are actually the wizards of the software world and the UIs are not as important.

Customers may not trust a brand that runs on AI. Being clear about it helps.

Because I think there's also, like, this idea of customers may not be ready to trust you if they think everything's AI. And so how do you, from a brand experience, how do you build that trust with the customer? By being very clear where AI is being used, but being very clear about what data is being shared, but being very clear of how their data is being used.

Companies that try AI early help decide what gets built.

Because right now, as a company, you have the opportunity to give feedback to the vendors. The vendors, all of us, we're testing the market on what's needed. And so if you become a practitioner, you start using it, you can report back and you can vote with your dollars, essentially, by paying for the technologies that are going to be built, right?

Most AI today is closer to brute force than clever algorithms. Data volume wins.

I always tell people it's not really about the algorithm because the way that most AI works now is almost like brute force. It's like trains on volume. So it's not really about how good the algorithm is.