00:00You're kind of bucking a trend that we've been talking about throughout the year in the context
00:04of AI. Actually, on-prem has kind of been back when AI is being run at the edge. You're going
00:10the other way from on-prem relying heavily on AWS. The rationale? Well, we are actually users
00:17of AI. We are not like some of these LLM companies that are creating and training their own models,
00:23although we have trained models in the past, but at a much smaller scale. So for us, investing in
00:28infrastructure and building out data centers for the size of our operation doesn't make sense.
00:32The work with AWS is focused on delivering personalized content, right? And is that at the
00:38foundation model level, you know, AWS out with Nova 2 today, or it's just simply that you're
00:43leveraging their scale, the multitude of data platforms they have? Yeah, for that particular
00:47use case, we're actually just leveraging their infrastructure and scale. We built all the
00:51personalization models ourselves, and we trained them in-house. And we've been doing that for the
00:56last probably three, four years, long before LLMs became a thing. We've had our own data science
01:03team, and we've been training models. So we use our own homegrown models for most of the
01:07personalization and recommendation work that we do. So herein lies the question of AWS reInvent,
01:12AWS number one in cloud computing, but they want to do more. They want to be number one in AI,
01:18you know, their own foundation models, the agentic tools that they release today. What would it take
01:24from Amazon in terms of the utility of that technology for you to rely on them more heavily?
01:30Well, actually, for some of the generative use cases, you know, that are LLM-based, we are using
01:36Amazon Bedrock. You know, we have a contracts management rights clearance system that we just
01:41launched. We are using Amazon's Bedrock capability for some of our moderation, you know, AI-based
01:47moderation of user-generated content. So we're looking more and more now towards using out-of-the-box
01:54capabilities that Amazon provides rather than build and train our own models, which we used
01:58to do in the past. I think the need for that is becoming less and less.
02:03Conde has a relationship with OpenAI. Conde was one of the first to make a deal with OpenAI.
02:08Early days, it seems, but how is that progressing?
02:11That's going well, actually. We're starting to use ChatGPT quite widely within the enterprise.
02:18Internally?
02:18Internally, yes. And for external use case, we've actually launched an AI-based recipe search
02:25on Bon Appetit on our website. And also, it's going to come out in our app, which allows customers
02:30to go in and do natural language search and also be able to modify the recipes according
02:35to their taste. So that's the first use case. We are looking at others with OpenAI as well.
02:40The broad theme of the program today here at AWS has been about companies of all sizes
02:47moving from using AI assistants internally to what AWS would call the AI co-worker, you
02:52know, a hybrid workforce of people and energetic AI. Conde has done layoffs in the past two years,
02:59financial years. How much has that been about AI tools changing productivity, eliminating certain
03:06roles changing certain roles?
03:08Well, I don't think most of it has been about AI, actually. Some of it, you know, especially
03:12in tech, you know, we've had, we use extensively, we use AI for our work on a day-to-day basis,
03:18which eliminates the need for some roles. We can do more things with fewer people. But other
03:24than that, across the organization, we've not really had any AI-based impacts.
03:28I've got to ask, in a different part of the Amazon universe, I've been experimenting at
03:35home with Alexa and Alexa Plus. Do we get some kind of Conde-NAS Alexa integration? Is there
03:41work going on there?
03:42Well, actually, we have actually already integrated with Amazon Alexa.
03:45So how does that work?
03:46So, you know, you can query and you can get content, you know, read out to you from some
03:51of our publications, not all. So, but I think it's pretty cool. We should try it out.
03:55But at the heart of that question is something a bit more existential. Conde sees itself more
04:00as maybe entertainment. And if you look at the revenue streams, very different from saying
04:05you're a news organization. Just explain how you see this company transitioning right now.
04:10Yeah, I think we're definitely in the entertainment space, because if you look at our brands, you
04:14know, we mostly cover leisure, fashion, lifestyle. We're not a daily news outlet. So people don't
04:19come to us on an everyday basis. So we are competing with other entertainment outlets, whether
04:24it is, you know, streaming media like Netflix or Hulu or Amazon Prime or social media, you
04:30know, TikTok and Instagram and others. So people have limited spare time. So we are competing
04:36for a slice of that. So we definitely have to do way better in terms of our personalization,
04:41our user experience, and also our great journalism that we have, I think, is what is going to take
04:45us forward.
04:46Sanjay, in the year ahead, what's the one big change you want to make on the technology side,
04:50the one thing you're still yet to do, be it AI or something else?
04:53Yes, I think it's mostly definitely around AI. We have a pretty solid data infrastructure
04:59that we've built on Amazon with Databricks. And our next task is really to figure out how
05:03do we vectorize all of our content and make it readily available in real time to LLMs. I
05:10think that is probably our number one challenge.
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