Introduction - Estefanía Valero Merkt
🫛 This edition of AI, Software, and Wetware features an audio interview on everyday ethical AI with Estefanía Valero Merkt, a 🇨🇱 Chile-based 🇻🇪 Venezuelan entrepreneur. Estefanía is a senior creative director, specializing in audio-visual production, and the founder of CÁLIZ Studio. We discuss:
What changed in her audio/video post-production work after generative AI tools became commonplace a few years ago
The exhaustive research she does on AI tools for ethicality as well as functionality before she includes them in a production workflow
Using AI vibe coding tools, as a self-described amateur, to write her own mini-app for coordinating time zone differences on her global projects
Why she started her Machine Angels community initiative and Big Sister AI sessions to help other women become more comfortable with using AI tools
How CENIA created a regional LLM and a community in Quilicura created a “human LLM” in Chile
Concerns in Chile about data center water usage
and more. Check it out, and let us know what you think!
This post is part of our AI6P interview series on “AI, Software, and Wetware”. Our guests share their experiences with using AI, and how they feel about AI using their data and works.
This interview is available as an audio recording (embedded here in the post, and later in our AI6P external podcasts). This post includes the full, human-edited transcript. (If it doesn’t fit in your email client, click HERE to read the whole post online.)
Note: In this article series, “AI” means artificial intelligence and spans classical statistical methods, data analytics, machine learning, generative AI, and other non-generative AI. See this Glossary and “AI Fundamentals #01: What is Artificial Intelligence?” for reference.
Interview - Estefanía Valero Merkt
Karen: I am delighted to welcome Estefanía Valero Merkt as my guest today on “AI, Software, and Wetware”. Estefanía, thank you so much for joining me on this call today. Please tell us about yourself, who you are, and what you do.
Estefania: Hi Karen. Thank you for the invitation. My name is Estefanía. I am originally from Venezuela and I am currently based in Santiago, in Chile. And I am a senior AI-empowered creative director working with mostly video. I am also the founder of an AI design studio called CÁLIZ.
Karen: Can you say a little bit about the industries that you have worked in or that you are working in now as a creative director? What kinds of videos do you make?
Estefania: Well, I have over 13 years of experience in post-production, mostly editing and overall visual storytelling. I have worked, for example, in VICE [Media] and coordinating post-production for feature films. I work a lot in commercial and also educational content. And lately I’ve been deeply focused on AI driven filmmaking, building scalable generative video workflows and integrating AI to expand the narrative possibilities of storytelling and tools that help along the post-production and creation process. And I also run training programs to help other audio-visual professionals and creators in general to integrate AI into their pipelines as well.
Karen: That sounds very cool. It sounds like you’re definitely using AI professionally. I’d like to hear a little bit about whether you’ve used it personally or if you studied the technology, and just tell us a little bit about your overall level of experience with AI and machine learning and analytics.
Estefania: Yeah, I started using it personally three years ago when I first started with GenAI and ChatGPT and all the LLM fever. But then I started integrating it to my workflows, experimented a bit with video generation and image generation as well. And right now I use AI extensively in my professional life every single day.
As a video lead, I manage heavily-researched AI stacks that include many tools, both for visuals and audio as well. Some of the ones I use are Google Flow and Whisk. I also use Sora, Runway, Kling, Seedance, and Nano Banana. Those are my main stack for video and image generation.
And I also use as well ElevenLabs and Suno for audio creation, and mostly Gemini and Claude for other processes along the LLM suite. And mostly I design these pipelines that allow creative teams to go from concept to final cinematic delivery, as I said, mostly working on commercial work.
And beyond my own creative work, I also study the technology very closely, so I can teach it better and help other professionals integrate in it as well. And for the last months, I’ve been running one-on-one consulting and group courses in AI audio-visual production. And I’ve been having the pleasure to teach professionals on how to master prompt engineering and overcome the technical frustrations of generative AI.
And currently, I am also developing a community initiative called Machine Angels, which started first for my girlfriends to kind of democratize AI tools for women and remove that tech bro gatekeeping that sometimes gets around this technology. So yeah, that’s mostly where my focus is right now using this tech.
Karen: Great. Yeah, I’d love to hear more about the Machine Angels community for women – I hope we’ll talk more about that. You mentioned earlier that you spent 13 years in post-production, and the generative AI tools that you’ve mentioned have all come along more in the last three to four years. So what’s the biggest change that you’ve noticed between the way you used to do post-production and what you do now or what you can do now with these new generative AI tools?
Estefania: That’s a great question. I think one of the main differences would be the creative process itself, the brainstorming. It’s very different for me. At least, I get to use AI as a collaborator in some processes, where sometimes I didn’t have human collaborators to work with. And the flow of ideas was slower when I was working with myself. And now I have this feedback, where my ideas can develop a little bit faster and maybe get to a solid outcome much faster as well.
Also being able to get into production. Working in post-production, I usually was handed all the content or all the data that I was going to edit. And now I get to create, from the start, many pieces by myself or with other human collaborators. You can get hands on most of the whole workflow now with this tool.
And the work is also, I wouldn’t say faster, because especially for commercial work there, there’s still many feedback loops – coordination between the agency, the client, and the creative team as well. But it flows in a different way, so I think that’s where the main difference is.
Karen: Okay. I’m always curious to hear if people find that it changes the speed of the work or it just changes what you’re able to do that you maybe couldn’t do or do easily before. You’ve got quite a toolbox of tools. Can you share a specific story on a way that you’ve used one of them and how it worked well for you? And then another story on something you did that did not work so well?
Estefania: In general, I think a great example in my workflow would be for creating commercial video assets, especially for short films or video campaigns. Also pitching creative concepts to agency clients. It works very well to have a visual representation of ideas early on in the process. I use a lot of coding with Gemini or Claude to present ideas in the sketch or brief stage so they get moving faster in the production process. I think that has worked very well for me in my work.
And I would say something that doesn’t go as well would be: at first, when I started generating video, I usually went straight from prompting to video generation, and that was very frustrating. The tools weren’t as developed as they are right now. It was like two or three years ago. So it was very frustrating. I’ve also talked to other creatives that are now starting with AI generation, and they also found it a very frustrating process at the beginning. Because you have an idea, and you want that you just write something, and you get it done automatically. And it doesn’t work that way. You do have to make a lot of iterations and you have to have your own process or workflow to get to a result that you are very happy with. And I think learning to go from text to image and then to video was one part that alleviated a lot of frustrations in my work process. And it’s one of the main tips and tricks that I teach other professionals as well.
Karen: That’s a good story. Sounds like you learned a good lesson about not going straight to video from prompting, and that’s good to hear about. You mentioned vibe coding. Are you actually writing software code, and is that part of what you teach in Machine Angels?
Estefania: I write code in a very amateur way. I’ve never worked with coding before AI, so sometimes I use it, as I said before, just to get to a brief where I can show some ideas in a visual or interactive way that is more straightforward than the old way of creating a deck or a presentation. I use it a lot in those cases.
And I also like to create mini-apps for my daily tasks. Those are mostly for myself, but I also enjoy doing them. And sometimes I also create party games with my friends, based on, I don’t know, conversations we have or some silly ideas that we’ll figure out along the way.
So yeah, that’s what I use it for. It’s not like for a final product itself, or I haven’t created an app or anything. But I do enjoy creating little things that help me get ideas in a visual and interactive way.
Karen: So you’ve been using the vibe coding tools to create mini-apps, really tools for helping you with your daily tasks. What would be an example of a daily task that it’s helped you? Something small and very specific.
Estefania: Sometimes I get very confused with time zone differences. They’re like these translator clubs sometimes, very confusing visually. So for specific projects where I’m working with creatives around the globe, I have created a time zone differentiator or a time zone clock that I can use to track better our differences between each of the members of the project. That’s one I enjoyed using.
Karen: Time zones are always a challenge. It always changes the alignments. Some states don’t change (just a few). And other countries may change to daylight savings, but on a different day of the year. It can be quite complicated. So I could see how having a tool for that would be useful. That’s a great example.
So there’s a lot of ways that you’ve used AI tools. Are there any things that you avoid using AI tools for? Whether it’s a large language model or an image or anything else like that?
Estefania: I think I consciously avoid relying on AI to dictate the core narrative or rhythm or sometimes human emotion of a video piece. For instance, in my editing work, I think the timing and the pacing and the restraint needed for visual storytelling are inherently human skills. So I do use AI tools in my own editing process. Sometimes it helps me reduce noise in some audio or extend some clips. They’re very useful tools for video editors. But the storytelling itself remains being very human. And I think that’s important for me.
It’s an incredible tool for building visual worlds and generating assets or assisting with complex compositing. But it doesn’t feel an emotion or understand the subtle shifts on a character’s journey. So I avoid using purely AI-generated concepts in that way without human creation.
“The technology must always serve the story, and not the other way around.”
Karen: Yeah. Great. I completely agree with that. I’m curious, when you make your videos, do you ever use music in them? And is it always human-created music, or have you tried some of the AI music creation tools?
Estefania: I’ve been using an AI creation tool that is called Suno. I’ve been using it sporadically here and there. I do like compositing sound in a more artisan way, we could say. So I mostly use human music in my video pieces. But I’ve been experimenting with it. I think it’s very fun to use and it does help a lot also when creating video pieces. It speeds the process sometimes. It helps the budget some other times. And it’s just an interesting tool. All around I’ve heard of music composers and producers that also use these tools. And they just generate short assets that they use in their human compositions. And I think that’s a similar way of what I do with video. It’s most like an asset generator rather than a final piece generation or generator.
Karen: Yeah. One thing that I’ve noticed that is interesting is that there’s different rulings, different regulations around the world as far as whether or not something that was created in part with AI can be copyrighted to the person who was using the tools. Has that ever come up? Or what are the current rules in Chile?
Estefania: I think everywhere we are writing the legislation along the way. Here in Chile, there are very interesting proposals on how to tackle AI ethics and usage in general. But I think the commercial aspect of AI generated assets has not been addressed so far. I’ve been in panels and discussions where creators are looking at it very closely. But I haven’t heard of a formal proposal to address this. I’ve heard some regulations around the voice usage and voice cloning with AI to protect voice creators for their voices being used without their consents. But at least in the commercial part, that’s something that usually comes up in the first stages of some projects with clients, because sometimes we’re not able to protect IPs, working with AI. It’s a point that is highlighted every time we start a project. At least, the people I work with are very cautious about it and trying to be very detailed on the limitations and the resourcing of all the assets that we use, both for starting and training our tools, as well as the final outcomes that we create.
Karen: Have you ever had a client say that they didn’t want you to use AI tools in creating the work for them?
Estefania: No, I haven’t. Mostly the clients I’ve had in the last couple of years are very eager to try AI generation tools in their pieces. I’ve heard of creators that are not willing to work with AI tools and that are taking stands on it, like a very anti-AI stance on it. Because there’s all these things about the training of AI that sometimes it’s stealing from creators. So it’s an ethics and political stance on it.
Karen: You mentioned creators that don’t want to work with AI tools. I’ve certainly talked with a number of people who feel that way, and as you mentioned, some of them feel like it’s stealing from the creators. And that is one of the main concerns: where AI and ML systems get the data and the text-based and video-based works that people have put into online systems or they published online. One of the big examples with Suno is YouTube – that they scraped a bunch of YouTube videos, which were copyrighted, but they ignored that.
And so this is one of the reasons I think that a lot of people feel like their work has been stolen, and then it’s being used to compete against them by selling licenses to the tools and selling what comes out of them. And a lot of companies aren’t very transparent about what they’re going to do with our data when we sign up to use the services.
So I’m wondering in general, how you feel about companies that are obtaining and using these works for training their systems and tools. There’s a concept called the 3Cs. It’s consent, credit, and compensation. And many people advocate that creators should be entitled to have to consent, and to be credited and compensated if their works are used.
On the one hand, there are the tools that you’re using. Then on the other hand, there are the works that you are creating, and how you would feel about AI companies scraping your work, your outputs, your work, and using it to train their tools?
Estefania: I think in most cases transparency is definitely lacking in terms of data provenance. And I think with most of the generative tools, whether it’s for audio sentences or image generation or video models, the training data is essentially a black box. As a creator, I often rely on my own research on the tools I work with, or I try to keep up with industry news or leaks to find out if a model might have been trained on scraped videos or massive downloaded internet data sets. But I think the companies themselves rarely provide a clear, accessible breakdown of their data sources. And I think as creators, trying to build sustainable and ethical workflows, this lack of transparency leaves us in the dark most of the time about these ethical implications of the tools we depend on.
I also think that it’s good to remember that we’re still in an initial phase of development for many of these tools and companies. I think these ethical concerns must be discussed in all these phases, because that’s what’s going to move the needle in one way or another.
And I think there have been companies giving great examples of actively developing clear policies for AI and data research. The recent Anthropic news – I know they have been handling very carefully all their ethics in data research as well. So I think there’s still a long way to go, industry-wide. And I think it’s good to highlight positive examples that can show a path forward for more transparent and responsible AI development. I think that’s very important from a creator’s side.
Karen: I’m impressed that you do this research into the tools that you use and where the company’s got the data that they used to train them. It’s hard to figure out sometimes, but it’s good that you’re looking into that. Anthropic is kind of a mixed bag. They do some things well and on the other hand, they stole a bunch of books and they’ve been subjected to lawsuits for that. And you probably heard about the big settlements. But on the other hand, they do at least talk about trying to keep some ethical lines and look at safety. Have you found any tools that you feel are actually doing this well, that are behaving ethically?
Estefania: Well, the research is definitely exhausting sometimes. Just keeping up with the updates on the tools every day. It’s very heavy as a creator, I think. In one week the whole stack that we use changes because of updates or workflow needs. And yeah, it’s very exhausting in that sense to keep up with all this research as well. It shouldn’t lay on the creators to manage who is doing the right things or the right choices and who’s not.
But I’ve seen some examples. Let’s see. I think the Adobe Creative Suite has done very good work protecting creators with their own creations. They try to manage copyrights for creators generating with their tools. That’s an example I can think of. But that protects the final outcome, not the input generation that we are working with. It has to be both ways. We should take into consideration what data or assets are we starting the projects with, and how do we protect the final outcomes as well?
In general, we are always constantly feeding algorithms. Social media are the most obvious examples. Everything we post, or how long we linger on a video, or our engagement patterns are always absorbed to train recommendation engines. And that has influenced how the work with AI is going as well. Like, facial recognition tools or spatial data that is often used to train computer vision models. And even the text we type into online grammar checkers or translation apps, it’s frequently ingested to refine large language models. So, I would say we do need these conversations, and we need spaces so each of us can understand better, where we are going and where we are.
Actually, it’s kind of how media education was very needed in the eighties and the nineties for TV usage. I can think of that as an example. Like, we do need digital media that has been widely informed the last 20 years with social media usage, and now we do need to create our own educational spaces for AI usage and consumption.
Karen: You brought up a lot of really good points there. I’m not sure which one I want to go to first! But yeah, with constantly feeding the algorithms, social media companies are a great example – the ones that are generally not transparent about how they use our data when we sign up. Or we think we’re giving them permission to show photos to our family and friends, and in reality, they’re also using them to train their models, and pictures and information can leak out. And so many other examples.
Even LinkedIn — which is where you and I connected — LinkedIn was in some trouble, not quite two years ago, when they decided they were going to opt into using everything we’d ever put in up to that point. And not everybody had a chance to opt out going forward.
What are the laws like in Chile, as far as data protection? Did you have any rights when LinkedIn said they were going to start using our information, or when Meta said they were going to start using our pictures in our posts? Did you have any rights for opting out?
Estefania: I don’t think it was discussed, like, countrywide legislation.
Karen: Yeah, we didn’t have the right to opt out here. Our friends in Europe who had the GDPR and other countries where they had something similar, like I believe Nigeria has something similar, they were able to opt out. But in the US, I put in my opt-out request with Meta and they said, “Yeah, we don’t have to do this, so we’re not going to. We’re not going to respect your request.” So I went ahead and deleted my stuff. If you won’t respect it, then I’m not going to let you keep it. Not that they didn’t keep a copy somewhere else and use it anyhow, but at least I tried! But yeah, I’m always wondering what the regulations are like elsewhere. As you said, they are still evolving and trying to catch up with the technology.
Estefania: Yeah, I don’t know if they are a foundation or an enterprise, but there’s the work of a group called CENIA [Centro Nacional de Inteligencia Artificial]. They’re doing a great job on legislation and also developing tools inside the country for AI usage. They created an LLM for regional usage and it’s fed with academic research and collaborators from Chile and South America. I also think they do have a great vision on it.
Definitely it’s a good source of knowledge as well. Because we are all using these massive worldwide LLMs sometimes. And they all come with their own bias and interpretations of the world. So it’s good to also have a more Latin American perspective on some subjects. In general, as a creator, I find that very valuable. And I think the outcomes that we create also reflect what tools we work with. They bring their own data sets into the mixture. So I think more than just focusing on the legislation, there’s also this discussion around data governance that’s very interesting as well.
I use a lot, for example, the Google AI suite. I use it on a daily basis. And as you were saying, we always agree on policy agreements. They’ve been consistently collecting all our data and using it as their main asset in exchange for giving us free tools, like our Gmail accounts for example. And we’re only complaining right now about that data usage. Obviously, the end consumer and users are never to blame here. But we do need to create our own spaces for this kind of conversation and education.
So I think that way we are more protected and we can act as informed beings in this discussion. One of the main reasons I created this community group, the Machine Angels, is to spark those kinds of conversations in everyday spaces. Because you do not need to be working with these tools or being an active creator with technology for them to affect you. You are, finally, always involved in these decisions, whether you want it or not. So the better information we can have on it, I think, we can be more informed actors in everything that is developing worldwide with AI.
Karen: So when you mentioned the Google AI suite, you mean tools like Gemini and Nano Banana, things like that?
Estefania: Yeah. I use Gemini and I use Nano Banana for image editing and generation. I also use Flow for video generation with their V3 model. And I also like a lot a tool that they now removed from their suite. That makes me very sad. It’s called Whisk for image generation.
Those are the main ones I use. I’ve also experimented with Lyria for creating music as well inside Gemini. It’s been very initial experiments. I haven’t used it in any work yet. But yeah, they do have a very big stack of tools there.
Karen: Yeah. As you mentioned, even outside of the AI suite of tools, they’ve really been trying to push their AI features into the Google Workspace tools everywhere. Every time you go to write an email, they want to know if you want to use AI. Or they try to summarize. And I’ve gone through and turned those off, where I can. But there’s still a good chance that they’re using our information for training their systems. Do you know any cases where your information has leaked out or been used in a way that you’re not comfortable with?
Estefania: Fortunately no. I haven’t. Well, maybe I’m not informed, but I haven’t had those cases yet. I’ve heard of people that I know that there have been deepfakes made with their images and their identities and stuff like that. And I think that’s very critical and the worst use of AI would be using somebody’s image for purposes they have not consented with. But yeah, I haven’t been consciously involved in any of that.
Karen: I hope it stays that way! Here, I probably get multiple notices of a data breach every year. It’s not a good thing, obviously. But the deepfakes, as you said, are one of the worst features. Especially for the way that women and children are being treated. It’s just really appalling. And it makes it hard to know what’s real and what’s not.
Estefania: Yeah, I agree. We as women and also the children are, well, for everything, we’re always the most vulnerable targets. And yeah, with the deepfakes, I think it’s getting worse day by day.
Karen: Yeah. So I’d like to hear a little bit more about Machine Angels. Can you talk a little bit about that?
Estefania: Yeah. Well, at the beginning of the year there were a couple friends, and it happened in different settings where my friends started to ask me about the tools I use, how I use them, if I could help them start learning to use some of them. Or they were just curious all around with AI tools.
Some of them were using it in their workspaces. And some of them were using it recreationally. And I just started feeling this need to have a space where I could collect all this information. Because if I was having daily conversations with all of them, I just thought that it could be a good resource itself, having a group where we can hold all this information and all this curiosity and tutorials or general conversations about AI.
So I first started creating an idea, an outline idea for it. And I created an Instagram group, or a channel where I could have all of them there and we could share about our experiments or our current needs with technology. And I just started sharing there what I was sharing with them in personal settings, and it started growing. There were more people involved that were also curious. And then we started creating biweekly sessions that are called Big Sister AI, where I could teach them a specific tool or how to use a setting of one tool.
For example, in the first one it was NotebookLM. We learned how to use it for educational purposes and research. It was also Gamma AI that’s for creating decks and presentations. And all of that started because one specific friend was like, “Oh, do you know any tools where I can create presentations for my work?” So I just told her, “Let’s get on a call and I’ll teach you. And if other friends can join, they will also learn about it.”
So right now I’m talking with one of them because she started using Lovable over the weekend for Women’s Day. It was open for free usage without paying for the credits. So I shared it in Machine Angels and two of them were very eager to try. One of them started by coding an app and the other one coded her own portfolio. So now I’m trying to get them together to teach one of these Big Sister AI sessions, so they can teach us back what they’ve learned: how was their process, what they created, how did they like it?
And I think it’s going pretty well on that side. Some of them have also reported back that they are using many new tools in their workflows and in their personal lives. And I think more than using it for work, it’s also how we can make life easier sometimes with them. I have a friend that always uses it for asking recipes with what she’s got in the fridge, or if their kids have a specific activity at school, she can get very creative with some ideas for helping them that she didn’t have time for before using AI. Or that she didn’t have community support to help her with. So now she can use that time more resourcefully, because she solves daily problems quicker.
I think that’s very interesting, that community we are creating together – like, how to integrate this in, in our daily lives. How can we solve limitations we have in our own life with time usage and practical help? And also, we’ve been having very interesting conversations around ethics and concerns as the AI fabrics, water consumption stuff. Or the future of work, stuff like that, that we are all very concerned about. So it’s good to have a space to talk about that as well. That’s mainly the focus of our community right now.
Karen: Yeah. Water consumption is one of the aspects of the environmental impact that comes up frequently. And I know some regions are much more affected by this, if they’re already in drought or if agriculture depends on it. Is that a big concern in Chile with companies trying to start up big data centers that would use power and water and other resources in the area?
Estefania: Yes, it’s actually a big thing right now because there are many data centers here in Chile, from especially American companies that have those data centers here. And in Chile, there’s a problem with water consumption, that there’s a draw that’s very quick, and our water resources are very impacted. There are fires every year because of this. The agricultural impact is huge, and also the water for human consumption. It’s a major problem.
But now with AI, I haven’t had access to information on if there are AI fabrics themselves here in Chile yet? I know there’s been data centers for the last decade, but I don’t know if there are any of them being used for AI usage or data processing right now?
There was a community project that was an experiment just to address this situation. I don’t remember specifically where they are located, but it was a group of people who got together in one place, and they started like a human LLM. And you could interact with this application and ask them questions, and there were people answering the questions.
Karen: Oh, interesting!
Estefania: Yeah, so obviously, it’s not an efficient tool. It wasn’t to compete with the efficiency of our team, but to highlight how we sometimes use, unconsciously, those tools when you can rely on other humans. For having support, for having information, to doing other stuff that you can’t. And it was very interesting to hear about it. I think it did start many conversations in the country. I think that was very successful in that way.
And I’m also always trying to research on technology advances on how to better build these AI fabrics so they are more sustainable, especially now that more people are using AI. We will need a solution in a short time, alright, so I try to follow up on conversations from engineers and know people that’s actually building this technology rather than focusing on the impact itself. I always try to look forward for the solution rather than the problem. So that’s my approach in that. And since I am using it a lot, that’s something I always have in the back of my head as well.
Karen: Yeah, I’d like to hear more about that human LLM experiment. Maybe we can find a link that we can share for our audience. [References: Quili.ai and “How much water does AI consume? The Quilicura experiment that replaced algorithms with “neighbor-servers””, in Spanish]
I wonder if your friend who’s looking for recipes could use that to have someone suggest what recipe to use for what she has in her fridge?
Estefania: Yeah, that would definitely be a good usage of that project. They were also promoting, like, “You need a drawing, you can ask Pedro for your drawing.” Human skills that can be sourced to other humans. And sometimes we do not because we are getting very used to just going with the machine.
Karen: Yeah, on Substack, which is where I write and where this interview will be initially posted, there’s a group of artists there who say, “Please don’t use an AI-generated image. I’m an artist. I will draw one for you, no charge. I just really want to see good art.” Some people actually offer to do it for free. It’s quite interesting. And of course there are open-source image repositories. I tend to use those.
My last question is: we hear a lot about trust and about whether or not people feel that they can trust these AI and tech companies with their data and with what they do with the tools. How do you feel about that aspect of trust? Do you feel like you could trust these companies? Or you don’t trust them, but you’re cautious about how you use them? Or what are your thoughts about trust?
Estefania: I try to approach my selection of tools with not trusting firsthand. So it’s something that it’s built over time. As I said, I like to research the tools I use. It’s not just if they’re useful for me and my workflow, but also if I align with their ethics and the overall company sets of values they work with. It’s like a conversation. It’s a process that’s built over time.
And obviously as users of technology, sometimes we don’t have a choice at the end. Sometimes I do have to work with some tools. I’m not aligned with their ethics, but I still have to use them in my everyday job. So I just try to experiment with ones that are more evolving alongside this global conversation and that are actually drawing a path ahead for us to follow.
So, yeah, I don’t think I trust any company on that matter. But I do try to always work with the ones that I feel most aligned with. And also when I teach other people how to use them, I always try to present them the ones that I am aligned with. So it doesn’t impact me as well, how I am using it. That’s kind of the approach I like to teach as well.
Karen: I appreciate you making time for this interview. I’m really enjoying hearing about how you’re using these tools and especially about how you’re helping other people learn how to use them effectively with your Machine Angels initiative and doing the work in your studio. Is there anything else that you were hoping I would ask you that I didn’t ask, but you would like to share with our audience?
Estefania: I think we covered pretty much all of it. I would like to share about always trying to be soulful about the content we create and the interactions we have. And I think that’s something we can grab along the way on this exploration path with artificial intelligence as well.
Karen: Yeah. That’s a great summary. Well, thank you so much, Estefanía. I appreciate you making time for the interview and I’m looking forward to seeing more of what you do with these tools with your Machine Angels group. How many people is it up to now?
Estefania: We are 32 members of Machine Angels right now.
Karen: Nice. Nice.
Estefania: Yeah. Thank you very much for the invitation and for the time for this conversation as well.
Karen: You’re welcome.
Estefania: Very grateful.
Karen: I enjoyed getting to know you. Thanks so much.
Estefania: Thank you.
Interview References and Links
Estefania Valero Merkt on LinkedIn
CÁLIZ Studio on LinkedIn
About this interview series and newsletter
This post is part of our AI6P interview series on “AI, Software, and Wetware”. It showcases how real people around the world are using their wetware (brains and human intelligence) with AI-based software tools, or are being affected by AI.
And we’re all being affected by AI nowadays in our daily lives, perhaps more than we realize. For some examples, see post “But I Don’t Use AI”:
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Series Credits and References
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Audio Sound Effect from Pixabay
Microphone photo by Michal Czyz on Unsplash (contact Michal Czyz on LinkedIn)
Credit to CIPRI (Cultural Intellectual Property Rights Initiative®) for their “3Cs' Rule: Consent. Credit. Compensation©.”
Credit to Beth Spencer for the “Created With Human Intelligence” badge we use to reflect our commitment that content in these interviews will be human-created:
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