Pax (02:23)
All right, Nick, thank you so much for joining us today. I'm excited to talk with you.

Nick Cawthon (02:27)
Hey Pax, thanks for having me on.

Pax (02:29)
⁓ So I think it'd be great to talk about ⁓ your maybe career and what you're doing today as the context for this conversation.

Nick Cawthon (02:38)
Yeah, I have been fortunate enough to be raised in the San Francisco Bay Area. came out of school in the late 1990s. I was a child who had exposure to the early models of Apple computers being in our backyard. And in coming back from university, there was this upswing of this thing called the internet, where in the garages and workshops of Silicon Valley, we're starting to really take hold in the industry amongst the community.

amongst my fellow graduates. And I remember coming and sitting home at the dinner table and telling my dad, hey, I got this job at this company called AltaVista. ⁓ And look how much I'm making. And my dad was in the last five years of his career as a university professor. And he said, you're making more than I am now. At the end of my career, you're making it more than the first job that you've gotten out of college. Now that job didn't last for more than 18 months because as the nature of the internet and dot-com boom and bust,

you know, things happen and AltaVista becomes Google and the rest is history. But it was that kind of upswell and feeling that there's change at foot that I'd love to talk about from a strategic perspective today because I feel that there are similar motions going on 2024, 2025, and that's really exciting to me to live it all over again.

Pax (04:02)
I love that. Let's talk about those motions. What do you feel like is going on that's similar to those days?

Nick Cawthon (04:07)
Yeah, if you've ever flown into San Francisco International Airport and taken the short highway ride into downtown, it is this ⁓ array of billboards. And ⁓ somebody who travels frequently and see these billboards often, there is a certain amount of groupthink and echo chamber that happens on these billboards where every six months there's a new buzzword. I'm sure you can name as many as I can.

⁓ cloud, Bitcoin, NFTs, ⁓ edge, ⁓ peer peer. It's all this sort of cycle of people who want investment for their companies, buying at billboard or using the buzzwords that they know are going to get investors excited and the technology that we're still trying to grok and understand how it works. And so naturally, ⁓ I, myself and others ⁓ were jaded to what this

hype cycle was going to produce because say what you will about Bitcoin, it hasn't really changed my life very much. Not a speculative guy, ⁓ regardless of what you might hear. so with AI, ⁓ it really wasn't anything that registered up until the ability to see the wide ranging impact that it could have, the breadth of acceptance potentially that it might have across different kinds of organizations and workflows.

felt different, it feels different. And that is something that is fairly new, at least in my world. I've worked with language models and a lot of research qualitative data and how to parse things that are normally imperceptible to human beings as a measure of strategy and design decisions. But this is our ability to now transform our workflow. ⁓ Pax, you're old enough to remember digital transformation.

1.0 when we went from pen and paper to spreadsheets and Word docs and how there were those who said, you know, I think I'm just going to stay the way I am and keep my business comfortable. And then 2010, a gentleman by Luke Roblasky gave this quote about mobile first and from a UX and design community, it was this mind shift of we're not designing for 640 by 480 screens anymore. We're designing for a new handheld device and a new medium and a new ⁓ form factor.

And so as we think about interaction design and UX and marketing and positioning and media, like how does this mobile first mantra start to shift our approach and our strategy to digital? And then lastly, this notion of, well, what if there is no interface? What if it is a language model instead of a UI? And how does that position the way we position ourselves and our brands?

Pax (06:55)
Right. Yeah. I mean, chat GPT agent that just launched a couple of weeks ago, ⁓ has implications, like some pretty big implications for the future of the internet as that continues to develop. I'm reminded of Seth Godin famously tells this story of when the internet came about and he said, ⁓ Larry Page and Sergey Brent, they, they saw the internet and said, let's, let's index it. Let's build a search engine.

And then he said, ⁓ what, what Seth Godin was working on at the same time was an index, but it was a book and it was like a catalog. And he said, they saw the internet and said, let's build a search engine. saw the internet and said, let's let me, I'm going to write a book. And he said, you know, guess who won obviously them. And so I, you know, that brings to mind this concept of. You can't look at the new with, you know, the eyes of where you've been.

Nick Cawthon (07:40)
Wow.

Pax (07:54)
Like a complete clearing of the slate and building from scratch. So my question for you is if you were leading, leading a team within a company in this time where, you know, we're looking at potentially like a big pivot to how commerce works online, ⁓ how might you build that team today? Who would you have on your staff to ensure that your organization is going to be best set up for what's coming?

Nick Cawthon (08:23)
I'm going to challenge the question because as a leader, I can't just clean slate. I've got a staff that's present and engaged and comfortable with what they're doing. It's how would I level up that staff? How would I repurpose those skills that are already in place so that they can be up skilled for the future? We have a County of San Mateo, which is the in between SFO and San Francisco. That's the County of San Mateo, ⁓ where you

Pax (08:43)
Great. Yeah, I love it.

Nick Cawthon (08:54)
If you have to do work with the county, if you want to do work with the county, you cannot eliminate any jobs through AI. There's a mandate to saying you need to repurpose the people that would be transcribing and translating town hall meetings. You need to retrain them to edit transcripts and publish documents on the web. You can't eliminate positions, despite San Mateo County being where a lot of these companies got their start.

And I love that manner of thinking because it is to acknowledge what we're good at doing as individuals, as a team, and then figuring out how we can make our new tools amplify that.

Pax (09:38)
Yeah. Well, and for the record, like I feel like the clearing the slate is more figurative than literal. ⁓ and I think it's a good exercise, not necessarily say, okay, clear the slate, but it just say, hypothetically, if you were starting from scratch, if you had nobody on staff, who would you need? And then say, now with my existing staff, what do they need to get to that ideal of where we're going? And that may be augmenting their skills or maybe hiring

Additional people or whatever so just for the record that that's kind of where I was coming from on that.

Nick Cawthon (10:09)
Yeah, sure.

And you used a good word there, augmenting. I think that if we look at this from a textual standpoint, is that from copywriters and strategists and those who have to make ⁓ signal from noise, is that how do you augment their process? I have had a client come across my desk in the last six months that has a business that I know nothing about. And in a former day, what I would do is I would

spend a lot of time Googling and downloading files and printing them out and highlighting them. And that process has changed considerably. Where in this case, ⁓ because we want the transparency and the citation of source, the nature of the business demands it, is that we're spinning up our own knowledge base, our own knowledge graph in a retrievable augmented generation. So it's a rag.

which is your own language model, that you can manipulate and change and have it change to be the voice of the client. And in our case, citations and reference can be transparent about how it's coming up with these outputs. And so if there's anybody that works in the world of words, I'm going to say that fast three times, that needs to make common sense to be able to augment them with a model that can amplify them.

would be how I would transform that aspect of it. And that takes a junior to go and find the right sources and the right files for which to index and to tag and to put in the references so that it's clear. That takes the manual work of somebody that might be coming out of school and wants to know that process and how that these words get parsed.

Pax (11:39)
Yeah.

Nick Cawthon (12:01)
And that there's a more mid or senior level to know the prompt engineering involved with trying to get to a campaign concept or a marketing concept or to see an audit. The share of the voice it's called, it's the language model's voice to know that you've got different models that say different things about different brands. And so the voice about our brand is suggesting this so we can then emphasize a strategy to amplify or to go in a different direction. So I think that.

Pax (12:29)
Yeah.

Nick Cawthon (12:30)
kind of parsing of how, you believe ⁓ that prompting is going to replace searching in the next five to 10 years, and the statistics show that it's increasingly being adopted, then figuring out the strategy and the copy and how to position it is going to be something that I would train on that side of the coin. On the other side of the coin is the visual and the interactive. And that's where my world has been. ⁓

Pax (12:53)
Mm.

Nick Cawthon (13:00)
And it's been something that we've seen a degradation, I'll say, back ⁓ when I described that utopian early 2000s of ⁓ this windfall of a new medium called the internet. ⁓ Subjectivity was running rampant and your design was awesome because it has all these skeuomorphic leather patterns and it looked like this and it felt like that. And everybody had their own subjective expression of how they wanted the internet to look.

It was beautiful and it was weird and it was very non-standard. But with mobile, with the consolidation of the industry, with the commoditization of design and interaction design, things began to take shape in the form of patterns. And Google began Project Kennedy, which was to say, it's a Google product, it has to look the same. It has to follow these patterns. And Microsoft had the same thing. IBM had Carbon. And we've seen these.

patterns get figured out, whether it be tab rows or buttons or drop-downs, there's just a standard now to how we communicate visually on the web. And I think even more so, which is a separate conversation about how we design for agents versus human beings. But as we stay in the sort of building of a team, to know what tools are required for which place in the process, wireframes are now at our fingertips.

where if we don't want to attach to a brand extension visually and we just want to showcase the structure of the app, that can be done in minutes or hours rather than days or weeks. ⁓ And so a technologist, a digital technologist that knows creatively, here's how we go from this part of our thought process of how might we design for something to near production ready code ⁓ is something that I think is an essential skill set.

for visual designers. I had a conversation with the creative director, executive creative director. This guy's in his 50s, he works in New York. ⁓ And he came into a freelance gig at another agency as he's in between jobs. And he said that he brought in image generation tools like Mid Journey in the storyboarding concept where he was amplifying his own process to showcase ideas for, in this case, TV campaigns.

and knew how to keep the characters consistent in between shots of the storyboard, which was something that was hard to do maybe nine to 12 months ago, and ⁓ was able to feel like he had a much better generative process of here's all these ideas. And I know I've been rambling for the last minute or two, but I want to punctuate this with a concept that one of my colleagues, Jesse James Garrett, had mentioned.

just last week, and that's this notion of amnesty, of AI amnesty in whatever field or process that you're in is to allow it not to feel like you're cheating because you do these things, because you do this thing, ends to be transparent and be a mentor and to be questioning of the process and to say, we're trying to figure out this transformation together. I'm not making it seem like anything other.

⁓ because amnesty is important for adoption.

Pax (16:28)
⁓ yeah, I think that's a really good point. I've been talking with some people about that concept of adoption within a team and, ⁓ amnesty is not one of the points that has been brought up. ⁓ it's been presented as kind of like endorsement or, ⁓ in some cases, kind of like mandate, which I don't necessarily love, but that concept of amnesty I think is great.

They also talked about, ⁓ dedication of resources and

I still ha I feel like I've seen within organizations when you have leaders stand up and say, Hey, listen, please use AI, please use it. Here's resources to use it. There's still, ⁓ resistance and friction to it. And, ⁓ I've been wondering about where that friction can come from within teams. I think there's going to be some, you know, a concept of inertia that they have to fight against. ⁓

There's also incentive. I think it's extremely inefficient to begin with, even though the whole promise of AI is efficiency. It starts off as very inefficient as you start to figure out how I can use this. So overcoming that hurdle. ⁓ And then I think there's potential for a lot of people in the workforce to feel like, why do I want to drive this forward when it seems like the end game here is you don't need me anymore?

You know, so like what's the incentive ultimately for them to help pioneer this and how do you overcome this? So I'm curious, have you seen that same friction? Why do teams still not kind of like dive in even though they're given like permission, they're given resources to do it and asked to do it? Like what else causes those, that resistance?

Nick Cawthon (18:13)
No,

it's hard. As anybody who's tried to cheat on a test, it's hard to cheat. You've got to sort of like, you're worried about it and you're fighting against it. Using these generative tools to make the fidelity of the idea that you want to communicate is extremely difficult. And it's a learning curve and you have to stay curious and patient and confident that your process will see its way through to an end deliverable that makes sense.

Pax (18:20)
You

Nick Cawthon (18:40)
but the next time I'll be faster and the next time I'll be faster and the next time I'll faster. So there is a fallacy of speed with this technology. And I may be talking too general because there are so many different tools within this tool belt, but there is a fallacy that we can go much faster now. It may be true in five years, that productivity scale may increase drastically. But that notion of

That notion of speed will be automatic, think is not quite true yet. As for the resistance, the story I'd like to tell is as a consultant, as an agency like yourself, we get exposed to a lot of different people who have different backgrounds. And there's one that I work with that doesn't trust the cloud. And that wave of transformation with SaaS products ⁓ and cloud-based platforms was one that she met with distrust.

Pax (19:28)
Yeah.

Nick Cawthon (19:38)
much like you see a lot of people not trusting these tools, these generative tools or the transparency and privacy aspects of them, is that when working with this individual, it had to be attachments to email. It had to be version file. There needed to be a trust of seeing an icon on a desktop and knowing that your data is safe. And so I think that we're feeling that same thing as like, no, I, in this case, it constrained her because she used spreadsheets for time tracking and budgetary.

needs and things that now there are CRM platforms and management software that can take the place of a cell-based approach. Her tool set was much more limited. I think the analogy is true again today, now 15 years later after maybe that cloud transformation was taking place, is that there are things that can do this for you much faster ⁓ if you allow that trust and that transparency to be a suspense of disbelief.

Pax (20:35)
Hmm. That's interesting. ⁓ that, that cloud transformation. Yes. I do remember when that was big and that was everywhere and people were moving to, you know, getting their servers out of their buildings. And, ⁓ that was big. And today it's not even talked about, know, like people entering in the workforce, many of them probably don't even realize that they're in, you know, everything's in the cloud, but yeah, I mean, if I were to

throw my laptop away, I literally lose nothing other than the inconvenience of having to replace my laptop, but no data whatsoever stored locally. ⁓ and so that leads me to this, this thought of with AI adoption and staying ahead of the curve, the common advice is to say, take what you're doing and see, you know, what can be augmented by AI. And I don't think that's wrong, but what's not sitting well with me on that is

When we, when the internet came along, there were people who said, okay, can I take what I'm doing and what, how can the internet play with that? And you got these kind of like weird archaic kind of versions of websites that, you know, eventually a generation came along and they didn't say, what can I do with the internet? It was just the internet is what is it's like, it was a complete paradigm shift rather than.

an application of technology, if that makes sense. so I'm wondering, is there a way to shortcut that in your mind? I mean, I don't know if anyone really has the answer. That's kind of a big question, but what's the best way to kind of approach that and not get left behind.

Nick Cawthon (22:06)
Yeah.

Yeah.

Not to be that generation, that old man on the porch who shakes their fist at clouds. ⁓ Not cloud computing, just clouds in general. ⁓ Yeah, it's interesting. I've got two young boys here and their first exposure to technology is a voice assistant, which within the next couple of months is going to have Gemini built in where it can do more simple things than turning off and on devices within our house.

Pax (22:20)
Yeah.

Nick Cawthon (22:45)
And I'm, with these adoptions of a lot of these agents, a lot of the instructions are coming through voice. There's certainly been a trend on Mac whisper or speaking to an algorithm to execute a prompt versus typing it out. And it's interesting to see a younger generation being ⁓ increasingly detached from the awkwardness of pointing and clicking and typing and having a mouse. And I alluded to it earlier about this.

Are we designing for agents or are we designing for humans anymore? And with this notion of, well, there are services that we can stand up and execute while we're walking around our daily lives. That's a big leap of faith for me. I like the point, click, drag type metaphor that I've been doing my entire career, but there are others that may reject that. And so my new...

My level of patience and adoption, I like to consider myself ⁓ somebody who's at the adopting edge, ⁓ but I don't know how to overcome those internal perceptions of I'm not ready for this or I'm not comfortable with this. ⁓ Going back to the organizational standpoint, I think what we'll see now is ⁓ greater collaboration between departments. ⁓

where if we have traditional agencies and development shops, if we're all gonna be swimming in the same pool using the same tools, there's gonna be a much tighter iteration of how is this working, how does it affect this? And it's not so much of a handoff anymore as it is a side by side and a self mentorship to say, this is how I would see this working and this is how it's actually working. That's been crucial for me over the last year.

is to have that mentorship of application development and UX development and all of that prototype stuff that I didn't traditionally have to worry about.

Pax (24:41)
Yeah.

Yeah. Yeah. Tapping into group kind of groups of minds is going to definitely help us move faster. And, you have been working on a tool to help teams. Do you want to tell us about that?

Nick Cawthon (25:01)
Yeah.

Yeah, I'll tell you about it from a couple different levels. Design are my people. UX and interaction design is what I know. And as you alluded to about the ⁓ organizational leader who has to look at what their team looks like, I was coming off an engagement where I had a profound sense of dread for the design team. I was brought in by the VP of product.

I used generative tools based upon the code base of the company to create production level prototypes that we could test and sort of explore the UX around and was able to skip the whole Figma process. And they had a team of eight to 10 that were used to that manufacturing process of abstractions of interfaces for which were to be handed off to developers. And I said that

Flow is resource intensive and slow and low fidelity. Where how do you get an assessment of where they are in their maturity, AI adoption maturity, and what kind of barriers might be in place, and what kind of recommendations you could make in terms of training and adoption for that. And so that was the impetus to say, look, these are my people and don't let them out.

Don't leave them to die. Make sure that they know where they can be improved. And that's the retraining aspect versus the redoing. ⁓ And so it's been an examination of what kind of recommendations and comparisons can be made across industries, across similar team sizes and organization sizes. And then the categorization, is it culture? ⁓

Pax (26:30)
Yeah.

Nick Cawthon (26:56)
Is it fear? Is it the process? Is it that the data's not right? Is it that we don't have integrations into other services so that we can use these tools? And those are some areas of investigation that this survey goes into. Now, that being said, I've taken it on myself to spin this up using generative tools and seeing where that fault lies, especially in the qualitative responses about motivations and reflections. And so that's been a learning process for me as well.

Pax (27:26)
Hmm. So what is the output for somebody, ⁓ taking this survey? Like what can they expect to gain from this?

Nick Cawthon (27:35)
Well, that's a great question, Paxton. It's never going to cost money to take, but the reflection is going to be where we stand as an industry. Where do I stand as an organization or as a team, but what are we seeing industry-wide based upon levels of seniority, based upon size? And to be able to say this is something that we're finding in common, we can segment our answers and our responses to say that this adoption is occurring here.

among sizes that have the resources? Are they nimble enough to be able to adopt new tools? Or are these enterprise companies just able to take it on the chin until that workforce turns over and maybe they bring in those new perspectives and new tools and change from within?

Pax (28:16)
Hmm. Interesting. So what are the main, ⁓ areas that you're evaluating in this survey, ⁓ to, to measure whether or not somebody is like AI ready.

Nick Cawthon (28:27)
Yeah, first of all, what have you done currently? Are you doing it in ideation or are you going, workflows are you using it in production? And so that understanding of what's current landscape. ⁓ There's also perceptions of reasons of why this isn't working. Is it the process? Is it approval? ⁓ I talked about the data, the maturity of how you can integrate UX design team tools with other portions of your agency.

Is it a licensing thing? Are you just able to use co-pilot? And that's the extent of your exposure because your enterprise has that relationship with Microsoft. And there's really no room for all of these startups that are now pushing, pushing, pushing innovation at a very fast rate. And then lastly, this notion of culture. And you and I have touched upon it in our conversation of like, we've seen cultures ⁓ resistant to change.

not feeling like they have amnesty to fail or to try or to innovate on their own. And that are their ethical considerations with the use of AI at their organization. And if so, how? And I try to ask for some success stories as well as some failure stories and get some opinions on how they feel about different aspects of each of these categories. And so it gives you a nice tidy score ⁓ per section as well as altogether.

and then breaks everything down in comparison to similar industries, similar organization size, and similar team size. ⁓ And yeah, by the time this podcast hits your ears, faithful listener, ⁓ we will make sure that the link is in the show notes and that you can contribute to the cause. And we will then again, share back out what we're seeing from a segmentation and factor analysis.

Pax (30:20)
I love it. ⁓ so I have seen, ⁓ I've seen the landing page for the survey. love the design. ⁓ I haven't had a chance to take it yet. So I'm curious, ⁓ are the, ⁓ just, this is maybe a little too nuts and bolts, but are the questions for the survey, are they, ⁓ like open text or are they a rating? And I'm curious. Yeah. How does the structure of that?

Nick Cawthon (30:42)
Yeah.

A

good survey has a mix of both, has both sort of demographic questions as well as behavioral and perception questions. And then the open input, and this is a great avenue of exploration, can then start to take the language and the verbiage that people are using to describe different conditions and aspects of their workplace and use of AI and start to train itself. It's like, this is how people are talking about

different categories. And that's the exciting part about it to me is that, again, with a model that you can have this sort of flywheel is that to really speak in the language that you're receiving. ⁓ And I've looked, that's the open AI integration. And I look for that to be really interesting is how we begin to describe our current workplace, that transformation.

Pax (31:31)
Yeah.

But

I love it. mean, that's kind of why I was asking. was, I was curious if you're using AI to interpret open, ⁓ like freeform answers. It was like, man, that the whole, hadn't even thought the entire survey industry has an opp like massive opportunities here, because it's always been qualitative or quantitative and there's restrictions and limitations to both AI kind of breaks down those walls. So anyway.

Nick Cawthon (32:03)
It

does. So yeah, thank you for helping me plug that. I it comes from a good place in my heart because my people are your people and that I want to see us upskill and all adopt through training and leveling up.

Pax (32:20)
Yeah. Well, Nick, thank you so much for being on show. This has been a great conversation. ⁓ What is the best way? So again, we're going to plug the ⁓ survey. We will put those when that's available. We'll put those in the show notes and make that available for listeners. ⁓ How else would you like people to connect with you and learn more?

Nick Cawthon (32:38)
You can

find me at gage.io, G-A-U-G-E dot I-O, ⁓ or on LinkedIn or Medium. ⁓ I'm around. ⁓ So again, thank you for having me. It's been a pleasure to talk to you and your audience.

Pax (32:53)
Likewise, thank you so much, Nick.