From Prompts to Product: How AI is Changing Software Development
In this episode of "How Many CTOs Does It Take?" podcast, hosts Scott Porad and Brad Hefta-Gaub explore the evolving reality of AI productivity, and what it means for software engineering, product management, and the future of work.
They kick things off with a look at recent data showing how the complexity of tasks AI can handle is rapidly increasing—measured by the length of human-equivalent work AI can now complete. This sparks a lively discussion about breaking down work for AI agents, the art of writing effective prompts, and the surprising thoroughness of AI-generated specifications.
From there, Brad and Scott share hands-on stories from their own podcast website, revealing how AI coding assistants like Codex and Claude are transforming everything from feature development to bug fixing. The conversation expands into the challenges and opportunities of integrating AI into real-world workflows, the role of design systems, and the shifting responsibilities of engineers in an AI-powered world.
Along the way, Scott and Brad wrestle with big questions:
- How do you break down complex projects so AI agents can succeed?
- What makes a prompt or ticket "AI-friendly"?
- Will AI coding agents drive more standardization in software development?
- How does the engineer's role change when AI handles the heavy lifting?
- Can AI bring back the joy and creativity that first drew us to technology?
Whether you're a CTO, engineer, or product leader, this episode offers a candid look at the practical realities of working with AI, the new skills required, and why embracing these tools might just make your job more fun.
- 00:00 Introduction and Welcome
- 00:42 The AI Task Complexity Curve
- 03:27 Breaking Down Work for AI Agents
- 08:00 WayMo Traffic Jams
- 09:05 Real-World AI Coding Stories (Podcast Website Features)
- 18:04 Standardization, Design Systems, and AI
- 22:30 A More Complex Example: Episode Transcripts
- 25:00 AI Agent as Product Manager for AI Coding Agent
- 28:12 AI Coding Fail, Time for AI Debugging
- 31:25 Finding Joy in AI-Driven Work
- 34:50 Outro and Credits
Resources:
- How Many CTOs Pod: https://howmanyctospod.com
- Scott Porad: https://www.linkedin.com/in/scottporad/
- Brad Hefta-Gaub: https://www.linkedin.com/in/bradheftagaub/
#TechPodcast #EngineeringPodcast #DevTalks #PodcastForDevs #HowManyCtos #Podcast #Ctos #CtoPodcast #Technology #Engineering #SoftwareDevelopment #TechLeadership #AIProductivity #AIAgents #PromptEngineering #ProductManagement #Standardization #Coding #AI
Brad: The robot product manager did a way better job of specifying to the robot software engineer what I, as the chief product officer,
just described as, I just want transcripts on my podcast website.
Another episode of the How Many CTOs Does It Take podcast?
I'm Brad Hefta-Gaub, and my trusty co-host is...
Scott: got all rad and we are here today for a little mini episode to talk to you about a bit of AI data that I saw recently
Brad: AI data,
the latest and greatest on AI.
Scott: the latest and greatest on AI which
Brad: By
Scott: will yes
Brad: the way, by the time this episode comes out, GPT-5 will have been released.
So that will be interesting.
Scott: this data will be still but I saw this brand I felt like I had to call you right away and get your take on this
Brad: Yes.
Tell me.
Scott: I saw a chart, a piece of data on the internet on someone's blog.
I don't have it in front of me handy,
but it basically showed the x-axis was time.
The Y axis was the number of minutes,
the duration of a human task that AI was able to complete successfully.
So, you know, the X axis is 2020,
2021,
2022,
et cetera, et cetera. You know, by 2023.
AI could complete a three,
a two minute task that a human could complete.
And by 2024,
it could complete a three minute task.
And now we're in mid 2025 and it can complete a task of, you know, say 12 minutes or maybe 15 minutes at the most, right?
Brad: So can we assume that like a length of time of the task is
some kind of a rough estimate for the complexity of the task?
Scott: I think that is an implication here,
which is longer tasks just are more complex and humans can do more complex things.
So, you know, can the computer do what the human can do?
And so this was pointed out, this data was pointed out to me by someone who was kind of talking about how it really illustrates.
for all this AI-assisted coding and all the agentic coding,
which
Brad: I know you love that word.
Scott: I've been thinking about that word actually.
Agentic, is agentic really just a fancy word for asynchronous?
Brad: I don't know.
I think it was just somebody wanted to say agent,
but sound fancier.
You should.
Scott: So,
look,
I know you spend a lot of time dealing with AI,
exploring it,
et cetera,
et cetera.
So I want to get your take.
To me, this really spoke and what this personal conversation I was having was like, how small, it's a signal of how small we have to break down the tasks for the AI or the AI agents to succeed at them.
Like if you want to code something,
you can't just say.
make me a mobile app you got to just break it down into like three minute four minute five minute tasks for the agent to do and has that been your experience
Brad: Okay, so I have a couple things to say about this.
So first of all,
Scott: Let's
Brad: I
Scott: hear
Brad: hadn't never put it in the form of like,
do I think this task is going to take the agent X minutes to solve?
But it has definitely been the case that I have found that you want to break things down into small enough tasks that when it starts to go off the rails,
it doesn't go too far off.
are off the rails and
Scott: Mm-hmm.
Brad: that's kind of the way i think about it and in fact oftentimes i would give my like here's a here's a here's a really i think funny example of where i've seen the ais go off the rails where i have to give it a time box and say if you
If you bump up against this time box,
then stop what you're doing and come and ask me for help.
And that is, no joke,
fixing linter errors.
So,
Scott: Hmm.
Brad: you know, a lot of times if you have really strict lint rules,
you can have these almost rules that are competitive with each other where it's like I fix one rule and the easy way to fix the rule is essentially to break another rule.
But if you have both those rules in place, then coming up with the correctly formatted code that achieves that meets both requirements sometimes is hard.
Right.
And I have seen I've seen.
seen i just like i've got like cursor with the agent running in the in the in the sidebar and i'll just watch it it will like and it's like literally flipping between two different implementations over and over and over again going like oh i i fixed the lint error oh i added more lint errors wait i fixed those lint errors i came and it's just like it it's you know even today um it's still pretty bad at that
Um,
and so that's an example where I've kind of stopped it wasting its own time and stopped it wasting my time by giving it very clear instructions about don't go off the rail.
If you, if you, if you may, if you fail to fix this in two attempts,
stop what you're doing and ask me for help.
Right.
So I think that kind of reads on the point about break things down into small pieces.
Now, of course, we've also seen that, you know, Michael Natkin and.
Kevin,
our friend from Pioneer Square Labs,
they often are using this technique of giving the agents, asking the agent to produce a checklist and then asking the agent to work through that checklist.
And I think that's a really good paradigm that's essentially achieving that same goal of like, have it only focus on small pieces at a time.
Scott: Mm-hmm.
Brad: Another thing that is kind of, I think, related to this, two years ago or two,
three years ago, one of the first like prompt engineering hacks that somebody discovered and then announced to the world and everyone immediately adopted it was if you gave it the prompt where you asked it the question,
but then said, think about the steps you're going to take.
to answer this question and complete those steps one by one like that original prompt all
Scott: Mm
Brad: of
Scott: -hmm.
Brad: a sudden unlocked a whole bunch of results on the exact same llms right so it wasn't that the llm got better it was that somebody just discovered that if you pre-prompted the llm to say think this through step by step
It just performed better. And I think that's very much in line with this.
It's just something about the nature of the way the LLMs work where telling it to go step by step keeps it from hallucinating basically or
Scott: Mm-hmm.
Brad: makes it less likely to hallucinate.
Scott: It's fascinating.
how these llms like how we're learning to interact with them you
Brad: Right?
Scott: know they're they're obviously not living beings but they're almost like they're they're like alien life forms where we don't speak the same language entirely or we don't we don't have the same way of interacting with each other
And we're kind of learning, which is it's just fascinating.
Your point about the this is a tangent,
if I could just share a tangent for a second, your point about the linter.
Have you seen the videos like on Instagram or wherever YouTube where the Waymo traffic jams?
Brad: I have not, but I can picture them in my mind's eye.
Scott: Yeah,
so I've seen a few of these, you know, it'll be like, you know, two Waymos like get, you know, Waymos are very bright,
they know how to not crash into stuff and they'll get, you know, get in some space with another Waymo and they'll just get stuck.
because neither Waymo will neither Waymo will move and you know if you if you or I get at a four-way stop and there's a little confusion eventually one person waves the other one on but the Waymo doesn't know how to do
Brad: They
Scott: that and
Brad: don't have arms to stick their arms out of waves.
Scott: and I've if you go on YouTube or Instagram you can find these videos of like parking lots full of Waymos like all the Waymos are trying to like leave in the morning to go do their routes but somehow something just got off a little bit
bit in their system you know they're programmed to leave all the parking lot in some order but they've gotten in a total traffic jam i'm going to find one and give it to you to put in the show notes
Brad: I love it. I love it.
Scott: it'll be terrific
Brad: So I want to hear something I think would be interesting,
Scott.
So.
I, you know, I told you a while ago that one of the things I've been using as like my experimentation and play around with to try to learn how to better interact with these coding agents is our podcast website.
Scott: Yeah.
Brad: And so yesterday or yesterday or two days ago,
I got a bee in my bonnet and I decided I came up with like three features I wanted to add to our podcast website.
Scott: Oh,
Brad: And
Scott: we got to do another live coding then.
Brad: well,
I already did them.
So but we'll use these as we'll use these
Scott: So you're
Brad: as.
Scott: like my AI agent.
Brad: Yeah,
so let me tell you an example of three things that it successfully did.
And and I think that we can kind of think through the scope of it.
Okay, so here is a prompt that I gave.
I was using Codex, but I think this would have worked just as easily with GitHub Copilot or Quadcode.
Like this, this feels like a very in the sweet spot.
So I asked Codex, the episode component.
This is my exact prompt.
The episode component uses the episode description from the podcast RSS feed,
which often,
not guaranteed,
will include time codes in the format of mm:ss for topics in the episode.
I would like to make those time codes be hyperlinks to fast forward the audio player to that time stamp in the audio file.
Scott: Okay.
Brad: So let me just ask you.
If you gave that instruction to a junior engineer,
how many story points do you think they would estimate that at? How hard would they say that was?
And, you know, do you feel like that's an appropriately described specification?
Scott: I feel like it is certainly enough.
for the engineer to have an outline of what you want, and then to maybe go ask about some edge cases they could potentially think of.
Like, okay, well, what about this brand?
What about that? What about this, you know, et cetera, et cetera.
Brad: Okay.
Scott: It's a great starting point.
Brad: Okay.
Scott: Actually,
let's even put it this way.
In regular work at many of the companies I've been at,
that is...
That is more specificity than most of the tickets that get written.
Brad: Yeah, fair enough.
All right.
So code X worked.
So my codec setup,
it has direct access to our podcast GitHub account.
It
Scott: Mm-hmm.
Brad: has the development environment for the podcast website is set up with lint. It has end-to-end tests. It has its just, you know, node NPM packages. So it's like a pretty robust, straightforward,
you know, any developer that we.
hired off the street to work on it. They'd read the readme, they'd know how to work in it, right?
So it has all that access to it.
Scott: Mm-hmm, mm
Brad: With that prompt, it worked for 5 minutes and 28 seconds and produced a pull request that added 31 lines of code and subtracted one line of code.
Scott: -hmm.
Brad: and ran fully linted, fully compiled,
and fully passed end-to-end tests.
Scott: Pretty good,
I'd say.
Brad: And I opened the pull request that generated a ephemeral environment for me to test in.
It worked exactly as I imagined it would work. And...
Boom,
I accepted the change.
Scott: I think if they're a human,
that would have taken 30 to 45 minutes.
Brad: Testing that PR,
our episodes page, by the way, has like all the episodes listed and they all have audio players that you can go click on and play.
In testing that PR,
I noticed a bug that I had never noticed.
And that is.
If if you wanted to on our episodes page,
you could just start clicking and playing all the episodes at once and then you would have this like cacophony of chorus of Brad and Scott talking in your ears.
And I thought to myself,
that's weird.
That's probably a bug. Like if you start playing one of the episodes, it probably should just pause the one that you were listening to. Right.
It feels like.
Scott: Probably, yeah.
Brad: All right. So I said.
Gave Codex a new command.
I said, it is possible for multiple episode components to be visible on the same page.
For example, on the episodes page,
we show all the episodes as episode components.
Episode components have audio players on them.
Right now, it is possible to click or...
Click or to click play or click a time code link on multiple episodes at the same time and end up with multiple audio players playing at the same time.
We would prefer that if you start playing an episode or a time code of an episode,
that it stops any other episode audio player that is already playing.
Scott: Very specific.
Brad: How do you feel about that?
Scott: I feel great about it. I thought you were going to say we would prefer that only one is allowed to be played at a time and let it figure out how to handle that scenario.
But that was great. That was a perfect ticket.
Somebody should be able to figure that out.
Brad: All right, what do you based on based on the previous example,
how long do you think chat GPT codecs worked on that problem?
Scott: I want to say one thing first.
You know, when we write bugs, at least the way I was taught,
you sort of write a background and then you write repo steps and then you write actual and then you write expected,
you know, that's sort of a format.
And then there's other formats like I think there's like the Gherkin format for like a way of describing features.
And I wonder if that type of.
structure. There might be different kinds of structures that are needed for LLMs,
but like a more formal type of structure is not only going to be valuable, but our industry will start to coalesce around that as just a more effective way of communicating.
But to answer your specific question,
I'm going to say your first one was about five and a half minutes.
And so just a little like strategizing here,
it could have been a lot faster.
So you were impressed. So I could say 30 seconds or it could have been abysmal and I could say, you know, 90 minutes. But since since earlier, I think you just told me sometimes you.
Given a time limit,
I'm going to say 90 seconds.
Brad: I think that's a pretty good guess, but I will tell you that you're wrong,
but not because of why I think you're wrong. I think it's impractical for it to take 90 or 30 seconds to do something only because it's got to check out all the code.
Because every single one of these,
it sets up a whole new environment.
Scott: Okay, understood.
I did not totally rock that part.
Okay,
I'm going to go back and I'm going to say seven minutes.
Brad: It took four minutes and 10 seconds.
um and
Scott: That's that's another 45-minute task.
Brad: right and here's what's interesting by the way about this one um this one is a little complex right because it had to solve a like the episode component doesn't know about the existence of other episodes so
Scott: Yeah,
I was thinking about that
Brad: it had to solve this problem of well how am i going to make the other the episode know about other episodes
Right.
And honestly,
it did it in a really clever way.
It actually introduced a new audio player Svelte store,
which is basically Svelte stores allow you to kind of exchange information between two different pieces of code.
And so it just basically changed it so that every time.
the the episode starts to play an audio player it records it puts in the story like i'm the audio player you know and
Scott: Yeah.
Brad: so then it's able to know the last audio player and so it solved the problem in a pretty ingenious way and i honestly think that had i given had i given this to like a junior engineer they would have had to think kind of hard about that part of the problem like how am i gonna how am i gonna let these things know about each other
without like making the code really ugly and complicated of like having some list of all audio players bullshit or
Scott: Well,
Brad: something like that
Scott: it's interesting.
Or do you have a design which says there is one audio player on this page,
and as you click play,
play, play, it updates the source in the single audio player.
Brad: why that maybe would be the answer that it would have had another another but
Scott: answer
Brad: then i would but then i would have been like i still want there to be a little play button down there like i want each of the episodes to be able to have a nice little play button that you could just you know start playing it
Scott: it's interesting what this is making me think of something do you think
Okay, there's a lot of potential solutions to that problem,
each of which are fine,
pros and cons to each, but do you think that this kind of coding is going to lead to standardization?
Like one of the complaints,
I don't know if complaints is the right word, one of the observations I've made about software engineering is, I mean, we have standards, but so little standards, right?
I mean, you go build a house.
Every single way the
Brad: Yeah.
Scott: wires and plumbing and everything is standard, we've got nothing relatively speaking.
And I wonder if all of this agentic coding is going to lead to more standardization.
Brad: I don't know if I can answer that question,
but let me give you a part of the answer that I think maybe I think this speaks to what you're asking.
So you recall a couple episodes ago we talked to Vince Dineen about design
Scott: Mm-hmm.
Brad: and,
you know, he kind of talked a lot about design systems, you know, like tailwind or or skeleton or whatever,
get a design system. And Greg Conklin, when we interviewed him, he.
And he he mentioned that as one of like the early things that startups should do is like get a design system early.
So design systems are an example of if you are using it in your development organization, then it standardizes the work within your development organization.
So a button is always a button.
Right.
Scott: Mm-hmm.
Brad: This particular code base that we have for our podcast,
I've chosen to use Svelte, which is a framework not dissimilar from React.
I could have used React,
but I used Svelte instead.
And there is a paradigm for doing Svelte development,
just like there's a paradigm for React development,
right?
And Angular has its own paradigm and, you know, all the different frameworks kind of have their own paradigms.
And what is interesting is that the AI code.
assistance,
I believe are quite good at following programmatic paradigms for the particular framework that the code base is.
Scott: Mm-hmm.
Brad: So Svelte is a relatively opinionated framework.
There's ways to do Svelte things right.
And there's ways to, and if you're not doing it the Svelte-like way,
it's obviously, to anybody who's familiar with Svelte, it's obviously not the Svelte-like way to do it.
Scott: Mm-hmm.
Brad: I didn't have to tell ChatGPT Codex that this was a Svelte code base.
I did use the word component,
so I kind of hinted to it, but it downloads the code, looks at it and goes,
ah,
this is spelled and immediately then.
So so to answer your question about like standardization, I think out of the box, the agents write code consistent with the paradigm of the framework that the code exists as.
And it's quite good at that.
Scott: So it almost takes like linting to the next level.
Brad: Or it's, you know, it's almost like taking,
like you don't have to teach it.
Like if a brand new junior developer shows up on my code base and they've never coded in Svelte before,
their first couple projects are all about a senior engineer saying,
don't do it that way. That's not the Svelte way to do it, right?
Scott: Right,
Brad: You
Scott: right.
Brad: don't spend any time with the coding agents to teach them how to code in Svelte in the Svelte way.
Scott: Yeah, it's interesting.
I mean, how many times in your career,
Brad,
have you had to go,
there's been some piece of software that wasn't working right and the person who wrote it isn't around and it's in some language you're not all that familiar with like,
oh, you know.
there's a shell script a batch file like okay well i've done these before but i'm not a shell script or batch file like expert um and you go in there and you hack some stuff in and you solve the problem and like you know you maybe do a little stack overflow to figure out how to do the thing you want to do and like then you move on right you're done and how ai is going to be a game changer for that
Brad: 100% agree.
Scott: Yeah.
Brad: All right. I have one last example I want to share with you.
Scott: Let's hear it.
Brad: You want to hear this one?
Scott: Yeah.
Brad: Okay.
Scott: Okay, let me guess. Before you even tell me,
15 minutes.
Brad: Ooh, good question.
Nope.
Scott: Okay, let's hear it.
Brad: Okay.
All right. So this one.
I got to be in my bonnet to say, what if we added transcripts to our show episodes?
Scott: Mm-hmm.
Brad: You know,
we use Descript as our recording platform,
and Descript has the ability to output transcripts. And our hosting platform has a little link when we publish episodes where it's like add your transcript here.
And I don't really know that much about transcripts, but apparently they're...
Apparently, there's a standardization around like VTT files or video transcripts and something called an SRT file is essentially an audio transcript and it includes time codes, name of the speaker and then whatever they said, right?
And I thought to myself, would that be cool if we could have in that episode thing,
if we have a transcript for it.
have it like have a little fold down that shows the transcript scott said this brad said this vince said this and um it should i don't know it like it should follow along it should be clickable all that stuff right but i didn't know anything about how transcripts work like i honestly didn't know so the first thing i did was i went to chat gpt and said
explain teach me everything about your
Scott: Right.
Brad: audio podcast transcripts you know and the stuff i just told you about srt files that was stuff that it it did it told me about and
Scott: And now that's going to clutter your brain for the rest of your life.
Brad: exactly i won't be able to get rid of it so um so it turns out that like
dscript will not output srt files or vtt files it just has these uh so it doesn't follow the standards so that kind of sucks but i went ahead and exported a transcript and i asked chat gpt like what's the best way to get me a transcript that would be easiest to convert to one of these srt files so it kind of told me you know i gave screenshots of dscript said here's some options tell me which ones to choose and so it said choose this and this and then it wrote me
me a Perl script or Python script to convert that output into an SRT file and I was like okay cool and
Scott: I'm liking it so far.
Brad: so then I'm like okay I want to ask my coding agent to add this and so what I did was I asked ChatGPT after having this big long conversation with it about transcripts I said will you please
Give me instructions that I can give my AI coding agent to add the transcript feature to our website.
Scott: Very meta.
Brad: Very meta.
Now, I am not going to read this to you because it's really long.
It's like, you know, a page at like 10 point font of instructions,
but it included.
feature name transcript display and playback sync with podcast episodes it included two paragraphs of context it gave requirements around fetching the so it turns out the transcript is embedded in the rss feed as another url that you've got to go download and then it's going to be in this srt format and you're going to have to convert that so it gave it like explaining how to fetch it
It gave it rendering instructions,
right? Display the transcript and a scrollable list of blocks,
human readable timescale,
speaker's name in bold,
transcript text.
You know, use standard styling,
audio player integration.
When the person clicks on a piece of the transcript,
jump the audio player to that point.
Accessibility, it had excessive,
I didn't come up with this, it came up with accessibility instructions to make sure that it's using article style blocks, which are HTML meta tags,
keyboard navigation,
performance requirements.
It added all that.
Scott: So what I'm taking from this is compared to the human-created tickets and specifications,
it does a really good job of specifying exactly what to build,
including a variety of considerations that...
We often forget that we should always be considering that are often a pain for companies to go back and deal with.
I've been involved in accessibility lawsuits where we've been sued because our product was not accessible.
We have to go back and fix these things.
But the robots get that stuff out of
Brad: The
Scott: the gate.
Brad: robot thought of it.
Scott: Yep. Amazing.
Brad: The robot product manager did a way better job.
of specifying to the robot software engineer what I, as the chief product officer, just described as,
I just want transcripts on my podcast website,
right?
Scott: Wow.
Brad: Now, what is your guess on how long the robot coder took to solve this problem?
Scott: 15 minutes.
Brad: Five minutes and 29 seconds.
Wait, there's a little bit more to the story.
Five minutes and 29 seconds,
it produced a pull request.
It was a brand new transcript component,
so it did a great job of being svelt-like.
The transcript component was about 171 lines of code.
It modified the episode component to embed the transcript component,
and it had to modify the sort of like the fetching functions that download that podcast data to recognize transcript URL.
Opened up the pull request.
launched the ephemeral domain it didn't write it didn't write new end-to-end tests for me which is interesting open
Scott: Hmm.
Brad: the pull request launched
Scott: Bad robot.
Brad: the uh bad
Scott: Bad robot.
Brad: robot i didn't tell it to make new tests uh launched the domain and it did not work it did not show the transcript it showed no transcript at all for the episode that i knew had a transcript because i specifically added a transcript so it could do it
and i was like what the heck like it clearly didn't maybe you know i was like ah maybe the rss feed doesn't yet include it so then i had to go debugging and i downloaded the rss feed oh yeah no it's in there i double check yeah it's there there was a bug in the original code where it did it did not know how to pull out the transcript url out of the rss feed
Um,
but so it was obvious to me that it was, that was the bug.
So then I basically brought that code base,
that pull request,
that, that branch into cursor.
And I told cursor,
Hey,
this code has a bug.
I know there's a transcript on episode one.
Tell me why is this not working?
And cursor figured out.
that cursor probably Claude Sonnet figured out that ChatGPT 3.5 or whatever it codex used
didn't know how to do that correctly and cursor fixed that bug and that took it because now I was kind of driving it took a couple minutes for me to get it to fix that bug um and
Scott: Did you actually have one model talk shit about another model to you?
Is that what they're doing? They're pointing fingers at each other?
Brad: They were basically pointing fingers at each other.
Scott: I have two observations
Brad: But
Scott: to
Brad: I always
Scott: make.
Brad: – I want to keep the robots happy with me and happy with each other, so I never name names.
I always ever say,
like, this was written by another expert.
Please tell me what that expert did wrong.
Scott: All right, so I have two observations before we wrap.
The first is, and I'm not trying to toot my horn by saying this, I'm trying to just say this is the data point to go with it.
I've kind of envisioned for a long time that all,
when I say a long time, a long time in the AI landscape,
which is like a couple of years at the most, right?
All software engineering,
not all software engineering, a lot of software engineering is going to actually become QA engineering.
And that's what I just heard you talking about,
which is like what's going to become product management is going to become QA, right?
Brad: Yep.
Scott: Now, there will be lots of people working to make the middle part.
We're going to have to write the robots,
although maybe the robots will start writing the robots,
right?
And it's turtles all the way down.
So that seems to be true,
you know, and that gets to the point about how like you're a very experienced engineer and so you know how to do this. How is our...
How are the new entrants to our field going to get this experience?
But the other observation I want to make before we wrap is, it seems like you're having fun.
Brad: Yeah.
Scott: And I think I hear a lot of people being
are feeling threatened,
their jobs and their livelihoods and their passion feeling threatened by AI,
but I also hear you having a lot of fun making stuff with it.
I don't know. I think that's a good thing.
Brad: Yeah,
I think that I'm having fun. The engineers that are on my team that have really embraced AI,
I think they're having a lot of fun too.
I think they feel empowered and they feel more powerful.
Scott: Yeah.
Brad: And that feels fun.
Scott: So overall soup to nuts that last one,
how long did it take you?
Brad: Probably total time.
Well,
starting out me learning what how how they actually
Scott: You
Brad: worked,
I'm going to say 30 minutes total.
Scott: easily did a day's worth of work, a half day to a day's worth of work.
including coffee breaks and lunch and the whole thing right in 45 minutes yeah
Brad: And it was, you mean for all three of those projects?
Scott: yeah
Brad: I would say it was probably two days worth of work in 45 minutes because I had to go learn about like if I hadn't had ChatGPT to teach me about transcripts, that would have been even more time, right?
that that was that was a big piece that i would have to go learn a lot about that like how are we going to learn about srt and vtt files like
Scott: You would have just been perusing blog posts and reading RFCs and stuff like
Brad: and
Scott: that.
Brad: plus i'm horrible with like writing like you know text processing things you know like you you know like the fact that it was able to convert some random text file into a specific format for me that was like
That
Scott: It's
Brad: was definitely
Scott: change
Brad: extra expert.
Scott: changer.
Brad: Yeah,
Scott: it's making it made you powerful.
Brad: For sure. It was easily a day to two days worth of work across three different disciplines,
product engineering and QA, that I spent 45 minutes on.
Scott: So the same joy that we got in the seventh grade when we saw the logo turtle scored across the screen.
Brad: Yep.
Scott: And we're like, look,
cool,
I made this, right?
And when we made a little video game, which was stupid,
you know, made something that added two numbers together or whatever in seventh grade computer class. And we're aging myself probably just like you age yourself.
But that was fun.
and because you were like as a kid you were being like this is cool check out what i can do and you know as a result of that now we've got self-driving cars but like um if somebody
Brad: Stuck
Scott: could
Brad: in a parking lot.
Scott: get stuck in a parking lot so um yeah maybe this is going to bring some of the joy back to what we're doing because i sense this is maybe this is me projecting i feel like in the last
It's 15 years, a lot of the joy has been squeezed out of what we do.
As I know why exactly, but it seems that's a topic for another time,
but maybe this is going to bring the joy back.
Brad: There you go. Bring the joy back.
I love it. Well,
thanks for talking to me about this one. This was fun.
I think it's always great that we do these recaps on kind of the state of AI and yeah.
As you know, I'm quite bullish on it.
Scott: Well, it was nice talking to you, Brad.
Let's talk again soon.
Brad: All right.
Bye. Have a good one.
This episode of How Many CTOs Does It Take Podcast was edited by Safran Epigow.
Our theme song was AI Generated by Brad.
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