Latest / The Learning Corner by Precursor / Episode #82: What Will Be Scarce in an AI World, The Real Cost of Vibe Coding, The End of the Software Engineer

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Episode #82: What Will Be Scarce in an AI World, The Real Cost of Vibe Coding, The End of the Software Engineer · The Learning Corner by Precursor

• 0:00
  A presentation for our team tomorrow, which is about the 20 agents that I have in production
• 0:04
  and what they actually do, what they've done for me.
• 0:07
  And in building the presentation, Cloud Code was like, oh, this one's been failing for a
• 0:11
  week.
• 0:12
  I was like, what do you mean?
• 0:12
  Yeah.
• 0:13
  It just stopped working.
• 0:14
  It's a problem I have to solve, and I think that's the thing I'm learning is that most of
• 0:17
  the things you need to know, it's far easier to learn them when you encounter them and bump
• 0:21
  your toe.
• 0:22
  It's the week of June 1.
• 0:24
  Welcome to the Learning Corner Precursor Ventures podcast.
• 0:29
  Welcome to the Learning Corner by Precursor.
• 0:31
  I'm Mia Farnam, and I'm joined by our GP and managing partner, Charles Hudson, where we will
• 0:36
  be talking through the articles and Goodreads that caught our team's attention over the past
• 0:40
  week.
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  Our first piece is a really well researched essay by economist Alexey Moss titled What Will
• 0:47
  Be Scarce?
• 0:48
  And it makes a really counterintuitive case for what happens to human labor when AI can produce
• 0:54
  nearly everything a person can.
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  The core argument is that as automation drives down the cost of commodities, real incomes
• 1:00
  rise, and people predictably shift their spending towards things that automation cannot
• 1:05
  cheapen.
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  Goods and services where the human element is the value.
• 1:08
  He calls this the relational sector, which includes nurses, therapists, personal chefs,
• 1:14
  bespoke craftspeople, hospitality and care workers.
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  He leans on a behavioral insight from Rene Girard called mimetic desire, the idea that
• 1:22
  people want things more when others can't have them.
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  Research he ran found that willingness to pay nearly doubled when people knew others were
• 1:30
  excluded from the same product.
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  The kicker in this argument is that AI involvement actually undermines that
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  exclusivity premium, making human made things feel scarcer and more valuable by contrast.
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  His conclusion is that economics does not become irrelevant in an AI world.
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  Scarcity just relocates.
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  He opens with this Starbucks example, which I think is such a good gut check on all the
• 1:53
  automation assumptions we make, especially with AI.
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  But Starbucks, if anyone remembers, they had technology that essentially removed a large
• 2:00
  portion of the human element entirely.
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  And then they tried it out, and they actually reversed a lot of those changes because they
• 2:06
  found out the handwritten cups, the interactions with the baristas and ceramic
• 2:10
  mugs, those things drove more people to sit and stay in their cafes.
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  Charles, do you buy the logic at scale that hospitality or so much of this intangible
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  interaction becomes a genuine competitive moat as opposed to a soft differentiator, which I
• 2:27
  feel like a lot of people are overlooking today?
• 2:29
  I think we already have examples of this in life.
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  Getting a human customer service person for most businesses and services that are scaled is
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  a VIP experience.
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  For everybody else, you get either the automated phone tree or you get the AI chatbot.
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  And I do think because human labor is expensive, people will say, oh, if that's done
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  by a human, it's bespoke.
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  Remember we talked about was that your brother who's the glassblower?
• 2:52
  Yeah.
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  Mhmm.
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  Like, yeah, your brother's onto something.
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  We are gonna see a resurgence.
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  And I think we already see this, like, you think about, watches or there's already a lot
• 2:59
  of handbags.
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  There are lot of luxury goods already where there's a premium on handmade by humans at low
• 3:04
  volume with great care.
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  I think that will accelerate.
• 3:07
  And I do think I consider myself an economist as someone who studied the economy in school.
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  Do I think one of the things I've been trying to figure out, if you think about, like, a lot
• 3:14
  of these conversations, particularly around AI businesses and services, modes, defensibility,
• 3:19
  it's I think it's all a question about what's scarce.
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  Because historically in software, computer engineering development cycles were scarce.
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  So if you could accumulate a lot of them and turn their energy into product, it was harder
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  to compete with that.
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  And now computer engineering cycles are less scarce.
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  I wouldn't say cheap.
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  I wouldn't say free.
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  They're less scarce.
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  And I think everyone's trying to figure, well, what what is scarce?
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  I don't think we we know.
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  So people have gravitated towards taste or energy.
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  These fuzzier.
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  The things that I do think we will see are resurgence.
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  It's funny.
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  I went to two coffee shops in Palo Alto.
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  This is obviously a very Bay Area thing.
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  They're both on University Avenue.
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  One coffee shop has one person working behind the bar, and a machine makes almost all of the
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  drinks.
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  Very capital efficient.
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  I think I said two words to the person in the coffee shop.
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  Coffee shop was pretty full.
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  The people were all working on laptops.
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  They weren't really talking to each other.
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  It felt like a library with a coffee shop in it.
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  The other one I went to is a really old Italian coffee shop on the other University Avenue that
• 4:13
  has no takeaway.
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  They make all of it on, like, traditional espresso machines.
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  And you go there and everybody's sitting there and talking.
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  And the coffee is made by the people behind the bar.
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  Many of them have been working there for a long time.
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  It's not the fastest coffee shop in the world, but I think it's the most pleasant.
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  And I think the people who go there go there for the experience.
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  I don't know how you invest against that theme, but not everything that happens in society
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  needs to be a money makings.
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  So I do think we're gonna see what he described.
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  And because human things are expensive and hard to procure, I think people are gonna value them
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  even more than they do today.
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  I think in your example, one thing that's true is both can exist at the same time.
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  For my own coffee shop, it's right across the street.
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  There's only one.
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  They had five locations, and they sized down during COVID.
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  It's very neighborhood feel.
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  There's always a line.
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  It always you don't get your coffee immediately, but everyone draws there.
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  And there's a Starbucks a block away, and it's way less crowded mostly.
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  And it's just a different group of people who go there.
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  I was talking to the manager, and their their Square tablet had broke.
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  And so they really could only take cash.
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  And there were still lines.
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  Like, people were still lines waiting.
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  And so that was, in their world, a defensible moat that worked for them.
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  People still showed up, and a lot of that had to do with the human element of it, the more
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  rational sector piece that Imas talks about of just the extra intangible aspects.
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  That's a pretty spot on comparison, I feel like, or example that we're making because it's
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  talking directly about hospitality.
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  I guess one thing that got me thinking when I was reading this article is the rational
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  sector, which he kind of argues is going to grow or more people might enter that space.
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  He gives the examples of service jobs.
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  So think in hospitality, nurses, teachers, etcetera.
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  Those people have historically underpaid for old times.
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  And so I'm wondering, does something like this cause those people to actually be paid
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  correctly?
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  Or does it just have more people enter the space because their jobs that they previously
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  worked in are no longer in high demand?
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  We had COVID happen.
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  Say that created demand for teachers and learning experiences that we hadn't seen prior,
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  but that didn't really change the salaries that they were paid.
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  So I like the framing of his his piece because it is optimistic and that things relocate,
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  which is great.
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  But the way he talks about the rational sector, I'm just not so sure if those people will get
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  the premiums that they've always deserved, I guess.
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  I would love to believe that they will.
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  I'm skeptical that they will.
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  But I do think people who are at the very high end of those services are gonna make more
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  money.
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  And I think, you know, you are gonna end up with the star economy, where I think the people
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  at the very top in some of these other professions are gonna find that they can
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  command more.
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  If you're a really great tutor, there might be wealthy families who say, you know, as great as
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  AI tutors are for my kids, I really want a human.
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  But I want, like, the best human I could find, and my willingness to pay for the best human
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  will be, like, much higher.
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  In the same way that if you are in hospitality and you work at a really good hotel or a really
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  good restaurant, you can make more money.
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  The standards are higher.
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  The expectations are higher.
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  You have to be better at your job, but you can make more money.
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  But that's more money within relative to that profession.
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  I I'm very curious to see how this all plays out.
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  It is interesting because I think the piece does focus also on elitist is a strong word,
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  but higher income power to drive where the relational sector is gonna have the most
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  demand.
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  That feels a little bit ironically dystopian when you think about the article and the
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  takeaway being so optimistic.
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  And then I think, oh, all the power and attention is truly being driven by the people
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  who have the money to kind of position it in that direction.
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  And the question that remains is who will have the money at the end?
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  I think there's this undercurrent conversation that it will be the plumbers, the electricians
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  who've been put behind, and now they will be at the forefront.
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  But I don't I don't know.
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  I think people who can continue to go to the high luxury resorts Yeah.
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  Will continue to go to those resorts and will pay even more premiums to go to the best ones.
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  I immediately thought of White Lotus when I read his art.
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  Yeah.
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  That's what I was thinking of.
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  Yeah.
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  I think the other thing that I've been thinking about, which is related to what you said is if
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  AI is as deflationary as we think it is for digital services, that means those things
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  become cheaper.
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  But if you're already sitting on quite a bit of wealth, you're gonna find yourself paying a lot
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  less for stuff that used to be expensive, which means maybe your willingness to do weird things
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  with your money that are esoteric could go way up.
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  Might just be like, well, you know, I don't need to spend as much money on x y z thing
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  anymore because of AI.
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  What are other passion things I could do with my money, things I'm interested in?
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  Hey, I'm gonna have a sommelier come to my house and hang out with me for a week and like,
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  teach me stuff.
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  Like, new ways to monetize expertise or human experience are gonna show up.
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  Do you think they're gonna be mostly accessible to people who made money in the pre IA era who
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  are sitting on it.
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  And suddenly, like, AI is making all of the other things in my life cheaper and easier, so
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  I have more money and time for leisure.
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  Like, they will be the Keynesian beneficiaries of all this stuff.
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  I do love the way that you worded it earlier, the star economy.
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  This is the time to lean into if you had a hobby that you're good at.
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  If you wanted to perfect it, it would be the time.
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  Our next piece is by Lisa Kustova titled 40 buyers and nothing to sell, describing an
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  honest take on vibe coding and what happens when you follow the hype production.
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  Lisa spent nine months trying to build an AI powered content system for financial advisers.
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  She showed up to a major industry conference in Huntington Beach with 40 people ready to buy,
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  exactly what the title says, and nothing to sell them because the product was structurally
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  broken in ways she could not see or diagnose on her own.
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  The piece is organized around this metaphor of footprints on a glacier in New Zealand.
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  Hikers were essentially following tracks, hopefully looking like they were going
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  somewhere, not realizing that tracks they were following were actually someone's practice labs
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  on the same path.
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  And the technical failure was really stark in her product she was creating.
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  Her AI coding assistant had quietly created three separate services to handle the same API
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  call, never flagged it.
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  And when things did break, it confidently blamed other tools rather than its own output.
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  What actually fixed things was, surprise surprise, a Bulgarian engineer who looked at
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  her code the way a plumber analyzes a wall leak and declared that it was a total mess, and that
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  if he had received this code from an actual software engineer, he was like, I would kick
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  this back to them and, like, fix your crappy code.
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  The real lesson she lands on is that the map everyone is following in Vibe coding is upside
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  down.
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  Prompting feels like progress, things we've all talked about before, but engineering an actual
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  system that works in production does require a fundamentally different orientation.
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  I read this piece, and I was thinking that Lisa is one of the few people willing to publicly
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  document what actually went wrong when vibe coding meets production.
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  In reality, even when there's a lot of excitement and demand coming her way.
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  But for every Lisa, I think there's probably, like, a 100 founders or operators who don't
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  realize that their product is structurally broken until customer does, which is far more
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  dire.
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  How long, Charles, do you think it will take before the market starts separating the
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  products that were genuinely engineered from the ones that just look like they are on the
• 11:03
  surface?
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  My current thinking is hype moves three times as fast as reality, which means it's gonna take
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  a while because everybody's AI psychosis, AI Pilled CEO, believes that these things can be
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  vibe coded into production.
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  And it's gonna take some failure before people say, well, wait a minute.
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  Maybe this isn't such a good idea after all.
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  And maybe we overestimated what we could do, and we need to go back and take a more
• 11:27
  fundamental look.
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  And I hope you didn't fire all of your software engineers.
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  And I think some of the stuff people will realize, you know what?
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  Maybe vibe coding an internal wiki or a software tool that's used by two people that we
• 11:37
  would never pay to bill, maybe that's an okay use.
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  Maybe our core production service that we use to make money that has some complexity
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  shouldn't be vibe coded by someone who's not a developer.
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  Whenever I talk to friends who are actually trained software developers, the way that they
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  use coding systems is different.
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  You know, the coding assistance help them write code, but they're still making judgments.
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  Or they're giving the coding assistant far more instructions on architecture and design, and
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  they're not just letting the coding assistant make a bunch of really important architecture
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  decisions on its own.
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  And I think that's why you get better code versus, I think, using coding system when you
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  don't understand the fundamentals of software development or architecture.
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  It will build you something.
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  It will probably work, but that doesn't mean it's maintainable.
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  I was just doing some math.
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  It's about 10 to one for me.
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  Support, tinker, upgrade, debugging to build.
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  A lot of my friends are like, for someone with your ability, that's probably right.
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  They're like, if you were better, you'd make better decisions upfront.
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  You'd plan a little better.
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  And it wouldn't be 10 to one.
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  It'd maybe be three to one.
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  But anybody who tells me, oh, I just like one shot of this thing, I've never touched them.
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  Like, it's probably broken.
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  You don't even know it.
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  So I have a real appreciation for the craft of software development and, like, what it takes
• 12:46
  to fix things when they break and how you diagnose the problem and how you figure out
• 12:49
  what about it you wanna fix.
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  And so I think this is the first of many of these stories we'll hear.
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  And they're gonna get a lot of attention, just like the the robots that trash those people's
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  houses and a desire to get cleaning robot training data.
• 13:01
  There there's a a deep audience for, like, AI mishap stories.
• 13:05
  Oh, absolutely.
• 13:06
  I like the metaphor of the hikers in the glacier because it showed that, like, we're
• 13:09
  kind of in unexplored territory, not for software engineers.
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  They've been doing this job forever, but for everyone else who's, like, jumping into this
• 13:16
  role and upscaling themselves.
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  She had two really good graphics.
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  One was kind of really invariable looking terrain, like if you were looking across a
• 13:23
  valley.
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  And in different pockets, she outlined, over here, it works well.
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  You're up on the top of the hill.
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  Over here, it doesn't work as well, but I'm not quite sure.
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  Over here, it's a a deep valley into a pit.
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  It definitely doesn't work.
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  But you're walking the same plane.
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  It could be all one product that you're working in, and in different pockets, it's working in
• 13:39
  different times, it's working.
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  Previously, at least for nontechnical people, it's quite binary.
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  I'm building something, and it works or it doesn't.
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  And the customer sees that too.
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  It's like the experience, what it is, and then it's working or it's not working.
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  I think what's scary is you could be getting an output that just looks like it's correct
• 13:57
  because it came in a pretty package.
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  It came at the right times.
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  Like, it could be spat out in five minutes, and you don't know if it's incorrect.
• 14:05
  And I think that's where it just takes a little bit more of a technical eye.
• 14:08
  And my question is, it does seem like people do need to understand the difference between when
• 14:13
  to use the LLM and when to use more deterministic script.
• 14:17
  That muscle, I feel like, does derive from having experience of being technical.
• 14:21
  Do you think that that skill can be taught broadly, or does it just naturally select for
• 14:25
  people who already have an engineering foundation?
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  And how could a nontechnical person upskill themselves to get there?
• 14:31
  I assume the question is you just continue have to build and actually take your time and be
• 14:35
  thoughtful.
• 14:35
  So funny.
• 14:36
  So I have a presentation for our team tomorrow, which is about the 20 agents that I have in
• 14:41
  production and, like, what they actually do, what they've done for me.
• 14:44
  And in building the presentation, Cloud Code was like, oh, this one's been failing for a
• 14:49
  week.
• 14:49
  I was like, what do you mean?
• 14:50
  It's like, yeah.
• 14:51
  Just stop working.
• 14:52
  It's not a critical one, or I would have noticed it.
• 14:55
  And so my project this week was I went back and put in much better logging and monitoring into
• 15:01
  all of my agents' skills so that I get regular updates.
• 15:05
  Software development, you would put in logging and monitoring.
• 15:08
  It is really interesting how I feel like every week, and I'm like, oh, I have a greater
• 15:11
  appreciation for this element of I understand this concept now in a much more visceral way
• 15:18
  because it's something that I want.
• 15:19
  It's a problem I have to solve, and I think that's the thing I'm learning is that most of
• 15:23
  the things you need to know, it's far easier to learn them when you encounter them and bump
• 15:26
  your toe.
• 15:27
  But how funny is that example that you could
• 15:29
  you would share that presentation to us tomorrow, and even if things weren't working,
• 15:33
  we'd be like, oh, how incredible.
• 15:35
  Amazing.
• 15:35
  Can you send me over your script so that I can copy it and start running it myself for my own
• 15:39
  workflows?
• 15:40
  And Claudine was like, do you wanna include the ones that aren't working in a I'm like,
• 15:43
  absolutely.
• 15:43
  Include the broken ones because I wanna talk about the broken ones and what I'm doing to fix
• 15:47
  them because I think that's, like, part of the lesson too.
• 15:50
  Yeah.
• 15:50
  And I think that's the growth muscle that we're going through collectively is there's this
• 15:55
  instantaneous satisfaction you get when you build something and it works.
• 15:59
  And you're like, I am amazing.
• 16:01
  And then when you have to do maintenance on it, it's just so much less fun because the output
• 16:06
  is the same.
• 16:07
  It's I already created this two weeks ago, and now I have to dedicate specific hours to
• 16:11
  continue to make it work.
• 16:12
  It's so much of a less fun task.
• 16:14
  But, again, that's where, I guess, real craft taste judgment is built in those hours as
• 16:19
  opposed to just spitting out something that works in that first go.
• 16:22
  Our last piece is a really good tie to what we just talked about now.
• 16:26
  It's an interview with Casey Newton and Boris Churney, the creator of Cloud Code at
• 16:29
  Anthropic, and it reflects Boris' take on the software engineering job market from someone
• 16:34
  who's actively automating his own role.
• 16:36
  Boris hasn't written a single line of code himself in over six months, and he's really
• 16:40
  clear eyed that the title of software engineer may start dissolving by the end of this year
• 16:44
  into something closer to builder as designers, PMs, and managers all start shipping code of
• 16:50
  their own.
• 16:51
  But his overall forecast is quite optimistic.
• 16:53
  He thinks that there will be 100 times more people writing or directing code in the near
• 16:58
  future than there are engineers today, just not people with that specific title.
• 17:01
  He uses the tractor analogy to ground the rate of change for everyone who feels uneasy,
• 17:07
  pointing out that it took seventy years from the tractor's invention to tractors
• 17:12
  outnumbering horses on American farms, and draws a parallel to how companies that
• 17:17
  reorganized entirely around computers in the nineties, so completely throughout their files,
• 17:23
  throughout all their paper, and just decided we're gonna focus on this only, saw massive
• 17:27
  productivity gains, while others who were half in, half out saw none or much lower.
• 17:32
  His closing point that stuck out was about access.
• 17:36
  The winners of some recent Claude Code hackathons that they ran weren't senior
• 17:42
  engineers, but it was like an electrician.
• 17:45
  It was a doctor.
• 17:46
  It was a carpenter.
• 17:47
  That anecdote really landed for me because it does flip the assumption that AI tools are a
• 17:53
  gift to people who are already only so close to the space.
• 17:56
  I think we spend a lot of time thinking about Claude Code is amazing for the best engineer
• 18:00
  because it makes them 10 times more productive.
• 18:02
  I would have loved to have seen what the electrician, the doctor, and the carpenter kind
• 18:05
  of came up with because I would assume that what they created was actually probably closer
• 18:10
  to something that is actually needed, because they are kind of building off of their own
• 18:14
  experience than somebody who's like, amazing.
• 18:16
  I have these tools.
• 18:17
  I can be productive.
• 18:18
  Let me just start creating things for the sake of creating.
• 18:21
  I was wondering that was surprising to you, the hackathon anecdote.
• 18:25
  I'd read a write up they did in the hackathon, and it was surprising.
• 18:28
  But I think for me, I thought a lot about Excel.
• 18:31
  And I think it used to be a long time ago that, like, computation was expensive and computation
• 18:36
  we had little people called computers who did computation free.
• 18:39
  And then Excel came out and everyone was like, woah woah.
• 18:41
  Can an investment banker use Excel better than the average person?
• 18:45
  Absolutely.
• 18:46
  Maybe they can.
• 18:46
  Can a consultant use PowerPoint better than the average person?
• 18:49
  Of course, they can.
• 18:50
  The average white collar worker assumes that they will have to use a PowerPoint or
• 18:54
  PowerPoint like product, whether it's Google Slides or PowerPoint or whatever, or and some
• 18:59
  kind of app based computation product like Excel or Numbers or whatever as part of your
• 19:02
  job.
• 19:03
  Those are just skills now that you must have in order to be effective as a white collar worker.
• 19:08
  But it doesn't require that you have the design skills of a designer and attention to detail of
• 19:12
  a consultant or the what's to do with macros and shortcuts of a banker.
• 19:16
  You can use your mouse.
• 19:17
  It's okay.
• 19:18
  You're expected to have some base level familiarity and facility with those tools or or
• 19:22
  you probably won't get hired.
• 19:23
  And I think the same is gonna happen.
• 19:24
  I think I'm an example of what Boris is describing.
• 19:27
  The code I've written in my life, I've basically written in the last nine months.
• 19:31
  Other than the code I wrote learning the basics of Python, 95% of the code I've written, I've
• 19:36
  written with the coding system.
• 19:37
  It's the only way I know how to really ship code.
• 19:40
  But I I would never call myself a software developer in the same way that business schools
• 19:44
  started teaching people Python and R for data analysis.
• 19:47
  I don't think most of those people would say, oh, I'm a data analyst.
• 19:49
  They're like, oh, I'm an MBA student who's, doing analytical work.
• 19:51
  And I think that's where we're headed.
• 19:53
  There will be, I think, people who are professional software developers because of
• 19:56
  everybody shipping code with variable in the same way that you would never let an analyst
• 20:00
  ship their final version of a model to a client or people who know what the model's supposed to
• 20:05
  do and, like, reviewing it.
• 20:07
  I think this notion that everybody's gonna ship to production with no oversight, it strikes me
• 20:12
  as, like, very dangerous.
• 20:13
  So I do think there will be people who are very skilled at software development, who understand
• 20:17
  architecture, who understand design, who are gonna look at what people ship and say, we're
• 20:21
  not committing this.
• 20:22
  Or this is pretty close.
• 20:23
  This needs some massaging and fixing and and it's gonna be a little bit more craftsman to
• 20:28
  make sure that, like, the body stays coherent.
• 20:30
  They're gonna tinker with these things as they come in.
• 20:32
  But many more people are gonna be contributing to software products, and it's just gonna
• 20:36
  become another skill you have to have just like Excel and PowerPoint.
• 20:40
  The one thing that I was noodling on, which was this tractor example, which I think he was
• 20:44
  using as a way to calm people's fears that things are moving too quickly and and in saying
• 20:48
  that it took many, many years before tractors were everywhere and people had really started
• 20:52
  relocating from farming to the back house and in in the factories themselves.
• 20:56
  I think that one works for the tractor example back then because it was the high barriers to
• 21:01
  entry to get a tractor is expensive.
• 21:03
  It was difficult to get ahold of them.
• 21:05
  You really couldn't start the tractor unless you knew how to work it.
• 21:08
  This feels different because there are low to no barriers to entry to start getting smart on
• 21:14
  code, and you can just go running with no oversight in one direction.
• 21:18
  So that's where I feel like I think the dissemination and, like, training of
• 21:21
  it is it's gonna happen a lot quicker, obviously.
• 21:23
  I think, like, half a year ago, I don't think most people didn't know what Claude was outside
• 21:26
  of the tech bubble.
• 21:27
  Now they do, which shows you that they've done a great job in terms of distribution.
• 21:31
  I do think that what you said, how it goes through the funnel all the way to production,
• 21:34
  that's gonna be what's more similar to the tractor analogy than just the dissemination of
• 21:38
  who has access to the tractor or to plot code.
• 21:41
  Also, for seventy years, you could be like, I'm just gonna ride this out.
• 21:44
  That's three generations.
• 21:45
  You could have been a horse farm where you're like, I see the tractor coming.
• 21:48
  I don't want change.
• 21:49
  My child or the person who takes over my farm, I'm a let them deal with the tractor.
• 21:52
  I'm gonna keep farming the way.
• 21:54
  And I think this is why people rightly have more anxiety over this.
• 21:58
  You know, IBM has been talking about the paperless office for fifty years.
• 22:02
  We're only kinda now just there.
• 22:04
  But this idea of a paperless office has been around for a long time.
• 22:06
  And ironically, the introduction of computers is for some people let let to more paper
• 22:10
  because people start printing stuff.
• 22:12
  So instead of having one copy, now you have, like, 10 copies or you print the same thing.
• 22:16
  So I think previous tech waves, you could see it coming, and you're like, I might just get
• 22:21
  out the door before I have to deal with this.
• 22:24
  This is not that.
• 22:25
  This is here right now.
• 22:27
  It's funny.
• 22:27
  The presentation I made for our team, I basically did a Cloud Code session and created
• 22:31
  a markdown file with all of the outputs I wanted from my agent development work.
• 22:36
  I gave that markdown file to Cloud Design, and that was the presentation.
• 22:39
  I didn't touch PowerPoint.
• 22:41
  It was like Claude code to Claude Design, and then some tweaking and polish.
• 22:44
  Is it a presentation that would ship to our LPs?
• 22:46
  No.
• 22:46
  Is it good enough for an internal discussion?
• 22:48
  Absolutely.
• 22:49
  I think the reason people are scared is, like, if you told them you have seventy years to
• 22:52
  learn this stuff, he would be like, okay.
• 22:54
  I have something I have some anxiety.
• 22:55
  But have anxiety for my children and grandchildren.
• 22:57
  It'd be fine.
• 22:58
  I'm gonna, like, retire without having had to learn this.
• 23:00
  That's not what's happening here.
• 23:02
  That's the scary part.
• 23:02
  I think it's also the people I mean, mostly millennials, I think, feel like we grew up in a
• 23:07
  time that was able to grasp the tech wave and figured all of that out.
• 23:11
  And then right when you're hitting middle management or management your own, you're
• 23:14
  having to relearn everything.
• 23:15
  And then bestow that knowledge onto a younger generation who doesn't have any of context.
• 23:19
  And I think that's where I feel the most anxiety is within that generation specifically.
• 23:24
  For sure.
• 23:24
  Well, we are all out of time.
• 23:25
  Thank you, Charles, for stopping in.
• 23:27
  Thank you to our listeners for jumping in as well.
• 23:29
  Please like and subscribe, and we'll talk with you all soon.
• 23:32
  Thank you.
• 23:33
  Bye.