WEBVTT
00:00:07.839 --> 00:00:10.400
Welcome to Human X Intelligent.
00:00:10.640 --> 00:00:15.679
Welcome to Human X Intelligent, a podcast about what it means to be human in the age of AI.
00:00:15.759 --> 00:00:24.559
From all of us living through it, I am Madalena Kosta, and every Monday I bring you a conversation about how AI is changing our lives, our work, and the way we relate to each other.
00:00:24.719 --> 00:00:27.440
AI is changing how quickly we can build software.
00:00:27.600 --> 00:00:33.359
But building something faster does not mean that it's answering the question of whether anyone needs it.
00:00:33.520 --> 00:00:41.600
So today we are exploring what happens to product work when writing code becomes easier and why deciding what to build still matters.
00:00:41.840 --> 00:00:46.560
My guest is Rich Miranoff, a product management leader, advisor, and author.
00:00:46.799 --> 00:00:48.479
I will make sure to share all the links below.
00:00:48.640 --> 00:00:58.320
But in this conversation, we talk about what makes a product worth building, the differences between faster engineering and business results, and the human judgment behind product decisions.
00:00:58.560 --> 00:00:59.600
So let's get into it.
00:00:59.840 --> 00:01:00.399
Hello, Rich.
00:01:00.640 --> 00:01:02.880
Welcome to the Human Access Intelligence Show.
00:01:03.119 --> 00:01:04.319
Thank you so much for being here.
00:01:04.560 --> 00:01:11.680
I want to start with somewhere specific, actually, because you've spent this year putting your thinking out in public.
00:01:12.079 --> 00:01:13.599
I met you during product ties.
00:01:14.079 --> 00:01:16.000
Not only that, but also on your own blog.
00:01:16.239 --> 00:01:19.599
So let's start with a number you published yourself.
00:01:19.920 --> 00:01:21.840
Most new products already failed.
00:01:22.319 --> 00:01:23.519
Something like six out of ten.
00:01:23.840 --> 00:01:31.599
And if AI lets uh lets a team build 10 times more products in the same time, the naive math says 10 times more winners.
00:01:31.760 --> 00:01:36.239
But you did the actual math and got a 96% failure rate instead.
00:01:36.400 --> 00:01:39.519
Walk me through how building faster makes things worse.
00:01:39.760 --> 00:01:40.000
Sure.
00:01:40.079 --> 00:01:45.760
And it doesn't make things worse from an engineering point of view, it just makes things worse from an economic point of view.
00:01:46.640 --> 00:01:49.760
So let's imagine we have an existing market, right?
00:01:49.920 --> 00:01:56.319
We're selling software to manage beauty salons or personal gyms or health spas, right?
00:01:56.480 --> 00:02:00.319
That market doesn't get any bigger because we can ship faster, right?
00:02:00.400 --> 00:02:11.919
In fact, if if we and our competitors launch 10 times as many products into an existing market, there can't be 10 times as much money in there to buy those 10 times as many products, right?
00:02:12.080 --> 00:02:15.120
In fact, with more products, prices are going to come down, right?
00:02:15.360 --> 00:02:18.960
So instead of asking how many do we win, I ask how many do we lose.
00:02:19.120 --> 00:02:26.080
So if if today we're finding that four of our 10 products succeed, and by succeed, I don't mean that we ship them.
00:02:26.240 --> 00:02:31.199
I mean that we found a market, made money, found customers, and generated revenue, right?
00:02:31.439 --> 00:02:44.719
So if four of our 10 products found market fit and found revenue and found success, and we ship 10 times as many products, we're shipping 100, we haven't created any conditions that make more than four of those successful, right?
00:02:44.960 --> 00:02:55.520
We still have the same health spas or personal gyms, whatever, looking for software to manage their day, but we have 10 times as many products fighting for those same four wins, right?
00:02:55.680 --> 00:03:00.719
So we move from four out of 10 wins to four out of a hundred wins.
00:03:00.879 --> 00:03:05.280
And of course, everybody's gonna say, well, I'll be there first and I'll take share away from everybody else.
00:03:05.759 --> 00:03:07.759
Pretty short-term play, right?
00:03:07.919 --> 00:03:12.719
There won't be more winners in that market unless we can create entirely new markets.
00:03:13.360 --> 00:03:24.960
Which, by the way, is hard because at least I've been building software for 40 years and we're mostly going into the same markets and improving the things we're selling and displacing existing competitors, right?
00:03:25.280 --> 00:03:32.240
The the world software budget doesn't get 10 times as big because we can ship 10 times as fast, right?
00:03:32.400 --> 00:03:41.680
The distinction I try to make here that nobody wants to listen to is the difference between can we generate code faster and can we generate money faster?
00:03:42.000 --> 00:03:44.639
Because products are more than just their code.
00:03:44.800 --> 00:03:50.240
They're the pricing and the positioning and the channels and the messaging and getting folks to try them, right?
00:03:50.400 --> 00:03:54.319
There's a lot more in the box than just can we generate more code.
00:03:54.639 --> 00:03:54.960
Yeah.
00:03:55.199 --> 00:04:06.479
And I guess something that came to mind, that's why I also ask about the number, is that I'm sure every time you're in the room and say something in the round of that number, every time the room just stays in the city.
00:04:06.719 --> 00:04:07.439
It gets very quiet.
00:04:07.680 --> 00:04:09.599
Or people start to yell, right?
00:04:10.560 --> 00:04:11.039
Right.
00:04:11.199 --> 00:04:24.160
Uh and and and for me, it's again, it's the distinction between an between being a software engineer and getting really great tools that let me build, well, maybe generate code times ten times as fast.
00:04:24.319 --> 00:04:30.800
I would say we're delivering workable, you know, useful products twice as fast or three times as fast.
00:04:31.040 --> 00:04:36.720
The whole rest of the company isn't moving faster, and even more than that, our user base isn't growing that fast.
00:04:36.879 --> 00:04:41.519
So so somehow we have to think not just about code generation.
00:04:41.680 --> 00:04:44.319
We got about think about the exchange of money on the other end.
00:04:44.720 --> 00:05:02.720
If we focus, or let me come down out of the theory for a second, because it it is in easy to nod along the these are the bottlenecks or the button that moves right as an abstract idea and lose the thread of what that actually looks like inside a team, a real team.
00:05:03.279 --> 00:05:09.279
So my question to you, yeah, let's say like this let's paint the current picture of what is happening.
00:05:09.680 --> 00:05:18.480
What are the products and engineering teams actually doing different right now, this year, even, because of artificial intelligence?
00:05:19.360 --> 00:05:23.279
So I have to tell you, I'm not building products lately, right?
00:05:23.439 --> 00:05:27.839
So I am not on the front lines of people who are living through this and figuring it out.
00:05:28.000 --> 00:05:29.759
I'm getting it secondhand, right?
00:05:30.000 --> 00:05:38.800
And again, I'm hearing from people that they are dramatically accelerating the step in the process where we create code.
00:05:38.959 --> 00:05:44.800
And people argue and their tools, and is it three times faster or 30% faster or 10 times faster?
00:05:44.959 --> 00:05:45.279
I don't know.
00:05:45.360 --> 00:05:48.879
And that varies an awful lot with context and situation.
00:05:48.959 --> 00:05:50.639
And do you have existing customers?
00:05:50.800 --> 00:05:54.079
And will people die if your application goes offline?
00:05:54.240 --> 00:05:54.480
Right.
00:05:54.639 --> 00:06:06.079
So, you know, if you're building uh an online dating app, you probably are a lot less concerned with some of the quality and testability than if you're building software for spaceships and you can't bring them back, right?
00:06:06.240 --> 00:06:11.439
Or medical devices where if your CPAP machine stops working, people die, right?
00:06:11.680 --> 00:06:13.759
So there's very different contexts here.
00:06:13.920 --> 00:06:24.240
And again, almost all the write-up is about the the middle of the engineering process where we've already decided what to build and we haven't yet shipped it to people who need it to work, right?
00:06:24.399 --> 00:06:27.920
If we expand that to what I think of as the maker team, right?
00:06:28.160 --> 00:06:36.560
Design, product management, support, uh, the other parts of the technical organization, you know, different people are getting different amounts of acceleration here.
00:06:36.800 --> 00:06:43.040
If I think about designers for a minute, I believe that good designers can generate 40 prototypes in an hour.
00:06:44.079 --> 00:06:48.560
But I can't figure out who's going to look at 40 prototypes in an hour, right?
00:06:48.800 --> 00:06:52.160
There aren't more users who are willing to spend more time doing this, right?
00:06:52.319 --> 00:07:04.319
I believe that product plus engineering plus design could add 10 small features a week to an existing product, but I don't know any users who are willing to try 10 new features a week because they've got a job to do, right?
00:07:04.560 --> 00:07:13.519
So, you know, the bottlenecks move from the the raw throughput of raw engineering to everything downstream and maybe everything upstream.
00:07:13.680 --> 00:07:18.800
Upstream for for me, the most important one is we ask, is this a good thing to build?
00:07:19.040 --> 00:07:20.000
Is it good for the world?
00:07:20.160 --> 00:07:21.120
Is it good for our users?
00:07:21.279 --> 00:07:23.439
Is it good for our company economics?
00:07:23.600 --> 00:07:24.399
Can we sell it?
00:07:24.560 --> 00:07:24.800
Right.
00:07:24.959 --> 00:07:28.079
I've many years I've talked about product waste, right?
00:07:28.319 --> 00:07:30.720
Engineering waste is when we don't do a good job building it.
00:07:30.879 --> 00:07:35.279
Product waste is when we build something the world isn't going to use or buy.
00:07:35.600 --> 00:07:43.600
And and if we're looking downstream to the bottlenecks of users and buyers and marketing and sales, we moved over to that side of the problem, right?
00:07:43.759 --> 00:07:48.560
I think product managers are starting to spend a lot more time on the front end.
00:07:49.279 --> 00:07:49.920
What should we build?
00:07:50.000 --> 00:07:50.639
Should we build it?
00:07:50.800 --> 00:07:56.720
And on the back end, how are we gonna sell it, package it, find partners, turn it into money, position it?
00:07:57.839 --> 00:07:58.079
Yeah.
00:07:58.319 --> 00:08:01.439
Because engineering's gonna do a better job of building it faster.
00:08:01.680 --> 00:08:03.199
Let me take another slice of this.
00:08:03.360 --> 00:08:12.399
If the part of the problem we're worried about is can we fix obvious bugs in the software faster and ship patches to known problems faster?
00:08:12.560 --> 00:08:14.000
I'll sign up for 10X there.
00:08:14.160 --> 00:08:14.399
Okay.
00:08:14.560 --> 00:08:17.920
We look through the tickets, we find all the ones where that are obvious to fix.
00:08:18.079 --> 00:08:27.839
We have our engineers turn them over to their, you know, their agents, and we don't worry about shipping bugs, about f shipping fixes to bugs, because we know they're bugs, right?
00:08:28.000 --> 00:08:31.279
Not so much with new features and not at all with new products, right?
00:08:31.439 --> 00:08:38.480
So I I'm trying to pull the product folks out of the engineering whirlwind here so we can ask the product questions.
00:08:38.960 --> 00:08:49.679
Even with what you just said about the clients versus speed and how it can actually do the opposite of what they were looking for, yes, how r right now the goal is all acceleration, right?
00:08:49.919 --> 00:08:52.559
For most of the team, specifically to startups.
00:08:53.039 --> 00:09:03.759
I was discussing the other day with a CTO, and he was very worried about the design part and what was my opinion on how designers would enter this area.
00:09:04.240 --> 00:09:17.039
My question to you is based on your experience throughout all these years, how would you say that each different individual in a product team can continue showcasing their value?
00:09:18.000 --> 00:09:18.240
Sure.
00:09:18.320 --> 00:09:21.360
And I love that you use the word value, really important.
00:09:21.519 --> 00:09:24.639
So, because it's not about throughput anymore, right?
00:09:24.799 --> 00:09:26.480
So let's think about our designers.
00:09:26.559 --> 00:09:42.399
And let's think about a senior designer who's been down the road and gotten their nose bloodied a few times and shipped them things that really didn't work, and so has taste and judgment and insight and continues to do a lot of real end user validation of what's working and not working, right?
00:09:42.480 --> 00:09:46.399
We're not going to turn that item over to the scripts and the and the agents, right?
00:09:46.559 --> 00:09:55.600
I think that designer no longer has to do all these pixel perfect color selection layout stuff that's gone, right?
00:09:55.919 --> 00:10:04.720
But if you if you had your agent spin up four or six versions, someone has to look at that and say, I know which ones of these are good, right?
00:10:04.960 --> 00:10:16.080
And I'm gonna use use a careful word here, plausible, which means they're believable, especially to somebody who doesn't know any different, versus good, which means it's actually gonna meet the need, right?
00:10:16.240 --> 00:10:30.960
So I think lots of CEOs are busy playing with prototyping tools and they're creating dozens of prototypes, and they're confusing those with good design because they don't actually know the difference between good design, mediocre design, and terrible design, right?
00:10:31.200 --> 00:10:34.000
And so I see the designers levering up.
00:10:34.159 --> 00:10:50.000
So they're gonna waste a lot less time with drawings and layouts and maybe even workflows and have the agents figure out where folks are getting stuck in the workflows, but they're gonna bring taste and judgment and insight and human factors to the design problem as they always have, right?
00:10:50.080 --> 00:10:53.679
On the engineering side, we're gonna spend a lot less time generating code.
00:10:53.919 --> 00:11:01.120
I'm old enough to remember when compilers came along and I stopped writing ones and zeros and I could start writing code, right?
00:11:01.360 --> 00:11:06.159
It made code writing faster, but we all levered up and now everybody could write simple programs.
00:11:06.320 --> 00:11:12.879
And you didn't differentiate your product by being able to put a SQL statement in there, because everybody could do that, right?
00:11:13.039 --> 00:11:19.919
You had to think of something bigger, and so now we get engineers who worry about scalability and security and meeting the need, right?
00:11:20.080 --> 00:11:29.919
So I think we may have smaller teams, but I think we have more interesting work because we're gonna give the bottom 60% off to our agents, but not the top 40%.
00:11:30.799 --> 00:11:35.600
Where are you at when it comes to judgment between artificial intelligence?
00:11:35.759 --> 00:11:36.480
Does it have judgment?
00:11:36.720 --> 00:11:37.679
Does it not have judgment?
00:11:38.000 --> 00:11:47.279
Should it have judgment versus human judgment when it comes to creating not only products, but everything around what a product is for a company?
00:11:47.759 --> 00:11:50.000
Again, I think that's a hard question, and there's layers.
00:11:50.480 --> 00:11:51.120
Okay, right.
00:11:51.440 --> 00:11:57.600
So if we ask the question of generic technical support documents, okay, first of all, nobody ever read them, right?
00:11:57.679 --> 00:11:59.600
And second of all, nobody's gonna read them in the future.
00:11:59.759 --> 00:12:12.799
So what we really want is we want our AI tools to work through all the software and figure out how it actually works and write all the steps and interpret all the questions from our users that say, well, how do I, right?
00:12:12.960 --> 00:12:15.279
Because honestly, it's not very interesting, right?
00:12:15.519 --> 00:12:18.879
When the question is, what should the application do?
00:12:19.120 --> 00:12:21.039
Not what does the application do?
00:12:21.279 --> 00:12:28.399
So I would use, I do use a bunch of my AI tools to figure out what's existing in the world.
00:12:29.120 --> 00:12:29.279
Okay.
00:12:29.519 --> 00:12:35.919
And actually to do a lot of help getting me using software that has terrible documentation, because then I can ask how it's supposed to be how it works, right?
00:12:36.159 --> 00:12:46.159
But let's think of a world where all the LLMs have all of the existing data about all the existing markets, and we're trying to launch a new product that's gonna open up a new market.
00:12:46.320 --> 00:12:46.639
Okay.
00:12:46.799 --> 00:12:49.360
We're gonna ask all the LLMs what the new market should be.
00:12:49.440 --> 00:12:57.679
They're all gonna give us the same prediction as they gave to all of our competitors, and we're all gonna rush into the same market, build the same product, and they're all gonna fail, right?
00:12:57.919 --> 00:13:02.399
So I think we have to ask the more interesting questions of ourselves, right?
00:13:02.559 --> 00:13:05.440
What are our users actually trying to accomplish?
00:13:05.600 --> 00:13:06.799
Not what we told them to.
00:13:06.960 --> 00:13:08.799
How do they really measure success?
00:13:08.960 --> 00:13:11.039
How does the buying process really work?
00:13:11.360 --> 00:13:28.480
Of all the messages that our LLM gave us, which ones are actually gonna resonate with real users, not the synthetic users that we used with all of our biases to give us the answers we wanted quickly, so we could move on and skip all the hard steps of knowing what's happening in the world, right?
00:13:28.960 --> 00:13:32.480
You've written about teams that want to automate the whole pipeline.
00:13:32.720 --> 00:13:32.960
Yes.
00:13:33.360 --> 00:13:44.000
This is perfect when I'll straight for, for example, a customer ticket to life code, and about teams outsourcing their actually user interviews, like you were saying, to AI bots and quick surveys.
00:13:44.159 --> 00:13:50.480
So where do you see people quietly handling over their thinking and they should be doing themselves?
00:13:50.960 --> 00:13:51.919
Again, pieces, right?
00:13:52.080 --> 00:13:57.840
So when I think of the discovery process, right, there's an awful lot of it that is no value add, right?
00:13:58.000 --> 00:14:07.919
Looking into our user base and figuring out who the specific users are for features or functions or products, sending them requests to book time with us, getting on their calendars, right?
00:14:08.159 --> 00:14:09.039
A lot of stuff now.
00:14:09.200 --> 00:14:15.360
I should myself write the email that's gonna go out to those thousand people because if it's wrong, nobody's gonna respond, right?
00:14:15.600 --> 00:14:22.399
And I think the moment we have to have humans in the room is the moment when we're actually asking the real questions of the real users.
00:14:22.559 --> 00:14:28.159
So show me what worked, tell me what you didn't work, why are you unhappy, right?
00:14:28.320 --> 00:14:34.480
And I'm always saying that we should have a product manager and a designer and an engineer on the call together, right?
00:14:34.639 --> 00:14:37.919
Endlessly, because you don't get to do that interview a second time.
00:14:38.000 --> 00:14:44.159
And so when the user says something that's really very workflow-oriented, and as a product manager, I missed it.
00:14:44.240 --> 00:14:49.759
And my designer, if I'm running the interview, my designer sends me the quick text that says, ask this question, right?
00:14:50.000 --> 00:14:54.639
We have a moment to diverge from the script to ask the interesting stuff.
00:14:54.799 --> 00:14:58.960
Somebody says to me, Well, we never run those reports on Thursdays.
00:14:59.120 --> 00:14:59.440
Wow.
00:14:59.679 --> 00:15:00.720
Never would have imagined.
00:15:00.799 --> 00:15:02.399
Okay, tell me more, right?
00:15:02.639 --> 00:15:08.639
When we script up the interview for our agents or our bots, it asks all the questions we planned.
00:15:08.799 --> 00:15:10.960
It may or may not good get get good answers.
00:15:11.039 --> 00:15:13.440
So they may be the completely wrong questions, right?
00:15:13.519 --> 00:15:18.879
And it doesn't, it doesn't react in the moment to something interesting, right?
00:15:19.200 --> 00:15:28.559
The reason I want my engineer or one of my engineers on the line is somebody says something that's all about data cleanliness and scalability, and I missed it because I'm the stupid product manager, right?
00:15:28.639 --> 00:15:35.039
And my engineer, because we've been trained, doesn't blurt in but writes it on a note and says, no, no, go dig deeper, right?
00:15:35.120 --> 00:15:35.840
Ask the question, right?
00:15:35.919 --> 00:15:37.600
Or maybe we switch chairs, right?
00:15:37.919 --> 00:15:41.759
Because discovery is about learning something we don't know.
00:15:42.000 --> 00:15:51.440
This would seem obvious, but everybody wants to everybody wants to automate it, and so we're gonna ask all the standard questions and get all the standard answers and learn all the standard things, which is nothing, right?
00:15:51.600 --> 00:15:57.120
Now, do I want my my uh AI tools to run through those transcripts and summarize them?
00:15:57.279 --> 00:15:57.840
Absolutely.
00:15:58.000 --> 00:15:59.039
Score them, right?
00:15:59.200 --> 00:16:00.639
Figure out which ones are interesting.
00:16:00.799 --> 00:16:03.440
Do I give up my judgment about what's interesting?
00:16:03.600 --> 00:16:05.039
I do not, right?
00:16:05.200 --> 00:16:10.799
If I'm not walking down the street in the mind of my user, we're generating slot.
00:16:11.519 --> 00:16:20.960
Before we go further, if you're enjoying this conversation, subscribe to HumanX Intelligent wherever you're watching or listening, so you can join me for a new episode every Monday.
00:16:21.120 --> 00:16:27.360
And I would love to know if your team could build anything faster, how would you decide what was actually worth building?
00:16:27.519 --> 00:16:29.919
Tell me in the comments or send me a message directly.
00:16:30.080 --> 00:16:32.639
I'd love to hear how this shows up in your own life.
00:16:32.799 --> 00:16:34.159
Now, let's get back to reach.
00:16:34.480 --> 00:16:52.799
Something that I found very interesting when using these tools when I go to interview clients is that uh our users, is that the way it analyzes it's very mathematical-based or very constructed way while I'm thinking during the call of something that oh, this is interesting.
00:16:52.960 --> 00:17:05.440
I was not expecting that this person said this and this way, and I write that, but when I see what the the AI summarized, it's never there, or at least not with the same strain that the user gave.
00:17:05.920 --> 00:17:07.759
At least before the current AI boom.
00:17:20.559 --> 00:17:31.599
To go to these interviews or to these conversations with clients, and they don't believe that uh anyone from product should go because it's a waste of time and they should just ship it, ship it, sheep.
00:17:31.839 --> 00:17:38.640
It's a really hard argument to win because if you've not seen the failures, if you haven't missed out on these, you don't believe they exist.
00:17:38.720 --> 00:17:41.839
It's uh for anybody who remembers the Dunning Krueger effect, right?
00:17:42.000 --> 00:17:45.279
The less I know about your job, the easier your job is, right?
00:17:45.599 --> 00:17:45.839
Yeah.
00:17:46.079 --> 00:17:49.279
So we've talked about uh what goes wrong when people move fast.
00:17:49.759 --> 00:17:56.559
So the right question is if we got a hundred or a hundred thousand tickets telling us to make this change, yeah, what was the problem?
00:17:57.920 --> 00:17:58.960
Not what was the solution.
00:17:59.119 --> 00:18:03.119
What was the underlying problem that caused all these people to raise this issue?
00:18:03.279 --> 00:18:07.519
And is the proposal that some of them are making the right solution?
00:18:08.160 --> 00:18:08.480
Right?
00:18:08.720 --> 00:18:15.119
Judgment, thought, get our engineering and design and product folks together, and we say, well, we're getting 50 complaints a day about this.
00:18:15.279 --> 00:18:16.720
What's really broken?
00:18:17.039 --> 00:18:18.960
Maybe not what they're seeing.
00:18:19.200 --> 00:18:24.559
An honest answer says, we understand the problem that you're having, we sympathize.
00:18:24.799 --> 00:18:30.000
Here are some things you might consider that are not the solution that you gave us, right?
00:18:30.720 --> 00:18:34.079
And and I think that's as humble and as polite as you can be.
00:18:34.160 --> 00:18:43.440
Now, of course, there's other issues here, but I want to zoom out now to the shape to the shape of the of the job itself, of product management itself.
00:18:43.599 --> 00:18:51.039
Because you've actually put the name on how it's changing, and I love reading what you write, just parenthesis.
00:18:51.279 --> 00:18:56.559
But you've described the shape of a product manager's job uh changing in what you call a barbell.
00:18:56.720 --> 00:18:58.720
And I really want people to learn more about it.
00:18:58.880 --> 00:18:59.440
Walk me through that.
00:18:59.680 --> 00:19:04.240
So and I put two pictures in the post, and it was on a sort of weightlifting theme, right?
00:19:04.400 --> 00:19:10.160
And so so if we think of the product management work, maybe not today, a year ago, three years ago as a circle, right?
00:19:10.240 --> 00:19:14.000
And we superimpose it on the picture of like a five kilogram weight, right?
00:19:14.160 --> 00:19:17.920
The middle 60% has traditionally been with engineering, right?
00:19:18.079 --> 00:19:26.480
Roadmaps and specs and standing over them and protecting them and keeping the CEO from wandering through and helping and scrum or Kanban, right?
00:19:27.039 --> 00:19:30.400
All the sort of software development cycle stuff, right?
00:19:30.559 --> 00:19:33.359
Now, do I think that's really much smaller this year?
00:19:33.599 --> 00:19:35.519
Not so much, but let's pretend.
00:19:35.680 --> 00:19:39.519
Let's pretend that that gets from 60% of our job to 10%, right?
00:19:39.759 --> 00:19:44.000
Because engineering is getting a lot more done and we don't have to write so many specs, right?
00:19:44.160 --> 00:19:46.160
We still have to understand what we're building, right?
00:19:46.319 --> 00:19:49.440
And so that takes the middle, a lot of the middle out of this circle.
00:19:49.519 --> 00:19:51.039
And what we're left with is the ends.
00:19:51.119 --> 00:19:53.680
So the picture of the barbell with the weights on the ends.
00:19:53.839 --> 00:19:58.480
And uh at the front, which we always called the fuzzy front end, it's the hard questions.
00:19:58.720 --> 00:19:59.680
Who's going to use this?
00:19:59.839 --> 00:20:00.799
How is it positioned?
00:20:00.880 --> 00:20:01.680
Will it make money?
00:20:01.839 --> 00:20:03.279
What problem are we solving?
00:20:03.440 --> 00:20:04.640
Where's the competition?
00:20:04.799 --> 00:20:07.119
Is this a good thing for us and the planet?
00:20:07.279 --> 00:20:10.400
Does our CEO end up in jail if we ship this?
00:20:11.200 --> 00:20:11.440
Right?
00:20:11.680 --> 00:20:14.640
All of the should we, is this a good idea?
00:20:14.960 --> 00:20:16.480
Do we have validation?
00:20:16.720 --> 00:20:19.119
Do people seem excited about buying this?
00:20:19.279 --> 00:20:19.599
Right?
00:20:19.759 --> 00:20:23.680
Because if we build the wrong thing, it doesn't matter how fast we build it, right?
00:20:23.839 --> 00:20:39.440
And on the back end, back to our other bottleneck, it turns out that if we just have a bunch of beautiful code in a box, nobody buys it because we need channels and messaging and a sales team and a marketing team and maybe PR and partners.
00:20:39.599 --> 00:20:47.759
There's all this stuff that turns code into money, and engineers don't see it because they're working on the code, at least the product.
00:20:48.000 --> 00:20:52.079
And so we as product folks are going to have to spend an awful lot more time.
00:20:52.559 --> 00:20:56.319
Because remember, everybody's shipping 10 times as many products now or 50 times.
00:20:56.480 --> 00:20:59.839
So how do we differentiate it and what's our key selling proposition?
00:20:59.920 --> 00:21:05.200
And right, and almost all that, by the way, comes from the interviews with the end users, okay?
00:21:05.440 --> 00:21:13.920
Because they tell us what's important and they tell us what messages matter, and they tell us how they manage, they measure success, right?
00:21:14.079 --> 00:21:24.480
And if we're not channeling our end customers, then it's positioned wrong, it's priced wrong, it's packaged wrong, it's through the wrong channels, the messaging is terrible, right?
00:21:24.720 --> 00:21:26.880
And we built something beautiful.
00:21:27.119 --> 00:21:32.880
Gosh, there's nothing sadder than a beautiful product that doesn't earn its keep and gets shelved because there's no money.
00:21:33.200 --> 00:21:36.640
We get the water bottles and the little tote bags, right?
00:21:36.720 --> 00:21:39.599
But it turns out that most of the company doesn't care about those.
00:21:39.680 --> 00:21:41.119
They care about where their money comes in.
00:21:41.359 --> 00:21:41.680
Yeah.
00:21:41.920 --> 00:21:42.880
And for sure.
00:21:43.039 --> 00:21:51.680
And if the job is generally reshaping, like you're saying, more weight on both ends and less in the middle, right?
00:21:51.920 --> 00:21:57.359
I like to say that a few years ago I was so sure that what I was saying was correct.
00:21:57.440 --> 00:21:59.440
And for the time it was correct.
00:22:00.079 --> 00:22:04.319
Because we cannot think of the knowledge that we are not have we don't have right now.
00:22:04.480 --> 00:22:06.240
We have to think of the knowledge that we have now.
00:22:06.480 --> 00:22:10.640
That doesn't make it wrong because it is right for the time that we are in.
00:22:10.799 --> 00:22:13.039
I would like to ask in terms of guardrails, right?
00:22:13.119 --> 00:22:21.440
Because if a product wanted to move fast with artificial intelligence without losing themselves in the process, what are the actual rules you put in place for this?
00:22:21.839 --> 00:22:23.759
Rules is a really strong word.
00:22:23.920 --> 00:22:26.240
I don't I'm not sure I believe in rules, right?
00:22:26.400 --> 00:22:30.559
In fact, uh if I go way, way back in time to the early agile days, right?
00:22:30.799 --> 00:22:34.160
Turns out agile wasn't a method, it was a way of thinking, right?
00:22:34.400 --> 00:22:37.920
And and and it was summed up to try something.
00:22:38.640 --> 00:22:40.000
If it works, do more of it.
00:22:40.160 --> 00:22:42.559
If it doesn't work, try something else, right?
00:22:42.720 --> 00:22:47.359
So, you know, the idea that we know what the rules are, I think is reaching it.
00:22:47.440 --> 00:22:48.960
But some suggestions, right?
00:22:49.119 --> 00:22:56.559
So one of them is, you know, one of them is is to say we want to have a sort of proportional investment in discovery, right?
00:22:56.720 --> 00:23:07.359
There's some little things which we know are right, and we're just gonna go ahead and do them and put them in the hopper and ship them and not worry about it because they're obviously right and we could back them out if we needed to, right?
00:23:07.519 --> 00:23:13.599
You know, the the bigger things we want to be more thoughtful about, right?
00:23:14.240 --> 00:23:24.799
If there's risk involved, if we can't undo something and it's big, then we want to spend more time up front thinking, doing discovery, modeling it out, uh, you know, pre-mortems, right?
00:23:24.960 --> 00:23:25.440
What else?
00:23:25.599 --> 00:23:30.799
I think that we shouldn't ever decide that a feature works until a human's tried it.
00:23:31.039 --> 00:23:35.279
This would seem obvious, but everybody's automating their testing, right?
00:23:35.440 --> 00:23:39.119
And I'm worried that we're gonna ship a lot of features that actually don't work, right?
00:23:39.279 --> 00:23:47.119
So anyway, I think humans need to be in on the important part of discovery, and I think humans need to be responsible for the outcome, right?
00:23:47.279 --> 00:23:52.319
So if we ship something and it fails, we're not allowed to blame it on our agents, right?
00:23:52.480 --> 00:23:59.359
And if we ship something and it's wally successful in the market, we should be able to stand up and say we were the coordinating thinkers there, right?
00:23:59.519 --> 00:23:59.680
Yes.
00:23:59.920 --> 00:24:16.000
I'm thrilled by a lot of sort of product ops and DevOps stuff where we're automating the creation of tests, we're automating the mining of data, we're automating the, you know, a lot of this stuff was hard but not higher value, right?
00:24:16.240 --> 00:24:20.960
And so, you know, I see us stepping up to do the more interesting work.
00:24:21.200 --> 00:24:22.240
Do I know where this is going?
00:24:22.400 --> 00:24:22.960
I really don't.
00:24:23.119 --> 00:24:36.400
I'm pretty skeptical about whether there's any new ideas being found in the the averaging and and median data that comes back from LLMs because they're trying to find the thing that is most likely not the thing that's true.
00:24:36.720 --> 00:24:38.640
They don't have that capability of the internet.
00:24:38.880 --> 00:24:43.279
No, but but there's an awful lot of information out in the world which I have trouble finding, right?
00:24:43.440 --> 00:24:47.440
I'd much rather have it uh that's working much better for me than Google search ever did.
00:24:48.319 --> 00:24:55.119
You know, I've been enjoying right now the the new addition to Google, like the Gemini being inside of the research.
00:24:55.279 --> 00:24:58.240
That has been helping me a lot to to focus.
00:24:58.400 --> 00:24:59.440
So I agree with you.
00:24:59.519 --> 00:25:10.480
I think since this tool started to be more mainstream, it started to help to make the process of discovering a competitor matrix, competitor now, wherever it is, more quickly and more easily.
00:25:11.680 --> 00:25:14.880
Let's zoom out or zoom all the way out now.
00:25:15.039 --> 00:25:24.319
Uh, you've done been doing this for 40 years, which means you already lived through more than one moment where everyone was certain that the job was about to disappear.
00:25:24.799 --> 00:25:26.640
Dangerous industrial time, forever.
00:25:27.440 --> 00:25:30.640
I want you to take me to 2031.
00:25:31.039 --> 00:25:31.599
No, no, no.
00:25:31.680 --> 00:25:36.319
I want you to take me back to the future based on what you experienced in the back.
00:25:36.559 --> 00:25:41.519
Like let's say only five years out, because now they're saying that every month there's something new.
00:25:41.759 --> 00:25:46.319
But from what you experienced in the past, let me fast forward again to maybe 2008.
00:25:46.480 --> 00:25:55.519
Okay, launched the iPhone, launched Android, and for about a year and a half, every board of directors asked every company whether they had a mobile app and whether it was in the app store.
00:25:56.000 --> 00:25:57.359
Nobody knew what they were supposed to do, right?
00:25:57.519 --> 00:26:01.039
And there were all these tools that came out that let you build mobile apps, right?
00:26:01.279 --> 00:26:09.039
And every product management job that was posted was for a mobile product manager or an app product manager, right?
00:26:09.279 --> 00:26:22.240
So when I when I look ahead to 2030, right, or 2031, I believe deeply that we're gonna have figured out what the AI tools are good for, what they're not good for, right?
00:26:22.400 --> 00:26:23.759
Everyone will be using them.
00:26:23.920 --> 00:26:28.240
I mean, I I'm like the last person on the planet who uses a laptop for anything instead of my phone.
00:26:28.480 --> 00:26:31.119
They will be commoditized, right?
00:26:31.279 --> 00:26:36.799
All of the AI tools vendors will mostly have been collapsed to down to a few survivors, right?
00:26:36.880 --> 00:26:38.799
The price point will be near zero.
00:26:38.960 --> 00:26:40.559
Tokens won't matter anymore.
00:26:40.720 --> 00:26:51.680
We will once again discover that product managers need to figure out not how to generate code faster or how to add AI, but what their users are trying to accomplish with their tools and their applications.
00:26:51.839 --> 00:26:58.079
I think we're gonna have more software engineers than we did before because we're gonna open up lots of new cool stuff to do.
00:26:58.319 --> 00:27:00.160
I hope that's true of designers too.
00:27:00.319 --> 00:27:07.599
And we're gonna get back to the fundamentals of well, what are we using our AI tools and all of our other tools and where do they go?
00:27:07.759 --> 00:27:08.799
Where do they fit?
00:27:08.960 --> 00:27:10.799
And we won't be talking about it anymore.
00:27:10.880 --> 00:27:14.640
I don't know anybody who asks me whether my app is gonna run on a phone.
00:27:14.799 --> 00:27:15.119
Yeah.
00:27:15.359 --> 00:27:17.200
I mean, what are you stupid, right?
00:27:17.759 --> 00:27:21.039
That's become an assumed thing for every application.
00:27:21.119 --> 00:27:23.440
It's got to run on a smartphone, right?
00:27:23.599 --> 00:27:28.400
Or or you didn't do your homework, but it's not a differentiator, it's not value add.
00:27:28.720 --> 00:27:31.039
I see product managers back to basics again.
00:27:31.200 --> 00:27:43.039
I see product managers on finance applications and product managers on dating applications and product managers on ecological applications, all of which use AI in the places that we know it adds value.
00:27:43.200 --> 00:27:45.519
And we don't talk about it anymore because it's not interesting.
00:27:45.680 --> 00:27:54.480
Everybody has the doomsday scenario where you know we we saw last week two of the big companies let their hacking tools out of the boxes and they attacked other companies.
00:27:54.640 --> 00:27:59.519
So, you know, maybe, you know, maybe this is Terminator and you know we're just looking at the smoke.
00:27:59.680 --> 00:28:04.880
But assuming that doesn't happen, I I think this is gonna look a lot more like the last few.
00:28:05.279 --> 00:28:05.519
Yeah.
00:28:05.680 --> 00:28:08.480
But let's focus for now on today.
00:28:08.559 --> 00:28:14.079
Okay and to g to give the people listening one thing that they can actually try this week.
00:28:14.400 --> 00:28:14.720
Okay.
00:28:15.200 --> 00:28:16.799
I'm thinking, right?
00:28:17.119 --> 00:28:18.799
Things that we could try this week.
00:28:18.960 --> 00:28:29.680
So one, I think I I put out a book called Money Stories Last Year, which is all about can you explain in less than 25 words why someone's gonna buy your product, right?
00:28:30.000 --> 00:28:45.839
And I think every product manager should sit down and and you don't have to get the book, but you know, you should be able to explain to the to the revenue side of your company whether your product makes money and how it makes money without ever talking about the tech, right?
00:28:46.160 --> 00:28:54.480
Because it's gonna turn out in the six more months when the CEOs are tired of overspending on tokens, this is the question they're gonna ask, and we should be prepared, right?
00:28:54.720 --> 00:29:00.160
The other thing I think people should do next week that isn't anywhere on my list, but why not, right?
00:29:00.319 --> 00:29:20.960
You should find somebody in another department that you don't know much about, and you should take that person out to lunch, and you should learn what they do and interview them a little bit and find out what they care about and how they're measured and how success is for them, and get a little humility about how everybody else in the company does something that we don't do that we think is easy and turns out not to be easy, especially in finance, right?
00:29:21.039 --> 00:29:27.599
Because the money stories are gonna require that we know something about the finance of the company, and the finance folks are really smart and don't care about product.
00:29:27.759 --> 00:29:47.440
And if you have a friend over in the finance department who will help you out on something because you've asked about their kids or their favorite sports team and you've been humble and listened for a half hour the same way we listen to users, you're building a network in your company that's gonna be hugely valuable and you're gonna understand whether your next product's gonna fail or not.
00:29:48.240 --> 00:29:48.720
Thank you.
00:29:48.960 --> 00:29:56.480
Before we close, there's one more thing I ask every every guest, and it has nothing to do with product management or product or CPO.
00:29:56.640 --> 00:30:01.680
It's what does it mean to you right now, in this moment of your life, to be more human?
00:30:02.079 --> 00:30:02.640
More human.
00:30:02.880 --> 00:30:11.519
I think of that a lot in terms of doing wonderful doing things that bring me joy, and then doing things that either bring joy to somebody else or pay it forward, right?
00:30:11.680 --> 00:30:25.519
I'm at the place in my career, which is at the end, where I've helped pull a lot of people up the ladder behind me, and my my plea to them is to reach down the ladder and pull somebody else up behind them, right?
00:30:25.599 --> 00:30:29.440
And if you're walking down the street and somebody drops a package, you help pick them up, right?
00:30:29.519 --> 00:30:33.279
And when somebody's having trouble walking across the street, you help them walk across the street.
00:30:33.440 --> 00:30:41.440
I think for me, the human is to get out of our own way and to leave the agents behind for a little bit.
00:30:41.599 --> 00:30:44.720
And there was a solar eclipse here a couple of nights ago, right?
00:30:44.960 --> 00:30:52.240
And uh my wife, who's brilliant, uh was needed to buy some of those glasses, you know, and Amazon only had them in packages of 40.
00:30:52.559 --> 00:30:54.000
So we and they were cheap.
00:30:54.079 --> 00:31:06.160
So we ordered a package of 40, and we went down the hill here to the Atlantic because I live in and she gave out 38 pairs of glasses to kids and people and whoever who didn't have them, right?
00:31:06.240 --> 00:31:10.160
And it was just like, you know, what are we doing today to bring a smile?
00:31:10.319 --> 00:31:15.440
What are we doing today to make somebody else, you know, uh I'm very privileged to have a lot of things, right?
00:31:15.519 --> 00:31:17.839
What what can we do today with what we have?
00:31:18.079 --> 00:31:29.200
You know, my my kids grown up, so it's not about kids anymore, but you know, gosh, how do we be good neighbors and good citizens and appreciate art and read a good book and do the thing?
00:31:29.440 --> 00:31:30.400
It's it's perfect.
00:31:30.559 --> 00:31:31.759
And thank you for doing that.
00:31:31.920 --> 00:31:33.759
That's such a good a good thing to do.
00:31:33.920 --> 00:31:37.440
Uh and thank you also for sharing that with me and sitting here today with me.
00:31:37.599 --> 00:31:41.200
I want to close something with something more practical now.
00:31:41.359 --> 00:31:48.319
Uh is there anything you'd like to share uh with the people listening and where they can find you, your writing, and Miranov consulting.
00:31:48.559 --> 00:31:48.799
Right.
00:31:48.880 --> 00:31:50.079
I'm really easy to find.
00:31:50.160 --> 00:31:53.119
I I put the links in your notes so you might send them out later.
00:31:53.279 --> 00:32:01.440
But everything I've written for the last 25 years, because it's a long-running blog, is on my Marinop.com site and it's echoed to my Substack and it's free.
00:32:01.599 --> 00:32:02.960
I've got a couple of books out there.
00:32:03.119 --> 00:32:06.319
The most recent one is called Money Stories.
00:32:06.480 --> 00:32:07.279
Guess what it's about?
00:32:07.440 --> 00:32:08.960
They're on Amazon and they're cheap.
00:32:09.039 --> 00:32:12.960
And if somebody really can't afford it, drop me a note, I'll send you the bits for free.
00:32:13.119 --> 00:32:16.319
And you know, I'm on LinkedIn and WhatsApp and whatever.
00:32:16.400 --> 00:32:20.079
If somebody needs my help for something, drop me an email, right?
00:32:20.160 --> 00:32:23.759
I mean, it's it's not about the money, it hasn't been cool.
00:32:23.839 --> 00:32:24.160
Thank you.
00:32:24.319 --> 00:32:25.359
Thank you again so much.
00:32:25.599 --> 00:32:26.559
It was a pleasure.
00:32:26.960 --> 00:32:30.559
Thank you so much for spending this time with me on Human X Intelligence.
00:32:30.880 --> 00:32:38.880
Today, Rich and I talked about the difference between building software and building a product people need and the digits behind deciding what comes next.
00:32:39.039 --> 00:32:43.279
For me, it's a reminder to ask a better question before reaching for a faster tool.
00:32:43.440 --> 00:32:44.799
What problem are we solving?
00:32:44.880 --> 00:32:45.359
And for whom?
00:32:45.599 --> 00:32:48.079
Speed is useful when we know where we are going.
00:32:48.240 --> 00:32:53.920
I would love to hear what stayed with you if someone came to mind while you were listening to share this episode with them.
00:32:54.079 --> 00:32:58.960
You can find Rich Work in the description and follow HumanX Intelligence on Instagram and LinkedIn.
00:32:59.119 --> 00:33:04.079
And I'll see you next week for another conversation about what it means to be human in the age of AI.
00:33:04.319 --> 00:33:07.039
Until then, stay curious and stay human.
00:00:07.839 --> 00:00:10.400
Welcome to Human X Intelligent.
00:00:10.640 --> 00:00:15.679
Welcome to Human X Intelligent, a podcast about what it means to be human in the age of AI.
00:00:15.759 --> 00:00:24.559
From all of us living through it, I am Madalena Kosta, and every Monday I bring you a conversation about how AI is changing our lives, our work, and the way we relate to each other.
00:00:24.719 --> 00:00:27.440
AI is changing how quickly we can build software.
00:00:27.600 --> 00:00:33.359
But building something faster does not mean that it's answering the question of whether anyone needs it.
00:00:33.520 --> 00:00:41.600
So today we are exploring what happens to product work when writing code becomes easier and why deciding what to build still matters.
00:00:41.840 --> 00:00:46.560
My guest is Rich Miranoff, a product management leader, advisor, and author.
00:00:46.799 --> 00:00:48.479
I will make sure to share all the links below.
00:00:48.640 --> 00:00:58.320
But in this conversation, we talk about what makes a product worth building, the differences between faster engineering and business results, and the human judgment behind product decisions.
00:00:58.560 --> 00:00:59.600
So let's get into it.
00:00:59.840 --> 00:01:00.399
Hello, Rich.
00:01:00.640 --> 00:01:02.880
Welcome to the Human Access Intelligence Show.
00:01:03.119 --> 00:01:04.319
Thank you so much for being here.
00:01:04.560 --> 00:01:11.680
I want to start with somewhere specific, actually, because you've spent this year putting your thinking out in public.
00:01:12.079 --> 00:01:13.599
I met you during product ties.
00:01:14.079 --> 00:01:16.000
Not only that, but also on your own blog.
00:01:16.239 --> 00:01:19.599
So let's start with a number you published yourself.
00:01:19.920 --> 00:01:21.840
Most new products already failed.
00:01:22.319 --> 00:01:23.519
Something like six out of ten.
00:01:23.840 --> 00:01:31.599
And if AI lets uh lets a team build 10 times more products in the same time, the naive math says 10 times more winners.
00:01:31.760 --> 00:01:36.239
But you did the actual math and got a 96% failure rate instead.
00:01:36.400 --> 00:01:39.519
Walk me through how building faster makes things worse.
00:01:39.760 --> 00:01:40.000
Sure.
00:01:40.079 --> 00:01:45.760
And it doesn't make things worse from an engineering point of view, it just makes things worse from an economic point of view.
00:01:46.640 --> 00:01:49.760
So let's imagine we have an existing market, right?
00:01:49.920 --> 00:01:56.319
We're selling software to manage beauty salons or personal gyms or health spas, right?
00:01:56.480 --> 00:02:00.319
That market doesn't get any bigger because we can ship faster, right?
00:02:00.400 --> 00:02:11.919
In fact, if if we and our competitors launch 10 times as many products into an existing market, there can't be 10 times as much money in there to buy those 10 times as many products, right?
00:02:12.080 --> 00:02:15.120
In fact, with more products, prices are going to come down, right?
00:02:15.360 --> 00:02:18.960
So instead of asking how many do we win, I ask how many do we lose.
00:02:19.120 --> 00:02:26.080
So if if today we're finding that four of our 10 products succeed, and by succeed, I don't mean that we ship them.
00:02:26.240 --> 00:02:31.199
I mean that we found a market, made money, found customers, and generated revenue, right?
00:02:31.439 --> 00:02:44.719
So if four of our 10 products found market fit and found revenue and found success, and we ship 10 times as many products, we're shipping 100, we haven't created any conditions that make more than four of those successful, right?
00:02:44.960 --> 00:02:55.520
We still have the same health spas or personal gyms, whatever, looking for software to manage their day, but we have 10 times as many products fighting for those same four wins, right?
00:02:55.680 --> 00:03:00.719
So we move from four out of 10 wins to four out of a hundred wins.
00:03:00.879 --> 00:03:05.280
And of course, everybody's gonna say, well, I'll be there first and I'll take share away from everybody else.
00:03:05.759 --> 00:03:07.759
Pretty short-term play, right?
00:03:07.919 --> 00:03:12.719
There won't be more winners in that market unless we can create entirely new markets.
00:03:13.360 --> 00:03:24.960
Which, by the way, is hard because at least I've been building software for 40 years and we're mostly going into the same markets and improving the things we're selling and displacing existing competitors, right?
00:03:25.280 --> 00:03:32.240
The the world software budget doesn't get 10 times as big because we can ship 10 times as fast, right?
00:03:32.400 --> 00:03:41.680
The distinction I try to make here that nobody wants to listen to is the difference between can we generate code faster and can we generate money faster?
00:03:42.000 --> 00:03:44.639
Because products are more than just their code.
00:03:44.800 --> 00:03:50.240
They're the pricing and the positioning and the channels and the messaging and getting folks to try them, right?
00:03:50.400 --> 00:03:54.319
There's a lot more in the box than just can we generate more code.
00:03:54.639 --> 00:03:54.960
Yeah.
00:03:55.199 --> 00:04:06.479
And I guess something that came to mind, that's why I also ask about the number, is that I'm sure every time you're in the room and say something in the round of that number, every time the room just stays in the city.
00:04:06.719 --> 00:04:07.439
It gets very quiet.
00:04:07.680 --> 00:04:09.599
Or people start to yell, right?
00:04:10.560 --> 00:04:11.039
Right.
00:04:11.199 --> 00:04:24.160
Uh and and and for me, it's again, it's the distinction between an between being a software engineer and getting really great tools that let me build, well, maybe generate code times ten times as fast.
00:04:24.319 --> 00:04:30.800
I would say we're delivering workable, you know, useful products twice as fast or three times as fast.
00:04:31.040 --> 00:04:36.720
The whole rest of the company isn't moving faster, and even more than that, our user base isn't growing that fast.
00:04:36.879 --> 00:04:41.519
So so somehow we have to think not just about code generation.
00:04:41.680 --> 00:04:44.319
We got about think about the exchange of money on the other end.
00:04:44.720 --> 00:05:02.720
If we focus, or let me come down out of the theory for a second, because it it is in easy to nod along the these are the bottlenecks or the button that moves right as an abstract idea and lose the thread of what that actually looks like inside a team, a real team.
00:05:03.279 --> 00:05:09.279
So my question to you, yeah, let's say like this let's paint the current picture of what is happening.
00:05:09.680 --> 00:05:18.480
What are the products and engineering teams actually doing different right now, this year, even, because of artificial intelligence?
00:05:19.360 --> 00:05:23.279
So I have to tell you, I'm not building products lately, right?
00:05:23.439 --> 00:05:27.839
So I am not on the front lines of people who are living through this and figuring it out.
00:05:28.000 --> 00:05:29.759
I'm getting it secondhand, right?
00:05:30.000 --> 00:05:38.800
And again, I'm hearing from people that they are dramatically accelerating the step in the process where we create code.
00:05:38.959 --> 00:05:44.800
And people argue and their tools, and is it three times faster or 30% faster or 10 times faster?
00:05:44.959 --> 00:05:45.279
I don't know.
00:05:45.360 --> 00:05:48.879
And that varies an awful lot with context and situation.
00:05:48.959 --> 00:05:50.639
And do you have existing customers?
00:05:50.800 --> 00:05:54.079
And will people die if your application goes offline?
00:05:54.240 --> 00:05:54.480
Right.
00:05:54.639 --> 00:06:06.079
So, you know, if you're building uh an online dating app, you probably are a lot less concerned with some of the quality and testability than if you're building software for spaceships and you can't bring them back, right?
00:06:06.240 --> 00:06:11.439
Or medical devices where if your CPAP machine stops working, people die, right?
00:06:11.680 --> 00:06:13.759
So there's very different contexts here.
00:06:13.920 --> 00:06:24.240
And again, almost all the write-up is about the the middle of the engineering process where we've already decided what to build and we haven't yet shipped it to people who need it to work, right?
00:06:24.399 --> 00:06:27.920
If we expand that to what I think of as the maker team, right?
00:06:28.160 --> 00:06:36.560
Design, product management, support, uh, the other parts of the technical organization, you know, different people are getting different amounts of acceleration here.
00:06:36.800 --> 00:06:43.040
If I think about designers for a minute, I believe that good designers can generate 40 prototypes in an hour.
00:06:44.079 --> 00:06:48.560
But I can't figure out who's going to look at 40 prototypes in an hour, right?
00:06:48.800 --> 00:06:52.160
There aren't more users who are willing to spend more time doing this, right?
00:06:52.319 --> 00:07:04.319
I believe that product plus engineering plus design could add 10 small features a week to an existing product, but I don't know any users who are willing to try 10 new features a week because they've got a job to do, right?
00:07:04.560 --> 00:07:13.519
So, you know, the bottlenecks move from the the raw throughput of raw engineering to everything downstream and maybe everything upstream.
00:07:13.680 --> 00:07:18.800
Upstream for for me, the most important one is we ask, is this a good thing to build?
00:07:19.040 --> 00:07:20.000
Is it good for the world?
00:07:20.160 --> 00:07:21.120
Is it good for our users?
00:07:21.279 --> 00:07:23.439
Is it good for our company economics?
00:07:23.600 --> 00:07:24.399
Can we sell it?
00:07:24.560 --> 00:07:24.800
Right.
00:07:24.959 --> 00:07:28.079
I've many years I've talked about product waste, right?
00:07:28.319 --> 00:07:30.720
Engineering waste is when we don't do a good job building it.
00:07:30.879 --> 00:07:35.279
Product waste is when we build something the world isn't going to use or buy.
00:07:35.600 --> 00:07:43.600
And and if we're looking downstream to the bottlenecks of users and buyers and marketing and sales, we moved over to that side of the problem, right?
00:07:43.759 --> 00:07:48.560
I think product managers are starting to spend a lot more time on the front end.
00:07:49.279 --> 00:07:49.920
What should we build?
00:07:50.000 --> 00:07:50.639
Should we build it?
00:07:50.800 --> 00:07:56.720
And on the back end, how are we gonna sell it, package it, find partners, turn it into money, position it?
00:07:57.839 --> 00:07:58.079
Yeah.
00:07:58.319 --> 00:08:01.439
Because engineering's gonna do a better job of building it faster.
00:08:01.680 --> 00:08:03.199
Let me take another slice of this.
00:08:03.360 --> 00:08:12.399
If the part of the problem we're worried about is can we fix obvious bugs in the software faster and ship patches to known problems faster?
00:08:12.560 --> 00:08:14.000
I'll sign up for 10X there.
00:08:14.160 --> 00:08:14.399
Okay.
00:08:14.560 --> 00:08:17.920
We look through the tickets, we find all the ones where that are obvious to fix.
00:08:18.079 --> 00:08:27.839
We have our engineers turn them over to their, you know, their agents, and we don't worry about shipping bugs, about f shipping fixes to bugs, because we know they're bugs, right?
00:08:28.000 --> 00:08:31.279
Not so much with new features and not at all with new products, right?
00:08:31.439 --> 00:08:38.480
So I I'm trying to pull the product folks out of the engineering whirlwind here so we can ask the product questions.
00:08:38.960 --> 00:08:49.679
Even with what you just said about the clients versus speed and how it can actually do the opposite of what they were looking for, yes, how r right now the goal is all acceleration, right?
00:08:49.919 --> 00:08:52.559
For most of the team, specifically to startups.
00:08:53.039 --> 00:09:03.759
I was discussing the other day with a CTO, and he was very worried about the design part and what was my opinion on how designers would enter this area.
00:09:04.240 --> 00:09:17.039
My question to you is based on your experience throughout all these years, how would you say that each different individual in a product team can continue showcasing their value?
00:09:18.000 --> 00:09:18.240
Sure.
00:09:18.320 --> 00:09:21.360
And I love that you use the word value, really important.
00:09:21.519 --> 00:09:24.639
So, because it's not about throughput anymore, right?
00:09:24.799 --> 00:09:26.480
So let's think about our designers.
00:09:26.559 --> 00:09:42.399
And let's think about a senior designer who's been down the road and gotten their nose bloodied a few times and shipped them things that really didn't work, and so has taste and judgment and insight and continues to do a lot of real end user validation of what's working and not working, right?
00:09:42.480 --> 00:09:46.399
We're not going to turn that item over to the scripts and the and the agents, right?
00:09:46.559 --> 00:09:55.600
I think that designer no longer has to do all these pixel perfect color selection layout stuff that's gone, right?
00:09:55.919 --> 00:10:04.720
But if you if you had your agent spin up four or six versions, someone has to look at that and say, I know which ones of these are good, right?
00:10:04.960 --> 00:10:16.080
And I'm gonna use use a careful word here, plausible, which means they're believable, especially to somebody who doesn't know any different, versus good, which means it's actually gonna meet the need, right?
00:10:16.240 --> 00:10:30.960
So I think lots of CEOs are busy playing with prototyping tools and they're creating dozens of prototypes, and they're confusing those with good design because they don't actually know the difference between good design, mediocre design, and terrible design, right?
00:10:31.200 --> 00:10:34.000
And so I see the designers levering up.
00:10:34.159 --> 00:10:50.000
So they're gonna waste a lot less time with drawings and layouts and maybe even workflows and have the agents figure out where folks are getting stuck in the workflows, but they're gonna bring taste and judgment and insight and human factors to the design problem as they always have, right?
00:10:50.080 --> 00:10:53.679
On the engineering side, we're gonna spend a lot less time generating code.
00:10:53.919 --> 00:11:01.120
I'm old enough to remember when compilers came along and I stopped writing ones and zeros and I could start writing code, right?
00:11:01.360 --> 00:11:06.159
It made code writing faster, but we all levered up and now everybody could write simple programs.
00:11:06.320 --> 00:11:12.879
And you didn't differentiate your product by being able to put a SQL statement in there, because everybody could do that, right?
00:11:13.039 --> 00:11:19.919
You had to think of something bigger, and so now we get engineers who worry about scalability and security and meeting the need, right?
00:11:20.080 --> 00:11:29.919
So I think we may have smaller teams, but I think we have more interesting work because we're gonna give the bottom 60% off to our agents, but not the top 40%.
00:11:30.799 --> 00:11:35.600
Where are you at when it comes to judgment between artificial intelligence?
00:11:35.759 --> 00:11:36.480
Does it have judgment?
00:11:36.720 --> 00:11:37.679
Does it not have judgment?
00:11:38.000 --> 00:11:47.279
Should it have judgment versus human judgment when it comes to creating not only products, but everything around what a product is for a company?
00:11:47.759 --> 00:11:50.000
Again, I think that's a hard question, and there's layers.
00:11:50.480 --> 00:11:51.120
Okay, right.
00:11:51.440 --> 00:11:57.600
So if we ask the question of generic technical support documents, okay, first of all, nobody ever read them, right?
00:11:57.679 --> 00:11:59.600
And second of all, nobody's gonna read them in the future.
00:11:59.759 --> 00:12:12.799
So what we really want is we want our AI tools to work through all the software and figure out how it actually works and write all the steps and interpret all the questions from our users that say, well, how do I, right?
00:12:12.960 --> 00:12:15.279
Because honestly, it's not very interesting, right?
00:12:15.519 --> 00:12:18.879
When the question is, what should the application do?
00:12:19.120 --> 00:12:21.039
Not what does the application do?
00:12:21.279 --> 00:12:28.399
So I would use, I do use a bunch of my AI tools to figure out what's existing in the world.
00:12:29.120 --> 00:12:29.279
Okay.
00:12:29.519 --> 00:12:35.919
And actually to do a lot of help getting me using software that has terrible documentation, because then I can ask how it's supposed to be how it works, right?
00:12:36.159 --> 00:12:46.159
But let's think of a world where all the LLMs have all of the existing data about all the existing markets, and we're trying to launch a new product that's gonna open up a new market.
00:12:46.320 --> 00:12:46.639
Okay.
00:12:46.799 --> 00:12:49.360
We're gonna ask all the LLMs what the new market should be.
00:12:49.440 --> 00:12:57.679
They're all gonna give us the same prediction as they gave to all of our competitors, and we're all gonna rush into the same market, build the same product, and they're all gonna fail, right?
00:12:57.919 --> 00:13:02.399
So I think we have to ask the more interesting questions of ourselves, right?
00:13:02.559 --> 00:13:05.440
What are our users actually trying to accomplish?
00:13:05.600 --> 00:13:06.799
Not what we told them to.
00:13:06.960 --> 00:13:08.799
How do they really measure success?
00:13:08.960 --> 00:13:11.039
How does the buying process really work?
00:13:11.360 --> 00:13:28.480
Of all the messages that our LLM gave us, which ones are actually gonna resonate with real users, not the synthetic users that we used with all of our biases to give us the answers we wanted quickly, so we could move on and skip all the hard steps of knowing what's happening in the world, right?
00:13:28.960 --> 00:13:32.480
You've written about teams that want to automate the whole pipeline.
00:13:32.720 --> 00:13:32.960
Yes.
00:13:33.360 --> 00:13:44.000
This is perfect when I'll straight for, for example, a customer ticket to life code, and about teams outsourcing their actually user interviews, like you were saying, to AI bots and quick surveys.
00:13:44.159 --> 00:13:50.480
So where do you see people quietly handling over their thinking and they should be doing themselves?
00:13:50.960 --> 00:13:51.919
Again, pieces, right?
00:13:52.080 --> 00:13:57.840
So when I think of the discovery process, right, there's an awful lot of it that is no value add, right?
00:13:58.000 --> 00:14:07.919
Looking into our user base and figuring out who the specific users are for features or functions or products, sending them requests to book time with us, getting on their calendars, right?
00:14:08.159 --> 00:14:09.039
A lot of stuff now.
00:14:09.200 --> 00:14:15.360
I should myself write the email that's gonna go out to those thousand people because if it's wrong, nobody's gonna respond, right?
00:14:15.600 --> 00:14:22.399
And I think the moment we have to have humans in the room is the moment when we're actually asking the real questions of the real users.
00:14:22.559 --> 00:14:28.159
So show me what worked, tell me what you didn't work, why are you unhappy, right?
00:14:28.320 --> 00:14:34.480
And I'm always saying that we should have a product manager and a designer and an engineer on the call together, right?
00:14:34.639 --> 00:14:37.919
Endlessly, because you don't get to do that interview a second time.
00:14:38.000 --> 00:14:44.159
And so when the user says something that's really very workflow-oriented, and as a product manager, I missed it.
00:14:44.240 --> 00:14:49.759
And my designer, if I'm running the interview, my designer sends me the quick text that says, ask this question, right?
00:14:50.000 --> 00:14:54.639
We have a moment to diverge from the script to ask the interesting stuff.
00:14:54.799 --> 00:14:58.960
Somebody says to me, Well, we never run those reports on Thursdays.
00:14:59.120 --> 00:14:59.440
Wow.
00:14:59.679 --> 00:15:00.720
Never would have imagined.
00:15:00.799 --> 00:15:02.399
Okay, tell me more, right?
00:15:02.639 --> 00:15:08.639
When we script up the interview for our agents or our bots, it asks all the questions we planned.
00:15:08.799 --> 00:15:10.960
It may or may not good get get good answers.
00:15:11.039 --> 00:15:13.440
So they may be the completely wrong questions, right?
00:15:13.519 --> 00:15:18.879
And it doesn't, it doesn't react in the moment to something interesting, right?
00:15:19.200 --> 00:15:28.559
The reason I want my engineer or one of my engineers on the line is somebody says something that's all about data cleanliness and scalability, and I missed it because I'm the stupid product manager, right?
00:15:28.639 --> 00:15:35.039
And my engineer, because we've been trained, doesn't blurt in but writes it on a note and says, no, no, go dig deeper, right?
00:15:35.120 --> 00:15:35.840
Ask the question, right?
00:15:35.919 --> 00:15:37.600
Or maybe we switch chairs, right?
00:15:37.919 --> 00:15:41.759
Because discovery is about learning something we don't know.
00:15:42.000 --> 00:15:51.440
This would seem obvious, but everybody wants to everybody wants to automate it, and so we're gonna ask all the standard questions and get all the standard answers and learn all the standard things, which is nothing, right?
00:15:51.600 --> 00:15:57.120
Now, do I want my my uh AI tools to run through those transcripts and summarize them?
00:15:57.279 --> 00:15:57.840
Absolutely.
00:15:58.000 --> 00:15:59.039
Score them, right?
00:15:59.200 --> 00:16:00.639
Figure out which ones are interesting.
00:16:00.799 --> 00:16:03.440
Do I give up my judgment about what's interesting?
00:16:03.600 --> 00:16:05.039
I do not, right?
00:16:05.200 --> 00:16:10.799
If I'm not walking down the street in the mind of my user, we're generating slot.
00:16:11.519 --> 00:16:20.960
Before we go further, if you're enjoying this conversation, subscribe to HumanX Intelligent wherever you're watching or listening, so you can join me for a new episode every Monday.
00:16:21.120 --> 00:16:27.360
And I would love to know if your team could build anything faster, how would you decide what was actually worth building?
00:16:27.519 --> 00:16:29.919
Tell me in the comments or send me a message directly.
00:16:30.080 --> 00:16:32.639
I'd love to hear how this shows up in your own life.
00:16:32.799 --> 00:16:34.159
Now, let's get back to reach.
00:16:34.480 --> 00:16:52.799
Something that I found very interesting when using these tools when I go to interview clients is that uh our users, is that the way it analyzes it's very mathematical-based or very constructed way while I'm thinking during the call of something that oh, this is interesting.
00:16:52.960 --> 00:17:05.440
I was not expecting that this person said this and this way, and I write that, but when I see what the the AI summarized, it's never there, or at least not with the same strain that the user gave.
00:17:05.920 --> 00:17:07.759
At least before the current AI boom.
00:17:20.559 --> 00:17:31.599
To go to these interviews or to these conversations with clients, and they don't believe that uh anyone from product should go because it's a waste of time and they should just ship it, ship it, sheep.
00:17:31.839 --> 00:17:38.640
It's a really hard argument to win because if you've not seen the failures, if you haven't missed out on these, you don't believe they exist.
00:17:38.720 --> 00:17:41.839
It's uh for anybody who remembers the Dunning Krueger effect, right?
00:17:42.000 --> 00:17:45.279
The less I know about your job, the easier your job is, right?
00:17:45.599 --> 00:17:45.839
Yeah.
00:17:46.079 --> 00:17:49.279
So we've talked about uh what goes wrong when people move fast.
00:17:49.759 --> 00:17:56.559
So the right question is if we got a hundred or a hundred thousand tickets telling us to make this change, yeah, what was the problem?
00:17:57.920 --> 00:17:58.960
Not what was the solution.
00:17:59.119 --> 00:18:03.119
What was the underlying problem that caused all these people to raise this issue?
00:18:03.279 --> 00:18:07.519
And is the proposal that some of them are making the right solution?
00:18:08.160 --> 00:18:08.480
Right?
00:18:08.720 --> 00:18:15.119
Judgment, thought, get our engineering and design and product folks together, and we say, well, we're getting 50 complaints a day about this.
00:18:15.279 --> 00:18:16.720
What's really broken?
00:18:17.039 --> 00:18:18.960
Maybe not what they're seeing.
00:18:19.200 --> 00:18:24.559
An honest answer says, we understand the problem that you're having, we sympathize.
00:18:24.799 --> 00:18:30.000
Here are some things you might consider that are not the solution that you gave us, right?
00:18:30.720 --> 00:18:34.079
And and I think that's as humble and as polite as you can be.
00:18:34.160 --> 00:18:43.440
Now, of course, there's other issues here, but I want to zoom out now to the shape to the shape of the of the job itself, of product management itself.
00:18:43.599 --> 00:18:51.039
Because you've actually put the name on how it's changing, and I love reading what you write, just parenthesis.
00:18:51.279 --> 00:18:56.559
But you've described the shape of a product manager's job uh changing in what you call a barbell.
00:18:56.720 --> 00:18:58.720
And I really want people to learn more about it.
00:18:58.880 --> 00:18:59.440
Walk me through that.
00:18:59.680 --> 00:19:04.240
So and I put two pictures in the post, and it was on a sort of weightlifting theme, right?
00:19:04.400 --> 00:19:10.160
And so so if we think of the product management work, maybe not today, a year ago, three years ago as a circle, right?
00:19:10.240 --> 00:19:14.000
And we superimpose it on the picture of like a five kilogram weight, right?
00:19:14.160 --> 00:19:17.920
The middle 60% has traditionally been with engineering, right?
00:19:18.079 --> 00:19:26.480
Roadmaps and specs and standing over them and protecting them and keeping the CEO from wandering through and helping and scrum or Kanban, right?
00:19:27.039 --> 00:19:30.400
All the sort of software development cycle stuff, right?
00:19:30.559 --> 00:19:33.359
Now, do I think that's really much smaller this year?
00:19:33.599 --> 00:19:35.519
Not so much, but let's pretend.
00:19:35.680 --> 00:19:39.519
Let's pretend that that gets from 60% of our job to 10%, right?
00:19:39.759 --> 00:19:44.000
Because engineering is getting a lot more done and we don't have to write so many specs, right?
00:19:44.160 --> 00:19:46.160
We still have to understand what we're building, right?
00:19:46.319 --> 00:19:49.440
And so that takes the middle, a lot of the middle out of this circle.
00:19:49.519 --> 00:19:51.039
And what we're left with is the ends.
00:19:51.119 --> 00:19:53.680
So the picture of the barbell with the weights on the ends.
00:19:53.839 --> 00:19:58.480
And uh at the front, which we always called the fuzzy front end, it's the hard questions.
00:19:58.720 --> 00:19:59.680
Who's going to use this?
00:19:59.839 --> 00:20:00.799
How is it positioned?
00:20:00.880 --> 00:20:01.680
Will it make money?
00:20:01.839 --> 00:20:03.279
What problem are we solving?
00:20:03.440 --> 00:20:04.640
Where's the competition?
00:20:04.799 --> 00:20:07.119
Is this a good thing for us and the planet?
00:20:07.279 --> 00:20:10.400
Does our CEO end up in jail if we ship this?
00:20:11.200 --> 00:20:11.440
Right?
00:20:11.680 --> 00:20:14.640
All of the should we, is this a good idea?
00:20:14.960 --> 00:20:16.480
Do we have validation?
00:20:16.720 --> 00:20:19.119
Do people seem excited about buying this?
00:20:19.279 --> 00:20:19.599
Right?
00:20:19.759 --> 00:20:23.680
Because if we build the wrong thing, it doesn't matter how fast we build it, right?
00:20:23.839 --> 00:20:39.440
And on the back end, back to our other bottleneck, it turns out that if we just have a bunch of beautiful code in a box, nobody buys it because we need channels and messaging and a sales team and a marketing team and maybe PR and partners.
00:20:39.599 --> 00:20:47.759
There's all this stuff that turns code into money, and engineers don't see it because they're working on the code, at least the product.
00:20:48.000 --> 00:20:52.079
And so we as product folks are going to have to spend an awful lot more time.
00:20:52.559 --> 00:20:56.319
Because remember, everybody's shipping 10 times as many products now or 50 times.
00:20:56.480 --> 00:20:59.839
So how do we differentiate it and what's our key selling proposition?
00:20:59.920 --> 00:21:05.200
And right, and almost all that, by the way, comes from the interviews with the end users, okay?
00:21:05.440 --> 00:21:13.920
Because they tell us what's important and they tell us what messages matter, and they tell us how they manage, they measure success, right?
00:21:14.079 --> 00:21:24.480
And if we're not channeling our end customers, then it's positioned wrong, it's priced wrong, it's packaged wrong, it's through the wrong channels, the messaging is terrible, right?
00:21:24.720 --> 00:21:26.880
And we built something beautiful.
00:21:27.119 --> 00:21:32.880
Gosh, there's nothing sadder than a beautiful product that doesn't earn its keep and gets shelved because there's no money.
00:21:33.200 --> 00:21:36.640
We get the water bottles and the little tote bags, right?
00:21:36.720 --> 00:21:39.599
But it turns out that most of the company doesn't care about those.
00:21:39.680 --> 00:21:41.119
They care about where their money comes in.
00:21:41.359 --> 00:21:41.680
Yeah.
00:21:41.920 --> 00:21:42.880
And for sure.
00:21:43.039 --> 00:21:51.680
And if the job is generally reshaping, like you're saying, more weight on both ends and less in the middle, right?
00:21:51.920 --> 00:21:57.359
I like to say that a few years ago I was so sure that what I was saying was correct.
00:21:57.440 --> 00:21:59.440
And for the time it was correct.
00:22:00.079 --> 00:22:04.319
Because we cannot think of the knowledge that we are not have we don't have right now.
00:22:04.480 --> 00:22:06.240
We have to think of the knowledge that we have now.
00:22:06.480 --> 00:22:10.640
That doesn't make it wrong because it is right for the time that we are in.
00:22:10.799 --> 00:22:13.039
I would like to ask in terms of guardrails, right?
00:22:13.119 --> 00:22:21.440
Because if a product wanted to move fast with artificial intelligence without losing themselves in the process, what are the actual rules you put in place for this?
00:22:21.839 --> 00:22:23.759
Rules is a really strong word.
00:22:23.920 --> 00:22:26.240
I don't I'm not sure I believe in rules, right?
00:22:26.400 --> 00:22:30.559
In fact, uh if I go way, way back in time to the early agile days, right?
00:22:30.799 --> 00:22:34.160
Turns out agile wasn't a method, it was a way of thinking, right?
00:22:34.400 --> 00:22:37.920
And and and it was summed up to try something.
00:22:38.640 --> 00:22:40.000
If it works, do more of it.
00:22:40.160 --> 00:22:42.559
If it doesn't work, try something else, right?
00:22:42.720 --> 00:22:47.359
So, you know, the idea that we know what the rules are, I think is reaching it.
00:22:47.440 --> 00:22:48.960
But some suggestions, right?
00:22:49.119 --> 00:22:56.559
So one of them is, you know, one of them is is to say we want to have a sort of proportional investment in discovery, right?
00:22:56.720 --> 00:23:07.359
There's some little things which we know are right, and we're just gonna go ahead and do them and put them in the hopper and ship them and not worry about it because they're obviously right and we could back them out if we needed to, right?
00:23:07.519 --> 00:23:13.599
You know, the the bigger things we want to be more thoughtful about, right?
00:23:14.240 --> 00:23:24.799
If there's risk involved, if we can't undo something and it's big, then we want to spend more time up front thinking, doing discovery, modeling it out, uh, you know, pre-mortems, right?
00:23:24.960 --> 00:23:25.440
What else?
00:23:25.599 --> 00:23:30.799
I think that we shouldn't ever decide that a feature works until a human's tried it.
00:23:31.039 --> 00:23:35.279
This would seem obvious, but everybody's automating their testing, right?
00:23:35.440 --> 00:23:39.119
And I'm worried that we're gonna ship a lot of features that actually don't work, right?
00:23:39.279 --> 00:23:47.119
So anyway, I think humans need to be in on the important part of discovery, and I think humans need to be responsible for the outcome, right?
00:23:47.279 --> 00:23:52.319
So if we ship something and it fails, we're not allowed to blame it on our agents, right?
00:23:52.480 --> 00:23:59.359
And if we ship something and it's wally successful in the market, we should be able to stand up and say we were the coordinating thinkers there, right?
00:23:59.519 --> 00:23:59.680
Yes.
00:23:59.920 --> 00:24:16.000
I'm thrilled by a lot of sort of product ops and DevOps stuff where we're automating the creation of tests, we're automating the mining of data, we're automating the, you know, a lot of this stuff was hard but not higher value, right?
00:24:16.240 --> 00:24:20.960
And so, you know, I see us stepping up to do the more interesting work.
00:24:21.200 --> 00:24:22.240
Do I know where this is going?
00:24:22.400 --> 00:24:22.960
I really don't.
00:24:23.119 --> 00:24:36.400
I'm pretty skeptical about whether there's any new ideas being found in the the averaging and and median data that comes back from LLMs because they're trying to find the thing that is most likely not the thing that's true.
00:24:36.720 --> 00:24:38.640
They don't have that capability of the internet.
00:24:38.880 --> 00:24:43.279
No, but but there's an awful lot of information out in the world which I have trouble finding, right?
00:24:43.440 --> 00:24:47.440
I'd much rather have it uh that's working much better for me than Google search ever did.
00:24:48.319 --> 00:24:55.119
You know, I've been enjoying right now the the new addition to Google, like the Gemini being inside of the research.
00:24:55.279 --> 00:24:58.240
That has been helping me a lot to to focus.
00:24:58.400 --> 00:24:59.440
So I agree with you.
00:24:59.519 --> 00:25:10.480
I think since this tool started to be more mainstream, it started to help to make the process of discovering a competitor matrix, competitor now, wherever it is, more quickly and more easily.
00:25:11.680 --> 00:25:14.880
Let's zoom out or zoom all the way out now.
00:25:15.039 --> 00:25:24.319
Uh, you've done been doing this for 40 years, which means you already lived through more than one moment where everyone was certain that the job was about to disappear.
00:25:24.799 --> 00:25:26.640
Dangerous industrial time, forever.
00:25:27.440 --> 00:25:30.640
I want you to take me to 2031.
00:25:31.039 --> 00:25:31.599
No, no, no.
00:25:31.680 --> 00:25:36.319
I want you to take me back to the future based on what you experienced in the back.
00:25:36.559 --> 00:25:41.519
Like let's say only five years out, because now they're saying that every month there's something new.
00:25:41.759 --> 00:25:46.319
But from what you experienced in the past, let me fast forward again to maybe 2008.
00:25:46.480 --> 00:25:55.519
Okay, launched the iPhone, launched Android, and for about a year and a half, every board of directors asked every company whether they had a mobile app and whether it was in the app store.
00:25:56.000 --> 00:25:57.359
Nobody knew what they were supposed to do, right?
00:25:57.519 --> 00:26:01.039
And there were all these tools that came out that let you build mobile apps, right?
00:26:01.279 --> 00:26:09.039
And every product management job that was posted was for a mobile product manager or an app product manager, right?
00:26:09.279 --> 00:26:22.240
So when I when I look ahead to 2030, right, or 2031, I believe deeply that we're gonna have figured out what the AI tools are good for, what they're not good for, right?
00:26:22.400 --> 00:26:23.759
Everyone will be using them.
00:26:23.920 --> 00:26:28.240
I mean, I I'm like the last person on the planet who uses a laptop for anything instead of my phone.
00:26:28.480 --> 00:26:31.119
They will be commoditized, right?
00:26:31.279 --> 00:26:36.799
All of the AI tools vendors will mostly have been collapsed to down to a few survivors, right?
00:26:36.880 --> 00:26:38.799
The price point will be near zero.
00:26:38.960 --> 00:26:40.559
Tokens won't matter anymore.
00:26:40.720 --> 00:26:51.680
We will once again discover that product managers need to figure out not how to generate code faster or how to add AI, but what their users are trying to accomplish with their tools and their applications.
00:26:51.839 --> 00:26:58.079
I think we're gonna have more software engineers than we did before because we're gonna open up lots of new cool stuff to do.
00:26:58.319 --> 00:27:00.160
I hope that's true of designers too.
00:27:00.319 --> 00:27:07.599
And we're gonna get back to the fundamentals of well, what are we using our AI tools and all of our other tools and where do they go?
00:27:07.759 --> 00:27:08.799
Where do they fit?
00:27:08.960 --> 00:27:10.799
And we won't be talking about it anymore.
00:27:10.880 --> 00:27:14.640
I don't know anybody who asks me whether my app is gonna run on a phone.
00:27:14.799 --> 00:27:15.119
Yeah.
00:27:15.359 --> 00:27:17.200
I mean, what are you stupid, right?
00:27:17.759 --> 00:27:21.039
That's become an assumed thing for every application.
00:27:21.119 --> 00:27:23.440
It's got to run on a smartphone, right?
00:27:23.599 --> 00:27:28.400
Or or you didn't do your homework, but it's not a differentiator, it's not value add.
00:27:28.720 --> 00:27:31.039
I see product managers back to basics again.
00:27:31.200 --> 00:27:43.039
I see product managers on finance applications and product managers on dating applications and product managers on ecological applications, all of which use AI in the places that we know it adds value.
00:27:43.200 --> 00:27:45.519
And we don't talk about it anymore because it's not interesting.
00:27:45.680 --> 00:27:54.480
Everybody has the doomsday scenario where you know we we saw last week two of the big companies let their hacking tools out of the boxes and they attacked other companies.
00:27:54.640 --> 00:27:59.519
So, you know, maybe, you know, maybe this is Terminator and you know we're just looking at the smoke.
00:27:59.680 --> 00:28:04.880
But assuming that doesn't happen, I I think this is gonna look a lot more like the last few.
00:28:05.279 --> 00:28:05.519
Yeah.
00:28:05.680 --> 00:28:08.480
But let's focus for now on today.
00:28:08.559 --> 00:28:14.079
Okay and to g to give the people listening one thing that they can actually try this week.
00:28:14.400 --> 00:28:14.720
Okay.
00:28:15.200 --> 00:28:16.799
I'm thinking, right?
00:28:17.119 --> 00:28:18.799
Things that we could try this week.
00:28:18.960 --> 00:28:29.680
So one, I think I I put out a book called Money Stories Last Year, which is all about can you explain in less than 25 words why someone's gonna buy your product, right?
00:28:30.000 --> 00:28:45.839
And I think every product manager should sit down and and you don't have to get the book, but you know, you should be able to explain to the to the revenue side of your company whether your product makes money and how it makes money without ever talking about the tech, right?
00:28:46.160 --> 00:28:54.480
Because it's gonna turn out in the six more months when the CEOs are tired of overspending on tokens, this is the question they're gonna ask, and we should be prepared, right?
00:28:54.720 --> 00:29:00.160
The other thing I think people should do next week that isn't anywhere on my list, but why not, right?
00:29:00.319 --> 00:29:20.960
You should find somebody in another department that you don't know much about, and you should take that person out to lunch, and you should learn what they do and interview them a little bit and find out what they care about and how they're measured and how success is for them, and get a little humility about how everybody else in the company does something that we don't do that we think is easy and turns out not to be easy, especially in finance, right?
00:29:21.039 --> 00:29:27.599
Because the money stories are gonna require that we know something about the finance of the company, and the finance folks are really smart and don't care about product.
00:29:27.759 --> 00:29:47.440
And if you have a friend over in the finance department who will help you out on something because you've asked about their kids or their favorite sports team and you've been humble and listened for a half hour the same way we listen to users, you're building a network in your company that's gonna be hugely valuable and you're gonna understand whether your next product's gonna fail or not.
00:29:48.240 --> 00:29:48.720
Thank you.
00:29:48.960 --> 00:29:56.480
Before we close, there's one more thing I ask every every guest, and it has nothing to do with product management or product or CPO.
00:29:56.640 --> 00:30:01.680
It's what does it mean to you right now, in this moment of your life, to be more human?
00:30:02.079 --> 00:30:02.640
More human.
00:30:02.880 --> 00:30:11.519
I think of that a lot in terms of doing wonderful doing things that bring me joy, and then doing things that either bring joy to somebody else or pay it forward, right?
00:30:11.680 --> 00:30:25.519
I'm at the place in my career, which is at the end, where I've helped pull a lot of people up the ladder behind me, and my my plea to them is to reach down the ladder and pull somebody else up behind them, right?
00:30:25.599 --> 00:30:29.440
And if you're walking down the street and somebody drops a package, you help pick them up, right?
00:30:29.519 --> 00:30:33.279
And when somebody's having trouble walking across the street, you help them walk across the street.
00:30:33.440 --> 00:30:41.440
I think for me, the human is to get out of our own way and to leave the agents behind for a little bit.
00:30:41.599 --> 00:30:44.720
And there was a solar eclipse here a couple of nights ago, right?
00:30:44.960 --> 00:30:52.240
And uh my wife, who's brilliant, uh was needed to buy some of those glasses, you know, and Amazon only had them in packages of 40.
00:30:52.559 --> 00:30:54.000
So we and they were cheap.
00:30:54.079 --> 00:31:06.160
So we ordered a package of 40, and we went down the hill here to the Atlantic because I live in and she gave out 38 pairs of glasses to kids and people and whoever who didn't have them, right?
00:31:06.240 --> 00:31:10.160
And it was just like, you know, what are we doing today to bring a smile?
00:31:10.319 --> 00:31:15.440
What are we doing today to make somebody else, you know, uh I'm very privileged to have a lot of things, right?
00:31:15.519 --> 00:31:17.839
What what can we do today with what we have?
00:31:18.079 --> 00:31:29.200
You know, my my kids grown up, so it's not about kids anymore, but you know, gosh, how do we be good neighbors and good citizens and appreciate art and read a good book and do the thing?
00:31:29.440 --> 00:31:30.400
It's it's perfect.
00:31:30.559 --> 00:31:31.759
And thank you for doing that.
00:31:31.920 --> 00:31:33.759
That's such a good a good thing to do.
00:31:33.920 --> 00:31:37.440
Uh and thank you also for sharing that with me and sitting here today with me.
00:31:37.599 --> 00:31:41.200
I want to close something with something more practical now.
00:31:41.359 --> 00:31:48.319
Uh is there anything you'd like to share uh with the people listening and where they can find you, your writing, and Miranov consulting.
00:31:48.559 --> 00:31:48.799
Right.
00:31:48.880 --> 00:31:50.079
I'm really easy to find.
00:31:50.160 --> 00:31:53.119
I I put the links in your notes so you might send them out later.
00:31:53.279 --> 00:32:01.440
But everything I've written for the last 25 years, because it's a long-running blog, is on my Marinop.com site and it's echoed to my Substack and it's free.
00:32:01.599 --> 00:32:02.960
I've got a couple of books out there.
00:32:03.119 --> 00:32:06.319
The most recent one is called Money Stories.
00:32:06.480 --> 00:32:07.279
Guess what it's about?
00:32:07.440 --> 00:32:08.960
They're on Amazon and they're cheap.
00:32:09.039 --> 00:32:12.960
And if somebody really can't afford it, drop me a note, I'll send you the bits for free.
00:32:13.119 --> 00:32:16.319
And you know, I'm on LinkedIn and WhatsApp and whatever.
00:32:16.400 --> 00:32:20.079
If somebody needs my help for something, drop me an email, right?
00:32:20.160 --> 00:32:23.759
I mean, it's it's not about the money, it hasn't been cool.
00:32:23.839 --> 00:32:24.160
Thank you.
00:32:24.319 --> 00:32:25.359
Thank you again so much.
00:32:25.599 --> 00:32:26.559
It was a pleasure.
00:32:26.960 --> 00:32:30.559
Thank you so much for spending this time with me on Human X Intelligence.
00:32:30.880 --> 00:32:38.880
Today, Rich and I talked about the difference between building software and building a product people need and the digits behind deciding what comes next.
00:32:39.039 --> 00:32:43.279
For me, it's a reminder to ask a better question before reaching for a faster tool.
00:32:43.440 --> 00:32:44.799
What problem are we solving?
00:32:44.880 --> 00:32:45.359
And for whom?
00:32:45.599 --> 00:32:48.079
Speed is useful when we know where we are going.
00:32:48.240 --> 00:32:53.920
I would love to hear what stayed with you if someone came to mind while you were listening to share this episode with them.
00:32:54.079 --> 00:32:58.960
You can find Rich Work in the description and follow HumanX Intelligence on Instagram and LinkedIn.
00:32:59.119 --> 00:33:04.079
And I'll see you next week for another conversation about what it means to be human in the age of AI.
00:33:04.319 --> 00:33:07.039
Until then, stay curious and stay human.
