WEBVTT
00:00:00.000 --> 00:00:01.330
Welcome back to Dev Interrupted, everyone.
00:00:01.409 --> 00:00:02.959
I'm your host, Conor Bronsdon.
00:00:02.990 --> 00:00:06.969
Today, I'm joined by Ashish Aggarwal, founder and CTO of Productiv.
00:00:07.059 --> 00:00:11.279
Ashish is not only a successful founder, but also an active investor in over 30 companies.
00:00:11.619 --> 00:00:14.179
He's someone who's deeply involved in the startup ecosystem.
00:00:14.519 --> 00:00:19.368
And today we're excited to hear his insights on scaling teams, the impact of AI on startups.
00:00:19.669 --> 00:00:22.300
And how to navigate the current macroeconomic climate.
00:00:22.750 --> 00:00:23.829
Welcome to the show Ashish.
00:00:25.000 --> 00:00:25.579
Thank you, Conor.
00:00:25.579 --> 00:00:26.250
Glad to be here.
00:00:26.929 --> 00:00:41.539
Yeah, I'm really excited for this conversation because I think you bring a lot of insight, both from your personal experiences and the kind of awareness you have of what's happening within tech startups, within software engineering broadly, but before we jump into the conversation.
00:00:41.749 --> 00:00:51.000
I do want to remind our audience that if you enjoy our episodes, please just take a brief moment to rate and review Dev Interrupted on your podcasting app, Spotify, Apple Podcasts.
00:00:51.299 --> 00:00:57.299
Your feedback helps us reach more listeners and continue to bring you more great conversations with engineering leaders like Ashish.
00:00:57.758 --> 00:00:59.067
now we've got that a little bit out of the way.
00:00:59.067 --> 00:01:01.198
We all know the startup ecosystem has changed.
00:01:01.408 --> 00:01:07.067
it's becoming increasingly difficult for companies to receive enough funding, let alone become unicorns.
00:01:07.147 --> 00:01:17.438
In fact, there's data from Carta that shows that of startups that raised their seed round in Q3 2020, only about 40 percent have graduated to Series A in two years.
00:01:17.608 --> 00:01:23.927
But that's actually quite a high rate compared to the folks who raised their seed cohort in Q1 2022.
00:01:23.938 --> 00:01:25.248
So that's just 15.
00:01:25.248 --> 00:01:29.078
4 percent of those folks have actually raised a series A in the 8 quarter set.
00:01:29.078 --> 00:01:30.617
So we're seeing this cash crunch.
00:01:31.087 --> 00:01:34.658
And then on top of that, Gen AI is reshaping the tech landscape.
00:01:34.918 --> 00:01:39.097
And leaders have to reckon with how to keep their teams motivated and happy in these challenging times.
00:01:39.878 --> 00:01:41.067
So with that in mind.
00:01:41.373 --> 00:01:45.983
Ashish, as someone who's deeply involved in the startup world, what are you seeing in the ecosystem right now?
00:01:46.332 --> 00:01:49.683
And how do you think startups should think about growing in this environment?
00:01:51.563 --> 00:01:52.438
Right, Conor.
00:01:52.438 --> 00:01:54.373
So you are right, right?
00:01:54.563 --> 00:01:55.832
Things have changed.
00:01:56.423 --> 00:02:01.513
I, I would not say things have changed for, for, you know, better or for worse.
00:02:01.543 --> 00:02:03.352
It's just that it's a new environment.
00:02:03.832 --> 00:02:09.143
And I think, uh, you've heard of this, that it's no longer growth at any cost.
00:02:09.312 --> 00:02:12.002
It's now the name of the game is efficient growth, right?
00:02:12.372 --> 00:02:13.223
And, and so.
00:02:13.478 --> 00:02:26.487
That's what I am seeing in, in, in Productiv, but also across the market in different companies that I am involved with, um, investors and rightfully so are saying, Hey, look, money's not cheap.
00:02:26.967 --> 00:02:36.397
We want, we want you to judiciously use the money, uh, you know, invest, build products, which are useful.
00:02:36.437 --> 00:02:39.897
Don't just go chase, you know, some pie in the sky kind of thing.
00:02:39.897 --> 00:02:43.377
So efficient growth, uh, is, is the name of the game now.
00:02:44.022 --> 00:02:45.473
Yeah, definitely.
00:02:45.772 --> 00:02:52.413
There's been a shift from this aggressive growth, growth at all costs, as you put it, to more sustainable, more efficient strategies.
00:02:52.742 --> 00:02:56.443
And it's definitely a trend I'm noticing in conversations with industry leaders.
00:02:56.842 --> 00:03:00.622
What challenges or opportunities do you see this creating for startups today?
00:03:02.793 --> 00:03:17.612
It, it really, uh, you know, to some extent, it, uh, to some extent, it, it, it, it solves that problem of saying this, the market, the startup markets, the startup ecosystem had become too crowded, right?
00:03:18.112 --> 00:03:23.483
Um, everybody running after, you know, any, any reasonable opportunity.
00:03:24.092 --> 00:03:35.927
Now it is, you know, uh, you know, sometimes they say, What's the saying there that, you know, you, you, you separate the milk from the water or what, what have you eat from the shaft?
00:03:36.348 --> 00:03:39.918
Um, I think that's what we are seeing now.
00:03:39.918 --> 00:03:42.758
So the opportunity there now is, is, is, is clear.
00:03:42.978 --> 00:03:46.897
The opportunity for startups is clear, especially for founders.
00:03:47.758 --> 00:03:52.957
Have, have, have a clear vision of what customer problem you're solving.
00:03:53.427 --> 00:03:55.307
How you can actually solve it.
00:03:56.138 --> 00:04:06.758
In without requiring, you know, a giant boatload of money to make a small amount of money, are being forced in, in, and this is good.
00:04:06.758 --> 00:04:13.157
Actually, the founders are being forced to think like businessmen, like business people.
00:04:13.747 --> 00:04:18.807
They're like, look, you are creating a company, but after all, you are creating a business.
00:04:19.288 --> 00:04:21.187
You have to solve a real problem.
00:04:21.197 --> 00:04:22.838
You have to have real customers.
00:04:23.108 --> 00:04:27.737
You have to have a real plan of how you will make money.
00:04:28.377 --> 00:04:30.228
And it could be that the money will come in the long term.
00:04:30.228 --> 00:04:32.858
That is an okay plan, right?
00:04:33.137 --> 00:04:34.997
Doesn't need to be profitable from day one.
00:04:35.007 --> 00:04:43.742
But you need to think about that, um, instead of the good old plan of Well, we'll just get more money from the market.
00:04:43.773 --> 00:04:45.093
We don't need to actually make money.
00:04:45.512 --> 00:04:48.843
Uh, so, uh, and I think that's, that's good for the startup ecosystem.
00:04:48.872 --> 00:04:52.653
Overall, it makes everybody more healthy.
00:04:52.963 --> 00:04:56.673
Uh, if, if we think of first principles in building a business
00:04:57.497 --> 00:04:58.307
I agree with you.
00:04:58.307 --> 00:04:59.737
I think it's a positive.
00:05:00.088 --> 00:05:02.088
For the ecosystem in the long run, though.
00:05:02.088 --> 00:05:05.278
I know it can feel hard for founders and employees in the moment.
00:05:05.737 --> 00:05:21.038
it's like that we're not trying to downplay how hard this is for many companies, for many people, but the long term impacts on having better businesses that are more efficient, that hopefully have a better impact for the dollars spent on them, uh, should be positive.
00:05:21.548 --> 00:05:24.158
And I wonder if, yeah, sorry, go ahead.
00:05:25.062 --> 00:05:25.742
No, absolutely.
00:05:25.742 --> 00:05:30.483
And I just want to echo that this is not to, uh, to, to downplay at all.
00:05:30.882 --> 00:05:35.512
Uh, how hard, you know, employees, founders, even investors are feeling in this market.
00:05:35.862 --> 00:05:36.983
Uh, absolutely.
00:05:36.983 --> 00:05:38.603
Everybody is feeling the heat.
00:05:39.012 --> 00:05:43.952
Um, but the more people I talk to, the more they say, Hey, Ashish, you know what?
00:05:45.362 --> 00:05:47.312
We are that company.
00:05:47.333 --> 00:05:51.762
We are those people who are solving a real problem, who have real customers.
00:05:52.083 --> 00:06:14.903
In fact, the fact that we are, uh, we are forced to, or we are more encouraged now to talk to the customers, to understand the problem a little bit deeper, to, to understand the revenue potential, to show the value to our customers and then to the market, it's helping us build a good product and a good sales motion much faster than, than if we were not under this pressure.
00:06:15.153 --> 00:06:17.543
So the pressure in some sense is bringing out the best.
00:06:18.242 --> 00:06:19.137
In a lot of people,
00:06:20.548 --> 00:06:22.127
you think that a lot of this.
00:06:22.502 --> 00:06:26.132
Kind of rationale of, Hey, we have to be like the full winners.
00:06:26.132 --> 00:06:27.403
We have to monopolize a market.
00:06:27.403 --> 00:06:28.992
We have to just push for growth, growth, growth.
00:06:29.333 --> 00:06:48.293
I mean, I know it was related to cheap money, obviously, like that's a huge part of it, low interest rates, but do you think part of it also was related to seeing these outsize winners and getting obsessed with the Googles, the metas, uh, and the kind of massive outcomes they had versus realizing, Hey, I can, I can run an efficient business and I, maybe I'm not going to own an entire massive market.
00:06:49.348 --> 00:06:50.918
it's, it's somewhat true.
00:06:50.918 --> 00:07:07.588
I, you know, again, I cannot speak to who was thinking like what, um, but, but I think in general that, that, uh, that mentality was, uh, I did see that a lot of saying, Hey, we'll figure out the moneymaking part later.
00:07:08.447 --> 00:07:13.858
Uh, uh, you know, uh, uh, you know, people were always, I think, I think they were disciplined enough.
00:07:14.362 --> 00:07:18.523
To say, hey, are we solving a good problem, a real problem, or at least a realistic problem?
00:07:19.692 --> 00:07:24.622
But that part about efficiency wasn't part of the picture because nobody's asking for it.
00:07:24.622 --> 00:07:32.603
They're like, go keep spending money, get to, you know, get to a hundred million customers or something, and then figure out how to make money, right?
00:07:33.372 --> 00:07:37.853
And only a very few people in the in the world, or very few companies in the world now can afford that.
00:07:38.668 --> 00:07:40.827
I mean, Facebook can clearly still afford that.
00:07:40.858 --> 00:07:44.427
It doesn't make any money from WhatsApp, but, but hey, it's a good business.
00:07:44.593 --> 00:07:45.663
Brand play, yeah.
00:07:46.288 --> 00:07:49.247
can play, uh, but not everybody is Mark Zuckerberg.
00:07:49.267 --> 00:07:50.098
So, you know,
00:07:51.202 --> 00:07:51.473
Very
00:07:51.708 --> 00:07:54.588
so, so we, we, we have to have a different strategy than Mark.
00:07:55.523 --> 00:08:07.312
So, given that need to adapt to more efficient growth, are you seeing more teams looking for talent from nontraditional backgrounds or in nontraditional geographies?
00:08:07.798 --> 00:08:08.218
yes.
00:08:08.298 --> 00:08:12.158
And, and, and to tell the truth, I am, I'm hearing.
00:08:12.932 --> 00:08:30.583
Um, that this is a trend, not just in small companies or startups, but also a trend in in much bigger companies, uh, uh, also, you know, uh, I'm hearing that most of the new budget that comes in is first allocated to, uh, quote, unquote, offshore.
00:08:31.747 --> 00:08:37.528
Offshore growth, whether it is Latin America or India or wherever, right?
00:08:37.857 --> 00:08:40.317
Wherever is your favorite location for getting the talent.
00:08:41.488 --> 00:08:44.817
And I think a lot of factors have combined into that, right?
00:08:46.067 --> 00:08:56.778
One, we all know that, you know, COVID as bad it was, it also showed all of us how we can work remotely from anywhere.
00:08:57.587 --> 00:09:01.398
And so that opened the doors to say, well, you know, if you have good talent.
00:09:02.388 --> 00:09:07.668
In fact, the top talent specially knows how to work from anywhere in the world.
00:09:08.357 --> 00:09:14.927
The tools have become more mature, the mentality has become more mature of working with each other from anywhere.
00:09:15.447 --> 00:09:17.207
So the teams can be geo distributed.
00:09:18.668 --> 00:09:31.258
And so now, and because of the cost pressure, now the name of the game has become, Hey look, get the top talent, because after all, that is also part of the pressure to say, Hey, You have to solve a real problem.
00:09:31.258 --> 00:09:32.317
You have to solve it fast.
00:09:32.317 --> 00:09:36.878
You have to make sure the customer value is there and so on and so forth, which means you need top talent.
00:09:37.538 --> 00:09:40.227
But the good news is there's talent all over the world.
00:09:40.592 --> 00:09:48.192
Including in the United States, by the way, we are part of the world and we have a lot of, a lot of top talent right here in the United States.
00:09:49.812 --> 00:09:54.423
But it doesn't need to be limited to the locality that you happen to be in.
00:09:54.832 --> 00:10:03.793
So, so yes, I am seeing startups and big companies, but especially startups move more towards geo distributed teams.
00:10:04.158 --> 00:10:07.988
Instead of central, uh, you know, co located teams.
00:10:08.452 --> 00:10:13.503
And to your point, that's really been enabled by us being forced to figure out working remotely.
00:10:13.503 --> 00:10:21.153
Like, I had been working remotely or hybrid for years before COVID, but now everyone has had to make that transition and at least experience it for a couple of years.
00:10:21.543 --> 00:10:27.472
Um, and it's really, I think, made companies aware of the opportunities to acquire talent worldwide.
00:10:27.482 --> 00:10:31.342
And it's made the job market competitive worldwide, it seems like as well.
00:10:32.107 --> 00:10:33.398
Absolutely, absolutely.
00:10:34.837 --> 00:10:45.337
Look, yes, this is happening, but also I'm seeing on the opposite side, uh, you know, there is still benefit in pockets, right?
00:10:45.378 --> 00:10:50.538
Depending upon the role, depending upon, you know, what you want to do, what problem you are solving specifically.
00:10:50.717 --> 00:10:56.707
Some roles, some teams just work better when they are co located, when they are co located.
00:10:57.008 --> 00:11:03.587
Brainstorming on a whiteboard, uh, it just makes higher efficiency or higher productivity for the company.
00:11:03.878 --> 00:11:12.227
Uh, again, reminder that the problem we are trying to solve is just higher efficiency for the company, not geo distribution for the sake of geo distribution.
00:11:12.658 --> 00:11:18.677
Uh, it is really the top talent which is driving things, not just that you can get things for lower cost.
00:11:18.738 --> 00:11:21.018
Nobody wants to sacrifice productivity here.
00:11:21.518 --> 00:11:37.118
Uh, so, uh, so in some cases I'm also seeing that people are saying, you know, uh, this job, you know, whatever, it can vary from company to company situation to situation, uh, but these kind of jobs should be co located because they just work better together.
00:11:38.107 --> 00:11:47.618
I'm curious if you think that is true of like product and engineering roles, because there are many C suite leaders who are kind of empathize or emphasizing a return to office.
00:11:47.868 --> 00:11:49.077
So I'm curious on your perspective there.
00:11:49.748 --> 00:11:52.118
Yes, product engineering is, is interesting.
00:11:52.118 --> 00:11:58.158
I mean, it's, it's, uh, uh, we, we, uh, It's unfortunately not a single kind of a role, right?
00:11:58.158 --> 00:12:01.827
Product engineering has, you know, dozens of sub roles in it.
00:12:02.467 --> 00:12:25.863
So, for example, when your web engineer or front end engineer is trying to work with your UX designer, um, and they want to, you know, brainstorm things, And if they want to use a whiteboard, which they typically do, uh, and co create, does it help if they are in the same room compared to a Zoom call?
00:12:26.133 --> 00:12:26.682
I think so.
00:12:26.712 --> 00:12:29.472
I think it helps that they are in the same room, right?
00:12:30.528 --> 00:12:40.988
But if you're an infrastructure engineer who's trying to, you know, deploy the latest version of the OS on your AWS machines, um, right?
00:12:41.337 --> 00:12:48.837
In fact, even, even their teams, the infrastructure team, uh, do they say that, hey, man, we'll, we'll, we'll work well?
00:12:49.863 --> 00:12:58.123
Even if we are remote, in fact, we might work better if, you know, if we are, uh, if we are free of distractions and nobody is like interrupting us.
00:12:58.663 --> 00:13:06.102
So it varies even within product and engineering, what kind of role and by the way, even those roles in some other company might be flipped, right?
00:13:06.133 --> 00:13:12.982
Somebody might say my infra team needs to be in person and my product design can be, can work, uh, you know, remotely with each other.
00:13:14.783 --> 00:13:17.993
I empathize with the leaders who, who say.
00:13:18.293 --> 00:13:22.842
You know, back to work is necessary for for some roles in some situations.
00:13:23.133 --> 00:13:28.253
Hell, I am one of those leaders, uh, who says not everybody needs to be co located.
00:13:28.432 --> 00:13:31.653
Uh, we have a, we have a geo distributed team here at Productiv.
00:13:32.363 --> 00:13:36.643
Uh, you know, we have engineers and other people in, in India.
00:13:36.682 --> 00:13:42.452
We have people in New York, in Denver, in Seattle, in, uh, in Palo Alto and so on and so forth.
00:13:42.773 --> 00:13:45.082
So we are a geo distributed team.
00:13:45.432 --> 00:13:47.842
Um, but then within these hubs.
00:13:48.307 --> 00:13:53.597
Uh, when I see some people, you know, come together to work and, and brainstorm ideas.
00:13:55.342 --> 00:14:02.033
You know, whiteboard or not, I think they often call me back and say, you know, it was a good idea to meet at work.
00:14:02.243 --> 00:14:03.452
You know, a couple of times a week.
00:14:03.793 --> 00:14:05.423
It made us go faster.
00:14:06.467 --> 00:14:20.768
it's really interesting as someone who, I think, personally, I thrive in a remote work environment, but like, even as someone who is very pro remote work in general, there are clear benefits to in person collaboration.
00:14:20.778 --> 00:14:24.238
There are clear benefits to co location at times.
00:14:24.518 --> 00:14:28.118
Um, and like, I've been lucky to work in an environment the last several years where.
00:14:28.802 --> 00:14:39.232
I am generally remote, but then I'll spend a week or two every quarter in person with my teams, in person with my colleagues, collaborating, collaborating across teams, collaborating on like key projects.
00:14:39.232 --> 00:14:41.373
And then we go out and execute on that work afterwards.
00:14:42.332 --> 00:14:48.163
But to your point, I think it does really depend on the culture of the company, the type of folks you have involved, and the type of roles.
00:14:48.192 --> 00:14:51.523
And like, it's a really tough balance.
00:14:51.793 --> 00:15:02.732
Right now to both try to acquire global talent, get access to all these incredibly talented people worldwide, while also retain some of those collaborative benefits and find the right mix.
00:15:03.173 --> 00:15:16.202
So, uh, yeah, I think your, your point about the variation between roles is one that I think a lot of folks need to hear, uh, in what too often becomes an almost like religious debate of remote versus hybrid versus in office.
00:15:16.202 --> 00:15:21.682
And there's, it's, it's one or the other, instead of this, like, Series of greys, which I think is most things.
00:15:22.143 --> 00:15:23.273
Yeah, it's true.
00:15:23.273 --> 00:15:45.462
And, you know, for a product of engineering, product and engineering, my philosophy has evolved into, um, to reuse the word a hybrid philosophy, which is, It's not pure remote that, you know, if there are, whatever, a hundred people in the team, they are in a hundred different locations and, you know, everybody's just pure remote, but it's also not that all hundred are in the same location.
00:15:45.712 --> 00:15:46.592
So what's the hybrid?
00:15:46.613 --> 00:16:07.778
The hybrid is create Create, you can call it hubs, create pockets where you put a team together of, you know, of the right roles and say you people can collaborate in person again in, in, in multiple times a week or whatever works for you.
00:16:08.107 --> 00:16:12.538
Um, and you have this certain, certain goals that you are trying to solve.
00:16:12.538 --> 00:16:14.158
You have a certain problem that you are trying to solve.
00:16:14.628 --> 00:16:19.477
And, and if you, if you distribute this properly, you'll create multiple hubs and each hub is solving the problem.
00:16:19.597 --> 00:16:22.967
an important problem and they have the choice to collaborate in person.
00:16:23.317 --> 00:16:35.577
If and when they need to, and I think that works a little bit better, more flexibility on both sides, you get the global talent, but you also have the choice of working in person, especially for product and engineering.
00:16:35.577 --> 00:16:36.967
I think that works pretty well.
00:16:38.413 --> 00:16:47.802
So Obviously, there's this massive alchemy people are trying to solve right now of how do you set up your teams for this new geo distributed world with global talent?
00:16:48.052 --> 00:16:50.312
How do you make sure you retain some of that in office collaboration?
00:16:50.312 --> 00:16:51.863
How do you set up the right infrastructure?
00:16:52.253 --> 00:16:59.143
So it's awesome to hear about your approach to this, but there's this entire other element of change that's coming through too, which is obviously AI.
00:16:59.153 --> 00:17:03.123
You know, speaking of efficiently scaling a business, I'd be kind of remiss if I didn't bring this up.
00:17:03.503 --> 00:17:10.357
But it's interesting because if you ask You know, individual developers, many will say, at least senior devs, I don't really use AI that much.
00:17:10.627 --> 00:17:14.347
Like it's not, it's not helping me with the things that are actual problems for me, like meetings.
00:17:14.367 --> 00:17:20.087
It's not helping me with the things that make me less efficient, which is some of the, it's not the coding that's the problem for me.
00:17:20.417 --> 00:17:23.317
Now, I think you talked to junior devs who are coming up and they're like, Oh, I rely on this.
00:17:23.327 --> 00:17:24.428
It's, it's very important to me.
00:17:24.738 --> 00:17:29.778
And if you talk to leaders, I think leaders are all think there are massive opportunities with AI.
00:17:29.778 --> 00:17:36.887
And even many devs will talk about, you know, tests are a great thing that I can, you know, uh, can offload out to an AI to help me write some tests and do these other things.
00:17:37.117 --> 00:17:39.907
What's your perspective on this kind of AI wave?
00:17:39.907 --> 00:17:41.978
How are you utilizing AI at Productiv?
00:17:42.928 --> 00:17:45.557
Um, so, so many questions in one, right?
00:17:45.778 --> 00:17:46.367
Yeah, sorry.
00:17:46.367 --> 00:17:46.637
I threw
00:17:46.657 --> 00:17:49.567
AI, I will, I will, I'll take them one at a time.
00:17:49.567 --> 00:17:51.938
I think, uh, is AI useful?
00:17:51.988 --> 00:17:52.708
Absolutely.
00:17:52.788 --> 00:17:53.117
Right.
00:17:53.288 --> 00:17:54.448
Is there a disconnect?
00:17:54.448 --> 00:17:54.617
Correct.
00:17:55.087 --> 00:18:14.357
Between, you know, how individual developers might be, uh, uh, might, might be using AI to, to improve coding or testing, uh, or what have you, versus what's the, you know, versus what's the marketing pitch saying, Hey, AI will solve word hunger.
00:18:14.688 --> 00:18:15.407
Absolutely.
00:18:15.407 --> 00:18:16.817
So, I am, let me agree with you.
00:18:17.228 --> 00:18:26.678
Uh, you know, the, the you know, the, the promise of AI today is way bigger.
00:18:27.093 --> 00:18:33.702
Then the actual reality of how people are using AI, um, including developers.
00:18:35.452 --> 00:18:38.573
Has AI made Productiv developers 30 percent better?
00:18:39.663 --> 00:18:40.313
It hasn't.
00:18:40.573 --> 00:18:44.222
We, uh, have we tried looking at it?
00:18:44.252 --> 00:18:44.742
Yes.
00:18:45.563 --> 00:18:48.452
In its current form, we said, let us wait a little, right?
00:18:48.452 --> 00:18:54.212
We are We are not finding it as useful as it's hyped to be.
00:18:55.423 --> 00:19:04.333
I'm hearing the same thing from many other, many other engineering leaders saying we, as a leadership level, we want to try it just in case there is something here.
00:19:04.653 --> 00:19:19.992
But individual engineers are saying that trying is one thing, but I have my day job here, uh, you know, I, I am, uh, it's not helping if I have to review the entire output of GenAI to see what did it miss and what did I get, uh, right.
00:19:20.182 --> 00:19:26.303
Um, and plus I have to maintain all the code anyway, so I have to understand the whole code and I have to correct mistakes.
00:19:26.303 --> 00:19:28.692
So it helps, but not really that much.
00:19:29.573 --> 00:19:31.932
From, and, and we have explored the ideas.
00:19:32.147 --> 00:19:38.597
Behind testing, uh, test generation or, you know, code reviews or code generation naturally.
00:19:40.268 --> 00:19:41.228
Is it going to get there?
00:19:41.248 --> 00:19:41.867
I think so.
00:19:41.877 --> 00:19:49.657
I think AI is, uh, is, is one of those things that we are early, right?
00:19:49.798 --> 00:19:57.468
And as we are building and learning, um, you know, you were asking earlier, how is AI impacting the startup ecosystem?
00:19:58.498 --> 00:20:05.057
It's, it's, it's, it's a sword or it's a knife that's cutting both ways, right?
00:20:05.298 --> 00:20:07.208
On one hand, every investor says.
00:20:07.903 --> 00:20:14.563
If you are not a pure AI company, well, then, you know, I need to think twice about funding you.
00:20:14.992 --> 00:20:17.692
Not everybody is thinking like that, but many are.
00:20:17.758 --> 00:20:19.127
Plenty are, we could name some names.
00:20:19.127 --> 00:20:20.928
Yeah,
00:20:20.982 --> 00:20:25.282
But on the other hand, AI is such a giant opportunity, right?
00:20:25.492 --> 00:20:34.863
Some people were saying to me, um, you know, some founders were saying to me, like, this is, this is, The wave of internet all over again, right?
00:20:35.323 --> 00:20:39.492
Uh, and, and sure it's early days.
00:20:39.512 --> 00:20:41.262
We don't know how internet will play out.
00:20:41.823 --> 00:20:47.032
You know, if you remember back in the 90s, we didn't know how it would play out, but eventually we know how it played out, right?
00:20:47.113 --> 00:20:50.462
So is it this wave of internet that will completely transform the world?
00:20:50.502 --> 00:20:53.653
We might be in a very early stage of that and people will figure it out.
00:20:54.232 --> 00:20:59.432
Or it could be those, one of those waves that did not play out like a blockchain wave where we thought it would change the world.
00:20:59.432 --> 00:21:01.752
And, uh, and it didn't.
00:21:02.913 --> 00:21:06.353
I personally think AI is one of the internet kind of waves, right?
00:21:06.413 --> 00:21:08.722
I think it will take a few years for us to figure it out.
00:21:10.192 --> 00:21:16.222
But we will figure it out and it will enable all of humanity, including engineers, including developers.
00:21:16.978 --> 00:21:20.387
to do their jobs in a different way, in a better way.
00:21:21.577 --> 00:21:28.958
So, Gen AI especially, apart from on top of AI, I think is a really, really important innovation that has happened.
00:21:29.498 --> 00:21:30.488
And so it will help us.
00:21:30.607 --> 00:21:32.048
How are we using it at Productiv?
00:21:32.357 --> 00:21:33.837
I think that's one of the questions you asked.
00:21:34.167 --> 00:21:37.768
Yes, there is a general purpose thing that everybody knows.
00:21:38.468 --> 00:21:43.077
That, you know, you use it to write marketing emails, or you use it on the go to market.
00:21:43.877 --> 00:21:46.428
Uh, uh, function side, whether it's customer support or whatnot.
00:21:46.428 --> 00:21:52.488
So we are dabbling with that, some of those tools in there on the product side.
00:21:52.837 --> 00:22:08.083
Uh, you know, we are more involved with AI and, and, uh, and Gen AI in building our product or rather in the product features we enable for our customers instead of, you know, having our developers use Gen AI to write code.
00:22:08.373 --> 00:22:09.222
So we don't do that.
00:22:09.272 --> 00:22:12.772
Uh, the, the second part, our developers are not using Gen AI to write code.
00:22:12.883 --> 00:22:27.842
Everybody's, everything is done literally by hand in the, in the plain old fashion, we are using Gen AI to, to provide human, uh, human friendly or human friendlier interfaces to interact with our product.
00:22:28.162 --> 00:22:39.801
So that people can, our customers can benefit from Gen AI to, to, to understand our product better, to get insights faster and better, to, to get their jobs done faster, uh, using, using product.
00:22:40.886 --> 00:22:41.176
Yeah.
00:22:41.196 --> 00:22:57.106
I wonder if right now we're seeing the most efficiency gains when you have enterprise scale with AI on the product side of things, because I look at like the recent announcement by Amazon that they see their generative AI tool internally saving.
00:22:57.686 --> 00:23:12.977
I believe it was 4, 500 years of work and 260 million annually with it focused specifically on code trans code transformation capabilities, uh, around taking them from like Java 8 and 11 and applications to Java 17, these kinds of things where it's like, okay, this is a migration.
00:23:13.307 --> 00:23:15.037
Devs really don't want to spend their time on this.
00:23:15.037 --> 00:23:20.636
Most devs are not excited by that, but it's something that we want to get done for efficiency sake, long term.
00:23:22.217 --> 00:23:45.386
My, my take right now is that like, to your point, like a lot of these tools that we're leveraging right now in AI are so experimental, especially on the code side, that in order to have the challenge of making sure it all works and the trust side of it, the safety side of it makes sense, you need this level of scale where you can have a pull platform engineering team that's devoted to that and say, Hey, look, yes, we're going to take this first pass of the AI, and then we're going to review it.
00:23:45.676 --> 00:23:48.467
And it's going to save us a ton of time if you have the scale to apply that.
00:23:49.011 --> 00:23:56.872
But for a team of a hundred engineers, it's a lot harder to have a bunch of folks that you can throw at a problem like that when you still have to deliver other business critical work.
00:23:57.692 --> 00:23:58.951
I think you're onto something there.
00:23:59.071 --> 00:24:07.977
Uh, I think, uh, you know, when you have a large amount of repetitive work, Which you can, you know, say there's a standard way to do this.
00:24:07.997 --> 00:24:10.916
We can teach an AI agent on how to do this.
00:24:10.916 --> 00:24:12.896
And then, you know, do the repetitive work.
00:24:13.176 --> 00:24:18.977
Um, it's, it's better to do it via AI than just plain old automation that we used to do in these cases.
00:24:19.277 --> 00:24:38.541
So yes, the power of automation, uh, for standard repetitive work, uh, has been, uh, I guess there's more power if you use AI to do that work, but where most of the work is net new, whether it's, you know, uncharted, Waters, um, with a typical small development team, you have that.
00:24:39.352 --> 00:24:41.721
Then it's harder to use AI as of today.
00:24:41.951 --> 00:24:45.352
It might get easier as we go along, but harder as of today.
00:24:45.902 --> 00:24:46.122
Yeah.
00:24:46.122 --> 00:24:56.011
To your point, I think the innovative work, the kind of innovation piece of technology and startups is where AI is not yet as strong, uh, from my viewpoint, at least.
00:24:56.521 --> 00:24:57.481
I agree with your point.
00:25:00.231 --> 00:25:05.692
You did say you think AI is a transformative internet level wave though potentially.
00:25:05.781 --> 00:25:10.342
Um, what do you kind of see as the future of AI as we go into the next couple years?
00:25:10.432 --> 00:25:15.711
Uh, as maybe it gets better at solving those technical problems or as it gets applied in other areas of technical expertise?
00:25:16.652 --> 00:25:17.271
Absolutely.
00:25:17.632 --> 00:25:17.892
So.
00:25:18.701 --> 00:25:21.892
You know, there are multiple layers of, of Gen AI and AI.
00:25:21.971 --> 00:25:28.021
Uh, and we have all seen those charts, I think, you know, the core of LLMs, which only very few big companies can build.
00:25:28.021 --> 00:25:44.132
And then there are tools on top of that, but then the outermost layer in some sense is this, you know, creating agents, uh, and the simplest way to say this is, is, you know, take any human or any human doing any job and say, we will have an AI agent who can do this job.
00:25:44.571 --> 00:25:46.961
We can have an AI agent who can interview.
00:25:47.237 --> 00:25:51.916
The interviewers or we'll have an AI agent who will respond to you, Connor.
00:25:52.217 --> 00:25:53.987
Uh, or, or, or what have you.
00:25:53.987 --> 00:25:54.227
Right.
00:25:54.227 --> 00:26:04.291
I mean everything, um, uh, and I think that is, no, we may not be able to replace humans completely, but I think what will happen is as AI gets better.
00:26:06.832 --> 00:26:13.561
We will be able to assist almost all humans in almost all jobs that they are doing,
00:26:15.041 --> 00:26:15.731
So do you think this
00:26:15.801 --> 00:26:27.951
a grain of salt, you know, not literally all humans with literally all jobs, but most humans in most jobs will say, we have an AI assistant and it makes us go faster, whether it's in generating content.
00:26:28.606 --> 00:26:37.916
Whether it's in generating insights, whether it's in understanding the word, whether it's in operating machinery or actually doing things via robotic arms or what have you.
00:26:38.622 --> 00:26:49.491
Yeah, I have to say, I think Microsoft's kind of perspective here about like, not just co pilot for engineering, but co pilot for every kind of role is one that makes sense to me, uh, broadly.
00:26:49.491 --> 00:26:54.142
So I'm really interested to see how that is continued to be implemented because I agree.
00:26:54.142 --> 00:26:55.682
I think there's an opportunity to.
00:26:55.882 --> 00:26:57.741
Create those efficiency gains for folks over time.
00:26:57.761 --> 00:27:01.182
It's just a question of, you know, when do you get the scale to make it work?
00:27:01.221 --> 00:27:03.801
And when does it get strong enough to really be impactful?
00:27:03.832 --> 00:27:07.852
Um, and that obviously will vary depending on job areas, depending on how things are set up.
00:27:08.362 --> 00:27:19.082
Um, and I hope that it will kind of reduce the need for some technical expertise in using complex products at times, giving, letting folks learn faster.
00:27:19.402 --> 00:27:21.422
Um, and I'll say like just on a personal level.
00:27:21.856 --> 00:27:30.987
Like, leveraging AI to help me learn something faster has been a great use case, and so I can see how that can potentially scale, uh, across a lot of different areas.
00:27:31.636 --> 00:27:32.557
No, absolutely.
00:27:32.557 --> 00:27:49.557
You know, when, when humans, uh, are freed from, let me say the mundane task or the repetitive task or even some of the creative tasks, but if somebody else is helping them in this case, a co pilot, um, my belief is that humanity just, you know, ups.
00:27:50.326 --> 00:27:58.346
You know, levels up and says, now that I have free time and, and, you know, my, my mind is free to, to do more innovative things.
00:27:58.606 --> 00:28:02.906
We just do it, whether it's learn faster, whether it's do the next level of things.
00:28:03.277 --> 00:28:07.797
Uh, so it will be good for innovation all along that some tasks.
00:28:08.426 --> 00:28:11.836
are appointed to AI co pilot to do for us.
00:28:11.906 --> 00:28:31.612
And, and, and learning is absolutely, uh, absolutely one of those things where it becomes a lot low pressure, in my opinion, when you are talking to an AI based teacher instead of You know, somebody who's pounding you over the head, uh, as, as one example of learning.
00:28:32.126 --> 00:28:42.047
Yeah, I'll be fascinated to see how it transforms the educational sector, uh, over the coming years, because there's a huge opportunity there to improve a lot of, of learning outcomes if done well.
00:28:43.321 --> 00:28:43.922
Absolutely.
00:28:43.922 --> 00:28:49.902
And, uh, you know, uh, and again, uh, none of us are saying that it will replace teachers, right?
00:28:50.251 --> 00:28:58.261
Uh, not saying that because there's a lot of human element of, you know, an emotional element of how a teacher grows a child.
00:28:58.626 --> 00:29:25.386
or whoever is trying to learn, um, uh, but yes, some parts of the repetitive work that, uh, that a teacher is doing, uh, or an educator is doing, if they are taken away by AI, whether it's, you know, whether it's checking the, you know, the scorecards or whether it's, you know, putting together new material or saying the material in different languages or, you know, answering questions at any time in point, right?
00:29:25.386 --> 00:29:29.896
There's a, there's an always available teacher co pilot that a student can talk to.
00:29:30.656 --> 00:29:32.896
All of these things, and there are tons and tons of ideas there.
00:29:33.237 --> 00:29:39.227
Um, I think it will help humanity in every, in every facet, including in teaching.
00:29:39.882 --> 00:29:47.102
I cannot wait until I can have a very efficient earpiece in that lets me live translate languages, uh, and we're getting very close to that.
00:29:47.102 --> 00:29:49.771
So, uh, it is definitely super exciting.
00:29:50.152 --> 00:29:54.692
And I think it's interesting to look at all these kinds of transformational waves together.
00:29:54.692 --> 00:29:59.521
We have obviously this remote work wave, we have changes on the monetary macroeconomic environment.
00:29:59.521 --> 00:30:07.061
We have changes on the technology side with AI following kind of close on the heels of this, like mobile transformation we saw in the 2000, early 2000s.
00:30:08.257 --> 00:30:15.497
How are you kind of navigating things as a founder and bringing this all together in what is a challenging transformational time?
00:30:17.682 --> 00:30:20.592
It is a challenging time, but it's also an exciting time, right?
00:30:20.652 --> 00:30:24.041
I mean, I, I see all of these as opportunities, right?
00:30:24.102 --> 00:30:43.632
Uh, you know, the, the investment landscape, for example, where there is, you know, money is tighter, is forces us to be more disciplined in solving real problems with our customers and talking more to our customers, uh, in, in not taking things for granted, right in, in taking a look under every rock to say, Hey, what did we miss?
00:30:43.991 --> 00:30:45.672
Um, when we look at ai.
00:30:48.047 --> 00:30:57.047
I don't think the days and sure, we are not using the full power of AI today because it's not mature, but I think it's, I think it's coming.
00:30:57.207 --> 00:31:03.106
And we are looking every single day, if not every single week, or rather other way.
00:31:03.326 --> 00:31:05.416
We are looking every single week, if not every single day.
00:31:05.481 --> 00:31:21.061
Um, to see how AI can help us build a better product for our customers, which we are already doing, but also make our engineers, our designers, our salespeople, marketing people, everybody more efficient at their job.
00:31:21.342 --> 00:31:29.682
So in some sense, Yes, the wave of AI and the macroeconomics are pressing us to be more efficient.
00:31:30.781 --> 00:31:34.751
In the same sense, they are also helping us be more efficient.
00:31:35.162 --> 00:31:53.156
And, you know, Productiv is actually in a very unique spot, if I may take a minute there, because, uh, you know, the whole wave of the macro wave of, uh, asking companies to get more efficient with their spend, Turns out that's actually what we do.
00:31:53.386 --> 00:32:06.537
Productiv is, uh, you know, we increase productivity of, uh, of our customers, specifically, you know, the IT teams and the procurement teams in our customers when they are, you know, buying software or managing.
00:32:06.967 --> 00:32:08.757
Software from third parties.
00:32:08.997 --> 00:32:19.186
So when, when the CFO is coming to, uh, our customer CFO is coming to the CIO or the IT teams and saying, Hey, look, let's, let's make sure we are spending money wisely.
00:32:19.416 --> 00:32:22.287
Let's reduce our spend by 10 percent or what have you.
00:32:22.287 --> 00:32:23.896
Productiv is the tool that helps them.
00:32:23.896 --> 00:32:27.626
So in some sense for us specifically, this whole macroeconomic wave.
00:32:28.446 --> 00:32:35.227
of, of saying cost efficiency, um, is actually helping us, uh, because that's what we, that's the problem to solve for our customers.
00:32:35.936 --> 00:32:36.267
Yeah.
00:32:36.267 --> 00:32:38.176
And definitely go check out Productiv.
00:32:38.196 --> 00:32:40.487
com for folks who are interested in learning more.
00:32:40.497 --> 00:32:41.727
It's a really interesting company.
00:32:41.727 --> 00:32:53.076
And, um, I saw you actually put out a trend report on the 2024 state of SaaS consolidation trends, uh, that looked really fascinating as well, and maybe speaks to some of these technology trends that are happening.
00:32:53.957 --> 00:32:54.636
Absolutely.
00:32:54.646 --> 00:33:01.507
And, and, and it goes to the point that you were saying earlier, in fact, the trend is not that people are adopting less technology.
00:33:02.247 --> 00:33:09.656
The trend is that every enterprise is adopting more technology because more technology is needed.
00:33:10.136 --> 00:33:15.106
To, to enable a geo distributed workforce to work more efficiently, you need better tools.
00:33:15.497 --> 00:33:22.886
Uh, and yes, we are seeing a giant uptick in the use of, uh, AI based technologies, starting with, you know, ChatGPT.
00:33:22.886 --> 00:33:31.817
Obviously, we are seeing ChatGPT has suddenly become the thing that everybody, you know, Most people in most companies are using, whether or not, you know, their leaders know about it.
00:33:31.876 --> 00:33:34.817
Everybody is actually trying to use it.
00:33:34.817 --> 00:33:39.076
But, uh, but people are using more tools.
00:33:39.156 --> 00:33:40.946
Enterprises are using more tools.
00:33:41.007 --> 00:33:44.727
It's just that they are trying to do it more efficiently.
00:33:45.297 --> 00:33:47.896
They are trying to manage their workforce more efficiently.
00:33:47.896 --> 00:33:51.247
But technology is the solve for
00:33:52.326 --> 00:34:08.447
Yeah, it's going to be really interesting, speaking of OpenAI, to see how their next raise goes, because they, they are like such a market leader, but they're also burning through cash and have a, like, potentially the highest ever private valuation for a raise in this next raise they're going to go through.
00:34:08.447 --> 00:34:14.916
So I'll be fascinated to see, you know, how Microsoft handles them continuing to give them Azure credits and everything else to go into it.
00:34:14.916 --> 00:34:17.237
It's going to be a really interesting ride for them.
00:34:18.686 --> 00:34:18.887
it.
00:34:18.887 --> 00:34:19.836
It must be.
00:34:19.867 --> 00:34:24.166
This is way above my pay grade to figure out what Microsoft and OpenAI will
00:34:24.181 --> 00:34:26.521
we're just, we're just, we're just interested to see.
00:34:26.521 --> 00:34:27.492
Yeah, absolutely.
00:34:27.532 --> 00:34:27.922
Absolutely.
00:34:28.012 --> 00:34:38.192
So, uh, well, let me ask you, like, this is, I mean, we're seeing these technology trends, we're seeing more and more technology purchases, we're seeing this consolidation effect you mentioned, uh, in Productivs research.
00:34:38.742 --> 00:34:48.251
How do you keep your team, you know, motivated, focused, and, you know, maintain this, like a high context, high agency culture in these challenging times?
00:34:51.067 --> 00:34:54.976
it's really, uh, you know, that formula hasn't changed, right?
00:34:54.996 --> 00:35:08.376
And throughout my years, uh, of working at many, many companies, you know, whether it's Microsoft or Amazon, big names or, or small, smaller companies like Postmates or, or Productiv itself, uh, for the last six and a half years.
00:35:09.067 --> 00:35:10.442
What I've found is that.
00:35:10.931 --> 00:35:15.791
You know, top talent, especially in product and engineering, but really across the board in any discipline.
00:35:16.291 --> 00:35:18.172
Uh, but I'll speak to product engineering.
00:35:19.211 --> 00:35:24.041
You know, top talent product and engineering really wants just three things, right?
00:35:24.532 --> 00:35:28.121
Um, they, they, they know they can solve big problems.
00:35:28.211 --> 00:35:34.871
So, so a, they, they want big problems to be available to them to be solved.
00:35:35.362 --> 00:35:36.271
They say, challenge me.
00:35:36.641 --> 00:35:40.851
I want to use my time and my skills in solving something meaningful.
00:35:41.586 --> 00:35:55.556
So if you, if we can, if we can put together a, you know, a good vision on saying these are the set of problems that our customers may want solved over many, many years, hopefully a multi year vision, uh, and then a short term vision part of that.
00:35:55.586 --> 00:35:56.967
And then those problems are difficult.
00:35:57.047 --> 00:35:58.706
They're not trivial to be solved.
00:35:59.387 --> 00:36:02.146
That is first piece of the puzzle, right?
00:36:02.726 --> 00:36:13.387
The second piece of the puzzle is Uh, you know, top, uh, top performers or top talent understands that hard problems they cannot solve by themselves.
00:36:13.527 --> 00:36:18.536
So they need a team of other, you know, whatever top talent around them.
00:36:19.056 --> 00:36:21.067
Uh, a lot, lot of engineers.
00:36:21.416 --> 00:36:33.896
Uh, uh, you know, recently and in the past years, uh, consistently saying, sheesh, the, the biggest reason I'm, I'm excited coming to work every day is look at the team around.
00:36:33.956 --> 00:36:35.567
I love working with these people.
00:36:35.896 --> 00:36:38.876
I love solving hard problems with these people.
00:36:38.887 --> 00:36:43.947
There's always an aha moment that I say, Oh my God, I'm so smart.
00:36:43.987 --> 00:36:46.317
I used to think I'm so smart, but I'm clearly not smart enough.
00:36:46.797 --> 00:36:50.487
Because these are the people that have figured this out better and they're like, I love it.
00:36:50.836 --> 00:36:51.117
Right.
00:36:51.396 --> 00:36:58.516
Uh, and everybody gets those aha moments almost every day where they find somebody solving somebody with a better suggestion than they were thinking of.
00:36:59.056 --> 00:37:07.197
Uh, so having a team of top talent around them, uh, uh, uh, and the third thing is, is actually a very interesting thing.
00:37:07.226 --> 00:37:07.976
So if you have.
00:37:08.467 --> 00:37:24.956
If you have hard problems, and if you identify and if you gather a team of people who can solve those hard problems, then the only thing for a leader to do is really let that team actually solve the hard problems, which is what we call culture.
00:37:25.257 --> 00:37:28.976
Broadly speaking, which is saying, hey, how much bureaucracy do you insert in the middle?
00:37:29.387 --> 00:37:31.637
How many forms do they have to fill in triplicate?
00:37:31.867 --> 00:37:34.047
How many roadblocks are you putting in front of them?
00:37:34.067 --> 00:37:36.016
Or how many roadblocks are you taking out?
00:37:36.257 --> 00:37:40.086
And my philosophy has been, you know, take out as many roadblocks as you can.
00:37:40.097 --> 00:37:47.976
Let these, let these high powered people actually solve the hard problems that our customers want to get solved.
00:37:48.956 --> 00:37:59.711
If they see that impact and they see that they can actually create that impact, Uh, then, you know, uh, you know, these, these things that you were talking about, how do you motivate them to come to work every day?
00:37:59.722 --> 00:38:03.172
Or how do you motivate to put their best foot forward?
00:38:03.612 --> 00:38:04.541
I don't need to.
00:38:04.992 --> 00:38:07.101
Uh, it comes naturally.
00:38:07.501 --> 00:38:09.612
Uh, I don't even have to ask.
00:38:09.612 --> 00:38:10.882
I see this in the work.
00:38:10.882 --> 00:38:12.001
I see is in the smiles.
00:38:12.492 --> 00:38:13.371
I see this.
00:38:13.927 --> 00:38:23.427
When, um, when every single time, you know, an engineer cannot look past, you know, the next month of good work on their plate.
00:38:23.867 --> 00:38:26.447
They actually ping me and say, Ashish, we have a problem.
00:38:26.447 --> 00:38:27.447
I'm like, what's the problem?
00:38:27.836 --> 00:38:32.507
Ashish, I only have one month of, you know, good work left on my plate.
00:38:32.567 --> 00:38:34.737
I will be like, I'll be idle.
00:38:35.467 --> 00:38:37.496
I'm like, no, no, this is not a problem, it's plenty of things.
00:38:38.086 --> 00:38:41.947
But imagine somebody saying, I need more work, more hard work.
00:38:42.407 --> 00:38:45.597
Uh, and, and that's, I love that culture.
00:38:46.447 --> 00:38:53.617
And, and I think if you get to that state, um, things just happen, you know, good things happen for a company and for a team.
00:38:53.987 --> 00:38:57.177
And fortunately, it has happened for me over many years now.
00:38:57.817 --> 00:39:09.266
I love to hear that because I'll say as like a team member, like you want to learn if you're a high performer, you want to be around people who push you and, you know, help make you better and who you respect deeply.
00:39:09.266 --> 00:39:21.121
And so this kind of perspective of like, not only am I going to bring in the right people to have that kind of high learning, high impact culture, but also going to give them autonomy and agency to Go do these things and not feel micromanaged.
00:39:21.402 --> 00:39:22.922
I mean, those are the best kind of teams.
00:39:22.952 --> 00:39:27.442
Um, uh, she says, this has been a fantastic conversation before we wrap up.
00:39:27.481 --> 00:39:31.652
Do you have any other closing advice that you want to share with, uh, the leaders who are listening today?
00:39:36.047 --> 00:39:41.777
Look, the closing advice as a founder, I would, I would say, yes, you've heard everything about how, you know.
00:39:42.907 --> 00:39:48.666
How the markets are tough, the macro is tough, the, you know, the AI is taking over the world, which is, by the way, an opportunity.
00:39:49.007 --> 00:39:56.286
Uh, but, but my closing advice is, is, is to, is to stick to the first principles, right?
00:39:57.556 --> 00:40:02.916
Always stick to saying, hey, look, do we really understand a customer problem?
00:40:02.916 --> 00:40:11.237
If you, if you understand a customer problem, if you're solving a real problem, and if you have, if that problem is hard, that cannot be just solved by somebody else after you solve it.
00:40:11.927 --> 00:40:15.856
So find a customer problem, which is real, which is hard enough.
00:40:16.262 --> 00:40:21.222
Which is unsolved, uh, And then you know Get a good team.
00:40:21.231 --> 00:40:51.467
You can't do it yourself If you have a good customer problem and a good team to help you solve that problem Everything else will come don't worry about whether people will invest You know what if you're solving a good problem, there'll be a lot of investors lined up outside your door Uh, if you're solving a good problem with a good team or a good seed team Uh, there'll be a lot of other good team members lined up outside your door whether you use ai or not is your choice Uh, uh, that's a solution looking for a problem.
00:40:51.467 --> 00:40:53.706
So if you go problem first, you'll be fine.
00:40:54.106 --> 00:40:59.356
If you invent a solution looking for a problem later, um, then that's a path of trouble.
00:41:00.202 --> 00:41:00.731
I love it.
00:41:01.231 --> 00:41:02.291
Thank you so much, Ashish.
00:41:02.291 --> 00:41:07.021
It's been a fantastic time having you on the show and I'm sure our listeners will take a lot away from this conversation.
00:41:08.286 --> 00:41:19.626
So for more insights about the software engineering landscape, what's happening in the startup ecosystem and more from Ashish, be sure to check out this week's edition of our Dev Interrupted newsletter on Substack, where we dive deeper into the topics we discussed today.
00:41:19.967 --> 00:41:22.047
We'd also love to hear your thoughts on today's discussion.
00:41:22.047 --> 00:41:28.336
So join the conversation on Twitter or LinkedIn by tagging at Dev Interrupted, and let us know how you're navigating these challenges in your own work.
00:41:28.666 --> 00:41:30.527
Ashish, thank you so much for joining me today.
00:41:30.527 --> 00:41:31.097
It's been a pleasure.
00:41:32.007 --> 00:41:32.536
Thank you, Conor.
00:00:00.000 --> 00:00:01.330
Welcome back to Dev Interrupted, everyone.
00:00:01.409 --> 00:00:02.959
I'm your host, Conor Bronsdon.
00:00:02.990 --> 00:00:06.969
Today, I'm joined by Ashish Aggarwal, founder and CTO of Productiv.
00:00:07.059 --> 00:00:11.279
Ashish is not only a successful founder, but also an active investor in over 30 companies.
00:00:11.619 --> 00:00:14.179
He's someone who's deeply involved in the startup ecosystem.
00:00:14.519 --> 00:00:19.368
And today we're excited to hear his insights on scaling teams, the impact of AI on startups.
00:00:19.669 --> 00:00:22.300
And how to navigate the current macroeconomic climate.
00:00:22.750 --> 00:00:23.829
Welcome to the show Ashish.
00:00:25.000 --> 00:00:25.579
Thank you, Conor.
00:00:25.579 --> 00:00:26.250
Glad to be here.
00:00:26.929 --> 00:00:41.539
Yeah, I'm really excited for this conversation because I think you bring a lot of insight, both from your personal experiences and the kind of awareness you have of what's happening within tech startups, within software engineering broadly, but before we jump into the conversation.
00:00:41.749 --> 00:00:51.000
I do want to remind our audience that if you enjoy our episodes, please just take a brief moment to rate and review Dev Interrupted on your podcasting app, Spotify, Apple Podcasts.
00:00:51.299 --> 00:00:57.299
Your feedback helps us reach more listeners and continue to bring you more great conversations with engineering leaders like Ashish.
00:00:57.758 --> 00:00:59.067
now we've got that a little bit out of the way.
00:00:59.067 --> 00:01:01.198
We all know the startup ecosystem has changed.
00:01:01.408 --> 00:01:07.067
it's becoming increasingly difficult for companies to receive enough funding, let alone become unicorns.
00:01:07.147 --> 00:01:17.438
In fact, there's data from Carta that shows that of startups that raised their seed round in Q3 2020, only about 40 percent have graduated to Series A in two years.
00:01:17.608 --> 00:01:23.927
But that's actually quite a high rate compared to the folks who raised their seed cohort in Q1 2022.
00:01:23.938 --> 00:01:25.248
So that's just 15.
00:01:25.248 --> 00:01:29.078
4 percent of those folks have actually raised a series A in the 8 quarter set.
00:01:29.078 --> 00:01:30.617
So we're seeing this cash crunch.
00:01:31.087 --> 00:01:34.658
And then on top of that, Gen AI is reshaping the tech landscape.
00:01:34.918 --> 00:01:39.097
And leaders have to reckon with how to keep their teams motivated and happy in these challenging times.
00:01:39.878 --> 00:01:41.067
So with that in mind.
00:01:41.373 --> 00:01:45.983
Ashish, as someone who's deeply involved in the startup world, what are you seeing in the ecosystem right now?
00:01:46.332 --> 00:01:49.683
And how do you think startups should think about growing in this environment?
00:01:51.563 --> 00:01:52.438
Right, Conor.
00:01:52.438 --> 00:01:54.373
So you are right, right?
00:01:54.563 --> 00:01:55.832
Things have changed.
00:01:56.423 --> 00:02:01.513
I, I would not say things have changed for, for, you know, better or for worse.
00:02:01.543 --> 00:02:03.352
It's just that it's a new environment.
00:02:03.832 --> 00:02:09.143
And I think, uh, you've heard of this, that it's no longer growth at any cost.
00:02:09.312 --> 00:02:12.002
It's now the name of the game is efficient growth, right?
00:02:12.372 --> 00:02:13.223
And, and so.
00:02:13.478 --> 00:02:26.487
That's what I am seeing in, in, in Productiv, but also across the market in different companies that I am involved with, um, investors and rightfully so are saying, Hey, look, money's not cheap.
00:02:26.967 --> 00:02:36.397
We want, we want you to judiciously use the money, uh, you know, invest, build products, which are useful.
00:02:36.437 --> 00:02:39.897
Don't just go chase, you know, some pie in the sky kind of thing.
00:02:39.897 --> 00:02:43.377
So efficient growth, uh, is, is the name of the game now.
00:02:44.022 --> 00:02:45.473
Yeah, definitely.
00:02:45.772 --> 00:02:52.413
There's been a shift from this aggressive growth, growth at all costs, as you put it, to more sustainable, more efficient strategies.
00:02:52.742 --> 00:02:56.443
And it's definitely a trend I'm noticing in conversations with industry leaders.
00:02:56.842 --> 00:03:00.622
What challenges or opportunities do you see this creating for startups today?
00:03:02.793 --> 00:03:17.612
It, it really, uh, you know, to some extent, it, uh, to some extent, it, it, it, it solves that problem of saying this, the market, the startup markets, the startup ecosystem had become too crowded, right?
00:03:18.112 --> 00:03:23.483
Um, everybody running after, you know, any, any reasonable opportunity.
00:03:24.092 --> 00:03:35.927
Now it is, you know, uh, you know, sometimes they say, What's the saying there that, you know, you, you, you separate the milk from the water or what, what have you eat from the shaft?
00:03:36.348 --> 00:03:39.918
Um, I think that's what we are seeing now.
00:03:39.918 --> 00:03:42.758
So the opportunity there now is, is, is, is clear.
00:03:42.978 --> 00:03:46.897
The opportunity for startups is clear, especially for founders.
00:03:47.758 --> 00:03:52.957
Have, have, have a clear vision of what customer problem you're solving.
00:03:53.427 --> 00:03:55.307
How you can actually solve it.
00:03:56.138 --> 00:04:06.758
In without requiring, you know, a giant boatload of money to make a small amount of money, are being forced in, in, and this is good.
00:04:06.758 --> 00:04:13.157
Actually, the founders are being forced to think like businessmen, like business people.
00:04:13.747 --> 00:04:18.807
They're like, look, you are creating a company, but after all, you are creating a business.
00:04:19.288 --> 00:04:21.187
You have to solve a real problem.
00:04:21.197 --> 00:04:22.838
You have to have real customers.
00:04:23.108 --> 00:04:27.737
You have to have a real plan of how you will make money.
00:04:28.377 --> 00:04:30.228
And it could be that the money will come in the long term.
00:04:30.228 --> 00:04:32.858
That is an okay plan, right?
00:04:33.137 --> 00:04:34.997
Doesn't need to be profitable from day one.
00:04:35.007 --> 00:04:43.742
But you need to think about that, um, instead of the good old plan of Well, we'll just get more money from the market.
00:04:43.773 --> 00:04:45.093
We don't need to actually make money.
00:04:45.512 --> 00:04:48.843
Uh, so, uh, and I think that's, that's good for the startup ecosystem.
00:04:48.872 --> 00:04:52.653
Overall, it makes everybody more healthy.
00:04:52.963 --> 00:04:56.673
Uh, if, if we think of first principles in building a business
00:04:57.497 --> 00:04:58.307
I agree with you.
00:04:58.307 --> 00:04:59.737
I think it's a positive.
00:05:00.088 --> 00:05:02.088
For the ecosystem in the long run, though.
00:05:02.088 --> 00:05:05.278
I know it can feel hard for founders and employees in the moment.
00:05:05.737 --> 00:05:21.038
it's like that we're not trying to downplay how hard this is for many companies, for many people, but the long term impacts on having better businesses that are more efficient, that hopefully have a better impact for the dollars spent on them, uh, should be positive.
00:05:21.548 --> 00:05:24.158
And I wonder if, yeah, sorry, go ahead.
00:05:25.062 --> 00:05:25.742
No, absolutely.
00:05:25.742 --> 00:05:30.483
And I just want to echo that this is not to, uh, to, to downplay at all.
00:05:30.882 --> 00:05:35.512
Uh, how hard, you know, employees, founders, even investors are feeling in this market.
00:05:35.862 --> 00:05:36.983
Uh, absolutely.
00:05:36.983 --> 00:05:38.603
Everybody is feeling the heat.
00:05:39.012 --> 00:05:43.952
Um, but the more people I talk to, the more they say, Hey, Ashish, you know what?
00:05:45.362 --> 00:05:47.312
We are that company.
00:05:47.333 --> 00:05:51.762
We are those people who are solving a real problem, who have real customers.
00:05:52.083 --> 00:06:14.903
In fact, the fact that we are, uh, we are forced to, or we are more encouraged now to talk to the customers, to understand the problem a little bit deeper, to, to understand the revenue potential, to show the value to our customers and then to the market, it's helping us build a good product and a good sales motion much faster than, than if we were not under this pressure.
00:06:15.153 --> 00:06:17.543
So the pressure in some sense is bringing out the best.
00:06:18.242 --> 00:06:19.137
In a lot of people,
00:06:20.548 --> 00:06:22.127
you think that a lot of this.
00:06:22.502 --> 00:06:26.132
Kind of rationale of, Hey, we have to be like the full winners.
00:06:26.132 --> 00:06:27.403
We have to monopolize a market.
00:06:27.403 --> 00:06:28.992
We have to just push for growth, growth, growth.
00:06:29.333 --> 00:06:48.293
I mean, I know it was related to cheap money, obviously, like that's a huge part of it, low interest rates, but do you think part of it also was related to seeing these outsize winners and getting obsessed with the Googles, the metas, uh, and the kind of massive outcomes they had versus realizing, Hey, I can, I can run an efficient business and I, maybe I'm not going to own an entire massive market.
00:06:49.348 --> 00:06:50.918
it's, it's somewhat true.
00:06:50.918 --> 00:07:07.588
I, you know, again, I cannot speak to who was thinking like what, um, but, but I think in general that, that, uh, that mentality was, uh, I did see that a lot of saying, Hey, we'll figure out the moneymaking part later.
00:07:08.447 --> 00:07:13.858
Uh, uh, you know, uh, uh, you know, people were always, I think, I think they were disciplined enough.
00:07:14.362 --> 00:07:18.523
To say, hey, are we solving a good problem, a real problem, or at least a realistic problem?
00:07:19.692 --> 00:07:24.622
But that part about efficiency wasn't part of the picture because nobody's asking for it.
00:07:24.622 --> 00:07:32.603
They're like, go keep spending money, get to, you know, get to a hundred million customers or something, and then figure out how to make money, right?
00:07:33.372 --> 00:07:37.853
And only a very few people in the in the world, or very few companies in the world now can afford that.
00:07:38.668 --> 00:07:40.827
I mean, Facebook can clearly still afford that.
00:07:40.858 --> 00:07:44.427
It doesn't make any money from WhatsApp, but, but hey, it's a good business.
00:07:44.593 --> 00:07:45.663
Brand play, yeah.
00:07:46.288 --> 00:07:49.247
can play, uh, but not everybody is Mark Zuckerberg.
00:07:49.267 --> 00:07:50.098
So, you know,
00:07:51.202 --> 00:07:51.473
Very
00:07:51.708 --> 00:07:54.588
so, so we, we, we have to have a different strategy than Mark.
00:07:55.523 --> 00:08:07.312
So, given that need to adapt to more efficient growth, are you seeing more teams looking for talent from nontraditional backgrounds or in nontraditional geographies?
00:08:07.798 --> 00:08:08.218
yes.
00:08:08.298 --> 00:08:12.158
And, and, and to tell the truth, I am, I'm hearing.
00:08:12.932 --> 00:08:30.583
Um, that this is a trend, not just in small companies or startups, but also a trend in in much bigger companies, uh, uh, also, you know, uh, I'm hearing that most of the new budget that comes in is first allocated to, uh, quote, unquote, offshore.
00:08:31.747 --> 00:08:37.528
Offshore growth, whether it is Latin America or India or wherever, right?
00:08:37.857 --> 00:08:40.317
Wherever is your favorite location for getting the talent.
00:08:41.488 --> 00:08:44.817
And I think a lot of factors have combined into that, right?
00:08:46.067 --> 00:08:56.778
One, we all know that, you know, COVID as bad it was, it also showed all of us how we can work remotely from anywhere.
00:08:57.587 --> 00:09:01.398
And so that opened the doors to say, well, you know, if you have good talent.
00:09:02.388 --> 00:09:07.668
In fact, the top talent specially knows how to work from anywhere in the world.
00:09:08.357 --> 00:09:14.927
The tools have become more mature, the mentality has become more mature of working with each other from anywhere.
00:09:15.447 --> 00:09:17.207
So the teams can be geo distributed.
00:09:18.668 --> 00:09:31.258
And so now, and because of the cost pressure, now the name of the game has become, Hey look, get the top talent, because after all, that is also part of the pressure to say, Hey, You have to solve a real problem.
00:09:31.258 --> 00:09:32.317
You have to solve it fast.
00:09:32.317 --> 00:09:36.878
You have to make sure the customer value is there and so on and so forth, which means you need top talent.
00:09:37.538 --> 00:09:40.227
But the good news is there's talent all over the world.
00:09:40.592 --> 00:09:48.192
Including in the United States, by the way, we are part of the world and we have a lot of, a lot of top talent right here in the United States.
00:09:49.812 --> 00:09:54.423
But it doesn't need to be limited to the locality that you happen to be in.
00:09:54.832 --> 00:10:03.793
So, so yes, I am seeing startups and big companies, but especially startups move more towards geo distributed teams.
00:10:04.158 --> 00:10:07.988
Instead of central, uh, you know, co located teams.
00:10:08.452 --> 00:10:13.503
And to your point, that's really been enabled by us being forced to figure out working remotely.
00:10:13.503 --> 00:10:21.153
Like, I had been working remotely or hybrid for years before COVID, but now everyone has had to make that transition and at least experience it for a couple of years.
00:10:21.543 --> 00:10:27.472
Um, and it's really, I think, made companies aware of the opportunities to acquire talent worldwide.
00:10:27.482 --> 00:10:31.342
And it's made the job market competitive worldwide, it seems like as well.
00:10:32.107 --> 00:10:33.398
Absolutely, absolutely.
00:10:34.837 --> 00:10:45.337
Look, yes, this is happening, but also I'm seeing on the opposite side, uh, you know, there is still benefit in pockets, right?
00:10:45.378 --> 00:10:50.538
Depending upon the role, depending upon, you know, what you want to do, what problem you are solving specifically.
00:10:50.717 --> 00:10:56.707
Some roles, some teams just work better when they are co located, when they are co located.
00:10:57.008 --> 00:11:03.587
Brainstorming on a whiteboard, uh, it just makes higher efficiency or higher productivity for the company.
00:11:03.878 --> 00:11:12.227
Uh, again, reminder that the problem we are trying to solve is just higher efficiency for the company, not geo distribution for the sake of geo distribution.
00:11:12.658 --> 00:11:18.677
Uh, it is really the top talent which is driving things, not just that you can get things for lower cost.
00:11:18.738 --> 00:11:21.018
Nobody wants to sacrifice productivity here.
00:11:21.518 --> 00:11:37.118
Uh, so, uh, so in some cases I'm also seeing that people are saying, you know, uh, this job, you know, whatever, it can vary from company to company situation to situation, uh, but these kind of jobs should be co located because they just work better together.
00:11:38.107 --> 00:11:47.618
I'm curious if you think that is true of like product and engineering roles, because there are many C suite leaders who are kind of empathize or emphasizing a return to office.
00:11:47.868 --> 00:11:49.077
So I'm curious on your perspective there.
00:11:49.748 --> 00:11:52.118
Yes, product engineering is, is interesting.
00:11:52.118 --> 00:11:58.158
I mean, it's, it's, uh, uh, we, we, uh, It's unfortunately not a single kind of a role, right?
00:11:58.158 --> 00:12:01.827
Product engineering has, you know, dozens of sub roles in it.
00:12:02.467 --> 00:12:25.863
So, for example, when your web engineer or front end engineer is trying to work with your UX designer, um, and they want to, you know, brainstorm things, And if they want to use a whiteboard, which they typically do, uh, and co create, does it help if they are in the same room compared to a Zoom call?
00:12:26.133 --> 00:12:26.682
I think so.
00:12:26.712 --> 00:12:29.472
I think it helps that they are in the same room, right?
00:12:30.528 --> 00:12:40.988
But if you're an infrastructure engineer who's trying to, you know, deploy the latest version of the OS on your AWS machines, um, right?
00:12:41.337 --> 00:12:48.837
In fact, even, even their teams, the infrastructure team, uh, do they say that, hey, man, we'll, we'll, we'll work well?
00:12:49.863 --> 00:12:58.123
Even if we are remote, in fact, we might work better if, you know, if we are, uh, if we are free of distractions and nobody is like interrupting us.
00:12:58.663 --> 00:13:06.102
So it varies even within product and engineering, what kind of role and by the way, even those roles in some other company might be flipped, right?
00:13:06.133 --> 00:13:12.982
Somebody might say my infra team needs to be in person and my product design can be, can work, uh, you know, remotely with each other.
00:13:14.783 --> 00:13:17.993
I empathize with the leaders who, who say.
00:13:18.293 --> 00:13:22.842
You know, back to work is necessary for for some roles in some situations.
00:13:23.133 --> 00:13:28.253
Hell, I am one of those leaders, uh, who says not everybody needs to be co located.
00:13:28.432 --> 00:13:31.653
Uh, we have a, we have a geo distributed team here at Productiv.
00:13:32.363 --> 00:13:36.643
Uh, you know, we have engineers and other people in, in India.
00:13:36.682 --> 00:13:42.452
We have people in New York, in Denver, in Seattle, in, uh, in Palo Alto and so on and so forth.
00:13:42.773 --> 00:13:45.082
So we are a geo distributed team.
00:13:45.432 --> 00:13:47.842
Um, but then within these hubs.
00:13:48.307 --> 00:13:53.597
Uh, when I see some people, you know, come together to work and, and brainstorm ideas.
00:13:55.342 --> 00:14:02.033
You know, whiteboard or not, I think they often call me back and say, you know, it was a good idea to meet at work.
00:14:02.243 --> 00:14:03.452
You know, a couple of times a week.
00:14:03.793 --> 00:14:05.423
It made us go faster.
00:14:06.467 --> 00:14:20.768
it's really interesting as someone who, I think, personally, I thrive in a remote work environment, but like, even as someone who is very pro remote work in general, there are clear benefits to in person collaboration.
00:14:20.778 --> 00:14:24.238
There are clear benefits to co location at times.
00:14:24.518 --> 00:14:28.118
Um, and like, I've been lucky to work in an environment the last several years where.
00:14:28.802 --> 00:14:39.232
I am generally remote, but then I'll spend a week or two every quarter in person with my teams, in person with my colleagues, collaborating, collaborating across teams, collaborating on like key projects.
00:14:39.232 --> 00:14:41.373
And then we go out and execute on that work afterwards.
00:14:42.332 --> 00:14:48.163
But to your point, I think it does really depend on the culture of the company, the type of folks you have involved, and the type of roles.
00:14:48.192 --> 00:14:51.523
And like, it's a really tough balance.
00:14:51.793 --> 00:15:02.732
Right now to both try to acquire global talent, get access to all these incredibly talented people worldwide, while also retain some of those collaborative benefits and find the right mix.
00:15:03.173 --> 00:15:16.202
So, uh, yeah, I think your, your point about the variation between roles is one that I think a lot of folks need to hear, uh, in what too often becomes an almost like religious debate of remote versus hybrid versus in office.
00:15:16.202 --> 00:15:21.682
And there's, it's, it's one or the other, instead of this, like, Series of greys, which I think is most things.
00:15:22.143 --> 00:15:23.273
Yeah, it's true.
00:15:23.273 --> 00:15:45.462
And, you know, for a product of engineering, product and engineering, my philosophy has evolved into, um, to reuse the word a hybrid philosophy, which is, It's not pure remote that, you know, if there are, whatever, a hundred people in the team, they are in a hundred different locations and, you know, everybody's just pure remote, but it's also not that all hundred are in the same location.
00:15:45.712 --> 00:15:46.592
So what's the hybrid?
00:15:46.613 --> 00:16:07.778
The hybrid is create Create, you can call it hubs, create pockets where you put a team together of, you know, of the right roles and say you people can collaborate in person again in, in, in multiple times a week or whatever works for you.
00:16:08.107 --> 00:16:12.538
Um, and you have this certain, certain goals that you are trying to solve.
00:16:12.538 --> 00:16:14.158
You have a certain problem that you are trying to solve.
00:16:14.628 --> 00:16:19.477
And, and if you, if you distribute this properly, you'll create multiple hubs and each hub is solving the problem.
00:16:19.597 --> 00:16:22.967
an important problem and they have the choice to collaborate in person.
00:16:23.317 --> 00:16:35.577
If and when they need to, and I think that works a little bit better, more flexibility on both sides, you get the global talent, but you also have the choice of working in person, especially for product and engineering.
00:16:35.577 --> 00:16:36.967
I think that works pretty well.
00:16:38.413 --> 00:16:47.802
So Obviously, there's this massive alchemy people are trying to solve right now of how do you set up your teams for this new geo distributed world with global talent?
00:16:48.052 --> 00:16:50.312
How do you make sure you retain some of that in office collaboration?
00:16:50.312 --> 00:16:51.863
How do you set up the right infrastructure?
00:16:52.253 --> 00:16:59.143
So it's awesome to hear about your approach to this, but there's this entire other element of change that's coming through too, which is obviously AI.
00:16:59.153 --> 00:17:03.123
You know, speaking of efficiently scaling a business, I'd be kind of remiss if I didn't bring this up.
00:17:03.503 --> 00:17:10.357
But it's interesting because if you ask You know, individual developers, many will say, at least senior devs, I don't really use AI that much.
00:17:10.627 --> 00:17:14.347
Like it's not, it's not helping me with the things that are actual problems for me, like meetings.
00:17:14.367 --> 00:17:20.087
It's not helping me with the things that make me less efficient, which is some of the, it's not the coding that's the problem for me.
00:17:20.417 --> 00:17:23.317
Now, I think you talked to junior devs who are coming up and they're like, Oh, I rely on this.
00:17:23.327 --> 00:17:24.428
It's, it's very important to me.
00:17:24.738 --> 00:17:29.778
And if you talk to leaders, I think leaders are all think there are massive opportunities with AI.
00:17:29.778 --> 00:17:36.887
And even many devs will talk about, you know, tests are a great thing that I can, you know, uh, can offload out to an AI to help me write some tests and do these other things.
00:17:37.117 --> 00:17:39.907
What's your perspective on this kind of AI wave?
00:17:39.907 --> 00:17:41.978
How are you utilizing AI at Productiv?
00:17:42.928 --> 00:17:45.557
Um, so, so many questions in one, right?
00:17:45.778 --> 00:17:46.367
Yeah, sorry.
00:17:46.367 --> 00:17:46.637
I threw
00:17:46.657 --> 00:17:49.567
AI, I will, I will, I'll take them one at a time.
00:17:49.567 --> 00:17:51.938
I think, uh, is AI useful?
00:17:51.988 --> 00:17:52.708
Absolutely.
00:17:52.788 --> 00:17:53.117
Right.
00:17:53.288 --> 00:17:54.448
Is there a disconnect?
00:17:54.448 --> 00:17:54.617
Correct.
00:17:55.087 --> 00:18:14.357
Between, you know, how individual developers might be, uh, uh, might, might be using AI to, to improve coding or testing, uh, or what have you, versus what's the, you know, versus what's the marketing pitch saying, Hey, AI will solve word hunger.
00:18:14.688 --> 00:18:15.407
Absolutely.
00:18:15.407 --> 00:18:16.817
So, I am, let me agree with you.
00:18:17.228 --> 00:18:26.678
Uh, you know, the, the you know, the, the promise of AI today is way bigger.
00:18:27.093 --> 00:18:33.702
Then the actual reality of how people are using AI, um, including developers.
00:18:35.452 --> 00:18:38.573
Has AI made Productiv developers 30 percent better?
00:18:39.663 --> 00:18:40.313
It hasn't.
00:18:40.573 --> 00:18:44.222
We, uh, have we tried looking at it?
00:18:44.252 --> 00:18:44.742
Yes.
00:18:45.563 --> 00:18:48.452
In its current form, we said, let us wait a little, right?
00:18:48.452 --> 00:18:54.212
We are We are not finding it as useful as it's hyped to be.
00:18:55.423 --> 00:19:04.333
I'm hearing the same thing from many other, many other engineering leaders saying we, as a leadership level, we want to try it just in case there is something here.
00:19:04.653 --> 00:19:19.992
But individual engineers are saying that trying is one thing, but I have my day job here, uh, you know, I, I am, uh, it's not helping if I have to review the entire output of GenAI to see what did it miss and what did I get, uh, right.
00:19:20.182 --> 00:19:26.303
Um, and plus I have to maintain all the code anyway, so I have to understand the whole code and I have to correct mistakes.
00:19:26.303 --> 00:19:28.692
So it helps, but not really that much.
00:19:29.573 --> 00:19:31.932
From, and, and we have explored the ideas.
00:19:32.147 --> 00:19:38.597
Behind testing, uh, test generation or, you know, code reviews or code generation naturally.
00:19:40.268 --> 00:19:41.228
Is it going to get there?
00:19:41.248 --> 00:19:41.867
I think so.
00:19:41.877 --> 00:19:49.657
I think AI is, uh, is, is one of those things that we are early, right?
00:19:49.798 --> 00:19:57.468
And as we are building and learning, um, you know, you were asking earlier, how is AI impacting the startup ecosystem?
00:19:58.498 --> 00:20:05.057
It's, it's, it's, it's a sword or it's a knife that's cutting both ways, right?
00:20:05.298 --> 00:20:07.208
On one hand, every investor says.
00:20:07.903 --> 00:20:14.563
If you are not a pure AI company, well, then, you know, I need to think twice about funding you.
00:20:14.992 --> 00:20:17.692
Not everybody is thinking like that, but many are.
00:20:17.758 --> 00:20:19.127
Plenty are, we could name some names.
00:20:19.127 --> 00:20:20.928
Yeah,
00:20:20.982 --> 00:20:25.282
But on the other hand, AI is such a giant opportunity, right?
00:20:25.492 --> 00:20:34.863
Some people were saying to me, um, you know, some founders were saying to me, like, this is, this is, The wave of internet all over again, right?
00:20:35.323 --> 00:20:39.492
Uh, and, and sure it's early days.
00:20:39.512 --> 00:20:41.262
We don't know how internet will play out.
00:20:41.823 --> 00:20:47.032
You know, if you remember back in the 90s, we didn't know how it would play out, but eventually we know how it played out, right?
00:20:47.113 --> 00:20:50.462
So is it this wave of internet that will completely transform the world?
00:20:50.502 --> 00:20:53.653
We might be in a very early stage of that and people will figure it out.
00:20:54.232 --> 00:20:59.432
Or it could be those, one of those waves that did not play out like a blockchain wave where we thought it would change the world.
00:20:59.432 --> 00:21:01.752
And, uh, and it didn't.
00:21:02.913 --> 00:21:06.353
I personally think AI is one of the internet kind of waves, right?
00:21:06.413 --> 00:21:08.722
I think it will take a few years for us to figure it out.
00:21:10.192 --> 00:21:16.222
But we will figure it out and it will enable all of humanity, including engineers, including developers.
00:21:16.978 --> 00:21:20.387
to do their jobs in a different way, in a better way.
00:21:21.577 --> 00:21:28.958
So, Gen AI especially, apart from on top of AI, I think is a really, really important innovation that has happened.
00:21:29.498 --> 00:21:30.488
And so it will help us.
00:21:30.607 --> 00:21:32.048
How are we using it at Productiv?
00:21:32.357 --> 00:21:33.837
I think that's one of the questions you asked.
00:21:34.167 --> 00:21:37.768
Yes, there is a general purpose thing that everybody knows.
00:21:38.468 --> 00:21:43.077
That, you know, you use it to write marketing emails, or you use it on the go to market.
00:21:43.877 --> 00:21:46.428
Uh, uh, function side, whether it's customer support or whatnot.
00:21:46.428 --> 00:21:52.488
So we are dabbling with that, some of those tools in there on the product side.
00:21:52.837 --> 00:22:08.083
Uh, you know, we are more involved with AI and, and, uh, and Gen AI in building our product or rather in the product features we enable for our customers instead of, you know, having our developers use Gen AI to write code.
00:22:08.373 --> 00:22:09.222
So we don't do that.
00:22:09.272 --> 00:22:12.772
Uh, the, the second part, our developers are not using Gen AI to write code.
00:22:12.883 --> 00:22:27.842
Everybody's, everything is done literally by hand in the, in the plain old fashion, we are using Gen AI to, to provide human, uh, human friendly or human friendlier interfaces to interact with our product.
00:22:28.162 --> 00:22:39.801
So that people can, our customers can benefit from Gen AI to, to, to understand our product better, to get insights faster and better, to, to get their jobs done faster, uh, using, using product.
00:22:40.886 --> 00:22:41.176
Yeah.
00:22:41.196 --> 00:22:57.106
I wonder if right now we're seeing the most efficiency gains when you have enterprise scale with AI on the product side of things, because I look at like the recent announcement by Amazon that they see their generative AI tool internally saving.
00:22:57.686 --> 00:23:12.977
I believe it was 4, 500 years of work and 260 million annually with it focused specifically on code trans code transformation capabilities, uh, around taking them from like Java 8 and 11 and applications to Java 17, these kinds of things where it's like, okay, this is a migration.
00:23:13.307 --> 00:23:15.037
Devs really don't want to spend their time on this.
00:23:15.037 --> 00:23:20.636
Most devs are not excited by that, but it's something that we want to get done for efficiency sake, long term.
00:23:22.217 --> 00:23:45.386
My, my take right now is that like, to your point, like a lot of these tools that we're leveraging right now in AI are so experimental, especially on the code side, that in order to have the challenge of making sure it all works and the trust side of it, the safety side of it makes sense, you need this level of scale where you can have a pull platform engineering team that's devoted to that and say, Hey, look, yes, we're going to take this first pass of the AI, and then we're going to review it.
00:23:45.676 --> 00:23:48.467
And it's going to save us a ton of time if you have the scale to apply that.
00:23:49.011 --> 00:23:56.872
But for a team of a hundred engineers, it's a lot harder to have a bunch of folks that you can throw at a problem like that when you still have to deliver other business critical work.
00:23:57.692 --> 00:23:58.951
I think you're onto something there.
00:23:59.071 --> 00:24:07.977
Uh, I think, uh, you know, when you have a large amount of repetitive work, Which you can, you know, say there's a standard way to do this.
00:24:07.997 --> 00:24:10.916
We can teach an AI agent on how to do this.
00:24:10.916 --> 00:24:12.896
And then, you know, do the repetitive work.
00:24:13.176 --> 00:24:18.977
Um, it's, it's better to do it via AI than just plain old automation that we used to do in these cases.
00:24:19.277 --> 00:24:38.541
So yes, the power of automation, uh, for standard repetitive work, uh, has been, uh, I guess there's more power if you use AI to do that work, but where most of the work is net new, whether it's, you know, uncharted, Waters, um, with a typical small development team, you have that.
00:24:39.352 --> 00:24:41.721
Then it's harder to use AI as of today.
00:24:41.951 --> 00:24:45.352
It might get easier as we go along, but harder as of today.
00:24:45.902 --> 00:24:46.122
Yeah.
00:24:46.122 --> 00:24:56.011
To your point, I think the innovative work, the kind of innovation piece of technology and startups is where AI is not yet as strong, uh, from my viewpoint, at least.
00:24:56.521 --> 00:24:57.481
I agree with your point.
00:25:00.231 --> 00:25:05.692
You did say you think AI is a transformative internet level wave though potentially.
00:25:05.781 --> 00:25:10.342
Um, what do you kind of see as the future of AI as we go into the next couple years?
00:25:10.432 --> 00:25:15.711
Uh, as maybe it gets better at solving those technical problems or as it gets applied in other areas of technical expertise?
00:25:16.652 --> 00:25:17.271
Absolutely.
00:25:17.632 --> 00:25:17.892
So.
00:25:18.701 --> 00:25:21.892
You know, there are multiple layers of, of Gen AI and AI.
00:25:21.971 --> 00:25:28.021
Uh, and we have all seen those charts, I think, you know, the core of LLMs, which only very few big companies can build.
00:25:28.021 --> 00:25:44.132
And then there are tools on top of that, but then the outermost layer in some sense is this, you know, creating agents, uh, and the simplest way to say this is, is, you know, take any human or any human doing any job and say, we will have an AI agent who can do this job.
00:25:44.571 --> 00:25:46.961
We can have an AI agent who can interview.
00:25:47.237 --> 00:25:51.916
The interviewers or we'll have an AI agent who will respond to you, Connor.
00:25:52.217 --> 00:25:53.987
Uh, or, or, or what have you.
00:25:53.987 --> 00:25:54.227
Right.
00:25:54.227 --> 00:26:04.291
I mean everything, um, uh, and I think that is, no, we may not be able to replace humans completely, but I think what will happen is as AI gets better.
00:26:06.832 --> 00:26:13.561
We will be able to assist almost all humans in almost all jobs that they are doing,
00:26:15.041 --> 00:26:15.731
So do you think this
00:26:15.801 --> 00:26:27.951
a grain of salt, you know, not literally all humans with literally all jobs, but most humans in most jobs will say, we have an AI assistant and it makes us go faster, whether it's in generating content.
00:26:28.606 --> 00:26:37.916
Whether it's in generating insights, whether it's in understanding the word, whether it's in operating machinery or actually doing things via robotic arms or what have you.
00:26:38.622 --> 00:26:49.491
Yeah, I have to say, I think Microsoft's kind of perspective here about like, not just co pilot for engineering, but co pilot for every kind of role is one that makes sense to me, uh, broadly.
00:26:49.491 --> 00:26:54.142
So I'm really interested to see how that is continued to be implemented because I agree.
00:26:54.142 --> 00:26:55.682
I think there's an opportunity to.
00:26:55.882 --> 00:26:57.741
Create those efficiency gains for folks over time.
00:26:57.761 --> 00:27:01.182
It's just a question of, you know, when do you get the scale to make it work?
00:27:01.221 --> 00:27:03.801
And when does it get strong enough to really be impactful?
00:27:03.832 --> 00:27:07.852
Um, and that obviously will vary depending on job areas, depending on how things are set up.
00:27:08.362 --> 00:27:19.082
Um, and I hope that it will kind of reduce the need for some technical expertise in using complex products at times, giving, letting folks learn faster.
00:27:19.402 --> 00:27:21.422
Um, and I'll say like just on a personal level.
00:27:21.856 --> 00:27:30.987
Like, leveraging AI to help me learn something faster has been a great use case, and so I can see how that can potentially scale, uh, across a lot of different areas.
00:27:31.636 --> 00:27:32.557
No, absolutely.
00:27:32.557 --> 00:27:49.557
You know, when, when humans, uh, are freed from, let me say the mundane task or the repetitive task or even some of the creative tasks, but if somebody else is helping them in this case, a co pilot, um, my belief is that humanity just, you know, ups.
00:27:50.326 --> 00:27:58.346
You know, levels up and says, now that I have free time and, and, you know, my, my mind is free to, to do more innovative things.
00:27:58.606 --> 00:28:02.906
We just do it, whether it's learn faster, whether it's do the next level of things.
00:28:03.277 --> 00:28:07.797
Uh, so it will be good for innovation all along that some tasks.
00:28:08.426 --> 00:28:11.836
are appointed to AI co pilot to do for us.
00:28:11.906 --> 00:28:31.612
And, and, and learning is absolutely, uh, absolutely one of those things where it becomes a lot low pressure, in my opinion, when you are talking to an AI based teacher instead of You know, somebody who's pounding you over the head, uh, as, as one example of learning.
00:28:32.126 --> 00:28:42.047
Yeah, I'll be fascinated to see how it transforms the educational sector, uh, over the coming years, because there's a huge opportunity there to improve a lot of, of learning outcomes if done well.
00:28:43.321 --> 00:28:43.922
Absolutely.
00:28:43.922 --> 00:28:49.902
And, uh, you know, uh, and again, uh, none of us are saying that it will replace teachers, right?
00:28:50.251 --> 00:28:58.261
Uh, not saying that because there's a lot of human element of, you know, an emotional element of how a teacher grows a child.
00:28:58.626 --> 00:29:25.386
or whoever is trying to learn, um, uh, but yes, some parts of the repetitive work that, uh, that a teacher is doing, uh, or an educator is doing, if they are taken away by AI, whether it's, you know, whether it's checking the, you know, the scorecards or whether it's, you know, putting together new material or saying the material in different languages or, you know, answering questions at any time in point, right?
00:29:25.386 --> 00:29:29.896
There's a, there's an always available teacher co pilot that a student can talk to.
00:29:30.656 --> 00:29:32.896
All of these things, and there are tons and tons of ideas there.
00:29:33.237 --> 00:29:39.227
Um, I think it will help humanity in every, in every facet, including in teaching.
00:29:39.882 --> 00:29:47.102
I cannot wait until I can have a very efficient earpiece in that lets me live translate languages, uh, and we're getting very close to that.
00:29:47.102 --> 00:29:49.771
So, uh, it is definitely super exciting.
00:29:50.152 --> 00:29:54.692
And I think it's interesting to look at all these kinds of transformational waves together.
00:29:54.692 --> 00:29:59.521
We have obviously this remote work wave, we have changes on the monetary macroeconomic environment.
00:29:59.521 --> 00:30:07.061
We have changes on the technology side with AI following kind of close on the heels of this, like mobile transformation we saw in the 2000, early 2000s.
00:30:08.257 --> 00:30:15.497
How are you kind of navigating things as a founder and bringing this all together in what is a challenging transformational time?
00:30:17.682 --> 00:30:20.592
It is a challenging time, but it's also an exciting time, right?
00:30:20.652 --> 00:30:24.041
I mean, I, I see all of these as opportunities, right?
00:30:24.102 --> 00:30:43.632
Uh, you know, the, the investment landscape, for example, where there is, you know, money is tighter, is forces us to be more disciplined in solving real problems with our customers and talking more to our customers, uh, in, in not taking things for granted, right in, in taking a look under every rock to say, Hey, what did we miss?
00:30:43.991 --> 00:30:45.672
Um, when we look at ai.
00:30:48.047 --> 00:30:57.047
I don't think the days and sure, we are not using the full power of AI today because it's not mature, but I think it's, I think it's coming.
00:30:57.207 --> 00:31:03.106
And we are looking every single day, if not every single week, or rather other way.
00:31:03.326 --> 00:31:05.416
We are looking every single week, if not every single day.
00:31:05.481 --> 00:31:21.061
Um, to see how AI can help us build a better product for our customers, which we are already doing, but also make our engineers, our designers, our salespeople, marketing people, everybody more efficient at their job.
00:31:21.342 --> 00:31:29.682
So in some sense, Yes, the wave of AI and the macroeconomics are pressing us to be more efficient.
00:31:30.781 --> 00:31:34.751
In the same sense, they are also helping us be more efficient.
00:31:35.162 --> 00:31:53.156
And, you know, Productiv is actually in a very unique spot, if I may take a minute there, because, uh, you know, the whole wave of the macro wave of, uh, asking companies to get more efficient with their spend, Turns out that's actually what we do.
00:31:53.386 --> 00:32:06.537
Productiv is, uh, you know, we increase productivity of, uh, of our customers, specifically, you know, the IT teams and the procurement teams in our customers when they are, you know, buying software or managing.
00:32:06.967 --> 00:32:08.757
Software from third parties.
00:32:08.997 --> 00:32:19.186
So when, when the CFO is coming to, uh, our customer CFO is coming to the CIO or the IT teams and saying, Hey, look, let's, let's make sure we are spending money wisely.
00:32:19.416 --> 00:32:22.287
Let's reduce our spend by 10 percent or what have you.
00:32:22.287 --> 00:32:23.896
Productiv is the tool that helps them.
00:32:23.896 --> 00:32:27.626
So in some sense for us specifically, this whole macroeconomic wave.
00:32:28.446 --> 00:32:35.227
of, of saying cost efficiency, um, is actually helping us, uh, because that's what we, that's the problem to solve for our customers.
00:32:35.936 --> 00:32:36.267
Yeah.
00:32:36.267 --> 00:32:38.176
And definitely go check out Productiv.
00:32:38.196 --> 00:32:40.487
com for folks who are interested in learning more.
00:32:40.497 --> 00:32:41.727
It's a really interesting company.
00:32:41.727 --> 00:32:53.076
And, um, I saw you actually put out a trend report on the 2024 state of SaaS consolidation trends, uh, that looked really fascinating as well, and maybe speaks to some of these technology trends that are happening.
00:32:53.957 --> 00:32:54.636
Absolutely.
00:32:54.646 --> 00:33:01.507
And, and, and it goes to the point that you were saying earlier, in fact, the trend is not that people are adopting less technology.
00:33:02.247 --> 00:33:09.656
The trend is that every enterprise is adopting more technology because more technology is needed.
00:33:10.136 --> 00:33:15.106
To, to enable a geo distributed workforce to work more efficiently, you need better tools.
00:33:15.497 --> 00:33:22.886
Uh, and yes, we are seeing a giant uptick in the use of, uh, AI based technologies, starting with, you know, ChatGPT.
00:33:22.886 --> 00:33:31.817
Obviously, we are seeing ChatGPT has suddenly become the thing that everybody, you know, Most people in most companies are using, whether or not, you know, their leaders know about it.
00:33:31.876 --> 00:33:34.817
Everybody is actually trying to use it.
00:33:34.817 --> 00:33:39.076
But, uh, but people are using more tools.
00:33:39.156 --> 00:33:40.946
Enterprises are using more tools.
00:33:41.007 --> 00:33:44.727
It's just that they are trying to do it more efficiently.
00:33:45.297 --> 00:33:47.896
They are trying to manage their workforce more efficiently.
00:33:47.896 --> 00:33:51.247
But technology is the solve for
00:33:52.326 --> 00:34:08.447
Yeah, it's going to be really interesting, speaking of OpenAI, to see how their next raise goes, because they, they are like such a market leader, but they're also burning through cash and have a, like, potentially the highest ever private valuation for a raise in this next raise they're going to go through.
00:34:08.447 --> 00:34:14.916
So I'll be fascinated to see, you know, how Microsoft handles them continuing to give them Azure credits and everything else to go into it.
00:34:14.916 --> 00:34:17.237
It's going to be a really interesting ride for them.
00:34:18.686 --> 00:34:18.887
it.
00:34:18.887 --> 00:34:19.836
It must be.
00:34:19.867 --> 00:34:24.166
This is way above my pay grade to figure out what Microsoft and OpenAI will
00:34:24.181 --> 00:34:26.521
we're just, we're just, we're just interested to see.
00:34:26.521 --> 00:34:27.492
Yeah, absolutely.
00:34:27.532 --> 00:34:27.922
Absolutely.
00:34:28.012 --> 00:34:38.192
So, uh, well, let me ask you, like, this is, I mean, we're seeing these technology trends, we're seeing more and more technology purchases, we're seeing this consolidation effect you mentioned, uh, in Productivs research.
00:34:38.742 --> 00:34:48.251
How do you keep your team, you know, motivated, focused, and, you know, maintain this, like a high context, high agency culture in these challenging times?
00:34:51.067 --> 00:34:54.976
it's really, uh, you know, that formula hasn't changed, right?
00:34:54.996 --> 00:35:08.376
And throughout my years, uh, of working at many, many companies, you know, whether it's Microsoft or Amazon, big names or, or small, smaller companies like Postmates or, or Productiv itself, uh, for the last six and a half years.
00:35:09.067 --> 00:35:10.442
What I've found is that.
00:35:10.931 --> 00:35:15.791
You know, top talent, especially in product and engineering, but really across the board in any discipline.
00:35:16.291 --> 00:35:18.172
Uh, but I'll speak to product engineering.
00:35:19.211 --> 00:35:24.041
You know, top talent product and engineering really wants just three things, right?
00:35:24.532 --> 00:35:28.121
Um, they, they, they know they can solve big problems.
00:35:28.211 --> 00:35:34.871
So, so a, they, they want big problems to be available to them to be solved.
00:35:35.362 --> 00:35:36.271
They say, challenge me.
00:35:36.641 --> 00:35:40.851
I want to use my time and my skills in solving something meaningful.
00:35:41.586 --> 00:35:55.556
So if you, if we can, if we can put together a, you know, a good vision on saying these are the set of problems that our customers may want solved over many, many years, hopefully a multi year vision, uh, and then a short term vision part of that.
00:35:55.586 --> 00:35:56.967
And then those problems are difficult.
00:35:57.047 --> 00:35:58.706
They're not trivial to be solved.
00:35:59.387 --> 00:36:02.146
That is first piece of the puzzle, right?
00:36:02.726 --> 00:36:13.387
The second piece of the puzzle is Uh, you know, top, uh, top performers or top talent understands that hard problems they cannot solve by themselves.
00:36:13.527 --> 00:36:18.536
So they need a team of other, you know, whatever top talent around them.
00:36:19.056 --> 00:36:21.067
Uh, a lot, lot of engineers.
00:36:21.416 --> 00:36:33.896
Uh, uh, you know, recently and in the past years, uh, consistently saying, sheesh, the, the biggest reason I'm, I'm excited coming to work every day is look at the team around.
00:36:33.956 --> 00:36:35.567
I love working with these people.
00:36:35.896 --> 00:36:38.876
I love solving hard problems with these people.
00:36:38.887 --> 00:36:43.947
There's always an aha moment that I say, Oh my God, I'm so smart.
00:36:43.987 --> 00:36:46.317
I used to think I'm so smart, but I'm clearly not smart enough.
00:36:46.797 --> 00:36:50.487
Because these are the people that have figured this out better and they're like, I love it.
00:36:50.836 --> 00:36:51.117
Right.
00:36:51.396 --> 00:36:58.516
Uh, and everybody gets those aha moments almost every day where they find somebody solving somebody with a better suggestion than they were thinking of.
00:36:59.056 --> 00:37:07.197
Uh, so having a team of top talent around them, uh, uh, uh, and the third thing is, is actually a very interesting thing.
00:37:07.226 --> 00:37:07.976
So if you have.
00:37:08.467 --> 00:37:24.956
If you have hard problems, and if you identify and if you gather a team of people who can solve those hard problems, then the only thing for a leader to do is really let that team actually solve the hard problems, which is what we call culture.
00:37:25.257 --> 00:37:28.976
Broadly speaking, which is saying, hey, how much bureaucracy do you insert in the middle?
00:37:29.387 --> 00:37:31.637
How many forms do they have to fill in triplicate?
00:37:31.867 --> 00:37:34.047
How many roadblocks are you putting in front of them?
00:37:34.067 --> 00:37:36.016
Or how many roadblocks are you taking out?
00:37:36.257 --> 00:37:40.086
And my philosophy has been, you know, take out as many roadblocks as you can.
00:37:40.097 --> 00:37:47.976
Let these, let these high powered people actually solve the hard problems that our customers want to get solved.
00:37:48.956 --> 00:37:59.711
If they see that impact and they see that they can actually create that impact, Uh, then, you know, uh, you know, these, these things that you were talking about, how do you motivate them to come to work every day?
00:37:59.722 --> 00:38:03.172
Or how do you motivate to put their best foot forward?
00:38:03.612 --> 00:38:04.541
I don't need to.
00:38:04.992 --> 00:38:07.101
Uh, it comes naturally.
00:38:07.501 --> 00:38:09.612
Uh, I don't even have to ask.
00:38:09.612 --> 00:38:10.882
I see this in the work.
00:38:10.882 --> 00:38:12.001
I see is in the smiles.
00:38:12.492 --> 00:38:13.371
I see this.
00:38:13.927 --> 00:38:23.427
When, um, when every single time, you know, an engineer cannot look past, you know, the next month of good work on their plate.
00:38:23.867 --> 00:38:26.447
They actually ping me and say, Ashish, we have a problem.
00:38:26.447 --> 00:38:27.447
I'm like, what's the problem?
00:38:27.836 --> 00:38:32.507
Ashish, I only have one month of, you know, good work left on my plate.
00:38:32.567 --> 00:38:34.737
I will be like, I'll be idle.
00:38:35.467 --> 00:38:37.496
I'm like, no, no, this is not a problem, it's plenty of things.
00:38:38.086 --> 00:38:41.947
But imagine somebody saying, I need more work, more hard work.
00:38:42.407 --> 00:38:45.597
Uh, and, and that's, I love that culture.
00:38:46.447 --> 00:38:53.617
And, and I think if you get to that state, um, things just happen, you know, good things happen for a company and for a team.
00:38:53.987 --> 00:38:57.177
And fortunately, it has happened for me over many years now.
00:38:57.817 --> 00:39:09.266
I love to hear that because I'll say as like a team member, like you want to learn if you're a high performer, you want to be around people who push you and, you know, help make you better and who you respect deeply.
00:39:09.266 --> 00:39:21.121
And so this kind of perspective of like, not only am I going to bring in the right people to have that kind of high learning, high impact culture, but also going to give them autonomy and agency to Go do these things and not feel micromanaged.
00:39:21.402 --> 00:39:22.922
I mean, those are the best kind of teams.
00:39:22.952 --> 00:39:27.442
Um, uh, she says, this has been a fantastic conversation before we wrap up.
00:39:27.481 --> 00:39:31.652
Do you have any other closing advice that you want to share with, uh, the leaders who are listening today?
00:39:36.047 --> 00:39:41.777
Look, the closing advice as a founder, I would, I would say, yes, you've heard everything about how, you know.
00:39:42.907 --> 00:39:48.666
How the markets are tough, the macro is tough, the, you know, the AI is taking over the world, which is, by the way, an opportunity.
00:39:49.007 --> 00:39:56.286
Uh, but, but my closing advice is, is, is to, is to stick to the first principles, right?
00:39:57.556 --> 00:40:02.916
Always stick to saying, hey, look, do we really understand a customer problem?
00:40:02.916 --> 00:40:11.237
If you, if you understand a customer problem, if you're solving a real problem, and if you have, if that problem is hard, that cannot be just solved by somebody else after you solve it.
00:40:11.927 --> 00:40:15.856
So find a customer problem, which is real, which is hard enough.
00:40:16.262 --> 00:40:21.222
Which is unsolved, uh, And then you know Get a good team.
00:40:21.231 --> 00:40:51.467
You can't do it yourself If you have a good customer problem and a good team to help you solve that problem Everything else will come don't worry about whether people will invest You know what if you're solving a good problem, there'll be a lot of investors lined up outside your door Uh, if you're solving a good problem with a good team or a good seed team Uh, there'll be a lot of other good team members lined up outside your door whether you use ai or not is your choice Uh, uh, that's a solution looking for a problem.
00:40:51.467 --> 00:40:53.706
So if you go problem first, you'll be fine.
00:40:54.106 --> 00:40:59.356
If you invent a solution looking for a problem later, um, then that's a path of trouble.
00:41:00.202 --> 00:41:00.731
I love it.
00:41:01.231 --> 00:41:02.291
Thank you so much, Ashish.
00:41:02.291 --> 00:41:07.021
It's been a fantastic time having you on the show and I'm sure our listeners will take a lot away from this conversation.
00:41:08.286 --> 00:41:19.626
So for more insights about the software engineering landscape, what's happening in the startup ecosystem and more from Ashish, be sure to check out this week's edition of our Dev Interrupted newsletter on Substack, where we dive deeper into the topics we discussed today.
00:41:19.967 --> 00:41:22.047
We'd also love to hear your thoughts on today's discussion.
00:41:22.047 --> 00:41:28.336
So join the conversation on Twitter or LinkedIn by tagging at Dev Interrupted, and let us know how you're navigating these challenges in your own work.
00:41:28.666 --> 00:41:30.527
Ashish, thank you so much for joining me today.
00:41:30.527 --> 00:41:31.097
It's been a pleasure.
00:41:32.007 --> 00:41:32.536
Thank you, Conor.
