So do you see that? Walk me through, like, what are the other big phrases that, you know, the investors say, the CTO head spins and vice versa in your experience?

I think, first of all, there's a fundamental disconnect between what done means.

Software is never done, right? So software is almost like a living thing that you work on.

And I think one of the key things we assess when we do a DD is also Harvard's view is perfection is an anti-pattern in technology.

It just doesn't exist, and it's a BS thing to even discuss, right? It needs to get the job done, and it needs to enable you to continue iterating on it, right? architecture needs to be malleable, right? It needs to be something that can evolve over time.

So I think ultimately the big topics we look at is trying to understand, are we talking to a team that is able and willing to commit to making decisions and learning from such decisions, right? And having the data to actually be able to learn from any decisions they made in the past, right? So I think this is something that's both difficult and fun.

It's actually the funnest thing about engineering, right? You get paid to learn, you get paid to be in this engineering playground and sandpit and do fun things, now with the help of agents also, so that's also fun.

think these things have never worked?

So do you see that? Walk me through, like, what are the other big phrases that, you know, the investors say, the CTO head spins and vice versa in your experience?

I think, first of all, there's a fundamental disconnect between what done means.

Software is never done, right? So software is almost like a living thing that you work on.

And I think one of the key things we assess when we do a DD is also Harvard's view is perfection is an anti-pattern in technology.

It just doesn't exist, and it's a BS thing to even discuss, right? It needs to get the job done, and it needs to enable you to continue iterating on it, right? architecture needs to be malleable, right? It needs to be something that can evolve over time.

So I think ultimately the big topics we look at is trying to understand, are we talking to a team that is able and willing to commit to making decisions and learning from such decisions, right? And having the data to actually be able to learn from any decisions they made in the past, right? So I think this is something that's both difficult and fun.

It's actually the funnest thing about engineering, right? You get paid to learn, you get paid to be in this engineering playground and sandpit and do fun things, now with the help of agents also, so that's also fun.

think these things have never worked?

That's, I think, a key one.

I think ultimately private equity guys or investors care about a handful of things, right? They care about top line growth, they care about risk mitigation, care about cost efficiency, and they care about multiple expansion, right? So I think technology can play a massive and quite pivotal role in almost all of these things, right? And obviously it really depends on the sector, right? Their sector, so just more prone to innovation, others are...

if you look at it from a modern perspective, a little bit behind because, you know, due to regulation and whatnot.

But I think, you know, let's stick with the example of everybody's, you know, favorite thing called AI these days.

AI is massive.

It's a massive, you know, potential efficiency lever, right? And internally in terms of Since Opus 4.5 or 4.6, agentic development is real.

It's actually doable.

That's an American

Reflect only ParkerGale's partners and none of the other partners in the business.

We need confidence in the team's ability.

They're not just the next cloud feature, ultimately, right? And two, do you have the foundations in place to innovate and things like that? And only once there's sufficient comfort, we're going to play offense, right? We're going to look at...

you know, we're going to do the whole thing from product and product operations, roadmap, architecture, infrastructure, scalability, open source, cybersecurity, and all these things.

And also understand, hey, not only how resilient are you, but, you know, what can you do? Are you, you know, deeply embedded in your customer workflows, right? Maybe do you have access to, you know, proprietary self-renewing data, which could be a flywheel and inform data, you know, machine learning features or AI features.

I think ultimately what a lot of people are sleeping on today still is the miracle of SaaS is potentially dead, right? Or at least it's severely, I don't think necessarily it's dead, but at least it's challenged, it's changes.

You will have deeply unprofitable users.

It was a lot of bombs, man.

But I mean, you guys, how has it evolved?

Yeah, I mean, you said it yourself, right? It's, it has shifted left dramatically.

It's become more important, um, given, you know, unless we have sufficient confidence or clients has sufficient confidence in the company's ability to exi- exist, you know, and not be the next, uh, Claude feature, um, there's no point in looking at scalability, architecture, cybersecurity, and all that fun stuff, right? So, um, I think for us it's, it's, uh, been a very rewarding time in many ways because, as you said, um, the rate of innovation has never been faster, so also our rate of learning has never been faster, and innovation has become such a quickly moving target.

It's insane, and it's so fun.

And as such, you know, today our work begins with an outside-in analysis looking for web signals and stuff like that and, and identifying signals, but given it's not yet based on a lot of evidence, it's signals, right? And then we want to make those proper insights and that hopefully match our client's investment thesis, and we get, get to do this via workshops, document analyses, code scans, and, and, and whatnot.

And, and, you know, phase one is typically now top three items of ICs is AI, AI, and then guess what?

[laughs]

All right.