Everything, everywhere, but not all at once: Hg’s Matthew Brockman on what's really happening in software right now
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What is the current state of software and AI hype versus reality in early 2026?
The sort of classic tenor at the moment, in particular sort of the venture community and the podcasts we all listen to, is I could whip up a payroll system in an afternoon using, you know, claw code or replit, that thing will work, I can use an open source machine that will give me the taxation rules for a European country, and off I go. That is pretty hard, right? We know that's pretty hard for many reasons, right? There's a whole bunch of you know judgments based into some of those assessments. There's a whole bunch of habit in terms of the way people. to do things there's a trust factor which is obviously huge in terms of how people use the product, where they're really going to pay people, how the tax office gets notified, all those
Welcome to Orbit, the HG podcast series where we talk to leaders and hear how they've built some of the most successful technology companies in the world. I'm David Toms, head of research at HG, and in this special episode I'll be interviewing HG's Chief Investment Officer Matthew Brockman to talk about the future of software in an AI world. Matthew, we're sitting here in mid-February 2026 because days seem to matter now in the public. Yeah. What's going on in software?
Lead him with a big question. I mean we can talk about what's happening in technology and we could talk about what's happening in the markets that obviously value software. Maybe if I I start with the latter 'cause that seems to be the most sort of immediate topic. I think you're at an interesting confluence between the sort of sustained momentum of the model codes and the sense of IPO model codes and some of the applications have been built on the basis of that technology and a I guess a real sort of sense of excitement about what the potential will be. I would still characterize it as that. I mean it's evident in some areas like coding and so on, but there's a lot of areas in application where it's still coming, frankly, versus what's been achieved.
versus the counterfactual for existing SaaS, where people are looking to see what their action is going to be. Like what are the AI products that are going to get built? And that at the moment is a sort of almost a bit of an unproven sort of negative. You know, how do I know something which I haven't yet evidenced? So you've got this sort of sharp sell-off, frankly, in the public markets for existing software, complemented with continued excitement, perhaps peak excitement, dare I say. for what the model capability is and where that's going to influence enterprise software in particular over the over the long term.
And do you think that there's an element of catch-up going on? That that there's been a lot of progress for three years and people have only now realized have we passed a tipping point?
I think there was some degree of an acceleration in the model capability during the latter part of last year. And it it was some of it was around obviously computes and data, and a lot of it was around sort of the the the the learning process for the models. And so I think with the What was it, claw four point five release and same with OpenAI and then the four point six release, people have seen the performance of that step up meaningfully in application. So I think there was always an expectation, certainly in HG, we had an expectation this was coming. And you would see this kind of level of performance arriving. The question was kind of when. And so when I talk to people in our in our team, when I talk to people in our portfolio companies, the the sort of capability, particularly in software development, but obviously that will then start to cascade into other applications.
has sort of meaningfully stepped up really even only in only those periods. And so I think what your the sort of catalyst, if you like, for, you know, for recent events has been that, on one hand, and then sort of announcements, if you want to call it that, from Anthropic and others on co work and these sort of collaboration spaces where essentially it's trying to make it easier for the average executive in a company to essentially build a genetic application.
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Chapters
8 chapters
1
What is the current state of software and AI hype versus reality in early 2026?
0:00–5:05
2
How have recent model releases (e.g., Claude 4.5/4.6) changed enterprise AI capabilities?
5:05–10:27
3
Why is ‘vibe coding’ useful for sales demos but not yet ready for production?
10:27–15:39
4
How are investors’ venture money subsidising inference costs and when will economics shift?
15:39–20:43
5
What does a winning enterprise AI product look like and how should incumbents embed agentic layers?
20:43–24:41
6
How does HG’s Catalyst program accelerate AI product builds within portfolio companies?
24:41–29:38
7
What are the emerging economic models for AI‑first SaaS (pricing, compensation, labor substitution)?
29:38–34:37
8
What would be the ‘virtual Matthew’ Turing test for AGI in private‑equity investing?
34:37–39:36
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