LLM AI, the fourth pillar of software: Chris Kindt speaks to Guido Appenzeller Part 1
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Why does Guido consider AI the fourth fundamental pillar of software?
My current gut feeling is we've created a fourth component here, which in the future will also be in every software that we build and that we that we make, right? And if that is true, that's a very major disruption, right? If you look at the last couple of ones when they built CPUs, they created Microsoft, created Intel, uh you know, when we created databases, they created Oracle, when we created networking, we created, you know, the Ciscos and Googles of the world. And so we probably have something of that scale ahead of us, right? Something that will create trillion dollar companies.
Welcome to Orbit, the HG podcast series where we speak to leaders and innovators from across the software and tech ecosystem to discuss key trends in building businesses that endure. My name is Chris Kint, I'm the head of the value creation team in HG, and I'm delighted to be joined today by Kido Appenzeller. Kido is a special advisor to Drayson Horowitz, former CTO at Intel and VMware, and a leading technology expert. At HG, We've been fortunate to hear kido speak most recently on AI and its effect on a world of B to P SAS. Thank you, kido, for joining us. It's great to be here today. Thank you. So, Guido, I can't believe that it's now almost a year since Chat GPT was launched and hit our world. And I must admit I didn't and I think many of us didn't quite see the powerful impact that it would have and how much discussion has been sparked around it since.
be really kind of helpful to have your perspective on how we got here and really what took us
So the place where we are today. I think it surprised everyone how suddenly AI sort of made a leap forward. And you know, when when I did my PhD at Stanford, AI was known as this thing which looked fantastic in demos. But then when you actually tried to put it in production, it never quite worked. Right. And uh continued like that, you know, pretty much until the the twenty tens, the mid twenty tens, like twenty fifteen or so, when some of the large hyperscalers, Google, Facebook, uh, you know, Uber, Tesla, it's not really figure out how to use deep learning type techniques and really create value, right? They build these massive neural networks to, for example, um optimize advertising and and and and similar tasks.
But there's still something which only the very large hyperscale style companies could do. You would needed a a large amount of investment, you needed very specialist like my my fellow Stanford PhDs, right? These kind of people that are very expensive and they would work for a long time and build a model that could solve one particular problem. And then suddenly, you know, I want to say mid-last year and roughly, we had a a couple of major breakthroughs where we made models bigger. Suddenly we saw these new emergent behaviors. And nobody quite understands why, but basically once you crossed certain size thresholds, you saw new capabilities that previous models didn't exhibit. And those made these models massively more useful, enabled different users to model with this idea of foundation models, and that really changed the unit economics and the advantage.
ads option of this newer AI. I guess the question that people are
now grappling with is what analogy do we use for this technology change? What would you kind of draw the kind of parallel to in terms of kind of previous technology changes that we've seen hit our
It's a great question. I mean, uh to me it looks right now like this is a very big technology wave. So something that creates immediate value, right? I'm using this technology uh every day, like some other technology waves where the value took much, much longer to develop. But we're also seeing a very rapid uptake, right? If you look at at statistics, how quickly people are adopting this, like OpenAI, for example, being possibly the fastest company from the first dollar to the first two billion dollar run rate in in the history of tech, right? So we're seeing these tools being adopted much quicker than previous sort of revolutions of similar type.
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Chapters
8 chapters
1
Why does Guido consider AI the fourth fundamental pillar of software?
0:00–3:54
2
How did the sudden leap in LLM capabilities happen in the last year?
3:54–7:08
3
What analogy does Guido use to compare the AI wave with past technology revolutions?
7:08–10:01
4
How are AI models being integrated into existing software products today?
10:01–13:13
5
What are the emerging risks and regulatory challenges for AI deployment?
13:13–16:08
6
How will chip shortages and compute capacity affect AI’s growth?
16:08–19:27
7
What cost‑reduction and efficiency tricks are driving AI performance forward?
19:27–22:27
8
What does the future look like for AI as a ubiquitous building block in software?
22:27–26:47
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