Episode summary
Goldman CIO Marco Argenti on the Warp-Speed Improvements in AI | Odd Lots
Odd Lots
· · Hosts: Joe Weisenthal, Tracy Alloway · Guests: Marco Argenti
Summary
Goldman Sachs CIO Marco Argenti says AI has moved well beyond experimentation and is now embedded across the firm, from a GSA assistant used by 47,000 people to developer tooling that is changing how engineers work. He argues that the biggest shift is not just faster coding, but a new workflow where developers write specs, explain intent, delegate to agents, and supervise outcomes.
Argenti says Goldman has already terminated some third-party software contracts and that the “buy versus build” equation is shifting for smaller applications as AI lowers the time and cost to build internally. For larger, highly regulated systems, he thinks vendors tied to stable processes like accounting remain durable, while software closely linked to changing workflows faces more disruption.
He also says Goldman centralizes model access through a gateway to manage token spend, prefers to hide token anxiety from users, and expects per-token costs to fall even as usage rises. On regulation and security, he frames AI as an evolution of prior model-risk practices: banks still rely on human review, risk tiering, info barriers, and controlled deployment pipelines rather than letting models act unchecked.
Key takeaways
- Argenti says Goldman has already terminated some third-party software contracts as internal AI tools improve the economics of building in-house.
- He expects developers to become more like product managers and supervisors, because AI shifts work from coding mechanics to explaining, delegating, and reviewing.
- Goldman uses a central model gateway to route requests by quality and cost, which Argenti says is essential to controlling token spend.
- Argenti argues that accounting and other tightly regulated processes are more durable than software tied to rapidly changing workflows like developer tooling.
- He says regulators are approaching AI like previous model-risk problems: limit actions, add human supervision, and classify applications by risk tier.
Notable quotes
“we have terminated contracts already. Yes, absolutely”
“the most important thing for a developer today is to be able to explain things rather than jumping and coding things”
“I think the trend will continue with regards to more and more of those. But the cost the the the per unit cost of token, I pretty I'm pretty sure that is going to go down also”
“we have the concept of info barriers, okay. And the info barriers are, are enforced, throughout the entire system”
How the episode opens
If you look at the software developer lifecycle, a lot of that is changing. Developers are or are developing software by developing specs today. And so if you are too much in the weeds down there in that mechanics, and you don't adapt for AI or agents doing that work in software development lifecycle deployments, rollbacks, monitoring, observability and all that.
I think that path will be very much disrupted. Or if you're adding a sort of a UX on things, that's another class. So you have a very simple process.
I don't know, you're doing surveys or expense reports or whatever. And now people are going to start expecting, you know, their personal assistants or agents to kind of do all that mechanic for them. And so I think that path is probably something that has a bigger question mark on top.
And so I always ask myself the process question first. The process transformation section, the question first, and then consequently what's the tool that is going to support that? Just to press on this point though.
Have you replaced any third party software providers with something that's been developed internally through AI? We have we have terminated contracts already. Yes, absolutely.
Hello, and welcome to another episode of the All Thoughts podcast. I'm Tracy Alloway. And I'm Joe Weisenthal.
Joe. The thing about AI, I feel like it's accelerated all of our timelines, right? Like, it's phenomenal to me to think back that, like back to the days of ChatGPT.
And when did that come out? 20 2020 2220. 2222?
That's just crazy to think. And then really unbelievable the gap. And I've been thinking about this like just a the explosion of capabilities.
Yeah. And the thing I've been thinking about is that, you know, after the first year or so and it came out, you know, we talked to executives and we were like, how are you using AI and your workflow?
From the episode’s captions: roughly the first two minutes, lightly cleaned. Keep watching on YouTube →
Topics
- agentic ai
- ai coding
- data governance
- token economics
- model risk management
Companies and tickers mentioned
- GS
- Goldman Sachs
- Claude
Watch the episode
Watch “Goldman CIO Marco Argenti on the Warp-Speed Improvements in AI | Odd Lots” on YouTube
Summary, takeaways and quote selection by Octagon. Quotes are short excerpts; the full conversation is in the episode.
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