The Ledger
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LeCroix: Enterprise value comes from multi-agent workflows, not single agents
“What we see in enterprise is rarely things that are solved with agents because that's not necessarily where you would expect an FDE to be most useful. Where there is more values, value is in more complex workflows where you will have several agents interact th…”
Lacroix: AI agents use file systems to replace long context windows
“And that I think that was the big change in and realization through vibe coding is that agents are good enough at manipulating file systems that they can use this as a replacement for their Context window, basically. They can select parts of what they want to …”
LeCroix: Generating thinking traces and calling tools are fundamentally identical in AI
“There's no real difference between Creating a new thinking trace or calling the right tool. It's all the same to me, because what you're optimizing at the end is what is the best output for the model to create before it gets results to me.”
Lacroix: Data quality improvements yield 10x the gains of model architecture tweaks
“Getting the data perfect, because we knew this was potentially not the most exciting part of the work, but it was absolutely critical, and any improvement on the data quality would, Tenex, the improvements that we would get by really improving on the model arc…”
LeCroix: Banks would not adopt AGI without enterprise governance controls
“Requirements I see for control and governance in enterprise make me think that even if I had some AGIS model on my servers right now, if I were to go into a large bank and say, Here is a thing. Please let it control everything for you. They wouldn't be happy t…”
Lacroix: Thousands-GPU AI training has a much smaller margin for error
“When you run inference on a few GPUs, or when you run small scale trainings on hundreds of GPUs, margin for error is a lot larger than when you run trainings on thousands of GPUs at the same time.”
Lacroix: Getting AI models into enterprise production requires extensive technical expertise
“What we have seen in terms of success is that given the current stack, it still requires a lot of expertise to manage to come to actual value and things that go to production, basically.”
Lacroix: Enterprise software should avoid moving customer data across environments
“The reason we do this is that it lets clients build where their data is, and without having to shuffle things around, which, as I've learned as a CTO, is something that you don't want to do ever. Because it asks, it raises a lot of questions, and it's quite a …”
Lacroix: Full enterprise AI coding ROI requires model customization
“To me to get the full ROI of coding, you need customization because a lot of ROI is unlocked on like sprawling code bases that are completely impossible to know for something that's been trained on the web.”
Lacroix: Focused use cases allow for significantly smaller AI models
“The more focused your use case is, the smaller you can make the model through fine-tuning or through just distillation in an even smaller architecture.”
LeCroix: MoE architectures improve AI training efficiency by lowering required FLOPs
“MOEs are really nice systems to train because of the lower amount of flops, which makes us able to push performances a lot more during training.”
Lacroix: MoE models need high throughput across GPUs for efficient on-prem deployment
“They are not necessarily the best format for on-prem deployment, because As of today, if you want to get the best efficiency out of a mixture of experts model, you require a lot of volume, because you're looking at deployments across dozens of GPUs usually, an…”
LeCroix: Full sandboxing is only necessary when AI agents execute code
“Typically, if the file system is just representing textual context, and you're not expecting the agent to do much action on it, then you don't really need a full sandbox. You just need some representation of that context as a file system, and it can be Any sor…”
LeCroix: AI training has shifted from acquiring world knowledge to acquiring know-how
“So before it was about accruing world knowledge and the web helps a lot with this. Now it's more and more about acquiring know-how.”
LeCroix: Enterprise AI customers reject deploying multiple models for tasks
“Customers aren't happy if you require them to deploy five different models to get their job done.”
LeCroix: File-system-capable AI is the basis of enterprise intelligence
“Having a system that is good at handling a file system is more generally very interesting. Even if you're not using it to code, you can use it to reason about enterprise knowledge, you can use it to connect to enterprise systems, and it's, to me, it's the basi…”
LeCroix: Small OCR models are often cheaper than large multimodal models
“Sometimes it's a lot cheaper to use a small OCR model to just get the text that you care about and then potentially post-process it or deal with it with another system than to run it through a large multimodal model that will basically do the same thing but at…”