The Week of the Open Model

TLDR;

Welcome back! This week, we have a huge theme: open models, with several news items putting them at the center of the entire industry, including new open models achieving SOTA results, research that will make their adoption easier, and even some companies in the space publicly attacking Frontier Labs.

We’ll also cover new research, like Meta’s mind reader, market data, and new products and models, including an incredibly realistic AI-generated video and a chores robot that may come to you as soon as this year.

Enjoy!

RESEARCH

PorTAL, a New Way to Automatically Update Models

I’ve long sustained that the future of enterprise AI is companies training open models in their own data while giving an enthusiastic ‘goodbye’ to OpenAI and Anthropic, at least for the vast majority of enterprise use cases.

And it seems several companies agree with me, including key players in the AI space like Palantir and Microsoft, and are becoming increasingly vocal about it (more on that below).

The appeal of lower costs, better governance, and tighter security makes this a no-brainer once open models are reaching a level of capability that warrants adoption.

The strongest argument against this idea has always been obsolescence, meaning why would I spend a couple thousand dollars fine-tuning an open model if it’s going to be obsolete by next week?

And while that argument is already not particularly strong when you realize that it’s okay to have legacy models for some tasks because these tasks do not need new levels of intelligence (e.g., I continue to use Gemini 3 Flash for a lot of my enterprise tasks despite this model being more obsolete than dinosaurs relative to the frontier, but it’s way cheaper), this new work by Ramp, an expense management company that is starting to look more and more like an AI Lab these days, proves how you can also ‘port’ your fine-tuning efforts from one model to the next.

In layman’s terms, you can amortize training costs by transferring the outcomes on one model it to a new, smarter base model without having to write off the training run.

But how? Leveraging one of the most fascinating pieces of research I’ve ever come across, Text-to-LoRA, which I’ve talked about in the past.

Fine-tuning is the practice of taking a pre-trained model and retraining it for a specific task you’re interested in. This is great because the model becomes incredibly good at that task (at the expense of losing performance in other areas).

The problem with fine-tuning is that you’re still retraining a large model, which can incur prohibitive costs, especially considering that the industry moves so fast that new, superior models come out every week, reducing the incentive even further because getting a return on that training run feels very complicated.

An alternative is to use LoRAs, low-rank adapters. My previous link goes into detail, but the idea is that most tasks that a model has to learn are “low rank,” meaning only a very small subset…

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