I rejected his rejection of technology. I think he didn't appreciate the power of information technology to support exactly the dreams he held. If people can understand each other, if they can learn from the whole world how to care for their bit of earth, surely that's a good thing? Because he rejected technology he never learned of free, open source, culture. He didn't appreciate that there is another path to liberation of humans from the machines that dominate us—that we embed them in our communities, bend them to our will, and reject exploitation. There's still hope...

I however am permanently touched by his perspective on the land as our foundation.

I was so inspired that I helped start a farm on the Kentucky River. It's a journey, but we are on our way to implement a little reflection of his vision near where he dreamed us these beautiful dreams of past and future.

There is the more likely reason they are not differentiating. They use almost exactly the same class of model. Everything is linear, parallelizable. It's incredible path dependence that's now invisible enough we think it's a natural law. Nature is not linear.

At best, you get much, much better ability to understand existing literature. the model itself has a very poor understanding of the physical world and that's masked by its knowledge of things people write about the physical world but it intrinsically doesn't have the same kinds of intuition and perspective that are really required to drive integration and completion in this space.

Is AI an important new tool in biology? Well, yes. Does it cause so much uplift in capacity that some rogue actor without biological research background could somehow destroy the world with a super-bio-nuke? I don't think so. I think that's just as logical a conclusion as the idea that next year one of the new frontier models will be told to make as many paperclips as possible and accidentally boil lake Michigan in pursuit of its goal.

As for the future... today the LLMs are "trained on biology", in that they read the textbooks, the research, the web.

They aren't trained on biology in the sense of being embodied, autonomously or semi-autonomously driving actual biological experiments. If you come from software, the timescale of these experiments is outlandish. Yes, I am partly saying the LLMs are not good enough today because they need to be embodied and trained for literal decades of lab time before there is even the _remote_ possibility that they could present a novel risk profile that is even a shadow of what the current fearmongering suggests the current models can enable.

And they aren't trained on biology in the sense that they've read the literature, but even 100T token training run only begins to touch the data scales that rather mundane bioinformatics operate at. True multimodal models that work on DNA and human language at high quality haven't yet emerged. We're talking new architectures which are going to arise after the next AI winter.

All of this ignores an even more fundamental point. Cost. If someone wants to make a bionuke, they don't need to use AI. They can set up the right evolutionary context and run quadrillions of parallel explorations of the design space. Directed evolution like this is cheap, well-understood, and insanely powerful. If you actually care about biosafety, we should be doing hard work to surveil gain of function research. Different flavors of LLM use are not going to be a differentiator for the foreseeable future.

Imo, this shouldn't be embedded in the executor layer. Orchestration should handle this.