inciampati

Earlier this month, we went to the mailbox to get his last book together. Will never forget it. Dolly's video note brought me to tears too. She brought brilliant, moving positivity into the world that we'd all be lucky to learn to emulate.

You know, it's not just cost-effectiveness, it's also iteration time and speed. DeepSeek V4.1 Flash is astoundingly fun to use because it's so quick. Tens of times faster than the standard Frontier models. With, in my experience, practically the same results. Because most of the trouble is not the intelligence of the model. It's the feedback from the world. The quicker you can get feedback from the world, the better your process is, irrespective of raw intelligence metrics of the AI you're using.

It's a commodity, and research into how to make it good will be done in the open. What this means is that we should doubt the justifications offered by magnificent-N-whatever companies slurping up all investment dollars to take over the world by being the first to get "general" "super" intelligence. No, there won't be a runaway improvement. Sadly, AI is just a clone of our inner world. Getting interesting information into it is still going to be just as slow as it is in the regular world. You have to build, tinker, experiment, get data, get knowledge, etc. The only thing getting sped up is how much time needs to be allocated to thinking and translating.

Isn't finding this out from an LLM somewhat... complex and non reproducible.

He was a friend of many people in my community. I met him a few times at public lectures. But his way of thinking has had a profound impact on my life.

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 no reason to believe that generalist models need to be transformer (or transformer hybrids) it's just that people are risk averse and not spending their resources on the many scaled risky bets needed to explore the possibilities. There is more there.

> We're not seeing them differentiate from each other, implying that the design space might not be that large.

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.

And this business of rates is also why getting the immune system—such as via personalized vaccine—to recognize a tumor is functionally "curing cancer". The problem is it isn't one disease in the sense that different tissues, cell types, and systems are affected leading to different parameters in the design and delivery of said vaccines.

It is amazing how fast it happened. Right now one of my main projects is fully running on DeepSeek flash. My reason was that I was blocked by both of the main US AI labs from working on it because it involves viruses. DeepSeek flash has been killing it since I switched it on, completing the first phase of the project and setting up an iteration in another application space. It isn't the most brilliant model, but it is reliable and I don't have to manage my weekly token allowance. I just spend freely and end up spending only a few dollars a day. Intelligence is going to become a basic commodity. Only special stuff is going to drive us to use special models. And maybe not even that.

Oak Ridge huh? What other small town would be a target?

I think I'm less bothered by the risk because I've actually used these systems to do biological research, to work in bioinformatics and to work in the lab. And while I think that the leverage you get in bioinformatics is very significant, the laboratory work has never felt the same.

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.

My point is more about conflict of interest in Anthropic, and certain techpeople types thinking that biology is a useful scapegoat because biologists don't use or understand this tech (they claimed 0.03% of users would be affected by biotech security stuff). So, arguing the world will fall apart because someone can ask your superduper powerful tool for a bad bad virus sequence, or how to do some technique in the lab, and get an answer that's plausible (oh but you never bothered to actually test if it's legit because you don't have the capability to do so).

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.

Anyone can write out the code for a bad virus. You can go download it from an open repository. It's only through deep interface with the world that the idea for the bad virus turns into an actual bad virus. The fever dreamers will say the LLMs will help you interface with reality to do the bad thing™ which their model let's you do. But you still need thousands to millions of times the effort And once made how do you deliver it in a way that might further your (bad) objectives? Presumably it just makes humans sick. There aren't "targeted" bioweapons, and among humans we are too damn similar for there ever to be. And all of this said, there is almost nothing special about the LLMs' abilities in biology. They only know what we know. They're not being trained autonomously with RL and a robotic wetlab. When that's a thing I'll start to take claims of biology risk more seriously. Right now they have the same logic as the paperclip theory of superintelligence risk. And cynically, it would seem that Anthropic purchased a biotech company and almost immediately decided to lock down biology work with their models.

Please post this!

but that's what we use the agent "swarm" to do .. it's also not scarce. what's scarce is imagination.