My current model for understand for how AI will scale out is that we'll move through the following choke points:
AI chip makers -> Data center infra and construction -> regional power companies
Right now we're firmly in the "AI chip makers" part of the expansion, with everything else in the beginning stages. AI is useful, but whether it's hyped or not, it's hard to deny that not being able to build and power data centers will impact how this plays out.
Me too! I'm really interested in seeing an LLM coding platform treat those documents (spec, architecture, decision logs) as first-class objects: collaborative, persistent, and outside the codebase itself.
Right now they live in repos and it's easy for them to get lost later, and harder to share them.
I've heard this feedback from a number of users: "If this AI agent is just doing a bunch of API calls, and I know how to do the calls myself, why would I use this?".
I don't have a great answers to that, besides, "well it's easier to onboard the agent than teaching someone how weave together 3 API calls", but that's the institutional logic, not really convincing an end user.
Either way, if the AI actually works, and we can predict when it needs to be used, the direction we're going in is to just run the process and keep the user informed.
I wrote this article after working on chatbots over the past year. The pattern I kept seeing was that the hard part wasn't getting agents to work, but getting busy people to use them.
Author here. I wrote this after working with AI systems over the past few years in an enterprise environment. The main idea is that the limiting factor isn’t model capability or even features, but the tools impact on human attention. Interested if others have seen similar patterns, especially with agent workflows or routing systems.
It's possible that the "functional" aspect of non-coding RNA exists on a time scale much larger that what we can assay in a lab. The sort of "junk DNA/RNA" hypothesis: the ncRNA part of the genome is material that increases fitness during relative rare events where it's repurposed into something else.
On a millions or billions of year time frame, the organisms with the flexibility of ncRNA would have an advantage, but this is extremely hard to figure out with a "single point in time" view point.
Anyway, that was the basic lesson I took from studying non-coding RNA 10 years ago. Projects like ENCODE definitely helped, but they really just exposed transcription of elements that are noisy, without providing the evidence that any of it is actually "functional". Therefore, I'm skeptical that more of the same approach will be helpful, but I'd be pleasantly surprised if wrong.
Such an advantage that is rare and across such long time scales would be so small on average that it would be effectively neutral. Natural selection can only really act on fitness advantages greater than on the order of the inverse of effective population size, which for large multicellular organisms such as animals, is low. Most of this is really just noisy transcription/binding/etc.
For example, we don't keep transposons in general because they're useful, which are almost half of our genomes, and are a major source of disruptive variation. They persist because we're just not very good at preventing them from spreading, we have some suppressive mechanisms but they don't work all the time, and there's a bit of an arms race between transposons and host. Nonetheless, they can occasionally provide variation that is beneficial.
You never go on vacation. Your family gets sick. Your friends need your help. You want to travel. You want to go to funerals for people who aren't in your direct family. You want to explore hobbies.
I'm not opposed to business books. It's true that most of them are in narrative form and try to extract anecdotal lessons into broad strategy, but I've found them useful for framing my own thinking about teams, strategy, and leadership. Thinking about your work, just in a different way, or though a different lens, I believe is helpful. How helpful? Probably not as much as making more decisions yourself, but at least in my environment I'm rate limited by circumstances beyond my control!
That said, there are a couple of "good" business books, and I agree with the author on the works of Michael Porter (esp. On Competition) and ET Jaynes, Probability Theory: The Logic of Science. The later was a major influence on my life and pivotal book I read as a young scientist, and it moved me into the direction of data science!
reply