In the savanna it’s not about who outruns the lion but who outruns their peers escaping the lion.
Frontier labs need these headlines not for human progress but as beauty pageant for investors and government agencies. If they don’t do maths they’ll just go after other fields.
So Terrance Tao here might be able to hold them off math but he won’t stop them from speedrunning STEM with similar consequences.
We may be locking people out of these fields instead delegating everything to machines, and I don’t think the machines are good enough to assume that responsibility.
Code cost is almost down to zero. If you move the point of “just leave it to the machine” from the compiler (where humans used to do the coding) to the high level logic (now with LLMs) then in most cases more code does not really matter. Like, why build and maintain an abstraction where the LLM could implement this many times over each time with different subtleties? Why use a library with its own constraints when you could have exactly what you want? Why use cross platform frameworks when you can just one shot the thing to N different platforms?
It’s not even slower. You can have code that’s larger yet more performant (stripping away abstractions can do that).
From time to time I try to do a pass of coalescing flows and cases and removing dead code to reduce the context and prevent the LLM from tripping over itself. But if it’s exclusively LLM maintained code I don’t care too much if there’s more of it.
Just last two weeks I had to slap Fable, three times, to stop writing 1000-2000 lines of defensive code... because of DB columns I just forgot should be NOT NULL. That was it. Nothing else. I told it that, boom, -4800 coding lines: gone.
LLMs defend the status quo and they regularly lose sight of everything bigger than the current PR they are working on.
I too am gradually making peace with the fact that LLM-maintained code does not have to be 100% readable for humans.
But this is not about readability. It's about the data model. So one concession I am willing to make is: don't care too much about the code _BUT_ manually curate the data model. So far: small wins on iteration turns and code volume producing. Too early to tell but for now I am happy with the results.
As a pretty avid Star Trek watcher from childhood, I found most of the things there technologically plausible other than the conversational nature of the ship's computer. Well, LLMs now are far more impressive conversation counterparts than those ships were ever depicted.
> As a pretty avid Star Trek watcher from childhood, I found most of the things there technologically plausible other than the conversational nature of the ship's computer.
I share the sentiment but the question here is how long can we maintain the balance point where the human in the agentic loop is required. It might be a window lasting only a few years, or for the foreseeable future; I think the answer lies the opaque compute economics of the frontier lab: how well models keep scaling and how economically sustainable is serving those models under the current market conditions.
If my job gets automated, I'll find something else to do. I wouldn't have wanted lamplighters to succeed in preventing electrification, so it would be unfair for me to prevent the automation of my job if it can be done.
That's life. I'm lucky to be interested in and also good at a profession that pays well. If I lose that and in exchange everyone gets to be good at making software, then that's a sacrifice I feel obligated to make for the benefit of humanity, and I'll still have more savings than someone not in this profession.
I use Astra to drive Fable; I drivel into my phone while walking in the forest and it builds. I don’t need to check; I do as clients need to pay, but it always is great. And it surprises me with things I did not know were possible even (never encountered them before so why would I know). We are at the point where our clients send voice messages and they get what they want without humans basically. This costs 10+10 max2 subs but that’s nothing compared to hiring people. We didn’t fire anyone; we just have 100+ more clients and make almost 50x more money. It’s boring but great as long as it lasts, we are already where you say for what enterprises generally need for the boring parts. That’s 99%.
I find I still add value, but I don't know if my value is real. Am I just biased and expect things to be done a certain way and penalize the model for doing something different? and am I providing the model with enough high quality context to align with my expectations in one-shot?
These inventions all stopped disrupting the world and just became a part of it. The question is whether LLMs are going to just take their quiet place, or profoundly change (or eliminate) humanity in a self-feeding frenzy towards singularity.
We need purpose, as a species; after all we left the caves for a better life. Desires make us human, the pursuit keep us busy and entertained.
There is no passion without wanting, and without passion what is the reason for living?
People need purpose. Workplaces provide a very structured, highly managed, perpetual treadmill of purpose. Not purpose in the "self growth" sense, but just something to keep people occupied. "The devil looks for idle hands", and with a large swath of the population* on welfare, there's a good chance he'll find plenty.
[0] that said, population in developed countries is naturally declining so maybe it won't be a problem in the long term either; or maybe, with less need to work, people will be more inclined to raise other people, leading us to both more machines and more humans.
Zitron is providing a service: he's running the train for those who want to believe, or sincerely believe, AI is a bubble. His interviews are apocalyptical one sided rants that confuse what is with what Zitron thinks should be. I'm not sure he's wrong, by the way. As LLM performance is becoming more and more commoditized I fail to see how spend catches up to expectations to keep running this market as it does, but that's beside the point.
One annoying side effect is that YouTube's algorithms will always try to force feed you more of the things you last searched, to amplify your biases and send you down the doomscroll rabbit hole. So if you search for Zitron, next time you visit you'll be flooded senselessly with naysayers, contrarians and skeptics from all courses of life.
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