Comment by llmslave2
18 hours ago
Pretty neat how this exponential progress hasn't resulted in exponential productivity. Perhaps you could explain your perspective on that?
18 hours ago
Pretty neat how this exponential progress hasn't resulted in exponential productivity. Perhaps you could explain your perspective on that?
Because that requires adoption. Devs on hackernews are already the most up to date folks in the industry and even here adoption of LLMs is incredibly slow. And a lot of the adoption that does happen is still with older tech like ChatGPT or Cursor.
What’s the newer tech?
Claude Code With Opus 4.5
Writing the code itself was never the main bottleneck. Designing the bigger solution, figuring out tradeoffs, taking to affected teams, etc. takes as much time as it used to. But still, there's definitely a significant improvement in code production part in many areas.
I think this is an open question still and very interesting. Ilya discussed this on the Dwarkesh podcast. But the capabilities of LLMs is clearly exponential and perhaps super exponential. We went from something that could string together incoherent text in 2022 to general models helping people like Terrance Tao and Scott Aaronson write new research papers. LLMs also beat IMO and the ICPC. We have entered the John Henry era for intellectual tasks...
> LLMs also beat IMO and the ICPC
Very spurious claims, given that there was no effort made to check whether the IMO or ICPC problems were in the training set or not, or to quantify how far problems in the training set were from the contest problems. IMO problems are supposed to be unique, but since it's not at the frontier of math research, there is no guarantee that the same problem, or something very similar, was not solved in some obscure manual.
> But the capabilities of LLMs is clearly exponential and perhaps super exponential
By what metric?
BS metric... /s
Sir, we're in a modern economy, we don't ever ever look at productivity graphs (this is not to disparage LLMs, just a comment on productivity in general)
It has! CLs/engineer increased by 10% this year.
LLMs from late 2024 were nearly worthless as coding agents, so given they have quadrupled in capability since then (exponential growth, btw), it's not surprising to see a modestly positive impact on SWE work.
Also, I'm noticing you're not explaining yourself :)
I think this is happening by raising the floor for job roles which are largely boilerplate work. If you are on the more skilled side or work in more original/ niche areas, AI doesn't really help too much. I've only been able to use AI effectively for scaling refactors, not really much in feature development. It often just slows me down when I try to use it. I don't see this changing any time soon.
Hey, I'm not the OG commentator, why do I have to explain myself! :)
When Fernando Alonso (best rookie btw) goes from 0-60 in 2.4 seconds in his Aston Martin, is it reasonable to assume he will near the speed of light in 20 seconds?
> Hey, I'm not the OG commentator, why do I have to explain myself! :)
The issue is that you're not acknowledging or replying to people's explanations for _why_ they see this as exponential growth. It's almost as if you skimmed through the meat of the comment and then just re-phrased your original idea.
> When Fernando Alonso (best rookie btw) goes from 0-60 in 2.4 seconds in his Aston Martin, is it reasonable to assume he will near the speed of light in 20 seconds?
This comparison doesn't make sense because we know the limits of cars but we don't yet know the limits of LLMs. It's an open question. Whether or not an F1 engine can make it the speed of light in 20 seconds is not an open question.
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I'm noticing you're not responding to my claim that producivity has been impacted
If you're not going to explain yourself, at least stay on topic. We're talking about exponential growth, so address the points I'm making.
LLMs a year ago were more able to do a complex project I've repeatedly tried to do than they are now.
Try Antigravity with Gemini 3 Pro. Seems very capable to me.
How long before introduction of computers lead to increases in average productivity? How long for the internet? Business is just slow to figure out how to use anything for its benefit, but it eventually gets there.
The best example is that even ATM machines didn't reduce bank teller jobs.
Why? Because even the bank teller is doing more than taking and depositing money.
IMO there is an ontological bias that pervades our modern society that confuses the map for the territory and has a highly distorted view of human existence through the lens of engineering.
We don't see anything in this time series, because this time series itself is meaningless nonsense that reflects exactly this special kind of ontological stupidity:
https://fred.stlouisfed.org/series/PRS85006092
As if the sum of human interaction in an economy is some kind of machine that we just need to engineer better parts for and then sum the outputs.
Any non-careerist, thinking person that studies economics would conclude we don't and will probably not have the tools to properly study this subject in our lifetimes. The high dimensional interaction of biology, entropy and time. We have nothing. The career economist is essentially forced to sing for their supper in a type of time series theater. Then there is the method acting of pretending to be surprised when some meaningless reductionist aspect of human interaction isn't reflected in the fake time series.
> How long before introduction of computers lead to increases in average productivity?
I think it never did. Still has not.
https://en.wikipedia.org/wiki/Productivity_paradox