[pear01]

This is temporary. What is the SKILL.md equivalent going to be in five years? In ten? You don't already see a pattern emerging around solutions to encode that "professional experience" into the tools themselves?

These LLMs can already incorporate our entire cultural corpus yet your "professional experience" is the threshold they won't cross?

[datsci_est_2015]

The word “incorporate” is doing some very heavy lifting in your assertion. These LLMs already have access to the whole corpus of architectural knowledge and software best practices, and yet they’re unable to reliably implement those best practices. Why not? Why do they often make completely unintuitive decisions, even when repeatedly prompted to ask clarifying questions?

[pear01]

To be clear by that and "cultural corpus" I meant their skill with natural languages. It is well known for instance that early LLMs were curiously better at composing sentences in English than doing basic math.

Regarding such formal reasoning we have already seen marked improvement in the last year or two alone. The question is how this weighs on your prediction re their capabilities in the next two, five, ten, etc years.

[datsci_est_2015]

What are the properties of LLMs that have convinced you that there remains emergent complexity (e.g. the “ability” to formally reason) that we have not yet seen?

[pear01]

There may be gains to be had in such emergence but that is not where I see the gains in the next five years. Those gains will be made by connecting LLMs more robustly with formal reasoning, which computers are already very good at. Continued iteration on connecting these right/left brain faculties could then lead to further emergence down the line.

The present notions of harnesses, structured output or looping in the LLM to some external state or sandbox be it debugger output or embedding into a runtime already show early promising results along these lines. I see no reason to believe these gains will not continue over the next five years.

If you have some theories in the converse in that regard I am all ears.

[datsci_est_2015]

Extraordinary claims require extraordinary evidence, not the opposite. There’s no current evidence to suggest limitless progress, or even superlinear progress with regards to compute and energy. My guess would be sub linear or even logarithmic progress vs. linear growth in compute and energy, as that’s how most physical systems behave.

[pear01]

No one said unlimited progress. Let's not revert to straw man claims.

If you think the potential of LLMs is overblown feel free to short the market. I don't pretend to know the future. But if I may, I don't think you are framing the debate in the correct terms. Evidence is an important facet of human affairs. So is risk. Best of luck with your predictions.

[antonvs]

> Why do they often make completely unintuitive decisions

Most likely because you haven't constrained their behavior in your prompt. You're making the assumption that they "understand" that using best practices is what you want. You have to tell them that, and tell them which practices they should use.

[datsci_est_2015]

They already fail consistently follow very simple and concrete instructions like “Please do not ever mock this object, always properly construct it in your tests”, so I’m not sure how they’re going to adhere to more vague and conceptual architectural paradigms. This is a problem with generative AI in general - image generation has similar limitations.

[antihipocrat]

Senior developers know what behavior to constrain.

If incorrect LLM output is a prompt issue then demand for experienced developers will remain, and demand may actually increase as time passes.

[ecocentrik]

The capacity of the person prompting it to understand is the threshold they won't cross. They can squeeze the gap as much as possible by dumbing down answers or slowly ramping up information complexity but there is a limit to comprehension.

[pear01]

This is an interesting answer for questions about human agency and accountability/personhood questions but I don't see how it leads to increased confidence in the role of human as SWE.

If LLMs get good enough, one might be tempted to ask so what if most humans can't understand the output? Human civilization has by and large been a constant exercise in us collectively accomplishing more and more while individually comprehending less and less.

Our ancestors likely understood more about hunting live game or murdering each other than we do. Most of us do not consider that a great loss. Most of us living in the modern world depend on things we don't fully comprehend. I'm just not sure how this would lead to being reassured re the human as SWE.

[npodbielski]

Do you really want to live in a world when nobody understand software that manages nuclear power plant? Or medical devices? Or financial software? Or radio transceivers firmware? Even something so boring like databases not understood could lead to disastrous effects if this would be the government database for managing people IDS. Hmm even if this would be working fine for years what would happen if bad actor would influence models to generate code if security issues? If nobody can comprehend the output how anybody would be able to think about the danger? This is even more grim then this https://www.citriniresearch.com/p/2028gic

[esafak]

Maybe the tables will turn and people will ask, do you really want to live in a world where things aren't designed by machines (smarter than us)?

[pear01]

We live in a world with nuclear weapons. Somehow we all cope and get up every morning. I think you are missing the point - the world is already grim. It always has been. What about human affairs say in the last century alone makes you think human oversight is some panacea? The impetus for civilization was not some innate desire for financial systems or medicine. It was not having other humans murder you. The Leviathan is already here.

The article you shared has little to do with this. Questions of how to divide up gains technology creates are a separate question from that of the technology itself. Tbh I found what you shared so boring I could barely finish it. I already in this thread made an exhortation to support politicans who commit to erasing inequality. The idea that LLMs can only exist with inequality is nonsensical. The only thing grim about what you shared is the lack of political imagination. It's boring.

[jimbokun]

At least we have people who understand the technology underlying nuclear weapons!

[ecocentrik]

We don't need as many hunters because we've domesticated sources of meat. We still need ranchers, butchers... an entire supply chain to get meat to consumers. We didn't remove humans from the loop, we just created specializations.

Software specialization might look very different in 10 years but I doubt that technically specialized humans will be completely removed from their professions. We might not be carrying bows and arrows anymore but we will be carrying the equivalent of a rope and a Stetson.

[pear01]

Ranchers, butchers... and factory farms. Most meat Americans consume have had very little interaction with a person until they are being devoured on the plate.

I appreciate your points. I agree with you that not all "technically specialized humans will be completely removed" but let's not pretend the comparison is going from a caveman with a spear to a cowboy with a lasso. If you concede it is likely to be very different at some point calling it SWE is no longer useful.

I think SWEs would be better off realizing they have enjoyed a relatively extreme level of privilege, and rather than trying to hold onto it, use what time they still have to advocate for a more egalitarian society, even if that means giving up some of their gains. Otherwise speaking of farming, the mass layoffs to come when software has been disrupting blue collar jobs for decades will really be a chickens coming home to roost moment.

[ecocentrik]

Now you're arguing against your own analogy? Hunter was ubiquitous position in human society prior to the domestication of animals. 50% of the workforce in hunter-gather societies. Today, 12 millennia after the domestication of wildlife, that number is down to 9-14% of the global workforce dedicated to the production, distribution, processing, sales of meat (not including cooked food) according to opus.

Considering that only 1% of the US workforce was a software engineer I expect similar workforce optimization to occur in software engineering specializations over the next 12,000 years. /s But seriously, it's never going to zero.

[pear01]

No one said it's going to zero. It doesn't have to go to zero for lives to change. Would you rather be a cowboy or a factory farmer? The latter are some of the least desirable jobs in the entire world. The fact that millions of people still do them isn't the point in your column you think it is.

[jimbokun]

The software specialists may be replaced entirely by subject matter experts.

No need for specialized commercial software, if everyone can just explain to the computer what they want in English.