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The problem with this is that it's supposedly about predicting the future but actually bases everything on LLM's current capabilities. It's incredible that peo

by ilaksh 2y ago

The problem with this is that it's supposedly about predicting the future but actually bases everything on LLM's current capabilities. It's incredible that people still haven't figured out or won't accept or plan for technology to continue to improve. Especially given how obvious the rapid improvement in this area has been. Having said that the article seems to accurately reflect what it's like using the current tools. But how could anyone reasonably expect the situation to be similar 3-5 years down the line? If they just didn't frame it as a prediction then it would make sense.

rileymat2 2y ago
> But how could anyone reasonably expect the situation to be similar 3-5 years down the line? We can’t, that’s why we are talking about the current capabilities. I remember using Dragon Natural Dictation software before the year 2000, I was young and it blew my mind away with the possibilities. 27 years later it still has not lived up to my young imagination. (It has gotten a ton better, no doubt, and will continue to with more AI)
- tessierashpool 2y ago
  spoiler alert: this always happens with new technologies, to some extent. the possibilities that materialize are always an odd subset of the possibilities that people envision when the tech is new. you can get better at predicting that subset by studying economics or the humanities, but randomness also plays a crucial role.
  - mooreds 2y ago
    Or it could be because low hanging fruit is low hanging.
- ilaksh 2y ago
  AssemblyAI's most recent models are incredible. I think you aren't accurately assessing leading edge STT or how your young self would judge this capability level.
CharlesW 2y ago
> The problem with this is that it's supposedly about predicting the future but actually bases everything on LLM's current capabilities. Is it possible you missed some of it? The entire last half is dedicated to predicting the future based on an agentic future that's (as the author notes) "a big unknown" as we have this conversation.
- ilaksh 2y ago
  It's not the entire last half. He does mention agents, but inaccurately says that Devin is the only agentic software engineering tool. But he does not override his initial premise of doubting that things will change significantly from the current uses in the future. It's not really unknown, there are several popular tools, and we can expect them to be more reliable and useful as better models continue to be rolled out. Also if you are writing about the future then you should look beyond next year. Everyone should anticipate that there is a strong possibility that we continue on a similar trajectory of improvement. Therefore it is NOT reasonable to expect that things won't change dramatically in software engineering, because the trajectory is very rapid progress. Within the next 3, 5, maybe 10 years, many existing jobs including software engineering may be replaced by AI. That's the direction we are headed and any article should take the possibility seriously by now.
swiftcoder 2y ago
> But how could anyone reasonably expect the situation to be similar 3-5 years down the line? That just depends how optimistic you are about rate of improvement. Folks who think we are on the cusp of AGI predict that improvement will be exponential. Folks who think that Moores law will keep up with training costs predict improvement that is somewhat linear. On the other hand, folks who are looking at the rate of change of current LLMs think we're running out of training data, predict that improvements are more likely to be logarithmic, and we're already in the flattening section of the curve...
- ilaksh 2y ago
  There are many curves. It's a series of sigmoids. There are many innovations to keep up with the expected constant performance increases and demand. They started running out of data, then curated it, then spent more time on inference. There are many small and large innovations that continue to improve performance as each improvement gets implemented and maximized and the gains flatten out and then increase again. Some are using giant SRAM chips. They will then connect them with optics. When that doesn't keep up with demand, probably memristors or something will be scaled to be the next paradigm. Look at the massive improvements made by the DeepSeek team to the open source SOTA recently.
- godelski 2y ago
  I believe growth is exponential. The growth of complexity. The author mentions how it can get you 70% of the way there, well Pareto is a bitch. Once the details matter more the difficulty grows evidentially (well power series). What people fail to realize is despite what we've done is incredibly and no easy task, it's "the easy part". And progress is unfortunately constantly like this. Because as we progress we are effectively solving higher order solutions. Solve your Taylor series problems. If you've done up to 4th order and still need 5th, everything previously is (usually) "easy" in comparison. Getting to the 6th is no different, and so on. And of course it gets harder, we made progress! The naive part is to think the future will be as easy as the past looks in retrospect, not as hard as the current state looked a priori.
  - intended 2y ago
logicchains 2y ago
>It's incredible that people still haven't figured out or won't accept or plan for technology to continue to improve Because it's incredibly difficult for people to accept that the career they put years into and love might not exist in 5-10 years. At the current pace, in a few years we'll have something smarter than o3 while no more expensive than o1. Then all it would take is someone to find a nice way to rig it up with short-term memory and wrap it in something like Devin, and then companies would be able to hire the equivalent of a top 1% remote dev for less than 10% of a current dev salary.
intended 2y ago
To be fair - the gap between present capabilities and future capabilities is the issue. Having a system that is accurate a random number of times, is very different than a system that is predictable. There’s just too many use cases where GenAI looks like a great fit, but then you have to have the mental overhead of figuring out if this is the time you lose the lottery. It’s just… the kind of mental overhead I wanted a machine to take away from me, not add to my life.
PaulDavisThe1st 2y ago
> It's incredible that people still haven't figured out or won't accept or plan for technology to continue to improve. Especially given how obvious the rapid improvement in this area has been. If you wrote this about aeroplanes in the mid-1960s, despite the previous 40-50 years of rapid improvement, you'd be wrong. The same would be true for bicycles, and automobiles - despite decades of incremental and noteworthy-for-specific-context improvements, there's been no fundamental changes in these (and many other technologies) that reflects the early progress. Yes, modern cars are safer, more comfortable, more fuel efficient (sometimes), but they get nothing done that wasn't possible with a car from the mid-1950s. Why would you assume that the recent past of LLMs provides an outline of what the future of AI in general (not just LLMs) is going to be?
- dwaltrip 2y ago
  The question is, are current LLMs more like wooden biplanes or early passenger jets? It feels like we are closet to the wooden biplane era, imo.
  - PaulDavisThe1st 2y ago
    Or are they more like a video of flying that we're watching and saying with glee "we're flying!" ?
DanHulton 2y ago
The situation certainly won't be similar 3-5 years down the line, but it also won't necessarily be different in the way that people are predicting. In the section about the 70% problem, the author writes: > The good news? This gap will likely narrow as tools improve. This is not a fact, it's a prediction, an article of faith. It's a common prediction, that these tools will only get better over time, but that's not guaranteed! I think it's likely as well, provided you define "improve" incredibly pedantically, but the unspoken part of this prediction is that the tools will improve _significantly,_ and _that_ part is one I have doubts about. Honestly, I think it's pretty likely that we've just about hit the local maximum of our current techniques - training newer, bigger models is fantastically expensive and doesn't seem to have the same jumps in capability as earlier generations did, and stringing together a bunch of models in an agent produces frankly only modest improvements for the cost increase. I've written about "the 70% problem" before (not by name, though), and my biggest worry there is that you require experience and good judgement to be able to use these tools effectively, and these tools by their nature erode that good judgement and deny you the experience. Juniors won't have to work through "the tough parts" of programming and build up the skills required to understand when an LLM is leading them astray, and even experienced programmers can lose familiarity with their own codebases as they rely on LLMs to provide the "understanding." Think of all the times where you haven't had to interact with a service for months, how long it takes to warm back up to it -- what about when that's _most_ of your code, because LLMs have been preventing you from needing to gain any deep understanding about it? What happens if, in 3-5 years, LLMs don't get significantly better, but we've stopped producing properly capable Intermediate and Senior developers, and even started atrophying the skills of existing Seniors? (Couple this with LLM-using developers who can move mountains in a day to kickstart new projects and get promoted, but leave behind a trashfire of a codebase for more "classically" experienced developers to clean up and maintain at a much slower pace. That's a problem that's _always_ been in the industry, just magnified due to the power of LLMs.)
- MrMcCall 2y ago
  [flagged]
  - 2y ago

by ilaksh 2y ago

rileymat2 2y ago

> But how could anyone reasonably expect the situation to be similar 3-5 years down the line? We can’t, that’s why we are talking about the current capabilities. I remember using Dragon Natural Dictation software before the year 2000, I was young and it blew my mind away with the possibilities. 27 years later it still has not lived up to my young imagination. (It has gotten a ton better, no doubt, and will continue to with more AI)

tessierashpool 2y ago
spoiler alert: this always happens with new technologies, to some extent. the possibilities that materialize are always an odd subset of the possibilities that people envision when the tech is new. you can get better at predicting that subset by studying economics or the humanities, but randomness also plays a crucial role.
- mooreds 2y ago
  Or it could be because low hanging fruit is low hanging.
ilaksh 2y ago
AssemblyAI's most recent models are incredible. I think you aren't accurately assessing leading edge STT or how your young self would judge this capability level.

CharlesW 2y ago

> The problem with this is that it's supposedly about predicting the future but actually bases everything on LLM's current capabilities. Is it possible you missed some of it? The entire last half is dedicated to predicting the future based on an agentic future that's (as the author notes) "a big unknown" as we have this conversation.

ilaksh 2y ago
It's not the entire last half. He does mention agents, but inaccurately says that Devin is the only agentic software engineering tool. But he does not override his initial premise of doubting that things will change significantly from the current uses in the future. It's not really unknown, there are several popular tools, and we can expect them to be more reliable and useful as better models continue to be rolled out. Also if you are writing about the future then you should look beyond next year. Everyone should anticipate that there is a strong possibility that we continue on a similar trajectory of improvement. Therefore it is NOT reasonable to expect that things won't change dramatically in software engineering, because the trajectory is very rapid progress. Within the next 3, 5, maybe 10 years, many existing jobs including software engineering may be replaced by AI. That's the direction we are headed and any article should take the possibility seriously by now.

swiftcoder 2y ago

> But how could anyone reasonably expect the situation to be similar 3-5 years down the line? That just depends how optimistic you are about rate of improvement. Folks who think we are on the cusp of AGI predict that improvement will be exponential. Folks who think that Moores law will keep up with training costs predict improvement that is somewhat linear. On the other hand, folks who are looking at the rate of change of current LLMs think we're running out of training data, predict that improvements are more likely to be logarithmic, and we're already in the flattening section of the curve...

ilaksh 2y ago
There are many curves. It's a series of sigmoids. There are many innovations to keep up with the expected constant performance increases and demand. They started running out of data, then curated it, then spent more time on inference. There are many small and large innovations that continue to improve performance as each improvement gets implemented and maximized and the gains flatten out and then increase again. Some are using giant SRAM chips. They will then connect them with optics. When that doesn't keep up with demand, probably memristors or something will be scaled to be the next paradigm. Look at the massive improvements made by the DeepSeek team to the open source SOTA recently.
godelski 2y ago
I believe growth is exponential. The growth of complexity. The author mentions how it can get you 70% of the way there, well Pareto is a bitch. Once the details matter more the difficulty grows evidentially (well power series). What people fail to realize is despite what we've done is incredibly and no easy task, it's "the easy part". And progress is unfortunately constantly like this. Because as we progress we are effectively solving higher order solutions. Solve your Taylor series problems. If you've done up to 4th order and still need 5th, everything previously is (usually) "easy" in comparison. Getting to the 6th is no different, and so on. And of course it gets harder, we made progress! The naive part is to think the future will be as easy as the past looks in retrospect, not as hard as the current state looked a priori.
- intended 2y ago

logicchains 2y ago

>It's incredible that people still haven't figured out or won't accept or plan for technology to continue to improve Because it's incredibly difficult for people to accept that the career they put years into and love might not exist in 5-10 years. At the current pace, in a few years we'll have something smarter than o3 while no more expensive than o1. Then all it would take is someone to find a nice way to rig it up with short-term memory and wrap it in something like Devin, and then companies would be able to hire the equivalent of a top 1% remote dev for less than 10% of a current dev salary.

intended 2y ago

To be fair - the gap between present capabilities and future capabilities is the issue. Having a system that is accurate a random number of times, is very different than a system that is predictable. There’s just too many use cases where GenAI looks like a great fit, but then you have to have the mental overhead of figuring out if this is the time you lose the lottery. It’s just… the kind of mental overhead I wanted a machine to take away from me, not add to my life.

PaulDavisThe1st 2y ago

> It's incredible that people still haven't figured out or won't accept or plan for technology to continue to improve. Especially given how obvious the rapid improvement in this area has been. If you wrote this about aeroplanes in the mid-1960s, despite the previous 40-50 years of rapid improvement, you'd be wrong. The same would be true for bicycles, and automobiles - despite decades of incremental and noteworthy-for-specific-context improvements, there's been no fundamental changes in these (and many other technologies) that reflects the early progress. Yes, modern cars are safer, more comfortable, more fuel efficient (sometimes), but they get nothing done that wasn't possible with a car from the mid-1950s. Why would you assume that the recent past of LLMs provides an outline of what the future of AI in general (not just LLMs) is going to be?

dwaltrip 2y ago
The question is, are current LLMs more like wooden biplanes or early passenger jets? It feels like we are closet to the wooden biplane era, imo.
- PaulDavisThe1st 2y ago
  Or are they more like a video of flying that we're watching and saying with glee "we're flying!" ?

DanHulton 2y ago

The situation certainly won't be similar 3-5 years down the line, but it also won't necessarily be different in the way that people are predicting. In the section about the 70% problem, the author writes: > The good news? This gap will likely narrow as tools improve. This is not a fact, it's a prediction, an article of faith. It's a common prediction, that these tools will only get better over time, but that's not guaranteed! I think it's likely as well, provided you define "improve" incredibly pedantically, but the unspoken part of this prediction is that the tools will improve _significantly,_ and _that_ part is one I have doubts about. Honestly, I think it's pretty likely that we've just about hit the local maximum of our current techniques - training newer, bigger models is fantastically expensive and doesn't seem to have the same jumps in capability as earlier generations did, and stringing together a bunch of models in an agent produces frankly only modest improvements for the cost increase. I've written about "the 70% problem" before (not by name, though), and my biggest worry there is that you require experience and good judgement to be able to use these tools effectively, and these tools by their nature erode that good judgement and deny you the experience. Juniors won't have to work through "the tough parts" of programming and build up the skills required to understand when an LLM is leading them astray, and even experienced programmers can lose familiarity with their own codebases as they rely on LLMs to provide the "understanding." Think of all the times where you haven't had to interact with a service for months, how long it takes to warm back up to it -- what about when that's _most_ of your code, because LLMs have been preventing you from needing to gain any deep understanding about it? What happens if, in 3-5 years, LLMs don't get significantly better, but we've stopped producing properly capable Intermediate and Senior developers, and even started atrophying the skills of existing Seniors? (Couple this with LLM-using developers who can move mountains in a day to kickstart new projects and get promoted, but leave behind a trashfire of a codebase for more "classically" experienced developers to clean up and maintain at a much slower pace. That's a problem that's _always_ been in the industry, just magnified due to the power of LLMs.)

MrMcCall 2y ago
[flagged]
- 2y ago