"Prediction: AI will cause the price of work that can happen in front of a computer to decrease much faster than the price of work that happens in the physical world. This is the opposite of what most people (including me) expected, and will have strange effects"
And I was like yeah I gotta start preparing for next decade.
I'm skeptical.
The envelope of "programming" will continue to shift as things get more and more complex. Your mother-in-law is not going to install Copilot and start knocking out web apps. Tools like this allow programmers to become more productive, which increases demand for the skills.
Reminds me of something I read that claimed when drum machines came out, the music industry thought it was the end of drummers. Until people realized that drummers tended to be the best people at programming cool beats on the drum machine.
Every single technological advancement meant to make technology more accessible and eliminate expertise has instead only redefined what expertise means. And the overall trend has been a lot more work opportunities created, not less.
So the winners were those that adapted earlier and the losers were those that didn't/couldn't adapt.
This translates to: If you're mindlessly doing the same thing over and over again, then it's a low value prop and is at risk. But if you're solving actual problems that require thought/expertise then the value prop is high and probably going to get higher.
We can automate the mundane but that’s usually the stuff that requires creativity, so the automated stuff becomes uninteresting in that realm. People will seek crafted experiences.
It's just concentrated into the hands of a very few super specialists, it's much harder to get to their level but their work is much much more important.
See: https://www.youtube.com/watch?v=ZSRHeXYDLko / Preventing the Collapse of Civilization / Jonathan Blow
So, during this period, a ~10% increase in population saw a 250% increase in drummers.
It does not appear that the drum kit killed the drummer.
Big caveats about what these surveys defined as "drummer" and that this doesn't reflect professional drummer gigs, just the number of drummers.
[1] https://m.facebook.com/Bumwrapdrums/posts/how-many-drummers-...
[2] https://www.quora.com/How-many-people-play-drums-in-the-US
By the same line of thinking, If you can get by with AI generated code did you really require a seasoned, experienced developer in the first place? If your product/company/service can get by with copy pasta to run your CRUD app (which has been happening for some time now sans the AI aspect) did you ever really need a high end dev?
I think its like anything else, 80% is easy and 20% is not easy. AI will handle the 80% with increasing effectiveness but the 20% will remain the domain of humans for the foreseeable future.
Worth considering maybe.
I think the analog to programming is a bit more direct in this sense; most companies aren't going to go with something like Copilot unless it's supplemental or they're on an entirely shoestring budget; it'll be the bigger companies wanting to squeeze out that extra 10% productivity that are betting hard on this - same with where larger bands would do this to have an extremely clean studio track for an album.
AI will be similar -- it will not just give more tools to people already in a given field (programming, writing, whatever), but also bring new people in, and also create new fields. (I personally can't wait for the gardening of AI art to catch on. It'll be so weird[1].)
Nowadays everyone can make professional looking photos so the demand for photographers has shrunk, as the supply has increased.
Why would an architect bother with sending some work overseas if tools like this would enable them to crank out the code faster than it would take to do a code review?
1) AI tools increase developer productivity, allowing projects to get completed faster; and
2) AI tools offset a nonzero amount of skill prerequisites, allowing developers to write "better" code, regardless of their skill level
With those in mind, it seems reasonable to conclude that the price to e.g. build an app or website will decrease, because it'll require either fewer man-hours 'til completion and/or less skill from the hired developers doing said work.
You do make a good point that "building an app" or "building a website" will likely shift in meaning to something more complex, wherein we get "better" outputs for the same amount of work/price though.
Yes, and this in turn increases demand as more people/companies/etc.. can afford it.
And even that could still be fine for programmers, as other firms will be enticed into buying the creation of software -- firms that didn't want to build software when programming was less efficient/more expensive.
And actually, I really don't see AI in the next decade making more of a difference than what Github did (making thousands of man-hour of works available for free). Around 2040 or 2050, maybe. But not soon, AI is still really far.
[systems] Designing data intensive applications - kleppman
[programming] SICP - sussman & abelson
Last one is an old scheme book. No other book (that I read) can even hold a candle to this one, in terms of actually developing my thought process around abstraction & composition of ideas in code. Things that library authors often need to deal with.
For example in react - what are the right concepts to that are powerful enough to represent a dynamic website & how should they compose together.
But on the other hand, it also can mean that.
programmers, on the other hand, are wage laborers, individually selling their labor to employers who profit by paying them less.
industry is sitting on the opposite side of the equation here. I wonder what will replace "learn to code". whatever it is, the irony will be almost as rich as the businesses that profit from all this.
There are exceptions -- fancy weapons don't widely raise standards of living -- but the trends are strong.
On the first though, we have little reason to think tech will systematically diminish the roles people can fill. In the broad, the opposite has tended to happen throughout history -- although the narrow exceptions, like factory workers losing their jobs to robots, are real, and deserve more of a response than almost every government in the world has provided. For political stability, let alone justice.
Replacement of programmers will follow these lines. New tools, like copilot (haven't tried, but will soon), new languages, libraries, better IDEs, stack overflows, Google, etc will make programming easier and more productive. One programmer will do the work that ten did. That a hundred did. You'll learn to become an effective programmer from a bootcamp (already possible - I know someone who went from bootcamp to Google), then from a few tutorials will.
Just like the secretary's role in the office was replaced by everyone managing their own calendars and communications the programmer will be replaced by one or two tremendously productive folks and your average business person being able to generate enough code to get the job done.
This would scale to support any number of economists. This would also be a simpler model and that simplicity might lead to a better product. In your model, the economists must explain to you, then you must write the code. That adds a layer where errors could happen - you misunderstand the economists or they explain poorly or you forget or whatever. If the economists could implement things themselves - less room for "telephone" type errors. This would also allow the economists to prototype, experiment, and iterate faster.
That doesn't mean anything. The last 20 years have seen an absurd chase of more and more stupidity in job titles to make people feel they are "executive assistants" instead of secretaries, "vice presidents" instead of whatever managerial role, etc, etc.
There are more "secretaries" than ever, and they get to do far more productive things than delivering phone messages.
Well like everything in life I guess it depends? The only iron rule I can always safely assume is supply and demand.
But for programming especially on the web, it seems everyone has a tendency of making things more difficult than it should be, that inherent system complexity isn't going to be solved by ML.
So in terms of squeezing out absolute efficiency from system cost, I think we have a very very long way to go.
Initially, they will appear more productive with CoPilot. Businesses will decide they do not need anybody other than those who want to work with CoPilot. This will lead to adverse selection on the quality of programmers that interact with CoPilot ... especially those who cannot judge the quality of the suggested code.
That can lead to various outcomes, but it is difficult to envision them being uniformly good.
I don't think software engineers will get much cheaper, they'll just do a lot more.
Otherwise I am struggling explaining why there is such a great demand for devs that short courses (3-6 months) are successful, the same courses that fail at teaching the fundamental of computing.
Maybe many. If the cost/benefit equation doesn't work, it makes no sense to do the project.
> I don't think software engineers will get much cheaper, they'll just do a lot more.
If they do more for the same cost, they are cheaper. You as a developer will be earning less in relation to the value you create.
Doesn't matter as long as I create 5x value and earn 2x for it. I still am earning double within the same time and effort.
Welcome to the definition of productivity increases, which is the only way an economy can increase standard of living without inflation.
It is certainly impressive to see how much the GPT models have improved. But the devil is in the last 10%. If you can create an AI that writes perfectly functional python code, but that same AI does not know how to upgrade an EC2 instance when the application starts hitting memory limits, then you haven't really replaced engineers, you have just given them more time to browse hacker news.
You're not replacing the engineer, but you're giving every engineer a tireless companion typing suggestions faster than you ever could, to be filled in when you feel it's going to add value. My experience with the alpha was eye opening: this was the first time I've interacted with an AI and felt like its not just a toy, but actually contributing.
But you are clearly more knowledgeable with your 0 drivers replaced comment.
https://blog.waymo.com/2020/10/waymo-is-opening-its-fully-dr...
I think full self driving is possible in the future, but it will likely require investments in infrastructure (smarter and safer roads), regulatory changes, and more technological progress. But for the last decade or so, we had "thought leaders" and VCs going on and on about how AI was going to put millions of drivers out of work in the next decade. I think it is safe to say that we are at least another decade away from that outcome, probably longer.
[1] https://finance.yahoo.com/news/number-american-taxi-drivers-...
That's exactly the kind of thing "serverless" hosting has done for a while now.
But over the coming decades AI could dominate coding. I now believe in my lifetime it will be possible for an AI to win almost all coding competitions!
They feed you all these algorithms in college and your brain suggests new algorithms based on those patterns.
An AI agent can interact with an environment and learn from its environment by reinforcement learning. It is important to remember that pattern matching is different from higher forms of learning, like reinforcement learning.
To summarize, I think there are real limitations with this AI, but these limitations are solvable problems, and I anticipate significant future progress
Also Generative Adversarial Networks original implementation was to pit neural networks against each other to train them , they don't need human intervention.
Some come from the other end of the process.
I want to solve that problem -> Functionally, it’d mean this and that -> How would it work? -> What algorithms / patterns are there out there that could help.
Usually people with less formal education and more hands on experience, I’d wager.
More prone to end up reinventing the wheel and spend more time searching for solutions too.
Which fits the pattern matching described by the grandparent.
A few people I know, most of which haven’t been to college, or done much learning at all, but are used to work outside of what they know (that’s an important part), tend to solve problems with things they didn’t know at the time they set out to solve said problems.
Which doesn’t really fit the pattern matching mentioned by the grandparent. At least not in the way it was meant.
(Some call me heterodox, I prefer 'original thinker'.)
This exists: https://en.wikipedia.org/wiki/Automated_theorem_proving
It's like saying that calculators can solve complex math problems; it's true in a sense, but it's not not strictly true. We solve the complex math problems using calculators.
I would very much like GPT-f for something like SMT, then it could actually make Dafny efficient to check (and probably avoid needing to help it out when it gets stuck!)
This looks like a clever example of supervised learning. But supervised learning doesn't get you cause and effect, it is just pattern matching.
To get at cause and effect, you need reinforcement learning, like AlphaGo. You can imagine an AI writing code that is then scored for performing correctly. Overtime the AI will learn to write code that performs as intended. I think coding can be used as a "playground" for AI to rapidly improve itself, like how AlphaGo could play Go over and over again
AlphaGo learns a game with fixed, well-defined, measurable objectives, by trying it a bazillion times. In this autocomplete idiom the AI's objective is constantly shifting, and conveyed by extremely partial information.
But you could imagine a different arrangement, where the coder expresses the problem in a more structured way -- hopefully involving dependent types, probably involving tests. That deeper encoding would enable a deeper AI understanding (if I can responsibly use that word). The human-provided spec would have to be extremely good, because AlphaGo needs to run a bazillion times, so you can't go the autocomplete route of expecting the human to actually read the code and determine what works.
Then we shall be reaching singularity.
Helping devs go through "boring", repetitive code faster seems like a good way to increase our productivity and make us more valuable, not less.
Sure, if AI evolves to the point where it reaches human-level coding abilities we're in trouble, but that's the case this is going to revolutionize humanity as a whole (for better or worse), not merely our little niche.
The hard part has always been writing an API that models external behavior correctly.
Knowing what code to generate with the AI: 200k/yr
Or maybe I'm thinking of tech leads. I don't know, my org is flat.
This is the problem with things like Spreadsheets, dag-drop programming, code generators.
Its not easy to tell a program what to change and where to change.
I've been saying something like that for a while, but my form was "If everything you do goes in and out over a wire, you can be replaced." By a computer, a computer with AI, or some kind of outsourcing.
A question I've been asking for a few years, pre-pandemic, is, when do we reach "peak office"? Post-pandemic, we probably already have. This has huge implications for commercial real estate, and, indeed, cities.
But yes, getting some kind of legal opinion will probably be cheaper with an AI.
https://www.suls.org.au/citations-blog/2020/9/25/natural-lan...
> I would
Well you're going to have a problem describing actual automation when you encounter it. What would you call it when NLP results are fed into an inference engine that then actually executes actions - instead of just providing summarized search results? Super-duper automation?
But I don't think AI will become capable of complex thought in the next one/two decades, so if you're training to be a software architect, project manager, data analyst I think you should be safe for some time.
Over time it will certainly do more, but it's probably quite a long time before it can be completely unsupervised, and in the meantime it's increasing the output of programmers.
I think I’ll side with his expectation, but then again, my salary depends on it.