Magic AI Secures $117M to Build an AI Software Engineer(maginative.com) |
Magic AI Secures $117M to Build an AI Software Engineer(maginative.com) |
Business analysts are also incredibly vulnerable - why have a middle man if the machine understands your requirements in English/French/whatever?
Can you imagine how depressing it will be to your market salary?
The opinionated thing is how to implement these within the boundaries of the existing codebase, skills, etc.
Until we have ASI (imho we're not remotely close) then there is plenty of work to be done. It will just involve fewer menial tasks.
These tools will be incredible and change how we do work forever.
I've done the kind of heavy hitting active-active, five nines engineering you'd think would be safe. I'm not so sure that doesn't change eventually.
Providing detailed instructions to computers to accomplish human/business objectives is the hard part about being a programmer.
But the level of abstraction has been increasing. It started as physically flipping switches then machine code then assembly then structured programming then object oriented programming and so on.
I remember during the 1990s with objects and VB custom controls people were talking that businesses would just hire a bunch of high school students to work part time just snapping components together like Legos.
Prompt: AI, production is down. Fix it. AI: working...
3 days later.
CEO: Hey, dear contractor, our Production is down for 3 days - can you fix it ? Contactor: Sure, give me $500/hour and within 3 weeks it might work again. You know, you have 10mio SLOC, 1mio npm dependencies so it will take a 'bit' longer...
Yes, its bit oversimplified, but imagine it ;)
Being the person who comes in to salvage a software disaster is a hell of a different career than a greenfield cloud startup dev or FAANG proto-pusher, but it can be extremely profitable. and yes, I suspect the demand for that type of software expert will be on the rise.
145mn raised to date.
No demo at all.
https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...
Healthy skepticism is pragmatic. Sequoia and FTX comes to mind as well. Don't assume an adult was in the room doing due diligence.
This is how the majority of companies actually build and release products.
You do demos behind closed doors to investors/board before showing the public.
I certainly hope that’s the case. I really like programming and building things for money, fun, and status.
It'd probably be best if we all learned some agriculture and opted for simpler lives, rebuilt our social capital (aka community) and learned to do leisure like sane people. The rat race has never thanked anyone for participating, not even software engineers.
Of course, such a solution isn't impossible. It may be that such a village, if left alone by circumstance and/or because it has an insider champion, may create such psychologically healthy and intellectually talented people that they may eventually challenge the greater status quo. Sounds like the premise of a YA mild dystopia novel!
I think a realistic timeframe is about 15 years from the time someone releases a viable tool to complete replacement of the last engineer. About half way through the pressure on salaries will become noticeable. I base this guess on how companies typically operate and how long practical adoption of smaller technology changes take.
In the first half there should be both a downward pressure due to the threat of replacement but at the same time an upward pressure for the same reason. New grads will stop coming and existing engineers can look forward to being phased out and command a premium for as long as they can.
Another possibility is that it doesn‘t quite get good enough, but regardless students start picking other subjects, creating a temporary shortage that sustains existing employees. Employees will just be reduced through retirement with half being retired after 20 years of this process anyway.
Yet another possibility is that a half-assed tool only reduces demand - again that can be fixed through retirement plus less or no new people joining the field.
Would our industry create a new job role such as "prompt engineer" aka "ai engineer supervisor" ??
Or would this ai developer be able to read jira/kanban tickets, cooperate with other "teammates", deploy fixes, etc with no major oversight?
Generally curious.
That is...exquisitely false. Especially under the tensions of scale, architecture matters a great deal. Yes, computers are ridiculously fast these days, but it is quite easy to architect yourself into a situation that cannot be solved by more hardware, not even theoretically. There are plenty of problems that would require more memory than there are fundamental particles in the universe if you model them wrong.
As for your offered examples, you seem to be thinking in terms of glue code and how well it could be replaced by an intelligent agent with its attention focused on a browser, reading one tab and entering data in another. This approach is going to break at any kind of scale greater than 1, and probably be quite brittle at 1. I'd argue that such an agent, if properly trained, will quickly realize that the cost/benefit ratio of it doing the job manually is not nearly as good as if it wrote a program to perform this mechanistic task. In which case, architecture still matters but the agent doing the architecting has changed.
I'm afraid if i run a 'rm -rf' command i might take out the entire electrical grid!
This is a pretty wild take. The job that is 99% dealing with human interactions is easier to automate than the job where you make a computer do what you want?
These transformer models are much better at soft skills than hard skills.
(have done technical due diligence for M&A, but also realize a greater fool can be found; ask lots of questions, and ensure the people you're trusting to validate the responses to those questions have the necessary domain expertise)
Plus if you kill off many engineers jobs, who is left to buy your products?
I want LLMs to fail at my profession as much as everyone at risk of losing their jobs, but unless Google is lying, things are looking pretty grim.
If you have a back-and-forth conversation, with the previous conversation chunks prepended as context to the next interaction, it will rapidly lose track of where you instructed it to spend its attention.
The manner in which the context is used seems to make a huge difference.
There is a reason why we still have people working at McDonald's even though fully automating it has been possible for a couple of decades now.
https://en.wikipedia.org/wiki/Ice_trade
It was more economical to send people out to cut ice from a lake in Maine and ship it by rail to Chicago than it was to just freeze water from a local supply. It was also more reliable since the technology was mature, versus ice plants that often broke down when meatpackers needed a consistent supply.
There's no reason why this won't be the case for AI unless semiconductor manufacturing continues its exponential performance/cost growth. The demand for technologically obsolete goods and services do not instantly disappear when a superior product enters the market.
Human software engineers right now are more reliable than AIs for most price-points. This is true for most industries in which machine learning is present.
How did you come up with this number? It seems pretty unrealistic.
> There is a reason why we still have people working at McDonald's even though fully automating it has been possible for a couple of decades now.
Maybe the low salary is the reason? If it is a bit more costly to automate certain aspects of manual labor, then the low salaries might remove the incentive to do so. This is not the case for software engineering.
If it costs $1m p/y to run a machine that cooks burgers and fries, or $30k for an employee who can do that _and_ cover something else when someone else is ill, it's a no-brainer. But businesses had to discover that the hard way; until the 80s, most people were still convinced automation would win everywhere, because it had won (and won big) in manufacturing. A combination of factors, from the '80s onwards, made labor costs effectively fall, which created our reality where certain jobs are so cheap that automating them makes no sense.
The "problem" is that, in certain regions, software development costs reached a point where automation looks very, very appealing. If a machine costs 500k p/y to replace a few 150k p/y SWEs without all those pesky employment complications, businesses will happily choose "AWS AI CloudDeveloper"...
https://www.theregister.com/2023/10/11/github_ai_copilot_mic...
"Make it profitable" appears a secondary concern in the AI space.
If 1M context uses 32x the memory of 32k, its a non-starter. Even a smallish LLM like Mixtral uses 4-8gb of memory just for your prompt. You would have 256+GiB at 1M...
I read somewhere that there was a recent breakthrough that enabled this.
Even if it costs a lot to run inference with 1M token context, it is hard to imagine it would cost anywhere close to a software engineer salary.
And it was meant to highlight that even if you have the tech (which we don't - the cheap tricks chatgpt or copilot do are impressive but still cheap tricks - are super expensive when it comes to actually training the models) it may not make economic sense to deploy them.
Even if it makes sense to deploy them the social unrest and volatility that will result in society may not end up well. (What's the point if all the consumers go away or they cannot actually buy the shit you're producing)
Still not good? Ok, maybe this is too radical but hear me out: how about we take this incredible increase in productivity as an opportunity to start paving the way for a true post-scarcity future that can benefit most people, eliminate intellectual property and take everything produced by robots to fund UBI, instead of worrying about our cushy jobs and (relative) wealth/status obtained through bullshit jobs?
yes I think feudalism 2.0 is how this will probably turn out
- Get rid of corporations by adding a cap size to the maximum number of people employed by a company. Corporations make sense when we need to coordinate large groups of people around a common project, and when we need the maximum efficiency from each individual. Nowadays most people's work is coordination and the actual labor is done by machines. Getting rid of corporations will lead companies to be smaller and more numerous, and we reduce the complexity of overall coordination needed.
- Patent / Copyright law reform. Anyone wanting to have some type of IP protected must put in some type of insurance bond. If (e.g) a book author wants to receive $100k for their work, they need to pay into the system to ensure copyrights. Once the value is reached, the bond can be reset (for a larger amount) or it goes automatically into public domain. The higher the amount desired and the longer the policy is in place, the more expensive it becomes to hold it. For software, there is an additional clause: the source code must be made available 3 years after the first release, failure to be able to reproduce the object code from the source code leads to an automatic fine equal to the value of bond.
- Make tie-in of sales and services illegal: if Amazon wants to provide a software-based service (AWS) then the software should be able to run on any commodity networked computer. If Apple wants to sell a hardware device, people should be allowed to install whatever operating system they want.
Now, please don't respond with "it's not realistic", because it very much is. There is no "boil the oceans with a candle" here. They may be difficult, but very well within the realms of possible. For my first point, one common objection is "if you limit the size of the company, then you'll have people being employed as contractors" and my response is "you can write the law in a way that anyone that spends more of 50% of their working time working for the same client should be counted as an employee.
Do you mean an AI programmer would cost $500k per year? If so I think you greatly overestimate the cost.
Recently I did some text processing with GPT-4 turbo (128k context) and I reached the daily limit of 5 million tokens. IIRC it cost me around $70 bucks for the day.
I think $70 is the hourly rate of a SE with $150k salary working 40 hours per week. Note that we are at early stages with this tech, it will probably only get cheaper from here.
Sure, for you that was the price. Enterprise cost would be way different.
"Note that we are at early stages with this tech, it will probably only get cheaper from here."
Haha people who pay for these ai tools can only hope...Ask any cloud provider, streaming service, or utility company if their prices are cheaper now than before.
As these ai tools get better, they will require more resources to run (according to altman's 7 trillion dollar request) and most likely drive up the costs.
But hopefully you are right though, as i believe we as humanity would be best served spending as little money and resources as possible on AI.
I suspect you underestimate it. Raw engine cost is one thing; what businesses downstream will actually pay, is another. Look at AWS: a lot of businesses don't even touch it directly, their vendor ISPs do. If "AIDev" really becomes a thing, businesses will buy specialized services (e.g. "ApiBuilder.io", "YAMLCrusher.io", etc etc), which will obviously command a premium on top of top-tier, 5-9s guaranteed, "raw" ml engines.