You can scale them up and down at any time, they can work 24/7 (including holidays) with no overtime pay and no breaks, they need no corporate campuses, office space, HR personnel or travel budgets, you don't have to worry about key employees going on sick/maternity leave or taking time off the moment they're needed most, they won't assault a coworker, sue for discrimination or secretly turn out to be a pedophile and tarnish the reputation of your company, they won't leak internal documents to the press or rage quit because of new company policies, they won't even stop working when a pandemic stops most of the world from running.
> deep learning doesn't allow models to generalize properly to out-of-distribution data—and that is precisely what we need to build artificial general intelligence.
I think even (or especially) people like Altman accept this as a fact. I do. Hassabis has been saying this for years.
The foundational models are just a foundation. Now start building the AGI superstructure.
And this is also where most of the still human intellectual energy is now.
These statistical models don’t generalize well to out of distribution data. If you accept that as a fact, then you must accept that these statistical models are not the path to AGI.
They're risky in that they fail in ways that aren't readily deterministic.
And would you trust your life to a self-driving car in New York City traffic?
Imagine you have a self-driving AI that causes fatal accidents 10 times less often than your average human driver, but when the accidents happen, nobody knows why.
Should we switch to that AI, and have 10 times fewer accidents and no accountability for the accidents that do happen, or should we stay with humans, have 10x more road fatalities, but stay happy because the perpetrators end up in prison?
Framed like that, it seems like the former solution is the only acceptable one, yet people call for CEOs to go to prison when an AI goes wrong. If that were the case, companies wouldn't dare use any AI, and that would basically degenerate to the latter solution.
Even temporary loss of the drivers license has a very high bar, and that's the main form of accountability for driver behavior in Germany, apart from fines.
Badly injuring or killing someone who themselves did not violate traffic safety regulations is far from guaranteed to cause severe repercussions for the driver.
By default, any such situation is an accident and at best people lose their license for a couple of months.
But we do know the culpability rests on the shoulders of the humans who decided the tech was ready for work.
Pretty bloody time for labor though. https://en.m.wikipedia.org/wiki/Haymarket_affair
The one minor risk I see is the cat being too polite and getting effectively stuck in dense traffic. That's a nuisance though.
Is there something about NYC traffic I'm missing?
Same with any company that employs AI agents. Sure they can work 24/7, but every mistake they make the company will be liable for (or the AI seller). With humans, their fraud, their cheating, their deception, can all be wiped off the company and onto the individual
I mean, this is an incredible moment from that standpoint.
Regarding the topic at hand, I think that there will always be room for humans for the reasons you listed.
But even replacing 5% of humans with AI's will have mind boggling consequences.
I think you're right that there are jobs that humans will be preferred for for quite some time.
But, I'm already using AI with success where I would previously hire a human, and this is in this primitive stage.
With the leaps we are seeing, AI is coming for jobs.
Your concerns relate to exactly how many jobs.
And only time will tell.
But, I think some meaningful percentage of the population -- even if just 5% of humanity will be replaced by AI.
Sure, if a business deploys it to perform tasks that are inherently low risk e.g. no client interface, no core system connection and low error impact, then the human performing these tasks is going to be replaced.
This reminds me of the school principal who sent $100k to a scammer claiming to be Elon Musk. The kicker is that she was repeatedly told that it was a scam.
https://abc7chicago.com/fake-elon-musk-jan-mcgee-principal-b...
Or - worse - there is no accessible code anywhere, and you have to prompt your way out of "I'm sorry Dave, I can't do that," while nothing works.
And a human-free economy does... what? For whom? When 99% of the population is unemployed, what are the 1% doing while the planet's ecosystems collapse around them?
Your concerns about mysterious AI code and system crashes are backwards. This approach eliminates integration bugs and maintenance issues by design. The generated TypeScript is readable, fully typed, and consistently updated across the entire stack when business logic changes.
If you're struggling with AI-generated code maintainability, that's an implementation problem, not a fundamental issue with code generation. Proper type safety and schema validation create more reliable systems, not less. This is automation making developers more productive - just like compilers and IDEs did - not replacing them.
The code works because it's built on sound software engineering principles: type safety, single source of truth, and deterministic generation. That's verifiable fact, not speculation.
what are you using for deterministic generation? the last i heard even with temperature=0 theres non determinism introduced by float uncertainty/approximation
People talking like this also, in the back of their minds like to think they'll be OK. They're smart enough to be still needed. They're a human, but they'll be OK even while working to make genAI out perform them at their own work.
I wonder how they'll feel about their own hubris when they struggle to feed their family.
The US can barely make healthcare work without disgusting consequences for the sick. I wonder what mass unemployment looks like.
There is absolutely no reason a programmer should expect to write code as they do now forever, just as ASM experts had to move on. And there's no reason (no precedent and no indicators) to expect that a well-educated, even-moderately-experienced technologist will suddenly find themselves without a way to feed their family - unless they stubbornly refuse to reskill or change their workflows.
I do believe the days of "everyone makes 100k+" are nearly over, and we're headed towards a severely bimodal distribution, but I do not see how, for the next 10-15 years at least, we can't all become productive building the tools that will obviate our own jobs while we do them - and get comfortably retired in the mean time.
This is interesting because it's both Oddly Specific and also something I have seen happen and I still feel really sorry for the company involved. Now that I think about it, I've actually seen it happen twice.
The wild part is that LLMs understand us way better than we understand them. The jump from GPT-3 to GPT-4 even surprised the engineers who built it. That should raise some red flags about how "predictable" these systems really are.
Think about it - we can't actually verify what these models are capable of or if they're being truthful, while they have this massive knowledge base about human behavior and psychology. That's a pretty concerning power imbalance. What looks like lower risk on the surface might be hiding much deeper uncertainties that we can't even detect, let alone control.
You can reply that AI researchers are smart and want to survive, so they are likely to invent alignment techniques that are better than the (deplorably inadequate) techniques that have been discussed and published so far, and I will reply that counting on their inventing these techniques in time is an unacceptable risk when the survival of humanity is at stake -- particularly as the outfit (namely the Machine Intelligence Research Institute) with the most years of experience in looking for an actually-adequate alignment technique has given up and declared that humanity's only chance is if frontier AI research is shut down because at the rate that AI capabilities are progressing, it is very unlikely that anyone is going to devise an adequate alignment technique in time.
It is fucked-up that frontier AI research has not been banned already.
I admire your optimism about the goals of all humans, but evidence tends to point to this not being the goal of all (or even most) humans, much less the people who control the AIs.
Do you mean they lie because of bad training data? Or because of ill intent? How can an LLM have intent if it’s a stateless feedforward model?
I know that I don't know a lot, but all of this sounds to me to be at least hypothetically possible if we really believe AGI is possible.
Less risky to deploy question will probably come once it is closer to 10x the cost. Considering the model was even specifically tuned for the test and doesn't involve other complexity I will say we are actually 10^4 cost off in terms of real world scenario.
I would imagine with better algorithm, tuning and data we could knock off 10^2 from the equation. That would still leave us with 10^2 cost to improve from Hardware. Minimum of 10 years.
For AI example(s): Attribution is low, a system built without human intervention may suddenly fall outside its own expertise and hallucinate itself into a corner, everyone may just throw more compute at a system until it grows without bound, etc etc.
This "You can scale up to infinity" problem might become "You have to scale up to infinity" to build any reasonably sized system with AI. The shovel-sellers get fantastically rich but the businesses are effectively left holding the risk from a fast-moving, unintuitive, uninspected, partially verified codebase. I just don't see how anyone not building a CRUD app/frontend could be comfortable with that, but then again my Tesla is effectively running such a system to drive me and my kids. Albeit, that's on a well-defined problem and within literally human-made guardrails.
This is a big downside of AI, IMHO. Those offices need to be filled! ;-)
That one isn’t guaranteed. Many examples online of exfiltration attacks on LLMs.
The rhetoric of not needing people doing work is cartoon'ish. I mean there is no sane explanation of how and why that would happen, without employing more people yet again, taking care of the advancements.
It's nok like technology has brought less work related stress. But it has definitely increased it. Humans were not made for using technology at such a pace as it's being rolled out.
The world is fucked. Totally fucked.
The framing of the question misses the point. With electric lighting we can now work longer into the night. Yes, less people use and make candles. However, the second order effects allow us to be more productive in areas we may not have previously considered.
New technologies open up new opportunities for productivity. The bank tellers displaced by ATM machines can create value elsewhere. Consumers save time by not waiting in a queue, allowing them to use their time more economically. Banks have lower overhead, allowing more customers to afford their services.
Digital banks
Cashless money transfer services
Self service
Modern farms
Robo lawn mowers
NVR:s with object detection
I can go on forever
