> Yet we're what? 5 years into "AI will replace programmers in 6 months"?

Realistically, we're 2.5 years into it at most.

No, the hype cycle started around 2019, slowly at first. The technology this is built with is more like 20 years old, so no, we are not 2.5 years at most really.

we're 2.5 years into the current hype trend, no way was this mainstream until at least 2022

GPT3 dropped in 2020. That's when it hit mainstream

GPT3 wasn't that impressive. GPT 3.5 is when it became "oh wow, this could really change things," and that was 2022.

It certainly made a heavy impression on this tiny new outlet called NYT in 2020 : https://archive.is/QtpMT

I feel like no one was really talking about this stuff until midjourney and dalle, but I can agree to disagree

GPT-3 shook the research world but it was by no means mainstream until the ChatGPT release in Nov 2022.

If you can quote anyone well-known saying we'd be replacing programmers in 6 months back in 2019, I'd be interested to read it.

Neural networks go back a lot further than 20 years ago. It was considered a research dead end for a long time though.

It’s hilarious how absurdly wrong you are here. Both of those things have happened and you don’t even know it.

I named 3 things...

You're going to have to specify which 2 you think happened

Where did it happen?

They try it, but it’s not reliable

I consulted a radiologist more than 5 years after Hinton said that it was completely obvious that radiologists would be replaced by AI in 5 years. I strongly suspect they were not an AI.

Why do I think this?

1) They smelled slightly funny. 2) They got the diagnosis wrong.

OK maybe #2 is a red herring. But I stand by the other reason.

I know a radiologist and talk a decent bit about AI usage in the field. Every radiologist today is making heavy use of AI. They pre screen everything and from what I understand it has led to massive productivity gains. It hasnt led to job losses yet but theres so much money on the line it really feels to me like we're just waiting for the straw that broke the camels back. No one wants to be the first to fully get rid of radiologists but once one hospital does the rest will quickly follow suit.

One word - liability.

The quote appears to be “We should stop training radiologists now, it’s just completely obvious within five years deep learning is going to do better than radiologists.”

So there's some room for interpretation, the weaker interpretation is less radical (that AI could beat humans in radiology tasks in 5 years).

I have a fusion reactor to sell to you.

Some people are ahead of you by 3.5 years [0]:

> Helion has a clear path to net electricity by 2024, and has a long-term goal of delivering electricity for 1 cent per kilowatt-hour. (!)

[0] https://blog.samaltman.com/helion

You're missing the big picture. Helion can still make their goal. Once they have a working fusion reactor they can use the energy to build a time machine.

Of course, silly me. I should put more practice time into 4D chess.

We're halfway into 2025 and you're cutting a goal they should have reached by 2024. Did they reach that goal?

They didn't reach that goal. Why would they bother reaching an easier goal when they could shoot for a bigger one? /s Their new goal is to build a fusion plant by 2028 [0].

[0] https://observer.com/2025/01/sam-altman-nuclear-fusion-start...

did you by any chance send money to nigerian prince ?

As far as I've seen we appear to already have self driving vehicles, the main barriers are legal and regulatory concerns rather than the tech. If a company wanted to put a car on the road that beetles around by itself there aren't any crazy technical challenges to doing that - the issue is even if it was safer than a human driver the company would have a lot of liability problems.

This is just not true, Waymo, MobilEye, Tesla and Chinese companies are not bottlenecked by regulations but by high failure rate and / or economics.

> the main barriers are legal and regulatory concerns rather than the tech

they have failed in sfo, phoenix and other cities that rolled red carpet for them

Pretty solid evidence that self driving cars already exist though.

When people say “we'll have self-driving cars next year”, I understand that self-driving cars will be widespread in the developed world and accessible to those who pay a premium. Given the status quo, I find it pointless to discuss the semantics of whether they exist or not.

Especially considering it would be weird to say "we'll have <something> next year" when we've technically had it for decades.

And more specifically, I'm referencing Elon where the context is that its going to be a software push into Teslas that people already own

As prototypes, yes. But that's like pointing to Japanese robots in the 80's and expecting robot butlers any day now. Or maybe Boston dynamics 10 years ago. Or when OpenAI was into robotics.

There's a big gap between seeing something work in the lab and being ready for real world use. I know we do this in software, but that's a very abnormal thing (and honestly, maybe not the best)

Waymo is doing 250k paid rides/week.

You're confusing "exist" with "viable".

When someone talks about "having" self-driving cars next year, they're not talking about what are essentially pilot programs.

I don't think that is a reasonable generalisation. A lot of people would have been talking about the first person to take a real trip in a car that drives itself. A record that is in the past.

Not to mention that HN gets really tetchy about achieving specifically SAE Level 6 when in practice some pretty basic driver assist tools are probably closer to what people meant. It reminds me of a gentlemen I ran into who was convinced that the OpenAI DoTA bot with a >99% win rate couldn't really be said to be playing the game. If someone can take their hands off the wheel for 10 minutes we're there in a common language sense; the human in the car isn't actively in control.

Good point. On the "exist" interpretation, we've "had" flying cars for several decades.

I remember one reason phoenix was chosen as a trial location coz it was supposed to be one of the easiest places to drive.

It's pretty damning that it failed there.

Yeah, it’s a big grid with wide streets. Did it fail there? If so I imagine it’s just due to lack of business—there are almost no taxis in Phoenix. Mostly just from the airport.

They are only self-driving in a very controlled environments of few very good mapped out cities with good roads in good weather.

And it took what like 2 decades to get there. So no, we don't have self-driving even close. Those examples look more like hard-coded solution for custom test cases.

What? If that stuff works, no liability will have to be executed. How can you state that it works and claim liability problems at the same time?

Four years into people mocking "we'll have self driving cars next year" while they are on the street daily driving around SF.

They are self driving the same way a tram or subway can be self driving. They traffic a tightly bounded designated area. They're not competing with human drivers. Still a marvel of human engineering, just quite expensive compared with other forms of public transport. It just doesn't compete in the same space and likely never will.

They are literally competing with human uber drivers in the area they operate and also having a much lower crash and injury rate.

I admit they don't operate everywhere - only certain routes. Still they are undoubtedly cars that drive themselves.

I imagine it'll be the same with AGI. We'll have robots / AIs that are much smarter than the average human and people will be saying they don't count because humans win X Factor or something.

How are they competing, if their routes are limited?

The cotton gin processed short fiber cotton, but not long fiber cotton.

Did the cotton gin therefore not compete with human labor?

Self-driving vehicles can only exist in cities of extreme wealth like SF. Try running them in Philadelphia and see what happens.

I'm quoting Elon.

I don't care about SF. I care about what I can but as a typical American. Not as an enthusiast in one of the most technologically advanced cities on the planet

They’re in other cities too…

Other cities still isn't available to average American.

You read the words but missed their meaning

They're driving, but not well in my (limited) interactions with them. I had a waymo run me completely out of my lane a couple months ago as it interpreted 2 lanes of left turn as an extra wide lane instead (or, worse, changed lanes during the turn without a blinker or checking its sensors, though that seems unlikely).

Yes, but ...

The argument that self-driving cars should be allowed on public roads as long as they are statistically as safe as human drivers (on average) seems valid, but of course none of these cars have AGI... they perform well in the anticipated simulator conditions in which they were trained (as long as they have the necessary sensors, e.g. Waymo's lidar, to read the environment in reliable fashion), but will not perform well in emergency/unanticipated conditions they were not trained on. Even outside of emergencies, Waymos still sometimes need to "phone home" for remote assistance in knowing what to do.

So, yes, they are out there, perhaps as safe on average as a human (I'd be interested to see a breakdown of the stats), but I'd not personally be comfortable riding in one since I'm not senile, drunk, teenager, hothead, distracted (using phone while driving), etc - not part of the class that are dragging the human safety stats down. I'd also not trust a Tesla where penny pinching, or just arrogant stupidity, has resulted in a sensor-poor design liable to failure modes like running into parked trucks.

In my lens, as long as companies don't want to be held liable for an accident, the shouldn't be on roads. They need to be extremely confident to the point of putting their money where their mouths are. That's true "safety".

That's the main difference with a human driver. If I take an Uber and we crash, that driver is liable. Waymo would fight tooth and nail to blame anything else.

Mercedes is doing this for specific places and conditions.

  I'd not personally be comfortable riding in one since I'm not senile, drunk, teenager, hothead, distracted (using phone while driving), etc - not part of the class that are dragging the human safety stats down.

The challenge is that most people think they’re better than average drivers.

I'm not sure what the "challenge" is there, but certainly true in terms of human psychology.

My point was that if you are part of one of these accident-prone groups, you are certainly worse than average, and are probably safer (both for yourself, and everyone around you) in a Waymo. However, if you are an intelligent non-impaired experienced driver, then maybe not, and almost certainly not if we're talking about emergency and dangerous situations which is where it really matters.

How can you know if you're a good driver in an emergency situation? We don't exactly get a lot of practice.

Sure, you don't know how well any specific driver is going to react in an emergency situation, and some are going to be far worse than others (e.g. panicking, or not thinking quickly enough), but the human has the advantage of general intelligence and NOT having to rely on having had practice at the specific circumstance they find themselves in.

A recent example - a few weeks ago I follow another car in making a turn down a side road, when suddenly that car stops dead (for no externally apparent reason), and starts backing up fast about to hit me. I immediately hit my horn and prepare to back up myself to get out of the way, since it was obvious to me - as a human - that they didn't realize I was there, and without intervention would hit me.

Driving away I watch the car in my rear view mirror and see it pull a U-turn to get back out of the side road, making it apparent why they had stopped before. I learned something, but of course the driverless car is incapable of learning, and certainly has no theory of mind, and would behave same as last time - good or bad - if something similar happened again.

Well, it depends on the details. I'd trust a Waymo as much as an Uber but I'm pretty skeptical of the Tesla stuff they are launching in Austin.

I basically agree with you, and I think the thing that is missing from a bunch of responses that disagree is that it seems fairly apparent now that AI has largely hit a brick wall in terms of the benefits of scaling. That is, most folks were pretty astounded by the gains you could get from just stuffing more training data into these models, but like someone who argues a 15 year old will be 50 feet tall based on the last 5 years' growth rate, people who are still arguing that past growth rates will continue apace don't seem to be honest (or aware) to me.

I'm not at all saying that it's impossible some improvement will be discovered in the future that allows AI progress to continue at a breakneck speed, but I am saying that the "progress will only accelerate" conclusion, based primarily on the progress since 2017 or so, is faulty reasoning.

  > it seems fairly apparent now that AI has largely hit a brick wall in terms of the benefits of scaling

What's annoying is plenty of us (researchers) predicted this and got laughed at. Now that it's happening, it's just quiet.

I don't know about the rest, but I spoke up because I didn't want to hit a brick wall, I want to keep going! I still want to keep going! But if accurate predictions (with good explanations) aren't a reason to shift resource allocation then we just keep making the same mistake over and over. We let the conmen come in and people who get too excited by success that they get blind to pitfalls.

And hey, I'm not saying give me money. This account is (mostly) anonymous. There's plenty of people that made accurate predictions and tried working in other directions but never got funding to test how methods scale up. We say there's no alternatives but there's been nothing else that's been given a tenth of the effort. Apples and oranges...

> What's annoying is plenty of us (researchers) predicted this and got laughed at. Now that it's happening, it's just quiet.

You need to model the business world and management more like a flock of sheep being herded by forces that mostly don't have to do with what actually is going to happen in future. It makes a lot more sense.

Practically no one is herded by what is actually going to happen, hardly even by what is expected to happen. Business pretends that it is driven by expectations, but is mostly driven by the past, as in financial statements. What is the bonus we can get this year? There is of course the strategic thinking, I don't want to discount that part of business, but it is not the thing that will drive most of these, AI as a cost saving measure, decisions. This is the unimaginative part of AI application and as such relegated to the unimaginative managers.

> It is difficult to get a man to understand something, when his salary depends on his not understanding it.”

It's all a big hype bubble and not only is no one in the industry willing to pop it, they actively defend against popping a bubble that is clearly rupturing on its own. It's endemic of how modern businesses no longer care about a proper 10 year portfolio and more about how to make the next quarter look good.

There's just no skin in the game, and everyone's ransacking before the inevitable fire instead of figuring out how to prevent the fire to begin with.

  > mostly don't have to do with what actually is going to happen

Yet I'm talking about what did happen.

I'm saying we should have memory. Look at predictions people make. Reward accurate ones, don't reward failures. Right now we reward whoever makes the craziest predictions. It hasn't always been this way, so we should go back to less crazy

Sure, you were right.

But if you had been wrong and we would now have had superintelligence, the upside for its owners would presumably be great.

... Or at least that's the hypothesis. As a matter of fact intelligence is only somewhat useful in the real world :-)

I am not sure the owners would keep being that in case of real superintelligence, though.

> What's annoying is plenty of us (researchers) predicted this and got laughed at. Now that it's happening, it's just quiet.

Those people always do that. Shouting about cryptocurrencies and NFTs from the rooftops 3-4 years ago, now completely gone.

I suspect they're the same people, basically get rich quick schemers.

I dont see any wall. Gemini 2.5 and o3/o4 are incredible improvements. Gen AI is miles ahead of where it was a year ago which was miles ahead of where it was 2 years ago.

The improvements have less to do with scaling than adding new techniques like better fine tuning and reinforcement learning. The infinite scaling we were promised, that only required more content and more compute to reach god tier has indeed hit a wall.

I probably wasn't paying enough attention, but I don't remember that being the dominating claim that you're suggesting. Infinite scaling?

People were originally very surprised that you could get so much functionality by just pumping more data and adding more parameters to models. What made OpenAI initially so successful is that they were the first company willing to make big bets on these huge training runs.

After their success, I definitely saw a ton of blog posts and general "AI chatter" that to get to AGI all you really needed to do (obviously I'm simplifying things a bit here) was get more data and add more parameters, more "experts", etc. Heck, OpenAI had to scale back it's pronouncements (GPT 5 essentially became 4.5) when they found that they weren't getting the performance/functionality advances they expected after massively scaling up their model.

The actual LLM part isn't much better than a year ago. What's better is that they've added additional logic and made it possible to intertwine traditional, expert-system style AI plus the power of the internet to augment LLMs so that they're actually useful.

This is an improvement for sure, but LLMs themselves are definitely hitting a wall. It was predicted that scaling alone would allow them to reach AGI level.

> It was predicted that scaling alone would allow them to reach AGI level.

This is a genuine attempt to inform myself. Could you think to those sort of claims from experts at the top?

There were definitely people "at the top" who were essentially arguing that more scale would get you to AGI - Ilya Sutskever of OpenAI comes to mind (e.g. "next-token prediction is enough for AGI").

There were definitely many other prominent researchers who vehemently disagreed, e.g. Yann LeCun. But it's very hard for a layperson (or, for that matter, another expert) to determine who is or would be "right" in this situation - most of these people have strong personalities to put it mildly, and they often have vested interests in pushing their preferred approach and view of how AI does/should work.

I basically agree with you also, but I have a somewhat contrarian view of scaling -> brick wall. I feel like applications of powerful local models is stagnating, perhaps because Apple has not done a good job so far with Apple Intelligence.

A year ago I expected a golden age of local model intelligence integrated into most software tools, and more powerful commercial tools like Google Jules to be something used perhaps 2 or 3 times a week for specific difficult tasks.

That said, my view of the future is probably now wrong, I am just saying what I expected.

100% this. I always argue that groundbreaking technologies are clearly groundbreaking from the start. It is almost a bit like a film, if you have to struggle to get into it in the first few minutes, you may as well spare yourself watching the rest.

Over ten years for the we'll have self driving car spiel