The winner that deserved to win might turn into the complacent monopoly pf tomorrow. It might vow to Not Be Evil for a while, but the investors will demand that it does whatever it takes to grow.

You have gotta stop cherrypicking. The massive influx of hyperbolic articles about how electricity will change everything started in the 19th century. It became a common theme in fiction (including classics like Frankenstein) and became an enormous media hype war, which historians call the War of the Currents.

Yes, electricity was useful. And it had hyperbolic articles talking about how transformative it would be. Like all prognostication, some of those articles were overblown, but, in some ways, they understated the transformative effect electricity would have on human history.

And cars? Did you somehow miss the influx of hyperbolic articles about how cars will change everything? Like, the whole 20th century?

What was your approach to researching the history of media hype? You somehow overlooked the hype around air travel, refrigeration, and antibiotics…?

Calculators are a particularly bad example for your case. There was absolutely hyperbole against calculators when they were introduced. [1]

With similar sentiment as well "They make us dumb" "Machines doing the thinking for us"

Cars were definitely seen as a fad. More accurately a worse version of a horse [2]

If you looked through your other examples, you'd see the same for those as well.

Some things start as fads, but only time will tell if they gain a place in society. Truthfully it's too early to tell for AI, but the arguments you're making, calling it a fad already don't stand up to reason

[1]: https://www.newspapers.com/article/the-item/160697182/ [2]: https://www.saturdayeveningpost.com/2017/01/get-horse-americ...

The personal computer, laptops, web browsers, cell phones, smartphones, AJAX/DHTML, digital cameras, SSDs, WiFi, LCD displays, LED lightbulbs. At some point, all of these things were "overhyped" and "didn't live up to the promise." And then they did.

The good thing is that local models are catching up very fast.

What if they’re just good for a while and then you go back to the old way?

Sure! First, here are references, in case you want to deep dive:

1. http://lucacardelli.name/Papers/Binary.pdf

2. https://www.researchgate.net/publication/221321423_Parasitic...

Second, asymmetric multimethods give something up: symmetry is a desirable property -- it's more faithful to mathematics, for instance. There's a priori no reason to preference the first argument over the second.

So why do I think they are promising?

1. You're not giving up that much. These are still real multimethods. The papers above show how these can still easily express things like multiplication of a band diagonal matrix with a sparse matrix. The first paper (which focuses purely on binary operators) points out it can handle set membership for arbitrary elements and sets.

2. Fidelity to mathematics is a fine thing, but it behooves us to remember we are designing a programming language. Programmers are already familiar with the notion that the receiver is special -- we even have a nice notation, UFCS, which makes this idea clear. (My language will certainly have UFCS.) So you're not asking the programmer to make a big conceptual leap to understand the mechanics of asymmetric multimethods.

3. The type checking of asymmetric multimethods is vastly simpler than symmetric multimethods. Your algorithm is essentially a sort among the various candidate multimethod instances. For symmetric multimethods, choosing which candidate multimethod "wins" requires PhD level techniques, and the algorithms can explode exponentially with the arity of the function. Not so with asymmetric multimethods: a "winner" can be determined argument by argument, from left to right. It's literally a lexicographical sort, with each step being totally trivial -- which multimethod has a more specific argument at that position (having eliminated all the candidates given the prior argument position). So type checking now has two desirable properties. First, it design principle espoused by Bjarne Stroustroup (my personal language designer "hero"): the compiler implementation should use well-known, straightforward techniques. (This is listed as a reason for choosing a nominal type system in Design And Evolution of C++ -- an excellent and depressing book to read. [Because anything you thought of, Bjarne already thought of in the 80s and 90s.]) Second, this algorithm has no polynomial or exponential explosion: it's fast as hell.

4. Aside from being faster and easier to implement, the asymmetry also "settles" ambiguities which would exist if you adopted symmetric multimethods. This is a real problem in languages, like Julia, with symmetric multimethods. The implementers of that language resort to heuristics, both to avoid undesired ambiguities, and explosions in compile times. I anticipate that library implementers will be able to leverage this facility for disambiguation, in a manner similar to (but not quite the same) as C++ distinguishes between forward and random access iterators using empty marker types as the last argument. So while technically being a disadvantage, I think it will actually be a useful device -- precisely because the type checking mechanism is so predictable.

5. This predictability also makes the job of the programmer easier: they can form an intuition of which candidate method will be selected much more readily in the case of asymmetric multimethods than symmetric ones. You already know the trick the compiler is using: it's just double-dispatch, the trick used for "hit tests" of shapes against each other. Only here, it can be extended to more than two arguments, and of course, the compiler writes the overloads for you. (And it won't actually write overloads, it will do what I said above: form a lexicographical sort over the set of multimethods, and lower this into a set of tables which can be traversed dynamically, or when the types are concrete, the compiler can leverage monomorphize -- the series of "if arg1 extends Tk" etc. is done in the compiler instead of at runtime. (But it's the same data structure.)

6. It's basically impossible to do separate compilation using symmetric multimethods. With asymmetric multimethods, it's trivial. To form an intuition, simply remember that double-dispatch can easily be done using separate compilation. Separate compilation is mentioned as a feature in both the cited papers. This is, in my view, a huge advantage. I admit, this I haven't quite figured out generics will fit into this -- at least if you follow C++'s approach, you'll have to give up some aspects of separate compilation. My bet is that this won't matter so much; the type checking ought to be so much faster that even when a template needs to be instantiated at a callsite, the faster and simpler algorithm will mean the user experience will still be very good -- certainly faster than C++ (which uses a symmetric algorithm for type checking of function overloads).

To go a bit more into my "vision" -- the papers were written during a time when object-orientation was the dominant paradigm. I'd like to relax this somewhat: instead of classes, there will only be structs. And there won't be instance methods, everything will be a multimethods. So instead of the multimethods being "encapsulated" in their classes, they'll be encapsulated in the module in which they're defined. I'll adopt the Python approach where everything is public, so you need to worry about accessibility. Together with UFCS, this means there is no "privileging" of the writer of a library. It's not like in C++ or Java, where only the writer of the library can leverage the succinct dot notation to access frequently used methods. An extension can import a library, write a multimethod providing new functionality, and that can be used -- using the exact same notation as the methods of the library itself. (I always sigh when I read languages, having made the mistake of distinguishing between free functions and instance methods, "fix" the problem that you can only extend a library from the outside using free functions -- which have a less convenient syntax -- by adding yet another type of function, an "extension function. In my language, there are only structs and functions -- it has the same simplicity as Zig and C in this sense, only my functions are multimethods.)

Together with my ideas for how the parser will work, I think this language will offer -- much like Julia -- attractive opportunities to extend libraries -- and compose libraries that weren't designed to work "together".

And yeah, Claude Code and Gemini are going to implement it. Probably in Python first, just for initial testing, and then they'll port it to C++ (or possibly self-host).

> The real debate isn't whether AI is transformative.

No, the debate is very much whether AI is transformative. You don't get to smuggle your viewpoint as an assumption as if there was consensus on this point. There isn't consensus at all.

The problem is in the middle of such a change it's hard to recognize if this is a real change or if this is another Wankel motor.

Plenty a visual programming language has tried to toot their own horns as being the next transformative change in everything, and they are mostly just obscure DSLs at this point.

The other issue is nobody knows what the future will actually look like and they'll often be wrong with their predictions. For example, with the rise of robotics, plenty of 1950s scifi thought it was just logical that androids and smart mechanic arms would be developed next year. I mean, you can find cartoons where people envisioned smart hands giving people a clean shave. (Sounds like the making of a scifi horror novel :D Sweeney Todd scifi redux)

I think AI is here to stay. At very least it seems to have practical value in software development. That won't be erased anytime soon. Claims beyond that, though, need a lot more evidence to support them. Right now it feels like people just shoving AI into 1000 places hoping that they can find an new industry like software dev.

  > 39% adoption in two years (internet took 5, PCs took 12).

> Gen AI reached 39% adoption in two years (internet took 5, PCs took 12)

You're comparing a service that mostly costs a free account registration and is harder to avoid than to use, with devices that cost thousands of dollars in the early days.

There's another perspective you can see in the comparison with the dot com boom. The web is here to stay, but a lot of ideas from the beginning didn't work out and a lot of companies turned bankrupt.

> Gen AI reached 39% adoption in two years

Source?

The four technologies I look at are 3D televisions, VR, tablets, and the electric car. 3D televisions and VR have yet to find their moment. Judging tablets by the Apple Newton and electric cars by the EV1, this time is different turns out to be the correct model looking at the iPad and Tesla, but not for 3d televisions or VR (yet). So, it could be, but my time machine is as good as yours (mine goes 1 minute per minute, and only forwards, reverse is broken right now.), so unless you've got money on it, we'll just have to wait and see where it goes.

Did google not rely on Gemini to do their ISA changeover?

https://arxiv.org/abs/2510.14928

Was Gemini worse than no tool at all there?

Do you really think that Opus 4.6 hallucinates to exactly the same degree as GPT-3.5? I am mystified how you can hold this perspective.

So type errors are not hallucinations in your book, but "a reference to something which doesn't exist at all" is?

In the context of AI most people I know tend to mean wrong output, not just hallucinations in the literal sense of the word or things you cannot catch in an automated way.

Maybe you're lucky. I had Opus 4.6 hallucinate a non-existing configuration key in a well known framework literally a few hours ago.

Granted, it fixed the problem in the very next prompt.

ChatGPT 5.2 kept gaslighting me yesterday telling me that LLMs were explainable with Shapley values, and it kept referencing papers which talk about LLMs, and about SHAP, but talk about LLMs being used to explain the SHAP values of other ML models.

I encounter stuff like this every week, I don't know how you don't. I suppose a well-structured codebase in a statically typed language might not provide as much of a surface for hallucinations to present themselves? But like you say, logical problems of course still occur.

>> I don't think an AI agent has left a hallucination in my code

I literally just went on Gemini, latest and best model and asked it "hey can you give me the best prices for 12TB hard drives available with the British retailer CeX?" and it went "sure, I just checked their live stock and here they are:". Every single one was made up. I pointed it out, it said sorry, I just checked again, here they are, definitely 100% correct now. Again, all of them were made up. This repeated a few times, I accused it of lying, then it went "you're right, I don't actually have the ability to check, so I just used products and values closest to what they should have in stock".

So yeah, hallucinations are still very much there and still very much feeding people garbage.

Not to mention I'm a part of multiple FB groups for car enthusiasts and the amount of AI misinformation that we have to correct daily is just staggering. I'm not talking political stuff - just people copy pasting responses from AI which confidently state that feature X exists or works in a certain way, where in reality it has never existed at all.

That's very true in terms of how capable these chatbots clearly are, but I believe the author was using 'underwhelming' to refer to the societal impact.

So far, life goes on roughly the same as it did five years ago. This can feel 'underwhelming' in contrast to the onslaught of public discussion about, and huge investments in, AI.

Most of us here on HN are programmers, and we all know how radically LLMs have changed our code projects. Even so, the change to our everyday lives (aside from our work or hobby project) is not, just yet, glaringly obvious. This year, it's mainly... every website shoves an AI box on its site that nobody seems to want!

Not true. I'm a really heavy user of AI. And it's improved my productivity dramatically as a developer, but it doesn't work in every situation even in programming. I see it as an indispensible tool, but its not, right now, a tool that will replace me as a programmer or product manager or salesperson, or marketer. or (in my case) an owner and investor.

Will that happen in the future, maybe. but I don't have enough insight into how AI is evolving in the labs to make a judgement on that.

This statment is really annoying and getting boring. There are A LOT of us who have built careers evaluating technology with healthy skepticism, finding where it works and were it doesn't, excited to share & learn - and we've heard "this time it's different" many times. Now because we refuse to jump in without that same nuance and thought, and proclaim "everything's different over night!" we're branded as ludites when we're really trying find a balance.

I don't hear people saying "nothing is going to change", but I do hear questions about the timeline and if the current levels of investment match returns. Branding these people as stuck in some sort of negative identity is bullshit.

You’re creating a false dichotomy to alienate perceived opponents. Frankly, it’s really annoying and close-minded, and you haven’t contributed anything to the conversation.

You're likely to find more nuance in opposing views than your "underwhelmed by AI" generalisation could represent.

it literally describes the gartnerhype cycle. this article is pointless, the only thing that matters is what survives it with over 1m users. AI will have billions of users when GHC is on the back end.

I think this is exactly right. I've been thinking of "this time" as similar to the advent of digital spreadsheets. Spreadsheets existed for thousands of years but spreadsheet programs transformed spreadsheet work that took hours or weeks into seconds. You still had to know what you were doing, and if you knew what you were doing you were easily 10x faster than those that didn't.

I think we are in a similar situation with code generation now, then only difference in my mind is that LLMs come with a massive platform risk. Who's to say that one day anthropic decides my company is too much of a competitor to use their tool (like they've already done with openai) or what if they decide that instead of pulling their product from my use they just make it generate worse code, or even insert malicious payloads. A dependence on these tools is wildly more risky than dependency on a word processor or a spreadsheet program. It reminds me of the arguments around net neutrality and I cannot fathom how people building on top of, and with, these tools do not see the mountain of risks around them.

Maybe AI is useful to you, but the US economy is currently buoyed by promises of AI replacing the workforce across the board.

Most of Mag-7 are planning to spend over 500B on capex this year alone on building out datacenters for AI pipelines that have yet to prove that it can generate a sustainable profit. Yes, AI is useful in some environments, but the current pricing is heavily subsidized. So my point stand, the hype is not warranted.

i think the point is AI has to go much further and faster than it has in the past 3 years to justify the investments being made from the hype. The hype did its job now the AI industry has to execute and create the returns they promised. That is still very much up in the air, if they can't then the tech was over hyped.

> It's laziness because they have little CS fundamentals to base such claims on

So, what CS fundamentals do you need to evaluate if AI is the real thing, or will disappoint in the future? Until a few months ago, coding agents were met with skepticism, until Anthropic introduced their new model and, with it, a hype train that cannot be rationally justified. Look, SOTA LLMs, and coding agents in particular, are impressive. However, current predictions about the future of software development (and the world in general) are speculative. There is little to no data showing whether AI can deliver on its promises. How could there be in this short time frame? No one knows what the future will hold, no one knows how coding agents will be integrated into our work life and everyday life in the long run, or what hard limitations they will reveal. No one can tell you how professions will change in the coming years; every prediction is purely speculative, and anyone making prophecies is either trying to cope with the uncertainty themselves or has some stakes in the AI bet. It would be nice if people were actually humble enough to admit that they have no idea what will happen in the future, instead of writing the hundredth doom and gloom post.

Porting tons of untyped js legacy front end code to vue with typescript and figma designs. Highly configurable business to business app (i.e lots of permutations). Everyone seems to have a “system”. I recommend looking at the OpenAI Cookbook for long running plans and do TDD to the extreme. https://developers.openai.com/cookbook/articles/codex_exec_p...

The 40k lines of code a day crows are amusing. In solving any problem solvable by code, there's a ratio of non-coding work to coding work, and codex et al all help immensely with the coding work but help less with the non-coding work.

Non-coding work is thinking about the system architecture, thinking about how data should flow, thinking about the problem to be solved, talking with people who will use it, discovering what their objectives are.

Producing 40k lines a code per day simply means you're not doing any of that work: the work that ensures you're building something worth building.

Which is why the result is massive, pointless things that don't do the things people actually need, because you've not taken any time to actually identify the problems worth solving or how to solve them.

It's a form of mania that recalls Kafka's The Burrow, where an underground creature builds and builds an endless series of catacombs without much purpose or coherence. When building becomes so easy when it was so hard -- and when it becomes more fun to build and watch codex's streams of diffs fly by, than to plan -- we forget the purpose of building, and building becomes its own purpose, which is why we usually so little actual productive impact on the world from the "40k lines of code a day" cohort.

What job are you in where you can even come up with problems that -need- 30-40k lines of code a day?

If you are pushing 40k lines of code per day you are an idiot and should be fired.

> "I am able to push 30-40K lines of nearly perfect code a day now."

It is physically and physiologically impossible for anyone to be reviewing "30-40K lines of nearly perfect code a day" to the extent needed to push it with confidence in a sensible development process.

Are you really reviewing 30-40k lines of code a day?

Why do you and many of your industry friends conveniently never actually post their 'perfect code' when asked for proof? I've asked like five different people now that make these claims and they just vanish into the ether.

do you understand every line of code you churn out?

> I dunno if the author realizes, but all the things they mentioned did materialize in one way or another, just not exactly how the hype described it.

From the post, which is not a very long one: "All of the above technologies are still chugging along in some form or other (well, OK, not Quibi). Some are vaguely useful and others are propped up by weirdo cultists"

But there's a spectrum of responses to these technologies, from knee-jerk cynicism to genuine moral disgust. "Useful" and "good for people/society/humanity" don't always go hand-in-hand, particularly if you take origins and power into account.

The guy who died on one was Jimi Heselden, who was a British entrepreneur who bought the company from the American inventor, Dean Kamen. Dean is alive, however he was recently found to have hung out with the "disgraced financier".

That’s dark. But.. accurate.

OP here. If you're interested, here are my thoughts on Google Glass from 12 years ago.

https://shkspr.mobi/blog/2014/04/quick-thoughts-on-google-gl...

I am looking forward to 2028 matching the hype of 2014.

No-one thought the internet was a failed technology in 2006. It was a vital tool by then.

Handheld computers were an expanding market, dominated by Blackberry.

EVs were an immature technology but hybrids like the Prius were selling.

I have found that to be utterly untrue

Short of insider trading, betting on things not happening is apparently the best way to make money on those shady betting sites.

You claim to cite 'technologies' but include a few brands and companies for some reason.

The one you keep citing, here and in the article, Quibi, lives on in technology-form (the spirit of your article we must presume) as an 8 billion dollar business in China and is rapidly upending every Hollywood film studio.

So, arguments about substantiation or even 'this time' fall flat in the face of not even understanding your own message.

Just chiming in to say thanks for the Pratchett quote! I dare say he's about to beat out Douglass Adams for my top author. Feets of Clay and Hogfather should be must reads for people dealing with AI right now imo.

You're not really saying anything, though. For every tech hype that has failed, there is another that's changed the world. This IS changing the world and our industry, regardless of whether it reaches the heights of the hypers.

I mean you're just stating that sometimes tech doesn't meet it's hype. What's insightful about that? It's a given; cherry-picking examples doesn't prove your case.

Or have an LLM write it and then we can judge whether the OP is wrong about whether "this time is different".

Usually HN only auto-edits on first submission. If you go in and undo it manually as the submitter, you can force it to read how you intend.