The only people underwhelmed by AI in February 2026 are people who have formed an identity around being AI skeptics over the last couple years and are struggling to shed it. I haven't met anyone who has seriously used the new models who isn't a at least a bit awed and disturbed.
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!
There is also that contrast about it being genuinely useful for work/programming and the fact that, for now, it changes the rest of my life in a negative way - by making PC hardware unavailable, by hearing every day I'll be out of work in 6-24 months, and by having to deal with people taking the information from Chat for granted.
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.
in a nutshell: AI - even if transformative and in the future a widely used general-purpose technology - is normal technology. I reject the technological determinism that is being fed to us, especially the idea that AI itself is an agent in defining its own future. I think adoption and the post-adoption spread will be slow & uncertain (relative to the current messaging) regardless of where it ultimately takes us. I think the absolute societal impact is grossly overstated, and the roles of institutions shaping the path underestimated or ignored.
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.
What disturbs me is the speed of improvement, moreso then the capability.
Maybe it will plateau in the next 6-24 months, in which case it will “only” be as disruptive as the computer or industrial revolutions, albeit at a faster pace.
Software jobs have been steadily outpacing other white collar jobs for the past year, but it's unlikely you will find one unless you work on your attitude and your ability to communicate respectfully.
This is a great point, because when you ask it (Claude) if it has any questions, it often turns out it has lots of good ones! But it doesn't ask them unless you ask.
You can define "ponder" in multiple ways, but really this is why thinking models exist - they turn over the prompt multiple times and iterate on responses to get to a better end result.
Well I chose the word “ponder” carefully, given the fact that I have a specific goal of contributing to this debate productively. A goal that I decided upon after careful reflection over a few years of reading articles and internet commentary, and how it may affect my career, and the patterns I’ve seen emerge in this industry. And I did that all patiently. You could say my context window was infinite, only defined by when I stop breathing.
That is to say, all of that activity I listed is activity I’m confident generative AI is not capable of, fundamentally.
Like I said in a cousin comment, we can build Frankenstein algorithms and heuristics on top of generative AI but every indication I’ve seen is that that’s not sufficient for intelligence in terms of emergent complexity.
Imagine if we had put the same efforts towards neural networks, or even the abacus. “If I create this feedback loop, and interpret the results in this way, …”
Probably the lack of external stimuli. Generative AI only continues generating when prompted. You can play games with agents and feedback loops but the fundamental unit of generative AI is prompt-based. That doesn’t seem, to me, to be a sufficient model for intelligence that would be capable of “pondering”.
My take is that an artificial model of true intelligence will only be achieved through emergent complexity, not through Frankenstein algorithms and heuristics built on generative AI.
Generative AI does itself have emergent complexity, but I’m bearish that if we would even hook it up to a full human sensory input network it would be anything more than a 21st century reverse mechanical Turk.
Edit: tl;dr Emergent complexity is a necessary but insufficient criteria for intelligence
This has nothing to do with burden of proof, it has to do with journalistic accuracy, and this is obviously a hit piece. HN prides itself on being skeptical and then eats up "skeptic slop."
You can literally go look at some of antirez's PRs described here in this article. They're not seeing it because it's not there?
Honestly, what you're describing sounds like the older models. If you are getting these sorts of results with Opus 4.5 or 5.2-codex on high I would be very curious to see your prompts/workflow.
People have been saying "Oh use glorp 3.835 and those problems don't happen anymore" for about 3 years at this point. It's always the fact you're not using the latest model that's the problem.
I agree. I've seen people insist moving to a newer model or fine tuning will make the output more clever, "trust me", sometimes without providing any evidence of before and after for the specific use case. One LLM project I saw released was prettymuch useless, but it wasn't the use case or the architectural limitations that were the problem, nope the next thing on the roadmap was "fixing" it by plugging in a better LLM.
"You can use AI but you are responsible for and must validate its output" is a completely reasonable and coherent policy. I'm sure they stated exactly what they intended to.
If you have a program that looks at CCTV footage and IDs animals that go by.. is a human supposed to validate every single output? How about if it's thousands of hours of footage?
I think parent comment is right. It's just a platitude for administrators to cover their backs and it doesn't hold to actual usecases
I don't see it so bleakly. Using your analogy, it would simply mean that if the program underperforms compared to humans and starts making a large amount of errors, the human who set up the pipeline will be held accountable. If the program is responsible for a critical task (ie the animal will be shot depending on the classification) then yes, a human should validate every output or be held accountable in case of a mistake.
I take an interest in plane crashes and human factors in digital systems. We understand that there's a very human aspect of complacency that is often read about in reports of true disasters, well after that complacency has crept deep into an organization.
When you put something on autopilot, you also massively accelerate your process of becoming complacent about it -- which is normal, it is the process of building trust.
When that trust is earned but not deserved, problems develop. Often the system affected by complacency drifts. Nobody is looking closely enough to notice the problems until they become proto-disasters. When the human finally is put back in control, it may be to discover that the equilibrium of the system is approaching catastrophe too rapidly for humans to catch up on the situation and intercede appropriately. It is for this reason that many aircraft accidents occur in the seconds and minutes following an autopilot cutoff. Similarly, every Tesla that ever slammed into the back of an ambulance on the back of the road was a) driven by an AI, b) that the driver had learned to trust, and c) the driver - though theoretically responsible - had become complacent.
Theoretical? I don't see any reason that complacency is fine in science. If it's a high school science project and you don't actually care at all about the results, sure.
The problem is that the original statement is too black and white. We make tradeoffs based on costs and feasibility
"if the program underperforms compared to humans and starts making a large amount of errors, the human who set up the pipeline will be held accountable"
Like.. compared to one human? Or an army of a thousand humans tracking animals? There is no nuance at all. It's just unreasonable to make a blanket statement that humans always have to be accountable.
"If the program is responsible for a critical task .."
See how your statement has some nuance? and recognizes that some situations require more accountability and validation that others?
If some dogs chew up an important component, the CERN dog-catcher won't avoid responsibility just by saying "Well, the computer said there weren't any dogs inside the fence, so I believed it."
Instead, they should be taking proactive steps: testing and evaluating the AI, adding manual patrols, etc.
NAND2TETRIS is fun. For an experienced programmer the difficulty is almost akin to a game. Highly recommend it to programmers who have been in high level land for too long.
They would need 250 million GPT Plus $20 subscribers to recoup a $5 billion expense. They're far from that even when we count the free users (which are likely 99% of the user base?)
The math just doesn't work. They're hemorrhaging money as far as I can tell (not counting the Azure computing deal).
We can only guess, but my guess is that inference is still a good chunk of their costs. That's why they're trying to get the mini/turbo models into a usable state.
Even then, training is still an expense. And it's not like you can train and forget. Even if your model is already trained you still need to incorporate new knowledge over time.
I don't think so, I think it's more about openness. I've noticed older software engineers tend to be more anti-LLM and quick to dismiss.
The shortcomings are aplenty, but they don't bother me. The things it can do weren't possible 2 years ago. I'll leverage those and take the bad with the good.
Similar experience with Tesla FSD. I know other Tesla owners who tried it a few times and think it's trash because they had to disengage. I disengage preemptively all the time but the other 90% of my drive being done for me is not something that used to be possible. I tried to give up my subscription because it's expensive and couldn't hold out two days.
