“How do we know they’re right?” I hear you ask. We know because we who are wrong and these ones aren’t them. So they can’t make wrong predictions because those who make wrong predictions have been sacked.
Schools needed to be punished too. There was a branch of knowledge called Science, and it was badly run and terribly elitist. These people would talk for hours and never in ALL CAPS. They used alot of big words and produced alot of theories. Forms of lies that they literally called form-you-lies. They were fanatics and would never agree on anything. Very frustrating.
Then a new kind of brave, talented politician made Science accountable to our heroic leadership. Suddenly there were new medications and rocket ships everywhere, enough for every person, woman, man, camera, TV. It was literally called a golden age because the right people acquired mountains of gold. Elections were no longer necessary. They were all rigged anyway.
There was a magazine called The Expertist or The Economix. Not really very good, but sometimes it did praise our heroic leadership, which was good.
That magazine published a long text that said,
> It is common thinking in Silicon Valley and Washington, DC that any regulation would put American firms at a disadvantage because they cannot trust Chinese competitors to abide by the rules.
And this was true, because no one else can be trusted, they should all be punished. Punishing others is how you get the best deals. Only our heroic leadership makes good rules for everyone else in the world to follow.
I.e. the AI 2027 guys were memed for being AI lunatics on a lot here and they have been pretty on the money in terms of pace of progress accelerating/ gov moving towards nationalization/ coding agents
[1] - https://garymarcus.substack.com/p/breaking-the-ai-2027-dooms...
Gary Marcus’ takes have aged really poorly over time ironically he is the “AI expert” who has to constantly move the goal posts.
From my perspective there’s very slow but very real progress happening in the AI space. I see people making wild predictions in both directions, but in terms of actual unsupervised utility there’s definitely progress abet wildly slower than most hype.
"The bet of using AI to speed up AI research is starting to pay off.
OpenBrain continues to deploy the iteratively improving Agent-1 internally for AI R&D. Overall, they are making algorithmic progress 50% faster than they would without AI assistants—and more importantly, faster than their competitors. The AI R&D progress multiplier: what do we mean by 50% faster algorithmic progress?
Several competing publicly released AIs now match or exceed Agent-0, including an open-weights model. OpenBrain responds by releasing Agent-1, which is more capable and reliable.28
People naturally try to compare Agent-1 to humans, but it has a very different skill profile. It knows more facts than any human, knows practically every programming language, and can solve well-specified coding problems extremely quickly. On the other hand, Agent-1 is bad at even simple long-horizon tasks, like beating video games it hasn’t played before. Still, the common workday is eight hours, and a day’s work can usually be separated into smaller chunks; you could think of Agent-1 as a scatterbrained employee who thrives under careful management.29 Savvy people find ways to automate routine parts of their jobs.30
OpenBrain’s executives turn consideration to an implication of automating AI R&D: security has become more important. In early 2025, the worst-case scenario was leaked algorithmic secrets; now, if China steals Agent-1’s weights, they could increase their research speed by nearly 50%.31 OpenBrain’s security level is typical of a fast-growing ~3,000 person tech company, secure only against low-priority attacks from capable cyber groups (RAND’s SL2).32 They are working hard to protect their weights and secrets from insider threats and top cybercrime syndicates (SL3),33 but defense against nation states (SL4&5) is barely on the horizon."
That's precisely where we are.
This is eerie. It's like a time traveler. The only delta is Anthropic is in the role of OpenAI.
That seems to me to be the most concrete and least obvious prediction in the quoted text.
I don't think that's happening. If that were generally accepted as true I would expect OpenAI to be unable to successfully IPO.
https://asteriskmag.substack.com/p/before-he-wrote-ai-2027-h...
https://www.lesswrong.com/posts/6Xgy6CAf2jqHhynHL/what-2026-...
Anyone who wants to dismiss the LessWrong / X-Risk / "doomers" should link their accurate predictions from 2021.
I’ve heard people say older models can’t do X, when I used that way etc. I suspect people are applying their own learning curve as part of their assessment of progress, you get better at writing prompts and it feels like the model improved.
Which is why I’m saying we need some objective metrics to judge predictions of actual capacity.
These companies aren’t just making stuff up, they really do want to improve the models, and the models really are improving.
I’m aware of multiple cases of benchmark cheating/“optimization”. So, taking benchmarks a face value seems laughable.
3 * N < 42 * N
42 - 3 < N * (42 - 3)
It helps to know layer you're working on.
People seem to make mistake of thinking how good LLMs are around tasks that they are familiar with and extrapolating it to whole population.
It's good mental exercise to think about how little you can do compared to expert on tasks you never thought of working.
Ie. if you're programmer or know something about finance, don't think how much it enables you to do better coding or investing, think instead how much it doesn't enable you to work on something you don't know like maybe molecular biology or visual special effects – it's all there but it's much better multiplier for people who do know their shit.
Knowing layer you're working on helps a lot, it gets multiplied.
Knowing programming is becoming more fundamental skill than ever before as it lies at the foundation of almost everything else.
I would however agree there are no experts. Not because of some prediction made on a short time scale not landing 100% but that there are literally no experts because expertise takes experience which takes time. There are no experts and no one knows what happens next. We are on the verge of where the foresight of science fiction effectively -ends-, the advent of AI is when things go a thousand possible directions and the stories stop there (sans a few like accelerando, but even then the story just plays out the end of thinking mass). No one knows what’s next, or when.
In fact I’d assert in many areas being discussed -it has already happened- and we don’t know it, and by the time we do it’ll be over. Not to be breathless, but there’s no reason to believe today some AI researcher somewhere didn’t build the first AGI and not be totally aware. And once they are there’s no reason to believe it’s going to be on the evening news or hacker news. By the time it’s ready for commercializing and disclosing it’ll be around for a while. Likewise with general purpose robots, autonomous weapons (btw already tested by Ukraine), etc.
Yes, autonomous weapons were explored and were found to be poor performers compared to actual pilots. The breakthrough is in terminal guidance and dozens of other little techniques to get quality human control extended into the far reach of the battlefield. And of course, AI assistance in logistics and analysis. But actual autonomous weapons making any more of a choice beyond "something is moving, kill it" have been, at least for now, mostly a dead end.
This is because it's very difficult to economically load the rather sizable compute requirement into the compact one-use weapons, and of course reliable communications aren't assured either.
That will probably change some day, but for now, cheap automous command drones making battlefield analysis e.g. mapping out enemy movements from afar and launching cheap autonomous kamikaze drones is not a thing beyond occasional limited testing.
I would note that a lot of drones are quite large too - small airplanes. They can carry more than enough hardware for autonomy. You can also have motherships doing planning and smaller kamikaze drones for combat.
These aren’t hard problems to solve, the harder problem is the reluctance to give over to automation. But that’s going to happen faster and faster.
Long spooling optical fibers.
We will have a fully autonomous Siri that is actually good before Ukraine gets autonomous motherships making targeting choices to repel Russian assault corps. However, given the pace of the war and the pace of AI development, that may actually happen. But it isn't happening today.
I agree that change happens on a longer timeline. This is why I’m so tired of statements like this…
“Within a couple of years, possibly much sooner…”
These “experts” are pulling timelines out of the sky, and these predictions are leading to reckless behavior from CEOs and executives which have a material impact on people’s lives. But they get clicks on their blogs and funding for their startup… I guess that’s all that matters.
If you have a 5% chance of thermonuclear war each decade for 10 decades, you'll:
- Hear similar annoying statements
- They'll be true
With AI, we don't know if it's one week or one decade. This means we should assign probabilities and consider all possibilities, not get annoyed.
If I predict the world will end every single day from now until forever, I will most certainly be right eventually. That doesn’t make me an “expert” or someone worth listening to on the topic. That’s the playbook of a doomsday cult, not anyone that should drive world markets.
There's no reason to report on the odds of a meteor falling on my head tomorrow. There is every reason to consider the odds of:
- Nuclear apocalypse (esp. Cold War era)
- Bioweapon
- Climate change leading to disaster
- AI apocalypse
- Etc.
None of those are infinitesimal odds. That contrasts with, say, a zombie virus.
https://fortune.com/article/why-microsoft-ai-chief-mustafa-s...
Altman and Amodei recently hard to start walking back their earlier predictions.
https://fortune.com/2026/05/26/sam-altman-dario-amodei-walki...
> Anthropic CEO Dario Amodei, who once claimed AI could eliminate 50% of white-collar jobs, now says automation may actually expand the work people do.
Is pivoting from 50% elimination to actually expanding work not a drastic enough?
Do you think AI has eliminated writing code? I still write code every day. The AI is more a thing I ask questions and it gives me right answers about 40% of the time.
Programmers still write code. They have enough expertise from actually programming to understand that the untouched output from AI is not good enough to be professionally responsible for.
I'm talking about legit programmers which are around 90% imho. Out of those programmers 65-70% no longer write code.
There's nothing that I can't tell Claude Opus or GPT 5.5 to do for me instead.
At work, where I don't have cheap AI subscriptions, access to the SOTA, and where code needs to follow conventions more strictly, I still have to write the code by hand often.
Your second article says something different, but this is because it's full of misquotes. The link supporting "50% of jobs" specifically says entry level, and the link supporting "reframed automation... not as a destroyer of jobs" has Amodei saying not that jobs won't be destroyed but that new jobs may be created to replace them. If AI moves sufficiently slowly to let that happen, which he explicitly cautions it may not.
Then you are dead wrong. Anyone who gives a shit about doing a good job is still writing code.
Literally all HN talks about is AI these days, and it is very, very clear that plenty of people are still writing lots of code, many finding AI makes them slower and causes more problems, and many making the reverse claims. There is always a rich mix of opinions and experiences here.
You would have to believe that literally everyone on HN is a bot (and also everyone on Reddit, Twitter, astralcodexten, or anywhere else online) to discount all these differing opinions in favor of "everyone I personally know" .
> I used Claude Code and Codex for the translation. This was human-directed, not autonomous code generation. I decided what to port, in what order, and what the Rust code should look like. It was hundreds of small prompts, steering the agents where things needed to go. After the initial translation, I ran multiple passes of adversarial review, asking different models to analyze the code for mistakes and bad patterns.
Sounds to me like "wrote their code using AI only".
And from the creator of Redis, antirez: https://antirez.com/news/158
> It is simply impossible not to see the reality of what is happening. Writing code is no longer needed for the most part.
There's a big difference between "not writing any code by hand" (which still might involve lots of non-manual-coding effort!), and "vibecoding", and as I understand this thread was about the former.
edit: yes, I'm a software developer, and if I have a change in mind larger than 2 lines I've been using Claude Code (with English input) rather than a text editor to do it, since about January.
"Write a function that will <do a thing the code does in 3 places>, put it in utils, and refactor the 3 locations that need to do that thing to use that function instead." (and then hundreds more prompts like that)
vs.
"Write an app that lets you trade crypto"
Just totally different activities.
Which ones? Please be specific.
The predictions of these "experts" have been drastically wrong for the last 3 years. At what point does someone lose their "expert" title?