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Posted by Tenoke 4/3/2025

AI 2027(ai-2027.com)
949 points | 621 commentspage 5
ahofmann 4/3/2025|
Ok, I'll bite. I predict that everything in this article is horse manure. AGI will not happen. LLMs will be tools, that can automate away stuff, like today and they will get slightly, or quite a bit better at it. That will be all. See you in two years, I'm excited what will be the truth.
Tenoke 4/3/2025||
That seems naive in a status quo bias way to me. Why and where do you expect AI progress to stop? It sounds like somewhere very close to where we are at in your eyes. Why do you think there won't be many further improvements?
PollardsRho 4/3/2025|||
It seems to me that much of recent AI progress has not changed the fundamental scaling principles underlying the tech. Reasoning models are more effective, but at the cost of more computation: it's more for more, not more for less. The logarithmic relationship between model resources and model quality (as Altman himself has characterized it), phrased a different way, means that you need exponentially more energy and resources for each marginal increase in capabilities. GPT-4.5 is unimpressive in comparison to GPT-4, and at least from the outside it seems like it cost an awful lot of money. Maybe GPT-5 is slightly less unimpressive and significantly more expensive: is that the through-line that will lead to the singularity?

Compare the automobile. Automobiles today are a lot nicer than they were 50 years ago, and a lot more efficient. Does that mean cars that never need fuel or recharging are coming soon, just because the trend has been higher efficiency? No, because the fundamental physical realities of drag still limit efficiency. Moreover, it turns out that making 100% efficient engines with 100% efficient regenerative brakes is really hard, and "just throw more research at it" isn't a silver bullet. That's not "there won't be many future improvements", but it is "those future improvements probably won't be any bigger than the jump from GPT-3 to o1, which does not extrapolate to what OP claims their models will do in 2027."

AI in 2027 might be the metaphorical brand-new Lexus to today's beat-up Kia. That doesn't mean it will drive ten times faster, or take ten times less fuel. Even if high-end cars can be significantly more efficient than what average people drive, that doesn't mean the extra expense is actually worth it.

ahofmann 4/3/2025||||
I write bog-standard PHP software. When GPT-4 came out, I was very frightened that my job could be automated away soon, because for PHP/Laravel/MySQL there must exist a lot of training data.

The reality now is, that the current LLMs still often create stuff, that costs me more time to fix, than to do it myself. So I still write a lot of code myself. It is very impressive, that I can think about stopping writing code myself. But my job as a software developer is, very, very secure.

LLMs are very unable to build maintainable software. They are unable to understand what humans want and what the codebase need. The stuff they build is good-looking garbage. One example I've seen yesterday: one dev committed code, where the LLM created 50 lines of React code, complete with all those useless comments and for good measure a setTimeout() for something that should be one HTML DIV with two tailwind classes. They can't write idiomatic code, because they write code, that they were prompted for.

Almost daily I get code, commit messages, and even issue discussions that are clearly AI-generated. And it costs me time to deal with good-looking but useless content.

To be honest, I hope that LLMs get better soon. Because right now, we are in an annoying phase, where software developers bog me down with AI-generated stuff. It just looks good but doesn't help writing usable software, that can be deployed in production.

To get to this point, LLMs need to get maybe a hundred times faster, maybe a thousand or ten thousand times. They need a much bigger context window. Then they can have an inner dialogue, where they really "understand" how some feature should be built in a given codebase. That would be very useful. But it will also use so much energy that I doubt that it will be cheaper to let a LLM do those "thinking" parts over, and over again instead of paying a human to build the software. Perhaps this will be feasible in five or eight years. But not two.

And this won't be AGI. This will still be a very, very fast stochastic parrot.

AnimalMuppet 4/3/2025|||
ahofmann didn't expect AI progress to stop. They expected it to continue, but not lead to AGI, that will not lead to superintelligence, that will not lead to a self-accelerating process of improvement.

So the question is, do you think the current road leads to AGI? How far down the road is it? As far as I can see, there is not a "status quo bias" answer to those questions.

bayarearefugee 4/3/2025|||
I predict AGI will be solved 5 years after full self driving which itself is 1 year out (same as it has been for the past 10 years).
ahofmann 4/3/2025|||
Well said!
arduanika 4/4/2025|||
...not before I get in peak shape, six months from now.
mitthrowaway2 4/3/2025|||
What's an example of an intellectual task that you don't think AI will be capable of by 2027?
jdauriemma 4/3/2025|||
Being accountable for telling the truth
myhf 4/3/2025||
accountability sinks are all you need
kubb 4/3/2025||||
It won't be able to write a compelling novel, or build a software system solving a real-world problem, or operate heavy machinery, create a sprite sheet or 3d models, design a building or teach.

Long term planning and execution and operating in the physical world is not within reach. Slight variations of known problems should be possible (as long as the size of the solution is small enough).

lumenwrites 4/3/2025|||
I'm pretty sure you're wrong for at least 2 of those:

For 3D models, check out blender-mcp:

https://old.reddit.com/r/singularity/comments/1joaowb/claude...

https://old.reddit.com/r/aiwars/comments/1jbsn86/claude_crea...

Also this:

https://old.reddit.com/r/StableDiffusion/comments/1hejglg/tr...

For teaching, I'm using it to learn about tech I'm unfamiliar with every day, it's one of the things it's the most amazing at.

For the things where the tolerance for mistakes is extremely low and the things where human oversight is extremely importamt, you might be right. It won't have to be perfect (just better than an average human) for that to happen, but I'm not sure if it will.

kubb 4/3/2025||
Just think about the delta of what the LLM does and what a human does, or why can’t the LLM replace the human, e.g. in a game studio.

If it can replace a teacher or an artist in 2027, you’re right and I’m wrong.

esafak 4/3/2025||
It's already replacing artists; that's why they're up in arms. People don't need stock photographers or graphic designers as much as they used to.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4602944

kubb 4/4/2025||
I know that artists don’t like AI, because it’s trained on their stolen work. And yet, AI can’t create a sprite sheet for a 2d game.

This is because it can steal a single artwork but it can’t make a collection of visually consistent assets.

cheevly 4/4/2025||
Bro what are you even talking about? ControlNet has been able to produce consistent assets for years.

How exactly do you think video models work? Frame to frame coherency has been possible for a long time now. A sprite sheet?! Are you joking me. Literally churning them out with AI since 2023.

pixl97 4/3/2025||||
> or operate heavy machinery

What exactly do you mean by this one?

In large mining operations we already have human assisted teleoperation AI equipment. Was watching one recently where the human got 5 or so push dozers lined up with a (admittedly simple) task of cutting a hill down and then just got them back in line if they ran into anything outside of their training. The push and backup operations along with blade control were done by the AI/dozer itself.

Now, this isn't long term planning, but it is operating in the real world.

kubb 4/4/2025||
Operating an excavator when building a stretch of road. Won’t happen by 2027.
programd 4/3/2025|||
Does a fighter jet count as "heavy machinery"?

https://apnews.com/article/artificial-intelligence-fighter-j...

kubb 4/4/2025||
Yes, when they send unmanned jets to combat.
Philpax 4/4/2025||
It's already starting with the drones: https://www.csis.org/analysis/ukraines-future-vision-and-cur...
coolThingsFirst 4/3/2025|||
programming
lumenwrites 4/3/2025|||
Why would it get 60-80% as good as human programmers (which is what the current state of things feels like to me, as a programmer, using these tools for hours every day), but stop there?
burningion 4/3/2025|||
So I think there's an assumption you've made here, that the models are currently "60-80% as good as human programmers".

If you look at code being generated by non-programmers (where you would expect to see these results!), you don't see output that is 60-80% of the output of domain experts (programmers) steering the models.

I think we're extremely imprecise when we communicate in natural language, and this is part of the discrepancy between belief systems.

Will an LLM model read a person's mind about what they want to build better than they can communicate?

That's already what recommender systems (like the TikTok algorithm) do.

But will LLMs be able to orchestrate and fill in the blanks of imprecision in our requests on their own, or will they need human steering?

I think that's where there's a gap in (basically) belief systems of the future.

If we truly get post human-level intelligence everywhere, there is no amount of "preparing" or "working with" the LLMs ahead of time that will save you from being rendered economically useless.

This is mostly a question about how long the moat of human judgement lasts. I think there's an opportunity to work together to make things better than before, using these LLMs as tools that work _with_ us.

kody 4/3/2025||||
It's 60-80% as good as Stack Overflow copy-pasting programmers, sure, but those programmers were already providing questionable value.

It's nowhere near as good as someone actually building and maintaining systems. It's barely able to vomit out an MVP and it's almost never capable of making a meaningful change to that MVP.

If your experiences have been different that's fine, but in my day job I am spending more and more time just fixing crappy LLM code produced and merged by STAFF engineers. I really don't see that changing any time soon.

lumenwrites 4/3/2025||
I'm pretty good at what I do, at least according to myself and the people I work with, and I'm comparing its capabilities (the latest version of Claude used as an agent inside Cursor) to myself. It can't fully do things on its own and makes mistakes, but it can do a lot.

But suppose you're right, it's 60% as good as "stackoverflow copy-pasting programmers". Isn't that a pretty insanely impressive milestone to just dismiss?

And why would it just get to this point, and then stop? Like, we can all see AIs continuously beating the benchmarks, and the progress feels very fast in terms of experience of using it as a user.

I'd need to hear a pretty compelling argument to believe that it'll suddenly stop, something more compelling than "well, it's not very good yet, therefore it won't be any better", or "Sam Altman is lying to us because incentives".

Sure, it can slow down somewhat because of the exponentially increasing compute costs, but that's assuming no more algorithmic progress, no more compute progress, and no more increases in the capital that flows into this field (I find that hard to believe).

kody 4/3/2025||
I appreciate your reply. My tone was a little dismissive; I'm currently deep deep in the trenches trying to unwind a tremendous amount of LLM slop in my team's codebase so I'm a little sensitive.

I use Claude every day. It is definitely impressive, but in my experience only marginally more impressive than ChatGPT was a few years ago. It hallucinates less and compiles more reliably, but still produces really poor designs. It really is an overconfident junior developer.

The real risk, and what I am seeing daily, is colleagues falling for the "if you aren't using Cursor you're going to be left behind" FUD. So they learn Cursor, discover that it's an easy way to close tickets without using your brain, and end up polluting the codebase with very questionable designs.

senordevnyc 4/4/2025|||
GPT-4 was released almost exactly two years ago, so “a few years ago” means GPT-3.5.

And Claude 3.7 + Cursor agent is, for me, way more than “marginally more impressive” compared to GPT-3.5

lumenwrites 4/3/2025|||
Oh, sorry to hear that you have to deal with that!

The way I'm getting a sense of the progress is using AI for what AI is currently good at, using my human brain to do the part AI is currently bad at, and comparing it to doing the same work without AI's help.

I feel like AI is pretty close to automating 60-80% of the work I would've had to do manually two years ago (as a full-stack web developer).

It doesn't mean that the remaining 20-40% will be automated very quickly, I'm just saying that I don't see the progress getting any slower.

coolThingsFirst 4/3/2025||||
Try this, launch Cursor.

Type: print all prime numbers which are divisible by 3 up to 1M

The result is that it will do a sieve. There's no need for this, it's just 3.

mysfi 4/3/2025||
Just tried this with Gemini 2.5 Pro. Got it right with meaningful thought process.
boringg 4/3/2025||||
Because ewe still haven't figured out fusion but its been promised for decades. Why would everything thats been promised by people with highly vested interests pan out any different?

One is inherently a more challenging physics problem.

mitthrowaway2 4/3/2025|||
Can you phrase this in a concrete way, so that in 2027 we can all agree whether it's true or false, rather than circling a "no true scotsman" argument?
abecedarius 4/3/2025||
Good question. I tried to phrase a concrete-enough prediction 3.5 years ago, for 5 years out at the time: https://news.ycombinator.com/item?id=29020401

It was surpassed around the beginning of this year, so you'll need to come up with a new one for 2027. Note that the other opinions in that older HN thread almost all expected less.

kristopolous 4/3/2025|||
People want to live their lives free of finance and centralized personal information.

If you think most people like this stuff you're living in a bubble. I use it every day but the vast majority of people have no interest in using these nightmares of philip k dick imagined by silicon dreamers.

jstummbillig 4/3/2025|||
When is the earliest that you would have predicted where we are today?
rdlw 4/3/2025||
Same as everybody else. Today.
meroes 4/4/2025||
I’m also unafraid to say it’s BS. I don’t even want to call it scifi. It’s propaganda.
wg0 4/4/2025||
Very detailed effort. Predicting future is very very hard. My gut feeling however says that none of this is happening. You cannot put LLMs into law and insurance and I don't see that happening with current foundations (token probabilities) of AI let alone AGI.

By law and insurance - I mean hire an insurance agent or a lawyer. Give them your situation. There's almost no chance that such a professional would come wrong about any conclusions/recommendations based on the information you provide.

I don't have that confidence in LLMs for that industries. Yet. Or even in a decade.

polynomial 4/4/2025|
> You cannot put LLMs into law and insurance

Cass Sunstein would very strongly disagree.

ImHereToVote 4/4/2025||
"The AI safety community has grown unsure of itself; they are now the butt of jokes, having predicted disaster after disaster that has manifestly failed to occur. Some of them admit they were wrong."

Too real.

resource0x 4/4/2025||
Every time NVDA/goog/msft tanks, we see these kinds of articles.
siliconc0w 4/4/2025||
The limiting factor is power, we can't build enough of it - certainly not enough by 2027. I don't really see this addressed.

Second to this, we can't just assume that progress will keep increasing. Most technologies have a 'S' curve and plateau once the quick and easy gains are captured. Pre-training is done. We can get further with RL but really only in certain domains that are solvable (math and to an extent coding). Other domains like law are extremely hard to even benchmark or grade without very slow and expensive human annotation.

amarcheschi 4/3/2025||
I just spent some time trying to make claude and gemini make a violin plot of some polar dataframe. I've never used it and it's just for prototyping so i just went "apply a log to the values and make a violin plot of this polars dataframe". ANd had to iterate with them for 4/5 times each. Gemini got it right but then used deprecated methods

I might be doing llm wrong, but i just can't get how people might actually do something not trivial just by vibe coding. And it's not like i'm an old fart either, i'm a university student

hiq 4/3/2025||
> had to iterate with them for 4/5 times each. Gemini got it right but then used deprecated methods

How hard would it be to automate these iterations?

How hard would it be to automatically check and improve the code to avoid deprecated methods?

I agree that most products are still underwhelming, but that doesn't mean that the underlying tech is not already enough to deliver better LLM-based products. Lately I've been using LLMs more and more to get started with writing tests on components I'm not familiar with, it really helps.

jaccola 4/3/2025|||
How hard can it be to create a universal "correctness" checker? Pretty damn hard!

Our notion of "correct" for most things is basically derived from a very long training run on reality with the loss function being for how long a gene propagated.

hiq 4/6/2025||
You don't need a full correctness checker to get a useful product though. New code generated by the current generation of LLMs, which also compiles and passes existing tests, is likely to be somewhat useful in my experience. The problem is that we still get too much code that doesn't pass these basic requirements.
henryjcee 4/3/2025||||
> How hard would it be to automate these iterations?

The fact that we're no closer to doing this than we were when chatgpt launched suggests that it's really hard. If anything I think it's _the_ hard bit vs. building something that generates plausible text.

Solving this for the general case is imo a completely different problem to being able to generate plausible text in the general case.

HDThoreaun 4/3/2025||
This is not true. The chain of logic models are able to check their work and try again given enough compute.
lelandbatey 4/3/2025||
They can check their work and try again an infinite number of times, but the rate at which they succeed seems to just get worse and worse the further from the beaten path (of existing code from existing solutions) that they stray.
9dev 4/3/2025|||
How hard would it be, in terms of the energy wasted for it? Is everything we can do worth doing, just for the sake of being able to?
VOIPThrowaway 4/3/2025|||
You're asking it to think and it can't.

It's spicy auto complete. Ask it to create a program that can create a violin plot from a CVS file. Because this has been "done before", it will do a decent job.

suddenlybananas 4/3/2025||
But this blog post said that it's going to be God in like 5 years?!
juped 4/3/2025|||
You pretty much just have to play around with them enough to be able to intuit what things they can do and what things they can't. I'd rather have another underling, and not just because they grow into peers eventually, but LLMs are useful with a bit of practice.
dinfinity 4/3/2025|||
Yes, you're most likely doing it wrong. I would like to add that "vibe coding" is a dreadful term thought up by someone who is arguably not very good at software engineering, as talented as he may be in other respects. The term has become a misleading and frankly pejorative term. A better, more neutral one is AI assisted software engineering.

This is an article that describes a pretty good approach for that: https://getstream.io/blog/cursor-ai-large-projects/

But do skip (or at least significantly postpone) enabling the 'yolo mode' (sigh).

amarcheschi 4/3/2025||
You see, the issue I get petty about is that Ai is advertised as the one ring to rule them all software. VCs creaming themselves at the thought of not having to pay developers and using natural language. But then, you have to still adapt to the Ai, and not vice versa. "you're doing it wrong". This is not the idea that VCs bros are selling

Then, I absolutely love being aided by llms for my day to day tasks. I'm much more efficient when studying and they can be a game changer when you're stuck and you don't know how to proceed. You can discuss different implementation ideas as if you had a colleague, perhaps not a PhD smart one but still someone with a quite deep knowledge of everything

But, it's no miracle. That's the issue I have with the way the idea of Ai is sold to the c suites and the general public

pixl97 4/3/2025||
>But, it's no miracle.

All I can say to this is fucking good!

Lets imagine we got AGI at the start of 2022. I'm talking about human level+ as good as you coding and reasoning AI that works well on the hardware from that age.

What would the world look like today? Would you still have your job. With the world be in total disarray? Would unethical companies quickly fire most their staff and replace them with machines? Would their be mass riots in the streets by starving neo-luddites? Would automated drones be shooting at them?

Simply put people and our social systems are not ready for competent machine intelligence and how fast it will change the world. We should feel lucky we are getting a ramp up period, and hopefully one that draws out a while longer.

pydry 4/3/2025||
all tech hype cycles are a bit like this. when you were born people were predicting the end of offline shops.

The trough of disillusionment will set in for everybody else in due time.

zurfer 4/4/2025||
In the hope of improving this forecast, here is what I find implausible:

- 1 lab constantly racing ahead and increasing the margin to other; the last 2 years are filled with ever-closer model capabilities and constantly new leaders (openai, anthropic, google, some would include xai).

- Most of the compute budget on R&D. As model capabilities increase and cost goes down, demand will increase and if the leading lab doesn't provide, another lab will capture that and have more total dollars to back channel into R&D.

osigurdson 4/4/2025||
Perhaps more of a meta question is, what is the value of optimistic vs pessimistic predictions regarding what AI might look like in 2-10 years? I.e. if one assumes that AI has hit a wall, what is the benefit? Similarly, if one assumes that its all "robots from Mars" in a year or two, what is the benefit of that? There is no point in making predictions if no actions are taken. It all seems to come down to buy or sell NVDA.
danpalmer 4/3/2025||
Interesting story, if you're into sci-fi I'd also recommend Iain M Banks and Peter Watts.
kittikitti 4/4/2025|
This is a great predictive piece, written in sci-fi narrative. I think a key part missing in all these predictions is neural architecture search. DeepSeek has shown that simply increasing compute capacity is not the only way to increase performance. AlexNet was also another case. While I do think more processing power is better, we will hit a wall where there is no more training data. I predict that in the near future we will have more processing power to train LLM's than the rate at which we produce data for the LLM. Synthetic data can only get you so far.

I also think that the future will not necessarily be better AI, but more accessible one's. There's an incredible amount of value in designing data centers that are more efficient. Historically, it's a good bet to assume that computing cost per FLOP will reduce as time goes on and this is also a safe bet as it relates to AI.

I think a common misconception with the future of AI is that it will be centralized with only a few companies or organization capable of operating them. Although tech like Apple Intelligence is half baked, we can already envision a future where the AI is running on our phones.

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