ngl, the obsession with just making LLMs bigger and hoping they stop lying to us is getting old. it feels like we’ve reached the limit of what "fancy autocomplete" can actually do for society. like, u cant run a power grid or design a microprocessor on a model that might decide to hallucinate just because the prompt was worded weirdly
I was checking out the speaker list and panel notes for the Milken Conference and it’s pretty telling who they’ve got on stage this year. seeing the ASML and Google guys sit down with Logical Intelligence to talk about "deterministic" AI makes it feel like the pivot is finally happening in the background
the future isn't just a smarter chatbot. it's gonna be about these energy-based models that actually understand constraints and mathematical logic. The industry is finally moving from "AI for fun" to "AI for stuff that literally cannot fail" bit of a reality check for the silicon valley hype cycle but honestly, it’s a relief to see some focus on correctness for once
why I think the "chatgpt era" of AI is already hitting a wall
byu/GodBlessIraq inFuturology
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The current AI trajectory is shifting from probabilistic Large Language Models (LLMs) to deterministic, energy-based reasoning architectures. This post explores how major institutional players at the 2026 Milken Institute Global Conference are prioritizing “correctness” over generative “vibes” suggesting a future where AI is integrated into mission-critical infrastructure with mathematical certainty rather than just next-token prediction.
I think just like the dot com bubble era, there are too many companies in the same space. Eventually, some of them need to fall for the strongest to survive. Open AI was first out of the gate, but we see it’s now faltering a bit to Anthropic & Google.
Don’t just look at the large language models, these massive data centers have the ability to train all kinds of neural networks to do all kinds of different things.
Note we’ve long had the other type of AI, deterministic reasoning models – it was LLMs that were novel. The self driving cars are a good example of pre-LLM AI and they work very well. The strong chess algorithms are another example that have been around for a long time. I don’t really think it’s appropriate to call either AI. They aren’t really emulating intelligence in any way. The holy grail is AGI where a computer is actually emulating intelligence not just doing fancy probabilistic pattern matching or deterministic algorithms.
Hit a wall a while ago. The progress we’ve been seeing recently is more in applying the tech rather than the tech improving.
Meanwhile we’re starting to see signs of model collapse in GPT so things might actually get worse.
Yeah I think determinism will win in the end. You can’t just leave things to chance and probabilities.
You’d want answers to be consistent 100% of the time, and not “well it could be this or that” and hope that the answer will be correct.
Why are anything right or wrong? Because we have theories, beliefs (which are a kind of theories), and they determine within those frameworks whether something is right or wrong. If something is wrong, then it’s because the theory is wrong.
It has nothing to do with chatbots probabilistically determining the answer is A instead of B. Chatbots don’t come up with theories.
No one ever claimed that hallucinations would disappear with scaling. Hallucinations are part of the architecture. They won’t go until we have new arch that isn’t generative. Gen ai is a stepping stone, not the final boss.
Largest use-case for AI is definitely gonna be personal assistants. Think stuff that any big corporate guy would have their secretary do. “schedule this meeting”, “Call this place and make an appointment.” “Add this thing to my calendar.”, “Look up this random thing and find me an answer by tomorrow.” On my morning commute I can ask gemini “what’s going on with this topic this week?” and it will do all the open source research for me, and given the right prompts in the “preferences” section of the app, I get back what is basically a customized daily news summary and update, which is pretty cool. Agentic research and basic secretary/assistant work is the big one. People used to have someone following them around with a phone and a PDA to do all sorts of things manually, now you can just ask your little pocket buddy to do it for you. I’m hoping with the rapid increase in renewable energy coming online, and maybe with some more federal support, we’ll be able to have access to these things in the future with as little environmental impact as possible.
For a future focused sub the posters here seem to be very poorly informed on what the majority of companies are actually doing with AI and how they are using it.
Where might I go to read more about “deterministic AI” and/or “energy-based models?”
There is no “understanding” in LLMs, that would infer some kind of sentient awareness. These are statical machines, they were designed to be convincing to trick ppl into believing they are aware. It’s a bit like recording all combinations to an answer on a recorder then playing it back to someone when they ask it. This is Plato’s Cave and Jean’s simulacra and simulation. And the more modern Frankfurt on bullshit:
“The liar cares about the truth and attempts to hide it; the bullshitter doesn’t care whether what they say is true or false”
LLM’s seem to be actually getting worse, perhaps because I’m getting better at detecting its lies or incongruencies
Yeah…. within the scope of the commonly talked about LLM models, but it’s always good to keep in mind that the vast majority of AI is going to be narrow scope, AI models that you’ve never heard of and a lot of of those are accurate and energy, efficient and much much faster than anything. LLM can do.
Those are the purpose built AI that aren’t trying to ever become AGI and generally, they do their jobs a lot better.
So one solution is just to stop trying to start from the top of complexity and start from the bottom and work up. The stock pumping ability of selling people to dream of AGI makes a lot more money than any actual AI model can. That seems to be the real dilemma here.
When it comes to achieving AGI, they have no idea what they’re doing or what approach is gonna work. It’s probably gonna wind up being some combination of approaches on top of that. It might not even be that useful because you already have 8 billion humans on the planet that effectively are already AI, but do so with way less energy input..
What you don’t have is a ton of cheap labor. It’s really the robotic labor coupled with AI. That’s good enough to run the robotic labor that’s gonna make the big difference, not the big data center is trying to be AGI that we don’t really need.
The entire history of AI since the 60’s has been “if only we had more processing / storage / nodes / connections / money / etc. then I’m sure that THIS time the magical statistical box will suddenly become intelligent through some form of critical mass via a mechanism that has never been witnessed, hypothesised or proven, ever.”
Every time, it just plateaus that little bit higher while consuming exponentially more resources.
And you know what that means? When it takes exponentially more resources to get a logarithmic improvement? It means it ain’t ever gonna happen. Certainly not when we can’t even describe any kind of method where that would occur.
As far back as the origins of the entire area of study, through neural networks and genetic algorithms and so on… it’s been the same nonsense based on the same flawed premise. That “intelligence” is just a matter of accumulating a critical mass of dumb systems. It’s never been true.
And now we have pumped a vast portion of worldwide GDP into it, caused a glut of datacentres, full of millions of specialist AI processors, causing worldwide shortages, trained on the entire digital history of the human race, and you know what… it still hasn’t happened.
One day, AI people will get off their arses and actually think about the problem they’re trying to solve, instead of just blatting out another identical PhD paper to all those before it, but with a slight twist, and then fleeing as soon as the inevitable plateau in their models starts to show.
Until then, apparently, we’re just gonna piss away the world’s resources (computing and otherwise) on another thing that has “funny” conversations but can still never infer, innovate or intuit… just apply statistics in a brute force manner of all automation that came before it.
/tinfoil hat on
I don’t think the point of LLM’s was for making anything useful, I think the point was to test run public perception, fine tune models, and gather training data for whatever is next. When something is free, you’re the product, and LLM’s have predominantly been free to use.
Of course, this is merely a gut feeling of mine and not actual evidence. And I don’t know what’s next, either.
/tinfoil hat off
Deterministic AI is reinventing programming languages
A lot of companies have figured out how to keep it constrainted. And you can see the difference in output produced by it. But in the end making machine learning algorithms 100% is not possible. 80-90% range is the max they can do.
OCR is really old and well done machine learning algorithm. It still has 80% accuracy in the real world.
The wall is compute efficiency. Sam Altman promised an improvement in tokens/compute, but the development has been mostly linear. Cost is going to ground expectations and reality in the next couple of years. This wont be a tool everyone can afford.
Google was always going to be the “winner” in all of this. They have the infrastructure already in place and have been working on building it for two decades.
It does seem that with LLMs the current situation is throwing more hardware to make bigger/faster/better models.
But it does seem that the models are much the same. Yes, more capable at what they’re designed to do, but not really anything different. Yes, there’s advances in making stuff more efficient etc.
For sure, one of the issues is people just using it inefficiently. Like for example people doing basic manipulation of a csv file that could be done with a single line of awk, sed and grep. Or asking what the weather is when you can just go to the website that shows it in more detail. But I guess when you have a hammer, everything looks like a nail.
LLMs have been game changing, but I think more advanced AI is going to be much different.
Yes, they do things extremely fast compared to humans, but also often they are not as good as humans.
For me the big issue is the sheer amount of data that they need. If you compare to humans for example, a 10year old hasn’t read the entirety of human literature to be able to reason and hold a conversation. Or a programmer doesn’t need to read every single open source project to be able to code.
Personally I think the big breakthrough will be in reduction of necessary training data, self learning (without re training) and some kind of actual reasoning. For example instead of training it on billions of lines of code, train it on just the language specification and with the reasoning it can actually code, rather than how it is currently.
The other glaring issue is cost. Right now everyone’s AI usage is heavily subsidized. What happens when actual costs are passed to consumers? Will they just say fuck it, not worth it? Will businesses say the same (I’m not talking about he big corporates that have money to burn, but the masses of small and medium businesses who can’t afford shitloads in AI subscriptions).
>it feels like we’ve reached the limit of what “fancy autocomplete” can actually do
This couldn’t be farther from the truth. Your entire post is just “ignore all the evidence and data and just pretend it’s the exact opposite.”
We can have hallucination free inference it just costs 3x as much. Just like how auto-pilot works where a signal comes in and three different computers verify it, 2 out of 3 votes check if it’s a false signal. It’s a thing many of them do, running an inference and then running the check and then double checking if it doesn’t match expected output.
The machines that keep lying to you are just cheap. The hallucinations are how they work but their errors aren’t world ending.
The “next gen” ones just have tons of redundancy and human oversight. It’s just way faster with a smaller risk paired with a brand new skill set. Just like how a practiced pilot knows why an autopilot could interpret humidity in the sensors as turbulance or whatever we’re going to see that be the default for most knowledge work.
this is like saying it’s finally winter after autumn. guys. autumn has ended. the last leaf has fallen. it’s finally happening, all in the background. winter is coming 🐺🦌🗡️🐉
I’m confident the technology will plateau, not there yet. Not because of LLM capability expansion, but more so the accompanying technologies. We’re about to see an explosion of vulnerabilities due to AI detection, but will level off as it gets integrated into SDLC. We’re going to see tons of agentic use cases, most will fail but some will survive. Localized LLM use cases due to data sovereignty concerns, etc.
A lot of specialists have been saying this for a while now. That’s why Yann Le Cun left Meta, because he wants to concentrate on world models. There are all sorts of other types of AI being researched. LLMs just happen to have the hype at the moment.
The transition to ai systems is what will push things forward. An llm with bad tools and no context is dumb. An llm with a treasure trove of accessible mcp tools, a KG, and a large enough context window is insanely effective, imho. The systems are what need to really catch up and are what most in my industry are focusing on now.
It hit a wall back in 2015 thats why it was given for free to the massess to crowd source development essentially.
The data is largely the same: Internet archives. LLMs are just fancy code nothing actually generally hollywood.
Hollywood has warped the idea of realistic scifi and everything we have been exposed to has one way or another oversold the future.
LLMs still have room for improvement, but as they are they are what we were promosed like 8 years ago… Just janky code that runs a fine line between probability, truth, and hellucinations. It only has room to imrpove.
‘Understanding constraints and mathematical logic” IS a smarter chatbot. Just because LLMs size doesnt stop hallucinations doesnt mean theres not a bunch of other solutions for fixing these problems. LLMs have been available to developers for about 5 years. This is like thinking the World Wide Web was as advanced as it was ever gonna be in 1996. You wouldve been in the “streaming will never work, bandwidth is too low, Blockbuster will always be boomin” crowd.
The viability of large language models growing exponentially is also reliant upon their continued usage of more and more energy. It isn’t genuine artificial intelligence. It is slowly built intentionally through programming until it basically “acts right”. Real artificial intelligence would develop on its own inputs over time. Like a child. The only difference would be that it would have robotic efficiency that humans don’t possess in our hardware. But it would also be programmed differently.
LLMs were always an s curve. Their performance growth was bound to flatten out, and that’s about where we are now, at the top of the s curve. Throwing more data at them doesn’t help. There needs to be a fundamental change in how they are built to get to the next level.
Reminds me of the early 1900s where there were too many companies using electricity so the space was crowded. Oh wait, it’s not the tool that’s useless, some companies will go bust because other companies monopolize in a product space better.
I’ve been seeing significant improvements, especially regarding coding intelligence. Nothing that makes me think we’re hitting a wall
th
e hype cycle moved insanely fast and expectations probably became unrealistic
> The industry is finally moving from “AI for fun” to “AI for stuff that literally cannot fail” bit of a reality check for the silicon valley hype cycle but honestly, it’s a relief to see some focus on correctness for once
No idea what OP is talking about. The AI industry has been discussing and trying to solve LLM hallucinations nonstop for like 3 years now.
OP thinks they just now discovered this issue? Lmao what?
Ta vivendo fora do planeta?
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Wait what makes you think the industry is moving there? What can we point to that even indicates deterministic AI is within reach, that we have any better ideas than LLM autocomplete? What’s the optimism of your post based on?