Imagine yourself the CEO of a big AI company. It takes about a month to develop and train a model, so you release a new model every month. A startup says they can 10x your efficiency. What does that get you? You can't release a new model every three days. You can't 10x R&D either. You definitely can't tell investors that you are growing at the same rate, but selling off assets and cancelling purchasing contracts. So you just never improve efficiency enough to use less energy than the previous model version.
I agree, but I also think as AI gets better we're going to see Jevons paradox in full swing, which might delay lower costs. We've seen this with Astra according to Tibo: https://x.com/thsottiaux/status/2097559315150426222
Cost will keep dropping, but the frontier will keep getting pushed. The whole "give me today's model 10x cheaper and I'm good" line is a fallacy. It isn't true now and it never will be for the top 1% of tasks, which will create the most economic gains.
There are an enormous number of tasks that can get by on good enough.
If you need image recognition, and a 30B model saturates the use case with 100% accuracy, you absolutely wouldn't continue to use the next frontier model as they come out.
And I'd argue most economically meaningful tasks will be saturated by cheaper models than those requiring frontier.
Think about what today's models can do with pretty close to 100% accuracy, and then consider that they will be orders of magnitudes cheaper over the years.
5.6 Sol can already obviate tons of labor, and why would you pay 2x or more for no meaningful gain?
The relative gap between frontier and non frontier also continues to shrink, so it's not like you take a meaningful performance loss by rewinding to models from 3-6 months ago. And soon that gap will expand to 12-24 months.
I get the impression the majority of people on here only think about coding, which net net will be a tiny volume of overall AI use in the end.
That can all be true but the frontier models will still have a huge market. You're thinking of all tasks as a fixed pie. The top 1% of intelligence opens up a whole new pie, stuff nobody does today because it's too expensive: daily cancer scans instead of one every few years, asteroid mining missions that need ten thousand PhD-hours of planning, custom drugs designed for your specific tumor, a personal lawyer and doctor for every person on earth, auditing every line of code in every bank and hospital continuously and so on.
Yes, agree that token consumption will increase exponentially for the next while.
Disagree that the frontier model is where the economic gains will be realized.
The smaller the relative gap between frontier and non-frontier/open weights, the less pricing power.
This gap has shown only to shrink over time, not expand.
Businesses will pay more for frontier, but not meaningfully more to justify the economics. It's always going to be a low margin business, perhaps outside of cyber security, warfare/intelligence and perhaps drug discovery.
Though the expensive and time consuming part of drugs is doing the trials and getting approval, not coming up with ideas
Sounds kind of like another K shaped economy. Low end models will be highly competitive and low profit. Problems that can be solved by low end models will be highly competitive and low profit too.
Where the interesting work will be is at the median point where cheap models do almost all of it but need to hand off some parts to the SOTA/more expensive models. Seems like there's money to be made by maximizing low end use while maintaining quality.
Certainly there's still a business there, I'm not saying they won't exist. But it's not going to be a monopoly-esque business with so many players in the ring, OpenAI, Anthropic, Google, Meta, Deepseek, Alibaba, GLM, Kimi etc. It will be cutthroat and a race to the bottom on price. And the difference from today -> 6 months ago intelligence will not be very meaningful.
Investors are largely treating these as future monopolies though.
We can already do so much with existing models. Harness improvements are probably more meaningful at this point.
e.g. say most image recognition can get saturated by a model of size xB parameters, so your tool for that can handoff to a smaller model. Document text extraction can use a model of size yB parameters. A model of size zB for summarizing text.
We are starting to get to a point where you can reasonably scope out an upper bound of required size/effort for many common tasks, and if you string these together, the frontier will largely act as an intelligent invoker of more efficient models.
Up until now there have been meaningful gains to each of those types of workstreams by using newer models, but that is starting to no longer be the case.
Yes, I do believe token consumption will rise exponentially from here in the near term. But cost of switching is low, and substantial profitability will be difficult.
how much would you pay for a prompt that could cure cancer? if you're a pharma company you would pay millions to get there days faster than your competitor. as intelligence rises the marginal value it can deliver rises with it.
Something like curing cancer (more realistically, curing a specific kind of cancer) has to interact with much slower real-world processes. The most expensive part of drug development is Phase 3 clinical trials in humans. Even the smartest model in the world can't accelerate that meaningfully. Even much earlier when they're just testing in cell cultures, it's a lot slower to run lab tests than to run software tests or mathematical proof checkers.
Or to put it another way, there's enough natural variation in real-world bottlenecks that no pharma company can assume they'll beat competitors to market by using a smarter model.
A really smart model could significantly improve the pharma business if it could identify promising approaches to cancer treatment that are less likely to fail in clinical trials, but I don't think that the frontier labs have data to make that work yet. Much of the biomedical literature is poorly reproducible ("replication crisis") and much of the drug-development-specific data is proprietary, never published in the first place.
I think there's an intelligence limit, or at least asymptote. It may be above human intelligence, but I don't think it's miles above it (at least not the kind of intelligence humans can create, recognize, or use). For example in Go, most estimates place God or "perfect play" three ranks above top professionals[0]. In the latest human-AI Go match, the human got a 2 stone handicap. So it's not like we have a lot more frontier to push there.
Intelligence is spiky. In some things humans may play near the limit (Go possibly), but if you look at parts of mathematics like addition, humans can add in their head just fine but its a few trillion times faster to use a computer on addition problems of any size.
And that's not even really touching societal/network intelligence. A single human isn't that smart and can't accomplish that much. Hence we form families, and companies, and societies, and governments. What does a society of AIs look like?
Note that addition that's "trillion times faster" is purely an optimization, not greater intelligence.
And a society of humans does not increase our overall intelligence. It allows all of humanity to access the accomplishments of our most intelligent members throughout history (which is huge, don't get me wrong!) but it seems almost self-evident that our civilization as a whole is not smarter than Newton/Einstein/von Neumann/etc.
this assumes our whole universe and what we can do in it is a finite go board. maybe it is. but we are no where close to exploring even a fraction of it. lots of things need to happen before any limit is reached. the cavemen would also probably think we saturated tools once they saw bows and arrows.
I don't know enough about the specific models they're comparing against to say this definitively, but it looks to me like they're comparing their pre-trained models with others' post-trained models.
The metric upon which their 10x claim is based (bits-per-byte) is exactly the metric which is optimized during pre-training. Post-trained models are fine-tuned to optimize other metrics, which is known to be detrimental to performance on bits-per-byte evaluations. So bits-per-byte evaluations will always make a pre-trained model look favorable in comparison to a comparable model which has also undergone post-training.
Can someone confirm whether the models they are comparing against (DeepSeek V4, Kimi K2, and Nemotron 3 Ultra) have been post-trained?
This is great. All algorithmic efficiencies are amazing!
One thing I'd remind all scientists and the wonderful people here is this wonderful meme/line from Jurassic Park: "Your scientists were so preoccupied with whether they could they didn't stop to think if they should."
What is the actual amount of data that needs to be pre-trained and what is not? Nobody has come up with great answers to this question, and I'm already seeing amazing 0.5b-2b parameter models working very well with n-Gram corpuses of data. So, how many parameters do you really need for a given workload?
this is basically the only thing pre-training teams work on in labs. compute efficiency is the metric, the assumption that scaling = intelligence is considered a given.
Super awesome. Wish they would release the paper about what they did to achieve this. I remember nous released the token superposition paper which improved pretraining FLOPs some, but not 50x: https://nousresearch.com/token-superposition. Wondering if they also found some cool tokenization strategiesa
If you're like me, a SWE who is curious about ML/LLM training but unfamiliar with the terms, I got an agent to explain to me how to read the charts.
Basically, you can think of a LLM as a function which generates a probability distribution of words. If the next word in a series is "they", and one model predicts that word 40% of the time, and another model predicts that word 1% of the time, the latter model is worse as it is more surprised by the true distribution.
You can convert these probabilities into "bits":
surprise in bits = −log₂(probability of the actual token)
Probability of actual token,Surprise
1,0 bits
1/2,1 bit
1/8,3 bits
1/1024,10 bits
This is then normalised by text length:
Bits per byte = total next-token surprise in bits / number of bytes in the evaluated text
So the lower you go on the charts, the less surprises in the LLMs distribution (a better model).
I don't believe this is actually happening.
Cheaper model training runs? Ability to scale training to larger model sizes without extending training time?
If this holds up that's a really big deal.
I think cost will decrease forever.
If you need image recognition, and a 30B model saturates the use case with 100% accuracy, you absolutely wouldn't continue to use the next frontier model as they come out.
And I'd argue most economically meaningful tasks will be saturated by cheaper models than those requiring frontier.
Think about what today's models can do with pretty close to 100% accuracy, and then consider that they will be orders of magnitudes cheaper over the years.
5.6 Sol can already obviate tons of labor, and why would you pay 2x or more for no meaningful gain?
The relative gap between frontier and non frontier also continues to shrink, so it's not like you take a meaningful performance loss by rewinding to models from 3-6 months ago. And soon that gap will expand to 12-24 months.
I get the impression the majority of people on here only think about coding, which net net will be a tiny volume of overall AI use in the end.
Disagree that the frontier model is where the economic gains will be realized.
The smaller the relative gap between frontier and non-frontier/open weights, the less pricing power.
This gap has shown only to shrink over time, not expand.
Businesses will pay more for frontier, but not meaningfully more to justify the economics. It's always going to be a low margin business, perhaps outside of cyber security, warfare/intelligence and perhaps drug discovery.
Though the expensive and time consuming part of drugs is doing the trials and getting approval, not coming up with ideas
Where the interesting work will be is at the median point where cheap models do almost all of it but need to hand off some parts to the SOTA/more expensive models. Seems like there's money to be made by maximizing low end use while maintaining quality.
Investors are largely treating these as future monopolies though.
We can already do so much with existing models. Harness improvements are probably more meaningful at this point.
e.g. say most image recognition can get saturated by a model of size xB parameters, so your tool for that can handoff to a smaller model. Document text extraction can use a model of size yB parameters. A model of size zB for summarizing text.
We are starting to get to a point where you can reasonably scope out an upper bound of required size/effort for many common tasks, and if you string these together, the frontier will largely act as an intelligent invoker of more efficient models.
Up until now there have been meaningful gains to each of those types of workstreams by using newer models, but that is starting to no longer be the case.
Yes, I do believe token consumption will rise exponentially from here in the near term. But cost of switching is low, and substantial profitability will be difficult.
Or to put it another way, there's enough natural variation in real-world bottlenecks that no pharma company can assume they'll beat competitors to market by using a smarter model.
A really smart model could significantly improve the pharma business if it could identify promising approaches to cancer treatment that are less likely to fail in clinical trials, but I don't think that the frontier labs have data to make that work yet. Much of the biomedical literature is poorly reproducible ("replication crisis") and much of the drug-development-specific data is proprietary, never published in the first place.
[0]https://senseis.xmp.net/?HandOfGod
And that's not even really touching societal/network intelligence. A single human isn't that smart and can't accomplish that much. Hence we form families, and companies, and societies, and governments. What does a society of AIs look like?
And a society of humans does not increase our overall intelligence. It allows all of humanity to access the accomplishments of our most intelligent members throughout history (which is huge, don't get me wrong!) but it seems almost self-evident that our civilization as a whole is not smarter than Newton/Einstein/von Neumann/etc.
The metric upon which their 10x claim is based (bits-per-byte) is exactly the metric which is optimized during pre-training. Post-trained models are fine-tuned to optimize other metrics, which is known to be detrimental to performance on bits-per-byte evaluations. So bits-per-byte evaluations will always make a pre-trained model look favorable in comparison to a comparable model which has also undergone post-training.
Can someone confirm whether the models they are comparing against (DeepSeek V4, Kimi K2, and Nemotron 3 Ultra) have been post-trained?
One thing I'd remind all scientists and the wonderful people here is this wonderful meme/line from Jurassic Park: "Your scientists were so preoccupied with whether they could they didn't stop to think if they should."
What is the actual amount of data that needs to be pre-trained and what is not? Nobody has come up with great answers to this question, and I'm already seeing amazing 0.5b-2b parameter models working very well with n-Gram corpuses of data. So, how many parameters do you really need for a given workload?
Going to be interesting to see what happens to discoveries like this in the future.
https://en.wikipedia.org/wiki/Jevons_paradox
Doesn't mean we should waste energy; it does mean that we have crossed a threshold beyond which energy concerns change shape.
Basically, you can think of a LLM as a function which generates a probability distribution of words. If the next word in a series is "they", and one model predicts that word 40% of the time, and another model predicts that word 1% of the time, the latter model is worse as it is more surprised by the true distribution.
You can convert these probabilities into "bits":
surprise in bits = −log₂(probability of the actual token)
This is then normalised by text length:Bits per byte = total next-token surprise in bits / number of bytes in the evaluated text
So the lower you go on the charts, the less surprises in the LLMs distribution (a better model).
For more info: https://smalldocs.org/s/DfvdGuFsiR3LlzXw1H5J0K#k=AJ8V1AQECYj...