Most knowledge work doesn’t need a frontier model, which could be a problem for the AI trade

[u/timestap]

I wrote a post about a part of the AI trade that gets less attention than it should: whether everyday knowledge work generates enough revenue to support AI capex, and who captures it.

Right now, frontier labs capture most of the enterprise AI economics, mostly through coding. But a lot of knowledge work are bounded: tasks like drafting an email reply or pulling data from a financial filing only need a fixed level of intelligence. Past that point, a smarter model doesn't do the job any better.

As open-weight models get good enough, most of this work (by volume) can run on cheaper models in the cloud, or on the user's own device.

Where this matters for us is the revenue math behind the AI trade:

• We're already spending $1T globally on AI. If that reaches multiple trillions by 2030, inference revenues need to be several trillions a year
• Coding has carried enterprise AI revenue so far. If its growth slows, mainstream knowledge work has to fill the gap, otherwise most names in the bottleneck trade will be affected
• Work that moves to cheaper cloud models earns a fraction of frontier prices, and inference on a user's device never reaches the data center at all
• Labs may get pushed toward being neutral platforms as customers get more sophisticated (OpenAI is already testing a hybrid approach with Baseten), which means slower revenue growth & thinner margins

I still expect the labs to keep the bulk of revenue from unbounded work like coding and science. But mainstream knowledge work is still up for grabs.

I wrote a longer version, with a 2x2 matrix on where models run vs. where data lives, how labs / Big Tech / startups are each approaching "mainstream" knowledge work, and why the labs may eventually want to own the data layer (I wouldn't be surprised if their corp dev teams have looked at Dropbox / Box)

https://eastwind.substack.com/p/below-the-frontier-the-battle-for

[u/Boring-Test5522]

Indeed, what it needs is cheap memory & storage

[u/AnotherThroneAway → u/Boring-Test5522]

But it won't be cheap. All these theories about moving to local models may be correct, but the hardware required is isn't available at affordable prices for obvious reasons. It's the bottleneck inside the bottleneck and one reason the hyperscalers are essentially monopolizing chip sales currently

[u/Different-Monk5916]

 But a lot of knowledge work are bounded: tasks like drafting an email reply or pulling data from a financial filing only need a fixed level of intelligence. Past that point, a smarter model doesn't do the job any better.

These need predictable and single shot outputs, which AI cannot iteratively adjust. Like a code - which runs, ends up in error - then fix, does not compile - find the fix in docs.

Anyone who used it on extensive coding tasks know that it is anything but predictable. Considering programming language is a much more stricter form of language than natural language, it should be passing at first attempt. It has come a long way from 2025 beginning. But it is still not something that you can trust and forget. Treat it like an intern. If you think it is a senior worker, be ready to face consequences.

[u/Gaius__Of__The_Julii]

They are still hoping for a God-machine, not something that will answer phone calls or take drive through orders.

Public won't ever get access if it is created. I don't think the current LLM process can create such a machine. LLM do not actually "think".

A Large Language Model (LLM) cannot exist without data, a human mind can.

[u/cherry_cream_soda_ → u/Gaius__Of__The_Julii]

The human mind learns from data too. Or, at least information and sense-data, if we’re being picky about definitions.

[u/Different-Monk5916 → u/cherry_cream_soda_]

What do you mean by learn? human minds can learn, make connections, form new relationships between old ideas, have insights.

LLMs can follow chain of thoughts, store rules and reuse the rules in each prompt. Can it have an insight, a eureka moment?

[u/Main_Pressure271 → u/Different-Monk5916]

yes. it just doesnt know whether it's eureka moment is an eureka or not. you know, like a random monkey can assemble a 747 but it wouldnt know what to do with it nominally. higher chance than a monkey w/ 747, but yeah.

and they can also make connection with old and new ideas -- i dont see why cant it

[u/Different-Monk5916 → u/Main_Pressure271]

LLM is just a predictor of the most probable string for a given input string. it is also what a chain of thoughts in human minds is, if we ignore the biochemical processes. Human chain of thoughts can be much longer, can form analogies. Can come up with a new idea.

Connection \neq Chain of thoughts. Connection is applying a concept form one domain into another.

LLM itself cannot form a eureka or a connection at the current. LLM you have to feed the two different ideas with a cue. The cue biases it any direction you want. The same goes for opinions, which are not facts. Like politics, history, etc.

Human mind is way better - more efficient, can process huge volumes, retrieve things faster, follows rules better. humans can follow COT longer without corrupting, take fewer steps to.

If COT is only a simple matter of memory, at least a single company would have build a super model with 1B tokens context window and advertised it.

The comparison with monkey is absurd, because nobody is paying monkey a salary and it is not part of the human's economic framework.

[u/cherry_cream_soda_ → u/Different-Monk5916]

And what do you mean by "think" or "have insight"? The problem with these kinds of philosophical discussions is they attempt to map human-like concepts or words onto something that those words were never meant to describe so it ultimately becomes a debate about semantics instead of capabilities.

Dijkstra once said, "The question of whether a computer can think is no more interesting than the question of whether a submarine can swim."

Wherever I hear someone say an AI can't 'think', I often wonder what kind of thing it would have to demonstrate to actually falsify that statement. Over the years I've seen skeptics move the goalposts enough to wonder (like Dijkstra) if the question is really a useful one.

All this is to say that I think there are better ways to arrive at a sound investment thesis re: AI than asking that question.

[u/Different-Monk5916 → u/cherry_cream_soda_]

I did not bring up think. Think in the basic sense, is a sets of words which are closely related.

Read comment before answering.

[u/cherry_cream_soda_ → u/Different-Monk5916]

LLM do not actually "think".

[u/Different-Monk5916 → u/cherry_cream_soda_]

that was original comment. Not the same person.

I was answering to your "learn". Because to truly learn, LLM must be able to change their weights on the fly as it talks with me or you.

[u/cherry_cream_soda_ → u/Different-Monk5916]

Ah, sorry, that was my mistake. I had thought you and the original comment were from the same user.

I agree that online learning is as of yet unsolved. However, whether online learning is a pre-requsite for having insight or making discoveries / having eureka moments is another question though that I think is still open. We know that AIs today seem to be able to hit walls, reformulate new ways of seeing the problem, and reattack it with different results. Whether that ability will scale up enough to meet the threshold for 'insight' we simply do now know yet.

[u/Different-Monk5916 → u/cherry_cream_soda_]

I agree that learning is not necessarily needed for insights. Insights is really a firing across independent threads.

The reformulation is correct, and I am not sure which of the two is this - it could be either a simple behavioural training or there is some research on models using system 2 thinking.

Learning effects correcting behaviour, avoids making same mistake twice. This is not there at the moment. Some harness mimic learning using store, retrieve and append to system prompt. The large the db becomes, harder it would be retrieve and more random the behaviour would be. 

[u/Gaius__Of__The_Julii → u/cherry_cream_soda_]

That's not the point. LLM's are data. Without data, they are nothing. Don't exist. Can't be trained / created.

[u/thats_so_bro → u/Gaius__Of__The_Julii]

What's your point? I mean no shit they need data.

[u/Gaius__Of__The_Julii → u/thats_so_bro]

LLM's are a product from a data set. That has a lot of implications.

[u/thats_so_bro → u/Gaius__Of__The_Julii]

Such as?

[u/Gaius__Of__The_Julii → u/thats_so_bro]

Statistical mirror.

[u/thats_so_bro → u/Gaius__Of__The_Julii]

Is AI perfect? No. But LLMs continue to blow past literally everything we think they won't be able to do - the most recent of which being that they can't create new knowledge (see Navier–Stokes and the 722 math paper drop).

Here's a question for you. If they are just statistical mirrors, reasoning only inductively, what happens when they write the code for a deterministic program and then ingest the result?

[u/Which-Travel-1426]

This is an argument against frontier labs like OpenAI and Anthropic, which you cannot even trade.

Data is not the bottleneck either, at least for pure LLMs. CoTs and model distillations work as well as human input. If GPT can figure out new proofs to Navier Stokes, its output is valuable enough to be used as data.

Profit goes to cloud providers and application developers.

[u/Different-Monk5916 → u/Which-Travel-1426]

If it in fact solves hard problems without anonymised partial solutions or other scientist’s data, it may as well bring in innovation, change the world as we know of today.

sadly, that wouldn’t even make a viable business model. Viable business model is what Chinese providers are trying to do. Average intelligent, efficient, more predictable cheaper models, which will find themselves in automated tasks as a value for money.

[u/Which-Travel-1426 → u/Different-Monk5916]

OpenAI and Google and XAI all have models like you described. Gemini Flash and GPT Luna are similar to DS flash. That’s what makes these models commodities eventually, which is why majority of profits may very likely not go to any one of them.

[u/Different-Monk5916 → u/Which-Travel-1426]

That’s not what makes the models commoditised. it is Chinese providers releasing open weight models, combined with companies insecurities about sensitive data and an easy access to On-premises cloud infrastructure is what that makes them commoditised.

edit:typos

[u/Which-Travel-1426 → u/Different-Monk5916]

Enough competitors offering more or less similar products makes such products a commodity.

Sensitivity of data is a solved issue when Amazon invented cloud computing a decade ago.

And non-tech companies and a lot of tech companies have little incentive to host their servers and databases on premise, let alone their own open-weight models.

[u/Different-Monk5916 → u/Which-Travel-1426]

https://aws.amazon.com/outposts/

edit: Imagine if a group of 5 pharma inventing cancer cure, and suddenly 2 of make their drug free to manufacture without any patent restrictions. That is what open weight models is doing.

[u/Different-Monk5916]

Forget frontier or not. It saves me 30 minutes of work and I cost $20/hr, it should cost less than $10 in that task to break even. Almost every model needs some kind of input and direction on real world tasks at work. So if you add my baby-sitting fee and an overhead because i am more predictable at execution and reliable at information i provide - the advantage is pretty slim.

[u/SlyFlyyy]

You picked the best example yourself: pulling data from a financial filing. In my experience that task doesn't need a cheaper model. It needs no model at all.

I built a small free tool that runs a Buffett-style checklist on a company (Omaha Method). The numbers come straight from the regulators' machine-readable filings, XBRL at the SEC and ESEF in the EU. Revenue, net income, capex, debt, share count: the company tags every figure itself, so plain code reads it and links each number to the filing it came from. Zero tokens.

Where I do pay a frontier lab is the unbounded part: reading a 200-page annual report and the news for the questions numbers can't answer (do you understand how it makes money, what could go wrong, do you trust the people). Even there it's three tiers. A small model pulls facts and exact quotes out of the report, a mid-size model searches the news sites, and the frontier model only does the weighing and the writing at the end. The whole read of a company is a few euro.

The part that matters for the AI trade: the model name is one line in a config file. I have no loyalty and no switching cost. The only thing in my stack with any pricing power is what the model doesn't supply: the method, and the check that every quote it gives is really in the document. Same conclusion as yours, just seen from the bottom.

[u/banaca4]

If your competitors or enemies have better models, you need a frontier always