Trouble is, everyone inside their buildings seems to believe that no one will be working like that in a year or two.

Offhand, I’m not even certain whether a model like that could justify the constant retraining we’re doing on the agentic models.

It doesn’t make a lot of sense to spend millions or billions on training to reduce hallucinations by 0.3% if your model assumes a human is in the loop to course-correct them.

This source claims that knowledge workers alone (probably because they are paid much more) account for 35 - 50 Trillion of that: https://github.com/danielmiessler/Substrate/blob/main/Data/K...

If LLMs can boost their productivity even by an average of 5% (studies from ~2024 put it in the ~30% range depending on task) that is ~1.5 - 2.5T in value annually. Even if the AI industry can capture a fraction of that, that is a huuuge monetization opportunity.

Note, at 5% productivity boost, humans are not just in the loop, they are the loop. AGI or large-scale replacement of humans is not even needed, but the financial opportunity is already immense, and it scales with how much human productivity can be improved (i.e. how much work can be offloaded to LLMs.)

Now, I don't think AGI will happen soon (or has already happened, depending on how you define it) but I do think humans will be a much smaller part of the loop and large-scale job displacement will happen once companies figure out how to properly use AI.

At this point, the financial upside for the AI industry is extremely high but will be limited by the social turmoil that will inevitably ensue (which we're already seeing brewing in the data center backlash.)

However, these frontier labs are also making moves that could let them capture a disproportionate share of the upside. One possibility is a situation analogous to the smartphone manufacturing space, where there are dozens of players but just a handful (e.g. Apple, Samsung in smartphones) capture the lion's share of the revenue.

Samsung the same. And is the best android device.

If tomorrow comes a Nokia os will be dead in the water: it has no apps.

But with a new llm that doesn’t matter. There is nothing sticky about typing Gemini, Claude or codex in a cli.

The AI labs are also making moves to secure long-term enterprise presence, such as their Forward Deployed Engineer strategy. I think that is a trojan horse play that could make enterprises dependent on them forever, much like so many companies are still dependent on IBM's mainframes. As an extreme example, you could imagine a company's core business logic encoded in the weights of a proprietary model custom-trained and hosted by one of these model providers, something even more inscrutable and sticky than ancient COBOL codebases.

The frontier labs, on the other hand, are thinking about replacing all human labor, ending death, and the risk of it causing human extinction. Most of the apparatus we're talking about approach it very parochially; it's almost like they're embarrassed to take the grander ideas even a little seriously, for being too nerdy/sci-fi.

They'll show up after the fact and whinge endlessly about how they should have been involved.

Or maybe every cultural group has its own set of whiners and we always think the ones we disagree with are the loudest.

Minus the cost of inference, that might not be the boon you're making it out to be. I hear what people around here are spending on their api and I'm skeptical that these tools are making me that much more productive.

Personally, for assisted development, I haven't seen much progress in a while.

If you are a software contractor, yes more productivity turns directly into money. But it is the same for all other contractors, so there is no gain there either. This is really a structural transformation that crosses industry boundaries, not unlike when we all got on the internet. All I know from living through that is certain changes are obvious even if they take a decade (there will be more AI and more deeply integrated in our lives) but most of the secondary effects are unknowable.

If living through the dot com era taught me anything is that transformative tech routes around walled gardens (i.e. AOL/Prodigy) which is bad news for Anthropic/OpenAI. The wild west that comes after is a window of opportunity for founders but investors will overcommit early (like now) and undercommit when the dust is settled (post bubble).

Look at what happened to twitter. So many companies aren't 'productive', they are simply parked somewhere where they can charge a toll, and then they pretend legitimacy.

The studies I've seen recently (at least in the software space) put it at something like a 10% increase in coding speed, which for me would probably translate to something like a 3% increase in productivity. I spend a lot more time on things like getting agreement between teams, documenting approaches to things that don't exist on the wiki, etc, that LLMs are significantly less effective at. Or just can't do; no one will be happy if I send an LLM instead of me to meetings.

I suspect a lot of roles are like that. They give a 10-30% boost to the core role function, but that core role is still only 30-50% of what you do.

> that is ~1.5 - 2.5T in value annually

That seems really large, but it's ~2-3x Walmart's yearly revenue, and OpenAI and Anthropic both have estimated valuations that compare to Walmart's market cap. And this is before we consider that they need to do it for cheaper or why would anyone bother. Realistically, potential revenue is probably half that at best.

It's also before cutthroat pricing really kicks in. People are willing to pay for Claude right now; I still suspect that as time goes on people will start looking towards Deepseek/GLM/etc models that provide 95% of the performance at 10% of the price. That'll cut the market even further.

The question is how much demand for knowledge work swells as prices fall, and whether that's a soft landing or a crash.

It's also before cutthroat pricing really kicks in.

Right, that's more of an estimate on the value proposition of the overall AI industry, rather than valuations of the industry or specific players. While I don't think OpenAI and Anthropic will capture all of the potential upside, I do suspect they will do much better than other players despite the competition (https://news.ycombinator.com/item?id=48740472)

> And this is before we consider that they need to do it for cheaper or why would anyone bother.

Typically yes, but there are reasons companies may be willing to pay the same amount or even more, such as "AI doesn't need sleep, holidays, insurance, or benefits" and "AI is easier to procure and replace than humans."

> The studies I've seen recently (at least in the software space) put it at something like a 10% increase in coding speed...

Curious to see which studies you're looking at, the studies I'm thinking of (some here: https://news.ycombinator.com/item?id=45379452) are from 2024 - 2025, so already old and before agents really took off.

However, your point about meetings and agreements and documenting is much more germane. My theory is that the largest productivity gains -- and subsequent labor displacement -- will come from reducing coordination overhead: https://news.ycombinator.com/item?id=48040999

> While I don't think OpenAI and Anthropic will capture all of the potential upside, I do suspect they will do much better than other players despite the competition

We may have a fundamental disagreement here because I think you see reasons for businesses to prefer LLMs beyond price, but my point was that I think a substantial portion of that top line number will be eaten up by needing to be cheaper than humans.

Like 30% cheaper than humans for the same task isn’t an unreasonable demand for onboarding, and that would lower the addressable market to .75-1.25 trillion. That’s also revenue, not profit. Their best case is probably 30-50% margins (even that might be high, I suspect it will become low margin as models are commoditized), so $600 billion-ish in profit spread across all the players and that feels pretty generous to me.

I suspect a lot of general knowledge work tasks could be done with fairly dumb local models. The small Qwens could probably respond to my Slack and emails for me.

> Typically yes, but there are reasons companies may be willing to pay the same amount or even more, such as "AI doesn't need sleep, holidays, insurance, or benefits" and "AI is easier to procure and replace than humans."

It’s also an enormous supply-side risk. Your AI provider could charge basically anything they want and you have to pay it, for the same reason people still pay bonkers Oracle licenses: too hard to migrate away.

Unless companies maintain their ability to swap providers, but then I can’t see why prices for inference don’t crash down to the cost of GPU time and electricity. If Claude doesn’t have some secret sauce that keeps me locked in, I don’t see why I wouldn’t use Deepseek or Qwen or one of the other dirt-cheap inference providers.

I just haven’t seen anything that seems to keep people locked in so far. Even the non-tech people I know that like AI are rotating providers to find their ideal price:performance ratio, many are finding success outside of Anthropic/OAI. I do know a couple holdouts that refuse to use anything Chinese. They mostly stick with OAI or Anthropic, though they do moan about paying more.

Plus the big factor in my mind for why these frontier labs will succeed -- and this is very fuzzy and hand-wavy -- is that they are very business- and government-savvy and execute extremely fast. They have the most powerful AI models at their fingertips with sharp people who know how to use them, along with insane levels of funding, and are showing the world how a truly AI-empowered organization can operate. I suspect they will thrive despite all the forces arrayed against them.

Anyone can google it /shrug

But for tech I get the impression that valuation has been fundamentally unmoored from actual production for decades already. Its less about 'productivity' and more about vibes.

Pre-bubble pricing: $1400 gets a 128GiB iGPU optimized for inference. Glm and kimi need 800-1000GiB. Call it 1TiB. The $1400 boxes could be ganged into sets of 4-8, with a switch. Call the switch $1000.

Each box has a TDP of 250W. 8 x 250/120V = 16.666A, or one household circuit in the US, so no new power infrastructure is needed.

$1400 x 8+1000=$12,200. Assuming standard five year depreciation, that’s $2440 a year. There are a billion knowledge workers alive today. So that’s $2.4T annual revenue. Average net profit margins on computer hardware are 4.3%. That works out to $105B net income, globally.

So, I guess the question is whether the (currently #2) open weight models provide $1.4-2.4T less value per year than the #1 and #3 models, and, if so, if customers can measure this, or are willing to spend 2x more and deal with censorship, data theft, intentional enshitification, sabotage, ads, product placement, etc, to get the slightly “better” model.

Also, note that my numbers assume moore’s law stopped for all time in 2024, but we’ve seen HW improvements since then.

I do think open weight and other competitor models, especially with better harnesses, will play a significant role in the equation and will result in less concentration in the market. However, I do also think the big AI companies will capture a lot of that value. Partially for the same reasons that the cloud industry has been growing like gangbusters, even pre-AI, despite on-prem being much cheaper: companies will outsource anything that is not deemed a "core competency" for their business.

A lot of the problems you mentioned will be relegated to the consumer market and won't apply to enterprise contracts -- which is where the real money is.

Mate your logic is insanely broken, underpinned by wishy washy assumptions mixed with shaky predictions about the future.

Aka a whole lotta noise

Pls stop posting you are creating noise.

I think this sort of thinking is a trap, because it presumes that all software has the same constraints.

There's a spectrum of requirements between "chuck this over the wall at Claude, it only has to work once" and "this is a literal rocket ship, formally verify the whole thing".

I've made some things with Claude I don't understand and don't control. It's fine, they're still useful to me. Things for the house that I wasn't going to build manually, some dashboarding stuff and scripts for work, stuff that can crash and burn and I'll be fine.

They won't justify trillions in investment, but they are useful.

Equally, I do agree with you on some things. Sometimes I hand-hold the LLM or forgo it entirely because I want to be 100% sure I know how something works, and can justify a decision if it causes a production outage.

I think the future is probably multiple different tools with different goals. Better IDE integration for some uses, an entirely separate "LLM herd controller" kind of thing for when you're okay with vibe-coding, and the most interesting is something in the middle where you're more in the loop than pure vibe-coding, but don't see the full context like in an IDE. Something where it surfaces changes to key components, but hides things like test changes.

The thing is though, for that purpose, AI is just an incremental improvement. I mean, in the 80s and 90s people were quickly building crappy-but-useful things with basic or visual basic or spreadsheets and access databases, in the 2000s people built awful but functional websites in PHP, etc. I think AI is an improvement on those "scratch my itch quickly" things, but it's not a replacement for good practices.

Those tools tend to suck in the sense that they were written for a single person, so the tool assumes that you wrote it and have the context of the author. Help text output often sucks, the UI often sucks or doesn’t exist, it might require 15 packages to be installed that no one has documented because it was never meant to be distributed.

I like it as an MVP thing to trial whether other people would use it and I need to write a “real” version of it, or if I’m the only one that will use it and I can just vibe-code the hell out of it permanently.

You used to have to decide that pretty early on, which meant a lot of talking to other people but also trying to prevent it spinning into a whole project with a PM that will never actually be done.

Dunno, ymmv, I like it a lot in the domain of “stuff that doesn’t have an oncall rotation”. I do find it super useful in that space. Still useful but much less so if the code is something I could be paged for.

As you said, building a script that only you use personally or a very simple thing that just accomplishes one task and it’s easy to test require almost no engineering, and an LLM can often build those with very little downsides.

That's a key point. Keeping knowledge and know how inside the company is strategic. For most people GPS did not result in better sense of direction, spellchecking did not help to write without making mistakes, and delegating translation to deepl does help to be better in a foreign languages. I don't see the gain for an individual, a company, a society if a technology reduces the ability to think, do stuff, understand complex problem, working hard at something. Hiring junior also matters, what is boring for a senior dev is useful for a junior, like the "wax on wax off" in Karatekid. Then when the senior dev retired the junior is not junior anymore and the know how is still here. I want to to transfer my knowledge to a junior, not to anthropic or google or openai.

Ideally, working hand in hand with an AI could be like driving a motorcycle vs riding a bicycle. Both are fine, but you go much faster with a motorcycle and you don't lose any ability. But prompting a motorcycle auto-pilot by voice sound a bit stupid and boring. Insane use of energy rarely comes into the equation, which is a bit weird. Personally it is why I am never tempted to use AI. However I see value in AI for finding weakness in a code (inverse of flattery), writing tests with all the edge cases based on specs since tests are often sloppy, asking a fresh view on a very difficult problem. I'd love to hear about the equivalent of move#32 in game 2 (AlphaGo vs Lee Sedol) in a difficult programming task. But I think that massive delegation of code writing is how you lose the knowledge and the know how: what keeps us sharp.

Final word: I asked once a review to claude, the codes involved a db transaction. Nothing complicated, Claude said everything was fine. However the transaction isolation level was not set (I did it on purpose, like if I did not know about isolation levels). He did not ask me if it was my intention to keep the default level. I would have preferred a challenging feedback: why did you chose the default isolation level ? Is it on purpose ? Do you know that the default depend on the db ? Do you know about isolation ? Tell me about the business use case and I'll explain which one would be the best.

Contrary to what some people suggest, I have not hit any maintenance or reliability dead ends. If something breaks, the agent fixes it.

If it cannot, I have the agent instrument the code and work through the logs to check hypotheses, until the source of the issue is found.

If even that would fail, which did not yet happen, I can still do some old fashioned digging and learning, like I always have.

This is for native mobile app development, and the code base is around 100k LOC.

Now, we can't know if this is true unfortunately, but it's not directly contradicted by anything that's known publicly at least. I thought it was an interesting way to frame it and makes the whole situation look marginally less bad.

But isn't this like a Jevon's paradox thing, also? If I'm able to become vastly more productive, and that value produces more sellable output for my company, there's no reason to cut anywhere to fund it. This is the same reason a company like Microsoft can hire 80 000 developers, it's because each dev pays for themselves in value (on average). I guess the same can be true for AI spend?

FCFF = EBIT(1-t)-Reinvestment

I dont care about your gross profit - this kind of cash profit determines the value of operating assets.

Whether he's lying is another question, but seems unlikely.

That being said, Anthropic did report being profitable this review quarter (Q2), so it's not as unreasonable as you claim.

Also Google is a pretty major AI company, and they're _insanely_ profitable.

Unfortunately (from my perspective) it seems like the US companies are increasingly stuck in their current model. I think it's a competitive disadvantage.

But obviously most of the real insiders seem to disagree with me, so I'm probably wrong :)

Chinese models are quickly commodifying frontier inference, the US Gov is preventing domestic SOTA models access to the public and without those models why would consumers still spend $200/month to use the best models?

It’s such a mess and isn’t inspiring confidence as a non-investor.

It all comes down to whose prediction of the future is closer to correct. I think the most likely future is commodification of inference and "agent-assisted" rather than "agent-driven" workflows dominating the future of work. But insiders - who both know way more than me, and also have more skin in the game, both for better and worse - seem to really think I'm wrong about that.

So I dunno! Could go either way!

What insiders are you talking about? They're going to be hot towards the possibilities so they can exit to a massive windfall. I dont know why they would want to be publicly critical of these technologies that could make millions on IPO.

My point is that actually it would be worse for these people if the valuations are only high during this period - which will last awhile longer from now! - where their equity is not liquid, but crashes as the market figures out this commoditization thing.

But if we're wrong about how that's going to go, then this isn't a concern because there won't be any devaluation. And to me that seems to be what they honestly think is going to happen. And they know more than me (and I think they're a lot smarter than me), so this does temper my confidence in my own predictions.

But is your impression that this is the strategy of people like Amodei? My impression is that it isn't, that they are actually true believers, and not just trying to hit the timing right and flip it.

https://www.cerebras.ai/blog/gemma-4-on-cerebras-the-fastest...

I think there is. Pair today doesn’t mean they’re locked into that forever.

Yup. I think we agree. These valuations aren’t made or unmade by whether their tools are being used as vibe agents or pair programmers.

go ahead m8 we are all waiting... the stage is yours. lets see your model.

Honestly I still don't see how they justify their valuations, period. If anything they're serious liabilities.

Open-weight models are improving and reaching "good enough" levels for more and more tasks. They're also known quantities; you know what you're getting with them and don't have to worry about the model silently (or not so silently) being switched out from under you (whether that's because Anthropic/OpenAI decides you're not worthy of their latest and greatest for one reason or another, or they switch you to a quantized model to save on compute, or they simply sunset the specific model you've been relying on).

And if the open-weight model doesn't run on your local hardware already, there are any number of hosting providers that will handle that for you (so you're back to just paying for colocation/cloud usage instead of nebulous tokens).

Closed models are improving as well, sure, but diminishing returns will eventually kick in (as they already have for various tasks, as I said).

So if not their models, where does their value come from? Just simple network effects/lock-in? "Normal" users will drift to other options if they start showing more and more ads, and enterprise customers will surely be looking for opportunities to avoid lock-in and reduce risk.

I think the last argument I've heard is that these valuations are basically a bet that Anthropic and/or OpenAI will achieve AGI that can fully replace human labor, so they'll essentially be able to sell that replacement labor to everyone. They haven't managed to pull that off, yet, however. Businesses that have tried to replace humans almost immediately realized either that the AI's capabilities were oversold or that they at least needed a human in the loop still, to some degree. And even if they do achieve AGI, that would surely become an issue of national security (they're already flirting with that today), so who's to say governments won't simply nationalize the best AI labs and either remove them from the economy entirely or perhaps even provide models as a public service to level the playing field?

That all sounds like a giant gamble, if anything. And it's incredibly frustrating to watch as someone that's been unemployed for a year because (a) budgets are being burned on tokens and (b) LLM-generated applications are flooding hiring teams and preventing real people from being seen. (Not to mention, as someone that spends a lot of time in gaming circles, the fact that DRAM and flash storage is quickly becoming inaccessible is just an additional frustration that means people can't even find temporary relief in entertainment.) I can only hope this bubble finally implodes before I lose my house.

<banned>

Not the first one to come up with that likely outcome either. I mean, if you're being restricted from SOTA models now, how long do you expect before the FBI kicks in your door for using an 'illegal' open model?

Today's news that Amazon is hiring 11k interns. I think part of the AI story was used as a convenient excuse to get rid of some "fat" and some covid overhiring and gave companies an out to change course.

For the non-bleeding edge they have a lot of competition with more competitors showing up every day.

The way this is playing out is not surprising, it's similar to any other technological breakthrough as it becomes commercialized. Eventually those means of production will become commoditized as well.

However the result is exactly the same, concentration of power.

Also about "creating" means of production, these companies actively try to sabotage this as another profit maximization strategy. They buy all the ram, so others cannot compete. They buy startups who succeed, so they stop competing.

It's not aspersions, it's just describing the phenomenon.

Even if I take your suggestion to heart, once my company would be big enough, if I wanted to optimize for profit, I would have to do the same as these companies.

The end result is concentrated power.

I don't know if it's a matter of just requiring a tiny amount of optimization or wholesale redesign.

Fable was amazing as a vibecoder but as an assistant it can't resist jumping into implementation and filling chats of pointless jargon.

It's really grim if you're looking for assistance instead of an implementor.

GPT 5.5 Pro and Fable are gorgeous bullshitters that pretend to be right (often convincingly because they are very smart) even when they are wrong and I need tons of energy to process their information.

I don't like it but don't know what to do, Anthropic models especially increasingly ignore instructions whether in memory or agents files.

The problem is obviously who will be left. There’s a lot of scifi to catch up on.

I recently migrated a very large web app to Tailwind and Opus kept screwing up over and over, refactoring and changing the design, the more complex the component became.

I ended up asking Haiku to do it and it managed to do everything correctly, pretty much without intervention.

"I just cloned this repo, investigate how to set it up, don't install anything, just collect information"

_spews information_

I proceed with the setup, but get a Linux specific dependency in a bash script, so I want to evaluate whether it can be rewritten...

"There's this error on MacOS, I think it's because we need linux-utils from brew, verify whether the script can be written in bare posix"

_proceeds installing linux-utils and all the rest_

"Didn't I tell you to not install anything?"

_you're absolutely right_

F*k me..

And now in a heavy coding week rather than bumping up against my spend limit by late Wednesday or Thursday I'm comfortably below it all week.

That said if anything I feel like I have to reign in K2.6 much more than Opus, actually. If I want to just ask it a question without it inferring some coding task to immediately start doing, it takes a lot more care to prevent it from just running off half-cocked off of an only 3/4s-cocked idea of my own. I use "plan" mode with both but it's somewhat more defensive with K2.6 than Opus.

I've moved completely to local models that I run with my M1 Mac Studio (64gb ram) some time ago. But for the rare times when I feel the local, quantized Qwen3.6 isn't enough, I just connect to Openrouter and use something like Kimi, GLM or Deepseek for a fraction of the price of Anthropic et al.

I currently don't see a world where it makes sense to run a local model that will eats up 60% of my RAM, 20-30% of my disk space while providing worse quality output than a $20/month subscription.

For me, those things are nice benefits but not the motivation. It's just a desire to own my tools and remove the magic wherever possible, and not pay a big corporation who is constantly tweaking/changing things that I might not like. It's the same reason I migrated everything I host off of Azure and onto a VPS, and why I moved from Jetbrains Rider to Neovim, and so on.

https://huggingface.co/mlx-community/Qwen3.6-35B-A3B-OptiQ-4...

Most of my work involves "Agentic engineering" instead of fire-and-forget. I like to stay involved during the planning as well as review and ask a lot more questions from the agent than I've seen others doing. In a way, I'm using the agent in a sort of "hyper auto-complete" mode to fill in the blanks (rather big blanks) once I've set out the requirements, scope and design (sometimes specific module boundaries). This works best for me.

I use Composer (since we use Cursor) or GPT 5.3-codex as my workhorse models and only break out the big guns when I have a genuinely difficult problem to solve.

IMO somewhat weirdly 5.3-codex might be the best overall coding model OpenAI have ever released. It's 90% as good as 5.5 and costs about 20% as much, since it's both cheaper per token and uses fewer tokens for the same task.

I'll miss it when they inevitably deprecate it, but hopefully I can use Kimi K2.7 by then

OpenAI claims to have made their new Terra model as good as GPT 5.5, but with half the cost per intelligence. Hopefully, this will bring it closer to the price you're expecting (or even better considering GPT models have good acceptance/success rates according to benchmarks).

Imo MiniMax and MiMo are a lot more reliable (and cheap)

Not opus level, but close enough and cheap enough to get the job done

If this was the last model I could ever use I think I would be happy.

the incentives aren't there sadly

(I mean, I have some ideas, but I don't know if they are good.)

I can't help but feel this is intentional towards the 'Agentic' workflow.

For the 'safety' argument (Re: Fable), they need these models to have basically a 2-tier instruction system, but given LLMs aren't great with actual Logic unless they program it out to test, this runs afoul and we get one or the other.

Feels like optimizing for either precision or recall, but can't have both

By observing how in 4 workdays it achieved more than Opus in ~11 days. I am my team's backend lead and the Fable 5 model finally turned the tide on my overwhelming backlog. Back to Opus and I have to treat it like special-education kid multiple times a day.

If you set off a classifier, that's how it looks to Claude.

IMO, they were quite good with checklists even a year ago, and tried to tick off each one.

There are so many models, and I personally ignore benchmarks so it takes some time to try different models on my use cases. Fortunately, it is ‘good enough’ to do the work to find a few models that work for me, and just use them for a month or two before re-investing time for my own evals to possibly change models.

People should evaluate what works for them and ignore other people and benchmarks. (Apologies if that sounds snarky.)

I give AI an image and just it what's wrong, and then it goes on to fix the bug in the codebase for me ( and write the tests), is this agent-assist or agent-driven?

Sometimes I just give the AI my description, and mockup, and it creates a plan and implements the details for me, and I verify visually ( this is the weak spot of AI), is this agent-assist or agent-driven?

The quite useful tool is to use /opusplan along with /codex:rescue (https://github.com/openai/codex-plugin-cc) means you get quite a strongly reviewed plan using native claude + codex without having to implement the mostly useless trust-me-bro plugins and other bs.

I ask “where did you get that?” … too often if I’m not constantly guiding it, and even then it still goes off the rails.

Sonnet as an autonomous agentic model is silly. We already have other models for that if you want something weaker and cheaper than Opus.