I want to propose alternative reality where 1.5-2.5T in value doesn't go to a handful of companies. Instead it turns out to be like restaurants where this gets distributed to lots and lots of small, local, mostly interchangeable teams. There will of course be some super star "chefs" leading the industry and setting trends and some "restaurant chain" like big businesses and supply chain for all of this.

I am deeply surprised by the silence of philosophers, sociologists, liberal arts majors, economists. Where are the think tanks who contemplate and debate the societal aspects? The tech is advancing full steam but the "other side" doesn't feel anywhere nearly ready.

> 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.)

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.

I’d also point out that LLM inference revenue already totals more than 100B annually based on publicly reported numbers. Almost none of that is replacing knowledge workers. Almost all is increasing their productivity. So empirically what you describe is already happening to a nontrivial degree.

You’re trying to apply value based pricing (infinite margin upside) to a commodity.

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.

> 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

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.

What a load of nonsense lmao.

Pls stop posting you are creating noise.

> I'm thinking the future of this tech will likely be better tooling with better IDE integrations rather than "Claude plz make me a SaaS kthx"

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.

> while also not losing understanding

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.

I am thinking the opposite. I've been having great results with handing more and more responsibilities to the agent.

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.

A common extreme misconception is that inference is expensive and that providers are loosing a lot of money. Inference is extremely lucrative and profitable.

why are you listening to these idiots who have every incentive to spin the story as much as possible

FCFF = EBIT(1-t)-Reinvestment

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

The insiders disagree because they are benefiting greatly from the insane valuations, right?

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.

I would not take "space data centers" as a given! from most to least likely these will be vaporware, vaprorized-ware, rubble-ware, loss leaders.

you always post about valuations but never share your own.

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

Their valuations don't make sense as just programming tools, period. Forget about if they are still human driven.

>Open-weight models are ...

<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?

There is no nefariousness in owning all the means of production, it's the endgame of maximizing profit.

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

Sam Allan has said some things that would make Lenin blush

You still have to manage/fight with the post-training that is baked into the model itself.

People keep making comments about fable like this? You could only use it for what like a week? How is that at all enough time to evaluate? Opus 4.6 didnt suffer from this problems for a hot minute and then when newer models were released it got worse. I think they change a ton behind the scenes and allocate compute however they want, so the model you use today may behave much differently than how it behaved yesterday

I've been seeing LLMs act lazy from the very beginning. They got a little better but smaller models really only want to have a single task given to them. Mythos at least does work. RIP

It was something that was used for token efficiency. Most of the settings and use cases are quite poorly communicated but asking Claude to review the latest release changelog (https://github.com/anthropics/claude-code/blob/main/CHANGELO...) is quite useful. Combined with @"claude-code-guide (agent)" to read it's own docs for settings/configs is super helpful.

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.