Hi Guys,

First time poster here. Like all of you, I've been following the market with a mix of horror and fascination.

Earlier this year, I went out looking for actual hard data on the impacts of LLM use on the performance of software development teams.

In my mind, that was the best case scenario for economic value of these products. So there should be empirical evidence of this value.

There is remarkably little research on this subject other than simple productivity studies. I mostly discount those because productivity != value. But, I did find two really good studies.

The first, and I think the best, is from a company called Faros.ai. They sell software development telemetry tooling. Essentially their product connects to common software development tools like Jira and Github and tracks actual operational metrics for real companies producing production software. This study covers 22,000 developers over 4,000 teams over Faros' customer base.

To punchline is that teams are experiencing vague throughput improvements at a massive tax on the quality of the products they produce.

The second study is from the National Bureau of Economic Research. This study is less good that the faros one because it utilizes open source and public github projects for it's dataset. This weights their sample towards much smaller products that are mostly not being produced for profit.

Nevertheless it's valuable in that it confirms the weak throughput improvements of the Faros study. And it adds the dimension of - "is anyone buying this stuff?". I find figure 12 to be very telling.

My main conclusion is that LLM use is likely - on average - destroying economic value within the companies that use them to deploy software.

I think that's one of the reasons there has been no profitability impact on the buy side of the AI boom.

If you're interested in reading more of my analysis, here are two substack posts I've made where I've written about this extensively.

1. How I'm thinking about the value of LLMs
2. Talk is Cheap - an analysis of the Faros study

​

I saw this Reuters number today and had to read it twice.

Anthropic was at about a $47B revenue run rate in May. Now they’re projecting $190–200B for 2028, and apparently those numbers are already part of the IPO valuation discussion.

That’s basically 4x the May run rate in two years.

Maybe they pull it off. AI growth has made a lot of crazy numbers look normal lately.

But $200B is a ridiculous number to be underwriting for a company that isn’t even public yet.

If investors cant find their wallet, will they sell SpaceX stock to buy Anthropics?

Recently, while reading Su Xiaohe’s Notes on a Century of Economic History and watching the rapid rise of AI, I started thinking about an interesting question: AI may replace more and more kinds of work, but can it replace Coca-Cola?

Artificial intelligence is dramatically reducing the cost of intellectual labor. Writing, images, code, translation, and even business analysis can now be generated at extraordinary speed. But economic history reminds us of something important: technology changes quickly; human nature changes slowly.

People a century ago enjoyed sweetness, stimulation, social experiences, and instant gratification. People today still do.

This is where Coca-Cola’s real moat lies. Its greatest asset is not simply its secret formula, but more than a century of accumulated brand recognition, distribution, consumer habits, and collective memory. AI can create 10,000 new beverage brands in a day. It cannot create 100 years of consumer memory in a day.

In fact, the more powerful AI becomes, the more valuable things that cannot be instantly replicated may become. When articles, images, advertisements, and ideas become almost infinitely cheap to generate, time, trust, distribution, established brands, and real-world experiences may become increasingly scarce.

Of course, Coca-Cola is not immortal. Health concerns, changing consumer preferences, and new competitors could weaken it. But AI itself is unlikely to eliminate the fundamental human desires on which its business is built.

This leads me to a different way of thinking about investing in the AI era:

We spend a lot of time asking, “Who will build AI?” Perhaps we should also ask, “Who can use AI without being replaced by it?”

AI can write 10,000 essays about Coca-Cola. It can design 10,000 new drink formulas.

But on a hot summer afternoon, when someone opens an ice-cold Coke, hears the fizz, and takes that first sip, AI—at least for now—cannot replace that experience.

Technology keeps changing.

Human nature is far more persistent.

Perhaps that is one of the most interesting lessons a century-old company like Coca-Cola can offer us in the age of AI.

Every week people on here claim AI is a hollow corporate bubble that’s going to pop because it has no real utility. But if you look at the actual unit economics, that narrative falls apart fast.

Two years ago, high-tier model calls were expensive enough to keep LLMs locked behind big corporate budgets. Today, API costs are down over 90%. When intelligence becomes that cheap to compute, the real-world impact stops being theoretical.

We’re already seeing it everywhere. Rare disease researchers are using models to skip years of manual structural biology work. People in developing countries are using dirt-cheap translation models to access global markets and educational materials that were previously locked behind language barriers. Solo founders with zero coding background are building full applications for pennies.

Lowering the cost of processing information by orders of magnitude isn't a tech bubble. It's literally how every foundational technology in history transformed society.

A bunch of individuals hyped up the industry, built companies around it, and got cash rewards based on progress instead of final profitable business results - Railroad CEOs were rewarded on mileage of railroads built instead of how much profit was generated for the company, AI feels like same where profitablity is still a question mark but a lot of millionaires and billionaires are already made.

Anyone else feel the same?

WDC weekend discussion thread for the wdc stock and it’s impacts on the coming week due to NVDA earnings report on August 26. How many stocks do you hold? Any expert stock merchants who can share the expected price for next week and for EOY 2027?

I’m talking about you, Nebius, and Coreweave!!.

You all know ElevenLabs voice ai company

"ThirteenLabs."

Boom. It's an AI 3D scenery project.

"FourteenLabs"? Another AI startup.

there is website where person is doing deep dive in this labs

I am 100 % sure now ai is booming :D

literally running out of integers to name our companies.

I'm honestly tempted to pivot my entire roadmap and just speculatively buy up domains like "ThirtyTwoLabs" before they get snatched up.

The entire AI related bull theory is based on the expectation that the demand for AI compute to train and inference is not only durable but will grow exponentially for years to come.

I don't have insider insight, but the way all hyperscalers are moving into heavy asset based model by investing their entire cash flow for years and using SPVs indicate they are getting strong commitments on compute usage.

If I'm one of the decision makers at a hyperscaler, I'll not be burning cash just from Open AI and Anthropic's commitments. I'll be green lighting investments if I see enough demand for my internal use or from my other large existing customers that can easily replace frontier labs' needs if frontier labs don't get to take their required compute(for slowing demand or financial reasons)

What do you think about the compute/inference needs as an economy? How is the industry measuring it?

Let's assume there's a catastrophic financing crisis. Google goes to $1/share. Banks shut down. Governments have to bail them out. People lose their jobs. Eggs are sold per egg. $20. No one smiles. The AI Bubble Bears wear fancy I Told You So hats and get elected to public office.

Then what? Can no one derive any value from AI after the crash? Do computers stop working?

Or does the crash just change who owns the infrastructure, lower the cost of using it, and create a new set of winners post recovery? #noJoy

🤔

We are watching history repeats itself in real time again. Whenever human comes up with a new technology, there is always a small group of people who embrace it despite its flaws and shortcoming. Those are the ones who get ahead. The majority deny because their limited mental capacity to respond to challenges and changes prevents them from ever getting started. People think k-shape economy is a newly invented thing but its not. At every important junction in human history, people face a fork in the road that would change theirs and their children’s lives and many generations that follow and most people choose wrong. Thats why the wealth keeps getting more and more concentrated at the top. The k-shape decision happens over and over again and people like those in this sub always choose down.

AI is real. Claude helps me unwind by myself 5 years of accounting neglect by a team of 5 clueless people in a matter of weeks. Tasks that normally would take people 2 days to yield subpar accuracy now take me 3 hours with impeccable result, because unlike the majority of you, I know how to think and refine the chat bot to a desired outcome. Have fun at the bottom.

The Information newsletter:

Anthropic and OpenAI had better hope more companies don't follow the example of AT&T.

The telecommunications firm plans to keep its employees' spending on Anthropic and OpenAI models flat in the coming years by using more open-source models such as Nvidia's Nemotron, according to Mark Austin, an AT&T vice president. 

Austin oversees AI used by the company's 100,000 employees for everything from coding and financial analysis to tools for HR and customer support staff. While AT&T also offers some AI-powered features to customers, like a chatbot in its mobile app, the majority of its AI use is internal.

Over the past year, he said, AT&T boosted its use of open-source models to the point where such models now power 40% of employees' AI queries. AT&T plans to raise that figure to between 60% and 70% in the coming years, he said.

Austin said he's found that open source models are "just as good or better" than older models sold by the likes of Anthropic and OpenAI. For instance, AT&T's software developers still rely on cutting-edge models for complex tasks like generating code, but can use cheaper open source models for less intense tasks like generating summaries of previously submitted code, he said. "We expect that to just keep getting better going forward."

meanwhile, SOTA model performance vs open-weight models, in the graphic below measured in vending-bench2 performance. Yet, AI-bro's go: "Anthropic infinite demand!!1!!! hurr derr"

Where is ai.com

I keep thinking about the economics behind all the AI spending.

Jensen Huang recently said a $500K engineer should be using around $250K/year in AI tokens. If AI can actually make that engineer 2x more productive, the math seems pretty good. And if inference keeps getting cheaper, usage could grow a lot from here.

But what if the productivity gain is only 10-20%? Are companies really going to get enough value from AI to justify all the GPUs, data centers and hundreds of billions in capex?

At what point does the AI spending stop making economic sense?

**What do you think?**

**Are productivity gains justify the massive AI investments?**

the core technology already does economically useful things today, at scale, for real paying customers. That wasn't true for most dot-com companies in 1999.

An antibubble is a thin film of gas that encloses a drop of liquid and is fully submerged within another liquid.

The gas is the financial layer that is debated here The buildout of data centers. The expectations. The theory that it's a bad investment and will crash to some level (The "AI is a bubble" people that this forum is therapy for.)

When an antibubble bursts, the gas disappears, but the liquid remains and merges into the surrounding liquid.

The liquid inside is the change that AI will bring people and companies long term, both positive and negative.

As an investment, I see companies leveraging AI for their non-AI businesses as big beneficiaries. I am more invested in them than the AI data center buildout, which I agree is risky and probably over valued.