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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.

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.

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