Categories
Articles

The $2 Trillion Question About Anthropic

Anthropic at $2 Trillion: Right About AI, Wrong About the Price?

The technology is real. The question is who captures the value — and Buffett answered this one about cars and planes a century ago.

Anthropic is heading for a public listing at a reported $2 trillion.

I want to start with what I actually believe, because it matters.

This technology is real, and it will unlock an enormous amount of economic value. Anthropic went from $386m of revenue in 2024 to $4.59bn in 2025 — twelve-fold growth. Q2 2026 revenue hit $11.5bn, with two consecutive profitable quarters and 300,000+ business customers.

Anyone calling AI “all hype” isn’t paying attention.

So the interesting question isn’t whether AI creates value. It’s who captures it — and whether the price leaves anything for you.

The most under-discussed fact in this debate is Anthropic itself

Anthropic was founded in 2021. Within roughly four years it went from nothing to overtaking OpenAI — the company that invented this market, with the multi-year head start, the Microsoft partnership, and the ChatGPT brand.

On enterprise LLM API spend, Anthropic now leads with about 40% share. OpenAI has slipped to the high-20s.

Impressive. But read it again as an investor:

If a newcomer can reach parity with the leader in five years, the leader did not have a moat.

That is what a moat is supposed to mean — protection against exactly this. We have instead watched model leadership change hands repeatedly between OpenAI, Anthropic, Google, DeepSeek and xAI, with benchmark leads lasting months, not years.

The cost structure says the same thing. Anthropic spent $7.33bn on compute in 2025 — 1.6× its entire revenue — and has committed roughly $518bn to infrastructure over the next decade, about 80% reportedly non-cancelable.

That is usually described as a moat. I’d call it the opposite: the price of staying in the game, which every serious competitor is also paying. Capital intensity that everyone must match doesn’t protect returns — it raises the stakes and converts flexible costs into fixed ones owed even if pricing collapses.

Buffett’s lesson, not mine

This framing belongs to Warren Buffett, who has made the argument for decades — most memorably at Sun Valley in 1999, at the height of the dot-com bubble.

His automobile example: roughly 2,000 car companies were founded in America. Around 780 are documented between 1895 and 1969; the count peaked at 206 manufacturers in 1908, fell to 24 by 1929 and just 8 by 1940.

And the early leaders? Duryea, Olds, Studebaker, Packard, Pierce-Arrow. Almost none are what you’d have wanted to own. Ford, GM and Chrysler weren’t the obvious early champions either.

His aviation version, from the 2007 Berkshire Hathaway letter, is blunter:

““The worst sort of business is one that grows rapidly, requires significant capital to engender the growth, and then earns little or no money. Think airlines.”

““If a farsighted capitalist had been present at Kitty Hawk, he would have done his successors a huge favor by shooting Orville down.”

Airlines went from ~7 carriers in 1930 to 31 by 1950 and back to ~7 majors today. The industry transformed the world — and its investors in aggregate lost money from its birth at Kitty Hawk. Pan Am, TWA, Eastern, Braniff: household names, all gone.

Buffett’s point is not that aviation was a bad idea. It’s that the industry and its investors are different questions. A transformative technology attracts enormous capital, and that capital competing for the same prize is what erodes the returns.

Now the arithmetic

Against 2025 revenue of $4.59bn, $2 trillion is 435× sales. Against the current run-rate — $65bn in July 2026 — roughly 31×. Against Anthropic’s own 2028 projection of $190–200bn, about 10×.

For context: NVIDIA trades at ~18–21× trailing sales. OpenAI — valued at $852bn in March, now targeting $1.4 trillion on at least $30bn of fresh capital — is in a similar range. Even Cisco at the absolute top of the dot-com bubble was only mid-teens to low-twenties. And Cisco was profitable, dominant, and entirely real. It just got bought at a price that assumed the good times would never normalise.

Where I actually land

The bull case isn’t stupid. If you genuinely believe Anthropic reaches $200bn of revenue by 2028 at software-like margins, $2T is about a 10× multiple on 2028 sales — defensible.

But that means you’re underwriting something very specific: that this company holds its position through a shakeout that hasn’t happened yet, converts $518bn of fixed commitments into profit, and defends margins against rivals who have repeatedly closed the gap in months.

The technology will work. Anthropic is an exceptional company. Neither of those settles the valuation.

“A transformative industry and a good investment are different things. The car industry transformed the world and destroyed most of its early investors’ capital. It is entirely possible to be right about AI and wrong about the price.

Not a prediction. A description of the risk in a 435× trailing multiple — and a reminder that the company which ends up owning an industry is often not the one that was first, or biggest, at the start.

If you disagree on the moat point, I’d genuinely like to hear why — that’s the crux of it.

Sources: Anthropic’s confidential S-1 as reported by Reuters; Berkshire Hathaway 2007 letter; Buffett’s 1999 Sun Valley remarks; Federal Reserve Bank of Kansas City research on US automobile entry/exit (1895–1969); Reuters/Bloomberg on OpenAI’s $30bn round (29 Sep 2026); companiesmarketcap.com for market data.

Categories
Articles

Scaling AI at Controlled Cost: What the Numbers Say

Everyone is benchmarking which model is smartest.

From a finance seat, the more useful question is: what does one unit of work cost — and does that cost hold as you scale?

I’ve spent months running my own AI models and applications, pushing hundreds of millions of tokens through them. The real story turned out to be the economics.

Per million tokens (output), top-tier frontier models cost up to ~$50.

The cheapest open-weight options: $0.60.

That’s up to 80x.

No negotiation or optimisation closes that gap. It’s a different cost structure — and it changes which projects are viable.

On capability: open-weight models hold their own on the majority of tasks a finance function would actually automate

– document extraction

– classification

– reconciliation support

– first-pass analysis

– code assistance

The gap only opens at the edge of the distribution: the hardest few % of problems.

So I treat model choice as procurement, not preference:

Standard tier — open-weight models by default, wherever the task is well understood.

Premium tier — frontier models, reserved for high-complexity, edge-of-distribution work.

That routing discipline is how I’ve scaled my AI workload 5–7x over the last few months — while expanding the scope of applications running on it. Hundreds of millions of tokens. Dollar-spend still well under control.

The finance takeaway: inference is a variable cost. And like any variable cost, it responds to design.

The winners won’t be the teams with the biggest AI budgets. They’ll be the ones getting far more work out of the budgets they already have.

I pulled list prices for the most widely used models — input vs output side by side, per 1M tokens, with the cost multiple against the cheapest open-weight option. Sources in the comments.

How is your organisation treating inference: a line item to manage, or a design decision to own?

Categories
Articles

The Most Valuable Asset On Your Balance (that most people forget)



We talk endlessly about portfolios, property, and net worth. But for most of us, the single largest asset we will ever own never appears on a financial statement.

Your human capital — the net present value of your future earnings.

Here’s the simple fact: if you earn for another 25 years, that’s not an abstract idea — it’s a number you can compute. And at a 5% discount rate, a steady $100k a year for 25 years is worth ~$1.41M today. That’s a real, quantifiable asset sitting at the top of your personal balance sheet, whether you acknowledge it or not.

How to compute it:
– Take your gross income
– Deduct taxes, non-recoverable contributions, and insurances
– Deduct employment-related costs — training, commute, anything work forces you to spend
– Discount the resulting net annual cash flow to present value over your expected working horizon

That’s your human capital. A number, not an abstraction.

Why it’s worth modeling:
1. It’s a mental tool for opportunity cost. Every career decision becomes a comparison of present values, not a hunch.
2. It surfaces tradeoffs. A better-paying role that costs more in tax, commute, and burnout isn’t automatically a raise.
3. It enables bigger decisions. Career moves, new roles, earlier retirement, sabbaticals — all become clearer when you can weigh what you’re giving up against what you’re buying.

The reality check:
Next time someone casually floats a project that would cost a million dollars — frame it properly. That’s roughly the equivalent of an entire working lifetime for someone on a median US salary. A $1M commitment is a lifetime’s worth of labor, concentrated into one decision.

(A caveat for precision: the exact number depends on the discount rate — ~$0.94M at 5%, ~$1.27M at 3% — but the framing holds either way.)

Human capital is the asset you can’t diversify, can’t insure fully, and can’t buy back once spent. Know its value. Manage it like the balance-sheet anchor it actually is.

Categories
Articles

Why, as a finance professional, I use Python instead of Excel



Finance is a lot of process. Specific data, specific steps, specific decision points. And most of that process is rule-based and logic-driven — which means it’s automatable.

Over the years, the share of my analytical work done in Excel has steadily shrunk — in favour of code editors. Today, less than 5% of my analytical work happens in Excel. The other 95%+ runs in Python and purpose-built analytical tools.

What Python gives me that Excel can’t:
– ⚡ Speed — fetch & process data in seconds, not spreadsheets
– ✅ Reliability — same logic runs the same way, every time
– 🧠 Complexity — models scale cleanly beyond a grid of cells
– 🔄 Flexibility — adjust & update in code, not a rebuild

The workflow: Fetch → Process → Structure → Act — each step automatable.

Excel is still great at what it’s great at. But for the process of finance, Python is the better tool. I’m a realist: I use both. The more complex and repeatable the work, the more I reach for code.

Categories
Articles

I hired a (personal) AI employee.

I hired a (personal) AI employee. It works for me in the background, and it’s now a permanent part of my “staff”.

For over a month I’ve run Hermes Agent — an autonomous AI that lives on my hardware, has its own memory, and does things. Not a chatbot. A worker.

What it actually does for me:

🎬 1,500+ videos & articles → downloaded, transcribed, summarized, and filed into a searchable learning library. I just drop a link; it does the rest. And it structures the whole process — turning a chaotic stream of random finds into a clear curriculum and learning plan I can actually work through. A very different outcome from what the social media algorithm will feed you.

🔎 A research assistant that knows my standards. I throw topics at it and it scours various sources — with references, context, and analysis. It’s learned what matters to me in research, so the output comes back the way I’d want it, not generic.

📚 5 Anki (language learning) collections (66,901 media files) synced to my phone via a self-hosted server it deployed — and debugged when the SQLite schema broke.

🔐 My VPN — added a TCP fallback, managed the firewall, and diagnosed a carrier-NAT issue that was blocking me on mobile.

🧠 A private digital coach — its own persona, memory, and Telegram chat, checking in on my career and health. Runs on local models on my own hardware. My conversations never leave my infrastructure.

📝 A notes repository it maintains and cross-references automatically.

What I learned about managing it:

  1. Autonomy is the unlock. I brief it, it executes end-to-end, and reports back. I don’t babysit it.
  2. It fails — and self-corrects. Crashed containers, locked databases, retries. Resilience beats perfection.
  3. Memory compounds. It remembers my personal targets and goals, my health habits, my taste in music. Every interaction gets smarter.
  4. Privacy is a feature. My most personal conversations — with my coach — run on local models. No cloud, no training on my data.
  5. You still need judgment. It’s a tool, not a replacement for thinking. I review the important stuff.

The honest take: I’m a realist — I know what technology can and can’t do, and I don’t chase hype. But AI is maturing, and the potential is very significant. After weeks of real use, Hermes Agent has earned its place as permanent infrastructure, not a novelty. It’s the difference between having an assistant and having a staff.

Curious about hiring your own agent — and your own private coach — on your own hardware? Happy to share what I’ve learned.

Categories
Videos

Exponential growth and epidemics

A brilliant video explaining the math involved in the exponential growth of epidemics and how measures to reduce exposure can reduce the spread by orders of magnitude. Video credit: 3Blue1Brown

Categories
Videos

What is the Fourier Transform? A visual introduction.

Credit for the video: 3Blue1Brown

Categories
Videos

Introduction to Monte Carlo methods

Credit for the video: Alon Honig

Categories
Videos

Space: The Next Trillion Dollar Industry

Categories
Videos

Ever wonder how block-chain actually work?

Concise video summary explaining the core ideas of block chain and the mechanics of cryptocurrencies.

Specifically on how need for trust in executing transactions is eliminated in this protocol.