where zichang writes

1. New technologies will not be uninvented - generative AI will have a place in corporate OPEX. There are projects where it is rather cost-effective to justify the incrementally adapting AI can bring. 
2. But it won’t justify the tokenmaxxing strategy, indefinitely. It simply doesn’t make financial sense - not all tasks are cost effective to use the SOTA model on it.
3. In a lot of ways, LLMs right now are like expensive Swiss knives. They are great, but question mark on whether everyone in the company needs it.
4. A justification for tokenmaxxing strategy, is that it serves as a priority discovery tool. People don’t know what will be the most productive use case of AI, and would be worried that early curtails hurts potential innovations. So they let the puppy fly for a while, collect the data and estimate the distribution of values/cost. Then finance will come in, and there will be cost management coming in.
5. The market will find an equilibrium. But the AI Capex, building of data centers, will take years to complete - and in cases already facing delays and challenges. https://www.wsj.com/tech/ai/americas-data-center-build-out-is-falling-way-behind-schedule-e408a9a8
6. When these projects are not yielding cash flows early enough, what would happen? Things will turn ugly, pressures will come in, cost cutting underway, assets sales may happen to raise liquidity, etc.
7. Adaptation of innovations like this, however, is interesting that it is relevant to everyone, not just pockets of work force. The last innovation like this is probably PC, or internet. 
8. Prudence. The best is yet to come.

1. Principle is, the economics of AI investment need to make sense. ROI needs to make sense.
2. I am seeing equilibrium being established.
3. On one end, running inferences, even large scale, is relatively cheap, it is a cost that corporates should be able to foot. 
4. One the other hand, “token-maxxing” strategy is raising concerns and make CFOs questioning their ROI: Uber already burnt their annual budget for AI in April, anecdotes coming everywhere that they can’t get enough of their tokens to complete their jobs (/ambitions/ideas).
5. But eventually, these AI projects need to be valuable products. Need to contribute. Otherwise, it would be an expensive hackathon.
6. So an equilibrium is being figured out - we can pay for it, but not infinitely. 
7. Reminded me of the case of dark fiber. In the early ages where fiber was introduced to the world, there was a avid investment in building out fiber networks throughout the country. Not all of them, are commercially viable to be “lit up”. As such, there are miles and miles of built-and-ready sleeping beneath the ground, waited to be waken by new players willing to pick them up.
8. Maybe compute will be like that. Yes, it will not be “uninvented”, demand for HPC will always be there. But there may be excess.
9. Will these “dark HPC” be relevant and usable in 20 years? That, we don’t know.  My understanding is likely not. 
10. If they will somehow still be in the game in 20 years, there will be a market for it.  

我在火车上坐下，看见身旁的女人已经在喝第二瓶香槟。好奇顿起。

原来她是要北方的城市见一位朋友。“我已经二十年没约会过了，”她说。在女儿上大学、自己刚完成了离婚之后，她开始了新的生活。

“今早我六点就起床了。”她有点紧张，又抿了一口香槟。

“你看着很棒，别担心，”我说，“享受今晚就好了！”

人的感受、害羞、紧张，不被时间磨去。

• Went to the “Raphael: Sublime Poetry” at Met today. The exhibition started with his father and his masters work - you can see his years of pupil, and what led to his style and crafts.

• He used rulers and compasses as tools to design his composition - there are solid methodical approaches backing the mastery and genius of composition - a theme that recurs. You can see his precise measurement in his sketches. Impressive and maybe a bit demystifying that there are traces of engineering in perfection in beauty. 

  

  

  

• However, rulers and compasses are just technique - but the balance and harmony are truly genius.

  

  

  

• very methodical - he would start with simple red chalks for sketches and build from there - but even simple sketches are captivating.

  

  

  
• Seeing his finished work reminds me seeing the first retina screens from Apple. It was meticulously complete. 

  

  

• He was a very prolific painter.  He started with a ferocious, ambitious, and extremely diligent study of the past masters, and later the raining orders from his patrons including the Popes. The sheer magnitude of his work was foundational of his impact. It was impressive to see how greatness unfolded and were practiced, and humbling to be reminded that diligence was the foundation of humankind’s finest. 
• He seems to be a humble student from the greats of his time, adapting his work and practices with advices from Leo Da Vinci and studies from Michelangelo (who were more direct rivalry of Raphael). 
• The surging projects from the Palace and the Pope - albeit led to the bankruptcy of the papacy of Leo X eventually - may signal the splurging activity and power of the renaissance age.
• His success, and managing to replace an earlier generation of painters at the Vatican palace involved his artistic virtuosity, but his personal charisma, savvy, and political support from his friends from Urbivo played a key role in earning the keen of the Pope. Raphael himself never shies away from getting in with the culture - in his sketches you can see lines of sonnet reflecting his attempts to rhyme with the poetry making culture of the court at that time.
• Renaissance artists were supported by patrons - thus their ability to serve different clientele is crucial. Raphael’s style and emphasis changed from Julius II to Leo X - partly because of the different preferences of the clients. 
• Reputation and impacts are an results of timing, culture, personal dedication and interpersonal impact (天时地利人和.） It’s hard to say that without the scale and popularity of Raphael and his studio among his contemporaries, or without the demands and cultural support of his clients, he would have so many prolific work and thus be this impactful. He had indeed succeeded in scaling his work.
• Key to his success of the scale, with some degree of controversy, was the partnerships with his fellow artist and pupils. He may design the composition, and leave the work to his studio and teams to complete, often leading to controversy of ownership. A modern enterprise.

A valuation multiple perspective:

the rent/price ratio (annual rent / home price) in Shenzhen is about 1%, much lower than major American cities that enjoy such ratios of 6%+.

It seems that the valuation of real estate in China (major metro) is much higher. however it may be explainable: the expense ratio in China is much lower than in the USA.

- property tax: China doesn't have a property tax yet as of 2026 (0% property tax rate per year)

- insurance: the insurance policy for homes are under-developed and are not hugely required.

- maintenance: the labor cost and manufactured equipments cost much less.

- property management / HOA fees: these may be the largest holding expense. In a 10M house (assuming 200 sq meters), annual expense is 400/sq meters = 80K = ~1%. This is on par with a lot of homes across both countries.

Thus - the high cash expenses hurt the valuation of the US property - leading to a lower price/rent ratio, or equivalently, higher rent/price ratio.

A cash flow perspective:

For primary residence: you need to consider the potential of asset appreciation and expense ratio - because that's what matters to the cash flows. For this perspective - China house is slightly more attractive, mainly due to its reduced expense ratio.

• Look for minimized expense ratio, but high upside for asset appreciation for primary residence. 

For rentals, you need to consider rent ratio and expense ratio. The cash flows are more important here.

• The greater the rent exceeds the expense, the better. 
• High expense ratio in the US can be compensated by high rent ratio. Low rent ratio in China is matched by the low expense ratio - from the operating cash flow perspective, they are equivalent.
• Avoid high expense, low rental property as investments.

Jiu-jitsu is honest, man. Or sports is.

you have to work for it. A stripe in your belt. Details of a move, direction of your toes. You saw issues in practice, you thought to fix them. There is no shortcuts of it.

consistency is the basic key. Show up. Practice when you don't feel like it. You need the time in gym. 

You earn the respect in compete. 

It's honest as fuck, maybe an antidote to too many other things in life. I am so in love with it.

Presence. 

“Hey darling, check out this song,” I forwarded a Spotify song to my girlfriend, “I love it.” Two weeks later, I shuffled to this song, fell in love with it and brought it up again to her. This time, we started an interest on the artist and googled her.

There was nothing.

Well, there were some reddit notes mentioning that the artist (Mary of Gold), could be an AI artist because no picture, no history can be found about her.

I didn’t want to debate about AI copyrights in this blog; I am clearly not an expert. What intrigued me was my reaction. Clearly, the song was still very good, and I did enjoy the voice and the progression. But knowing that I got personally engaged with a song that unexpectedly turns out to be generated by AI - it all felt different to me. 

I started to wonder: should I not be listening to this song? Is that “discrimination”? But clearly it was so good. If there was a R&B version of Turing test, this would have passed, maybe. 

If there are two songs, one written by a human and another written by AI, how should we judge them. Should they be judged by the same standards? I think eventually they will be. It is just like man-made vs machine-made redwood furnitures - eventually the market will judge its value. Maybe there was never really one standard of “judgement” - there have always been tiers, sometimes non-conforming standards to price things.

I later read a social media post from one of my non-technical friend, sharing her first experience of “vibe-coding.” It became clear to me that execution, or making a demo-ready app is now going to proliferate. People would no longer be bogged down by, “I have this wonderful idea, I just don’t have time / know how to put it into work.” Now everyone can do it. 

What’s going to be more valuable? 

1. An easy answer: “better” idea is going to be valuable. Ideas that really solve people’s problems and helpful, are going to be important
2. Resources, like capital, distribution, infrastructure, political powers, energy: those are still going to be scare and will impact the influence of your app.
3. Laws of economics will still rule: people may run to publish their app online - but is it going to generate cash flows that feeds its operations? If not, a lot of the development will end up vanishing. 
1. Caveat: will the concept of “money” still stand in an extreme version of merchandise proliferation?
4. Laws of physics will still rule: to assemble things, you still need to put a bunch of articles together. That’s a lower bound of resources you will need, and the resources are not infinite.  

I tried to think of a metaphor in previous history where there was such a proliferation of merchandise and individual “makability”, the Industrial Revolution comes into my mind. We are, in the very beginning of an industrial revolution. A lot of new things will be built, many of it will not sustain the laws of economics, but human creativity will take us to an era that we couldn’t imagine before.

I am excited about it.

Using Claude, I was able to prototype my idea of putting several financial metrics in a dashboard in under 2 hours. I probably had 4 or 5 versions.

It does unleash productivity. Code is the first tool that it learns well. People who understand code obtain initial, albeit huge, leverage (the concept of the 10x engineer). Productivity increases for these folks.

Eventually the UI will get better, and it will learn more tools people are using. The AI inference need there is real.

When building becomes easy, deciding what to build becomes more important. “Taste” and “design” will differentiate.

[drafting]

We can teach subject-matter experts prompt engineering—but then what is our value-add as a data scientist?

1. Technical connector. We serve as the bridge between SMEs and engineers. We build PoCs, run MVPs, and prove technical viability.
2. Safeguarding CI/CD. We act as stewards of the CI/CD process, enforcing best practices that may be unfamiliar to SMEs, such as proper version control.
3. Fluency in frontier research. We stay well-versed in cutting-edge research, understanding how the latest developments can help us iterate on models. This requires deep familiarity with deep learning and neural network literature.
4. Disciplined model development. Evidence-driven development, edge-case handling, and robust test cases are essential to ensuring feature stability. Today, almost anyone can build a demo—but systematizing it reliably remains a core expertise.