Category: lenS & thoughtS

Some thoughts of Tariffs

I think tariffs is just another form of tax.

It’s shared by buyers and sellers and is a tax on consumption, especially lower-end consumption – as the outsourced/imported products should proportionally be more lower-end.

It’s actually a more onerous “tax” on low to mid income US people, assuming that high-end consumers care more about high-end products which are not produced in China etc.

And it does increase gov income. Instead of higher taxes on wealthier people, this form of “tax” is more shared across average consumers.

Separately, tariff will increase US mfg jobs.

However, who will do these jobs? With the current wave of AI, you could imagine more average “white collar” workers will be replaced by AI and maybe they shall go to manufacturing.

I think we should say all jobs are created equal – however, if you look back, when those manufacturers jobs were transferred out of the US, US was in the dominating position in the world. No one could force the US to get rid of “good jobs” and keep “bad jobs” back then – I could only imagine that those outsourced jobs were considered “worse”.

So now more people in the US will pick up those “bad jobs”. That’s not a very comforting vision for many average people in the US – probably more dangerous, more exposed to health issues, more repetitive (well white collar jobs can be repetitive). However, this should do good for US overall.

Where are talents?

If talents are born randomly, each year he or she could be anyone that is born anywhere in the world.

Even if China has 1/6 of global population, its newborn is less than that. In 2024, 132mn babies were born. In 2023, China has 9mn newborns.

That makes ~7% of global new born, less than half of the 17% population weight.

 

Investing in China: common fallacy

A common fallacy, especially in the past, is to find a US asset in the similar industry and use it as a reference for valuation.

Why this is less useful, especially in rmb assets?

To put it simply,

asset value = earnings power (e.g. EPS) x multiple (e.g. P/E)

Then we look at these two components:

1/ Industry dynamics can be vastly different.

Demand side can be very different – e.g. work from home has never been a thing in China vs. only ~24% of workers don’t WFH in 2022 in the US.

Supply sides can also be very different in terms of entry barriers, the level of competition etc.

There are just too many different things, thus the projection based on a US company’s past is usually not a good reference.

2/ valuation can be vastly different.

 

Partially, difference in multiple is reflecting terminal growth,  etc., therefore it’s similar to the first point, which is about fundamentals.

Additionally, If you think about bond prices – US treasury yield vs. China gov bond yield, they are on two diverging roads.

What’s good though? Assets in China can produce less correlated return vs. US assets, therefore providing additional benefits to a portfolio.

—

See others posts

Investing in China: regulation and justice

Enron – not alone

Recently finished the book The Smartest Guy In The Room.

Shockingly, you could find many of Enron’s problems in other industries in China during the go-go era (property):

1/ focus on doing projects/deals with early monetization. less focus on the real economics over the entire horizon

2/ lots of off balance sheet financing

3/ weak audit; can’t put a check on mgmt

4/ mgmt takes more early profits out, with potential conflict of interests in the form of SPV, etc.

It’s also similar in WeWork!

The same playbook. Remember that Adam Neumann owns some buildings WeWork leases.

Random thought on China’s role in US debt

Historically, China was a big buyer and still a big holder of US gov debt, which to some extent led to the ultra-low interest rate environment.

Nowadays, China is still helping keep the US interest rate low, indirectly. I think by not providing good return opportunities, China is forcing money to go to other places, indirectly providing more money (with low return alternative) in the global market for the US gov.

It’s not just impacting foreign capital interested in China, but Chinese money as well.

Alternatively, if China has attractive opportunities all over the place, it will compete for global money one way or another.

One thing US is envying.. inflation

China Oct CPI is out, core CPI excl. food and energy, there was 0% increase month over month, and only 0.2% increase year over year.

Almost all kinds of food price dropped MoM, with pork dipping 3.7% MoM (+14.2% yoy).

Including food and energy, Oct CPI dropped 0.3% MoM and increased 0.3% YoY.

In Sep, the core CPI YoY increase was at 0.1% while MoM is -0.1%.

 

Thoughts?

a) Powell would love US CPI / PCE look more like China’s… not exactly the same, otherwise that might indicate a problem with demand..

b) The end of Sep & early Oct China’s stock market rally didn’t move the needle / has limited impact so dar. Day to day consumption still looks weak.

c) Rental price dropping 0.1% MoM and 0.3% YoY.

 

If RMB depreciates

One thing that might happen after additional US tariff is a weaker RMB, which can make Chinese export more appealing.

This may diver export from US to other counties, which gives pressure to producers globally.

What to do if you have rmb?

– buy houses in China

especially given that rmb borrowing rate is low, and housing prices have declined for 3 years, if you have confidence in chinese macro, buying a property in good locations is not a bad choice.

– buy overseas companies that are making money overseas

then the earnings power is not priced in rmb, and the value of the company should be rather uncorrelated to rmb.

 

Xiaomi’s strength

Besides Xiaomi’s scale, supply chain capability, IoT strategy, etc., I think the most underestimated strength comes from its competitors.

For all those merchants or companies who are “bullying” consumers, they will find themselves outcompeted by Xiaomi’s products – simply better, cheaper.

Xiaomi is not copying. Xiaomi doesn’t enter a new category if it thinks the product is good enough. Xiaomi usually executes with better efficiency, offers more value, or adds some differentiation.

Another noticeable change for Xiaomi in recent years is its brand value. It used to be more associated with low to mid income consumers as its products offer value.

However, as its car business picking up, people find its brand attractiveness quickly expand into the premium segment. Those who won’t buy Xiaomi phone can buy SU7 or SU7 Ultra etc. – this greatly expanding Xiaomi’s consumer base.

It’s like Walmart + Sam’s Club in terms of capturing more consumers.

Xiaomi could be China’s Tesla.

After Trump was elected

High-level things that people/market celebrates:

1/ more M&A in the US

2/ certainty in taxes: Harris proposed raising corporate income tax, stock buyback tax, unrealized capital gains tax

3/ near-term possibility of ending wars globally

 

Rates / Treasury yields

bad for long-term bondholders – higher inflation, higher deficits (more supply). but long-term treasury can’t rise too much – it can hurt US domestic mfg etc. if rates are high.

Trump would want Fed to cut rates. Even if Fed cuts, long-term bond yield could be hard to come down.

Thus, we should see a steepened yield curve, which is good for banks! Banks are borrowing current (cheaper cost) and lending long-term (higher yield).

 

China

1/ less probability of near-term war on TW strait (less idealogical driven; deal maker)

2/ higher tariff, less export – China needs to rely more on domestic consumption

 

Industries

1/ obviously, IRA could be changed. Then bad for renewables like solar, etc. It’s such an interesting industry.. no real demand if no IRA?

2/ crypto obviously, can boom especially if a new pro-crypto SEC chair comes in power

 

Waymo vs. Tesla

People sometimes simplify the differences between Waymo autonomous driving and Tesla FSD as Lidar-based solution vs. a vision-based solution, especially as Tesla has been saying it doesn’t use any lidar.

But there are several other important distinctions.

For example, the production & scale is different. Tesla owns the mfg and has been selling cars to consumers, a lot of cars. Meanwhile, Waymo deploys the solution on other carmakers’ cars. As Waymo’s fleet is much smaller than Tesla’s annual delivery, the cost structure can be very different. Tesla can enjoy better economy of scale vs. Waymo, even if the hardware is the same.

Secondly, the responsibility is different, which is a key difference and debating point for robotaxi going forward. Tesla is reluctant to take on responsibility for its FSD solutions as the cars are sold, but Waymo owns the car and operates the ride-hailing service. Waymo takes the responsibility if there is accident due to the autonomous software.
On a separate note. if you think about Uber, that’s actually is a very good business model. It can gain from the value creation of autonomous driving potentially, but because it’s just a platform, it’s just matching cars and passengers and take a cut from fares, so it doesn’t need to take responsibility for a autonomous driving software failure. Uber is not a provider of the traveling service but is just providing the matching services.

There is a third element, which I am not hundred percent sure about. It is said that Waymo relies on hard-coded rules and local data vs. Tesla currently more like a blackbox. So the programmers write specific instructions to tell Waymo cars what would do in different (extreme) scenarios. Instead, in Tesla’s current version (end-to-end), it is using a lot of data the train the AI mode without human specifying what to do in each case. Tesla is only feeding data to the AI, and let AI learn from human drivers. 
This is also why some argue that Waymo is much harder to scale.

However, I do think Waymo is underestimated –

1/ it can actually take a similar approach as Tesla as well, because Waymo also has a lot of data – maybe not as much as Tesla, but Waymo for example has a lot of data in San Francisco. Waymo probably already has the best driver (or at least very good) for San Francisco. If you have the “best driver” in San Francisco and all the related data, you can probably train an AI model with what you have – Waymo can train an AI driver w/o specific rules, but based on data from its current fleet in San Francisco, which is running w/ handwritten rules by human programmers. And from that you are also replicating what Tesla was doing – basically this AI driver for San Francisco is just a learning the best practices of how to drive in San Francisco from the existing Waymo cars. I don’t think Tesla has significant data advantage if we are just talking about San Francisco.

2/ and here comes another bold assumption, which is that if you are the best driver (AI version, not specific-rules based version) in San Francisco (plus Phoenix), you are probably not a bad driver in other cities. Of course you don’t know about the specifics about other cities and other countries, so you are not as good in NYC as you are in SF, but you also won’t be as bad as people expected. And over time maybe in just a few weeks this “San Francisco guy” can do a decent job in New York City as well. 
If that’s true, and that’s probably a big if, then Waymo’s solution can actually be more scalable than people would have expected.

What I’m trying to say is that, at current stage, I don’t think Tesla’ choice technology has already won this autonomous driving competition with huge data advantages. The jury is still out.