Mar 3 – both Nvidia and Broadcom shall use Intel as a foundry to produce chips.
This should add additional pressure to gross profit margin to both companies.
Feb 25 – Apple shall invest $500bn in the US in the next 4 years, including a new advanced manufacturing facility in Houston to produce servers for Apple Intelligence and Private Cloud Compute.
Apple also supported billions of $$ for TSMC’s Fab 21 facility in Arizona.
Not to mention the rollback of those DEI policies across companies, e.g.
For the past 8 quarters (2023 & 2024), incremental gross profit margin is well above 80%, more like 90%.
This is easy to understand, e.g. one more successful ads sale (click etc.) on Instagram won’t cost Meta more.
What’s even more amazing is the incremental operating profit conversion, which on average is like 100% for the past 6 quarters!
What does 100% mean? It means one dollar of additional gross profit earned by Meta was converted into one dollar of operating profit – how amazing is that!
The operating profit margin increased from 20% in 2022 Q4 to 48% in 2024 Q4, growing at 91% CAGR.
Although there were some one-off opex optimization efforts, thus incremental operating margin should definitely fall, it’s still a very very powerful business franchise, demonstrated by this amazing incremental earnings power.
Sometimes, it does seem that even SOEs in China doesn’t have monopoly. For example, there are 3 telecom companies, and multiple banks.
However, there are also no real market competitions. E.g. you don’t see China telecom operators competing for customers by offering differentiating product offerings.
Consumers didn’t get a wider range of choices via these seemingly “competing” businesses.
How these businesses “split” profits looks more like a “political” question, rather than a economical one.
Eli Lilly:
$41bn LTM global revenue
China MRQ rev = 4% of total revenue
Starbucks:
$36bn LTM global revenue
China MRQ rev = 9% of total revenue
Nike:
$47bn LTM global revenue (Nike brand)
China MRQ rev = 14% of total revenue
Lululemon:
$10bn LTM global revenue
China MRQ rev = 15% of total revenue
As the go-go period ended in China, it’s no longer the era of the fastest runner.
Surprisingly, good culture now matters in China.
1/ Culture matters to employees.
Trip.com founder & CEO James Liang advocates for “hybrid” work mode – employees can choose to have two days WFH during a week.
2/ Culture matters to customers.
PDL (Pangdonglai) is very popular and has a growing influence in China as a retailer – an industry that most people ignore nowadays. PDL is famous for its “customer service, quality and integrity“; the at PDL work 7-hour days, have weekends off, get a string of perks and are entitled to 30-40 days of annual leave.
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.
Reading Enron’s story from The Smartest Guys In The Room
It seems that one particular problem from Enron’s business model can be found elsewhere easily – developers focus a lot on up-front calculations (present value of all the expected future cash flow from a project), getting deals done, and moving on the next one.
In the process, banks lend money based on the similar calculations before real projects finish and generating cash flows, employees of developers get paid based on the formula linked with similar calculations, etc…
Things are good when they are good. But when “unexpected” things happen, this business model can be troublesome.
Similar dilemmas can be found in property, solar, etc..
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.
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.
In 2017, Texas passed a bill to allow driverless cars on the road.
Later that year, Waymo started to bring driverless cars to the road in Texas.
California introduced rules around driverless testing on public roads in Feb 2018.
Waymo won the first driverless permit to test in California in Oct 2018 for ~3 dozen cars.
…
In 2020, Waymo started to open its fully driverless service to the general public in Phoenix.
…
In 2024, Waymo offered the service to anyone in SF.
Lots of small steps.
Each state/city can be different. Requirements can be different & definition of “driverless” can be different.
Area can be limited.
Target passengers can be limited.
etc.
Where was Waymo’s technology at?
In 2018, Waymo’s miles per disengagement was 11,154 miles.
In 2023, Waymo’s miles per disengagement was 17,311 miles.
On average, people may drive 10k+ miles per year in the US.
So on average you will only experience one “Disengagement” in a year in 2018, which is a decent rate.
Where is Tesla FSD at?
The latest 12.5 seems to have 1 critical disengagement per 123 miles?
This needs to iterate & improve over time to be fully driverless.