Tag: Tech

AI to disrupt games?

AI has created chaos in many areas, including the gaming industry.

Google Genie was a case in point a few weeks ago.

TakeTwo (-10%), Roblox (-10%), and Unity (-20%) all down after Google debuts AI Game Creation Tool
byu/Rukuba inValueInvesting

However, I don’t think code alone is what makes a game successful.

Many successful games are like basketball or soccer.

It’s a cultural and social thing.

Shooting is fun but that’s not basketball is all about.

I bet AI can create and update new games easily but it’s the same for sports.

There can be new “sports” coming up – they can be fun to play as well. However, the number of players, the audience, the whole league/industry around a classic sport are the moat.

In the pre-AI era, I don’t think the studio that has top-tier coders is guaranteed to have blockbuster games.

$RBLX $TTWO $TCEHY

Big capex is not longer welcomed

US big tech continue to post higher capex outlook for 2026 and those figures are surprisingly large.

However, you now start to negative reactions.

1/ Their own stocks respond negatively

2/ Nvidia stock, which presumably is a beneficiary for higher capex, hasn’t responded very positively

#Why capex is less welcomed?

1/ It could just be higher inflation across the chain. higher price for infrastructure, power equipment and construction workers etc. Therefore, it’s a less-efficient use of money

2/ Investors don’t see immediate growth. The 2026 growth outlooks, which should be supercharged by already massive capex in 2025, is not impressive enough. Investors fear that marginal incremental growth coming from additional capex looks small, at least in the current year.

Xiaomi smartphone GP may drop 30% given rising memory cost

Some simple calculation:

Xiaomi smartphone GPM was 12.6% in 2024, with 192bn revenue.

Xiaomi sold 1.64 billion smartphones that year.

The GP per handset is about 147 RMB in 2024

Across different smartphone models, memory cost is different, ranging from 50-500 per handset.

But in a nutshell, it’s about 12-18% of BOM.

It’s could be about 150 memory cost per handset for Xiaomi, which is similar to GP per handset.

Then if memory cost is rising 50-100%, the entire GP per handset could be at risk.

To offset, Xiaomi may increase prices for customers.

And as a large customer for memory chips, it may not receive full mark-up immediately.

In the end, maybe 1/3 of the memory cost impact of 120 need to be absorbed by Xiaomi.

Then GP per handset could be more like 100-110 RMB.

And as the price increases, volume could be impacted, plus the RMB appreciation recently (two-thirds of Xiaomi smartphone volume is overseas).

Total impact to Xiaomi smartphone GP could be like 65-75bn, or 25-30% negative impact from 2024 level.

Human brain vs AI model

Human brain has 86 billion neurons, which forms 100 trillion synaptic connections.

That 100 trillion is the first-order proxy of model “weights”.

Currently, SOTA AI models could have ~2 trillion parameters or model weights.

For example, OpenAI’s GPT 5.2 model is estimated to have 1-5 trillion parameters, while GPT 3 has 175 billion parameters. Meta Llama 4 Behemoth (MoE) has nearly 2 trillion parameters.

Thus AI models now are closer to human brains. Only 50x difference.

However, human brains is not just 100 trillion synaptic connections.

  • A synapse isn’t a single scalar. It has multiple properties (strength, short-term plasticity, release probability, receptor composition, timing effects, etc.). So raw physical degrees of freedom per synapse could be >1.

  • Not all synapses are independently controllable. Biology adds constraints and correlations (developmental wiring rules, local learning, neuromodulators, homeostasis). That means the effective independent DoF is likely lower than “#synapses × variables”.

  • The brain has lots of additional state beyond synapses. Neuron membrane potentials, ion channel states, neuromodulator concentrations, glial regulation, oscillations, etc. That adds dynamic DoF that don’t map cleanly to “parameters” the way a static model does.

Another thing need to keep in mind how energy-efficient a human brain is.

A typical adult brain runs on about ~20 W.

How to operate a SOTA model?

ChatGPT gives me this

for a dense FP16 2T model, 32 H200 GPUs is the “it loads and runs” baseline, while 48–64+ GPUs is where you start getting reasonable headroom + throughput, depending on your target context and requests/sec.

So about 40 kw.

That would be about 2000x energy consumption than human brain.

Of course human is not just about brain, so about 400x.

GPT 5.2 estimated parameters

The previous 40x P/S sector was SaaS

Cloud and SaaS received premium valuation from 2020 to 2021.

The outperformance started in 2016 and lasted 5-6 years. Watch that outperformance here: https://cloudindex.bvp.com/

Back then, “rule of 40” is the king of valuation metrics, which means “um of revenue growth and profit margin should equal 40%+”. The higher the better of course.

While market fluctuates, you can find the P/S or revenue multiple in the past.

Here, you see that in 2016 companies at rule of 40 receives ~6-7x current year revenue multiple.

Here, you see that in 2020 companies at rule of 40 receives 17x LTM revenue, or 12.8x forward revenue.

During this 2020-21 period, it’s normal to see 30-40x P/S for hot SaaS companies. I remembered Shopify was 40x P/S.

Looks at these charts from here – the evidence of 30-40x P/S glory days.

We all know what happened next.

 

 

That multiple fell back to ~6x for the regression line in 2022, with Fed raising interest rates. See here for the chart.

 

The multiple has stabilized afterwards, from 2022 till now.

Currently, the valuation (forward revenue multiple) is ~4-5x for 2nd and 3rd quantile companies followed by BVP, including the names like Salesforce, Hubspot, Workday, Nutanix, etc.

BVP has introduced the new the Rule of X to give growth more credit btw.

I think some bubble is brewing now, with AI model companies or even chip companies.

However, investors keeps dancing, expecting that Trump will appoint new Fed Chair this year and the new chair won’t raise rates. Trump wants lower rates, not higher.

Maybe we should see another around of crazy valuation first.

And if SaaS outperformed 5-6 years (2016-2021), maybe AI-related stuff should outperform till 2027/28.

Nvidia to become new android+qualcom

In the smartphone era, Google’s Android and Qualcom chips is a powerful solution for many hardware makers.

Nvidia looks to resemble that status alone in self-driving, with this announcement. The Alpamayo + Thor combo provide self-driving software + chip for cars.

More than that, it looks that Nvidia might also do that in robots.

Obviously, Tesla would be the Apple of this era.

Btw, Nvidia’s evaluation data set includes lidar data  https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles-NuRec

This dataset has a total of 1727 hours of driving recorded from planned data-collection drives in 25 countries and 2500+ cities. The data captures diverse traffic, weather conditions, obstacles, and pedestrians in the environment. It consists of 310,895 clips that are each 20 seconds long. The sensor data includes multi-camera and LiDAR coverage for all clips, and radar coverage for 163,850 clips.

 

Robots should be capex?

Technically, capex should be more one-off than recurring.

Phone used to be a “capex” item. You won’t buy a phone every other year, before the iPhone era. Apple’s P/E multiple expanded when it transformed the category into a more “recurring business”.

In the early stage of AI training, people spend whatever is needed on chips capex. This is due to the increased performance of AI GPUs and thus the efficiency of training. However, after this growth era, I think this is still more of a “capex” item thus the growth should normalize later. Inference is another thing though.

Robots should be capex ultimately. However, in the initial adoption stage, which hasn’t arrived yet, we should see a growth that makes people forget this is a capex category. Then there will be a period of doubt, like when Buffett purchased Apple. And hopefully, the leading robot company by then can transform robot into a “recurring” category like Apple did for smartphone.

 

What I don’t understand about robotaxi…

For the same destination, Baidu’s Apollo robotaxi in Shenzhen will charge RMB 125 (before coupon) vs RMB 40 on Didi express (affordable tier, before coupon) and RMB 50 for regular taxi.

Didi charges 125 before coupon
Didi Express charges 40 before coupon
Regular taxi charges 50

What’s also interesting is that Baidu’s robotaxi estimates that it will take 79 minutes!

Meanwhile Didi estimates it’s about 31 minutes, which is in-line with other map apps’ estimates.

Baidu robotaxi charges more than 2x the taxi price and takes more than 2x the time…

Well done.

Attaching the breakdown of Baidu robotaxi fare (before coupon)

FAANG to MAG 7

Netflix was out, Microsoft, Nvidia and Tesla are in.

 

When FAANG was first coined, it was 2013. Netflix provided significant return in that mobile Internet and cloud era.

ChatGPT was out in Nov 2022 and swept the world in 2023.

 

Netflix doesn’t touch hardware and AI.

Nvidia was neglected as it’s not close to consumers except for the gaming PC. Later people may know it as a crypto mining machine, but not the backbone of an entire era.

Tesla was neglected as people can easily live without an electric vehicle. Now it seems that autonomous driving will eventually serve many people and be deployed in more use cases in the form of robots.

Microsoft was neglected because it was mature and old? Not sexy.