Hang Seng TECH index (800700) has been lagging the AI trade because many of the big China AI/semis winners were not even in the index.

Now Hang Seng is proposing to redesign HSTECH from 30 to 50 stocks, with much more exposure to AI, semis, robotics, hardware and cloud.

Official proposal:
https://www.hsi.com.hk/static/uploads/contents/en/news/pressRelease/20260810T180000.pdf

Timeline:

• Consultation ends 18 Sep
• Final decision on the new HSTECH methodology expected by end-Sep
• New stocks will be determined from the 30 Sep index review
• New HSTECH expected to take effect in the Dec 2026 rebalance

If HSTECH starts becoming the main China AI index, adding these AI winners could give the whole index a much stronger push and potentially attract more money into HSTECH.

BABA then gets both: China AI rising tide + its own Cloud/Qwen monetisation.

Basically instead of BABA trying to rerate alone, it could finally have the broader China AI trade pushing behind it too.

Alibaba Cloud just opened its 3rd data center in South Korea and launched its Agentic AI services there.

The part I find interesting is actual usage. Gendive is using Alibaba’s Wan/HappyHorse models inside its agentic workflow product, while Zepeto is using Alibaba Cloud for real-time 3D avatar generation.

Not saying this alone will move BABA, but it’s another sign that AI demand is moving from just training models to real production workloads that keep consuming inference and tokens.

This is the part of Alibaba Cloud I think the market may still be underestimating.

Abit late to this news, but I think this is pretty interesting for MSFT and the whole AI bubble/capex debate.

CME just announced that H100 and B200 compute futures are expected to start trading on October 5.

Basically, these futures track the rental price of Nvidia H100 and B200 GPU compute. You are not buying the actual GPU. You are trading what the market thinks that compute will be worth in the future.

Think of it like electricity futures, but for AI compute.

This is where I think it gets interesting for MSFT.

Microsoft is already saying Azure demand is higher than available capacity and new capacity is getting used quickly.

So what happens if GPU supply keeps increasing, but compute futures also keep going higher?

To me, that would be strong evidence that AI demand is still growing faster than supply.

And for MSFT, wouldn't that be a pretty good position to be in?

They are spending huge amounts building AI capacity. If compute stays scarce, that capacity should stay highly utilized and MSFT should also have more pricing power when selling GPU compute through Azure.

The setup I would watch is:

GPU supply ↑

Compute futures ↑

Azure growth stays strong

MSFT still says demand > capacity

If all of that happens together, I think the argument that hyperscalers are massively overbuilding AI infrastructure gets much weaker aka AI bubble is not real yet..

If these compute futures becomes a proper market, we may finally have a forward-looking market price for AI compute instead of just relying on what MSFT, NVDA, GOOG etc are telling us.

Could be a very useful leading indicator for Azure demand and MSFT's AI capex ROI before it fully shows up in earnings.

News: https://www.prnewswire.com/news-releases/cme-group-and-silicon-data-to-launch-compute-futures-on-october-5-to-unlock-new-way-to-hedge-ai-risks-302848593.html

Reuters reported on August 10 that Microsoft could unveil Maia 300 as early as September and is discussing manufacturing capacity with TSMC for more than 300,000 chips for delivery in 2027. Longer term, Microsoft reportedly wants capacity for more than 1 million Maia 300s.

That scale is the important new information. Maia 200 production was only in the tens of thousands, so 300,000+ Maia 300s would represent roughly an order-of-magnitude increase. Microsoft also reportedly hopes to attract major Azure customers such as Anthropic to the accelerator.

Thesis impact: moderately bullish, potentially more important for FY28+. If Maia 300 performs competitively, Microsoft can internalize more of the AI-compute economics currently going to Nvidia, potentially improving Azure AI performance-per-dollar, expanding capacity faster, and eventually supporting cloud margins. The reported >300K production target makes this more than a small experimental chip program.

https://www.reuters.com/business/microsoft-plans-unveil-its-new-maia-300-ai-chip-this-fall-information-reports-2026-08-10

Microsoft job postings explicitly mention the “new Maia-300 AI accelerator,” with teams building the software stack around both AI training and inference.

Maia 200 is mainly focused on inference and Microsoft already claims ~30% better performance/$ versus the latest hardware previously in its fleet.

If Maia 300 expands further into training + inference, this is a pretty big deal for MSFT. They’re slowly internalising more of the AI stack instead of sending a huge portion of AI compute economics to Nvidia.

https://preview.redd.it/myznbl9kliih1.png?width=447&format=png&auto=webp&s=783bc704f79ce98f9f46da45cf8405d019e13de2

https://preview.redd.it/sbjrqqv3suhh1.png?width=611&format=png&auto=webp&s=8eee716fb1b63918ccd44de16871205acda2965b

Alibaba may finally monetize Qwen even when other cloud providers host and sell it. Instead of earning only when users run Qwen on Alibaba Cloud, BABA could collect a share of third-party revenue, turning Qwen’s global open-source adoption into a high-margin licensing business.

https://qwenwork.cn/

Alibaba just launched QwenWork, its new enterprise AI work platform.

It is probably easiest to understand it as Alibaba’s version of Microsoft Copilot, but with more of the agentic workflow built directly into one product.

QwenWork can create files, search company knowledge, connect to tools and complete multi-step tasks.

The DingTalk integration is the part that matters most.

DingTalk is one of China’s biggest workplace platforms, with around 200M monthly active users. Companies already use it for messaging, meetings, attendance, approvals, tasks and daily operations.

Employees can assign work to QwenWork directly through DingTalk chats, send it files, receive completed outputs and trigger processes such as reports, calendars, tasks and approvals.

So Alibaba does not need to build adoption from zero. It can introduce QwenWork directly to companies and employees already using DingTalk.

With Microsoft, building a full agentic workflow often means combining Copilot, Copilot Studio and Power Automate. QwenWork appears to package more of that directly into one product.

The revenue potential looks promising too with the enterprise plan listed at RMB198/user per month.

Microsoft just shared some FY26 customer updates:

• Atos: 56k Copilot users + 19k AI agents
• EY: expanding AI across 400k+ employees
• NHS England: rolling out Copilot to 500k+ staff after 30K users trial
• Chow Tai Fook: 400+ AI agents already in use

This is what I meant when I said enterprise AI still feels very early.

Not long ago, most companies were doing small POCs. Now we’re starting to see those turn into real deployments at massive scale.

Capex is still the risk, no doubt.

Follow-up: some data behind my earlier post on Microsoft AI demand

Morgan Stanley’s latest CIO survey shows 72% of CIOs expect to increase spending on Microsoft over the next year, the highest among the major software vendors they track.

They’re also expecting around 41% Azure growth, citing strong demand and improving GPU capacity.

This is the kind of data I was looking for.

My point before wasn’t that Microsoft’s capex isn’t a risk. It clearly is.

It’s that we shouldn’t look at the capex without also looking at the demand that may be driving it.

From what I’m seeing at work, AI still feels very early. Companies are only starting to move from POCs into actual workflows, coding agents, automation and AI-related KPIs.

If CIOs are still planning to increase Microsoft spending while Azure is growing around 40%, it seems to support the idea that enterprise AI demand still has a long way to run.

Still early, but interesting to see the data starting to line up with what many of us are seeing in our own workplaces.

I get the concern around Microsoft’s rising AI capex. The spending is huge.

But I’m not convinced demand is anywhere close to peaking yet.

Just look at your own workplace compared with 12-18 months ago.

AI used to be mostly demos and POCs. Now teams are being pushed to use it, developers are using coding agents, and AI is starting to show up in KPIs and real workflows.

And this still feels very early.

Most employees still don’t really know what AI can do. Most companies haven’t redesigned their processes around it yet.

Microsoft also already sits deeply inside most enterprises through Office, Teams, Azure, GitHub, security, identity, etc. This is their biggest moat imo.

Even if open-source models keep getting much better and cheaper, running them at scale still needs expensive hardware, infrastructure, security, maintenance and support. Most companies are not going to manage all of that themselves.

Looking at what is happening in workplaces today, I feel like enterprise AI demand is still far from peaking.

What am I missing?

https://preview.redd.it/sx65w6abj72h1.png?width=759&format=png&auto=webp&s=d51cf88e02824bd788148beacf1047fe61cb7473

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