Tech Stories

Issue #63

Hello Reader,

It's working again.

After frantically trying and failing failing to fix it last weekend, the abrupt domain-wide blacklisting of Tech Stories by Microsoft suddenly evaporated. Mid-week, Sunday's newsletter started streaming into Office 365 and Hotmail inboxes, and open rates actually jumped beyond the usual 50%. If you received it late, that's why.

While its great things are back to normal, I've already started diversifying my content further - and I have no plans to stop. More on that later.

This week, I want to talk about the data centres next door in Johor, how AirTrunk probably got the go-ahead for its new 70MW data centre in Singapore, and the direction of enterprise AI deployments.

The data centres next door

A data centre project in Johor earned the dubious distinction of becoming the first to receive a stop-work order this week. I wrote about it on Friday and was surprised by the responses - including from people who'd normally avoid commenting on sensitive topics.

Dirty roads, construction at ungodly hours, missing safety hoardings six months into construction. Clearly, this struck a nerve.

So what happened? From what I can piece together, Johor's state housing and local government committee chairman Datuk Mohd Jafni Md Shukor decided to go nuclear on Thursday. He called the media, filmed himself issuing the order on-site, then held a press briefing. For good measure, the 30-minute video was also uploaded to Facebook, where it promptly went viral.

While he carefully avoided naming ZData - and mainstream media followed suit - glimpses of personnel in ZData attire gave the game away. The data centre community already knows. Which means the data centre community is already aware.

There is a lot to unpack here. Why go public so dramatically, burning all bridges? Why not issue a private warning first? And is ZData the only offender here? I'll let you connect the dots.

Looking at this situation, ZData could certainly benefit from professional crisis communications help at this point.

AirTrunk's 70MW Singapore play

On Monday, AirTrunk confirmed that it is building a new data centre in Singapore. At 70MW, AirTrunk SGP2 is the largest data centre approved since the 2019 moratorium. (Nxera's 58MW DC Tuas doesn't count because it was technically approved before the moratorium.)

I wrote about what little details we know about SGP2 here. Unfortunately, AirTrunk wasn't in a position to take questions. I asked when I first saw the announcement under embargo on Friday.

Still, how did AirTrunk secure the go-ahead for such a large data centre when the pilot DC-CFA only offered 20MW to each winner?

The answer, as I wrote last year, is that the DC-CFA isn't the only pathway to data centre capacity in Singapore. That became clear when I clarified with IMDA how the 300MW will be allocated. What I know for certain: DC-CFA isn't the only pathway.

There are several pathways: data centres powered by renewables, those supporting highly strategic initiatives, and of course, the DC-CFA.

But as I said before, the era of small data centre operators is effectively over. The capital requirements, access to talent, ecosystem play, and in Singapore at least, the clout needed to deploy novel fuel, are simply out of reach for smaller operators today.

The future of AI

"Future proof with AI" by AI Singapore and Elastic.

What will the future of AI bring? This is a topic I've often written about. As an AI realist, I've explored both the opportunities and the risks, honestly evaluating AI without the fluff.

This week, I was at "Future proof with AI" organised by AI Singapore and Elastic, which I wrote about here.

It was a rich session that I'm still thinking over. But perhaps the most impactful was the final session where Elastic's senior customer architect Han Xiang Choong shared his generative AI journey and how it solved a particularly daunting problem at his previous organisation.

Tasked to solve long servicing times at the customer service centre, the team explored multiple approaches including traditional AI/ML and even building an LLM from scratch. Nothing worked.

Turns out the problem was completely human. Long training times, undocumented best practices, and poor knowledge transfer were the real culprits. Instead of building an impossibly good AI solution, the team built a co-pilot tool that helped customer service personnel quickly access the answers they needed.

The results? Call handling time dropped by 20% and accuracy went up to 95%. Sometimes the best AI solution is knowing when humans need better tools, not replacement.

Platform-native content

I’m switching things around. As I wrote last week, this editor’s note will also appear on both Clearly Tech and LinkedIn to reach more readers. I like to think the version here will be the most personal though, so do stay around.

I’ve also made some tweaks for better platform-native content. Stories on my blog are now written as full articles rather than direct duplicates of my LinkedIn posts.
My content on LinkedIn will stay in the current style. For what it's worth, it's actually harder to write LinkedIn posts that are concise, clear, and easy to read without compromising accuracy.
I shall also be writing some longer stories on Clearly Tech soon, so do check it out if you haven't already done so.

By the way, someone suggested that some readers might be interested in improving their writing with my help. Is that true? What are your thoughts?

As usual, you can reach me by hitting reply.

Regards,
Paul Mah.

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