Lifestyle Updates: CNA Explains: What is the Gale-Shapley algorithm and how is it powering GovTech’s new dating platform?

Source:

https://www.channelnewsasia.com/singapore/firstdate-gale-shapley-algorithm-govtech-dating-platform-6424666

ChatGPT:

🇸🇬 CNA article: Singapore's government is using a 60-year-old matching algorithm to play Cupid

The CNA article is essentially a deep dive into the technology behind GovTech's new FirstDate dating pilot — particularly the Gale-Shapley “stable marriage” algorithm.

The bigger story is not really the algorithm itself. It's whether a dating system designed around fewer choices, mutual compatibility and mathematical matching can address some of the problems created by today's swipe-heavy dating apps.

CNA published the article on 2 October 2026, shortly after FirstDate launched its pilot. (CNA)

---

💘 What is FirstDate?

FirstDate is a GovTech-developed dating platform currently being piloted among single public officers aged 21–35.

Users:

1. Log in using Singpass

2. Complete a questionnaire covering interests, lifestyle, values and relationship preferences

3. Receive one recommended match per cycle

4. Have 72 hours to decide whether to connect

5. Only reveal contact details if both sides accept

The idea is deliberately different from Tinder-style infinite swiping.

FirstDate's philosophy is essentially:

Less choice, more consideration.

GovTech says the project originated from an internal hackathon and is testing whether giving people fewer, potentially more compatible matches encourages more thoughtful connections. (CNA)

---

🧮 What is the Gale-Shapley algorithm?

This is the genuinely interesting technology angle.

The algorithm was developed by mathematicians David Gale and Lloyd Shapley in 1962.

Imagine there are 100 men and 100 women, each with their own ranked preferences.

The algorithm works roughly like this:

1. People propose to their preferred choices

↓

2. Each recipient keeps their preferred proposal

↓

3. Rejected people try their next preference

↓

4. The process continues

↓

5. Eventually you get a “stable” matching

A stable matching means there isn't another pair who would both prefer each other over their current partners.

This isn't the same as:

“Everyone gets their No. 1 choice.”

It doesn't.

The objective is to find a stable allocation, not necessarily the globally most desirable couples. CNA explains that the outcome can also depend on which side is doing the proposing. (CNA)

---

🏆 Why is everyone calling it “Nobel Prize-winning”?

Because the broader matching theory and market-design work associated with Lloyd Shapley and Alvin Roth won the 2012 Nobel Memorial Prize in Economic Sciences.

And this isn't just a dating algorithm.

Variants of matching theory are used for things such as:

• medical residency placements

• school admissions

• kidney exchanges

That's actually one of the coolest aspects of the story.

Singapore is essentially taking mathematical machinery originally developed for allocating scarce resources and matching people to institutions and applying it to romantic relationships. (CNA)

---

🤔 But there's a catch

The algorithm can produce a mathematically stable match.

It cannot produce:

chemistry.

It doesn't know:

• whether someone has a great sense of humour

• whether two people click face-to-face

• whether their physical attraction exists

• whether their conversation flows

• whether they actually want to meet again

Even FirstDate explicitly acknowledges that compatibility on paper doesn't guarantee chemistry or a relationship. (FirstDate)

So:

Stable match ≠ successful relationship.

That's probably the most important limitation of the entire concept.

---

📱 Why people are actually talking about this

The online reaction has been far bigger than you might expect for a GovTech hackathon project.

The original discussion on r/singapore attracted 500+ upvotes, while subsequent threads continued generating hundreds of votes and comments. (Reddit)

And the story has gone international.

A post on X about FirstDate reportedly reached more than 2.8 million views and 14,000+ likes, according to coverage of the viral reaction. (Nestia News)

That is a huge amount of attention for what is currently only a limited public-sector pilot.

---

😂 The dominant social-media reaction: “Government dating app?!”

This is where the story becomes quintessentially Singaporean.

A lot of the viral conversation isn't actually about Gale-Shapley.

It's:

“Singapore has a government dating app.”

Memes include:

• “We got Singapore government dating app before GTA 6”

• jokes about government-assigned girlfriends/boyfriends

• “government-mandated GFs”

• jokes about BTO priority being awarded to successful matches

• jokes about government incentives for having babies

• comparisons with the old Social Development Unit (SDU)

MustShareNews documented the same reaction, including jokes about the government effectively becoming Cupid. (MS News)

---

💬 Reddit reaction is actually quite nuanced

This is where things get more interesting.

🟢 “This is actually a good idea”

Some Redditors argue that conventional dating apps have a fundamental conflict:

Dating apps make money when you keep using them.

FirstDate has almost the opposite incentive:

If you find someone and leave the platform, that's success.

One of the most upvoted comments essentially made this argument — that commercial apps can be designed around maximising engagement, whereas FirstDate is designed around actually getting users off the platform. (Reddit)

Another Reddit discussion argued that identity verification through Singpass could eliminate one of the biggest dating-app problems:

fake profiles / catfishing. (Reddit)

---

🟡 “The algorithm is interesting, but humans aren't spreadsheets”

This is the other major theme.

Redditors with knowledge of game theory are digging into the actual mechanics.

One detailed discussion pointed out that stable matching isn't necessarily the same as optimal romantic matching.

There can be multiple stable solutions.

And who gets to propose matters.

That's a legitimate technical concern rather than just a joke. (Reddit)

---

🔴 “Why is the government involved in dating?”

There is also significant scepticism.

Some commenters describe the concept as:

• dystopian

• unnecessary government intervention

• treating relationships like an administrative problem

HardwareZone has a similar split.

One HWZ commenter questioned:

“using tax payers monies?”

while another defended the concept by arguing that shy people might benefit from having a mechanism that introduces them to compatible singles. (HardwareZone Forums)

That's probably the fundamental debate:

Is this a useful public-service experiment, or is government trying to solve something that should remain private?

---

🇸🇬 The age restriction is getting plenty of attention

The pilot is restricted to:

21–35-year-old unmarried public officers.

That produces a lot of jokes from people outside the eligibility group.

One Reddit comment joked about being over 35 and therefore effectively being abandoned by the government. (Reddit)

But there's also a serious reason to question the restriction.

If the experiment eventually expands, the government would need to determine:

• whether non-public servants should participate

• whether people above 35 should participate

• whether the same algorithm works across a much larger population

• whether different relationship preferences require different matching models

For now, GovTech says there is no decision to expand FirstDate beyond the pilot. (CNA)

---

🧑‍💻 The most interesting technical criticism

There's an important distinction between Gale-Shapley itself and how FirstDate implements it.

The classic Gale-Shapley problem assumes:

• clearly defined preference rankings

• two groups

• reasonably well-defined preferences

• participants willing to participate in the matching process

Real humans are much messier.

For example:

“I like people who enjoy travelling.”

That's not necessarily a strict ranking.

And:

“I don't want someone who smokes.”

isn't necessarily equivalent to saying:

“I prefer non-smokers by exactly X amount.”

FirstDate therefore has to translate messy human preferences into structured data before the algorithm can work.

That is arguably the more difficult engineering problem.

---

📱 X / Facebook / Instagram / TikTok / Threads

The strongest publicly indexed evidence is currently from X and Reddit, rather than Facebook/Instagram/TikTok/Threads.

The X reaction is clearly viral, with the FirstDate story reportedly reaching millions of views. (Nestia News)

The themes appearing in social-media coverage are:

😂 Humour

“Government-assigned girlfriend.”

🤖 Tech fascination

“Nobel Prize algorithm for dating.”

😬 Dystopian jokes

“Government decides who you date.”

❤️ Genuine interest

“Maybe fewer choices actually produce better relationships.”

👶 Fertility discussion

“Is this really about dating, or is it another response to Singapore's declining birth rate?”

🔐 Privacy/security

“Singpass means you know the person is real.”

The last one is particularly interesting because government verification could actually be a meaningful differentiator from commercial dating apps.

---

🧠 My assessment of the article

I'd rate this differently from the SIA/crypto articles you asked about.

Factor	Score
Positive sentiment	7/10
Negative sentiment	4/10
Humour/meme potential	10/10
Social-media engagement	9/10
Singapore relevance	10/10
Technology interest	9/10
Practical impact today	4/10
Potential long-term impact	8/10
International interest	9/10
Overall article impact	9/10

⭐ Sentiment: 7/10 positive

The overall mood is surprisingly positive underneath the jokes.

A lot of people are basically saying:

“This sounds ridiculous... but actually, why not try it?”

That's quite different from outright rejection.

🔥 Impact: 9/10

Not because FirstDate itself currently affects millions of people — it doesn't.

It's because the story hits three extremely powerful Singapore topics simultaneously:

Dating + Government + Technology

and then adds:

AI/algorithms + Singpass + fertility + Nobel Prize mathematics.

That's an almost perfect recipe for viral discussion.

---

🎯 The real story behind the story

The CNA article is ostensibly:

“How does the Gale-Shapley algorithm work?”

But the actual viral story is much bigger:

“Singapore built a government dating app that uses Nobel-winning mathematics to decide who gets introduced to whom.”

That's why this story has travelled internationally.

And there's an even more interesting question underneath it:

Could fewer choices actually make people happier?

Modern dating apps give users potentially hundreds or thousands of options.

FirstDate essentially says:

“Stop swiping. Here's one person. Talk to them.”

That's a surprisingly radical design philosophy.

Commercial dating apps generally optimise for engagement and matching activity.

FirstDate is explicitly experimenting with getting users to stop using the platform.

That makes this much more interesting than just another Singapore government app.

---

🔥 For YOLOFOMO101: 9.5/10 content opportunity

I'd actually rate this as one of the stronger stories you've sent me recently for your blog/Shorts.

The best hook isn't the technical explanation.

I'd use:

“Singapore built a dating app that wants you to stop swiping.”

Then:

No Tinder-style endless scrolling.
One match at a time.
Singpass verified.
Nobel Prize-winning algorithm.
72 hours to decide.

Then finish with:

“The weirdest part? The algorithm was originally designed for things like doctor-hospital matching and kidney exchanges.”

That's much more likely to generate comments than simply explaining the Gale-Shapley algorithm.

(CNA)

Technology Updates: Low on code, high on miles: S’porean uses AI to create tool to maximise credit card rewards

Source:

https://www.straitstimes.com/tech/low-on-code-high-on-miles-sporean-uses-ai-to-create-tool-to-maximise-credit-card-rewards

ChatGPT:

This article is particularly relevant to Singapore’s miles/card community because it describes KiasuMiles, an AI-powered tool built specifically around the problem of remembering which card to use at which merchant.

1. What the Straits Times article says

The Straits Times article was published on 26 September 2026 and profiles Hosan Swee, a Singaporean engineer and AI educator who built KiasuMiles despite not having traditional software-development skills. (The Straits Times)

His problem was familiar to serious miles chasers: he and his wife had around a dozen cards, each with different:

• bonus categories

• minimum-spend requirements

• monthly caps

• exclusions

• payment-method rules

• merchant/MCC treatment

Remembering all of this at the point of payment became cumbersome.

His solution was to build an AI-powered recommendation system that answers questions such as:

Which card should I use at Cold Storage with Apple Pay?

or

Which card should I use for GrabFood?

The important difference from simply asking ChatGPT

KiasuMiles isn't intended to be a generic chatbot that guesses an answer.

It has a structured database of Singapore credit-card rules, currently covering roughly:

• 50 credit cards

• 3,300+ merchants

The system matches the user's cards against the merchant, spending category and payment method, while taking into account caps and conditions. (The Straits Times)

For example, the article uses Cold Storage and says KiasuMiles may identify the DBS yuu card, while also warning about its conditions such as the monthly spending requirement and participating yuu merchants. (The Straits Times)

---

2. The really interesting part: AI did the coding

This is arguably the bigger story than the miles tool itself.

Swee had the idea but wasn't a conventional programmer. He used:

• ChatGPT

• Codex

• ChatGPT Work

to turn natural-language instructions into working software.

One of his prompts essentially told the AI to build a structured knowledge base containing:

card → earn rate → minimum spend → categories → caps → exclusions.

The AI then gathered information from bank and merchant websites and structured it.

He says something that is quite important for the current AI era: the AI is not replacing the need to think about the product; it is dramatically reducing the cost of implementation. (The Straits Times)

He also built it incrementally rather than asking AI to create everything in one giant prompt, testing the recommendation logic with realistic scenarios before expanding it.

---

3. Why KiasuMiles is potentially more useful than asking ChatGPT directly

This is the strongest technical point in the article.

A normal ChatGPT question might be:

"What's the best card for Cold Storage?"

The problem is that a general-purpose AI may have:

• outdated card rules

• incomplete information

• incorrect MCC assumptions

• forgotten spending caps

• incorrect exclusions

• incomplete payment-method rules

KiasuMiles instead maintains a specialised knowledge base and uses that to produce the recommendation. (The Straits Times)

The current KiasuMiles site also says it returns the card from your own wallet, rather than simply recommending the theoretically best card available in Singapore. (KiasuMiles)

That's an important distinction.

---

4. What KiasuMiles actually looks like now

The product has evolved beyond the description in the newspaper article.

Its current site describes a workflow where you connect it to a compatible AI agent and tell it which cards you own. It then answers:

Merchant + payment method → your eligible cards → best card + conditions + fallback. (KiasuMiles)

Its open-source GitHub project describes the same concept as a Singapore credit-card rewards optimisation MCP. (GitHub)

The project's published architecture is interesting because the hosted service says it doesn't require card numbers, expiry dates, CVVs or banking credentials. It works with card names and reward rules rather than payment credentials. (GitHub)

The current project reports 48 cards and about 3,308 merchants, which is broadly consistent with the figures reported by the article. (GitHub)

---

5. What the online miles community is saying

I searched specifically for discussion around KiasuMiles + the article, rather than treating general Singapore credit-card discussions as reactions to this particular story.

Important caveat

The article is only a couple of days old, so there isn't yet a huge amount of independently indexed discussion.

I could find much more substantive material around KiasuMiles itself than around the article on Facebook/Instagram/X/Threads/TikTok.

That means I wouldn't claim there is already a broad social-media consensus.

The reaction so far is better understood through several themes.

---

🟢 Theme 1: “This is exactly the problem miles hackers have”

This is the most obvious appeal.

Singapore's credit-card ecosystem has become sufficiently complicated that even experienced miles collectors can make mistakes.

The creator himself explains that the losses aren't necessarily one giant mistake; they're lots of small incorrect taps over time. His own earlier write-up describes discovering that he was sometimes using the wrong card despite following the miles community. (Hosan's Substack)

The current KiasuMiles product page makes the same argument visually:

many cards → one cashier → a few seconds to decide. (TinkerHaus)

That is a very Singapore-miles-community problem.

---

🟢 Theme 2: The interesting innovation isn't “AI recommends a credit card”

This is where I think the article has more significance than it initially appears.

People have been asking ChatGPT questions like:

"Which card gives the most miles for dining?"

for a while.

The more interesting idea is:

AI + structured data + personal wallet + merchant database

rather than simply:

AI chatbot + question

The current KiasuMiles architecture even breaks the recommendation into steps such as:

identify merchant → determine MCC → filter user's wallet → rank eligible cards → return recommendation. (TinkerHaus)

That is much closer to an actual decision-support system than a chatbot.

---

🟡 Theme 3: Accuracy is going to be the biggest concern

This is the biggest weakness that experienced miles users are likely to focus on.

Credit-card rewards change constantly.

Banks can change:

• earn rates

• minimum spend

• caps

• exclusions

• MCC eligibility

• wallet/payment-method treatment

• merchant partnerships

So the question isn't merely:

"Can AI build the database?"

It's:

"Can the database stay correct?"

KiasuMiles says it regularly updates its rules, and the creator explicitly warns that simply asking a general AI chatbot can produce incomplete or outdated information. (The Straits Times)

That makes data maintenance and verification arguably more important than the AI itself.

---

🟡 Theme 4: MCC/payment-method edge cases remain difficult

This is an especially important issue for Singapore miles collectors.

For example:

Merchant → payment method → payment processor → MCC → card rules

can all affect the actual earn rate.

Apple Pay isn't necessarily equivalent to every other payment method.

Online transactions aren't necessarily equivalent to physical transactions.

A merchant's actual MCC may differ from what consumers expect.

The KiasuMiles approach attempts to explicitly incorporate merchant category and payment method rather than simply saying "use Card X for groceries." (The Straits Times)

That is probably one of the strongest aspects of the idea.

---

🟢 Theme 5: Open-source makes it more interesting

The project is publicly available on GitHub under an MIT licence. (GitHub)

That potentially allows developers/miles enthusiasts to:

• inspect the implementation

• identify bugs

• contribute improvements

• see how the recommendation engine works

• build their own integrations

The creator's Tinkerhaus page describes it as an open-source AI card-recommendation skill. (TinkerHaus)

That gives it more credibility than a mysterious black-box "AI miles optimiser."

---

6. Facebook / Instagram / X / Threads / TikTok

I specifically searched these platforms for the article and KiasuMiles.

The indexed public discussion is currently quite thin, especially compared with Reddit and specialist Singapore miles communities.

That's important because social-media search engines often don't expose all posts, especially:

• private Facebook groups

• Instagram comments

• Threads posts

• TikTok comments

• X posts behind indexing/login restrictions

So I wouldn't interpret the lack of indexed posts as "nobody is interested."

What is visible points to the story being picked up primarily as an AI/product-building story, rather than becoming a major controversy around credit-card rewards.

---

7. The bigger discussion this article has triggered

I think there are actually two stories hidden inside this article.

Story A — Miles

Singapore's credit-card optimisation game is becoming so complicated that there is now a need for a dedicated AI decision engine.

That's a sign of how sophisticated the local miles ecosystem has become.

Story B — AI

A non-traditional programmer can now go from:

"I wish there was a tool that did this"

to

a functioning specialised financial application

using AI coding tools.

That second story is arguably more significant.

The creator himself says that learning basic software-development terminology improved his ability to communicate with AI. In other words, you don't necessarily need to become a professional programmer; you need enough technical understanding to direct, test and challenge the AI. (The Straits Times)

---

8. My overall read of the article

I'd summarise it this way:

The article isn't really about getting more miles. It's about the transition from "AI answers questions" to "AI operates specialised decision systems."

KiasuMiles takes a particularly Singaporean problem — "Which of my 8–12 credit cards should I tap right now?" — and turns it into a structured AI workflow.

The strongest parts are:

• Personalised: only considers cards you actually own.

• Merchant-specific: rather than generic category recommendations.

• Condition-aware: considers caps/minimum spends.

• Payment-aware: attempts to account for payment method.

• Structured: relies on a maintained database rather than pure chatbot memory.

• Open-source: implementation can be inspected. (KiasuMiles)

The biggest thing I'd watch is data accuracy. A miles tool can be 95% right and still give you a wrong recommendation at exactly the merchant where the remaining 5% matters. Credit-card T&Cs also change frequently.

---

And there's a funny Singapore angle

The name KiasuMiles is actually quite appropriate.

The traditional miles enthusiast workflow is:

carry 8 cards → remember 20+ rules → check Telegram/Reddit/blogs → remember monthly caps → remember MCC → stand at cashier → panic → tap card → later check whether you got the right miles.

KiasuMiles tries to turn that into:

"I'm at Sheng Siong. Which of my cards?" → one answer.

Its own current demo illustrates this with merchants such as Sheng Siong and Shell, showing the recommended card, earn rate, cap and fallback. (TinkerHaus)

Given that you already use a fairly sophisticated Singapore miles stack involving Citi Rewards, UOB cards, Amaze/HeyMax etc., this is actually much closer to your real-world use case than a generic credit-card comparison site.

Lifestyle Updates: Is standing still or walking on the escalator more efficient?

Source:

https://www.instagram.com/reel/Ddiye96FWZR/?stkn=MWptNnJ2dWRtMm5xbw%3D%3D

ChatGPT:

The Reel is about a surprisingly counter-intuitive question: during busy periods, is an escalator more efficient if people stand on both sides, or if one side is kept open for walking?

I researched the underlying London trial and Singapore discussions around the same issue. The key finding is real, but there is an important qualification: standing on both sides improves total passenger throughput in sufficiently crowded situations; it does not necessarily make the journey faster for someone who wants to walk up.

🎬 What the video is saying

The Reel's main argument is:

Standing on both sides of an escalator can move more people through the system than having everyone stand on one side and reserve the other for walkers.

The conventional arrangement seems logical:

• Left: people stand

• Right: people walk

• Walkers get to the top faster

• Everyone else stands

But there is a capacity problem.

If most passengers want to stand, reserving roughly half the escalator for the relatively small number of walkers means that a large amount of usable capacity is deliberately left unused.

When the escalator is crowded, filling both sides with standing passengers can therefore move more people onto the escalator at once.

The London experiment

This wasn't just a theoretical calculation.

Transport for London trialled standing on both sides at Holborn Underground station, where the escalators are particularly long. An earlier trial found that standing on both sides could increase escalator capacity by about 30%. TfL subsequently conducted a longer six-month trial involving two escalators. (TfL Content)

The reason Holborn was particularly suitable is important: its escalators are around 23–24 metres high, and relatively few passengers wanted to walk up such a long escalator. Consequently, under the conventional arrangement, a substantial portion of the escalator's capacity could be unused. (The Independent)

TfL reported that during the earlier test, congestion was reduced by about 30%, and station-control measures to stop passengers entering because of crowding were needed much less frequently. (TfL Content)

---

🧠 Why does it work?

Imagine an escalator with 100 passengers waiting.

Conventional system

Suppose:

70 people want to stand
30 people want to walk

If everyone stands on one side, only around half the physical width is being used for standing passengers.

The walking lane may also not be continuously occupied because walkers arrive at different rates.

Standing on both sides

You can put standing passengers across the entire width.

So instead of:

🧍 🧍 | 🚶 →

you effectively get:

🧍 🧍 | 🧍 🧍

More people enter the escalator per unit of time.

That's why system-wide throughput can increase even though an individual walker loses the ability to climb.

---

⚠️ The important catch

This is where social-media versions of this story can be misleading.

"Standing on both sides is more efficient" does NOT mean "everyone reaches the top faster."

Those are different measurements.

A person who normally walks up the escalator may actually take longer under a standing-only arrangement.

The benefit is primarily:

More people can be transported through the bottleneck per unit of time.

A Reddit discussion about the Holborn experiment captured this distinction particularly well. Some London commuters recalled that it made things faster for people who would normally stand, while making the experience slower for people who specifically wanted to walk. (Reddit)

So there is a genuine individual-vs-system trade-off.

---

🇸🇬 Why this is particularly relevant to Singapore

This is where the Reel gets interesting for Singapore.

Singapore has a deeply established keep-left / walking-right escalator culture. The Straits Times reported in 2024 that the "stand on one side, walk on the other" practice has been longstanding here, while also publishing a letter arguing that standing on both sides could improve safety and efficiency in crowded locations. (The Straits Times)

And Singaporeans have actually been debating this for years.

HardwareZone

A 2025 HardwareZone thread titled "We shld go back to standing onboth sides of escalators" generated discussion specifically about the queues created by everyone squeezing onto one side. Several posters supported using both sides, particularly during peak periods. (HardwareZone Forums)

Another HWZ discussion asked why the majority should squeeze into one lane for the sake of the relatively small number of people walking. One poster argued that the main concern isn't escalator wear but safety, because walking on moving steps increases the chance of tripping. (HardwareZone Forums)

There are also Singaporeans who strongly defend the existing convention because they regard keeping left as basic pedestrian etiquette.

---

Reddit Singapore: surprisingly divided

A 2024 r/Singapore discussion about escalator etiquette had hundreds of votes, and the comments show the cultural conflict very clearly. (Reddit)

One camp essentially says:

"Keep left; right is for people who need to walk."

Another camp points out:

"If almost everyone wants to stand, why waste half the escalator?"

Some Redditors specifically cited the efficiency argument and said standing on both sides could clear a crowded entrance more quickly. Others said that the existing convention is simply more considerate because people have different urgency levels.

There is also a practical Singapore problem:

At places such as MRT stations, people can end up forming a long queue for the standing lane while the other half of the escalator is relatively empty.

That's exactly the situation where the standing-on-both-sides model becomes attractive.

---

🇸🇬 There's actually evidence behind the Singapore debate

One thing I found particularly interesting is that Singapore's debate isn't merely based on social-media opinions.

The Straits Times previously reported that experts did not consider walking on escalators to be a significant cause of mechanical wear, despite claims that concentrating passengers on one side damages escalators. The more established safety concern was the possibility of people falling while walking. (The Straits Times)

So I would separate three claims:

Claim	What the evidence says
Standing on both sides can increase throughput	Yes, in appropriate crowded conditions
It makes each individual passenger reach the top faster	No
Walking on one side necessarily causes major escalator damage	Not supported by the Singapore expert evidence I found
Walking introduces additional fall/safety risk	Experts cited by ST have identified safety as a concern
It works equally well at every escalator	No — escalator length, crowd density and number of walkers matter

The TfL research itself also suggests this is context-dependent. Its own commentary said many normal-length escalators could benefit from having people walk, whereas Holborn was unusual because its very long escalators discouraged walking. (YouTube)

---

🌏 What I found across social media/forums

HardwareZone

Leans toward: "Use both sides when crowded."

There are multiple threads complaining about queues created by the one-side-standing convention. But there are also people who consider keeping left/right walking to be basic etiquette. (HardwareZone Forums)

Reddit Singapore

Very divided.

People understand the capacity argument but many still prefer keeping one side available for walkers because they value the option to move faster. (Reddit)

Reddit London

The original Holborn experiment remains controversial years later. Some Londoners remember the 30% capacity improvement, while others remember the trial as frustrating because people who wanted to walk were prevented from doing so. (Reddit)

X / Facebook / Instagram / TikTok / Threads

I couldn't find sufficiently reliable, directly indexed discussion of this specific Reel on those platforms, so I wouldn't manufacture a "social-media consensus." The strongest searchable Singapore discussion is currently Reddit/HWZ, plus the Straits Times coverage.

---

🤔 So is the video correct?

Broadly yes — but the viral version needs a qualification.

The London experiment genuinely found roughly 30% higher escalator capacity when people stood on both sides. TfL subsequently used a standing-only arrangement at Holborn based on that earlier result. (TfL Content)

But the correct statement is:

When an escalator is heavily congested and most passengers are willing to stand, standing on both sides can increase total throughput.

It's not:

"Everyone should always stand on both sides because it's faster."

That's too broad.

For Singapore, I think the most logical interpretation of the evidence is:

Off-peak + normal crowd → keeping a walking lane makes sense if people actually use it.

Peak-hour + huge queue + mostly standing passengers → filling both sides can make better use of the escalator's capacity.

Very long escalator → the case for standing on both sides becomes stronger, because relatively few people may actually want to walk all the way up.

And that's essentially why Holborn was chosen for the London experiment in the first place. (YouTube)

So the fascinating part isn't really "walking vs standing." It's that an escalator's optimum operating rule changes depending on crowd density and how many people actually want to walk.

Technology Updates: Singapore PC prices surge as growing AI infrastructure competes for components

Source:

https://www.straitstimes.com/tech/singapore-pc-prices-surge-as-growing-ai-infrastructure-competes-for-components?utm_medium=social&utm_source=facebook&utm_campaign=stfb

ChatGPT:

I checked the Straits Times article and the discussion that has developed around it, especially on HardwareZone and Reddit. The article is getting a surprisingly strong reaction because the price increases are not theoretical — Singapore buyers are reporting that they are already seeing them.

1. The article in a nutshell

The Straits Times' main finding is:

Singapore PC prices have jumped sharply in 2026 because AI data-centre construction is competing with ordinary consumers for memory, storage and graphics components.

IDC data cited by ST shows average PC prices in Singapore rose by up to 40% year-on-year in Q2 2026. More importantly, the shortage is concentrated in components that consumers actually notice. (The Straits Times)

Component / product	Approx. price change
Windows laptop	+26%
Normal desktop	+28%
Gaming desktop	+39%
Gaming laptop	+17%
32GB DDR5 RAM	~4×
1TB SSD	+120%
4TB HDD	+64%
RTX 5060 8GB	+44%

The really shocking number is RAM.

A 32GB DDR5 dual-channel kit that averaged around S$195 in August 2025 was around S$763 one year later. (The Straits Times)

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2. Why is AI causing PC prices to rise?

This is the important part.

It's not simply:

AI companies buy Nvidia GPUs → GPUs become expensive.

The supply-chain effect is much broader.

AI data centres require enormous amounts of:

• HBM memory

• conventional DRAM

• SSD/NAND storage

• GPUs

• networking equipment

• CPUs

• power infrastructure

Memory manufacturers can make substantially more money selling components into the AI/data-centre market than the traditional consumer-PC market.

So manufacturers have an incentive to allocate scarce production capacity toward enterprise customers.

That leaves less supply for:

PCs → laptops → DIY upgrades → consumer SSDs → gaming PCs.

IDC's Ho Jin Wei describes this as manufacturers shifting memory production toward AI infrastructure because of the higher margins. (The Straits Times)

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3. The problem isn't only RAM

This is where I think the article is particularly useful.

RAM

The biggest problem.

32GB DDR5: S$195 → S$763

That's approximately a 291% increase, or almost four times the old price. (The Straits Times)

SSD

1TB internal SSD:

S$149 → S$328

That's more than double.

HDD

4TB HDD:

S$205 → S$336

Still a substantial 64% increase.

GPU

RTX 5060 8GB:

S$572 → S$824

About 44% higher.

And ST says graphics-card manufacturers could raise prices further in the second half of 2026. (The Straits Times)

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4. The really bad news: don't expect a quick recovery

This is probably the most important takeaway from the article.

IDC doesn't expect a meaningful return to cheaper PC hardware soon.

Its view is roughly:

2026 → expensive

2027 → still expensive

Late 2027 → some easing

2028+ → meaningful relief more likely

IDC therefore says meaningful PC-price relief is more likely a 2028-and-beyond story. (The Straits Times)

That is much more serious than a normal temporary shortage.

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5. Singapore retailers are already hurting

This creates an unusual situation:

Components are becoming more expensive while retailers are selling fewer computers.

ST spoke to four Sim Lim Square retailers, who reported sales declines of around 20% to 80% compared with 2025. (The Straits Times)

One retailer selling new/refurbished PCs reportedly saw sales fall 80% and said it was becoming difficult to cover rent.

So retailers are being squeezed from both sides:

higher wholesale costs

customers refusing to buy

= terrible margins

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6. HardwareZone reaction: "Yes, this is real"

The HardwareZone thread is particularly interesting because these aren't just people discussing an article — many are comparing it with what they personally paid for hardware.

The thread has already accumulated roughly 69 replies and 4,000 views. (HardwareZone Forums)

One of the recurring observations:

RAM and SSDs are at all-time highs.

Users specifically mention:

• RTX 5060 previously around S$500 → now S$800+

• RTX 5060 Ti 16GB → above S$1,200

• RTX 5080 → approaching S$3,000

• RTX 5090 → around S$7,000–8,000

HardwareZone users are therefore broadly agreeing with the article rather than dismissing it as sensationalist. (HardwareZone Forums)

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7. The "I should have bought earlier" effect

This is probably the strongest sentiment across the forums.

People who bought PCs in 2025 are basically saying:

"I didn't realise how lucky I was."

One Reddit user bought a high-end PC in November 2025:

• RTX 5090

• Ryzen 9800X3D

• 64GB RAM

• approximately S$6,700

They subsequently tried to replicate the same configuration and estimated it would now cost approximately S$9,800. (Reddit)

That's roughly a:

S$3,100 / 46% increase.

Another user said they bought a 5080 around the same period and similarly felt they had bought just before the surge. (Reddit)

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8. Reddit: "Delay the upgrade"

This is where the discussion gets particularly interesting.

A June Reddit discussion asked how Singapore PC users were coping with the RAM crisis.

One user said they were considering repairing an old PC because reputable builders were already charging around S$1,500–2,000 for a decent build. (Reddit)

Another user said:

• 32GB DDR5 previously cost about S$300

• now S$200 could barely buy 16GB DDR4

The conclusion from many commenters was essentially:

Don't upgrade unless you actually need to.

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9. And now people are asking: should I buy before 11.11?

This is especially relevant for Singapore shoppers.

A Reddit thread posted just two days ago asks exactly that:

Buy a PC now or wait for 11.11?

The user was worried that Shopee sellers might raise prices before 11.11 and then offer artificial discounts.

One commenter said their prebuilt PC went from:

S$3,200 → S$3,900 in three months.

The general sentiment was:

Don't assume 11.11 will magically make PCs cheaper.

There may be promotions, but if the underlying hardware price keeps rising, a S$100–200 sale voucher isn't necessarily meaningful. (Reddit)

That's an important distinction.

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10. Some users think the article is too simplistic

There is also a more technical counterargument.

It's not literally true that:

"AI companies are buying all the RAM."

The supply chain is more complicated.

AI accelerators primarily use HBM, while PCs use conventional DDR4/DDR5 DRAM.

However, these products ultimately compete for semiconductor manufacturing capacity and related resources.

A Reddit hardware discussion explains the economic incentive well: manufacturers can obtain much better margins from HBM/AI-related memory than conventional consumer DRAM. (Reddit)

So the real story is:

AI demand changes the economics of memory manufacturing.

That causes manufacturers to prioritise higher-margin products, tightening supply for consumer hardware.

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11. Another major debate: "Is this just another AI bubble?"

This is probably the most interesting social-media argument.

Some Redditors believe the current pricing is unsustainable because AI infrastructure spending is becoming excessive.

The argument is:

AI investment explodes → memory demand explodes → manufacturers expand capacity → AI investment eventually slows → memory oversupply → prices crash.

There are already people comparing it to the dot-com bubble and waiting for the AI boom to burst. (Reddit)

But there's an equally strong counterargument:

AI demand isn't fake.

Even if some AI companies fail, the surviving hyperscalers may continue spending enormous amounts on infrastructure.

So nobody knows whether we're heading toward:

2000-style AI bust

or

a sustained structural increase in computing demand.

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12. HardwareZone has a very Singaporean solution

One of the funniest themes in the discussion is basically:

"Just don't buy."

People are saying:

• keep your existing PC

• repair it

• buy used

• look at Carousell

• postpone upgrades

• hope the AI boom eventually ends

One HardwareZone user essentially said they were holding out until 2028 for hard-drive prices to normalise. (HardwareZone Forums)

Another said they were still using a 2016-era Asus laptop, having only upgraded its SSD/RAM.

That tells you how consumers are adapting: extending hardware lifespans.

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13. Social media beyond Reddit/HWZ

I also searched for discussion on X, Facebook, Instagram, TikTok and Threads.

The important finding is that there isn't yet a huge independent conversation around the specific ST article.

The discussion is much more fragmented:

X

Mostly global discussion about:

• RAM shortage

• Nvidia GPU prices

• AI infrastructure spending

• "AI bubble" arguments

• memory manufacturers prioritising HBM

Facebook

More Singapore-focused sharing of the ST story, with discussion generally centred on:

"AI is making everything expensive."

Instagram/TikTok

Much less substantive discussion. The topic is more likely to appear as short-form PC-building/AI content rather than detailed analysis.

Threads

Similar situation — discussion exists around AI infrastructure and hardware inflation, but it hasn't generated anything close to the HardwareZone/Reddit level of technical discussion.

So if you're looking for actual Singapore consumer sentiment, I would weight:

HardwareZone + Reddit > Facebook > X > Threads/Instagram/TikTok

for this particular story.

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14. The biggest takeaway

The article isn't really about "PCs becoming 40% more expensive."

The bigger story is:

AI infrastructure is beginning to compete directly with ordinary consumers for semiconductor manufacturing capacity.

And that affects much more than PCs.

The same supply-chain pressure can potentially flow into:

PCs → smartphones → SSDs → game consoles → networking equipment → servers → electronics.

This is why the story is particularly interesting given the current AI boom.

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My overall reading of the situation

I'd rate the article's thesis as credible, but I'd separate three things:

🟢 Very real

Consumer RAM/SSD/GPU prices are already much higher.

The Singapore retail data and forum users independently corroborate this. (The Straits Times)

🟡 Likely to persist

Prices probably won't return quickly to 2025 levels.

The combination of AI infrastructure demand and manufacturers prioritising higher-margin products makes a rapid correction unlikely. IDC's 2028+ warning is therefore worth taking seriously. (The Straits Times)

🔴 Still uncertain

Whether today's prices will remain permanently high.

If AI investment eventually slows substantially, semiconductor capacity expands and consumer demand weakens, we could get another major hardware price correction.

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And for an ordinary Singapore PC buyer...

My conclusion from the article + forums would be:

If your PC is working fine → don't upgrade simply because of FOMO.

If you genuinely need a new PC → don't assume waiting until 11.11 will produce a dramatically cheaper machine.

If you're buying high-end RAM/SSD/GPU → price-locking a good deal may be more valuable than chasing a small sale voucher.

If your existing PC can survive another 1–2 years → that's probably the cheapest strategy.

The irony is that AI is making AI-enabled services cheaper and more accessible while simultaneously making the hardware needed to run ordinary computing more expensive.

And the Singapore forum reaction is essentially: "Fine, then I'll keep using my old PC."