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Energy Stocks Are Printing Cash Again
Something has quietly changed in energy.
Supply tighter, demand steady.
LNG exporters, pipelines, infrastructure getting paid on volume.
With AI data centers climbing gas demand and export capacity expanding, cash flow grows.
This briefing breaks down steady producers, midstream players, export names quietly compounding while tech obsesses over headlines.
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The AI Boom Is Creating Billionaires Nobody Has Heard Of
When people talk about AI billionaires, the names are usually obvious. Jensen Huang. Sam Altman. The founders of the companies building increasingly powerful models.
But somewhere in southern Taiwan, an 89-year-old industrialist briefly became the country's richest person by making something that looks almost embarrassingly ordinary: metal rails.
King Slide's rails allow enormous AI servers to slide in and out of racks for maintenance. They are not glamorous. They don't run a language model. They don't appear in the demos that have defined the AI boom.
Yet without them, some of the world's largest data centers cannot function.
That is the more interesting AI story now emerging across Asia. The software race has created an enormous physical infrastructure buildout, and the companies quietly supplying its smallest components are capturing a surprising share of the economics. The question is whether this creates a durable industrial opportunity or simply another speculative corner of the AI boom.
For the first few years of the generative AI boom, the story sounded almost entirely digital.
Better models. Larger training runs. More parameters. More data. More intelligence.
But AI eventually runs into a very physical constraint: machines have to exist somewhere.
Servers need racks. Racks need power. Power generates heat. Heat requires cooling. Data centers need electricity, transformers, networking equipment, cables, storage systems and thousands of specialised components that rarely make headlines.
The numbers reveal how quickly this has changed.
The world's biggest technology companies are expected to spend roughly $700 billion on AI infrastructure in a single year. Goldman Sachs has projected that annual AI infrastructure spending could reach between $920 billion and $1.4 trillion by 2027.
At that scale, AI stops looking like a software industry.
It starts looking like the largest industrial construction cycle in decades.
And once you look at it that way, the obvious companies aren't necessarily the only companies that matter.
The Billion-Dollar Opportunity Can Hide Inside a Drawer
King Slide is a perfect example.
The Taiwanese company began in the 1980s making furniture handles and drawer slides for American and European brands. Then Chinese manufacturing became cheaper and more competitive.
One major American customer reportedly demanded that King Slide cut prices by 30% or lose the business to a Chinese supplier.
King Slide chose neither. It walked away.
Instead, the company pivoted toward server infrastructure, eventually winning orders from companies such as IBM and Compaq for server rail kits.
That decision looks almost mundane in hindsight. But it gave King Slide decades to develop expertise in a niche that would later become strategically important.
When AI servers became dramatically larger, heavier and more expensive, the humble rail suddenly mattered.
The company that had once been competing to make better drawer slides was now supplying infrastructure for the AI data center boom.
In less than four years, King Slide's market capitalisation increased more than 3,000%, reaching roughly $43 billion.
The lesson isn't that every obscure manufacturer will become a $43 billion company.
It is that technological revolutions rarely distribute value only to the companies consumers can see.
The Picks and Shovels Are Getting More Interesting
King Slide is only one example.
Bloomberg identified at least seven companies across Taiwan, China, Japan and Switzerland that dominate specialised corners of the AI supply chain. Collectively, their major shareholders have gained more than $62 billion in additional wealth.
Some make specialty drill bits used in printed circuit board manufacturing.
Another, Guangdong DTEK Technology, operates in this kind of highly specialised manufacturing niche.
Then there is Toto, better known globally for toilets, which also manufactures electrostatic chucks used in memory-chip production.
Ajinomoto is famous for umami seasoning and soup bases. Yet it also produces insulating materials critical to advanced semiconductor packaging.
This is what makes the current AI cycle different from a conventional technology boom.
The winners aren't necessarily the companies with the most recognisable products.
Some are selling components that most investors would struggle to identify in a photograph.
But they occupy positions that are extremely difficult to replace.
And that can be a much better business than it sounds.
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The Real Moat Was Built Before AI Arrived
There is a temptation to assume that these companies simply got lucky.
AI demand exploded, their products happened to be useful, and their stock prices followed.
The reality is more interesting.
Many of these companies spent decades building capabilities that AI suddenly made valuable.
King Slide, for example, expanded its engineering team from just seven people to nearly 500. It has accumulated more than 3,800 patents.
That matters because specialised manufacturing knowledge is not something that can be purchased overnight.
You can build a factory relatively quickly.
You cannot instantly build the engineers who know how to solve the factory's problems.
You cannot instantly recreate relationships with hundreds of suppliers.
You cannot instantly reproduce decades of manufacturing tolerances, process knowledge, quality systems and customer trust.
This is the part of the AI boom that investors often miss.
The most valuable infrastructure companies may not have been created by AI.
They may have been waiting for AI to make their accumulated knowledge enormously valuable.
Asia's Biggest Advantage Is an Ecosystem, Not a Factory
The geographical concentration of this supply chain is perhaps the most important part of the story.
Five of the seven companies identified in Bloomberg's reporting are based in the greater China region.
That isn't necessarily because these companies are uniquely brilliant.
It is because Taiwan and China have spent decades building manufacturing ecosystems around electronics, semiconductors and specialised components.
Taiwan has TSMC and a dense network of suppliers around it. South Korea has similarly deep semiconductor capabilities. China has an enormous manufacturing base and supplier network.
When demand for a new technology suddenly explodes, these ecosystems can respond because the pieces are already nearby.
Engineers are nearby.
Suppliers are nearby.
Factories are nearby.
Customers are nearby.
Knowledge moves quickly between them.
That is extraordinarily difficult to replicate.
America can subsidise a semiconductor factory.
It can offer incentives to build a data center.
But money alone cannot manufacture 30 years of accumulated industrial knowledge.
This may ultimately be one of the most important economic lessons of the AI boom: industrial ecosystems can be harder to build than individual companies.
The AI Supply Chain Has a Second Problem: What Happens If the Boom Slows?
The same concentration that makes the system efficient also makes it vulnerable.
Taiwan sits at the centre of the global semiconductor ecosystem. China controls important parts of the manufacturing chain and has demonstrated that export controls can become geopolitical tools. The US has imposed restrictions on advanced chip technology, while China has responded with controls on strategically important materials.
The AI supply chain is therefore becoming entangled with geopolitics.
But there is another risk that may be just as important.
What if AI infrastructure spending eventually slows?
The numbers being invested are enormous, while many AI model companies are still spending heavily to build and operate their systems. Revenue is growing, but the long-term economics remain uncertain.
That creates an unusual situation for the companies supplying the infrastructure.
Their businesses can be extraordinarily strong while demand is accelerating.
But if hyperscalers suddenly decide they have built enough data centers, specialised suppliers that expanded aggressively to meet the boom could discover that their biggest strength has become their biggest vulnerability.
King Slide is already expanding in Taiwan and trialling production in Texas.
Its management is effectively making a long-term bet that the next generation of AI servers will require even more infrastructure.
That may prove exactly right.
But industrial companies live with a different reality from software companies. A software company can scale down a product. A factory is a commitment.
Closing Thought
The most interesting part of the AI boom may not be artificial intelligence at all.
It may be the industrial infrastructure being built underneath it.
For decades, investors learned to look for software margins, network effects and intellectual property. AI is forcing them to look again at factories, components, manufacturing clusters and engineering knowledge.
The next generation of AI billionaires may therefore look very different from the last one.
They may not be sitting in Silicon Valley.
They may be running a factory in Taiwan, Japan, China or South Korea that makes something almost nobody outside the industry has heard of.
That is how industrial revolutions often work.
The world remembers the person who builds the machine.
The person who quietly makes the tiny component that allows the machine to work can become even richer.
Missed reading the recent editions? Check out our previous coverage here:
Blu Dot surpasses 2,000% ROAS with self-serve CTV ads
Home furniture brand Blu Dot blew up on CTV with help from Roku Ads Manager. Here’s how:
After a test campaign reached 211,000 households and achieved 1,010% ROAS, the brand went all in to promote its annual sales event. It removed age and income constraints to expand reach and shifted budget to custom audiences and retargeting, where intent was strongest.
The results speak for themselves. As Blu Dot increased their investment by 10x, ROAS jumped to 2,308% and more page-view conversions surpassed 50,000.
“For CTV campaigns, Roku has been a top performer,” said Claire Folkestad, Paid Media Strategist, Blu Dot. “Comping to our other platforms, we have seen really strong ROAS… and highly efficient CPMs, lower than any other CTV partner we've worked with.”
Using Roku Ads Manager, the campaign moved from a pilot to a permanent performance engine for the brand.
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Disclaimer: The views, thoughts, and opinions expressed in the text belong solely to the author, and not necessarily to the author's employer, organization, committee or other group or individual.