This blog post is disseminated on behalf of Chip AI Data Centers, Deep Sea Minerals (CSE: SEAS | OTCQB: DSEAF | FSE: X450), and The FUTR Corporation (TSXV: FTRC | OTCQB: FTRCF | FSE: QA20).
At A Glance
- AI growth is increasing demand for data centers, electricity, cooling systems, electrical equipment, and critical minerals.
- Supply constraints across electricity, equipment, materials, and talent could shape how quickly new AI infrastructure can be built.
- Critical minerals such as copper, nickel, manganese, and cobalt support data centres and their associated electricity, grid, and battery-storage infrastructure, while also serving demand from defense, energy, and other industries.
- Data represents another layer of the AI economy, creating opportunities around ownership, control, and monetization.
- Growing interest in AI sovereignty is connecting computing infrastructure, critical mineral supply chains, and data with broader geopolitical considerations.
The artificial intelligence boom is often viewed through the companies developing the latest models, semiconductors, and applications. Yet as AI adoption accelerates, some of the most important opportunities may be emerging further down the value chain.
Every new AI workload depends on physical infrastructure. Data centers require reliable power, cooling systems, and electrical equipment, all of which require significant quantities of critical minerals. AI systems also depend on enormous amounts of data, raising questions about ownership, privacy, and how to distribute the economic value of that information.
These themes were explored in investorTV’s panel titled “Future-Oriented Companies: Technology & Mineral Innovation for the Data Center Economy,” featuring Chip AI Data Centers Chairman and CEO Kenneth Cotiamco; Deep Sea Minerals CEO James Deckelman; and The FUTR Corporation CEO Alex McDougall. The discussion examined the bottlenecks emerging as AI scales and where opportunities could develop across the value chain.
AI Is Becoming a Physical Infrastructure Story
For all the attention given to software and chips, the expansion of AI increasingly depends on whether enough physical infrastructure can be built to support it.
Asked which bottleneck matters most, Kenneth pointed to pressures across virtually the entire supply chain, particularly electricity.
“Power is a big bottleneck. At some point as these data centers come online, there isn’t enough power currently in the grid to support what’s planned out.”
That constraint extends beyond electricity generation itself. James highlighted the importance of physical infrastructure such as transformers, switchgear, thermal management, and cooling systems.
Kenneth noted growing supply constraints, explaining that equipment is backordered and that shortages of materials and talent can further delay data center development. According to him, demand for data center space is already running ahead of the industry’s ability to deliver it.
“There is absolutely no way that we can fill that pipeline in the next year. You can throw us copious amounts of money. There is not enough talent in the space. There are not enough materials.”
AI demand can expand rapidly, but the physical infrastructure required to support it cannot necessarily scale at the same speed.
More Data Centers Could Increase Demand for Critical Minerals
Once AI is viewed as an infrastructure buildout, critical minerals become part of the same story.
Data centers themselves contain significant amounts of electrical equipment, but their material footprint extends further into the power systems required to connect them to the grid. James described mineral supply as “absolutely critical” to scaling AI infrastructure, highlighting copper in particular while also pointing to nickel and cobalt for battery storage and manganese for grid infrastructure.
The challenge extends beyond AI. Defense, electric vehicles, batteries, renewable energy, and other industries depend on many of the same resources. Even if computing becomes more decentralized, the devices supporting that model will continue to require critical minerals.
James highlighted this diversification during the discussion:
“If and when we begin producing, we can potentially benefit from multi-sector market demand, demand other than that related to AI alone.”
Deep Sea Minerals is focused on polymetallic nodules containing copper, nickel, manganese, and cobalt, seeking to position itself within a domestic U.S. supply chain extending from resource to market.
As James explained, the company views these resources as a potential addition to existing sources of mineral supply:
“We don’t see deep sea minerals as being a replacement for terrestrial mines at this point, but rather one that augments the existing supply as demand continues to increase.”
The AI infrastructure boom therefore adds another source of demand to mineral supply chains that were already becoming strategically important.
From Critical Minerals to Infrastructure
Meeting the infrastructure demands of AI requires progress across the entire supply chain.
Critical minerals must be extracted, processed, and eventually used to manufacture the equipment needed to build power grids and data centers. Transformers, switchgear, and other components can become bottlenecks even when the underlying commodities themselves are available.
This makes both resource development and downstream capacity important to the broader AI infrastructure buildout.
For Deep Sea Minerals, advancing its U.S. deep-seabed mineral exploration license application represents an important step toward potentially bringing a new source of critical minerals into that supply chain. The application has received a substantial-compliance determination from the National Oceanic and Atmospheric Administration, meaning it is sufficiently complete to continue through the federal review process. The company has not yet received an exploration licence or commercial recovery permit.
Data: Another Part of the AI Supply Chain
The chain does not end once the infrastructure is built.
AI systems need data, and the growing value of that information creates another question: who controls it and who captures the economic benefits when it is used?
That is where The FUTR Corporation and CEO Alex McDougall enter the picture.
FUTR has developed a consumer-facing, financial-services-focused AI agent designed to help people collect and control their data while potentially participating in its economic value. Alex said the company had approximately 44,000 users and around $10 million in revenue at the time of the panel.
His thesis is that data should increasingly be viewed as an asset in its own right.
“Our main thesis, again, is that data is one of the most valuable asset classes that is out there and it is an actual asset class on its own.”
Instead of information flowing primarily toward large technology platforms that monetize it on the back end, FUTR’s model seeks to give consumers private, secure data vaults that can monitor how their information moves and how it creates commercial value.
The company distributes its technology through high-touch channels such as auto dealers and financial advisors rather than relying exclusively on a direct-to-consumer approach.
This creates another layer in the same AI ecosystem.
Critical minerals help build the infrastructure. Data centers provide computing capacity. But data gives AI systems the information they need to generate useful outcomes.
The Geopolitics of AI Infrastructure
Once power, minerals, computing, and data are considered together, the AI investment story also becomes increasingly geopolitical.
Governments have reasons to care about where computing infrastructure is built, where strategically important materials originate, and where citizens’ information is stored.
Kenneth pointed to the growing push for sovereign AI and domestic data center capacity as governments seek greater control over where data and computing infrastructure are located.
Alex extended the same argument to individual data.
“It matters to people where their data is actually housed and held.”
For critical minerals, sovereignty is tied to where strategically important resources come from and how reliably countries can access them. Deep Sea Minerals is pursuing a strategy centered on supplying the United States and developing a domestic critical mineral supply chain from resource to market. This strategy aligns with U.S. efforts to develop a domestic supply chain for critical minerals derived from seabed resources.
The three issues are increasingly difficult to separate.
A country seeking greater AI sovereignty needs computing infrastructure. That infrastructure requires reliable electricity and physical resources. The International Energy Agency has identified copper, aluminium, silicon, gallium, rare earth elements, and battery minerals among the materials used across data centres and their supporting energy infrastructure. The systems operating on that infrastructure also require data, while governments, businesses, and consumers increasingly care about where that information is stored and who controls it.
Looking Beyond the Biggest AI Names
The panel discussion highlighted how much broader the AI investment landscape has become. Chip AI Data Centers, Deep Sea Minerals, and The FUTR Corporation approach the AI economy from different parts of the value chain, but each reflects how the technology is creating opportunities beyond the companies developing models and chips.
Despite the rapid pace of investment and adoption, Kenneth argued that the AI cycle remains relatively early, comparing its development with the internet’s gradual move from early adoption to ubiquity:
“AI has kind of been around in the background for a while, but really it came to the forefront two to three years ago… We’re still early.”
For investors, the discussion points toward a more comprehensive way of viewing the AI opportunity. Rather than focusing solely on the companies attracting the biggest headlines, attention can also turn to the businesses enabling the technology to scale.
AI may appear digital at the point of use, but the investment story surrounding it extends much further. Companies involved in building, supplying, and supporting that ecosystem represent another part of the broader AI investment landscape.
Watch the full panel discussion on investorTV’s YouTube channel to hear more about the infrastructure, critical minerals, and data needed to support AI growth.