Gene editing, quantum computers, energy, and even copper. The next wave of AI could transform industries we rarely associate with artificial intelligence.
When we think about AI, names like Nvidia, Microsoft, and Google immediately come to mind.
But what if some of the biggest beneficiaries of AI over the next three years aren’t traditional technology companies?
What if they’re developing new medicines, building electrical infrastructure, or mining the materials needed to power the digital world?
The next chapter of AI may be less about chatbots and more about transforming the physical world.
Here are six areas worth watching.
1. Biotechnology: Could AI Help Rewrite Human Biology?
Imagine developing medicines faster, designing proteins that don’t exist in nature, or treating genetic diseases by correcting faulty DNA.
That’s the promise of combining AI with biotechnology.
AI can help scientists analyze enormous genetic datasets, predict protein structures, and design more precise gene-editing tools such as CRISPR.
The potential is extraordinary, particularly in drug discovery, personalized medicine, and genetic therapies.
Of course, discovering a promising treatment is different from proving that it works safely in humans. Clinical trials remain expensive and time-consuming.
But the pace of scientific discovery could accelerate significantly.
Companies to watch: Thermo Fisher Scientific and Danaher provide essential research tools, while Roche and AstraZeneca are exploring AI-driven drug discovery.
The interesting opportunity may not be limited to the company that discovers the next breakthrough medicine. It could also extend to those supplying the technologies behind thousands of experiments.
2. Quantum Computing: Can AI Accelerate the Next Computing Revolution?
Quantum computers could eventually help solve complex problems in chemistry, materials science, and physics.
But building them is incredibly difficult.
Their biggest obstacles include maintaining stable quantum states, correcting errors, and scaling the technology.
AI is already helping researchers improve error correction, optimize quantum hardware, and develop better control systems.
Companies such as IBM and Google are investing heavily in this field.
Will quantum computing become mainstream by 2029? Unlikely.
But significant advances could bring us closer to practical applications, particularly in scientific research.
And there’s an intriguing possibility: AI could help build better quantum computers, which could eventually accelerate scientific discoveries themselves.
3. Semiconductors: Beyond the Memory Bottleneck
Everyone knows AI needs powerful chips.
And the shortage of high-bandwidth memory (HBM), essential for feeding data to AI processors, is already a well-established industry challenge.
But the more interesting question is: Where will the next bottleneck appear?
As AI processors become more powerful, manufacturers must connect more computing components, move data faster, and manage increasing power consumption.
Several technologies could become even more important:
- Advanced packaging: Connecting multiple processors and memory components efficiently.
- Optical interconnects: Using light to move information between computing systems.
- Power delivery: Supplying enormous amounts of electricity to increasingly dense computing hardware.
- Advanced cooling: Preventing powerful processors from overheating.
Companies such as ASML, TSMC, Broadcom, and Applied Materials operate across critical parts of this ecosystem.
The future semiconductor race may not simply be about building faster chips. It could increasingly depend on how efficiently we connect, power, and cool them.
Beyond traditional data centers, decentralized AI networks like Bittensor are exploring alternative ways to distribute computing power.
4. Electricity: The Unavoidable AI Infrastructure Boom
AI may live in the cloud, but the cloud runs on electricity.
And lots of it.
The International Energy Agency projects that global data-center electricity demand could nearly double between 2025 and 2030.
Building enough computing capacity requires far more than additional power plants.
It also means expanding electrical grids, installing transformers, managing power distribution, and developing sophisticated cooling systems.
This puts traditional industrial companies in an unexpectedly exciting position.
Companies to watch: Eaton, Schneider Electric, GE Vernova, and Vertiv.
Nuclear energy, natural gas, renewables, and battery storage could all play roles in meeting growing demand.
The surprise?
Some of the most important AI infrastructure companies may manufacture equipment that has existed for decades.
The AI revolution might be powered as much by transformers as by transistors.
5. Commodities: Could Copper Become the New AI Metal?
AI’s physical infrastructure creates demand for raw materials.
And not all metals will benefit equally.
Copper stands out because it’s essential for electrical grids, transformers, cables, and industrial equipment.
Silver is another interesting candidate, thanks to its exceptional electrical conductivity and its use in electronics and solar technologies.
Platinum could benefit from specialized applications in data storage and hydrogen technologies, although its relationship with AI is less direct.
Gold remains important in certain electronic components, but its price is driven primarily by investment and monetary demand.
And palladium is still much more closely tied to the automotive industry than to AI.
For the companies supplying these materials, AI could become an additional source of demand alongside electrification and industrial development.
But there’s a catch: growing demand doesn’t automatically mean rising prices. Supply, substitution, and economic cycles still matter.
6. The Wild Card: AI-Discovered Materials
This could be one of the most fascinating developments of all.
Imagine AI helping scientists discover:
- Batteries that charge faster and last longer.
- New materials that make computer chips more efficient.
- Better catalysts for industrial processes.
- Improved superconducting materials.
- Cooling technologies that dramatically reduce energy consumption.
AI allows researchers to explore enormous combinations of chemical structures and materials, helping identify promising candidates before conducting expensive experiments.
A single breakthrough could have consequences across multiple industries.
Better batteries could transform energy storage. New materials could improve semiconductors. More efficient catalysts could change how chemicals are produced.
This field may not attract as much attention as generative AI, but its long-term potential is enormous.
The Bigger Picture: The Winners May Be Hiding in Plain Sight
There’s a common thread connecting these industries.
Scientific breakthroughs rarely happen in isolation.
A gene-editing company needs research instruments. A chip designer needs manufacturing equipment. A data center needs electricity, cooling, and raw materials.
This creates an interesting possibility:
Some of the biggest beneficiaries of AI may be the companies enabling innovation, rather than the companies making the headlines.
Think ASML in semiconductor equipment, Thermo Fisher in biotechnology, or Eaton and Schneider Electric in electrical infrastructure.
They don’t need to invent the next revolutionary chatbot to participate in AI’s expansion.
Of course, technological importance doesn’t guarantee commercial success. Some breakthroughs will take years, and others may never become economically viable.
But one thing seems increasingly clear.
The first wave of AI transformed how we create and process information.
The next wave could transform how we discover medicines, design materials, build computers, and produce energy.
And perhaps the next big AI winner won’t be a software company at all.
It might be a biotechnology laboratory, an electrical equipment manufacturer—or even a copper mine.
The real AI revolution is only beginning.
While AI is transforming physical industries, another revolution is unfolding around privacy, data ownership, and decentralized AI systems
This article explores emerging technology and industry trends, not investment recommendations. Future scientific and commercial outcomes remain uncertain.