Data centers are eating the economy — and we’re not even using them

ary·Artificial IntelligenceData centers are eating the economy — and we’re not even using themBy Baris SaydagBy Baris Saydag Baris Saydag is CEO of Kinesis Network, a platform that transforms underutilized computing resources into scalable, on-demand compute services

The industry’s current apach, throwing money at massive infrastructure jects, resembles adding two more lanes to a congested highway

It might offer temporary relief, but it doesn’t solve the underlying blem

The numbers are staggering

Meta is reportedly exploring a $200 billion investment in data centers, while Microsoft has committed $80 billion for 2025

OpenAI, SoftBank, and Oracle have announced the $500 billion Stargate initiative

Yet here’s the uncomfortable truth

Most of these resources will remain dramatically underutilized

The highway analogy holds true When faced with traffic congestion, the instinctive response is to add more lanes

But transportation reers have documented what’s known as “induced demand.” It’s a counterintuitive finding that ves additional capacity temporarily reduces congestion until it attracts more drivers, ultimately returning traffic to previous levels

The same phenomenon applies to data centers

Building new data centers is the easy solution, but it’s neither sustainable nor efficient

As I’ve witnessed firsthand in compute orchestration platforms, the real blem isn’t capacity

It’s allocation and optimization

There’s already an abundant supply sitting idle across thousands of data centers worldwide

The challenge lies in efficiently connecting this scattered, underutilized capacity with demand

While giants are purchasing entire nu power plants to fuel their data centers, cities across the country are hitting hard limits on energy capacity for new facilities

This energy crunch highlights the significant strains on our infrastructure and is a subtle admission that we’ve constructed a fundamentally unsustainable system

The fact that companies are now buying their own power plants rather than relying on existing grids reveals how our exponential appetite for computation has outpaced our ability to power it responsibly

The distributed alternative The solution isn’t more centralized infrastructure

It’s smarter orchestration of existing resources

Modern software can aggregate idle compute from data centers, enterprise servers, and even consumer devices into unified, on-demand compute pools

This distributed apach offers several advantages: Immediate availability: Instead of waiting years for new data center construction, distributed networks can utilize existing idle capacity instantly

Cost efficiency: Leveraging underutilized resources costs significantly less than building new infrastructure

Environmental sustainability: Maximizing existing hardware utilization reduces the need for new manufacturing and energy consumption

Resilience: Distributed systems are inherently more fault-tolerant than centralized mega-facilities

The nical reality The nology to orchestrate distributed compute already exists

Some network models already demonstrate how software can abstract away the complexity of managing resources across multiple viders and locations

Docker containers and modern orchestration tools make workload portability seamless

The missing piece is just the industry’s willingness to embrace a fundamentally different apach

It’s not a hardware blem requiring more infrastructure

Nor is it a capacity issue

It’s an orchestration and allocation blem requiring smarter software

Instead of building our way out with increasingly expensive and environmentally destructive mega-jects, we need to embrace distributed orchestration that maximizes existing resources

This requires a fundamental shift in thinking

Rather than viewing compute as something that must be owned and housed in massive facilities, we need to treat it a utility available on demand from the most efficient sources, regardless of location or ownership

The nology exists today to orchestrate distributed compute at scale

What we need now is the vision to implement it

Explore this year's list.