SUMMARY: We  explore one of the most overlooked bottlenecks in the AI boom: energy and infrastructure and  why power availability is becoming the limiting factor.

GUEST: Wannie Park, Founder/CEO of PADO AI

SHOW: 1026

SHOW TRANSCRIPT: The Reasoning Show #1026 Transcript

SHOW VIDEO: https://youtu.be/satMQRxKQC8

SHOW SPONSORS:

• ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!
• Nasuni - Activate your data for AI and request a demo

SHOW NOTES:

1. AI’s Hidden Constraint: Power

• AI growth is no longer limited only by GPUs and compute
• Power generation, cooling, and grid interconnects are emerging as major bottlenecks
• Data centers could account for 10–12% of North American power demand in coming years

2. Why Data Centers Are Being Reimagined

• Traditional data centers were built for enterprise IT, not AI-scale workloads
• AI infrastructure introduces:
  • Massive power density needs
  • Advanced cooling challenges

3. The Grid Wasn’t Built for AI

• Utilities are designed around peak demand scenarios
• Most grids run well below peak capacity most of the time
• AI workloads create volatile and unpredictable consumption patterns
• Long interconnection timelines are pushing companies toward alternative infrastructure models

4. GPU Utilization Is Surprisingly Low

• GPU clusters are often underutilized because of:
  • Scheduling inefficiencies, Cooling limitations, SLA constraints
  • Effective GPU utilization may be as low as 12–13% in some environments

5. Cooling as a Major Optimization Layer

• Legacy data centers often cool entire zones inefficiently
• Pado AI aligns
• AI workloads, Cooling systems, Power allocation
• Workload-aware orchestration helps optimize cooling and compute efficiency

6. The Rise of “Compute Forecasting”

• Pado forecasts compute demand instead of energy demand
• The platform models:
  • GPU workloads, Power consumption, Cooling requirements, SLA priorities
  • Goal: maximize “compute per megawatt”

7. AI Workloads Become Time-Aware

• AI providers may increasingly:
  • Shift workloads to off-peak periods
  • Incentivize delayed non-urgent jobs
  • Dynamically balance compute demand
  • Users are already seeing variable inference latency in real-world AI systems

8. Sustainability vs Reliability vs Profitability

• Operators must balance:
  • Uptime expectations, Infrastructure costs, Sustainability goals
  • Renewable adoption is growing, but reliability still drives investment in natural gas and battery-backed systems

9. Brownfield vs Greenfield Opportunities

• Pado AI is focused primarily on existing (“brownfield”) data centers
• Existing enterprise infrastructure can often be extended and optimized instead of rebuilt
• Enterprises may gain significant AI capability without hyperscale GPU deployments

FEEDBACK?

• Email: show @ the enterprise ai show dot come
• Bluesky: @TheEntAIShow.bsky.social
• Twitter/X: @TheEntAIShow
• Instagram: @TheEntAIShow