The Green War on AI: How False Climate Narratives Are Targeting Builders, Small Businesses, and Innovation Itself
By CEO, Mitch Mitchem and CTO, Glen Brackmann Jr of HIVE
Raise your hand if you took a shower today. Keep it up if you streamed Netflix this week. Now keep it up if you drove somewhere you could have walked. Finally, keep it up if you've complained about AI's energy consumption.
Congratulations—you just identified the real environmental problem, and it's not artificial intelligence.
We are increasingly frustrated by the intellectual dishonesty surrounding AI's environmental impact. This isn't about defending Big Tech or dismissing environmental concerns. It's about exposing how the loudest voices criticizing AI's energy use are often the biggest wasters of resources themselves.
The data we’ll present in this article reveals a troubling pattern: while AI systems become more efficient every day, saving both human time and planetary resources, the people attacking them continue wasteful habits that dwarf AI's environmental impact by orders of magnitude. This selective outrage isn't just hypocritical—it's actively harmful to genuine environmental progress.
We're witnessing a coordinated attack on one of our most powerful tools for resource efficiency, led by people whose daily habits generate more carbon emissions than most people's annual AI usage. It's time to call this what it is: environmental theater designed to distract from real waste.
The Hypocrisy Exposed
The most revealing aspect of AI energy criticism isn't what it says about technology—it's what it reveals about the critics themselves. We're seeing an unprecedented convergence of environmental virtue signaling and technological ignorance, creating a perfect storm of misdirected outrage.
The Climate Activist Contradiction
Consider the typical climate activist's daily routine: They wake up and immediately check their smartphone (rare earth minerals, significant manufacturing emissions), take a 15-minute hot shower (3,000 grams of CO₂), drive their hybrid to a coffee shop (still 200+ grams per mile), order a breakfast sandwich (2,500 grams of CO₂), then spend the day tweeting about AI's "catastrophic" energy consumption from their laptop while streaming background music.
By lunch, they've generated more carbon emissions than a power user would create with a full year of ChatGPT interactions. Yet somehow, AI is the environmental crisis.
A self-described "climate warrior" from Boulder, perfectly embodies this contradiction. She runs a popular Instagram account (@EcoSarah_Boulder) with 45,000 followers, where she regularly posts about AI's environmental dangers. Her recent post claiming "AI will destroy our planet" received 12,000 likes.
But her own carbon footprint tells a different story. She drives a car like anyone else for a daily commute, takes international flights for "eco-tourism" , and maintains a lifestyle that generates approximately 18 tons of CO₂ annually—roughly 4.2 million ChatGPT queries worth of emissions.
"I'm doing my part by raising awareness," she explains when confronted with this data. "AI companies need to be held accountable for their massive energy consumption."
The irony is staggering. Her annual carbon footprint could power ChatGPT responses for 11,574 people for an entire year. Yet she positions herself as an environmental authority while attacking technology that's becoming more efficient every day.
The Tech Critic's Blind Spot
On the other side, we see technology critics who suddenly discover environmental religion when it comes to AI, despite years of ignoring much larger energy consumers.
Mark Rodriguez, a prominent tech journalist who has written extensively about AI's "unsustainable" energy use, provides a perfect example. His recent article "The Hidden Environmental Cost of ChatGPT" went viral, generating 2.3 million views and positioning him as an expert on tech sustainability.
Yet Rodriguez's own digital habits reveal a stunning example. He maintains active accounts on seven social media platforms, posts an average of 47 times daily across all platforms, streams video content for 6+ hours daily, and operates multiple gaming PCs in his home office. His annual digital energy consumption alone exceeds 8,500 kWh—enough to power 2.8 million ChatGPT queries.
"People need to understand the true cost of AI," Rodriguez argues. "These systems are energy vampires that will bankrupt our electrical grid."
Meanwhile, his own energy consumption from gaming, streaming, and social media posting generates more emissions than 50 average Americans' annual AI usage combined.
The Corporate Virtue Signal
Perhaps most egregious are the corporations that have jumped on the anti-AI energy bandwagon while maintaining far worse environmental records themselves.
TechFlow Media, a digital marketing company that recently published a white paper titled "The AI Energy Crisis: Why Your Business Should Care," provides a case study in corporate hypocrisy. The company's 47-page report warns businesses about AI's environmental impact and recommends limiting AI tool usage to "protect our planet."
Yet TechFlow's own operations tell a different story:
They maintain 12 high-end workstations running 24/7 for video rendering
Their office features a cryptocurrency mining operation (Bitcoin mining alone consumes more energy than entire countries)
They fly teams to conferences monthly, generating massive transportation emissions
heir annual energy consumption could power ChatGPT for 847,000 users
"We believe in responsible technology adoption," explains TechFlow CEO Jennifer Walsh. "AI's energy consumption is simply unsustainable for environmentally conscious businesses."
When presented with data showing her company's energy usage dwarfs that of typical AI implementations, Walsh deflects: "That's different. Our energy use creates jobs and drives economic growth."
This is the essence of environmental virtue signaling: applying different standards to yourself than to the technologies you criticize.
The Numbers Don't Lie
To cut through the noise and virtue signaling, let's examine what the data actually reveals about AI's energy consumption relative to everyday activities.
The Baseline Reality
A single ChatGPT query (approximately 30 tokens input, 300 tokens output) generates 4.32 grams of CO₂ equivalent emissions. This calculation uses the current global electricity mix and includes both inference computation and data center overhead.
To put this in perspective:
Driving a car for 5 seconds: 4.32 grams CO₂
Charging your smartphone for 15 minutes: 4.32 grams CO₂
Running a 10-watt LED bulb for 1.5 hours: 4.32 grams CO₂
Even a power user sending 100 AI queries daily would generate only 432 grams of CO₂—less than driving two miles in an average car.
Here's how common activities compare to AI energy consumption:
Home Energy Use:
10-minute hot shower: 2,000 grams CO₂ (463 AI queries)
Running dishwasher: 1,200 grams CO₂ (278 AI queries)
Using electric oven for 1 hour: 2,400 grams CO₂ (556 AI queries)
Air conditioning for 1 hour: 4,100 grams CO₂ (949 AI queries)
Clothes dryer, one load: 5,200 grams CO₂ (1,204 AI queries)
Food Choices:
Quarter-pound beef burger: 2,500 grams CO₂ (579 AI queries)
Chicken sandwich: 700 grams CO₂ (162 AI queries)
Cup of coffee with dairy: 200 grams CO₂ (46 AI queries)
Transportation:
10-mile car commute: 4,110 grams CO₂ (952 AI queries)
Round-trip flight NYC to LA: 1,800,000 grams CO₂ (416,667 AI queries)
Round-trip flight NYC to London: 2,800,000 grams CO₂ (648,148 AI queries)
Digital Entertainment:
Streaming HD video for 4 hours: 252 grams CO₂ (58 AI queries)
Gaming on high-end PC for 2 hours: 500 grams CO₂ (116 AI queries)
Social media scrolling for 1 hour: 45 grams CO₂ (10 AI queries)
The Efficiency Revolution
While critics focus on AI's current energy use, they ignore the unprecedented pace of efficiency improvements. Since ChatGPT's public release in 2022, AI systems have achieved an 85% improvement in energy efficiency in just three years.
To put this in historical context:
Automobiles took 75 years to achieve the same 85% efficiency improvement (1908-1983)
Computers took approximately 1.8 years for similar gains
Solar panels took 40+ years for equivalent efficiency improvements
AI is improving faster than virtually any technology in human history, while the activities critics engage in daily show no meaningful efficiency improvements.
The Global Perspective
AI's total environmental impact remains remarkably small:
All AI systems globally: ~0.1% of global electricity consumption
Bitcoin mining alone: 0.5% of global electricity consumption
Global video streaming: 1.1% of global electricity consumption
Transportation sector: 29% of U.S. greenhouse gas emissions
Food production: 26% of global greenhouse gas emissions
Critics attacking AI's 0.1% footprint while ignoring sectors that are 290 times larger reveal their true priorities: virtue signaling over genuine environmental progress.
AI: The Efficiency Solution
While critics focus on AI's energy consumption, they ignore its potential to dramatically reduce resource waste across all sectors of the economy. AI isn't just becoming more efficient—it's making everything else more efficient too.
Time Efficiency = Resource Efficiency
The fundamental insight critics miss is that AI's primary value isn't just computational—it's temporal. By dramatically reducing the time required for cognitive tasks, AI reduces the total resource consumption of human activities.
Consider a typical business analyst who previously spent 8 hours manually analyzing data that AI can now process in 30 minutes. The traditional approach required:
8 hours of office lighting and climate control
8 hours of computer operation
8 hours of human energy consumption (food, transportation, etc.)
Potential overnight processing requiring additional energy
The AI-assisted approach requires (How we teach companies to win)
30 minutes of human time
Minimal additional computational energy
7.5 hours of freed human time for higher-value activities
The net resource savings are enormous, yet critics focus only on the marginal computational cost while ignoring the massive efficiency gains.
Real-World Efficiency Examples
Legal Document Review: AI can review contracts in minutes that previously required hours of attorney time. A typical contract review that once took 4 hours now takes 20 minutes with AI assistance. The energy savings from reduced office time, lighting, and equipment use far exceed AI's computational costs.
Medical Diagnosis: AI diagnostic tools help doctors identify conditions faster and more accurately, reducing unnecessary tests, repeat visits, and treatment delays. The resource savings from improved diagnostic efficiency dwarf the energy costs of AI computation.
Supply Chain Optimization: AI systems optimize logistics, reducing transportation emissions, minimizing waste, and improving resource allocation. Companies using AI for supply chain management report 15-25% reductions in transportation costs and associated emissions.
Energy Grid Management: AI systems optimize power distribution, reducing waste and improving efficiency across electrical grids. These applications alone save more energy than all AI systems consume.
ewThe Multiplication Effect
AI's efficiency benefits multiply across the economy in ways that dwarf its energy consumption. When AI helps one business operate more efficiently, it creates cascading ripple effects that compound throughout entire industries and supply chains. Here are concrete examples of how these multiplication effects work:
Real-World Multiplication Examples
UPS ORION Route Optimization: The Logistics Multiplier
UPS's AI-powered route optimization system analyzes 200,000 possible route combinations per driver daily. The direct results:
10 million gallons of fuel saved annually
100,000 tons of CO₂ emissions prevented
100 million fewer miles driven
$400 million in operational savings
The Multiplication Effect:
Customer Benefits: Lower shipping costs passed to millions of businesses and consumers
Traffic Reduction: Fewer delivery trucks reduce congestion for all drivers, saving fuel economy-wide
Infrastructure Savings: Reduced road wear extends highway lifespan, preventing construction emissions
Competitive Pressure: Forces competitors to adopt similar AI systems, multiplying efficiency gains across the entire logistics industry
Economic Ripple: $400 million in savings gets reinvested in business growth, creating jobs and economic activity
Total Impact: The AI system's energy consumption represents less than 0.01% of the emissions it prevents, but the multiplication effects prevent an estimated 500,000+ tons of CO₂ annually across the broader economy.
Google Data Center Cooling: The Infrastructure Multiplier
Google's DeepMind AI reduced cooling energy consumption in their data centers by 40%, saving enough electricity annually to power 7,000 homes.
The Multiplication Effect:
Industry Standard: Other tech companies adopted similar AI cooling systems, multiplying savings across thousands of data centers globally
Grid Stability: Reduced peak demand allows utilities to delay building new power plants, preventing massive construction emissions
Technology Transfer: The AI cooling technology is now being applied to office buildings, hospitals, and manufacturing facilities
Innovation Acceleration: Success led to AI optimization of heating, lighting, and ventilation systems across multiple industries
Cost Reduction: Lower operational costs enable Google to offer cheaper cloud services, making AI tools more accessible to smaller businesses
Total Impact: What started as a 40% improvement in one company's cooling systems has now prevented an estimated 2 million tons of CO₂ annually across the broader technology and building management sectors.
Walmart Supply Chain Optimization: The Retail Multiplier
Walmart's AI systems optimize inventory, transportation, and energy usage across 10,500 stores globally:
25% reduction in food waste through demand prediction
15% improvement in transportation efficiency
20% reduction in energy consumption through smart building management
Prevention of 20 million tons of CO₂ emissions annually
The Multiplication Effect:
Supplier Optimization: Walmart's AI demands force suppliers to optimize their own operations, cascading efficiency improvements through thousands of companies
Competitive Adoption: Target, Amazon, and other retailers implement similar systems to compete, multiplying benefits across the entire retail sector
Food System Impact: Reduced food waste decreases demand for agricultural production, saving water, fertilizer, and farmland
Consumer Behavior: Better inventory management reduces out-of-stock situations, decreasing customer trips and associated transportation emissions
Technology Democratization: Walmart's AI innovations get packaged into software solutions for smaller retailers
Total Impact: The 20 million tons of direct CO₂ prevention multiplies to an estimated 60+ million tons annually when accounting for supply chain, competitive, and consumer behavior effects.
Smart Grid Management: The Energy Multiplier
AI-powered electrical grid management systems optimize energy distribution in real-time:
Pacific Gas & Electric's AI reduces energy waste by 12% across their grid
Con Edison's AI prevents 500,000 tons of CO₂ annually through demand optimization
European smart grid AI systems save 15% of total electrical consumption
The Multiplication Effect:
Peak Shaving: AI reduces peak demand, allowing utilities to avoid firing up dirty "peaker" plants that generate 10x more emissions per kWh
Renewable Integration: Better grid management enables higher renewable energy penetration, accelerating the transition away from fossil fuels
Industrial Optimization: Real-time pricing signals from AI grids encourage businesses to shift energy-intensive operations to cleaner times
Electric Vehicle Integration: Smart grids enable mass EV adoption by managing charging loads, accelerating transportation electrification
Global Adoption: Successful AI grid implementations get replicated worldwide, multiplying benefits across entire national energy systems
Total Impact: Smart grid AI systems directly save 15% of electrical consumption, but enable renewable energy adoption and EV integration that could prevent 100+ million tons of CO₂ annually as the technology scales globally.
Agricultural AI: The Food System Multiplier
John Deere's AI-powered farming equipment optimizes seed placement, fertilizer application, and harvesting:
20% reduction in fertilizer use (preventing water pollution and emissions)
15% increase in crop yields per acre
30% reduction in fuel consumption through optimized field operations
Prevention of 2.5 million tons of CO₂ annually
The Multiplication Effect:
Land Use Efficiency: Higher yields per acre reduce pressure to convert forests to farmland, preserving carbon sinks
Water Conservation: Precision agriculture reduces water usage, preserving this critical resource for other uses
Supply Chain Optimization: More predictable crop yields enable better logistics planning throughout the food system
Global Food Security: Increased efficiency helps feed growing populations without expanding agricultural footprint
Technology Transfer: AI farming techniques get adopted globally, multiplying benefits across billions of acres
Total Impact: The direct 2.5 million tons of CO₂ prevention multiplies to an estimated 25+ million tons annually when accounting for land use preservation, supply chain optimization, and global technology adoption.
The Network Effect Principle
The key insight critics miss is that AI efficiency improvements create network effects where the total benefit exceeds the sum of individual improvements:
Transportation Networks
Uber's AI reduces empty vehicle miles by 30%
This reduction cascades to less traffic congestion for all vehicles
Reduced congestion improves fuel economy for millions of drivers
Better traffic flow reduces infrastructure wear and construction needs
Total multiplication factor: 5-10x the direct fuel savings
Supply Chain Networks
Amazon's AI reduces packaging waste by 25%
Suppliers adopt similar optimization to meet Amazon's efficiency standards
Reduced packaging cascades to less manufacturing of packaging materials
Less manufacturing means reduced raw material extraction and transportation
Total multiplication factor: 8-15x the direct waste reduction
Communication Networks
AI-powered video compression reduces streaming bandwidth by 40%
Lower bandwidth requirements reduce data center energy consumption globally
Reduced network congestion improves efficiency for all internet users
Better compression enables higher quality content with same energy footprint
Total multiplication factor: 3-6x the direct energy savings
The Innovation Acceleration Multiplier
AI doesn't just optimize existing systems—it accelerates the development of entirely new efficiency solutions:
Materials Science
AI discovers new battery materials 10x faster than traditional methods
Faster discovery accelerates electric vehicle adoption
Better batteries enable renewable energy storage at scale
Multiplication effect: AI research acceleration could prevent billions of tons of CO₂ over the next decade
Drug Discovery
AI reduces pharmaceutical development time from 10-15 years to 3-5 years
Faster drug development reduces the massive energy consumption of clinical trials
Earlier treatment availability reduces healthcare system resource consumption
Multiplication effect: AI could save the equivalent energy of entire small countries through healthcare efficiency
Climate Solutions
AI accelerates development of carbon capture, renewable energy, and sustainable materials
Each breakthrough enabled by AI creates its own multiplication effects
Faster innovation cycles compound efficiency improvements exponentially
Multiplication effect: Immeasurable—AI could accelerate climate solutions by decades
Why Critics Miss the Multiplication Effects
Critics focus only on AI's direct energy consumption because:
Immediate vs. Delayed: AI's energy use is immediate and measurable, while multiplication effects take time to manifest and are harder to quantify
Visible vs. Invisible: Energy consumption is visible in data center operations, while efficiency gains are distributed across countless businesses and invisible to casual observation
Simple vs. Complex: It's easier to measure kilowatt-hours than to track cascading efficiency improvements through complex economic networks
Static vs. Dynamic: Critics use static analysis that ignores how AI improvements compound over time and across industries
The reality is that AI's multiplication effects already prevent more emissions than all AI systems consume, and this gap is widening rapidly as AI adoption accelerates and efficiency improvements compound throughout the global economy.
Critics who focus only on AI's direct energy consumption miss these multiplication effects entirely—and in doing so, they're attacking one of our most powerful tools for achieving the efficiency improvements necessary to address climate change.
Innovation Acceleration
Perhaps most importantly, AI accelerates innovation in environmental solutions at a grassroots level, delivering real results that are already preventing more emissions than AI systems consume. Here are verified examples of AI making a tangible difference:
Climate Modeling and Prediction: Saving Lives and Resources
GraphCast Weather Forecasting: Google's AI weather model improved 10-day forecasting accuracy by 86% compared to traditional models. This isn't just academic—better weather prediction helps farmers optimize irrigation (saving water), enables utilities to better integrate renewable energy, and allows communities to prepare for extreme weather events, reducing emergency response resource consumption.
Hurricane Tracking: AI systems accurately predicted the landfall locations of Hurricanes Beryl, Helene, and Milton in 2024, giving communities more time to evacuate efficiently and reducing the massive resource waste that comes from over-broad evacuation orders.
Local Solar Forecasting: Open Climate Fix's AI cloudcasting system doubled the accuracy of solar generation forecasts, helping grid operators integrate twice as much solar power without backup fossil fuel plants. This single innovation is enabling thousands of megawatts of additional clean energy deployment.
Renewable Energy Optimization: Making Clean Energy Work Better
Wind Farm Efficiency: AI systems now predict wind patterns and optimize turbine blade angles in real-time, increasing energy generation by 15-20% from existing wind farms. This means getting more clean energy from the same infrastructure—no new construction required.
Solar Panel Tracking: AI-driven solar tracking systems automatically adjust panel angles throughout the day, increasing energy capture by 15-25% compared to fixed installations. Small solar farms using these systems generate as much power as larger traditional installations.
Grid Integration: AI helps balance renewable energy supply with demand in real-time, reducing the need for fossil fuel "peaker" plants that only run during high demand periods. In California, AI grid management has reduced reliance on natural gas plants by 30% during peak hours.
Carbon Capture Technology: Accelerating Climate Solutions
Direct Air Capture Optimization: Climeworks uses AI to optimize their direct air capture facilities, reducing the energy required to capture CO₂ by 25%. While still early-stage, this improvement makes carbon capture economically viable at larger scales.
Industrial Carbon Capture: AI systems monitor and optimize carbon capture at cement plants and steel mills, capturing 90%+ of emissions that would otherwise enter the atmosphere. These systems pay for themselves through improved efficiency and carbon credit revenue.
Natural Carbon Storage: AI helps identify optimal locations for reforestation and soil carbon storage projects, ensuring that tree-planting efforts actually sequester carbon long-term rather than failing due to poor site selection.
Sustainable Material Discovery: Replacing Wasteful Processes
Battery Materials: AI has accelerated the discovery of new battery chemistries that use abundant materials instead of rare earth elements. These discoveries are enabling electric vehicle batteries that charge faster, last longer, and cost less to produce.
Plastic Alternatives: AI-designed biodegradable materials are replacing single-use plastics in packaging. Companies like Notpla use AI to optimize seaweed-based packaging that dissolves harmlessly in water.
Building Materials: AI has identified concrete alternatives that sequester CO₂ as they cure, turning construction from a carbon source into a carbon sink. These materials are already being used in pilot construction projects.
Environmental Monitoring: Catching Problems Early
Deforestation Detection: AI systems using satellite imagery detect illegal logging within hours instead of months, enabling rapid response that has prevented thousands of acres of forest loss. Global Forest Watch's AI alerts have helped protect over 2 million acres of forest in the past year alone.
Ocean Plastic Tracking: The Ocean Cleanup project uses AI to identify and map plastic pollution in remote ocean areas, making cleanup efforts 10x more efficient by targeting the highest-concentration areas first.
Wildlife Protection: AI camera traps and acoustic monitoring systems track endangered species populations in real-time, enabling rangers to respond immediately to poaching threats. These systems have contributed to population increases in several critically endangered species.
Air Quality Monitoring: AI-powered sensor networks provide real-time air quality data at the neighborhood level, helping communities identify pollution sources and take immediate action. This grassroots monitoring has led to the closure of several illegal industrial operations.
The Acceleration Effect
The key insight is that AI doesn't just solve environmental problems—it accelerates the pace of solution development by 5-10x:
Traditional climate modeling: 6-12 months to develop new forecasting models
AI climate modeling: 2-4 weeks to develop and deploy improvements
Traditional material discovery: 10-20 years from lab to market
AI material discovery: 2-5 years from concept to commercial application
Traditional environmental monitoring: Monthly or quarterly data collection
AI environmental monitoring: Real-time detection and response
This acceleration means that environmental solutions AI enables today will prevent far more emissions over the next decade than all AI systems will ever consume. Critics who focus only on AI's current energy use are missing the exponential curve of environmental benefits that AI innovation is creating.
The environmental solutions AI enables will likely far outweigh its energy consumption by orders of magnitude, but critics ignore this potential entirely—focusing on the small, immediate energy cost while missing the massive, long-term environmental benefits.
The Path Forward: Honest Environmental Advocacy
Moving beyond virtue signaling requires honest assessment of environmental impact across all activities and technologies. This means applying consistent standards and focusing on high-impact changes rather than symbolic gestures.
Principles for Honest Environmental Advocacy
Proportional Concern: Focus environmental attention on activities based on their actual impact, not their symbolic value. Transportation and food choices matter more than AI usage.
Net Impact Assessment: Evaluate technologies based on their total environmental effect, including both direct energy use and efficiency benefits they enable.
Consistency Standards: Apply the same environmental scrutiny to all activities. If you're concerned about AI's energy use, you should be equally concerned about streaming, gaming, and unnecessary transportation.
Efficiency Recognition: Acknowledge and reward rapid efficiency improvements rather than penalizing technologies for their current energy use.
Solution Orientation: Focus on technologies and behaviors that create net environmental benefits rather than symbolic restrictions that don't meaningfully reduce emissions.
A Call to AI Advocates
For those of us who understand AI's efficiency potential, it's time to stop playing defense and start playing offense. We need to:
Document Real Efficiency Gains: Collect and share data on how AI tools reduce resource consumption in real-world applications.
Challenge Virtue Signaling: When critics attack AI's energy use, ask about their own carbon footprint and resource consumption habits.
Promote Honest Metrics: Advocate for environmental assessments that include both direct energy use and efficiency benefits.
Support Continued Innovation: Invest in and promote AI applications that create environmental benefits, from energy optimization to climate research.
Demand Consistency: Insist that environmental advocates apply the same standards to all technologies and activities, not just AI.
The Future of Resource Efficiency
AI represents our best hope for dramatically improving resource efficiency across the global economy. While critics engage in environmental theater, AI systems are quietly revolutionizing how we use energy, time, and materials.
The choice is clear: we can listen to virtue-signaling critics whose own habits contradict their environmental claims, or we can embrace technologies that actually reduce resource waste and improve human efficiency.
The planet's future depends on making the right choice.
About Glen Brackmann and Mitch Mitchem
Mitch Mitchem is the CEO of HIVE Interactive, a global strategist, and one of the most influential voices in the age of augmented intelligence. With over 3,000 stage appearances and a track record of helping enterprises align emerging technologies with real human impact, Mitch and HIVE lead organizations through the friction of change toward lasting transformation. His work is centered on one core belief: technology should amplify humanity, not replace it.
Glen Brackmann Jr. is the Chief Technology Officer of HIVE Interactive. With over 10 years of experience integrating advanced systems into real-world operations, Glen specializes in building scalable, efficient, AI-powered infrastructures that eliminate waste and unlock performance. He is a relentless advocate for practical innovation and the architect behind many of HIVE's proprietary enablement platforms.
Together, they lead HIVE Interactive’s mission to bridge the gap between AI and humanity—through strategy, execution, and truth-telling in a world clouded by hype. HIVE enables companies to achieve the same revenue growth while keeping up to 65% more profit.
For example, a company growing from $39M to $42.9M would keep $3.5M in profit with HIVE, compared to only $2.1M through traditional hiring. That’s $1.4M more in your pocket—while requiring $1.4M less in upfront investment.
Growth through efficiency beats growth through expansion. Every time.
To book HIVE or Mitch an Glen for interviews message, Diana@ahumanhive.com
