THE INVISIBLE REVOLUTION: HOW AI AGENTS ARE TRANSFORMING DAILY LIFE

Much like the early days of personal computing, we’re seeing an explosion in both capability and adoption. Early AI assistants like Siri and Alexa were essentially sophisticated command-response systems. Today’s AI agents are different and much more sophisticated. They can understand context, plan multi-step tasks, and even work together in teams to solve complex problems. 

This rapid evolution is being fueled by a perfect storm of technological advancement. The latest large language models from companies like OpenAI, Google, and Anthropic have achieved unprecedented levels of reasoning capability. Meanwhile, a new infrastructure stack is emerging to support these advanced agents, with companies developing specialized silicon, agent orchestration platforms, and sophisticated memory systems to enable more complex agent behaviors.

UPCOMING IMPACT OF AGENT DEPLOYMENTS

The impact on business operations is already profound. For example, Bank of America’s AI-driven virtual assistant, Erica, has handled millions of customer requests, from simple account inquiries to complex loan applications. Amazon has deployed AI agents across its customer service operations, seamlessly handling returns, refunds, and support inquiries. These aren’t just cost-saving measures—they’re transforming how businesses operate and interact with customers.

According to a McKinsey study, GenAI agents could drive productivity of customer care functions. More importantly, these agents are taking on tasks that humans either couldn’t do efficiently or couldn’t do at all, such as analyzing vast amounts of data in real time or coordinating complex multi-step processes across different systems and departments.

PREPARING FOR THE INVISIBLE REVOLUTION

The exponential growth of AI agents is creating unprecedented demand for computing power and digital infrastructure. Digital infrastructure providers are seeing surging demand for high-performance computing facilities as companies rush to deploy and scale their AI agent networks. This infrastructure boom isn’t just about raw computing power—it’s about creating the sophisticated, reliable computing environments these advanced AI systems require to operate effectively.

Just as the AI boom is driving unprecedented demand for power, the rise of AI agents is creating its own infrastructure challenges. Companies are racing to build the foundational technologies needed to support networks of autonomous agents. This includes:

• Specialized chips optimized for agent-based computing
• New platforms for managing and orchestrating multiple agents
• Advanced systems for maintaining long-term memory and context
• Secure communication protocols for agent-to-agent interaction
• High-performance computing facilities capable of running thousands of agents simultaneously

Companies are investing heavily in this infrastructure and recognizing that the future of AI lies not just in individual models, but in networks of specialized agents working together.

As AI revolutionizes industries worldwide, technology CEOs and boards face a defining moment. Federal AI spending has grown rapidly over the last few years, and the CHIPS Act injected $52.7 billion into semiconductor development, so the foundation for AI innovation in corporate America has never been stronger.

Yet the real work lies ahead: demonstrating AI’s transformative value to secure continued investment and ensure corporate America’s competitive edge in the global AI race.

The Stakes For CEOs And Boards

The numbers tell a compelling story. Federal AI R&D investment has surged to $1.8 billion in FY 2023, while the global AI market is projected to reach $190.61 billion by 2025.

Leading technology CEOs and boards are already capitalizing on this momentum. Microsoft and OpenAI are investing billions of dollars and building large-scale digital compute infrastructure to train the next generations of advanced language models. Google’s DeepMind has been working to improve weather forecasting accuracy while also enhancing its data models and large language models (LLMs).

To continue these trends and gain further federal investment, here are four critical priorities for CEOs and boards:

1. Demonstrate real-world impact for corporate America.

Success in navigating the AI landscape starts with understanding its value. For example, Google’s deployment of DeepMind AI in its data centers yielded a 40% reduction in energy used for cooling, demonstrating AI’s potential for significant operational efficiency gains in enterprise infrastructure.

By showing how these advancements will impact the country, companies can make a strong case for future investment.

2. Build corporate and ecosystem trust through radical transparency.

In an era of AI uncertainty, demonstrating trust and transparency will also be essential.

IBM set an early standard by publishing their AI Ethics Board proceedings and impact assessments for the last five years, while Salesforce created a transparent AI governance framework that can be adopted by other companies.

Rather than simply racing to deploy new capabilities, these companies are prioritizing responsible development through clear governance structures and regular impact assessments. Most importantly, they’re changing the narrative around AI-powered workforce transformation.

3. Foster strategic partnerships and a collaborative path.

The future of AI innovation depends on collaboration across the ecosystem. DeepMind’s partnership with Moorfields Eye Hospital NHS Foundation Trust, for instance, led to an AI system that can recommend the correct referral decision for over 50 eye diseases with 94% accuracy, matching world-leading eye experts.

These types of partnerships are creating powerful ecosystems that multiply the impact of AI investments and ensure that corporate advances in AI have multifaced and multifold benefits.

4. Invest in tomorrow’s corporate talent.

The AI skills gap is a major challenge, and companies must show they are solving it to continue to earn federal investment.

Several companies are already demonstrating how they can overcome this challenge. IBM’s AI Apprenticeship program, for instance, has created a new pathway for nontraditional tech workers to enter the field. Likewise, Microsoft’s AI for All initiative has partnered with community colleges to bring AI education to underserved communities.

On top of demonstrating the value of AI, these types of programs are setting up their companies for future innovation by building a robust pipeline of skilled talent.

CEOs And Boards: Making The Business Case For Continued AI Investment

Successful CEOs and boards are mastering the art of effective advocacy through strategic engagement.

When OpenAI CEO Sam Altman testified before Congress, he combined compelling demonstrations of AI capabilities with clear proposals for responsible oversight. The most effective CEOs and boards are taking a three-pronged approach to securing continued support:

1. They’re documenting success through detailed case studies and ROI analysis, using data and results to demonstrate AI’s impact on productivity, innovation and shareholder value creation.

2. They’re proactively addressing societal concerns by showcasing corporate innovation, actions and investments, explaining how AI can augment corporate capabilities while benefiting multiple stakeholders.

3. They’re building broad coalitions that span industry, academia and government, creating a unified voice for responsible AI advancement. Regular company roundtables, technology demonstrations and stakeholder forums have become essential tools in this effort.

CEOs and boards are learning to translate technical capabilities into tangible benefits that resonate with shareholders, employees, customers, partners, policymakers and the public. They’re also increasingly transparent about challenges and limitations, building credibility through candid dialogue about AI’s potential and risks.

The Path Forward For Long-Term Success

While the CHIPS Act and increased investments provide momentum, the future of AI innovation depends on CEOs and boards driving the next steps.

Long-term corporate success requires a delicate balance between pushing technological boundaries and ensuring fiscally responsible development. The most effective leaders are those who can navigate this complexity while building trust and delivering measurable value to both businesses and communities.

The AI revolution is no longer approaching—it’s here. The question facing CEOs and boards isn’t whether to invest in AI capabilities, but how to invest thoughtfully and responsibly.

Those who step up to this challenge today will shape the corporate innovation landscape for decades to come. By embracing transparency, fostering collaboration and demonstrating tangible shareholder and stakeholder impact, companies can ensure they maintain a competitive edge while creating lasting and transformative value for all their stakeholders.

In the annals of technological innovation and history, we stand at a pivotal moment. Just as the internet revolution democratized information and knowledge, we are now witnessing AI drive a societal transformation that can redefine how we live, work, and interact with the world around us.

THE AI RENAISSANCE: FROM IVORY TOWERS TO MAIN STREET

Just a few years ago, training a state-of-the-art language model like GPT-3 was a feat that required immense resources, both in terms of computational power and capital. However, according to a recent analysis, training a model with similar capabilities to GPT-3 would now cost approximately $1.4 million, which represents a reduction of about 70% in just three years. If we consider the latest advancements in AI research, particularly the insights from the “Chinchilla” paper by DeepMind, we can achieve even better performance with smaller, more efficient models.

The Chinchilla researchers found that a 70 billion parameter model trained on 1.4 trillion tokens could outperform GPT-3, despite having less than half the parameters. This finding has profound implications for the economics of AI. It suggests that the path to better AI isn’t necessarily through ever-larger models, but through more efficient training on larger, high-quality datasets.

KEY PLAYERS IN THE DEMOCRATIZATION OF AI

Tech Giants: Increasingly Providing A Platform To Build Upon

Companies like Google, Microsoft, Amazon, and Meta, amongst others through their cloud platforms (Google Cloud AI, Azure AI, and AWS AI services), now offer pre-trained models, development tools, and scalable infrastructure that allow businesses of all sizes to implement AI solutions without massive upfront investments. Google’s TensorFlow and Meta’s PyTorch, open-source machine learning frameworks, have become the go-to tools for AI developers worldwide. These platforms have significantly lowered the barrier to entry for AI development.

Startups: Driving Agile And Nimble Innovation

A new breed of AI startups is focusing on making AI more accessible and applicable to specific industries. Companies like Hugging Face are democratizing natural language processing by providing open-source models and easy-to-use tools for developers. Other startups are creating no-code/low-code platforms that allow non-technical users to implement AI solutions. For instance, Obviously AI enables users to build and deploy machine learning models without writing a single line of code.

Open-Source Communities: Enable Collaborative Progress

The open-source movement has been crucial in democratizing AI. Projects like scikit-learn, Keras, and OpenAI’s GPT models have made powerful AI tools freely available to developers worldwide. These communities foster collaboration, knowledge sharing, and rapid innovation.

Educational Institutions: Nurturing Talent

Universities and online learning platforms are playing a vital role in democratizing AI education. Carnegie Mellon is offering accessible courses on AI and related subjects. Other institutions and platforms like Coursera, edX, and fast.ai are making AI education accessible to millions worldwide. Stanford Professor Andrew Ng’s “AI For Everyone” course, for example, has introduced AI concepts to over 1.4 million learners from diverse backgrounds.

THE ROAD AHEAD: SHAPING OUR AI-DRIVEN FUTURE

The democratization of AI is not just a technological shift; it’s a societal one. As we move forward, different stakeholders have crucial roles to play:

For CEOs And Business Leaders:

• Invest in AI literacy for your workforce.
• Ensure your team understands AI fundamentals and its potential applications in your industry.
• Start small, but think big. Begin with pilot projects that can demonstrate quick wins, but have a long-term strategy for AI integration.
• Prioritize ethical AI use. Develop clear guidelines for AI implementation that address issues of fairness, transparency, and accountability.

For Policymakers:

• Develop adaptive regulatory frameworks that encourage innovation while protecting public interests.
• Invest in AI education and infrastructure to ensure your jurisdiction remains competitive in the AI economy.
• Address the potential workforce disruption by supporting reskilling and upskilling programs.

For Educators:

• Integrate AI literacy into curricula across disciplines, not just in computer science programs.
• Collaborate with industry to ensure that AI education aligns with real-world needs.
• Use AI tools to enhance your own teaching methods and provide personalized learning experiences.

For Researchers:

• Focus on developing AI systems that are not just powerful, but also interpretable and fair.
• Collaborate across disciplines to find novel applications of AI in various fields.
• Engage with different stakeholders to increase AI literacy and address concerns about AI’s societal impact.

For Students:

• Embrace lifelong learning to stay relevant in an AI-driven economy.
• Engage in public discussions about AI ethics and governance.
• Experiment with accessible AI tools to understand their potential and limitations.

Jensen Huang, Nvidia’s CEO believes AI is “reshaping industries and opening new opportunities for innovation and growth.” Satya Nadella, Microsoft’s CEO, “can’t imagine life without artificial intelligence.” Sundar Pichai, Google’s CEO, recently commented that this is a “once-in-a-generation opportunity to unlock human potential, for everyone, everywhere.”

AI can certainly be all of that and more. However, our driving of the democratization of AI is what will truly represent a pivotal moment in human history. It’s not just about the technological advancement; it’s about ensuring that AI empowers individuals and organizations to solve problems, create value, and shape our collective future. AI will transform our world, but how we can ensure we harness its potential to create a more innovative, equitable, and prosperous society for all?

The future of AI is not just in the hands of tech giants, innovative startups, universities, and researchers; it’s ultimately in your hands, in the hands of educators, entrepreneurs, artists, dreamers, and problem-solvers such as you across all walks of life. As we democratize AI, we’re not just distributing technology; we’re distributing the power to shape our future. The AI revolution is here, and it belongs to all of us.

Imagine a city that never sleeps—pulsing with energy 24/7—with millions of people and tens of thousands of businesses. Now, picture this city running entirely on clean energy. This isn’t science fiction; it’s the future driven by data centers, the hidden powerhouses driving our digital world. As the demand for computing power skyrockets—driven by technologies such as AI, robotics and autonomous vehicles—so does our environmental footprint. The tech industry now faces a pivotal challenge: How do we feed our insatiable appetite for computing power while ensuring a sustainable future for our planet?

In this article, I’ll explore how data centers are not just adapting to the sustainability challenge but also how they are leading the charge in creating a greener, more efficient future for all of us.

The Imperative For Sustainable Data Centers

From a technical standpoint, the exponential growth of AI and high-performance computing presents unique challenges. According to Goldman Sachs, data center power use is on the rise worldwide. The impact is already visible in key markets. For instance, in Virginia, a hub for U.S. data centers, commercial power consumption rose by 37% from 2016 to 2023. This dramatic increase in energy demand underscores the urgent need for innovative and energy-efficient technologies in data centers.

This surge in power consumption is not just a technical challenge; it’s reshaping the energy market landscape. Goldman Sachs notes that hedge funds and asset managers are increasingly interested in power derivatives, seeing opportunities in the AI-driven boom. The cost of power on the U.S. East Coast has been rising since early 2024, with the spark spread increasing by roughly 50% this year and nearly doubling over the past two years. These market shifts highlight the growing economic implications of data center energy consumption and the potential for innovative solutions in power generation and management.

Faced with these challenges, the tech industry is responding with groundbreaking innovations in data center designs and operations.

Here are some key areas where significant progress is being made:

Energy Efficiency Innovations

Advanced cooling technologies are at the forefront of energy efficiency efforts. Liquid cooling systems, once reserved for supercomputers, are becoming mainstream in data centers. These systems can reduce cooling energy consumption by over 18% compared to traditional air cooling methods. Companies like Google and Microsoft are utilizing these technologies, implementing liquid cooling in their data centers to handle the heat generated by AI workloads more efficiently.

AI-driven thermal management is another game-changer. Machine learning algorithms can predict heat patterns and adjust cooling systems in real time, optimizing energy use while maintaining ideal operating temperatures. DeepMind, for instance, reduced Google’s data center cooling energy by 40% using AI-powered recommendations.

Renewable Energy Integration

The shift toward renewable energy in data centers is accelerating. Most data center operators have committed to 100% renewable energy goals by 2030. The digital infrastructure company where I am CEO is carbon-free.

Amazon has also emerged amongst the leaders in this space, announcing in July 2024 that it has already achieved its goal of powering its operations with 100% renewable energy, a full seven years ahead of its original 2030 target. The company has become the largest corporate purchaser of renewable energy in the world for four consecutive years, investing billions of dollars in more than 500 solar and wind projects globally. To provide perspective, these projects generate enough energy to power the equivalent of 7.6 million U.S. homes.

Intelligent Workload Management

Carbon-aware computing is emerging as a powerful tool for sustainability in cloud services. Google Cloud exemplifies this approach with its comprehensive suite of tools designed to help customers reduce their carbon footprint. By integrating sustainability considerations into its platform, Google Cloud enables customers to make informed choices that balance performance needs with environmental impact. This approach not only helps companies achieve their sustainability goals but also optimizes resource usage, potentially leading to cost savings.

The Future Of Data Center Sustainability

As we look to the future, there are three key developments poised to revolutionize data center sustainability that leaders in the tech industry should keep a close eye on:

1. Next-Generation Sustainable Cooling Technologies: Emerging technologies like two-phase immersion cooling, where servers are submerged in a dielectric fluid that boils to remove heat, promise to dramatically reduce cooling energy requirements. This technology could enable even higher-density computing while significantly lowering energy consumption.

2. AI-Optimized Energy Management Systems: The next generation of data center management will leverage AI not just for workload distribution but for holistic energy optimization. These systems could predict energy demand, integrate renewable sources and manage storage solutions in real time, maximizing efficiency and minimizing carbon footprint.

3. Integration Of Data Centers Into Smart Grid Systems: Future data centers could likely play a more active role in broader energy ecosystems. By integrating with smart grids, data centers could serve as energy storage facilities, helping to balance the grid during peak demand periods and storing excess renewable energy when demand is low.

Shaping The Future: The Road Ahead For Sustainable Data Centers

The path to truly sustainable data centers continues to have challenges ahead, but the stakes couldn’t be higher. As tech leaders, we have a responsibility to drive innovation in this critical area. The solutions we develop today will shape the environmental impact of the digital economy for decades to come.

To stay ahead of the curve, tech leaders should invest in research and development of energy-efficient technologies specific to high-performance computing and AI workloads, collaborate with energy providers and policymakers to accelerate the transition to 100% carbon-free energy sources and implement and continually refine carbon-aware computing practices across their operations.

By prioritizing sustainability and carbon-free energy in our data centers, we’re not just reducing our environmental impact—we’re building a more resilient, efficient and responsible digital infrastructure for the future. The revolution in data center sustainability is underway, and it’s up to us to lead the charge toward a greener, more sustainable digital economy.

The artificial intelligence (AI) revolution is transforming our world, but it comes with an insatiable surge in data centers that are powered by an ever-growing appetite for energy. As AI and high-performance computing (HPC) applications proliferate, the tech industry faces a growing challenge: building the next generation of data centers and finding suitable locations with the scale of power and infrastructure needed to support the expected growth.

Ironically, the solution to our future may lie in our past—the very industrial heartland that powered America’s historical economic dominance can provide the solutions to our AI-driven future. Across the Rust Belt, former steel mills and legacy manufacturing plants are finding new life as digital infrastructure hubs. This renaissance is breathing economic vitality into communities hit hard by the decades-long reduction in domestic industrial manufacturing, while helping to meet the insatiable demand for global compute power.

The Symbiosis of Old and New

As an example, in Pennsylvania’s Allegheny Mountains, the town of Bellefonte tells a compelling story of industrial evolution. Once home to thriving iron furnaces and manufacturing, Bellefonte saw its economic fortunes decline as heavy industry moved overseas. Today, however, the hum of digital compute servers has replaced the clang of machinery, as former industrial sites have been repurposed into data centers and hubs of digital infrastructure.

The advantages of these locations are multifaceted—many retain robust and large-scale electrical infrastructure from their industrial past, including high-capacity power lines and substations. Their strategic positions near major population centers and transportation hubs provide multiple low-latency and high-speed fiber optic connections. Perhaps most critically, they often have access to competitively priced and large wholesale electric grids like the PJM market, a vital consideration for energy-intensive AI and HPC applications.

Nuclear Power: The Unsung Hero of the AI Age

The resurgence of nuclear power is also playing a pivotal role in this transformation. Once-struggling nuclear plants are finding new purpose as primary power sources for data centers. This symbiosis addresses two pressing needs: it provides carbon-free baseload power for compute-intensive applications, while enhancing the economic viability of nuclear facilities.

In Midland, Pennsylvania, which is about 30 minutes from Pittsburgh International Airport, we’ve witnessed this firsthand. Our company has deployed digital infrastructure across an approximately 350,000 square feet lot where we operate and manage tens of thousands of compute machines powered by carbon-free energy, including from nuclear plants within sight of our facilities, creating a win-win scenario. Our digital infrastructure facilities benefit from reliable, scalable carbon-free electricity, while the nuclear plants build long-term and 24/7/365 stable customers that can help optimize demand and supply, and secure the ongoing economic prospects of the grid.

In a groundbreaking move, Microsoft recently announced an exclusive 20-year deal to use all the power from a reactor at the Three Mile Island nuclear plant in Pennsylvania. Similarly, Amazon Web Services (AWS) is investing up to $650 million in a data center campus adjacent to another nuclear power plant in Pennsylvania, with plans to develop a 960-MW facility. These partnerships between technology companies and nuclear facilities underscore the growing importance of sustainable and scalable power sources in the new AI era. They also demonstrate how historic and legacy industrial sites in areas such as the Rust Belt can play a pivotal role as we continue to build our collective digital future.

The Scale of AI’s Energy Demand

AI computing has staggering energy requirements. With the advent of AI, particularly the power-hungry GPUs required for large language models (LLMs) and large-scale machine learning, this power used by data centers is projected to increase dramatically. Some researchers estimate that within the next three years, the energy needed to power global data centers could jump by over 50% driven by AI-applications alone.

The impact of this digital transformation extends far beyond the tech industry. In Ohio’s Mahoning Valley, once synonymous with steel production, we’re seeing a new generation of jobs emerge. Former steelworkers are retraining as data center engineers and site technicians, network engineers, and cybersecurity specialists. Community colleges are partnering with tech companies to develop curricula and internships tailored to these new opportunities.

The economic benefits ripple outwards. Local businesses, from restaurants to hardware stores, are seeing increased activity and prosperity. Property values are stabilizing, and even increasing, in areas that had long been in decline. Perhaps most importantly, there’s a renewed sense of optimism and purpose in communities that had felt left behind by the digital economy.

I am seeing this transformation firsthand. Our recent expansion into regions such as Ohio and our recent growth in Pennsylvania is not just about increasing operational capacities of our digital infrastructure. It’s about contributing to the economic revitalization of these communities. We’re not just building digital infrastructure and compute facilities; we’re helping to build a bridge to the future for these regions with rich industrial histories. And by bringing high-tech jobs to areas with strong industrial traditions, we’re helping create a bridge between America’s iconic manufacturing heritage and its AI-driven future.

Looking Ahead

As we stand on the cusp of this exciting AI revolution which will transform how we live and work, the demand for compute power shows no signs of slowing. Former industrial sites across Pennsylvania, Ohio, and beyond stand ready to meet this challenge. By reimagining these areas as hubs of digital infrastructure, we’re not just powering the technologies of tomorrow – we’re revitalizing communities and creating new economic opportunities in areas that need them most.

The future of AI may be written in silicon, but its foundations are being built on the bedrock of America’s industrial past. It’s a powerful reminder that innovation often comes from unexpected synergies, and that the next chapter of our next industrial revolution is being written in the very places where the first ones began.

As we continue to expand our digital infrastructure footprint, we all should look at doing so in a way that honors the industrial heritage of these regions while propelling them into the digital future. By harnessing the power of AI, HPC, and digital infrastructure in these reimagined industrial sites, we’re not just driving technological progress – we’re fostering economic resilience and innovation renewal in the heart of America’s industrial heartland.

In the heart of Council Bluffs, Iowa, a colossal three-million-square-foot Google data center hums with activity, its vast array of servers and networking equipment working tirelessly to power AI applications used by millions worldwide.

This facility is just one piece of the complex AI infrastructure puzzle that’s rapidly reshaping our technological landscape. From the silicon chips that crunch the numbers to the cloud services that deliver AI capabilities to users, understanding the AI infrastructure stack is crucial for anyone looking to navigate the AI revolution.

THE AI VALUE CHAIN: SILICON, SOFTWARE, AND SERVICES

The AI stack is comprised of three main layers: silicon, software, and services. Each layer in this stack plays a crucial role in the AI ecosystem, with strong interdependencies between them.

Silicon: The Foundation Of AI Computing

At the base of the AI stack lies silicon—the physical hardware that powers AI computations. This market is evolving rapidly and incredibly competitive due to the exponential growth in AI workloads. The key players in this space include:

• NVIDIA: The market leader manufacturing GPUs, especially the H100 and A100 series. NVIDIA’s success stems from its comprehensive ecosystem, including the CUDA programming model and cuDNN library, which have become de facto standards in AI development.
• AMD: Rapidly expanding its presence, especially with the recent announcement of its $4.9 billion acquisition of ZT Systems. AMD’s CDNA architecture, embodied in its Instinct MI300 series, is positioning the company as a strong challenger in the AI accelerator market.
• Apple: Developing custom AI chips for its devices, with the Neural Engine in its A-series and M-series chips showcasing the potential of AI hardware in edge devices.
• Google: Creating custom Tensor Processing Units (TPUs) for its AI workloads, demonstrating the value of application-specific integrated circuits (ASICs) in AI computation.
• TSMC (Taiwan Semiconductor Manufacturing Company): The world’s largest dedicated independent semiconductor foundry, TSMC plays a crucial role in manufacturing chips for many of these companies, including NVIDIA and Apple. Its advanced process nodes are key to enabling more powerful and efficient AI chips.
• Supermicro: A leader in high-performance, high-efficiency server technology, Supermicro is playing an increasingly important role in the AI infrastructure space. Its GPU-optimized servers are widely used in AI and deep learning applications.
• Startups like Cerebras, Graphcore, and SambaNova: These companies are pushing the boundaries of AI chip design with novel architectures, challenging the established players and driving innovation in the field.

Other tech giants are also making significant strides in AI chip development. Amazon, through its AWS division, has developed custom AI chips like Inferentia for inference and Trainium for training machine learning models. Meanwhile, Meta (formerly Facebook) is working on its own AI hardware, including the Meta Training and Inference Accelerator (MTIA), designed to optimize recommendation models and other AI workloads specific to its needs.

How companies can rebuild trust in an era of fraud and spam
An epidemic of fraud and theft has eroded consumer confidence, while the rise of AI and the never-ending hype cycle on social media.

Software: The Brain Of AI Systems

The software layer forms the crucial bridge between raw computing power and practical AI applications. This is where much of the innovation in AI occurs, driving advances in natural language processing, computer vision, and reinforcement learning. While these developments leverage increasingly powerful hardware, it’s the software that truly defines AI capabilities.

Open-source frameworks have become the cornerstone of AI development, fostering innovation and democratizing access to advanced AI tools. Google’s TensorFlow and Facebook’s PyTorch lead the pack, providing robust platforms for building and deploying AI models. Microsoft’s ONNX (Open Neural Network Exchange) aims to improve interoperability between different frameworks, while IBM’s Watson offers a suite of enterprise-grade AI services. Newer entrants like Hugging Face are revolutionizing the field with their focus on transformers and easy-to-use APIs for state-of-the-art models.

As the field evolves, we’re seeing the emergence of AI-specific operating systems and middleware designed to optimize hardware usage. These software innovations are key to unlocking the full potential of AI hardware, enabling more efficient and powerful AI applications across various domains.

Services: Bringing AI To The Masses

At the top of the AI infrastructure stack, we find the services layer, where AI becomes tangible for most businesses and consumers. This layer is dominated by cloud providers like Amazon (AWS), Microsoft (Azure), and Google (GCP) and complementing these are AI-as-a-Service platforms such as OpenAI, Anthropic, and Cohere, which provide access to cutting-edge AI models through simple APIs.

As the AI landscape evolves, here are five key developments AI leaders should monitor:

1. Specialized AI Chips: Expect a proliferation of task-specific AI chips (e.g., for inference, training, or edge computing) from companies like Groq, Cerebras, and SambaNova, offering improved performance and efficiency. These could dramatically reduce costs and energy consumption while increasing AI capabilities.
2. AI-Native Cloud Architectures: Cloud providers will offer more AI-optimized infrastructure, integrating hardware and software layers for better resource utilization and cost-effectiveness. This trend could reshape how organizations deploy and scale AI solutions.
3. Edge AI Acceleration: The expansion of 5G and more powerful IoT devices will drive growth in edge AI, enabling real-time applications in autonomous vehicles, smart cities, and industrial IoT. This shift could open new markets and use cases for AI technologies.
4. Quantum AI: While still in early stages, the convergence of quantum computing and AI could revolutionize complex problem-solving in fields like drug discovery and financial modeling. Early movers in this space may gain significant competitive advantages.
5. AI Development Democratization: Improved tools like AutoML, visual programming interfaces, and AI-assisted coding will make AI development more accessible to non-experts, potentially spurring innovation across various sectors. This could lead to a surge in AI applications and change the skill sets required for AI development.

The AI infrastructure stack—encompassing silicon, software, services—is the force propelling AI forward. As we’ve seen, this ecosystem is characterized by intense competition, rapid innovation, and complex interdependencies. Looking ahead, the AI infrastructure landscape will continue to evolve—specialized AI chips, edge computing, and quantum AI are just a few trends that will continue to reshape AI.

Picture a city where artificial intelligence (AI) algorithms complement the hum of steel mills, where robotics startups collaborate with century-old manufacturers, and where the next big tech breakthrough is as likely to come from a university lab as from a hospital research center.

Welcome to Pittsburgh, an example of the emerging cities that will become the epicenter of America’s AI revolution, where tradition and innovation coexist and thrive.

In the shadow of its storied industrial past, Pittsburgh is orchestrating a technological renaissance that’s reshaping the digital landscape while honoring its roots. As the CEO and president of a publicly traded digital infrastructure company that also has sites in the greater Pittsburgh area, I’ve witnessed firsthand how this resilient city is forging a new identity at the intersection of AI, digital infrastructure and innovation—all while maintaining its industrial heritage.

Re-Emerging Cities And The Steel City’s Multifaceted Makeover

Pittsburgh’s journey from steel titan to tech hub is a story of resurgence. While the city once produced nearly half of the nation’s steel, it has now diversified its economy without abandoning its industrial roots. According to the Pittsburgh Regional Alliance, Pittsburgh is now home to over 100 AI and robotics companies while still boasting a significant manufacturing presence.

The announcement of 72 Steel’s plans to build a massive 44-acre steel fabrication site underscores the enduring importance of manufacturing in the region. This juxtaposition of traditional industry and cutting-edge technology is what sets Pittsburgh apart from other tech hubs.

Venture capital and private equity investment in robotics in the region has totaled more than $3.3 billion since 2012, underlining the financial world’s confidence in Pittsburgh’s tech future. This influx of capital has been crucial in fueling the city’s rapid transformation and supporting the growth of its burgeoning tech ecosystem.

At the heart of this ecosystem are Carnegie Mellon University (CMU) and the University of Pittsburgh, both renowned for their computer science and robotics programs. CMU’s School of Computer Science, ranked among the best in the world, has been a training ground for America’s leading AI innovators. The university’s partnership with Uber to develop self-driving car technology put Pittsburgh on the map as a serious contender in the AI race.

The Digital Infrastructure Backbone

As impressive as Pittsburgh’s AI credentials are, they would be nothing without the robust digital infrastructure to support them. The exponential growth of AI and machine learning applications demands unprecedented computing power and data storage capabilities.

According to a report from Statista, the volume of data created, captured, copied and consumed globally is projected to grow from 120 zettabytes in 2023 to 181 zettabytes in 2025, representing a compound annual growth rate (CAGR) of 23%. This explosive growth, driven largely by AI, IoT devices and cloud services, is putting unprecedented pressure on existing digital infrastructure—which is why our company recently expanded our digital infrastructure in the greater Pittsburgh area, given our optimistic view of the region and its future promise.

Pittsburgh’s strategic location, with its proximity to major East Coast markets and diverse energy sources, makes it an ideal hub for data center operations. Pittsburgh’s energy mix includes nuclear power from the nearby Beaver Valley Power Station, abundant natural gas from the Marcellus Shale, and growing renewable sources like wind and solar. This varied energy portfolio provides the reliable and sustainable power needed to support the region’s burgeoning data infrastructure.

The Edge Computing Revolution

One of the most exciting developments in digital infrastructure is the rise of edge computing, which brings data processing closer to the source of data generation. This is particularly crucial for AI applications that require real-time processing and low latency.

Pittsburgh is at the forefront of this edge-computing revolution. The city’s Advanced Robotics for Manufacturing (ARM) Institute, a public-private partnership founded by CMU, is pioneering the use of edge computing in industrial settings. By processing data at the edge, manufacturers can make split-second decisions, optimize production in real time and improve overall efficiency.

Challenges And Opportunities

Despite its impressive progress, Pittsburgh’s tech ecosystem faces challenges. The city still struggles with talent retention, as some graduates of its prestigious universities are lured away by the allure of Silicon Valley or the East Coast tech hubs. Additionally, while venture capital investment in Pittsburgh has grown significantly in recent years, it still lags behind more established tech centers.

These challenges, however, also present opportunities. The lower cost of living and high quality of life in Pittsburgh can be increasingly attractive to tech professionals, and the city’s strong sense of community and collaborative spirit also set it apart from other tech hubs. Moreover, Pittsburgh’s focus on AI ethics and responsible innovation positions it well for the future. As concerns about the societal impacts of AI grow, Pittsburgh’s interdisciplinary approach to tech development could become a model for other cities to follow.

The Road Ahead For Re-Emerging Cities

As we look to the future, Pittsburgh’s trajectory as a tech hub seems set to showcase a new model for the re-emerging cities that were previously hubs of traditional industry, powered by the unique synergy between its tech and tradition. The city’s blend of industrial know-how, building for scale, cutting-edge research and a collaborative spirit creates a powerful ecosystem for innovation that sets a model for other cities across the Rust Belt to adopt.

As the CEO of a company that builds digital infrastructure that helps power these innovations, I’m excited about the possibilities that lie ahead. Pittsburgh’s renaissance is more than just a local success story—it’s a blueprint for how cities across America and the world can reinvent themselves in the age of AI while honoring their industrial heritage.

The steel mills that once solely defined Pittsburgh now hum alongside servers and AI algorithms, signaling a new era of innovation that doesn’t forget its roots. In this new landscape, re-emerging cities such as Pittsburgh aren’t just keeping pace but instead are leading the charge into a future where traditional industry and cutting-edge technology coexist and thrive together.

Imagine a world where your car makes life-saving decisions in milliseconds, where your smartphone predicts what you need before you ask, and where factories optimize production in real time. This isn’t science fiction—it’s the promise of edge computing, and it’s happening right now.

In an era where data is the new oil, edge computing is the high-octane fuel powering the engines of innovation. As the CEO and President of a NASDAQ-listed digital infrastructure company, I’ve witnessed firsthand how this technology is beginning to truly revolutionize industries, from autonomous vehicles to smart cities. But what exactly is edge computing, and why should it matter to you?

At its core, edge computing brings data processing closer to the source of data generation. It’s the difference between your smart home responding in real time versus waiting for a distant server to process your request. And in a world where every millisecond counts, edge computing isn’t just an upgrade—it’s a necessity.

THE POWER OF PROXIMITY AND PROCESSING IN REAL TIME

The primary advantage of edge computing lies in its ability to process data closer to where it’s generated. By moving computation and data storage to the network’s edge—closer to users and devices—we can dramatically reduce latency and improve real-time processing capabilities. This is particularly crucial for applications that require split-second or near-instant decision-making, such as autonomous vehicles, logistics and transportation, and industrial automation.

Tesla is an example of a company seeking to leverage the power of edge computing. Elon Musk has outlined an ambitious plan to turn Tesla’s fleet into a distributed network of mobile data centers using the powerful onboard computers in Tesla vehicles to perform distributed inference tasks when the cars are not in use.

This innovative approach could potentially create a network of 100 million Teslas, which, when combined, could result in 100 gigawatts of distributed inference computing power around the world. By processing data from multiple sensors in real time at the edge, Tesla’s cars can make instantaneous decisions about navigation and safety, without relying on a constant connection to a central cloud server.

ENHANCING IOT CAPABILITIES

Internet of Things (IoT) is another sector that stands to benefit significantly from edge computing. As the number of connected devices continues to skyrocket, edge computing enables IoT devices to operate more efficiently, even in areas with limited connectivity, by reducing their reliance on constant cloud communication.

To put this growth into perspective, Statista projects that by 2030, there will be 32 billion IoT-connected devices in use worldwide. This massive proliferation of devices will generate an unprecedented amount of data, making edge computing not just beneficial, but essential for efficient operations.

The Port of Barcelona’s 5G Maritime pilot project exemplifies the transformative power of edge computing in enhancing IoT capabilities within a complex maritime environment. Picture a massive container ship approaching the harbor. Barcelona’s port now has the ability to track and manage the ship’s movements in real time, and its exact location is known and updated instantly on the port authority’s systems. This precision is made possible by processing data from IoT sensors at the edge and its benefits are manifold: real-time ship tracking improves traffic management, reduced latency enables split-second decision-making, and enhanced data processing allows for immediate responses to potential incidents.

CHALLENGES AND OPPORTUNITIES

While the potential of edge computing is immense, it’s not without challenges. Security concerns, standardization issues, and the need for robust management tools are hurdles that the industry must overcome. However, these challenges also present opportunities for innovation and growth.

One of the primary concerns with edge computing is security. As data processing moves closer to the source, traditional security perimeters become less effective. This necessitates a new approach to cybersecurity, one that focuses on securing devices and edge nodes rather than just centralized data centers.

Standardization is another challenge. With multiple platforms entering the market, there’s a risk of fragmentation that could hinder widespread adoption. However, industry consortiums like the Edge Computing Consortium (ECC) are working to address this issue by developing common standards and frameworks.

Despite these challenges, the edge computing market is poised for explosive growth. According to Grand View Research, the global edge computing market is expected to reach $140 billion by 2030, with a growth CAGR of 37% from 2023 to 2030.

PREPARING FOR THE EDGE COMPUTING REVOLUTION

As we look to the future, it’s clear that edge computing will play a crucial role in shaping our digital landscape. To prepare for this shift, consider the following actions:

1. Assess Data Processing Needs: Identify areas across your organization where low latency and real-time processing could provide tangible benefits. This could include improving customer experiences, optimizing operations, or enabling new product features.
2. Invest In Skills And Expertise: Edge computing requires a different skill set compared to traditional cloud computing. Invest in training your IT teams or hire specialists with experience in areas like distributed systems, IoT protocols, and edge-specific security practices.
3. Partner With Edge Specialists: Consider partnering with companies that specialize in edge computing infrastructure with a track record of deployments in your industry. These partnerships can help you leverage expertise and resources that might be challenging to develop in-house.
4. Stay Informed On Standards And Best Practices: The edge computing landscape is evolving rapidly. Learn about emerging standards, best practices, and security protocols. Participate in industry forums and consortiums to ensure your edge strategy aligns with broader industry trends.
5. Prioritize Security: As you move towards edge computing, make security a top priority. Implement robust security measures at every level of your edge infrastructure, from individual devices to edge nodes and the network connecting them.

By embracing edge computing, businesses can unlock new levels of efficiency, innovation, and customer experience. The potential for transformative change is immense. It promises a future where real-time data processing and decision-making become the norm, driving unprecedented advancements across industries. The edge computing revolution is not just coming—it’s here, and it’s set to reshape our digital landscape in ways we’re only beginning to imagine.

Back in the 19th century, railroads revolutionized our world, connecting places, enabling transportation at scale and igniting a new wave of industrial growth. Today, we stand at the threshold of a similar transformation, as digital tracks are being laid across the United States. This digital infrastructure—a network of data centers, fiber optic cables, and cutting-edge hardware and software—is the modern equivalent of those transformative railroads, but with even greater potential.

This digital infrastructure is not just a supporting player in the upcoming AI revolution—it is the very foundation upon which our AI-driven future is being built. Just as the railroads of the past opened new frontiers and possibilities, today’s digital infrastructure is enabling unprecedented advancements and scale in artificial intelligence (AI) and its applications.

Digital Infrastructure And AI Adoption

The explosive growth of AI applications, particularly large language models like ChatGPT, has catapulted AI into the mainstream. OpenAI’s ChatGPT reached 100 million monthly active users just two months after its launch, making it the fastest-growing consumer application in history. To put this in perspective, ChatGPT’s user growth outpaced Instagram, which took two and a half years to reach 100 million users, and TikTok, which achieved the same milestone in about nine months, each holding the previous record for rapid adoption.

What users don’t see, however, is the vast infrastructure that powers these transformative applications. Behind every chat response or image generation lies a complex network of data centers housing hundreds of thousands of powerful computers. These facilities are the engine rooms of the AI revolution, processing enormous amounts of data and performing complex calculations at unprecedented speeds.

The energy demands of AI are staggering when viewed on a global scale. Training a single large language model like GPT-3 is estimated to consume about 1,287 megawatt hours (MWh) of electricity, equivalent to the annual energy usage of 130 average U.S. homes. One study estimates that by 2027, the AI sector could consume between 85 to 134 terawatt hours (TWh) annually. That is more than the annual power consumption of all but around 30 countries in the world.

This immense computational demand is why digital infrastructure has become so critical. Just as railroads needed extensive networks of tracks and stations, AI requires a vast ecosystem of data centers, networking equipment and specialized hardware. These digital rails are the conduits through which data analysis and compute flows, enabling the rapid processing and analysis that powers AI applications. Today’s AI data centers are the colossal engines powering our digital age. For example, Google’s data center in Council Bluffs, Iowa, spans nearly three million square feet—equivalent to about 50 football fields.

The AI Value Chain: From Silicon To Services

At the heart of the AI ecosystem is a value chain that begins with chip manufacturers like NVIDIA, whose GPUs have become the de facto standard for AI computation. NVIDIA’s data center revenue exploded to $22.6 billion in the first quarter of 2024, a 427% increase year over year. This exponential growth reflects the surging demand for AI-capable hardware and underscores the critical role that specialized chips play in the AI revolution.

GPUs, however, only represent the tip of the value chain iceberg. Between chip production and AI services lies a critical intermediary layer: the companies that house, operate and maintain these GPUs. These digital infrastructure companies play a crucial role in making AI accessible and are responsible for the day-to-day operations of the data centers that power AI applications. They manage everything from the physical security of the facilities to the intricate workflow of load balancing and resource allocation that keeps AI services running smoothly.

While the end users of AI applications may never see or interact with the infrastructure and data centers directly, they are the tracks that keep the digital trains running, ensuring that data flows smoothly and that computational resources are used efficiently.

The investments of OpenAI’s Sam Altman and others in energy production are a forward-thinking approach to one of the biggest challenges facing the AI industry: the enormous energy demands of large-scale AI computations. As AI models grow larger and more complex, their energy needs increase correspondingly. This has led to growing concerns about the environmental impact of AI and has spurred efforts to develop more energy-efficient AI hardware and to power data centers with carbon-free energy sources.

Building Towards The Future Of AI

As the AI revolution continues to unfold, we have a crucial role to play in ensuring its sustainable growth. Here are five actionable steps that we can take to contribute to energy efficiency and responsible AI development:

1. Invest in energy-efficient hardware.

Prioritize the development of energy-efficient AI hardware, including working closely with chip manufacturers to design more power-efficient GPUs or exploring alternative computing architectures like neuromorphic chips that mimic the energy efficiency of the human brain.

2. Optimize AI models for efficiency.

Encourage R&D efforts to create more efficient AI models that require less computational power without sacrificing performance, including techniques like model compression, knowledge distillation or the development of smaller, task-specific models instead of large, general-purpose ones.

3. Implement smart data center management.

Utilize AI itself to optimize data center operations. Machine learning algorithms can predict and manage workloads, adjust cooling systems in real time and optimize energy distribution, significantly reducing overall energy consumption.

4. Support research in carbon-free AI.

Allocate resources to support academic and industry research in carbon-free AI technologies, including funding research projects and partnering with universities to emphasize sustainability in AI development.

5. Develop sustainability standards.

Actively participate in industry collaborations to establish standards and best practices for energy efficiency in AI and digital infrastructure, including targets for reducing the carbon footprint of AI training and inference models or improving the power usage effectiveness (PUE) of data centers.

As we continue to lay the digital tracks for AI’s transformative growth, we will enable the immense impact of the AI revolution. However, its true potential can only be realized if we ensure its growth is sustainable and beneficial across all its stakeholders.