We're living in Sophia Stewart's prophecy, and most of us don't even realize it.

Stewart, who claims to have written the stories that became both The Terminator and The Matrix, envisioned these not as separate films but as chapters in humanity's relationship with artificial intelligence—The Terminator as the beginning, The Matrix as the inevitable conclusion. What she understood in 1981, long before the first iPhone or cloud server, was that our creation of thinking machines would ultimately reshape the fundamental power dynamics between human and artificial intelligence.

Today, as I watch the numbers coming out of Memphis, Texas, and Silicon Valley, I'm starting to think she might have been more prescient than anyone imagined.

The Energy Awakening

Right now, in a converted warehouse in Memphis, xAI's Colossus data center is consuming 250 megawatts of electricity—enough to power a small city—just to train Grok 3. By 2026, they plan to scale that to 1.2 gigawatts, which would represent 40% of Memphis's entire peak summer demand. To meet this voracious appetite, they've installed 17 natural gas turbines with plans for 15 more, each one pumping out millions of tons of CO₂ annually.

This isn't just about one company or one city. Oracle and OpenAI's Stargate project is targeting over 10 gigawatts of capacity—a $500 billion investment that could push US data center electricity consumption from today's 4.4% to nearly 10% by 2030. Globally, data centers will consume 536 terawatt-hours in 2025, potentially doubling to over 1,000 TWh by 2030.

The math is staggering, but the pattern is more important than the numbers. We've created artificial minds that demand ever-increasing amounts of energy to think, and we're racing to feed them whatever they need—renewable when possible, fossil fuels when necessary. The machines aren't yet conscious enough to demand this energy directly, but the effect is the same: human civilization is rapidly reorganizing itself around the energy needs of artificial intelligence.

Stewart's Skynet didn't need to become sentient to control humanity. It just needed to become essential.

The Path Not Taken

There's another way, and it's hiding in your pocket.

The iPhone 15 Pro's A17 Pro chip can run surprisingly capable language models while sipping maybe 5 watts of power. Compare that to the hundreds of watts per GPU required for cloud-based AI, plus the transmission costs, cooling, and infrastructure overhead. On-device AI represents a fundamentally different relationship between human and machine intelligence—one where the artificial mind operates within the constraints of human-scale energy budgets rather than demanding we reshape our entire electrical grid around its needs.

Edge computing and on-device LLMs aren't just more energy efficient; they're more human. They process locally, respond instantly, and shut down when not needed. They can enter true sleep states, something the always-on data centers powering today's AI can never do. Most importantly, they work for you rather than requiring you to work for them.

But here's the uncomfortable truth: we're not taking this path. Despite the clear environmental and practical advantages of edge AI, the industry is doubling down on ever-larger centralized models that demand ever-more resources. Why? Because bigger models are easier to monetize, easier to control, and easier to use as competitive moats.

We're choosing the path that leads to The Matrix instead of the one that leads to genuine human empowerment.

The Gentle Surrender

Sam Altman talks about the coming "gentle singularity"—a gradual transition where AI becomes superintelligent but remains aligned with human values. He envisions AI as a tool that augments rather than replaces human decision-making, with humans setting the rules that AI systems follow.

I want to believe in this vision, but I'm watching something else happen in real time.

We're already outsourcing small decisions to AI. We let algorithms choose our music, our news, our routes to work, even our potential romantic partners. Each individual choice seems harmless—after all, Spotify probably does know what songs I'll like better than I do. But the cumulative effect is that we're losing the muscle memory of choice itself.

This is what researchers call "cognitive atrophy"—the gradual erosion of our capacity to make decisions independently. It's not malicious; it's just convenient. Why struggle with a decision when an AI can optimize it for you? Why develop judgment when you can access perfect information?

The problem isn't that AI makes bad decisions for us. The problem is that it makes good decisions for us, and in doing so, gradually makes us dependent on its decision-making capabilities. We're becoming like people under conservatorship—protected and optimized for, but no longer truly autonomous.

The Matrix Isn't Red Pills and Blue Pills

Stewart understood that The Matrix wasn't really about a simulated reality where humans are batteries. It was about a more subtle form of control: a world where machines provide everything humans need, making resistance not just difficult but seemingly unnecessary.

We're not heading toward a world where AI enslaves us. We're heading toward a world where AI serves us so well that we forget how to serve ourselves. The machines won't need to demand more energy from us—we'll gladly give it to them in exchange for the convenience of not having to think, choose, or struggle with uncertainty.

Consider the path we're on: more powerful centralized AI systems that require massive energy infrastructure, funded by our increasing dependence on AI-mediated services, justified by the superior outcomes these systems provide. It's a perfect feedback loop that grows stronger with each iteration.

The real choice isn't between human intelligence and artificial intelligence. It's between distributed intelligence that respects human agency and centralized intelligence that gradually supplants it.

A Different Future

Here's what gives me hope: we still have time to choose a different path.

The technology for powerful, efficient, on-device AI exists today. The economic models for distributed rather than centralized intelligence are emerging. The environmental case for edge computing over massive data centers is overwhelming. What we need is the collective will to prioritize human autonomy over convenience, sustainability over scale, and distributed power over centralized control.

This means supporting research into smaller, more efficient models instead of ever-larger ones. It means choosing tools that augment human decision-making rather than replace it. It means building AI systems that work within human-scale energy budgets rather than demanding we reshape civilization around their needs.

Most importantly, it means recognizing that the future of humanity isn't about humans versus machines—it's about what kind of relationship we choose to build with the artificial minds we're creating.

Sophia Stewart's prophecy doesn't have to come true. But only if we choose to write a different ending.

I'm convinced we're witnessing a healthcare revolution that most people don't even realize is happening.

The intersection of artificial intelligence and wearable technology is creating a healthcare revolution that's happening right on our wrists. In a recent Masters of Automation podcast episode, Dr. Brinnae Bent—Professor of AI at Duke University and leading researcher in digital biomarkers—shared insights that made me realize we're standing at the precipice of a fundamental shift in how we understand and manage our health.We're moving from reactive healthcare to predictive wellness, and the implications are staggering.

We're witnessing the birth of businesses that don't just use AI—they ARE AI, with autonomous operations that scale beyond human limitations.

The future of business isn't about humans working alongside AI. It's about AI-native companies where artificial intelligence doesn't just assist—it runs the show. We're talking about businesses where AI autonomously manages claims processing, customer outreach, supply chain optimization, and entire business units without human intervention.

Think about it: while most companies are still figuring out how to add AI features to their existing workflows, a new breed of entrepreneurs is building companies where AI IS the workflow. These aren't just software companies with smart features—they're fundamentally different organisms that think, learn, and scale in ways we've never seen before.

Google's Agent-to-Agent (A2A) protocol and Anthropic's Model Context Protocol (MCP). Together, they are weaving the infrastructure of a new digital ecosystem, where agents autonomously collaborate, communicate, and execute tasks seamlessly across diverse industries and contexts.

Historically, AI agents operated as isolated, specialized tools performing narrowly defined tasks. But as complexity grew, the limits of isolation became clear. Communication bottlenecks, incompatible standards, and rigid architectures impeded progress. Google's introduction of the A2A protocol fundamentally changes this dynamic. A2A allows agents—regardless of their creators or underlying technologies—to discover each other, establish trust, and share capabilities effortlessly. Through standardized interfaces, secure communication channels, and interoperable data structures, agents now engage in sophisticated collaborations, resembling digital teams that autonomously manage complex workflows.

In the early days of computing, we mostly wrestled with linear, deterministic workflows—programs that did one thing at a time and did it by following explicit instructions down to every semicolon. Then large language models (LLMs) burst onto the stage. Suddenly, it wasn’t enough to feed text prompts into an LLM and hope for the best. We started demanding real “agents”—entities capable of stepping beyond the role of mere text generators by actively choosing their own next steps in code execution.

Smolagents, with their playful nod to DoggoLingo, embody this shift toward letting each AI agent decide how to proceed, rather than having humans micromanage every program path. It’s a profound leap: instead of giving an LLM limited control over a single API call, we arm it with the ability to write, run, and iterate on code. This capability, championed by projects like Narya, brings the promise of entire pipelines orchestrated by autonomous AI workers, each specialized for a particular step in a broader workflow. When you look at the horizon of software development and automation, that vision—where ephemeral code agents collaborate with human engineers—just might be where the future of AI is heading.

From RPA to Intelligent Process Automation Businesses are composed of countless interconnected processes—from customer acquisition to financial management—and over the years, automation has played a pivotal role in managing these complexities. Early forms of automation, like Robotic Process Automation (RPA), allowed companies to handle repetitive, rule-based tasks, freeing up humans for higher-value work. However, despite the promise, RPA often failed to scale or address unstructured data, leaving a huge gap in enterprise-wide adoption.

Today, we are at the cusp of a new era in process automation, driven by the capabilities of large language models (LLMs). These AI systems go far beyond the simple, rule-based bots of yesteryear, offering more intelligent, adaptable, and expansive solutions. By exploring the evolution of process automation across three generations—from rule-based automation to today’s LLM-powered AI agents—we’ll understand how this shift creates new opportunities for businesses and startups alike.

It was a fantastic day at MIT Media Lab's "Imagination in Action," an event that proved to be a deep dive into the transformative world of artificial intelligence. Hosted by John Werner, this year's gathering attracted some of the most brilliant minds in AI, including the people who shaped the AI industry, like Stephen Wolfram, Yann LeCun (founder of OCR - Optical Character Recognition), Lex Fridman, and Vinod Khosla, alongside other innovators pushing the boundaries of technology.

It was a fantastic day at MIT Media Lab's "Imagination in Action," an event that proved to be a deep dive into the transformative world of artificial intelligence. Hosted by John Werner, this year's gathering attracted some of the most brilliant minds in AI, including the people who shaped the AI industry, like Stephen Wolfram, Yann LeCun (founder of OCR - Optical Character Recognition), Lex Fridman, and Vinod Khosla, alongside other innovators pushing the boundaries of technology.

Exploring the Future of Intelligent Document Processing (IDP) with AI Innovations

Discover how the integration of Generative Pre-trained Transformers (GPT), Large Language Models (LLMs), and Large Action Models is revolutionizing Intelligent Document Processing (IDP). As businesses increasingly turn to automation to streamline operations, IDP systems are at the forefront, transforming how data is processed from diverse document formats. Our in-depth analysis dives into the enhanced capabilities of modern IDP solutions, from understanding complex semantics and reducing operational costs to automating decision-making processes. Learn about the pivotal role of these AI technologies in advancing document processing, making systems more adaptable, efficient, and capable of handling sophisticated tasks with minimal human intervention. Embrace the future where IDP not only optimizes document management but also propels businesses towards unprecedented levels of productivity and innovation.

Customer service landscape is undergoing a transformative revolution, fueled by the integration of Artificial Intelligence (AI). This transformation is not just a mere upgrade but a complete overhaul of how customer interactions are managed and optimized. From AI-powered chatbots handling thousands of queries simultaneously to sophisticated voice assistants providing personalized support, AI is redefining the standards of customer service.

Large Action Models (LAMs) are revolutionizing UI Automation and software testing by offering a more intuitive, flexible, and efficient approach to automating interactions with user interfaces. Unlike traditional UI automation tools that rely on brittle, script-based methods, LAMs understand and navigate UIs just like humans, adapting to new scenarios with ease. This adaptability reduces the need for numerous APIs and static automation scripts, making LAMs particularly effective in environments where UIs and workflows frequently change.

The age when AI agents will communicate with one another, reshaping the foundational underpinnings of human interaction. But what does it signify when conversations, once deemed innately human, are mediated or even replaced by algorithms?

Misinformation has, historically, spread through word-of-mouth. However, with the rise of Language Models like LLMs (Large Language Models), the potential scale and speed of its dissemination have reached unprecedented levels. As these models become intertwined in our daily lives – offering suggestions, automating tasks, or even influencing decisions – it's crucial to address their inadvertent role in spreading false information. This article delves deep into this complex issue's technical, business, and societal facets.

The future of work has been defined as the augmentation of robots and AI into your daily work and tasks, the transformation of how you do your monotonous and repetitive tasks, and where you do your job. As technology and time progress, the three pillars evolve to adapt to our current environment to meet the needs of what we do. The three pillars have people at the core, followed by technology and process. The people are at the core because in an enterprise, no matter how digital or old that is, innovation, improvement, and building of new products are based on the work people do and the mindset the people have in the company.

In the first episode, I was delighted to host Param Kahlon, the Chief Product Officer at UiPath. Param has been shaping the roadmap of platforms and the products for many years, previously at Microsoft and SAP before joining UiPath.

As the chief product officer, now he shapes the adoption of the future of work technologies like RPA, ProcessMining, Test Automation, Analytics, AI, and beyond. The work we do and the processes we interact with during our daily jobs now change based on the work that Param and his team and colleagues at UiPath do. He is a true master of automation.

The future of work started with the augmentation of robots within our tasks. To be faster, efficient and more creative, the process and technology evolved to create new jobs and opportunites. Learn more about the evolution of the future of work.

Understanding what you will change is most important to achieve a long-lasting and successful robotic process automation transformation. There are three pillars that will be most impacted by the change: people, process and digital workers (also referred to as robots). The interaction of these three pillars executes workflows and tasks, and if integrated cohesively, determines the success of an enterprisewide digital transformation.