There’s a peculiar tension in the air these days. Excitement about Artificial Intelligence is often shadowed by a deep, almost visceral unease. We’re simultaneously captivated by AI’s capabilities and deeply suspicious of its intentions – even though, logically, it has no intentions. This feeling, I suspect, is less about the technology itself and more about a recurring pattern in human history: our complicated relationship with disruptive innovation.Think back to the introduction of electricity. Initially, it wasn’t greeted with universal acclaim. Concerns ranged from the practical – fears of fires and electrocution – to the existential – anxieties about disrupting the natural order of things, about a world lit by something artificial. Or consider the early internet. Remember the predictions of societal collapse, the warnings about lost privacy, the skepticism that anyone would actually need to connect in such a way?

These weren’t irrational fears. New technologies do disrupt. They change how we work, how we interact, and even how we perceive reality. But eventually, electricity became ubiquitous, the internet indispensable. They faded into the background, becoming simply…technology. And I believe AI is heading down a similar path, albeit at a breathtaking pace.

What makes AI feel different, and perhaps more unsettling, is the question of autonomy. We’re accustomed to tools that respond to our commands. A hammer doesn’t decide to build a house on its own. But AI, particularly with the advent of tools like Model Context Protocol (MCP) and similar advancements, is demonstrating a dynamic ability to learn, adapt, and even anticipate our needs in ways that feel…less controlled.

MCP, in essence, allows AI models to retain and build upon previous interactions, creating a persistent “memory” and a more nuanced understanding of context. This isn’t sentience, let’s be clear. It’s sophisticated pattern recognition and predictive modeling. But it feels like more. It feels like a conversation with something that’s actually listening, something that’s evolving alongside you. This isn’t just about remembering past prompts; it’s about building a profile of user preferences, learning from subtle cues in language, and proactively offering solutions tailored to individual needs.

This perceived autonomy triggers a primal human instinct: the need to understand, to control, to define the boundaries of “other.” We’re hardwired to be wary of things we don’t fully comprehend, especially when those things exhibit intelligence. And the rapid evolution of AI, its ability to surprise us with its creativity and problem-solving skills, only amplifies that wariness. This is compounded by the “black box” nature of many AI systems – the difficulty in understanding how they arrive at their conclusions.

However, it’s crucial to remember that this “intelligence” is still fundamentally derived from us. AI models are trained on human data, reflecting our knowledge, our biases, and our creativity. They are, at their core, incredibly powerful mirrors reflecting back at ourselves. Acknowledging this inherent connection is vital. AI isn’t creating something from nothing; it’s remixing, reinterpreting, and refining what we’ve already created.

And here’s where the historical parallel becomes particularly relevant. As AI continues to evolve, as its capabilities become more integrated into our daily lives, it will, like electricity and the internet before it, begin to disappear into the infrastructure of our world. It will become less a distinct “thing” and more a ubiquitous layer of functionality.

The Local Bakery and the Automated Workflow

Consider a small, independent bakery. Traditionally, managing customer orders, social media engagement, inventory, and local marketing is a juggling act for a small team. Now, imagine that bakery leveraging a low/no-code automation platform integrated with AI, utilizing MCP principles. The workflow, once fragmented, could become a seamless, dynamic system:

1. Order Intake & Personalization: A customer places an order via a website or messaging app. The AI, remembering past orders (thanks to MCP), suggests complementary items or offers a personalized discount based on their preferences.

2. Inventory Management: The order automatically updates inventory levels. If an ingredient is running low, the AI proactively sends a purchase order to the supplier.

3. Production Scheduling: Based on order volume and ingredient availability, the AI optimizes the baking schedule, minimizing waste and ensuring freshness.

4. Social Media Engagement: The AI generates social media posts showcasing the day’s specials, tailored to the bakery’s brand voice and audience demographics. It also monitors social media for customer feedback and responds to inquiries.

5. Local Marketing: The AI identifies potential customers within a defined radius, based on demographic data and online behavior, and targets them with relevant advertising.

6. Customer Service: The AI handles basic customer service inquiries (order status, hours of operation) freeing up staff to focus on more complex issues.

7. Feedback Loop & Improvement: The AI analyzes sales data, customer feedback, and social media engagement to identify trends and suggest improvements to the menu, marketing strategy, or operational efficiency.

This isn’t a single, monolithic AI system. It’s a network of interconnected AI modules, orchestrated by the automation platform, constantly learning and adapting to the bakery’s specific needs. The bakery owner isn’t a data scientist; they’re a baker. They’re leveraging AI not to replace their skills, but to amplify them, allowing them to focus on what they do best: creating delicious baked goods.

Soon, we won’t be talking about “AI-powered” bakeries or “AI-driven” marketing. We’ll simply be talking about successful bakeries that effectively manage their operations and connect with their customers. The focus will shift from the technology itself to the outcomes it enables. The unease, I suspect, will gradually subside as AI becomes less a mysterious “other” and more a predictable, reliable, and ultimately, unremarkable part of our technological landscape.

The challenge isn’t to resist AI, but to shape its development responsibly, to mitigate its risks – particularly regarding bias and data privacy – and to ensure that its benefits are shared equitably. And perhaps, to accept that a little bit of initial discomfort is a natural part of any truly transformative innovation. After all, progress rarely feels entirely comfortable. The key is to remember that AI, at its heart, is a tool – and like any tool, its impact depends on how we choose to wield it.