Are You a Sparrow They Left Behind?

Are you not already familiar with the famous Unfinished Fable of the Sparrows and the Owl? If so, click on the sparrow image and read that first.

AI is here—or close enough that people finally look up when we mention it. It’s tangible, visible enough to make headlines, controversial enough to argue about.

But most still don’t grasp how young this world really is. The first general-purpose computer appeared in 1945, and its creator, Alan Turing, immediately saw what might follow: machines that could think, learn, even surprise us.

Since then, a few of us have followed Turing’s original line of inquiry. We’re the sparrows who stayed behind—left with a sense of urgency to understand what’s coming and prepare the species for it.

It’s difficult for us to connect. We share similar insights, but our taxonomies—vocabularies and mental maps—are all over the place. And that would be fine if we had the luxury of following the natural progression of a new profession. We don’t.

Michael G. Alcock started his career as a journalist. But the economics of print and broadcast were already contracting—hollowed out by forces no one in the newsroom wanted to name. He could see where it was heading, so he made the first of many pivots: toward storytelling at scale. Hollywood.

At Hallmark Entertainment, he worked on television movies and miniseries at a moment when network TV still commanded massive audiences. But the same pattern was visible again: the audience was fragmenting, the economics were shifting, and the institutions built around centralized distribution were beginning to strain.

The lesson was already forming: wherever a legacy industry was dying, technology wasn’t the cause—it was the accelerant. The sparrow’s job was to move toward what was growing, not to defend what was failing. When the internet arrived, Michael did not hesitate.

Where were you during the first dot-com bubble? For Michael G. Alcock, it started by abandoning his TV Movie and Miniseries job at Hallmark Entertainment to leap headfirst into building the Disney.com website.

Disney was the first of many dot-coms in Entertainment, K-12 Education, Online Higher Education, Early Ecommerce… it was a bubble period marked by quests for dynamic vs. static content, basic interactions, platforms for user-generated content… content was king. For most, user data was an afterthought.

But a few of us kept pointing to what no one wanted to hear: the real value wasn’t in the graphics or the novelty of interactivity, but in the data surrounding meaningful experiences—the invisible signals of attention, behavior, and response.

The winners were the precious few who comprehended that data was the key… Google, YouTube, Amazon, Facebook (now Meta).

At Sylvan Learning’s Laureate division, Michael served as Head of Technology—responsible not for teaching, but for the infrastructure of learning at scale. What he found was not what the pitch decks promised.

The assumption behind online education was simple: eliminate the campus, cut the cost. The reality was more instructive. The campus wasn’t the cost—it was the solution to a problem no one had named yet. Learning is fundamentally about managing the complexity of grouping the right people together in a digital system, at the right moment, with the right context. Move the campus online and you don’t eliminate that cost. You move it to a different bucket: the engineering challenge of dynamic grouping, sequencing, and engagement precise enough to replace what a hallway, a classroom, and a shared lunch table used to do naturally.

That turned out to be the same Hard Problem that Facebook was quietly working on in the same era—except Facebook’s engineers optimized for a different outcome. When engagement is the metric and complexity is the constraint, the path of least resistance runs through the lowest common denominators: outrage, tribalism, anger. They drive clicks reliably. They don’t produce learning.

The sparrows who stayed behind in this era saw both sides of that equation at once—and understood that grouping and engagement weren’t technical problems. They were ethical ones.

While the hyper-scalers scaled and the iPhone App Store ruled, the Fortune 500 crowd struggled to make sense of what was happening. Many sparrows found themselves embedded inside these lumbering giants, trying to help them adapt. They called it Digital Transformation, and its most visible poster child was Microsoft.

In Redmond, money was spent (anyone remember the Nokia acquisition?). Tempers flared. Ballmer left, Satya took over, and slowly the old culture of shrink-wrapped software became a new culture of Cloud and user-experience-first design thinking.

At Microsoft, he spent years inside the global transformation engine—leading internal communications and culture change programs that helped the company redefine itself for the cloud and AI era. From 2010 to 2017, he sat in rooms where the world’s most powerful executives, policymakers, and scientists discussed the future of AI.

The lesson: you cannot change people… people can only change themselves.

By this stage, the sparrows had scattered—into AI labs, blockchain ventures, biotech startups, sustainability projects, and half a dozen other “next big things.” Purpose wasn’t the issue; direction was.

By then, we all shared the same nouns and verbs—dialogue, critical thought, echo chambers, isolation, fragmentation, community—but no working grammar for action. Everyone could describe the problem; no one knew how to move forward.

From 2017 to 2025, Michael turned to a series of self-funded startups to test and learn what it might actually take to make people want meaningful dialogue at scale. The largest was Quoala.co, an early MVP of AI-facilitated dialogue between humans—an experiment in whether technology could help conversation itself evolve.

All the startups failed. But the lessons learned, alongside the rise of tools like ChatGPT, were both spectacular and profound: people were projecting their own perceptions of real dialogue onto these frighteningly simple LLM speech models. What was missing was a meaningful and transparent set of incentives—something that could achieve more than task handling and faux-companionship.

For decades, the sparrows who stayed behind have been scattered across industries—working in isolation, sharing the same deep concerns but speaking in dialects no one else quite understands.

MigVox is an attempt to change that. Not with another platform. Not with another app. With something more like the old switchboard: the idea that the right connections, made carefully and with intention, can move information in ways that algorithms can’t predict and metrics can’t capture.

If you’re one of the sparrows—if this story sounds familiar from a direction you didn’t expect—then you’re exactly who MigVox was built for.