Universal basic meaning

How we might offer people meaning by connecting individual actions to national strategies

2040 CE

Mara tilted her phone, scanning the alley behind the corner café. On her screen, the AI overlay filled the space with potential: wildflowers sprouted in cracks, climbing ivy draped across brick walls, and a birdbath gleamed under a patch of imagined sunlight.

“This could work,” she murmured.

“Not bad,” her AI assistant, Lila, replied, her tone upbeat. “Shall I flag it for pollinator-friendly plants and urban cooling potential?”

“Do it.” Mara tapped to log the location in the GreenPrint app, where the dots of her neighbourhood’s rewilding ideas blinked like constellations. She’d already logged seven sites that week—cramped alleys, abandoned lots, even a forgotten strip of soil between bus stops.

“Your contributions this week have helped map 22 square meters of potential green space,” Lila said. “Not bad for a city girl.”

Mara smirked. “And what’s that worth in the grand scheme of things?”

“Every square meter counts,” Lila replied. “Here’s your aggregation update.”

The screen zoomed out, shifting from Mara’s tiny patch of the city to the national map. Dots lit up everywhere—parks expanded, rooftops went green, and verges teemed with wildflowers. Her city alone had contributed thousands of locations, each flagged by citizens like her, stitched together into a seamless vision for urban biodiversity.

She stared at the glowing patches. It was strange and wonderful to see herself as part of something so massive.

Mara checked her notifications later that evening. The Elm Street parking lot had made it to the top of her neighbourhood’s rewilding proposal list.

Lila chimed in as Mara opened the app. “The community consensus process is live. Shall I walk you through it?”

“Go ahead,” Mara said.

“Right now, 57% of residents favour converting the lot into a wildflower meadow,” Lila explained. “16% prefer it stays a parking lot, while the rest are undecided or proposing alternatives.”

Mara swiped to see a breakdown of the data. The app displayed colourful bars and percentages, supported by aggregated comments and personal stories. A driver shared how parking in the area was already a nightmare; a retiree reminisced about the wildflowers they’d seen as a child; a young mom explained how shaded greenery would make walks with her toddler more bearable in the summer heat.

“You haven’t weighed in yet,” Lila said.

“Not sure what to say,” Mara muttered. “I mean, I’d like the meadow, but people do need parking.”

“Why not propose a hybrid solution?” Lila suggested. “You can suggest keeping some parking while rewilding the rest. Based on the current input, it’s a popular compromise.”

Mara tapped a few buttons, adjusting the lot’s proposed layout. A simulation rendered her suggestion, showing a meadow blooming alongside a reduced number of parking spaces. Her idea joined the pool of contributions, where others could upvote, tweak, or comment.

“Submission logged,” Lila said. “You’re part of the 23% advocating for a mixed-use solution.”

Mara leaned back, letting the app’s quiet hum of activity unfold. The process didn’t feel like arguing; it felt like weaving. Every comment, vote, and idea strengthened the tapestry, moving the neighbourhood closer to a collective decision.

Two days later, Lila brought Mara the results.

“Elm Street consensus has finalised,” she said. “The neighbourhood favours your hybrid proposal, with an 84% approval rating.”

Mara raised an eyebrow. “84%? That’s… high.”

“The decision incorporates weighted insights,” Lila explained. “Experts highlighted the meadow’s environmental benefits, while community input emphasised the need for equitable parking. Your solution balanced both priorities.”

The app displayed a visualisation of the process, showing how individual preferences, facts, and expert insights combined into a clear and actionable decision. It wasn’t about voting someone down—it was about synthesising the best ideas.

“And it doesn’t stop here,” Lila added. “Your neighbourhood’s data feeds into the city’s rewilding strategy, which in turn shapes national targets. Want to see how it connects?”

Mara nodded, and the screen zoomed out. Her hybrid proposal became a single glowing thread, weaving into the broader movement of urban rewilding. Cities across the country lit up with similar threads, each decision contributing to a living, breathing network of green spaces.

“You’re one of 1.8 million participants,” Lila said. “Together, you’ve identified 30,000 hectares of urban land to repurpose—enough to offset 12% of the nation’s carbon footprint.”

Mara stared at the glowing map, her chest tightening with pride and responsibility. It wasn’t just about her little parking lot. She could see her efforts join something vast and interconnected.

The next weekend, Mara walked past the Elm Street lot. A group of kids chalked outlines on the pavement where the meadow would grow, their laughter spilling into the cool morning air. Parents chatted nearby, pointing at the designs and speculating about what flowers might bloom there.

“It’s funny,” Mara said aloud. “I didn’t even talk to most of these people about this, but it still feels like we did it together.”

“That’s the point,” Lila replied. “Consensus doesn’t mean everyone has to speak—it means everyone is heard.”

Mara smiled. For the first time, she felt like she wasn’t just living in her neighbourhood. She was shaping it—one small decision at a time.

Analysis

Imagine a society where small efforts—repurposing an empty lot, reducing household energy use, or helping the elderly—feeds into a greater whole. People’s actions ripple outward, aggregating into strategies that shape not just local communities but entire nations. This is the promise of a society with a hive mind, one where AI connects individual contributions to collective goals, fostering a sense of purpose and unity while supporting diversity.

At its core, this vision redefines how we relate to work, identity, and value. In a world moving away from traditional work norms, people face a profound question: how do we define purpose beyond personal gain? A system like Universal Basic Meaning offers a framework for answering this, encouraging individuals to align their efforts for the common good. Whether through rewilding urban spaces, contributing to local policies, or mentoring others, people shift from measuring success by what they own to what they contribute.

AI acts as a bridge in this transformation, mediating between individual desires and collective needs. Unlike traditional governance, where decisions are often imposed from above or reduced to binary choices for easy public consumption, AI promises to support nuanced, participatory processes. People engage in ways that suit their lives—some submitting detailed proposals, others offering a simple preference. These inputs are synthesised, revealing patterns and trade-offs that enable informed collective action.

This system’s strength lies in its inclusivity. Parents juggling childcare, workers with little free time, or those with accessibility challenges can all participate meaningfully, without being constrained by rigid schedules or in-person meetings. Their voices are not diminished but amplified, ensuring that consensus reflects the true diversity of thought and feeling in a community. This adaptability allows governance to evolve from a hierarchical model prone to systemic bias to one that feels deeply participatory and highly democratic in a modern, nuanced way.

This shift isn’t just about governance. It has profound implications for identity and belonging. In the hive-mind model, individuals see themselves not as isolated recipients downstream of policy but as integral parts of something larger. Their unique contributions enrich the whole, while the whole, in turn, reinforces the significance of those contributions. This dual perspective—personal and collective—has the potential to address the disconnection and loneliness that often define modern life.

There’s also the challenge of cultural transition. Moving from a society driven by having to one rooted in giving requires a shift in values, which may be met with resistance. For some, the idea of deriving meaning from community work or ecological stewardship might feel imposed or overly idealistic. But once people can sense the impact of their contribution, skepticism can give way to a sense of shared achievement.

What makes the hive-mind model so compelling is its resilience through diversity. Urban and rural communities, for instance, may prioritise different needs, but AI ensures these priorities are understood and respected, framing them accurately and reliably within the bigger debate. This approach is founded on the multiplicity of people, rather than being thwarted by it.

Ultimately, the promise of a hive-mind society is not the absence of conflict but a new way of navigating it—one that sees it as necessary to reach full representation. By framing disagreements as opportunities for synthesis rather than division, it shifts the focus from winning arguments to building understanding. Progress becomes a collective endeavour, rich with meaning and value.

Thinking points

• How will trust in AI as a mediator be built and maintained?

• While AI simplifies complexity, there’s a fine line between facilitating decisions and influencing them. What do the safeguards look like?

• It seems like this could usher in an age of decision by committee at a national scale. Intuitively that sounds problematic, but is it?