Machines That Manage: The AI Swarms That Could Reshape Community & Capital

How Hierarchies of AI Bots Could Rewire Work, Wealth & Community Leading To An Agentic Future. A MyÜberLife Consulting Group × WÜLF Engineering x Cültüre is Data™ Series

The Swarm Is Sovereign

TL;DR:

Artificial intelligence has entered a new era. We’re moving from AIs as isolated tools to AIs as autonomous agents that can collaborate, adapt, and take initiative.

This post breaks down “agentic AI” in plain language – what it is, how it’s different from traditional AI, and why it matters – using a new kind of ‘5C’s™’ framework (Context, Code, Collisions, Computation, Culture).

Key insight: No one is as smart as everyone. In the age of AI, a swarm of networked intelligences can outperform any lone system. Power is shifting toward collective intelligence – and forward-thinking leaders and cultural designers need to understand and embrace this swarm to stay ahead.

That said, broad‑based adoption is still early: McKinsey finds that only ~50 % of firms have any AI running in production and < 1 % have scaled it enterprise‑wide, with most pilots yet to deliver measurable ROI (WSJ). So while the swarm trend is real, its industrial ubiquity remains an open empirical question, not a foregone conclusion.

Context: From Sci-Fi to the Boardroom

Artificial Intelligence can seem intimidating or overhyped – especially if you’re new to the conversation. In reality, AI is simply software that learns from data. If you’ve used a smartphone, searched on Google, or let Netflix recommend a show, you’ve already experienced AI in a basic form. But today’s context is different: AI has evolved from behind-the-scenes algorithms into something more visible, conversational, and agentic.

Just a couple of years ago, most AI systems were like savants in a box – very good at a narrow task, waiting passively for a command. You ask a question, it answers. Useful, but ultimately a tool responding to a user. Now we’re witnessing a shift in context. AI is starting to step out of the box and take on a life of its own (don’t worry, not in a sci-fi villain way). What’s changing is that AI can initiate actions, not just respond. Instead of a one-off Q&A, we have AI programs that can keep working on a problem, adjust their approach, talk to other AIs, and come back with a result – all with less hand-holding from us.

Btw, Visa just turned that thought experiment into consumer infrastructure: its new Intelligent Commerce APIs let autonomous agents swipe your card for you—booking the flight, comparing seat prices, even auto‑filing the expense—without you ever opening a browser tab. That’s agentic AI not as sci‑fi, but as Tuesday‑morning checkout flow. However, with this potentially giant leap forward innovation comes additional vigilance. Regulators are watching: the CFPB’s 2025 digital‑wallet rule and a parallel lawsuit from NetChoice signal that agent‑run payments will be contested terrain, with oversight tightening even as innovation accelerates.

This is what we mean by “agentic AI”: an AI that behaves more like an agent or assistant with a mission, rather than a static tool. Traditional AI might be like an encyclopedia – you look up an answer, you get some information, and you move. Agentic AI is like a proactive analyst on your team – you give it a goal, and it figures out how to get there, checking in along the way. The context driving this evolution is a perfect storm of technological progress and need: Vast amounts of data, cheaper and faster computing, and business problems that simple automation can’t solve.

Importantly, agentic AI isn’t a brand-new invention out of nowhere – it’s a natural next step. Think of how work gets done: a decade ago, software only did exactly what you programmed it to do, no surprises, no vibe no additional input. Now, with AI everywhere, and a surplus of high level cognition at your fingertips, software can learn from examples (like how your email filters learn to spot spam). The next evolutionary step is software that not only learns but also acts autonomously on that learning. This shift in context – from passive to active AI – is why everyone from executives to strategists to cultural engineers and creative professionals should pay close attention. It’s not just a tech industry story; it’s becoming a business and society story.

“No one is as smart as everyone.” When intelligence is decentralized and shared, the hive mind outperforms the lone genius. The swarm is becoming sovereign – and that changes everything.

For leaders and innovators, the big context to grasp is this: collective intelligence is the new leverage. Whether it’s networks of people or networks of AI (or, most powerfully, people and AI together), problem-solving is trending toward distributed, swarm-like approaches. In practical terms, this means that the old top-down way of doing things – one brain making all decisions – is giving way to bottom-up systems. From open-source software communities to crowdsourced design challenges, we’ve seen how swarms of contributors can outperform even the best centralized teams. Now imagine swarms of AIs augmenting those human networks – that’s the context we’re entering. The swarm is sovereign: control and creativity are emerging from the many, not the few.

Code: The Building Blocks of Agentic Intelligence

If collective intelligence is the new power, code is its currency. But don’t let the term scare you – by “code” we simply mean the instructions and algorithms that make AI tick. In the traditional sense, code was something only engineers worried about. You wrote a program line by line, and it did exactly what you told it to do. End of story. With modern AI, however, code has evolved into something more flexible and surprising. We don’t always spell out every rule anymore; instead, we create learning systems that develop their own rules by training on data. In other words, we write code that writes itself in a way – or at least adapts itself.

This matters for agentic AI because an AI agent needs a different kind of “brain” than a static program. It needs code that can handle goals, make decisions, and even generate new code or instructions on the fly. For example, consider a simple task: “Schedule meetings for next week with my top clients.” A traditional program might handle a fixed list of steps (check calendar, send emails at set times). An agentic AI, might dynamically figure out each client’s time zone, prioritize the most important clients, draft a friendly personalized email using an AI language model, adjust if someone replies with a conflict, and maybe even update a CRM system with the new meeting info – all through a combination of coded abilities and on-the-spot reasoning. The code driving an AI swarm isn’t a single linear script; it’s a set of modules and models that work in concert, often created by different teams (or even by AIs themselves), interacting like a digital ecosystem. Visa’s architecture is a great example of this: one module authenticates the card token; another scans merchant inventories; a third negotiates shipping options; a fourth triggers fraud‑detection heuristics— all orchestrated by an agent that rewrites its shopping strategy in real time if inventory or price signals shift.

One practical example of this is how AI-enabled workflows and toolchains are being built in modern organizations. Instead of one monolithic software, companies are stitching together multiple AI services: one piece of code summarizes reports, another scans incoming emails for important signals, another makes predictions from data – and then an orchestration agent code (think of it as the conductor) directs these pieces to work together towards a larger task. This modular approach to code is very much like a swarm: simple units, when properly connected and curated, produce sophisticated behavior. Even if you’re not an engineer, the takeaway is that coding in the age of agentic AI is about creating feedback loops and adaptability. We give these systems scaffolding, framework and objectives, but we also give them leeway to figure out the details, which is a huge departure from the old hard-coded way of the past. This approach more closely matches the management principle, Commanders Intent. You give one or more people on your team a clear goal and set of intentions for a task, essentially a “why,” but you afford them the space and autonomy to unlock their genius to figure out the “how” to get there.

From a strategic standpoint, code becoming more autonomous changes how we approach problem-solving. For executives and strategists, it means when you invest in technology, you’re increasingly investing in systems that can learn and improve on their own. You're not just about buying a software tool off the shelf; you're cultivating a codebase or an AI model that evolves. In fact, businesses are starting to open up parts of their code and data through APIs to let third-party AI agents innovate on top of them (imagine your company’s platform is a garden, and outside developers and their AI agents are the pollinators). Code is no longer a sealed vault – it’s a living asset that participates in a broader digital economy. To put it simply: in the age of sovereign swarms, code is less about strict commands and more about collaborative instructions.

Collisions: Where Ideas and Agents Intersect

Innovation often happens at the collision of ideas – when different perspectives, disciplines, or technologies bump up against each other. In the context of AI swarms and agentic systems, “collisions” are the sparks that fly when autonomous agents interact. Picture a busy intersection in a city: lots of independent agents (people, cars) moving with their own goals. Collisions (hopefully metaphorical, in the case of cars!) are those unexpected encounters or conflicts that force everyone to adapt. In AI terms, collisions can be incredibly productive: one agent’s output might become another agent’s input in a way the original programmers never anticipated, leading to a creative solution or a new insight. On the other hand, we need also acknowledge that many collisions fizzle; Amazon’s celebrated warehouse‑robot swarm is still the exception, not necessarily the norm, across manufacturing.

That said, it’s still encouraging to think of a theoretical example relevant to cultural strategists: You could have one AI agent scanning social media for emerging cultural trends, and another agent tasked with brainstorming product ideas for your brand. If they work in isolation, each does a decent job in its lane. But let them collide – i.e. let them talk to each other – and something interesting happens. The trend-watcher agent might throw a curveball insight (“Gen Z is repurposing old video game aesthetics in fashion”), which collides with the product ideation agent’s process and suddenly you get a totally fresh campaign idea that a human team might not have connected so quickly. The collision of two different knowledge domains (culture & product design) via AI agents creates a novel outcome. In essence, 1 + 1 = 3 when collisions are managed well.

Collisions aren’t always comfortable. In business, a collision might look like the marketing team’s data AI suggesting a strategy that collides with the intuition of a veteran creative director. Or two autonomous trading algorithms on Wall Street might collide, causing bizarre market behavior. The goal isn’t to avoid collisions, but to harness them constructively. This is where human oversight and cultural savvy come in. As executives or cultural engineers, you must design the rules of engagement for your AI agents – much like setting traffic rules in that busy intersection to minimize crashes but allow purposeful interactions. For example, you might program your swarm of customer service chatbots to occasionally share learnings with your data analysis bot (a planned collision of customer sentiment data with sales numbers), but also put guardrails so they don’t reinforce each other’s errors in a feedback loop. Or, returning to the Visa example for a second, consider the coming clash between a consumer’s budgeting bot and a retailer’s dynamic‑pricing bot, both transacting over Visa’s rails. Their algorithmic tug‑of‑war—price drops vs. spending caps—creates the productive friction that surfaces fair‑market prices faster than either side alone.

In more theoretical terms, collisions drive emergence. If you’ve heard of emergence in complex systems, you know it’s when a system shows behavior or solutions that none of its parts had alone. A flock of birds wheeling in unison or an ant colony forming bridges out of their bodies are examples from nature – no single bird or ant planned the outcome, it emerged from many small interactions. Similarly, AI swarms might discover strategies or patterns that no single model would find on its own. There’s a famous real-world adjacent analog in chess: individually, a human grandmaster or an AI can play great chess; but when humans and AIs started teaming up in “freestyle chess” tournaments, the human-AI team (a mini-swarm) could beat either humans alone or AIs alone. Why? Because of the constructive collisions between human intuition and machine calculation. The lesson for us: welcome interdisciplinary, inter-agent collisions. By intentionally mixing AI agents with different specialties – and mixing AIs with humans – we create fertile ground for breakthroughs. The key is establishing a culture where those collisions are seen as creative friction, not chaos.

Computation: Fueling the Swarm

Underneath all the context, code, and collisions, there’s a more concrete foundation: computation. This is the raw power that makes AI tick, the hardware and processing that allow complex algorithms to run. Why devote a section to something as unsexy as processing power? Because computation is to agentic AI what oxygen is to fire – without enough of it, even the best ideas suffocate. The past few years have seen an explosion in computational power (think cloud computing, GPUs, specialized AI chips, etc.), which is a major reason why concepts like agentic AI are actually practical now. Ten or twenty years ago, the ideas were floating around, but the hardware couldn’t really support a swarm of AI agents running in real time on affordable budgets. Now it can.

For decision-makers, it’s important to grasp that computation is a strategic resource. Just like oil or electricity powered the Industrial Revolution, agentic AI promises step‑function productivity gains, yet, unlike steam or electricity, it unfolds in digital rather than physical infrastructure—meaning its diffusion could be both faster (software scales instantly) and slower (governance, skills, and data quality are stubborn bottlenecks).

Companies like Google and OpenAI have access to immense compute and thus can train enormous models (the ones behind things like ChatGPT). But the trend is not just about giant models – it’s about ubiquitous compute. We’re heading toward a world where every device, every interface, every network has some form of intelligence baked in. In practical terms, that means the swarm (of AI agents, IoT devices, etc.) has a vast playground to operate in. Your smartphone alone can host multiple AI agents working on your behalf (one managing your schedule, one monitoring your health metrics, one curating your news). In an organization, cloud servers can scale up dozens of AI processes simultaneously – one reason agentic AI is exciting is because you’re not limited to one AI at a time. You can deploy a whole team of digital agents in parallel if you have the computation to support it.

Another angle to computation is how it changes our approach to problem-solving. In the past, limited compute forced us to simplify problems or ignore data. Now, with abundant compute, we can attempt brute-force approaches or highly detailed simulations that were previously impossible. This means as a strategist or cultural engineer, you might lean on AI to simulate scenarios fully before deciding. For instance, if you’re planning a new product launch, an AI swarm could simulate thousands of marketing strategies in a virtual environment (using lots of computation) to identify which one might resonate best – an expensive experiment if done in the real world, but cheap as a simulation. High computation lets us ask “What if?” in ways we never could before. We can let AI agents loose in a sandbox to see what they come up with, iterating rapidly.

However, with great power comes great responsibility (and cost). Leaders, consider where to allocate your computational resources for the biggest impact. There’s also the consideration of sustainability – those massive data centers draw a lot of power. The future might belong to those who can be efficient in computation, not just throw brute force at problems. This is where WÜLF Engineering sensibilities come in: engineering smarter algorithms, optimizing code, and using distributed computing (maybe tapping into idle computers in a network like a true swarm) to get more done with less. The bottom line is, computation is the enabler of the sovereign swarm. It’s turning ambitious AI concepts into daily reality. Keep an eye on it, invest in it wisely, and ensure your team has the computational thinking skills to leverage it – not everyone needs to be a coder, but understanding what the machines can and can’t do at scale will be a baseline leadership skill.

Also, while you’re pushing the throttle on computation remember that more compute can often mean bigger blast radii for mistakes. Under the EU AI Act, any agent touching credit scoring or payments is “high‑risk,” subject to pre‑deployment conformity assessments and fines of up to $38 million or 7 % of global turnover for non‑compliance (ModelOp). Designing fault‑tolerant pipelines—rate‑limiting agents, mandating human sign‑off on critical actions—is as strategic as the models themselves. So, apply some prudence here, but don’t shortcircuit the innovation cycle. Enterprises that over‑corrrect—locking down agent autonomy to prevent hallucinations—risk a “control‑loop freeze” where the system is technically safe yet strategically useless. The art is calibrated constraints: enough freedom for discovery, enough oversight for trust.

Culture: Designing for a Swarm Future

Ultimately, all the technology in the world means little without the human element: culture. Culture is the collective mindset, values, and behaviors of a group – essentially, the operating system for any organization or community. As AI becomes more agentic and swarm-like, culture becomes both a driver and a product of this change. We shape our tools, and thereafter our tools shape us. The rise of AI swarms is already influencing culture, and vice versa. The key question for cultural designers (whether you’re an HR lead, a community builder, a strategist, or a CEO shaping company culture) is: How do we co-create a culture where humans and intelligent agents thrive together?

Visa’s rollout further underscores why cultural trust frameworks must evolve in lock‑step: the CFPB is already debating opt‑in consent screens and liability waterfalls for agent‑driven payments, while EPIC warns that an always‑shopping wallet could morph into a “surveillance throttle” on consumer freedom. Leaders designing swarm ecosystems need parallel governance swarms—legal, ethical, UX—to keep agency and autonomy balanced.

First, consider openness and collaboration. A culture that hoards information or siloes innovation will never fully harness swarm intelligence. Swarm dynamics thrive on sharing – just as open-source software communities thrive by openly exchanging code, an organization ready for agentic AI encourages data and insights to flow across departments. For example, if your marketing AI agent discovers a new customer behavior pattern, is there a cultural norm for it to feed that insight to the product development team’s AI? Culturally, this might mean breaking down internal barriers and encouraging interdisciplinary teams (mix your engineers with your creatives, your data scientists with your ethnographers). When your human teams operate like a well-connected swarm, your AI agents can plug into that network much more effectively.

Second, we need a culture of curiosity and experimentation. AI agents, especially in their early days, will produce surprises – some brilliant, some nonsensical. Newcomers and skeptics may feel uneasy with an autonomous system making decisions or suggestions. The antidote is fostering curiosity: treat the AI’s output as a starting point for exploration rather than a final verdict. Leaders can set the tone here. Instead of reacting with fear (“Why did the AI do that? This is scary!”), encourage a response of wonder (“That’s interesting – what can we learn from what the AI did?”). Teams that play with AI, poke at its weird edges, and find creative uses for its quirks will advance faster than those who simply treat it as a strict tool or, conversely, fear it. In practice, you might run hackathons in your company for employees to team up with AI agents on passion projects, or have an “AI sandbox” where anyone can try out new agent-driven ideas without formal approval. A curious culture turns AI from a threat into an opportunity.

“Culture is the operating system, and technology is the app.” No matter how advanced our AI tools become, it’s our cultural settings – our mindsets and norms – that determine what we do with them. To design the future, we must update both.

Third, as we integrate AI agents into workflows, human agency must remain front and center. It may sound ironic (agency for humans in the era of agentic AI!), but it’s crucial. Just because the swarm is sovereign doesn’t mean individual humans lose their autonomy or purpose. Rather, we are redistributing agency: freeing people from routine tasks so they can exercise higher-level decision-making and creativity. A healthy culture will celebrate this. Instead of the old narrative “AI is here to take your job,” the narrative in a forward-thinking culture is “AI is here to level you up.” Yes, some rote roles will sunset—but history shows that every automation wave also births net‑new categories (prompt engineers, agent‑orchestration managers, AI risk auditors) that were previously uneconomical because of time or cost constraints. The policy challenge is smoothing that transition, not stopping it. In a practical sense, that might involve re-skilling programs, where employees are trained to work alongside AI – learning to supervise a team of AI agents, to interpret AI-driven analytics in context, to do the uniquely human things like relationship-building and strategic vision which AIs aren’t suited for.

Finally, cultural designers should see themselves as the choreographers of human-AI collaboration. Just as an orchestra needs a conductor to bring out harmony from various instruments, our emerging human-AI societies need conscious cultural design. We have to set ethical norms (e.g. what decisions we do or don’t delegate to machines), ensure diversity and inclusion of cultures (e.g. making sure our AI agents aren’t all trained on one cultural perspective, which could lead to bias), and craft narratives that inspire people to embrace new ways of working. If “the swarm is sovereign,” then leadership becomes about guiding the swarm rather than dictating from the top down. This means leaders will facilitate networks, curate interactions, and empower teams (hmm…Kind of sounds like what true leadership was meant to be) – human and AI alike – to find solutions from the ground up. It’s a shift from top-down commander to thoughtful gardener: you can’t boss a swarm around, but you can cultivate an environment where it flourishes in the direction you want.

Closing: Embrace Agency, Curiosity, and Forward Motion

We’ve journeyed through Context, Code, Collisions, Computation, and Culture – five lenses to understand why the swarm (of humans + AIs) is increasingly sovereign in our world. The takeaway for executives, strategists, cultural engineers, and creative professionals is clear: this isn’t science fiction – it’s a strategic reality unfolding right now. And you have a choice in how to respond. You can stand on the sidelines, skeptical or hesitant, or you can lean in with agency and curiosity.

Choosing agency means seeing yourself not as subject to technological change, but as an agent of change (pun intended). Just as we empower our AI systems to act, we must empower ourselves and our teams to proactively integrate these tools. Play with that new AI workflow app, encourage your team to delegate a project to an AI agent duo and see what happens, redesign a process around human-AI cooperation.

Take a page from leaders like Tobias Lütke from Shopify, Luis Von Ahn, from Duo Lingo, and Aaron Levie of Box. All 3 CEOs have implemented AI-first approaches to productivity and growth at there organziations. In Tobias’ case, he essentially gave a company-wide mandate to limit hiring, pushing teams to use AI first to do additional work, and only hire a new person if a team could explain why they needed a human over AI. Likewise, Luis haulted all contact work at Duo Lingo, opting for more content creation speed from AI, which allows the company to capitalize on its recent jump in subscriber growth over the last year. And Aaron, issued an internal “AI‑first” mandate—publicly noting that he now prototypes products, drafts press releases, and even preps earnings‑call Q & A with Box AI before looping in staff, and that this shift is “raising my expectations for everyone I work with.” By examining Levie actions alongside Lütke and Von Ahn, we see a pattern: diverse, high‑growth companies are operationalising agentic workflows from the C‑suite down, not waiting on IT to bless the experiment.

These moves may sound drastic from a traditionalist view, but it’s the kind of swift action that allows leaders to gain a competitive edge in the early adoption cycle. Small experiments lead to big breakthroughs, but it takes that forward motion – the willingness to start.

Choosing curiosity means approaching this swarm era with questions and openness. Ask “How might this help us solve X?” rather than “Will this make my role irrelevant?” The most future-proof skill isn’t coding or data science (though they’re useful) – it’s learning. In a landscape shifting this fast, the best cultures are those that learn and adapt continuously (autodidacts for the win). Luckily, agentic AIs are great learning partners; they’ll happily iterate with you as long as you let them.

Finally, forward motion for cultural designers and leaders means setting a vision and taking concrete steps. The vision: a world where human creativity and AI intelligence enhance each other, where organizations operate more like living networks than machines, and where innovation emerges from all levels. The steps: start with today’s tools (they’re more powerful than you think), build cross-functional teams that include AI in the loop, and don’t be afraid to rewrite old rules. Yes, the swarm can be unpredictable, but that’s exactly why it’s powerful. With the right guidance, those unpredictabilities become innovations.

As we conclude, remember that sovereignty in this context isn’t about losing control – it’s about acknowledging a new form of distributed power and learning how to ride the wave. The swarm is here, and it’s learning fast. Will you join it, guide it, and co-create with it? For those willing to embrace this moment, the reward isn’t just staying relevant – it’s shaping the next chapter of our culture and industry with intentionality and imagination.

When a $15‑trillion network like Visa invites autonomous agents to the till, the swarm isn’t coming—it’s already tapping ‘Pay now.

Call to action: If this discussion sparked ideas or questions, keep the conversation going. Share this essay with a colleague who’s curious (or anxious) about AI. Start a dialogue within your team about how you might leverage agentic AI in your next project. And if you’re eager to dive deeper, consider reaching out to us at myuberlife.com or subscribing below for future insights – we’re exploring this new frontier together. The future belongs to the curious, and it’s time to swarm. Let’s move forward.