A few months ago, I found myself deep in the Amazon rainforest on an expedition with The Pachamama Alliance, a nonprofit close to my heart. Immersed in the Ecuadorian jungle, I witnessed ecosystems that thrive not because of domination or competition, but because of interdependence. Every tree, insect, and current of water plays a role in sustaining the whole—adapting, evolving, and strengthening one another in ways that ensure collective survival.
It was a living metaphor for the world we are building with artificial intelligence today. The systems we create are no longer just tools to extend human will; they are networks of intelligence that must be designed with reciprocity and shared value at their core. Just as the rainforest’s vitality depends on balance, so too does our future with AI.
In recent months, we’ve been reminded as leaders of the stakes of this moment. What we’re building with AI isn’t just new tools; they are new systems for how decisions get made, where power concentrates, and how we live, create, learn, and thrive in the modern world. Or, as the White House’s recent AI report put it—“an industrial revolution, an information revolution, and a renaissance—all at once.”
This will be an era defined by collective intelligence: the thoughtful choreography between human and machine systems of intelligence in service of amplifying creativity, problem-solving, and the capacity for growth in ways previously unimaginable.
Collective Intelligence 101
For centuries, our systems have prized human intelligence as the pinnacle of existence, a mindset that has fomented ecological degradation, systemic inequities, and increasingly brittle digital ecosystems. Yet, advances in artificial intelligence have reshaped our view of what non-human systems can know, create, and intuit. In tow, the cracks in our worldview are beginning to show. If AI can create art that mimics the outputs of human ingenuity, how long before Picasso lives entirely in the machine?
It’s a lofty question, but one that once explored convulses the schema for modern society and enterprise. Consider, to start, that the industrial era made new forms of blue collar work defunct while reifying economic power in white collar structures. Now, AI promises to upend knowledge work; what new economics might come of that change, and shape the next century of industry?
We enter, now, an era where collective systems of intelligence will proliferate. Where human intelligence mingles purposefully with artificial intelligence to shape brand new approaches to thinking, solving problems, extracting value, and yes, growing businesses. This era will require purposeful choreography between machine and human intelligence – and in doing so, greatly expand the capacity for people and organizations to impact the world around them.
As much as this is a technical shift, it’s a reorientation of values as well. For leaders, it requires we move from reactive problem-solving to ideal-driven design; that we harness these new interdependent systems to build solutions from the future we aspire to, rather than merely patching existing faults.
Importantly, we must hold a more productive tension between the perceived opposites of human and machine intelligence to find a collective ideal that transcends and integrates both. This calls for a worldview rooted in the pursuit of intelligent systems for collective benefit, not naked extraction.
At Left Field Labs, these principles guide our every endeavor. Technology should democratize capability, allowing human creativity and machine intelligence to mutually amplify one another. Implementation should be intentional, and focused on mirroring our values, not chasing shiny tactics. Technology should be positioned within the broader continuum of collective intelligence, ensuring it serves mutual and regenerative benefit, rather than reinforcing traditional power dynamics.
There's a clear split emerging in the broader business landscape. On one side are those who approach AI primarily as a mechanic for cost savings and incremental efficiency gains; on the other, those who see an epochal opportunity to fundamentally transform how we tackle bigger, more meaningful challenges.
People frequently ask me, "What's next?" My answer is straightforward: given the efficiencies AI provides, we need to leverage this surplus of capacity and time to solve bigger problems.
How We’re Building What’s Next — Now
We’re taking a few immediate steps as an organization to accomplish this:
Launching “Build What’s Next,” a bold call to visionaries across the U.S. to embrace this moment with urgency and propose challenges or unmet needs that can be met with creative technology. We’ll unleash our team of strategists, engineers, and designers on collaborating towards a working solution – free of cost. More on that initiative here.
Developing “The New Green Loop,” a cross-disciplinary initiative that uses AI, blockchain and digital monitoring systems to unify climate data across corporate, governmental, and community sources. By bringing creative technologists and climate scientists together, we aim to transform ESG reporting from a fragmented compliance exercise into a unified platform for real-time action.
Doubling down on impact in all dimensions across Left Field Labs – with an organization-wide goal of 10% of our projects consisting of pro bono work by 2027; committing to embedding sustainability themes into at least 10% of creative campaigns annually; and dedicating a percentage of our annual R&D toward projects that expand digital access and opportunities in underserved communities, aligning with our legacy in digital inclusion initiatives.
Our focus is in crafting mutually beneficial outcomes — win-win-win-win scenarios for our clients, their consumers, the communities they exist in, and the planet.
If all we do with AI is make existing systems run leaner, we’ll miss the point entirely. The moment calls for leaders willing to turn efficiency gains into bold experiments, new products, and systemic fixes that our current playbooks can’t deliver. Collective intelligence gives us the capacity—what we build with it will decide whether this becomes an era of shallow optimization or one of transformative progress.