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The Edge of Intelligence

How Neuromorphic Engineering Will Humanize the Future of Innovation

The Edge of Intelligence

GUEST POST from Art Inteligencia

The Hidden Friction of Modern AI

We are living through an era of breathtaking algorithmic capability. Generative AI, large language models, and autonomous agents are reshaping organizational agility and rewriting the rules of experience design. Yet, behind every seamless digital interaction lies an unsustainable truth: our most advanced intelligence is kept on an exceptionally short leash. It remains tethered to hyperscale data centers drawing massive, environmentally costly energy footprints from the power grid.

The root of this problem isn’t the software; it is the physical architecture of the silicon itself. For decades, computing has relied on the traditional von Neumann architecture — a system where data must constantly shuttle back and forth between a separate memory unit and a processing unit. This structural bottleneck means that as AI tasks grow more complex, chips run hotter and consume more power simply moving data around, creating an architectural barrier to true scaling.

For innovation leaders and strategists, this creates a profound friction. True, human-centered innovation cannot be fully realized if every localized tool, remote system, or smart device requires a high-bandwidth, high-carbon umbilical cord tied to a centralized cloud. To design experiences that are genuinely resilient, ambient, and privacy-first, we must liberate intelligence from the server farm. Neuromorphic engineering represents the paradigm shift that will finally break this bottleneck, moving us away from centralized computing and toward a decentralized future where intelligence lives efficiently at the edge.

What is Neuromorphic Architecture? (The Organic Paradigm Shift)

To break the computational logjam, engineering is turning to the most efficient computer ever created: the human brain. The brain operates on roughly 20 watts of power — barely enough to illuminate a dim closet bulb — yet it manages complex cognitive tasks, sensory processing, and real-time learning simultaneously. Neuromorphic engineering sheds the rigid rules of traditional computing to mirror this organic brilliance, designing physical silicon structures that mimic the nervous system’s architecture.

Synapses in Silicon: The Structural Shift

Standard processors think in binary code, executing instructions sequentially according to a strict, rhythmic system clock. This means every component is continuously running and drawing power, whether it is actively processing fresh data or not. Neuromorphic computing fundamentally upends this design through two key innovations:

Asynchronous Processing: Instead of relying on a system clock, neuromorphic chips operate based on event-driven spikes. Individual artificial neurons remain quiet and consume virtually zero energy until a change in data triggers them to “fire” and transmit information. If nothing is happening, no energy is wasted.
Co-located Memory and Processing: By embedding hardware-based artificial synapses directly next to artificial neurons, these chips eliminate the structural divide that defines traditional processing. The hardware does the thinking and the remembering in the exact same physical space, completely bypassing the data-shuttling bottleneck.

By shifting from sequential logic to a highly parallel, brain-inspired network, we achieve a massive leap in energy efficiency. This architecture transitions our systems from energy-hungry server clusters down to self-contained edge components operating on mere milliwatts of power.

The Futurology & Scaling Angle: Unlocking Edge Intelligence

The true value of neuromorphic engineering lies far beyond a simple reduction in corporate utility bills. For innovation strategists and futurologists, this hardware pivot represents a massive scaling catalyst: the liberation of artificial intelligence from centralized cloud architectures. When advanced cognitive processing requires milliwatts instead of megawatts, the mathematical parameters governing system design, operational reach, and deployment speed change overnight.

Decentralizing the Innovation Landscape

For years, the industry trajectory has favored hyper-centralization. Complex models required massive, multi-billion-dollar data centers to function, creating a digital dependency that stifles agility. Neuromorphic computing completely flips this model, enabling advanced AI agents to execute local, highly sophisticated inference tasks on small, distributed devices for weeks at a time on a single milliwatt of power. Intelligence ceases to be a destination you connect to; it becomes an ambient feature embedded directly into the environment.

Breaking the Umbilical Cord: The Strategic Advantages

By migrating intelligence directly to the physical edge, organizations can unlock three critical operational advantages that traditional cloud setups simply cannot match:

Zero Latency for Real-Time Action: Eliminating the round-trip journey to a distant cloud server means local systems can analyze, decide, and act in milliseconds. In time-sensitive scenarios, this immediate local processing makes the difference between predictive success and operational failure.
Absolute, Privacy-First Security: Because data is processed entirely on the local neuromorphic chip without ever being transmitted over a network, the attack surface shrinks dramatically. This architecture allows organizations to build deep, trust-based customer experiences that naturally safeguard sensitive personal and enterprise data.
Carbon Dematerialization: Moving computing workloads away from massive, grid-dependent server farms and onto ultra-low-power edge silicon provides a tangible, measurable path to reducing an organization’s digital carbon footprint, aligning aggressive technology scaling with vital sustainability goals.

Human-Centered Design Scenarios: Neuromorphic in Action

To fully appreciate the impact of brain-inspired computing, we must look past the spec sheets and examine how it alters human experiences. Moving advanced intelligence to the edge allows us to design environments and tools that are profoundly responsive, resilient, and unobtrusive. By removing the constraints of power consumption and cloud connectivity, we can bring three critical, human-centered design frontiers to life:

1. Extreme Field Resilience

Traditional digital tools fail the moment they lose their connection to the network. Neuromorphic engineering allows us to design industrial and humanitarian tools that maintain full cognitive capabilities in the most isolated environments on Earth. Imagine search-and-rescue drones or deep-sea research equipment operating autonomously for weeks on a tiny battery, analyzing complex visual data and making critical safety decisions entirely offline. By placing autonomous decision-making directly into the hands of field teams, we build operational resilience where it matters most.

2. Privacy-First Smart Cities

Current smart city frameworks frequently rely on a centralized “surveillance state” model, shuttling massive streams of public data back to central servers for analysis. Neuromorphic chips allow us to redesign urban infrastructure from the street corner up. Traffic signals, public utility grids, and safety systems can process visual and environmental changes locally and instantly. A street corner camera can identify a traffic hazard or an emergency situation and adjust local systems immediately — all while completely discarding the raw footage locally to preserve citizen anonymity.

3. Ambient and Intimate Experience Design

The next generation of wearables, medical tech, and smart home systems must integrate naturally into the background of daily life without demanding constant attention, frequent charging, or invasive data sharing. Neuromorphic architecture enables sub-milliwatt, continuous contextual awareness. Medical implants can monitor heart or neurological patterns and predict adverse events locally in real time. Consumer devices can subtly adapt to user habits, preferences, and physiological states over time, delivering deeply customized experiences without draining battery life or transmitting personal routines to a corporate mother ship.

The Commercial Landscape: Market Leaders and Startups to Watch

Neuromorphic engineering has officially breached the perimeter of academic research and entered the commercial fast lane. As traditional silicon hits the physical and economic boundaries of Moore’s Law, a vibrant ecosystem of semiconductor giants and venture-backed startups has emerged to productize brain-inspired architecture. For strategists building long-term roadmaps, these are the key players shaping the hardware landscape:

The Semiconductor Giants (Research & Infrastructure Scale)

Intel (Loihi Platform): Intel remains a primary institutional driver of neuromorphic development. Their massive Hala Point system uses Loihi 2 processors to pack over one billion artificial neurons into a single research chassis, demonstrating up to 100× the energy efficiency of conventional hardware for complex optimization workloads.
IBM (TrueNorth & NorthPole): A true pioneer in the space, IBM’s foundational neurosynaptic research continues to push the boundaries of ultra-low-power digital image and sensory recognition, aiming squarely at defense, aerospace, and high-performance computing (HPC) environments.
Samsung & Qualcomm: Both tech giants are aggressively integrating neuromorphic mixed-signal IP and co-processors into their commercial system-on-chip (SoC) portfolios, targeting the next generation of smartphones, advanced driver assistance systems (ADAS), and consumer electronics.

The Pure-Play Pioneers & Edge Disruptors

BrainChip (Akida): As one of the few publicly traded pure-play neuromorphic companies, BrainChip has achieved widespread commercial traction. Their Akida event-based neural processor brings ultra-low-power, on-device machine learning to millions of IoT systems, smart sensors, and autonomous vehicles via recent integrations with LiDAR and edge perception platforms.
Innatera: This European innovator is making major waves at the micro-scale. Having commercialized their Pulsar neuromorphic microcontroller, Innatera has partnered with original design manufacturers (ODMs) to drive mass production of always-on intelligent consumer wearables, health tech, and smart home sensors that operate entirely within sub-milliwatt power budgets.
SynSense: Specializing in the fusion of sensing and computing, SynSense designs mixed-signal processors that process sparse, real-time data streams instantly. Their chips are purpose-built for ultra-low-latency processing in smart cameras, bio-signal analysis tools, and auditory devices.
Rain AI & Unconventional AI: Representing the heavy-hitting venture tier, these companies are building analog-in-memory neuromorphic architectures leveraging memristors to simulate biological synapses. Backed by massive financing rounds from top-tier technology visionaries, they are actively aiming to scale these brain-inspired chips into the billions of units required for the future edge economy.

The Change Management & Strategic Roadmap for Leaders

The transition to neuromorphic architecture will not be a passive hardware upgrade handed down by IT; it requires a fundamental recalibration of enterprise strategy. Innovation leaders who fail to adapt their roadmaps now risk locking their organizations into rigid, energy-intensive architectures just as the rest of the market decentralizes. Embracing edge intelligence demands proactive change management across infrastructure, design philosophy, and team capabilities.

Rethinking the Ecosystem and Auditing Roadmaps

The immediate task for strategists is to audit current digital transformation initiatives for cloud dependency. Are you over-indexing on centralized architectures that will soon become costly technical debt? Leaders must begin identifying where ultra-low-power, offline inference can replace grid-dependent processing, actively pivoting investment toward distributed systems that scale without exponential carbon costs.

Designing for the Peripheral

For decades, experience design has been built around active engagement: prompting the user to look at a screen, click a button, or initiate a connection. Edge intelligence requires a shift in design mindset from “active center-stage interaction” to “ambient, peripheral support.” When a device can process context constantly on less power than a digital watch, the goal is to build systems that anticipate and resolve friction quietly in the background, minimizing the cognitive load on the human.

Bridging the Skills Gap

The move to asynchronous, spike-based processing breaks many of the traditional rules of software engineering. To fully leverage this technology, organizations must rebuild their cross-functional teams, bridging the historically siloed worlds of hardware design, software engineering, and customer experience. Preparing teams for this shift requires cultivating new competencies in event-driven programming and decentralized systems, ensuring your workforce is ready to build the next generation of human-centered tools.

Conclusion: Designing a Smarter, Sustainable Tomorrow

Neuromorphic engineering is far more than a technical upgrade for hardware developers or an efficiency metric for infrastructure teams. It represents the foundational unlocking mechanism for the next grand era of human-centered innovation. By fundamentally breaking the decades-old von Neumann bottleneck, brain-inspired processors allow us to shift from a world where intelligence is an expensive, centralized luxury to one where it is a cheap, ubiquitous, and sustainable utility.

The ultimate goal of any transformative technology must be to blend seamlessly into the fabric of daily life, amplifying human potential while strictly respecting the ecological boundaries of our planet. Centralized cloud reliance has carried us far, but its massive power grids and high latency are reaching their natural scaling limits. Embracing an asynchronous, edge-first architecture is our path forward to resolving that tension.

For innovation leaders, futurologists, and experience strategists, the challenge ahead is clear: stop designing exclusively for the cloud and start preparing for the perimeter. By liberating artificial intelligence from the data center and bringing it directly to the edge, we finally gain the architectural freedom to build an enterprise landscape — and a society—that is as profoundly resilient as it is impactful.

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Frequently Asked Questions

To help both human readers and search indexing engines quickly parse the foundational concepts of brain-inspired computing, here is a clear breakdown of the most common questions regarding neuromorphic engineering.

What is the difference between traditional von Neumann architecture and neuromorphic engineering?

Traditional von Neumann architecture separates memory and processing, requiring data to constantly shuttle back and forth, which creates an energy-intensive bottleneck. Neuromorphic engineering co-locates memory and processing on physical artificial synapses and neurons, allowing the chip to process information asynchronously only when data spikes or changes occur, drastically cutting energy consumption.

Why is neuromorphic computing critical for the future of decentralized AI and edge devices?

Modern AI requires massive, centralized data centers to handle complex processing workloads due to high power demands. Neuromorphic chips require orders of magnitude less power — often operating on just a single milliwatt — allowing advanced AI agents to run locally and independently on small edge devices for weeks at a time without cloud connectivity or high carbon footprints.

What are the primary real-world use cases for neuromorphic engineering in experience design?

Key use cases include extreme field tools that operate entirely offline in remote areas, privacy-first smart city infrastructure that processes data locally to protect citizen anonymity, and ambient consumer wearables or medical implants that seamlessly adapt to human behavior in real time without draining battery life.

Disclaimer: This article speculates on the potential future applications of cutting-edge scientific research. While based on current scientific understanding, the practical realization of these concepts may vary in timeline and feasibility and are subject to ongoing research and development.

Image credits: Gemini

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Why Intuition is the Maximum Form of Intelligence

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Why Intuition is the Maximum Form of Intelligence

GUEST POST from Robert B. Tucker

Have you ever had a bad feeling about a relationship or business deal, but you went ahead with it anyway? Later, after things went awry, you remembered a faint alarm that went off in your mind — your intuition.

During a 35-year career studying and coaching business innovators, I have witnessed firsthand how intuition is an indispensable navigational compass, if only we understand it and listen to this subtle, often misunderstood form of intelligence.

“Every time I’ve made a bad decision about a person,” one business owner observed, “I eventually recalled how much I knew about that person before I made the decision. I knew perfectly well that person couldn’t be trusted. And yet greed or something made me believe the opportunity was so great that I didn’t listen to what my intuition was trying to tell me.”

Your intuition can help you sense when a problem exists. It can slow you down and help you avoid making rash, emotional decisions. Intuition can help you spot both threats and opportunities in today’s fast-changing world.

“I don’t care if the numbers say we’re going to make a billion dollars,” one CEO in the natural foods industry explained. “If I don’t feel good in my gut about a decision, I won’t go ahead. When it doesn’t feel right, it’s like a nervous stomach. When a decision feels right, it’s like a great meal. It just feels good.”

If We Are “Needy” or “Greedy,” Our Intuition Lacks Ignition

Intuition is a kind of “sixth sense” early warning system. If we are attuned to it, our intuition can help us in all sorts of ways: to sense when a problem exists, to alert us to changes in the environment, and, most importantly, to prevent us from making rash, emotional decisions, especially about people. However, if we are needy (“I need to fill this position right now”) or greedy (“This deal is too good to pass up”), our intuition has no impact on our thinking.

In today’s exponential world, the earliest indicators of change sometimes show up first in premonitions: “something is not right,” or “something has changed.” Despite the raging popularity of data (“In God we trust. Everyone else bring data”), dashboard metrics are lagging indicators and may not yet reflect a marketplace change. Leading indicators, on the other hand, could be anything from a seemingly random comment at a conference or a feeling of unease that merits further attention and investigation. For example, this quarter’s numbers may still look normal, yet you sense something important has changed in the market for your services, or imminent danger bubbling beneath the surface.

Intuitive customer-facing employees often pick up on changing customer expectations well before surveys or quarterly reports capture the trend. When leadership listens to those employees and treats their intuitions as valuable inputs rather than useless hunches, such weak signals can become an early warning that changes are afoot and action is required. While competitors wait for lagging data collection to catch up, those who recognize early signals are often the ones who ride the waves of change.

How Some Leaders Tap Intuitive Intelligence

Over the years, the popularity of intuition in business has waxed and waned. University of Texas professor Weston Agor’s seminal research measured intuitive intelligence, using the Myers-Briggs test. He discovered that successful executives used intuition in a variety of ways: to size up people they are dealing with; to make quick decisions, such as in negotiating sessions when they must “think on their feet” and especially in making decisions when all the facts simply aren’t available.

Agor’s research has been confirmed by further studies. Harvard Business School professor Daniel Isenberg reached similar conclusions after studying the thought processes of 16 senior managers in large corporations and smaller mid-sized firms. Isenberg followed each executive for an average of two days, observing them as they went through their day.

When he interviewed each executive, he found that they tapped intuitive judgment at almost every turn to support and interpret whatever data was relevant. “Particularly when faced with highly complex problems or issues for which there are no precedents,” Isenberg reported.

Five Ways to Activate Your Intuition

As academic studies have proven, leaders who consistently make better decisions don’t rely on gut instinct alone—they train their minds to tune in to subtle, often faint alarm bells. Here are five techniques for how to tune in and maximize this high form of intelligence.

1. Create White Space to Detect Signals.

Intuition emerges when the mind is allowed to connect dots beneath conscious awareness. Continuous input crowds out insight. Leaders who deliberately step away from screens and meetings give their brains time to integrate weak signals—subtle shifts in markets, people, and timing that data has not yet captured. Resilience coach Eileen McDargh starts each year with a one-week silent retreat in Mexico and recommends starting each day quietly with no screens.

2. Elevate Intuitions to Hypothesis Status

Intuitive impulses should neither be obeyed blindly nor ignored completely. Treat them as an early hypothesis. When something feels “off,” that is a cue to explore assumptions, scan for confirming or disconfirming evidence, and run small experiments.

3. Broaden the Signal Field

Strong intuition depends on exposure. Leaders with the sharpest instincts read widely, listen across boundaries, and stay close to customers and frontline realities. Their pattern recognition improves because their mental models are continuously refreshed with diverse inputs.

4. Listen to Somatic Signals

Experience often registers in the body before it reaches conscious thought. Tension, hesitation, or unexpected premonitions are not answers—but they are data. Ignoring them because we are needy or greedy reduces decision quality; noticing them expands it.

5. Think Future-Back

Intuition sharpens when leaders mentally rehearse multiple futures. By imagining success and failure two or three years out, hidden risks and overlooked opportunities surface early—when action is still possible.

In the final analysis, intuition is not guesswork. It is a compressed experience, activated by reflection, tested by foresight, and converted into results through human agency. Wise decision-makers treat both as inputs, not conclusions—signals that prompt foresight, sense-making, and deliberate action rather than blind acceptance.

This article originally appeared in Forbes

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The AI Commons: Could Intelligence Become a Public Utility?

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The AI Commons: Could Intelligence Become a Public Utility?

GUEST POST from Art Inteligencia

Section I: The Shifting Baseline — From Innovation to Infrastructure

1. The Utility Pattern: Lessons from Historical Infrastructure

Throughout history, fundamental advances follow a predictable evolutionary arc. Technologies that begin as specialized, high-cost innovations—whether clean running water, electrical power, telecommunications, or the internet—gradually transition into quiet, essential infrastructure. In their early stages, competitive advantage stems from simply possessing or accessing the technology. Over time, however, as adoption broadens and delivery mechanisms standardize, the baseline shifts. The core technology becomes a utility: ubiquitous, reliable, and expected.

Artificial intelligence is undergoing this exact transition, but at an unprecedented velocity. We are moving rapidly past the phase where merely having an AI capability offers a distinct edge. Instead, we are entering an era where basic machine intelligence is assumed to be running invisibly in the background of every tool, workflow, and human experience.

2. Intelligence as Flow, Not Stock

To understand intelligence as a utility, we must rethink how we conceptualize cognitive compute. Historically, organizations treated intelligence as a “stock”—a resource to be accumulated, hoarded, and stored in proprietary databases, specialized teams, or custom algorithms.

As foundational models mature and compute capacity scales globally, intelligence shifts from a stock to a “flow.” Like electricity running through a grid or water through a municipal pipe, machine intelligence becomes an atmospheric, ambient resource. It flows into interfaces, automated systems, and decision-making environments in real time. Value no longer resides in owning the reservoir of intelligence, but in how intentionally and humanely we direct its flow to solve meaningful human problems.

3. The Abundance Paradox

As the raw marginal cost of processing power drops, we encounter the Abundance Paradox: the exponential increase in raw cognitive compute does not automatically translate into democratized human benefit or equitable value creation.

Without deliberate architectural and policy choices, abundant intelligence risks being concentrated within a handful of private silos. When access to high-tier cognitive capabilities, low-latency infrastructure, or specialized domain models is gated behind high economic barriers, technological abundance can actually widen the gap between those who leverage intelligent systems and those who are displaced or underserved by them.

4. The Human-Centered Imperative

Reframing artificial intelligence as public infrastructure elevates the stakes of experience design and change management. Infrastructure is not passive; its design actively shapes human behavior, agency, and societal health.

If intelligence is to become a true utility, human flourishing, psychological safety, and individual agency must be treated as foundational design requirements rather than secondary considerations. Building an AI utility requires designing systems that amplify human potential, preserve dignity, and remain intuitive and trustworthy for every individual who relies upon them.

Section II: Architecture of the AI Commons

1. Defining the Public AI Utility

If machine intelligence is to serve as a foundational utility, its underlying infrastructure must be reimagined as a public common. Just as a municipal water system relies on reservoirs, filtration plants, and piping networks, a functional AI Commons requires three core layers:

Open-Weight Base Models as Digital Public Goods: High-capacity, foundational models maintained transparently and made universally accessible to prevent monopoly control over baseline reasoning.
Sovereign Compute Grids & Energy Alignment: Distributed, clean-energy-backed computational infrastructure managed with public oversight to ensure reliability, security, and environmental sustainability.
Standardized Interoperability Layers: Universal protocol standards that allow diverse devices, applications, and human-facing interfaces to connect seamlessly to core intelligence channels.

2. The Human Experience Design (HXD) Layer

An infrastructure is only as effective as its point of interaction with human lives. In an AI Commons, experience design becomes the primary vehicle for equity and trust.

We must design interfaces that uphold human dignity and autonomy—ensuring people retain clear choice and agency rather than becoming passive consumers of automated outputs. This demands intuitive cognitive ergonomics, universal accessibility across physical and economic capabilities, and cultural adaptability that respects local contexts. Without deliberate design at this layer, we risk creating a severe “cognitive divide” where advanced capabilities remain usable only by the tech-literate elite.

3. Governance & Ethics in the Open

Managing shared resources historically carries the risk of the “Tragedy of the Commons”—where individual overconsumption or misuse depletes the shared resource for all. In the context of intelligence, this manifests as algorithmic bias, deliberate disinformation, or resource monopolization.

Governance Pillar	Traditional Proprietary Model	Public AI Commons Model
Model Auditing	Closed-door internal reviews behind corporate secrecy	Participatory, continuous public auditing and open lineage tracking
Access Rights	Tiered monetization based on capital and market power	Guaranteed baseline access as a civic right, paired with tiered usage limits
Alignment & Values	Optimized for shareholder engagement and profit retention	Co-designed with multi-stakeholder governance representing broad societal values

By embedding democratic oversight, clear lineage, and ethical stewardship directly into the architectural blueprints, the AI Commons can maintain its integrity as a sustainable resource for universal human empowerment.

Section III: Orchestrating Human-Centered Change

1. Organizational Transformation: The Shift to Application Density

When baseline intelligence becomes a universally accessible public commodity, the traditional sources of competitive advantage evaporate overnight. Organizations can no longer rely on hoarding proprietary algorithms or controlling isolated pockets of technical expertise.

Instead, value creation shifts dramatically from intelligence scarcity to application density. Victory belongs to organizations that excel at weaving raw cognitive flow into deeply empathetic, frictionless, and impactful human experiences. Success is no longer measured by how much intelligence an organization owns, but by how effectively it applies that intelligence to solve real-world human problems.

2. Reimagining Change Management for Collaborative Intelligence

Legacy change management frameworks were designed for linear transitions and discrete tool adoption. In an era of atmospheric AI infrastructure, change management must evolve from an exercise in structural disruption to an ongoing discipline of human empowerment.

Leaders must active-steer their cultures away from anxiety over job displacement toward a mindset of collaborative intelligence. This requires redesigning workflows so that human curiosity, creative synthesis, strategic judgment, and empathy are amplified—not replaced—by underlying AI utilities.

3. Co-Creating the Future Through Multi-Stakeholder Ecosystems

Building an equitable AI Commons cannot be left to technologists or policymakers alone. Sustainable infrastructure requires continuous co-creation across diverse ecosystems:

Citizens & Communities: Defining local needs, cultural guardrails, and real-world priorities.
Experience Designers & Human-Centered Innovators: Ensuring interfaces promote human agency, clarity, and trust.
Technologists & Engineers: Delivering resilient, scalable, and environmentally aligned technical architectures.
Policymakers & Ethicists: Establishing transparent oversight, fair access protocols, and public accountability.

4. Unlocking Continuous Innovation on Stable Foundations

A common misconception is that public utilities stifle innovation through standardization. History proves the exact opposite: standardizing foundational layers unleashes unprecedented waves of higher-order creativity.

When the power grid became a reliable utility, it didn’t eliminate innovation—it enabled the creation of modern computing, global manufacturing, and home appliances. By turning baseline intelligence into a stable, trusted, public foundation, society frees innovators to focus their energies on solving higher-order challenges in healthcare, education, climate resilience, and human flourishing.

Section IV: Futurology — Scenarios for 2035 and Beyond

As we project a decade into the future, the structural choices we make today regarding cognitive infrastructure will branch into radically different societal landscapes. By exploring plausible future scenarios, leaders can better navigate today’s decisions to steer humanity toward shared prosperity and resilient design.

1. Scenario A: The Enclosed Gardens (Status Quo Extended)

In this scenario, intelligence remains trapped behind proprietary walls controlled by a small oligopoly of technology behemoths. Advanced reasoning capabilities are heavily monetized, resulting in extreme digital and socio-economic stratification.

Siloed Ecosystems: Proprietary “cognitive ecosystems” dictate user experiences with high lock-in costs and minimal interoperability.
The Expanding Cognitive Divide: Wealthy institutions and individuals access hyper-optimized cognitive amplifiers, while public services, non-profits, and developing regions rely on legacy, second-tier models.
Erosion of Public Trust: Black-box algorithms and profit-driven optimization lead to widespread algorithmic bias, deepfakes, and societal polarization.

2. Scenario B: The AI Commons (Public Utility Realized)

Here, global standards establish foundational intelligence as a public utility—accessible to all as a basic civic right, backed by transparent governance and green compute infrastructure.

Universal Baseline Intelligence: High-quality reasoning, translation, and problem-solving models are freely accessible to every school, clinic, small business, and community globally.
Explosion of Hyper-Local Innovation: Unburdened by model licensing fees, grassroots innovators design tailored solutions addressing specific cultural, environmental, and regional needs.
Human-Centric Flourishing: Interfaces prioritize user agency, mental well-being, and cognitive extension, turning technology into a seamless partner in human potential.

3. Scenario C: The Hybrid Infrastructure Network

Borrowing from modern transportation and logistics, this scenario pairs a publicly funded base layer with specialized commercial layers running on top.

Infrastructure Layer	Primary Function	Governance & Access
Public Foundation Layer	Provides baseline reasoning, general knowledge, standard ethics, and civic services (e.g., public transit / public roads).	Publicly funded, open source/open weights, universal free access up to standard quota limits.
Commercial Specialty Layer	Delivers ultra-high-precision, specialized domain expertise for medicine, advanced engineering, and high-frequency finance.	Market-driven, subscription/metered pricing, enterprise SLA guarantees and specialized security.

The Hybrid Model offers a pragmatic bridge—protecting public interests and equitable baseline access while preserving powerful economic incentives for high-stakes, specialized technological breakthroughs.

Section V: Call to Action — Designing for Shared Flourishing

The trajectory of artificial intelligence is not preordained by technology itself; it is determined by the choices we make today as leaders, creators, policy makers, and human-centered innovators. Moving from proprietary enclosure to an intentional AI Commons requires a fundamental pivot in mindset: shifting our focus from building higher competitive walls to designing broader, more inclusive tables.

1. Core Design Principles for the AI Commons

To ensure that public intelligence infrastructure truly serves humanity, every system, policy, and user interface should adhere to three non-negotiable principles:

Human Agency First: Intelligent infrastructure must elevate human decision-making and intentionality, serving as a cognitive lever that expands capability rather than an opaque engine that quietly replaces human choice.
Radical Transparency & Lineage: Every public baseline model must offer verifiable data provenance, open auditability, and clear explainability, building lasting societal trust through radical open-source visibility.
Equitable Frictionlessness: Quality of access and baseline cognitive capability must be uniformly excellent across zip codes, socioeconomic strata, and physical capabilities, eliminating artificial digital barriers.

2. A Strategic Directive for Leaders

For business executives, public officials, and innovation leaders, preparing for the utility era requires immediate action across three fronts:

Stakeholder Group	Immediate Strategic Shift	Core Focus
Business Leaders	Pivot from hoarding algorithms to maximizing application density.	Differentiate through superior experience design, trust, and solving real human problems.
Policymakers	Invest in public compute infrastructure and open data standards.	Ensure baseline intelligence is treated as a core civic right with multi-stakeholder oversight.
Designers & Innovators	Champion cognitive ergonomics and human dignity.	Build intuitive interface layers that bridge the cognitive divide and empower every individual.

3. Conclusion: The Ultimate Benchmark of Innovation

Intelligence is rapidly becoming the core substrate of modern human existence—as fundamental as light, power, or communication. As we stand at this crossroad, we must remember that true progress has never been measured by the raw power of our tools, but by how broadly and equitably those tools expand human potential.

By designing an AI Commons anchored in human-centered change, shared stewardship, and intentional experience design, we can weave machine intelligence into the common fabric of society—unlocking an unprecedented era of shared prosperity, continuous innovation, and collective human flourishing.

Frequently Asked Questions: The AI Commons

1. What is the AI Commons, and why is it necessary?

The AI Commons is a proposed model where foundational artificial intelligence—such as open-weight base models, sovereign compute grids, and interoperability layers—is treated as a shared public utility rather than a closed private asset. It is necessary to prevent severe socio-economic stratification, bridge the cognitive divide, and ensure universal baseline access to essential intelligence.

2. Won’t making intelligence a public utility stifle commercial innovation?

No. History shows that standardizing foundational infrastructure—like electricity and the internet—actually accelerates innovation. By providing a stable, reliable, and universally accessible base layer of intelligence, organizations and entrepreneurs are freed to focus on higher-order value creation, specialized commercial applications, and solving complex human problems (“application density”).

3. What role does Human Experience Design (HXD) play in the AI Commons?

Human Experience Design (HXD) serves as the critical bridge between raw computational power and human flourishing. In an AI Commons, HXD ensures that interfaces prioritize human agency, dignity, cognitive ergonomics, and universal accessibility, preventing technology from overwhelming or displacing human intentionality.

SPECIAL BONUS: Braden Kelley’s Problem Finding Canvas can be a super useful starting point for doing design thinking or human-centered design.

“The Problem Finding Canvas should help you investigate a handful of areas to explore, choose the one most important to you, extract all of the potential challenges and opportunities and choose one to prioritize.”

Image credit: Gemini

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The Neo-Feudal AI Economy: What Happens When Intelligence Becomes Infrastructure?

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The Neo-Feudal AI Economy: What Happens When Intelligence Becomes Infrastructure?

GUEST POST from Chateau G Pato

I. Hook & Core Premise

The Shift from Tool to Terrain

For decades, technology existed as a suite of tools—discrete applications designed to execute specific tasks at the command of human operators. Today, we are witnessing a fundamental paradigm shift: artificial intelligence is no longer merely a tool we deploy, but the underlying terrain on which modern society operates. Intelligence is transitioning into foundational infrastructure, as essential and ubiquitous as electricity, clean water, or physical transit networks. As organizational workflows, decision-making systems, and value creation models are woven directly into algorithmic fabrics, we cease to simply use AI; we inhabit its ecosystem.

The Neo-Feudal Metaphor

This structural evolution brings a striking historical parallel into focus: the emergence of a Neo-Feudal AI Economy. In this emerging landscape:

Digital Lords: A small group of hyperscale platform monopolies control the essential raw materials—massive GPU clusters, energy resources, proprietary datasets, and foundational models.
Digital Fiefdoms: Walled ecosystems dictating the terms of access, compute distribution, and algorithmic reach.
Digital Serfs: Knowledge workers, creative professionals, and enterprises who build their livelihoods on top of these proprietary models, perpetually dependent on platforms they neither own nor control.

The Central Thesis

When intelligence becomes infrastructure, power inevitably concentrates among those who own and govern that infrastructure. Left unchecked, this concentration risks creating systemic economic disenfranchisement and eroding human agency. However, technology is not destiny. By intentionally applying human-centered design, open innovation frameworks, and proactive change leadership, we can transform intelligence from a consolidated gatekeeping mechanism into an empowering public utility—one that amplifies human ingenuity rather than replacing it.

II. The Four Pillars of the Neo-Feudal AI Architecture

To understand how power consolidates within an economy powered by synthetic intelligence, we must analyze the structural mechanics of its architecture. Much like medieval feudalism rested on land ownership, lord-vassal contracts, and agrarian rents, the Neo-Feudal AI Economy is built upon four foundational pillars that dictate how value, leverage, and agency are distributed.

1. Compute & Data as the New Land (The Digital Estates)

In this new paradigm, compute capacity and frontier data are the primary assets of economic production. The capital expenditures required to build, power, and maintain gigawatt-scale data centers create immense barriers to entry. Hyperscalers and platform sovereigns effectively own the modern equivalent of prime arable land:

Infrastructure Enclosure: Access to cutting-edge silicon clusters, cooling infrastructure, and power grids is concentrated among a handful of tech monopolies.
Data Enclosure: Raw human culture, intellectual property, and institutional knowledge have been harvested into proprietary training corpuses, transforming public information into privatized intelligence platforms.

2. Algorithmic Rent-Seeking (Tolls on the Knowledge Superhighway)

The traditional software business model—purchasing licenses or building in-house capability—is giving way to perpetual cognitive rent. As artificial reasoning becomes embedded in every business process, organizations must pay a continuous toll to access foundational intelligence:

API Rents & Model Dependencies: Companies pay per token or per agent interaction, shifting operational budgets toward foundational platform owners.
The Platform Paradox: Businesses build solutions on top of third-party platforms, only to see their margins squeezed as platform lords adjust pricing, usage terms, or capabilities at will.

3. The Erosion of Middle-Tier Innovation

In a standard market, mid-sized enterprises and specialized software vendors serve as engines of competition and job growth. In a Neo-Feudal AI Economy, these middle-tier players face severe structural vulnerability:

Tenant Farmer Dynamics: Mid-tier innovators become tenant farmers, cultivating niche applications on land owned by platform lords.
Sherlocking & Platform Absorption: When a tenant develops a lucrative application or workflow, the underlying platform lord can natively integrate the feature, rendering the tenant’s business model obsolete overnight.

4. The Frictionless Dislocation of Human Capital

The most critical human-centered impact of this architectural shift is the rapid commoditization of cognitive labor. When foundational intelligence becomes an abundant commodity, traditional knowledge-work career ladders dissolve:

Skills Obsolescence Velocity: Cognitive tasks—from coding and synthesis to analysis and design—are automated faster than traditional educational or organizational systems can adapt.
Erosion of Autonomy: Knowledge workers are increasingly managed by, or forced to conform to, algorithmic systems, reducing human experts to mere supervisors of platform-driven workflows.

III. Experience Design in a Gated Intelligence World

As intelligence becomes embedded infrastructure, the practice of experience design reaches a pivotal crossroads. For decades, human-centered design has focused on reducing friction, optimizing usability, and fostering delight. However, when every interface, service touchpoint, and operational workflow is mediated by proprietary AI models, experience design must evolve to confront deeper systemic challenges: the preservation of human agency, qualitative nuance, and authentic empathy.

Customer & Employee Experience (CX/EX) Under Algorithmic Sovereignty

When organizations rely on standardized, off-the-shelf foundation models to drive customer service, onboarding, internal communications, and decision support, a subtle homogenizing effect takes hold. The experience layer risks becoming flattened into a uniform, algorithmically optimized grayness:

Homogenization of Brand Identity: As AI agents handle end-to-end interactions using shared base models, brands lose their unique voice, cultural resonance, and distinct human character.
Algorithmic Bureaucracy in Employee Experience: Internal workflows dictated by prescriptive AI managers can reduce workers to execution units, stripping away autonomy, personal craftsmanship, and intrinsic motivation.

The Threat to Human Agency

Modern experience architecture too often prioritizes hyper-convenience over human empowerment. By delegating cognitive choice, critical reflection, and creative synthesis entirely to background algorithms, design patterns risk inducing cognitive passivity:

Illusion of Choice: Users and employees are presented with algorithmically curated options that direct outcomes toward platform-preferred paths rather than genuine human intent.
Atrophy of Critical Thinking: Over-reliance on predictive suggestions creates a “deskilling feedback loop,” where human operators gradually lose the capacity to question, validate, or override automated outputs.

Designing for Intentional Friction

To counter the passive drift of gated intelligence, experience designers must champion “intentional friction”—deliberate design interventions that pause the automated flow to invite reflection, human judgment, and moral reasoning:

Human-in-the-Loop Architecture: Designing explicit checkpoints where human intuition, ethical assessment, and qualitative context are required before high-stakes decisions are finalized.
Designing for Co-Creation over Automation: Shifting the design paradigm from passive task-completion engines to active cognitive scaffolds that augment, challenge, and elevate human capability rather than replacing it.

IV. Human-Centered Change Leadership for the AI Era

Navigating the transition into an economy anchored by intelligent infrastructure requires a fundamental reset in how we approach organizational transformation. Technocratic approaches to change management treat technology adoption as an end in itself—forcing human systems to bend around software architectures. In a world of exponential platform shifts, true leadership demands a human-centered change framework that prioritizes human dignity, organizational agility, and strategic resilience.

Moving Beyond Technological Determinism

Technological determinism suggests that the trajectory of artificial intelligence is fixed and unyielding—that society and organizations must passively adapt to whatever AI platforms dictate. Human-centered change leaders reject this premise:

Agency Over Automation: Technology defines what is technically possible, but human leadership, policy, and design determine what is desirable, ethical, and valuable.
Intentional Adoption: Leaders must evaluate AI capabilities not through the lens of cost-cutting alone, but through how well they strengthen core organizational values, customer trust, and long-term capability.

Reframing Change Management for Accelerated Shifts

Linear, episodic change models (e.g., Unfreeze-Change-Refreeze) are woefully inadequate for an era defined by continuous model updates, autonomous agentic systems, and rapid platform evolution. Organizations require a persistent capability for continuous adaptation:

Continuous Change Capability: Transitioning from viewing change as a disruption to viewing change capacity as a core operational muscle and competitive advantage.
Adaptive Organizational Architecture: Building flexible team structures, modular workflows, and psychological safety nets that allow employees to experiment, iterate, and pivot alongside evolving tools.

Preserving Human Dignity and Purpose

When cognitive tasks are automated at scale, the primary risk to organizations is not just skill gap dislocation, but a crisis of human purpose and engagement. Leadership must consciously orchestrate the repositioning of human talent:

Elevating Humans to Choreographers: Shifting worker identity from task executioners to strategic choreographers—guiding AI output, injecting qualitative context, and applying ethical judgment.
Value Creation Through Uniquely Human Qualities: Doubling down on human-centric traits that algorithms cannot replicate: empathy, contextual curiosity, moral reasoning, and cross-disciplinary innovation.

V. A Manifesto for Open Innovation & Distributed Intelligence

If the current trajectory of centralized AI infrastructure goes unchecked, the concentration of economic power will inevitably yield a modern digital feudalism. Reclaiming human agency and economic dynamism requires an intentional strategy: counterbalancing gated platforms with open, decentralized, and human-centered innovation models. Below is a framework for resisting algorithmic enclosure and building a more resilient, distributed intelligence ecosystem.

Neo-Feudal Risks vs. Human-Centered Innovation Countermeasures

Neo-Feudal Risk	Human-Centered Innovation Countermeasure
Monopolization of Reasoning: Power concentrated in proprietary, black-box foundational models.	Open-Source & Decentralized AI: Investing in open weights, localized compute networks, and open-source foundation models to democratize access to reasoning.
Algorithmic Dependency: Complete reliance on third-party platform rules, APIs, and shifting pricing models.	Data Sovereignty & Custom Architectures: Curating proprietary domain data assets and deploying fine-tuned, owned agent architectures.
Worker Displacement: Commoditization and elimination of human knowledge roles.	Continuous Capability Mapping: Building co-creative human-AI workflows that treat synthetic intelligence as an augmentative capability, not a substitute for human ingenuity.
Homogenized Output: Bland, standardized customer and employee experiences driven by generic models.	Differentiated Experience Design: Double down on human empathy, craft, contextual nuance, and distinct brand character.

Fostering Public & Open-Source Intelligence Assets

Just as physical infrastructure—highways, water systems, and communication grids—relies on public oversight to prevent monopoly exploitation, foundational cognitive infrastructure must be treated as a public good:

Digital Commons & Shared Datasets: Establishing public digital trusts and open-data repositories that ensure equitable access to non-proprietary training data.
Democratized Compute Utilities: Supporting public compute initiatives, open-source frameworks, and edge-device processing to reduce total reliance on hyperscale monopolies.

Ecosystem Co-Creation

Challenger enterprises, mid-tier companies, and independent innovators cannot compete against tech sovereigns in isolation. Resilience lies in collaborative networks:

Open Innovation Ecosystems: Pooling industry-specific expertise, data, and specialized agents across organizations to create shared value networks rather than walled gardens.
Interoperable Standards: Championing open standards for AI agent communication, data portability, and model evaluation to ensure enterprise mobility and prevent platform lock-in.

VI. Conclusion & Call to Action

We stand at a critical inflection point in the history of human enterprise. The transformation of artificial intelligence from discrete software applications into foundational infrastructure is not merely a technological shift; it is a profound economic and social restructuring. Left to default market forces and hyper-concentrated capital, this transition will naturally gravitate toward digital feudalism—a world where a small oligarchy of platform sovereigns extracts rent on human thought while workers and organizations operate as precarious digital tenants.

The Choice Ahead

However, the outcome of this transformation is not predetermined. Technology reflects the values, structures, and choices of those who design and deploy it. We face a defining fork in the road:

The Passive Path: Allowing intelligence to become a gated, consolidated monopoly that homogenizes human experience, erodes agency, and accelerates economic inequality.
The Intentional Path: Harnessing intelligence as an empowering, accessible utility—a cognitive scaffold designed to expand human potential, foster open innovation, and democratize economic opportunity.

The Leader’s Imperative

Transforming intelligence infrastructure into a force for broad-based human empowerment requires immediate, proactive leadership across every discipline:

Executive Leaders: Resist total algorithmic reliance. Invest in proprietary domain assets, build adaptive organizational architectures, and champion business models that share created value across your human ecosystem.
Innovators & Strategists: Build beyond the walled gardens. Engage in open ecosystems, leverage decentralized models, and reject zero-sum competition in favor of co-created industry value networks.
Experience Designers & Change Leaders: Champion the human element. Design for intentional friction, preserve critical human reflection, and construct workflows that elevate knowledge workers into strategic choreographers of synthetic intelligence.

As intelligence becomes the infrastructure of our world, our highest mandate is clear: we must ensure that human ingenuity, dignity, and agency remain the ultimate purpose of that infrastructure.

Frequently Asked Questions

What is the Neo-Feudal AI Economy?

The Neo-Feudal AI Economy refers to an economic model where artificial intelligence transitions from a standalone tool into foundational infrastructure. In this system, power and capital concentrate among a small group of platform monopolies (“Digital Lords”) who control compute, energy, proprietary datasets, and foundation models, while businesses and knowledge workers operate as dependent tenants (“Digital Serfs”) paying ongoing cognitive rent.

Why is AI considered an infrastructure rather than just software?

AI is becoming infrastructure because modern workflows, business logic, customer interactions, and decision-making systems are being built directly on top of foundational models and compute grids. Like electricity or water utilities, access to artificial intelligence is becoming essential for basic participation in the modern digital economy.

How can leaders prevent algorithmic enclosure and protect human agency?

Leaders can counter algorithmic enclosure by adopting human-centered change leadership, investing in open-source foundation models and decentralized compute, maintaining proprietary data sovereignty, and designing experiences that preserve intentional friction and human-in-the-loop decision-making.

SPECIAL BONUS: Braden Kelley’s Problem Finding Canvas can be a super useful starting point for doing design thinking or human-centered design.

Image credit: Gemini

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That’s Innovation with Two V’s

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innovvation starts here

Should innovation be spelled with two v’s instead of one?

With so many words starting with the letter v being so central to innovation and rolling up into two even more powerful v words, maybe we should spell it innovvation to help organizations remember to focus on the two keys to successful innovation:

Value
Veracity

The Importance of Value to Innovation

The first v in innovvation is value. Value is such an important v that you will hear me say often that Innovation is All About Value and is composed of three elements:

Value Creation is pretty self-explanatory. Your innovation investment must create incremental or completely new value large enough to overcome the switching costs of moving to your new solution from the old solution (including the ‘Do Nothing Solution’). New value can be created by making something more efficient, more effective, possible that wasn’t possible before, or create new psychological or emotional benefits.

Value Access could also be thought of as friction reduction. How easy do you make it for customers and consumers to access the value you’ve created. How well has the product or service been designed to allow people to access the value easily? How easy is it for the solution to be created? How easy is it for people to do business with you?

Value Translation is all about helping people understand the value you’ve created and how it fits into their lives. Value translation is also about understanding where on a continuum between the need for explanation and education that your solution falls. Incremental innovations can usually just be explained to people because they anchor to something they already understand, but radical or disruptive innovations inevitably require some level of education (often far in advance of the launch).

All of the details of what each of these three keys to innovation success are reside in my previous article Innovation is All About Value. It’s well worth the investment of time to read it.

The Importance of Innovation Veracity

A few years ago I wrote about the importance of innovation veracity and how when it comes to innovation, veracity is all that matters. As a quick refresher, let’s look at the definition of the word veracity from our friends over at Merriam-Webster:

Veracity

1: devotion to the truth : truthfulness
2: power of conveying or perceiving truth

So, veracity is the second v in innovvation. There is probably no better way to spend your innovation time than to spend it identifying the right customer insight and the right way to communicate the solution with customers which you’ve created to serve the insight.

For the three components below that will help you get to veracity, I must give credit to the documentary A Good American (a must see). I will stay away from the politics of the movie so I don’t give away the punchline, and just say that it profiles the work of a group of computer scientists who designed a system to protect American citizens from terrorism at a lower cost and with greater privacy protections than the system that was eventually funded and profiled in a different movie – Snowden.

It’s probably no coincidence that Oliver Stone was involved with both films, but that fact is of course irrelevant to the discussion here. What is important is that the film A Good American contains great monologues by the protagonist that provide insight into the power of meta data.

As an innovation coach what’s important to me so I can help a wide variety of clients, is not the particular context of any particular client, so much as the meta information in a variety of applicable tools and frameworks that I collect or create to serve them. What’s important are the insights that can be applied across all industries and all innovation contexts.

What I’ve been able to extract out of this particular film and into the innovation context, so that I can share them with you, are the protagonist’s view of three components that help you identify signals and drive the transformation of DATA into INTELLIGENCE (or innovation veracity in our context), and they are:

Volume – in order to derive meaningful conclusions you need a lot of data inputs, in this case, lots of idea fragments (ideas come later)

Variety – multiple perspectives are necessary to avoid blind spots and increase the potential for connecting idea fragments

Velocity – volume by itself is not enough, momentum is important too. You have to keep Stoking Your Innovation Bonfire

Conclusion

By focusing on value and veracity in our innovation efforts we will arrive at the most important goal of our innovation pursuits – and yet another v – Victory!

So dig deeply to understand where value comes from in your context, and then keep digging. Only when we truly understand the sources of value in any particular context can we create new sources of value for the value consumers in that context, create the proper means of accessing the value, and translate that value for people in the context and in adjacent contexts that might expand the consumption of the new value.

And strive to also understand the indisputable truths in your context that will enable you to achieve the veracity and the resonance you will need to renew the organization’s permission to exist in your context, fuel your continued success, and ensure your next victory.

Download the PDF version here:

Keep innovating!

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What We Can Learn About Innovation From Crows

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What We Can Learn About Innovation From Crows Crows are everywhere. There is no denying it. What is fascinating is that unlike many animal species, crows live in harmony with humans (along with rats and cockroaches), and in fact choose to live with humans instead of away from them, continuing to evolve in order to thrive in areas where humans dominate the landscape.

I came across an interesting Ted video with hacker and writer Joshua Klein about his fascination with crows. It is about ten minutes long and documents examples of a variety of academic research and observations highlighting the intelligence of crows, including his own experiments with a crow vending machine.

What’s also evident in the video and interesting is how much we underestimate crows, which may go back to the popular saying ‘bird brain’. Joshua Klein highlights in the video that far from being ‘bird brains’ crows have the same ratio of brain power as a chimpanzee and possess complex communications, socialization, and memory capacity.

If you watch the video above you’ll see that crows are creative problem solvers (creativity having also been proven as a trait of dolphins) and much more persistent than a squirrel (and many humans for that matter).

So, what can we learn about innovation from the crows in the video (or in real life)?

Well, two things for sure:

1. Goals help.

Crows are inspired to use their creativity in the pursuit of food. Rewards are not a panacea for humans, but can be useful from time to time, when properly framed and reinforced with the right inspiration.

2. We must encourage people to be persistent.

If we let people be squirrels, people won’t push through the difficulties that wicked problems present, and won’t get to the other side where wicked solutions are achieved. Look at how the crows in the video were led step by step to overcome smaller challenges that accumulated into a complex solution that people wouldn’t normally think the crows would have been capable of.

These are the links between the crows and innovation that I identified from the video.

What did you see?

Image credit: HuffingtonPost.com

This article was originally featured on Linkedin