What Can We 'Really' Learn From AI?
What can we ‘Really’ learn from AI
‘AI is giving us a blueprint for how to redesign work, and Cities’
Steam Power, Electricity, the Internal Combustion Engine, Computers and the Internet - five ‘General Purpose Technologies’ that changed the world. Each of them far more than point solutions, like most technologies, that address small, discreet problems. These technologies upended HOW societies worked. They impacted on everything, transformed economies and led to massive social transformation. Despite, over time, disappearing into the background, where we no longer gave them a second thought, they permeated our lives completely. They each represented an ‘Age’.
Now we are entering the ‘AI’ age. Sure, AI has been around since the term ‘Artificial Intelligence’ was coined at a summer workshop at Dartmouth University in 1956. But it has been the rise of Generative AI (as opposed to predictive, Analytical AI) that has marked the dawn of this new age. With ChatGPT’s release in November 2022, the world suddenly saw how each and every one of us was going to have access to unparalleled intelligence via natural language. Andrej Karpathy has written that ‘English is the new programming language’, and it is this fundamental redefinition of what it means to ‘compute’ that is opening the floodgates.
AI is acting as an accelerant, revealing and widening the gap between our industrial-era infrastructure—designed for stability, hierarchy, and predictability—and the fluid, networked nature of the modern economy. Traditional systems were built to support linear, process-driven workflows, but AI thrives in environments that are dynamic, decentralised, and non-linear.
Key areas of mismatch include:
Urban Planning & Real Estate: Most cities are structured for an era when work was location-dependent, but AI enables distributed, asynchronous work, rendering many commercial spaces underutilised.
Education & Workforce Development: Industrial-era education systems focus on static skillsets, but AI demands continuous, adaptive learning.
Regulation & Governance: Many policies were designed for slow-moving technological shifts, whereas AI evolves at an exponential pace, making traditional governance models ineffective.
AI Architecture as a Model for Future Cities, Workplaces, and Institutions
AI models, particularly large-scale neural networks, provide useful metaphors for designing future systems:
We are entering a world where the price of intelligence is trending towards zero. And we have much to learn, and are already learning, from the technology that is delivering this, AI.
For example modern AI systems —especially large language models (LLMs) built on “transformer” architectures— are highly modular and layered. Each layer processes information in a distinct but interconnected way, creating flexible outputs that can adapt to various contexts.
Cities and Infrastructure: Urban planners increasingly talk about “modular urbanism,” where components of the city (transport, energy grids, data centres, housing) are designed to be upgraded or reconfigured without overhauling the entire system. This modular approach parallels how AI layers can be retrained or fine-tuned without redesigning the entire model.
For example:
Smart Grids are modular and able to integrate renewable energy sources, manage distributed energy resources, and adapt to changing demand.
Prefabricated Housing uses modular construction techniques that allow for faster, more flexible, and potentially cheaper building.
Modular Transportation Systems such as bike-sharing, scooter programs, micro-mobility solutions are modular additions to existing transport networks.
Data Centers are often built in modular units, allowing for scalable expansion.
Workplaces: Just as AI systems separate tasks (e.g. natural language understanding, image recognition) into specialised modules, workplaces are moving away from rigid departmental silos to agile, cross-functional teams. In practice, this can mean project-based “squads” that form and dissolve as needed—mirroring the flexible architecture of modern AI. Other manifestations include:
API-fication of Work: Treating teams and departments as "APIs" that can be plugged and played together for different projects.
Skill-Based Teams: Forming teams based on specific skills needed for a project, rather than fixed departmental structures.
Flexible Workspaces: Designing offices that are modular and adaptable to different team sizes and project needs (hot-desking, flexible meeting rooms).
Software Tools as Modules: Increasingly businesses are using modular software suites where they can add or remove functionalities as needed. In a world of millions of ‘AI Agents’, designing these temporary or ongoing networks and ecosystems will be one of the highest skilled, and paid, human jobs.
2. Data-Driven, Learning-Oriented Ecosystems
AI models depend on continuous data input and feedback loops to refine performance.
Cities: Smart cities increasingly gather real-time data on traffic, pollution, and public health to make policy decisions on the fly. This learning cycle allows municipal governments to experiment, measure outcomes, and pivot quickly—akin to how AI continuously refines its internal weights.
Institutions: Traditional organisations (governments, universities, corporations) are recognising the value of continuous feedback. This shift from top-down planning to iterative, data-driven decision-making will transform institutional cultures, much like the shift from rule-based AI to machine learning has transformed computer science.
Contemporary early adopters are good examples of where this is going:
Estonia transformed itself by digitising government services, adopting a secure digital identity framework, and fostering an entrepreneurial tech ecosystem. This nimble governance model shows how legacy bureaucracies can be re-engineered around data-driven processes.
Singapore’s Smart Nation Initiative:
By integrating AI into urban planning (e.g. advanced traffic management, digital services), Singapore demonstrates how a city-state can become a “living lab” for next-generation infrastructure.
Platform Economy in China:
Tech giants (e.g. Alibaba, Tencent) have used AI to drive innovations in fintech, e-commerce, and urban services. The speed and scale of adoption offer lessons in how platforms can reconfigure entire economic sectors and consumer behaviour.
3. Network Effects and Distributed Intelligence
AI architectures often rely on distributed processing (cloud computing, edge devices) to handle large-scale tasks efficiently.
Future Cities: We see an emerging trend toward “polycentric” or multi-nodal cities, where multiple urban centres interconnect rather than relying on one central business district. This networked structure allows for distributed resources (e.g. satellite innovation hubs) that share data and resources across the region.
Future Workplaces: Remote and hybrid work models enable distributed teams operating across different time zones and geographies. This mirrors AI’s capacity to run distributed computations, pooling resources from multiple nodes (cloud servers, edge devices) to achieve a collective outcome.
Modular architecture and workplaces, data driven decision making, feedback loops, distributed networks of ‘offices’, and edge computing (intelligence in our buildings and our devices). Our industry, without realising it, is mimicking how AI works. And, slowly, developing into a constantly self-learning system. We’re becoming less reliant on centralised, rigid structures and more fluid, adaptive and ‘anti-fragile’.
Now, some of the required changes to accommodate AI will, as above, sort of happen by osmosis but a lot more, structurally, needs to be done. And the first two Industrial Revolutions (1760 - 1840 and then 1870-1914) offer us many lessons. Such as:
Infrastructure Investment: The need to invest in new infrastructure (railways, factories, electricity grids) to support the new economy. For AI, this means digital infrastructure, data centers, and potentially new forms of energy infrastructure to power AI.
Education and Skill Development: The importance of adapting education systems to prepare workers for new jobs and industries. We need to focus on AI literacy, data skills, and adaptable skill sets.
Social Safety Nets: The need for social safety nets to cushion the impact of job displacement and inequality during periods of rapid change. See dickens for evidence of how brutal this can be!
Regulation and Governance: The necessity of developing new regulations and governance structures to manage the ethical and societal implications of new technologies.
The Rise of Electricity: Looking at the history of electricity has three distinct lessons, all of its own -
Gradual Adoption and Integration: Technologies are not adopted overnight but are gradually integrated into existing systems. With AI, we’ll likely see small, agile, ultra-productive superteams leaning in heavily, but across the board the cadence, is likely to be more ‘slowly, then suddenly’, though I expect this process to be faster than historically (8-28 years according to McKinsey)
Unexpected Applications: The full impact of a technology is often realised through unforeseen applications and innovations. No-one thought of Uber, Airbnb or Netflix before the technology that enabled them arrived. And even then was years before they seemed ‘obvious’. We’re very bad at guessing future jobs.
The Need for Standardisation: Standardisation is crucial for widespread adoption and interoperability. AI is turning out to be more ‘open-source’ than many expected but this needs to be encouraged to underpin the universality that is needed for real impact.
There are also more contemporary developments we can learn from:
The Internet and Mobile Revolution: This demonstrates the speed and scale of digital disruption. Lessons include:
The Power of Network Effects: The value of technologies increases exponentially as more people adopt them.
The Rise of Platform Economies: The emergence of platform-based business models that leverage networks and data.
The Importance of Cybersecurity and Data Privacy: The growing importance of protecting data and ensuring cybersecurity in a networked world.
Companies Adapting to Remote Work Post-Pandemic: This shows organisational agility and the rapid adoption of digital tools in response to a crisis. Lessons include:
Flexibility and Adaptability: The ability to quickly adapt organisational structures and processes.
The Importance of Digital Infrastructure and Tools: The necessity of having robust digital infrastructure and tools to support remote work and distributed operations.
Focus on Employee Well-being and Connection: The need to address the social and emotional challenges of remote work and maintain employee connection.
Smart City Initiatives (both successes and failures): These provide real-world examples of attempts to integrate technology into urban environments. Lessons include:
Focus on Citizen Needs: Successful initiatives prioritise citizen needs and solve real problems.
Data Privacy and Security Considerations: The importance of addressing data privacy and security concerns in smart city deployments.
Interoperability and Open Standards: The need for interoperable systems and open standards to avoid vendor lock-in and promote innovation.How can we thrive amid extreme uncertainty and rapid change?
Thriving in this era requires a multi-faceted approach at individual, organisational, and societal levels:
Individual Level:
Cultivate Lifelong Learning and Adaptability: Embrace continuous learning and be willing to adapt to new skills and roles throughout your career.
Develop "Future-Proof" Skills: Focus on skills that are less likely to be automated, such as critical thinking, creativity, emotional intelligence, complex problem-solving, and communication.
Embrace Agility and Resilience: Develop the ability to navigate uncertainty, bounce back from setbacks, and embrace change as an opportunity.
Build Strong Networks: Cultivate diverse networks of connections for support, learning, and opportunity.
Focus on Purpose and Meaning: Find work and activities that provide a sense of purpose and meaning in a rapidly changing world.
Organisational Level:
Foster a Culture of Innovation and Experimentation: Encourage experimentation, learning from failures, and continuous improvement.
Embrace Agile Methodologies and Flexible Structures: Adopt agile methodologies and organizational structures that allow for rapid adaptation and response to change.
Invest in Employee Development and Reskilling: Provide opportunities for employees to learn new skills and adapt to evolving roles.
Prioritize Data-Driven Decision Making: Leverage data and analytics to understand changing trends and make informed decisions.
Build Resilient and Diverse Supply Chains and Operations: Develop robust and adaptable supply chains and operational models that can withstand disruptions.
Societal Level:
Invest in Education and Reskilling Infrastructure: Create accessible and affordable education and reskilling programs to prepare the workforce for the future.
Strengthen Social Safety Nets: Provide robust social safety nets to support those displaced by technological change and ensure a more equitable distribution of benefits.
Develop Ethical and Regulatory Frameworks for AI: Establish clear ethical guidelines and regulatory frameworks to guide the development and deployment of AI in a responsible and beneficial way.
Promote Digital Literacy and Inclusion: Ensure that everyone has access to digital technologies and the skills needed to participate in the digital economy.
Foster a Culture of Collaboration and Dialogue: Encourage dialogue and collaboration between government, industry, academia, and civil society to navigate the challenges and opportunities of AI-driven change.
In summary, thriving in an era of AI-driven uncertainty and rapid change requires:
Adaptability and Learning: At all levels, from individuals to societies, we need to prioritize learning, adaptation, and agility.
Investment in Infrastructure: We must invest in both physical and digital infrastructure, as well as "human infrastructure" (education, skills, social safety nets).
Ethical Considerations and Governance: We need to proactively address the ethical and societal implications of AI and develop appropriate governance frameworks.
Collaboration and Inclusivity: Navigating this complex landscape requires collaboration across sectors and ensuring that the benefits of AI are shared broadly.
2.3 Key Takeaways
• Regulatory Foresight: Countries or cities that proactively shape regulation (rather than reacting to disruption) create more stable environments for AI-driven transformation.
• Public-Private Collaboration: Successful transformations often hinge on close collaboration between governments, private industry, and academia—mirroring how AI breakthroughs typically result from collaborative, interdisciplinary research.
• Infrastructure Investment: Building the “rails” for AI—cloud computing, data security frameworks, broadband networks—remains a critical enabler.
Defining Your Own Fate: A Call to Action
Does this make you more or less nervous about the future? Can you, will you ‘Look after Number One’. Does it feel harsh, or pragmatic? Now is the time to act—before the AI transition defines your fate for you. Explore the resources in the 'Thrive in Tumult' framework and start implementing one strategy this week.