These seven transformations form a continuous chain of capability: from semiconductor physics to global connectivity, from compute to deployability, from modern product methods to learning algorithms, and finally to executable market design for complex systems.
Each transformation builds directly on the foundations created by those that came before it.
Together they define the socio-techno-economic architecture of the modern world.
Transformation #1 — Material Science and Electronic Design
When: 16 December 1947
Where: Bell Labs, Murray Hill, New Jersey, USA
Who: John Bardeen, Walter Brattain, William Shockley
The invention of the point-contact transistor made it possible to switch and amplify electronic signals using precisely structured semiconductor materials.
This enabled scalable, rugged, and miniaturised computing and marked the beginning of an industry capable of embedding logic into everyday objects.
The roots of robotics originated here: sensors, actuators, motor drivers, and control electronics are all built on semiconductor physics.
Impacts:
- Computing became scalable, rugged, and miniaturised.
- A new industry formed around embedding logic into physical devices.
- A foundational stack was created for later advances in communications, compute, and autonomy.
Relation to others:
Transformation #1 provides the primitive building blocks required for connectivity (#2), mass compute (#3), deployability (#4), rapid product cycles (#5), learning algorithms (#6), and executable markets (#7).
Transformation #2 — Networking and Internet Protocols
When: 1 January 1983 (TCP/IP transition)
Where: United States (DARPA, Stanford, university networks) → global internet
Who: Network researchers, academic institutions, defence bodies
The switch to TCP/IP unified previously incompatible networks into a single interoperable internetwork.
Digital communication became globally routable across heterogeneous systems, enabling the modern internet.
Robotics capabilities expanded through remote sensing, telemetry, distributed coordination, and teleoperation.
Impacts:
- Interoperable global communication for research, commerce, and institutions.
- Real-time software distribution and collaboration.
- Rich data streams that later feed learning systems.
Relation to others:
Transformation #2 consumes the hardware of #1, enables the utilisation of compute (#3), powers cloud deployability (#4), accelerates product loops (#5), and provides essential data for #6 and system steering under #7.
Transformation #3 — Compute and Advanced Manufacturing
When: 1970s–1990s → present
Where: Silicon Valley (Intel), Hsinchu (TSMC), Europe (ASML), global semiconductor supply chains
Who: Intel, TSMC, AMD, ASML, and the global fabrication ecosystem
Microprocessors standardised computing, while semiconductor foundries industrialised fabrication with high-yield, repeatable processes.
Compute capacity increased exponentially and became affordable and widely deployable.
Robotics benefited from cheaper embedded controllers, higher throughput perception, and reliable motor control silicon.
Impacts:
- Compute became ubiquitous and economically accessible.
- Hardware cycles drove software innovation across industry.
- Specialised silicon emerged for graphics, machine learning, and actuation control.
Relation to others:
Transformation #3 monetises the connectivity of #2, enables #4 cloud and mobile deployability, accelerates #5 development loops, and makes large-scale model training (#6) and dynamic markets (#7) possible.
Transformation #4 — Deployability: Cloud, Mobile, and Energy Storage
When: 2000s–2010s
Where: Seattle and Silicon Valley (AWS, iOS, Android), East Asia and global battery supply chains
Who: Amazon Web Services, Apple, Google, battery and power-electronics innovators
Cloud computing turned infrastructure into an elastic utility, and mobile platforms put networked computers into billions of hands.
Advances in energy storage made autonomous devices and robots practical outside laboratory environments.
Robotics moved from research labs to warehouses, factories, homes, and cities.
Impacts:
- Software could be deployed continuously with global reach.
- Hybrid edge-cloud architectures enabled low-latency coordination.
- Physical autonomy became economically feasible.
Relation to others:
Transformation #4 operationalises #3 at scale, depends on #2 for reach, strengthens iterative delivery (#5), and provides both the runtime and distribution layer for #6 and #7.
Transformation #5 — Construction Methods for Goods and Services
When: 2001 (Agile Manifesto) → 2010s (DevOps, serverless)
Where: Snowbird, Utah; Silicon Valley; global product organisations
Who: Agile and DevOps pioneers, platform engineering teams
Organisations shifted from large, static projects to continuous product evolution guided by telemetry and user feedback.
Serverless computing and managed services reduced operational overhead, enabling rapid experimentation.
Robotics adopted similar methods through firmware CI/CD, simulation-in-the-loop, and coordinated fleet updates.
Impacts:
- Faster translation of ideas into delivered value.
- Operating models grounded in measurement and adaptation.
- Higher-level innovation enabled through platform reuse.
Relation to others:
Transformation #5 leverages #2 and #4, monetises #3, accelerates the training and refinement of #6, and provides the execution cadence for #7.
Transformation #6 — Learning Algorithms and Foundation Models
When: 2017–present (Transformers → foundation models)
Where: Global AI labs (Google/DeepMind, OpenAI, Anthropic, academia, open source)
Who: Vaswani et al., Hinton, Sutskever, LeCun, Bengio, and global contributors
Self-supervised learning at scale enabled models to learn general patterns, representations, and behaviours from raw data.
Foundation models can read, write, classify, generate, plan, and assist, lifting the baseline capability of software.
In robotics, these models improve perception, planning, simulation-to-real transfer, and high-level reasoning for control.
Impacts:
- Intelligence assistance becomes accessible across domains.
- Research and development cycles accelerate.
- Interfaces become conversational and intent-driven.
Relation to others:
Transformation #6 consumes the connectivity, compute, and data produced by #2–#4, is shaped by the rapid iteration of #5, and becomes the decision-making core within #7.
Transformation #7 — Market Design and Executable Regulation
When: 2020s–
Where: London, UK (research and pilots) and Silicon Valley, USA (two-way marketplace pioneers)
Who: Imperial College London collaborators, civic-tech builders, Amazon, Etsy, Uber, global platform innovators, Robin Hanson (LMSR, 2002), and distributed-ledger researchers
Two-way marketplaces such as Amazon and Etsy demonstrated programmable matching between buyers and sellers.
Uber operationalised real-time Automatic Market Maker behaviour using dynamic pricing, decentralised dispatch, and telemetry-driven allocation.
AMM theory builds on Robin Hanson's Logarithmic Market Scoring Rule, which formalised continuous, bounded, information-based market making.
Distributed Ledger Technologies introduced verifiability, privacy controls, and decentralised participation to support transparent and democratic coordination.
Modern AMMs, combined with learning algorithms, can govern and direct the behaviour of socio-techno-economic systems using real-time signals while ensuring core economic properties:
- Individual rationality
- Incentive compatibility
- Budget balance
- Economic efficiency
- Revenue adequacy (financial investability)
Shapley-value allocations enable fair pricing and fair cost recovery.
Nash-equilibrium rate-setting ensures stable system behaviour.
Cyber-physical constraints such as network limits, congestion, safety, and quality-of-service are encoded directly into the system logic.
Human heterogeneity is recognised explicitly, avoiding the homogeneity assumptions of neoclassical economics.
This transformation draws on:
- Graph theory for representing networks and propagation
- Holarchies for coordinating nested local–regional–global behaviour
- Socio-techno-economic modelling for capturing interactions between people, institutions, and physical systems
These capabilities unlock:
- Circular-economy optimisation and full-value resource recovery
- Climate security and avoidance of catastrophic outcomes
- Shippable global deployment through software-based governance
- Growth in democracy, fairness, institutional listening, and peace
- Stable financial and infrastructure systems with real-time feedback
Relation to others:
Transformation #7 synthesises connectivity (#2), compute (#3), deployability (#4), rapid iteration (#5), and intelligence (#6).
It establishes market design as an engineering discipline that aligns incentives, physical realities, human diversity, and desired system outcomes.
Overall Structure
Transformation #1 provides the physical substrate.
Transformation #2 connects the world.
Transformation #3 scales computation.
Transformation #4 deploys compute and power everywhere.
Transformation #5 builds adaptive systems and delivery methods.
Transformation #6 adds general-purpose intelligence.
Transformation #7 coordinates resources, incentives, and outcomes in real time.
Together, these transformations underpin the modern capabilities of robotics, autonomy, digital governance, and system-scale control.