The AI Value Chain: Research and Policy Priorities

Introduction: The AI Value Chain and Information Flows

Artificial General Vehicle

Locked-In Intelligence: The Human Condition

• Humans have locked-in intelligence
• Limited by our physical embodiment
• Our communication abilities constrain us

The Communication Bandwidth Disparity

• Human vs. machine: disparity in communication rates
• Digital systems process information orders of magnitude faster than humans
• This disparity creates structural power imbalances

Bandwidth vs Complexity

bits/min	\(100 \times 10^{-9}\)	\(2,000\)	\(600 \times 10^9\)

Changing Flows of Information

New Flow of Information

Evolved Relationship

Evolved Relationship

• UK policy responses:
  • Online Safety Bill
  • Digital Markets, Competition and Consumer Act

Human Analogue Machines and Information Flow

• AI systems as Human Analogue Machines (HAMs)
• From humans to HAMs to humans
• Exponentially increases power differentials

HAM

Trustworthiness, Intelligent Accountability, and the AI Value Chain

• Digital systems can’t encode
• Digital systems don’t have “intelligent accountability”
• Trustworthiness precedes trust

A Question of Trust

A Question of Trust

Again Univesities are to treat each applicant fairly on the basis of ability and promise, but they are supposed also to admit a socially more representative intake.

There’s no guarantee that the process meets the target.

Onora O’Neill A Question of Trust: Called to Account Reith Lectures 2002 O’Neill (2002)]

• Public dialogue reveals citizen priorities
• Trustworthiness requires public engagement
• Technical governance needs democratic legitimacy

Public Dialogue on AI in Public Services

• September 2024 convened public dialogues.
• Perspectives on AI in priority policy agendas.

“I think a lot of the ideas need to be about AI being like a co-pilot to someone. I think it has to be that. So not taking the human away.”

Public Participant, Liverpool pg 15 ai@cam and Hopkins Van Mil (2024)

AI in Healthcare: Public Perspectives

Key aspirations include:

• Reducing administrative burden on clinicians
• Supporting early diagnosis and prevention
• Improving research and drug development
• Better management of complex conditions

AI in Healthcare: Public Perspectives

“My wife [an NHS nurse] says that the paperwork side takes longer than the actual care.”

Public Participant, Liverpool pg 9 ai@cam and Hopkins Van Mil (2024)

AI in Healthcare: Public Perspectives

“I wouldn’t just want to rely on the technology for something big like that, because obviously it’s a lifechanging situation.”

Public Participant, Cambridge pg 10 ai@cam and Hopkins Van Mil (2024)

Innovation Models: From Productivity to Attention

The Traditional Productivity Flywheel

• Traditional innovation focused on productivity gains
• Clear connection between macro interventions and micro incentives
• Created broadly shared prosperity in 20th century

Productivity Flywheel

New Productivity Paradox

• Current productivity flywheel relies on measurement to translate innovation into productivity.
• Without measurement how does the wheel spin?

The Attention Reinvestment Cycle

• AI economy increasingly driven by attention rather than productivity
• Disconnection between macro interventions and micro incentives
• Leads to different power dynamics and distribution of benefits

The Attention Reinvestment Cycle

• AI creates time savings for professionals
• Freed attention reinvested in knowledge sharing
• Organic growth through professional networks

Benefits of Organic Growth

• Solutions spread through peer-to-peer learning
• Frontline workers become technology champions
• Sustainable, scalable implementation

Attention Reinvestment Cycle

Policy Implications for the AI Value Chain

• Data flows and governance as central policy concerns
• Need for new institutional models to address power imbalances
• Focus on bridging macro interventions to micro incentives

Data Readiness Levels

https://arxiv.org/pdf/1705.02245.pdf Data Readiness Levels (Lawrence, 2017)

Three Grades of Data Readiness

• Grade C - accessibility
  • Transition: data becomes electronically available
• Grade B - validity
  • Transition: pose a question to the data.
• Grade A - usability

Supply Chain of Ideas

• Ideas flow from creation to application like physical supply chains
• Parallels with traditional economic supply chain management
• Particularly relevant for IT and AI solutions

Supply Chain of Ideas

• Current imbalance between supply and demand sides
  • Mismatch between macroeconomic interventions and microeconomic need
• Over-Focus on solutionism
  • technologies/companies
• Under-focus on real-world needs … disconnect between government and citizens … disconnect between companies and customers

Supply Chain of Ideas

• Need to map idea problems demand to idea supply
• Need to understand … problems (demand) … current “stock” of solutions (supply)
• Requires active management of idea resources
• Shape supply to meet demand

Research and Policy Priorities

• How to connect macro policy to micro incentives in AI
• Institutional innovations for data governance
• Methods for effective public participation in AI governance

Innovation Economy Challenges

• Over emphasis on “macro economic supply interventions”
• Under emphasis on maping “micro economic demand” to “micro economic supply”

Digital Failure Examples

The Horizon Scandal

The Lorenzo Scandal

Commonalities

• Policy-reality disconnect
• Poor local needs understanding
• Missing feedback loops

Implementation Issues

• Inflexible top-down approach
• Limited stakeholder input
• Inadequate technical oversight

Key Lessons - Engagement

• Engage stakeholders at all levels
• Design flexible systems
• Build feedback mechanisms

Key Lessons - Implementation

• Use phased rollouts
• Ensure technical competence
• Set realistic timelines

Innovation Economy Conclusion

• Interact directly with micro-demand
• Release quality attention
• Reinvest human capital in more innovation

Conclusion: Serving People, Science and Society

• AI value chain creates new power dynamics and challenges
• Need to bridge between macro policy and micro incentives
• Public engagement is essential for legitimate governance