Cognitive Moats

In this podcast we investigate the massive physical and financial expansion of artificial intelligence infrastructure, projecting that completing a U.S. data center buildout by 2032 will cost nearly $9 trillion and require substantial portions of the national GDP. Because major technology firms are increasingly outgrowing their internal cash flows, financing has shifted heavily toward leases, joint ventures, private credit, and specialized debt structures. While these financial arrangements successfully expand overall funding capacity, they simultaneously obscure asset-level leverage and hidden contingent obligations. Ultimately, investors face significant exposure to risks such as tenant concentration, rapid technological obsolescence, execution delays, and uncertain residual values, which could amplify systemic vulnerability if future AI demand falls short.

Many organizations mistakenly believe that providing artificial intelligence training automatically guarantees a boost in workforce productivity, but recent research reveals a significant disconnect between tool adoption and actual output. While company-sponsored instruction successfully drives behavioral adoption by turning workers into regular users, it is a surprisingly weak indicator of the actual amount of time saved during daily tasks. Instead of reducing workloads, increased digital efficiency often fills the schedule with extra coordination and review, creating a reinvestment gap where companies must strategically decide how liberated capacity is utilized. To achieve sustainable benefits rather than employee burnout, leaders must focus on designing supportive ecosystems that integrate approved technologies with human judgment. Ultimately, translating technology adoption into genuine business value requires treating implementation as a human-centred design challenge rather than a simple software procurement exercise.

As organizations are adopting AI @ Scale, measuring productivity gains attributable to AI is also a problem statement that requires deeper research.
A recent NBER paper by Diane Coyle and John Lourenze Poquiz reveals why business leaders struggle to quantify AI’s real value.
Link to the paper - https://lnkd.in/dhMm4_PV
Here is what practitioners need to know:
📉 The Efficiency Paradox: By automating tasks like customer service or scheduling, AI reduces recorded transactions. This means internal efficiency gains can look like a decline in official output, even as organisational effectiveness and customer value soar.
⏳ Time Reallocation is the Key: AI’s true benefit lies in saving time on routine cognitive tasks (like data cleaning or drafting), freeing up your team for higher-value, creative work.
💡 Quality over Quantity: Standard metrics track volume, but AI drives dynamic, unpriced quality improvements and process re-engineering.
By automating routine cognitive and logistical tasks, these organizations are capturing massive time savings and quality improvements that legacy reporting structures simply cannot see.
For business leaders, the takeaway is clear: if you are only measuring success by traditional headcount or volume metrics, you are blind to the quiet productivity boom happening inside your workflows.
To measure your AI ROI, design internal reporting around task-based time savings and outcome-focused quality gains.

As organizations rush to adopt Generative AI to boost productivity, a silent threat is emerging in the workplace.A groundbreaking study published in the Journal of Service Management reveals that when employees collaborate with AI, they frequently fall into the trap of AI complacency—the tendency to intentionally neglect verifying AI-generated output, even when it contains systematic errors.And here is the kicker: It has nothing to do with how tech-savvy or experienced your employees are.

This paper explores the potential emergence of an economy driven by autonomous AI agents capable of executing complex tasks with minimal human supervision. The authors examine how these digital entities might function as consumers and producers, potentially leading to market collusion or a decoupling of prices from true human preferences. They suggest that AI could drastically alter firm structures by reducing coordination costs, though this may also introduce systemic fragility through correlated errors. To manage these risks, the text argues for the creation of new digital institutions and legal frameworks to handle agent identity and accountability. Ultimately, the researchers call for a distinct economic theory to address the "alignment problem," ensuring that autonomous systems remain beneficial to human society.

This article by Seidl, Ma, and Splitter examines the concept of strategy-as-practice by addressing the fundamental ambiguity regarding what makes an activity "strategic." The authors propose a new framework that categorises strategic activities into four distinct perspectives: those with significant consequences, those formally labelled as strategy, those performed by recognised strategists, and those constituting a recurrent pattern of action. Each viewpoint introduces unique research questions, ranging from how specific outcomes are produced to how professional identities are constructed. By differentiating these views, the researchers aim to integrate strategy-as-practice with broader management theories such as managerial cognition and dynamic capabilities. Ultimately, this framework provides a structured approach for scholars to expand the boundaries of strategic management and develop more cumulative knowledge. This multidimensional perspective ensures that the field captures the full complexity of how strategy is actually performed within organisations.

This academic article proposes a significant ontological shift in how we understand artificial intelligence, moving away from viewing it as a standalone entity or autonomous agent. The authors argue that AI is more accurately defined as an organizing capability that is fundamentally connective, codependent, and emergent. Rather than residing solely within software, this capability arises through the complex system of relations between human participants and learning algorithms. By focusing on these human-algorithm relations, the research highlights how organizational tasks like analysing, learning, and acting are collectively produced rather than pre-programmed. Ultimately, this perspective suggests that AI is a doing and becoming process that reconfigures organizational structures, power dynamics, and intelligence.

This National Bureau of Economic Research paper investigates the productivity impact of generative AI on software development by tracking three generations of coding tools. The authors analyse data from over 100,000 GitHub developers to compare simple autocomplete features, interactive sync agents, and autonomous async agents. While these tools dramatically increase task-level activity, such as code volume and commit frequency, the study reveals that these gains diminish significantly as they move toward final output. This attenuation is attributed to a "weak-link" effect, where human bottlenecks in code review and project management limit the overall speed of software releases. Finally, the research finds that while AI has increased the supply of new applications in digital marketplaces, there has been no corresponding increase in total user engagement.

The future of customer experience is no longer just conversational—it’s deeply vocal. 🎙️Many organizations still rely on robotic, inflexible Interactive Voice Response (IVR) systems that frustrate customers. While digital assistants like Siri or Alexa set the stage for basic voice interaction, modern service organizations need to aim much higher

This research investigate the psychological and social effects of integrating artificial intelligence into collaborative work teams. Through three empirical studies, the authors explore how an AI teammate's social presence influences a person's motivation to contribute to collective tasks. The results indicate that while people initially perceive AI as having less presence than humans, this gap can be narrowed by increasing the transparency and familiarity of the system. Crucially, the studies reveal that social presence does not always drive productivity directly; instead, it works by fostering a willingness to depend on others and a sense of team-oriented commitment. Ultimately, the findings suggest that successful human–AI collaboration relies less on making robots look human and more on building interpersonal trust and clear operational understanding.