Latest Research Papers:
The Attention Merchants
> How Commercial Distraction Creates Drift in Entrepreneurial Execution
Executive Summary
Entrepreneurs compete for customers, capital, and market position. They also compete for something less obvious: control over their own attention.
This paper examines what Tim Wu called the Attention Merchant – the commercial system that captures human attention, measures it, and sells access to it. This system didn’t start with social media. It goes back to:
- The mass newspaper, where readership became inventory for advertisers
- The advertising agency, where persuasion became a professional discipline
- Radio and television, where human time was scheduled, rated, and sold
Digital platforms intensified the pattern. Attention became continuous, personalised, portable, and algorithmically shaped. AI may extend it further. The question is no longer just what people see – it may become what they ask, trust, compare, and choose.
The central argument: the cost of the attention economy isn’t just wasted time. For entrepreneurs, the deeper cost is drift.
Drift is the gradual separation between what a founder says matters and what the founder’s week actually proves. Attention merchants rarely destroy execution through one dramatic interruption. They work through accumulation – notifications, feeds, metrics, competitor comparison, dashboards, research loops, and the pull of “next.” The founder stays busy. Stays informed. Stays stimulated. But the work that matters most stays unfinished.
The paper draws on media history, advertising, audience measurement, platform design, task-switching research, and attention residue. The argument is that entrepreneurial attention should be treated as an execution asset. Being more disciplined isn’t enough. Spending less time online isn’t enough. Entrepreneurs need a system of attention governance:
- Protected work blocks
- Bounded platform use
- Deliberate re-entry after interruption
- Scheduled review
- One correction at a time
- Proof-based execution
The conclusion is straightforward but hard to live: attention must be assigned before it gets captured. An entrepreneur who cannot govern attention cannot reliably govern execution.
Why Insight Does Not Become Execution
> How success advice fails without feedback, rhythm and correction
Executive Summary
Modern founders face an unprecedented supply of success advice: podcasts, books, interviews, frameworks, courses, routines and motivational content. This abundance appears beneficial. Yet repeated exposure to such advice often fails to produce lasting behavioural change. This paper examines why.
The core failure is rarely a lack of insight. The failure is conversion. Ideas remain at the level of inspiration, intention or passive familiarity. They do not become action, feedback and correction. Drawing on research in training transfer (Baldwin and Ford, 1988), motivation, implementation intentions (Gollwitzer and Sheeran, 2006), goal-setting (Locke and Latham, 2002), feedback (Kluger and DeNisi, 1996), deliberate practice (Ericsson, Krampe and Tesch-Römer, 1993), habits (Wood and Neal, 2007), self-regulation (Zimmerman, 2002), survivorship bias (Denrell, 2003) and entrepreneurship (Frese and Gielnik, 2023), this paper distinguishes information exposure from learning transfer and motivational intensity from behavioural architecture.
Success advice can interrupt drift, supply language and inspire experiments. It becomes misleading when consumed as a substitute for structured execution. Founders do not need an endless stream of motivational inputs. They need a correction rhythm: a recurring process in which intention is compared with reality, reality is analysed, analysis becomes planning, and planning returns to action with visible proof.
AI as a Time Trap
> A systems-thinking review of how artificial intelligence can consume the time it promises to save.
Executive Summary
The AI time trap is the pattern in which AI lowers the time cost of an individual task, but the surrounding system adapts so that the saved minutes are swallowed by higher expectations, extra review, more inbound work, and new coordination burdens.
In the same way that the two-income trap turns a seeming household surplus into a new baseline of fixed costs, AI can turn a labour-saving tool into a new minimum standard of responsiveness, output, and availability (Warren and Tyagi, 2003).
The trap is not that AI makes people slower in general. The evidence is more exacting than that. In bounded tasks, AI can create genuine gains. Noy and Zhang’s controlled writing study found completion times fell by 40% and output quality rose by 18% (Noy and Zhang, 2023). Brynjolfsson, Li and Raymond found 14% higher productivity on average in customer support, with larger gains for novice agents (Brynjolfsson et al., 2023). Dillon and colleagues found regular users in a cross-industry field experiment spent substantially less time on email (Dillon et al., 2025).
Yet broader workplace evidence shows why these gains often fail to become free time. In Humlum and Vestergaard’s Danish worker panel, average reported time savings among users were only 2.8% of work hours, and 80% of those gains were reallocated into other job tasks (Humlum and Vestergaard, 2025). Workday’s employer survey found nearly 40% of AI time savings lost to rework and only a minority of employees reporting consistently clear net benefits (Workday, 2026). Upwork’s workforce survey found many workers saying AI had increased workload in at least one way (Upwork Research Institute, 2024). METR’s randomized study of experienced open-source developers found that early-2025 AI tools made them 19% slower on measured tasks (Becker et al., 2025).
The most important mechanisms, in order, are the throughput ratchet, the verification tax, workflow mismatch, new meta-work and fixed costs, task misfit along AI’s jagged frontier, loss of backup human capability, AI debt, and status or competitive pressure. The best solutions are correspondingly systemic: measure net end-to-end time rather than draft speed; redesign workflows instead of only adding tools; use AI to reduce inbound load, not merely accelerate outbound load; restrict AI first to bounded tasks; train users to verify selectively and critically; preserve human judgment and skill; and convert some savings into real slack rather than into higher quotas (Calvino et al., 2025; Workday, 2026).
The Hidden Cost of Drift
> A comparative review of deviation from goals, standards, and missions across economics, business, psychology, health, and classical thought
Executive Summary
This review examines drift as the gradual movement away from a chosen goal, standard, regimen, or strategic path. The hidden cost of drift is larger than the visible loss that appears in delayed revenue, weaker health, missed milestones, or reduced output. It also includes the value that would likely have been created on the best feasible on-track path, the added cost of later correction, the loss of options caused by delay, and the spillover damage to motivation, trust, coordination, and judgment. The term itself is used unevenly across disciplines, so the argument here is synthetic. It draws together adjacent ideas that describe the same underlying pattern from different angles.
Across the contemporary literature, four mechanisms appear repeatedly. First, people and institutions often fail to represent what is being displaced by present choices. Second, immediate friction tends to carry more psychological weight than later benefit. Third, weak feedback allows small deviations to become normal before they become measurable. Fourth, repeated deviation changes the system itself. Habits settle, defaults harden, routines become entrenched, clinicians delay intensification, and organizations begin to defend what they should have corrected. The result is that drift rarely stays small. It compounds through time and becomes harder to reverse (Samuelson and Zeckhauser, 1988; Madrian and Shea, 2001; Carver and Scheier, 1982; Sabaté, 2003).
The classical traditions did not quantify these losses in the language of effect sizes or hazard ratios, yet they identified many of the same mechanisms. Aristotle treated akrasia as failure to act in line with right reason. Epictetus narrowed moral attention to what is within one’s control. Confucian thought stressed self-examination and ritualized correction. Buddhist writings on heedfulness warned that negligence ripens into suffering. Daoist texts added the neglected insight that loss of proportion can itself be a form of deviation. Sun Tzu and Kautilya connected disciplined judgment with successful action and linked disorder with poor preparation and weak self-command. Modern research is more descriptive and intervention-focused, but the old traditions are often sharper on what drift does to character, prudence, and institutional legitimacy.
What is the Intention-Execution Gap?
> Landscape, Theories, Evidence, Practical Interventions and Applications
Executive Summary
The intention–execution gap is the problem where people often fail to do what they genuinely planned to do. Researchers have found this pattern across health, education, work, business, and everyday life, – i.e. it is not confined to any single domain but appears to be a fundamental feature of human action. Intentions matter, but they do not fully explain behaviour. Planning something is important, but it is not enough by itself. As Sheeran and Webb (2016) and Conner and Norman (2022) have observed, – the act of deciding is merely the beginning; something else is required to carry that decision into the world.
Meta-analytic research shows a clear relationship between intention and behaviour, but also a large unexplained remainder, – a gap that persists even when motivation is strong. Experimental studies show the same basic result: when researchers strengthen intentions, the change in actual behaviour is often smaller than the change in stated motivation. As Webb and Sheeran (2006) have demonstrated, this suggests that the gap is real, not just a measurement problem, – i.e. people genuinely intend and genuinely fail.
Modern research has shifted away from asking only why people form intentions and toward asking how intentions become action. That shift has increased attention to what might be called ‘action control’, – i.e. the processes of planning, monitoring, habit formation, emotional regulation, and environmental design that govern execution. The strongest evidence supports tools such as if–then plans, progress monitoring, and friction reduction (Gollwitzer and Sheeran, 2006; Harkin et al., 2016; Michie, van Stralen and West, 2011; Mertens et al., 2022).
The field still contains major debates, especially about how intention should be interpreted, how much weight should be given to willpower models, and how effective or ethical nudges really are. As Hagger et al. (2016) and Dang et al. (2021) have shown, some long-held assumptions about self-control have recently come under serious scrutiny. Newer methods increasingly study behaviour in real time, which may help explain the exact moments at which follow-through breaks down (Klasnja et al., 2015), – thus moving the science closer to the lived experience of action and inaction.
On Drift
> Why People (Especially Entrepreneurs) Slide Away From Stated Goals – and How to Prevent Unintended Deviations
Executive Summary
This essay will look at the phenomenon of drift – the gradual deviation from stated goals through accumulated small decisions rather than dramatic reversals. It is a problem that cuts across psychology, behavioral economics, organizational theory, and strategic management, yet no unified treatment exists. This essay attempts such a synthesis.
At its core, drift represents what I believe is a failure of governance over one’s own attention and resources. As James MacGregor Burns observed, “leadership endeavors to induce people to be aware of what they feel – to define their values so meaningfully that they can be moved to purposeful action.” (Burns, 1978) Drift is the opposite movement: action without awareness, values that blur, purpose that diffuses.
But here lies a critical distinction we must address: How do we differentiate between drift-reactive, unreviewed, rationalized after the fact – and legitimate strategic pivoting? Research on pivots describes “reorientation through accumulated decisions that restructure activities, resources, and attention over time.” (Kirtley & O’Mahony, 2023) The accumulated-decisions structure is identical in both cases. The difference is governance: explicit review, evidence-gating, and authorized reallocation versus gradual, unexamined erosion.
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AI Can Save You Time.
But It Cannot Tell You What Your Time Is For.
Join John Angheli for a free two-day online forum on how to combine Artificial Intelligenceand Human Intelligence to create a Decade Year – as to accomplish in one year, what takes most people ten years, or never happens at all.
- Speed is not wisdom.
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- Output is not progress.
- A full calendar is not a meaningful life.
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