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Peanut-Buttering-Anti-Pattern

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Peanut-Buttering Anti-Pattern

The peanut-buttering anti-pattern occurs when an engineering organisation spreads its capacity too thinly across too many simultaneous initiatives — like spreading peanut butter across a large slice of bread: the wider the coverage, the thinner (and less effective) each layer becomes.

What It Looks Like

  • Every stakeholder has “their” engineering project — everyone gets a little
  • Engineers are assigned to 3-5 projects concurrently, each at partial capacity
  • Every project technically has people working on it, but none has enough to build momentum
  • Teams are perpetually context-switching between competing priorities
  • Progress is glacial on all fronts; nothing ships on time or with quality

Why It Happens

  • Stakeholder pressure: executives use resource allocation as proxy for organisational influence
  • Fear of saying no: managers avoid the difficult conversation of explicit trade-offs
  • Optimistic planning: each project “only needs one engineer for a sprint”, but totals compound
  • Accountability diffusion: when everything is a priority, nothing is — and no one owns outcomes

Why It’s Harmful

The core damage is cognitive. Research on task-switching (Meyer et al., 2001) demonstrates that switching between non-trivial tasks incurs executive control overhead — the brain must unload the current task context, retrieve the next, and re-engage. Gerald Weinberg quantified the organisational equivalent: each additional simultaneous project absorbs approximately 20% overhead per person from context-switching alone. With five concurrent projects, up to 80% of capacity is lost to switching costs rather than productive work.

At the team level, peanut-buttered teams are identifiable by their position in the Four-States-of-a-Team model: they are almost always stuck in Treading Water — not drowning, but unable to invest in improvements because all capacity is consumed by reactive work across too many fronts. Hiring more engineers does not fix it; it merely gives more people to spread thinly.

The anti-pattern also compounds Organizational-Debt: because no project reaches meaningful completion, technical shortcuts accumulate and half-finished work creates hidden maintenance burden.

The Fix: Concentration Principle

Larson’s remedy is deliberate resource concentration:

  • Fewer projects, more people each: give each initiative enough capacity to build momentum
  • Explicit deprioritisation: commit publicly to what you will NOT work on this quarter
  • Outcome measurement: assess projects by shipped value, not by whether headcount is assigned
  • WIP limits: Kanban literature (Anderson, 2010) formalises this as constraining work-in-progress to match real throughput capacity

Identifying Peanut-Buttering in Your Organisation

  • Count how many projects each engineer touches per sprint — more than two is a warning signal
  • Look for teams stuck at Treading Water with no path to recovery
  • Check whether “priority” lists have more items than the team can meaningfully advance

Sources

  • Larson, Will (2019). An Elegant Puzzle: Systems of Engineering Management. Stripe Press. ISBN: 978-1-7322651-8-9.

    • Chapter 2.2: Peanut-buttering as a resource allocation anti-pattern

  • Weinberg, Gerald M. (1992). Quality Software Management, Vol. 1: Systems Thinking. Dorset House Publishing. ISBN: 978-0-932633-22-5.

    • Quantified context-switching overhead: each simultaneous project costs ~20% of productive capacity; available estimate widely cited in software management literature

  • Meyer, David E., Joshua S. Rubinstein, and John E. Evans (2001). “Executive Control of Cognitive Processes in Task Switching.” Journal of Experimental Psychology: Human Perception and Performance, Vol. 27, No. 4, pp. 763–797. DOI: 10.1037/0096-1523.27.4.763.

    • Foundational experimental research establishing task-switching costs and executive control overhead in the human brain

  • Anderson, David J. (2010). Kanban: Successful Evolutionary Change for Your Technology Business. Blue Hole Press. ISBN: 978-0-9845214-0-1.

    • Chapter 4: Work-in-progress limits as the operational mechanism for avoiding peanut-buttering in software teams

  • Mark, Gloria, Daniela Gudith, and Ulrich Klocke (2008). “The Cost of Interrupted Work: More Speed and Stress.” Proceedings of the ACM CHI 2008 Conference on Human Factors in Computing Systems, pp. 107–110. DOI: 10.1145/1357054.1357072.

    • Found that after an interruption, workers take an average of 23 minutes to fully return to a task, quantifying the recovery cost of context-switching at the individual level

Note

This content was drafted with assistance from AI tools for research, organization, and initial content generation. All final content has been reviewed, fact-checked, and edited by the author to ensure accuracy and alignment with the author’s intentions and perspective.

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