One of the most inescapable edicts when leading a team is the order to optimize the system towards the organization’s goals.
relearn Leadership - the over-optimisation problem in organisations
The Optimization Trap: Why Too Much Efficiency Makes Us Fragile with Olivier Hamant
The Value of Getting Closer to the Work
Scaling The core problem of scaling a team is that not everyone can know everything. Scaling is the work of systematizing that – so that things are understandable, knowable. It’s turnin…
The Optimization Trap: Why Too Much Efficiency Makes Us Fragile - The Great Simplification
In this episode, Nate is joined by biologist and biophysicist Olivier Hamant to explore why living systems prioritize robustness over performance, and what that means for a civilization built almost entirely in the opposite direction.
The Illusion of Decisiveness
Why product teams spin in chaos despite a "clear" strategy
Review: Team Topologies
Many organizations are struggling with their business agility transformation. One of the reasons is the way they have organized their teams. The focus was probably on efficiency and if these teams …
Adopt and scale Team Topologies: platform-as-a-product, templates, more. — Team Topologies - Organizing for fast flow of value
Accelerate value with Team Topologies: measure cognitive load, define team boundaries, apply inverse Conway, readiness assessments, and more for fast delivery.
Paging Charity? How do I get my leaders to stop running teams Into the ground? - Stack Overflow
I'm a senior staff engineer, and I’m wondering what advice you might have for a person in my role in a large company to affect priorities and the speed at which work is "expected" to be executed.
3 tactics for leading a team through uncertainty
Lessons learned leading a team through a bumpy acquisition.
Solipsistic Superintelligence is Unlikely to be Cooperative
AI's central challenge is shifting from capability to coexistence. The dominant paradigm in AI research focuses on developing powerful agents that treat the world as an exogenous and stationary source of feedback. We contend that superintelligence, an extremely capable task solver, born out of such a solipsistic approach to AI design, is unlikely to be cooperative. Deploying AI systems induces endogenous non-stationarity, resulting in a train-test-deploy gap where historical distributions diverge from the deployment context. We refer to this as the self-undermining property of unilateral optimization. Closing this gap requires AI that participates in cooperation: the equilibrium-selection process through which multiple actors navigate their interdependence. We call for a non-solipsistic research paradigm that treats this interdependence as a core design principle rather than approaching cooperation as a task to solve. This entails building dynamic evaluation testbeds involving adaptive counterparties, treating institutions as design primitives, and preserving human agency as a structural feature of the systems we build.
Selling the American People: Advertising, Optimization, and the Origins of Adtech
How marketers learned to dream of optimization and speak in the idiom of management science well before the widespread use of the Internet.Algorithms, data
Reward is not the optimization target — LessWrong
TurnTrout discusses a common misconception in reinforcement learning: that reward is the optimization target of trained agents. He argues reward is b…
Case Studies — Team Topologies - Organizing for fast flow of value
Examples of Team Topologies used in industry
