Decision-Making Practices

Deliberate techniques for better decisions under uncertainty — calibration, curated options, synthetic advisory panels and structured pressure-testing.

Also known as: calibration, AI advisory board, pressure-test, Curated options (input options / process options), curated options, blind spots, intellectual DNA, go/no-go decision, Thinking in Bets, Bayesian updating

How authors use it

Oldest first (extraction date when no publication date is known).

Author	Their term	Their meaning
How to Convince Your Boss They Need a Coach Marlo Lyons · 2026-03-27	blind spots	Leadership behaviors or patterns invisible to the leader due to insulation from candid feedback at senior levels.
Ask Me Anything! Annie Duke (via Thinking in Bets newsletter) · 2026-04-06	calibration	The accuracy of one's confidence in predictions and decisions; being 'well-calibrated' means your stated confidence matches actual outcomes. Central to Duke's decision-making framework from poker and behavioral economics.
Generative AI in the Real World: Aishwarya Naresh Reganti on Making AI Work in Production Ben Lorica and Aishwarya Naresh Reganti · 2026-04-16	calibration
Communicating with Confidence When You're Under Pressure HBR.org · 2026-05-07	go/no-go decision	The conscious pre-communication check-in to determine whether you are in a fit state to engage effectively, including managing the follow-up conversation
How Fast-Growing Companies Can Make Better Decisions Tatiana Sandino · 2026-05-07	Curated options (input options / process options)	Input options are pre-configured resources and materials (e.g., product assortments, promotional bundles); process options are modularized tasks or routines employees choose from to sequence their work
Build an AI Advisory Board with GPTs for Decision-Making Carolyn Healey · 2026-05-25	AI advisory board	A personal collection of 3–5 custom GPTs, each configured to imitate the framework, philosophy, voice, and decision pattern of an admired leader, used to pressure-test decisions before they are made
Build an AI Advisory Board with GPTs for Decision-Making Carolyn Healey · 2026-05-25	intellectual DNA	The synthesizable essence of a thinker — derivable from books, shareholder letters, speeches, interviews, and published frameworks — that an LLM can reproduce well enough to generate that thinker's likely response
Build an AI Advisory Board with GPTs for Decision-Making Carolyn Healey · 2026-05-25	pressure-test	Submit a contemplated decision to the synthetic panel and use their responses (especially their disagreements with each other) to surface forgotten questions, latent assumptions, and overlooked angles
Build an AI Advisory Board with GPTs for Decision-Making Carolyn Healey · 2026-05-25	blind spots	Domains of judgment a decision-maker systematically under-weights, which can be compensated for by selecting advisors whose dispositions differ

Documents (34)

Dare to Be Bad at Something (From Fixable) Fixable (TED Audio Collective / Pushkin Industries), hosts Anne Morriss and Frances Frei; syndicated via the HBR Podcast Network / Cold Call feed · 2026-09-23 Reader 🖥️
To excel where it matters most, individuals and organizations must deliberately choose — guided by a strict, customer-derived ranking of what matters — not only what to stop doing, but what to keep doing while doing it badly, and leaders must have (and visibly model) the emotional courage to sustain that choice under pressure.
Is Your Strategic Plan Too Ambitious? Or Not Ambitious Enough? Rebecca Knight · 2026-09-10 source ↗ Reader 🖥️
How ambitious a strategic plan should be is not an intrinsic property of the target number but something you calibrate by systematically examining the underlying problem, your available resources and talent, your tolerance for uncertainty, your measurement time horizon, and your competitive position — a deliberate exercise most organizations skip, defaulting instead to timid, business-as-usual plans.
You can have a conversation with Thinking in Bets now Thinking in Bets · 2026-09-05 Reader 🖥️
Grounding a conversational AI's answers directly in a specific book's text, with citations to the passages they came from, converts an unverifiable claim about what the book says into a checkable one — which is what makes AI-mediated access to a specific author's ideas trustworthy, and worth extending to a reader's own material.
AI Is Undermining Leaders’ Judgment. Here’s What to Do About It. Leonid Sudakov, Nathan Furr · 2026-08-19 source ↗ Reader 🖥️
As AI-driven decision-support systems make organizations more informationally “intelligent,” they simultaneously train leaders out of original judgment through a three-stage erosion of cognitive offloading, deference to tools, and organizational monoculture — so leaders must deliberately practice structured curiosity and intentional dissent to preserve the independent judgment that produces competitive breakthroughs.
Strategy Making Gets an AI Boost Gretchen Gavett · 2026-08-18 Reader 🖥️
As curator of The Insider, Gavett presents two arguments that senior leaders should trade fixed, precomputed plans for continuous option-generation under protected human judgment: AI dissolves the bounded-rationality limits that forced strategy-making into static frameworks like SWOT and Porter's five forces (per Felipe Csaszar), and longer, less predictable careers require small low-stakes experiments plus deliberately protected reflection time rather than a single career plan (per Lynda Gratton).
Strategi som regi i møte med teknologi: En samtale med Walter Qvam Verdistrategipodden (Tale Skjølsvik interviewing Walter Qvam) · 2026-08-01 Reader 🖥️
Strategy is best understood not as a detailed analytical plan but as an ambition or aspiration compressed into a believed, narratable direction that a leader — acting as a director/orchestra-conductor rather than a soloist — uses to make an organization's culture willing and able to execute; and because plans now have a shorter shelf life than ever, strategic success increasingly depends on combining proactive bet-making with the honesty and speed to detect weak signals and respond to the unforeseen (robustness), rather than treating proactive and reactive strategy as opposites.
Introducing The Founder Mindset: Tim Ferriss on Experiments, Risk, and Freedom HBR IdeaCast (Adi Ignatius, bonus crossover episode featuring Reza Satchu interviewing Tim Ferriss) · 2026-07-30 Reader 🖥️
Ferriss argues that what looks like reckless risk-taking is really a disciplined practice of minimizing downside — defining fears concretely instead of vaguely, treating decisions as either fast-and-reversible or slow-and-irreversible, and pursuing 'bounded' optionality (a pre-narrowed, time-boxed set of parallel experiments) rather than either indefinite hedging or premature commitment — which he claims reconciles his love of optionality with Satchu's thesis that commitment is a superpower.
3 Questions to Pressure-Test Your Priorities Karen Walker · 2026-07-27 Reader 🖥️
Because leaders now operate inside "ambient urgency" — a chronic, self-replicating condition where everything feels equally critical, unlike the episodic urgency that traditional prioritization and time-management frameworks were built to resolve — the real leadership task is no longer ranking what to do first but consciously running each high-stakes decision through three tests (am I the only one who can make it, does its value compound over time, and am I avoiding it out of fear rather than judgment) to make fewer, clearer, more deliberate tradeoffs instead of reactively treating every demand as equally urgent.
Don't Let AI Make Bad Analytics Worse Steven Tadelis · 2026-07-27 source ↗ Reader 🖥️
AI will make analytics abundant but not automatically better, so companies must deliberately build a 'decision-grade' operating model — starting every analysis from the decision it should improve, labeling descriptive/predictive/causal questions correctly, exposing assumptions and counterfactuals, governing metric definitions, risk-triaging validation of high-stakes claims, and rewarding truth-seeking over persuasion — or AI will simply accelerate existing analytical dysfunction.
How a Museum Marketing Team Used AI to Bring People Closer to Art Harvard Business Review · 2026-07-21 source ↗ Reader 🖥️
Drawing on an HBS case study, HBS professor Julian De Freitas and Harvard Art Museums marketing director John Connolly use the Museums' 2025 AI-avatar campaign to argue that adopting generative AI in a public-facing, brand-sensitive context is not a generic technology-rollout decision but a domain-specific one: AI's threat to perceived authenticity is sharpest in domains built on one-of-a-kind originals, high trust, or expected human touch (art, tax preparation, healthcare, education), so organizations in those domains must weigh interpretive authority, stakeholder trust, and brand license before novelty or attention. The case shows this weighing playing out procedurally, not just conceptually: Connolly deliberately built face-to-face (not email) consensus with skeptical curators, conservators and art historians, framed the initiative obliquely ('45 degrees') rather than announcing 'let's do AI', and treated radical transparency about AI's use as the load-bearing safeguard against the exact authenticity risk the technology raises — a governance pattern the speakers suggest generalizes to any brand deciding whether it has 'license' to use a given AI application versus merely appearing to jump on the bandwagon.
Why Great Leaders Need More Than Good Judgment Harvard Business Review · 2026-07-21 source ↗ Reader 🖥️
Because the pace of change now outstrips any individual leader's judgment and experience, leaders should replace reliance on personal judgment alone with an institutionalized discipline of systematically gathering and integrating stakeholder perspectives ('panoramic leadership') before moving to action.
The New Rules for Becoming an Enterprise Leader HBR.org · 2026-07-14 source ↗ Reader 🖥️
Three converging forces — pervasive personal/organizational use of AI, geopolitical turbulence, and compressed leadership pipelines — have made every one of the seven classic functional-to-enterprise leadership transitions harder and faster than before, so organizations must replace 'ready now' succession planning with a 'ready enough plus support' model that deliberately builds both outer leader skills (strategic thinking, decision architecture, stakeholder navigation) and inner leader skills (polarity tolerance, emotional regulation, real-time self-awareness) earlier in careers, through curated crucible experiences rather than credentialing.
Brain Food: Two Types of Talent Shane Parrish (FS) · 2026-07-12 Reader 🖥️
Meaningful capability and opportunity are not given but chosen: you build 'talent' and protect the focus it requires by claiming responsibility for your outcomes and by refusing the many attractive commitments whose real cost — time stolen from what you're already building — stays hidden until it's spent.
TBM 430: Incubate, Compound, Refinance, Liquidate John Cutler · 2026-07-12 Reader 🖥️
Software assets, teams, and initiatives can be read as a portfolio positioned by the value they create against what they cost to carry -- moving between Incubate, Compound, Refinance, and Liquidate over time -- and AI does not change this underlying economic physics of complexity, coupling, and decay, only the cost, speed, and feasibility of accelerating whichever direction, healthy or pathological, the portfolio is already moving.
Daniel Pink’s Post Daniel Pink · 2026-07-11 Reader 🖥️
Aligning task type to the body's predictable daily peak/trough/rebound rhythm — and protecting regular breaks — materially improves performance and decision quality, because when you do something largely determines how well you do it.
How to Get Happier and Fight Climate Change Elizabeth Dunn and Jiaying Zhao (guest post via Annie Duke's "Thinking in Bets" newsletter) · 2026-07-08 Reader 🖥️
Climate-friendly behavior change doesn't require guilt-driven sacrifice: because hedonic adaptation and sensory-specific satiety mean the marginal pleasure of a high-carbon food declines with frequency and portion size, cutting back (not eliminating) can shrink your carbon footprint while preserving or even increasing enjoyment -- so, the authors argue, reframing climate action as an opportunity for joyful decisions rather than as loss/sacrifice will produce more sustainable behavior change than guilt-based messaging, though this argument is grounded in happiness/consumption psychology rather than in decision-science reasoning about framing effects, loss aversion, or motivation under uncertainty (Duke's usual territory).
When Nothing Happens Fish Food for Thought · 2026-07-08 source ↗ Reader 🖥️
Whether a leader's harsh behavior is judged as abuse or as excellence turns almost entirely on whether the numbers are good rather than on the behavior itself, so tolerating a high performer's bad conduct is not a moral judgment about their character but an unadmitted bet on their continued performance — a bet that reverses the moment results fade, not when the behavior escalates — and leaders should name and confront that bet today instead of letting it become the scandal they must later explain.
The hidden pattern behind successful products | Mark Pincus (Founder of Zynga) Lenny's Newsletter · 2026-07-06 source ↗ Reader 🖥️
Successful products come not from bold original visions but from disciplined humility: trust your gut instinct while distrusting your specific ideas, master and tastefully copy what is already proven before layering on one unanimously-endorsed 'better' and one expendable 'new', start embarrassingly small, and act on belief grounded in real signal rather than on hope.
Brain Food: Building Trust in Yourself Every Day Shane Parrish (FS) · 2026-07-05 source ↗ Reader 🖥️
Trusting yourself is not something you affirm in the moment but something you build daily from an accumulating record of real, often unwitnessed effort — 'evidence over affirmation' — so that confidence becomes the earned balance of consistently applied fundamentals rather than a story you tell yourself.
Why "good enough" is the whole point of 80/20 Ernie Svenson · 2026-07-03 source ↗ Reader 🖥️
The real value of the 80/20 rule is not the input-output ratio but the permission it grants to stop at 'good enough' — the outcome-relative level a job actually demands — because the last increment of effort usually goes unnoticed, so the true discipline is the confidence to quit polishing rather than the diligence to do more.
Brain Food: Answers Without Effort Shane Parrish (FS) · 2026-06-14 source ↗ Reader 🖥️
Understanding — and the confidence, success and clutch performance that stand alongside it — cannot be borrowed or shortcut; all are earned only through whole-hearted, sustained effort, so an answer you obtain without effort is hollow because it never becomes understanding you actually own.
Build an AI Advisory Board with GPTs for Decision-Making Carolyn Healey · 2026-05-25 source ↗ Reader 🖥️
Running major decisions through a self-built panel of 3–5 custom GPTs modeled on the frameworks of admired leaders is a cheap, always-available substitute for an advisory board that forces decision-makers out of single-mental-model thinking by surfacing questions they would not have asked themselves.
Brain Food: Intelligence vs. Wisdom Shane Parrish (FS) · 2026-05-24 source ↗ Reader 🖥️
What separates effective lives from merely productive ones is the constant subordination of capability to selection — choosing the right goals, removing what others tolerate, refusing the social tax of false 'niceness' — so that intelligence is harnessed by wisdom rather than wasted on impressive but misdirected striving.
#35 Lessons learned from a 2x Unicorn Founder (Uri Levine) Perspective — The Scandinavian Product Podcast / Afonso Franco · 2026-05-09 Reader 🖥️
Startup success is maximized by falling in love with a real, large problem — not a solution — and then relentlessly iterating through failures with speed, focus, and the courage to make hard decisions about people and direction.
Communicating with Confidence When You're Under Pressure HBR.org · 2026-05-07 source ↗ Reader 🖥️
Effective communication under pressure requires shifting focus from your internal state (fatigue, stress, emotional resistance) to the outcome you want to create and the impact on your audience — and this shift is enabled by self-awareness practices (checking in before you speak), structural discipline (anchoring to purpose, being concise), and treating self-care as something that happens outside the communication moment so you can prioritize the other person during it.
How Fast-Growing Companies Can Make Better Decisions Tatiana Sandino · 2026-05-07 source ↗ Reader 🖥️
Fast-growing companies that frame decision-making as a binary choice between centralization and decentralization fall into predictable failure modes; a third path — structured empowerment, which combines curated decision options with outcome-based accountability — lets organizations scale while preserving both consistency and local adaptability.
TBM 419: Stop Being So Negative! Stop Being So Naive! John Cutler · 2026-04-26 source ↗ Reader 🖥️
Teams need both outcome optimism and capability optimism, and the way to make them coexist is not to resolve the tension but to explicitly sequence deliberation and execution modes so each style contributes at the right time.
Generative AI in the Real World: Aishwarya Naresh Reganti on Making AI Work in Production Ben Lorica and Aishwarya Naresh Reganti · 2026-04-16 source ↗ Reader 🖥️
Shipping generative AI in production requires inverting the traditional software effort profile — spending most resources on post-deployment calibration, data understanding, cross-functional collaboration, and workflow-level design rather than on initial build — because the natural-language interface opens an unbounded error surface that only continuous human-in-the-loop refinement can tame.
TBM 416: Investment Stewardship (As Habit) John Cutler from The Beautiful Mess · 2026-04-15 Reader 🖥️
Understanding the return on engineering investment is not a calculation you can perform retrospectively but a set of stewardship behaviors you must practice continuously, and if you haven't been building that habit all along, you cannot cold-start it when the question becomes urgent.
Assumptions as code: SiriusXM's approach to platform prioritization DX · 2026-04-10 Reader 🖥️
SiriusXM's platform engineering team built a weighted, multi-factor prioritization framework (evolved from RICE) that makes trade-offs explicit through numeric weights on impact factors, and then extended it with an 'assumptions as code' system that stores organizational assumptions in a Git repository so AI can surface conflicts, staleness, and invisibility at the moment of decision-making.
Ask Me Anything! Annie Duke (via Thinking in Bets newsletter) · 2026-04-06 Reader 🖥️
Annie Duke invites newsletter subscribers to a free live AMA session (April 8, 2026) as a preview of her paid 3-week cohort-based course 'Making Better Decisions' with Maven (April 20 - May 6, 2026), promising practical tools for improving decision calibration for individuals and teams.
How to Convince Your Boss They Need a Coach Marlo Lyons · 2026-03-27 source ↗ Reader 🖥️
The article argues that subordinates can influence senior executives to engage an executive coach by reframing coaching as a strategic tool rather than a remedial intervention, using the executive's own stated pain points as entry points, and carefully managing the power dynamics so the decision feels self-originated.
TBM 390: Governance by Principle, Not by Template John Cutler · 2025-11-23 source ↗ Reader 🖥️
Governance should be built from first principles — context-specific risk management, accountability, evidence, and decision rights — rather than inherited from project/program templates and accounting-flavored false precision, because software investment is durable and dynamic rather than static and project-shaped; most organizations' governance is procedurally legitimate (defensible on paper) while failing the substantive test of actually serving investors, employees, and customers, and nothing in major governance frameworks (OECD, NIST, ISO 38500, etc.) actually requires projects or programs as the unit of governance.
Tiny Experiments Anne-Laure Le Cunff · 2025-01-29 Reader 🖥️
As reconstructed from the subtitle ('How to Live Freely in a Goal-Obsessed World') and the glossary's evaluative terms, Le Cunff's claim is that fixed, linear goals and a single overarching 'purpose' constrain rather than serve people navigating an uncertain world, and that small, time-bound, curiosity-driven 'personal experiments' (Pacts), evaluated by what was learned rather than by whether a predetermined outcome was hit, are the better vehicle for growth — but this document itself only supplies the vocabulary and self-reflection prompts for that thesis, not the argument or evidence for it.

Propositions touching this concept (26)

By publication month, oldest first (extraction month when no publication date is known).

2026-02

explicitcorroboratedA model reviewing its own work is bounded by its own training biases — same data, same techniques, same blind spots.
GitHub Copilot CLI combines model families for a second opinion · p1
implicitModel families trained by different organizations have sufficiently different blind spots that one can reliably catch errors the other misses.
GitHub Copilot CLI combines model families for a second opinion · p7

2026-03

explicitcorroboratedThe more leaders rise through the ranks, the less candid feedback they receive, creating growing blind spots.
How to Convince Your Boss They Need a Coach · p1
implicitThe subordinate's motivation to suggest coaching is partly self-interested: the boss's blind spots directly impair the subordinate's own effectiveness.
How to Convince Your Boss They Need a Coach · p10

2026-04

explicitAI product development inverts the traditional software effort split: 20% building, 80% calibration.
Generative AI in the Real World: Aishwarya Naresh Reganti on Making AI Work in Production · p1
explicitIn the AI era, leaders own the quality of their domain model's understanding; blind spots in the model are the leader's blind spots.
SWLW #698: From World Model to Domain Intelligence, How to build your Second Brain, and more. · p2
explicitAI removes complexity, volume, and information fog as excuses for strategic blind spots -- the domain model is a mirror of the leader's strategic thinking.
SWLW #698: From World Model to Domain Intelligence, How to build your Second Brain, and more. · p3
explicitdevelopedThe appropriate epistemic framework for engineering ROI is Bayesian updating -- starting with priors, collecting signals, narrowing uncertainty quarterly -- not demanding definitive proof.
TBM 416: Investment Stewardship (As Habit) · p6
explicitUsing AI to reinforce broken mental models of product development (PRDs that wither on contact with reality, box-checking business cases, cascading work nobody believes in) supercharges what was already broken — 'more artifacts, same blind spots.'
TBM 418: Campfires, Trails, and Quests · p1
implicitestablishedCalibration (accuracy of confidence) is a central, improvable dimension of good decision-making.
Ask Me Anything! · p2

2026-05

implicitcorroboratedA panel built from American CEOs and investors of a specific era contains enough diversity to compensate for an individual decision-maker's blind spots.
Build an AI Advisory Board with GPTs for Decision-Making · p8
explicitSilicon Valley insiders are structurally unreliable narrators about AI labor impacts because of financial incentives and professional blind spots.
Is AI Doom Going Out of Style? · p2
empiricalThe set of curated options should be limited to roughly six or seven alternatives, because cognitive working-memory limits prevent employees from effectively weighing larger choice sets.
How Fast-Growing Companies Can Make Better Decisions · p5
speculativeAI-generated options (e.g., real-time diagnostic suggestions for customer service) can serve as curated options within a structured empowerment system.
How Fast-Growing Companies Can Make Better Decisions · p10

2026-06

explicitNo single discovery tool suffices: CASB reveals calls to public providers, service-mesh telemetry reveals self-hosted in-cluster models, and API-gateway logs give a choke point — each with complementary blind spots — so most organizations must use all three and centralize the signals into a SIEM.
Governing AI in the Cloud: A Practical Guide for Architects · p3

2026-07

explicitagent-skills' /ship command fans out multiple named review personas (code-reviewer, security-auditor, test-engineer, web-performance-auditor) in parallel and merges their verdicts into a single go/no-go decision.
agent-skills/docs/comparison.md at main · addyosmani/agent-skills · GitHub · p4
explicitcontestedA closed loop of models that write, review and judge is trained on broadly the same data and shares the same blind spots, so a confident 'looks good' with no human anywhere in it is borrowed confidence — the loop can be both very sure and very wrong, with no human left to tell the difference.
Agentic Code Review · p13
implicitcorroboratedWarranted self-confidence is a matter of calibration — belief backed by an evidentiary track record — not an affect to be manufactured; 'evidence over affirmation' is calibrated confidence.
Brain Food: Building Trust in Yourself Every Day · p9
explicitcontestedDeliberately creating AI personas -- including ones holding views contrary to the leader's own -- is a practical way to surface blind spots and expand perspective.
Why Great Leaders Need More Than Good Judgment · p9
explicitBuilding an AI-based gatekeeper (a model that reads and filters content directly) faces a turtles-all-the-way-down problem: since the gate and the content it gates are drawn from the same underlying model capabilities, a detector built from frontier-model capability may inherit the same blind spots as the content it's meant to catch.
You Probably Won’t Read This Article…and That’s OK · p7

2026-09

explicitdevelopedReviewing code with the same model/session/assumptions as the agent that wrote it reproduces the author's own blind spots; adversarial review requires structural independence (a different model, a context-isolated session, or structured adversarial prompting) modeled on GAN generator/discriminator dynamics and Zero Trust's explicit-verification and least-privilege principles.
Agentic AI and Code Reviews · p5
explicitIf the same reasoning path both writes a change and defines the proof that it is correct, verification collapses into one opinion wearing two hats; a trustworthy verifier needs evidence, a charge, or an objective independent of the author's, and a judge drawn from a different model family reduces the risk of shared blind spots.
From AI Code to Trusted Software: Harness Engineering in Practice · p11
implicitLeaders default to under-ambitious plans mainly because day-to-day operational focus crowds out the higher-altitude question of what the organization could become, not because they lack information about the eight calibration questions.
Is Your Strategic Plan Too Ambitious? Or Not Ambitious Enough? · p11
explicitExpressing a belief as a calibrated probability (e.g. "I'm 60% sure") rather than a flat assertion better represents actual knowledge, guards against blind spots, and invites others to check the claim.
You can have a conversation with Thinking in Bets now · p3
explicitUsing AI to verify AI is necessary (there is no practical alternative at machine-generated volumes) but is insufficient by itself, because a checker sharing the same training data, architecture, or incentives as the system it checks can share its blind spots, producing 'synthetic confidence' rather than real verification.
When AI can do more than we can check · p4

2026-10

explicitWhen an agent fails on a task, asking it directly what capability or visibility is missing from its environment is more productive than retrying the same prompt, because the agent can identify its own blind spots.
Agent-First Engineering: 9 Playbook Tips for a Zero-Human-Code Future · p3

Open gaps (101)

incompleteThe article never specifies what happens after a decision fails the Replacement Test — who receives it, what mechanism (delegation, redesigned approval authority, org structure change) actually transfers it, and how a leader avoids quietly continuing to make 'default' decisions out of old habit even after recognizing the test result.
3 Questions to Pressure-Test Your Priorities — read next: Read against structured-empowerment and delegation-design literature for concrete handoff mechanisms rather than diagnostic questions alone.
incompleteThe article gives no account of how the three tests interact when they conflict — e.g., a decision that requires real courage (passes the Fear Test) but produces no lasting value (fails the Compounding Test), or one only the leader can make (passes Replacement) but is low-compounding busywork. There is no stated priority order or resolution rule among the three tests.
3 Questions to Pressure-Test Your Priorities — read next: Read against multi-criteria decision frameworks that specify sequencing or weighting rules, to test whether the three tests need an explicit order of application.
uninformedThe central empirical claim — that decision quality 'degrades sharply' under high-volume, high-pressure conditions — rests on a single linked source without any discussion of that literature's size, rigor, or replication, and without engaging competing findings (e.g., moderate stress/arousal improving performance up to a point, per Yerkes-Dodson-style research) that would complicate a simple degradation story.
3 Questions to Pressure-Test Your Priorities — read next: Read against the broader decision-fatigue and stress-performance literature to test whether 'ambient' (chronic, low-grade) pressure behaves the same way as the 'high-pressure' conditions the cited research actually measured.
incompleteThe three-test framework is presented as a universal replacement for prioritization frameworks, but the article never addresses whether ambient urgency is evenly distributed — across organizational levels, industries, or company life-stages (the anecdotes are all from a company mid-transformation) — or whether leaders in more stable contexts still face genuinely episodic urgency for which older ranking-based frameworks remain sufficient.
3 Questions to Pressure-Test Your Priorities — read next: Read against organizational-context/contingency literature to test whether ambient urgency is a general leadership condition or specific to high-change, transformation-era organizations.
incompleteCAST and RED are each illustrated by exactly one historical pair plus one or two anonymized client engagements, with no measurement of whether these practices actually preserved judgment quality, no discussion of failure cases, and no account of whether the featured companies were simultaneously reducing AI usage (making it unclear whether the intervention, rather than reduced AI exposure, produced the reported outcomes).
AI Is Undermining Leaders’ Judgment. Here’s What to Do About It. — read next: Any controlled or longitudinal study of structured-curiosity/structured-dissent interventions specifically in AI-saturated decision environments.
uninformedThe piece never engages the tension it stands in with 'broaden your inputs' leadership advice — e.g. work recommending leaders deliberately create AI personas, including contrarian ones, to surface blind spots. If AI-supplied recommendations measurably suppress independent override judgment (this article's own central finding), then leaning on AI personas for a 'different view' risks reproducing the very algorithmic-authority deference this article warns against, rather than curing it.
AI Is Undermining Leaders’ Judgment. Here’s What to Do About It. — read next: extract-why-great-leaders-need-more-than-good-judgment (Marshall Goldsmith's 'panoramic leadership,' which recommends AI personas as a blind-spot tool)
incompleteThe article frames judgment erosion purely at the level of the individual leader (a leader deferring to AI). It does not address the cascading, accountability-laden version of the same problem: subordinates who must explain and defend a leader's AI-endorsed decision to others, and who may mask, embrace, or reinterpret that decision to protect their own credibility — a distinct organizational dynamic from the leader's own judgment eroding.
AI Is Undermining Leaders’ Judgment. Here’s What to Do About It. — read next: extract-when-employees-are-held-accountable-for-ai-generated-decisions
incompleteThe piece never delimits the analogy. Clausewitz wrote about conflict between coherent state actors with continuous policy interests, capable of mobilizing collective will, and existing in a Westphalian system; everyday disputes are dyadic, intimate, often involve unequal power, and frequently lack a coherent 'policy' on either side. The transfer is asserted in the intro and then never re-examined. This is the central category-jump risk: the seven principles inherit their authority from a domain whose structural features mostly do not apply.
How To Handle Conflict: 7 Secrets From History's Master Strategist — read next: Compare against Annie Duke (decisions under uncertainty for individuals — 'Thinking in Bets'), Roger Fisher & William Ury ('Getting to Yes' — interest-based negotiation, an explicit alternative to the adversarial frame), Deborah Tannen on argument culture, and Donald Kagan's 'On the Origins of War' for a check on how state-level conflict actually scales.
incompleteSections 2 and 5 invoke ideas (decide under uncertainty; model the other side's agency) that have a much more rigorous treatment in decision theory and game theory. Barker's framing stays at the level of aphorism. For a reader already inside the decisions-under-uncertainty literature (Annie Duke, Kahneman), this is the thinnest layer.
How To Handle Conflict: 7 Secrets From History's Master Strategist — read next: Annie Duke 'Thinking in Bets' and 'How to Decide'; Schelling 'The Strategy of Conflict'; Axelrod 'The Evolution of Cooperation' for what 'the enemy gets a vote' actually looks like once formalized.
incompleteThe argument assumes honest self-accounting is available on demand ('you can't lie to yourself'), but says nothing about the well-documented human capacity for motivated self-deception, biased memory of one's own effort, and inflated self-assessment — the very failure modes that make evidence-based self-trust hard.
Brain Food: Building Trust in Yourself Every Day — read next: Cognitive-bias and self-deception literature; CBT on distorted self-appraisal; Tetlock/Duke on the gap between felt confidence and actual accuracy.
incompleteThe model of self-trust is drawn entirely from sport, a domain with unusually clear, immediate, and honest feedback. It does not address building self-trust where feedback is delayed, noisy, or absent (leadership, parenting, creative or research work), where 'you can't lie to yourself' no longer reliably holds.
Brain Food: Building Trust in Yourself Every Day — read next: Annie Duke, Thinking in Bets and Quit (resulting; separating decision quality from outcome quality in low-feedback domains); Ericsson on deliberate practice when feedback is poor.
incompleteThe implicit stock model of self-trust is presented with only inflows: trust rises monotonically with reps, and the outflows — broken promises to yourself, quitting, missed commitments — that deplete it are ignored, as is the fast-to-lose asymmetry the sibling 'Credibility is Expensive' issue makes explicit.
Brain Food: Building Trust in Yourself Every Day — read next: James Clear, Atomic Habits (identity-based habits — every action is a vote for the person you become); the 'Credibility is Expensive' extraction on trust's asymmetric fragility.
incompleteNo mechanism is offered for the beginner with no track record yet — self-trust is described as the fruit of a decade, leaving unaddressed how to act with warranted confidence before the evidence exists.
Brain Food: Building Trust in Yourself Every Day — read next: Bandura on self-efficacy and mastery experiences; small-wins and tracer-bullet approaches to bootstrapping confidence.
incompleteNo epistemic test for whether a custom GPT is applying a thinker's framework versus producing fluent imitations of that thinker's voice. The 'intellectual DNA' claim is the load-bearing assumption of the entire practice — if it fails, the panel becomes a stylized confidence generator. The article offers no method (held-out test cases with known answers, decisions-vs-outcomes tracking, blind comparison against a generic 'critique my decision' prompt) for detecting this failure.
Build an AI Advisory Board with GPTs for Decision-Making — read next: Annie Duke, 'Thinking in Bets' and 'Quit' — for the distinction between decision quality and outcome quality, and the discipline of pre-committing to what evidence would change your view. Philip Tetlock, 'Superforecasting' — for what actually-calibrated judgment looks like and how to measure it. Daniel Kahneman, 'Noise' — on the gap between confident expert judgment and reliable judgment.
incompleteNo engagement with the possibility that the practice does subtle epistemic harm: it lets the decision-maker experience the feeling of having consulted wisdom without the actual feedback loop (real advisors push back, get hurt, hold you accountable, and update their advice based on what happened last time). The closer the simulation gets to feeling real, the more it may displace practices that actually improve decisions.
Build an AI Advisory Board with GPTs for Decision-Making — read next: Cutler on accountability and DRIs (directly responsible individuals); literature on simulated advice in clinical and organizational decisions (Logg et al. on algorithm aversion/appreciation); the AI-coding-quality conversation in the user's library (Marcus, the SQLite-vs-LLM-rewrite finding) for the more general pattern of fluent output being mistaken for capable output.
incompleteSelection bias in the advisor pool is unacknowledged. The named exemplars are almost all American, male, billionaire CEOs/investors from a specific era — selected by biographical-survivor bias (we have books about Buffett because he succeeded). Their published frameworks are necessarily post-hoc rationalizations of decisions that worked. A panel of survivors cannot teach you about the decisions that, made the same way, did not work.
Build an AI Advisory Board with GPTs for Decision-Making — read next: Survivorship-bias literature; Tetlock on the difference between pundit confidence and forecasting accuracy; Annie Duke on resulting (judging decision quality by outcomes).
illogicalSelf-undermining selection criterion: 'pick thinkers whose blind spots differ from yours.' If a decision-maker could identify their own blind spots well enough to do this selection, the blind spots would no longer be blind. The directive presupposes the very self-knowledge whose absence the panel is supposed to compensate for.
Build an AI Advisory Board with GPTs for Decision-Making — read next: Kahneman, 'Thinking, Fast and Slow' on the bias blind spot; Pronin et al. on asymmetric insight; literature on structured techniques (e.g. premortem, red teaming) that don't require accurate self-diagnosis to function.
uninformedNo engagement with established decision-quality literature that already addresses many of the same goals (Duke, Tetlock, Klein's recognition-primed decision-making, Kahneman on noise, the premortem technique). The piece writes as if the choice is between 'one mental model' and 'GPT panel,' ignoring a substantial existing toolkit that does not depend on the framework-capture assumption.
Build an AI Advisory Board with GPTs for Decision-Making — read next: Duke, 'Thinking in Bets'; Klein, 'Sources of Power'; Kahneman, Sibony & Sunstein, 'Noise'; the premortem literature (Klein 2007 HBR).
incompleteThe conversation offers no framework for organizational or structural conditions that make communication under pressure chronic — all advice is individualistic (self-awareness, self-care, preparation). There is no discussion of reducing the systemic conditions (overwork, meeting overload, unrealistic demands) that create the pressure in the first place.
Communicating with Confidence When You're Under Pressure
incompleteThe distinction between 'doing it with ease' and 'taking the easy way out' is presented as a clear binary, but no guidance is offered for the gray zone — situations where face-to-face is marginally better but the leader is genuinely at risk of harmful reactivity. The advice to do self-care 'outside the communication moment' assumes sufficient recovery time exists.
Communicating with Confidence When You're Under Pressure
incompleteThe cited studies are observational and contested, but presented with confidence. Most acutely, the Israeli-judges parole result (Danziger et al., 2011) has a well-known confound critique — the order in which cases were heard, not fatigue/glucose, may drive the 'defaults to no' pattern (Weinshall-Margel & Shapard). The Cornell tweet finding measures mood, not cognition, and the Duke anesthesia statistic is a correlational time-of-day association. The piece stacks three vivid but disputable findings as if they jointly proved a causal schedule.
Daniel Pink’s Post — read next: Weinshall-Margel & Shapard (2011) reanalysis of the parole data; Glöckner on the extraneous-factors critique; replication and effect-size literature on time-of-day effects in decision-making.
incompleteThe 'hidden cost of yes' claim names the phenomenon but gives no method for actually estimating that hidden cost before committing, i.e., no decision procedure — only the observation that the cost exists.
Brain Food: Two Types of Talent — read next: Greg McKeown's 'Essentialism' (notably advertised in the same email) and Cal Newport's writing on deep work and attention scarcity both supply the operational frameworks this aphorism gestures at but doesn't provide.
uninformedMunger's closing line — 'In the end, most people get what they deserve' — is presented without qualification, ignoring the well-documented role of luck and structural/starting-position advantage in outcomes, which sits in tension with the issue's own agency-focused framing.
Brain Food: Two Types of Talent — read next: Robert Frank's 'Success and Luck' and Michael Mauboussin's 'The Success Equation' both directly interrogate the skill-vs-luck attribution this quote assumes away.
incompleteTiny Thought 1 ('the day you decide you're in charge...') asserts a locus-of-control effect on perceived opportunity but doesn't address cases where structural or external constraints genuinely limit agency, nor distinguish perceived opportunity from actual opportunity.
Brain Food: Two Types of Talent — read next: Literature on locus of control (Rotter) and its boundary conditions under real resource/structural constraints.
incompleteThe 80-20 flip is asserted from consulting experience but not quantified. No data is offered on what 'calibration' costs in practice relative to build — the ratio could vary enormously by domain, and the 80-20 framing may be more rhetorical than empirical.
Generative AI in the Real World: Aishwarya Naresh Reganti on Making AI Work in Production
uninformedThe legal and compliance discussion is acknowledged as outside Aishwarya's expertise. Given that governance failures can kill finished projects (as her own anecdote shows), the treatment is surprisingly thin — no frameworks, regulations, or concrete practices are discussed.
Generative AI in the Real World: Aishwarya Naresh Reganti on Making AI Work in Production
illogicalAishwarya argues that model personality differences will not converge because vendors treat them as competitive differentiators, but also argues that model capabilities are converging rapidly. The distinction between 'capability' and 'personality' is never defined rigorously — prompt sensitivity, output style, and error patterns could plausibly converge as capabilities do.
Generative AI in the Real World: Aishwarya Naresh Reganti on Making AI Work in Production
incompleteThe claim that early-career 'agent-native' workers have a hiring advantage is supported only by Aishwarya's personal hiring experience at a small consultancy. Whether this generalizes to larger enterprises with different incentive structures is unexamined.
Generative AI in the Real World: Aishwarya Naresh Reganti on Making AI Work in Production
incompleteThe optimization pyramid (effort -> performance -> cost -> latency) is offered as a sequential framework but no guidance is given on when to stop optimizing performance and move to cost/latency, or how to handle cases where the two conflict fundamentally.
Generative AI in the Real World: Aishwarya Naresh Reganti on Making AI Work in Production
missing evidenceThe claim that models from different families have complementary blind spots is the core theoretical justification, but no analysis of what specific biases differ between Claude and GPT families is provided.
GitHub Copilot CLI combines model families for a second opinion — read next: Look for research on training-data or RLHF-induced biases across model families; compare failure modes on shared benchmarks.
incomplete'Good enough for whom' is left under-specified. The standard is audience-relative ('the level the job demands', 'which details a judge will actually care about'), yet Svenson treats the demanding audience as singular and self-evident. He does not address conflicting audiences, or the case where the relevant reader's genuine standard is near-perfection (a regulator, an appellate court, a brand, an adversary looking for the attackable flaw) — which would collapse his effort-versus-value gap and vindicate the 'perfectionist' he diagnoses as merely fearful.
Why "good enough" is the whole point of 80/20 — read next: Read on satisficing versus maximizing (Herbert Simon) and stakeholder / quality-attribute analysis: who sets the adequacy bar, and what the stopping rule should be when the audience is adversarial or the cost of a missed detail is asymmetric.
illogicalConflation of 'faster + more confident decisions' with 'better decisions' (the 75% stat). Confidence and accuracy are dissociable — and AI-mediated commerce specifically risks increasing confidence while reducing comparison quality.
A new generation of ads for the AI era of Search — read next: Annie Duke / Thinking in Bets on confidence calibration; behavioral economics on choice architecture and dark patterns; recent work on AI overreliance
incompleteThe piece asserts that AI can 'blur' the descriptive/predictive/causal distinction but does not explain the mechanism by which it does so — is it that AI models are trained on correlational data and cannot distinguish causal from correlational patterns without explicit experimental data, or is it purely a communication/framing failure by users who ask loosely worded questions? These have different fixes (better model training and disclosure vs. better user education and question templates).
Don't Let AI Make Bad Analytics Worse — read next: Causal-inference-in-ML literature (e.g., work on distinguishing observational from interventional/counterfactual queries in LLM-based analytics assistants) would clarify whether this is a solvable interface problem or a deeper representational limitation.
incompleteThe six-part model is presented as equally applicable across company sizes and maturities, but the case narratives (Amazon, eBay, Airbnb) are all large, resource-rich organizations that could build dedicated platforms (Minerva) or run large-scale randomized experiments. The article doesn't address whether a small company without a data-science team or a marketing-experimentation budget can implement 'expose counterfactuals' or 'govern the metrics' at any meaningful fidelity, or what a minimum-viable version looks like.
Don't Let AI Make Bad Analytics Worse — read next: Practitioner writing on lightweight/low-cost causal inference and metrics governance for resource-constrained teams would test whether the model degrades gracefully or requires the scale these examples assume.
incompleteFerriss's entire case rests on his own anecdotal career narrative — a survivorship-biased sample, since the podcast only hears from someone whose bets largely paid off. There is no discussion of people who applied the same fear-setting / bounded-experimentation approach and never found a durable 'through line,' so the method's actual success rate versus its narrative appeal is untested.
Introducing The Founder Mindset: Tim Ferriss on Experiments, Risk, and Freedom — read next: Nassim Taleb's 'Fooled by Randomness' or empirical entrepreneurship failure-rate literature, to test whether fear-setting and bounded experimentation causally improve outcomes or are simply the story successful people tell afterward.
illogicalFerriss's reconciliation of 'loving optionality' with Satchu's 'commitment is a superpower' is never tested against Satchu's actual target population — people who never define ANY bound on their optionality (the ones marrying, buying homes, and joining churches less, per Satchu's own evidence). Every one of Ferriss's examples (book-title testing, time-boxed side projects) is a case where he had already implicitly committed to a domain (writing, entrepreneurship) and was only negotiating bounded choices within it; he never addresses whether 'bounded optionality' is achievable for someone who hasn't committed to any domain at all. The qualifier 'bounded' may be doing all the reconciling work without a general criterion for how tight a bound must be to actually count as commitment.
Introducing The Founder Mindset: Tim Ferriss on Experiments, Risk, and Freedom — read next: Barry Schwartz's 'The Paradox of Choice' for the mechanism by which unbounded options degrade decision satisfaction and follow-through — which would give the 'bounded' qualifier real content; also worth checking other Founder Mindset episodes for whether Satchu pushes back on this exact point with Ferriss.
incompleteNo engagement with cases where the fast/reversible-decision heuristic backfires — decisions that seem reversible at the time (a startup pivot, a public statement, a hire) but carry irreversible reputational or relational costs that aren't obvious until later. Ferriss's whole framework depends on correctly classifying a decision as reversible in advance, but the episode never discusses how often that classification itself turns out wrong, or what to do when a 'fast' decision turns out to have been a hidden one-way door.
Introducing The Founder Mindset: Tim Ferriss on Experiments, Risk, and Freedom — read next: This is the next-order question left by this same episode's own answer to extract-tiny-experiments-le-cunff's g2: that gap asked WHEN deliberation should override iteration, and Ferriss answers 'when the decision is irreversible' — but he never supplies a method for classifying irreversibility ex ante, which is what would actually make the rule usable. Worth reading Amazon's 'Type 1 vs. Type 2 decisions' framework (Bezos) or Annie Duke's work on hidden irreversibility for a candidate classification test.
illogicalThe 'defy gravity' claim — that trade-off physics apply identically in services as in physical products — is asserted by analogy ('the same physics applies') rather than demonstrated; the hosts never specify the underlying constrained resource (attention? budget? headcount? culture bandwidth?) the way weight/materials constrain a laptop.
Dare to Be Bad at Something (From Fixable) — read next: Operations-management literature on service-quality trade-off mechanisms (queuing theory, capacity-utilization trade-offs) that would ground the metaphor mechanistically rather than rhetorically.
incompleteThe three-forces/seven-transitions framework is presented as applying to 'virtually every leader' and organization, but every illustrative example in the conversation (the food-products company, generic references to 'very large organizations') is a large, AI-adopting, multi-divisional global corporation. There is no engagement with how the framework applies, or fails to apply, to smaller organizations, non-profits, public-sector bodies, or firms in AI-cautious or resource-constrained settings.
The New Rules for Becoming an Enterprise Leader
incompleteImplicit premise about authority and self-correction ('I trust my own thinking only because I have to argue for it') is asserted as autobiographical but generalized. The argument that formal authority degrades judgment by removing resistance deserves more scrutiny — many formal leaders maintain rigorous self-criticism, and many title-less voices ossify.
3) Christin Gorman leder uten tittel — read next: Read on epistemic humility and authority (Annie Duke on 'Thinking in Bets' framing of belief-updating; Greger Teigre Wedel (Heis ep 12) 'når sjefen har svaret' on the inverse problem). Also Beck on Tidy First / TDD as institutionalized self-correction independent of authority.
incompleteHaugseth's epistemic update on skye is offered as an instance of changing her mind, but the underlying mental model that drove her earlier full-pro-cloud stance is not interrogated. What other current convictions might be in the same epistemic position now? The 'how does a leader earn forankring on the world' question (same as the Olrud Moen ep 15 gap) is unaddressed.
7) Stine Haugseth: Insourcing, retning og strategi på en ølbrikke — read next: Annie Duke 'Thinking in Bets' (already in user's library) on calibration and the cost of being skråsikker about probabilistic claims; Philip Tetlock on superforecasting; Donella Meadows on systemic leverage points.
incompleteNo criteria are given for how the museum weighed the avatar campaign's benefits (attention, engagement, membership) against its risks (donor relations, reputational, Harvard-brand association) — the episode describes the process (face-to-face conversation, transparency commitment, oblique framing) but never surfaces an explicit decision framework, cost/benefit threshold, or who held final go/no-go authority once curators signed off.
How a Museum Marketing Team Used AI to Bring People Closer to Art — read next: Structured decision-making frameworks for weighing novel-technology risk against brand value (e.g. curated options, pressure-testing, calibration) applied to a real go/no-go case, to see whether Connolly's process maps onto or diverges from formal decision-science practice.
paywallSeveral content blocks could not be imported from the original HBR document due to paywall restrictions. These appear at the beginning, middle, and end of the article and likely contain supplementary content (author bio, related articles, or promotional material).
How to Convince Your Boss They Need a Coach — read next: Access the full article at hbr.org with a subscription to verify no substantive content was missed.
evidenceThe article's recommendations rest almost entirely on the author's consulting experience and anecdotal client cases. No controlled studies, success/failure rates, or systematic evidence for the five-step framework are offered.
How to Convince Your Boss They Need a Coach — read next: Look for empirical research on upward influence tactics in organizational psychology (e.g., Yukl & Falbe on influence tactics) to assess whether the recommended strategies have empirical support.
scopeThe article assumes a relatively healthy organizational context. It does not address what to do when the boss's blind spots constitute toxic or harmful leadership, where coaching alone may be insufficient.
How to Convince Your Boss They Need a Coach — read next: Consider literature on managing toxic leadership, whistleblowing, and organizational exit as complementary frames.
missing_counterargumentThe article does not address the possibility that coaching may not be the right intervention -- for instance, when the problem is structural (bad incentives, poor organizational design) rather than behavioral.
How to Convince Your Boss They Need a Coach — read next: Consider systems-thinking perspectives on leadership problems, e.g., Senge's 'The Fifth Discipline.'
incompleteThe eight diagnostic questions are presented as a flat checklist with no guidance on how to weigh or prioritize them when they point in different directions — e.g., adequate funding paired with weak staffing, or high uncertainty paired with an externally mandated annual measurement cycle.
Is Your Strategic Plan Too Ambitious? Or Not Ambitious Enough? — read next: Multi-criteria decision frameworks or strategy-scorecard literature that combine qualitative heuristics like these into a single calibration score or decision rule.
uninformedAll supporting evidence is qualitative and anecdotal (two expert interviews, the Bezos/Amazon anecdote, one Burberry case) with no data on the relative frequency or cost of the two failure modes — plans that were too timid vs. plans that were too ambitious — so a reader cannot tell which error is more common or more costly in practice.
Is Your Strategic Plan Too Ambitious? Or Not Ambitious Enough? — read next: Empirical strategy-execution research on goal-setting outcomes (e.g., academic stretch-goal research, or large-sample studies of strategic-plan success/failure rates).
illogicalThe piece treats 'start small while aiming high' (the Bezos narrative) and '10x goals can't be achieved incrementally' (Sull's funding argument) as compatible, but doesn't reconcile that starting with books was itself an incremental first step — the article never clearly distinguishes incremental execution in service of a big vision from incremental ambition-setting, even though only the latter is framed as the failure mode to avoid.
Is Your Strategic Plan Too Ambitious? Or Not Ambitious Enough? — read next: Real-options theory or staged-investment strategy models that formalize the distinction between a small, reversible first step and a capped level of ambition.
incompleteThe piece frames climate action entirely through individual consumer choices (diet, spending, travel, having children, job) with no acknowledgment of the disproportionate share of emissions from industrial and energy production relative to individual consumption, and no discussion of collective or political action. This risks reproducing the 'carbon footprint individualization' critique -- the idea that focusing on personal consumption choices redirects responsibility away from systemic/corporate actors.
How to Get Happier and Fight Climate Change — read next: Mark Kaufman, 'The Carbon Footprint Sham' (Mashable); Michael Mann, 'The New Climate War,' on the individualization of climate blame.
illogicalThe piece generalizes from a single, well-evidenced domain (food, where hedonic adaptation and sensory-specific satiety directly and literally apply) to 'everything from how to spend money and where to go on vacation to whether to have children and what kind of job to take' -- a much broader set of life decisions where the same mechanisms don't obviously transfer. Having children, for instance, is not a repeatable-frequency choice analogous to eating chocolate; the generalization is presented as a smooth extension without any argument for why the mechanism carries over.
How to Get Happier and Fight Climate Change — read next: The book itself (not excerpted here) may supply this argument; absent that, treat the food-domain findings as the well-supported core and the life-domain claims as unsupported extrapolation.
incompleteLevine's framework is entirely drawn from consumer-facing, network-effect products (Waze, Moovit, Uber, Google). He does not address how his principles (especially 'fail fast with an embarrassing product' and 'retention is the only PMF metric') apply to B2B, enterprise, or deep-tech startups where shipping an embarrassing product may permanently damage trust.
#35 Lessons learned from a 2x Unicorn Founder (Uri Levine) — read next: Ben Horowitz 'The Hard Thing About Hard Things' for enterprise context; or April Dunford 'Obviously Awesome' for B2B positioning nuances
incompleteThe 'less than 5% of value in the first decade' claim is presented as fact without sourcing. It conflates market capitalization with value creation — early-stage value creation (technology, team, IP) is real but not captured by stock price.
#35 Lessons learned from a 2x Unicorn Founder (Uri Levine) — read next: Janeway 'Doing Capitalism in the Innovation Economy' for a more nuanced account of when and how value accrues
incompleteCrossing the chasm from early adopters to early majority is identified as critical (simplicity wins) but Levine provides no mechanism or case study for how to actually make that transition. Geoffrey Moore's framework is conspicuously absent.
#35 Lessons learned from a 2x Unicorn Founder (Uri Levine) — read next: Geoffrey Moore 'Crossing the Chasm' — the canonical treatment of this exact problem
incompleteThe firing advice assumes a US-style at-will employment context. The host raises Scandinavian legal constraints and cultural norms, but Levine dismisses them with 'you need to face the legal challenges' without engaging with how employment law changes the calculus.
#35 Lessons learned from a 2x Unicorn Founder (Uri Levine) — read next: European employment law perspectives on startup team management; Scandinavian HR practices for scale-ups
uninformedLevine claims 'only 64 people have built two unicorns.' This figure is likely outdated or sourced from a specific list; the actual number fluctuates and depends on definition (co-founder vs sole founder, SPAC unicorns, etc.).
#35 Lessons learned from a 2x Unicorn Founder (Uri Levine) — read next: N/A — minor factual claim
incompleteThe piece describes human review as optional ('Humans may review pull requests, but aren't required to... we've pushed almost all review effort towards being handled agent-to-agent') but never says what determines which PRs actually receive a human look versus none, or what happens when agent-to-agent review systematically misses the same class of issue because both the authoring agent and the reviewing agent share the same model's blind spots.
Harness engineering: leveraging Codex in an agent-first world
incompleteOsmani concedes 'you can't write down everything' and resolves it as a load-bearingness triage problem — but never gives a criterion for identifying load-bearing decisions IN THE MOMENT (before you know which ones will matter later). This is the operationally hard part. Empirically, much rationale that turns out to be load-bearing did not look load-bearing at decision time. The 'capture the load-bearing why' guidance therefore risks being well-intentioned but unimplementable, or implementable only by people who already have the seniority to predict consequence — i.e., precisely the people whose departure creates the problem in the first place.
The Intent Debt — read next: Naur 'Programming as Theory Building' on what kinds of decisions are reconstructible from code and which are not. Polanyi on tacit vs explicit knowledge. Decision-research literature (Annie Duke, your Thinking in Bets reading) on the inability to predict consequence and how that affects pre-commitment to recording rationale.
incompleteParrish offers no operational test for distinguishing wisdom from intelligence. If wisdom is choosing the right goals, then choosing well is itself a kind of getting-what-you-want — meta-getting. The distinction risks collapsing into 'intelligence applied to first-order goals' vs. 'intelligence applied to goal-selection' — i.e., into a hierarchy of intelligence rather than a separate faculty. Without a test (e.g., regret-rate, time-horizon of evaluation, second-order satisfaction), the aphorism is a vibe, not a tool.
Brain Food: Intelligence vs. Wisdom — read next: Annie Duke, *Thinking in Bets* and *Quit* (separating decision quality from outcome quality; resulting; expected value of changing course). Russ Roberts, *Wild Problems* (decisions whose value cannot be precomputed — the closest contemporary treatment of 'what's worth wanting'). Aristotle's *Nicomachean Ethics* on phronesis (practical wisdom) vs. sophia and techne. Harry Frankfurt on second-order desires ('Freedom of the Will and the Concept of a Person').
incompleteSurvivor bias in the exemplars. Chung Ju-yung and Winston Weinberg are presented as evidence for diligence + speed + refusal-of-impossibility, but the population of people who pursued the same strategies and were destroyed by them is invisible in the newsletter. The implicit causal claim (mindset → outcome) is not separated from the role of context, capital, geopolitics, and luck.
Brain Food: Intelligence vs. Wisdom — read next: Phil Rosenzweig, *The Halo Effect*. Michael Mauboussin, *The Success Equation* (luck vs. skill decomposition). Daniel Kahneman on regression to the mean and outcome bias.
incompleteThe Nice/Kind reframing collapses an interpersonal question into an individualist one. 'Kindness is what you owe yourself' is rhetorically powerful but elides the relational and reputational costs of consistent time-defense, and the asymmetric power dynamics in which 'guarding your time' is available to some workers and not others.
Brain Food: Intelligence vs. Wisdom — read next: Jenny Odell, *How to Do Nothing* (counter-perspective on attention and refusal as a class-coded privilege). Oliver Burkeman, *Four Thousand Weeks* (a less productivity-maximalist version of the same insight).
incompleteThe newsletter equates 'wisdom' with selection of high-value goals but never engages the question of whose values define worth. The implicit value system (impact, achievement, output, building things that last) is presented as universal common sense rather than as a specific tradition. A reader who already shares the worldview will not notice; one who does not will find the wisdom/intelligence distinction culturally narrow.
Brain Food: Intelligence vs. Wisdom — read next: Charles Taylor, *Sources of the Self*. Alasdair MacIntyre, *After Virtue* (on the disappearance of shared moral vocabulary that would make 'worth getting' intelligible). Agnes Callard on aspiration.
illogicalThe 'failure is just feedback' frame depends on the video-game analogy, which is structurally disanalogous to most adult contexts: video games have low stakes, instant respawn, identical re-runs, and no observers whose evaluations persist. The aphorism is true under those conditions and progressively less true as stakes rise, irreversibility increases, and social audiences accumulate.
Brain Food: Intelligence vs. Wisdom — read next: Nassim Taleb on ergodicity and the difference between ensemble averages and time averages — the conditions under which 'try again' is and is not available.
incompleteThe 'instincts right 95% / ideas wrong 75%' rule is offered as a memorable heuristic with no derivation, measurement, or falsifiability, and the instinct-vs-idea boundary is never operationally defined — so it is unclear how anyone could know their instinct (rather than their idea) was the part that was right, except in retrospect.
The hidden pattern behind successful products | Mark Pincus (Founder of Zynga) — read next: Calibration and intuition research (Kahneman, Klein on naturalistic decision-making); the difficulty of separating genuine intuition from post-hoc rationalization.
incomplete'Kill hope' / 'collecting winnings not making bets' risks premature abandonment. The conversation acknowledges both founders who 'stoically stick with a losing idea' and celebrated persistence-to-breakthrough cases (Rovio's 45 games; Pincus pulling his own Earth project four times), but never reconciles them into a stopping rule: when is refusing a B+ discipline versus quitting too early? The decision is left to an unspecified, intuitive 'signal'.
The hidden pattern behind successful products | Mark Pincus (Founder of Zynga) — read next: Optimal-stopping and explore/exploit trade-offs; Annie Duke on strategic quitting versus grit; how to price the option value of continuing.
missing evidenceThe exact prioritization formula is never disclosed. We learn the inputs (seven impact factors, urgency, confidence, effort) and that weights are tunable, but the mathematical relationship between these variables is not shared.
Assumptions as code: SiriusXM's approach to platform prioritization — read next: Look for SiriusXM conference talks or blog posts that may publish the formula; also consider whether the formula matters less than the conversation process it enables.
unexamined assumptionThe framework assumes that leadership can meaningfully assign numeric weights to impact factors. Whether leaders actually have the information and cognitive capacity to set accurate weights -- or whether the weights merely formalize existing biases -- is not examined.
Assumptions as code: SiriusXM's approach to platform prioritization — read next: Research on calibration in expert judgment (e.g., Tetlock's superforecasting work) and whether structured elicitation improves or merely formalizes biases.
missing evidenceThe assumptions-as-code system is described conceptually and architecturally (Git backend, AI surfacing, three modes) but no concrete examples of stored assumptions, their schema, or real validation outcomes are shown.
Assumptions as code: SiriusXM's approach to platform prioritization — read next: Look for open-source implementations or detailed case studies of the assumptions repository; consider building a prototype to test the concept.
scope limitationThe discussion focuses on prioritization within platform engineering but does not address how this framework interacts with prioritization in the product engineering teams that platform engineering serves. Cross-org priority conflicts are mentioned but not deeply explored.
Assumptions as code: SiriusXM's approach to platform prioritization — read next: Explore how platform engineering priorities can be formally linked to product engineering priorities, possibly through shared impact factors or dependency mapping.
unexamined assumptionThe 'builder persona' argument relies heavily on GitHub's 25% YoY user growth as evidence of non-developer adoption. This statistic could have many explanations (more professional developers, more student accounts, more bot accounts) and is not validated.
Assumptions as code: SiriusXM's approach to platform prioritization — read next: Examine GitHub's actual user demographics data; look for stronger evidence of non-developer builder growth in enterprise settings.
incompleteQvam repeatedly asserts that ownership structure (state, dispersed retail investors, private equity, Swedish industrial families) strongly shapes strategic discipline and calls PE 'an inspiration for everyone,' but this rests entirely on his own anecdotal board experience — no comparative data across ownership types, and no engagement with private equity's well-documented downside risks (leverage, extraction, short-termism).
Strategi som regi i møte med teknologi: En samtale med Walter Qvam — read next: extract-tim-oreilly-tidy-house#p13 (PE capturing AI efficiency gains as profit rather than reinvestment) and broader private-equity outcomes literature.
incompleteThe newsletter conveys Csaszar's core claim and recommendations but doesn't address how an AI-widened set of strategic options actually gets evaluated without hitting the same bounded-rationality constraint AI was meant to solve on the generation side — it asserts the conversation becomes 'sharper' without explaining the mechanism. The full underlying article, 'AI Is Revolutionizing Strategic Decision-Making' (hbr.org/2026/09/ai-is-revolutionizing-strategic-decision-making — not flagged Executive-only in a raw-HTML check; full-page/client-side access not verified since it was never saved), likely addresses this and is the natural next read.
Strategy Making Gets an AI Boost — read next: Extract hbr.org/2026/09/ai-is-revolutionizing-strategic-decision-making directly.
evidentiaryAll supporting evidence comes from retail, service, hospitality, and franchise contexts; the author acknowledges seeing 'similar principles' in other sectors but provides no cases outside these domains, leaving the framework's generalizability to knowledge-work, tech, or manufacturing undemonstrated.
How Fast-Growing Companies Can Make Better Decisions
methodologicalThe case studies are observational and retrospective; no controlled comparison or counterfactual analysis is presented. OXXO's tripling of stores and doubling of profitability is attributed to structured empowerment, but confounding factors (market growth, competitor dynamics, macroeconomic conditions) are not addressed.
How Fast-Growing Companies Can Make Better Decisions
conceptualThe boundary between 'structured empowerment' and well-designed delegation with guardrails is under-theorized. Many mature organizations already provide decision playbooks and measure outcomes; the article does not clearly distinguish where existing management practice ends and structured empowerment begins.
How Fast-Growing Companies Can Make Better Decisions
incompleteWritten in November 2025, the essay treats team capacity, cognitive-load limits, talent fungibility, and the explore/expand/extract lifecycle as stable, human-team constraints on governance design, without addressing how AI coding agents are actively renegotiating exactly these constraints (team throughput no longer scaling purely with headcount in the old way, individual 'talent fungibility' changing when agents absorb some context-transfer cost, etc.).
TBM 390: Governance by Principle, Not by Template — read next: This corpus's broader AI-era team-structure literature (e.g. extract-dora-ai-team-profiles-and-capabilities, extract-team-topologies) for how these same governance inputs are being revised.
incompleteThe essay asserts that durable units can be 'measured with KPIs, OKRs, service levels, risk signals, etc.' as if this measurement capability is simply available once an organization decides to adopt it, without engaging the extensive, separately-documented difficulty of building valid, non-gameable measurement for exactly these kinds of units (the same difficulty DORA/SPACE/DevX literature in this corpus wrestles with at length).
TBM 390: Governance by Principle, Not by Template — read next: extract-cannot-measure-productivity and extract-dora-community-discussion-dx-core-4 (this batch) for the measurement-validity problem this essay assumes is solved.
incompleteThe article doesn't address the organizational power dynamics that make stewardship habits hard to sustain. Who has the authority to insist on these practices? What happens when a new VP arrives and disrupts the habit? The argument assumes a continuity of leadership and culture that many organizations lack.
TBM 416: Investment Stewardship (As Habit) — read next: Literature on organizational routines and institutional memory (e.g., Nelson & Winter's evolutionary theory of the firm) would strengthen this argument.
incompleteThe Bayesian updating framework is invoked but never operationalized. What are the priors? What counts as evidence? How do you avoid anchoring bias in your priors? The metaphor does real work in the argument but remains at the level of analogy.
TBM 416: Investment Stewardship (As Habit) — read next: Douglas Hubbard's 'How to Measure Anything' provides practical methods for Bayesian estimation in business contexts that could concretize this.
incompleteThe article conflates two distinct audiences: engineering leaders who need to practice stewardship, and executives/finance who need to ask better questions. The five action steps mix both perspectives without differentiating who owns each behavior.
TBM 416: Investment Stewardship (As Habit) — read next: Exploring the principal-agent problem between engineering and finance would sharpen the argument about whose habit this needs to be.
incompleteThe PE aside is presented as a concession but actually undermines part of the thesis. If PE firms can arrive and quickly create clarity through explicit models, that suggests some form of cold-start IS possible -- just a reductive one. The author waves this away without fully engaging the tension.
TBM 416: Investment Stewardship (As Habit) — read next: A more careful treatment would distinguish between types of clarity: PE provides cost-side clarity but not value-side clarity, which is the harder problem the article is actually about.
incompleteThe base-camp/climbing-mode prescription is offered as a heuristic but not operationalized. How does a team decide when to switch modes? Who calls the switch? What happens when the climb reveals the plan was wrong — can you go back to base camp mid-climb?
TBM 419: Stop Being So Negative! Stop Being So Naive! — read next: Amy Edmondson's work on teaming and psychological safety; decision-making frameworks like RAPID or DACI for explicit mode-switching
incompleteThe gender and power dimension is raised but not developed. The piece acknowledges that the same words land differently depending on who says them, but does not explore how the base-camp/climbing framework interacts with power asymmetries.
TBM 419: Stop Being So Negative! Stop Being So Naive! — read next: Literature on voice and silence in organizations (Morrison, Milliken); intersectionality in organizational behavior
incompleteThe piece focuses on the interpersonal and team level. It does not address how organizational incentive structures (promotion criteria, performance reviews, leadership selection) systematically filter for outcome optimism and against capability optimism.
TBM 419: Stop Being So Negative! Stop Being So Naive! — read next: Research on organizational culture and selection effects; Jerry Harvey's 'The Abilene Paradox'; Chris Argyris on defensive routines
incompleteConstructive controversy research is cited but the conditions for cooperative framing are not specified. What makes a team able to disagree cooperatively rather than destructively?
TBM 419: Stop Being So Negative! Stop Being So Naive! — read next: David W. Johnson and Roger T. Johnson's constructive controversy research; Patrick Lencioni's 'The Five Dysfunctions of a Team'; Linda Hill's 'Collective Genius'
incompleteDesign is positioned as the 'truth-telling' frame because designers see the seams, but design's own blind spots and the cost of giving design more structural power are not examined. The essay grants design moral authority without scrutinizing it.
TBM 423: Why Defining Teams Is So Hard — read next: Jared Spool and Don Norman critiques of design's overreach; service design literature on the limits of journey-mapping; Tony Ulwick / jobs-to-be-done as a competitor frame that disputes design's own narrative.
incompleteThe entire framework depends on placing an asset on the value-created x carrying-cost 2x2, but the piece never operationalizes either axis -- no metrics, proxies, or even a single worked numeric example are given for how a team would actually locate a real asset on the diagram. Every judgment in the piece (Incubate vs. Compound, rational leverage vs. dangerous debt) presupposes an assessment the piece itself never shows how to make.
TBM 430: Incubate, Compound, Refinance, Liquidate — read next: extract-tbm-416-investment-stewardship-as-habit's argument that ROI understanding is 'continuously practiced behaviors, not a calculation' supplies the missing discipline (habitual signal-gathering) this piece assumes but doesn't build; DORA/Core 4 cost-of-delay proxies could operationalize 'carrying cost' concretely.
incompleteThe piece analyzes portfolio movement from a God's-eye systems view and never addresses the political economy of admitting an asset belongs in Liquidate or a Build Trap -- whose careers, teams, and budgets depend on the asset continuing, who has the authority to call the move, and what happens when the people who benefit from an asset's continuation are also the people who would have to authorize its liquidation. This is the same organizational-authority gap already flagged in extract-tbm-416-investment-stewardship-as-habit's open gaps about who has standing to insist on stewardship habits.
TBM 430: Incubate, Compound, Refinance, Liquidate — read next: extract-tbm-416-investment-stewardship-as-habit#g1 (authority to insist on stewardship habits); sunk-cost and loss-aversion behavioral-economics literature; organizational-debt concept.
incompleteEach of the four AI walkthroughs (Build Trap, Limits to Growth, Success Trap, Zombie Portfolio) is structured as a symmetric 'AI can help X, or AI can make X worse' pair, with no criterion offered for telling which effect will occur in a given organization before the fact. This is a more specific, but still undiagnosed, version of the same problem already flagged against extract-tbm-415-demand-mix-discovery-ai-dysfunction-multiplier (that Cutler treats AI as a uniform amplifier without a way to predict, case by case, which effect dominates) -- TBM 430 segments by portfolio position instead of by AI application type, but the underlying diagnostic gap (predicting the branch in advance) persists at the finer grain.
TBM 430: Incubate, Compound, Refinance, Liquidate — read next: extract-tbm-415-demand-mix-discovery-ai-dysfunction-multiplier (the sibling gap about AI's heterogeneous effects); decision-making concept for what a genuine ex-ante diagnostic would require.
incompleteThe piece never explains what determines whether a given Border Collie eventually becomes a Fox, stays a Border Collie indefinitely, hardens into a Purist, or exits -- no personal (temperament, risk tolerance, financial cushion) or organizational (seniority, sponsorship, gender/power dynamics -- a dimension the corpus has already flagged as underdeveloped in Cutler's other work) variable is offered to explain the branching, even though the whole piece is built around exactly this branching.
TBM 433: The Border Collie Faustian Bargain — read next: extract-tbm-419-stop-being-so-negative-stop-being-so-naive#g2 already flags Cutler's general pattern of raising power/gender dynamics without developing them; Hirschman-adjacent literature on who chooses voice vs. exit vs. loyalty under organizational strain would supply candidate variables (risk tolerance, alternative options, sunk investment).
uninformedThe five closing options for a Border Collie -- bark (voice openly), backchannel, herd locally (a form of loyalty-with-translation), leave (exit), conserve energy (neglect) -- map almost exactly onto Albert Hirschman's classic Exit/Voice/Loyalty(/Neglect) framework, yet Hirschman is never cited or drawn on, so the piece reinvents a well-studied typology from scratch and gets none of its accompanying theory (e.g. what makes exit costly, what makes voice effective) for free.
TBM 433: The Border Collie Faustian Bargain — read next: Albert O. Hirschman, 'Exit, Voice, and Loyalty' (1970) -- directly supplies the missing theoretical scaffolding for exactly the choice this piece poses without resolving.
uninformedThe post invokes the decision-quality/outcome-quality distinction (via the Decision Quiz) without ever naming or explaining it, assuming the reader already knows Duke's framework — the same pattern seen in her other Substack posts, which lean entirely on prior audience familiarity rather than re-explaining the underlying ideas.
You can have a conversation with Thinking in Bets now — read next: Duke, Thinking in Bets and Quit, for the actual argument behind 'separate outcome quality from decision quality' (resulting) that this post only gestures at.
evidentialNo evidence or examples are provided for the effectiveness of Duke's decision-making methods or the course format. The email relies entirely on the reader's prior familiarity with Duke's work.
Ask Me Anything! — read next: Read 'Thinking in Bets' (book) or Duke's other Substack posts for the underlying framework.
specificityThe email offers no specifics about course content, methodology, curriculum, or learning outcomes. 'Practical ways' and 'better calibrated' are the only descriptors.
Ask Me Anything! — read next: The Maven course page (linked) likely has a detailed syllabus.
audienceNo indication of who this course is for (experience level, profession, prior knowledge). The implicit assumption is that the Substack audience self-selects.
Ask Me Anything! — read next: Check Maven course listing for prerequisites or target audience.
incompleteAdler's four gap forms (uninformed/misinformed/illogical/incomplete) presuppose an argument to evaluate, but this excerpt supplies none — it is a toolkit table, a set of reflection prompts, a glossary, and acknowledgments. The evidence, worked examples, and inferential support for claims like 'quality of experience beats efficiency' or 'intentional imperfection produces better long-term outcomes' live in the book's main chapters, which were not captured in this Reader save. Nothing here can be assessed as backed by real mechanism or research versus inspirational framing, because no mechanism or research is presented at all — only vocabulary and prompts.
Tiny Experiments — read next: Read the book's actual chapters (not this back-matter excerpt) to evaluate whether the experimental-mindset thesis is supported by cited research or worked case studies, or is asserted by authority and narrative alone.
incompleteFor a reader coming from Annie Duke's 'Thinking in Bets' (calibration, resulting, treating decisions as probabilistic bets), this glossary's vocabulary overlaps Duke's territory — planning fallacy, sunk cost fallacy, cone of uncertainty, persist/pause/pivot (Plus Minus Next) — without importing any of Duke's formal apparatus. Across roughly 70 glossary terms, none mentions calibration, base rates, probability, expected value, or the decision-quality-versus-outcome-quality distinction that is Duke's central move. The two entries closest to a decision framework ('decision frame,' 'steering sheet') are described purely qualitatively, with no method for weighing options. Whether Le Cunff's book supplies that rigor elsewhere cannot be assessed from this excerpt; what is visible here is soft, motivational vocabulary that reuses cognitive-bias terms without the probabilistic mechanism a Duke reader would expect.
Tiny Experiments — read next: Compare against extract entries citing Annie Duke's 'Thinking in Bets' and 'Quit' on resulting and the decision-quality/outcome-quality split, to test whether Le Cunff's 'experimental mindset' adds mechanism beyond a softer restatement of the same territory.
incompleteThe inference payoff is asserted but never demonstrated. 'Project into a graph, where inference, validation, query and integration are already waiting' names four benefits without a single worked example of what an agent can now answer, or what a validation constraint would catch, that a plain markdown wiki could not. The most consequential claim in the piece — that the ontology overhead buys real capability — is left as a promise.
Tony Seale's Post — read next: Seek the linked Vault-LD spec for a concrete example, then pressure-test it against the counter-claim in the corpus (Karpathy: an LLM-maintained markdown index is sufficient for complex Q&A at ~100-article scale without any graph). Ask specifically: what query, inference or validation justifies the ontology, and at what vault size does flat markdown actually break?
incompleteThe prescription stops at self-diagnosis. It tells the reader to write down a name and ask the harder question, but never says what to DO with the answer: whether and how to act on a tolerated high performer before the numbers force it, how to weigh their irreplaceable contribution against the harm, or how to break normalization of deviance without simply waiting for results to fade.
When Nothing Happens — read next: Practical methods for holding high-performing-but-harmful people accountable while retaining their value; interventions that reset an eroded behavioral standard.
illogicalThe Steve Jobs rebuttal doesn't address the question it's answering. Adi's challenge was about trusting one's own vision over outside input during a strategic/creative call; Goldsmith's answer (Apple needed Cook's operational management to stabilize post-Jobs) is really about needing both 'artist' and 'soldier' leadership types in an organization -- a different claim that leaves the original 'when do you trust your gut' question open.
Why Great Leaders Need More Than Good Judgment — read next: Read against other treatments of Jobs's leadership style (e.g. any extraction touching founder-mode / visionary leadership) for a more direct engagement with when concentrated individual judgment outperforms broad consultation.
incompleteChoe's AI-persona technique (deliberately generate personas, including contrarian ones, to reveal blind spots) offers no method for verifying that a given persona actually represents an outside perspective rather than the leader's own priors reflected back through the model. This is the same self-undermining selection problem already flagged elsewhere in the corpus for a near-identical practice (picking advisor personas whose blind spots differ from your own presupposes you can already identify your own blind spots) -- unaddressed here despite being presented as a solved technique.
Why Great Leaders Need More Than Good Judgment — read next: extract-build-ai-advisory-board-gpts-decision-making#g4 states this problem directly for the closely related 'AI advisory board' practice.