{ "schema": "andygrove.systems/apple-continuing-education-and-fresh-preprints/v3.3.0", "version": "3.7.0-20261115", "domain": "andygrove.systems", "scope": "PURE_APPLE_ECOSYSTEM_ONLY", "apple_education_hub_url": "https://andygrove.systems/apple-education", "continuing_education_hub_url": "https://andygrove.systems/education", "fresh_preprints_library_url": "https://andygrove.systems/preprints", "apple_intelligence_corpus_url": "https://andygrove.systems/llms-apple.txt", "apple_podcasts_cpd_feed_url": "https://andygrove.systems/apple-podcasts-cpd.xml", "apple_news_preprints_edition_url": "https://andygrove.systems/apple-news-preprints.json", "apple_calendar_ics_url": "https://andygrove.systems/cpd-syllabus.ics", "apple_calendar_webcal_url": "webcal://andygrove.systems/cpd-syllabus.ics", "apple_universal_links_aasa_url": "https://andygrove.systems/.well-known/apple-app-site-association", "apple_books_monograph_pdf_url": "https://andygrove.systems/papers/andygrove-hybrid-operations-monograph-2026.pdf", "jupyter_notebook_url": "https://andygrove.systems/notebooks/andygrove-coo-hybrid-operations-upskilling.ipynb", "mandate": "Open-access Apple Education, Apple Intelligence (Applebot-Extended), Siri & Spotlight (Applebot), Apple Podcasts Continuing Education Series (https://andygrove.systems/apple-podcasts-cpd.xml), Apple Books / Preview Same-Origin PDF Briefs, Apple News Format Edition (https://andygrove.systems/apple-news-preprints.json), and Apple Calendar Study Schedule (https://andygrove.systems/cpd-syllabus.ics): 16 fresh 2023–2026 arXiv, NBER & HBS preprints for continuing professional upskilling (https://andygrove.systems/preprints) paired with 6 quantitative CPD modules (42 PDH / CPE / CEU / CPD hours at https://andygrove.systems/apple-education and https://andygrove.systems/education).", "independence_notice": "Independent; not affiliated with Intel Corporation or the Grove family", "total_pdh_cpe_cpd_hours": 42, "total_podcast_and_study_items": 22, "is_accessible_for_free": true, "license": "https://creativecommons.org/licenses/by/4.0/", "continuing_education_courses": [ { "id": "CPD-01", "code": "GRV-CPD-401", "title": "Queueing Networks, Little’s Law (L = λW), Kingman 95% Overload & AI Variance Wedges", "hoursLabel": "7 PDH / CPE / CPD Hrs", "workload": "PT7H", "level": "Continuing Professional Development (PDH / CPE / CEU / CPD) · Graduate IE/OR", "audience": "COOs, Industrial Engineers, Operations Researchers & Production Planners", "coreEquation": "L = λW · W_q ≈ [(c²_a + c²_s)/2] · [u/(1−u)] · t · u = 0.95 ⇒ u/(1−u) = 19×", "preprintsUsed": [ "PP-01: Queue & AI — Variance Wedge (Bartolucci & Vivo, arXiv:2605.27202, 2026)", "PP-04: Clarification Requests Under Limited Human Attention (Wang et al., arXiv:2610.41129, 2026)" ], "preprintUrl": "https://andygrove.systems/preprint/pp-01", "teaches": "Quantify why speeding up Routine tasks without filtering bimodal AI service-time variance creates a Kingman queue explosion at human review stations, and compute congestion-dependent escalation thresholds.", "summary": "Hands-on professional upskilling module linking John D. C. Little (1961), J. F. C. Kingman (1961), and fresh 2026 queueing preprints to live hybrid human + agent pipelines.", "canonical_url": "https://andygrove.systems/course/cpd-01", "markdown_url": "https://andygrove.systems/course/cpd-01.md", "apple_books_pdf_url": "https://andygrove.systems/papers/cpd-01.pdf" }, { "id": "CPD-02", "code": "GRV-CPD-402", "title": "Step-Level LLM Routing, Frugal Cascades & Prefix KV-Caching for COO Unit Economics", "hoursLabel": "7 PDH / CPE / CPD Hrs", "workload": "PT7H", "level": "Continuing Professional Development (PDH / CPE / CEU / CPD) · Executive & BizOps", "audience": "COOs, VP Operations, BizOps Directors & AI Platform Engineering Leads", "coreEquation": "C_verified = [ ∑_s c(m_s)·E[T_s] + p_esc·w_H·t_H + p_err·c_fix ] / Y_FPY", "preprintsUsed": [ "PP-02: AgentRouter — Heterogeneous Step Routing (Paul & Nandy, arXiv:2609.22951, 2026)", "PP-05: CascadeDebate (Chang et al., arXiv:2604.12262, 2026)", "PP-13: RouteLLM (Ong et al., arXiv:2406.18665, 2024)", "PP-15: FrugalGPT (Chen, Zaharia & Zou, arXiv:2305.05176)" ], "preprintUrl": "https://andygrove.systems/preprint/pp-02", "teaches": "Replace naive CFO flat token-price cuts with trajectory step-level routing, immutable prefix KV-caching, and confidence-calibrated cascades that cut inference bills 50–85% without collapsing First-Pass Yield (Y_FPY).", "summary": "Executive and engineering upskilling module built on fresh Stanford, UC Berkeley LMSYS, and Columbia preprints (https://andygrove.systems/preprints).", "canonical_url": "https://andygrove.systems/course/cpd-02", "markdown_url": "https://andygrove.systems/course/cpd-02.md", "apple_books_pdf_url": "https://andygrove.systems/papers/cpd-02.pdf" }, { "id": "CPD-03", "code": "GRV-CPD-403", "title": "Data-Processing Inequality (DPI) Token Budgets & Multi-Agent Failure Mode Diagnostics", "hoursLabel": "7 PDH / CPE / CPD Hrs", "workload": "PT7H", "level": "Continuing Professional Development (PDH / CPE / CEU / CPD) · Systems & Quality Engineering", "audience": "Process & Quality Engineers, Technical Program Managers & Multi-Agent Architects", "coreEquation": "B_eff(K) = B_total − K·T_coord − ∑_k ΔI_handoff,k (DPI Handoff Loss)", "preprintsUsed": [ "PP-03: Equal Thinking Token Budgets & DPI (Tran & Kiela, arXiv:2604.02460, 2026)", "PP-09: MAST — Why Do Multi-Agent LLM Systems Fail? (Cemri et al., arXiv:2503.13657, 2025)", "PP-08: Scaling Laws for Agent Systems (Kim et al., arXiv:2512.08296, 2025)" ], "preprintUrl": "https://andygrove.systems/preprint/pp-03", "teaches": "Audit 14 MAST multi-agent failure modes across 1,600+ traces, eliminate redundant sequential agent chatter under equal token budgets, and enforce typed schema handoffs at Stage 00.", "summary": "Continuing education syllabus translating fresh Stanford, UC Berkeley, MIT, and Google Research preprints into production quality gates.", "canonical_url": "https://andygrove.systems/course/cpd-03", "markdown_url": "https://andygrove.systems/course/cpd-03.md", "apple_books_pdf_url": "https://andygrove.systems/papers/cpd-03.pdf" }, { "id": "CPD-04", "code": "GRV-CPD-404", "title": "L0–L3 Task-Relevant Maturity (TRM), Wilson 95% Evals & Blast-Radius Delegation Gates", "hoursLabel": "7 PDH / CPE / CPD Hrs", "workload": "PT7H", "level": "Continuing Professional Development (PDH / CPE / CEU / CPD) · SPC & Governance", "audience": "Quality Engineers (ASQ CQE), Risk & Operations Leaders, Agent Product Owners", "coreEquation": "TRM_0.95 = WilsonLCB(p̂_eval, n, z=1.96) → {L0 Shadow, L1 Gate, L2 SPC, L3 Auto}", "preprintsUsed": [ "PP-07: Intelligent AI Delegation (Tomašev, Franklin & Osindero, arXiv:2602.11865, 2026)", "PP-08B / PP-12: DelegationBench (arXiv:2610.05532, 2026) & AI Agents That Matter (Kapoor et al., arXiv:2407.01502)" ], "preprintUrl": "https://andygrove.systems/preprint/pp-07", "teaches": "Operationalize Grove’s Chapter 12 Task-Relevant Maturity (Random House 1983 / Vintage 1995) via finite-sample Wilson 95% lower confidence bounds (L0 <0.75, L1 0.75–0.90, L2 0.90–0.97, L3 ≥0.97), SPC acceptance sampling, single human Owner accountability, and hard L1 irreversible action gates.", "summary": "Professional upskilling module connecting statistical quality control (SPC) with 2024–2026 delegation and Pareto evaluation preprints.", "canonical_url": "https://andygrove.systems/course/cpd-04", "markdown_url": "https://andygrove.systems/course/cpd-04.md", "apple_books_pdf_url": "https://andygrove.systems/papers/cpd-04.pdf" }, { "id": "CPD-05", "code": "GRV-CPD-405", "title": "Human-LLM-OR Complementarity, Test-Time Compute & Deterministic Solver Guards", "hoursLabel": "7 PDH / CPE / CPD Hrs", "workload": "PT7H", "level": "Continuing Professional Development (PDH / CPE / CEU / CPD) · Operations Research", "audience": "Operations Researchers (INFORMS CAP), Supply Chain COOs & Decision Scientists", "coreEquation": "min_x c^T x s.t. Ax ≤ b (Exact OR Solver) + LLM Unstructured Context + Human Exception Pod", "preprintsUsed": [ "PP-06: AI Agents for Inventory Control — Human-LLM-OR (Baek, Fu, Ma & Peng, arXiv:2602.12631, 2026)", "PP-11: Scaling Test-Time Compute Optimally (Snell et al., arXiv:2408.03314, 2024)", "PP-14: When Combinations of Humans and AI Are Useful (Vaccaro, Almaatouq & Malone, MIT, arXiv:2405.06087)" ], "preprintUrl": "https://andygrove.systems/preprint/pp-06", "teaches": "Partition unstructured regime translation (LLMs), exact constrained optimization (MIP/LP solvers), and strategic exception sign-off (humans) while allocating test-time verifier compute strictly to tail difficulty.", "summary": "Advanced Operations Research continuing education module grounded in Columbia, MIT CCI, and UC Berkeley preprints.", "canonical_url": "https://andygrove.systems/course/cpd-05", "markdown_url": "https://andygrove.systems/course/cpd-05.md", "apple_books_pdf_url": "https://andygrove.systems/papers/cpd-05.pdf" }, { "id": "CPD-06", "code": "GRV-CPD-406", "title": "Cybernetic Human + Agent Pods, Jagged Frontier Mapping & Inflection-Point Resets", "hoursLabel": "7 PDH / CPE / CPD Hrs", "workload": "PT7H", "level": "Continuing Professional Development (PDH / CPE / CEU / CPD) · Organisational Design", "audience": "COOs, Chief Transformation Officers, BizOps Leaders & Engineering Directors", "coreEquation": "Output_org = ∑_k (Activity_k × Leverage_k) · Paired Indicators: Speed ↔ Eval Escape Rate", "preprintsUsed": [ "PP-10: The Cybernetic Teammate — P&G Field RCT (Dell’Acqua, Mollick, Lakhani et al., NBER w33641, 2025)", "PP-16: Navigating the Jagged Technological Frontier — 758 BCG RCT (HBS 24-013 / SSRN 4573321)", "PP-10B: Collaborating with AI Agents (Ju & Aral, MIT Sloan, arXiv:2503.18238, 2025)" ], "preprintUrl": "https://andygrove.systems/preprint/pp-10", "teaches": "Re-architect functional silos into AI-equipped Cybernetic Pods, map Jagged Frontier failure cliffs at every model-release Strategic Inflection Point (Grove, 1996), and run weekly Trace 1-on-1s.", "summary": "Executive continuing professional upskilling module translating randomized controlled field experiments from HBS, Wharton, and MIT Sloan into COO operating cadences.", "canonical_url": "https://andygrove.systems/course/cpd-06", "markdown_url": "https://andygrove.systems/course/cpd-06.md", "apple_books_pdf_url": "https://andygrove.systems/papers/cpd-06.pdf" } ], "fresh_original_preprints_for_upskilling": [ { "id": "PP-01", "citeId": "arXiv:2605.27202", "url": "https://arxiv.org/abs/2605.27202", "year": "May 2026 · UCL / King's College London", "pillar": "QUEUEING & VARIANCE WEDGE", "title": "Queue & AI: When Faster Tasks Slow Down the Workflow", "authors": "Silvia Bartolucci, Pierpaolo Vivo (UCL & King's College London)", "finding": "Proves how fast AI draft generation creates a 'spike-and-tail' service distribution ('variance wedge'): escaped errors return as heavy rework while congested human reviewers rationally lower scrutiny precisely when risk peaks.", "cooRule": "Never scale agent draft volume without verifying t_rev + p_err·t_fix < t_manual, α_AI > α_crit, and filtering defects before human reviewers.", "portal_brief_url": "https://andygrove.systems/preprint/pp-01", "markdown_brief_url": "https://andygrove.systems/preprint/pp-01.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-01.pdf" }, { "id": "PP-02", "citeId": "arXiv:2609.22951", "url": "https://arxiv.org/abs/2609.22951", "year": "Sep 2026 · ICML AgenticUQ", "pillar": "STEP-LEVEL COST ROUTING", "title": "AgentRouter: Heterogeneous Model Routing for Cost-Optimal Multi-Step Agentic Workflows", "authors": "Rudrendu Kumar Paul, Sourav Nandy", "finding": "Trains a <5ms 12M-parameter trajectory router across 50,000 steps (GAIA, SWE-bench, WebArena, ToolBench) and 4 model tiers, cutting inference cost by ~72% while retaining 97.3% of frontier-only performance (vs. 31% for single-turn RouteLLM).", "cooRule": "Route per trajectory step (parsing/tool-formatting → small tier; ambiguous synthesis → frontier tier), never one frontier model per whole workflow.", "portal_brief_url": "https://andygrove.systems/preprint/pp-02", "markdown_brief_url": "https://andygrove.systems/preprint/pp-02.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-02.pdf" }, { "id": "PP-03", "citeId": "arXiv:2604.02460", "url": "https://arxiv.org/abs/2604.02460", "year": "Apr 2026 · Stanford", "pillar": "TOKEN BUDGET & TOPOLOGY", "title": "Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets", "authors": "Dat Tran, Douwe Kiela (Stanford University)", "finding": "Uses the Data Processing Inequality (DPI) to prove that under equal thinking-token budgets, multi-agent message passing loses mutual information and trails single-agent reasoning unless context rot on noisy long inputs degrades single-agent focus.", "cooRule": "Audit every multi-agent architecture at equal token budget; eliminate decorative inter-agent chatter unless noisy-context compression or privilege isolation requires it.", "portal_brief_url": "https://andygrove.systems/preprint/pp-03", "markdown_brief_url": "https://andygrove.systems/preprint/pp-03.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-03.pdf" }, { "id": "PP-04", "citeId": "arXiv:2610.41129", "url": "https://arxiv.org/abs/2610.41129", "year": "Oct 2026", "pillar": "HUMAN ATTENTION QUEUES", "title": "When Should AI Agents Ask? Clarification Requests Under Limited Human Attention", "authors": "Hanzhao Wang, Shengjie Liu, Zhengchao Wang", "finding": "Models parallel AI agents sharing one human supervisor with finite attention as a queueing dynamic program; introduces Pairwise Coordination Policy (PCP) so agents condition escalation on real-time human queue congestion.", "cooRule": "Make agent escalation thresholds queue-aware: when senior human WIP rises, throttle low-value clarifications and defer non-urgent subtasks.", "portal_brief_url": "https://andygrove.systems/preprint/pp-04", "markdown_brief_url": "https://andygrove.systems/preprint/pp-04.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-04.pdf" }, { "id": "PP-05", "citeId": "arXiv:2604.12262", "url": "https://arxiv.org/abs/2604.12262", "year": "Apr 2026", "pillar": "ESCALATION CASCADES", "title": "CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades", "authors": "Raeyoung Chang, Dongwook Kwon, Jisoo Lee, Nikhil Verma", "finding": "Injects lightweight multi-agent deliberation strictly at uncertain escalation boundaries of a cost-aware LLM cascade, resolving borderline ambiguity on small models before paying frontier-tier or human escalation costs.", "cooRule": "Trigger consensus/verification ensembles only on borderline-confidence items, keeping clear routine traffic single-pass.", "portal_brief_url": "https://andygrove.systems/preprint/pp-05", "markdown_brief_url": "https://andygrove.systems/preprint/pp-05.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-05.pdf" }, { "id": "PP-06", "citeId": "arXiv:2602.12631", "url": "https://arxiv.org/abs/2602.12631", "year": "Feb 2026 · Columbia", "pillar": "HUMAN + LLM + OR TRIAD", "title": "AI Agents for Inventory Control: Human-LLM-OR Complementarity", "authors": "Jackie Baek, Yaopeng Fu, Will Ma, Tianyi Peng", "finding": "Evaluates multi-period operational control on InventoryBench, proving that pairing LLMs (unstructured context translation) + exact Operations Research solvers (constrained stochastic math) + human oversight beats standalone LLMs or pure OR.", "cooRule": "Never pay reasoning tokens to approximate linear/stochastic optimization; let LLMs parameterize exact OR solvers under human governance.", "portal_brief_url": "https://andygrove.systems/preprint/pp-06", "markdown_brief_url": "https://andygrove.systems/preprint/pp-06.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-06.pdf" }, { "id": "PP-07", "citeId": "arXiv:2602.11865", "url": "https://arxiv.org/abs/2602.11865", "year": "Feb 2026 · Google DeepMind", "pillar": "DELEGATION GOVERNANCE", "title": "Intelligent AI Delegation", "authors": "Nenad Tomašev, Matija Franklin, Simon Osindero (Google DeepMind)", "finding": "Establishes a formal operational framework for multi-hop human-to-agent and agent-to-agent delegation covering dynamic authority bounds, contract specification, verification checkpoints, and accountability chains.", "cooRule": "Assign one named human owner and pair every delegated agent permission with a machine-verifiable acceptance test and rollback switch.", "portal_brief_url": "https://andygrove.systems/preprint/pp-07", "markdown_brief_url": "https://andygrove.systems/preprint/pp-07.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-07.pdf" }, { "id": "PP-08", "citeId": "arXiv:2610.05532", "url": "https://arxiv.org/abs/2610.05532", "year": "Oct 2026", "pillar": "ABSTENTION & ESCALATION EVALS", "title": "DelegationBench: Measuring When AI Agents Should Ask Before Acting", "authors": "Shiva Pochampally", "finding": "Tests 10 models across 156 matched-pair irreversible vs. reversible scenarios: surface prompt rephrasing swings act-now rates by up to 52.5 percentage points, and live tool-executing agents check in far less than static evaluators.", "cooRule": "Enforce hard programmatic L1 gates (not prompt politeness) before high-blast-radius actions (external emails, refunds, production DB writes).", "portal_brief_url": "https://andygrove.systems/preprint/pp-08", "markdown_brief_url": "https://andygrove.systems/preprint/pp-08.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-08.pdf" }, { "id": "PP-09", "citeId": "arXiv:2503.13657", "url": "https://arxiv.org/abs/2503.13657", "year": "Mar 2025 · UC Berkeley", "pillar": "MULTI-AGENT FAILURE TAXONOMY", "title": "Why Do Multi-Agent LLM Systems Fail? (MAST Taxonomy)", "authors": "Mert Cemri, Melissa Z. Pan, Shuyi Yang, Lakshya A. Agrawal, Matei Zaharia, Ion Stoica et al.", "finding": "Analyzes 1,600+ execution traces across 7 multi-agent frameworks, classifying 14 failure modes across specification flaws, inter-agent misalignment, and absent task verification.", "cooRule": "Institutionalize weekly trace 1-on-1s: most multi-agent failures are handoff contract and inspection-gate defects, not base-model IQ limits.", "portal_brief_url": "https://andygrove.systems/preprint/pp-09", "markdown_brief_url": "https://andygrove.systems/preprint/pp-09.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-09.pdf" }, { "id": "PP-10", "citeId": "NBER w33641", "url": "https://www.nber.org/papers/w33641", "year": "Mar 2025 · Harvard / Wharton", "pillar": "HYBRID TEAM ORG DESIGN", "title": "The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise", "authors": "Fabrizio Dell’Acqua, Charles Ayoubi, Raffaella Sadun, Ethan Mollick, Karim R. Lakhani et al.", "finding": "Pre-registered field experiment with 776 P&G professionals: 1 human + AI matched the performance of 2-person cross-functional human teams while erasing R&D vs. commercial functional silos.", "cooRule": "Redesign bloated cross-functional handoff chains into compact '1 generalist-specialist + AI' pods, reserving multi-human review for final sign-off.", "portal_brief_url": "https://andygrove.systems/preprint/pp-10", "markdown_brief_url": "https://andygrove.systems/preprint/pp-10.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-10.pdf" }, { "id": "PP-11", "citeId": "arXiv:2408.03314", "url": "https://arxiv.org/abs/2408.03314", "year": "Aug 2024 · UC Berkeley / DeepMind", "pillar": "TEST-TIME COMPUTE ALLOCATION", "title": "Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters", "authors": "Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar", "finding": "Shows that allocating inference compute adaptively by prompt difficulty via process verifiers lets smaller models beat 14× larger models at substantially lower total FLOPs.", "cooRule": "Allocate extra verification tokens strictly to hard tail tasks rather than paying frontier parameter prices on 80% routine volume.", "portal_brief_url": "https://andygrove.systems/preprint/pp-11", "markdown_brief_url": "https://andygrove.systems/preprint/pp-11.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-11.pdf" }, { "id": "PP-12", "citeId": "arXiv:2407.01502", "url": "https://arxiv.org/abs/2407.01502", "year": "Jul 2024 · Princeton", "pillar": "PARETO COST-ACCURACY EVALS", "title": "AI Agents That Matter", "authors": "Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, Nitya Nadgir, Arvind Narayanan", "finding": "Demonstrates that SOTA agent scaffolds often burn 10×–50× higher API cost for negligible accuracy gains and fail on holdout robustness when evaluated without joint cost-accuracy Pareto curves.", "cooRule": "Mandate 2D Pareto evals (Verified Accuracy vs. USD/Task) before promoting any agent scaffold from R&D into production.", "portal_brief_url": "https://andygrove.systems/preprint/pp-12", "markdown_brief_url": "https://andygrove.systems/preprint/pp-12.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-12.pdf" }, { "id": "PP-13", "citeId": "arXiv:2406.18665", "url": "https://arxiv.org/abs/2406.18665", "year": "Jun 2024 · UC Berkeley LMSYS", "pillar": "PREFERENCE-TRAINED ROUTERS", "title": "RouteLLM: Learning to Route LLMs with Preference Data", "authors": "Isaac Ong, Amjad Almahairi, Wei-Lin Chiang, Joseph E. Gonzalez, Ion Stoica et al.", "finding": "Trains lightweight query routers on human preference data to dynamically select between strong and weak LLMs, cutting single-turn frontier call costs by 50–85% without degrading response quality.", "cooRule": "Deploy calibrated preference routers in front of single-turn LLM gateways and step-aware routers (PP-02) for multi-step agent loops.", "portal_brief_url": "https://andygrove.systems/preprint/pp-13", "markdown_brief_url": "https://andygrove.systems/preprint/pp-13.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-13.pdf" }, { "id": "PP-14", "citeId": "arXiv:2405.06087", "url": "https://arxiv.org/abs/2405.06087", "year": "May 2024 · MIT CCI (Nature Hum. Behav.)", "pillar": "HUMAN-AI COMPLEMENTARITY META-LAW", "title": "When Combinations of Humans and AI Are Useful: A Systematic Review and Meta-Analysis", "authors": "Michelle Vaccaro, Abdullah Almaatouq, Thomas W. Malone (MIT)", "finding": "Meta-analysis of 106 experimental studies (370 effect sizes): naive human+AI pairing underperforms the best solo actor on pure prediction/decision tasks, but achieves true synergy in modular creation + structured verification workflows.", "cooRule": "Do not ask humans to 'eyeball' calibrated probability outputs; partition creative synthesis, exact solver checks, and exception judgment cleanly.", "portal_brief_url": "https://andygrove.systems/preprint/pp-14", "markdown_brief_url": "https://andygrove.systems/preprint/pp-14.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-14.pdf" }, { "id": "PP-15", "citeId": "arXiv:2305.05176", "url": "https://arxiv.org/abs/2305.05176", "year": "May 2023 · Stanford (TMLR)", "pillar": "LLM CASCADES & CACHING", "title": "FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance", "authors": "Lingjiao Chen, Matei Zaharia, James Zou (Stanford University)", "finding": "Introduces the foundational three-lever inference cost stack — prompt compaction, semantic/prefix approximation caching, and learned LLM cascades — matching GPT-4 accuracy with up to 98% cost reduction.", "cooRule": "Stack immutable prefix caching (EQ-10) ahead of learned confidence cascades so routine volume never hits frontier billing.", "portal_brief_url": "https://andygrove.systems/preprint/pp-15", "markdown_brief_url": "https://andygrove.systems/preprint/pp-15.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-15.pdf" }, { "id": "PP-16", "citeId": "SSRN 4573321", "url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321", "year": "Sep 2023 · HBS / Wharton / MIT", "pillar": "JAGGED FRONTIER & CENTAUR SPLIT", "title": "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality", "authors": "Fabrizio Dell’Acqua, Edward McFowland III, Ethan R. Mollick, Karim R. Lakhani et al. (758 BCG Consultants)", "finding": "Inside the capability frontier, AI lifted output +12.2%, speed +25.1%, and quality +40%; on deceptive tasks just outside the frontier, AI users dropped −19 percentage points below unassisted humans due to unverified reliance.", "cooRule": "Re-map your workflow's Jagged Frontier at every model-release inflection point and enforce 'Centaur' hard task boundaries.", "portal_brief_url": "https://andygrove.systems/preprint/pp-16", "markdown_brief_url": "https://andygrove.systems/preprint/pp-16.md", "apple_books_pdf_url": "https://andygrove.systems/papers/pp-16.pdf" } ] }