Complete references
References use APA 7 author–date conventions. Each chapter supplies the sources for its own claims; this list collects the cited works in one place. TFIS assets provide the primary project philosophy and maturity-model context. Research and university or laboratory publications support the specific claims described in the chapters, within their stated limits.
Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn? A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637. https://doi.org/10.1037/0033-2909.128.4.612
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), Article e2422633122. https://doi.org/10.1073/pnas.2422633122
Camuffo, A., Cordova, A., Gambardella, A., & Spina, C. (2020). A scientific approach to entrepreneurial decision making: Evidence from a randomized control trial. Management Science, 66(2), 564–586. https://doi.org/10.1287/mnsc.2018.3249
Camuffo, A., Gambardella, A., Messinese, D., Novelli, E., Paolucci, E., & Spina, C. (2024). A scientific approach to entrepreneurial decision-making: Large-scale replication and extension. Strategic Management Journal, 45(6), 1209–1237. https://doi.org/10.1002/smj.3580
Debenedetti, E., Zhang, J., Balunović, M., Beurer-Kellner, L., Fischer, M., & Tramèr, F. (2024). AgentDojo: A dynamic environment to evaluate prompt injection attacks and defenses for LLM agents [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2406.13352
Dell’Acqua, F., McFowland, E., III, Mollick, E., Lifshitz, H., Kellogg, K. C., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2026). Navigating the jagged technological frontier: Field experimental evidence of the effects of artificial intelligence on knowledge worker productivity and quality. Organization Science, 37(2), 403–423. https://doi.org/10.1287/orsc.2025.21838
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58. https://doi.org/10.1177/1529100612453266
Erik S., & Zhang, B. (2024, December 19). Building effective agents. Anthropic. https://www.anthropic.com/engineering/building-effective-agents
Grace, M., Hadfield, J., Olivares, R., & De Jonghe, J. (2026, January 9). Demystifying evals for AI agents. Anthropic. https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents
Gruber, M. J., Gelman, B. D., & Ranganath, C. (2014). States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit. Neuron, 84(2), 486–496. https://doi.org/10.1016/j.neuron.2014.08.060
Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Gutierrez, C., Kirrane, S., Labra Gayo, J. E., Navigli, R., Neumaier, S., Ngonga Ngomo, A.-C., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4), Article 71. https://doi.org/10.1145/3447772
Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2024). Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics, 12, 157–173. https://doi.org/10.1162/tacl_a_00638
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586
OpenAI. (n.d.). A practical guide to building agents. https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
Packer, C., Wooders, S., Lin, K., Fang, V., Patil, S. G., Stoica, I., & Gonzalez, J. E. (2023). MemGPT: Towards LLMs as operating systems [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2310.08560
Rajasekaran, P., Dixon, E., Ryan, C., & Hadfield, J. (2025, September 29). Effective context engineering for AI agents. Anthropic. https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Roediger, H. L., III, & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255. https://doi.org/10.1111/j.1467-9280.2006.01693.x
Rozenblit, L., & Keil, F. (2002). The misunderstood limits of folk science: An illusion of explanatory depth. Cognitive Science, 26(5), 521–562. https://doi.org/10.1207/s15516709cog2605_1
Stobierski, T. (2023, February 2). Cash flow vs. profit: What’s the difference? Harvard Business School Online. https://online.hbs.edu/blog/post/cash-flow-vs-profit
The Future Is Solo. (n.d.-a). The functional life (Field Manual No. 01). Retrieved September 10, 2026, from https://life.thefutureissolo.com/
The Future Is Solo. (n.d.-b). The future is solo. Retrieved September 10, 2026, from https://thefutureissolo.com/
The Future Is Solo. (n.d.-c). The meaning of life: A techno-optimist’s response. Retrieved September 10, 2026, from https://witmol.thefutureissolo.com/
The Future Is Solo. (2026). The Future Is Solo 20260910 [Google Slides presentation]. https://docs.google.com/presentation/d/1dvbBGs22QvtGogevZ6f5mf8YQE7WDAPBwYlclhTyctI/edit
The TFIS build deck records reported experiments and next steps. Its citation does not establish independent verification of implementation, learning effects, or commercial outcomes. The fictional Himura Inc. materials are original teaching specimens and are not empirical sources.