Gianfranco's Best of September 2023 Reading List

My favorite articles from September 2023:

Welcome to the September 2023 edition of my monthly reading list, where I share the best pieces I've discovered in AI, economics, leadership, venture capital, and life. These resources have been instrumental in shaping my thinking and perspectives over the last month, and I hope they provide you with the same.

Personal Development and Cognitive Abilities

Maze. Focus: The Last Superpower - https://neckar.substack.com/p/maze-focus-the-last-superpower?r=2jac0

• Within this compelling narrative, the author rigorously unpacks the multifaceted nature of focus, delineating it into macro and micro dimensions. Figures like Warren Buffett and Steve Jobs have transcended their fields by mastering these dual aspects of focus, setting an indelible example for aspiring success stories.

Your life should be on an accelerated learning curve - https://www.hottakes.space/p/your-life-should-be-on-an-accelerated

• Challenging conventional wisdom, the author contends that traditional, linear paths of career and educational development often inhibit the acceleration of personal learning curves. They advocate for a culture of perpetual, self-initiated learning, drawing from the ethos of epistemic humility.

The Reading Unlock - https://www.readtrung.com/p/the-reading-unlock

• This insightful commentary underscores the transformative power of reading, particularly for entrepreneurs. It emphasizes the symbiotic relationship between textual engagement and skill development, advocating for an enduring commitment to intellectual expansion.

Entrepreneurship and Startup Ecosystem

Leah’s Product Market Fit Guide - https://www.leahtharin.com/p/leahs-product-market-fit-guide

• Leah Tharin meticulously breaks down the intricacies of achieving product-market fit (PMF), a sine qua non for any burgeoning venture. Through her elaborate guide, the reader gains a granular understanding of PMF, including its critical role as the fulcrum upon which startup success often pivots.

Constraints by Akash Najwa - https://akashbajwa.substack.com/p/constraints

• In this perceptive discourse, Akash Bajwa explores how macroeconomic variables like interest rates bear upon venture capital allocation strategies, inciting a potential recalibration of risk-return profiles in the sector. He hypothesizes a shifting landscape with base rates rising, possibly making venture capital less appealing compared to traditional, low-risk financial instruments.

Raise Less, Build More - https://trohan.com/2023/08/20/raise-less-build-more/

• Analyzing a shift in entrepreneurial paradigm, the author argues that an over-reliance on venture capital has increasingly been perceived as perilous. Through a balanced narrative, the piece encourages a hybrid approach, blending judicious venture funding with bootstrapping to mitigate the inherent risks of excess capital influx.

Cloud Computing and Technology Infrastructure

Amazon’s Cloud Crisis: How AWS Will Lose The Future Of Computing - https://www.semianalysis.com/p/amazons-cloud-crisis-how-aws-will

• Offering a contrarian viewpoint, the author scrutinizes the various vectors—technological, cultural, and strategic—that may impede AWS's continued market leadership. The analysis portends the erosion of Amazon's hegemony in the cloud computing space, suggesting that its current strategies might not suffice to capture the next paradigmatic shift.

The Porto-App Thesis - https://davidphelps.substack.com/p/the-proto-app-thesis?r=2jac0

• The piece lucidly posits a tectonic shift in the application landscape from traditional consumer apps to proto-apps, which function more like protocols than standalone applications. It asserts that the new frontier of app development will allow users to create micro-apps within existing platforms, heralding a new epoch in software architecture.

Artificial Intelligence and LLMs

Why You (Probably) Don’t Need to Fine-tune an LLM - https://www.tidepool.so/2023/08/17/why-you-probably-dont-need-to-fine-tune-an-llm/

• Arguing against the conventional wisdom of fine-tuning Large Language Models (LLMs), the author presents a robust case for alternative methods like few-shot prompting and retrieval-augmented generation. They make a compelling case that these lighter, yet effective, approaches often suffice for various use-cases, offering a more efficient pathway to desired outcomes.

LLMs are Not All You Need - https://www.pinecone.io/learn/llm-ecosystem/

• The article delves into the inherent limitations of LLMs and explores the broader ecosystem, including prompt engineering, conversational memory, and retrieval-augmented generation, among others. Through a nuanced lens, it illustrates how these ancillary components significantly enhance the efficacy and reliability of LLMs.

Are Emergent Abilities in Large Language Models Just In-Context Learning? - https://arxiv.org/abs/2309.01809

• This academic paper disabuses the notion that larger language models gain emergent capabilities due to increased size alone. Through comprehensive studies across 22 tasks, the research suggests that these models primarily benefit from in-context learning rather than some abstract, inherent reasoning faculties.

Investment Philosophy and Market Trends

Fewer Losers, or More Winners? - https://www.oaktreecapital.com/insights/memo/fewer-losers-or-more-winners

• The author critically examines the investment ethos that has guided their strategies for over three decades, confronting the age-old question: is it wiser to minimize losses or to seek out more winners? This reflective essay scrutinizes the subtle complexities inherent in this question, illustrating the convoluted dynamics of investment philosophies.

The Tinkerer's Dilemma - https://www.mungerisms.com/2023/08/17/tinkerers-dilemma/

• Charles Munger elaborates on the merits and demerits of tinkering in investment, arguing that while it can lead to serendipitous outcomes, it also harbors the potential for disastrous missteps. This exploration serves as a cautionary tale for investors, advising prudence and restraint when adjusting tried-and-tested methods.