Software and SaaS
Near-zero marginal cost, high gross margin and recurring revenue; the dominant destination for growth capital since the 1990s.
18 books · 34 people behind it · 1 reading lists
Start here
- The Hard Thing About Hard ThingsBen Horowitz, 2014
- Crossing the ChasmGeoffrey A. Moore, 1991
- The Mythical Man-MonthFrederick P. Brooks Jr., 1975
The people behind it 34
Each person holds a book in this entry or names it in their own tags.
Investors 5
ML
Mark Leonardnamed in their tagsPF
Philip Fishernamed in their tagsBM
Bill Millernamed in their tagsHH
Hamilton Helmernamed in their tagsJD
John Doerrnamed in their tagsFounders 12
Ben Horowitz1 book hereMB
Marc Benioff1 book here
Marc Andreessennamed in their tagsML
Mark Leonardnamed in their tags
Paul Grahamnamed in their tags
Peter Thielnamed in their tags
Brad Feldnamed in their tags
Reid Hoffmannamed in their tagsDH
David Heinemeier Hanssonnamed in their tagsGK
Guy Kawasakinamed in their tagsMD
Michael Dellnamed in their tagsSB
Safi Bahcallnamed in their tagsExecutives 3
AS
Andrew S. Grovenamed in their tagsML
Mark Leonardnamed in their tagsES
Eric Schmidtnamed in their tagsAcademics 1
Strategists 5
GA
Geoffrey A. Moore1 book here
Clayton Christensennamed in their tagsHH
Hamilton Helmernamed in their tagsDT
Don Tapscottnamed in their tagsSB
Safi Bahcallnamed in their tagsJournalists 8
SL
Steven Levy1 book hereBS
Brad Stonenamed in their tagsCA
Chris Andersonnamed in their tagsKK
Kevin Kellynamed in their tagsWI
Walter Isaacsonnamed in their tagsBM
Ben Mezrichnamed in their tagsDT
Don Tapscottnamed in their tagsRE
Randall E. Strossnamed in their tagsReading list — Essential Coding in the age of AI reading list · Superbook
10 books, in the order the list argues for
There is no canonical book on vibe coding yet. That is not a gap to apologize for — it is an honest description of where the field stands in 2025. The tools are changing faster than publishing cycles. What exists instead is a layered foundation: classics of software craft that turn out to matter more when an AI is generating your first drafts, and recent books on the nature and trajectory of AI systems that give that
Sources: Simon Willison's Weblog — AI-assisted coding posts · Ethan Mollick — One Useful Thing newsletter · Martin Fowler — Refactoring.com
Books 18
Frederick P. Brooks Jr. · 1975 · Why adding people to a late software project makes it later.
Harold Abelson, Gerald Jay Sussman · 1985 · The classic computer science text on abstraction and program structure
Geoffrey A. Moore · 1991 · The standard framework for technology adoption and go-to-market.
Marc Benioff, Carlye Adler · 2009 · Insider account of building Salesforce and the SaaS model.
Ben Horowitz · 2014 · Insider account of running and selling Loudcloud/Opsware.
Andrew WilkinsonNat EliasonAlfred LinAndrew ChenAustin RiefBen HorowitzBrad FeldBrett AdcockChris DixonDavid CancelDrew HoustonDustin MoskovitzFletcher RichmanJenny ChuJensen HuangJulie StevanjaJulie ZhuoKathryn MinshewKeith RaboisLuis von AhnMarc AndreessenMarty CaganMatt MullenwegMax LevchinNate O'BrienNick MolnarPeter AttiaPeter ThielRaoul PalReid HoffmanSam AltmanSatya NadellaSophie BakalarSuperbook reading listsTobi LütkeZack Onisko
G. Pascal Zachary · 1994 · The making of Windows NT as an anatomy of a large software project.
T3 Softwar
Matthew Symonds, Larry Ellison · 2003 · Oracle and Larry Ellison, with Ellison's own annotations.
Jessica Livingston · 2007
John Ousterhout · Ousterhout's core argument — that the enemy of good software is complexity, and that complexity accumulates through shallow abstractions and tactical shortcuts — maps directly onto the failure mode of AI-assisted development. Models are natural tactical coders. This book argues for the strategic counterweight.
unverified
T3 Clean Code
Robert C. Martin · Controversial in its prescriptiveness, but useful precisely because AI-generated code routinely violates every heuristic in it: long functions, unclear names, mixed levels of abstraction. Reading Martin sharpens the vocabulary for what to fix in a model's output, even if the rules are applied with judgment rather than dogma.
unverified
Ethan Mollick · Mollick is a Wharton professor who studies AI adoption and writes with unusual clarity about working alongside LLMs across knowledge domains. The framing — that effective AI collaboration requires treating the model as a co-worker with specific strengths and specific blind spots — is the most practically useful frame available for day-to-day coding with AI.
unverified
T3 Refactoring
Martin Fowler · Fowler catalogued the moves that make existing code better without changing its behavior. In an era where AI generates working-but-messy first drafts, refactoring is the core skill: not writing code from scratch, but reshaping what you have. The catalog of smells is a better prompt-debugging tool than most people realize.
unverified
Brian Christian · Christian's account of the gap between what AI systems are optimized for and what we actually want from them. The alignment problem is not only an AI-safety abstraction — it surfaces in every code generation session where a model produces code that passes the tests but misses the intent. The book builds the conceptual vocabulary for understanding why.
unverified
Mustafa Suleyman · Suleyman co-founded DeepMind and writes about AI and synthetic biology as a combined wave of general-purpose technologies. Relevant here not as a how-to but as context: the tools that make individual developers dramatically more productive are part of a broader technology transition whose governance is still unsettled.
unverified
David Thomas, Andrew Hunt · The craft baseline. Hunt and Thomas articulate the discipline that AI code generation does not replace: knowing when code is good enough, how to automate the right things, and what a working developer owes to the person reading the code next. The 20th-anniversary edition updated the examples but the reasoning has not aged.
unverified
Tracy Kidder · Kidder embedded with a Data General engineering team in 1978 and wrote the definitive account of what it feels like to ship hardware under pressure. The human dynamics — the obsession, the burnout, the pride in craft — are unchanged. Reading it alongside AI tools is a reminder that the emotional texture of building things has not been automated away.
unverified
Nadia Eghbal · Eghbal's study of open-source maintainers is partly about how software gets made and partly about what happens when a solo creator's work becomes infrastructure for thousands of strangers. Both questions bear on AI-assisted development: the tools lower the floor for creating software, but the maintenance and social questions remain human.
unverified