📖 Book
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Paid
Beginner
Why we picked it
The single best thing ever written on customer conversations. It teaches you to ask about the customer's life and past behaviour, not your idea, so you can't be lied to. If a founder reads one thing before talking to a single customer, it's this.
From
momtestbook.com
by Rob Fitzpatrick
~130 pages
- Talk about their life, not your idea.
- Ask about specifics in the past, not opinions about the future.
- 'That's so cool, I'd totally buy it' is a compliment, not data, dig for commitment and evidence.
Open
momtestbook.com →
✍️ Essay
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Free
Intermediate
Why we picked it
Evans is the clearest writer on separating what a technology can actually do from the story being told about it. He repeatedly walks through how a new platform gets overhyped in the short run and misjudged in the long run, and how to reason about adoption instead of headlines. Reading a few of these builds the exact instinct a non-technical founder needs: judging a trend by what it changes for real users, not by demo excitement.
From
ben-evans.com
by Benedict Evans
10 to 20 min each
- A trend matters when it changes user behaviour, not when it trends on social media
- Ask what the technology reliably does today versus what it might do later
- Adoption curves and unit economics tell you more than launch demos
Open
ben-evans.com →
📄 Article
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Free
Intermediate
Why we picked it
Twice a year Evans publishes a free deck reading the macro and strategic shifts in tech, strong on second order effects, not just that something changes but what it drags along with it. It is a worked example of how a careful analyst separates a real structural shift from noise. Study it as a model for how to think, not only for what to think.
From
ben-evans.com
by Benedict Evans
90+ slides
- Big shifts matter most for their second order effects.
- Ground trend claims in data, not vibes.
- Copy the reasoning method, not just the conclusions.
Open
ben-evans.com →
📄 Article
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Free
Intermediate
Why we picked it
Willison is a working engineer who writes plainly about what the technology can and cannot reliably do, which is the single hardest thing for a non-technical founder to gauge. He is honest about hallucinations, confidently wrong answers, and the gap between an impressive demo and a dependable product. This gives you the vocabulary to ask a builder sharp questions about what breaks in production.
From
simonwillison.net
by Simon Willison
~30 min
- Capable models still produce confident wrong answers, so reliability is the real test
- A model that dazzles in a demo can fail on messy real inputs
- Treat these tools as fast but fallible assistants, not oracles
Open
simonwillison.net →
📖 Book
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Paid
Beginner
Why we picked it
Mollick is a business professor who tests AI hands-on and reports what actually works, not what a vendor promises. The book gives a non-technical reader a grounded, first-hand feel for where these tools are strong and where they quietly fail. That felt sense of the frontier is what lets you judge a claim rather than take it on faith.
From
Penguin Random House
by Ethan Mollick
~256 pages
- Form your own view by using the tools, not by reading takes about them
- Keep a human in the loop and define the exact job you want done
- Capability is uneven, so test on your real task before you believe a claim
Open
penguinrandomhouse.com →
📄 Article
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Free
Beginner
Why we picked it
This is the ongoing, free version of Mollick's testing: regular posts that run real experiments and show the results, good and bad. It keeps you current without drowning you in hype, because each piece is grounded in something he actually tried. A good habit is to read it before you form a strong opinion about a new capability.
From
oneusefulthing.org
by Ethan Mollick
10 to 15 min per post
- Evidence from real tests beats confident predictions
- The frontier moves fast, so revisit assumptions instead of anchoring once
- Watch what practitioners demonstrate, not what marketing announces
Open
oneusefulthing.org →
📄 Article
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Free
Beginner
Why we picked it
A short, neutral explainer of the pattern nearly every technology follows: a burst of inflated expectations, a trough of disappointment, then slow real adoption. Naming the pattern helps you locate where a trend actually sits instead of reacting to the peak of the noise. It is a simple lens you can apply to any hot technology, not just AI.
From
Wikipedia
~10 min
- Peak excitement and real usefulness rarely arrive at the same time
- Map where a trend sits before deciding how urgently to act
- The trough of disillusionment is often where the real work begins
Open
en.wikipedia.org →
📄 Article
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Free
Intermediate
Why we picked it
Our answer tells you to ask builders what breaks in production, and this is a concrete checklist of what that actually means. It lays out the technical questions serious evaluators ask about architecture, dependencies, and risk, in language a non-technical founder can follow. Use it as a script when you sit down with the engineers you trust.
From
startupctobook.com
by Gareth Price
~20 min
- Ask where the single points of failure and key-person risks are
- A trend is only real for you if it survives your production constraints
- Self-aware honesty about risk matters more than a perfect answer
Open
startupctobook.com →
📄 Article
✓ Link checked
Free
Intermediate
Why we picked it
When you want to go deeper than headlines, this is a curated map of the resources that actually explain modern AI, sorted from gentle introductions to market analysis. Instead of trusting one loud take, you can read the primary sources and form your own view. Treat it as a menu: pick the introductions and the market pieces, skip the heavy research papers unless you are curious.
From
Andreessen Horowitz (a16z)
by Derrick Harris, Matt Bornstein, Guido Appenzeller
curated list
- Go to primary explainers instead of secondhand hot takes
- A weekend of reading can make you conversant enough to judge claims
- Separate the introductory pieces from the deep technical papers you can skip
Open
a16z.com →
✍️ Essay
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Free
Beginner
Why we picked it
The definitive essay on where good ideas come from: notice problems you personally have, don't force it. Use it as the lens for judging whether your idea is a real problem or a solution in search of one.
From
paulgraham.com
by Paul Graham
~20 min read
- Live in the future and build what's missing.
- The best ideas look like bad ideas at first (schleps and hard-to-explain).
- Start with problems you have, in a domain you actually know.
Open
paulgraham.com →
📄 Article
✓ Link checked
Freemium
Intermediate
Why we picked it
This is the canonical piece that put jobs to be done on the map, written by the people who coined it. It uses the famous milkshake story to show that customers do not buy products, they hire them to make progress in a specific situation, which is the exact lens this question is about. Read it as the clearest short starting point before going deeper into JTBD.
From
Harvard Business Review
by Clayton Christensen et al.
~20 min read
- Customers hire a product to make progress in a specific circumstance, so the job, not the customer profile, is the unit of analysis.
- The same product can be hired for very different jobs, which changes how you build and market it.
- You find the job by studying the struggle and the context, not by asking people to rank features.
Open
hbr.org →
📄 Article
✓ Link checked
India
Free
Intermediate
Why we picked it
A trend only matters against the real market you are selling into, and this annual report is the clearest sober picture of India's startup and consumer reality. It grounds you in who your customers actually are and what they can pay, which is the antidote to importing global hype wholesale. Read it to pressure-test whether a shiny trend fits the India you are building for.
From
Blume Ventures
by Sajith Pai and team
~130 slides
- Global trends land differently against India's real income and buyer segments
- Ground a trend in your actual addressable customers, not a headline market
- Data about your market beats excitement about a technology
Open
blume.vc →
📖 Book
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Paid
Advanced
Why we picked it
Perez shows that real technological revolutions run on 50 year cycles with a predictable frenzy, a crash, and then a long deployment where the durable value actually shows up. It reframes bubbles as a normal phase of a real shift rather than proof it was fake. Read it to tell the difference between a passing mania and the early froth of something structural.
From
Goodreads
by Carlota Perez
224 pages
- Real revolutions include a bubble, so hype alone proves nothing
- Durable value shows up in the long deployment phase, not the frenzy
- Structural shifts follow a recognizable multi decade pattern
Open
goodreads.com →
