CHAPTERS
- 0:00 – 0:41
Why 7% weekly growth is the wrong starting point pre-PMF
Dalton tees up the common startup myth that if you aren’t growing 7% week-over-week right after launch, you’re failing. Michael reframes the conversation: before product-market fit, growth targets can mislead because they distract from whether the product provides real value.
- •The "7% week-over-week" rule is often misapplied by very early startups
- •Pre-PMF companies should not anchor on growth graphs as primary evidence of progress
- •The core question is whether the product helps users achieve their goals
- 0:41 – 2:04
Big-company analytics instincts break when you only have a few users
Michael describes founders coming from Google/Meta who want to show graphs and A/B tests despite having only a handful of users. Dalton argues that post-PMF tactics don’t translate to the zero-to-one phase, and can even put experienced operators behind scrappier teams who focus on fundamentals.
- •A/B tests and dashboards are meaningless with tiny sample sizes
- •Weekly growth metrics are noise when user count is near zero
- •Operators trained in mature orgs may need to "unlearn" analytics habits
- •Early-stage advantage: focus on learning, not measurement theater
- 2:04 – 2:35
What 7% growth advice is actually trying to enforce (retention + value)
Michael concedes that 7% weekly growth can be a useful heuristic—but only as an outcome of doing many things right. The advice implicitly punishes churn and highlights that true growth becomes hard if users aren’t sticking around.
- •7% growth is easier in absolute numbers when you’re small
- •Sustained growth is difficult if users churn quickly
- •Growth should be treated as a result, not the cause, of a working product
- 2:35 – 3:06
Metric cheating and "good-hearting": how founders fool themselves
Dalton and Michael discuss how founders pick metrics that can be manipulated (like cumulative signups) to appear successful. They warn that doctoring metrics only delays confronting the truth: users aren’t getting value.
- •Founders often switch to vanity metrics to hit targets
- •Cumulative signups can hide churn and low engagement
- •"Good-hearting" a metric creates self-deception and wasted time
- •The goal is truth about value delivered, not optics
- 3:06 – 4:38
The "gotcha" user-value questions that reveal you have zero users
Michael shares a line of questioning: how much usage is required for users to get value, and how much are they actually using it? Founders often aren’t measuring or discover usage is far below what’s needed—leading to the punchline that they effectively have “zero users.”
- •Define the usage needed for a user to get value
- •Compare required engagement vs. actual engagement
- •Common failure modes: not measuring at all, or tiny usage levels
- •If users aren’t getting value, user count is effectively zero
- 4:38 – 5:31
Pre-PMF goal setting: get a few people to genuinely love the product
They argue that the right early goal is not graphs but love: do you have 3, 5, 10 people who would be upset if the product disappeared? Direct conversations with users are the primary instrument; the “love” signal precedes scalable growth metrics.
- •Replace growth goals with “how many users love this?”
- •Talk to users instead of staring at dashboards
- •If 100 people truly love a product, growth usually follows naturally
- •It can take time to learn what to build that earns love
- 5:31 – 6:40
Gmail’s early playbook: slow user adds, relentless iteration for love
Dalton tells the Paul Buchheit/Gmail story: build for yourself first, then add users one or two at a time while continually improving based on feedback. Gmail spent months under 100 users, but those users depended on it—setting up eventual breakout success.
- •Gmail began with the creator as the first user
- •Added users very slowly and iterated tightly on feedback
- •Months with <100 users can be normal—even for legendary products
- •The target is dependency and delight, not immediate hockey-stick growth
- 6:40 – 8:12
The fundraising trap: building for investors instead of users
Michael voices the founder anxiety: investors want graphs, and building something lovable takes time. Dalton calls this self-defeating—once the “customer” becomes the investor, product decisions degrade and the company’s odds of long-term success drop.
- •Founders can mistake fundraising needs for product goals
- •Treating investors as the customer leads to bad strategy
- •You might “win” a seed round while increasing long-term failure risk
- •Culture damage: deferring user focus becomes habit-forming
- 8:12 – 8:52
Why user obsession beats pitch optimization (and YC’s counterexamples)
They contrast teams that optimized for demo day with teams that kept working on customer value after mediocre fundraising. Dalton notes many companies succeed later by following the user-love path, outperforming batchmates who competed on hype and valuation.
- •Some startups fail at demo day but win by focusing on customers afterward
- •Pitch/valuation competition is a poor substitute for value creation
- •Time spent with customers improves hypotheses and product direction
- •Long-term outcomes favor user-centric execution
- 8:52 – 10:42
Stripe’s slow launch and the "Collison install" as pre-PMF superpower
Dalton explains that Stripe didn’t publicly launch for a long time and added customers slowly, because payments required depth and reliability. The founders’ hands-on onboarding—sitting with customers to implement—created exceptional early value and loyalty.
- •Stripe took years before broad public launch; early access was controlled
- •Some products can’t be safely “MVP’d” in weeks (e.g., global payments)
- •White-glove onboarding and direct implementation support can be decisive
- •Early customers felt intensely cared for—even by the CEO
- 10:42 – 13:07
Use AI to learn the real timelines of great companies—and reset expectations
Michael argues that “pop wisdom” about instant hypergrowth persists because research used to be hard. With AI making history and case studies easier to access, founders should test beliefs against real origin stories and timelines of top companies.
- •Common startup narratives overemphasize speed and hockey-stick graphs
- •AI lowers the cost of researching early stories and timelines
- •Compare assumptions to reality: time to launch, 100 users, meaningful revenue
- •The "fastest to $100M" race isn’t the only—or best—frame
- 13:07 – 15:03
Actionable pre-PMF goal framework: 1 → 10 → 50 → 100 users who love you
They close by directly answering the goal-setting question: start by ensuring you have one real user with no asterisks, then aim for 10, 50, and 100 users who genuinely love the product. Only around that point should you “graduate” to graphs, and love should be defined carefully to avoid cheating; where possible, quantify customer benefit (e.g., GMV, income, revenue dependence).
- •Set goals around real, loving users: 1, then 10, 50, 100
- •Start using graphs seriously after reaching ~100 true lovers
- •Define “love” explicitly to prevent vanity reinterpretations
- •Prefer measuring customer benefit (dollars/time saved) over proxy metrics
- •Examples of benefit metrics: marketplace GMV, seller income, restaurant revenue share, streamer livelihoods
