Lenny&#39;s Podcast

Our reading — their words are on the recording

0:04:40
Our reading
The only real case for product management as a specialist function is that it is a trade, not a qualification — something you get good at by doing.
Their wordsThe only argument as far as I'm aware for why you would want product management to be a specialist function is really it's a trade, not a qualification.
0:06:36
Our reading
The engineer-designer-PM pod ratio was an HR artifact of scaling, not something anyone showed a product needed.
Their wordssomewhere along the line this kind of HR ratio of like a pod popped into being. You know, every time you hire six engineers, you add a designer or you add a PM, you add an EM
design
0:07:10
Our reading
Hiring product managers by ratio infantilizes engineers and designers who were perfectly capable of deciding for themselves.
Their wordshiring so many PMs infantilizes the engineers and the designers who are perfectly capable of making good decisions, but just never had to because there was always a PM to babysit them.
design hiring management
0:09:53
Our reading
A company should hire a product manager only against a specific need, never by default.
Their wordswe articulate it that way to kind of force ourselves to remember that you don't hire a PM just for the sake of hiring one, you hire one with a really specific need.
0:10:36
Our reading
Product managers should be assigned to problems, not attached to engineering teams.
Their wordsrather than mapping PMs to teams where you kind of assume that the PM will always do that, we tend to map them to kind of problems or kind of like core projects.
management
0:14:22
Our reading
AI gives an individual contributor so much leverage that one very senior product manager beats three entry-level ones.
Their wordsyou can have one very senior PM across more things and they can be more efficient than having, you know, three relatively entry-level PMs.

Elizabeth Stone19 Jul 2026 · 1h 12m · 20 korrents

Why Netflix is betting on systems thinkers—not specialists—in the AI era | Elizabeth Stone (CPTO)

Elizabeth Stone

All 20 from this episode, along the running time →

Our reading — their words are on the recording

0:03:47
Our reading
The confusion about who does what is a storming phase every transformative technology causes, and AI is in the middle of it.
Their wordsI think anytime a new technology comes along, especially one that's as transformative as GenAI, you go through a storming phase before you go through the forming phase of things. And I think we are in the middle of that right now.
0:04:32
Our reading
That AI lets anyone prototype does not mean everyone should be shipping code to production.
Their wordsDo I believe that means anyone should be shipping code to production? That everyone should actually be doing everything? Probably not. But I think that it's good for people to be exploring what's possible.
0:12:26
Our reading
Craft excellence within each discipline is not going away, however much AI blurs the work across functional lines.
Their wordsI still see a craft excellence that's really important in the disciplines that I don't think is going away anytime soon. Even if there's fluidity or blurring of the work across the functional lines.
0:14:29
Our reading
What made Netflix fast — local teams building whatever stack they needed — stops working once agents operate across systems; common paved paths now matter more.
Their wordsIn a world of AI with agents operating across multiple systems, wanting source of truth data, the importance of having preferred paved paths that get the most of the benefits and produce some guardrails so we can make sure we're doing good work, common infrastructure, common paved paths, solving problems once with a core set of capabilities becomes more important.
0:14:53
Our reading
The rising hire in an AI world is the systems thinker who can look across every business domain and name the building blocks, not the local domain expert.
Their wordsSo, we are hiring more people who can look across all the business domains and abstract that to here's the building blocks we're going to need in a world with AI.
0:15:59
Our reading
Once non-designers can build products, a design team’s job becomes templates and design systems — otherwise the company ships Frankensteins.
Their wordsI get really nervous about having different design languages or different types of user interactions and shipping Frankensteins, basically. So, designers need to then be the people we're hiring again for design systems thinking.
design

Adam Mosseri9 Jul 2026 · 1h 08m · 20 korrents

The rise of taste, human authenticity and judgment in an AI world | Adam Mosseri (Head of IG)

Adam Mosseri

All 20 from this episode, along the running time →

Our reading — their words are on the recording

0:04:10
Our reading
Small product teams beat large ones for a reason that has nothing to do with AI: fewer people to coordinate means less design by committee.
Their wordsBut they just by virtue of having less people to coordinate they can often move faster and make um better decisions a little bit less design by committee.
design
0:08:31
Our reading
Designers will do well out of AI rather than badly, because taste is the hardest part of their job to imagine being automated away.
Their wordsyou've got these other generalists doing design, you've got engineers doing design, product staff doing design, but I'm actually pretty long on design or designers because they tend to have taste and I think that is something that is much more difficult to imagine being automated away.
design taste
0:14:42
Our reading
Grit, fast learning and self-awareness are enough to get good at anything eventually, and whenever somebody is not working out, one of the three is missing.
Their wordsBecause if you're those three things, if you got fire in your belly, you learn quickly, and you're self-aware, you can kind of get good at anything eventually.
0:17:40
Our reading
AI resets who succeeds at work: success goes to whoever's strengths happen to match what the new tools and the business need, not to who was good before.
Their wordsSo you know who who succeeds is a function of whose strengths are aligned with the tools needs and the business's needs.
0:22:30
Our reading
Within a year or two a strong engineer's token spend will rival their salary, and companies will have to cap it like any other resource.
Their wordsI think that you can imagine at least in an year or two coming that the the burn rate of a strong engineer might be the same as their salary or their cost of employment.
0:24:50
Our reading
Directing an AI will turn out to be like leading a team: too prescriptive stifles good ideas, too open-ended wastes time going the wrong way.
Their wordsI believe that if you are too prescriptive as a leader with a team you end up stifling good ideas but if you're too open-ended sometimes teams just waste time um going in the wrong direction.

Mark Pincus14 Jun 2026 · 1h 39m · 22 korrents

The hidden pattern behind successful products | Mark Pincus (FarmVille, Words with Friends, & more)

Mark Pincus

Our reading — their words are on the recording

0:04:29
Our reading
A founder's gut instincts are almost always right and the ideas built on top of them are usually wrong, so the work is to isolate the instinct and test many ideas around it.
Their wordswe have these instincts like in our gut, these human level instincts that are pretty much always right. And then we put these ideas on top of the instincts that are usually wrong. And my rule of thumb is your instincts are right 95% of the time.
0:08:17
Our reading
A team has not earned the right to innovate on a product until it is the world's leading expert on the best versions of that product that already exist.
Their wordsAnd I like to say we we don't we haven't earned the right to innovate on the camera until we are the world's leading PhD on the best mobile cameras that already exist.
0:10:10
Our reading
The genuinely new idea in a product will probably fail; its job is to give people a reason to try the product at all.
Their wordsAnd we have to accept that that new idea is probably going to fail. It's a reason to try it for people, but it's probably going to fail.
0:15:27
Our reading
The taboo against copying is a moral one, which is exactly what makes copying an arbitrage available to founders with less ego invested.
Their wordsin the Peter Teal sense it's almost a moral arbitrage because there's something in our gut as a product you you became a founder an entrepreneur because you wanted to go be an innovator and so it can feel like a beatdown that your path to innovation starts with copying other people's work
startups
0:16:17
Our reading
Ambition should be measured in the eyes of your customer rather than your peers, which means being willing to take the best ideas wherever you find them.
Their wordsif you're truly ambitious, burn your resume. If you and if you define your ambition in the eyes of your consumer, not your peers, you're not trying to win awards and respect from your peers.
ambition
0:17:04
Our reading
Taking something a person already loves and making it one inch better wins more of them than showing them something they have never seen and did not know they wanted.
Their wordsBut if you took something that she loves and you make it one inch better, she might love that more than if you showed her something she's never seen before and didn't wake up knowing that she wanted.

Tony Fadell7 Jun 2026 · 1h 35m · 23 korrents

Tony Fadell: How to build real taste (and why AI makes it matter more)

Tony Fadell

All 23 from this episode, along the running time →

Our reading — their words are on the recording

0:08:58
Our reading
A version 1.0 has no data to be driven by, so one or two people must make the opinion-based decisions and own them.
Their wordsAnd so, if most of your decisions are going to be opinion-based decisions for a 1.0, you have to have one or two or a very, very small set of people who are in charge with making the opinion-based decisions
0:10:41
Our reading
You cannot get real feedback on a new consumer product before shipping the whole thing; feedback starts when somebody spends their own money on it.
Their wordsAnd if you're doing a 1.0 and the world hasn't seen you, you're not going to get that from consumers, ever. You have to ship it, and you have to build the entire kind of ecosystem so those consumers see it in the fullness so that when they do the evaluation and they spend their own money, then you're getting real feedback.
0:14:29
Our reading
Micromanagement should be aimed at the few decisions that matter, never at how people carry the work out.
Their wordsAnd when I mean micromanaging, it means micromanage the decision, not necessarily operations of doing it.
0:19:22
Our reading
A home assistant is only as good as its context, and context comes from sensors placed around the house rather than from a better model.
Their wordsthe way you get best context is by having sensors properly placed around the home that don't necessarily invade privacy, but allow you to pick up a lot of comings and goings and who's who in the room and your voice and these kinds of things.
0:23:14
Our reading
What is worth building is found by starting from a long-standing pain and asking which new technology has just made it solvable.
Their wordsAnd so, I always kind of start with okay, where's our current pain and are there new technologies to solve that pain?
0:25:35
Our reading
What you have to invent is the whole system around a product, including how it is sold, bought and installed, not the one piece people remember.
Their wordsSo, you have to think about the full the full thing you're trying to build not just the one piece even though that's what you might remember. You have to remember it's it's a it's a system that you're going to innovate with.

Dan Shipper24 May 2026 · 1h 34m · 24 korrents

AI predictions: Job markets, Codex beats Claude, and the death of org charts | Dan Shipper

Dan Shipper

Our reading — their words are on the recording

0:00:40
Our reading
Every new model makes yesterday’s human competence cheap and therefore commoditised; the human job is to take that frozen competence and make something new with it.
Their wordsWhat models do in general is they make yesterday's human competence cheap. And so, it becomes commoditized. It's not valuable anymore. What humans do is we go in there and we're like, "Yeah, we have all this frozen human competence from yesterday. How do I use this like make something new and interesting?"
0:00:57
Our reading
Within about a year everyone will have at least one agent they talk to and offload work to.
Their wordsIt's going to bifurcate in two main ways. One is everyone's going to have at least one agent that they talk to, that they can offload work to.
0:12:05
Our reading
Most knowledge work will move inside a coding-agent environment like Codex or Claude Co-work, which becomes the operating system for email, documents and everything else.
Their wordsThe second is that most of the work that you do is actually going to happen on your computer in an environment like Codex or Cloud Co-work that becomes the sort of operating system for it becomes the sort of operating system for how how you do all of your work, whether that's your email, the documents you create, like all that kind of stuff.
Anthropic
0:13:44
Our reading
The workable model right now is one super agent for a whole company, not a personal agent for every person.
Their wordsAnd I have completely flipped. And I I really think that uh the the model for now is going to be a super agent, like one agent for the entire company.
0:14:44
Our reading
An AI agent is only useful while a specific human cares about it; sever that personal connection and it stops being useful.
Their wordsin order for an AI agent to be useful right now, it really needs a human who cares about it. It really needs it like a human personal connection with someone who's like watching what it does and make sure that it's doing the right thing and that it's useful for people.
0:21:03
Our reading
Codex, not Claude Code, is the right daily driver right now.
Their wordsbut I I think Codex right now is my daily driver. I like spend all all my time in it basically.
Anthropic

Eric Ries10 May 2026 · 1h 39m · 24 korrents

How Anthropic, Costco, and Patagonia all build incorruptible companies | Eric Ries

Eric Ries

Our reading — their words are on the recording

0:06:38
Our reading
Organizations are dragged into mediocrity by a force no one controls but everyone obeys, until their founders lose control of what they built.
Their wordsI call it the force that no one controls but everyone obeys that tends to drag organizations down into mediocrity to the point that we lose control of them. Now, sometimes we lose control of them because we get fired.
startups
0:08:15
Our reading
What destroys great companies is not competition but their own success, because the more golden the goose the greater the temptation to butcher it.
Their wordsall kinds of famous companies where the thing that destroyed them was not competition. It was not someone else came up with a better product. No, their very success became a liability because the more golden the goose, the greater the temptation to butcher.
0:12:12
Our reading
If you do not get your company's governance right, none of your other decisions will matter for the long term, because you will not be the one making them.
Their wordsIf you don't get this right, no other decision you make about your company will matter for the long term because you're not going to be the one making it.
0:12:20
Our reading
Only a fifth of venture-backed founders are still CEO three years after going public, and the standard documents their lawyers gave them are why.
Their wordsAccording to Harvard Law School, among venturebacked companies that have the standard best practices set up that you got from your lawyer, okay, only 20% of founders are still the CEO 3 years after going public.
startups venture capital
0:18:31
Our reading
There is never a right time to protect a company's mission: it is too early until it is suddenly too late, and success is what makes you a target.
Their wordsWas it ever the right time? No, it is never the right time to do this. If you put this off, you will eventually find yourself in a situation where you can no longer do it. You will have lost the leverage. Success will not protect you because success is what makes you a target.
0:23:58
Our reading
The corporate best practices lawyers and bankers push on founders are younger than the trees in the local park.
Their wordsso many of the best practices that your lawyers, your bankers, whoever advisers you have, they're going to be pushing best practices on you that are younger than the trees in your local park.
startups

Evan Spiegel26 Apr 2026 · 1h 10m · 22 korrents

How to win when software is not a moat | Evan Spiegel (Snapchat CEO)

Evan Spiegel

Our reading — their words are on the recording

0:04:42
Our reading
Distribution, not product-market fit, is the hard part of consumer technology, and almost nobody spends enough time on it.
Their wordsAnd I think people don't spend nearly enough time thinking about, you know, distribution and figuring out distribution. And that seems to me uh to be a huge differentiator.
0:05:25
Our reading
The value of a social network is in connecting you to the handful of people you care most about, not in how many people it connects.
Their wordswhat really mattered was connecting you to the right people. And so, if you could just connect someone not to all their friends, but to their best friend, to their partner, to their spouse, the people that they cared most about in the world, that that that's where the majority of the value is in the network.
0:07:00
Our reading
The next generation of great consumer companies will be born on new form factors like glasses, the way the last one was born on mobile.
Their wordsas we look forward to, you know, sort of these next generation form factors, things like glasses, there's going to be a whole new set of opportunities and a whole new surface for people to build generational consumer companies.
0:10:23
Our reading
Software is not a moat, and the AI industry is only now discovering what Snap learned fifteen years ago.
Their words15 years ago we essentially learned that software is not a moat, right? Which is something that everyone is discovering today with AI, right?
0:11:08
Our reading
A platform of creators and developers is a durable moat, because an ecosystem, unlike a feature, cannot be cloned.
Their wordsthe more that we could build a platform uh around our business, the more that we could build an ecosystem with creators and developers, the harder it would be for folks uh to to just copy that.
0:12:03
Our reading
Network effects are not a sufficient moat, because software features get copied however many patents you hold.
Their wordsnetwork effects are certainly an important piece of the puzzle for sure, um but I don't think they're enough when it comes to these sorts of software uh you know, software cloning.

Jason Cohen25 Jan 2026 · 1h 46m · 24 korrents

The surprising advice from a founder who built 2 unicorns | Jason Cohen (WP Engine)

Jason Cohen

Our reading — their words are on the recording

0:06:51
Our reading
Writing on a schedule is worth less than only publishing what is the best you can do, even if that means one post a year.
Their wordsmy attitude was was always I will only put out stuff if if it's the best that I can do. It's up to the reader to decide if it's good or useful. Um, and if so, if I don't have that, I'm just not going to publish.
0:11:00
Our reading
Diagnosing stalled growth has a strict order, because fixing a later problem does nothing while an earlier one is still broken.
Their wordsthere's a there's a series of of questions that I ask to to diagnose why is growth slowing in this order because it's one of these things where the first one that's a problem, if you don't fix that, it doesn't matter if you fix one of the ones below.
0:12:44
Our reading
A cancelling customer already survived an improbable gauntlet to buy, which makes churn an emotional emergency before it is ever a metric.
Their wordsThey actually had the budget and bought the stupid thing. Then they went through onboarding and invested their time etc etc. That is a crazy gauntlet that almost no one gets through.
0:15:38
Our reading
Cancellations always overtake marketing, because cancellations grow automatically with the company while marketing grows only as fast as you can improve it.
Their wordsMarketing grows only as fast as you can improve marketing. We all know that's quite hard actually. It's linear. It's hard to find new channels that aren't trivial. Like, it's hard. Of course, we're going to do it, but like it it's hard. Whereas, cancellations grow automatically as you grow, right? So, cancellations always overtake marketing for this reason.
0:16:42
Our reading
Every subscription business has a hard ceiling on its size: new customers per month divided by the cancellation rate.
Their wordsSo let's suppose you add 100 customers a month and you have 5% cancellation. So 100 divided by 5% is 2,000. So a company like that will never have more than 2,000 customers.
0:18:56
Our reading
A cancellation-reason drop-down measures list order rather than reasons: randomise the options and every one of them gets picked equally.
Their wordsAnd I realized it was the first one on the list and I thought, huh, I wonder if people are just picking the first one. So then then we randomized the list so everyone saw a different order of the list and now all the items were picked equally

Jason Lemkin1 Jan 2026 · 1h 42m · 24 korrents

We replaced our sales team with 20 AI agents—here's what happened next | Jason Lemkin (SaaStr)

Jason Lemkin

Our reading — their words are on the recording

0:00:30
Our reading
AI is not taking the jobs people wanted; it is taking the jobs nobody wanted and the careers of everyone who is merely average.
Their wordsAI is replacing the jobs people don't want to do today, and it is displacing the midpack and the mediocre.
0:09:33
Our reading
AI sales agents do not work out of the box; the training is the product.
Their wordsI think it's important that it takes time to train these agents. They don't work out of the box.
0:18:09
Our reading
In many software categories more than half of all prospects are now in market at once, against a historical three to five percent.
Their wordsSo, add all that up, 5%. In many categories, we're north of 50% in market. So, that just totally changes.
0:19:47
Our reading
The email-based sales development rep will be ninety percent displaced by AI within a year.
Their wordsThe emailbased cadence SDR will be 90% displaced by AI next year.
0:19:58
Our reading
Having a human qualify inbound leads is already obsolete, and the role should be extinct within a year.
Their wordsThe contact mess that we see, we have no need for them today. They should be extinct next year.
0:29:11
Our reading
Almost nobody should build their own AI go-to-market tools; buy them, the way you would buy any other software.
Their wordsDon't build it yourself. You're not Versel. You don't have a full-time wicked awesome engineer that wants to build this.

Dylan Field16 Oct 2025 · 1h 26m · 22 korrents

Figma’s CEO: Why AI makes design, craft, and quality the new moat for startups | Dylan Field

Dylan Field

Our reading — their words are on the recording

0:27:54
Our reading
Fun can be a product's differentiator, even when the team thinks that is an absurd thing to compete on.
Their wordsThe team was like what we're gonna make fun our differentiator and in retrospect it was absolutely the right move.
0:33:23
Our reading
After a decade of Swiss minimalism, design is about to swing back to something far more expressive.
Their wordsI think we're going to swing back to being way more expressive and draw as part of that story.
design simplicity
0:35:10
Our reading
AI cannot get a product to the quality bar on its own, because it still works in the realm of averages.
Their wordsAnd AI won't get you there alone. AI is uh still in the realm of kind of law of averages and uh better prompting can help of course
0:35:50
Our reading
In a product review a prototype beats a static mock, and a static mock beats a lot of words.
Their wordsat least my product reviews and uh product conversations, I feel like prototypes beat static mocks and static mocks beat lots of words.
0:40:24
Our reading
The thing to optimise at launch is time to value — how fast a new user reaches the moment that shows what the product can do.
Their wordsshortening the time to seeing and having that incredible moment and seeing, you know, the true value of the product, for example, in Figma design, can we get you to have a collaborative multiplayer moment?
0:42:22
Our reading
Striking down the boring blockers one by one moves retention and activation as visibly as any new feature does.
Their wordswe literally at some point had a team that would that was called blockers and they just went and one by one struck them down and each time we saw uh improvement in retention, improvement in activation the metrics for as we addressed each one you could literally see the change in the graph.

Albert Cheng5 Oct 2025 · 1h 25m · 26 korrents

Finding hidden growth opportunities in your product | Albert Cheng (Duolingo, Grammarly, Chess.com)

Albert Cheng

All 26 from this episode, along the running time →

Our reading — their words are on the recording

0:10:32
Our reading
A growth team that only explores turns scattershot, and one that only exploits saturates — the job is to keep oscillating between the two.
Their wordsIf you do too much exploration, you can have your team feel a little bit too scattershot, just trying a 100 different random ideas. What's the through line? What's the strategy? How do you pattern match, you know, successes across them? And if you do too much in exploitation, which is often the MMO of growth teams, it can lead to this like saturation and stagnation where you're just locally maximizing a thing.
0:12:03
Our reading
After a defeat people want encouragement, not a list of their mistakes — so a learning product should lead with what the user did well.
Their wordsWhen you lose a game now as opposed to surfacing your blunders and your like horrible stuff that you did, we flip it on its head. And so we show you your brilliant moves, your best moves. And we have coach say something encouraging. You know, losing just part of learning like keep it up, that type of thing. That change alone was pretty dramatic for us. It grew game reviews by 25%, subscriptions by 20%
0:13:34
Our reading
Only 30 to 50 percent of product experiments win, because consumer behaviour is too unpredictable to be predicted from a hypothesis.
Their wordsI mean in my experience the typical win rate and I hate to use that term for experiments is is often something like 30 to 50%. Like usually you're not actually get like you're trying a bunch of things a lot of hypotheses turn out not to be true. you know, consumer products are very unpredictable like that.
0:16:13
Our reading
A rising share of statistically insignificant experiments is the signal that a team has exploited an area too far and should go back to exploring.
Their wordsAnd if I'm starting to see like more and more experiments that are not statistically significant, that may be a signal to me to say, okay, we might have kind of tried to exploit a little bit too far. Like there might not be as much juice to squeeze.
0:17:36
Our reading
Most of the questions people have about a company's data are never asked, and giving them a machine to ask instead of a colleague uncovers them.
Their wordsAnd I think what's also kind of interesting is that just human nature is that if you have a question that you feel like, you know, you might be a bit embarrassed to ask or you don't want to bother someone, you just don't ask the question, right? And so by the nature of having these tools, you get actually a pretty large explosion of questions being asked.
0:19:48
Our reading
AI has made prototyping fast but has not yet joined it to the workflow, so at any real scale the handoffs between functions are still there.
Their wordsI would say though that something that is kind of annoying to me is that we haven't yet figured out the bridging from the tinkering to the workflow quite as seamlessly as I would like.

Julie Zhuo21 Sept 2025 · 1h 36m · 22 korrents

From managing people to managing AI: The leadership skills everyone needs now | Julie Zhuo

Julie Zhuo

Our reading — their words are on the recording

0:09:22
Our reading
Managing agents is the same job as managing people: fix the outcome, work out what your resources are good at, and design the process.
Their wordsmanagement is just about in my mind having an outcome. So you want to get something done. That's the thing. You have a northstar. You have a vision and you're just trying to figure out how to use the resources that you have to get that thing done.
design
0:10:14
Our reading
Models have personalities the way people do, and using them well means developing an intuition for which one to reach for.
Their wordsAnd you have to understand the strengths of of you know used to be people but now it's basically models and different models have different strengths. So it's like they have different personalities
0:13:49
Our reading
If you cannot say crisply what success looks like, you will not get great work — out of a team or out of an AI.
Their wordsif you're really unclear about what success looks like the prompt, you're probably not going to get the most amazing work. Um I think that's true for managing teams and it's very much true for managing AIs.
0:15:20
Our reading
AI will not make you world-class at a skill, but it takes a near-beginner to roughly the sixtieth or seventieth percentile of it today.
Their wordsbut if I was, you know, at the zero or 10th percentile, it can certainly get me even today very quickly up to like the 60th 70th in terms of um what the state of the art is.
0:15:36
Our reading
AI has made individuals capable enough that the boundaries between engineer, designer, researcher and product manager should be dissolved.
Their wordsAnd so the main thing that I felt so excited about and this is something I tell my team all the time is we need to dissolve the boundaries of these traditional roles.
design
0:17:17
Our reading
Putting a product manager on a team invites everyone else to delegate the product thinking away, which is why she does not hire them.
Their wordsI find that sometimes when you have like a designer or product manager and let's say I'm an engineer, then when I have a problem like, oh, I need to figure out the product definition, my default will be, well, I've got these people and that's kind of their job description, so I'm just going to delegate that to them.

Rahul Vohra23 Mar 2025 · 1h 25m · 24 korrents

Superhuman's secret to success | Rahul Vohra (CEO and founder)

Rahul Vohra

Our reading — their words are on the recording

0:10:12
Our reading
Referral growth is driven by whales — single users who invite fifty or a hundred people — not by many users each inviting a few.
Their wordswe've definitely found that there are whales to use the gaming terminology that one person is going to be responsible for inviting 25, 50, 100 people
0:15:25
Our reading
Everything a company can build is either solution deepening or market widening, and doing one means not doing the other.
Their wordsyou can classify anything that you build in a company into one of two categories. Solution deepening and market widening. Now solution deepening means making your product better for its existing users but not making it available to more users. Whereas market widening means making your product available to more users but not making the product itself any better.
0:17:35
Our reading
A company that looks to its users as though it has stopped shipping is usually widening its market, and grinding through that is worth it.
Their wordsBut this is the hard part. When you're when you're doing that market widening, you're not solution deepening. And so your perceived product velocity may decrease. You can avoid some of these things with some smart technology decisions, but mostly you just have to grind through it and it is worth it to get to the other side.
0:19:21
Our reading
A company deserves everyone in it, the chief executive included, to spend as much time as possible in their zone of genius — which is an org design problem.
Their wordsAnd number two, the superhuman opportunity deserves everyone who works at the company to spend as much time as possible in their zone of genius. And so that includes me as well as everybody else. So what I did is I hired a really great president. I went from eight direct reports to two.
design
0:20:46
Our reading
Your calendar records what you thought you were going to do; only a log of every task switch records what you actually did.
Their wordsSo, this is a technique that I call the switch log. And it's born out of the observation that your calendar says what you thought you were going to do, but it's really only your trail of work that describes what you actually did.
0:22:45
Our reading
The right way to work is to do whatever feels right for as long as it feels right, and read your time back at the end of the week — not to obey a timetable.
Their wordsWhat I'm saying is do what feels right for as long as as it feels right to do. Then at the end of the week you can see where your time is going.

Elena Verna19 Jan 2025 · 1h 35m · 26 korrents

10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey)

Elena Verna