Wilson Ding

Zynga sold for $12.7 billion. Its founder says startups fail due to this: They try to innovate too early (unless its deep tech). Mark Pincus calls it the “All new fails” rule: 1/ Don’t start with “new”: Start with what’s already proven to work. Copy it legally, study it deeply. 2/ Proven means 10 out of 10 users say: “Yeah, that works.” If they don’t, it’s not proven. 3/ Founders fail because they skip the proven phase: They chase novelty before they’ve earned the right to innovate. 4/ The Zynga story began with poker: Same table, same felt, same gameplay, nothing original. 5/ Then they made it better: No downloads. Just click and play. Friction cut in half. 6/ “Better” means 10 out of 10 users agree: Not your team, not your investors, real users. 7/ Only after proven and better do you earn “new”: That spark that surprises and delights users emotionally. 8/ Zynga’s “new” was showing your friends’ faces: It made games social, not solitary. 9/ Forget MVP, build a minimum viable idea: A concept that hits emotional resonance before metrics. 10/ Seek true signal: You’ll feel it viscerally before you can measure it. 11/ Silicon Valley worships originality: But originality without resonance is noise. 12/ Better founders are great students first: They copy what works before they try to change it. 13/ Most products die from founder ego: They’re “new” before they’re useful. 14/ Users don’t care about novelty: They care about ease, trust, and delight. 15/ “Proven → Better → New just works. And it built a $12.7B company. 📩 Get ai + vc intel in your inbox via my newsletter Startup Riders, link under my name ☝️ 📌 Source: Mark Pincus on Why Most Startups Fail a16z #ai #llms #agent #startups #founder #vc

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Hot take: I don't think MCPs are here to stay. I think AI will be smart enough to use the tools and services we have today (API, SDK, UI) to do anything a human/dev can.

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a friend just texted me: "Hearing there are 'senior devs' who are very skeptical of AI generated code (aka not using any AI tools) at a startup I’m interviewing with… interesting that they are still employed in a startup against this backdrop [see image]" my response: seems like there are just different games being played those who embrace AI as a medium for expressing ideas are playing the right game — they're not just treating it as a "better tool" if you've been in industry awhile, you may need an intellectual reboot to see it last night i went to a VentureBeat event; mostly enterprise folks… several people (from IBM, Walgreens, etc) asked about “change mgmt” and how to convince their teammates to adopt AI tools… someone raised the question of brown-field vs green-field opportunity w/r/t AI… is it worth it to upskill existing workers or better to just start fresh…? Great question! for people entrenched in a job fiefdom, e.g. where the power players and how to execute reliably, the perceived risks of novelty are infinite. they're not wrong, they're just not interested in changing the game they've mastered. aaand also, developers are the self-disclosed laziest people in the world. but they're incredibly productive because they'll happily spend a week automating some repetitive task so they never have think about it again. this is why generative AI and coding go so well together: the existing developer culture despises inefficiency, and is always looking for ways to cut corners in favor of a more elegant, efficient route. this is because developers are incredibly sensitive to the tiniest impediment to achieving persistent flow states. it's like noticing a typo in that green-on-black matrix screensaver and instead of ignoring it as a minor irritant, they'd prefer to rewrite the screensaver from scratch to fix it. but for non-developery roles, this gameplay is totally foreign. maintaining those impediments ensures job security. since they know how to navigate around known obstacles, their tacit knowledge is an insurance policy against obsolescence — for now. so it makes that sense that some senior devs, whose sunk costs could represent hundreds of thousands or millions of $$$ into existing, _stable_ systems might resist the quasi-non-deterministic output of AI coding tools. except (as they say) there is more than one way to skin a cat. here's a new game to invite resistants to play: have the skeptical senior devs review several blind PRs, all submitted by “new,” unfamiliar contributors (a mix of humans and AIs), and see if they prefer the human or AI-generated code. if the AI code really is as good as many of us believe, the experience might snap them out of their sunk-cost fallacies. because until you surprise yourself with what these tools can actually accomplish—in a blind taste test—you’ll never really see what winning this new game looks like, even if it's happening right in front of your eyes.

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