PLG was the backbone of some of the fastest-growing companies in history.

Slack grew by making team invites frictionless. Dropbox gave you free storage for every referral. Zoom let you host 40-minute meetings without a credit card. Those models worked because reducing friction was enough.

Pangram Labs ran AI detection on 30,000 front page Amazon reviews. About 3% were AI generated with high confidence, climbing to 5% in categories like beauty and baby products. Nearly three quarters of the AI generated ones were five star. 93% had the "verified purchase" label, so even that badge doesn't mean what people think it means anymore.

The part that got me: real reviews mention weird, specific, slightly annoying details. "The manual was useless but I figured it out." AI written ones glide right past that. They praise smoothly and specifically avoid describing anything a person would actually notice. Once you know to look for it, you start seeing it everywhere.

Feels like the entire signal system flipped. We used to tell people to be suspicious of typos and broken English. Now the polished, confident, well written review is the one worth a second look.

Anyone else noticing this in categories outside beauty and baby products? Curious where else this shows up.

What s the most ridiculous thing you ve blown startup money on?

Everyone has that regret: fancy office chair, logo redesign, too many SaaS subscriptions. What was yours?

Been running a lot of outreach experiments lately and the data keeps surprising me.

LinkedIn DMs are getting better reply rates across the board. But cold email still books more meetings when the list is tight and the message is specific.

Cold email inboxes are just flooded right now. AI-generated copy everywhere and buyers have learned to ignore it. Standing out is harder but the upside is still real if you get the targeting right.

LinkedIn DMs feel more personal so people actually respond. The problem is connection request limits and platform crackdowns are making it painful to scale.

Hey everyone - I'm launching my first product soon & would love to tap into this community's experience on price-setting for B2C software (ours helps job seekers).

The long story short is we ve been leaning towards entering a fairly established market at significantly lower pricing (like ~4-5x) than competitors. The thesis is this will help us A) attract users in general, and B) drive better unit economics by focusing more on capturing paid users and not focusing (much?) on total user acquisition (paid + free).

I am noticing two contradictories:

The growth of MVP services
The growth of AI tools that help you generate the code
What's the point of MVP services when the majority of people (with a very little knowledge about the code) can use AI to create their first MVP?
Yeah, you can now argument that some solutions are technologically more difficult and that's the point where the MVP service provider can step in, but anyway...

Each model is becoming more "skilled".

I'm asking because I don't have mine yet, so I have nothing to trade here except the question.

Every version of this story I read online is clean. Wrote a post, got a signup, closed a deal. The ones I hear in person are much messier and usually involve someone the founder already knew.

So which was yours? And the part I'm actually curious about, did customer number two come from the same place, or did you have to start from zero all over again?

I always assumed building the product would be the hardest part.

After launching my first app, I realised getting people to discover it is a completely different challenge.

For founders who've been through it, what was the biggest surprise after you launched?

I'm currently building in Social tech with no plan on integrating AI in the near future. It's an app that'll enable users to connect with others near them and attend/host events.

Are you also building a product that doesn't have an AI component? How has it been? Has the available funding decreased for "unsexy" ideas since the advent of AI?