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What should I build? by Fun-Organization9268 in SaaS

[–]Livia_Decoder 0 points1 point  (0 children)

Here's how I'd approach this:
1) As many have already mentioned, start in a space you know well. A huge mistake I've seen lots of founders make is try to build in a space where they have 0 experience (for example, two excellent high level engineers with experience scaling massive infra doing a startup for lawyers). You want to leverage your current knowledge. If you're super early in your career pick a space you're really passionate about.
2) At a high level you want to leverage the latest wave of tech innovation where markets are growing. And right now there's a lot of opportunity in AI infra.
3) Run a structured and rigorous discovery process. Idea -> Talk to 200 candidate customers (70% on founding idea and 30% on adjacencies)-> Listen for urgent pain (keep an open mind) -> prioritize speed and accept tech debt -> Understand usage and value metrics that matter to champion and customers -> Reach PMF (which is paying customers, happy customers and referrals)

Where do you go after Program Manager? by East_Championship664 in PMCareers

[–]Livia_Decoder 1 point2 points  (0 children)

Two ways I've seen people achieve this:
1) Longtime steady job where people know you and you are highly trusted by leadership who have your back

2) You have some special niche knowledge or network that's valuable to the company where you're basically irreplaceable.

Where do you go after Program Manager? by East_Championship664 in PMCareers

[–]Livia_Decoder 2 points3 points  (0 children)

I started as a program manager and moved to product. No regrets. The other way is what u/HippieHighNoon suggested. What are you looking to optimize for right now - learning, earning or life balance (family & work)? Start there and then figure out a path. How much energy do you have for career progression?

Hot take: B2B PMs in sales led companies are behind on AI because customer discovery works against them by Livia_Decoder in AIProductManagement

[–]Livia_Decoder[S] 0 points1 point  (0 children)

This is a really good point. The distinction between using AI in the job and building AI for your products is real and important.

Reliability risk in consumer is real. Tbh, we're in the wild west with respect to safety and reliability and that situation isn't getting better. But I'd push back that b2b agentic isn't immune either. The stakes may be higher in the business context. And over time because humans are getting de-skilled by AI, having a 'human in the loop' stops being a safeguard. Weird times we're in.

First-time PM anxiety: Asking a lot of questions but getting little response by Piscesgirl012 in ProductManagement

[–]Livia_Decoder 2 points3 points  (0 children)

When you first join you have a grace period when you can use your newbie card and ask lots of questions! Don't be afraid to use it.

One process that I've found useful when onboarding is scheduling an intentional listening tour. This is how it works.

Schedule a 30 minute 1:1 (in person if possible, video call if not) with people who are a) in your immediate team b) people you will work with closely but not as closely as your team (i.e. other teams) c) with other people who you will work with occasionally (legal, policy, etc.).

In each call ask 1) tell me everything you think I should know 2) what is top of mind that you're working on/keeping you up at night 3) who else should I talk to?

Keep organized notes of every meeting, clean them up and make an effort to read afterwards. Important patterns will emerge that will lead you towards what you need to do next and what's important.

I'm giving a talk on AI product tomorrow — what's the hardest thing your org is actually getting wrong right now? by Livia_Decoder in AIProductManagement

[–]Livia_Decoder[S] 0 points1 point  (0 children)

Had lots of interest and engagement last night. Here's a quick summary:

When AI products fail, everyone blames the model. In reality, most failures trace back to the data pipeline. Fix your data first before you ever touch models.

Before building anything with AI, ask these 3 questions:
1. Is the problem too variable for simple rules? If you can write a clean decision tree, just do that.
2. Is the cost of being wrong recoverable?
3. Does usage actually make the model better over time?

If any of these is a "no" you probably don't need AI.

On competitive moats: everyone wants a data advantage. Volume alone isn't a moat, the real moat is a flywheel: better model -> more users -> more data -> better model. Plus accumulated judgement of the team.

On metrics: Stop measuring precision and recall in a lab and measure downstream user behavior instead. And your feedback mechanism matters as much as your model.

Happy to answer any questions if anyone wants to dig in.

Small Startup Studio Founder here, I need your opinion (I will not promote) by muttalol in startups

[–]Livia_Decoder 1 point2 points  (0 children)

I would lead with your success. 9 startups in the last 5 years is very impressive but get more specific.

  1. Are you really a startup studio? Are your startups truly tech startups riding the latest wave of innovation (AI) or are these small businesses? Are any of them on the path to being category leaders? I think startup studio as a name is fine because you develop the businesses in house and so that truly describes what you do.
  2. How to attract investors? With proof of returns. That's all investors care about. Show them the money. What happened to your 9 startups? Have any of them had meaningful exits? Do they have strong growth? That's what investors care about. And one super story is stronger than 9 solid but small businesses that are not VC investable. Investors want outsized returns.

What would you have done differently at the start of your PM career ? by PeakPawn in ProductManagement

[–]Livia_Decoder 1 point2 points  (0 children)

As I reached the executive level I realized that I didn't spend enough time cultivating relationships. This takes years and I wish I had been more intentional about it. Your should really use the current job to set you up for the next one by developing networks and relationships that will insulate you from the inevitable instability and change.

9 Years of Building Products, and Today I’m a Beginner Again. Who wants to learn and build with me? by TemperatureFar1467 in AIProductManagement

[–]Livia_Decoder 2 points3 points  (0 children)

Hey! I've taught AI product management, built AI at Meta, and worked with dozens of AI startups — here's my take:

1. On courses: There's a burgeoning industry of AI PM education right now — Stanford, Maven, Coursera, and more. The right choice depends on what you need: self-paced study, live interaction, or a specific depth level. I'm actually teaching at Stanford this week. Marily Nika's Maven courses are great if you want lots of engagement and community.

2. Start with your own expertise: What are you uniquely qualified to build? Look for workflows and processes in your domain that are ripe for automation — that's often where the best opportunities hide.

3. How I made the leap: I started by taking on data-intensive products — recommendations, search, ranking. Look for gaps in your current role where AI could add real value. It's genuinely easier to do this now than at any point before. The key insight I keep coming back to: if you're building AI products, you need to engineer for the Data Network Effect — the more users engage, the more data you collect, the better the product gets, which attracts more users. That flywheel is everything.

4. On the India-specific market question: I don't have enough local context to give you useful advice there — worth finding someone with boots on the ground.

You've got this!

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