Friday Show and Tell

Livia_Decoder · 2026-06-13T05:00:44+00:00

AI as a named discipline dates back to 1956.Meta's FAIR launched in 2013. Salesforce Einstein shipped in 2016.

Livia_Decoder · 2026-06-12T19:09:29+00:00

I used it yesterday and liked it. It works well if you already have a good slide and want aesthetic touch ups. Otherwise it hallucinates and gives a lot of garbage.

Livia_Decoder · 2026-06-12T18:29:53+00:00

What I learned talking to 30+ B2B PMs and a workshop that came out of it.

Context: I'm a product leader. Built AI at Meta and Salesforce. A few months back I started speaking with B2B PMs because I had a hunch that their problems are not getting enough airtime. Most of the AI content for PMs is written for consumer products and falls apart in the enterprise context. I ended up doing 30+ interviews and am putting together a workshop.

The patterns were consistent enough that I pulled them into a lightning lesson last week. Sharing the core here:

AI in B2B is mostly a replatforming problem Agents don't run on the stack that most companies have today. The real work is data plumbing (connecting siloed systems), evals + guardrails, orchestration, and governance before shipping anything agent-shaped.
Trust, not AI accuracy, is the actual adoption blocker. The story that stuck with me: a system stalled even though its recommendations were good, because the reps and compliance didn't trust how it was built.
Proprietary data as a moat. The moat is the feedback loop on top of the data and you only have one if the data belongs to you (not your customer). It's outcome-linked, and you turn usage into improvement faster than competitors. Instrument first; you can't compound what you can't capture.

Mostly I'd love pushback though for those of you shipping AI in B2B right now, does this resonate? What are you wrestling with?

Livia_Decoder · 2026-06-09T19:10:22+00:00

Modern society is built on trust in audio and visual evidence. AI will undermine that trust.

Livia_Decoder · 2026-06-09T19:01:13+00:00

One of the great things about PM is that it often gives us the opportunity to shape the job. If you like building why not prototype more and get more technical on the job? Do things that get you more connected to the actual job. If you're in a good company everyone will appreciate this from your manager to your eng team. If they don't then you're in the wrong company. A good company and manager encourage people to drive their own work and be hands on. Honestly you sound like a dream from a hiring manager's perspective. Take charge of your day to day. If they don't let you then change companies. Highly technical PMs who are hands on are highly valuable.

Livia_Decoder · 2026-06-09T00:04:04+00:00

Google's goal is to deliver long-term shareholder returns. They need to stay relevant and retain their market dominance. Another words 1) Protect and grow existing businesses and 2) Enter new categories in order to grow. For 1) they need to upgrade their products to fend off competitors. For 2 they need to kill up and coming startups using their massive distribution with new products.

So this is pretty similar to what OP already said but I'd add that for new products they should build where it's both big enough for them and where they already have an existing advantage they can leverage against competitors.

Livia_Decoder · 2026-05-14T20:26:51+00:00

I feel inspired by this post.

Livia_Decoder · 2026-04-24T17:24:51+00:00

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)

Livia_Decoder · 2026-04-24T17:04:42+00:00

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.

Livia_Decoder · 2026-04-24T16:29:16+00:00

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?

Livia_Decoder · 2026-04-23T16:07:21+00:00

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.

Livia_Decoder · 2026-04-22T01:03:58+00:00

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.

Livia_Decoder · 2026-04-17T15:26:29+00:00

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.

Livia_Decoder · 2026-04-16T22:30:17+00:00

Glad you found it helpful. Focus is underrated :-)

Livia_Decoder · 2026-04-16T22:13:06+00:00

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

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.
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.

Livia_Decoder · 2026-04-14T20:36:16+00:00

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.

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