This is 20 Product with me, Harry Stebbings.
Now 20.
Product is the monthly show where we sit down with the best product leaders to reveal their tips, tactics and strategies to building great products and product teams in a world of AI.
I'm thrilled today to be joined by Nom Levinsky, CPO at Superhuman, formerly Grammarly.
Prior to Superhuman, Nom was a Senior Director of Product Management at Facebook, and in his earlier years he was a CPO at Thumbtack and spent five years as a Director of Product Management at Google where check this out?
He was responsible for all of YouTube's applications.
But before we dive into the show today, you know what's wild?
It's 2026 and so many product teams are still flying blind, buried in spreadsheets, chasing feedback across 10 different tools, trying to figure out what actually will move the needle.
I've spoken with hundreds of product leaders and the best teams all do one thing differently.
They build a system to capture ideas, validate them with real data and focus their roadmap on the right things.
That's why product teams at Canva, Deliveroo, Toast, Decathlon use Jira Product Discovery.
It pulls ideas and feedback into one place with built-in tools to prioritize what will actually have the biggest impact.
That's when a roadmap stops being an endless list of ideas and becomes a plan people actually believe in.
Join more than 20,000 teams already using Chira Product Discovery.
Head to Atlassian.com forward slash Harry.
Oh, I like it.
I get my name in there.
Atlassian.com forward slash Harry and start building the right thing today.
After Atlassian helps your team build and ship great products.
Intercom helps you support the customers using them.
If you're looking for a way to transform your customer service, let me introduce you to Finn, baby.
Finn is the number one AI agent for customer service, resolving up to 93 of customer queries automatically.
There is no other agent that can do that.
Not 93% of customer queries, okay?
No other agent can do that.
So why choose Finn?
Finn is the best performing AI agent for CS.
Finn doesn't just answer questions.
It takes actions.
It automates the most complex customer queries, like refunds, transaction disputes, technical troubleshooting, with speed and reliability.
I wish my team was speedy and reliable, Beats every competitor in every head-to-head bake-off.
Completely configurable and code-optional setup.
My word.
I mean, the benefits just go on and on.
It's easy and efficient implementation.
It works on any help desk with no tedious migration needs.
It's trusted by over 6000 customer service leaders, including top AI companies like Anthropic, Lovable Synthesia, Clay.
Hey, Vanta.
So if you're ready to transform your customer service team, scale your support and give team members time to focus on the really high level strategic work.
Learn more about Finn at finnai forward slash 20VC.
While Fit scales your support without losing speed, Reforge shows you how to translate that scale into durable, product-led growth.
Everyone's shipping faster than ever.
Cursor, Cloud Code, Codex, AI is making code and writing code faster than ever.
But here's the problem.
Speed means nothing if nobody uses what you ship.
That's where Reforge comes in.
Reforge is building the product discovery engine that sits upstream of your coding agents.
Not another prototyping tool, research repo or AI interviewer, but a product that will number one, ingest your customer data.
Number two, generate variations of product solutions.
Number three, validate the solutions before code is written.
And number four, hand off winning directions to your team.
Reforge kills product debt before it starts, because every unused feature you ship isn't just wasted engineering time, it's a maintenance burden, complexity tax and surface area that you cannot shrink.
Used by product teams at companies like Toast Vimeo, Klaviyo and many more.
Reforge helps teams ship more features that actually get used.
Try Reforge at reforge.com forward slash build and use the code 20VC, that's 20VC, for one month free of pro.
You have now arrived at your destination.
Norm, I'm so excited for this, dude.
I've heard so many good things.
I was literally just making you incredibly uncomfortable beforehand.
Normally with venture investors you say that you've got great references and they're like no, stop it.
Tell me more.
But you're like legit, like, no, it's making me uncomfortable, which shows your humility.
But thank you so much for joining me today, dude.
Oh, it's my pleasure.
Thank you for having me.
Not at all.
But I want to start with a little bit of a story, because I spoke to Glenn at Redfin before and he said you came into Redfin when they had a couple of different product leaders and you answered a question that he always remembers.
The question that you answered is, what is a product leader?
And he said the description that you gave was very simple.
But it was phenomenal.
And so when I ask you what is a product leader and a great product leader, what is that description?
I mean, I think fundamentally, a great product leader is a great storyteller, someone that is able to understand what the customers actually need, what problem that actually needs to be solved, and can form that into a story that is just well understood and well aligned not only with the market and the customer, but that gets everyone internally to row in the same direction.
There's all these interesting debates right now about roles collapsing and product and marketing and so on.
I just really don't see these things as separate.
I see product and marketing and, you know, it's the same thing.
And so maybe that's where where this comes from.
I think that fundamentally, a good product leader is just an excellent storyteller, and the best companies and the best brands are excellent storytellers.
This is where we don't send schedules in advance, because I just kind of lose interest in them the minute you say anything.
Our teams are like, we spent hours doing this research, Harry, and then you just go rogue.
But you said storyteller.
I get that.
But the challenge when you're a horizontal product is different customers resonate with different stories.
How do you think about being a great storyteller when you have such a broad customer base with a horizontal product?
That's an excellent question.
Not to like return some of the flattery to you really shows like kind of a depth of understanding that you have.
That I don't think is always common amongst this crowd.
So I appreciate that.
I think that you you go to different things, right?
You go to what is the feeling that you're trying to create?
What is the?
After your features are delivered, after you solve each of the micro problems, what is the overall feeling that you're leaving the customer with?
How do they feel supported?
How do they feel more in the flow?
Whatever you're going after.
I think a good example of that is Instagram.
Was Instagram about the features of the specific problems or was it more about catering to the feeling of the need for vanity?
Essentially, was the kind of the.
I think the product insight is that, you know, vanity is a much bigger market than we realized it was.
You know, latent demand, that kind of tapped into.
That's the feeling that you're that you're trying to address.
What is a bad story that product leaders often tell, do you think?
What do you see?
I mean, maybe this is like close to my heart right now, but I'm pretty tired of the like this is going to make you more productive story or this is going to make you like faster story.
I think we like lean to things like time and productivity when we don't know what the value is, what the true value is.
And so we're like, well, that's a catch-all.
It will save you time.
People want to save time.
Let's see if time spent will be the, the thing that resonates, you know, rather than kind of going a little bit deeper and understanding like, what problem are you actually tapping into beyond the, the feeling of, you know, wanting to be quick, wanting to be in the flow, and that's an anxiety problem.
A lot of people suggest that we're going to lose the design phase in a world where vibe coding and prototyping is so much quicker and real.
Do you agree with that?
No.
You know, it's interesting.
So many of these things that we're like talking about as if they're new things.
I don't I don't think they're very new.
I think what's changing is that the tool set is accelerating things that have been happening for a long while.
Like I'm sure, in all of your investments.
Like the best teams are one where like, talent is collapsed,
In a smaller number of people, people wear many different hats.
The idea that, like you, are a designer and therefore you stay in these lanes and you do these tasks and you don't do engineering work or you don't do product work.
Just like doesn't make sense.
Like it doesn't work that way.
And I get that as we scale.
There's the idea that, like everyone, needs to specialize and kind of get deeper in their lane.
I just I just don't think that that's ever been true.
And now the tools are making it even less true, because to specialize and do well in these, in these areas, to to be a unicorn that can do many things.
You're not required to learn the syntax or the tooling or the tactics of doing any one of those jobs in the way that you have before.
So is the design phase going away?
No, like you still have to do that sort of thinking.
And there are many different tools for doing that sort of thinking.
And sometimes.
But as a designer today, I had the CPO of Duolingo on the show.
He was fantastic.
And they said about actually chess and the integration or introduction of that from two designers.
And they vibe, coded it in a couple of days and then brought to life a working kind of prototype that everyone could play with.
Is there any excuse for a designer to bring a design anymore when you could vibe code your idea into reality as efficiently in the same amount of time?
So, I mean, that's interesting.
As efficiently in the same amount of time.
I don't know that where you are and kind of the design thinking stage necessarily leads to building a prototype is as efficient.
I think of what you're asking is, if you want people to empathize and understand your idea and feel your idea in the in the highest signal way, should you give them the highest fidelity approximation of that idea that you can, as quickly as possible.
That i would say yes, that is necessary as as a designer you know you should be able to produce that prototype, produce that working product If you have the time and the means when you're trying to get other people to empathize with your idea.
But that doesn't mean that when you're starting your thinking process, that maybe a whiteboard is actually like the best place to start, or a blank sheet of paper or a Figma canvas that works in the dumb old way that they used to work.
I just, I just think that the design thinking is can still benefit from those different mediums, those different ways of doing the thinking, because of the constraints that they apply, because of the space that they create.
I think that's different than saying hey, if you're at the point where you want to share your idea and get people to empathize with your idea as deeply as as they possibly can, what's the best way to do it?
Yeah, like have them use the thing and feel it in the highest fidelity approximation that you think it should be, and so that might be a working, working product or a working prototype, but i don't think that means that all design thinking should start in a vibe coding platform.
What tools do you see used most often within the product teams today?
Is it CASA?
Is it Cloud Code?
Is it Cognition?
What are you seeing internally that's interesting or surprising?
In terms of the AI coding, definitely Cloud Code is the far and away.
And I think just a lot of people make the transition from.
I want to start with something that has more familiar UX, like Cursor, and then move to the terminal.
And there's a lot of freedom in moving to the terminal and almost, like you know, having layers of the abstraction even less visible, not worrying about what files it's creating, not worrying about inspecting the files, like kind of gaining that confidence.
I think that's what I'm seeing most.
And then I'd say the next is starting with the more familiar applications for prototyping, like Lovable or Figma.
Make
But the transition that I typically see is you start with that Lovable.
It's more familiar, feels less scary.
You know it's like it doesn't feel as much like coding and you move to something like like a cursor and eventually in the terminal.
What do we do in product development today that we won't do in three years time?
Write specs for humans.
I hope most people aren't writing specs for humans any longer.
I think writing specs for agents is really helpful and smart.
And I think that also takes a different form.
And the way that we write them, I think then also becomes different.
But yeah.
How does the world change when you're writing specs for agents, not humans?
And what needs to be altered?
Some things are still helpful to be similar, like, you know, who am I building for?
What problem am I trying to solve?
Kind of like what like those fundamental things.
But when you're writing for a thing that's actually going to do the job, i think you just you you end up like creating different, different types of details and kind of also even just structuring what you're writing differently.
You it's good to use examples build up like a context library of things that have worked in the past.
That you want to emulate things that haven't worked in the past.
That you want to avoid All of these things.
That when you're writing to a human, you just assume that they have that tacit knowledge that you know, since you've been working on these things together.
They like, they understand that.
That you don't feel like you need to embed as much of like the context engineering effectively.
Into the spec that when you're going to have something that is actually bootstrapping and coding, you know kind of based on that, on spec and that and that plan file in a very direct and fundamental way.
I think it changes what what you put in there.
I also think it should change how you write it.
You should use the thing that's going to code it to help you write it.
Because it's going to end up creating a version of that output that is more compatible with what it understands to do the work.
I have so many questions.
I'm so enjoying this.
If we're writing specs for agent. are we going to see a normalization of products?
Like a kind of plateauing of creativity?
Because the wonderful thing about writing specs for a human is that a human brings in The experience they had from growing up in a kibbutz in Israel, where they think about whatever in a really different and cool way.
That influences how they think about collaboration features.
And they bring that really cool, anomalous experience to the product process.
And you lose that hallucinatory element when writing a spec for an agent that executes it.
Do we see that?
I would say we will see a little bit of both.
I actually think that this will lead to creativity and certainly like taste standing out more than ever, because I think you're going to have this like flattening, where you are going to have a lot of things that just like oh you know, here we go.
It's this, the same dialogue, it's the same flow, like this this is what works.
These things have learned that this is what works and this, this is what they're putting out.
But that actually, i think, gives more room for the standouts to shine, For the things that kind of have that taste and creativity to shine.
And fundamentally, that's still like the human the human job is to figure that out.
So I get the, maybe the analogy, that to describe that flattening, just to go back to Instagram and sort of like what Instagram did to did to photos.
Right.
We went through this period of like every photo looks amazing, it's filtered it's, it's touched up, like look at my amazing, shiny life like.
It was sort of like this creative flattening of what like makes a good photo and then you know slowly organically, over time, what emerged is that the things that that people you know thought were more interesting or or more compelling was the things that like looked a little bit messy or like a little bit or organic or you kind of like the trend of what was like in fashion changed after that kind of flattening period happened.
I think we'll see something similar with applications.
The joys of doing what I do is I get to ask really smart people for their wisdom to help me in my other job, which is also far more lucrative, I have to admit, than media, which is obviously using other people's money.
But great lesson, kids.
OPM, other people's money.
You see, it's this brilliant wisdom that we give on this show.
And vibe coding, I'm just stuck with it as a market.
Do you think it will be an enduring market to offer non-technical functions, the ability to spin up development sites?
You name it faster.
Will your lovables rat blitz base 44s be in every sales and marketing team or is it a moment in time hype cycle?
I think the idea that everyone can build is not a moment in time hype cycle.
We've seen it time and time again.
You can democratize the act of creation.
Many more people want and can create than we believe or than we currently observe.
And then that kind of expands.
So I don't think that that is going anywhere.
Where the like value capture will be in in that stack?
I think is a different question.
You know, is it in the kind of the deployment, hosting and distribution of the thing?
Is it in and will you be paying for the tool?
I think that that is a different question.
But I think fundamentally, what will happen is that these vibe coding tools will continue to march up the stack and they're not building an IDE anymore.
They're building a service, a thinking service that does things for you.
My friend, which one's easier to do?
Is it easier for Claw Code and Cursor to go down the stack and eat the consumer end?
Or is it easier for the consumer end lovables base44s replis to go up the intellectual stack and eat the developer end?
I think that the hardest thing right now is figuring out the user experience that is going to scale to the largest number of average users.
I think that we're already very much at the point where We have this kind of capability overhang that people talk about right where, like what these models can do and what people actually can do with them.
There's there's a big gap there.
So I think that that the question of is it easier for cloud code to figure out the the right user experience, or is it easier for someone that is working more actively in the kind of that UX application layer like a Manus or like a Lovable to figure out the user experience unlock?
I guess I would probably bet on people working at that UX application layer already.
But I think that the foundational labs are doing that as well and are trying to learn at that level as well. much would you say of net new code created today within superhuman entities all the different products you have is written by ai versus by engineers i think we're we're basically at the point where we're approaching about half that's that's there i think that it can go much further than that what do you think it is in 24 months In 24 months, I hope it's like 90%.
When it's 90%, what do we do?
Do you have less engineers?
Do you just create way more products?
How does that change when 40% more is taken?
I think it's absolutely the latter that we build more things.
I just this idea.
I don't.
I have never worked at a single company that doesn't have an infinite roadmap, that doesn't have like a
We're done here.
Like we only need this many people.
We're done.
We're done here.
I absolutely think we're going to go through a phase, and we're going through a phase where the number of people we need and what a good ratio on a product team looks like.
And all of that is going through like a fundamental shift.
And that's going to that.
That causes obviously some uncertainty disruption, that that doesn't always feel great, but then i think once that kind of normalizes and we have a better understanding of that, our desire to do more is not going to go anywhere.
And then we're going to like continue to scale, but basically, you know, divide up the work in a different way.
Can I ask you you said about the ratio on product teams changing.
What do you think it's changing from and to and how would you discuss that?
So I mean, I think that you know, up until like a couple of years ago, you'd probably like what is a kind of a decent product team look like if you were starting like a zero to one team.
You know, maybe you say it's like you know, depending on the problem, you know five, 10 engineers.
At the limit it's kind of like 1 PM, one designer, right.
I think now you're much more looking at.
It's like maybe 1 PM, one designer, two engineers is kind of like what you need and then also who is doing what is also very, very different, right.
Like everyone is in the code.
Everyone is building the thing.
And you just have a small number of people that have their hands on a much wider part of the product pipeline.
And I think that leads to better products. totally get you.
And so we have smaller teams, everyone still being in the code.
When we get a little bit further along, how has testing and deployment changed in the world of AI?
Or has this fundamentally remained the same?
Well, I mean, I think that for one, the AI can do a lot of the testing, certainly the first run, catching all the obvious things.
I mean even for on-call incidents, like when the kind of bad things happen.
I think that AI can do a lot of the triage, a lot of the first-run investigations, so that by the time it gets to an on-call engineer, it's like here's what I think is going on.
Here's, I think, the three options are to try to fix this.
Like, which one of these paths do you want me to take?
Go.
And I think over time, as you build up that context and that memory, then it's going to ask you less and less.
The whole stack is going to be automated in the same way.
How much faster does AI make your teams?
Are you able to ship twice as much, three times as much?
I know it's hard to quantify, but just to help someone who doesn't live in product.
I think that maybe for our teams, the fundamental thing that can get shrunk is the exploration phase and the rate of iteration through the exploration phase.
How quickly you can kind of get to this is the thing we actually need to build and we've, you know, we've learned that we've, we've tested that.
We've iterated through that exploration phase that's shrunk quite significantly.
I don't know how to like put, like a it's 2x, it's 3x on that, but i i do think that in the limit that speeds you up quite dramatically, because usually what does it really mean?
The exploration phase is shrunk like, I think, that the phase of going through.
We've observed this problem.
How do we build a solution for this problem?
Okay, let's try to iterate through what a solution might be.
Let's go and kind of test that with some of our customers.
Oh, that's not the right one.
Let's iterate and try this other one.
How much of that you can shrink?
How many of those you can do in parallel?
Who can do that kind of full pipeline of that exploration?
How many different people you need to do that?
And so basically it just increases the rate of learning like quite dramatically, which then kind of, you know, shrinks the whole kind of product development lifecycle.
And I think if the business is fundamentally learning much more quickly than you know, the whole thing has like a massive acceleration.
There's also obviously the.
I think when we think about this, we like quickly go to, like I used to type all these things with my hands and now, like I can you know?
This thing just types them for me.
Yes, there's that's that sort of speed up as well, obviously.
And that saves you like quite a bit of time.
But then again, I feel like you you just end up doing more also of other things.
You're you're reviewing code.
Right.
And then we need to, like, make that process more scalable.
You're then just kind of doing more things and in parallel.
And so anyway, I think that the shrinking of the exploration and the rate of learning is, at least right now for us, one of the biggest accelerators.
When we think about how it makes more efficiency and more productivity gains within engineers.
If an engineer say, is paid 250 grand a year I'm just making up numbers and it makes them 30 more efficient or better or whatever it does.
So it makes sense to pay 75 grand per engineer per year.
You know, it's interesting.
I just don't think that that's how things work fundamentally.
I think what happens is engineers just get to spend more time on other parts of the product development process.
And so they they get to flex their, their product skills more, or they get to flex their data analysis skills more.
The most valuable people will continue to be the people that can wear many hats, that you know have a more like kind of fulsome skill set that they can express, which was before, was just harder to express because of one where time had to go, but to also the time that your tooling required of you to kind of be able to express those skills.
No, I'm a VC.
We're very simple.
We're coin operating.
We're thinking dollars and great British pounds in the UK.
If we are going to make money from AI, we need to fundamentally see the transition from software spend to human labor spend in a way, like I mentioned.
30% of time, great.
We'll pay 30% of salary because otherwise we're still paying the per seat 25 bucks a month.
And then the TAM doesn't expand.
Can you help me understand?
Are we going to see the expansion of TAM with the movement of that software spend to human labor spend?
Or are we all getting way too high on our own supply and we're going to stay in a software spend world?
I mean, I guess, like TAM expansion that I see is that the number of problems, the number of things that you're able to solve with software and do for people, is just going to expand.
And so, yes, will you be able to have smaller teams, like kind of more productive, doing more?
Will that change kind of like the OPEX accounting fundamentals there?
Like yes, but I think then, like what happens is that you expand through just providing more service, solving more problems.
And does that lead to like basically fewer, bigger firms?
I don't know.
Perhaps, like maybe there are too many providers and what you actually need is fewer providers that just do more and cover much more, much more of the stack.
Do you have to be a platform today?
When we look at a superhuman, with all the entities that you have your coders, your superhumans, your grammaries and when we look at like a Notion I know Notion obviously a competitor, so forgive me for bringing in a competitor.
But it's like, you know, they obviously have calendar integrated now.
I think they're starting to do cool recordings.
They obviously have that cool kind of knowledge management system.
I think when you look at like an otter or a firefly, they're aware of the need to move from product to platform.
Is product no longer enough in a world where you need to be platform?
It depends how you define platform.
I think there's one definition of platform, which is basically other people can build businesses on top of your product.
I think that maybe that definition is not required for everyone.
I think that your customers are going to build on top of your product is a requirement for everyone.
That is.
That is that is serious, because customers needs are always nuanced.
And I think we're very quickly moving to a world where you're just going to have a lot more bespoke software.
And so you have to have a platform approach that and how your customers can build on and extend your platform, your products, in order to kind of meet their need.
What changes then for product leaders in the world?
When you exist in a world where customers need to build nuanced personalized, customized features, elements to your product, i think you just think of things pretty differently and the impact of things pretty differently when you know you have other people developing on your platform right, like how you roll out change, how you think about backwards compatibility, how you even kind of run experiments and kind of measure your experiments in terms of you know who's doing what with your product and kind of what you're making better or what you're breaking.
Like.
One of my favorite examples of that and this is more from Like YouTube is a platform and the definition that other people can build their business on top of YouTube.
And when we would kind of experiment and, you know, just observe things in terms of watch time, it's like hey, watch time is up.
This is a this is a big win.
And then we'd roll it out and we'd have like the community like, you know, enraged.
And why is that?
It's just like why it's not it's not equally distributed.
And so you have to, you have to look at, you know, by kind of cohorts of publishers and creators like whose business is that?
Am I hurting whose business?
Am I helping?
And how do I kind of try to, you know, normalize that across the base?
I just think you just you have to observe things differently.
Can I ask you a bit of a weird one?
But everyone is so excited, high on their own supply about how we're able to build more faster.
Is there anything that you are nervous or worried about in the way that AI is changing how we build product?
Maybe maybe I'm just giving us too much credit.
I would like to think that in areas of like security, sensitive data, that we're all still adding the observability and the controls, such that there is kind of like the right human in the loop.
For those sorts of decisions.
I suppose that we'll go through like kind of some bad phases where people are using these things irresponsibly.
And, you know, we have data leakage.
We have, you know, prompt injection, shorted sort of hacking and and so on.
But I think that's just part of the learning curve that we're going to go through.
I don't think that's something fundamental that's going to make software worse in the limit.
Code quality is also not one that I think I worry about too much in the limit.
I'm more worried about whether we actually use these tools to make our lives and our work lives actually easier and better, or whether we're on a trajectory that I feel like we've been on, which is, I'm not sure that a lot of the tools that we use for work and a lot of the ways that we work have actually made us better.
We certainly can work a lot more and all the time, which I think has benefits and also, for some folks, some downsides.
But, as an example, I used to really love Slack when I was in my startup phase and there were 10 people in the room and it was one of the best products that I'd ever seen.
But I have to say in my role now, I'm not sure that Slack actually makes me better at what i do and makes my day better and more effective, and i i do worry that some of these things that we're building could kind of go towards that, that trend, rather than actually helping us do things better and taking things off of our plate and kind of removing some of the drudgery of work.
I think one thing that we're going to see in 2026 that no one's talking about is 24-7 inference that everyone is going to have inference running all the time, not just on input into chat GPT, but consistently for the majority of people, especially in knowledge worker jobs.
You will have that.
Do you agree with that?
And does that factor in how you think about building product?
I think that's a very good observation.
And whether it happens widely in knowledge work by 2026, I think, is you know.
I'll take a bet with you on that.
Certainly for coding, I
I think in a lot of places, we're already there, right?
I mean you have like the Ralph Wiggum stuff that popped up over the holiday and now people are running in Friends 24-7 for their coding tasks.
For the sort of knowledge work that the majority of us do on Honda Day-to-Day.
Will we be there by the end of 2026?
I might disagree with you on timing, but I think that the insight is correct.
I so love to be proved wrong, and I'm wrong most of the time as a venture investor.
Why would you disagree on timing?
Because I think we're still at the stage where fundamentally we don't have the right UX.
Many people are still at the stage of I'm not sure what to do with this thing other than search and chat.
I still think that a lot of how people use these things, it doesn't feel like they actually make them better or that the output is better than kind of what they've done on their own.
I think in most cases that's not actually a problem with the model or a problem with the technology.
I think it's often a problem with the user experience.
And what I mean by that is, how do you add this sufficient context?
How do you prompt this thing correctly?
What should you use it for?
How should you change your workflow to adapt to it?
I think we're still at that stage for a lot of the work that we do.
Do you think AI will do more to harm or to help wealth inequality?
Oh, man, I think we're going to definitely go through a painful period.
Wealth.
Inequality in the US is something that I really am concerned, concerned about because it's higher than it's ever been, higher than the Gilded Age.
And that has a lot of very. potentially dangerous ramifications.
I don't think the answer, though, is to curtail things like AI.
I do still fall on the side of.
I think it ultimately creates a lot more abundance, that it's not a zero sum game and that it is an example of a technology that's going to lift all boats.
Just I don't think that the path from here to there is going to be as smooth as we might like.
I think.
Another prediction of mine for 2026 is I think we're going to see the demonization of tech and tech leaders like never before, as you see, the first real instantiation of job losses and job displacement in large parts of the economy, like in low-level law, in customer support, bookkeeping.
Yeah.
I think that that one.
I won't take a bet against you for it, or this year I think that is more likely to see that.
I want to do a quick fire round.
So I say a short statement, you give me your immediate thoughts.
What have you changed your mind on most in the last 12 months?
How close we are to what my version of AGI would be.
And I think specifically with Opus 4.5, that has changed quite significantly for me.
I think what Cloud Code can do is just incredible for coding or knowledge work.
I think it's made a huge leap with Opus 4.5.
I think we've crossed some invisible capability line and now what it can do and the outputs that it can produce for code and otherwise.
We just haven't found a way to package the right UX around it for most people, in my view.
You can have Anthropic at 360 or OpenAI at 500.
Which one would you rather buy?
I'm a buyer of Anthropic at this point.
What's your biggest prediction for 2026?
Like I said about inference or the demonization of tech leaders, I think we're going to crack the like continuous learning, self-improving.
These things can just be set on a task and given the context and build the memory and are just going to get better on their own.
For the majority of tasks that you put in front of them, that's likely already been cracked.
And we're just dealing with, like the implications of that and how do you roll that out safely, and so on.
What was your biggest takeaway from Matter?
We haven't discussed it, but Matter is an amazing place.
What was your biggest takeaway?
I think this is specific to the part of meta that I was in.
And to be fair, I didn't get to experience mainline meta.
I was in an incubator team that was explicitly shielded from the rest of the organization, for good reason.
And that's because...
I think doing zero to one product development.
I don't think this is an uncommon learning, but just seeing it so viscerally and understanding all the reasons as to why it is.
I think doing zero to one product development at scale is just really, really hard.
And I think that that place has...
Many, many strengths.
But like lots of companies at that scale, that's just a fundamentally hard thing to do.
Totally get you.
When you look back at your leaders over the years, as we said, from YouTube to Google, to Meta, to Thumbtack, you can't choose Shashir, by the way.
Which was the best leader you worked under and why?
So, like, I got to piss all the other ones off.
Because you're not saying one was bad.
You're just choosing one.
It's like when you have six kids you can say the favorite, because there's so many that five.
You know it's not like there's the least favorite.
Yeah this, this goes, this goes back to you know what I said about like what I think is like a good product leader.
And I do think that the best leader in the company that that I have ever experienced is is Glenn Kelman because of that not only his ability to empathize and understand the market, but how well he can communicate and describe what we should do and why it matters.
His ability to relate and get people to want to just run through walls for even seemingly meaningless things.
I just think he's in a class of his own.
If I look back to Plumtree, which was basically like corporate portals, like my Yahoo for the enterprise, like we felt we were on, like a messianic mission.
Why?
Like we were building corporate portals and Glenn was able to relate it in that way, in a very meaningful way.
And I think that he would be at the top of my list.
When you first joined up with Shashir first day of Grammarly.
What do you know now that you wish you could tell yourself then?
I think I didn't push for product expansion nearly as aggressively or fast enough.
I had the hunch and we sort of had the conversations of.
You know we must own a surface like fundamentally, if we want to be like a retentive product, you have to, you know, have a destination that is meaningful to people in some way.
It was sort of like a side chat.
And, like you know, every time I'd kind of bring, I was like no no no that's, that's a, that's a big, that's a big risk.
And anyway, lots of good lots of good reasons.
And I think we got there and now we are undergoing like quite a tremendous, tremendous like product expansion.
But I wish I had pushed harder and that we'd got there a year sooner.
Final one, what are you most excited for when you look forward to the next 12 to 24 months?
Personally, I'm most excited to build with AI and how I think it's going to change even my job.
How will it most significantly change your job?
Most significantly think that the people in, i think, my roles typically have a hard time finding time to build, to make, and that's like fundamentally why i got into this career in general is i like to make things and you know, as you progress you just you do that less and less, which i think is a real shame, And I think that it's going to give me the opportunity because of the amount of time and energy it takes now, the amount of space I can I need to carve out in my day, in my, in my weekend, etc.
I think I'm going to get to build a lot more than I have in a long time, where before it would have been like oh, I got that week, a quarter where we ran that sprint and I got to.
You know, actually be a designer again or be a PM again.
I'm very hopeful.
Maybe I'm projecting I really want more of that time in my week to week.
And then I hope through that to also help change how our teams work and kind of like our rhythms and our expected roles and accountability and rhythms such that more of our PMs and more of our designers and teams can work that way.
Because I don't think that the biggest thing that's in their way now is the tooling or the desire or the knowledge.
I think it's actually more the change management around just how we work and like what should the week look like, and you know who's responsible for what, and you know what do we use meetings for and you know, like all of those things i think really need to change in order to create the space and the permission for people to just do a very different thing in the majority of their day.
Norm, you figured this wasn't the traditional interview.
From how freewheeling I am.
I've so enjoyed this discussion.
You've been fantastic.
So thank you so much for doing it, man.
My pleasure.
Thank you so much for having me.
I really appreciate it.
But before we leave you today, you know what's wild?
It's 2026 and so many product teams are still flying blind, buried in spreadsheets, chasing feedback across 10 different tools, trying to figure out what actually will move the needle.
I've spoken with hundreds of product leaders and the best teams all do one thing differently.
They build a system to capture ideas, validate them with real data and focus their roadmap on the right things.
That's why product teams at Canva, Deliveroo, Toast, Decathlon use Jira Product Discovery.
It pulls ideas and feedback into one place with built-in tools to prioritize what will actually have the biggest impact.
That's when a roadmap stops being an endless list of ideas and becomes a plan people actually believe in.
Join more than 20,000 teams already using Chira Product Discovery.
Head to Atlassian.com forward slash Harry.
Oh, I like it.
I get my name in there.
Atlassian.com forward slash Harry and start building the right thing today.
After Atlassian helps your team build and ship great products.
Intercom helps you support the customers using them.
If you're looking for a way to transform your customer service, let me introduce you to Finn, baby.
Finn is the number one AI agent for customer service, resolving up to 93 of customer queries automatically.
There is no other agent that can do that.
Not 93% of customer queries, okay?
No other agent can do that.
So why choose Finn?
Finn is the best performing AI agent for CS.
Finn doesn't just answer questions.
It takes actions.
It automates the most complex customer queries, like refunds, transaction disputes, technical troubleshooting, with speed and reliability.
I wish my team was speedy and reliable.
Beats every competitor in every head-to-head bake-off, completely configurable and code-optional setup.
My word.
I mean, the benefits just go on and on.
It's easy and efficient implementation.
It works on any help desk with no tedious migration needs.
It's trusted by over 6000 customer service leaders, including top AI companies like Anthropic, Lovable Synthesia, Clay Vanta.
So if you're ready to transform your customer service team, scale your support and give team members time to focus on the really high-level strategic work.
Learn more about FIN at finai forward slash 20VC.
While FIT scales your support without losing speed, ReForge shows you how to translate that scale into durable, product-led growth.
Everyone's shipping faster than ever.
Cursor claw code, codex ai is making code and writing code faster than ever.
But here's the problem speed means nothing if nobody uses what you ship.
That's where Reforge comes in.
Reforge is building the product discovery engine that sits upstream of your coding agents.
Not another prototyping tool, research repo or AI interviewer, but a product that will number one ingest your customer data 2.
Generate variations of product solutions 3.
Validate the solutions before code is written 4.
Hand off winning directions to your team.
Reforge kills product debt before it starts, because every unused feature you ship isn't just wasted engineering time, it's a maintenance burden complexity, tax and surface area that you cannot shrink.
Used by product teams at companies like Toast Vimeo, Klaviyo and many more.
Reforge helps teams ship more features that actually get used.
Try Reforge at reforge.com forward slash build and use the code 20VC, that's 20VC, for one month free of pro.
