This CPO regrets that product management exists | Tom Verrilli (CPO of Whatnot)
In a Nutshell
Tom Verrilli, CPO of Whatnot, argues product management shouldn't exist as a default function—PMs should only be hired for specific needs because they "infantilize" engineers and designers who are capable of making decisions themselves. The company operates with just 21-22 PMs for massive GMV by mapping PMs to problems rather than teams, having managers do 90%+ IC work, and prioritizing senior PMs who execute rather than coordinate. This approach works because fewer, more senior people with strong judgment can leverage AI tools to move faster while maintaining ground truth across the product.
These notes were generated by AI and may contain inaccuracies.
Tom Verrilli, CPO of Whatnot, states that the company's product team was built on the premise: "We regret that product management exists." This is articulated to force the organization to remember that PMs should not be hired for the sake of hiring them, but only where there's a really specific need.
Product management didn't originally exist as a specialist function. In early internet businesses, the business, founder, or CEO types talked directly to engineering and design about what needed to be built. As internet businesses scaled faster than any other businesses in history, execution specifics were delegated. However, this delegation was rarely a specialist role - it was usually just fewer details communicated to engineers and designers who understood the context.
The idea of a specialist decision-making class in tech is a more modern function than a pure necessity. The optimal scenario would be if engineering and design had the context needed to make great decisions themselves, staying so in touch with users that they could make the same decisions a product manager would make.
The only argument for product management as a specialist function is that it's a trade, not a qualification - something you get good at by doing. It's a muscle built by reps. The more you do it, the better you get at it. However, the flip side is that the more you abstract engineers and designers from doing the same thing, their muscle gets underdeveloped.
Product management plays an important role and can be deployed for leverage on particular things that need to go really well. This allows design and engineering to be the best at their craft in those situations. But wherever possible, it's optimal to not have a product manager and instead have design and engineering building their muscles through direct involvement.
As tech companies scaled and hired many engineers, an HR ratio of "a pod" emerged: every time you hire six engineers, you add a designer, you add a PM, you add an EM. When serving billion DAU products, this resulted in many product managers. Engineers are perfectly capable of understanding infrastructure like notifications, yet PMs were assigned regardless.
Hiring so many PMs infantilizes engineers and designers who are perfectly capable of making good decisions but never had to because there was always a PM to babysit them.
Specialization works both ways. It's useful to have people well-honed in making decisions, and it's reasonable for someone to say they're an infrastructure lead focused on scale and don't want to spend time on alignment or minutiae. Certain skill sets don't necessarily translate well - being good at building mental models for infrastructure scaling may not translate to listening to customers and understanding core problems.
The "we regret" stance isn't saying product managers aren't wanted for this work. It recognizes that in some situations you need to help other functions specialize. The articulation forces the organization to remember not to hire a PM just for the sake of hiring one - only where there's a really specific need.
When the organization says "we regret product management exists," documents about shipping and work are written with clarity that anyone can bring change forward. DRI (Directly Responsible Individual) for new product development can be an engineer or designer, but everybody goes through the same level of function - product review and doing the work. If people don't want to do that work, they can move to product management.
Rather than mapping PMs to teams where the PM will always do the work, PMs are mapped to problems or core projects. This means there will be years where there isn't a PM attached to a particular engineering team even though there's ongoing product work to do.
This perspective is notable coming from Whatnot, which builds very consumer products requiring strong PM skills, as opposed to developer tools companies like Linear or Anthropic where the "we don't need PMs" perspective is more commonly heard. The insight that PMs aren't as necessary as people may think applies even to consumer products.
Tom spent seven years at Twitch prior to Whatnot, which was very Amazon pizza team ratio-driven. At Twitter, there was very tight alignment with many alignment meetings, which is why engineers didn't want to be in them. The evolution came from understanding that a lot of this is a function of management not being able to see what's going on, leading to hiring more people, building more layers, and systems begetting systems.
What's changing is the realization that the only way to get leverage isn't just hiring more PMs underneath you and being more senior. There's better understanding of what's happening in businesses now, and it's easier to converse directly with the codebase and talk to engineers than ever before.
AI makes it a lot easier to move fast with tooling if you have well-honed judgment. You can pull data on a hex thread that would have taken a week or two weeks with an Amazon L7 data scientist in 2017. If empowered with good judgment, you can make quick decisions and keep people moving.
Beyond AI, ratio pushes came with cultural pushes like "hire great people and get out of their way" and bottoms-up roadmaps. There's been a cultural shift in tech where top-down is recognized as pretty good because folks at the top can make quick decisions and remove alignment debate. They have more macro context, and assuming they're genuinely in touch with ground truth and are good enough, it's an efficient model to have senior leadership involved in decisions.
This means one very senior PM can be across more things and be more efficient than having three relatively entry-level PMs.
Whatnot has just passed 20 PMs - 21 or 22 PMs in the building. For the volume of GMV their sellers move, this is pretty small. They're loosely organized into three groups: buyer, seller, and trust and risk (standards, payments, safety, etc.).
Within those broad groups, PMs are reassigned pretty regularly. People have loose alignment - you might be broadly a growth PM or broadly work on discovery, but even amongst those groups, work gets allocated and moved around quickly.
Every six months, the CEO, CPO, some other folks, and senior leads sit down to define what needs to be true over the next six months - what has to get done as a company in terms of outcomes and critical projects. They go through that list and say who's the DRI, who's accountable for that. This is mostly how PM work gets allocated.
Quite regularly, after going through the list, they'll identify something that's second or third priority in a bunch of different teams' roadmaps that feels really important but has no owner. They'll grab a PM and say congratulations, this is the thing you need to deliver over the next six months. It's rarely "you need to build a feature that works exactly this way" - it's more high-level about how to work out what needs to be true, then mapping the human beings who can guide that.
Not every name is a PM name, but overwhelmingly when going through the planning process, it tends to be PMs with the right skill set - whether more financial, algorithmic and recommendations-based, or core user feature.
In the last two years, 31,832 people applied to be a product manager at Whatnot. They hired one.
What's definitely trending down: folks who spend a lot of their time in interviews talking about driving alignment and stakeholder management. There's a group of PMs whose specialty wasn't technical or customer-oriented - it was politics. Folks who naturally lean towards driving alignment and building relationships, and tell stories about failing because they didn't keep the CEO up to date with something that led to a pivot - this is an anti-pattern that trends down.
What really jumps in PM interviews is whether, over the course of interviews or case studies, both macro thinking and micro thinking can be seen. Can someone describe an end state system, but also exude impatience on how they'd validate that very quickly and where they'd push to get it done? Are they specific about the things they've built?
There's an awful lot of folks who've worked at FAANG or similar scale companies where they babysat things that existed and polished the edges, as opposed to being given a problem and having to come up with something unique and novel, or really iterating through a complicated change. The key is whether they made decisions along the way and moved, rather than going along with what's happening due to inertia in large organizations.
Systems thinking can be developed through mental exercises. One thing asked in product review: if someone says they want to run an experiment, ask what they do if it's green and what they do if it's red. If they don't know how their strategy would change, they haven't really thought about that.
The same mental exercise applies: what would happen if there was a thousand times more usage than expected? What are the unexpected knock-on effects? Doing this mental exercise before getting pen on paper or code on the system helps unblock people to move faster.
PMs are often slowed down by risk concerns - legal might, finance might, another team might. The internal moniker used is "know then go" - think through all the things that could go wrong, understand where scale will break, understand the things that might happen, and then make the move anyway. If you've thought through all the things that could happen at scale, you're probably going to preempt a bunch of them.
When running experiments in a high-growth environment, the focus should be on identifying things that could go wrong rather than trying to solve every potential issue. The goal is to think through all possible outcomes, then you end up solving more than anticipated. This approach means moving beyond asking whether an experiment will be impactful and instead considering what comes next and what comes next after that.
In higher growth environments, the target is not a 5% statistical win. Instead, the aim is something that moves the business and creates compounding effects over time.
The traditional promotion path created a problem where successful PMs were promoted to director level, then told to stop being hands-on. Their goal became coaching and guiding rather than doing the work. This led to a yo-yo development process where work gets done, reviewed, rejected, and sent back repeatedly.
At Whatnot, managers spend 90%+ of their time doing IC work. Tom himself spends approximately 50% of his time on IC work.
Advantages of Leadership Doing IC Work
- Speed of decision-making: With 10-15 years of experience, senior leaders can make decisions more quickly and have immediate impact without working through multiple layers of alignment
- Better leverage: A VP can handle the workload of multiple junior PMs because they are more efficient, creating broader visibility across the product
- Intuitively correct decisions: Leaders making decisions across multiple areas (like discovery algorithms and seller shipping issues) are more likely to make the right trade-offs
At Twitch, the discovery team and ads team were constantly competing for impressions. The discovery team worried about discovery metrics while the ads team focused on ad dollars. The solution was to have the same PM accountable for both, with the shared goal of GMV generated from the feed—one through organic discovery, one through paid substitution. This eliminated months of back-and-forth and shifted focus from career-first to company-first decisions.
IC work for PMs means whatever is required to most effectively ship products. This includes:
- Being in support tickets to understand customer problems
- Pulling data yourself to understand it
- Sitting with engineering and design
- Querying the codebase directly
- Writing specs
- Running standups
Tom has shipped production code at Whatnot, though he acknowledges that engineers likely cleaned up issues with linting, localization, and other nuances that decades of software engineering have established.
There will be a split across the industry. Some organizations are large enough that having everyone hands-on may not be right. The culture must match this approach—where people want to execute rather than discuss. Every startup reflects its founders' thinking about how to run the organization.
Many senior directors at companies like Meta, who spend their time in alignment meetings, miss talking to customers, talking to engineers, and shipping things.
A notable trend is CTOs and technical leaders moving to engineer roles at Anthropic, including the CTO of Workday becoming a member of technical staff, and CTOs from Instagram, Box, and Super.com doing the same. The goal would be for Whatnot's product bench to look similar, with experienced people building as ICs.
The compensation question is whether you can reach VP-level pay as an IC. The argument is that rather than having five L5s reporting to one L7 and four L7s reporting to one VP, you could have three senior people each paid D2 VP money, particularly if they are delivering the level of impact those multiple layers would provide.
The product ends up being better because fewer things get done but those things get executed exceptionally well. Having best people in the weeds means fixing issues as they arise rather than making trade-offs.
Growth can hide problems when teams work off averages. The cultural commitment is to "whole ass few things" and push best people into the details.
AI eliminates time spent asking engineers how hard something would be. PMs can now use tools like Claude to understand level of effort estimates and codebase logic without distracting engineering velocity.
This saves kickoff meetings, alignment meetings, PRD writing time, and experiment time by empowering someone to make decisions directly.
Senior PMs should start doing IC work in their current roles to keep those muscles honed. Pushing internally for hands-on work helps both where you are and where you might move to.
The focus should be on scoping the correct thing, understanding the problem, defining what good looks like, and leveraging the broader perspective that comes from director-plus experience.
There's a tendency to let junior PMs yo-yo back and forth on PRDs when senior leaders already know the answer. The coaching approach should help people understand what good looks like quickly rather than endless review cycles.
PMs have substantially more leverage enabling teams to ship better and faster than sitting in the minutiae of shipping production code themselves. Understanding the codebase and talking to someone who can execute well is often more valuable than doing the work directly.
Data Science
Using tools like Hex allows PMs to pull nuanced cohort reports, grab individual user data, understand user behavior, and build sensitivity models, forecasts, and regression models quickly.
Codebase Understanding
PMs can query Claude about how the codebase works rather than bothering engineers, understanding systems at the boxes-and-lines level and nuance layers.
Real-Time Product Observation
AI enables watching customers use products while simultaneously analyzing the live codebase to distinguish between bugs and comprehension gaps, creating a real-time feedback loop across customer side, code side, and observation side.
Whatnot operates a live commerce auction platform. One auction model is "sudden death" where the timer ends without extension. Another adds 10 seconds when someone bids in the last 5 seconds. A seller requested changing from 7-second to 3-second timers to move more product, and engineers updated the config in real time while in the seller's chat.
Data scientists are seeing non-data scientists doing half-formed analysis and asking them to validate it, often finding it wrong. This creates role confusion about what data scientists should be doing.
Having fewer, more senior PMs helps because they have seen more patterns. However, organizations have historically underinvested in data engineering, data structures, and data labeling. The best data scientists are pushing toward fixing these foundational systems rather than just answering questions.
Using AI for data analysis or code writing does not absolve responsibility for ensuring the output represents good analysis or good code—it simply provides leverage for those naturally inclined toward quality work.
Data science continues to be in demand due to the power of tracking, attribution, and measurement. The speaker notes they haven't spoken to a data scientist in a while but have hired plenty. Even when reducing headcount per output, macro hiring may not decrease because AI systems enable faster growth and more initiatives. Net fewer roles relative to customer impact is more likely than absolute reduction.
Tech leads that function as hybrid engineering managers are trending upward. These roles involve running very small teams and tackling initiatives directly. As the cost of experimentation decreases, organizations can afford to incubate smaller teams that sit in corners attempting historically difficult projects. This model offers high leverage without requiring full prototyping. The role sits between individual technical contributor and full engineering manager.
At Whatnot, product teams will likely maintain their historical composition of specialist designers, engineers, and product managers for high-confidence projects where clear paths forward exist. The formal structure won't morph significantly. However, significant free space will emerge around these teams where anyone—designer, engineer, product manager, or data scientist—can pursue meaningful improvements to customer problems they understand, codebases they're familiar with, and macro context they grasp. A Friday afternoon scenario where someone can't focus on planned work but can fix something else exemplifies this empowerment.
A post from two to two and a half years ago argued PMs are the best-positioned role in tech to thrive in an AI world. Core PM skills become increasingly valuable as AI handles building: identifying what to build, distilling and communicating requirements, prioritizing ideas for highest ROI, giving feedback to improve design impact, developing go-to-market strategy, and understanding business strategy. These skills matter whether performed by PMs, engineers, or designers.
Core PM skills—genuinely understanding customers, business, and technology, then translating them together for optimal efficiency—represent the point of leverage as execution becomes cheaper. However, many people with PM titles over the last five years have been rewarded for storytelling, alignment, and strategy rather than these fundamentals. Product theater describes doing the activities PMs should perform without the underlying substance. The speaker acknowledges being guilty of rewarding this behavior. With 32,000 applicants for jobs, many candidates excel at theater—alignment, documentation, meetings—but lack actual product-building understanding.
Every hire at Whatnot completes an actual hands-on case study regardless of role. Candidates present well initially but when given prompts and data to develop a POV and defend it verbally, thinking quality often decays rapidly among those skilled at theater versus specifics.
The common advice to hire great people and get out of their way requires nuance. Better hires enable greater trust, but the speaker operates in a verify-then-trust model rather than trust-then-verify. Leaders often hold superior understanding of how system components fit together across buyer and seller sides, while individual contributors understand feature nuances better. Leaders should periodically work alongside teams in the trenches to understand details. Grant, the founder and CEO, exemplifies this by clearing his schedule during reviews to sit with teams, pull up tickets, examine code, and analyze data line by line.
Planning aligns the company on important problems to solve and functions primarily as a resourcing discussion. Leaders remain accountable for ensuring resources sit in appropriate places. Without periodically going deep with teams on specifics—how referrals work, fraud logic for address validation, whether address signals use Google normalization or free text—macro decisions suffer without micro understanding. The T-shaped model involves going very deep when required while maintaining broad perspective across areas.
Most of the speaker's career involved working for product-minded founders. At Whatnot with two founders, the approach involves not double-teaming initiatives—if Grant or Logan own something, the CPO steps back to avoid unnecessary layers. The "two dads problem" describes conflicting instructions between multiple reviewers. When founders are accountable for initiatives, the CPO doesn't need day-to-day visibility. Alignment work consumes significant time. The CPO role in founder-led companies requires understanding it's not your company, finding the balance between offering feedback and recognizing when minds are made up, and providing coverage where founders aren't focused.
Top-down approaches work when leadership possesses sufficient ground truth to make correct decisions in the weeds. Micromanagement emerges when leaders lack ground truth yet attempt to manage from above. Junior engineers and entry-level designers appreciate working alongside CPOs or CEOs because it eliminates alignment meetings and unblocks progress. Real-time data querying enables leaders to engage with details more effectively than ever before. Leaders should focus on three or four most important things at a time, calling the ball on ownership while clearing schedules to nail those priorities.
Candidates should first understand their motivation, recognizing that CPO roles don't involve unilaterally deciding roadmaps. Spend time with founders understanding working styles—how they like getting pushed, their communication preferences. Before joining Whatnot, the speaker had five or six coffees with Grant discussing team dynamics and roadmap challenges, followed by a full day of problem-solving sessions. The goal is determining whether the ongoing debate and discussion style matches personal preferences. The CPO role involves translating founder vision and intuition into reality rather than competing for vision ownership.
Don't treat pushing back as a trick—seek truth rather than extract yes answers. When leaders push in unexpected directions, start from curiosity: confirm understanding of their prior, identify missing context. If minds are made up, don't fight. If openness exists but data is required, bring fresh data. Without data, CEO opinions win—check ego and accept outcomes. Debate only for substantive reasons, not framing or nomenclature unless genuinely consequential.
The accordion mental model counters two failure modes: iterating forward without directional understanding, and creating long roadmaps that lose learning advantages. Before playing notes, stretch the accordion fully out to understand the problem space. Then compress by building V1. Repeat the motion: re-evaluate understanding given new learnings, adjust direction, build the next iteration. Value creation happens during compression, not expansion. The model requires constantly zooming out to reassess plans, then zooming back in to execute. Both roadmapping and iteration are necessary—overindexing on either fails. The process involves having a plan, identifying the smallest experiment to prove hypotheses, and adjusting subsequent versions based on results.
An example right now is the core of most marketplaces is listings. If you try and think about Amazon.com without listings, there's basically nothing there. It's a bunch of, you know, it's a left rail or right rail and some videos. In video commerce and live commerce, whatnot, you don't really historically need listings. If I want to sell you a pair of AirPods, I could literally hold them up to screen and show you them and describe them and say they're AirPods. I'm going to start them at a dollar. And as a buyer, you now have all the information you need in order to kind of like make a purchase decision, which is great. And so you may not have to invest in making listings the way that someone else does. And that's probably net good for a seller because it takes like three and a half minutes to make a listing. And it takes zero minutes to describe a thing and hold it up. But you then zoom out and say, "Oh new buyers are going to come to live commerce and expect search to function. If I don't know what you're selling until after you've sold it, there's no possible way that I can put somebody in the right stream for earpods because they didn't we didn't know you have them and I therefore can't direct people to it. And so you go through this exercise of like we don't need to fix it. It works. And then you're like, okay, great. What does that have implications for other things? Okay, then what would we do? Oh, we're going to make everyone make listings. And you're like, zoom back out again. If every seller is now spending three minutes for every listing that they're making, the number of things they can sell per hour is now dramatically dramatically lower, which means it's bad for sellers. Okay, we can't do that again. And so you just you have to keep stretching through the like what are the longerterm implications or what are the knock-on effects of things we're building. That is extremely helpful.
What's interesting about whatnot is it's there's like this spectrum of like whatnot and then there's this whole trend of agentic commerce where agents are going to be buying the things for you and just working with each other create a whole new agentic economy and this is like the opposite humans live talking to each other buying from each other. Thoughts on that on that trend and and how may how that might impact you guys? Listen, I for one welcome our agentic overlords for things like I don't want to have to think about light bulbs, air filters, any of the programmatic stuff that I need to run my life, right? I'm also pretty great with it for like really high intent things. I need a particular cable for my computer. I'm looking for, you know, outdoor lights for my house. Things where there are really taxing searches. I think about this, you know, got to go to a wedding which means I need black shoes but delivered by Thursday because if they don't get here before Thursday they're a no good for me kind of things like absolutely but most of commerce in America isn't actually high intent like how many years are we into ecom now like 30 years into ecom and e-commerce has never exceeded 20% of retail spend in America right the vast vast vast majority of retail shopping in the United States is still people going in person and buying things uh in the UK somewhere. It's like 7525. And it turns out actually that a lot of shopping is low intent. I I'm going to the mall. I might be because I've got a wedding coming up and I don't have anything to wear and I'm going to wander around and see what people have available because I don't actually know specifically what I want. And actually the value of stores is that that person who runs that shoe store has agency and taste and she curates a selection of shoes. She has, you know, a level of customer service. She has a window display which can show me the types of things she has. And I can wander around the mall and actually work out what it is I want to buy or be educated by it. And as it turns out, it's quite pleasant. Like there's a reason that the mall is a social thing. And so I think you know that Agentic is going to be huge. Retail is a 7.5 trillion dollar industry in the US though. So I don't think it's a like winner takes all thing. What I think live commerce does is it's the first time that we've ever managed to kind of like bring together scale and convenience of the internet and also that same kind of like social cultural experience uh of physical shopping because you know at at Twitch we used to basically assume that if it's less than a thousand people in the stream it's non-economic because you're you're operating on CPMs. So but you can go into a stream on whatnot and see 30 50 people in that stream and you you imagine for a moment that you're running a shoe store at a mall and you had 50 people in your store, you'd never close. Like, you would literally never shut down the store because that's so much more foot traffic than you can ever imagine being in a store because commerce has totally different economics to entertainment and CPMs aren't the thing that you have to worry about. So, I think it's not really in kind of like competition with aic. I think it's a completely different kind of like customer need.
Amazing. There's room for everyone. I want to end maybe with a question about Twitter. So, you were a Twitter. Yes, sir. uh a PM at Twitter working on growth. I feel like everybody that worked at Twitter as a PM was just scarred from the experience of Twitter. Everyone said do not do things this way. What's uh what's something that stuck with you from that experience? What did you learn? What did you unlearn? It is funny. I uh I have said to a friend before that asking someone who was at Twitter in like the 2015 16 era is a little bit like a therapist asking somebody to tell them about their childhood. It's like you know the trauma that you're you're bringing up. Yeah. For those who not familiar with that law, I think we had nine heads of product in the two years that I was there. It was that level of kind of chaos. I think my favorite quote was from someone on the partnership team who said it felt like Carasa lived in the events. That was like how often drama was going on about the place. Um I think Lenny I probably took two overwhelming things away from from my time at Twitter and other than like some incredible friends and I will say that the the product diaspora from that era of Twitter is pretty incredible and and everywhere. The first one is like if you truly find product market fit, like if you manage to bottle lightning, doesn't matter how badly you screw up the organization uh of it, like it's huge. And like Twitter genuinely had that level of product market fit that you could emotionally feel how much people loved your product. And it's a really powerful litmus test to learn relatively early in your career. That's what PMF is. Not like, hey, the graphs look okay, right? There's like a level of further that goes in. Um, I think maybe on the flip side of like less positive is like what I definitely learned is like most of the time you hear it's really complex. It it isn't. Leadership is just weak. So, you know, the two years that I was there, everyone knew we were going to have to lift the 140 character limit, right? There was working group after working group. Like the project beyond 140 was like everywhere because we knew for example that like uh people in Japan tweeted six times more often than people uh in western markets and it was largely when we spoke to them because kanji let you say heaps more uh in the same number of characters than uh you know a romance language did. We knew that was the inevitable end state, but there's obviously a bunch of like work that was needed and trade-offs and just no one wanted to make the call. And so there was just like yet another design sprint, yet another cycle. And it was another year and a half after I left. I think it was maybe even almost two years after I left before someone actually did it. And it turns out, you know, nobody died. The soul of a place didn't fall apart. I imagine the same discussion went on for editing tweets, which took another two and a half years. Like sometimes things aren't actually that complicated. It's just weekly to Yeah, it's funny how far it's come now. You can write an entire blog post on Twitter like articles is a big bet. On the product market fit piece. I think even more important there is the network effects of a Twitter which you spent a lot of time thinking about building marketplaces. Like to me watching Elon basically change everything. I forget who tweeted this but just like everything changed the brand, the name, the website, uh number of people working there, everything. Yeah. people, the team, like what what was the thing that that stayed the same? And it was basically the network effects of Twitter. Yep. It's the place to be. So, everyone's there. And that's hard to break. And even in spite of how much you tried to mess it all up, uh it's still kicking. That's bottle lightning. Genuinely, you can tell.
Yeah. Okay. I'm going to take us to a recurring corner of the podcast. Fail corner. Why I like doing this is because people see you see see people like you coming on the podcast had have this illustrious career everything's going great constantly or just killing it and they don't see the things that don't work out and the times that uh you failed and in their life things often go wrong. So uh so the question for you is just what's an example of a time in your career where things failed something you build some career move you made that didn't work out and then what what did you learn from that experience? Honestly, mate, and it's it's very nice of you to say nice things, but I feel like I fail more often than I succeed across the course of my career. Genuinely, uh to the to the blog you referenced right at the start, I published in the in the back of that the actual document we use internally to talk about how we build and it starts with like batting 500 is like the goal. So, like you're hoping to be right as often as you're wrong. So there's probably just too many specific examples of of times I've screwed up in my career. But there probably is a really common thread to it and I think it's probably an easy trap for for any PM to fall into which is like um averages mean nothing to the individual is probably the thing that I've like really scarred by. uh in any sizable population, it's really attractive to go and look at like average utility or average adoption of something and then you find that like you know only 3% of people use something and you're like cool we can probably get rid of that feature. It's not used widely but if you don't go a layer deeper and be like actually for like you know it's only 3% of something but there's a group of people for whom that's 100% of what they do. this is their core use case and for expediency's sake because somebody doesn't want to maintain a feature anymore you're just going to deprecate it and then it turns out you like blow up the use case of that group of humans and then to your last point about network effects the ongoing spiral effect of that can be enormous you know I I think about it a lot in e-commerce of like this is somebody's business right if we're just not reliable we're like deprecating a feature it's kind of like a westfield mold just turning off the power in the leadup to Christmas without thinking about it. And so like there are real downstream impacts to people's businesses that often come from just like a lack of nuance in understanding metrics, particularly averages. Like they they just lie to you all the time. And I think, you know, I've probably screwed up in all of the ways in my career, but most of the time I've made genuinely like I'm disappointed in myself levels of decisions. It's typically that I've relied on averages without thinking about the individual use cases that that are hidden underneath. Makes me think about uh Jeff Bezos has a quote, "When you have data and an anecdote, trust the anecdote?" Yeah, that's exactly right.
Well, Tom, we've gone through everything I wanted to talk about. Is there anything else that you wanted to share? Anything you want to leave listeners with before we get to a very exciting lightning round? I think all I'd add, mate, and I've really enjoyed the discussion, so thank you. Is like I don't think there is one way to do product management, and I don't think there is one way that AI will shape the industry. So like we're pretty confident that for what not the product we're building and the culture of the company that we have that you know this model of like fewer PMs who are more senior with like a lot more autonomy is right for us. Uh I don't pretend to you know presume that that will be true for the entire industry but I do think there has never been a better time to go back to the roots of like the actual product work and getting out of that theater and I think that probably is true everywhere. um even if you are still you know there are people who are wonderful people managers and really derive their satisfaction from doing that and growing and coaching and I'm sure there'll be loads of places where that's still valuable. So uh assume that at least half of what I've said is wrong in the same basis that half of the things I've probably ever shipped are not correct. That's why I love these conversations and why I think this work that we do together is important is we are living through the wildest time in our careers. So much is changing. so much is being rethought as you've described and it feels like the only way we can make our way through this successfully is to learn from how other people are approaching it. See what they've learned, see what they've has not worked. Take bits that resonate, ignore the bits that seem bombastic or not applicable. Exactly. Because like no one knows exactly where uh Elizabeth Stone had this great way of putting it. We're in this kind of there's there's a storming phase and the norming phase and we're in this storming phase of holy Like I remember in my PM career as I started writing and stuff everyone was always asking me how has product management changed over the last decade. I'm like it hasn't changed. It's the same basically but it feels like now it actually significantly changed. Although to your point earlier actually the core things and also not all be the same. Yeah. Yeah. So so that's why these are so useful just for people to see here's how a team is operating and what they've learned and here's things to try and it may not work for you but this is how we learn from each other. With that, we have reached a very exciting lightning round. I've got five questions for you. All right, here we go.
What are two or three books that you find yourself recommending most to other people? Okay. Uh, not to be cliched, but The Hard Thing About Hard Things, I still think is the best book written about product management. Um, just from a like breadth of things that you have to go through and try and do and screw up a lot. Slightly left field. Um, but the best book anyone's ever recommended to me to read, which I now recommend to others, um, is The Purpose-Driven Church by a pastor called Rick Warren. Uh, Emtt Sheer, the CEO at Twitch, used to basically make sure that people read it. It's this wild examination of why people emotionally invest in something and how to engineer emotional investment into it from a group of people. Uh, it's literally a like how to go and build a church uh, guide book written in the '90s. Uh, definitely worth a read if you're in a community product uh, of any form. Uh, and the last one is if you're looking for kind of like fiction or fantasy, which is where I tend to go in the evening, uh, Babel by RF Qual. I love when books have never been mentioned before get added to the cannon of recommended books. It's very fun.
Favorite recent movie or TV show that you've really enjoyed? Uh, Star City on Apple TV. Uh, if you liked For Mankind, it's kind of like the flip of that, but it's the Soviet side. It's like watching the Americans and, uh, For All Mankind mixed together. favorite product that you have recently discovered that you really like. Uh, this is a very deep cut, so I will apologize to most listeners. Um, uh, hopefully you've detected the accent. I'm told constantly that my Australian accent is going, but one of the things that I love is every time I go home, I realize actually a bunch of the government services apps in Australia have become phenomenal. You ever had that kind of concept where you're like, I wish the government has all my data or I wish there was just like one place where I could with one click get my driver's license renewed. I could like transfer titles and do all of the admin that slows you down in life. Services New South Wales actually nailed bizarre to me that I would ever come on a podcast and say actually a government run app in Australia of all places is it? But I was home recently and had to do all of my life admin and it's incredible. Wow. Uh something I heard recently about Australia, while we're in that topic, real quick tangent from lightning round is uh with a solar panel buildout that has happened there, there's more uh electricity available in Australia than they can use. Yep. And they're giving people huge in Australia. So it's like uh they're giving people free electricity in the middle of the day because there's so much available and they're like use all your stuff in the middle of the day because this is otherwise it's going to go to waste. There is a pitch somewhere that says if AI actually needs loads of electricity in order to be data centers, Australia's entire 21st century economy should be power. Incredible. That's like such good news that we are finding ways to generate uh so much energy from solar panels. A small piece of regulation 20 years ago that said if you're building a new property, you got to put solar panels on the roof. And it turns out it works great. Oh my god, I love this. I love this optimism of the future because you know the way through, not not the problem. here. Here. Okay, two more questions.
Do you have a favorite life motto that you often come back to in work or in life? Uh in my uni days or uh back in college for American translation, uh I used to have a party trick if I'd memorized if by Roger Kipling because I thought it was like deep and really meaningful. Um but I think if if I'm being really honest, uh I'll figure it out is probably the closest. You know, like it turns out most things are not as hard as people think. Like we'll work it out. I'll figure it out. If you're willing to devote the required time, money, effort, energy, you can solve almost anything. Uh, and if you're not, then it's probably not that big of a problem. Final question. I imagine people ask you this a lot, but I'm also just curious. What's something you bought on whatnot in the past month or so? That was just awesome, delightful, surprising. The most fun thing I've bought recently, I'm not joking, is a live lobster. So, um, recently one of the things that's really taken off on whatnot is our like, um, fresh and specialty foods kind of category. And so, there's this wonderful seller who goes by EFish Co. who has a seafood store down on the dock in San Diego. And every morning as the boats come in, he literally goes out and live streams all of the crates of seafood coming in off the boats and then he auctions them off live on whatnot and and ships next day to your door. So, uh, I got a a California spintail lobster shipped direct to my door, courtesy of a live stream. And how does this work? They put an ice ship it next ice kind of like container and it's a UPS overnight on my door the next day. And that is my agent commerce is going to be great, but not all-encompassing because I no intention of buying a spiny California lobster that morning. Unless your agent decides you eating lobster today. And we I got to be honest with you, it was delicious. Amazing. I did not know you could buy stuff like that.
Tom, where can folks find you online if they want to follow your writing? Uh, slashhiring, talk about what you're hiring for, and finally, how can listeners be useful to you? If you're looking for me online, I'm TD Robo, TD R O BBO on Twitter, which is or X, I guess, uh, which is where I, uh, do most of my musing. Uh, it's a mix of product management, uh, and yelling about Warriors games. So, apologies in advance. Uh, and then on LinkedIn is the other place I've put most of my kind of like work writing. I will say it's, uh, I I try for quality over volume. So don't expect uh daily daily drops from me on either side. Just bangers. Just bangers once a year. That was the nicest thing anyone said about being ages. Uh just just dropping casual bangers. And how can folks be be helpful honestly? Uh would always welcome feedback around whatnot uh and how people are finding it and what more we can do better. So like hit me up on either of those platforms with you know hot takes uh feedback or thoughts. And then uh you're hiring PMs even in spite of the many strong opinions about product management. Maybe just talk about that and where folks can apply it. Sure. Uh listen, we are constantly looking for PMs. The the intent of kind of like explaining how many people applied versus hired was not to discourage uh people. It was more along the lines of like the proliferation of product management doesn't itself naturally lend to the people with the skill set that you and I have spent the better part of 90 minutes talking about right now. But you know, we hired two people yesterday. I'm very excited for them to come start. Um and we've always got a variety of of PM roles open. the whatnot if you just kind of Google whatnot jobs um the the right kind of application process will will pop up there or I've found that you know the valley is small enough and product management is not that kind of obscure that you can probably find one of the 20 or so humans who work at whatnot and and reach out to them but we're looking at a variety of things right now payments very high on on our list of what we're doing as well as logistics so if anyone is really excited to come work on the future of shipping uh lobsters overnight uh it turns out there's quite a lot of nuance product happen there amazing Tom, thank you so much for being here. It was a pleasure, mate. Thanks for having me. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennispodcast.com. [music] See you in the next episode.
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