Introduction and the four question categories (0:00)
Aakash: Companies are hiring AI PMs everywhere right now, but the problem is there isn’t enough data on the internet to know what they’re asking for. I’ve helped over 50 candidates get AI PM jobs paying over $300,000 in the last year. Here’s what I’ve learned about those processes. AI PM jobs are now over 30% of PM jobs. Top AI companies like Anthropic and OpenAI are paying over $1 million a year to these AI PMs.
Unfortunately, the tactics you use to get a PM job and an AI PM job are dramatically different. AI PM interviews ask questions like AI product experience, AI technical knowledge, ML team collaboration, and AI ethics and safety. Today’s episode is literally everything you need to master all these question types with real advice and real mock responses done by me and Bart.
Bart, as part of the Land PM Job program that we run together to help folks land jobs at OpenAI, Anthropic, Meta, Google, Stripe, what have you learned about the AI PM interview process since the program started?
Bart: The biggest finding is that case interviews only end up being 10% of the interviews you actually get. Yes, at OpenAI, at Meta, at Google, you will face the case interview. But for all of the other roles, you’re highly unlikely to face the case interview. You’re actually just going to face a series of behavioral interview questions. And even at the OpenAIs of the world, you are going to face behavioral interview questions. So realistically, everyone who wants an AI PM job has to master all of the categories of behavioral interview questions.
Aakash: So what are the AI PM interview questions people should know? What are the categories people should focus on?
Bart: There are four broad categories of questions. The first, have you actually shipped AI products? They’re going to ask you situational behavioral questions like tell me about a time when you shipped an AI product and it had a big impact. Tell me about an AI strategy you’ve created. What was a time when you shipped an AI feature and the eval did poorly?
The next is working with ML and AI engineers. What about a time you had a conflict with the AI research team? How did you build evals that helped the ML engineers hill climb?
The third category is AI-specific tradeoffs. How are you going to trade off accuracy versus cost? How are you going to trade off speed versus quality? How are you going to trade off hallucinations versus actually answering their questions?
And then the final category is graceful failures. How do you deal with bias? How do you deal with hallucination? A lot of times they’ll ask about ethics and safety in this category as well. So you really need to make sure that you can strongly answer each of these.
Mock interview setup (3:28)
Bart: How about we show folks how it’s done and do a mock for each of these. So let’s say I work at OpenAI and I’m interviewing you for a senior AI PM role for the ChatGPT apps team. I’ll be their head of product, Amy Vora. So Aakash, tell me about yourself.
Tell me about yourself (3:48)
Aakash: Amy, it’s such a pleasure to be in this interview with you. I’ve actually been reading your content in your Substack for years. I’ve been following all your podcast appearances and of course I’ve been a huge fan of OpenAI since ChatGPT launched in 2023.
About myself, I started in PM back in 2008 working in SaaS. I’ve worked my way up from product manager to VP of product. In my latest role at Apollo.io, I managed a team of up to seven PMs, leading a cross-functional team of over 30 engineers and five designers and three analysts. My charter there was really a culmination of everything I’ve learned throughout my career.
I started in SaaS in 2008. Then I worked at ThredUp where I worked on search relevance and applications of ML to improve our pricing algorithms because we had hundreds of thousands of items and we needed to price them in real time. Then I worked at Epic Games where I launched the very first AI in a major shooter. What these AI did is they acted as humans and they solved the critical problem that beginner players were getting stomped on by expert players. Now they had somebody to stomp on which were the AI. And the skill-based matchmaking that we launched along with that was the single biggest thing to move retention in my 3 years at Epic Games.
Then I worked at a firm where our ML risk algorithms were responsible for making loan decisions. Very high stakes decisions when it comes to an AI application. I led the activation team. Eventually I led the entirety of the growth experience team and sat on the senior leadership team.
Most recently at Apollo, I’ve been working on how do we grow this company from $800 million in valuation to $2.5 billion in valuation. And luckily we did so successfully with a bunch of AI features that we launched on the growth team. Some of the most successful were the AI email writer and the AI agent. So I’ve really spent my whole career applying ML and AI to build apps and that’s why I really think this role would be an exciting next step for me.
Feedback on tell me about yourself (6:02)
Bart: So dear viewer, pause the video for a second and tell me what do you think Aakash did great here.
Did you notice that Aakash didn’t really talk that much about himself? I didn’t hear about his children or hobbies. He didn’t really talk about himself as a PM. He actually was a skilled politician who answered the question, why would we hire you at OpenAI as an AI PM? Because that’s the goal of this interview. That’s what’s actually being asked between the lines.
While Aakash is a phenomenal person to spend time with, they didn’t invite him to evaluate whether he’s a good fellow to bring to a pizza party. They need to know whether he’s a fit for the AI PM position or not.
Aakash: Two or three things I attempted to do in this response. Number one, be concise. I tried to keep my response to less than 2 minutes. Number two, tell a really clear story. What I tried to do is say I’ve had this 16-year career arc, here are the steps that are relevant to an AI apps position within that. Then I tried to show that I’m at a certain level of seniority. I tried to name check the amount of PMs that I managed, the valuation, the titles so that I could position myself for a more senior, higher paying role.
And then finally, I infused a little bit of the question behind the question. Not just tell me about yourself, but why are you here today? That’s where the specific references showing that I’ve studied the interviewer Amy Vora and the specific references to ChatGPT are going to differentiate me just 1 or 2% compared to the other candidates. And that little 1 to 2% can mean the difference between “hey we liked you” versus “hey we gave you the offer.”
Category 1 – AI product experience (8:50)
Bart: Let’s move on to the first category of AI product experience. Aakash, tell me about a situation where you’ve shipped an AI product.
Aakash: As we kind of saw in my tell me about yourself, I’ve been shipping AI products for the last 10 years. But if I had to pick one story that I think was particularly fun that highlights how I can think innovatively about AI applications that can actually drive results, I would highlight the one I mentioned in Fortnite. I told you already it was the biggest change to retention we’d ever seen. Let me unpack that issue a little bit more.
The problem we faced, and I actually uncovered this in the data and made this the product strategy for the upcoming season, was we had an abnormally high new user churn rate. We were 3 years into the life cycle of Fortnite and when we started Fortnite the 30-day retention was something insane for a game. It was like 90%. But when I started at Fortnite two years later, it had dropped to like 75%. And then it continued to drop for a year to like 65%.
I said okay, this is a problem for us. We’re acquiring millions of new users a day sometimes, especially when we had a new season launch. And these millions of people, not all of them are coming back as frequently as they used to. So naturally, like any good product manager, I watched some of them in their sessions. I watched some of their game replays and I talked to some of them and there were a couple key themes that came out.
Number one, it’s hard for us to learn the mechanics. So we shipped things like walkthroughs and better videos illustrating the mechanics. Number two, I feel like I’m getting placed against people who are way better than me. So we shipped skill-based matchmaking. Unfortunately, those two weren’t quite enough. Even with the skill-based matchmaking, we faced this third problem, which is I feel like I’m not getting enough time to learn these mechanics. People are just killing me.
In Fortnite, people usually have some skill in first person shooters, but Fortnite has this very unique mechanic called building. What an experienced player would do is they would box up the new player. They would build around the new player and the new player would have no chance. The problem was people would learn building pretty fast like in their second or third month. There wasn’t a big enough pool for us to matchmake just amongst the first month players.
And you might say, oh gosh, why not? If there’s millions of people a day, why isn’t there a big enough pool? Well, it turns out in a first person shooter, the latency, what’s known as the ping, really matters. So we actually matchmake with people in your local region. People in New York State are not matching with California. People in Israel are not matching with people all the way in India. The pools were actually really small.
What I realized was that the fundamental problem was that our human pools for new players were too small. To compound onto this issue, Epic was fighting a war against Apple and Google and we got kicked off of mobile. Fortnite mobile which was generating 80% of our new players was kaput.
Then when I saw all these factors I realized we needed to think about how we can bring AI into the game. What if we had AI that was disguised as a human? That was the insight. Worked with some game engineers to mock up some prototypes. The initial prototypes were quite bad because they were based on mechanistic rules. If you get shot at, shoot at them. They felt like AI. But when we actually built AI into the system, when we used neural networks, we were able to create players that acted and felt like humans.
Once I saw this in play testing where people didn’t realize that they were playing against AI, I knew this was ready to ship. We launched this feature. I asked the AI engineers what percentage they thought we should launch. They said 1%. We launched at 1%, monitored it, ramped to 5%, 10%, 50%. It was looking great. We ramped it to 100%. And when we did that, that’s when we saw those amazing retention increases. Ultimately, it was something like a 7 to 8% retention lift, but that really compounded and it generated hundreds of millions of dollars in revenue.
Feedback on AI product experience (16:29)
Bart: What I liked about this reply was the storytelling. It wasn’t jumping to conclusion. It wasn’t about focusing too much on AI technically. AI was the element of using AI to solve real problems. In order for me to fully embrace and immerse in this story, I needed to understand the background, the reasoning, the core problem being solved and the evaluation metric which was given a name and a value. The metric got me as a backbone of the story in driving Aakash’s motivation on using AI, why AI was used, and why it was successful.
Aakash: As you can see I tried to have a concise response. This time I might have been 2 minutes 30 seconds but that’s because I was storytelling. I tried to keep it so that the interviewer was engaged. I’m not using ums and ahs. I’m also not reading off of a script. I’m actually looking at how is Bart reacting. What are his facial expressions? Since he seemed engaged, I said okay, I can go a little bit more than 2 minutes.
Actually, I chose the gaming story because in my tell me about yourself, I saw Bart light up on that story. So it’s really about reading the interviewer signal. Number two was putting in how I as a PM operate. Really leaning into user insights, teaming up with other PMs, building a strategy, teaming up with game designers and engineers. Some PMs, the way they tell stories, it’s like I did everything. Of course you didn’t. You worked in a team context. I tried to differentiate what I versus we drove.
Category 2 – Technical AI knowledge (19:57)
Bart: Let’s move on to the second category. Technical AI knowledge. Aakash, how would you evaluate if an ML model is performing well?
Aakash: Can I actually take a second to structure my response here?
Bart: Take your time. We’re in no rush.
Aakash: This is a big question. I’ve kind of written down for myself a three-level framework. The first level is offline evaluation. The second level is online evaluation. And the third level is business impact.
Offline evaluation. This is the topic that Kevin, the former CPO at OpenAI, has talked about a lot. Evals. Evals are the new PRD is what some people say. I’m not sure I quite agree with that but I do think evals are critically important. Personally as a PM I try to have a very active role versus just outsourcing the eval to my AI engineering team.
When I think about evals I take the Hamel Husain and Shreya Shanker approach. I’ve taken their class and what their class says is that you want to figure out what are the actual failure cases. A lot of people focus on precision and recall and yes those are important and they use generic eval packages like the ones OpenAI has. I think those are good and a baseline. But the higher level is doing what they call axial coding of responses.
What you do is you look at either synthetic or real data. For my AI email writer at Apollo, I looked at real data on the 1% group. I said what are the failure cases and I axially coded them. One example, it’s referencing the wrong name of the recipient. Another example, it’s referencing incorrect data about my company. Another example, it’s not using the best practices we know that are going to drive email open rates in the subject line.
I put together this whole list where I figured out all of the errors and I coded them into groups of errors and then I created an eval framework for my AI engineering team. I said here’s few-shot examples. Four or five really good, four or five okay, four or five bad. So I spent the time to generate these 15 examples across these groups so that they know what good looks like and then they could construct a metric-based eval to hill climb upon. That’s the first group, offline evals.
The second group, online evaluation. This is your standard product testing. Having A/B tests, ramping it up from 1%, 10%, 25%, 50%. Looking at how many people are accepting this email without edits, how many people are sending this email, what is the open rate compared to a human-generated email.
Then the final category, business impact. For the people that we have launched AI email writer, are we seeing more emails being sent which results in more credits being used? Are we seeing people buy more credits? Are we seeing people potentially upgrade from one plan to a higher tier? Are we seeing people retain more? Across these three levels then I really make a decision. Is the new AI email writer model good or do we need to iterate more?
Feedback on technical AI knowledge (23:44)
Bart: Notice how Aakash not only provided the theory correctly but structured it in a way that immediately implies that he knows it, understands it, and knows how to apply it. It’s not just something that he read but he actually lives and breathes, has his own interpretation and best practices, and leaves me with confidence that he knows how to evaluate an ML model.
Aakash: That’s exactly what I was trying to do. I was trying to not give a textbook ChatGPT or Claude response. I think that’s the baseline. Yes, you can get a 7 out of 10, but your entire competition group is using that too.
The two things I tried to do. Number one, reference my gurus. This is my philosophy on the topic. Hamel Husain and Shreya Shanker. I even mentioned that I know this is important because Kevin while at OpenAI has talked about it. So I’ve brought in some company-specific context and person-specific context. I’ve tried to make it a response that ChatGPT simply couldn’t create.
I also didn’t have a canned response. A lot of you are going to take the list of 80 questions, come up with a practice answer, and then try to use your practice response or AI to help you in the interview. That’s a huge mistake. I’m not using any AI. I did take a minute to write down my thoughts. And I think that’s very effective. It actually shows that you’re thoughtful about it.
I’ve mock interviewed people who give me a six, seven minute response to this. Mine was less than 2 minutes.
Category 3 – ML team collaboration (25:58)
Bart: For the third category, ML team collaboration. Tell me about a situation when you had a conflict with your AI team and how did you resolve it?
Aakash: Can I take a second to choose the right story?
Bart: Take your time.
Aakash: I want to talk about something we haven’t talked about yet. So I’m just thinking about all the details here.
Bart: It’s already promising if you have to decide which one is the fun one.
Aakash: So I actually come up with an interesting story. We haven’t talked much about my time at ThredUp yet. I did reference that in my tell me about yourself. One of the most significant projects I got to work on at ThredUp, and all credit to James Reinhardt, our CEO, who said invest on this because I wouldn’t have gotten the AI and ML engineer resources otherwise. I was the growth product lead. I wasn’t actually leading these engineers originally. But he said Aakash is the right person to work on this project because Aakash is the one building all the systems to customize the experience for new visitors. So Aakash, I want you to build out our new pricing system.
What was the conflict with the pricing system? The big conflict, and this is dating the time, this is back in 2015, 2016. Should we use person-level information? That was the conflict. AI engineering team says no. Me, I’m saying we have to do that.
The reason James put me on this project is because I had just shipped a massively successful project using person-level information to customize what items and what homepage we were showing to people. We were an online thrift shop. We had 100,000 brands. Some people are shopping Anthropologie, they’re buying $100 shirts. Other people are shopping Old Navy and they’re buying $1 shirts. There’s a huge difference in what an Anthropologie consumer wants to see versus an Old Navy consumer.
The AI engineering team had three concerns. People are going to feel it’s creepy. We’re going to run against the law. It’s not going to be ethical. So what I had to do was one by one figure out who really held which concern on the team. I had to take a person-level approach. We had seven engineers on this team and one designer who was functioning like an engineer. So eight people.
For each of them, I needed to figure out, are you in the creepy camp, are you in the legal camp, or are you in the ethics camp? And then for each, taking a person-by-person approach, not just calling a group meeting and saying guys, this is why your three things against this are wrong.
For the creepiness camp, I facilitated the conversation with James the CEO and our COO who was the other really important C-suite level executive for pricing. I got the CEO and COO to really say nope, we don’t care about the creepiness, we need to build it in a way that it’s not creepy. That influenced the AI engineers more than any evidence I could provide.
On the legal concern, a somewhat similar approach except not using the C-suite but using our legal team. Me again steelmanning the legal argument and then getting the legal team to tear it down. So I wasn’t the one tearing it down. They were. I felt like my engineers felt like I was on their team.
The final group, the ethics camp. This was harder. I didn’t rely on executives or outside groups. Once we had conquered the other two issues, I had us ourselves hold discussions around the ethics. We said well, probably what the ML is going to do is figure out you’re richer and charge you more, figure out you have less money and charge you less. Is that unethical? We looked at other retailers and I got the team to realize that actually we’re going to be charging people with less money less. The ethics could actually be quite good.
By taking this different approach to the different blockers, an individualistic approach, I was able to get everybody on board. Not just to build this system, but this was a year-long project. We shipped iteration after iteration and the whole team stayed on the project.
The metric we were most caring about was first visitor to purchase conversion rate. We were able to increase that on a relative basis by nearly 15%. It was one of the best improvements we had in my time there.
Feedback on ML team collaboration (31:54)
Bart: I really liked how you put me in the middle of the story with the right context, how you basically acted as a whip to get everyone’s concerns approached and addressed.
Aakash: What I was hoping to do in this reply was very clearly show what is the conflict. When I mock people sometimes it’s not clear what the conflict is. I gave a lot of weight to helping the interviewer really understand the conflict, the two sides, why we sat on those sides, who the conflicts were with, and to make it seem like a legit conflict. Too many people give me a fake conflict.
Number two, tie it back to huge impact. Sometimes people will just say I resolved the conflict and that’s it. I ended on a high note. We increased relative first visit to purchase conversion rate by 15%.
Finally, I tried to show that I used multiple methods. Not just like the C-suite backed me. I actually showed how I used three different techniques. I chose a story where I did a lot of politicking and I can talk about the politicking. The resolution was not shallow. It was ongoing and the team kept working on the product for a year. No one left. It was an actual flip so that the AI engineers were on board at the end.
Category 4 – AI ethics and safety (34:51)
Bart: Let’s move on. Tell me about advocating for AI safety versus shipping pressure.
Aakash: So we were just talking about ThredUp. Let’s continue the ThredUp story. I told you about the first 3 months of the conflict. Let’s fast forward 3 months to month six. ThredUp acquired a company in Europe. Europe. Regulations. More concern about ethics.
Two concerns came up. Concern number one, an AI engineer on my team uncovered that we were surfacing racial bias in our pricing algorithm. Certain zip codes with higher percentage of white people were seeing higher prices. Certain zip codes with higher percentage of people of color were seeing lower prices. This could be a big problem both for the ethics of what we were trying to do but more importantly also for the regulations. The EU has some very specific regulations around this. I believe this was pre-GDPR. It was something they had around a racial discrimination act. It varied by country.
For me, the first thing was I don’t want to become the PM who is just pushing aside these ethical and regulatory considerations. I’d already done that effectively 6 months ago. I already knew where the perception of me could be negatively seen. So I wanted to be the most upstanding for AI safety and ethics.
What I did is I said we’re going to have to delay this feature. I told everybody this feature is on pause. It’s on hold until the CEO of the acquirer, the legal team of the company we acquired, and the new AI engineer that we had gotten from them, all of them were happy, as well as the pre-existing AI engineer on my team that had surfaced the concern.
The first thing I did was accept the delay. But I kept it in the back of my mind. Bringing out this pricing system, 15% improvement in first visit to purchase, it’s really going to help us in Europe as well. So when the planning came up for the next quarter, for all of those people, I brought it up in exactly this way. Hey look, the data from America shows a 15% visit to purchase improvement rate. This is awesome, right? We want that, right? And they were all like yeah. Then I would voice their concern for them, the ethics and legal concern.
The AI engineers came up with an ingenious solution to solve the racial bias problem. They worked on the system so that we weren’t doing that. They made it so specific, so person-specific that the zip code correlation dropped out. Once we solved it because I stood for the AI ethics and safety and was willing to almost shelve the feature if needed, they themselves were then going to the legal team and pinging the legal team even without me. Saying hey look, we solved this issue, now is it legal? Can we do it?
We shipped it. It was a quarter late. The CEO wasn’t happy about that. I ate the blame. I just told him yeah, I basically delayed it. But he appreciated it. When it came around in 360 feedback, everyone said I loved how Aakash was on our side for AI ethics and safety. That ultimately led to me getting promoted.
Feedback on AI ethics and safety (39:24)
Bart: I love how you turned the story from what could have been a very boring technical legal drama into the way that you can change something that’s a showstopper into an actual success and opportunity to get promoted. That’s what you really want to do. If you ever are asked about your weakness, your failure, your struggle, you really want to turn that around and tell them about what you’ve learned, what you’ve achieved, and how you made a bad situation great for you, product, and the company.
Aakash: What I was hoping to do is show that my responses aren’t canned or highly practiced. I actually chose a brand new story that I had never thought of prior to this recording but I was just riffing off the last question. I was trying to make it a conversation. I took a little bit of time once I had chosen the story to figure out what details I wanted to surface.
One thing that’s very important for these companies and roles is they do not want the PM who is bulldozing AI ethics and safety. They actually want the PM who is making the righteous stand for these things.
Category 5 – AI product strategy (41:30)
Bart: For this final category, tell me about an AI product strategy that you’ve created.
Aakash: We talked a little bit about the AI email writer in the context of how I had evaluated an ML model, but I never got to tell you about the strategy context around that. I was pretty involved in building the strategy and it’s one of the strategies I’m most proud about.
To give you a little bit of recap. We said $750 million to $2.5 billion SaaS. What is the SaaS doing at Apollo.io? It was a sales engagement tool. It wanted to be basically the main software that a sales team lives in outside of Salesforce.
Apollo had built an amazing engine before I came around contact database. We had one of the world’s best contact databases to get the emails and phone numbers of the people in your account that you should be reaching out to. But when I looked into the data on this feature, I found that it was very much a one-time type of feature. It wasn’t a recurring feature.
This was actually one of the insights that got me hired. I had written a product strategy in the interview process. When it came around to planning a year later I said it’s time to really triple down on engagement. ChatGPT launched in 2023, you guys changed the world, and we knew we needed to use it. My critical insight pushing the team was let’s not just focus on the contact database, we need to improve retention.
The problem with Apollo was like everybody else, it started at the bottom of the market. Small and medium-sized businesses churn. We needed to take them from contact database to actual tool they are using to make the contact. At the time there were tools like SalesLoft and Outreach which were well established. So I said how do we replace Outreach? How is it Salesforce plus Apollo plus Apollo instead of Salesforce plus Apollo plus Outreach?
We realized that AI could be the big wedge. I helped us come up with three major vectors for our AI strategy. One was the email writer. The second was email warm-up. And the third was email responses.
Email writer was a big success. Email warm-up, we had some huge fits and starts. We actually had to turn off warm-up for a while. It was a bit too early. Some of the emails were a bit too ridiculous. But as OpenAI’s models evolved and as we improved our fine-tuning and our RAG pipelines, we relaunched email warm-up eventually. That led to a massive uptake in our engagement tool. The problem people faced before was they would create a new email address for their salesperson and the person would tank their own deliverability because they’d send bad emails that would get marked as spam. With the email warm-up tool, once we fine-tuned the models enough, we were able to build back-and-forth email conversations that would essentially create credibility for an email address before it was ever used.
Paired with the email writer, this led to a huge increase in adoption of our engagement products. As that adoption increased, something like 10 to 20%, the retention increased. We knew this mechanistically because we had shown that if we get people in higher retention products, they retain better ultimately. The end result was a huge improvement in gross revenue retention. It was part of the reason they scored that $2.5 billion valuation.
My role in the strategy was really pushing the team to think beyond the initial product they had, to understand the new avenues that AI enabled, and then help drive the execution.
Feedback on AI product strategy (48:05)
Bart: I love how it was not a strategy to introduce AI into anything per se, but to solve a problem. As an AI PM, you need to have that strong PM foundations and focus in order not to fall in the trap of putting AI for AI’s sake. In the era of AI, many solutions that were not possible in the past are now up for grabs. It’s all about choosing the right problems to solve, finding the right strategy, and using AI when it’s called for.
Aakash: What I tried to do was number one, show what was my role in contributing to the strategy. Little anecdotes like I built this in my interview, I’m following up, I worked with the team. Number two, showing that it’s a real strategy. Some people show a strategy that influences three months of work. I tried to show this influenced years of work. The final leg of this strategy is still ongoing. And then I tied it back to metrics. I didn’t remember the exact metrics on the top of my head, so I just gave a rough range, but it still helped people say okay, he’s focused on driving the actual business impact.
Six overarching skills for AI PM behavioral interviews (50:14)
Aakash: Those were all the categories we had, but tell me, what are the overriding skills for behavioral interviews AI PMs should internalize from all those categories?
Number one, see how specific I was? Real architecture, real outcomes, real stories. Number two, see how I was connecting technical to business? A lot of times I see people not have that ending where I connect it back. Number three, see how I showed iteration? I said we even paused the email warm-up for a while. Experimentation and failure are expected. If your stories don’t have that, people are going to think they’re fabricated.
Number four, I demonstrated collaboration. I talked about how I worked with the CEO and COO. In every response, I mentioned the ML engineers or the designers. Nothing is done alone as a PM.
Number five, include ongoing operations. I talked about how they’re still building it, how this is still happening.
Number six, use STAR+M. Situation, Task, Action, Result, Metrics. Why do we have that M at the end? You noticed in all my responses, I always ended on metrics. That’s because the PMs who are getting hired today, they have to drive impact. Boris Cherny said most software engineers are going to start to get the title product builder or manager. Most designers are going to start to become product managers. How do PMs differentiate as PMs anymore? A lot of it is metrics. And that’s why you need to have that business orientation, not just that user orientation.