The Ex-Google PM Secret to Landing the Offer

0:00 No interviewer knows everything, and they need you to connect the dots and actually tell the story. >> Gal Elsh has been a PM at Google for 6 years, and most recently was a principal [music] PM at Microsoft. >> Google is is is a lot about just being a good person, really being a good person, doing the right thing. I mean, Google's tagline for most of history was do no evil. >> A lot of the information about Google is probably [music] outdated. So, can you walk us through? What are you hearing on the ground?

0:26 >> These are the five types of interviews that are currently in the interview loop in 2026. The first one is product vision. This is the most classic product question. How would you build maps for blind people? The second one is product analysis. Anything that has to do with metrics, [music] analytics, experimentation. Next, we have strategic insights. The most common pitfall here is starting like the CEO, and after a few minutes becoming an L5 PM and really solving tactical things. Then, we have execute with judgment. It's all here.

1:00 It's about how you work with engineering team, and how you actually solve day-to-day problems. The last one, which is the newest one, which >> 99% of people actually fail [music] because of behavioral. >> Remember that it you're always telling a story, and you're coloring it with very specific colors. So, before starting the story, understand this question, which colors is it looking for? >> What's the right way [music] to prepare? >> Another really important thing to do >> Before we go any further, do me a favor and check that you are subscribed on YouTube and following on Apple and Spotify podcasts. And if you want to get access to amazing AI tools, check out my bundle.

1:42 Where if you become an annual subscriber to my newsletter, you get a full year free of the paid plans of Mobbin, Arise, Relay App, Dovetail, Linear, Magic Patterns, Deep Sky, Reforge, Build, Descript, and Speechify. So, be sure to check that out at bundle.akashg.com and now on to today's episode. Googliness. It's one of the most elusive words in the job search. Just about everyone wants to land a job at Google, but everyone's kind of at a loss to explain what Googliness is. That's why I'm really excited to have Gabi Shell on. Not only was he a PM at Google for 6 years and sat on the hiring committees to see whether they should hire people and whether they were Googley enough, but he's been a PM at Microsoft, at Melio, and now he actually coaches people, which gives him a level of insight that the average Google PM wouldn't have, which is how do you actually take people from not scoring well on Googliness to scoring well. The first half of this video will be for anybody who's interviewing for any role at Google. The second half will be specific to PM and APM roles. If you stay till the end, we will break down what Gabi has been seeing in his coaching experience is the latest Google PM interview loop so that you can actually prepare for what you can expect in 2026, not outdated advice from a long time ago. Gabi, welcome to the podcast.

3:06 >> Hi Akash, excited to be here. >> Gabi, one of the things that really stuck with me in our prep call was when you were talking about this book The Storytelling Animal and how more than memorizing different facts or frameworks, people being able to execute on storytelling is one of the keys to Googliness. Can you break this down for us? >> Yeah, okay. Now, this is something that I really um stress when working with candidates. Candidates many times come with the idea that their interviewer is a really smart person, which usually is correct, but they also come with the idea that their interviewer kind of knows everything and it's enough to just dump um a lot of facts and and they will connect the dots together. This is not true because no interviewer knows everything and they need you to connect the dots and actually tell the story.

3:57 Now, if you don't do it for them, they will do it and not necessarily in the way that you want them to do it. Because and this is what you mentioned regarding the storytelling animal, this is actually a a theory that I really like to refer to. Um it was actually coined in the in the '80s in the early '80s by a um a communication theorist called Walter Fisher. Now, what Walter Fisher said is um we all know about Homo sapiens. Homo sapiens is the wise man, the wise human.

4:32 This is we are Homo sapiens. But what what he said what Fisher said in the 1980s that after evolving and after learning to speak so and telling so, we have evolved into something different. We are no longer Homo sapiens, we are now Homo narrans. Homo narrans is the narrative uh human, the storytelling animal. This later became more popular in in a in a book in the early 2000s. So, basically we're a storytelling animal.

5:05 What that means is that um what all of these are talking about, we can't really have random pieces of data held in our mind. The moment we see data, we the moment we see facts, we try to turn them automatically in an instant of a second into this story. As an example, I come to my kitchen, I see the refrigerator door open, I see milk carton spilled on the floor, and I see my cat sitting happily in the sun.

5:36 These are three facts, but I as you most probably did have complete story now in my mind. I know exactly what what I know the timeline, I know what happened. Um this is not necessarily what happened. Maybe some other thing happened, but I create I always create a story. Um now um we don't want the interviewer to create some kind of story of random facts that that we tell them. Now, let me give an example.

6:08 Um the interviewer asks me to tell about myself. So, I tell them I'm a computer science major. I actually finished um third in class. I'm uh I worked a bit as an engineer. Then, I started working as a back-end PM in a small cloud company. Um very end heavy, very back end. I moved to Microsoft Azure, and I continued working in a cloud with back-end teams. Um then, I transferred to a more user thing facing role. I did that for a few years.

6:42 And then now I'm responsible now I'm in this uh new company again doing a user facing role. Now, I told about myself. I told all this these facts. Now, connecting these facts that I said together, I can imagine a few different stories. Someone who really loved computer science and somehow went with the flow to become a front-end PM, a user facing PM instead of what they love. I can imagine a different story. I can imagine many different stories, but let me tell this very quickly in a in a different way. This exact same story. I started as While I started as a computer science major, I always um I always was fascinated by how people perceive things, how users use products. So, immediately after graduating, I worked a bit as an engineer, but immediately moved to a PM role. I was still at a back-end role, but still was fascinated by how how everything every action that I do influences end users. Eventually, at Microsoft Azure, I got the first chance that I was really looking for to work with act on an actual user-facing role.

7:48 Um then in my last role, the role that I'm doing now, this is what I'm doing full-time and and this is really where I was always aiming. So I added a story. I colored the exact same facts. I colored it and turned it into a story that that tells a lot about me and my passions, where what I where I'm aiming my career, what makes me, what drives me. So um this is really important to turn the the the facts into a story.

8:18 >> So if you just give people facts or data, they're going to write the story themselves. >> Yes. >> Can you walk this through for us? Maybe the same situation, two stories, two different candidates, how this would look? >> Yeah. Okay, so so so let me give an example like um it's not a career example, it's a real-life example, but um but maybe I'm being asked um tell me about something interesting that happened in the last week, something that you're proud of. Now let me give this example.

8:46 Um I have this neighbor. Um yesterday I was coming to the driver, I saw the neighbor pulling in and pulling out 18 bags of groceries out of the um out of their car. Um now that neighbor I know lives in a four-story apartment and yesterday the elevator in our building was not working. What I immediately realized that I want to do is um I immediately went and and and offered my help because we needed to go many times up and down and then after going the first time up and down, I realized this is going to take a long time. I saw another neighbor, a teenager, asked them to join, so the three of us did it together and we finished kind of it was a lot of work. We went four times up and down eventually and and we and we finished and it was um I think the neighbor was really happy.

9:35 Let's summarize a bit what what I told here. Um I told about 18 18 bags. Um I talked I talked about a broken elevator. This was the situation. Um then I described um five or four trips up and down, which is a lot of work. I also told how I recruited a neighbor. This is more or less it. These These are the things that I talked about. Now, an interviewer listening to the story um would what would they learn about me? Um they would learn that I'm good at um heavy lifting. Heavy lifting, I'm not afraid uh Yeah, literally heavy lifting. I'm not afraid to do hard work.

10:24 Um also, I'm good at project management. I realized that we're we don't have enough people. I recruited the neighbor. Um interesting. Now, let me give let me talk in another way. The exact same story. But I'm I'm showing I'm pointing out to different things. Um yesterday, as I was coming back home, I saw my neighbor who lives on the fourth floor um pulling out 18 bags of um 18 bags of groceries. Now, this neighbor is an 83-year-old elderly widower.

11:03 Um I know him for for over a decade. His His wife passed away like 20 years ago. He's He's very independent. He does everything alone, but still he's 83 years old. So, I immediately thought that they might need some help there. I offered I politely offered to help them because I didn't want to make it seem as if they can't do it themselves. So, I politely offered to help them. They were actually very happy. I actually took most of the bags myself because what what to do? I'm a bit stronger. And actually joined the asked another neighbor to join, a teenager neighbor joined me. We did most of the lifting ourselves. Um and he was really thankful. He really thanked us. Um um it was something that I'm I felt really good after that.

11:51 Now, um from this story, what what are the fact that an elderly I have an elderly widower. Um I didn't want to um I didn't want to somehow imply that they're not independent. I came very politely. There was a big obstacle. Um this is more or less it. Now, um what do you learn about me? You learn that I'm a people's person.

12:22 I'm a people's person. I'm a empathetic. Maybe a few other things. Um but you see, it's exact same facts. I just um chose which facts to highlight and with which color to color my story. And we'll talk a bit more about how to color the story, how to color it actually in Google colors. Um the red the the red, yellow, green, and blue. Um Yeah. So, um this I think is is an example that that I like to show um of of how um how stories can differ.

13:01 >> What is the interviewer's brain doing differently in story A versus story B? >> Um okay, they take the facts and immediately, not only do they build the story, I mean, I told the story. They don't really need to build the story, but they they build a story about who I am. Now, in the first in the first story, they build a story about someone who get things done. Someone who's not afraid to to take responsibility.

13:26 And the second story, you have this in a way, but much more you have someone who can work in a team, someone who is a collaborator, someone who sees other people in the team, and uh and really takes note not to offend someone, although they are actually doing I'm actually doing the work for them. So, they build a completely different view of who I am. Now, this is important. When you're being asked in these behavioral questions, Google ask questions, you always say, "Okay." You tell the interviewer, "Let me take a minute to think." And dozens of stories start running in your head. So, before choosing a story, it is important to take the interviewer's question and break down what is the theme of this question? What are they trying to learn about me in this specific question?

14:21 What I suggest maybe we can do a more career style um um demo of of how this works. >> Yeah, I would love to. What if we took like Tell me about a time you had a conflict with a coworker. >> Okay. That's the classic, maybe the most classic behavioral question. Let's do the same. Tell me about a time you had a conflict with a coworker. Okay. Um I'll give an example a hypothetical example. I didn't really work at Gmail, but I'll give a Gmail example because everybody knows Gmail. Um so, let me tell you about a time that I was a uh PM in the Gmail core team, and how I managed to push our team to deliver live features which landed really well with our customers. Okay. So, we're working on Q1 planning. We just recently launched AI auto reply feature, and user reviews on the feature were mixed.

15:15 I was already working for a few weeks with our tech lead on ideas for significantly improving that feature, and and I wanted I wanted that to be the main focus for for the team in the coming quarter. However, when I presented these ideas to the eng manager, um he strongly pushed back. He mentioned the known fact that over the past quarters, um the rate of failed messages has been increasing steadily, and we we said that we would be working on it. Failed messages means that a Gmail that an email is being sent for some reason it doesn't reach the destination, and we do retries, and it can delay the message for for many minutes, sometimes maybe even up to an hour. Now, we both had the same goal of improving user satisfaction in the coming quarter. We just came from different perspectives.

16:08 Now, um I was really eager to to launch um these new AI features that users were um eager to have. So, what I did, I brought data that showed that actually less than 2% of the users have been affected by the slate messages issue in any given month. I also brought data that showed that nearly 60% of our users have tried automatic AI replies at least once in the past month, and less than 50% of them said they would actually recommend it to someone. So, we had a user satisfaction issue. Um So, I'm looking at these pieces of data, and it seemed seemed clear to me um that that I It's kind of convincing, but I didn't stop there. I actually um suggested that we both go um to the VP and present um both options.

17:00 We can either do this, and we can do that, and present them fairly. This is what we actually did, and um the VP was actually convinced with my data, and we ended up launching these new features for automatic AI responses. With 60 Within 60 days of launch, we increased user satisfaction by 23%. But, I I not only did this, I also um placed the the fixing the engine issue um as a P0 for the next quarter.

17:34 Um now, what I learned is I actually knew that that I coming with data is not something uh um is not something new to me. I knew that convincing with data almost always work. But, what I learned is is what worked really well is also paying attention to what the engine manager came up with and putting it P0 for the next quarter and making things work really smoothly. Okay. This was story number one. Now, intentionally, I didn't do a really bad answer. It was kind of an okay answer.

18:06 It was I can say it was I can score myself um above average. Yeah. Was it a good answer? It was a good answer. Um I convinced the end end manager with data. I took care of everything. It's a good It's a good answer, but um we will soon be talking about Googliness and let me demonstrate um another way to answer this question, another version. >> Let's do it. >> Okay. Again, um story about my um hypothetical time as a as a PM in the Gmail core team.

18:38 Um now, let me tell you about a time um when we were incorporating an experimental feature of an AI-sorted inbox. Instead of showing you the the mail as it as it came, we have all sorts of We have an AI algorithm that shows which emails we would care most about. So, sorting it um Now, solving the inbox problem might be considered one of the highest priority task for the Gmail team. So, we knew we were going to somehow put AI to this and and we came up with a sorting recommendation idea.

19:09 Um now, the question was are we going to build the feature completely in our team, which was the direction that I wanted to go, versus using I mean there are uh recommendation many recommendation algorithms within Google. We have of course um the search team does it a lot, the maps team does it, many other teams. Um so we could reuse um reuse a lot of the knowledge from other teams. Now, for me this would first of all um create a dependency, which I didn't like, on other teams, um because I want to run fast. I don't want to have these dependencies. And I thought that the problem space we were discussing was really different because they they deal with kind of if we talk about Google search, they deal with endless data, whereas we deal it's a completely different problem. We have a few dozens, maybe a few hundreds of emails. These are very very small numbers, and we can really go through 100% of the data. It's a completely different problem.

20:05 So this is what I presented, and I actually again I worked with our tech lead um and it it it was um the direction that I direction I was aiming for. What happened is that um um our eng manager um really pushed back. What he said is that um this was would take us around three to four quarters to launch, whereas using other teams other modules from other teams, we can launch within it the coming quarter with of course some um limitations.

20:38 Um So he actually showed the numbers, the number of engineers needed, the time to launch. I was still skeptical, and then I pushed I said, "Okay, let's do a quick POC." So what he did, he took one engineer and in two days he showed actually surprising results with a really quick and dirty solution and surprisingly not bad results. And actually there I had to pause for a second, and I said, "Okay, um I think actually he convinced me. I think his direction would get us to to um to what we want much quicker." What happened eventually is um that we did launch um using using other models and we added tooling first so we would be able to swap in the future really quickly.

21:28 To cut a long story short, it was live for a few months um and two things became clear. The feature had very little traction. Users kept turning it off. They didn't love it for various reasons. Um another thing during these these months we came up with a totally different inbox solution, which was actually um much better and ended up um getting a lot of love from users. Um what I learned here is um that it's many times import- important to um not find the perfect solution.

22:05 One, two, um really be open to your eng manager. Um because here uh yeah, opening to different perspectives. This is something that is difficult for all of us. It was difficult for me here. I did push back a lot, but um it was really worth it to um really gave us saved us a lot of time, got us much quicker to a better solution. So, which one of these wins at Google and what's the difference between the two stories we just heard? Okay. So, let let's um let's talk about the first one. In the first one, we're talking about auto reply auto reply new features versus um missed messages.

22:55 Choose missed messages. Um first one really loved by users. Second one, less common. Um I brought all this data escalated elegantly, I might say, because I did we did go together. Escalated elegantly. Um and the most important thing, managed to convince with data.

23:33 Data. Bottom line, I won. Users won. Seems like a great victory story. >> Yep. >> Um story Second story. In the second story, we're talking about uh um AI AI inbox sorting. Um it's um in-house in-house versus external modules.

24:08 Um eng manager brought data. I asked for an experiment, asked for a POC. I asked for a POC. I was convinced. I really listened. Everybody won. Users won.

24:39 So, I think these are the the main um differences. Um and when we will talk soon about Googliness, we'll see that the second one is is much more Googly. Now, I can't say for sure that in 100% of the companies, story B would be better. In Google, it would most probably be better. Um again, there are more than 100,000 Googlers. I can't promise that 100 that 100% of them would prefer story B, but most of the ones that I know, I would definitely prefer story B because it so shows how you actually work collaboratively, how you actually listen to people, how you have um intellectual humility, how you put your ego aside. A lot of really good traits. Now, an important thing to remember is in this interview, I don't want to see that you're a really professional PM that knows how to work with data, how how to work cross-functionally.

25:38 These things happen in other interviews. In this interview, I assume that you did you did well on the other ones. Here, I want to see who you are. How I want to see that you're a really a person that I want to work knows how to work with people and would be nice to work with, would be um Yeah, it's it's um it's a different it's it's important to understand what is what is being asked, what is the title of this interview.

26:04 >> So, Googliness is a word that's thrown around constantly, and I feel like there is been no real definition. Can you define it for us? How do we actually show that Googliness? >> Okay. Now, that's a great question that's being asked a lot. Now, Googliness is a term that has been around almost ever since Google has been around. Um Googliness is a real thing and is a really one of the more really important reasons that people love working at Google.

26:35 Um I can it really exists, and it's Let's say that I can give an example. I land landed at London um start to start working with a team in which I hardly knew anyone. We had a few email exchanges. Um Now, the moment you step into the room, then everybody is is listening and everybody is assuming that the other one has good intentions and knows what they're talking about. Another important thing that happens if we come with conflicting ideas, um I gave an example of an escalation, but escalations is very rarely happen at Google. 99% of the meetings at Google end with a consensus.

27:18 Now, this might seem surprising for for people working at some more competitive companies, um but this can actually happen and one of the important reasons that one of the important thing that makes it possible is Googliness. It's everybody is Googley and Google choose Googley people and they really act in a Googley way. Now, I said nothing almost nothing about um what Googliness is. Now, Googliness the official term, uh I think the first time that it was defined officially is back in 2015.

27:54 Um Laszlo Bock was um was back then head of people operations at Google and he actually wrote a book called Work Rules. Work Rules, um pun intended, I assume. Um Now, there he mentioned a a few things. The first one that I already mentioned uh in the example was is intellectual humility. Um so, when we talk about Googliness, um I don't really when I ask you a a behavioral question, I don't want to hear how smart you are. I don't want to hear about the data you brought. I want to hear that um you're confident, you know what you're talking about, but you never brag.

28:39 Um and you never um you never put down other people. You always come with humility to to each task and to each situation. Um This is one. Another one is being comfortable with ambiguity. Um This again, um we never it also came up in in in a way in the example that I that I brought. Um being comfortable with ambiguity and not immediately jumping to the solution, taking time, maybe listening to other opinions, maybe running a quick proof of concept, a small experiment, and being able to stay in the state of not knowing for a long while and waiting for the data to emerge. Um trying to find the data and and and not thinking that you have the solution and not immediately striving to find a solution as as soon as possible. You need to strive to find the best solution, not necessarily as soon as possible.

29:46 Um Collaborative spirit. Um this is another one. I think this kind of speaks for itself. Um This I can say really happens at Google. Everybody really works together toward um towards joint goals. So, collaborative spirit, this um exists. Um conscientiousness. Um conscientiousness. Did I get it right? Almost. Conscientious Close. >> Close. >> And Yeah, almost. Conscientiousness. Uh um Now, taking ownership of your work, maintaining high ethical standards, um challenging the status quo, but doing it constructively, like always being guided by your conscience. Um In a way, Googliness this is it it we're breaking down Googliness is is a lot about just being a good person, really being a good person, doing the right thing. I mean, Google's tagline for most of history was um do no evil. So, um it's really a lot about that. And then again, in a different perspective is doing the right thing. Um And this is it's kind of what is the right thing? It It's It's It's about approaching with integrity.

31:04 Um and being respectful both to your coworkers and to your users. Um it's kind of it's In a way, you feel like doing the right thing. Yeah, it's saying nothing, but it's not saying nothing because many times we know what the right thing is. We and not necessarily We have many reasons not to do the right thing. We do the second best, not necessarily. So, Googliness is always and willing being um willing to pay the price for doing the right thing because it is a core value to do the right thing.

31:38 Now, this is more or less I I I'm I hope I I tried I managed to um to paint a picture of what Googliness is. Now, um Now, it's really important when you choose your stories, you don't need to choose a Googly story. You don't need to to choose a story. I'm asking you tell me about a time. Don't look for a time where um you were intellectually humble. Um because an important thing, the facts of what happened to you are fixed. The meaning is not.

32:13 So, as I demonstrated in the first story, you can paint the same story with different colors. So, it's important when choosing your story, you choose a story and and you you paint it with the right colors. In in in in screenwriting, we many times they talk about plot versus theme. The things that happen are that happened are the plot. And as an interviewer, I don't really care about the plot because I'm sure that you had um I'm exaggerating, of course. I am interested in the plot, but um I'm I'm interested in the theme not less than I'm interested in the plot.

32:52 Um because you can choose many stories. Um everybody in their career had good moments, had bad moments, had different things happen. The theme of the story and the theme that you choose for the story is really important because it tells me also a thing about about you. Which theme are you choosing? What are the values that are important to you? In the in the example with my neighbor, if I choose to tell about how hard I work, this is what this is one goal. If I choose to tell about my heart going out to this 83-year-old widow widow, this is another goal.

33:29 And I choose which one and and and choosing means in a way which of these is more important to me. And this tells me a lot about tell the interviewer a lot about me. This is why in a way I say the theme is in a way more important than the plot. >> So, if you're not choosing Googly stories, you have a Google loop coming up. How exactly are you preparing to succeed on the Googliness dimension?

33:53 >> Okay. So, first of all, um it's important to try and understand as much as you can what Googliness is, these things that we discussed. When we prepare for behavioral interviews, um a recommendation that I usually give is prepare um eight, 10, or 12 stories in advance. Um these have to be good stories, stories that have um in which meaningful things happened to you during your career. Um now, the number of behavioral questions is endless. If you give me now 10 minutes, I can for 10 minutes just throw out um more and more and more behavioral questions. The number is endless, but the important thing to understand is if you have 10 interesting stories from your career, they would most probably cover 95% of the possible behavioral questions.

34:49 Um Now, so be because of course because each each story covers a lot of different of different questions. Um now while you practice after you do these eight to 10 to 12 stories, just you can think about your you can think yourself. You can go to question banks. Just just think about many different behavioral questions. And then in your head just quickly try to think how does this how do I color this story in Googly colors? How do I color this story in Googly colors to match this this question?

35:24 >> What's the single most common mistake people make when they're trying to demonstrate Googlyness? >> That's a good question. There the most common pitfall I think is over fitting and not being 100% honest. Like trying to really fit the story to what you think Googlyness is. And this shows. It is really important and it's it's a really great question that you asked. It's really important to do all the all these things while being honest.

35:58 Um do not invent a story because it shows. You can choose any story. It has so many perspective and so many ways to tell that and there are so many stories in every career. You don't need to invent anything. Be really honest. And if it's a story about ambiguity, you don't need to exaggerate how ambiguous the situation was. It was kind of ambiguous. Don't exaggerate. Don't be honest because the five traits that I that I mentioned here are just it's just an example. It's it's kind of there are many traits. Honesty is one of them. Many of them it's about being a good person and and everything wherever you feel that you're kind of not doing the right thing and not being honest, you are not being Googly.

36:43 >> That's a great point. So, if Googliness is actually testing for being a good person, let's make sure that we're using those same high ethical standards in terms of choosing the story and representing ourselves. Now, the final question I want to ask about Googliness is what's the right way to prepare, right? Is it I go read Laszlo Bock's book How Google Works? I go read The Storytelling Animal and I rewatch this video five times? Or is it I go practice and record myself? Or is it I go interview myself with ChatGPT? What's like the right study practice plan in order to really ace the Googliness dimension?

37:22 >> Okay, so technically what you need I kind of mentioned you need to to practice these stories and see how how do you come out as a Googly from out of them. But I think another really important thing to do you might want to do this is more on the really soft side of preparation is spend some time and and maybe write down what really is important for you as a person, what really is important for you as a product manager or whatever or an engineering manager. What is really important for you?

37:52 Do you and honestly ask yourself do you really believe in what Google believes? Now, I'm not saying that if the answer is I kind of feel better in a more competitive place, then the answer is not okay, so so drop the interview and say I'm not coming. This is not for me. But being honest and understanding how Googly you really are and how and what is the what is the gaps that honestly you have. You might have been working in some some company that you're not really proud of, that was not doing the right thing, that was kind of using dark UX patterns. This is something that you did in your career, and obviously it's not very googly. How honestly do you feel about that? Are you really proud of that? It's a thing. You probably shouldn't be really proud of that in the Google interview, but understand how you want to tell it again.

38:50 This is a gray area. You are proud of it, but you're not going to be proud of it here. So, really look deeply and see how googly you really are. And if there is a gap, how do you plan to honestly bridge it? >> Really awesome. Okay, guys. So, we just covered end to end how you can demonstrate googliness. We started with the importance of storytelling. We walked through what actual googliness is. We showed you how to prepare. Two things we want to quickly talk to you about. Number one, if you are loving Gail's advice as much as me, consider going to igotanoffer.com, finding his page, getting some credits. A call with him is three credits to start, and you can get his wonderful coaching. If you want to work with me on how to land a PM job, and how to create your LinkedIn, and how to create your resume, and how to create your GitHub, and your portfolio, then you can check out my coaching and go over at landpmjob.com.

39:45 Both of us have helped people through Google processes, and now we are going to shift into the next part of this video, which is about PM and APM. So, the first thing I want to understand, Gail, is you coach so many people. You get to see the market in a way that other people can't see it. You get to see what's actually happening here in 2026. And a lot of the information about Google is probably outdated. So, can you walk us through what are what are you hearing on the ground? Not, you know, revealing confidential info that when you were at Google, but what are you hearing on the ground now that you're an interview coach about the Google PM process? What does it look like today?

40:28 >> Okay. First of all, many things have changed in the past two or three years. The The PM loop was kind of steady for many years, at least for the decade that I knew it very closely. There's We had the product sense. We had analytical. We knew We knew exactly how the product loop loop would look like. In the past two, maybe three years, many changes have occurred. Now, the interview loop now consists of This is not confidential information. This is what Google sends to every candidate who starts the process.

41:05 Now, these are the the five types of interviews that are currently in the interview loop in 2026. The first one is product vision. This is what used to be called product sense. This is the most classic product product question. How would you How would you build maps for blind people? The second one is product analysis. This is the classic analytics question.

41:40 For instance, an example here, how many An estimation question might be how many messages per second does Gmail receive? Or you notice a 30% change in the usage of your product, what would you do? Or setting goals for the product. Anything that has to do with metrics, analytics, many times experimentations. Next, we have the strategic insights interview. Now, if in the If in the product vision interviewer we're meant to be NL5, L6, L7 PM and really solve day-to-day problems in a strategic insights interview we're more like the CEO or an SVP and we need to take a much higher level view of how things work, the forces that move the company, the things that keep Sundar awake at night. Yeah, here it might be question example question should Google offer a StubHub competitor.

42:51 This is really strategic deep strategic question. Now the most common as I'll just mention that a very common pit where we're not talking about strategic insights here but it's worth learning the most common pitfall I hear is starting like the CEO and and after a few minutes becoming NL5 PM and really solving tactical things. So this is the strategic insights interview. Then we have execute with judgment it kind of similar to what used to be cross-functional collaboration.

43:24 Um Here you have to to to show the ability to strike a balance between detailed and prioritization and how you execute and work with engineers. Um For instance you're about to launch a product one month from launch internal feedback shows that the app is really not ready. What do you do? So here it's about how you work with engineering team and and and how you actually solve day-to-day problems. The last one which is the newest one which kind of didn't exist and we have the least data about is problem space understanding.

44:10 Basically, problem space understanding is where um the the solution can come from many different directions. You need to be able as a product manager at Google to strike a balance between the technical, for instance, and business angles for solving something. Um for instance, we might have a bug that is um that a lot of users are affected. We might go the engineering way and see how we want to solve the bug, but we might go the UX way and because we can't solve the bug, we might go the UX way and just have a pop-up and show um this bug is now this feature is now only experimental, so you might expect bugs. So, um how do you resolve conflicting product requirements? There are sometimes like general questions. How do you resolve conflicting product requirements? So, really about um defining the problem space.

45:06 Um now, that being said, it constantly changes. Every few months we see different things. This is one thing. The second thing is that they're many times are combination. Yesterday, I worked with a candidate um who had a hiring manager interview, which um was the the um the recruiter told him it would be on product vision and problem space understanding, but also expect a lot of behavioral questions.

45:39 So, basically, it was um yeah, it's going to be almost about anything um except product analytics, maybe. So, So, yeah, many times there are combinations now and um an important I want to take a step back and um it's really important to be prepared to each for each and every one of these because majority of times, I don't know, 70, 80% I know I don't want to say a number. Majority of times you will have a product vision interview and it will be about product vision.

46:09 But, one of the nice things about working at Google is that Googlers get a lot of freedom. Now, this applies also to interviewing. When a Googler comes to an interview, they can basically ask whatever they want. They're not tied to any question bank. They can do whatever they want. So, I did see cases where a candidate came to a product vision interview and was asked something that was was 90% analytics, hardly any product vision. And the opposite. A strategic insights interview can be really short and then become a really long Googliness interview.

46:47 So, although majority of the times you will you will get the interview that you expected, it is really important when coming to Google, be prepared for the unprepared. Don't black out if suddenly you came to an analytics interview and you're being asked something completely different. Don't say, "Oh, no, no." Okay. You expected it. >> That's wild. And which these like I had gone through the Google interview process a couple times myself back in like 2014 and 2019, I believe, were the last two times I did it. And they had very specific case interviews that dominated everything. There was like a product sense round. It was like a 45-minute case. They maybe asked me like one question like tell me about myself, but then they just spent the whole time on the case. Are any of these rounds like in dedicated case interviews or are these all kind of these hybrid rounds now?

47:38 >> No, there are what you described is usually what happens in the in the product vision and in the strategic insights and the common is is to have one specific case for the whole interview. This is very common. It doesn't mean that you spend 45 minutes in a monologue answering the question. We would probably finish it around 30, 35 minutes. The interviewer might interrupt you in the middle with follow-up question. They might interrupt you in the middle and completely change course.

48:07 With which is something that I really like to do as an interviewer. Um many things can happen, but usually we stay on one case. For instance, you are a PM in Gmail or um design Google Maps for blind people. We would stay in the same area usually for um most of the interview. >> So just to confirm, product vision and strategic insights are mostly a single case. Are product analysis, execute with judgment, or problem space understanding mostly a single case, or those more multiple topics?

48:40 >> They can be either product analysis many times it can be a single case where we dive deeper and deeper and deeper. We might I don't define North Star metric for Waymo. We can talk about it for 3 weeks. It's a really difficult question. We can talk about it for 5 minutes. So um it can be that, but other times it can be really quick question in product analysis. Okay, let's just talk about some simple metric. Let's talk about a metric change. It It differs.

49:09 Um as for execute with judgment, it tends to be shorter. It tends to be shorter because there isn't that deep to go usually in this question. So this would tend to be two, three But again, anything can happen. It can be a very long one where we go deep. Um >> We're kind of describing the 80%, but there's the 20% of wild card cases. Is there any vibe coding or AI prototyping in the interview because there were some reports on Reddit and Blind, but then some Google director said, "No, it's a very scripted process.

49:42 We don't have that." What is the truth that you're seeing on the ground? >> Okay, from all the data that I have from candidates that I work with, and here I will say also with Googlers that are friends, I didn't hear anything about vibe coding interviews. But that doesn't mean as I said Google is really changing the interview process really quickly these days. It it doesn't mean that it will not appear tomorrow. So, um but right now to the best of my knowledge, it's not a real a thing.

50:18 Probably as I said 100,000 100,000 and something Googlers maybe few of them do ask about vibe coding. I don't know. >> Okay. >> So, they potentially could have the authority because Google isn't giving you an exact question bank, but it is definitely not part of what Google is prescribing as the rounds. >> To the best of my knowledge, I'm not a Googler anymore, but yeah, to the best of my knowledge right now it is not. >> What about technical and estimation rounds? Historically, there's definitely been some content written out there. I think I even remember like an I got an offer article from like 10 years ago or something that talked about how Google might ask these estimation questions or they might ask these system design or technical questions to PMs. Are either of those still up in the Google interview loop in 2026?

51:06 >> Okay, first of all, um there used to be a dedicated technical um technical interview. When I interviewed, I actually was interviewed by um by software engineer. And I was actually writing in my Google technical interview, I was actually writing Java code. Um this does not happen anymore. Um there is no dedicated technical interview, but technical knowledge is expected in a few of these. Um especially in in execute with judgment because you're working with engineers, you're expected to know how to work with engineers.

51:40 If I tell you if I ask you a question about a latency bug in um in search, I expect you to understand how search works. What are the possible because if you have no technical knowledge, you kind of cannot do anything about What would you do? Um you would call the engineers. So, yeah, and I you would be expected to have this technical knowledge, but it is part of a bigger question. >> Got it. All right. So, that is the interview loop, guys. Gal is as close to it as anyone in the world because he is regularly coaching people going through it. As he said, there's 100,000 Googlers out there. What one Googler really confidently says to you, there is some latitude out there. I was just speaking with Satyajit Salgar, he's a director of product at Google AI, and he said, "Yeah, we have full ability if we are hiring for a specific role to sculpt the interview process for that specific role now."

52:37 So, things at Google are changing where it was very fixed when I interviewed, I think, 12 years ago, and it had that technical round. And they even asked me estimation question. Now, technical and estimation is out as prescribed rounds, but it's up to your interviewer. They might ask you something along that. They might ask you something along vibe coding and air prototyping. We have put the five interviews that you are most likely to see in the five topics that should cover 80 to 90% of your prep, but with Google, never just assume this is exactly how it's going to be. As Gal said, sometimes people ask analysis questions in a vision round. And so, you really want to be prepared for the whole gamut.

53:18 Now, when I coach people on Google rounds, I tend to find that they are spending 99% of their time practicing product vision, execute with judgment, these case interviews. And 99% of people actually fail because of behavioral. >> [laughter] >> So, what I want to do is I would love to And those are exaggerated numbers. It's not 99% in both cases, of course. But if people are really failing in behavioral, which is what I see, I'd love for you, Gal, to like live coach me. So, I'm going to do the type of tell me about yourself that I frequently see in people I am coaching, and then I'd love for you to take my answer to a great answer. Are you ready?

54:02 >> Yeah. All right. Let's do it. >> So, we're going to do sort of a character version of myself. Okay. I'm really excited to be here, Gal, at Google today. Um my career in product management started back in 2008 when I was working in B2B SaaS at this company called Scout Force in Ann Arbor, Michigan. From there, I spent time as a founder working on an app called Rap to Beats for a few different for a few years and grew to a couple hundred thousand monthly active users. It was an iPhone app, not an Android app, but really learned a lot about mobile development.

54:45 And because of that, I went from B2B to consumer, and I continued down the consumer journey. I worked at ThreadUp, where actually joined as a growth manager, but I eventually became a director of growth product. And I moved over then to do my MBA and started working at Epic Games, where I was started as an associate producer, but eventually promoted into the product manager. And then I worked at a firm where I started as a group product manager, but I was eventually promoted into head of growth product on the senior leadership team.

55:21 And most recently, I was a VP of product at Apollo.io, which was, when I joined, worth $600 million, and when I left, it worth $2.5 billion. And we overall four x'd, and I led pricing and packaging, core activation, acquisition, retention. My job was to really own the product-led growth funnel at Apollo, and I feel in Google would just be the awesome next step for me. >> Okay, that's really interesting, Akash.

55:52 This was a good example of how you actually what do you actually did is walked me through your resume. I did read your resume, and I didn't learn anything new from what you have just told me. Now, I'm wondering for which for which role at Google are you interviewing right now? >> Let's say that I'm interviewing for within Google Brain, there's obviously research and apps. Let's say I'm on the apps team, like the labs, like they created notebook LM, they created Palm-E, they created Google AI Studio, AI prototyping, those types of roles.

56:25 >> Okay, so you're working on the apps team. Now, um I see that um your relevant your relevant experience for for really app development, you were in mobile development in your early career, you might want to highlight that, like sow the seeds of where you started wanting to go there. Um then things happened, you moved from you started to become growth product PM, I'm not sure why. Then you went to an MBA.

56:57 Um I would really want to hear why you went in in that direction. And um and I'm wondering this current role is actually really not business-oriented at all. So, this is something that really makes me think you went into the MBA direction, but now you're going back to really hands-on being a product manager in in the apps team. Um is this something that you really want, or as an interviewer, I'm wondering whether it's just an opportunity that you saw and you're jumping on it because it seems interesting.

57:37 Um And and and the the most um the most important weakness that I see um you did a lot of growth. You you um you went from your last um your last role was product-led growth. And I'm wondering is this a direction that you're no longer interested in? This is kind of a weakness that you started a direction. You really managed to grow um Apollo.io.

58:09 Um why not continue in that direction? So, this is something that I really want to would want you to to um highlight in your story. And now, do you want to take a second try or do you want me to try to do it? >> Let me try to incorporate your feedback and let's see if I need another round, guys. And by the way, I know that's not how I actually answer guys, but I wanted to show you how I am here tell me about yourself from most people when I ask them, which you just said, they restate their resume. So, what I'm currently doing in my mind is I'm saying, "Okay, Gal came up with these weaknesses." So, what I actually want to do is probably sort of flip some of these weaknesses.

58:48 And Gal said, "Hey, he already knew my resume." And so, what I want to do is I want to add on texture beyond my resume. So, let's see if I can do a better job of this in this next response. Gal, thanks for having me here today. Um I know my resume probably seems like I've done a lot of product-led growth, worked in B2B and B2C, so you might be asking why I'm here today. And the through-line I want to actually show you throughout all my background, whether it was B2B or B2C or growth, was that I was building a lot of core AI or before it was called AI ML products.

59:23 So, all the way back in one of my first growth products roles at thredUP, which is now a public company. And I had joined when they were Series C and went through Series E and had been gone from growth manager to director of growth product was I implemented these ML algorithms to influence the pricing, influence what is shown on the homepage. So, I was really building a lot of apps on top of ML models. And some of the results we achieved were pretty mind-blowing. We doubled visitor to first purchase conversion rate. We managed, as a result of doubling that conversion rate, to triple our new customers. Because once that conversion rate increases, we could spend more on ads, and the amount of ads we were able to spend more led to a tripling of new customers.

60:13 My CEO actually called out when he announced the Series D. He said, "The work that Akash and the growth product team have done really helped enable this raise." So, that was one example of using ML to build an app on top of it. At Epic Games most recently, we built AI characters into the product. So, what's the problem with Fortnite? Well, if you try Fortnite, you can often face people who are much, much better than you.

60:40 And so, what the AI solved this problem of let's get people to face people at their level so that they don't suddenly just lose the game and quit. And it was a crazy launch as well. We managed to increase day 30 retention, which was like our North Star metric at the time on a relative basis 50%. I can't share the exact amount that we went from X to Y, but we grew day 30 retention 50%, which was game-changer for the game.

61:12 And most recently at apollo.io, where I actually rose all the way to VP of product, we shipped an AI email writer, which was built on top of models like Gemini and it helped people send cold outreach cuz this was a tool for sales people. And we were able to increase the amount of emails people were sending, the open rates, and the reply rates. So, building all these AI apps throughout my career, I feel like what I want to do is this new trend in PM.

61:43 Away from managerial middle management into a senior IC role building AI apps. How did I do? >> That was much better. I would shorten it a bit. It was You went a bit too deep, I think, specifically in Epic Games. It was a bit too much information. Um all of them could have been shortened. Um really just highlighting in a sentence or two and and and keeping the theme of the ML algorithms that you developed in each of these.

62:14 That would make it almost there, but another thing which I usually look for and I usually ask follow-up questions if I don't get it is um your personal passion. I I see that you're really about ML algorithms. I understand that this is what you did, but I'm not sure why. Why do you love it? And this just adding in the beginning or the end, depends how you like to tell the story, just a few sentences about why this specific problem really keeps you awake at night.

62:45 Why at 3:00 a.m. you suddenly come up with an idea for an ML algorithm to solve the Epic Games problem. Um just really just 20-30 seconds on this on your personal passion. This really makes the difference for me and I know for a few other interviewers. >> There you go, guys. So, I probably started at like a three. Things seems like I took it to like maybe like an eight, but there's still room to go even further by shortening it. And I think people often do go too long and by showing your personal passion. Is that right?

63:16 >> Yeah. Yeah, eight, maybe even nine. It It a really good answer. Way to go. You could have shortened it mostly. Um but but yeah, that passion I think really if you want to be a 10 out of 10, you need, in my opinion, to have that really personal touch, which never a guy really comes up in the in the in the resume. I need to to hear and feel why you really love doing what you do.

63:38 >> There you guys go. So, a great tell me about yourself is beyond the resume. It's addressing weaknesses and it's showing passion. So, pause the video now. Try to iterate the same way we just did. If you want, throw the transcript of this video into an AI and throw your recording of your tell me about yourself and say, "Hey, how would Gal improve it?" Or you can book time with Gal or I and we'll help you with it. Now, Gal, I want to talk about AIPM roles. How does, based on what you've been doing coaching different people, how is Google looking for AIPMs? What are the things that they're looking at? Is the process different? Is the bar different for behavioral questions? Basically, what should people who are preparing for Google AIPM roles know?

64:20 >> First of all, an important thing to mention is that Google has only just recently started interviewing people for specific role. And for all of them, this is part of the reason. Until until not very maybe two, three years ago, you would interview as a generalist. You would interview to become a Google PM or a Google engineer. And only then you would have the team matching process. Now, the vast majority of candidates actually start their interview round with a hiring manager with a hiring manager interview.

64:56 This means this means two things. One, you're already assigned a team. You will not have the team matching process as before. It might happen, but not necessarily. But the more important thing is that since you're already assigned to a team, you're expected to have knowledge in that area. So, if you're assigned to an AI team, then you are expected to have knowledge in AI. Um now, what are the differences? Let's see what are the differences. It's kind of interesting to think in a behavioral or Googliness interview, what it what would be the differences, but let's start by talking about the role itself. What might be the differences? So, um for a classic PM, first of all, I think the clearest differences a the classic PM would have a deterministic um way to solve things.

65:50 Um and an AI PM would have a probabilistic way of solving things. Um we never know exactly how the AI um how the AI would solve the the problem. Next, uh um the the What is done? The definition of done. Um here, done means meets the spec. We have a spec. We did the We did everything that's written in the spec. It is done. But, what does that mean in AI?

66:22 Um in AI, I would say that done is something along meets certain um levels um of accuracy and failure rates. So, um so it's actually ongoing, and even if it was done, um it can become, in a way, undone. So, you never leave your product, which is something uh in a way different. Um Another thing, how do we even define the product? We used to know that a product is defined by a product requirements document, the classic PRD that we all know and love. Um but now it's more we define the outcomes, we define guardrails, it's not a classic PRD.

67:13 Um Next, how do we work with data? I mean, data is is um PM's best friend, but data used to be mostly is for analytics and understanding how your product performs, um but now data is a super important part of the product itself. For the learning algorithm, the data is crucial. So, data becomes something very different. You need to have deeper understanding of data.

67:44 Um A really important thing here, you had certainty. You would sit with your engine manager and you would break down and break down and break down, and you would have certainty in execution. You would know, yeah, you would you would say it would take 3 months, but then it would take um 6 months. Here you have a much higher level um of uncertainty because you never can predict how the model will perform. You can never predict how many iterations will be needed. Maybe this will change. I don't know, maybe AI PMs 30 years from now will already know all these things, but for now the uncertainty is is much higher.

68:30 Um another thing is failure. Failure um failure can be predicted. We used to in PRDs, we would write what we do in certain certain areas, certain modes of failure, and we would already offer solutions. Here um we can always be really surprised by the model. So, it becomes risk and trust um become um become much more important.

69:05 So, all that being said, um how does all that map into um a behavioral interview? Because this is what we're talking about, behavioral. We're talking about Googliness. Now, here I think um if let me go back to um to my Googliness slide. So, um I think all the things that we mentioned are most closely related first of all to um being comfortable with ambiguity. This is something that I would really look for in a candidate.

69:38 Number one thing, I want to know that they can accept ambiguity, that they can change course if nothing works. Ambiguity is the number one thing to stress in the Googliness list. Um the next thing um I would I would mention is intellectual humility because we need when you become you switch from being to an AI PM, you need to let go and relax in a way. Not really relax, but anyway, relax. You need to let go and because you don't really know, you don't control every single detail.

70:16 Um you are working you have an unpredictable collaborator that is working with you. And and you need and and and you actually need to be humble because that collaborator is really smart and really fast and can learn and iterate and can do a lot of things that you will never be able to do. So, intellectual humility in front of that collaborator is really important. You don't have that control that you were used to have as a classic PM. These are the things that I would probably stress.

70:49 >> What are the most common failure patterns when you're coaching people for these AI PM roles that they run into? >> Hm. Well, actually many of them in in the tell me about yourself, they don't have a good story like you had. And you had a you have a very good story about why this really why you're a good fit. Now, this many times um people miss it and they don't realize that actually they have been working with ML related because if you have been working in the past few years, then you have most probably your engineers were working with ML algorithms. You didn't directly work with ML algorithms like now, but you did have some relation to them. And understanding if you're interviewing for an AI role, then looking back and understanding where you actually did in a way work with AI is really important and this is something that candidates many time omit while they could um really easily do it.

71:54 >> Mhm. And what if you feel like you really don't have AI or ML experience? How do you interview for these AI PM roles? >> Don't pretend. This I think is a general um never pretend. Never try to hide your weaknesses. This This is constantly and I think this is also in product division question and analytical questions. Many times candidates they realize they made a mistake, they forgot something and they try to hide it. And I tell them in a way you are in the spotlight now.

72:23 The interviewers see you trying The interview sees you trying to hide something. You cannot really So, instead of trying to hide, just say it out front. This um the moment you sit down in the first 3 minutes say, "Actually, I don't really have deep AI background, but and then the but can be many other I don't know what is your um but I really love this. I really I'm really interested in this. In my free time, I do a lot of AI stuff. I don't know.

72:54 Whatever is the right answer for you. But, don't hide it. Don't try to tweak the story. No, just say it out loud. I I don't have official um background as being an AI PM. I'm really passionate about it. I'm really interested in it. I really learned about it. Be be honest about it. Again, honesty is really important in these interviews. >> Amazing, Gal. If I were to take away three things, guys, from the overall talk that we just had today. Number one, tell the story or the interviewer will build a story for you.

73:29 Number two, Googliness is fundamentally about doing the right thing and being a good person. So, hold that same standard for your story selection and your answers. And then number three, don't make your behavioral answers too long. Don't just restate your resume, but actually tell the story to help people see your passion and see you why you are a fit for the role. Is there anything else you'd add, Gal? >> Not exactly add, but I want to finish again with what I started. The story.

73:57 Remember that it you're always telling a story and you're coloring it with very specific colors. So, before starting the story, understand this question, which colors is it looking for? And understand how to color your story with these exact colors. Um and then the the job of your interviewers to to pass you would be really easy. They just need to fill in all the rubrics because you prepared everything for them and uh that's it.

74:30 You're good to go. >> All right, guys. He just dropped so much alpha. Go support him. If people want to find you online, where can they find you? >> Um I got an offer.com. >> All right, guys. Find him there and we'll see you in the next episode. Bye. I hope you enjoyed that episode. If you could take a moment to double-check that you have followed on Apple and Spotify podcasts, subscribed on YouTube, left a rating or review on Apple or Spotify, and commented on YouTube, all these things will help the algorithm distribute the show to more and more people. As we distribute the show to more people, we can grow the show, improve the quality of the content and the production to get you better insights to stay ahead in your career.

75:08 Finally, do check out my bundle at bundle.akashg.com to get access to nine AI products for an entire year for free. This includes Dovetail, Mobbin, Linear, Reforge Build, Descript, and many other amazing tools that will help you as an AI product manager or builder succeed. I'll see you in the next episode.