Inside Gong: How teams work with design partners, their pod structure, autonomy, trust, and more | Eilon Reshef (co-founder and CPO)

I want to start with talking about your pod model, which is a really unique way of building and organizing your product teams. That was probably 2016. We're trying to figure out how does the operating model look like for our product and engineering. The first bunch of people we had was essentially a pod. It was one product manager, a user experience designer, back-end engineers, a couple of front-end engineers. But at some stage, we're starting to scale. We're kind of contemplating we go like the traditional, the old school of front-end engineers, back-end engineers. And then we said, let's try to replicate what we have.

Let's talk about how these pods work with design partners. From what I've heard, it's very unlike how any other company works. We just took the pod concept to an extreme where every pod is working with sometimes a dozen design partners, sometimes two dozen design partners. And this feels like a cheap code of how to build new product lines. What are they like the percentage of success rate you have with new product? I would say a very close to 100% of the features we build end up being used by a significant number of people.

Does it feel crazy for companies not to operate this way?

Today, my guest is Elan Reshef. Elan is co-founder and chief product officer at Gong. He was also the longtime chief technology officer at Gong. As I share at the top of our conversation, it feels like basically every company that has a sales team uses Gong.

And it's really rare to build a product that is so ubiquitous and so loved across the tech ecosystem. In our conversation, Elon shares some of the secrets of what makes Gong so consistently successful, including how their product teams work with 6-12 design partners on every new product and feature that they invest in, how he creates a culture of autonomy and trust,

Why and also how he optimizes for making decisions quickly, even large one-way door decisions, what he and his team have learned about building AI-based products, since they've been building AI-based products longer than most other companies, and so much more, if you're building a B2B SaaS company or product, you will learn a lot from this conversation.

If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. It's the best way to avoid missing future episodes, and it helps the podcast tremendously. With that, I bring you Alon Resheft.

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Alon, thank you so much for being here. Welcome to the podcast. Thank you for having me. So it feels like on this podcast, every guest that I've had mentions Gong as a product they use, it feels like it's just like in the ether of tech companies these days. Also, I've heard so many times how unique it is that you all operate, how you all build your product teams and operate.

which is I especially love hearing on this podcast in just different ways of operating. So I'm really excited to dig into this, to hear about the journey and things you've learned along the way of building gong and essentially building something that is so ubiquitous and so loved, which is very rare. I want to start with talking about your pod model, which is a really unique way of building and organizing your product teams. And in particular, I work with design partners, but let's start with the pod model. You just talk about what this pod model is and how you organize your product teams.

Sure, and when we started the pod model that was probably 2016 before it became popular, I think that might have been before the Marty Kagan set up books. I don't remember exactly, but at some stage we're starting to scale. I mean, scaling is maybe from 50 people to 60 people. We're in the whole company or whatever. And we're trying to figure out how does the operating model look like for a product and engineering. And the first bunch of people we had was essentially a pod. It was one product manager.

years truly, a user experience designer, maybe back in engineers, a couple of front-end engineers and whatnot. And we're kind of contemplating, do we go like the traditional, the old school of like front-end engineers, back-end engineers, or however it is? And then we said, let's try to replicate what we have. So what we essentially did is really kind of replicated that. So up until now, we have the spot structure, product manager, UX,

fractional writing, fractional analyst, and then a team leader from an engineering extent, 0.5 to, I'd say, seven engineers. They get an agenda. They think, like, we launched a forecast product. That was a pod working on that. And then they get to be pretty autonomous in identifying how to solve the problems and also working with enough customers. So I can go to sleep knowing that they're not like hallucinating as the term that everybody uses now in different contexts, but going in a very reasonable direction.

Okay, so I want to talk about the autonomy piece. That's a really important point. But before we get there, let's talk about how these pods work with design partners. From what I've heard, it's very unlike how any other company works. And I think it's something a lot of people can learn from. Let's talk about kind of the scale of how these pods work with design partners. So I think we just took the pod concept to an extreme where every pod is working with sometimes it doesn't design partners, sometimes two doesn't design partners, maybe sometimes five if it's a very niche or fringe capability.

And they work with them head in hand. So an interesting story, the same forecast product I just mentioned. The product manager comes to me one day and he's like, hey, I just cannot play with a design partner on the product. And I kind of know what's going on and I know it's not built yet. So I asked the product manager, but we don't have that working. So he said, no, no, we don't. But I showed him this sort of the half built stuff. I asked him to hit save. He hit save and got an error message.

And I told him, let's meet again in a week and let's say, but we're going to work. So it's very extreme in terms of working hand-in-hand with the customer. Customers appreciate it. I later got feedback that they appreciate how the thing was going, making progress according to their feedback. Not what they said, but kind of interpreting what they said, digesting it and building something that makes sense. So every bot has this set of design partners.

So these pods essentially cross functional product teams. Each one has the organize it around like an outcome. How do you describe what each pod is responsible for? Is it like move this metric or build this product or something else?

We tend to be less metric driven maybe than your average, especially to see, but even like other maybe B2B companies, usually it's more around some sort of a job to be done. In our case, it could be sense engagement. How do you kind of prospect or conversation intelligence? How do you create a summary? How do you make it easier for people to remove dratory and consume information fast?

And once you get this agenda, you kind of pretty much have a lot of, you know, you mentioned autonomy, a lot of kind of control over how you kind of progress. Ideally, design partners guide you, not me. Awesome. Okay. So it's like, here's the outcome we want this pot to achieve.

they have autonomy to work with design partners to design this new product. So maybe let's stay on this example of this forecasting tool. He just briefly described with this tool, what it gives you, what it does. Yeah, it's a product we launched a couple of years ago, and it helps organization forecast where they land in terms of the sales organization. So every sales organization, B2B sales organization, has bottoms up forecast process, people submit numbers, people up kind of override that. AI helps you kind of, in our case, AI helps you protect the right number,

Usually there's an analytics component top of it that kind of helps you assess it at scale. So that's the product itself. Awesome. So you created this pod here, build this forecast product, make it successful. How do they find these design partners? Is it like reach into existing customer base and figure out who would be most interested in this? Usually it's existing customers, very, very rarely it would be an unexisting customers, but customers with some state expressed interest.

in this capability, not to over give a plug to go. But of course, we can listen. All of our conversations are recorded. So I can always look up our conversation database and which customers express the need for X, Y, and D. You can very easily reach out to them.

One of the maybe unique things we've done at some stage just became, as you can see, I think we have like 25 pods right now, maybe 30. It depends what you call a pod, but it's a lot of effort, like this whole management. At some stage, I board an idea that comes from talent acquisition.

And in talent acquisition recruiting, there's a person called recruiting coordinator. If you do that scale, which we had done over the years, and that person all they do is just set up the meetings for the recruiter who then sets up the meeting for the hiring manager. So in our product team, there's one person who's basically a research coordinator, and she's responsible for reaching out. She basically talks with the PMs like, what's your target market? ICP, whatever you want to call that. Give me some, what do you want to learn from them? She reaches us, we have a micro CRM for that.

and then sets up the meeting and a PM comes in, they have like already like a meeting in their calendar. And these are the design, people at these companies that are going to be their design partners. Exactly. So I might say, hey, what I want to speak is with a head of RevOps at, I don't know, mid-size companies or, I know, an IC seller at an enterprise company. And then she can kind of, of course, sift through our customer base, slice and dice it, run in micro email campaign and get those kinds of coming.

I love that detail, because as you said, coordinating 12 companies and people at these companies and timing is really stressful and complicated and gets the PM's life up. So that's really helpful. It's interesting. There are some companies where product teams aren't even allowed to talk to customers. Salespeople are like, no, don't mess with these people. Customer success, we got this. They complete opposite. Each pod is working directly with, say, a dozen customers helping build a new product for them.

Exactly. And in the early days, we didn't even like tightly coordinate with customer success. Nowadays, we do it much better because there's always going to be this customer is like frustrated about something or in the negotiation about something that's probably not the right time to ask them about to be a design partner. And so we kind of double check. But it's not like a process where we have to get signed off by three customer success managers to talk with a customer.

That makes sense, obviously. Yeah, you don't want to surprise the people and mess up relationships. So is there any structure to the design partner process or is it just teams have these people available when they talk with them when they want? Or is there more structure to like how to effectively build a product with design partners? It depends on the context. So when you build a new product, there's more linear path, right? It's ideally you want it to be launched sometime.

Ideally, I want to measure some progress. So usually what we've done in this case is some sort of a weekly meeting where we kind of show them progress and sometimes biweekly, depending on our own cadence. And some other capabilities that we build might be more, I want to say, freeform. Maybe it's an enhancement, maybe it's tweak, maybe it's an extension of the, a big example. One of the things we do right now is we let customers ask a question about an account. So coming to GONG, what's new with Cisco, if Cisco's a customer?

At some stage, you do it. We're doing it in all languages, right? So you want to recruit a specific design partner, a set of design partners who are non-English speakers. So it doesn't have a strict timeline. You want to get enough of them. So you see the thing works, gets your reasonable quality results. You're not going to get to all languages on the one hand, but at the same time, you don't have just Spanish. So that might be less structures. Maybe it's a couple of meetings with each one, and then when you move on, you launch the feature, and you move to the next corner, what project or feature?

So one thing that people might be thinking as they hear, this is how do you, all these customers are telling you, here's what I need to be to use this forecasting tool, for example. And as a PM, it's always this balance of doing what customers ask you to do versus like a way of this vision. And here's how we keep it simple. You have what God wants to give your teams for what to do with this feedback, essentially.

Yeah, I think this is going to core skill and expect PMs to have around this. This is exactly your kind of job, right? Try to figure out what request is like must have versus not must have. We typically they ask the customer, you know, what do you have right now? How happy are you between zero and 10 or whatever? So if you're at the six, we want to get you to an eight or a nine. So that's maybe a high level principle.

But at the same time, I expected to say, hey, this is a unique, I didn't hear it from anybody else. Maybe I'm gonna proactively reach out to more customers, but that might be a one customer thing. And we still do one customer thinks in a different context, right? So if we have a, I don't know, seven or eight figure deal that they have like one customization that they really, really need and we know that they can't work without it, I mean, like every enterprise facing company, we're gonna do that. But from a design partner perspective, it's the opposite. It's more like, let's try to build something that works across our customer base versus for a specific customer.

which is why it does it is probably smarter than one or two or three. Yeah, I think at some stage, I guess you get like seven or eight or nine at some stage. By some of my experience, the request starts to converge. There might be one outlier, but generally you're going to hear the same things.

I imagine this approach is rooted in how you all started. We worked on a post back in the day on how you all got your first 10 customers. And I remember the story was you got, I think, 12 design partners when you first design gong. And then, like, you told them, we're going to start charging now and 11 out of 12. We're like, we will buy this now, please charge us and we love it. Exactly. Exactly. So it's in a way, it's replicating this, but it was successful at the time. It wasn't

It was like maybe 80% intention at a time and at some point you take the stock that works and you make it 100% attention. What are the percentage of success rate you have with new products? Because you would think this approach is the best way to consistently build products people end up buying and using. Is it like 100% of the time you end up building things that people will buy and use? Is it something below that? What do you find?

I think it does increase significantly the utility of the products. I would say very close to 100% of the features we build end up being used by a significant number of people. We don't charge for all of them, most of them we don't charge, which doesn't mean other things couldn't happen. Maybe people use it, but the value is not huge. So it's like, yeah, design partner likes it like it, but it ends up being applicable to a smaller fragment or segment of our customer base than what we had hoped.

And it may be they don't not as willing to pay for it. Although that's a little bit of a different process, like real product launch. It could be the quality is not good enough because we're kind of focusing on, you know, is it providing value? Is it understandable versus like, you know, did you find a bug when you use that on a safari on this kind of computer?

We're not building the design partner program to kind of solve for this. Maybe we should, but we are not doing it right now. But generally speaking, I would say more than 95% of our capabilities we build are being used in a very kind of significant way.

which I think is probably higher than most companies. And this feels like a cheat code of how to build new product lines, expand product expansion, time expansion, like ways to add new ways to charge your existing customers. And it feels like a cheat code, basically. Just like, tell us what you need. We'll work with you and build it and it will sell it to you. It'll be great.

Yeah, respectfully, you know, you're truly, I do believe customers know much better than what they need. And then myself or my colleagues in the executive team, whoever else, it's, you talk to a customer and they kind of describe their pain. They might not know how to build it or what's the right way to implement it, but the pain should be there.

Coming back to something else you mentioned, this word autonomy. So when I asked people what to ask you and what stands out about you to them as a product leader, the most often term they came up as autonomy and trust. How much autonomy you give teams, how much you trust teams to do the right thing.

Can you talk about that way of working, where that came from, and why you think that is the way to operate? It's about selfish things, a very personal thing. So I think even beyond trust, it's just for me, it's selfish. I'll tell you why. I just think you get more from everybody if you kind of let them be themselves.

And do things in the way that they believe is the right way of force within limits, right? They're not going to develop, I don't know what it is, software in different business. But the story I always like to tell is, when my son was in primary school, which was a while back,

One of the parents told me, and we had this picnic where all the parents and the kids were gonna meet. And usually there's like a list of ingredients that people need to bring in, you know, to bring on a battle of order, whatever the thing is. And what's usually happened, there's even like people are joking about it, is people run to the list because it's usually like a physical list and then, or make night, probably now it's already in Google Sheets always, but, and people run to just like mark the item that is as easy to get as possible, like on a battle of order, and then I'm done.

And then you always get the lowest common denominator because everybody brings the sort of, I don't even see cheapest, like the easiest thing that you can bring to such a picnic. And then this lady told me, here's a different method. Just tell everybody, bring your own thing. Like, are you crazy? You know, people are just going to not bring anything or, I mean, whatever you want, right? Or people are going to read the same thing. Like, multiple people are going to, I don't know, bake some pie or do something, right?

And she's like, no, that's not going to happen. So I trusted her. That's maybe a trustworthy, but we tried it out. And what happened was really kind of fascinating. People were going out to the specialty stores and bringing like specialties, where I'm basing these around. So they were going to this homeless place, which is like, you know, an Israeli thing. And she's like driving 30 miles to your favorite thing. People were like baking and making stuff. So we had like literally a feast.

And if anything, two things happen. Everybody was much, much happier, right? They were happier because of course they got better food. And then you're like, and also most people kind of just their personality, they brought it to the table. It's like, I really like homeless. I don't like the whatever. The other thing I would have to bring. And we did it every year afterwards because we did this thing at least annually and it worked every single time. So if you take it to the software that you can't tell everybody, you know, just

Develop your old thing, but if you can guide them towards hey do the thing that you know if you more economy essentially bring yourself to the game be yourself don't try to sort of Put yourself in a box. I truly believe you're gonna get much better results short and even more importantly long term because it keeps people thinking it keeps them being motivated and they're like How do I kind of contribute in the way I think is the right way?

Reminds me, I'm looking for day cares for a son. He's like 17 months, almost 17 months now. And there's this Montessori approach to teaching kids. And it's a very similar approach, which is just let them, if they're ever busy with anything, don't even make eye contact. Don't interrupt them. Let them keep doing the thing and let them choose what they want to work on.

Yeah, there's many, many of these education systems or principles that are on this lines. I mean, the person who told me that I don't assume she's invented it, but we all err on the side of like one day more control. But I do the same thing with my kids. So I would never, I never installed any piece of software on my kids devices. So not like firewall protection, I don't know antivirus, air attack. And I think because I'm like, this is your problem.

And if you want to protect yourself, it's your responsibility. So this is autonomy. And there was one time where I had negotiated with my daughter. She was like, I told her, I think you're using your computer too much. We negotiated. She said, maybe an hour is enough. I told her maybe more. I think we agreed on a two-hour thing. And then she came to me three days in a row. Could you please install the software on my machine so I can help me control my limits?

And i love it when it's the other around because now she's responsible i'm helping her versus the other around so absolutely i take it to my personal life as well. So how does this look day to day at gong on the product team like when someone hears you give him a lot of autonomy what does that actually look like how people understand what that actually means.

It means that if you're working with design partners and you get like an idea from a customer, it's your responsibility to decide are you gonna do it or are you gonna talk to your manager or to me, you know, now I have force work managers, but it's your responsibility. So we're not gonna quote unquote punish you if you decided that, you know, you kind of took it an opinion from a customer and went ahead and did it. It's your responsibility to decide, you know, do I know enough? Do I need more input?

How up do I go? And that requires them to think, you know, how confident am I in my decisions? So the way is the culture, basically, you give them feedback and advice and the teams can operate the way they want.

build the features they think are important, work with design partners that they think are important. Yes, and of course, you know, you are expected to solicit feedback, right? So if you're going to build your own thing for six months and it's going to be, well, we're going to have, we're going to review it along the way, of course, but we expect you to initiate a review. You have like a, we have a weekly session where you can bring up your reviews, but it's not as forcing you to do it. You have to bring it, you have to solicit it, and you have to sort of drive the process.

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Is there an example of a product or a really important feature that came out of this way of working? Or a team just like, you don't think that's a good idea? I'm just going to do it anyway. I don't think it goes up to a whole product. It's kind of very hard to, because you've got to have resources to build a whole product.

But I do think there are substantial features that came out of it, even sort of the kind of the AI fine tuning example I gave you before is like something came up in a hackathon and people are like, let's start to build it, let's incubate it, and then they come and move forward. Of course, we had to give them resources at some stage, but it wasn't like a top down, let's do this. It was more like, hey, we're trying it out. Hey, we need a couple of more resources. We're trying more and in some stage, we realize it's super important. And then we kind of, quote unquote, funded it completely.

So when people listen to this and some product leaders might be thinking, oh, I want to work this way. I want to give my teams more freedom or trust. What do you, what needs to be true and your org for this to work well versus it become chaos? Firstly, you as a leader need to sign up to like.

sort of let go a little bit, not control everything, willing to sort of make some more mistakes than maybe you'd make otherwise. So that's the one thing. I think the harder thing is sort of, at least for me, is you also have to sort of get your peers in the same boat, right? Because the head of sales is going to ask you, hey, what's happening? How do I know what's happening if you don't have a control over every feature?

The CFO is going to ask you, hey, what's the, I don't know what is, the RRI of this, or how do you justify those type of decisions? So there has to be some fundamental trust within, you know, you're in your team, you're in your colleagues to at least experiment that way. And of course, if you do an ongoing basis, you lose some visibility. And I think that's maybe one thing you can acknowledge, right? Because if you keep keeping more control by definition, you're going to have less visibility in what you're doing. So give up a little bit of visibility.

hopefully get the benefit of higher velocity and higher what morale or engagement from people. And that should result in better products as well.

I love that. That's a really good example. With the sales example, which is great. Do you encourage the sales folks to talk directly to the pod to ask about these sorts of things? Or do you discourage that sort of communication? Oh, yeah. I think sales, from my perspective, are part of the virtual pod, right? So the core quad is, as I mentioned, you know, product engineering, but part of the virtual pod is their product marketing customer success.

In all fairness, sales people are usually busy doing their work versus actually sitting with us and helping us. Is this going to sell? Did you hear it from customers? Is the type of questions that product managers usually want to get? But if they're happy to spend time, they will be very vulnerable. Coming back to the design partner way of working, does it feel crazy for companies not to operate this way, to not work this closely with design partners on new products and features they're building?

I wouldn't go back right, so I think it's just like...

even like, I hate terms such as risks, because that's a very like, I don't know, like a big use term, but just the risk of building something you're not going to know if it's going to get used. And I was asked by sort of a very senior product manager of a very successful big SaaS company. It's like, why do you even do this? And like, what do you mean? Like, hey, we launch products and then we see if people like them. It's like, well, I don't think that's a great idea because in that company, successful and bigger than God, but at the same time,

I just think it's leaving too much in the hands of I would even call it luck, right? Because how do you know? Yeah, like I was thinking, it just feels like a cheat code and just feels like something a lot of companies can learn from how you all operate there. Something that has come up a bunch so far in our conversation is your focus on speed and optimizing for velocity. Something that I've heard about you is that you're really big on just making really quick decisions.

Even like one way door decisions that are really big, your philosophy is just make it quickly before you have all the information necessarily. Talk about that approach. That's maybe a little bit of a personal thing, but I would encourage people to look up. In Google, there's maybe a supporter for Isaac Asimov, he's a science fiction writer, I think, beginning of last century. And he has this short story that the machine that won the world, so you can look it up. It's a fun story, pretty short.

But it's basically the computer, the big computer at the time, supposedly won the war, but the only reason won the war is because they wanted the people to trust this superhuman machine. But what they realized in the machine was just giving crap, just like our anonymity nation. So they had the person, president, whatever it is, basically they ended up saying, I end up tossing a coin.

But people wanted to really believe that this is a smart machine thing is going to help us win the war. So it's kind of obviously a funny kind of story. But I think there is truth to it. So many, many decisions when it's not a close call, it's like should go and should go and open an office in China right now. Well, probably not. There's so many reasons why not we don't really be baited.

But, like, should we develop feature A and feature B and you look at them, they're kind of the same, I don't know, call it same value or same cost or whatever, however, whatever kind of mental framework you have for deciding, you end up being like 51, 48, 49. No decision is going to be like super wrong.

So yes, you can try to bring in more data and you can try to sort of like bring more people, but like both decisions are okay. So just go ahead with one. Hopefully it's not like a huge, huge mistake. I'll tell you I had this discussion with my co-founder, I meet with the CEO. And a few years ago, we were considering buying a company.

And it's like pretty strategic decisions, right? And we're like, oh, we don't know. There's pros and cons. We're like on the fence there. And we end up not buying the company to kind of look up gong. And we haven't bought big companies. And I asked him maybe it was a couple of months ago, it's like, what if we had bought this company? Do you think we would have been in a radically different position?

He was like, no. So it's like, I mean, it could have been better. It could have been worse, but it would not have made a huge difference. And the reason is, it was a 51-49 decision. It wasn't a 70-30 decision. So it's hard for humans to make decisions. You probably know. There's experiments that show it's almost like running or jogging or doing something that requires your physical capacity to make decisions. So just make the, you know, it's hard, then you want to postpone it, but just do it.

It's such a freeing way of thinking about it. It's interesting because there's been recent conversations on this podcast where Spotify has this kind of value. They call talk is cheap and it's meant to be a virtue. Talk is cheap, so let's just talk a lot before we make a decision. But it's specific to Spotify because there's like a lot of regulatory challenges and if they make it a big decision, it's a long term. It's like they put a lot of effort into it.

So it's interesting. There's such different ways of operating. There's like, let's just talk for months and make a decision versus we're just going to make it and then it'll be fine. Yeah, of course, like big, big one door. Yeah. I mean, this is, of course, you're going to spend more time on it, but people tend to over overthink I think decisions.

I also found out that personally, the quality of my decisions, if you sort of wake me up in the middle of the night and ask me, you know, what do you think about X? And I'm going to be like, I have no idea, I'm slipping. And then you're like, you know, you've got to force me into the decision, I'm going to make a decision. Now you're going to give me two weeks to ponder over it. I don't think the quality of the decision is going to be much, much higher, which is maybe personal, could be personal. But that's at least what I found over my too many years of existence.

I think something that's probably necessary for that to work out well is having a deep experience in that space. You've been at this for a long time, so I imagine your instinct often is trained based on your past experience of the market and customers. You feel like that's a necessary component of trusting your gut and instinct on these sorts of decisions. Yeah, of course. You've got to, at some stage, you know what you're doing. Yeah, if I were now to make a decision around entering a different space, there's no way I would be like, yeah, let's flip the coin, like the asset move story and go for it.

I go to a conference, learn it, whatever the thing is, and then make that decision. But most of the decisions all of it make on a day-to-day basis is now our domain of expertise versus totally new techniques. Awesome. Okay, let's talk about AI for a bit. You guys were very early on AI. You were working on... Basically, your product was built on machine learning back then, what it was called. Before it was cool, and everyone probably thought it was based of time, and now it's never going to work. Now everyone's building AI, building AI into their product,

What have you learned about working with AI over the years that you think people maybe are not yet aware of or that will likely cause them pain that you can help solve and avoid for them?

Yeah, funny, you know, when we launched Goan, we didn't use the term AI because people thought it was a bad thing. It makes wrong decisions or they just thought it's an action item, an acronym. When we founded Goan, I was in Sabbatical and I actually went to this deep learning course because I was bored, you know, and unfairness. And after that course, I ended up buying NVIDIA stock, which I wish I had kept up until now.

But I did send an email saying, hey, this is the next thing. But so we understood it's the next thing. Of course, we didn't know it's going to be an LMM and GPT and other acronyms that we've all thought for the years. And probably in now that we're talking at the end of 2020 forish, I think people should not go from one extreme, which is, hey, we need a bunch of data scientists for every small project, which was the case five years ago or three years ago.

to the other extreme, which is, hey, LLM is going to solve everything. Because LLM's don't solve everything. They have huge utility. We use LLM's over the place, most companies that develop AI kind of stuff. We use LLM's. It's a great thing. But at the same time, don't assume it does everything. You still need some of the core competencies of AI. So you do want to have expertise, people who actually know what they're doing.

and help guide us PMs around, is this something that can be built or known? Because if you're going to spend many, many hours on asking an LLM to do, I don't know what. In the case of Gone, for example, tell me what a good sales cycle looks like. LLMs don't do that. Maybe something else does, but we have a deal prediction model. LLMs cannot predict deals because it's very specialized. I think you need to have expertise. You still want to have some measurements.

So yes, version one, you can just go to an N11 and say, you know, create something, I don't know, whatever. But if you don't have measurements, like in the old machine learning, whatever metrics you use, you're not going to advance. You're going to have V1 and then you're going to have V2. And you have no way to know if you've made a progress. So we kind of pay a lot of attention to, we have people who are going to specialize, you know, how you measure use LOS system, which is kind of the one, you know, using chess as well. And we do have experts who kind of help us make the right decisions.

You can make a very good progress without these, but I think there's a glass ceiling if you don't figure out how to create a more operational rigor around this all day, I think. What I'm hearing is, don't assume you can just outsource all your AI magic model building to the foundational model companies. You need to have your own AI expertise and all expertise.

Yeah, or even if you end up outsourcing the core work, at least you have to have the expertise to understand what is doable, what is not doable, what's the right way to approach it, what's the input you give to the NLM, how is it going to be good quality or bad quality? There's even like, if you just take the product management aspect, if the NLM gives you something that is 90% accurate, or I don't know, people are going to think is good,

The products are a little different than if it's 50% good. So just the way you even think about it, the way I think Figma calls their AI feature like first draft.

Which is a term I like because they kind of realize it's not best, it's not great, but it's a good first draft. So if you know what it is, it's easier not just to name, but how to conceptualize, how to build a workflow around it, and what to train users to assume for it. And I think there is an expertise there that comes on top of LLMs, even if you just use LLMs and you can't afford or you don't want to go deeper.

For folks that want to do this at their company, what are the functions that you have that help you do this slash skills of people you hire that you think are important?

I think you still have to have this kind of quote unquote data scientist role. And data scientists could be in the company, could be advisors as well, right? Not everything has to be a full type in the company. And the role of a data scientist is help guide the company, right? Deal prediction model. Is this an LAM thing? Do you need to build a model if you need? What input do you need? How long it's going to take the data?

Also, in our world, these data scientists are the people who know how to measure these things. Is this model better? Is this model better? Is this problem better? Is this problem not better? And the judgment, right? So when GON creates an account brief, the data scientist is not going to know if that brief or this brief is the right one, but they can kind of guide us through what's the right tools that you need to put it in front of customers and how do you measure this and whatnot.

And then I think in the end of the day, you also need like this myth, you know, kind of now it's becoming a common, you know, sort of the prompt engineer, the person who's actually working with the NLMs and guiding them. That is like a, it's a bit of a technical skill, but you got to have it in a, it doesn't have to be a full-time person, but needs to be, there needs to be that expertise of somebody who's actually optimizing things. Many, many customers tell us that, you know, GONG AI is, well, it's more accurate than others.

Yes, there's some combination of models we build from scratch, fine-tuned, because we have AI expertise. But some of it is also how the prompts we give to the LLMs, how much rigor we put into optimizing them and finding the edge cases and ranking them and improving them over time. If you want to get really good AI, you have to invest in it as well.

As you're talking, I'm thinking about how your pod model matches really well with this world of things moving so quickly, AI changing constantly, just giving teams autonomy feels like a huge advantage in this world where things are just changing weekly.

Yeah, so we have a couple of maybe, you know, it's three different parts. We have like an embedded AI specialist team either as specialist or a team or, I don't know, a couple of people. And then they can iterate very, very quickly on using LLMs or using non-LLMs, you know, SLMs, people now say small language models, but whatever the thing is, they can iterate very, very quickly.

Awesome. Okay. Couple more things I want to touch on. One is the spiral model. So you mentioned that you just went to learn deep learning on your own. You like went off to the side. I'm going to understand this new thing that everyone's talking about deep learning. And you got really smart and machine learning basically really quickly. And

You have this thing you call the spiral method for how to learn something complex quickly. You wrote a medium post about this or a blog post. What is the spiral method? How does it work? How do people learn things really quickly that are really complicated?

Yeah, I think it's even beyond just the speed, but also like how do you even know that you actually learn it? So it's kind of there is a mathematical kind of or physical concept called annealing, which is how certain kind of material kind of becomes the way it is. And it's sort of the temperature goes slightly down, eventually become a crystal or whatnot.

There is an element to this, I think, in learning as well, which is you want to know what deep learning is, like, you know, nothing. You go find the person next to you, and you're like, what is deep learning? They tell you something. Of course, you don't know anything because you just heard it from one person. And the next question is, that's like, who else are they to be speaking with? They give you three other names. I think in tech, we all tend to be like this very, very kind of cool ecosystem of people who are willing to help as long as you don't ask too much of them. So then you speak with three other people, and then they give you like other names, and you sort of go around.

And ideally, at some stage, you feel like, you know, first person, you have no idea what they're talking about. You probably didn't even understand what they're saying. The fifth person, you might understand 50% or 50% is like new. At some stage, you're going to feel like, well, new stuff is 10%, or 5%, or 0%. I call it this partner, because it's going to go in circles around the target. And eventually, you feel like, well, I'm hearing the same thing again and again. And you're like, well, if I heard it from 3P, but I didn't learn anything new, I'm sort of at the bull's eye. Of course, at the level, I am. So I'm never going to be like a

deep learning specialists in the same way that true data scientists are. But as a product manager, I know it probably as well as I can, given that everybody I'd spoken with at the time was not giving me anything new at the level of diet and desire to decide. I love that. Is there anything you've been studying recently that you've either used this method or something else you're excited about learning that's new or on the cutting edge?

Usually I do this for use cases within our customer base. For example, if I wanted to go after a certain persona or a certain use case for the product. So we had this notion of can we do a better job for a specific persona within sales, people who are account managers.

So I would use a similar method. Like, hey, talk to what account manager it took to an analyst or whatever the thing is. And eventually when you start hearing the same thing, it's like, what do they care about? It's different than it says people like selling new business or different than contact center centers. When you start hearing the same thing, you're like, okay, I kind of got to where I need to be. Now I can make decisions.

I can always do another spiral and get one level deeper, which is, I don't know, do some user research go all in, but at least it's sort of the conversation level. I've got it. I've got where I need to be. I love how simple this is as you just start talking. Just find somebody to talk to you, ask about this. No pressure. And then just, okay, who else should I talk to? You just keep having conversations, spiraling deeper and deeper into knowledge and wisdom.

Okay, one day I wanted to touch on, which is always stuck with me about your approach initially when you were starting Gong, is how you found your initial ICP who to go after. And it's really funny how narrow you got when you all decided here's who we're focusing on for our first dozen customers. So I have the list here.

So when you decided, here's who we're targeting. Here's the list of constraints. We're going to target people selling their product in the US, in English, over video conference, using WebEx, which was the big one at the time, selling software that is worth $1,000 to $100,000.

And there was only 5,000 companies in this bucket. Can you just talk about why you found it was so important to get so narrow and just the power of getting really narrow, which is very counterintuitive to a lot of people where they're like, oh, we're just going to be for everyone. It's a huge market.

I think it's sort of the traditional, I call it the boning alley or whatever you want to kind of force. Crossing the chasm. Yeah, the crossing the chasm kind of methodology, which you want to start narrow. You want to create this kind of small pond where people talk about each other and you can kind of light the fire in there. In my previous company, by the way, I did read Crossing the Chasm.

And i told myself nah i can do way better than that. So we had one customer in i think it was lawyer or i know one of the cosmetics companies american express in sysca like different industries and there was no way we could scale it because everybody had their own lingo the way they thought about the technology whatnot.

So, by having a smaller set of customers or IC, and on the definition of customers, you can develop much more focused, and it's easier to light the fire, because people move, right? At some stage, I think it was you wanting to the business. We heard from a company that they interviewed a salesperson, and the salesperson asked, are you using gong? And they said, we are thinking about using gong, but we're not. Like, well, I'm only going to work for companies that use gong.

And that's the sort of the power of a small pond with companies that are like each other, because you get this viral effect that is not commonly B2B, but it's as close as you can because of those conversations. That other customer became a non-customer, literally because they interviewed a person who told them he's not going to come unless they bought gone. You can do this if you have a white market where people don't even talk to each other. And there is an assumption that you're not burying yourself at this market.

I love because because today, like I said at the very top of this conversation, you're just so ubiquitous. Like everybody seems to be using gong. And I love that you started with something with those like seven, I don't know, different constraints to narrow down who you're going after. And it's such a good example of the power of starting very focused and then expanding from that, which is what you've done.

Okay. Last question before we get to a very exciting lightning ground. We have a segment on the podcast called fail corner where so many of these podcast conversations, everyone's always sharing all the successes. Everything's always going great. We never, nothing ever goes wrong. When in reality does things often go wrong. Can you share a story from your career or just the journey of gong when things didn't go well, when there was maybe a failure? And if you learn something from that time, what you learned.

Yeah, I always kind of joke that in my previous company, we've done so many mistakes that if life limited you to a certain number of mistakes, I wouldn't have any left. I think I still do mistakes, but just so many. So every one of them probably done twice or and then it's like, oh, it's some stages like, you know, third time's a charm. So the one I just gave you is like probably the worst is, you know, crossing the cars and then you start a company, you have this like technology. I was thinking, let's go horizontal and that technology was whatever web integration, something.

eventually ended up being an e-commerce content syndication or content management SaaS software, which is the right way to go because you want to specialize in a certain market, but initially just going all in was ridiculously not smart. And the other thing we did together was that was

previous company started year 2000. So that was like the bubble, one of those very, very nice bubbles. And so like, you know what, we actually got three customers, admittedly in totally three different segments. Now let's go and scale. Now we only need like one salesperson, one SE and see kind of do what's now called product market fit. I don't know that the term even existed there.

And we're like, nah, you know what, investors told us he got to hire more people. So we hired 20 salespeople, all of them failing miserably. Because, hey, we didn't have a true product market feed, but even what's worse, we didn't have a true focused ICP with a very, very pitiful product market feed.

If you hear me talk about how we started going, I mean, there's the CEO and he kind of drove it out of that business strategy. But so me being a co-pider there, it definitely bringing the same, I'm not going to make that mistake again. I might do new and fun ones, but not that same mistake again. Awesome. Thank you for sharing that. With that, we reached our very exciting lightning round. Are you ready? Sure. Let's do it. First question, what are two or three books that you find yourself recommending most to other people?

There is a set of books. I think one that is sort of the starter one is, I think it's called right now the ideal executive.

Um, people don't really know itself a management book out around a team and whatnot. I think the original version is funny enough. I think it was called mismanagement, but nobody won't buy a book called mismanagement. Much rather by a book that's called ideal executive because you of course are not mismanaging your deal executive altogether. So you're just reinforcing yourself. Uh, the jokes aside is basically kind of gives you the, it's trying to sort of, um,

Define people by four characteristics. I think misnamed, but like, are you an administrator? Can you like, are you, he calls it a producer, basically get the job done, integrate it, which kind of brings people together. And the fourth one, it's basically kind of changed an agent.

You know, kind of do a lot of mess and change stuff. Usually entrepreneurs kind of include that part of course. And basically he's claims that nobody does the whole for it. Maybe you're good at one, maybe okay at the other. And personally, I'm horrible in administration. So I obviously acknowledge that and I try to sort of compliment myself. But so I think there's two things in it. Firstly, just those.

I thought there was the four good ways of looking at people as a manager, as a leader, of course. That's one. But I think even if you disagree with those four, just understanding that you want to look at the people in the organization yourself included, and it's through the prism of key characteristics, and you can select a different framework, helps you a lot with

creating high velocity discussions with others, because I can talk with somebody and say, hey, you're a P. Well, I'm not a P, I'm an I, whatever the thing is. And that makes a discussion that is much, much faster and more comprehensible than just trying to explain this from scratch, like, hey, you tend to do this, and you might want to do this, and you might want to strengthen that.

I'd recommend starting from this, but there's probably other methodologies you can pick and maybe some of the listeners here have already had one. But that's one I like because I found it useful. It's called the ideal executive. I think some pretty sure, yeah. Great. Any other books before we move on?

That one's going to probably work. I like crucial conversation that's going to end the beating path. It's like how to conduct conversations with people in your organization. I think it's never bad to sort of re-merse yourself into how to speak properly with other people.

We have an episode coming up where we're going to share script and phrases to use to have better, hard conversations. Ah, it's good. Yeah, I'm excited for that. Slash scared. OK, next question. You have a recent movie or TV show you really enjoyed. I didn't have TV like broadcast TV for many, many years. So nowadays there's Netflix so you can find stuff, but I'm.

in my taste in sort of TV and movie tend to be pretty esoteric, French. So I've recently watched this British TV series called Slow Horses with Gary Oldman and it's a really kind of funny, you know, sort of funny spy thing.

which I felt I'm using an intelligence at the same time so some kind of comedies tend to be pretty kind of lowest common denominator that one seems still fun and and with you at the same time so that's my latest that I kind of really kind of enjoyed watching even the third season.

I love slow horses. It's like, I don't think it's that obscure. I think it's like one of the ones Apple promotes often. I will say this last season was not not my favorite, but the other two roles. Yeah. I 100% agree, which I said, even the third season was OK, but the first two are really, really good. Yeah, it's super fun. There's a fourth.

It took me like three tries to actually get into the show initially because people kept telling me it's so good. And I started watching and it's just like, who's this old messed up guy just complaining endlessly, but you got to keep watching. Okay. Do you have a favorite product you recently discovered that you really like? I assume you have a silverware caddy in your dishwasher, right?

Oh, yeah, it's a put like forks and knives. So that's my favorite product as of lately. And I'll tell you why. It's a funny story. I lost mine and you can ask yourself, how can you freaking lose like one of those bastards? And it was in the dishwasher, of course. And for some reason, I couldn't find it. That's like you have to be really kind of out of you might do not find anyway. So I go to some Amazon or eBay wherever I just buy a new one. And then of course, a day later, I find it's like a fence somewhere within the dishwasher. And now I have two.

So this is my latest invention. If you have two of those baskets, you could put one of them in the sink and you can just like continuously load your cutlery or silverware while the thing is working or you haven't vacated it. So it's kind of changed how we organize our kitchen with something that probably costs 10 bucks.

No product managers ever thought about offering two of those with your dishwasher. I don't even try to upset anything of any of that. And I called it to some people and actually ended up buying a second one and suddenly was successful, which is the most ridiculous thing. It's like, you know, spend 10, 15 bucks, get something organized in a completely obscure and unintentional way.

I'll give you an even crazier idea that a previous guest suggests Rory Sutherland has this pitch that you should have two dishwashers. Everyone should have two dishwashers because one is your clean and one is dirty. And you just take your plates and things out of the clean one, use it and put it straight into the dirty dishwasher. And why are we just putting things away constantly? Just like go from one to the other and one to the other.

So there you go. Similar idea. Similar idea. Find the next level. Find the next level. Find the next level. Find the next level. Find the next level. Find the next level. Find the next level. Find the next level. Find the next level. Find the next level. Find the next level. Find the next level. Find the next level. Find the next level. Find the next level.

Yeah, there's so many razors, and there's one that I think came in in some Murphy book or whatnot, and it's called Hanon's Razor. You can look it up, Wikipedia, wherever. And it basically says, it goes like never attribute to malice, that which is adequately explained by stupidity.

Um, so it obviously sounds funny and it's trying to be funny, but it's so helpful because so often do we attribute like people's behavior, you know, thinking a company, a customer, I don't know if personal like sometimes to malice like, oh, this person's not returning my calls because X.

or this person hasn't given me feedback or has given me feedback because of X. And it's sort of like we all, I think there's a saying like always assume well or with intent or whatever. And this is sort of the more funny way to sort of say that. Yes, the person is, and again, stupidity is probably a funny way to do it, maybe inappropriate, but it's basic. Yes, they just didn't know, they didn't care, they didn't think about it, they weren't trained, whatever the thing is. And if you take this modern, new day to day life, at least I find that it's,

It's so true and so often true that is funny but inspiring. I really love that quote. I think of it often when somebody's doing something that's annoying me.

Final question, you mentioned that you are from Israel, you live in Israel. You mentioned delicious food. Hummus is one example. Is there another Israeli food that you think people are sleeping on that you think people should try when they have a chance? Israeli food has become a little bit, you know, kind of gotten a little bit to be in fashion lately. So people coming from the US to visit us in the office are like, oh, Israeli food is so good. And I'm like, what do you mean? It's the same food we've had for like 20 or 30 years. I think the taste changed because you kind of eat more healthy and less oily these days.

Most of the Israeli food is sort of Arabic in nature or Turkey. So there's great falafel, great hummus, peta bread, the Turkish delights of sorts, so a lot of those. And some very, very obscure. And if you come to Israel and show you're on some very less known food that only kind of special guests get to taste. What's here? OK, and say it here.

There is a thing called Sabif for example. Nobody knows of it. It's people claim it came from the people who came from Iraq, but my wife's father came from Iraq. He's like, we've never seen this before. It's sort of Peter Breadfield with hummus and eggplant and eggs. Now, maybe something else. I have no idea. Tahini, maybe. I don't know.

It tastes good, but it's such a weird combination and it's become a little bit of a thing and nobody knows what the origin is. I think it's somebody made a mistake and gave it a name and now it's like ubiquitous. You are making me hungry. Elon, this was amazing. Two final questions. Where can folks find you if they want to reach out and learn more? Maybe ask them follow questions and how can listeners be useful to you?

Pretty available in LinkedIn is probably the best way. I tend to read my inbox in LinkedIn and respond when I can. And then useful to me. I mean, if you want to come work for Kong, check out our careers page, of course. The product team is mostly based instead of even Dublin, Ireland.

Maybe a little bit remote for most people, but there's sometimes folks in the US and sometimes non-product. Of course, we're hiring quite a few people these days, so we'd love to at least give us a chance. Awesome. Alon, thank you so much for being here. Thanks for inviting me. Bye, everyone.

Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.