Transcript: Building the AI Operating System for Revenue: How Gong scales to 5,000+ customers | Eilon Reshef (CPO, Gong)
Full transcript: Building the AI Operating System for Revenue: How Gong scales to 5,000+ customers | Eilon Reshef (CPO, Gong).
2026-02-20
Host: transcription was a lot native recording calls was native, but like we didn't transcribe calls. even like and most of the majority of VC VC firms didn't want to they don't want to invest in calls because their hypothesis was people will not want to get recorded entering 2026 and obviously it almost feels like ridiculous at this stage, but it wasn't obvious at the time.
What we had to do was actually invent or develop the the call recording technology because most providers either could not record even calls. They they they did it with some clumsy process.
When was like the sort of like tipping part that you thought, okay, we've achieved uh crack market. Then we gave it to 12 design partners and we told them, could you please give us feedback?
And they started complaining and we fixed things of course that didn't work in the beginning. And then at some point they stopped complaining. I think we are as as as a universe, we're a little bit in a sort of uh, you know, post truth world.
And and what pros truth means also is um you get a lot of incentive of just like coming up with very bold, not necessarily true claims because they get you publicity and sometimes recognition.
And the press loves this because it gets them whatever condom clicks, right? What if I just told you AI is going to make your engineers 30% more effective? This is like boring, right? Who cares about 30% more effective, right?
So now people much are much kind of more excited about talking about maybe engineers is going to go away. Maybe the PM can code, maybe there's no need for stats. So I think these are wildly exaggerated.
I think I sometimes tell our marketing people when they read some of those very, very bold claims, if you see them coming from VCs, for example, I mean, does content marketing, right?
We tell people about how sales should behave because we assume that if people read about how sales should work, eventually they're going to look and see who is and then maybe they come to us as customers or as prospect.
Host: sales is sort of like an art. Does the coaching really make or give you the best sales person? There's effectiveness versus efficiency, both kind of contribute to productivity.
Efficiency, AI does this all the time helps you write an email, a lot of these things just takes take takes time off. Effectiveness on is more subjective in some ways because how how can you prove that somebody's better.
Gong has been, I think traditionally we've been able to show that you can move the curve. You're not going to make the excellent people excellent plus plus. You're never going to make the the the C players hate players. This is not going to happen.
You can make the C player C plus, the C plus B and Bs the A is going to thing. So you see the whole curve moving and you see it pretty consistently.
Host: Hello everyone. Welcome to Starup Project uh where we deep dive into the minds of innovators and entrepreneurs that are shaping the future of tech. Uh my guest today is Elon Reshev, uh the co-founder and chief product officer of Gong.ai.
Uh so Gong.io. Uh before co-founding Gong, uh in 2014, uh he was already a season entrepreneur having co-founded and led by Collage, uh a successful SAS platform which was acquired in 2013.
Uh Gong leverages advanced AI to analyze customer interactions and sales conversations and uh enables teams to boost their productivity, deliver revenue predictably uh and drive efficient growth.
Uh under Elon's leadership, uh Gong has evolved from Asia conversation intelligence offering into a sophisticated revenue AI operating system. Uh it remained a price uh customer insights through proprietary Gong revenue graphs.
Uh then most actionable intelligence and automatic critical worknotes. Um have over 5,000 customers including prominent links like design PayPal. Uh Gong's impact is undeniable.
Uh it helps companies achieve our codes back 57% higher win rates and saves thousands of operational hours. Uh today we'll explore uh the Genesis of Gong, how they achieve product market Seep uh and how AI changed their business and product.
Um and a lot of more really other interesting things. Now Elon, uh welcome to the show. Elon: Thanks for having me.
Host: Uh so I think the first question I wanted to ask was um what was that initial problem that sort of like caught you and your co-founder's attention uh that it led to gone?
Elon: It's a good question and actually Gong is one of those boring companies where not much has changed in terms of the overall vision from when we started and to date despite all of the revolution that happened within the AI technology world.
Obviously LMs and whatnot. Um when we started that was about 10 years ago 2016 and at the time, um we were looking at the revenue space. And what we noticed was people were it was treated like an art, right? Sales is art.
And we felt strongly that if you can uh uh introduce at the time we didn't even call it AI, it was data sa- science, data driven workflows, whatever you want to call it. Um you you can you can make things much more efficient.
And again today everybody's talking about productivity and AI, same idea. Uh and then we realized that in order for quote unquote AI to make sense and and help, first of all you have to have like quality data which hasn't changed.
AI is only as good as the data it gets. So what we started as a company was let's capture the core information which some people might think is the CRM, but in reality it's actually the conversations that that people have with customers.
So it could be sales sales could be both sales could be sales engineers, SDR, whatever.
And the idea is if AI has access to those conversation and we can start making like really really good decisi- decisions, recommendations and actually actually carry out actions for you. So that's exactly how we started.
A at the time, at the time, you know, we kind of hooked up to WebEx. That was our first video conference system. Zoom was barely starting. Um and then later we did email and text messages and other data sources and of course connecting to CRMs.
But the core the genesis was let's bring information, put it in some sort of and then we call it reviv graph, so there some sort of a graph system and then apply logic to it to help people be more productive and and leaders get more intelligence.
Host: I think in some sense uh a lot of these ideas are now a little bit more common uh but I think back then it was not that common like you know getting all the call data and transcribing it was not really native to any of these.
You know if you're using Zoom or anything like transcription was not native and like recording calls was native, but like we didn't transcribe calls.
Uh even like if you use teams or Google stack back like transcription and like getting that data and analyzing that data was not really common because there nobody is a lot more competition in that space.
Uh what was like the tech stack when you were using? We try to solve this problem? Elon: Yeah it's it's a very good point. Even the idea of recording at the time, people did not want to invest in back in 2015.
I mean obviously we did raise money, but and most of the majority of VC VC firms didn't want to they don't want to invest in calls because their hypothesis was people will not want to get recorded.
Which was of course great right now we're entering 2026 and obviously it almost feels like ridiculous at this stage, but it wasn't obvious at the time.
Um and what we had to do was actually invent or develop the the call recording technology because most providers either could not record even calls.
Even if they did it with some clumsy process where maybe a user had to manually click record, which of course wouldn't happen. Um so we started with the technology stack that's basically developing a bot that joins the calls.
And now it's so common that yeah, sometimes if you meeting like four people and then it bots or what not, but at the time we still have a patent on bots joining calls. So we we developed this back in 2015.
Um and it's joining the calls, it's sort of the automation around starting recording, capturing the screen and whatnot.
Of course keep bringing it to the back end, of course it's a was a robust kind of cloud first, you know, kind of system to begin with, 2015 modern stack.
And then in the beginning we used a third party transcription engine which really really sucked at the time.
It wasn't because of the system, it was just because technology in 2015 was like I think something like 30% word error rate, which means three out of 10 words are actually wrong. So you couldn't even read the transcript.
The first versions of Gong, we essentially heat the transcript, we had like a for clicks so people wouldn't find it. You could still search it and do statistics and like high level topic detection, but like reading it was really really hard.
And then very quickly moved to a homegrown system, which was better.
And of course nowadays we still use a homegrown system, but uh it's much more much easier to just kind of, I don't know, take Whisper or or any a major providers transcription off the shelf and then kind of use it and get I guess pretty good results.
Host: Um who who were the early customer adopters? And you mentioned like transcription being not that great, but like who which type of customers really leaned in on this stick?
Elon: Yeah, um so I'm a big believer in sort of the acrossing the cash with me my father both are like big believers in crossing the cash model. Which means you want to start with a very small niche.
Um so when we started, we basically said um who is adopting technology fast? It's like technology company's good. Which technology companies are more likely to use video conferencing?
Like guess what, software companies because they don't want to travel to uh, you know, destination because soft it's like easier to sell software online than that kind of physical goods.
And then so we pretty much said, hey, an enterprise we couldn't sell because we didn't have like a way to sell to enterprise, which just like we didn't have security, we didn't have scalability.
So we said let's start with software as a service companies in the United States of America, North America, selling in English over video conferencing, mid-sized companies, selling mid-sized ticket items because if you're just selling, I don't know, $5, you probably the conversation is not as important to you.
Maybe even the whole sales cycle is more like to see that of thing.
And then if you're selling, I don't know if you're Boeing and selling airplanes, my guess is you're probably not going to have like you're going to have much more a relationship selling and in-person selling, which at the time we couldn't support.
Um so the idea was like focus there. And then afterward we said, hey, it's not only video conferencing, it's phone conversing.
And then you pick an email and then you can start to more relationship understanding and then you can have like now we have an in-person recording and all sorts of other things. Um but the idea at the time was like focus, focus, focus.
There's probably 10,000 companies in the world, let's focus in this category.
But look to get from seed round to A round or to B round, whatever it does you only need like a of dozen, two dozen customers as long as you know there's enough companies in the future, we're very satisfied it just like starting with a very narrow customer base.
Host: What what kind of early insights was gone providing? You know at that time like in 2015, 16, uh for us adopting like because transcription itself was not accurate. So what does actually find valuable?
Elon: Yeah, um so if you think about what can you do with a transcription that is not accurate, um it it almost lent itself to what are the killer apps for this and and one killer app was search.
Um so even if you don't like some words are missing, you search for a competitor, you still find them. So we invented this idea called a tracker, which still by the way is available in every gong. I don't want to be or even like big.
I actually even Microsoft has some sort of a conversation this product that actually even use the word tracker inside it. And the idea was the tracker was like a safe search our safe keyword list.
And then what you could do as as as a as a an organization say, hey, I want to focus on those conversations that actually use, you know, up competitor Rex or a challenge Y or technology Z or whatever.
And then you can program the system for this to kind of drive many, many workflows. One is just like I'm a product manager. I want to know what my what what's happening in the field or very common use case.
Uh I'm a sales manager, I want to coach people, but I don't want to coach them on every call, I want to co- I want to call that just like talks about pricing.
Um or I want to help my team uh position against the competitor and I want to find only conversations that are uh about a certain competitor.
So search was a phenomenal use case or narrowing down filtering you this a you know, very big graph is sort of like super important. The other one is more of a collaboration use case, which is I'm I'm an I'm I'm I'm a sale- I'm a sales person.
I got asked a question, I need to bring in more people into the loop. And I can start tagging people and just having a very convenient interface, we can bring more people collaborate.
There's a chat window, there's a you know kind of mark moment 27 and and and and and it's making it very easy to people to kind of send as a team. And that was a very useful.
So these are maybe the two main ones when more AI might what let's say AI mainly.
Host: And when was like the sort of like tipping part that you thought, okay, we've achieved uh crack market, but was it like very early on or like a couple of years into the development of the product?
Elon: Yeah, that's one of our funnier stories in Gong. So this is this is going to be like maybe two different answers. So one is when should we have realized that we had product market fit and then maybe, you know, us being a little bit slow.
When we actually realize it, right? So to the point when we should have realized it, we uh raised money in October of 2025. I brought some of the team members who worked with me in my previous life.
So January, three month in, we had like a running prototype that could record many calls and transcribe them and do all of the things we just discussed.
So we started giving it to customers to get alpha customer, beta customers, whatever just like SAS companies, you know, kind of our size, I mean not like five people, but whatever, 500 people, maybe 1,000 people.
And then we gave it to 12 design partners and we told them, could you please give us feedback? And they started complaining and we fixed things of course that didn't work in the beginning. And then at some point they stopped complaining.
And then when they stopped complaining, we're like, why are you stopping complaining? And we watched obviously their behavior and they're like, we're using it, it's fine, why will we be complaining?
And then I meet my co-founder basically said, well, if they are not complaining and they're using it, maybe we start asking for money, right? Um so we had 12 design partners. We called them and we said the beta is over. There wasn't any beta, right?
It was just like we just gave through the software to try it out, right? And then 11 out of the 12 paid. And that was like May 2016, which is six, seven months into the company, right? And 11 out of the 12 actually went ahead and paid.
And we weren't cheap at the time. We were like charging initially maybe 750 per individual per year. So it like of our this price now is higher, but you know, for a young startup with a dozen employees that that's not small, right?
And 11 out of the 12 paid and then the 12th actually paid a year later. I mean bought a year later the CROs change jobs and like they couldn't buy it.
Um and I think that's probably the point where you should stop and be like shit this thing is actually working. Um because like 11 out of 12 is like I'm really an away. Um but we're like, okay, that makes sense.
Let's maybe, I don't know, uh why isn't the 12th buying? And then well, maybe it's time to hire first sense rep.
But that was definitely the moment where had we've been more maybe kind of attentive to the the process, we would have said, hey, this is this is a product market fit momentum.
Host: The the first time I actually sort of encountered Gong was in 201819.
Uh I was doing a pitch deck for an Indian company trying to do software for customer support um and sort of trying to do similar things of like, you know, collecting all the transcription and trying to improve their efficiency at on a call center level primarily targeting uh you know, full delivery companies.
Uh and then I was evaluating what the bigger players, you know, internationally were already doing some version of this and that that's my first sort of uh encounter of Gong and since then I sort of like kept tracking of what is Gong doing.
Um can you talk a little bit about like you know what types of different customers are using Gong because you know, I can easily imagine on the enterprise sales organizations.
Um but what are the types of customers you generally can sort of categorize them into?
Elon: I would say these days obviously uh the Gong has evolved from what uh we started using kind of conversation intelligence and then revenue intelligence which we added more capabilities I'll touch on this a little bit in a second and now kind of we call it iOS for revenue teams and obviously as the as the sort of the capabilities uh were were strengthened also the types of customers that you can serve is is growing.
So um the type of capabilities we added is is pipeline management, forecasting, sales intelligence, which is prospecting, uh coaching, enablement these sort of things.
So as you expand those suddenly more and more companies need they might no need also by they might not be doing I don't know forecasting using software, but they might still do prospecting over software or coaching using software.
So nowadays I I'd say we serve companies um anywhere from a small company of like 50 people all the way up to the world's largest organizations. Five of out of the top Fortune 10 companies are gong customers.
Um Cisco is one of our kind of, you know, kind of public references. They are deploying it to 20,000 sellers, which I think is the largest revenue deployment in the world, I don't know. Um but obviously large scale as well.
Um so I would say nowadays it's it's less it the the sort of the the industry, we're much more diverse now.
So there's financial services companies, there's healthcare companies, uh of course technology companies and even like communication companies, AT&T and such. Uh and then nowadays we also cover people think of Gong sometimes is selling.
Um but we're really kind of try to help everybody uh along the customer journey for anybody who's creating pipeline, prospecting, technical called SDRs, selling, um selling team, pre sales, solution, architects and whatnot, implementation, post sales, people responsible for retention and expansion.
Um and then sometimes the sort of the more strategic level even product managers and and sort of like uh non revenue related. Um so if you sort of look at these type of personas almost every company in the world has them.
We're still a lot of our business in North America just as this is where we started. Still a lot of our business maybe 50% is still tech or tech related. Just with is for where we started. Um but very diverse nowadays.
Host: What do you think about this idea that you know, anyone can code or software start up and like they're sort of seeing the commoditization of writing code.
In in an in that era like you know, getting an MVP version of Gong might be easy or easier than you know, what it would be like you know, eight years or nine years back when you started, right?
Um then we are seeing certain like transcription software are like companies are like everywhere where there's so many of them.
Well, but my general question is like how do you look at the commoditization of software and in in that scenario like what is the edge? What is the moreter? How do you approach just building companies?
I mean, you are an established player, but someone who's starting now, like how how would you think um or you know, what are your general thoughts on?
Elon: Yeah, maybe I'll answer this a little bit my at a zoom up level and and maybe even a little bit provocatively, right? I think we are as as as a universe, we're a little bit in a sort of uh you know, post truth world.
And and what pros truth means also is um you get a lot of incentive of just like coming up with very bold, not necessarily true claims because they get you publicity and sometimes recognition.
And the press loves this because it gets them whatever condom clicks, right?
I think some of the discussion around like, hey, we're going to vibe code everything kind of comes from this the idea of like what if I just told you AI is going to make your engineers 30% more effective. This is like boring, right?
Who cares about 30% more effective, right?
Host: Yeah. Elon: So now people much are much kind of more excited about talking about maybe engineers is going to go away. Maybe the PM can code. Maybe there's no need for sales. So I think these are wildly exaggerated.
I think AI is phenomenal for engineering productivity and I don't know if it's can save, I don't know. I I don't think anybody's saving even 50% these days, but I think 50% is within reach. Um 10% people are getting today, 20, I think maybe even more.
Um and I think it really good for software companies it's good for the universe because you can get more value by getting more software. I think yes, you can definitely do lovable or use any other tool for prototype very, very quickly.
But once you want to get like real software with all of the infrastructure, security, functionality, iterations, uh uh enterprise quality software, yes it could be cheaper, but I don't see majority of functions going away and then if you also need sales people to sell it and you need marketing people to market it.
Um so you 100% sure you can do it more effectively, but I don't think that fundamental is change. I also don't think that organizations should be bothering with, I don't know, codating our software.
They're going to get into the same cycle of like I got to retain it, I got to change it, it's not working, who's going to support it. Same challenge that people have had for maybe 40 years, I don't know, 30 years for sure.
Um which to me like doesn't make any sense, right?
Host: Yeah, I mean, I think the triple fiber for coding is you can get into production but once you get into production and people start asking I want this, I want that and uh I want to improve this and then you don't know what you've written and you know, then maintenance and improvement really becomes a challenge.
Um but yeah that that's actually probably what you said is right in terms of like a post- truth world.
You need to make an exaggerated claim and discuss it and um you know, promote it and it has its sort of I think it has validity in in Heaven built in it. Uh and which everyone sort of is striving for. Um Elon: I would even say more.
Now I'm going to insult a little bit my VC friends here, but you know, since I mean we are on record, I mean not going to beat me for it, but I I sometimes tell our marketing people when they read some of those uh very, very bold claims, um if you see them coming from VCs, for example, I mean, does content marketing, right?
We tell people about say how sales should behave because we assume that if people read about how sales should work, eventually they're going to look and see who is and then maybe they come to us as customers or as prosper. Everybody does marketing.
VC's content marketing, which is used for deal flow, right? Is is basically come up with very, very bold claims about AI because that gets you the the very eager entrepreneurs.
So I think you should also like reverse engineer who says what into what what are they kind of are they actually writing objectively or is there a goat behind it and we should all be more particular about how we interpret what's written out there um in the media because that's the 2026 world, right?
We can't change it, we can just be more aware of it.
Host: Uh we talked about this uh, you know, from conversational intelligence to this uh now you call yourself as like revenue operating system, AI revenue operating system, right? What is the difference?
You know, what are the features that make it different? Elon: Yeah, so when we started the whole notion was we're going to start with analyzing a single conversation. And there's lots of stuff to be said about a specific conversation.
Did you set up next steps? Did you did you just talk yourself to death, right? The Gong is probably invented the idea of measuring a talk ratio for the rep, right?
Should be like obvious but people say, uh you know, there's a reason why we have two ears and one mouth, you should just listen more than talk. Um but there's so much value you can bring by just focusing on the conversation.
So very quickly in the road we said we don't want to be the experts in how to handle a specific conversation. That might be good for an consumers and support, I don't know, like over the phone.
We want to help people really kind of realize their what it full potential in terms of being revenue professionals and revenue organizations.
So we we started understanding what are the key workflows that people have within revenue organization and started rethinking about them in what I would now call AI before and it was just like data and data science way. So I'll give you an example.
Every revenue organization on earth, there's some sort of a cadence where somebody reviews their pipeline and decides what to do next. Sometimes it's the rep, sometimes it's a one-on-one meeting, sometimes it's a big forecast call, right?
So we said, what does this process look like from in an AI centric world? be like, you know what AI actually shows you which deals are more relevant than others. So it shows you have you had conversation what's said in the conversation.
You know within team if you can ask a question about that deal or what not. And then it summarizes it for you and it just helps you kind of do it up job. So we gradually built more and more workflow.
So revenue intelligence and then revenue iOS is basically uh pipeline management and then we crew this to forecasting. What if I can help you forecast where you are, which is super important.
And then we took another key workflow which is uh making people better. So we have an enablement product that says, hey, I'm going to actually help you coach the team.
And of course nowadays AI can actually coach for you to a certain degree, right? score calls and understand the facets.
And then now we're launching a train of AI trainer which is going to talk with you and help you simulate the customer and coach you, right? So this is another angle.
And then we look at how do you prospect and the idea is like what if you can prospect to people but actually leverage your history with be like AI is going to write the emails for you, it's going to do most of the majority of the boring work for you, right?
Um so as you look at all of those things together, if you a revenue organization, be like, yeah, I want to sing a platform that does all of those things from to kind of go between systems and a different data layer that I need to sort of reconcile and you know several contract and what not.
Uh then um Gong nowadays at the position where we we we're like here we're a single OS for for everybody.
So you still going to need like a CRM, you might still need some sort of data from somewhere and other things it's not like I don't think there's a single company in the world where you know, you can just use that company's products and nothing else.
Uh but it is a sort of a central place where revenue professionals leaders can do the majority of their high quality work nowadays and of course more coming soon.
Host: You you're talking about AI training or coaching, you know, um so you also mentioned like in the setting of our conversation sales is sort of like an art. Um does the coaching really make or give you the best sales person?
Um like do you see that pattern as an outcome or are you saying that if someone is 50% that we are making them 80%, but really the 80 to 20 is still heard, like what what is your take on that?
Elon: Yeah, um so there's effectiveness versus efficiency both kind of contribute to productivity. Efficiency AI does this all the time helps you write an email and all of these things just takes take takes time off.
Effectiveness on is more subjective in some ways because how how can you prove that somebody's better. Gong has been, I think traditionally we've been able to show that you can move the curve.
You're not going to make the excellent people excellent plus plus. Yes, you help them another 10% for sure, but like they're already excellent. You're never going to make the the the C players hate players. This is not going to happen.
But you can make the C player C plus, the C plus B and B is the A is going to thing. So you see the whole curve moving and you see it pretty consistently, you just learn new skills, things that weren't aware of and and and just become better.
I don't think you can expect everybody to be an A player.
By the way you should also replicate the A players if you're going to try to shoot a three-pointer from like Steph Curry you're just not going to make it because you know, he shoots from I don't know, wherever like weird places.
You should probably you know, you can do better than what you're doing right now, but the A like the A plus players sometimes have such a unique pattern um that you don't even want to replicate.
Host: I think uh Gong is at also the intersection of this to infraziel like some sort of AI agent uh phenomenon that's happening.
Uh I've seen a lot of demos and products out there um you know, which are sort of AI taking a call uh for for the customer and you know, sort of doing better or sometimes same quality output. what is your general take on that kind of sort of like when AI is actually talking to customers and now AI agents basically taking over real some sort of responsibilities.
Like where are we in the curve of like an option of those kind of fun factors? Elon: We're we're getting close. I don't think uh uh those AI agents are going to replace a B2B seller.
Most of Gong customers are B2B sellers, like there's a limited of relationship, there's a limited of knowledge. There's an element of of just like uh um continuously understanding what's going on with with your customer.
Um AI kind of customer facing AI can do a sometimes a good job in sort of like especially like uh one and done B2C phone calls, you know, kind of anywhere from like a glorified IVR to just like, hey, let me qualify you a little bit and sell you something.
This is already happening. Um I think the equivalent in B2B might be um inbound leads especially ones that you don't have capacity to deal with.
And the other thing is this is kind of where I think the uh the market will be heading is outsource specific task from your day. So let me give you an example, right? I'm a salesperson and um I want to walk my customer through my proposal, right?
As an example, right?
You can suddenly quote quote agent to do this for you if the has been trained enough to sort of understand what does your contract look like and trained enough to understand what the customer need, they can probably do a reasonable job in walking you through the contract saves you like 30 minutes and to be honest probably the customer is going to be happier.
And because now they can do it whenever they want like 6:00 a.m. Pacific and the you know they're whatever the thing, 6:00 a.m. whatever some some time where the rep is not even available.
Um so um so I think it's going to be carving out those tasks, making sure that you can train the the agent to do a good job before this particular task versus everything and and chop away pieces from the rep's work.
Uh I think what's the way we're looking at it what's really nice about it is we when we kind of starting to kind of provide those things including the trainer by the way, we we train the system based on action conversations.
So we go we have a called AI Builder which is basically says, hey, let me look at historical patterns, identify what's working, build me something.
It could be a document for humans, it could be a script for agents and be like take based on that, which is really huge because if you start with like program this from scratch, you're probably not going to get much and then how are you even going to conceive of all of the issues can take months and months of training, whereas if you have all of this history, you can very quickly like iterate.
Host: I mean, it's also possible like if I'm if you're only dealing with customer support calls, you know, hey, I'm trying to do a refund or I'm like a very specific small problem.
I think it's much easier for someone like you who has all the training data available to create an agent targeting a specific problem and every time a customer asks some kind of question you can route it towards that particular agent.
I think that's a pretty possible scenario uh that I can see playing out. Uh do you have any thoughts of like how this AI agent space will evolve?
Host: Like what sorry?
Host: Uh the AI agent space evolve. Elon: Um how will it involve? Like in the in the future? Like Yeah. Um I uh um I I think it will gradually take away start from taking away very specific like you and I mentioned, right?
Just like take start taking away those tasks that are very specific. You know, like you said, ask for a refund or help me understand how I account works or maybe help me book travel for all I know.
A little gradually take more and more and more responsibilities. Um it might take away some of the um maybe kind of more junior jobs, which, you know, kind of AI usually is where it shines. Uh and then outsource pieces of of the more advanced jobs.
So even if you're a B2C seller, B2B seller sorry and you're selling very high end equipment, you might still use an AI centers like a sidekick.