Your support rep is also trapped in this call, with Des Traynor of Intercom

title: Your support rep is also trapped in this call, with Des Traynor of Intercom
author: Complex Systems with Patrick McKenzie (patio11)
contenttype: podcast
publication: Complex Systems with Patrick McKenzie (patio11)
published: 2026-01-15T08:31:58
sourceurl: https://pscrb.fm/rss/p/prfx.byspotify.com/e/media.transistor.fm/7d50f764/ba2cbea4.mp3

word_count: 11222

Welcome to Complex Systems, where we discuss the technical, organizational, and human factors underpinning why the world works the way it does. Hi, to everybody. My name is Patrick McKenzie, better known as patio 11 on the internet. And I'm here with desk trainer who is the co-founder of Intercom. Hey, Patrick, how's it going? Doing pretty well. Thank you. How about yourself? Really well, thanks. So for the five people in the audience who don't know what Intercom is, can you give them a brief presses? Sure. I like the idea of expanding our addressable market with these five people. Intercom began life as a customer messaging tool. It's predominantly used as a customer service help desk. And then over the last few years, obviously it's gone entirely an AI help desk combined with an AI agent called Finn that everyone knows the most. So the thing that I would most love to talk about today is customer service considered as a system and customer communications broadly. I think we at many companies in the capitalism are a little bit guilty of shipping the org chart where the customer perceives they have a relationship with their bank or their software company or similar. But there is one five-dom in charge of customer support and one five-dom in charge of marketing and the two five-doms never talked to each other. And at the scale of a large enterprise, there's really like 37 five-doms. But we ship the org chart. And so we have a Blinker conception of what happens. And a fun fact about my life, my first job was actually as a CS rep slash order entry representative at a company that sold pens and paper. And so I got to see from that side of the telephone, why it is difficult to do this well at scale. And would love to communicate that to the audience, maybe give some people some pointers on how to do it better at their companies or how to be a more proactive consumer about it and talk about how AI is changing things. Let's start with kind of like the industrial organization of customer support. You deal with customers who are across the spectrum from a company like the one I used to run with one person selling software from his bedroom all the way up to largest companies in the world. And I feel there's probably like some step changes in how customer support is run as an organization across that spectrum. But you've forgotten more than I will ever know. What does that like look like? I mean, the step changes are interesting because they almost always serve one God at the cost of another. Like companies love to say that it's all about the customer. But then when you look at how they actually designed or org chart designer customer experience journeys and flows, it's almost always taking business goals, somewhat in isolation, and prioritizing them over any sort of cohesive and user experience that they would like to profess to enjoy. So when you're an early stage startup, typically it's like it's founder level support. In the same way, it's like founder that sales and founder that kind of everything because it's usually as founders plus plus or whatever is what the entire team is. The early days of support are usually honestly good because you're kind of moonlighting your engineering and product people to do a bit of support. And they're fixing bugs as quickly as often as they're applying to customers. So as in they tend to see a problem, make it go away for everyone. Go back to the next issue on the queue and kind of like blitz through it that way. Even your early support team can be pretty good because it's usually like minded people who really want to start up. Who are still kind of like missionaries to start up in some sense. They really believe about like increasing the GDP of the internet or making internet business personal or whatever it is that they're kind of the good goal is. It's once you start to say that you do two things. You start to scale the support org and usually that necessarily has some like you know model breaking things in it like you have to give them hours to work unexpected workloads and quotas and all of that sort of stuff. Like it's easy to say, oh, we do really have to give some errors. The reality is you kind of do you have to actually say to people your job to stop a five o'clock because the tickets don't stop. But you have to go home and at some point up becomes a reality. You can't just rely on the sort of like the wide eyed altruistic you know missionaries of your startup to do that. You actually have to have a scalable system that you know that is robust to sort of individual motivation. So that's one area where it starts to get a little bit chopped up which is just the raw scale of people. Once you start saying, hey, you're a set working hours and then perhaps later on you'll start to set individual efficiency quotas. So exactly estimated average handling closed time while you're operative or whatever. That starts to change the individual's incentive. That can be survived. There's a new I know a lot of our scale companies that have this but still managed to deliver a cohesive customer experience. I think the thing people really get wrong is what I would call like the chopping up of the experience. So we've all had this horrendous experience where you know like if you ever bought a car you go in and the person who greets you when you're trying to sell you a car is all like you know like slick hair, shiny suit, shiny shoes and they're like, welcome Mr. McKenzie, let me walk you around the dealership. Then you come back to you later because there's something wrong with the car and not a sinner in the place we'll talk to you. You know like and that's the difference honestly between the sales or we're going to support or go off to the right like and you start to see incentives that work. I think what generally happens is people tend to chop up the experience. So you used to go from let's just say Fenderlet sales, Fenderlet support where I was like one person remembering all of your context. To some middle ground where I was like you know maybe somebody closed the deal but generally speaking it was a pretty cohesive experience because the company was still pretty united. Then you fast forward to the at scale kind of like fully efficiency maximized. Let's just say like PE a fight customer experience and what you have there as well is the SDR who talked to you to land UDA, did the deal day, you handed you over to the ORM, this is CSM involved and then this is support person. And you have a question and as the end user this is where the calm ways law kicks in right. This is where you really feel like you're bumping into the org chart. You have a question that might look like hey I would like to use such and such a feature but it's not working for me. That sounds like support until you realize that it's not on your plan and support hands you over. Support hands you over to say CSM, CSM realizes this is actually a contract, a new contract, a new contract, a new account of the original AED, AED, AED, AED, hands back to the ORM. It's the ORM hands to the CSM. I like to know none of this is in service of brilliant customer experience but it is the result of six, seven different orgs all kind of like goal maxing and does not really like most orgs. Sure we have metrics like CSAT and NPS which are extremely vague things that everyone likes it when they go up but when someone gives you like a two on an NPS survey, no one knows what to do. I think the metrics aren't great and because of that we don't have a very crisp way of saying how do we measure what or not we're just doing right by our customers. Obviously the long term measure of that is things like net revenue retention or anything like that. They stick around for many, many years and even thought that doesn't mean you're good at it. They could just be stuck. They could be vendor lock in or any other way to do it. But I think the natural tendency as you hire in more and more seasoned senior professionals what they'll want to do is build up their individual fiefdom as you said or like you know if we were to be plight or we might just say their own org they'll want to report up to their boss on their own org. They'll want to isolate goals the day can directly affect as much so that will mean ruling other things outside of their scope. So you end up with like support leaders who will talk about like handling time and take a close race and take a recidivism and stuff like that. Sales people talk about leads or like CSM's we talk about utilization or whatever. But like everyone's kind of chasing their own thing and it can at times appear kind of like either like bipolar or psychopathic to the end user who's actually receiving all of these various messages like use more no upgrade no don't upgrade fix your stuff you know and like none of it actually makes sense and that's that's ultimately it's the desire for efficiency comes with a cost of user coherency and I think that's the thing that people don't don't realize until it's too late and it's very hard to claw back then because it involves like dismantling rules engagement that have been like hard fought and won by previous leaders. I think we deal with good heart's law in the most painful way. Oh good. Sorry. Sorry. Oh yes. Sorry. No good heart says that the measure becomes a target right? Right. Yeah. Sorry for the people on listening that haven't heard of this. It's that you can measure statistics and that is useful. But as soon as you make a statistic into a goal the either individuals choice game the system or the systems organic incentives to cause gaming of itself will cause the fidelity of that metric tracking reality to decrease. I was going to say what we talked to it through there was actually kind of useful because like there's almost like a mashup of good heart and convey right that overlap right which is like orgs have goals. Those goals are usually set on targets those targets then no longer those targets were at once based on a measure not measures now it's efficient. So all of a sudden you end up with call centers where where people in their org are going to pick up and drop the phone rather than dealing with the customer support call because that will reduce the average handling time. Right. Even though you know unlike it's like these are the sort of perverse incentives that can occur when you kind of let individual orgs use measures as targets. Or one of the classic ones for me you mentioned net promoter scores. So we are institutionally as capitalist good at their jobs. We are aware that things like average whole time etc. We have fully captured the customer experience so we'll send you a survey afterwards. I was on an airline once and I got passed out a piece of glossy printed paper that had clearly been commercially manufactured saying you're going to receive a survey after you get off the airplane and remember five is the only acceptable score. And so it isn't merely the fact that the organization is attempting to defeat the organization's surveillance of its own efforts. Someone signed a purchase order to do that. There's also the bizarreness of the question as well. Like I don't know if you're familiar with it. There was a great meme of like the windows 2,000 dialogue. It was like would you recommend this? Someone just wrote in the comments box. I need you to understand the normal human stalker had recommending operating systems to each other. I think there's something valid about to like have a criticizing the ridiculousness of the question. A lot of things don't get like regularly recommended. And yet that's entirely what MPS is seemingly based on. Yeah. I think there is a bit of cargo culting and a bit of the built affordances that we put into software, create the culture of the organizations that use the software. And then that becomes industry bus practice. And so you end up measuring whatever is easy to measure. And so in web analytics, people spend a lot of time thinking about time on page. Not because that matters to anyone because the average, you know, obscures the difference between people who bounce immediately and people spend an hour reading the article. But simply because like Google Analytics puts it front and center because next to hits, that's one of the few value ads that they can put on every web page on the internet. Yeah. In unrelated news, did you notice that a LinkedIn have recently released games inside LinkedIn? I'm sure it's great for time on page. Oh, boy. I continue not engaging with that website. And despite being a gamer, I think I will continue with that strategy. I think as you were narrating the dysfunction of a company passing between various different orgs, one of the things that causes us to be particularly frustrating for customers is, it's not merely that you get seven messages in quick succession from different orgs that all have a different view on the elephant. But that it will take three days for the first person who gets your email or message or similar to say, hmm, I need to talk to that's the specialist about that one. And often there is a, you know, opaque to you conversation behind the curtains on. So what are we doing with this in Q3? Oh, that's a sales question now. They get back to you. Okay, so it's a sales question. And then the customer might write back and say, this wasn't a sales question, you know, two months ago. But no one who has touched the message yet is aware of that because the the org has through various incentive structures and the way that there is constant churn in policies and constant churn in people in these positions. They've forgotten what they knew about their own actions months ago. Totally. So this presents this further discombobulation to the to the user. Again, I paid for college being a customer support drone. I have the most respect for them. It's just a rough life. Not the only problems. But one of the problems is that there is a human capital issue in customer support orgs. You mentioned that oftentimes the early customer support people are superstars because they're, you know, missionaries trying to get into a startup by hook and by crook. And that is often the case in my experience. And often the thing that gets you sorted into like being an early customer support person at a startup versus being like an early engineer or a product leader at a startup is, well, I have no identifiable skills. You can still add value to the world with by having, you know, quote unquote, no hard skills. But as the company starts scaling to the moon on this, it starts to look for a profile and compress the salary band and career band, such that it is not merely picking from the bucket of people that do not have hard skills yet, but is like anti selecting for skill and diligence at a growth level. And the customer support orgs will organically identify people who are good at it and who like the job who like dealing with customers who are good at finding hard problems. But the customer support orgs don't have a way to retain those people internal to the function because unless you go into customer support management and there are many, many more people on the phones than there are managers. So if you, if 20% of the people on the phones are really good, then the brutal math of that funnel is going to be in that most of them will need to do something other than customer support for their career. And a happy case for the company and for the individual is we successfully identify that you're very diligent, very talented, why don't you move into product management? Why don't you cross-trained as an engineer? Or why don't you, et cetera, et cetera, et cetera, which has this evaporative cooling effect on the customer support org. And then the people who are good, but don't want to become an engineer, don't want to become a product manager, they don't stick in their role because the job that you're doing at 22 is the job that you're doing at 24, is the job that you're doing at 28, is the job that you're doing at 37. And the company will generally speaking not have a ladder for customer support that compensates or rewards or acknowledges people in the same way that it would compensate reward or acknowledge people in engineering or design or product or similar. So I would understand and agree your diagnosis there is correct. And this is a couple of things that we've bumped right into because obviously we sell FIN, which is a product that is AI that does customer support. And one of the most common of our friends we got is some version of, oh well, if this thing really does result 67% of my support, what am I going to do with my support team? And I'm like, if you literally just stop hiring for a year, like watch what happens. Like people like attrition in support orgs is extreme at the best of times in the best of companies because of a few different things. You're totally right. Most people see it as a front door end, but they actually want to get a job over to sales or product management or frankly anywhere, anywhere where they can kind of build a career. Noter said people will go into support management as they no longer take it. Hanzler has turned out like operations type people. And then to rest, yeah, generally tends to be like churn and burn over time. So that's just generally true. So the second thing we notice a lot is that like in the era post AI where you actually are now having an AI agent who's doing two turrets of the queue, that agent generally tends to feed on knowledge, context, scripts, scenarios, data integrations, etc. It actually does in a lot of our customers' cases create now to be clear. Smaller numbers, right? The support orgs are still smaller. And I'm not trying to, we'll never hide away from that fact in the term. But there are new interesting career paths that are look a lot closer to like engineering level salaries or whatever. We are like a principal support automation specialist or something like that. Now I'm making up the titles everyone has their own titles. But generally speaking, who owns the AI for our support experience? Is actually a job that needs doing. And what we find is some support people, like you can retain the best support people by offering a career path into being. I'm excellent at automating human conversations. That's what I do now. And actually, I think when we look into the next five, 10 years that will be a useful skill set to have acquired. So I think that your diagnosis is totally correct. I just think this will change over time. Like, should you start a startup tomorrow? In two years time, we'll be looking for somebody who's really good at AI support. Not normal support, but AI support. And that's the sort of person to be able to hire. I have been hoping this would happen for a while as I saw the career paths of people that were extremely effective in. Support and operations, which were quite tied at the hip at the company I was at at the time. And I think we've seen it in other fields and software before. For example, system administrators were the, you know, redheaded stepchildren of engineering for the longest time. And it was considered a job, which was barely white collar, if that. And then happened in a lot of places at roughly the same time. But I think Google deserves maybe the most credit for it saying, we don't have system administrators here. We have, you know, site reliability engineers. We are going to redefine the entire practice of system and registration to being DevOps now. And DevOps is on the engineering skill letter period. And then they dragged probably 80 to 90% of that industry at most software companies from the much put upon, you know, the stereotypes were. Yeah, the guys in the basement living on pizza. Guys in the basement living on pizza with the neck beards and et cetera, et cetera. And said, no, these are like valuable for professionals. We're going to manage compensate, recognize, et cetera. So that may have such. And cannot happen one day too fast for that to happen to people in customer support. I also think there's probably some surface area for AI helping the agent. Because like the physical reality of being a customer support rep is you're on the phone with someone or, you know, email or chat to our 30 chats at the same time. But you get a some sort of screen that cut that like summarizes what the system quote unquote the system thinks you need to know about this customer interaction. If you have a very good engineering team and you have many smart people who have done the processes, et cetera, et cetera. You get maybe three sentences of context on like what is probably happening in this customer's life or in their relationship with our company, which is causing them to get in touch with us today. And possibly as many of the two of those sentences will be true. People experience this as wild because wise capitalism so bad at capitalism. But when you're when you're dealing with, for example, a bank, there are just so many stitched together IT systems at the bank. And there are so many statuses your account could possibly be in that the bank is probably institutionally unaware of all the statuses an account could be and you know, are you under investigation by the feds. Do you have like a pending update to your beneficial ownership and you know, you have a negative balance in this thing which we are across across defaulting your checking account and that is the reason the balance shows negative. Yeah, yeah, yeah, yeah. And if this isn't displayed on the screen that customer support rep is seeing, they just have no way to help you other than that looks really weird. Most people in this situation like you probably bounce to check that's why your balance is negative so. A person might freestyle I think you bounce to check your balance is negative and customer who knows they haven't bounced to check but doesn't know that. There is this ponderous behemoth of a machine that is causing their customer support agent to not have the correct information perceives the institution is lying to them. I think the acknowledgement of an ad read sounds cooler in Japanese. 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And to like maybe do some real time debugging of okay, I'm aware like in my prompt there are 25 things that could cause a customer to be in negative account status at the moment. However, if it's not one of those 25 things, I'm aware I can like search X and Y and Z and maybe like surface that to the customer service agent faster than the customer service agent could possibly do when they're handling 10 to 20 phone calls an hour. Yeah, that's totally mean like the way we see support the way our best deployments work is like. Finn resolves it if it can and that that will include having a backup for with the customer to work out. Hey, did you recently whatever like logic check or have one bounce or whatever so it will interrogate it will also use data connectors to go look stuff up independently. But then then fin also exists as a copilot as well, which will help to support up and actually interesting either that's a harder job because. Finn has taken care of all the easy stuff so almost by definition of being in the inbox and having bypassed Finn something is definitely tricky at this point and then we do as much as we can to kind of like pre flight the answer. First for the rep but like it's it's a difficult it's like it's one of these interesting areas where the better Finn gets the worst our product will get because it's just it means it's exclusively working on extremely difficult problems. It's like it's definitely we're like you know augmenting the human is definitely the next thing to do once you try to like basically do all the human work and I think that we we see a lot of a lot of genuine value there. And like the other thing that we see a lot of is like that also decreases the time of training as well so like you can put a human into an inbox they can be a lot you know McDonald's are famous for what they call day one productivity like your first time after training you walk into a kitchen you should be able to make a big block or whatever. I think like what's with proper augmented co-pilot second kind of explain the context and explain the situation you can actually get four teams a key decrease the training time from on a complex B2B product it could be genuine months like is in on a property big broad that B2B massive product you could be like two or three months before you can. You can depend on the handle large chunks of tickets but when you can actually when you have a well stacked co-pilot effectively you can bring that down quite a bit the way we think about it is like what is the job of the human in that world is it is a judgment is it like seniority is it like some regulated industries that you need to have a name of who approves the quote or who. Who approves the refund or whatever but we look at that because like the way we see AI and support is like humans should answer the question for the first time and if possible make that the last time the human has to answer it as well so we kind of we like to solve both sides but like here's the policy that Finn didn't know you know the Black Friday coupon no longer exists or whatever so the human will say that we're part of the customer then the human will also confirm that with Finn and then Finn will be like right all you'll never see this one again and that's kind of where you want to get this we have this kind of really good flywheel. I love it. I like every question that makes it true creates a new rule that makes sure no other questions like that will make it true again. And this sort of concentration effect as one moves through the various layers of support is something which deeply responsible for a lot of the customer experience of going through support or where it's often misunderstood by the people who get to tier three because 100% of their interactions of this issue have have been with tier three they think like I must be the majority case. But in fact if you've gotten to tier three you are already like fourth or fifth sigma out for this org because all the easy cases have been taken by tier one or the gap to tier two and tier two did one of the like 20 blessed resolutions on it and did not need to break tier three and so at tier three. Asperationally speaking you're dealing with someone who is one of the best of the company they might not be managed on tickets per hour basis anymore et cetera et cetera they're you know capable of doing meaningful creative professional work on finding resolutions. But you only get to tier three when like this situation is already like on fire which direct implication of this like keeping people into your three when they are simultaneously like good enough to do the work. And this is a person who can go up and down the org chart talk with managers interrogate multiple IT systems et cetera et cetera and also the experience of their life is 24 hours a day constant firefighting for seven years in a row it's a tough strategy. And so exposing like let the a ideal with as much of the firefighting as much of the you mentioned data connectors like a stupendous portion of the of the work of high end customer service teams is just this organization likes to think that it has one IT department but it actually has 47 systems and we're integrating another 12 after the acquisition closes. And sometimes you don't get a choice on whether the acquisition closes should not you know finance like federal government has called you up and said there's a bank bank failing we need you to take one for the team and your IT team is like wonderful we we love having 20 systems that we know nothing about and by the way all the people who've been maintaining them for the last 10 years just quit. Just if there yeah so what are the sort of like interesting second order consequences of having fin or another agent talking to the user directly in terms of how users perceive that I think a lot of people assume that I don't want to talk to a robot it's going to be indifferent to my concerns it will give me heavily templated answers et cetera et cetera. My ambient oppression from talking to a lot of robots over the years and also being a CS rep is that getting a copy paste and answer feels a lot more robotic than actually speaking to a robot but what is your experience in that of users of fin. Yeah you're correct first of all that like I think people like does this weird. Deification of the copy paste answer to macro right where people believe that they were getting these artisanal hand types replies from the fingers of the founder themselves right and I think a lot of orgs I've kind of. Taken this on on and being like oh I could never expose the chat want to my user our users are so are so like a high end that we would insist on every user getting a hand type dancer. And then you're in practice looking at what to do what you're doing is like spitting a load of like control K macros out to like you know they're barely half reading the tickets like almost the funny thing I feel like five supporting you're like. Taking a blurry scan of the ticket that comes in seeing the word password and spitting out the password resetting and sending and somehow like that's like held in this pedestal as being like brilliant support. I think what one when we launched fin it was March 2023 now one thing I will say is at the time fin was resolving one and four its resolution rate was around 2324% Today it's like 67% so it has gone a lot better. But the thing we noticed most was the behavior of the end user was we'll serialized at the time they were talking to a bot they immediately dropped into bot speak. So so they might start off before they knew who was going to receive the question and say hey I have a couple of questions about this payments issue. It looks like on the 11 October it was a refund blah blah and they would have written a perfectly valid thing that by the way I love some will feed on really well. And then Finn would use that and Finn would then present itself with like derpy little chap or icon and say hey it looks like you blah blah and then once they saw this back at the time they immediately dropped into like bot payment issue human please. I'm like I'm with we're forever for a straight because it was just like dude it was actually about to solve your problem if you just read the thing that said it was like you know. And I will say thanks to like the good folks are like a Claude and chat GPT and all I need a rock I need to build a user-facing sort of a LLM agent. Everyone's kind of realizing that shit this stuff works. So now people are starting to like naturally speak to Finn in a way that actually Finn loves which is like here's a lot of detail here's a lot of context obviously Finn's got a lot better long away. And as a result they're actually getting brilliant fast answers we're getting answers in like six or seven seconds. A more interesting thing we see what our customers is like a temptation with Finn to like some high-end professional support people is well we'll use Finn for our free users and maybe our $9 or more users but our premium users they get to wait 37 minutes for their replies. And eventually they all kind of ever finds the reality of managing which is we should probably just use Finn by default and hunt over to human when we need to it's probably a far better experience because Finn will deliver an instant accurate answer. As a second order effect by the way of like providing real-time brilliant support is far better user behavior. So you can imagine you signed into a dashboard for the first time you have a question you ask the question now how does your user behavior change if you get an answer six seconds later or oh here's how you go and filter a report and drill down and explore his PDF or whatever it is. Well the chance there you're going to go and do that because you just go told you to do it complete with a little visual and all that sort of stuff. So immediately you're achieving your outcome that you came there for the person who's waiting even let's just say three minutes let alone 30 minutes for like the real-time hand-type human answer. They're probably gone they're probably in a different tab that they certainly lost whatever bit of influence they had to go and generate that report. And I think in general like you should be pushing every business should be pushing itself to provide real-time instant support because it actually is just a conduit for great user behavior which really matters. Funnily for us for it we launched Finn voice earlier this year and we thought exactly like we we now end up saying things like hi please tell us exactly we are problems we don't even bother saying we're an agent right now because all together human human human human human. Whereas when you say just describe your entire query and we will get you where you need to go we don't say it to a human but we imply that because it at least suggests that they should describe your issue. And then people started instead of speaking like bots to a phone they would start giving us a full mouthful of context like I am or you know blah customer I'm vlogging and I see this red arrow screen it tells us everything you know and then when they get to reply you're like yeah that's actually a text quick and you can almost hear to pose them into your voice of like how did you do that but I so I think there's a period of human adaptation is one thing I'd say the only is just I think one or one is just sort of what's the word I'm like. The performance of in drives more demand for Finn so like people are in viewed demand like people ask I'm asking questions get a great answer and then they go back with like five more questions because they're like oh wow this thing's paying out and I think that's like again a really positive for both the business and the user that like they didn't intend on becoming an expert on how to use whatever like a son or base cop or something but they asked a question got an answer didn't ask you more and all of a sudden they're getting everything they need in real time and they become a better user in the far side of it this is nice inversion from a pattern. That we see sometimes in software businesses specifically where there's always a question on like where is the line for customer support what should you be doing for a user and what you probably shouldn't be doing for a user like is that consulting services etc etc and I think that you can get told is a go get our customer support agent at a big software company is like look we're really happy that you put together this plan of action for the customer and that it completely solve their problem but if you keep doing that they're going to come back to you with like lots of more requests for your expert level advice on these things and we like our cost model gets totally blown out if you're doing like expert level advice and touching one ticket a day even if the customer is thrilled as a result of this and that customer is only paying $200 a month like here you know I've got an MBA here's the math doesn't pencil if on the other hand you have an agent that is capable of doing the you know page long analysis of what the customer is attempting to do and here's my consulting recommendations on it which opus at all very much are these days for a lot of intellectual labor then if the user like comes to the conclusion that weight and added benefit of this software is it'll like get me a long portion of the way towards being done just by like chatting with them and asking what I should do next tokens are cheap in the universe and they are they are cheaper than sad subscriptions even at like relatively high usage of the tokens I totally agree on just a few thoughts on one is like so the question of where to support support blur into sales or success whatever is is a really really good one and certainly one of the things we're doing will be like releasing in 2026 is like the city of fin moving beyond the support or into the adjacent functions like say sales say customer success etc so that's that's absolutely coming on and whatever inspirations for that is just realizing that like two things I guess one it's not obvious to the customer is where the walls of the world are anyway so like you could say like hey I'm using your tool to design a building is will this thing work right the support apps for me like I don't know I'm not an architect or whatever right like whereas we're in like actually can be a lot closer to an architect we've seen this happen time again so one of the features we're building into fairness this idea of an e-commerce buying assistant and what blew my mind about how we built it was it obviously has all the smarts from being like trained on the entire corpus of human intelligence but then also all the specific product detail that you feed it as well so you can go to fit and we had this one of our customers and say like hey I'm looking for like this is a furniture store and I was like hey I'm looking for a couch that's like five to six feet at dark blue but really important has to be easy to clean now there's no UI I've ever seen on an e-commerce store I had a check box for easy to clean right it's just not a thing and if you walked into this store and said to like they're like you know they're a young early career sales or pay what's easy to clean I don't think day no either whereas fin produced two different types of easy to clean the produced ones were removable covers on a produced one where the primary material was like a plastic sort of synthetic substance that's like easy to wipe whatever and it was able to like you know find the right size and what's in stock and what's not and like I actually you know I look at those experiences I'm like I don't like people still talk about AI like it's a it's a it's a it's a pure substitute for human I don't think there's a human India in that entire organization I could have answered in as much detail as fin did and we're so we're just going to continue to see this like fin kind of help like if you're designing a project fin can fins also trained in Prince 2 project management you know what I mean like I see you kind of get all these benefits of like all human intelligence plus all the scoping and and sort of whatever you feed to the rig their retrieval of my generation what well then you then get to like seamlessly blur from my fails to support to success and ultimately offer all your customers this sort of like white below top tier service that you might previously have reserved for like the operation on the top 5% here of your revenue this is like CSM's 30 bottom 95 percent 99 percent maybe in a lot of words and hopefully from the customer support agent experience like not you know there is that sort of crystallization effect where you're only dealing with the hardest problems now but and as a former CS representative sometimes the hardest problems are brought to you by the people that are the most difficult to deal with and one of the reasons for the pathologies people see but aspirationally speaking you know level up something past like tier 3 firefighting to being like a genuine counselor a genuine consultant for these companies versus simply being like I'm the person who knows how to work the state machine for you to like find an answer in this you know globe spanning org on how to do the thing that you obviously need to know how to do that kind of thing like move that to an AI that can query the same databases I can query and slack message the same people and use me the human intelligence has been like okay you have a strategic need in your business like let's talk about that strategic need in the business when the AI can't immediately bang out a you know first year McKinsey associate strategic need in the business plan for like you know three cents and tokens I think we do have the unsolved problem of what will humanity be doing when agents are good enough for large portions of white color work but we're a couple years from there yet at least from completely understanding the implications of it for the economy you mentioned fin voice which is fascinating to me and one people don't appreciate how much better all the AI tools get immediately once they go multimodal like the point your camera at something take a photo and it suddenly gets you know pictures worth thousand words it gets just radically more capable without in some cases like the a labs didn't even you know making announcement oh yeah it does it does photos now too and talking about the commerce adjacent things like I do a little bit of art I need to know more about paints than I do and I've taken photos of artwork and progress and asked an AI like hey this feels a little dark to me uh what color should I be using and it says oh yeah you know this shade of purple like great I don't have that uh here are the five paints that I have that sounds something like that and it can you know immediately come up with things like oh yeah three to one mix of paint a and paint paint see we'll get you pretty close to the bottle that I suggested and out of the box with no special training into paint ranges of like obscure Spanish paint companies they they know more about color theory than any sales rep at any hobby store or you know maybe like the 99th percentile sales rep of hobby stores and they just keep getting better yeah yeah I think it's like there are so many numerous examples I see like all my friends who work and I need to spend like nutrition they're all just quietly realizing they've commented you know two years ago I could take a photo of a plate and it would give me a rough start but it was always off by 50 percent now it's like correct down to like you know specific potassium levels and stuff on the dish or whatever like I think every single application of AI just seems to be getting as smarter and deeper to a point where like you know we are very much looking at superhuman capability and then weirdly at the same time like coming to a conclusion that it's still a per substitute for human and I think it'll take a while maybe one or two years I think for people's proper perception to realize that like you should try and do everything with AI and then work at where humans have value. I also loved the fact you mentioned that if you address an agent as like I'm a working professional I've done a lot of like I would when writing to a human I did a lot of work prior to pushing a send on this email it will respond to you in kind when you address them like they're a dumb bot they will respond to you in kind if you like go zoom or chat speak I eat it in an emotion into this you will get an emotion back yada yada that's all like a fun interesting quirk until you realize how much of customer support is successfully supporting people who are not you're like 90 percent all user and like it is extremely underappreciated that these things are multilingual out of the box and not just multilingual in that I speak Spanish I speak Japanese yeah but like no if you are a you know a like person who is learning the English language but need to dip in a Spanish a couple of times or I think the word is blah but I'm not positive it will meet you exactly where you are and the you know there are wonderful empathetic humans in the world it is difficult to keep them attached to a sport org for a long time particularly if they're multilingual etc etc but the range of situations that can cover is just unprecedented a multilingual support agent who is also really good at dealing with people who are in you know dealing with diminished mental capacity is just it's a unicorn and it comes out of a box and you pay tokens people don't realize how much of the work around the work in support is workforce management specifically by language by time zone by product line by like time of the day as well by covering lunches and like you know by covering like vacation policies and all sorts of stuff like that and in practice all those problems go away once you click the which is just bananas because like so just whole tools just whole roles built around orchestrating all of these things to never overlap in bad ways and all that sort of stuff and yeah like you know you get down to like dialect specific like multilingual age agents like you know and so like it just it really does like you know an interesting thing we see a lot a lot of startups say some version of hey we yeah we don't use AI because we're really close to our customers like I get it right I understand if I was a fan or I could imagine having similar thoughts but the very second there's a higher somebody speaking different language stuff it all just goes me you're like no I got a second you know because it's like once they realize hey I'm walking down a complex path here that's when I realize this is probably a better way to do this then like so my advice used to be like hey I understand startups should probably just not use in until they read another customers these days I'm like no no startups you intend to continue so if that means you want a close customer contact do that but also don't go building an org accidentally doubt like it's like barely fit for purpose when actually AI is right here I'm well it's not as far as customers immediately all the time yep and you always have the option of picking up the phone and talking to a customer or ghosting someone's chat or sorry I'm using the word ghosting like the internet does not use the word ghosting you know peaking in a conversation that someone is having a review in their account history or similar your choice is basically in in all the moments the founder is not directly interacting with the customer are they having a good out of the box experience or are they having like the standard capitalism support experience which I think many people have frustrations with have you seen much use of it in governments one of the I think this is probably as revolutionary for frontline work in government in places that it is for frontline support to have to suffer companies but I don't know what the adoption story looks like yeah I mean we've had adopted in pockets of government around Europe we haven't seen any sort of yet to see a sort of transformational case study I think to say so much bureaucracy that goes true and there's also so many and what's the word like one written policies like ultimately you still have to tell Finn what the actual thing you do is and if that's not written in terms of word up that's always a challenge and then separately well I will say when we are out re-invent just this year like a couple weeks ago I'm in Las Vegas Amazon's big event do us definitely a lot of interest from people and then you you bump into the old fed ram stuff but I mean it's definitely I suspect it's definitely coming and I you know door will be it'll probably be next year it's going to be like the first proper like AI native taxis and this my guess right we have some we had some big customers they'll have some in the in the tax space we're like they've seen phenomenal value again just from taking a little on switch where Finn's just like hey I saw what you did last year I'm guessing it's the same answer this year but maybe an updated PDF somewhere okay got it it's like it's shocking how much value you can deliver very quickly there is just a huge amount of intellectual effort at companies that have employees that is doing like very much below the bar cognition like in the tax case hassling people for okay like for each brokerage that you have I need a 1099 from it and if you if you missed chase then I need to like have a multi email like thread with you about like okay I didn't see chase like do you have last year did you close that or yada yada and no one is well served by that not the accountant who you know went to school and is now a partner at a firm but is wasting their time chasing down PDF files not the customer who is getting interrupted in their busy life and getting asked to like click a button I know a website somewhere and get it back to their accountant and not the tax agency yada yada and this thing where it's just like relatively minimal amounts of cognition minimal amounts of agency and a huge amount of I never get bored I never get tired I do not feel pity if I'll separate me and I will chase it to the heat death of the universe if you ask me to is just quite an upgrade I think that's also going to happen in mortgages for example which are another thing where we have a relatively highly paid individual who is getting paid to project manage someone doing a you know complex bit of finance and the most important transaction that they're going to have in their life statistically speaking and a huge amount of that is just like I need a w2 okay that isn't a w2 you know that isn't a w2 I need a bank statement a you know a screenshot of your cash app that I understand like you've perceived that as a bank statement that isn't a bank statement here's the button you need to click yada yada and I think it's in addition to being more efficient it's a lot more humane to the customer they don't like constantly feel like they're being judged by someone for well I would know what that you know the like underrated requirements were for a mortgage if I did this every day but since I don't and I only get a mortgage once every 10 years like a sane person I don't like have the you know fanny and Freddie like rulebook memorized and I feel like I'm being judged for not having it memorized and so the AI can be like maximally not judge mental if you train it too and people I think have a a prior for dealing with these AI's that yeah it's just a computer you know I don't really have to feel like annoyed at wasting the computer's time there's also a thing about the embarrassment thing is very real one of our customers is effectively a a you know loan company and obviously a part of loans is collecting payment and what we found was or what the customer found was a reporter to us was like the information they got from users because of not having to deal with saying it to another human such as hey I might miss this payment can I extend that to this people are like much more willing to speak in a in a way that might make them feel vulnerable to another human but knowing that they're talking to an agent they're they're just they they give up all the circumstances quite honestly in the same way frankly if you most people go to their doctor and say they're feeling fine if just GPT asks and they might actually offer up so I'm like well I've got this weird lumper you know it's just this way where people are more comfortable when they think no one else is around or when no one else really is around so there is a behavior change that in a lot of areas it's definitely worth topping into which is this idea of create a safe space for somebody to give you all the context that you need to do the job knowing that they're pretty much free from my human judgment of the sense and I think it's something quite valuable about that in a lot of specific industries yeah and I think there's a filter and distillation step which happens any time you're taking a patient history or trying to get someone's financial circumstances are similar where you the professional particularly if you've got a highly organized spreadsheet machine bearing down on you are trying to be maximally efficient and I just need like three data points that I need to make a decision here and everything that isn't one of those three data points that I'm listening for I'm throwing out immediately and I think that accounts for some of the people not feeling both not feeling heard in the like the emotional sense but also like no wait I have something that isn't one of the usual things and you're you're not hearing me when you say that you keep asking for like what my blood pressure reading is the AIs can say maximally non-judgmentally like if you want to you know give me the war and peace version of what brought you to the office today I'll listen to all of it and then I will distill that down into three paragraphs for the practitioner and you know like ideally I'll do a better job of distillation than you know AIs the last year but as people with complex situations know sometimes humans don't do a great job of distillation or don't make a space for it and you know perhaps the practitioner when it says you know when the like distillation from the AIs says warning this individual has classic signs of x y and z you probably want to click on that we'll say oh oh wait time out and I don't want to give you like the usual questions I'm sure I will ask those questions eventually but you know tell me more about the lump it's changed size recently what did it look like look like before do you have photos oh oh okay yeah and you've seen three people for this already yeah okay this the sound serious and and I feel an immense sense of optimism about how the world will change as a result of these extremely powerful technologies getting into all the things so I think we probably only have a few minutes left but as someone who deals with these and like the bleeding edge every day what's something that people who are maybe one or two rungs out like they've got open AI or opus are similar on their phone they've used them a bit but what are they underestimating right now in terms of capabilities in the future trajectory of AI in our companies and lives I think that the connectivity story of AI has yet to really land like I do think you know I think feelings like signing in with open AI or with like cloud or whatever will become a dominant thing and I think AI that is like you know when it is kind of on your phone in a real way and I don't mean in an opulent intelligence way but I mean like it has access to your email and your text to your location and you know your photo library and all that sort of stuff the amount like I think the amount of work that like people don't realize how much time they kind of waste changing tabs and copying and pasting and all that sort of stuff on their phone and so much of it is just done to try and like create a workflow where you're like jumping from up to up place to place etc and that that's only what we would call like the behavior that you want to do there's so much stuff you should be doing that you just don't know how to do or like we're done of time to do whatever I think when AI is like fully weaponized it will like you know at least one agent will have access to like all of your kind of core ideas and you know maybe at the very least your data sources and your permissions to take actions and I think that's when there'll be a huge unlock which you know we've seen browsers like from perplexity and open AI hint at this sort of parent it's coming but there's a world of difference between like I'll go here to book the restaurant and the restaurant's being booked for you and it's already in your calendar you didn't even need to tell me you know all the way true to like a new resource of your health and it's just like well obviously you need to leave for this you know like there's like everything in between I think you know we're probably only in my opinion about one to two years away from like an extremely fluid experience where you're not even aware how these apps are talking to a shooter but like you know you you know it's time to leave from one place and as I said like you know maybe the bill gets paid automatically maybe the car pulls up automatically cars certainly knows where you're going maybe the SMS is sent to say you're five minutes late or whatever I think like all of that will happen I think a lot of it right now is like I think genuinely is like quite possible here and there today but like similar to you know like the old meme of a Star Trek was like the least believable thing about Star Trek the next generation most of all these systems would actually work together I think like that's genuinely where we are right now but I do think a lot of folks focusing on it and when we think about like what IO produces or if Apple finally delivers an Apple intelligence like the capability or what's possible is like it's going to be like dramatically different from today today I think we're becoming experts at like deep Q&A as a as a species if we like right we've gone from like surface level one shot answers to like deep research all the way through to your side so like long running conversations back and forthing like most AI users have a lot of that going on I think the next stage will be like action driven stuff it's kind of paired part similar to what we went through with support early support was all just Q&A now we're all into world of taking taking orders issuing refunds changing names updating accounts etc I think that's what we're going to see happen in kind of kind of consumer AI and it's going to be it's going to be substantial in terms of behavior change that will happen yeah some some themes there and I wish we had another hour to discuss them like it you know let's have your agent talk to my agent is certainly going to happen sooner rather than later I think they're already possible of like finding opportunities that no human in the interaction was where existed my one sentence example of this I asked an AI to review my tax return for accuracy and it said hey you know based on your charitable contributions I think quite plausible you have your kids in a private school and I said yes there's a credit for that like no there's not and it said oh no you live in the state of Illinois there is see here I'm like I'm pretty good at this my accountant this is literally his job neither of us knew to ask that question and then you know free money subscription description pays for itself for a year yeah big time could keep chatting about this for forever but unfortunately the next person who needs the studio needs the studio so uh desk working people find you on the internet I'm just desk trainer basically everywhere at Twitter you know LinkedIn whatever you name it and then yeah Finn is our company it's also everywhere Finn. Well thank you very much for your time today and for those of you at home thanks very much for listening and we'll see you next week on complex systems thanks for tuning into this week's episode of complex systems if you have comments drop me an email or hit me up at patio 11 on Twitter ratings and reviews are the lifeblood of new podcasts for SEO reasons and also because they let me know what you like