Elio Narciso
ScaleStack
Elio Narciso, co-founder and CEO of ScaleStack, spent four years at Amazon as a certified Bar Raiser with veto power on hires. He watched firsthand how lowering the bar during COVID cascaded into missing the entire AI wave. Now at ScaleStack, he’s rebuilt his hiring system around a simple principle: follow-up questions expose BS, and the candidate who asks you questions first reveals their real curiosity.
At Amazon, Narciso became a certified Bar Raiser, a role that sounds smaller than it is. You keep your full-time job. You interview candidates alongside the hiring manager. But you have veto power. If you say no, the hire doesn’t happen. Period.
The system works because the hiring manager wants to hire. They need bodies. But the Bar Raiser has no skin in closing the role, only in protecting the standard. This tension creates the architecture that keeps bars from drifting.
When Amazon lowered standards during COVID to meet demand, Narciso watched the consequences unfold. The company missed the AI wave entirely. Engineers who would never have been hired under the old bar were making architectural decisions. The cascading effect proved the principle: hire at a lower standard and your future self pays the price.
If you have very high bars in hiring, you tend not to make mistakes down the line. People are owners, they drive results.
Elio Narciso, ScaleStack
Candidates now prepare for interviews with AI. They write answers. They memorize them. They deliver them during the call. The first answer sounds polished. That’s the problem.
Narciso recently interviewed someone who was clearly reading from a script. Instead of calling it out, he asked follow-up questions. Then more follow-ups. The script became useless. The candidate couldn’t adapt because they had no depth.
The system is simple: ask a question, get an answer, then dig. Why that answer? What did you learn? What would you do differently next time? What did the customer say? Each layer of questions forces the candidate to either go deeper into real experience or expose the surface-level performance.
If you’re just satisfied with the initial response, you lose the ability to find out true data points.
Elio Narciso, ScaleStack
Narciso starts many interviews by asking candidates to ask him questions. Most get confused. Some panic. They start pitching themselves when he’s already told them to ask instead.
The questions they ask reveal everything. Did they research the company? Do they ask about the business model, the customer problem, the technical approach? Or do they ask generic things like “What’s the team like?” or “What are the benefits?”
Someone who has done research asks specific questions. Someone who hasn’t asks shallow ones. The depth and specificity of their curiosity predicts their ability to operate at the level the role demands. This is a data point most interviewers ignore.
The difference between someone who builds for customers and someone who sounds good in interviews comes down to one question: What did the customer say?
Narciso asks candidates to describe a situation where they faced a tradeoff. Maybe their manager pushed for a metric but the customer would suffer. What happened? What was the outcome? What did the customer think about it?
Many candidates describe the situation and then stop. They don’t know what happened next. They don’t know what the customer said. This tells you they weren’t paying attention to the outcome of their own work. They did the task and moved on. That’s a red flag. Someone who is truly customer-centric tracks the impact all the way through.
Ask them what the customer said. That question opens up a whole new world of questions to dig deep into.
Elio Narciso, ScaleStack
Narciso’s first company succeeded to $50 million but started to fracture because he chose co-founders poorly. He picked someone older and more senior, thinking seniority guaranteed competence. He didn’t spend enough time with them first. The culture suffered.
Now he has one rule: know your co-founder well before you commit. Not years necessarily, but repeated, deep experience. Work together. Live through problems together. See how they act when things fail and when they succeed.
Complementary skill sets matter. If you’re exactly the same, you’ll agree on everything and miss blind spots. If you’re very different, you’ll fight. The fight is the point. Narciso and his current co-founder fight all the time. Out of that friction comes better decisions than either would make alone.
Look for kindness in failure and humility in success. When things go wrong, do they blame others or take ownership? When they win, do they own it or credit luck? The person who is kind in failure and humble in success is someone you can build with across multiple business cycles.
Two years ago, Narciso would have hired himself as a salesperson. Today, he wouldn’t. The expectations for everyone have moved up so dramatically that last year’s good performance is this year’s minimum.
This isn’t about replacing people with AI. It’s about the speed and quality AI enables. Expectations for developers, salespeople, solution architects have all fundamentally changed. Developers don’t write code anymore; they manage agents. Sales development roles that existed three years ago are gone. The jobs that remain demand people who can upskill and move with the technology.
When someone hands Narciso a first draft written entirely by AI, with no refinement, no loops of thinking, no evidence of actual judgment, he treats it as a no-hire. On his first day at Amazon, submitting a well-drafted document in an hour would have been impressive. Now it’s table stakes. The expectation is that you use AI as a starting point, then you think, refine, remove the slop, and make it yours.
If you see a quick first draft made by AI with no thought behind it, forget it. My expectations are so much higher.
Elio Narciso, ScaleStack
For founders making their third or fourth hire, when the product works and traction is real, the temptation is to scale fast. You need two more people. You’re interviewing five. You want to close. Don’t.
Be your own bar raiser. Keep the standard high even when you’re desperate. Yes, it costs time. Yes, you’ll do more interviews, screen more candidates, wait longer. That’s the job. Hiring someone who doesn’t fit costs more than waiting.
You’ll make mistakes anyway. Everyone does. But if you keep the bar high, your mistakes are less expensive and your wins are stronger. When you lower the bar to fill a seat, you’re not just hiring one person. You’re setting the precedent for everyone after them.
How do you actually find somebody who wants to build for the customer rather than somebody who can interview well, say the right things?
So you ask a question, you get an answer. You're not going to stop there. Okay. But why that? And so what did you learn from this yesterday? By the way, like a candidate clearly was reading. I went like into subsequent detailed questions and like essentially the script was unusful. If you're just satisfied with the initial response, you lose the ability to find out true data points. What's been your strategy to stay ahead in this AI revolution?
Now, essentially that intelligent layer is for the most part agentic and so we have a reasoning agent that helps you put together the workflow. A developer doesn't actually write code anymore. They just are managers of agents.
What's your system now for hiring?
One thing that I really learned at Amazon is the this is who we hire. I'm Luis Cent, your host, and today I'm joined by Elio Narciso, co-founder and CEO of Scalstack. He's also the host of the Revenue Engine Masters podcast. Elio, thank you so much for being on.
Yeah, thank you for having me. I was looking forward to this, Lu.
Likewise. Likewise. Um, we we connected at at Go to Market last year. Uh, and uh I'm glad we were able to to reconnect here. So you spent four years inside AWS running their global startup program. Um so front row seats to hundreds of companies trying to build a go to market uh revenue engine. Um and then Scale Stack isn't your first rodeo. You built two companies before joining AWS. Now you're on your third. Like what what's the biggest thing that's changed about how you hire this time around?
Uh well first of all thank you for having me. Uh, I think Amazon shaped and improved and perfected my not only process about hiring and I'll tell you more in a second, but also the very high bar that is needed. Um I mean everybody talks about like big companies as like oh they get slow they get like um you know complacent and bureaucratic and I think that that's true especially if you don't insist on very high standards on hiring and you know I don't I don't think I'm sharing anything uh confidential but uh when I joined uh AWS Uh this was 2018. Um the the bar was very high was still very hard. Despite the amount of uh people that they wanted to hire, the bar to get in was still very very high. And then what happened and and so the team that we built like 2017, 1819 was you know really strong despite the size. Um the minute that the standards were lowered and this happened because of COVID, you know, there was so much demand that like needed to be satisfied effectively the bar became lower and so people that probably would not have been hired were hired. Um and I think that that generated like a lot of cascading effects like uh including you know Amazon had invested in a IML for years and years and like you know AI flew by them and so it's both an example uh in terms of even a large company like Amazon already was back in 2017 2018
has like if you have very high bars in hiring, you tend not to make mistakes down the line because I mean the people are good and motivated, they are owners, they like drive results and all of that stuff and
so was that really reinforced at Amazon or were you already doing that at your first two startups before you you started scale stack? at Amazon. It was impressive the scale of this and then like the system that were built to sort of support and guarantee that the bar was higher. I became a bar raiser at Amazon that I don't know if you're familiar with the concept but essentially at Amazon there's this idea of like uh there is a hiring manager that has the motivation to hire and so maybe in the back of their minds there is sometimes oh well I mean I just need to have someone in that position and then there is the bar raiser who has veto power on the hiring if they think that the person that they are interviewing is not raising the bar.
Oh wow. So that was your that was your position there. You had the bar raised.
Not like a this is not like a a role independent of like your So I
Yeah. Yeah. You're still doing your full-time job, but you get to veto whoever you don't want.
Basically every process. So you have to go through uh I mean you have to have done lots of interviews number one. And then like you have to be sponsored by like uh people that think that you're doing a great job at interviewing and then you go through a program and once you qualify you become a bar raiser which is like a big thing basically you're treated as if you got a promotion inside to give you perspective of how important it is and then the processes the hiring processes that you get involved with
mean that like you have veto power. So if you think that candidate should not be hired, the hiring manager cannot do anything.
How many did you veto in your years there?
Uh not that many because like uh just the presence of how bad's gonna go vote no on this one. We can't push him through.
Yeah. And so that's why so if Amazon has reinforced these things I mean we all like have an intuition that like you know hiring is important but then if you have to hire like only a couple of people this is immediately evident but if you have to hire like you know dozens or hundreds then like you know it's easy for the standards to lower you know actual inertia
and so I think that Amazon bring forth like the importance of this but also what systems you have to put in place in order to make sure that the bar stays high. And so the system is really that like it's the interviewer's job to really collect a lot of data points, facts, not opinions, not like bias. uh when you
well like uh for instance like uh one thing that I really learned at Amazon is the the need to ask like several follow-up questions to the original question. So you ask a question, you get an answer, you're not going to stop there. Okay, but why that? And so what did you learn from this? And like you know what things would you not do or do like next time that you're faced in this situation? So like lots of things that like the candidate either can give you like a truthful answer like it cannot be rehearsed it cannot be prepared. So like you just get especially in the age of AI when like you know and we all do like a lot of remote interviews just yesterday by the way like a candidate clearly was reading and like you know I went like into subsequent detailed questions and like essentially the script was unusful.
Is this for an engineer or uh what kind of role are you in
local market position?
Oh wow.
Yeah.
Okay. Uh or were they using Cluey or what one of those tools to help you cheat on interviews or
I don't know what he used but uh he was clearly reading um and that's like you know already clearly not not a good sign.
Did you call it out? Did you say hey are you are you reading right now? It's where
No, I just asked questions and sub questions and follow-up questions that made the script that he had like uh unusful to be hon. But um not to make it like on this case, but I do think in the age of AI like you know the tools that we have like you know clearly you can rehearse on you know live and so then like you lose the ability to like um if you're just satisfied with the initial response you lose the ability to find out like uh true data points. And so a simple example is the follow-up questions. Never just ask one question just like follow the answer into because I mean there is a lot to unpack in an answer no so just the first answer is not going to be satisfied and then the the idea of like using the interviews interview essentially to collect a lot of data points if the interviewer doesn't think this way and you know it's easy to get into opinions bias like first reactions uh which are also very bad Because I mean maybe you get like a a positive impression just by the face and look of someone.
Yeah, we all have their biases,
right? And that's another thing that we looked into um in the bar raising program. So like you know eliminate trying to eliminate bias by like just focus on the data points. If you say that this guy can you know deliver results, what inputs and data points you can bring to the table to discuss with you know the rest of the interviewers?
Do you think you were a great interviewer before the bar raiser program or did that experience really?
I really enjoy interviewing. I really like you know I'm curious about people their history what they've done like you know why they're showing up to an interview. So I was always curious uh since my very first job I remember I applied to be one of the I was I started in management consulting and so also there you had to apply to be one of the people that interviewed so it's always been like interesting an interesting process for me know um but Amazon yeah Amazon reinforced the importance and then provided a platform and a system that like I we still use uh
totally what's your system Now for hiring
well the interviews are like essentially uh depending on the position there is always like a technical interview um where like you know for that position doesn't mean technical just development but like you know if you are a salesperson like okay let's look at the you know sales process and like you know do you think about like um pipeline and do you think how do you think about conversion rates you know all the technical aspects of selling but then We do cultural fit interviews which really is let's try to probe this person across like the values that we have in the company and that we see things that we care like you know we care about like you know delivering results. We care about like uh speed. We care about like uh being attentive to like details and um customer oriented. So like a few values that we test and that's cultural fit for us. No. And so the values that we did spend some time at the beginning and we try to check it uh check them like every once in a while. They don't stay just as like oh these are our values. they are used during the interview process because they are like you know values are like common beliefs right shared beliefs I think scale stack is not an easy place in terms it's very demanding right so and and so that's our culture I mean and but we're also very collaborative and people are very open and like you know so we don't we're very transparent we share
from people who do really well there and some that wouldn't fit within in the culture right?
Exactly. So that's that's the thing about cultural fit that it's not like oh this is uh bad or this is good. It's just like you know it's a better fit for what who we are, what we do, what we need to do for our customer. So it will depend and change by the company. No. So like uh and you're sharing those values in the interview process with them.
Well, if people if people do research, they can find out about these values. uh and you know I think you come better prepared about like not so much about like you know ways of thinking no so like before the podcast we look at quickly at your documents so like you know just a little bit of an understanding uh about where I should focus my attention and so we don't pre we tell people the process we don't tell them if this is a technical interview or a culture fit interview
but it's not that we don't talk about this like in podcast for instance or we don't write about this or it's not on our website. So if people do some research they will find out.
Yeah, every candidate interviewing should uh be listening to this podcast for sure if they want a job at it's going to help them. Um, so you know, we
they don't want to jump to which is
so you know we talked a lot about like the the hiring component, but now that you've talked about culture, Amazon is known to have this also extremely demanding culture and you know, you probably knew that going in there. Um, and now somebody going to work with you is also like, okay, he came from Amazon um, at one point. There's going to be a
a demanding culture. How do you like find those people during the interview process? Like how is you know how do you identify somebody that like wants demanding, wants to grow, wants to be challenged?
So uh I mean for one like you know you need to ask questions and sub questions like we were saying. Um but I think that like uh even like during the early stages of the interview I I for instance like you know typically start from them asking me questions and that's some people are totally confused
I've done that before they do they do get very confused
totally confused and they start like you know pitching themselves when I clearly have asked them asked me any question. So like you know maybe you've start you know like read a little bit about us maybe like you know you know I'm sure you there are lots of I mean there are lots of things that you don't know and so maybe you want to be um asking me and just that's a simple way to test for curiosity no and to see how they are thinking about the role what are important things um and instead like you know people that are just like you know not that curious or not that interested said like, you know, we'll be like asking very shallow questions and then we'll want to move the interview to the interview of them. And so that's a simple uh test that I use to see if people are um curious, interested, you know, maybe if they if they've done some research, they will ask they will ask very specific questions. If instead they haven't done any research, you know, maybe they will ask like, you know, super high level shallow questions. So that's there is a lot of data that you can extract by them asking you questions. Um and then also like you know sometimes these interviews are interesting for me as well. I mean maybe I learned something new. I read some somewhere like I think um the yeah the zoom info founder founder
yeah Henry
we both know um you know he said that like oh I if I don't learn something new in an interview like that's a no hire. I think it's too high of a bar and probably necessary, especially if you're talking to Henry. So, hi Henry if you're watching this. But
he's going to have to the second time he's been quoted for that exact quote on this this uh podcast.
I was shocked by that. I think it's um yeah I think it's interesting if I discover something new but that being like you know the differentiating factor
it it is it is a definitely a high a high bar maybe for some roles um you know or maybe if you expand just uh like I I think the good thing is is like it puts you in a curious mindset as well when you're interviewing and often I need to get reminded of that as well. Um, and I like, you know, I like the way you position it where like you've always liked interviewing because you're curious about people. And I think that that is the key, right, is to to and it's also going to help you figure out who's right for your culture. Uh,
so the thing is uh I I do come prepared to the interviews, not like you know like a script, but I do think about like you know what's the position they are interviewing and so what are the areas I want to check? what are the specific values that are more important or less important for this position that I want to probe and then definitely I have like a battery of questions that I use um that will change constantly and I'll shuffle them there are like hundreds that um that I use and that will I will be adapted and then you know just like uh when we prepare for this podcast you know maybe you have like a general high level like idea idea about the things that you want to touch but then it will go in its own direction uh on its own. No, so I do the same for the interviews. I do come prepared. Uh I do think about like what things are important for this position and you know I I typically have a pre-brief uh from the team um and that already has like you know some ideas strengths weaknesses things that you need to probe, things that we don't know that we should know and stuff like that.
So your team is preparing you for all those interviews beforehand with that. Is that like through Slack through a notion doc? How do you all keep that organized?
Uh, it's a Google doc, I think. Yeah, it's a pre-brief. So, there's a a screener uh that takes some initial notes, looks at the LinkedIn. I mean, nobody's really using the resume anymore. Is it just about linking with it? But like yeah so like it's collecting a few ideas
based always from like the there is always a screening process
and then uh after that you know maybe if I am the technical interviewer I'm typically the first if I'm not like you know I will have the inputs from the technical interviews as well. Um and so yeah there is a document that we maintain it's a live document through the steps of the interviews. Yes.
Love it. Is it is so are those processes that you were able to take from Amazon or did had you always implemented this kind of structured interviewing in in all your companies?
Uh there are some ideas that that came from Amazon but then some ideas from our own experience. My co-founder is also very good at this and he never worked at any big company but is just like really good at processes and operations and so um he has put his spin on some things for instance like at Amazon you couldn't really access um other people's feedback before you inputed your own
which I can see Why it it's useful for a company like Amazon so big where like you know there's the VP the director and so like very formal like structure where like you know maybe the you know the per the IC will feel intimidated by the feedback of like the manager or director or VP and so then be less truthful maybe I don't know if you hire the right people you should not have that problem but like I can see why in an organization like ours which is like pretty flat and so like you know we all talk like equals really like uh it doesn't make much sense I just want to know what they thought what are the areas that they want me to you know test
they deeper in exactly like see if they're actually a good fit and you know without that feedback doesn't really allow you
have like a shared like session like which we call the debrief but also before the interview like all the prior interviewers will have put some notes and so I do have inputs uh about things that we want to discover like uh and you know nobody cares if like uh I say that this guy is great and maybe the next interview will think like it's not great so it's fine so that's why I think for this is a very big difference versus Amazon like Amazon bus to feel like all of your feedback
and then you would see everybody else's
and then you would do the debrief So I I read your uh founders network piece. So customers f first guide for building innovative products. Really good article. Um like how you know so when you're hiring somebody how do you actually find somebody who wants to build for the customer know rather than somebody who can interview well say the right things like how do you find that customer centric person?
I mean if it's uh for like a slightly senior position they will have faced you know other situations where they had to choose. So I asked them for instance about tradeoffs you know when like you know interesting things come when like you have to choose your priorities or choose tradeoffs and so I want to see how they're thinking about that. Well, like is there like a situation that you can share with me where like your manager was putting a lot of pressure on you to reach a goal, but you knew that the customer would not have been served very well by that goal and maybe an alternative, you know, and so they faced this situation. Oh yes, I can give you this example. Oh, interesting. And so what did you do and what did you learn and what happened and what did the customers say? So again like go very
wow
no in 30 seconds there you open up a whole new world right of questions to dig deep into
because you will see if they are like BS in you. Yeah.
What did the customer say? That question itself is what
what did the customer say? What did they think? Oh, like you know and so uh those are all like uh important things. Um, do they know even like you know some people like they tell you like they describe like a situation that they find themselves in and then they don't know really what happened, what was the outcome and what was and even if they were not maybe in charge of the final outcome. The fact that they don't know means that maybe they're not as interested in like the final result of their own job. It's just like oh I did it you know done and and instead like you know it it is different if you achieve certain results or not or if your team that achieved them and if you know about them and if you can describe them in detail um they it tells you stuff.
Yeah it's uh it's crazy. I'm I'm looking at, you know, the the cursor cell for uh what 60 billion to SpaceX and then everybody's coming out of the woodwork saying, "Oh, you know, they were trying to get me to work there, trying to get trying to poach me to work there." Um I don't know why why that just popped popped up in my head. um you know as you were telling the story I dig in deeper but I think it's uh a a lot of founders I guess are are just they don't dig deeper right um and even when you get some outreach uh you can can dig deeper there and and uh maybe take that 15-minute call from the the recruiter reaching out to you
yeah but your you know your first company it cleared 50 million I read in that that same article and you said that you let the you know wrong people corrupt the culture and you know good ideas might have not got shipped um money could have been hiding the problem like how do you you know catch that at scale sacks so like so you know that doesn't happen
well uh I think the founding team is very important and so you know it's the first hire in many ways so like yeah choose your co-founders well
I I well let's Let's dig deep dig like dig into that right how you know how do you choose a co-founder well
I think you have to know them well meaning like time repeated experience it can be life experience it can be work experiences but I think that they need to be a known entity where like you know an interview quote unquote is not necessary and if you haven't spent time um you have to be very diligent and like in the process of deciding if doing this venture together like you know you probe for some things. I think that people like you know maybe they carried away from the enthusiasm uh initially it's very like thrilling not to start a new company but then like quickly after like only a few months like it becomes like heavy heavy lifting um if you don't have the right people. So I would say in my experience like you know back going back to the first experience we really didn't think that much about this like you know there was somebody that we knew and you know we did this together like you know they were older more senior than us and thought like ah they're going to be great and then they weren't.
Yeah. And so time, you know, that's why you see a lot of founders, oh, we did college together or like, you know, they they did like a PhD or a master or like they work together like Stripe or whatever it is. So like it's typically joint deep joint experiences that like will enable you with the knowledge that you need to think okay this person like you know first compliments me very important. Yeah. as if you are doing exactly the same you know and if you have like similar uh skill set I don't know if it's useful and then like you know will they be there in the long run if we share like failures and successes and are they like you know kind in failure and you know um humble in success so things like this those so
and are you getting a you know are are you sitting down and talking for hours before on like a bunch of what if scenarios and you know what if this happened
co-ounder I mean for your co-founder you mean
exactly
I mean I think that that's if you don't know them well if you do I mean like that's what I'm saying that like I think that I would like uh put emphasis on like do not embark on a new venture with people that you don't know well and I don't say that you can only start a company with somebody that like you know you have spent spent like uh uh years together but like
you have to spend some time and look at this from many different angles um before committed and by the way like my co-founder and I fight all the time so no but this is important because I do think that uh like if you choose well it's complimementaryary knowledge and skill set and that means also that there are like different perspectives and views that they need to be reconciled and so
it's painful is um time consuming it's tiring sometimes but typically I think that the outcomes are better um skills tech is a much better company because the two co-founders are like so very different um and we have different skill set and like you know we have to constantly align and reconcile the ideas the output or the decisions that we get to are way better than any decision that we would have taken individually.
I I love that
that perspective on it.
Yeah. And so scale stack AI infrastructure. You're a you know go to market layer powering billions of data points for for enterprise customers. you have like so so customers are analyzing your own go to market motion and and how you you know uh you know drink your own champagne. um how you know how do you stay on on top of this and you know now especially now with AI coming into play people trying to build um you know the or vibe code their own apps what you know what's been your strategy to stay ahead in this AI revolution
we are not our own ideal customer we do use scale stack but we are not our own ideal customers most of our customers use Salesforce and we use Hopspot
most
maybe. Yes. uh most of our customers have like you know yeah hundreds of sellers and we don't but so there is uh value in our own infrastructure when is deployed a larger scale and so but having said that AI I think has improved dramat well first of all we started the company when AI machine learning was not geni so like we started in like 22 I guess. So that's around the time like
you're always ahead of the curve. You had mobile products before the iPhone. Now you got you know AI before AI.
So no we were thinking about machine learning scoring stuff with machine learning. That's what we were doing and and thinking and then like Genai came and like so we sort of rebuilt the platform around that. We could not have done what we are doing without AI in its current incarnation or without a lot of people essentially because what we realized is that like there needed to be an intelligent layer in the orchestration infrastructure orchestration for gotom market data. Um and at the beginning we supplemented that with um you know forward deployment engineers uh aka consultant technical consultants. No. Uh but that would have been like very expensive for like you know hundreds of customers. And so luckily AI in its current incarnation came and so the intelligent layer now is provided by agents. They act as analysts. They act as uh like smart workers all
wait. So you would have had to hire a four deployed engineer previously and now you have an agent doing that job.
Yes.
Wow. That's incredible. Yeah. So basically like that's what we were doing at the beginning know we had like built an orchestrator we had built automation we had built like uh lots of different API connectors modules to transform the data but then we needed like you know smart people solution architect forward deployment engineers to put together these workflows think through map like the attributes that customers needed and that the workflows needed to be optimized for now. Essentially that intelligent layer is for the most part agentic and so we have a reasoning agent that helps you put together the workflow. Uh we have like agents that act as analyst on the data. So to validate, clean and rich using you know the platform. So like the agent is a worker using the platform. So you cannot do what we do without the platform or the agent. So like both have to work uh together. There is still like the need for solution architects because the workflows are very complex but that's more as just like a a developer today. A developer doesn't actually write code anymore. They just are managers of agents and so the solution architect you know has to just make sure that like the output is good and you know but not putting together the stuff from scratch. So the solution architect is managing the reasoning agent and
it's checking the work and then once like you know okay this is good like we deployed uh but for it's getting better and better like you can start from cloud you can start from codec so you can use our own reasoning agent so that's uh a very interesting development um that has
yeah crazy
dramatic over the last six months Yes.
And you and like for deployed engineers it's like a 700% increase in in hiring there like you know so you've been able to replace some way but there it's still the the most in demand it's ever ever been at least that that term um you know it's been called different things before uh
well I think in the age of AI uh you cannot afford uh to just do like a platform you need to do outcomes right so like it's not that you say okay hey takes this and figure it out. Take this platform and like learn it. It's just that customers are looking to solve problems and at speed the speed of things is like dramatically increased. And so I think that that's what's changed in the age of AI as well. No, that
your expectations are higher. You're not satisfied with a tool. You need outcomes and the speed at which you want to get outcomes and solve problems is way faster than before.
Yeah. I mean you even see open AI like you know building a consultancy and it's you know so it's not uh gone are the days where here's this offer go figure it out right it's it's a very
very much full full experience that uh folks need. So we you and I were both at SAS. We didn't connect there unfortunately. Um but I was listening to to another one of your podcasts where you mentioned um you know you you were there and got a lot of value from it. There there are some boosts there that are saying hey we replace people with AI right um from STRs to AEES. How how are you looking at AI in hiring? are you going and saying, "Hey, I'm going to try to hire with AI first, do this with AI first or or what what's your philosophy on that?"
So, I think that the again the bar is getting higher. So like uh it's not so for every one of us I mean like I I told someone the other day look if I had met Elio two years ago as a salesperson which is essentially one of my responsibility in the or is like to be part of the sales team and actually bring deals and stuff like that. uh I would not hire him you know so the expectations for each of us have like dramatically increased so it's not that AI is replacing work but the expectation for the way we do work and the speed at which we do things and the quality are just higher now we haven't seen yet I think in the job market broadly the so feared effect of oh people are hiring less you know like there is that fear
but I don't see it
yet maybe
yeah I mean the data doesn't point that way um but there
doesn't point that way what I do see even from our own microcosm is that the expectations are way higher so whatever was good like a year ago is definitely not good now and like for people inside the company like you know the speed at which they need to develop uh skill set and usage of tools and AI is like much higher. uh there was someone in the team that like you know 6 months I thought like oh I don't know like there is uncertainty if like they will be able to keep up and like they did and they started using more of the tools went faster better like their output is like meaningfully better faster than before and so I think expectations are higher I mean even our investors they are telling us like you know you need to hire like you know better developers today, right? The expectation like so the developer job which has totally changed like they don't write code like we were saying earlier. It doesn't mean that you don't need developers but you need like you know really good developers and so essentially is like you better upskill uh and uplevel your knowledge or else I think that that's really the message uh because if I see I mean like putting together a document today is nothing I mean like uh on Amazon people went crazy because you had to write like narrative and six pages and PR faqs and I would spend like days, weeks writing these documents now like you know writing a document a first draft is like nothing and so you better not show me a quick first draft made by AI where I can see what you are actually thinking or if you've done like multiple loops refine it improving, removing AI slop and all of this stuff. That's
because if you think that like so if we went back like to my Amazon day 2018 and somebody like after half an hour of me talking to them like came back with like a a document prepared by I I would have been impressed because they
did not like how did you do this in like you know an hour but they do this now today and it's like you know forget it like my expectations are like so much higher so I think that that's changed. I think that people need to really uplevel um and by people I mean starting in the math.
I think you need to be easier on yourself and go through the cultural interview first and see if you
So, so last one here so we can um you know start wrapping up. There's a founder listening. They're about to make their third or fourth hire. Um you know that their their product is good, their their traction's real. um what what's the piece of advice that you can leave them with to you know continue going on the right track when when it comes to hiring?
Uh I mean like uh nothing different than what we said before about like keeping the bar high. Um even if like you know be your own bar raiser. I know that I love it
but like you know be your own by razor and like be patient because I mean making mistakes I mean and still in all of this like even if you have the system the process the interviews the data points you will make mistakes 100%. and you know normal because we're all humans. Uh but um I would say yeah be your own bar razer and like you know really make sure that even if you have like a desperate need to have someone in position and like you know you have five people and you desperately need like two more to do X and Y. keep that bar high because you know if you don't even if you have to wait longer do more interviews select screen more people it's typically way better than you know hiring someone that is not a good fit
I love it quickly realize soon after
love it love it love it thank you so much for being on who we hire super valuable here where can people find you
pretty visible I think so people can email me at less skills.ai uh or on LinkedIn. Um so
wonderful. Well, thanks for being on and have the best day ever.
Thank you, Louis. Louis
