EP 143: Tech industry's hiring landscape and ethical quandaries of AI, with Daniel Chait, CEO of Greenhouse

Join host James Mackey as he discusses the latest in tech industry hiring with Daniel Chait, CEO of Greenhouse. They explore the state of hiring, increased applicant ratios, and the disconnect between hiring plans and budgets. Discover the impact of AI on recruitment, potential biases therein, and the future of work in a shifting market. 

    0:00 Tech industry hiring trends and market conditions
   6:44 Hiring trends and cost-cutting measures in the tech industry
16:01 AI's impact on work and decision-making
27:29 Active job search strategies

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Our host James Mackey

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Transcript

Speaker 1: 1:12

Hello, welcome to the Breakthrough Hiring Show. I'm your host, James Mackey, and we have Daniel Chait back on the podcast with us. Daniel, thanks for joining us again today.

Speaker 2: 1:21

Yeah, great to be here, Thanks for having me back.

Speaker 1: 1:23

Yeah, I'm really excited to dive into it. One of the first topic we talked about bringing up is what's happening in the employment market, specifically within tech. Are tech companies starting to rebound in terms of building their teams again, what challenges we're seeing there? And really just also, I think just making the general comparison to the overall employment market throughout the United States will be interesting too. But as a CEO of a company that does a lot of work within the tech industry and with tech companies, what are your customers seeing right now? Are companies starting to get back on track with hiring or is it still pretty slow?

Speaker 2: 1:57

Yeah, it's interesting, james, because you see the headlines in the paper and it's all about hey, interest rates are set to come down, unemployment's been at a low for a long period of time, really tight labor market Still persistent inflation, although it's been moderating, and so you hear all these kind of headlines of like really economic strength. But when you look in the tech press you still see stories of kind of layoffs and tech companies still struggling, like still venture capital has yet to rebound, except in a couple of sectors. And so what's really going on? I think from our business perspective we see a lot of those tech and tech adjacent businesses, because that's our customer base. So our 7,000 plus customers center around tech, financial services, biotech, marketing, advertising, healthcare management, consulting these types of firms that are very knowledge worker centric and very dependent on kind of growth orientation. And so it's a very different slice than you see in the overall headlines.

Speaker 2: 2:58

And what I can tell you is last year our customers hired in aggregate a little over a million people. It's a decent subset of what's going on in the economy, but it's a very different subset than you see overall and the story is very different in that world. What is hiring has not yet resumed at full strength, that it was in the either pre-pandemic leading up to 2019 or in that post 2020 boomlet for 21 and the early part of 22. But it's not still in that free fall period that we experienced, starting in late 22 and heading into early 23, where you basically saw tech companies and tech-related companies of all types really come to a screeching halt. It's not that anymore. It's like easing up a bit.

Speaker 1: 3:45

So over the past 18 months we've seen a pretty big decline in demand steadily. Last year it was pretty bad. Q3 and Q4 were honestly somewhat terrible. When it came to new business sales for us, again, we do contract recruiting, embedded recruiting, rpo search for primarily tech customers and we've worked with over 200 tech companies in the past decade, so we're pretty well connected within the space. So it was really interesting. I have hundreds of CEOs and executives that I was texting saying, hey, what's up, what can we do for you? And it was for the most part it was pretty much nothing, not a whole lot of demand.

Speaker 1: 4:24

What was interesting is in January and February we did start to see an uptick in demand for our services.

Speaker 1: 4:29

I don't know if that's so much in terms of the amount of jobs open had increased or they had just been open for, maybe in Q4. And as a result, they weren't filled and so they started to consider bringing on vendors. Sometimes there's a little bit of a delay for us in terms of when the market starts to heat up and then when companies realize they're struggling to fill roles in-house, then they start to leverage a service like ours. Now it's not a significant uptick, but it's the first time in well over a year that I actually feel like I can meet with my finance leader and start to do a little bit of forecasting and actually get an accurate pulse on the economics of the business and getting back on track. It's been so difficult trying to forecast and figure things out simply just because we didn't know where bottom was. It just seemed to continue to dip. So it does seem like it's a plateau, but at least the demand seems to be becoming a little bit more predictable and consistent, which it's been nice to see.

Speaker 2: 5:30

Yeah, there's a lot of different signals and trends that you can pick up on. I think the one you're highlighting is a really interesting one. From our perspective, when we look at the way our customers go to source candidates, what we see is that when they feel like it is an employer's market in other words, they open up a job, they get plenty of pipeline, they get plenty of applicants. These of them find applicants they really move away from using outside services and then, when that switches and they feel like it's hard for them to find candidates or get people interested, all of a sudden they ramp way up and they'll go find a specialist and say I need help, there's no answer to my calls, I can't get any candidates, and so that needle moving back and forth is a really important indicator. What we're seeing is, in the run-up to that happening, you're starting to see a big uptick since late 22 in the ratio of applicants in each job. So we track as just like one of many pieces of data we look at across all of our customers when they open up a job. If you just average out how many jobs there are and how many applicants are in those jobs, we look at that kind of ratio as a signal of how Canada rich or Canada poor is. The environment overall, and for about the last three years that number has really hovered in the same range, with a little bit of a spike in early 2020, but it hovered otherwise in that very same range Since late 22,. It has climbed steadily upward and has now more than doubled since the lows in early 2022. So that means that for every applicant that was in a pipeline in, say, june of 2022, there's now two applicants in that same pipeline, so way more applicants. So companies are feeling confident that they can find the people they want.

Speaker 2: 7:18

When they look just at that, now, that's all well and good. At the same time, we're also seeing a real rise in application activity per person. What does that mean? I'm old enough to remember that when I applied for jobs at a college, I had to go to Kinko's, print out a resume, fold it up, make a cover letter, put an envelope, put a stamp and mail it. It was hard and so I didn't apply to that many jobs. These days, the tools are so much easier to apply and with the rise of chat, gpt and AI-related tools, it's easier and easier for people to apply for more and more jobs, and so what's happening part of what we're seeing is a rise in applications for jobs faster than a rise in applicants for jobs, and so I think part of why someone may rely on an outside party is one of many ways that they can actually help filter through all that noise and actually find the right talent in that sea of mess.

Speaker 1: 8:15

Yeah, it's definitely taking a lot more time to go through inbound applications. There's really not a whole lot to do to speed it up, just if you're going to properly review a resume. It just does takes time to get through, particularly if you have hundreds of applications or even if you have to go through 20 to 30 a day or something like that's actually it takes a decent amount of time to do that properly. So it's it. What's interesting too, it's, like some of the, we are starting to see that uptick in demand in which more people need recruiting support. They're not really sure if it's going to be on an ongoing basis, which is another reason they might leverage us and so they come to us.

Speaker 1: 8:51

And we're starting to see these bigger hiring plans where growth stage tech companies, maybe series b for instance, could have as many as we're talking about 100 to 250 employee range. They might have throughout the year 50, let's just say 50 technical hires that need to be made. We just put a plan together for one where they had about 30 that they need to do in Q2. But what's really interesting is that there's these big kind of robust hiring plans are bigger than we were seeing before, but the budget for even just for HR and like delivering on the recruiting is is so tight, which is weird, because it's okay, we're the cost of payroll is going to increase so significantly, but we'll put together a proposal and we'll do.

Speaker 1: 9:35

We do capacity plans and put it all together on a spreadsheet and we say, okay, if you need to fill these 30 roles over a three month period, based on what we're seeing with our other customers and our calculations, it's going to take five and a half recruiters.

Speaker 1: 9:48

And then we're getting the cause. On average, like a lot of these tech recruiters are doing two high level technical hires per month and we'll still get the response like, oh, we were trying to do it with one and so there's still definitely is that disconnect there and we're still seeing a higher drop off when it comes to going to the capacity or proposal stage. So we ended up putting like, all right, you could just get one person for X rate and we can do that, but we put the recommended capacity of okay to actually achieve your hiring plan is we're going to require this. So there's still like a bit of a disconnect, I feel like from executive teams that it's like they some of them even seem like they want to get back on track with hiring, but they're not necessarily reflecting that in their budget or HR.

Speaker 2: 10:30

Yeah, I think that's right and I think that's. I think that's a trend that we're seeing across the board in companies, which is that we're all being pressured to do more or less and be more efficient, and so you're seeing a big shift from kind of growth at all costs to growth plus efficiency, and that's showing up in higher workloads and like slower growth in staff all in every area of many businesses. And again, when we look at our data from our customers hiring patterns we see this very much reflected. So we look at a metric called recruiter workload, which is simply just the number of applications queued up in a job pipeline per recruiter assigned to that pipeline. So it's basically a measure of workload. It's not perfect, but it's a metric and the specific number isn't so important as the direction of it.

Speaker 2: 11:17

That number tends to hover around 100 and has been for several years in that range, plus or minus a very small number. And again, since 2022, the metric is very clear straight up into the right. And now, if 100 is typical, that number is hovering over 300, approaching 350. Meaning, the average recruiter at a company using Greenhouse today is looking at a queue of about three times more resumes or applications than they were just a year and a half ago which tells us exactly what you're saying, which is that companies are ramping up their hiring, or at least getting more applications, but they're not ramping up their internal recruiting teams nearly as much, and I think that is reflective of the pressure that companies are feeling to keep costs down and to try to make things more efficient.

Speaker 1: 12:06

Yeah, we're just seeing some weird stuff. I put together a proposal for a customer. This might've been in yeah, it was in January and I had never seen this happen before but the day they started they kicked off the engagement. They gave notice the same day. So we negotiated notice period within the contract and literally on day one they sent the email just to keep the project to the certain length and it's, it's fine, cause it's okay, come on, that's not how that's supposed to be used, but it was just interesting. People are just so incredibly really tight right now, which I get. It's like a lot of this. Still stop, go, stop, go, stop, go where it's companies seem to want to get back on track. But then it's OK, wait, we're going to meet with the board again and we're going to evaluate.

Speaker 2: 12:58

And it's very kind of cl is what we're seeing in the tech world. When we look at you can just see it in like the total amount of venture capital dollars being invested that had been running up very dramatically in the years leading up to 2021 and into the early part of 22. That's come way back down and that VC money was fueling when you talked about a Series B company, before it starts at the early stage and goes through A, b, c and beyond. But that fuels all kinds of growth-oriented behavior that doesn't really worry so much about cost. The kind of venture model is lose money, get big and then figure out how to make money later. And when that money drops back down it cascades through a whole set of a whole economy and all the vendors selling to those businesses are feeling it.

Speaker 2: 13:39

We're seeing it and we sell to both kind of startups as well as like large established companies by and large. The larger they are, the more they've just been steady and carried on with things. Those small businesses they tend to be more venture backed and when they're in that run-up they're hiring and growing as much as they can. When that money gets shut off, they're the first ones to really slow things down and cut spend, because they're not. Because they're in the VC world, they call it like default debt or default alive. Are you making enough money at this point to sustain all your costs or are you actually losing money and if you don't figure something out, you're going to run out? I think a lot of the smaller ones. They just had to put the brakes on spending.

Speaker 1: 14:22

Oh yeah, I saw some stat I can't remember exactly, I think it was around 3,200 tech companies went out of business last year. Yeah, that's wild man.

Speaker 2: 14:34

That's real stuff, and most of those are the smaller ones. Right, if you're already at scale, you're already a public company. Yeah, you've got to mind your metrics and you want to make sure that you're spending appropriately, but it's not an existential question of whether you'd be around next month.

Speaker 1: 14:43

Yeah, for sure, it's the hyper growth right. It's like focusing on that J curve, compounding year over year revenue growth is what drives valuation. So the whole emphasis is on that and not actually scaling a profitable business. And then you go, obviously, public. You have different responsibilities and different metrics and also different access to capital at that point too. But yeah, it's been an interesting market. And I think just to pivot into AI, which we had briefly, I think we briefly touched on, but I want to dive more into that and I think it's there's I'm seeing a lot of startups, software startups, think about different solutions within talent acquisition for AI, and maybe some applications are more beneficial actually than others. But I'm curious if there's any AI application within talent acquisition right now that you're actually like, really excited about or you think is going to make a truly meaningful impact to help companies hire more effectively.

Speaker 2: 15:36

Yeah, let's talk about some of the bigger trends that are happening, right, because we talked about, obviously, the introduction of generative AI into the discourse has affected everyone.

Speaker 2: 15:47

Everyone's thinking about how to apply this stuff and where it goes first, and then you take some of the stuff that you and I have just been talking about, where what we're seeing in our customers collectively is this big increase in application volume without the same increase in staff, and so they're figuring out how do they solve this, and one of the ideas that everyone gets is oh, we should use AI, and one of the most kind of obvious things that companies want to do is I've got this big buildup of applicants in the top of my funnel.

Speaker 2: 16:17

Can the computer just automatically tell me who to pick? And I think that's an area that companies have to be really careful. Models and AI algorithms magnify and intensify the impacts of any human biases that are in the data sets that they're trained on, in the people that are. How is it filtering the candidate pool and is it intensifying existing biases that may exist in the world around us, making those problems worse? In other words, depriving you of top talent, because it's focusing on other aspects of who's in the pool. So that's a big risk that companies are thinking about.

Speaker 1: 17:13

So I actually I've been thinking a lot about that topic. What's interesting about how you said it is it's intensifying or magnifying bias, because one of the what I've been thinking back and forth on is more so that when it comes to biases, people are incredibly biased. Right, so it's. If we move to an AI model, is that necessarily? Is it a reason to avoid AI? Because, honestly, people are incredibly biased and at least we can hopefully train systems where it's potentially could be easier to train an AI system to do something properly, versus people that you're onboarding and they're leaving and it's there's knowledge gaps and there's just it seems like human error would ultimately could be a lot more challenging. But what's interesting is it's not necessarily you're trading one potential bias for another. When you say magnify or intensify, what I'm getting at here is the reason it's magnified because they can get through so many more applicants faster, or is it literally more bias or what's creating when you say magnify?

Speaker 2: 18:08

It's both right. First is just obviously, when you automate something, you speed it up, you can do it a lot more efficiently, and so if you're not doing the right thing, you do not do the right thing even faster. That's obviously one piece. But think about what these algorithms do. At the core of them is the idea of pattern identification, and the things that you and I are talking about right now. Those are patterns, and so what an AI algorithm can do is winnow out everything else and just find this one pattern, and often the pattern isn't what you intended.

Speaker 2: 18:38

There have been famous studies in other fields, for example in the medical field, where AI algorithms were showing ability to diagnose certain diseases through the imagery through an x-ray or an MRI image only to find out that the thing that the actual algorithm was phoning in on was nothing to do with the diagnostic image that was taken.

Speaker 2: 18:58

In other words, it wasn't the x-ray of the person's bone, it was the label at the bottom of the image, and the AI algorithm had figured out that patients coming from certain hospitals were sicker than others and was identifying those more as having these disease, and so it was like finding stuff that wasn't necessarily relevant to the these, and so it was like finding stuff that wasn't necessarily relevant to the diagnosis, and so an AI algorithm may decide that people who played lacrosse are more likely to get through it. Whatever these things are, they can have total unintended consequences in a lot of ways. So again, I just think that it's a bit worrisome to put these decisions in the hands of an algorithm when it's very unclear what those algorithm decisions are going to result in. So I think that's one big worry that companies have.

Speaker 1: 19:45

So one more question, follow-up question on that. So then that's a hard problem to solve there, right? Because we want to use these systems to work more efficiently, ideally to remove bias, and we look at this as, hopefully, a solution at some point in time. But in the meantime, how do we leverage it? Because it's okay if we have the system doing a lot of this work for us, but then we basically have to double check all of the work. It's really not doing anything for us.

Speaker 2: 20:12

Yeah. And again, I think if you're using AI to generate content, you always have to double check the work. You've seen this topic called hallucination, where it'll create facts out of thin air. It'll make things up that aren't true, and so if you're shipping AI output without a human in the loop, you're finding yourself having that issue. And then, of course, governments are figuring this out and quite a few places now have laws governing the use of algorithms in the loop around any decision for hiring someone. And so, again, if you're putting AI into the decision-making process, you're now subjecting yourself to a myriad patchwork of state, local and national laws that are changing all the time, governing your use of those type of algorithms. So, for those reasons, we're very cautious about putting in loops. So your question is okay, but how can we use these things?

Speaker 2: 21:08

At the same time as you should be careful about using them to make decisions about people, you can use them in myriad ways to spur creativity and brainstorming. For example, if you're writing a job description, often we're staring at that blank piece of paper. We want a starting point. You can use it to suggest new ideas or new ways of working. So if you're looking for patterns, I may want to find patterns in my hiring data of show me the places where candidates are getting stuck. Show me the things that are yeah, show me the most unusual criteria on my scorecard that I'm weeding people out based on. It can be used as a really smart researcher to identify patterns that you can then use to change your process or to improve things, or to generate lots of content that you can then act as almost like an executive editor to shape and then publish. So there are tons of ways in which you can get lots of productivity out of AI without taking the risk, before these algorithms are ready, to put it right in the middle of decision.

Speaker 1: 22:14

And I think like right now, there's this big fear that AI is going to replace a lot of skilled workers and there might be efficiency gains potentially on revenue teams or other places where maybe this could happen a little bit, maybe for, like, sales development or something. But I still feel like that's as the technology currently is. Today I don't see it. It's probably a huge potential, a threat for the future, but as of today, I don't really see it impacting headcount to a significant extent. What are your thoughts from that perspective?

Speaker 2: 22:38

Yeah, look, I think there's a lot of that's coming quick. So I saw a study recently around using large language models in a legal context. So these were lawyers reviewing contracts and trying to identify certain terms and certain risks and then they fed the same contracts to an algorithm and algorithm did it like 99 plus percent faster, made fewer errors. It's just like the productivity gains are incredible and I think any knowledge worker should be realistic that that you know that's happening in a lot of parts of work. So you mentioned somebody doing outbound sales.

Speaker 2: 23:16

Certainly in recruiting, even as a manager, I have a team that I manage. I, for example, do one-on-ones with them and I've used AI to help me think of better questions to ask in my one-on-ones. I've used it to help me flesh out a memo or an email that I'm writing. I've used it to give me alternative ways to say things. It's taking a big part of people's work away, without necessarily taking their job, if that makes sense and freeing you up to do the more high value and more strategic work. I think the question for everybody is like how much work are you doing that could be replaced by an algorithm and how much of the work that you're doing. That's uniquely you, and where's that balance gonna lie?

Speaker 1: 23:59

Yeah, I think it's also just as an executive too, leading a team. It's incredibly helpful. This is just a random application. But during the market correction we had to get creative with pricing because our customers are scrutinizing costs a lot more. So historically we for our RPO, we had just done a flat subscription rate where it's okay, x amount per recruiter and we had recruiter sources coordinated. We put together a package but it was essentially a hundred percent subscription based.

Speaker 1: 24:23

But then we were deciding okay, we want to introduce a hybrid model which is a lower subscription plus a success fee component, so the fixed cost is less but the success fee it actually makes it a little bit more expensive. So I just put in the parameters of the pricing model I want, saying I want a hybrid split between subscription to success fee. I want the success fee model to be 25% more expensive based on recruiters doing X amount of capacity. This and the other. I put in probably six to 10 different parameters and it punched out a pricing model that I double-checked the math but it was perfect. It was like okay, I could have paid a GTM consultant a thousand bucks an hour or just leverage this tech to help me think through stuff a lot faster. So that was probably the coolest experience that I've had as a leader, just leveraging it for that.

Speaker 2: 25:13

Yeah, these are the kinds of things, but it's not replacing your job.

Speaker 1: 25:16

No.

Speaker 2: 25:17

What I draw the example to you is because this is not a new fear. Every new technology that comes out, people always wonder whose jobs are going to take. And yet the more technology we have wonder whose jobs are going to take. And yet the more technology we have, the more work there is to be done. And so I think when cameras came out, they were expensive and they were clunky and you see the old timey things of a guy under the hood when pulling the. And then when cameras started to get cheaper and then become digital, they didn't take the job away from a professional photographer. In fact, there are millions and millions more times of as many photographers today as there was a generation ago, just because everybody has a camera in their pocket now. But the job of a professional photographer is just different and they use different tools. And every new technology that comes along is going to be the same.

Speaker 2: 26:01

And I think the job of an office worker in the future is not going to be to open up a blank window and type emails from scratch when it's sent. That job can be done by a computer, but higher order thinking and strategizing around that and other ways of showing up and adding value are going to become the job and a lot of the routine stuff is going to be increasingly automated. Even I saw the superhuman, the email tool they focus on. It's used by a lot of tech companies like focus on basically like getting your email done really quickly. They just announced a new feature using AI where basically you can, when it automatically draft for you or apply to every email in your inbox.

Speaker 2: 26:43

And so what if your emails were already replied to before you got to them? So the email comes in and you look at it and we're sitting right there as a draft of the email and there's little switches. Do you want this? Do you want the reply to be do you want to say yes or do you want to say no? Do you want it to be more professional or more casual? You just make some choices and it'll just like automatically rewrite the thing you just send.

Speaker 1: 27:01

Like it's pretty good good, that's cool, so they put it in a draft mode.

Speaker 2: 27:11

So you can, yeah, but it's already written essentially yeah and by the way, and it can just present you with some choices, and you can. It's easier to make those choices and they just actually write those emails. So you just make choices, let the computer write the emails according to your choices and hit send, essentially like an executive assistant you got it, it's our, except it's a really smart one that's super efficient with its time.

Speaker 2: 27:29

And so again, I get back to recruiting. I think about what are the possibilities here. I'm like, oh, every single day our customers are waking up and making a million decisions, and mostly they're doing it from scratch, with imperfect human memories. And what if they can make those decisions built upon all the data that we have, with perfect memory and never getting tired? Oh, it'd be really helpful. They think about you know, how are they writing job descriptions? How are they building interview plans? Where are they sourcing candidates from? Are they buying ads? Are they using outside agency firms? What is their mix? You know, how are they crafting offers? All of that stuff computers could just help them be so much more intelligent about than they are today. So that's the stuff that we're really excited about.

Speaker 1: 28:13

Yeah, it's really cool. And on the prep before we hit record, we discussed was how job seekers are also leveraging AI and the potential there. Let's talk about that.

Speaker 2: 28:26

Yeah, we mentioned this a minute ago. It's so easy now to apply that job seekers are applying for so many more jobs, and part of what they're able to do now is they can customize and tailor, using machine learning and natural language, their application to each job at scale automatically. And we actually see in our own data there has been a long-term decline in cover letter usage for a long time. Just people did people just they realize that people don't always read cover letters, so they're writing less cover letters than they used to, and that trend has also stopped declining Since 2022, when ChatGPT was launched. I don't know if it was a coincidence, but we started to see that where usage of cover letters was going down, it's now completely leveled off and there's probably a lot in there. Probably part of it is the job market and people are competing harder, so they find it that they want to write cover letters more. But part of it has to be that it's just easier to write a cover letter now because you can have.

Speaker 1: 29:25

I do it automatically for you. It would seem quite an interesting correlation. There probably is some actual relationship there. I would think it's interesting, though I never really loved reading through cover letters. Personally, I always wanted to see more just about the peer experience and then flesh out the other stuff over the phone.

Speaker 2: 29:44

That's right, and so the thing that is starting to happen is you're getting this kind of arms race where candidates are using AI to generate more applications with more in them, and then companies are wanting to use AI to winnow all that back down and get to where the real signal is, and so I actually think that the way out here if you remember the movie War Games from the 80s Matthew Broderick the famous line was the only way to win the game is not to play. I think we're seeing something similar here, where the escalating arms race between candidates and companies to who can use AI to overwhelm the other side I don't think is a winning game. I think there's got to be another way where companies can find the real signal in all of that noise and find out who they really ought to be talking to.

Speaker 1: 30:26

It's just going to force companies to use AI to go through resumes because there's going to be so many more of them that they're not going to hire a sourcing team of 10 people to go through inbound applications.

Speaker 2: 30:37

It's just not going to happen.

Speaker 1: 30:38

Right, that's right. So where does it end? Yeah, I don't know. I think, and that's it as a job seeker too. It's just important to remember that everyone's going to be doing that. So I'm not saying whether you should or shouldn't, I just think that most, a lot of people are going to be moving through their network, they're going to be moving through relationships, and I think taking a more and this is I just generally recommend this taking an active role in your job search is critically important. I think just the whole model of I'm going to put out my resume and wherever somebody's interest is where I'm going to invest the next several years of my life.

Speaker 1: 31:13

And to be clear, I understand a lot of people do not necessarily have several options and you have to take sometimes what you can get to pay the bills and all these types of things.

Speaker 1: 31:22

That's not necessarily. I think multiple things can exist at once what I'm getting at here. I think everybody should be, to the extent possible, playing an active role in their job search and being really thoughtful and critical about the type of company they want to work for, the type of role they want to go toward and essentially almost put like a target list together, if you will, and focus on relationships and even if you apply to a job, reaching out to the hiring manager and saying, hey look, I just applied for the job, I'm interested for X, y, z reasons Just do something to stand out, because there's just going to be so much more noise that just doubling down into relationships and taking an active, targeted approach is just increasingly more important, particularly not only with AI. But then it's also like this job market with still all the layoffs, since there's just so many incredibly bright people that are unemployed at the moment. So it's just so important to play an active role, right, 100%.

Speaker 2: 32:15

Yeah, I mean that energy is always going to pay off, but it's not as you said. It's not a one size fits all strategy. As a candidate in a competitive market, you need to try everything you can to give yourself an edge.

Speaker 1: 32:28

I totally agree with that advice. Yeah, and too, just keep in mind, too, recruiters are going through resumes faster. They have to. So even if you're applying for more using AI, you're going to need to do more than that. They're going through super fast and there's more applicants, so they're also going to scrutinize a lot more. For instance, if you're in tech, if it's not B2B consultative SaaS with X, y and Z deal size just for instance, for a salesperson X, y, z deal size and a sales cycle of X amount of months and selling into FinTech specifically for companies over 500 employees. Companies are getting that dialed into what they're looking for right now. So if you don't align perfectly, they're just going to hit no, go on that application and move on. So it's relationships. It's always where it's been at, honestly.

Speaker 2: 33:11

Really have an incredible career.

Speaker 1: 33:12

It's investing in that as much as possible, absolutely, yeah, hey, daniel, this has been a great time, as always. It's really great to have you on the show and thank you so much for sharing your expertise from every customer you've worked with over the years and everything you're doing at Greenhouse. Again, I'm a big fan of everything that you've built and I'm always recommending Greenhouse to our customers as go-to applicant tracking system. That'll help prepare them for scale. I really appreciate everything you're doing for the show. Thank you.

Speaker 2: 33:37

Thank you very much. Great to be here.

Speaker 1: 33:40

All right and everybody tuning in. Thank you so much for joining us today and we'll talk to you next time. Take care.