How AI Is Rewriting Hiring and Operations | Sean Griffith (Founder of Truffle)

Many companies are experimenting with AI.

But very few are restructuring their operations around it.

In this episode of Business AI Explained, Vladimir de Ziegler sits down with Sean Griffith, founder of Truffle, to discuss how AI is reshaping hiring, software workflows, and internal operations.

Sean explains how his team is able to produce the same level of output with fewer than 10 people compared to what he previously experienced inside a publicly listed company doing over $300M in ARR.

The conversation explores how startups and modern teams are beginning to replace traditional software tools with AI-driven workflows and how leaders can rethink operations to become fully AI-native.

Sean also explains how Truffle is redesigning the hiring process using AI-assisted candidate screening, transcription, and evaluation to help HR teams move from hundreds of applicants to a shortlist much faster.

The conversation covers:

• How startups can control costs when rolling out AI features
• Quick SEO opportunities to appear in LLM search results
• How developers are replacing traditional software with Claude Code
• How to structure operations for AI-native teams
• Which tools and workflows companies should automate first
• How AI is transforming hiring and recruitment processes

If you're building products, leading a startup, or exploring how AI can reshape your internal operations, this episode provides practical insights into the next generation of AI-native companies.

Episode length: ~30 minutes

👤 ABOUT THE GUEST

Sean Griffith

Founder of Truffle

→ LinkedIn: https://www.linkedin.com/in/griffithsean/

→ Company: https://www.hiretruffle.com/

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If you’re implementing AI in your operations and want hands-on help building real workflows:

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Transcript

SPEAKER_00 0:00

In today's episode, I am joined by Sean Griffith. Sean is the founder of HioTruffle, which is an AI applicant screening software. In other words, it allows companies to screen candidates using voice agents. I'm very excited to have Sean on this podcast to explore how he's actually running SEO and product-led growth as part of his new company and how he's using AI to help him with that. He was the VP Product and Operations at Cinch, which is a publicly listed company in Sweden, making more than$300 million in revenues. From my early conversation with him, what really uh stood out is that with a much smaller team today at Hyotruffle, he's managing to achieve equivalent levels of deliverables and output. He's basically using cloud code everywhere from growth, marketing to operations to even development. So I really want to understand what it took him to go from running a traditional business with traditional processes to being completely AI native, and what are the learnings for other entrepreneurs and other companies who want to do the same. And before I forget, this is Business AI Explain, where I help companies go from a shiny demo to building AI applications in production. What I've learned over the past two years running Elements Agents, where I build AI workflows for companies, is that going from a nice demo to something that you would feel comfortable presenting to clients and to having your team to use every day is a completely different game. And so the point of this podcast is to bring in experienced leaders as well as AI experts to truly tell us and share learnings on how we can implement AI in operations and the kind of workflows that you can copy. So enough about me, enough about the podcast. Let's go. Hi everyone. Today I'm joined by Sean Griffith. Sean Griffith is the founder at Hio Truffle. I'm gonna leave it up to Sean to describe the company himself to get a better sense of how the product is set up. And uh yeah, so thanks so much, Sean, for joining us today on the podcast. And uh yeah, very excited to have you here.

SPEAKER_01 1:45

Yeah, great to be here, Vlad. Thanks so much. Let me maybe just to take a couple, take a minute and uh cover truffle, right? So Truffle is an AI-powered hiring platform. Really, you know, sort of our our journey began in in the one-way video interview space. And this was really meant to be uh a way for companies to help optimize uh and find the right candidates, right? You know, sort of in a world where what we're seeing is just a flood of applications through an easy apply button or AI generated resumes and applications. How do you how do you know if somebody is good, right? And so the the signal from a one-way video interview, you know, we found with some good AI analysis was much better than a resume alone and helped teams save an absolute ton of time. Over the last little while, we've sort of continued to grow the business, adding some sort of AI-proof assessments in there. I think, you know, sort of a personality assessment or some skills-based assessments, and soon bringing in some resume analysis to give sort of a more complete picture and building, you know, uh helping teams build a better workflow.

SPEAKER_00 2:53

I think it it's very interesting because you're touching, I think, on two exciting topics uh around AI, not as a tool per se, but rather like with the workflow and the outcome. So by workflow, I mean the whole process, you know, there's a start and an end, and you want to automate as much of this as possible. And then there's this outcome. So you're kind of selling an outcome instead of just running interviews for the sake of it. So, first question for you is why do you think today we can build something like that? You know, what's the most exciting topic with Gen AI and the recent developments in the last six months that make it possible?

SPEAKER_01 3:24

Yeah, uh great questions. Now is probably the right time. I mean, look, the tools have just gotten so much better, right? We saw just, you know, this this month or last month, I guess, in February, a huge leap, I think, in uh with the release of some of the newer models. And so the power that we get from those, right? Uh, we see it, you know, probably exponentially larger than a chat, you know, GPT 3.5, right? So something that was not really that old, but you know, seems like ancient history now.

SPEAKER_00 3:48

Oh, if we just dig a little bit deeper into uh the pricing and the business model, you have a product-led growth motion, meaning that there's a self-serve with a free trial. Now we know that with AI, you pay per token. And I don't think that the you have, at least it's not advertised, you don't have like external funding. So how do you usually how do you go about offering a PLG motion when you're you know not like throwing millions to like just drive adoption uh and just putting your market, your product in the wide, basically?

SPEAKER_01 4:15

Yeah, that's right. So I think a couple of key things there. One is, you know, a lot of my past experience was like deeply embedded in in PLG, right? So we have a lot of sort of playbooks to work off of, and the team really understands how to bring customers to us, right? So I think one is the funnel itself, right? How do we get customers coming to Truffle rather than having to pay to go find customers? And so we built around SEO, AEO, GEO, um, you know, from the very first moment, right? So I think if we start at the very beginning, that was really key to our success, right? Is a lot of self-serve trials into us. And then if we look at, you know, once somebody arrives at Truffle, it is mostly, I would say, a touchless experience, right? So, you know, we do some outreach because, you know, we think it's really interesting to talk to customers, right? And uh to try and move them across the line when it makes sense. But many of our customers are able to self-serve, get started, right, and subscribe without ever having interact with a human. And a big part of that is being really thoughtful about the experience they have from the moment that they get to our website, sign up for an account, go through a really nice onboarding experience where they're shown value, hopefully very, very early, right? And then they're moved through additional sort of aha moments that make them go, wow, this is something that I, you know, I I I now see that I can't live without in the future.

SPEAKER_00 5:46

Yeah. Um, you touched on uh on two things which I'd like to maybe dive a little bit deeper into. The first one is AEO and GEO. So basically fitting traffic from different LLMs, such a GPT, and you want to show up in the recommendations. And then the second one is you've had experience uh once again like running PHG motions. Basically, operationally, maybe it's more it's costlier to like serve more customers who are not like on a paying license, but today you're actually paying whenever they use the product. So how does that differ in how you think about those channels today with an AI product?

SPEAKER_01 6:19

Yeah. So for Truffle, right? You know, we our our free trial is limited free trial, right? And so, you know, the idea there is it's sort of seven days, you get five candidates analyzed, and then you you'd sort of move to a subscription from there, right? So we we limit it. Now we're flexible, of course, right? You know, in making sure that customers get the opportunity to see. So it does limit sort of our exposure on the on the cost side of things, right, with a sort of limited free trial. So so that's one piece, certainly.

SPEAKER_00 6:48

And and and the second question is more about uh LLMs and showing up in the results. So how do you adapt the SEO play uh in the age of AI?

SPEAKER_01 6:57

Yeah, look, this one's tricky because like SEO, right? We had a lot of experience with SEO, right? Building very large brands previously. And so taking a lot of those playbooks in the very beginning to build the sort of SEO foundations of things worked really well. But we saw that change very rapidly. Um and AEO or GEO is also changing really rapidly, right? You know, there was a time where you could sort of game some of this, right? Just get listed in listicles and all of a sudden you're coming up in every LLM, right? But that changed very rapidly. And so now it it is more around mention rate, right? And getting uh featured, uh, you know, a breadth of feature, I would say, uh out in the wild, uh, which is really leading to more inbound traffic and more um exposure in the LLMs. And this is this is a totally different game, right? There was a world where you could just produce a lot more content. It was really good content and it lived on your site. And as long as you got a few backlinks, you were fine. Um, that world is a little bit different today, right? You still need to do those things, but then you also really need to be out there, whether it's you know, Reddit, you know, producing YouTube videos on podcasts and you know, a number of different areas, great reviews on different review sites, which is all leading to um, you know, the LLMs understanding your importance in the market.

SPEAKER_00 8:16

So basically the algorithm is rewarding anything that is hard to scale.

SPEAKER_01 8:21

Yeah, it is true, right? And one of the things that we have noticed, right? So there was a time where, you know, it was not that long ago, six months ago, it was a little easier to do, right? We've seen a little bit of a shift where it is rewarding incumbents and longevity a little bit more, right? You know, you've been around for a little bit longer. There does seem to be some reward there. So you've got to fight extra hard, right? And when you're in a in a space that is sort of upcoming and you're one of the sort of newer players out there, but we're finding ways to do it.

SPEAKER_00 8:49

Yeah. And amongst these ways, is there any specific tool that you use or any specific workflow that you're you know comfortable sharing that you know the audience could benefit from?

SPEAKER_01 8:58

Yeah. So a couple of things that we're doing is uh so we use AirOps, right? Which is really wonderful tool, right? Uh we've actually moved a little away from AirOps now that we've got sort of clawed code running the way that that we want it to do a lot of the things that AirOps once did for us. So, and then you know, we've we spent a lot of time building processes, right, or systems that have a lot, yeah. So AirOps is wonderful, right? It's really helped us to understand our our content and to create some really great workflows for producing new content. And and so that's helped to you know alleviate and and understand our mention rate out there, right? Where are we being mentioned? Where are we being featured? What can we double down on? So it's been really wonderful. That's a key. And then building, you know, sort of internal processes and systems, which allow us to sort of what we're doing in hiring, move some of the sort of lower leverage, so lower value tasks off of our plate so we can really dial in on some of these things, right? So our content pipeline, right, is largely being developed through scanning a library of Reddit comments from recruiters, for example, right? And we can pick up on key topics there that allow us to generate new blog posts pretty rapidly. Um and so it's feeding this content machine, which means that we can now spend more time on things like producing YouTube videos, right? Uh which is sort of being fed from this content machine as well. Yeah. And uh we find that there's sort of a lot of value in that.

SPEAKER_00 10:28

Amazing. Um uh I'm looking at truffle. Uh I can see your YouTube channel. It's amazing. Yeah. Yeah.

SPEAKER_01 10:37

So we we've just recently sort of started into this world, uh, but we have like a massive pipeline of content, right, which is is gonna start hitting next week, actually. And just from the early videos that we tested there, um you know, it's leading to more mentions in AEO, right? Uh in some of these. So we think that this is gonna be a big sort of boon over the next little while. Again, it's just breadth of content where you're being featured out in the wild.

SPEAKER_00 11:06

It's amazing how every conversation conversation that I have now since we're in 2026 is around cloud code, and everyone is moving away from paying licenses, moving everything to cloud code, and very fairly quickly, people are achieving amazing results. So, what's your take on cloud code? Do you use it across all departments? Oh, yeah.

SPEAKER_01 11:23

What's your take on the We are all in? And and when I say all in, I mean we are really all in. You know, we um Yeah, yeah. We, you know, about a month ago, we sort of doubled down with our development team and really you know bedded in with cloud code. And we've seen sort of an incredible acceleration there. Luckily, we had a team that was really open to it. Um, and we spent a lot of time, you know, sort of up front thinking through what does that process look like. You know, I'm running most of the product myself, and so you know, now we have a you know, in cloud.ai, there's a project, right, which you know contains all of our product and an upcoming roadmap and you know it takes a couple of minutes basically to log a an upcoming feature ticket now, right? Which then goes to the dev team, gets prioritized, they can pick it up, right? And they're just pulling it directly into cloud code and you know, we're shipping features faster than we ever have before, which is really wonderful, right? So from a development standpoint, but then also using it in marketing and operations. For example, we have just last week we developed a new release process, uh, which I think is really interesting. And so uh, you know, I'll share what that looks like. But we were having this challenge of we have a help center, which is pretty large today. And so you release new features, it used to take forever to go back through and update a help center to produce the email that goes out to customers to update our changelog in Canny. And so update our product catalog. And so now we have, you know, a claude which lives in, you know, we we use basically a Git repo to have it in we have an internal Git repo, which the team can all access, right? And so they can launch a cloud from there basically. And it goes through and it looks at, okay, everything since we last released, it builds all of our help center content, it will update all of our old help center content, it will update our change log, it will produce the email that goes out to customers, and then it's just a matter of us reviewing, right? This is something that would have taken teams days in the past and now takes an hour to do, which is really incredible.

SPEAKER_00 13:19

Yeah. So so basically your code code has access to different MCPs. You connect to the GitHub code base uh via the GitHub MCP, and then you can actually uh do you use resend maybe to like automate uh email communication or do you use another tool that you can we use intercom for for just about everything, right?

SPEAKER_01 13:35

So our help center lives there, our emails, uh our marketing emails lives uh live there, right? Um and so we're connected via MCP to um linear, right? Which pulls all of our tickets in so it can check the diffs, right? Uh from the last release. We're using Notion, right? So everything gets pushed to Notion for a check, and then you know, once we've had an opportunity to look through it there, Claude can then push it directly to um intercom via their API and everything's ready to just sort of press go and and you're done.

SPEAKER_00 14:03

I was t I was talking to the uh head of product marketing at Superbase, and he was saying I just know of Superbase, yeah, very well. So yes, I'm uh completely uh Superbase pilled. Yeah. I love it too. And he's saying that basically all the collateral creation is basically automated, and what remains now and what makes people stand out is their their ability to like lead and drive initiatives because the last bottleneck is making sure that things move in the right direction, people adopt tools and so on. And the reason I say that is I'm curious to understand your take based on your previous experiences, you know, senior VP at a large company where you have a lot of processes, large teams, and now you have a smaller team and you're just achieving maybe not the same level of output, but you know, I've just been curious to understand how has the work evolved and curious to understand a bit more like how you're organizing yourself at higher truffle.

SPEAKER_01 14:52

Yeah. It's a really great question, right? And and thinking back, you know, it wasn't all that long ago that that I was at Cinch and just thinking about the work that we're doing today compared to then, right? It was very different, right? Very different work, right? Many of those things, many of the struggles that I think we had previously, if done well today, right, and building systems that you could build today, they could really superpower an organization like Cinch, for example, where a lot, you know, there's just in a very large organization, the communications is so critical, right? And the system builds is really key. But it also is just, it's it's infinitely more difficult to do. And so if there's um, you know, if you had the opportunity to centralize some of those things, right, and build really great systems in the way that I think you can today, I think it really, really superpowers an organization. So, you know, we are organized around you know, sort of a marketing, you know, I look after product, you know, marketing, sales, development, and and some operations. And as a small team, of course, we're all sort of doing everything. But the idea here is, you know, can we democratize as much as possible, right? To give everybody access to the knowledge, right? And so can we centralize that, you know, in some ways so that way the team is always on the same page, right? And when I say always on the same page, I mean but the communication that we have as a team, right? As well as just access to information, which I think is, you know, is just it's much easier to do today than it has been in the past.

SPEAKER_00 16:26

Yeah. So you're probably uh more on the side of AI native companies that will rule the world because you have the, you don't have any of the legacy and the complexity that comes with like decades of running a business.

SPEAKER_01 16:37

Yeah, that's the idea, right? I think we have a lot of uh we have a lot of internal knowledge about you know what went really well in the past, but also where those struggles were. And so how can we remove those bottlenecks today, leveraging AI in sort of an AI native way? And look, honestly, we've seen this change, you know. I think when we started Truffle, you know, we were probably not as AI native as we could have or should have been, but we've gone all in right over the last six months, especially. Obviously, we were using AI, we were producing an AI product, right? But it's something that we've been really thoughtful about, you know, as it's uh as it's advanced to make sure that we're leading on the front lines of those things as well.

SPEAKER_00 17:17

Yeah. And maybe if we think about how could people implement AI across their operations, what are the you know, top two, three things you would you know highly encourage people to adopt?

SPEAKER_01 17:26

Yeah, look, I think it's um you've gonna, there's gotta be a lead from the front here for sure, right? I I recently listened to something and they were talking about how you know CEOs and many organizations are are still using AI in a way, especially larger organizations, right? AI in a way that is very much like check my email. When um I think you know, if they really understand sort of how AI is moving today, right, it would change how the organization used AI. And so I think uh for us, um, it's very much been a sort of expectation setting, but uh, uh, you know, an opportunity to teach people how to do it as well, right? I was the first one in our organization to use cloud code. Now I'm a bit technical, right? And I, you know, I like to play around. So maybe it's a bit of a different story. But I I think it's um, yeah, there's lots of resources out there today where anybody can pick it up and just see how it goes. So I think that's key, right? It's you've got to hold hold folks' hands in the in the beginning through using it, right? Making sure that they have a deep understanding of you know what the possibilities are. I think you've got to give them the right tools as well, right? Yeah. Everybody should have access to the newest models um, you know, on a on a good paid plan. I think that's like the most basic thing that you can you can possibly do um for for people.

SPEAKER_00 18:43

Yeah. And then you basically give them the freedom. I mean, you're supporting them to experiment and just learn as they go and make sure that's a lot of people.

SPEAKER_01 18:50

Yeah, that's right. Yeah, for sure. You know, when we really started using it internally, uh, sorry, when we started, you know, on the development side, we've used cursor in the past and and some other things, right? But you know, we said, okay, now's the time, claude is the thing, let's go. Um, then it was a matter of uh, you know, walking them through, hey, here's how you know you can leverage it, right? And um working with them together, but ultimately really empowering people to just go out um and explore it themselves, right? Giving them the tools and resources, but then you know, giving them the time as well to just experience what it can, what it can do. And I remember, you know, one of our developers came back to me pretty shortly thereafter and was like, you know, my mind is absolutely blown, right? But it's also a bit scary. You know, that I think you have to also acknowledge, right, that um for a lot of people, it's gonna be a very different world, right? You know, a front-end developer today, you know, one of the things he said was, you know, like this was what I I really love coding, right? And so, you know, he still codes, but in a very different way. And so managing that transition between the two things is really important, right? And I think being really you know just open and honest about it is uh is really key.

SPEAKER_00 20:03

Yeah. Yeah, I think I read on Twitter X today that someone also expressing that same feeling that they spent 20 years mastering, you know, a domain and now all of a sudden their skill becomes completely commoditized and it feels a bit like uh not soul sucking, but they're losing a little bit like their purpose.

SPEAKER_01 20:19

Um I think but you know, for the right people, um, and I think this is where hiring, you know, is really important, you know, are really dev hires, um, finding that right fit individual who's just very open, open to change, right? You know, they are uh deeply embedded in the team, in the business itself, I think was so helpful because although it was a huge change in their role, they could see it as they they see it's the future, right? They see that we're moving in this direction. And if anything, you know, we've really looked at it as this is an opportunity to help them grow in their role, as much as it may change, you know, sort of the specific thing they're they're doing. And so, you know, as part of this, it was, you know, you were a front-end developer previously, right? You still are doing front-end development, but actually now they're doing full stack development and they're starting to think about the product side of the business as well, right? And so they've given been given a great opportunity to grow in the role as much as it has been a huge change in what they were doing.

SPEAKER_00 21:22

Yeah. Yeah, it seems like the skill set now is going from working on the clean output, meaning the code, to actually having a clean output because of the stochasticity of AI, you basically spend more time doing evaluations, setting up guardrails, making sure that things don't go wrong. Is this like a pattern that you also see? Um, you know, especially when we think about the voice agent uh that is analyzing transcripts and so on. How do you think about all these topics on making sure that you know people can trust the output of interviewing people, which is quite important?

SPEAKER_01 21:53

Yeah, it's a really it's a really good point, right? So, you know, we spend a lot of time, sorry, two sides of that, right, on the development. Side. Absolutely right. Our CloudMD file is constantly, you know, sort of being refined and tweaked, right? And so I think that's a that that will forever be a thing, right? It's something that we're we're constantly thinking about now is what does that look like, right? To really optimize, you know, and make sure that we've got the right guardrails there. And then as far as the sort of analysis goes, um, you know, we're running tests on those constantly, right? To make sure that, you know, the analysis and the output that we're giving is truly valuable, right? It is not just AI slot because it's pretty easy to sort of get in that direction. And so we spent a lot of time in the very beginning, you know, sort of building the groundwork for this, right? The prompting and the sort of model usage so that way we can make sure that we're truly delivering value here, right? When our claim is, you know, you process 10 candidates and we rank and order them right based on your particular job description. You've got to be able to deliver on that promise.

SPEAKER_00 22:54

Yeah. And uh yeah, I think you touched on something very uh very valuable uh around like prompting. Yeah. Prompt engineering is uh is something that emerged, disappeared. Some people say that it's you know not even relevant anymore because you have those reasoning models and they kind of refine their output over time. What are the non-conventional ways do you think people can actually improve the output of their AI? You know, besides the prompting? Um, do you have like other things that you learned along the way, the hard way that you would suggest people to like watch out for?

SPEAKER_01 23:27

Um yeah, look, I think that this this definitely has changed, right? In the very beginning, a perfect prompt was like, you know, very, very much required. Um now, uh there a big part of this is human eyes on some of this stuff is still just really important, right? And that is, you know, sort of part of the hard work, I guess, is going back and looking at these things, right? So one of the things that we do pretty regularly is we will take candidates, right? And we will rate them internally and then look at how that measures against, you know, what the the models are producing. And so sometimes the hard work is not necessarily in the AI prompts, but it is in actually doing the thing, which I think is still really important, right? One of the big big things we focus on is, you know, we never make decisions, you know, in the hiring process for people, right? It ultimately is up to them. For us, it's providing better signal. And so for us, I think, you know, we've got to go back and do those things as well. Right, which is, you know, we're not making a decision here, but we're verifying the results. And you can do that in a lot of automated well ways as as well. But I think, you know, sort of truly trusting and checking is uh is really important today too.

SPEAKER_00 24:38

Yeah. Um yeah, I think you're touching on evaluations. Do you maybe do you use a specific tool for evaluations, or is it simply like mapping the transcript to the conclusion and then you have someone to review that or anything?

SPEAKER_01 24:52

Effectively, that that's that's what we're doing, right? So we we are taking the evaluation, we basically transcribe that the whole interview, right? And then we rank match the candidates on a number of attributes, and then uh from there, you know, we get an overall score on a candidate, uh, basically. And um yeah, it's it's shockingly accurate, I would say, most of the time. It's not perfect, right? There's a bunch of things that you can do to sort of change the the outcome, right? You can feed an intake into Truffle, for example, which you could say, hey, I want, you know, one of the things that's really important in this role is communication. And so, you know, let's upweight communication sort of thing. So there's things that you can do to change that, but I think going back and looking at those, reviewing those candidates, right, and making sure that the evaluation output that we're producing is consistently very good is um is really important.

SPEAKER_00 25:41

Yeah. Um there are two other things that you talked about uh when I looked you up online is the the the the importance of keeping humans and basically making the whole experience human.

SPEAKER_02 25:53

Yeah.

SPEAKER_00 25:55

And and then the second thing is around not being a worker collect and just you know getting more time for yourself and not just making the 996 like or like the core 996 like working 12 hours a day, six times a week. How do you actually use AI to actually have a better life? Yeah, where you can just have more personable interactions with your clients, with your product, as a founder, you know, as an entrepreneur, like you know, what's what's maybe recommendations and learnings that you that you have?

SPEAKER_01 26:26

Yeah, so um so you have two questions. I guess uh the let's start with the second one, I think, right? So is how am I using AI? Look, I think it's something really important. You know, I think there have been times in my life burnout is is uh super common, I think, these days, right? And so how do you prevent that burnout and make sure that you are really focused and you can do that deep work, right? Uh, I think is really critical. And so that's something that I've spent a lot of time on personally, is making sure that the time that I am sitting at my desk is the most valuable possible. So that way I can take lots of time separately. The ways that I do that, right? Certainly there's some AI in sort of the client side of things, right? As we look at a sales process, for example, I mean, you can pull so much together today, right? And so again, we have, you know, sort of a nice a nice internal process built for looking at prospects, right, for some of the follow-up that happens there. That's really key. And I think that saved us certainly a lot of time, but also just enable better conversations. You know, because you get a nice brief in the beginning and you can go into a conversation really well informed without having to spend two hours digging around the internet to find these things. So I think that's definitely something that uh, you know, we've we've leveraged, right? That I continue to leverage. Would say, you know, as part of this as well, it's really easy today to just sit in front of your computer and cloud code, you know, for hours on end. And so part of it is just like this internal thing where you've got to say, you know, hey, look, you know, I've done what I can do and enough is enough. And I think having that uh button, right, which says, okay, you know, I I've accomplished what I needed to today, I'm out is really important. I was reading this uh actually just something earlier today, the brain fry, right? Which is this concept of, you know, you could just literally, it's you know, it's it's cooking your brain from all of the sort of AI work that you're doing. And although I'm probably producing more than I ever have before, I am spending maybe a little bit more time the last couple of weeks than than normal doing it. It's real that you can just sort of get exhausted by it. And so I think it's really important to know when enough is enough to stand back and and say, hey, look, we've uh we're we're accomplishing a lot and this is gonna have to be good enough.

SPEAKER_00 28:42

Yeah. I think uh whenever I hear that people have 10 different you know, cloud code windows running in parallel uh on different repos, I'm like, okay, this is very impressive. But at the same time, when it comes to like multitasking, I know that you know your IQ literally just drops by 20 points whenever you're multitasking. So I really wonder not whether it brings like the best out of you know your input.

SPEAKER_01 29:04

You know, I totally agree, right? And maybe controversial of me to say, but it's like it's rare that I'm running more than one instance, right? Because I'd I'd rather be focused on the one thing that I'm doing and give it all of my attention. Of course, if it's like a long-running task, right? Maybe I'm maybe I'm doing something else. But you know, largely, you know, I'm a big believer in in just deep work, right? And that you need time to think and to and to really be um in there. And I think you can produce more um in in that way than running you know 10 windows simultaneously.

SPEAKER_00 29:32

Yeah, amazing. Is there anything else that's I haven't covered that's you know would be worth sharing or commenting?

SPEAKER_01 29:39

No, look, I think it's been really great, great conversation. I think we've talked about a lot in a pretty short period of time, so I appreciate that.

SPEAKER_00 29:46

Yeah, I try to basically just conscious of your time, uh, compile a bunch of questions into one, which you managed to unwrap and tackle individually. So I appreciate uh you know the the the rigor in that. Yeah, you bet. Happy to do it. All right. Thanks. Uh thanks so much, Sean. I will share all the details and the links to your YouTube channel, to Higher Truffle, and to your LinkedIn profile uh for those who want to learn more. Is there any other place where people can find you to learn more about uh your company and just your journey?

SPEAKER_01 30:15

I think the best place is just on the on the website itself, right? Uh lots of information there. Of course, on LinkedIn, happy to connect and and talk with anyone.

SPEAKER_00 30:23

Awesome. Perfect. All right, thanks, Sean, and uh yeah, we'll be in touch. Excellent. Thanks so much, Vlad. This was Business AI Explained. Thank you so much for listening. We are releasing new episodes every Tuesday. So don't forget to like and subscribe to make sure you don't miss out on our content.