Escaping AI Slop: How Atlassian Gives AI Teammates Taste, Knowledge, & Workflows, w- Sherif Mansour

Hello and welcome back to the Cognitive Revolution. Today, in honor of this

Revolution. Today, in honor of this week's Gemini 3 release, I'm going to do something that I've only done once before, and that is read an intro essay

exactly as it was drafted for me by AI.

I've spoken many times about my intro essay workflow. For the last two years,

essay workflow. For the last two years, going back to Claude 2, I've given the latest Claude model a collection of recent intro essays, plus the transcript of the current episode, and given it a

short prompt, something like adopting the style, tone, voice, perspective, worldview, values, rhythm, and cadence embodied in the attached intro essays.

Write a new intro essay for the attached transcript.

While I almost always edit its output extensively before recording, Claude has always done the best job of this. And to

be honest, no other model from any other provider has come particularly close.

That changed this week, though in a notable way with Gemini 3. While I've

not tested it broadly yet, the intro essay that follows immediately jumped out to me as one of the very best drafts I've ever received from an AI. And while

there are a few things that I might have said or framed a bit differently, it's clear to me that this model gets me from my writing samples as much and probably

more than any other model ever has. So,

here goes.

Hello and welcome back to the Cognitive Revolution. Today, I'm excited to share

Revolution. Today, I'm excited to share a conversation that bridges the gap between the frontier of AI agents and the reality of enterprise software

deployment at massive scale. My guest is Share Mansour, head of AI at Atlassian.

For those who might not know, Atlassian is a top 100 global tech company with a $40 billion market cap. And while they are perhaps best known for software development tools like Jira, I was

surprised to learn that today the majority of their users are actually non-technical knowledge workers in departments like marketing, HR, and finance.

This gives Sharief a unique vantage point. He's not just theorizing about

point. He's not just theorizing about how AI might change work. He is

observing how millions of users are actually beginning to adopt AI teammates in the wild. We cover a tremendous amount of ground in this conversation, including Sharief's framework for

avoiding AI slop, the generic low-v valueue output that comes from unrefined usage by injecting three specific ingredients: taste, knowledge, and

workflow. We cover the limitations of

workflow. We cover the limitations of rag in complex enterprise environments where permissions are granular and questions are broad and how Atlassian uses a teamwork graph to answer queries

like what did my team work on last week which vector search simply can't handle.

We touch on the evolving relationship between AI and UI, including my theory that the best interface is no interface.

But Sharief pushes back with a compelling historical analogy involving MS DOSs and the command line, arguing that while chat is the universal interface, it is often the worst

interface for specific tasks.

We also discuss Atlassian's recent acquisition of the browser company and the vision for a browser built specifically for the knowledge worker context. And finally, we debate the

context. And finally, we debate the concept of the oneperson unicorn with Sharief offering a skeptical take based on the sheer complexity of business orchestration.

I found this conversation particularly valuable because it moves beyond the hype of drop-in agents and gets to the nitty-gritty of process architecture. As

Sharief notes, we are moving from a world where we humans are the doers of work to one where we are the architects of how the work gets done. Whether

you're an AI engineer trying to solve memory and context window challenges or a business leader trying to figure out how to actually get your team to adopt these tools, there is a lot of practical

wisdom here. So please enjoy this deep

wisdom here. So please enjoy this deep dive into the future of the AI enabled organization with Sharief Mansour, head

of AI at Alassian.

Sharief Mansour, head of AI at Atassian.

Welcome to the cognitive revolution.

>> Thank you. Thanks for having me.

>> I'm excited for this conversation and I appreciate that we were able to arrange it. I believe 16 time zones apart, maybe

it. I believe 16 time zones apart, maybe even 17 hours ahead. you are uh in Australia where I'm here in Eastern time in the US. So, it's amazing what technology can do. Speaking of

technology, Atlassian, obviously a a big technology company, but I think probably a good chunk of the audience doesn't have necessarily direct experience with

it. So, just as a quick introduction,

it. So, just as a quick introduction, I'll let you do a fuller introduction.

$40 billion market cap company, one of the top hundred tech companies in the world by market cap. And that puts it ahead by the way of household names like

eBay, Reddit, SoFi, and Expedia, but very focused historically and certainly in the in the products that I've used on the software industry. So for those that maybe aren't from the software industry

and don't have so much background with the company, uh you've been there 16 years. How would you introduce the

years. How would you introduce the company? And as we think about the AI

company? And as we think about the AI future that we're stepping into, how would you describe Atlassian's positive vision for the AI future?

Yeah, that's a good question. Yeah, I've

been here a long time and certainly actually before joining Atlassian, I was a customer myself and so I saw how it transformed. I used to work for a large

transformed. I used to work for a large telco in Australia and I saw how the software changed how we work. So I would describe it pretty simply as uh we're we're a product company that builds

collaboration tools for teams and we specialize in different kinds of teams. So historically we were specializing for technical teams specifically software and IT teams and what do they do you

know they plan projects build software deploy it IT teams run service desks and help desks and you provide a service internally but today the vast majority of our user base are similar types of

work so I call them people who run projects been in just different departments marketing projects finance projects etc or people who provide a service to another team in the business

the procurement team inside a big organization provides a service to the rest of the business by helping review contracts or whatever it is and so today majority of our users are actually from

nontechnical departments HR finance marketing legal and all that uh and really just how about helping these teams collaborate uh with each other to get work done I guess the second part of your question how does AI fit into it

and all that you know we've got close to I think we're three and a half million users of our AI capabilities I think what's the big shift we're seeing in the change in how teams collaborate is teams

are now introducing their virtual teammates and bringing them to the team and not just at the I'm not talking about just the personal chat productivity level. It's about you know

productivity level. It's about you know a team working together to get a job done adding a team agent in their context to help them move that work together in a in a collaborative

fashion. So I think we're really big on

fashion. So I think we're really big on seeing AI just as a new teammate that helps speed up the collaboration process that teams do whether they be technical teams or nontechnical teams uh working

together. When you talk about

together. When you talk about introducing new kinds of teammates, the AI teammate that is obviously very I think like you know a little bit ahead of the curve or you know skating where

the puck is going to be so to speak in the sense that I think everybody including the the frontier model companies has this vision of the drop in AI knowledge worker something that you

could just deploy you know with all the sort of benefits of software and inference. of the

scalability, the copyability, the, you know, pay for what you need. It's, you

know, could be 24/7 or it could be, you know, five wide at a given time, but it can also be off when you don't need it.

There's all these advantages, but there's also these sort of rough spots that mean that in some ways they don't quite live up to the same

expectations that we have for a human teammate. How do you think about first

teammate. How do you think about first of all, maybe just what people most need to know about an AI teammate? you know,

where have you seen it done well or not well in implementation? And how do you think about anthropomorphizing these things in the first place? Like should

people be mapping AI? Should they be starting from the idea of a human teammate and kind of, you know, creating a fork for their understanding of an AI teammate or should are they so different that they should maybe, you know, start

with a different base understanding and and kind of allow that to evolve independently of of their expectations for humans?

That's a good topic we are constantly wrestling with. We have this value

wrestling with. We have this value inside at Lassin called build build with heart and balance which is just really about trying to find the fine line in making a decision or balancing two trade-offs. I think most people would

trade-offs. I think most people would agree nobody wants to talk to something that feels incredibly robotic.

And at the same time I think most people would also agree that nobody wants to talk something that tries too hard to be human and feels unauthentic. So, it's

like this weird balance of the two. You

know, I was just speaking at an event last week and the topic was how much does authenticity matter when you're talking to an AI? And and you have to kind of like pair the layers back behind

that question which is like why is why does someone see authenticity is important and all the way back down and one thing that comes across especially with our business customers is just the importance of trust especially in a

business context. Like if I ask you a

business context. Like if I ask you a question, I want you to be reliable and give me something trustworthy. And so

how do you achieve trust in a business context? For us, a lot of it's

context? For us, a lot of it's transparency. So if I give you an

transparency. So if I give you an answer, I need to give you as many citations as possible. You know, our AI rover, we spend we have a whole team that's just dedicated to focusing on improving citations, for example, to

make sure that the the quality of the response is, you know, is factual and is is pointing to the right sources. But

applying it to the agents topic you brought up. The same token happens here

brought up. The same token happens here which is if I deploy an agent and put in my workflow I want to know how it works.

I want to see its instruction. I want to see who created it. I want to I want to understand what are the last 10 things it's done. So I think transparency is

it's done. So I think transparency is key there to help building up trust which then helps builds up authenticity if that makes makes sense. But I do agree with you and it is a fine line

like it's it's we certainly not sure I'm like I'm on the boat of like oh yeah it should just feel like a human teammate.

We're not suggesting that at all. It

feels like a virtual teammate and I think humans have an understanding of what a virtual teammate can be in in a team context and how that would works. I

think we want them to feel like collaborators with us. I think already at the personal productivity AI level people are feeling like it's a personal collaborator. in the team level it

collaborator. in the team level it becomes more interesting because there's now a team dynamic here right so I always say every team in every company has like character and I'm not talking about characters in like human

personality character but like there's a set of in jokes they have and there's a way they show up to the office or virtual office and there is types of language they use voice and tone and if

you're a team serving customers in a bank you have a very different character team character to a team that's working on uh marketing campaign for a digital

agency for I don't know young youth or something and so it is important for teams to apply some level of character

to their virtual teammates otherwise you have this weird blend of like it just doesn't feel like it's part of our team and or the complete opposite of like I'm just feel like I'm talking to a robot and this is not useful. The other big

benefit that teams get out of that is you probably I'm sure most of your listeners have heard the phrase AI slop everywhere. My loose definition of AI

everywhere. My loose definition of AI slop because some people say I slop and they're referring to accuracy of responses and whatever. I think if I define AI slop as something like the

output is technically correct but it's uh it's creativity is like lazy like as in it's yeah everyone's getting similar results. Let's, you know, let's call

results. Let's, you know, let's call that AI slop. Then how do we combat AI slop in a world where everyone has access to the same tools? We all have access to the same large models, etc.

The one of the biggest techniques to do that is to apply your your team's character, your team's soul, if that makes sense. Like like what is it we

makes sense. Like like what is it we stand for? What are we trying to do? How

stand for? What are we trying to do? How

do we talk around here? How do we think creatively? What are the kinds of things

creatively? What are the kinds of things we we like and what what don't we like?

and you kind of, you know, you're effectively prompting your virtual teammate to try to speak to you your team's language. And I think that's

team's language. And I think that's critical in teams just not getting standard AI slop at AI. So there is this fine balance of not making it feel like a human, but also representing what the

collective group of people are trying to do.

>> Yeah, that I'm really glad you brought up the slot point because I was finding myself very curious about that in in preparing for this. I've done a lot of stuff to build AI features into products

and and done a lot of like workflow automation and you know making things scale that didn't previously scale. But

when reading about the work that you guys are doing to facilitate teamwork and you know brainstorming collaboration all that kind of stuff with AI that was the big thing that came to mind for me.

So I would love to dig a little deeper into how do you do it I guess maybe conceptually but also into the techniques that you're using like when you

and I can imagine a lot of different approaches right but when I bring one of these virtual teammates onto my team how much is on me as a customer to set it up do I like write a brief or give them my

employee handbook and say you know this is how you're supposed to act how much do you guys go into my data reservoir and and you look at the way we talk to each other and try to extract patterns

from that and you know have the thing sort of you know obviously we know like continual learning is not quite to the state that we think it ultimately probably will need to be for like you

know the most powerful transformative agents everyone can envision but we do have in context learning we do have the ability to go you know spol through the archives and kind of try to pick up on

trends and turn that into a prompt. So,

how do you think about mechanically making all that happen?

>> Yeah, it's it's a lot of layers. Maybe

the first and foremost layer is like you to answer your first part of your question, there's a lot you get out of the box, but the reality the real differentiator is applying your team's taste, knowledge, and workflows. Well, I

use those three ingredients. Taste, I

think we've just talked about, which is just like if everyone's typing the same thing, how do you how do what is your unique thing for your business or your team? You have to apply that. That

team? You have to apply that. That

literally just might be a lot of prompting or it might be a lot of context you give it, but you're applying your taste and your opinion to how something should work. Otherwise, we'll

end up with the same looking website, the same looking products and the same looking services and and humans just that's just not going to work in the long term. Knowledge is the second thing

long term. Knowledge is the second thing which is just that's where the customer comes in of hey, I'm building an agent.

What knowledge am I going do I want it to either have access to or be trained on? And there's a nuance difference

on? And there's a nuance difference between those two things. But you know, often it's like, hey, here's my I am I'm the procurement team. One of our customers just does this a lot. Was

talking to him the other day. And they

have a bunch of confluence pages that have all the things they look out for when they review a a new contract from a vendor. That is their organizational

vendor. That is their organizational knowledge. They have that documented.

knowledge. They have that documented.

They connect their SharePoint. It's got

some additional information there as well. And they pull that in and the

well. And they pull that in and the agent has access to that knowledge. So

the knowledge is the second big ingredient. And the third one's really

ingredient. And the third one's really just how they choose to deploy it in their business workflow. And they might put that into a a very rigid workflow like a Jurro ticketing system where

they're just going to go, "Hey, when a new work item comes in, I want you to review the attached procurement contract and write your first draft response on it and then pass the the work item to

the human to review and to go back and forth with you to then make a proposal for how it should be." And so you kind of have that taste. I always say like those are the three ingredients you kind of need in any business. I want to apply AI and you talk to a customer, you're

like, "What are you trying to do?" And

they're trying to think of a killer use case. And I was like, "Why don't you

case. And I was like, "Why don't you just stop by just telling me what you do today?" Like pick a team. Pick some

today?" Like pick a team. Pick some

tasks that the team does. What do they do? And you often find those three

do? And you often find those three ingredients of like how they apply their taste to that AI, what knowledge they give it, and where they deployed in in a workflow, some automation tool, Jurro or

Confluence or whatever it is. It doesn't

matter what it is. they just put in a in a workflow and and they kind of get the benefits there. So they really need

benefits there. So they really need those ingredients there. What we do with help is we give them obviously a lot of we have a lot of investment in our search solution and that's very important for AI in an enterprise

context because everything's permissioned. So when you ask a question

permissioned. So when you ask a question and I ask a question we get two you know Nathan gets very different results to share because the data you have access to is totally different. So search in

enterprise context becomes very important. Every percentage better our

important. Every percentage better our search engine gets the better the results get. And the second big thing we

results get. And the second big thing we have in our portfolio is we call it a teamwork graph which is just a map of all the entities that a customer has that they've worked on. So they didn't, you know, we know their teams and their

their pull requests or their work items or their conference pages or their Trello boards or their ideas and how that connects to their Figma designs or their Google document proposal with the

client or their, you know, amplitude dashboard where they're tracking metrics and we kind of had the relationship between all of those which helps us give better results to the user.

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So let's do maybe one double click on each of these taste, knowledge, and and workflow levels.

If I start with taste, I mean what I don't know if this is like uh something you guys keep a secret, if you know how um much you want to share, but in my

experience, I have found Quan has been the best at capturing my taste. And the

main task that I always do that I kind of use as my own personal benchmark for this is writing the intro essay to this show, which listeners have heard me talk about many times. But basically, I have

a big PDF with a bunch of previous essays. We'll take the transcript of

essays. We'll take the transcript of this one, put those two in there with a relatively simple prompt. It's really

the previous examples, of course, that are doing the bulk of the work >> and ask it to, you know, follow those examples and and write me something new.

I find that Claude has always done the best job at that for me. Obviously, you know, people's mileage varies, but how do you

think about like guiding users through the context creation process? You know,

that that kind of thing I find very few people do. The only reason I did it was

people do. The only reason I did it was cuz I believed it would work, but you know, if I didn't it was kind of annoying to go back to 50 different documents and I did that before I had an agent, you know, that I could potentially delegate it to. So I had to,

you know, it took me however many minutes and it's like, is this worth my time? Well, I believed it would work. I

time? Well, I believed it would work. I

think a lot of people don't even have the confidence that spending, you know, putting the elbow grease in to curate the context will pay off at all and so they just don't do it. So I imagine you have to guide people or encourage, you

know, nudge, maybe propose, whatever ways for them to assemble that context.

And then I'm really curious about what's working well for you, you know, and and how do you evaluate that because it's this also, you know, when you get to something like taste, right? What makes

it so valuable is also probably what makes it, I would think, very hard to subject to a standard eval. We've got

>> totally guys have so many customers, so many different taste profiles out there.

How can this thing adapt to all those taste profiles? How can you even begin

taste profiles? How can you even begin to get a handle on measuring that?

>> Oh, that's a really good question. But

your your example is a great one because you have a prompt and then you have your prior context that you've given it which is effectively your art. Like it's how your voice and tone, how you think about

things, how you write about these shows, etc. And so you've given it your taste in between those two things combined, which is a really good example. And

you'll talk to customers that will just do the prompt and just go, "Oh, well, it was it was okay. It wasn't that great."

Like it was just, you know, not not the thing. So I don't know how much is in

thing. So I don't know how much is in your example how much is clawed how much is I I do generally believe that all the models level out and they're they already are kind of getting the same to

answer your question on how much do we need to nudge customers a lot but we do have some unique advantages we're bit fortunate here for example the more you use our as you use our AI

tool most customers start with you I always give them like I'm Egyptian so I always think everything in pyramids so like a little simple framework of like AI maturity like bottom layer most

people just ask a question get an AI from an answer layer one layer two is like oh hang on a second AI can do some work for me and they'll ask it to generate an artifact like write a

proposal for me write the show notes for me write uh write the intro for me etc that's like the next big light bulb moment in most people's minds and layer three is like oh hang on a second in my

team's context I can take my knowledge and take my instruction and then package that in a repeatable workflow that will help me scale and uh give me either more ideas or improve the quality of

something, improve the effectiveness of something or efficiency of something. At

that bottom layer, when they start with just the question answers, you'll get some customers that'll be wowed with us doing little work. And I'm

always curious. I'm like, well, hang on a second. Why? Cuz you get some

a second. Why? Cuz you get some customers get terrible results. And then

I'm, you know, always telling me, why why are you getting terrible results?

the ones that happen to be using for example robo chat which is our chat tool in our products we've got inbuilt personal memory so over time we start to remember you know for me I like to write

with a lot of emoji that's just my writing style is a very simple example so every time I ask Rover to help me with some writing assistance it often will prefix my headings with an emoji that best reflects that heading stuff

like that right so I'm stay feeling it's more personalized because it's more applying my taste to that world so that's the personal memory is one big thing that we kind of give customers who already happen to be using our products

out of the box. The second big thing we get is, and this isn't talked about a lot, Nathan, in the world of AI, especially in the business context, our products are open by default. And I have

to explain that a little bit. When you

create a a documentation space in Confluence or a Jurro project or whatever it is, the default is open and you need to decide who you want to restrict it to. And we're trying to encourage transparency and fully flowing

information. Now, in a business context,

information. Now, in a business context, you'll always need very granular permissions. So you can go, you know, as

permissions. So you can go, you know, as much as you like and control everything.

But we have got over like 20 years of history of customers who have been open by default for many customers, right?

Like who use our products, have they've been using it for a while. And so when they when they're trying to apply taste and judgment and whatever in their AI, they're giving it some context, some

documentation spaces, or they're pulling in some extra context. Turns out O open by default is a huge advantage when working with AI. Well, let me give you two like a contrasting example. Imagine

you joined a company tomorrow. You asked

AI question. In most companies where you don't have any open by default tools, you'll probably get very little or no results because you're a new starter.

You have no access to the word documents that were just created or the Google drive, the Google documents that we just created, whatever. You just don't. Now,

created, whatever. You just don't. Now,

unless some IT admin's gone out of their way and changed the default permissions, which is super rare and very unlikely, when someone joins uh a company and they've got some open knowledge bases by default, all of a sudden they're getting

questions answered that would have had to require them to message 50 people to work out the answer to the question or at least they're getting pointers to the right right person. So, that new starter

there comes with organizational and teamwork knowledge in an open environment. I issue you they get a much

environment. I issue you they get a much bigger advantage to joining an organization where you haven't had that open by default. So just just a long answer of saying like I think there's a lot of ways we can prefix and bootstrap

that context for the customer to make that taste a little bit easier. But in

saying all of this, it's only as good as what the user decides to write.

like at the end of the day if they're not writing their opinions and their taste and their instruction whatever AI can't magically guess that unless you're again you're using personal memory which you are already writing a

lot and all that stuff. So it really comes back down to you have to put in effort to get outcome. And people always use the example of like oh 80% was okay but then to get to the last 20% it took

me another 5 six hours and that's true.

Now sorry I take a long answer but like to answer your eval question like so so then how do we measure success of this?

It's hard. It's like really hard because how at one team uses you know we have a platform where anyone can create an agent put in any business workflow. So

you're like what is success there? We

have some pretty good signals of success. So you often have did the user

success. So you often have did the user use the output of the agent? Was it

successful? We obviously have thumbs up, thumbs down, and all that kind of stuff as well. And you also have a lot of more

as well. And you also have a lot of more nuance signals like was there a lot of rephrasing required. You may ask the

rephrasing required. You may ask the agent to do something, but you're kind of like that's not really what I'm after. I'm going to ask it again in a

after. I'm going to ask it again in a different way or you got that wrong or that kind of stuff. So like there are other signals that give us that as well.

But the reality is um agent creators in a business that you know you often take our tools and then apply it and then give it to their teams they're the ones that really in the in the best place to

judge success for that and they usually will give us feedback on which parts are systems system things that we could approve or they'll realize that these are things that are knowledge that I need to add or instruction that I need

to add and so it's you know either one of those two buckets.

>> Yeah. Interesting. So it sounds like it's much more in terms of measurement, it's much more about presumably you're doing like small fraction tests and capturing all these

like actual feedback signals from users.

But what I notably didn't hear there is like we have a standard, you know, automated eval suite and we use, you know, an LLM as a judge. Maybe that's in there, too. But that didn't sound like

there, too. But that didn't sound like that's a huge driver of your confidence in what's working or not. No, no, we have that and so we use that to to to give you an example. We have like 20 or 30 out of the box agents that we ship

and so they run through the same system.

It's our advantage to model as many domain specific different examples as possible. But the reality is customers

possible. But the reality is customers will will create their own evals for the agents they create that are very verticalized in their use cases. You

know the example I gave you about the procurement contract. We could have an

procurement contract. We could have an out-of- the box example and our own evals and LM as a judge, etc., which we do in some of those scenarios, but again, it's really only as good as what the customer sets up. So, our agent

framework lets people test the agent, see the responses, set up their own eval, that kind of thing, and run through that themselves. So, it's

important that, you know, as you deploy an agent into your as you add a new teammate to your team, you define what success looks like for that teammate.

Hey, welcome to the team. This is what I expect of you. You know, I think this is a good response. I think this is a bad response. I think that same thing still

response. I think that same thing still applies. that doesn't go away when

applies. that doesn't go away when you're trying to apply AI in your team's context.

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[Music] I'd love to maybe get a little bit more into the weeds, a little more technical if you will, on some of the structures

that underlly all of this. Again, this

is just such a timely subject, right?

Where there's everybody has kind of come to the realization that we've got context window working memory and then we've got this like baked into the weights, you know, deep memory.

>> And the huge question is what sits between those two productively. And

we've got, you know, rag of course and we've got all kinds of different experiments and memory structures. Then

we've got the idea that like maybe context window can just scale it to the point where it kind of solves the problem and then there's like you know maybe new architectures are needed for continual learning to really solve that

problem in a more you know integrated way what are you guys doing today you know if I can get into like my personal memory is that you know just a list of

observations is it something more complicated than that and similarly when we go to like the knowledge structure that the agents are able to tap into, you know, especially with all this, you

know, knowledge and history that precedes them. Are you are they using

precedes them. Are you are they using like the same search affordances that human users are using or has there been you know a thought that like because

this is a theory that I have is that I think a lot of times people want to put them into the same thing and sometimes that's the right thing to do but then other times it's like a human user can only you know scroll through maybe the

10 blue links but the LLM user you know might be able to handle 200 results and you know maybe in processing a lot more they can actually do in the and uh a better job.

>> Yeah. Uh really good question. So

personal memory I see as uh just you know I just think it's a capability that everyone just ends up having over the large language models. What's unique

here? What's important to call out is how easy and how organic is it for a user to populate personal memory. And so

the phrase I use is what user proximity is the person close to the AI to doing it. So for example, a coder coding is

it. So for example, a coder coding is probably chatting to the AI all the time in their IDE and so they're implicitly populating personal memory or explicitly as they do it. So they have very good

workflow proximity in the coding use case. You know for us we have a we have

case. You know for us we have a we have a collaboration documentation tool called confluence. So as people are

called confluence. So as people are writing and using AI assisted writing they're implicitly and explicitly populating personal memory. Make it

shorter no you're too formal here etc. that kind of stuff that's happening organically and that's really just at the individual users preference. Then

there's organizational memory and I think there's a fork here in my mind of like two ways we can be effective with organizational memory. You've already

organizational memory. You've already touched rag. Rag gets in incredibly hard

touched rag. Rag gets in incredibly hard in enterprise context because everything is permissioned even at the field level.

You know with Robo you can pull in Salesforce and you can also pull in SharePoint and you can also pull in GitHub. you know, when I pick up a

GitHub. you know, when I pick up a GitHub issue or a Salesforce record, I might be able to see some fields and you might not be able to see other fields.

And so, there's a very large investment of like teams just trying to making making sure it has a semantic index behind it and it's also permission aware and all that stuff and it's as fast as performant as possible and all kind of

stuff. And that's the rag technique.

stuff. And that's the rag technique.

Then the other technique is really we have what we call the teamwork graph which is an understanding of all the users the teams their goals and how the work relates to that. When I say the

work it could be the Figma design the pull request in GitHub the the page in notion whatever it is they're they've got mapped out and it maps them all out together. The way the team graph works

together. The way the team graph works is both organically and inorganically.

So example is Jura is often seen as a a system of record in most organizations when you when you used to get your approved in the app store as an example

that goes through a ticketing system and that's a system of record you know where is the app where is it at what are all the related documentation for this app who's reviewing it etc and typically

systems of records don't track the work don't actually capture the work being done they track where the work is at and the links to the work. And so if you

pick up a you know our billing example where the call center team is disputing a bill, someone's called up to dispute a bill issue that goes through a ticket and the ticket captures the business workflow and all the linked objects

related to that workflow. Uh that

typically happens and that's that's the graph. So populated inorganically by

graph. So populated inorganically by customers just saying hey I want to connect GitHub and I want to connect Figma and I want to connect whatever it is to their to Robo whatever it is. But

it's also populated organically which is as I use Confluence or Jira or or Trello whatever it is I'm pasting links and I'm telling you that you know okay here's

the related design for it paste link etc. So we can as teams work in our products, we populate the graph and understand these nodes and the relationships between them. And so then

when it comes to users building an agent or asking a question, there's sort of like you know two or even three parts if you count just as general knowledge, but let's put that aside for a sec. They're

mostly two parts. It goes when you say give me a status update about everything my team did last week. Now when you you break that down, that's a pretty loaded statement. So status update needs cut.

statement. So status update needs cut.

What do you mean by status update?

Probably might use it LLM general knowledge. Maybe in your organization

knowledge. Maybe in your organization you've already got some documentation somewhere it has access to that says this is how we define what a status update is or this is where our status updates look like. Okay. So it'll

probably use some of that. Well my team did last week. My team okay how does it know what your team is? There's implicit

and explicit signals here. Now we have a team's construct in our portfolio. So a

lot of millions of customers on a daily basis create squads in our system to group work together and so we can traverse that okay my team you know you and there's the team relationship and

that team is connected to members in that team that week created these five jur work items these four conference pages these three Figma designs whatever it is and it kind of kind of traverses

that tree and so it could give you you know your blue links response rag would be a terrible solution to that problem because we'd give you like the top five documents and then try to summarize the

status of those. In scenarios where you might say what is my team status update that week the graph solution is a much better solution for that where we want to traverse all the objects give you the

summarize them you know etc apply the organizational context but in scenarios where you might like what's the process for taking annual leave in my company rag's probably a much better solution in

there if you don't traverse the graph for that so that you know I think really good AI in a business context kind of needs both of a rag set of techniques but also a way to traverse very structured information at a much bigger

scale.

>> Yeah, that's great. I that really reminds me a lot of a project that I studied a while back called Hippo Rag, which we did a full episode on. This is

maybe the closest thing that I've heard of in the wild to a implementation of that where you've got on the fly or maybe background processing entity

recognition. It sounds like if I'm just

recognition. It sounds like if I'm just randomly putting in links, you've got the job to do of figuring out like what is this in the first place those you know you've got a lot of different instances where an entity will pop up.

So you got to you got to do that disambiguation or or reconciliation.

Then you know the process in the background of mapping out all these connections as well and then when it comes to runtime what they were doing was pretty still early. Uh there was a

research project back in the hippo rag days, but they would sort of identify through like uh semantic search like what are the entities that are relevant and you may even in in the case of my

team that might just be fully explicit.

Um but then they expanded the search radius through the graph like you could expand it one node out or two nodes out or three nodes out and then that became the universe in which you would do your uh semantic matching

>> and you can have that I always thought that was hotspired. Yeah, you can have those single multihop travels through there. I think the other thing you

there. I think the other thing you overlay on top of that which gets even better again if you happen to be a vendor again we're fortunate here like us where we're capturing some collaboration graph as well. So we have a separate graph which is kind of the

collaboration signal. So when Nathan

collaboration signal. So when Nathan likes my pages or comments on my issues or etc. that could also be used as a waiting depending on your query to lean

into some nodes in the graph of other nodes especially when it comes to understanding people and work relationships. So you would layer any uh

relationships. So you would layer any uh user activity signals of collaboration who worked as well. I shared a page with you etc. You viewed this content but haven't viewed this other content. So

therefore we're more likely to show you the know depending on your query the content you hadn't viewed if you're asking for something like that. So you

an additional layer you can add on top of that is just those collaboration signals if you if you can it makes a huge difference to the results.

>> Yeah that's cool. How do you think about it might be too early for this but one thing I've occasionally noticed in products is sometimes they need to forget as well as remember you know if

like if you took my whole work history right you would find that in certain eras I've like collaborated very closely with certain people and you know in some cases those people are no longer at the company and other cases they still are

but we're not collaborating as closely as we used to be whatever most folks who've rushed out to build any sort of AI memory system, you know, retrieval or whatever, however far they've got.

They've mostly just tried to get it to work for like right now. And there

hasn't been much thought about how does this evolve over time as everything always does. How do we kind of know when

always does. How do we kind of know when to let go of things? You know, maybe you could get by just sort of saying last interaction with this person was whatever, then let the, you know, let the model handle it at runtime. But I

suspect that there's like a lot more.

You know, I certainly know that like my brain is like constantly clearing stuff out to make space. So I'm wondering, you know, what thought you've put into, you know, allowing the system to evolve to let go of things that it no longer needs

as much.

>> Yeah. Particularly hard for organizations. Again, open by default

organizations. Again, open by default actually makes this more challenging. So

you could argue it it makes it better in some ways but also makes it more challenges because now you're now getting 20 years of content and who knows if that like that page 19 years ago is still relevant or not etc. So I think that's even particularly

challenging but also you know there's also it's like classic pick any use case and there's probably a pro case and a con case for those. The two most obvious I actually ran into this in the early

days of Rover. I remember doing some testing and we're playing some things and I asked it something about rewriting my like we're kind of having a new team charter set and I said rewriting rewrite

my team's charter using my my goals and we have like a a goals app and you could track goals etc and you know OKRs and all that stuff and it picked up a goal I had from like four or five years ago in

the early days and I'm like wait where did you get this from and I'm like I haven't worked on that for ages and you know that's a really good example there and the goal was still active So I

hadn't cleaned up the data to mark it as inactive and so it's just assumed that was the case. Now since then we have done a bunch of signals just use that as an example touched on user activity and

collaboration activity being a key one that comes up a lot there. So there's a lot of decay on content that might be older that you may or may not have

collaborated on depending on the context query you you might ask for as well. But

it also highlights the importance of ensuring that the data you connect is is useful to some degree. You know, I had a customer the other day, I'm going to connect all my SharePoint to Robo. And

I'm like, sure, go ahead. But I was like, how big is your SharePoint? He's

like some ridiculous like 1 TB. I'm

like, do you even need that? He's like,

I don't know. You know, so you're like, okay, you could. And it'll it'll decay as you know, it'll try to decay and create gravity over time depending on what it is. But it does it does

highlight the whole garbage in garbage out like kind of motto. There are ways in the system where we try to decay things based on time based activity, your interactions with objects etc.

There's also the true definition of the object in the system. You know in this case I had a goal that was still active that I hadn't touched for a while.

Probably should just gone and archive it and I didn't as an example. So there's a mix of we try to do stuff but also I think as customers connect data to different systems it is something for

them to think about which is does a system you're connected to does it have any ways to decay history decay history is that even the right word you know what I mean fade create some gravity over time for old

stuff but also know when to pull it in if that's the right stuff. So I think that's an important aspect for folks to think about as they they pick their AI solution. Yeah.

solution. Yeah.

when it comes to kind of brute forcing this stuff and just being willing to pay for compute to do it versus

trying to be more efficient and obviously efficiency and latency relate too, but if you're willing to pay, you can also do a lot of this stuff in the background. How do you guys think about

background. How do you guys think about my starting position? I used to always say use the best model available. Don't

worry about cost. Maximize performance

and optimize cost and latency from there. And I think that was pretty good

there. And I think that was pretty good guidance for a while. Now though maybe not, you know, I don't think you want to use GPD5 Pro for everything if only

because of latency, right? So it's like I think we we do now have models that are for many use cases like overpowered.

Um and that wasn't the case when I developed that guidance. I guess how do you think about like the overall optimization problem of

>> cost latency performance other intangibles that you know are maybe also worth considering. I assume that that's

worth considering. I assume that that's something that has to start at kind of a value level but then obviously gets you know operationalized in detailed ways too.

>> Yeah. Uh I could probably answer that in two different ways. There is the what we do internally as teams build AI features in our products and then what what how that applies for customer application

and customer thinking if that's useful internally very similar to you uh especially in the early days just find fit like your priorities just make sure the thing's useful if you're spending

more time trying to optimize the cost and even in a and you can manage costs with controlled rollouts and all that kind of stuff and you haven't even found fit. the wrong, you know, I always say,

fit. the wrong, you know, I always say, you know, the riskiest assumptions test, the rat test, like start with your riskiest assumptions first and do whatever you can to debunk that and then move to your next one, move to your next one. And so most of it's like, is this

one. And so most of it's like, is this thing is the feature I'm building valuable? Go ahead, like test it with

valuable? Go ahead, like test it with customers and whatever. So there we are a bit more liberal in that process. Go

ahead and and do whatever you need. I

will say as we've yeah, I couldn't even count the number of AI features we have now in our portfolio. It's kind of rewriting the

portfolio. It's kind of rewriting the whole app, assuming AI is now always on at some point. probably over well over 70 80 things across my main apps. The

there are some clear patterns that we have learned for time and time again where you're like you know you don't need this. So the way we solve this

need this. So the way we solve this internally is when teams need to build an AI feature they'll often they get complete free reign in the in the early days in discovery process at some point

they will talk to our ML team we have AI gateway and we have a mix of locally hosted models models in the cloud there's like a ridiculous amount of models in the AI gateway which is a proxy so we can swap models and test

different things out and depending on the use case we'll probably take those workloads those AI workloads and and run them locally on an open source model or depending what it is that it's doing.

You know, a simple summarization feature no longer requires every single bell and whistle in the world. We probably

offload those workloads locally. The

more complicated ones would go to a more sophisticated model depending on what it is. So, they go through that process at

is. So, they go through that process at the tail end of their discovery process or what that applies there. The good

thing for us is it means that more and more of these I guess AI the cost keeps going down and we keep passing that value to to customers which is good and it's it's good for us that way. It also

means that teams can keep learning as quickly as possible and that doesn't slow them down too much. Now, when

customers apply AI in their workflows so that that's probably the biggest thing I see the explosion of token usage, whether they're using Revo dev for coding or they're actually adding an AI

agent in a judo workflow, which is probably the biggest our fastest growing use case of agents and deployment. They

just go to their existing workflows.

They've already got millions of workflows in Jurro and they're like, "An agent can help here, an agent can help here. This step here is really just

here. This step here is really just triaging the ticket coming in. An agent

could probably do that if we gave a good instructions and they they just go and just, you know, a orchestrate a bunch of agents in a in a business workflow. That

one is fascinating cuz you'll get customers that will probably will overuse AI when they don't need to. To

give you a very simple example, imagine a ticket comes in and you're reviewing the legal contracts or whatever coming in. There are ways where our

in. There are ways where our orchestration framework automation orchestration framework lets you do some strong more deterministic comparison of content of what's in the ticket. For

example, there's like some string like string string manipulation functions.

You can inspect the contents of it. You

can say if the juro ticket has these words in it, then do that. Now, using

the out of the box automation nodes that help you build these Lego pieces is a very effective and costefficient way to do that. Now, some customers will be

do that. Now, some customers will be like, I'm just going to switch that to an AI and you're like, "Okay, cool. That

can also get you the same outcome, but gez, the compute and the tokens on that, it's just like it's not worth it." Like,

you're using someone a customer described to me once is like, "I realized after putting this in this workflow, what was her phrase?" She said it was like, "I've got a rocket launcher

and I'm swatting mosquitoes with it."

And I was like, "Yeah, that's that's probably not a good use of AI there."

Uh, and so they'll use the more deterministic functions in our orchestration framework that let you do like specific string manipulation or comparison or whatever it is. That's a

way better solution there. So from a customer's lens, there is some skills to be learned on building tailored agents and deployment and workflows. is when do you need an agent and when is a large

language model appropriate and organizational knowledge appropriate or when could you do something a bit more deterministic on the functional side that is much cheaper to execute and run also more effective from performance

perspective as well because it it only does one job right and so that's a skill that I'm finding many many organizations are learning as they're deploying these agents and we try to help them with tips

and tricks and techniques and content but I I I see that as an ongoing journey for a while for a lot of because you're like, I can solve everything with AI and you're like, you don't need AI for that one. That one's like a simple just check

one. That one's like a simple just check the contents of, I don't know, some string value and do that and you're done. Yeah,

done. Yeah, >> it's another one of my mantras is anything that can be done with traditional code should probably be done with traditional code. It'll be faster, cheaper, more reliable. Though, even

things as simple as like writing a regular expression, you know, nothing makes me feel stupider than writing a regular expression. So it is sometimes

regular expression. So it is sometimes tempting to just be like okay just have the AI or the right way to go there is to have the AI write the regular expression for you but sometimes it's tempting to just you know have it do all the work itself

>> but that but on that on that example real quick like we do so our agent framework lets people create skills that the agent have the skills can be no code or code skills and that's just a really

good example where you can make sure the agent calls a very deterministic skill like a function or do math or check the bill details or whatever and that's very specific that can bespoke to the

business. So someone writes some Java

business. So someone writes some Java code or whatever it is they want and they cook that as a skill for the agent, put that in a business context and off you go. But it takes it's a process of

you go. But it takes it's a process of discovery for customers. I think people start go oh this is amazing. They're

like wait sometimes it didn't give me the right results but I you know in this specific scenario I wanted to follow this exact two steps and you're like you're right Nathan that's probably better written in code depending on what

it is they're trying to do.

I don't know how specific you want to be in your response to this, but could you give us a breakdown of how many tokens on a relative basis are flowing to which

different kinds of models? Like, you

know, where are you using proprietary?

If you wanted to name brands, I'd certainly be interested to hear that breakdown. So interested in your take

breakdown. So interested in your take and and maybe what your customers are signaling to you also that they care about when it comes to what different kinds of models are you know from

trusted enough sources to be used or not. Look, I don't I'm actually just

not. Look, I don't I'm actually just pulling up the dashboard to give you a like it's in the ridiculous millions, probably billion. I have no idea tokens.

probably billion. I have no idea tokens.

It's a ridiculous number. I remember we used to have a dashboard with all the tokens consumed and created. It's it's

kind of pointless now. But on the model thing, I am seeing a huge trend from a year and a half ago, two years ago. I

want to pick the model. I want to pick the model. I want to pick the model to

the model. I want to pick the model to now like like the care factor just it's like oh I realize it's like me saying I want you to use postgres not my SQL that's the case I mean all the big

expected chat GPTs there claude anthropics there all that stuff so I I couldn't go into details of all the the I'll probably get it completely wrong but name the model we're probably using

some variant of it in some way we have a fair bit of transparency with our main AI features they we publish docs on what the feature is what model it uses etc

as well for the big items but everything from GTP claude mistral like it's a mix mix of stuff it's a lot to do that but uh honestly maybe the takeaway that I

think I have for software vendors or software teams I always say is it does pay off to build a model gateway or have a solution where you can proxy models because if you believe that that layer

is being commoditized and there's lots of them and choose the right tool for the job then to help you move the fastest you therefore need to find a way to learn, switch, work out the best use

case and cost optimization and do that way. So, you know, that's certainly a

way. So, you know, that's certainly a something I would recommend to anyone building software like it's it's paid off orders of magnitude. Yeah.

>> So, you're on the models will be commoditized side of the will models be commoditized debate.

>> Little asterisk there. The general

purpose models for most general purpose use cases. Yes. And I can see the total

use cases. Yes. And I can see the total use case for verticalization of models, you know, like a DNA sequencing model that is just trained in some health for healthcare purposes on that kind of

stuff. I could totally see that. I just

stuff. I could totally see that. I just

think the average knowledge worker that most of our customers sitting on a desk using content to create content or like you know in in finance department, HR department, marketing, customer success,

whatever it is, the general purpose models are 80 90% easily solving the problem. And we're seeing that desire

problem. And we're seeing that desire for model selection just decline over time which is which has been pretty good. You will see but there is a little

good. You will see but there is a little nuance here is important which is like for those building agents that are setting up their own testing and eval whatever model stability is

important for them right so it's like API stability right like if you swap the bottle overnight you're probably going to get a ton of different results so that's a little slightly different scenario but an important thing for people to consider as they're thinking

about that. I've got a few questions on

about that. I've got a few questions on the future of software and also the future of work. Sometimes I ask myself why do we build UIs? And the answer I

came up with is we build UIs because we need input from the user. We need access to the user's intelligence or taste as you noted judgment. There's something

that only the user of the software can provide that the software can't continue to do its job without. And so I sort of see these UIs as kind of, you know, if it if it didn't need anything from the

user, then the software in general would run really fast. And it's this sort of moment where, okay, we we have a stop because we need something from the human

that is when, you know, a UI ends up getting created and presented to someone. I sort of think for people that

someone. I sort of think for people that are trying to who have have software products and they're trying to like figure out how to use their AI layer then I think well the AI can in many

cases be sort of a substitute for the human that would otherwise use your software and you know one kind of true north goal is like software you don't have to use as much as possible you know

people are mostly not like waking up in the morning super excited to use whatever SAS tools they're going to use throughout the day it's it's obviously like in most cases a means to an And so

if you can start to abstract those UIs into tasks for the AI to do and you know sometimes maybe you bundle them or kind of re redraw the borders around them for

who knows what various reasons. That

seems like a good mental model for people that are trying to figure out how to bring an AI layer to their software product. But beat that up for me. How

product. But beat that up for me. How

would you critique that? How do you think about it differently?

>> It is such a good topic. We tal we spent hours talking about this hours probably days weeks. I love to look at history

days weeks. I love to look at history tech history and try to find parallels.

Now nuances are important because some parallels don't apply and some do etc. But if I could rebank rewind back to

maybe some of your more mature listeners to the MS DOSs terminal days and back before Windows existed for those of us

that remember the terminal DOSs or whatever or Mac terminal whatever was the universal interface to the operating system. It was how you interacted with

system. It was how you interacted with the operating system. It's how you got stuff done. You could do maths. You

stuff done. You could do maths. You

could type a word document. you could

you could draw ASI art if you wanted to etc. But we learned very quickly that it was actually the worst interface for

some use cases. And so over the years we built verticalized apps on top of the terminal to do word processing, image

generation spreadsheets audio podcast recording, etc. This is a we are now me you Nathan we're talking in a dedicated interface that's built on top of the operating system on top of the browser

on top of the browser and on top of the operating system that solves this problem but it's all the universal interface is still just commands and a terminal with some high order level of programming language on top of it apply

that to AI I always go with the phrase of like well what's the universal interface to any large language model it's conversation that's what it is that's how it works today that's how the the the models were designed the

transformer model that was like the Aha moment we can predict the next thing. So

let's go a conversational UI is the best way to kind of get it predicting the next thing. Our UI construct for

next thing. Our UI construct for conversation is chat is just like society and history. It's chat for everything. I do believe and and I would

everything. I do believe and and I would argue I think we're already seeing it that chat is not the universal interface to all AI. I think what we'll see is a spike in a lot of crazy amount of chat

usage as the primary way to do anything in in UI. And over time, as we start building verticalized and more specialist solutions, which is still

humans interacting with AIS, they'll be in dedicated experiences on top of chat.

Doesn't mean chat will go away. It just

means chat the like I use terminal less and less every day. I don't code anymore, but every now and then I'll open it up to do something. But most of the time I have a dedicated experience that's far superior for me to solve my

particular problem to do that, etc. So I think the same thing will happen with AI. The the the the example I like to

AI. The the the the example I like to tell teams I see that are building any any building an AI feature for any of our apps. I always say what's your AI

our apps. I always say what's your AI feature? I bet you nine times out of 10

feature? I bet you nine times out of 10 you could build the poor man's version of it in a prompt without the UX on top of it. Is that true? And they would they

of it. Is that true? And they would they would like actually yeah we could we could just prompt our way through this but we can't expect users to type these crazy prompts every time or we can't we can't get the data in a more structured

way and whatever. But you can almost fake any AI feature out in any product through prompting. It's the worst

through prompting. It's the worst experience for it. And so then you run into this. Okay. So if that's the right

into this. Okay. So if that's the right conclusion, then you I reached the conclusion that we're going to build specialist interfaces on top of that.

Then the second thing people often say is ah okay. Well, but AI will dynamically generate a specialist interface all the time. I don't believe yes or no. Again, nuance is important

here. Like

here. Like for form input, yeah, potentially, but it has to be a pretty predictable form input. Like you try filling out a form

input. Like you try filling out a form via a chat interface, it's a disaster.

Like it's still how do you do form validation, conditional form fields, et like there's a ton of issues there. Now,

could AI dynamically generate something every time? Oh, it can and it probably

every time? Oh, it can and it probably will over time. Will humans want an interface that keeps changing? No. I I

think history has told me that as humans interact with software they want a some level of predictability in you know there is some variance in how they learn the tool and how they use it. So uh I I

do believe that there'll be lots of specialized user experiences built on top of AI that will effectively be a conversational backend like user conversational API back end because that's the interface to these live

jungle models but it'll be in a dedicated interface. The example I used

dedicated interface. The example I used last week was my son loves making vibing games and stuff and he's been geeking out with Leonardo AI. Like I don't know if you've played with that or not, but you to build to build image sprites for

games to make little characters, predictable characters and these little tower defense game and stuff and all that. Could you chat GPT that in a

that. Could you chat GPT that in a terminal and a chat interface? Totally.

Is that the worst interface for doing dedicated predictable UI graphics of slat variants and different sizes and what? Yeah, it's terrible. like you

what? Yeah, it's terrible. like you

would rather someone who's who's thought about the problem deeply design an experience that is still AI native but is really around building base level UI constructs with slight variances you know the character moves their hand here

or carries another weapon or you know whatever it is they're doing and it's a fairly sophisticated interface but it's it's designed for it's a good example of a vertical user interface that's built

just for AI long answer of of saying the chat is the universal interface but it's the worst interface in the long term And I do think that the uh humans will always be interacting with specialized

software on top of that. It's not saying that some of the software won't just you you don't need anymore. You can now use in chat. I think that will happen in all

in chat. I think that will happen in all things. But I just think like everything

things. But I just think like everything in like we just end up creating more and more software. The explosion of software

more software. The explosion of software is only increased with cloud and it's even increasing even more with SAS. So

people are just creating better interfaces. And so this is where like

interfaces. And so this is where like end up in this world where design matters and design ends up being a huge differentiator in any AI future where everyone has access to all the tools. I

wrestle with this question on on software the explosion of software you know do are we going to see a proliferation of SAS? So obviously

there's a lot of different reasons people build SAS software. I think one pretty common one and I think this sort of applies I think I've even heard you say that it sort of applies to some of

Atlassian's core products is that the software encodes a way of working that is known to be effective and so for

something like you know Jira it's like yes it sort of does these like functional things of it you know takes information it stores information allows you to search

whatever but What made that really transformative for so many teams was it kind of guided them to work a different way than they had been working before, right?

Instead of the old waterfall model, they were actually able to do agile because the tools they were using were sort of set up for it and naturally steered them in that direction. And so it kind of

created habits, reinforced habits, all these sorts of things.

If that is right, I wonder like to what degree does AI change that potentially a lot. When you first apply AI, you think,

lot. When you first apply AI, you think, okay, I have this task of triaging tickets. You know, what are the inputs?

tickets. You know, what are the inputs?

What are the outputs? How do we make this decision? Let's get all the

this decision? Let's get all the examples. You know, let's evalidate.

examples. You know, let's evalidate.

Let's do all this stuff. Great. Okay,

now we've got an AI that can triage a ticket. But then, you know, the next

ticket. But then, you know, the next level up obviously is like if we're using AI to answer the tickets, do we even need to triage the tickets? You

know, maybe we just answer them all immediately as they come in, right? We

don't necessarily need to have that step at all. So, we could sort of do the Elon

at all. So, we could sort of do the Elon Musk thing of like best part or best step is no part or step. So I guess I'm wondering how much of the SAS stuff that

we've seen is appropriate for agents. You know, how much should the agents be like jumping into all the structured workflows that have been created for humans? and how

much should we maybe be thinking like actually they might ought to work a quite different way from the way we work and maybe that you know that the SAS tools that we have actually don't

necessarily encode the right way for agents to work. I would agree with the observation that like the way we work is not necessarily the way an agent would

work over time. But the way we construct our units of action that an AI can take is usually giving it skills or tools, you

know, to that that it can call at a particular step in in its workflow.

That's typically what happens. I if I were to use Jura as an example with Jira the real value of Jira like it's funny like it's the classic like oh I vibe coded a to-do in progress done app okay

I've kind of made I've replaced Jira it's done you're like oh if that's what you're using Jira for yeah sure like you probably you know you weren't use you like that's what you're better off doing there's a bunch of other issues with

that like how do you maintain your own software and scale and whatever there's a bunch of issues but the real value of the SAS tools that model that let a He models some workflow is the value that

the customer has created by modeling it in their world. Jira's value is making I don't know I'm just picking some random companies here like the Coca-Cola the teams at Coca-Cola make it theirs and

they make it theirs by actually modeling their taste how they want their organization to work cuz otherwise everyone just ends up with the same organization. their business workflows,

organization. their business workflows, how they how they build products, how they do customer service, how they respond to change and track incidents, how they onboard a new employee, they

apply taste in modeling their workflows in some way. In a world where AI agents are helping us do more of our work, the

problem of modeling how these agents work in a workflow does not go away. I

would argue that employees, employers and employees want even more control to define if we said at the top of the call taste is important to define how they

would like the AI to work. So my logical conclusion there is therefore agent orchestration with human workflows is becomes a pivotal thing that every business needs to do at some point. And

so okay, what does that look like?

Humans need to decide what they would like to do and what they would like their agents to do and how they would like them to do them and what they will do and what the agent will do and what their other colleagues will do etc. That

problem definition space of applying AI to a business workflow. I don't

prescribe to that world where well you'll just give it to AI it'll work out everything cuz then in that world and I can't reason with that world because they end up in the AI slop world. you're

like, "Okay, well that's basically everyone has the same thing all the time, every time, and everyone's company works exactly the same thing." Which by definition isn't true because everyone's company builds different products and different services. And so like, well, I

different services. And so like, well, I just can't follow I can see how people follow that reasoning, but the reality is the most valuable thing that a

business needs to do is give their human teammates and their virtual teammates tools to do their jobs and design the workflows in which they would like to do that. You know, everyone goes from doing

that. You know, everyone goes from doing the thing to architecting the thing. I

think that becomes more important in that world. And in that world, will they

that world. And in that world, will they use different sets of SAS services? Oh,

totally. It may maybe they were using some SAS service yesterday and they use a different one tomorrow. That'll

change. That will always change. And

that has changed for the last, you know, as many years as I've been doing this for. But I think what doesn't change is

for. But I think what doesn't change is the the need for specialist tooling where humans will do work with AI. I

don't think that goes away in the SAS world. I I would argue that only

world. I I would argue that only increases if we're already if you believe that chat is not the only inter isn't it going to be the interface to all interfaces and it like that won't

work. I just I can't see that world

work. I just I can't see that world given the amount of crazy specializations there are in in the world. It's just unbelievable, right?

world. It's just unbelievable, right?

Like you just look at the the crazy explosion of legal AI tooling. It's so

specialist. Uh my brother's a M M&A lawyer and watching him use his tools I'm like it is just fairly sophisticated UI tooling on citations of previous

court cases related like legislation by country by geo and it's just like could he type a prompt and try to solve similar problems potentially but it's a disaster try to build any business that

does that job at scale you're going to need specialist tooling I I I just I can't see that well and then the only other counter argument to my argument is I always challenge myself of like oh but AI could dynamically generate that

interface. I'm like sure but at some

interface. I'm like sure but at some point like that needs to be predictable and scalable for employees and people to work with and then then you end up building a verticalized SAS software like you end up getting to the same

outcome of a verticalized solution on top of the conversational interface.

>> You know what what predictions would we make from that? It seems like there's this Silicon Valley notion of the oneperson unicorn which we haven't seen happen yet. You know, we're starting to

happen yet. You know, we're starting to see some interesting, you know, very small teams with like, you know, pretty notable scaling

success, but no oneperson unicorns yet to my knowledge. But I think the one person unicorn is sort of premised on an idea that obviously the AIS are going to continue to get better. You know, it probably isn't the case. You could build

a oneperson unicorn with today's models, but you know the next generation or the next generation that it starts to become at least people think that it becomes more realistic.

Maybe you think like nah that's just never going to happen. But I would not be comfortable putting a cap on, you know, how far the AI capabilities

>> curve will go before it bends over. I

always say, you know, it might be an S-curve, but the top of the S-curve can still be superhuman. And I kind that is probably my best guess. You know,

there's also the possibility of intelligence explosion. That's a whole

intelligence explosion. That's a whole other thing. But it seems safe to me to

other thing. But it seems safe to me to to believe that like we're going to get to something that is genuinely better than people at almost everything almost all the time. But then, yeah, I guess I

just wonder like enterprises want control. That's a deep value or a deep

control. That's a deep value or a deep reflex, but does that set us up for a world where, you know, does does that work against the enterprise at some point if they're

like, you know, we can we put these agents into these boxes and we have these workflows and we like we need them to do these sort of point things and these prescribed ways and meanwhile, you

know, with maybe Gemini 4 or whatever, you know, some kid is out there that's like, I'm just going to >> Yeah. provide like light guidance and

>> Yeah. provide like light guidance and you know a bit of a bit of genius but other than that you know the AIS are going to just do all this sort of stuff and I'm going to kind of let them figure it out. There's also this notion of, you

it out. There's also this notion of, you know, in in human teams obviously we're all different, right?

And so I heard a previous previous interview where you said that the hardest thing about scaling a company is scaling product teams because communication trust process and rituals all the way down.

>> And so I think again that's kind of like one of the reasons we have all this software is because people can't keep all that structure in their heads. you

know, they can't just have like a shared mental model of it. So, they need some more, you know, lasting instantiation that kind of says this is the way we

work. But the AIS are also going to be

work. But the AIS are also going to be like clones of themselves, right? They

can potentially collaborate, you know, with a lot fewer guard rails and kind of constraints and all these sorts of things in place. And I just wonder if that could be, you know, something that

could really sneak up on the incumbents.

There's a lot there, dude. Let's do the first part. The first part of just the

first part. The first part of just the one person unicorn, I think it's a good test, right? I was talking to a five

test, right? I was talking to a five person startup to kind of doing like a sales AI vertical.

The work that they do is the same work that I just described to you. To give

you a specific example, they have AI agents in their Jer workflow that automatically publish content to their WordPress or whatever their blog is on a regular basis, thought leadership content based on a bunch of inputs that

they have given it in a regular job and humans in the loop review the content and you know try to critique it and give it feedback etc. So they they got this content creation machine that's just pumping out content to do that. So first

point is even those oneperson unicorns they're orchestrating AI agents in a workflow to get stuff done. I think is my my first point like I don't think it's just an enterprise thing. I think

the ones that are getting high leverage are not just manually typing in a prompt all the time. They put it in some sort of frequent automation orchestration rule and are doing it there. The second

big thing they were is like those when you talk to those people at least when I talk to them they're the bottleneck and they they have a constant need to

hire more people. Can they do more with what they have? Oh, absolutely they can.

But the ones that are truly growing, like we have more like we have more ideas of what we want to build than people to build it and agents to help us build it like like unless they're happy

to stay stable and not grow, they're still like, "Hey, I'll I'll hire. I can

just get more with what I have, but I'm also still hiring." You hear that story all the time. So that one person unicorn, I've read just lots of references to that thing. I'm like,

sure, if that one person is happy to just remain as they are and be the bottleneck like like if they're running a smart business like they would realize that talent is a scarcity and they

probably want to grow at some point.

Doesn't mean they each person can't do a lot more than they ever could before. So

that's probably the second point there.

Third one's really just around often gets excluded but not not a major talking point but like there are so many regulations and industries and compliance that just needs to happen. So

like you know it there is a whole cohort of the market that like literally cannot move without some sort of intervention there from a domain expertise human stuff. Even with AI stuff I'm sure

stuff. Even with AI stuff I'm sure there'll be there are legatory implications. The last one I keep coming

implications. The last one I keep coming back to, Nathan, is like, I just still don't believe that one person unicorn is some guy or girl typing a handful of minor prompts

into things and not getting slop even when AI gets better. When again, my my definition of slop is it produces something good but just lacks any creative diversity. So like everyone's

creative diversity. So like everyone's getting similar things cuz that's that's the how these models are designed to do.

And you can do it today. you can go type the same prompt in seven different tabs of like different AI tools and you're getting 80% the same. So therefore, for that person to actually become a

unicorn, maybe they they just get to the most they capitalize on their slop the first, but then the second unicorn will look too similar. So they'll have to put in a lot more effort in avoiding that slop. And how do you do that? It's

slop. And how do you do that? It's

really just about the human process of just making AI yours. Like I I I don't see a solve for that. Like other than just making it yours in some way, how do you make it yours? There's a million

things of voice and tone, taste, content, context, etc. I I just don't see that going going away at all. Like I

I think that's what makes us human and that's what makes us crave different things and like different things because of how we think and how we feel and how we interact with things. Anyway, those

are my thoughts on that topic. You've

mentioned this kind of core skill throughout the conversation like figuring out how to build workflows basically. You might in other contexts

basically. You might in other contexts call that systems thinking or system architecture, process design, process architecture.

I have found that skill is like very natural to some people and very unnatural to other people. What have you learned about how to teach that skill?

How how should people practice it? What

can we be, you know, if this is the thing, right, where we're going to be going from doers of things to architects of how things get done. The uh also the the next logical question that people love is spicy. You probably you hear this all the time, I'm sure, is well,

does that mean I don't I don't need as many junior staff anymore because I like it's just the senior staff that get the architecture stuff, right? So, I think that that's also related to that question I find that comes up a lot. We

always forget that how good of a teacher AI is. It's arguably the best teacher of

AI is. It's arguably the best teacher of all time. You know, I always even I just

all time. You know, I always even I just think about like after the product management craft at Alassian as an example, you know, we've we've deliberately changed our our hiring profiles of like which seniority and

which which people I make the joke of like those kids that are cheating cheating in college and university. We

want more of them and they're coming in as AI native need a better phrase for that. But the reality is they are the

that. But the reality is they are the ones that are already starting to think how they can use AI to advantage them the most even though they have less domain industry experience. The ones

that are doing that are using it in their personal lives or their you know as a it feels native to them. My son if I observe him he's gone from he's 11 years old. He's gone from scratch

years old. He's gone from scratch coding, which is like a visual coding language, straight to vibing. And he

like he hasn't had that middle section of like learning about syntax and code constructs, whatever. You know, he geeks

constructs, whatever. You know, he geeks out every now and then, but he hasn't yet had that. But he's already, you know, his Roblox got up upgraded the other day. He's like he's got this

other day. He's like he's got this Roblox like IDE where he he codes stuff and he's like, "Dad, dad, that AI thing just appeared in Roblox." And he runs in my room and I'm like, "What are you doing?" And he's just typing and vibing

doing?" And he's just typing and vibing away and doing stuff. So he's that that's becoming what he the skill he's learning is how to instruct AI to get what he what he wants and if he doesn't

it doesn't get what he wants he's learning how to instruct AI to teach him to explain what he is he's after I will say this whole doing ar being the architect of the thing one logical

conclusion that's incorrect I would say is people say therefore I only hire or focus on senior staff no I would argue behavioral change is really hard I would argue a lot of senior staff actually

probably still doing things the old way because it's the quickest and easiest. I

struggle with this. Like when I get a new task, I could do it the way I currently do it or I could try this new way of doing it which may or may not work which will may require more time or

may fail but I may get a better outcome.

That behavior change is really hard.

Where is it in our workforce that we're we're easiest and best place to instill behavior change when someone's new to any role is great there. I will say the biggest thing to be aware of is just AI as a teacher is an excellent tool to

help with there. And the second thing is yes, we say more people go to to being the architect of the thing, but also there's an equivalent amount of people in the workforce that are reviewing the thing who aren't yet architecting the

thing that are also building knowledge to then become architects of the thing.

Right? So the person architecting the workflow for that procurement team I gave you as an example of, that's usually one person that sets that up and decides how it works. The humans are still reviewing the contract. the

proposals from the AI, but they're also building domain knowledge and skill of what's good and what's bad taste. And

they're in in doing so, they're also becoming architects over time. So, I

think I think their growth still continues even as people who are viewing the results of AI. They're speaking of new teammates, Alassium brought bought a

browser company, and it's literally the browser company. So, kind of a

browser company. So, kind of a two-parter around this one. one, the

goal, as I understand it, is to build the browser for knowledge workers. I'd

love to hear a little bit more about like what the vision is for that. You

know, what you imagine that ultimately looking and feeling like. And then

I'd be interested in your advice for businesses that are thinking about acquiring an AI startup in their space.

How should they be thinking about the value drivers? Classically you have like team

drivers? Classically you have like team tech and traction and you know but this is such an uncertain world for so many reasons right like is the tech that some startup has built

today you know going to endure in the way that we might have been confident it would in past eras is the traction even right I mean you know we see these like super fast basically vertical revenue

expansion stories but then we also kind of wonder like yeah what happens you know to cursor for example if you and propic decides that they're going to only allow their next model to be used

in claude code for a few months, you know, when it first comes out.

>> So, I just feel like the, you know, it's to so many companies the feeling is like it would be really nice to be able to acquire our way into this a bit, but you

know, are we comfortable paying, you know, what we would normally pay for the sort of traction that we see? Like, it

can be really hard, I think, for people to think about that.

>> Awesome. Uh, browser for knowledge workers. amazing team. Josh and team

workers. amazing team. Josh and team have done an incredible job. Maybe just

as analogy to help that uh that I kind of use in my head like we're not trying to compete against your everyday browser and your consumers browsing the web, doing shopping, whatever it is, planning

their next trip, buying some dress or something. When you reset the

something. When you reset the assumptions on which you're building software, you get a very different outcome. I keep telling the teams inside

outcome. I keep telling the teams inside of Lassian that are building our apps, I'm like, "Hey, we need to build assuming AI is always on and available and you'll get a very different Jura to what you did back then, right?" Like

that's our mental model. Now, in this particular example, early messenger days as a parallel example, uh, AOL Messenger, ICQ, I still remember my ICQ number.

There were messaging tools that were largely used in a consumer context. Now

we did the industry we as in we the industry did apply some of those messenger tools in a work context and they worked okay but they weren't great they didn't take off well etc. When we

reset the assumptions of hey in a business context how are the how is messaging different we ended up with a very different solution like we ended up with channels as a way to reflect org

and team structure we ended up with integrations as a way to integrate with different systems uh business workflows modeled in MS teams channels or slack channels etc permissions and control

became more important so we actually end up with very different products to the consumer messaging products and still I would argue today consumer messaging products, they look very different to business products. Like, and so we

business products. Like, and so we believe the same thing's happening in the browser context for work. Um, if you reset the assumptions on on how people work every day with a wide variety of

different SAS tools, why do they go to the different SAS tools to do what tasks, then there's a bunch of different things we need to do to to get to that outcome. So that's like a bit of an

outcome. So that's like a bit of an analogy of like hey like we generally believe that there will be a different work construct for using AI in a work context and and you'll still need a personal context and it'll be quite different and I think the same is

applying a little bit with just personal AI and work AI as well. You're seeing

more and more of that like you know the personal AI productivity in a personal world is quite different to to a work one. I think the browser companies

one. I think the browser companies already thinking about Josh and Tim thinking about like hey in a world where the browser has access to the tools that

you have access to permissioned per user it's a fairly complicated world in a world where the browser has access to the organizational knowledge and the graph that we talked to you about how

might we solve the similar problems that but in a work context we end up with quite a different interface quite a different experience that ends up happening there so I think the opportunity there and the The vision

there is really just around envisioning teams working in that collaborative context with their virtual teammates across many SAS tools. We believe what the browser looks like today will look

quite different to what it looks like tomorrow for knowledge workers. So stay

tuned there. That's a exciting and moving space but also just a fun space.

As someone who's been in tech for a long time, you're like when assumptions reset it's such a clean slate to like start thinking again. We went through this

thinking again. We went through this with e-commerce days, the cloud days, etc. mobile. So, it's always fun.

etc. mobile. So, it's always fun.

>> So, that sounds like mostly team is what I heard largely there. Any thoughts

about tech and traction if you're thinking about buying an AI startup? Oh,

yeah. So, your second part of your question, look, there's there's the mix of like there's that there's an AI fog of like investors and and companies are struggling with like, oh, look, how much

of this is like reproducible overnight?

how much of it's not etc like that that exists. I will say on that particular

exists. I will say on that particular topic and bucket what has standard the test of long-term AI long-term I always tell teams define long-term is 12 months

here in the world of AI understand the trajectory and define what long-term means because otherwise you don't make a decision you're in information pro analysis you go oh but the trajectory is here and so be like okay let's agree for the next 12 months what's the most

valuable step you can take as a business as a team whatever it is to get to 80% of something to 100% of something that's a marketspecific

vertical domain niche is a massive investment and I think that is certainly again depending on what your listeners are thinking about whatever that's certainly something you're like okay if it's a vertical and I can see that the

teams put in effort to get to that what I could do with a general model gets me 80% what I could do with a general AI tool but what I get to here is that 20% businesses are willing to pay if that's high value to get to that extra 20% and

that's where most of the value is that's one big thing to take away the second big thing to ask your in your teams is I use the phrase uh workflow proximity there's user workflow proximity and

bioximity I those two things are pretty important for thinking about these things user workflow proximity and asking is do we have a right to win in terms of how users work every day and

this is more for the PLG style of discussion for example I could go build a some AI tool to help with calendar AI automatic

scheduling tomorrow as me as a random individual who's probably going to win that space. People that already have

that space. People that already have people that are using calendars. So, I

would either, you know, build to get acquired or or or build to the hope that I could somehow steal market share and then start with a new calendaring system. That seems like an epic two big

system. That seems like an epic two big hops to take, right? So, asking workflow proximity is where do the users exist today? I always and then say for that

today? I always and then say for that job to be done, draw Maslo's hierarchy of needs for that job to be done. And

you want to be as close to the bottom of that pyramid as possible for that vertical because that player is in best position to move to the next layer of the stack and to the next layer of the stack. So the calendar example you're

stack. So the calendar example you're like you can I really want to earn calendaring and scheduling to to do that or you feel like your niche on top of that calendar will be a niche that those big vendors won't go after for a while

and you can earn some big bucks doing that and whatever and that's fine. So,

so I always think user user user and bio workflow proximity is also a good thing for these companies to have in mind when you're thinking about these things. Do

they help us? Do they give us more users workflow or more bio workflow? And then

where are they in the stack of that job to be done? Are they at the top? Once

I've done these four things, I need to do this fifth thing. Then that's a dangerous spot to be in. But it might be good if again it's so nuanced depending on the market's big enough. you think

what you're doing is so specialized they might go after that etc. There's a bunch of things to think about there.

Hopefully that's useful.

>> Yeah, a couple good frameworks there for sure. on the future of software. One

sure. on the future of software. One

mental model I have for like are we going to see a software explosion or you know might we five years from now have like fewer professional developers than

we have today because AI is doing a lot of the work is to ask and I I you know give you a spectrum right how much more of this would I buy if it were

functionally free on the low end is like dental work like I would buy no more dental work even if it were free right I I don't want it. You don't enjoy it. You

don't enjoy it.

>> I'd like to avoid it, you know. And

accounting is like less painful than dental work, but it's like still I would probably pretty much buy whatever I'm required to have and probably not much more. Some people might, you know, buy a

more. Some people might, you know, buy a little more, but I think most people have roughly that attitude. On the other hand, like massages, if they were, you know, freely available

all the time, I'd probably consume like a hundred times more than I currently consume. Maybe even more than that.

consume. Maybe even more than that.

Where is software on that spectrum I find quite hard to figure out and I would definitely separate software for the purposes of this from like AI inference generally

like how much more do we need you know what is the limiting factor on how much we can use is it our time is it something else but what's your intuition because I do think it's going to get a

lot cheaper and then in terms of like the future of the industry it seems like I just don't know how elastic demand really is totally take the mass massage

is one by the way that's such a good one look I feel like the only way to have a fruitful discussion is to be more nuanced on the category of software and we could so many ways we could slice

this so let's just say we slice it in B2B and consumer okay well consumer well that that honestly feels like a a massive pool look at our lives of

entertainment leisure home renovations uh like whatever pick cooking etc I feel like that continues continues to grow.

Um especially and sadly so there's also the the the business for attention which exists but it's it's what it is. So I I just can't see that shrinking in the

world of AI. I think in the world of AI we have to ask ourselves society is like what do we want it to become in the consumer space. The trajectory we're on

consumer space. The trajectory we're on is is extremely highly fabricated content and the the blend between reality and real is getting blurry. But

again, arguably you could go, okay, applying a photo filter to my phone 5 years ago was already somewhat AI, right? Like that was like already that I

right? Like that was like already that I anyway, I just can't see that shrinking in the business. Again, if you go business by specific, vertical, etc. But

the general thing again, I I go, okay, well AI looks better with the more tools it has access to. Tools being tools that humans need to build or tell AI to

build. I don't think that goes away to

build. I don't think that goes away to build those tools for it to have access to. So the developers as an example are

to. So the developers as an example are still going to need to build tools for AI to have access to or review or be involved in some architectural step of that that stop. And so in the business

context, I don't know why that would shrink. I still think that would

shrink. I still think that would continue to grow. If anything, the granularity of tools explodes.

You know, if I build an app tomorrow, let's use the calendaring after, you know, as an example. the app to a human that's one tool the calendaring app to an AI is like

that's like 50 tools it's like you know find time between Sharief and Nathan that works in this time zone is probably one tool that you give it find you know time between two people block busy time

is another tool public you know get public holiday like there's so many ways you could slice and dice that so I would argue that in that world designing software for AI

is incredibly more sophisticated ated and more complicated in terms of the quantity of the granularity of which we need to design things to get a better

outcome than it is for humans. And so I reached the conclusion of like I just can't see that shrinking anytime soon.

Now could a vendor's world change so that they go from building a UI to building a set of tools that AI calls?

Yeah, it will and it is changing and the business model will change over time.

You know, if I look at the app store as an example, you know, Apple's got a big push right now with their app vendors of like, hey, spend time focusing on building app intents over UIs. If your

app intents is their sort of their skills and framework, if your app kind of does these things, then yeah, then I guess CE or whatever the new AI ends up being can use that as a tool and there's

still value exchange done in that tool, but it depends on what the use case is and and how that works. So I just see the software continue to explode. But

the maybe the more useful discussion is having it by market, by domain, cuz then you could see, okay, you know, how automative software might be totally

different to software for reviewing and tracking official document signage or compliance or something like that. That

might change. Yeah, it might be different by by the actual market there.

>> Last question. leadership.

What do you think companies are not doing enough as they try to lead a process of encouraging their people to adopt AI? Things I've said in the past

adopt AI? Things I've said in the past which I think are like okay but maybe getting a little stale are like leaders should lead by example. Make sure

everyone gets hands-on. Highlight your

individual creativity and success stories. Make AI adoption a part of

stories. Make AI adoption a part of performance reviews and evaluations is maybe like as spicy as I've got with it.

What's the next level up from that in terms of, you know, the best ideas that you've seen for, you know, making especially bigger organizations

really rally and and catch this wave?

>> Look, they're all good points. I think

the nuance comes in the the tactical implication of how they apply them. Like

leadership modeling behavior is a great example, right? The leaders that do it

example, right? The leaders that do it well and inspire are the ones that can share stories that are more than I used AI to summarize this document and draft this email for me. That's the thing you

need to unlock. And so, okay, how do we help folks unlock that? I'm a firm believer when I again look at tech history when a technology wave came when we used it in our personal lives, it

ended up impacting our work lives a lot stronger. When we started buying stuff

stronger. When we started buying stuff online in e-commerce, it ended up changing how we exchanged software and bought software. When we started using

bought software. When we started using mobile phones personally, it changed how we use mobile for work. And so I always say look at your days as a leader to be very specific. You might be renovating

very specific. You might be renovating your I'm just going to run through my use cases. Like you might be landscaping

use cases. Like you might be landscaping your backyard and you could use AI to give you a bunch of ideas, visualize it for you, critique the different plants and the environment you're in, etc. like

actually use it aggressively there. If

you have children, oh boy, my use cases explode here. Like I use I have a agent

explode here. Like I use I have a agent that helps my son with his math homework that doesn't give away the answer but go knows the the school curriculum for his grade and year and he talks to it and it

helps you know walk through an answer and it's very personalized. We put some Fortnite jokes in there and stuff like that that he likes to do. My daughter,

she loves creativity and she she's geeks out with AI music all the time and makes her own prompts and all that kind of stuff. So using it personally for the

stuff. So using it personally for the model behavior I think is the important thing that leaders miss. They go look I press summarize or I wrote an email and

you're like that's a great start but just find I actually love asking this question in job interviews. Some of you personally use AI and you know you'll see things from planned a trip to you

know redoing a whole house renovation with it and it's basically running like the show for me. You know I recently just did a sporting injury to my MCL and I'm think

a physiotherapist but I'm also got an AI assistant that reminds me every day of exercise I should do and it checks in on me and I will it ask also what I ate and it will tell me like is that good or

bad? like I've spent time prompting it

bad? like I've spent time prompting it to say like are you helping me heal as quickly as possible? Uh and doing that kind of stuff. The second thing on like your mandating or or now getting others to do it like I always feel like we got

to show the examples ourselves.

Creating a safe space is the hardest thing people will need to do. What is

safe? I don't believe mandating is safe.

I think that sends a message of like do or do or like get out of here. I I just generally I just if I hear the mandate message I'm like okay it might help some people for motivation but I think the

vast majority of your workforce probably see that as a threat but I would I would again the nuance in these conversations I think is where all the value Nathan is. So like I always say find we you

is. So like I always say find we you know we just ran a AI builder week a thousand product managers designers engineering leaders researchers we

blocked time off synchronously across the crafts. So it was no longer in some

the crafts. So it was no longer in some team some person who's taking a few days to tinker with this thing. That person

feels guilty if they come back with a failed project. if they if they feel

failed project. if they if they feel like they're blocking the team when the team's asking them questions on their day jobs and they're like over here trying to explore the new thing. So I

always say like okay find times where multiple people that work together can block their times so they're not dependent on each other but what you're celebrating is the learnings not the not the actual outcome the outcome is the

learning not not a product outcome and because tinkering is the most important thing they need to be doing and then applying AI in their business workflows like whatever that may means in in in

their department is something for that team to go through the process of discovery themselves. That's the best

discovery themselves. That's the best way they'll be able to do that. So I

often say to teams don't go you'll get customers that will want a hight touch meeting and we'll meet with them whatever and they're sitting there exploring and trying to discover this killer use case and they'll pull in

someone who tries to imagine use cases.

I'm like and I'll often say who in your company is doing some step of that or that today? No one. And I'll be like

that today? No one. And I'll be like forget that. Just throw it in the bin.

forget that. Just throw it in the bin.

Throw in the bin yesterday. Find me a team of what they're doing today and let's write down exactly what they're doing and let's walk through I always say just write down for any specific task write down the step steps for each

task and then within each step what are the specific knowledge instruction and actions the human takes to solve that step and then we'll be able to identify opportunities in the more effective way

and actually get to a proof of concept like super quickly by you guys building your own agents deploying them in a much more effective way. So I I think your advice, Nathan, like high level tech, I

think where everyone struggles is the next click down. Like I I I couldn't tell you of any other killer advice, you know, other than saying the next click down is often what's missing, which is like help me actually do the model

behavior. Help me actually, you know,

behavior. Help me actually, you know, apply to a team. Like what does that look like? We have plays in our playbook

look like? We have plays in our playbook people can go look up. We try to open source some of these frameworks for how people can apply in their teams. But definitely go and and do that.

>> This has been great. I really appreciate all the time and uh many thorough answers. Anything that we didn't touch

answers. Anything that we didn't touch on or anything you want to leave people with before we break for today?

>> No, my only my encouragement is just look at your days outside work and and just play and try things. Uh I think that's what will change your behavior

and that's probably will also change your behavior in your workplace over time. You'll find because you'll start

time. You'll find because you'll start to use that level of thinking when you get to your work. So that would be my just my biggest encouragement is like ignore all the buzzwords and all that stuff and just try to try when you're

fixing your tap next and you need to take a photo and understand which washer is what washer or whatever it is or how that works or like whatever it is just find a way to to tinker. But yeah, thank you. Thank you for your time, Nathan.

you. Thank you for your time, Nathan.

Really appreciate it. Share Mansour,

thank you for being part of the cognitive revolution. See you.

cognitive revolution. See you.

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