---
title: "The Token Bill Came Due: What Comes After the AI Pilot Phase"
episode: 132
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Anish Chadalavada"
guest_title: "Co-Founder & Chief Growth Officer, Gradial"
date_published: 2026-10-05
date_modified: 2026-10-21
duration: 00:42:31
word_count: 7389
topics: ["ai-in-gtm", "revenue-operations", "gtm-strategy", "brand-positioning"]
canonical_url: https://www.leanscale.team/knowledge/podcast/anish-chadalavada-gradial-the-token-bill-came-due/
source: "LeanScale Knowledge Hub — https://www.leanscale.team/knowledge"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# The Token Bill Came Due: What Comes After the AI Pilot Phase — Full Transcript

> Episode 132 of The LeanScale Podcast, with Anish Chadalavada.
> Published October 5, 2026 · 00:42:31 · 7389 words.
> Machine-transcribed and **not diarized** — speaker attribution is inferred, so verify
> attribution against the audio before quoting a specific person.
> Structured breakdown: https://www.leanscale.team/knowledge/podcast/anish-chadalavada-gradial-the-token-bill-came-due/

## 00:00 — Cold open and intro

**[0:00]** My guest today is Anish Chhatalavada, Co-Founder and Chief Growth Officer at Gradial, the company building the enterprise AI harness that runs marketing work across all the tools an enterprise already owns.

**[0:13]** See this as not the end of SaaS, more like a reset that turns SaaS into infrastructure.

**[0:30]** It's really just the unit economics fundamentally don't make a lot of sense when it comes to purely thinking of it as a single player operation.

**[0:41]** Whereas if you just throw agents at the problem or you just throw people at the problem or you just throw tokens, like none of those is going to converge to the outcome that the organization needs.

## 00:58 — The claim: the marketing SaaS stack won't survive agents

**[0:58]** Anish, you said the traditional marketing SaaS stack won't survive the shift to AI agents. That's a pretty big claim. What makes you so convinced?

**[1:08]** Yeah, that's a good question, Anthony. So I'd say marketing has always been built around software that stores data but doesn't actually do the work.

**[1:18]** And over time we start seeing this create a bigger and bigger tax on marketers and marketing has become more about project management, handoffs, tickets, tools and tracking than it has about actually ideas and building great customer experiences.

**[1:37]** I think there's been this kind of black and white perspective in the market around the SaaS apocalypse like, oh, SaaS is dead. No, it's critical.

**[1:45]** And really when I talk about the marketing stack and how agentic is going to disrupt that, really what I'm referencing is a simplification or consolidation where all of these point solutions and tools that exist to do a small piece of a campaign

**[2:05]** that make another product easier to use, those are going to go away. Your core systems of record that have context are essentially going to turn into infrastructure.

**[2:17]** And so when you think about the agentic marketing stack, you have something that's a lot simpler than the messy web most enterprises are accustomed to dealing with today.

**[2:28]** And so that's why SaaS apocalypse isn't quite correct. There will be software products that are holding critical context that are a key part of this story for sure.

**[2:37]** But all the point solutions that are required today to get everything to work that are just storing data or adding another checkpoint to this long list, those aren't going to be necessary anymore when you have agents that can go actually execute tasks.

**[2:52]** So I see this as not the end of SaaS, more like a reset that turns SaaS into infrastructure so agents can use it for context to actually go do work.

## 03:05 — Anthony's pushback: it's a data model problem, not a model problem

**[3:05]** I was actually recently just at a CEO summit with up data partners and we have a really similar point of view where I think most people's AI strategy has nothing to do with their ability to use the models, build agents, build workflows.

**[3:23]** It all is a data model problem. So, hey, do you have a semantic layer context graph data infrastructure even built in a way that AI can use? Where do you think people tend to fit kind of on the spectrum that you're talking about?

**[3:37]** Like, as you look at their stack, what do you think they have in place and what moves do you think they're going to need to make in the near future?

## 03:45 — Phase one is over: the token bill came due

**[3:45]** Yeah, I'd say everyone is still in the early days when it comes to getting real value from agentic. I think we're wrapping up the end of a long period of experimentation and trying a lot of different things because over the past 12 months, you know, everyone's tried a lot of different AI pilots, products, tools, different AI models, building things themselves.

**[4:09]** And then the token bills came to you and you start to see, you know, at the end of the quarter, you get this massive token bill. You can't articulate all the outputs that came out of the token bill.

**[4:19]** It's so easy to build agents that everyone's built an agent and you're starting to see even the quality of content is turning into slop. It's really detrimental for the brand.

**[4:31]** And so as we kind of reach the end of what I think of as phase one with these agentic deployments, most customers are in the early days of phase two, which is how do I actually have a control tower or command center for agents that allows me to go focus on executing work for those hero workflows or those critical workflows?

**[4:54]** And when you start thinking about the problem in that way, it's not so much how do I build agents as quickly as possible. It's a much more organized top down approach that boils down to, like you said, context, guardrails, a brand and process context.

**[5:12]** So how do I think about, you know, my current way of doing things and what I want to preserve about it, like the approvals, the guardrails, the brand, what's the context that the agents need about, you know, my content, the performance of that content, my audiences, how I think about the newest products.

**[5:29]** If I can put those two pieces together, and that's why we think of Gradial as a harness is if I can put those two pieces together in a systematic way, then I've created the fertile soil for agents to go actually execute work.

**[5:42]** It's very similar to onboarding a new employee. You know, if you throw a ton of different things at them, it's not possible for them to be successful. But if you give them a really good cheat sheet of here's the 20 critical things, and then I want you to try and then learn from what you've tried.

**[5:58]** Those, those people can onboard much more quickly and so I think of agentic as as essentially very similar to that process of how do I go get agents in an organized way, operating within the guardrails of the business to go execute work.

## 06:14 — What an enterprise AI harness actually is

**[6:14]** And you described this as a enterprise AI harness for, I think I have a rough idea of what that is, and what it looks like but what does that all entail.

**[6:28]** Yeah, so a harness is essentially connecting the best LMS with the process brand context guardrails and tools and integrations that an enterprise uses, so that you can actually go execute the workflows that their teams are spending time on today.

**[6:49]** And in doing so you can unlock time for those marketers to focus on the creative, the strategic the impact and like I said, there's a huge amount of demand and need for that.

**[7:02]** These marketing roles need people in them because without people there at the steering wheel you get a lot of slop and a lot of high volume of content but not necessarily like stuff that's a creative for the brand and so a harness is essentially putting all of the pieces

**[7:19]** of the puzzle together and allowing agents to work within that framework by connecting all of a brand's context and systems and guardrails with the LMS that can actually reason and plan, so that you end up with this agent that can actually go and execute workflows at scale,

**[7:37]** launching a promotional campaign for back to school, for example, for some of the teams that think they're running this natively out of cloud or open AI they think they have their data organized or, hey, I've put everything into a filtering system or shared GitHub.

**[7:57]** What are some of the things that people like, oh my gosh I didn't realize that I'm actually not able to do XYZ with this infrastructure, and we're going to need something that really reels in all of the AI horsepower that I'm driving.

## 08:12 — Spend governance, and model lock-in as the multi-cloud story again

**[8:12]** Yeah, I'd say it's the, the number one thing is, you know, you touched a little bit on the spend controls and the governance I think that there's like fundamentally this incentive model difference between LLM providers that are looking to drive usage right

**[8:29]** into the product, and then the actual desire to deliver enterprise outcomes and that's where the vertical applications, the need for vertical applications comes from is that, you know, if I spend a lot of tokens in open AI I am, I am definitely becoming more productive

**[8:46]** in my work but how do I track that back to the things that my enterprise actually needs to be able to solve for and so we're starting to move past this phase of, you know, token maxing is, is the best thing I can do because those tokens are expensive and if you can't

**[9:01]** track that back to actual work outputs in a systematic way and artifacts in a multiplayer way, then you're going to struggle to justify the ROI and struggle to justify that spend within the business.

**[9:13]** But the even more important part is just about that the interoperability like this is the same as the multi cloud story if you're a big company.

**[9:20]** You don't want to be locked into one cloud so a lot of times you know you have infrastructure and different clouds and model providers are the same like we had a lot of, you know, we heard a lot about the desire to build around clock over

**[9:33]** the past six months and then when when chat GPT six when Astra came out, then there was a big shift from, you know, you can see this in the open router model calls there was a big shift from Claude to open AI very very quickly so if I've built over

**[9:48]** the past six 12 months all around Claude, and then it turns out that you know open AI now has the most intelligent model and then Gemini is the best at image creation then I've indexed myself into this, this risky position that I shouldn't be in and

**[10:03]** so being able to use different models for different tasks is another critical component of a harness and a critical piece of why you don't want to see this as a purely build out endeavor because you want to be able to operate across these different

**[10:19]** models you want to be able to rely on vendors who really specialized in a particular functional area, and you want to be able to have those guardrails and spend governance built in, which is, is just challenging to do when you're driving

**[10:35]** consumption as quickly as possible.

## 10:38 — Why single-player AI economics don't add up

**[10:38]** I think it makes a ton of sense, and I am just starting to feel the pain. So lean scale or about 30 people. We had everybody on a bunch of Claude max plans, and what ended up happening is a lot of people would run out and then they're running

**[10:56]** into usage, and, and I'm when we go dig in like hey why are you using so many tokens, and then this person's barely using any but seems like you're getting the right job done. It all came down to that infrastructure like oh they're indexing our

**[11:11]** entire CRM every single time they ask a particular query or they're using like the latest model on super super simple tasks, and just like letting it run. And I think that's, we're still in this period if we're here in September, 2026 where a

**[11:29]** lot of this is getting subsidized. I'm under the impression that's going to change relatively quickly and the prices are going to go up soon.

**[11:38]** Yeah, definitely. It's really just the, the unit economics fundamentally don't make a lot of sense when it comes to purely thinking of it as a single player operation because really we see a lot of people that are like you said we building

**[11:55]** a wheel over and over again where you know a lot of times, and within an organization or at least within a function. There's common problems that everyone's dealing with, and you want to solve for them holistically.

**[12:08]** So if you have a situation where everyone has a cloud max plan and everyone's kind of got the same problems around content review for example, and they're all going and building their own agents and workflows for content review.

**[12:20]** One, you're starting to break down on the brand and guardrails you really want to centralize that and two, you're starting to duplicate work and duplicate token spend to accomplish the same thing and that that's really why you want to think about something

**[12:36]** holistically at the functional level of like hey I want to think about a harness for marketing or for sales or for support, where all of the hero workflows that I know are slowing my team down can be executed in a more central governed way, where I can track and control

**[12:53]** the end. And then, you know, folks can still use AI for their own productivity but it's not like everyone needs to go build the same thing over and over again, 10 times within within the work.

**[13:07]** Yeah, it makes a ton of sense and I, I had just seen so much recreation of the wheel over and over again, and it's so inefficient.

## 13:16 — Governance and multiplayer architecture

**[13:16]** Now, one of the things I'd love your perspective on too. It's not the sexiest topic, but governance. I mean I'm running into a lot of potential issues where, hey you're storing really sensitive data over here and then connecting it over there and then

**[13:33]** somebody can potentially get access to it. Or it's a little overly restrictive and certain things that people need to do they can't get access to the right things. What is, what is your thought on the governance infrastructure, and how do you give people the right access

**[13:49]** to the right data and workflows and agents, compared to what kind of seems to be like a free for all right now.

**[13:57]** Yeah, totally. I we see this all the time where it's like, everyone just goes and connects all their different tools and then it's hard to reconcile against like, who has access to what and then are people even putting data in the right places and this is this

**[14:09]** is part of the reason why, you know, really focused on the enterprise, because this is a really hard problem and a really hard pain point in the enterprise and really what we're looking for here is, how do I essentially reflect the permissions that people

**[14:26]** have and the rights that people have to see different information in the way that they use the AI products and that's fundamentally a multiplayer question. And because, you know, a lot of these LMS are architected for for driving usage and for simplicity

**[14:41]** and they're like these super productivity apps, they are really designed around single player and because they're really designed around single player it's just hard, it's just hard to solve that problem for, you know, who has access and like how do we make sure that data

**[14:56]** is not being shared in a way that it shouldn't, you really have to design from the ground up for multiplayer, whether you're building or buying or working with a vendor you really need to solve from day one for that multiplayer approach and say hey here's, here's the

**[15:11]** shared organizational context that everybody has access to. And then here's the different like layers of permission or access within that. And then here's the individual data that people have access to which should not be shared and, you know, it can be like a relatively

**[15:25]** simple hierarchy we benefit from, you know, we're working with with marketing teams and so a lot of times, there's content is intended to be public or is already public. And so, you know, we still see the same issues but you really do need to think of it from first principles

**[15:41]** as like a multiplayer architecture, in order for in order for that data and governance to be built in the correct way, bringing this back to marketing and specific for their use cases.

## 15:56 — Which martech survives agents

**[15:56]** You mentioned, hey, some of the tools will remain some of them won't remain. Are there any specific marketing, tech infrastructure platforms that you think will still see in the next two, five years.

**[16:13]** And are there any ones that you think like a something like this is probably not going to survive unless they do something dramatically different.

**[16:22]** Yeah, I'd say like the, the barometer for me on that without without naming names here is that if you're a, if you're a key system for the business that stores a lot of critical context.

**[16:34]** I think that there's a lot of staying power around that because we know context is the fuel for agents and without good context that's organized in a structured way that the business recognizes and understands.

**[16:46]** It's difficult for agents to be successful so when you think of their core CRM systems where all of this customer information lives. I think that those systems are really valuable and critical for agents to work correctly.

**[16:59]** And, you know, what we're solving for is that how do you go interoperate across like, you know, CRM, and then a different, you know, CMS from another brand and then a ticketing system that comes from another product provider that interoperability across that martech suite that glue that that's really challenging and that's where we've, we've really focused.

**[17:19]** If you're a system that purely exists to make another system easier to use for example, like there's, you know, the email builders or like, you know, tools that are existing because configurability is hard like those I think don't have as much staying power because software is predominantly going to be used by agents and not by people.

**[17:41]** And so once agents can use the software effectively tools that exists solely for human usability because the original software was too hard to use.

**[17:51]** That isn't going to be something that we see anymore but that that's the reason for like the bloated web of software most people are dealing with is, you know, I start with some software that it's too hard to use so I add another tool that makes it easier to use and then another tool and then an agency.

**[18:06]** So we're going to start to see, you know, I don't need to do all of those things I can focus my work, my agency work on the strategic impact that matters, and not putting hours of my team's time or my agency's time towards help make this system work for me.

## 18:26 — Who owns AI inside go-to-market

**[18:26]** Makes a ton of sense. From your perspective, who tends to be, because I feel like this is an interesting thing I'm seeing, I'll share what I'm seeing first and then would love your take.

**[18:37]** The ownership of AI infrastructure abilities that the team that's putting in the tools managing them in a lot of the companies, at least from a go to market perspective, I am seeing rev ops take a leadership role in there.

**[18:54]** I was anticipating it's not what I was expecting. I was expecting engineering or product teams or the CTO to kind of own that.

**[19:03]** Are you seeing the same thing for these go to market teams who tends to be picking up the ball on these AI projects.

## 19:10 — Gradial put RevOps in the driver's seat of business development

**[19:10]** Yeah, I'd say, I'd say rev ops is definitely like a very critical piece of the puzzle because they are managing a lot of the data and context for the business and like how, how the revenue lifecycle is moving and how the funnel of the pipeline is moving and you know we've

**[19:28]** actually taken an interesting approach of connecting our rev ops team directly with our business development and sales team so that when we see like deals are sold or leads are not getting outreach to our rev ops lead can then go work directly with the BDR team to actually

**[19:43]** go reach out to those leads. And so we've actually gone and empowered our rev ops team to take on more execution responsibility because I think over time, we're starting to see this consolidation of roles we don't see a deletion of roles and go to market I think there's

**[20:01]** a lot of anxiety around that but we don't we don't see a deletion of roles. Like I said if anything it's clear that people are needed more than ever based on some of the content that we've all seen in our email inbox and on LinkedIn right it's very

**[20:14]** detrimental for the brand for that content to go out at scale. So we actually need more people in the driver's seat than before. It's just that the time that they're focused, the things they're focused on can start to shift to higher value higher impact work.

**[20:28]** And we're also seeing in parallel this, this kind of consolidation in the sense of more generalist roles, where people are able, because of AI they're able to span across multiple functions or focus areas whereas before they were maybe rev ops is

**[20:44]** just rev ops today rev ops can be rev ops and all the insights and reporting and data and analysis, but also business development, and maybe even some content because that rev ops lead knows better than anyone, what needs to happen in the pipeline for deals to move

**[21:02]** or for leads to turn into opportunities. And so why not go empower that person to say you're responsible for for biz dev then you, you should say, who we should reach out to and with what message and that's working really well for us and it's a small

**[21:16]** example of like our view of the future state of the world for the product that we sell as well where it's like, maybe yesterday you were a content author who is responsible for building web pages. Well tomorrow, you have the most intimate understanding of the website so you should be the person who's maybe

**[21:31]** also responsible for not just the build with the help of agents but actually the content and the, the strategy for the website as well for both an audience of agents and an audience of people.

**[21:41]** And so, that used to be three or four different roles of project management content authoring content strategy, and then audience strategy and now it's like, let agents do the hard part of like the actual build and analysis and the manual work that's

**[21:57]** describable, and then you focus on the strategy across all of those. That's really what we're starting to see so in a nutshell to kind of go back to what you were talking about a second ago.

**[22:07]** I definitely see rev ops as one of the kind of key, key audiences for like the strategic like entry point for a business, but also other functions within go to market can start to see themselves and operate in a more strategic way, where you know previously maybe

**[22:23]** the operational role like marketing ops, similar to rev ops it's like well now you can be a very strategic role that controls your destiny versus being an operational center that is constantly being asked to do more with less.

## 22:37 — Entry-level GTM roles in an AI world

**[22:37]** Do you think I I'm, I'm seeing something really similar to consolidating into that general type of role also moving the strategic skill needed higher. So, we're needing to see those skills. One, one question that always makes me jump to is what are the good

**[22:57]** entry level positions for people who are just coming out of school, just getting into the workforce, typically you don't have a lot of experience to pull from to have a strong opinion on strategy.

**[23:09]** Are there things that are opening up for people in that position that you're seeing.

**[23:15]** Yeah, definitely I think a generalist go to market role is one of the best things, folks can do, as they come right out of college for example and they're, if they're not a technical person if you're a technical person, then I think being a product builder,

**[23:30]** is kind of my vision of like the full stack product role of, I'm going to do some product strategy and I'm going to talk to customers but I'm also going to do some design and wire framing on what this product experience should look like because that interface is actually a really critical differentiator

**[23:45]** of AI, and then I'm actually also going to write code as well, whether or not I studied computer science that full stack product builder role. If you're technical, I think is a really good place to start and you learn the strategy as you go, you know there's lots of room and opportunity

**[24:00]** to use scrappy and brings in a new way of doing things because they don't have the experience and they'll learn things on the job and I think something in parallel on the go to market side like I don't want to say necessarily like a GTM engineers the manifestation

**[24:15]** but something akin to that where hey I come in, you know I, I'm not a technical person per se but that doesn't matter for my ability to write code anymore so I can come in and basically solve a go to market problem because my problem growth is it, you know, something

**[24:31]** granular like, hey I can't create opportunities fast enough I can't create meetings fast enough top of funnel mid funnel pipeline like there's a kind of flexible go to market role where you can put someone in who, again, doesn't have experience but brings a new perspective

**[24:45]** and new way of doing things, and has all the tools at their disposal that they can go, go figure it out. And if you want to test some ideas it's easier to do that now than it ever has been in the past.

**[24:56]** So go test something out and then come back with the results and really seeing that as a full stack, a full stack role and all it requires is, you know, the ability to reason and think critically and think strategically and, and really just be scrappy and use all the tools

**[25:13]** at your disposal to go solve problems, think problem solving is that ultimate, you know, ultimate cross functional domain that there, there will be space for you, even with all the progress with AI like someone who can think about solving problems

**[25:28]** be scrappy and go test ideas and then go iterate on those ideas, there's, there's plenty of room and opportunity for those types of roles and it's a good fit for folks who are early junior career, maybe just starting out in their careers.

**[25:43]** It gives me a lot of hope I have three kids another one on the way in November, and I'm just thinking like, what are their opportunities going to look like I know it's not going to look what it is today. I just hope there's something that they can aim towards.

**[25:57]** 100% I definitely think there is and like I said I see a lot more. I see more and more room and opportunity for people to do the roles like that, that we see as as being critical today, where it's just the, the role is evolving, right, it's just evolving

**[26:16]** because we don't need as much human time spent on lower impact tasks that are repetitive, which is good. I think that was a lot of the promise of the internet was to do that, which, you know, it's dubious like how much productivity was gained from the onset of the internet

**[26:34]** right like now there's a lot of research pointing to this idea that there's some productivity gains but a lot of cases, the work just shifted medium from like pen and paper or from Excel to like a software application but people were still doing repetitive row tasks over

**[26:52]** again there's still people as of, you know, a couple years ago who are still doing data entry but they were just instead of doing it in a database they were doing it in like a UI. And so now with AI it's like AI can actually do the function of data entry.

**[27:06]** But that doesn't mean that there's not people who should be looking at data goes back to the rev ops and marketing ops question of like, we still want people looking at the insights and coming up with the strategy of like, how should I operate my business, what's the best way to connect

**[27:20]** people. And that's not something that AI has eaten up or is about to eat up, in my opinion, if anything, we've gotten to a point of fatigue with AI and people are starting to like, you know, band slop content right or automatically market a spam and so we actually

**[27:37]** really need people there.

## 27:39 — The slop grenade and accountability

**[27:39]** Yeah, I have definitely seen that I love the LinkedIn. This is AI button. It's one of my favorites. Yeah, yep, it's everywhere now and I love it too. I think it's, I think it's great and I think it's like, you know, this, I forget who talked about this idea of like

**[27:54]** the slop grenade which is like, you know, the use of AI creates the use of AI on its own right creates attacks on people downstream because it's like I'm offloading the review and the bar setting that the standard setting I'm offloading that from myself to someone downstream

**[28:13]** or someone who reviews my work, if I just use AI to do it. And so, how do we then go and allow people to use AI for productivity but then empower them to say no, you're still the accountable person.

**[28:25]** At the end of the day, even if you use AI you're still the accountable person.

**[28:29]** So we really do need that mechanism of action in the enterprise and just everywhere because otherwise, you know, we risk this like broad tax that happens on on society and on teams where the use of AI by you know the people doing the work without guardrails

**[28:47]** and strategy, then creates attacks that eats up the entire organization's time, and then you end up with scale slop content that actually hurts your brand. And so at the end of the day you're actually worse off than you were 24 months ago because you've put out all

**[29:04]** the slop content you've fatigued and exhausted everyone internally and everyone externally, and people don't associate your brand with taste anymore and brand is the only thing that matters. So, it's a big risk.

**[29:20]** A CRO I used to work with, his name's Tom Miller, he used to say, "Practicing a bad golf swing more makes you worse than not practicing at all." And I always think about that when, like you mentioned, "Hey, if you're cranking a bunch of slop out into the market, it's incrementally making your brand worse."

**[29:42]** So, you'd be better off just doing nothing and just then continuing to throw a bunch of slop into the market.

## 29:52 — From Point72 Ventures to founding Gradial

**[29:52]** Exactly. I'd love to hear the story because you have a very decorated career, a long time at point 72, I think going from that investor position into an operator role. I'd love to hear how that happened and what were you seeing that made you think that this was such a big opportunity?

**[30:14]** Yeah, I was investing in early stage AI companies and it was a really exciting time to be working on AI. It was right at the cusp of chat GPT and the kind of six months proceeding and immediately after chat GPT came out.

**[30:29]** And so it was clear that there was this new paradigm shifting technology that was available, but a lot of the use cases and applications still wasn't clear. It also wasn't clear the pace at which AI would improve and what would become possible in what time period.

**[30:45]** But it was clear that there was a transformative new technology and there was an opportunity that hadn't existed before. And so that kind of got me thinking about starting a company and thinking about what might be some interesting problems to go work on.

**[31:01]** One thing I saw a lot of early stage founders start to a little bit fall victim to is the shiny object syndrome a little bit of, "Hey, I want to go just work on AI and I'm going to go solve for a pretty easy use case."

**[31:18]** I think, you know, Sam Altman and the Y Combinator folks have talked a lot about how solving a hard problem is actually better as a company than trying to go solve an easy problem because there's so much competition around the easy problems.

**[31:34]** And there's more room to explore, fail, iterate, and become an expert when you're going and solving a hard problem than it is when you're going and solving an easy problem. And so paradoxically, not that building a company is ever easy, but paradoxically, you should as a founder go solve something that's challenging and/or is close to home.

**[31:57]** And I think I really internalized that from seeing the companies that were successful is that they were going and solving a hard problem in most cases. And the companies that were solving easy problems got some early traction, but then it became really hard for them to scale as the market became super saturated.

## 32:14 — Why Gradial chose workflow over content generation

**[32:14]** That was a lesson that I took into building Gradial is that, you know, at the time there was a lot of focus on content creation. That was the thing people were thinking a lot about. I can automate content and images and video.

**[32:27]** And instead we decided to focus on process and workflow, which didn't seem really appealing at the time for many folks, but for us was like, this is what really is hard about work is all of these things I need to do in order to get something out the door.

**[32:47]** All of the approvals and the checks and the different steps and the dependencies and the teams and the agencies and all this stuff that is required to be able to do something simple.

**[32:57]** That's what we want to go solve for, because if we can go solve for that, we're really solving for reasoning at a more fundamental level and we're solving for process and, you know, eliminating the BS work.

**[33:08]** Why would you want to eliminate content from marketing? Like it's, you know, why not eliminate the things that people don't want to spend time on today and then empower them to go focus on the things that matter.

**[33:20]** So that's really like the big lesson that I took away from working with early stage founders is that we saw this frequently is, you know, when we saw founders chasing a really challenging problem, it was, you know, their success rate was above what was expected.

**[33:35]** And when we saw founders chasing a really relatively easier technical problem, their success rate was lower than what we expected. And so that was a big indicator for me and, you know, where founders should focus when they're building.

**[33:51]** I love that framing. And I think it also makes it worth doing. I think you can get more excited about it too. And because, you know, helping founders build their businesses, building one on your own, you're going to run into challenges left and right.

**[34:06]** And if you can really believe in what you're building, and when you see the impact you're making with one of your customers and knowing that you're really driving change, that's the rocket fuel to just keep it going.

**[34:17]** And I think it's really hard to do that if customers aren't that interested in it, or it doesn't seem like it's really changing their lives. I think it's hard to get that motivation to keep punching through.

## 34:28 — What happens next: the premium moves back to humans

**[34:28]** So I'm curious where you think this goes. It seems like there's been such an explosion of AI capabilities and then this need to really harness it in and build the infrastructure.

**[34:41]** What trends do you think, do your best at whatever crystal ball you're using, what trends do you think will will happen in the next couple years?

**[34:50]** Yeah, I'd say like, there's been a big, you know, focus on like the agent to agent communication and things like that like everyone will have their own agent it'll interact with brand agents and go do tasks for you.

**[35:03]** I increasingly think something like the opposite is going to happen, which is that people are increasingly fatigued with AI and increasingly seeking and placing a premium on human interactions.

**[35:15]** And so I think we're going to start to see something a little bit counterintuitive in that, you know, there's actually going to be a premium for the ability to access or work with a human on things that are important, you know, so if assuming I have the resources,

**[35:32]** things like shopping or travel or financial advice, like things that we would have said are ripe for automation or agent to agent type of things.

**[35:40]** I think we're actually going to see something like the opposite where we're looking for ways that we can spend more time talking to people and less time trying to automate things with AI.

**[35:52]** Now, that creates more opportunity on the B2B side of, hey, well, I want to automate all the painful logistical operational work of a financial advisor or a travel advisor or a nurse or a marketer.

**[36:07]** And so that that's where I think the world is going to trend towards is something similar to what we're seeing in marketing, which is operations and like routine tasks are going to be done with AI and then the actual kind of the face of the brand, the content, the things that are, you know, relationship building,

**[36:28]** things that matter when it comes to taste or helping define the strategy or the future, whether it's for like a trip I want to take or my financial future.

**[36:37]** I think that's actually going to create even more opportunity and work for people.

**[36:44]** And then we'll start to see something like, you know, whereas previously I might have been able to support 10 clients tomorrow, I could support 500 because all of my back office and operational work is done by AI.

**[36:57]** And I think in the past, it's been something like the opposite of like, how do I automate this job or this role or this function or this, you know, the travel agent itself is an AI.

**[37:08]** I don't think, I don't think like everyone wants the travel agent to be an AI I don't I don't think everyone wants that to be a chatbot anymore.

**[37:16]** So now we're going to start to see this countervailing trend that puts more pressure on the technology to go solve the complex, the operational, the workflow, and then actually enables and empowers people to be the front door and relationship holder of that experience.

**[37:31]** So I think that's a, that's a more rewarding and fruitful place to be in general, then, you know, that some of the trends we've seen in the past year where it's like, try to automate everything gear, like I said before that's clearly caused fatigue and, you know, people

**[37:46]** aren't that happy to see that the AI content everywhere they go.

**[37:51]** I know this might sound like an off metaphor for this but when people have talked about this one I agree to the area where I feel like we've seen it is in food.

**[38:03]** So food became increasingly faster, easier, more convenient.

**[38:09]** At the same time ultra processed. And then there was just a massive shift like no I'm not eating another process thing like I need real vegetables, real meat.

**[38:21]** And then the whole push to organic and I will pay double for it like I don't care how affordable it is.

**[38:27]** And I think you're right. I think there's a lot of things where it's like, I don't care if I have to pay more for it, but I want to know that a human is verifying this stuff.

**[38:37]** That's a really good, a really good analogy and that, you know, reducing the varied entry and making things cheaper is not necessarily like yielding the highest quality of output and it really the quality.

**[38:51]** The premium is being put on that quality. When it comes to what what people are really looking for.

## 38:59 — A harness for every function

**[38:59]** You approach this, the enterprise harness really focus on on marketing early in the conversation you mentioned.

**[39:07]** Hey, I think there's going to be opportunities for these verticalized platforms harnesses.

**[39:16]** Where do you see other potential opportunities where something you're working on can similarly be produced for another function or another type of company.

**[39:27]** Yeah, I'd say there's opportunities for every business function. Right.

**[39:31]** Like what we're seeing is that there's, you know, the LMS are fundamentally very horizontal.

**[39:36]** They're enabling like single player productivity to the max. That doesn't mean that they can or should do every function or any function like they they're, you know, if you try to put the LM and the task of being a marketer, then you get the problem with the content being slop, etc.

**[39:53]** So how do we go and enable like the function specific learning to be a part of what the AI is doing the way that a function operates is fundamentally different than another function or someone in a different part of the business.

**[40:07]** And we think that there's a lot of opportunity for like function specific products that allow for a unified experience to connect with and talk to all the tools and agents to accomplish the hero workflows of that function.

**[40:21]** And I think we're starting to see that happen with legal with support with sales and with marketing. I think there's opportunity in every function to do something similar because it's really like a control tower.

**[40:32]** You know, the reality is that it's easy to build agents. It's easy to buy agents, which means that you need a control tower that holds all the context that understands the workflow very well, the approvals and the guardrails very well and understands your organization and the function very well.

**[40:46]** Because then if you have that you can plug in best of breed agents and LLMs and intelligence and people everywhere and you get the best output because you get this organized harness that can orchestrate work.

**[41:01]** Whereas if you just throw agents at the problem or you just throw people at the problem or you just throw tokens like none of those is going to converge to the outcome that the organization needs.

## 41:12 — Wrap

**[41:12]** 100%. And I really like the way you laid it out Anish, I really appreciate being on the podcast because I think your approach and perspective is really, really enlightening for myself and I know a lot of people listening.

**[41:27]** And I think some of the things you mentioned, hey, how can you control the spend, take advantage of whatever model has the best capability for whatever job you're trying to get done, manage the spend, manage the governance of the data who has access to data, who doesn't have access to data.

**[41:45]** And then also just get people focused on the most human part of the work. I think that's a lot of what a lot of us have been trying to build for a long time. How do we take away the boring and just let them do the creative part or the part that requires accountability in the job and trust building and relationship building.

**[42:04]** I'm really impressed with what you've built. I'm really excited to watch you and Grady will grow. You've already done a fantastic job. Congrats on the Series C and all of the amazing companies that you're working with.

**[42:16]** And I hope you come back to give us an update on how things are going in the future.

**[42:21]** Awesome. Thank you so much, Anthony. Really appreciate you having me on and yeah, appreciate the conversation.