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Building on Data Roundtable #1 - Building AI Products That Make Money
Recorded: Feb. 12, 2026 Duration: 1:11:01Space Recording
Short Summary
In a recent roundtable discussion, industry leaders from Weiner, Hela Labs, NodeOps, and Inflective explored the evolving landscape of AI products, emphasizing the importance of economic viability and user engagement. Key highlights included the launch of innovative tools like Neutron and CreateOS, strategic partnerships aimed at enhancing ecosystem sustainability, and a shift towards practical applications that deliver real value to users.
Full Transcription
Thank you. Thank you. Hello everyone thanks for joining Gurley so we can test the mics please request a speaking slot Thank you. Let's also please share this roundtable a bit around, share it with friends. at the end of the round table we're gonna give away some free subscriptions to inflective.ai
so make sure to stay around hey David David, can you request to speak?
Hello, hello.
GM, GM.
How are you, sir?
All good on my side.
You?
All good, all good.
Perfect.
Can you hear me loudly and clear yes we can oh perfect perfect
I always struggle with those spaces you know this time I decided to go on mobile
I think I think spaces are always a gamble let's say it like that, because either it works or it doesn't.
I see
Weiner here. I see
Hella Labs.
I'm going to try to invite you again,
Hella Labs.
Also Weiner.
I see Sheena requested okay
okay let's get two three more minutes for people to to gather up
okay so hella labs i see that you're a speaker now can you please just mic check
hi hi everyone hello hello great to hear from you
can you hear me clearly very clearly all right hi everyone and thanks uh for inviting us here and talk about the ai products that make
money so we'll start in a bit right yes so just give it a minute or two more and then we'll then Thanks.
Hey, Sheena.
Hey, hey, everyone. Nice to be here.
Thank you so much for having us.
Thanks for joining.
OK, so the last one, I think it's going to be Ify from Vayner. Ify, can you please join as a speaker?
Okay, now I see your request.
It should be fine now
I think that's everyone yeah hey hmm okay now now now we can hear you yeah now we can hear
you awesome i'm not mute anymore no no no you're good you're good you're good okay cool uh awesome
i think we can uh we can start it up so hey everyone uh welcome to today's roundtable it's
a completely new series that we're starting on the inflective.
And it's going to be based around AI, AI agents, data, and all the hot topics currently around
crypto and even web to space. So the theme of today is building AI products that actually make money.
And I think this is one of the most important conversations in AI right now.
Over the past year, we've seen huge engagement numbers, viral growth and impressive technical demos.
But revenue has often lagged behind.
Usage alone is no longer a single of success.
longer a single of success the real question is economic viability and i think we see this
The real question is economic viability.
all the time when we talk with you know we talk with partners we talk with vcs whoever we talk
they are they are not into numbers as much anymore as much as they are into like is your product
making revenue does your product has a potential to make revenue and uh and and stuff like that so
welcome uh our guests uh we have um we have a project from we have helalabs joining veinar
and no dobs so welcome guys and uh joining us of course is also also David, Inflective's CEO.
So thanks, everyone.
And I think it's best if we just start giving you each project,
two, three minutes maybe, to tell us your latest updates,
like what are you working on, and introduce yourself a bit.
introduce yourself a bit so let's just start with uh with iffy weiner
So let's just start with Ify, Weynar.
hi guys i'm uh i'm the head of growth at weiner and we've been in the industry for about 20 years
uh launched a lot of products from starting from games and then apps, traditional Web 2 ones, then Web 3 ones.
And now the AI era is here.
And no surprise, I'm now working on a lot of AI products as well.
So I think there's a lot of good learnings I would love to share today with you guys currently. I think the part of keeping me most busy is my Neutron,
which is an AI memory tool, pretty nifty tool actually,
if you guys haven't checked it out,
do check it out at my neutron.ai.
But yeah, that and there's a bunch of other products
that are gonna be rolling out as well.
So it's very different, the AI, you know,
definitely than the traditional ones. So it's very different, the AI, definitely,
than the traditional ones.
There's a lot of learning phases still going on, majorly,
I would say.
So really excited to discuss all that today.
Awesome, awesome.
Hela, you want to go next?
Yeah, sure.
Hi, everyone.
And thanks for having us, as I've mentioned earlier.
I'm here, Kate, representing Hela Labs.
And I just want to introduce what Hela Labs is,
since we are a layer one,
which is mainly focused when it comes to building
and an ecosystem in the AI as well.
So it's much relatable since we are having that conversation now.
And right now, we're very focused on the track that really connect the same goal when it comes to helping builders.
when it comes to helping builders, as well as the real products
and also to grow when it comes to sustainability.
And right now, what we're actively working on is the strengthening of the ecosystem
by supporting the apps and other projects,
as well as when it comes to the theme today,
which is the AI that make,
when it comes to making money,
the AI products that making money,
we're also focused on that practical side as well.
How products get adopted and what makes them stick and also what models that will make
sense in the long-term run so that's what we're currently moving and right now we want builders to not just launch, but also keep the users, control the cost, and, of course, the product that will make the money.
Oh, sounds like a lot is going on at your place.
Yeah.
Yeah.
Okay, thank you.
And last one, last but not least,
NodeOps.
Yes, super excited to be here
because it's been like a full-off action week for NodeOps.
So about myself, by qualification,
I'm actually a chartered accountancy in India.
But yeah, I was in the tax and legal practices
for almost a decade before I
shifted to the Web3 space and joined a couple of Web3 projects, but been with NoreOps from day one.
Obviously, worked in Web2 sector, Web3 sector, I mean, worked across the globe, cross borders,
etc. So what is NoreOps? Well, everybody knows NoreOps is basically, you know, unifying the
decentralized compute and intelligent workflows.
And we just came up with a new product, which was on 4th of February, called as CreateOS.
We launched it on Product Hunt.
And of course, it was ranked as the number one project of the day.
So what is CreateOS is primarily it's an intelligent workspace where ideas will just move seamlessly from concepts to live deployments.
So we are eliminating the entire thing about context, switching across tools, infrastructure, workflows.
And we are giving like an immediate pathways to actually monetize your built-in apps through an integrated CreateOS marketplace distribution.
I know too many words that I tried to cover in like 30-40 seconds.
I know too many words that I tried to cover in like 30, 40 seconds.
But in very simplified terms, CreateOS has tried to give a unified workspace
where you can have the entire cycle of building to shipping to deployment
and actually putting it on a marketplace to get monetized.
That's what we just built.
And proud to share because just like we are on this AMA right now,
but just about a few hours back,
we actually went live with our documentary,
which actually shows the first real-life use case of CreateOS.
So though we are an infra company
and as a role of, you know, simplifying infra
and bringing in the AI intelligent workspaces,
we actually completed a 75-day pre-launch deployment
across the massive cotton textile industry in India.
So we transformed the traditional old CCTV cameras into an intelligent CCTV infra,
which is able to get a real-time operational and compliance dashboard to the factory owners.
So obviously everything is out. I mean, I know it's a very long thing to actually explain on this AMA
but in very simple terms that you know we have not only built another AI infrastructure support
but we have actually showcased that how it can creep into traditional sectors at factory floor
levels and it's the time that traditional factories who don't even understand what AI is are able to adopt it seamlessly.
Yeah, so that's just like a quick sneak peek about what NodeOps is up to.
Amazing. I mean, I'm really honored to be surrounded by this amazing project.
It's very clear that, you know, web 3 in general has moved away from the from the hype and
from the from from projects who are really shallow and only the the the best ones the ones that are
actually building are still staying around uh while we are building, David, I want to share some exciting stuff we have
at Inflective for those who don't know us yet. Hey, GMGM again. David here, CEO at Inflective.
Yes, for those who don't know us, what we are doing. Inflective basically transforms documents,
device signals and to expertise.
And we turn it into structure tokenized intelligence,
which is the core fuel for AI agents and robotics.
So in short, we liberate knowledge,
which is trapped in PDFs and in locked expertise.
You can imagine like SOPs or any PDF files, etc.
So we are liberating this knowledge.
Last week we really did a great update.
Actually, we have now the second build out and we introduced the marketplace.
So we are starting monetizing the whole system.
And I can explain a bit more in the next minutes.
Awesome. Yes, the new launch really adds a lot to our platform.
So if you haven't checked it out yet, go to inflective.ai and
check it out. Okay, now let's move to the roundtable and to the debate everyone is here for.
So with all the guests before this roundtable, I share the topics that we're going to cover,
and each of the guests has stepped into the topic that they would like to discuss and share their view on. So we're
going to start with the first one, where Hela, NodeOps, and David tapped in. So over the
past year, many AI products have shown strong engagement, but weak or inconsistent revenue.
Usage alone no longer feels like a reliable signal of success.
Why do so many products show real usage but struggle to convert that into sustainable revenue?
And maybe Hela, you can go first.
All right.
Thank you for that question.
And that's a very relatable one since we're talking about the AI products that really make money. So this one is very super
common. And honestly, it's not because the teams are not doing wrong or they are doing wrong or correct. But when it comes to AI products, it's just easy
that we'll try and easy to talk about. So when it comes to AI, you can So a lot of usage is, or a lot of user is more into curiosity.
So they are more into exploring things or they want to test it or use it for some small tasks.
But the moment that they connect the product to a real outcome it will like just
saving time and also improve the accuracy and also reduce the cost when it comes to paying
but when it comes to the real products at Hela I think it's more that we try to stay grounded when it comes to that real usage.
And because we love innovation, we always ask what's the outcome of every AI products that we will release and who will actually benefit from it. So because that usage alone isn't the finish line that we are only thinking, but it's more
of we are thinking in a long-term basis.
So for example, in Hela, if we build a tool that helps teams or people when it comes to launching their projects faster.
It's by simplifying integrations and also automation
so that we don't measure when it comes to how many people have tried the apps.
But we track it whether the team keep using it weekly
and because it reduces friction and also when it comes to speed up of the real system.
So what we really solve in Hela is that we help really the apps or the projects itself.
It's that we help them turn the AI into something impressive
and also we focus on the clear outcomes
when it comes to real usage of the AI product itself.
Yeah, and I think in these days,
a lot of products look really good on the outside yeah but what but what you just explained
makes complete sense right the the real usage is the problem the this is something that something
that's below the hood it's it's uh it has to be just as impressive as a nice ux ui to start with
so yeah awesome Thanks for this.
No dobs, you want to go next?
Why do you think so many AI products show real usage
but struggle to convert that
into sustainable revenue these days?
Maybe I can jump uh yeah i i've seen i've just seen that sheena dropped off and now she she came back but i think i was for a second too late okay ai product you know what typically i'm audible
yes yes you are now thank you okay all right, yeah. So what I feel is that, you know,
like this whole new vibe of vibe quotas
and people trying to build something new every day.
So whenever there's a new AI product that comes,
it's typically very amazing, right?
Because this is something that, you know,
we thought could happen, but it happens.
But it's like the whole curiosity,
which, you know, you just want to give it a try,
understand it, explore it.
But what really needs to be answered is that, can I like literally use it in my operational day-to-day work?
Is it something that I cannot run my business with?
So it's very essential that whatever you're building, whatever you're coming up with,
you should have a very clear, what is the problem statement?
What are you trying to solve?
For example, when we came up with CreateOS, we knew that there are like a lot of
AI tools already. We knew that people are already quite dependent and, you know, quite adaptive to
all the like GPTs or building up in Cloud, etc, etc. But what we felt was that there was a real
one bridge that was missing, that you're able to build something, but where are you deploying it?
How are you shipping it out? So that was a clear problem statement that we identified.
It was not just that we were in the heart
to create another AI tool,
try to give competitor analysis,
have some people try it.
You know, we make a hype for a couple of months
and then what's next?
So the moment you have a problem statement clear,
you know what you're trying to resolve,
you automatically know how to upgrade it with time.
You automatically know how to adapt it with the narrative that's going out.
So what happens in a lot of AI tools right now is that people give a try, they generate
few outputs.
They may even use it like weekly, like, you know, the free trial period and then just
cancel out the subscription before everything goes live.
But are they really trying to replace a real cost
or like a painful operational problem?
So let's pick up an example.
These days, there are like so many AI writing tools out there, right?
There are so many of them, like plethora of them.
Like everybody feels that, you know, they can try to experiment
and actually become the content writer
and replace the marketing team altogether.
But we are just
experimenting there with the post and we're just trying to see what goes well, what doesn't go well.
But unless it is directly trying to actually drive that lead or serve a marketing hire,
it still remains as an optional tool, right? It doesn't become like irreplaceable for something
which is already existing or what you already have. So what I feel is that usage is trying to show the curiosity. But anything that you're building out
is has to have that harder product challenge that you're trying to resolve. And I think the ones
that is very clear, you know what is going to survive, what is just going to come like a wave,
it's going to come like a trend and then just disappear.
It's not going to sustain.
So that's like my typical view on this point that you raised.
Yeah, that makes complete sense to me.
I'm still yet to see an AI tool that completely replaces marketing.
Because I think that marketing is not just writing social posts, but a lot of, a lot
of other things around it.
So, and it's just like with the, just like with the vibe coded tools, right?
That people, people go on the website website they see a good tool and they say
oh i can build that you know tool just like that but then they wonder like why is mine not as
successful as that one but they don't know the the you know the sweat and tears and how many hours
did the founder of that tool that they copied put into into that, put into promoting that, going to events and stuff like that.
So usually the successful ones, I think,
comes with someone who actually cares about the product as well
and knows it ins and ins and downs,
not just something that was put into AI.
So AI helps a lot,
but still there needs to be someone with passion behind it.
David, you wanted to say something?
Yeah, I agree with both girls that were talking before me.
I would add that, you know, we have AI products that, you know, have real usage but struggle to convert.
Yes, I agree. Why? I think because it's AI is still complex.
If you see, I mean, everyone knows JGPT. It's really easy to use, right?
It's really useful and it's not just nice to have I
use it daily but why I see with the you know with the products let's your own
crypto Twitter or on X fit right is that it's not easy to understand so yeah
there might be a real usage behind it, but, you know, it just doesn't convince me to click on it, to try it.
The last such product was OpenClaw.
I mean, I'm not a techie guy.
I see the value, a huge value in OpenClaw, but then I started reading how to, you know, download it, how to set it up, etc.
I was just like, bro, this is not for me.
It's just too complex.
It doesn't outweigh the value because it's so complex.
So I think that all the AI products must convert from nice to have and from buzz wording to must have that would be my take on this one
okay interesting interesting so we we are moving to to the next topic as ai systems become more
autonomous traditional pricing models start to feel strained costs scale differently and value
is not always linear with usage so if i'm gonna go to to you with this one are subscription models
still a good fit for ai products or are we forcing old sas logic onto new systems. What do you think?
I think a pretext to this would be that AI technology,
especially its costing for companies,
is still in a learning phase.
Like as David just said, it's complex. So, it's even more complex for users to understand
if tokens and compute and GPU cost and all that kind of stuff. They haven't been exposed to this
information before, this way of billing or anything like that before. So, are we kind of forcing the old subscription model onto this new AI product era?
Yes, we are.
I think we are literally forcing it.
It's not how most AI companies would be able to price their products,
products, like just a simple SaaS-based subscription model.
like just a simple SaaS-based subscription model.
Because traditional SaaS products, let's say a CRM system has transactions going in and
out and maybe invoices going in and out, there's not enough compute going on, whether somebody
does 10 messages or 10,000 messages, the infra doesn't explode or anything like that.
But with AI, it's different, right?
Like most of, there's a lot of processing going on
in the background for image generation, for content,
for just reading through code bases.
There's a lot of that happening.
So even a simple instruction from the user, you know,
just figure out what my code base does and, you know,
create a feature list or something, is one line from the user where it is tons of compute in the
background and that's what the company's cost is, right? So they have to, we are still in a
learning phase where I think most companies are trying to figure out how we can make it so it's a bit more predictable
for the end users because that's what the end users want, right? They want
you know that it's a hundred dollars per month or two hundred dollars per month or whatever it is.
Nobody really wants the pay-as-you-go style where, you know, it just,
at the end of the month, you get a message,
oh, you were super productive,
so now instead of $100,
you're gonna like pay me $500, surprise.
And we see that a lot.
Like, I mean, there's AI tools I use
where I honestly, I'm very experienced with these.
I, you know, would like to say
that I'm able to calculate
how much it's gonna cost for a product user.
But still, when you get the invoices,
they can range from like $400 to $4,000.
It depends on how much you're getting to use the system.
And not just you as a company, but the users as well.
You don't know what kind of work the users are going to put your system through and what
kind of stress they're going to put on it.
So I think subscription models like we're seeing right now in most of our products as
well, in MyNewtron as well. We do have subscriptions. But they are in a way where the user can actually,
first of all, try for free because acquisition is very important with new types of products like
these. And there's a lot of new products out there. Like Shina was saying, there's like
products popping up here and there. And you want to go and you want to try. And the ones that have
good onboarding experiences, you can just log in with Google and try like money, try on.
And that's where you're seeing a lot of people signing up
to these AI products, trying them out
to find out if that's their, like,
actually need what they need in their workflow,
in their business workflows, in their daily life workflows, right?
So just for that to happen
and for that onboarding to be seamless for users,
I think that's why subscription model is in place right now,
which I feel is the most successful
with of course usage clears on top, right?
So for power users,
they can then go ahead and purchase more credits
because I think without having credits or whatever
top ups you're kind of limiting the power users and they're the actual users they're going to
become your niche and they're going to stick with your products the rest of them probably
are just going to try it out for a few weeks and then leave which is fine you'll get a lot
of feedback from them but again they're not your actual customers long-term.
The actual customers are the power users,
and you need a way for them not to get blocked
from whatever they're doing.
They should have an option to just kind of top up,
continue using or whatnot.
So I think that's why right now for most products
that I see subscription plus user based pricing is in place.
I think in a future world where our customers are more educated
about these tokens compute GPUs, perhaps pay as you go might
work better.
It needs to for AI products to be successful because, again, as I said,
there's a lot more hidden costs associated with using AI tools from a business perspective
than there is in normal like traditional SaaS tools. So like for example, AWS tool, right? AWS has a pay-as-you-go tier that works today, but if, and because people understand what
they are getting, the technical terms in it, but most of the AI tools that are coming out
are not for technical people.
They're just for like normal people.
So if you have like pay-as-you-go pricing on like an artist that wants to use a system and you tell the artists about tokens and GPUs and CPUs, although some of them are well educated on this topic, but most of them won't be.
And that's why we are kind of forced to be stuck with subscription for a bit, I would say.
But definitely it's not probably the best thing yeah I
agree I agree and I also kind of agree with you that the AI tool needs to have
like a free tier or something you know to give you a taste before you commit so
so subscription model still feels like the most optimal for now but
as things evolve i think this will evolve as well uh david's what david what's your view on this
um i was listening to if you and i i um i mostly agree um however, I always look from, you know, from every Joe perspective, from normal user, you know, and we are used to pay for subscriptions like, you know, Netflix, Spotify, etc.
really the easiest way to you know get the get the uh the product in your hands right so
from user perspective subscriptions are really zero effort what's happening behind are all the costs
in in the you know internal costs of a company and i do agree that subscriptions are okay also for AI products, but they must be or should be
or will be some sort of modified
or some sort of hybrid.
Like we introduced last week
an inflective monetization part of a,
let's say, web two part of a solution.
So we also go with subs and credits.
So it's some sort of hybrid.
And, you know, we are acting as a startup
and pivots are the most common thing to do.
So from now on, we need to be data-driven
and just see what's the best mix of subscriptions
and credits, et cetera.
So looking forward to it.
Awesome.
Thank you very much for this. Moving to the third topic of the day is AI infrastructure
costs often look manageable early on. And I think if you touch this a bit with your previous response
and then escalate quickly once products move beyond experimentation. Many teams only
discover this after real users arrive. Where do teams most commonly underestimate the true cost
of running AI products in production? And I'm going to go with no doubts to start with this one.
Oh yes, definitely. Why not? I mean, just launching a new whole product yeah we did have a
good amount of r&d going behind this so i think typically what i feel is right now when a team
like a i think there is always an urge and a rush to release something so what teams typically do
is that they only budget for the model for the api billing and you know they try to create what
could be just like a simple wrapper solution or something but you know when the production like it's all into the production stage and you know
you are doing all the testings etc the model is just one piece of the stack it's not like the
final product that you're bringing out right so during testing probably you might have just spent
like a one thousand or something just for inference and getting you know, the early testing ideas, etc. But then
when the real users arrive, obviously, the prompts are going to get longer sessions are going to
increase. And definitely, it's a very good idea to have kind of a roadmap properly laid down.
And then, you know, there'll be concurrency spiking. And suddenly, you know, you just realize
that you're somewhere sitting around like an 8 to 10k. Your cloud usage is going to increase.
The backend support that you're providing, everything is going to increase.
It's going to multiply with the kind of scaling that you have imagined.
So from a simple testing, it's like a full-fledged product which is going out.
And that's even before adding any kind of monitoring, fallback models, the security layers that have to go in, right?
I mean, you just can't avoid these very key ingredients
to keep the product up for time, running,
and able to support the real demand that's coming in.
So I think what happens is in this whole process,
it's not that AI in production is not just anymore the model
that you want to bring out, right?
It's a running full operational system.
And I think when all this is not properly anymore the model that you want to bring out, right? It's a running full operational system. And I think when all this is not properly captured
and that's where the real, you know, backlash has come.
So where you imagine something that, you know,
would be going with the next price
and just, you know, in the hurry to just, you know, budget it,
just thinking that it's just a mere API costing.
That's where everything gets stuck.
So, I mean, I can tell you from my real example. So when Noros was doing, API costing, that's where everything gets stuck.
So I mean, I can tell you from my real example.
So when Noros was doing, so we knew that if this is what is coming for the initial testing,
everybody in the team, be it a tech person or not tech person, we had proper prototyping sessions.
We had a lot of discussions, a lot of R&Ds, people trying to build and understand what's
happening around.
So there is a lot of, you know, work that goes into the production stage.
So, yeah, that's like a clear, you know, trying to link up your question with even a real
experience that we have been seeing from the last few months.
Yeah, that makes a lot of sense.
Thank you very much.
Hello? You want to add something here? extreme cost in AI production or the cost of the production itself.
So the demo stage is kind of, they feel that it's kind of cheap and smooth.
But when it comes to the real users, when the real users hit it,
the hidden cost will pile up and shows up.
So it's not all about just the tokens or the model itself,
but we also recognize the retries,
the small issues that the team is having
when it comes to real projects,
and also that's where the real cost is.
So that's why we also here in Hela often use lighter models
when it comes to simple tasks and we only use heavier models
when it comes to tasks that is truly needed so that when it comes to higher cost,
it will not explode and the real users will not be a mess when it comes to the real users.
So, yeah.
So, I agree with Sheena when it comes to the second question.
I would just like to add that I've been also around now in the space for a while.
Previously with Vayner, when we were building AI, I've noticed basically everything that you two were just saying now,
so completely agree.
Moving to the next one.
Some teams prioritize rapid capability expansion
while others focus on controlling costs and narrowing scope.
Both approaches carry risks.
So Ify, I'm going with the uh this one with you first
when building ai products should teams optimize for capability growth first or or cost control first
ah so this is like a continuation of the last right so we know that there's a lot of hidden costs that's gonna show up later that people are not aware of.
So you could go ahead and start building a product
that is very cost efficient,
but then you have accidentally built
a really, really boring product, right?
And people don't fall in love with something
that's just efficient or something they they
want the wow moment um you know that something saved them like endless hours or they created a
beautiful video or something like that so i think i have a different opinion from uh some of the
other producers out there i guess uh. Mainly because I'm like,
I think I started with the entertainment products first,
like games and movies and shows in my earlier days.
So in there, I guess it kind of stuck to me till now,
where whenever I'm involved in a product,
I make sure that costs like running costs,
operational tools, that kind of stuff is not limiting us
from making an amazing experience for the user.
That should be the first priority because I think
the age-old business kind of model is,
I use like our model, I think you guys probably heard
about it, ARM, like acquisition, retention, monetization.
So the first step is our acquisition like what can i do uh to make this product look amazing
so people actually come in there they use it and they want to keep using it right uh kind of like
the click up versus jira right if you look at the you are like ui uh quite a bit different there
even in their ads and everything so So that's where I come from
it, yes. But that means that I am aware that I'm building something that is probably not
cost-efficient. So I want to make sure that when I'm developing, I'm not deeply integrating any of
the third-party APIs or any of the tools that i'm using which i get stuck with kind of so when
i'm building the product i make sure that the tech team is designing something that all of these
tools can be replaceable either by other third-party tools or by something we can build in
house for in the future basically but once the acquisition is happening and I see the potential of the product that yes,
people are signing up, they're using the product, that's when retention comes in.
And retention phase is where I usually do a yes, the product improvement in order for
users to stay in the product based on their feedback usually.
And also the cost optimization there.
That now that the real user is coming in,
now I actually have real data to look at
and real costs to compare against.
Before the retention phase, you know,
it's anybody's guess really.
It's just people sitting in a meeting
taking educational guesses, definitely.
But I think the industry is just too new
to have experts on this topic
of somebody trying to accurately estimating
that what the cost is gonna be
when a million users hit or something like that.
It's just gonna be really dependent
on how the users are gonna use your tool.
So I think in my opinion,
just make it magical first.
In the retention phase,
go ahead and make it a little bit more sustainable
so people keep using it and it starts costing you less.
And then once the monetization phase comes,
which is the third phase.
Now you're ready to actually just optimize like crazy.
Make your own in-house tools
for some third party API you're using maybe,
or use cheaper ones or do partnerships with people
to cut that cost down.
There's lots of incubation programs.
There's lots of partnerships that actually give you grants
that can also reduce your cost significantly or discounts.
So this can all happen if you have users that are using your app,
you're able to monetize, then all sort of these partnerships
will open up to you and you can reduce the cost from there.
But I would really, in my opinion, I discourage people
to cut the cost
as a first step.
Not good for the product.
Thank you, Ify.
Hala, to you.
OK, maybe we go to NodeOps.
Oh, sure.
So, yeah, I think I totally agree with what was expressed by Vanarchein.
So, obviously, you cannot have both of them getting up to each one
because essentially what you're doing is you're just either slowing down
the entire learning process or you're just making yourself a very constrained product. So that's true. But again, once, you know, I mean,
early, you need to obviously ensure that there's capability added to it much, you know, what you're
trying to design and deliver. So you'll be prudential about the cost that you're, you know,
incurring, but it's not that you try to control the cost on day one. But yes, once value is validated, you know, you have to definitely shift to a cost control
because that's what is going to make you sustainable.
If you decide to scale, you know, ensuring the cost is multiplying or multifold
you will end up not being
sustainable but you will try to quote a very
absorbent subscription product thing and that's
where the product moves from being active
actually getting into a common stage.
So here in
short in a single line
you have to try to do both
but you don't start to optimize both
at an early stage.
And the sequencing here will be really, really later.
So you go with the flow.
You try to aggressively control the cost,
not before you've proven the value.
Otherwise, you're just going to be slowing down the entire learning thing.
But after you have scaled,
you cannot be ignoring the cost factor.
I 100% agree with you here.
Hela Labs, do we have you here now?
The question is about the usage-based pricing, right?
I can repeat it.
I can repeat it.
No problem.
When building AI products, should team optimize for capability growth first
or control costs first?
I think for the startup, it's much better for controlled one
because not all projects have the same or a large amount of when it comes to budgeting. So it's much if you're in a startup or just starting up
and no funding at all and just by yourself,
it's much easier if the fund is controlled or the cost is controlled.
okay thank you very much moving to the fifth topic is usage-based pricing is often presented
Okay. Thank you very much.
as a fair and flexible model for ai in practice it can create unpredictability for both builders
and users and i think if, you touched on that point
in the previous questions.
So I'm gonna go with you first here.
Does usage-based pricing actually align incentives
for AI products or does it introduce new friction at scale?
So again, yeah, from the business perspective, super fair, right?
There's a cost that you're incurring and you're just putting a margin on top of it and just
forwarding it to the user.
But in reality, from the customer's perspective, it's pretty stressful as you go because psychologically, it changes how you use the tool.
And I think we've all experienced it when the image generation tools came out first.
When you know that you're paying for every image that you're generating, you're going to experiment a lot less. And if you, like, if we see from our chat chat
that we do every day, experimentation is what you do the most with AI. You know, you want
to have different variations of your docs, your images, your videos and whatnot. But
keeping it usage based from user's perspective users perspective, like they kind of discouraging
them to experiment because every experiment is now costing them. And that's not really
from AI's perspective specifically is not how AI is used. It is entirely that you experiment
with this AI buddy of yours.
You ask a question,
so you give you five different answers and try to please you.
You'll tell it don't please me,
that team all that kind of stuff.
But if,
if all of that is,
it's got a pay as you go thing on top of it,
then you'll try to be from a customer's perspective,
especially if it's using being used in workplaces,
you're going to be very like trying to be from a customer's perspective, especially if it's being used in workplaces, you're going to be very trying to be cost
efficient. And I mean,
you see it in workplaces even now where
project managers will be running around and
asking the design
team, hey, what did you do this month, man?
The bill is like $5,000 this
month. You got to stop
somehow and reduce the usage
or somehow. But from a designer's perspective,
this is a real creative killer. The whole reason why he's using AI is to quickly get
ideation going mostly and then using it to perhaps finalize in different things. But that's where I think usage-based pricing is very stressful,
to say the least, for the users.
But what can you do?
Because without it, I think no product will be able to sustain.
So I think it's still a riddle to be solved.
I think somebody will come along and give us the
magical answer to this problem but as it stands now i i don't see how we can avoid it for the
power users luckily chat gpt is not that, because otherwise I would probably leave my salary there.
Just work to chat with ChatGPT.
I think if they would be giving an award for most usage, I would probably get a platinum trophy already.
So, really happy that they have this tier system.
Okay, thanks, Ify.
David?
Yeah, when I was listening to Ify, oh gosh.
I mean, it's a chicken-egg problem here,
you know, whether this pricing reward outcome
or just penalize exploration of AI tool.
So it really depends from case to case, I believe. just penalize exploration of an AI tool.
So it really depends from case to case, I believe.
I would be lying if I say that we have figured out the perfect pricing model at Inflective.
For now, let's say that we are satisfied with it.
We have subs plus credits.
So also some sort of way as you go.
But yeah, I would say that it's from case to case
and it depends.
I can't really say much rather than this.
Thank you, David.
Who else do we have for this one?
Hela, Nebs, you want to add something here?
Yeah, I think for, because we mainly were into user perspective,
so usage-based pricing sounds fair at first,
but when it comes to the real usage
or the moment that the user started using the product seriously
and then the cost piled up.
So that's where the limit or the user ends the subscription on our end.
So on our perspective, it's very good that they or the project itself have a hybrid pricing.
And also, there's a base plan for it so that other users or the community or our community know what they are committing to and
how much they will pay for it.
So it's basically
easier to budget and also
it's basically
builds trust when
it comes to the real users.
Awesome. Thank you very much.
Moving to the next one and David I'm going to start with you once
for a change. Not all signals of product market fit look the same in AI. Traditional SaaS metrics
don't always capture what's really happening what signals tell you an ai product
is economically economically viable not just interesting or technically impressive
yeah that's a that's a good one um
when it comes to to product market fit um I know from previous Web2 projects, and I think that we're going to still use the same metric for Inflective 2.
knows that you reached a product market fit, right?
Is when you say to 40% of your users,
let's say four out of 10 users,
you just tell them the fact
that you removed the product from the market.
And if they are, let's say, angry and upset
because of such activity
and they, let's say, disapproval this move,
you know you have a product marketer because the user lives worse
without your product rather than with your product.
So this still might be the case also for the AI products.
And I'm really looking forward to
conduct such a research on
Inflective, but I think it's not the timing
right at the moment.
We'll wait a bit, but yeah.
Once you see that the users are
suffering more without your product
rather than with your product.
Well, if they suffer with your product, it's never a good sign, right?
I mean, without
it, sure.
Well, yeah, I need to make a disclaimer,
you know, English isn't my
name. Yeah, yeah, we always
use this one. We always
use this one. Everyone does.
Okay, okay, cool.
Thanks, David. Hela?
Yes. You want to contribute to this one
yeah I think
it's basically
I consider
a product to be
impressive
when
people are coming back
and they
you're no reason to
keep chasing people but they are always coming back.
And the second one for me is they are willing to pay.
And I think is most commonly if basically if the cost will not blow up, then you get then they get the value of it.
So it all comes back to people will always come back and also they are willing to pay how much or whatever price it is.
they are willing to pay how much or whatever price it is.
Yeah, and I also think if they are coming back without incentives,
that is also a very good sign, right?
That it's not that big of a problem to keep getting people back
if you're giving them candies for every stuff that they do.
But once that shuts down, if they still keep coming back
and use the product, then I think you actually have something there.
Now, Dops, you want to add something?
No, actually, you covered.
In fact, I was about to say the same point.
I was to speak after Hela Labs,
that if a user is renewing the subscription,
coming back to you,
irrespective of whether you're running a season discount
or you're running some freebies along with it,
but it doesn't matter.
He's repeatedly recharging the subscriptions.
So that definitely matters.
And yeah, I think for real economic
viability like going back to basic
economics the demand and supply right
so and obviously measuring
your ROIs
so that is definitely
the real signal the real usage
number of users like at least
for NodeOps case
we've always very you know
transparently even shared the dashboards and our revenues.
Like we have dashboards where you can see month on month revenues, you can see quarter to quarter comparisons.
So I think a very sustained graph is what looks beautiful.
For me, having a massive spike, say probably during the Black Friday sale and then having really nothing in December, I don't think so.
That's a very strong signal of an economic viability here.
I would love, of course, the love and, you know, the eyeballs that we get during a good
Black Friday sale.
But, you know, that not being one of the main factors to come back to my AI product,
that is what, in my eyes, actually shows real economic viability.
AI actually shows real economic viability.
Great.
Great.
So for the last and final question,
I'm going to go back to you again,
but I'm just going to read it out first.
So across both Web 2 and Web 3,
some AI products have quietly found
sustainable business models
that don't follow the dominant narratives.
What monetization approach
have you seen work for AI products that surprised you?
So I don't think so there's any particular playbook.
I mean, people have come up with, you know, great ideas, you know, trying to monetize the infra execution layers better than, you know, just
a simple AI output, right. But I think I wouldn't be like saying that there was any kind of a wow
moment. But wherever on products where users have been paying for reliability and control,
without, you know, having second thoughts on it, I think the stickiness that they try to bring in,
having second thoughts on it.
I think the stickiness that they try to bring in,
that is something which doesn't surprise me,
but that just ensures that users just don't pay for any tool
because it's intelligent.
They are primarily paying for the reliability and control
that they're able to get.
So I think for me, I think in simple terms
would be more about mapping the reason why they have to,
you know, be having a paid model to a particular
AI product. And just to finish it up, staying on this topic, I have a question for you. And I'm
going to ask the same question to everyone. What is the latest WOW AI tool that you're using right now? If you would have to say one, which one is that one?
Okay, so I'm pretty much loyal. Create OS. The reason is because Create OS is also able to
integrate your existing MVPs. So for example, if I have a real comfort zone in GPT, I can actually just directly integrate and deploy it.
So I start my day with the unified workspace
that CreateOS provides.
So that's like my go-to workstation.
And that's where currently my rest of the AI tools are sitting.
So for me, it's CreateOS.
Do you like when products keep evolving
and adding new features in?
Or are you more like a typer?
Like, I already have what I wanted.
I don't want new features.
I don't want to learn new stuff.
What's your position?
No, no.
So I, like I said, right, I'm like 90s kid.
I have like spent my 10 years of my experience working on Excel sheets.
But today I'm very confidently using all the AI tools out there.
And for me, an upgrade is always very exciting.
And though I'm a non-tech person, you won't believe,
but the moment CreateOS has an upgrade,
I am the first one to knock doors to my tech team.
And I actually end up making a video content,
trying to explain my rest of my folks who are like typically still working on Excel or just working in a very traditional Web2 environment.
I start training about them because I love the updates.
I'm so excited, so overwhelmed.
And I ensure that it's absolutely up to date.
Yeah, this reminded me when you said 90s kid, reminded when i was talking to my you know 15 year old
just the other day when he was playing some some games and he was like pushing 100 buttons at the
same time and i was like in my days we were using paint right and and we we got so stressed if we
wanted to color something but we had a small you know small dot that we didn't cover and then yeah and then
everything everything turned red and then you know yeah we were so stressed about it and they
are like now building and everything and just like so i i'll just tell you one example that
we're talking this the moment create os went for an alpha testing to us and they said she now why don't you build something i actually built the nokia game the snake game and i just
sent it yes and you won't believe like today's kids they were like wow this is so engaging i'm
like you guys are the minecraft kids how are you saying this typical game of mine is so engaging
so it was completely a very different experience. And yeah, I played the snake game
with my own UI UX experience.
That's crazy. Thank you. Thank you for sharing this.
Okay, so
we don't take too much of
your time anymore.
Hela, would you want to
add something here?
I think it's
already said and done by Sheena
when it comes to AI explorations
and what are the things that are really users
or what are the real usage of the AI products as of now for people.
So yeah, that's it for now. I won't do so much of the time. Same question for people. So yeah, that's it for now.
I won't lose so much of the time.
Same question for you, final one.
One AI tool that you're using right now
that it's like your wow tool
that you always go back to.
Currently, on a personal perspective,
since I'm into a tech system, I'm using an AI tool, N8N, wherein I build systems in one place.
So it will turn into a product and the workflows into one system.
close into one system.
It's like an AI feature
that you will be adding
to automate all of your systems
into one dashboard.
Oh, nice.
Looking forward to test that.
Yeah.
Okay.
David, and you,
I know you're still using
Nokia 3310.
So, what's your favorite tool on that thing?
To be honest, the last one I used was Inflective.
I don't want to shill it.
But no, this is basically the reason why I started the whole idea around Inflective was when I was writing my academia research and I was struggling with verify data, et cetera, et cetera.
But this might be the story for the next time.
But yeah, I do still use Inflective for my academia research and it's really helpful.
for my academia research, and it's really helpful.
Beside that, I was really impressed by OpenClaw,
but I failed due to lack of technical skills, to be honest.
So waiting someone on X to show me how to do it for dummies.
So I'm looking forward for this one for sure.
Well, maybe you can just ask one of our devs.
You don't need to wait for X to show you.
We have developers who know this.
Awesome.
Okay.
Thanks everyone for joining.
It was a great pleasure having you all.
We'll be doing this round tables every week, every Thursday, 3 p.m. UTC.
We'll, NodeOps and Hela, I know that we'll see you guys soon back on this once again.
Thanks everyone again for joining.
It was a great pleasure and speak soon.
Thank you very much. Have a great day. Thank you. Bye-bye. Thank you. Bye-bye. Bye. Thank you. Bye.
