Code Story: Insights from Startup Tech Leaders
S12 Bonus: Tobias "Tobi" Konitzer, Growthloop
26 Mar 2026
Chapter 1: What is the background of Tobi Konitzer?
But now we face the second big question, which is, okay, you have this thing that you want to build. Maybe you just build a new product and you call it Toby's Brilliant Autonomous Marketing Decisioning Agent. Now, as you can tell, I'm not a product marketer, obviously, because hopefully nobody calls it that.
But you build this product and you go to a dark room and you bring all the smart people, hire the PhDs, and you gravitate towards building this thing and writing the Bayesian reinforcement learning models on the whiteboard. You feel really good about yourself. Nobody wanted what we built. So I've really reworked the way that I'm thinking about tackling these big problems.
My name is Toby Konitzer. I am VP of AI for Growth Lab.
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Chapter 2: How has Tobi's career evolved in AI and decision-making?
Six months moonlighting. There's nothing on the back end. Who share what it takes to change an industry. I don't exactly know what to do next. It took many goes to get right. Who built the teams that have their back. A company is its people. The teams help each other achieve more. Most proud of our team. Keeping scalability top of mind. All that infrastructure was a pain.
Yes, we've been fighting it as we grow. Total waste of time. The stories you don't read in the headlines. It's not an easy thing to achieve, mind you. Took it off the shelf and dusted it off and tried it again. To ride the ups and downs of the startup life. You need to really want it. It's not just about technology. All this and more on Codestory.
Chapter 3: What is Growthloop and its unique approach to customer data?
I'm your host, Noah Labhart. And today, how Toby Conantzer is driving compound growth by introducing customer data and agentic intelligence. This episode is sponsored by Mesmo. If your team is collecting large volumes of logs, metrics, and traces, but still struggling to get timely answers, Mesmo can help.
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Chapter 4: How does Growthloop utilize AI for autonomous decision-making?
Just get a dot tech domain. It instantly tells investors and customers what you're about. Don't overthink it. Secure your dot tech domain today from any registrar of your choice. This episode is sponsored by Unblocked. Unblocked is the context layer your agents are missing. It synthesizes your PRs, docs, Slack, and tickets into organizational context that agents actually understand.
So they make better plans, write higher quality code, use fewer tokens, and require fewer correction loops. If you're running Cloud Code, Cursor, or any agentic workflow, Unblocked is worth a look. Learn more at getunblocked.com slash codestory. Toby Conitzer was born in Germany, studied cultural studies as an undergraduate student. Eventually, he went to Duke to get a PhD in political science.
And that eventually changed to become a PhD in computational social science at Stanford, which is basically writing code to answer social science questions. After graduating in 2017, he joined Facebook Research for a year, then founded two AI startups. But outside of tech, he has two young daughters who he likes to spend time with and take to the park.
He used to be an avid trail runner, but his favorite thing to do is think, and do so as often as possible. For the last 10 years of his career, Toby has been chasing optimized decisioning and outcomes using AI.
Five months ago, he decided to join his current venture and use AI to shift the conversation from tooling for marketers to using AI to build an autonomous decisioning system that learns and improves over time. This is Toby's creation story at Growth Loop.
So GrowthLoop, before I joined and still, is what is called a composable customer data platform. So essentially what that means in the marketing world, without having to pull any data out, so zero copy data, customers can use GrowthLoop, which unifies data automagically on the customer data warehouse and then serves two marketing applications on top of that. One is audience building.
The other one is journey canvas or journey building. So imagine you're a marketing person and you say, look, I really want to build an audience of people that have a very high transaction volume and they open my last email. Usually that data is siloed. The email engagement data lives over here and the transaction data lives over there.
And so the workflow would be you ask your data engineer to pull that data for you to unify it and write the SQL code to build the audience. Essentially, what GrowthLoop does is provide a UI that does not require any of these, as one of my colleagues would say, these bread lines. You, as a marketer, are having to wait for the data engineer to satisfy the ticket.
You can, through the UI, build these audiences directly, and it automatically merges these tables together from different parts of your data warehouse, and then also exports the audience to a destination.
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Chapter 5: What challenges does Growthloop face in scaling its technology?
Like Graze, like Iterable, like Facebook, like Google Ads. So that is a traditional world of composable CDP. Essentially, if you think about it, that's tooling for the marketer, right? It's tools to make your job more efficient. And the whole gain here is efficiency.
When I started talking to Tamim as our CTO, CPO, and Chris O'Neill as our CEO, these guys had laid out a vision of closing the loop. So essentially of using AI to build almost like an autonomous decisioning system that doesn't ask the marketer to build the journey, but actually builds the journey in such a way that the ROI is maximized, right?
And shift the whole debate from tooling, tooling for the marketer, to an opinionated, outcomes-optimized decisioning network that maximizes whatever return to pre-specify. And I have to tell you, these sort of closing the loop plans are not only ambitious, but I've been chasing them for the last 10 years of my career, both in AdTech and in MarTech. And that's why I signed on.
We can talk a little bit about where Growth Hub is now, what we're building towards, how we bring this vision to life. I'll stop here for a second.
That's actually a perfect segue. That's kind of the next place I want to go. So what is being built now? What is the next step with Growth Loop now that you've joined and you're going to tackle these ambitious problems?
It's an interesting problem because most companies have this problem. Oh, let me infuse AI into existing products or somehow into our offering and make our offering strong.
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Chapter 6: How does Tobi prioritize product development and team building?
I think GrowthLoop was a little bit ahead of that before I joined because there was a specific vision about the compounding marketing engine. Also, again, this autonomous, self-optimizing thing, breathing organism that automatically maximizes the outcome. But in general... I think it's a hard question to answer.
And if you look at most companies out there, particularly with the emergence of generative AI, one of the ways or very early things that most companies, many companies build is something like a chatbot. Because it's so obvious, right? You can build a chatbot that is powered by Gen AI. And I always, I scoff at that a little bit. Why?
Because for most companies, it's actually completely orthogonal to the product offering, right? So I'm a car marketplace. And my first investment in AI is to build something that is entirely orthogonal for my product. So that's not the mistake that we wanted to repeat here. But now we face the second big question, which is, okay, you have this thing that you want to build.
Maybe you just build a new product and you call it Toby's Brilliant Autonomous Marketing Decisioning Agent. Now, as you can tell, I'm not a product marketer, obviously, because hopefully nobody calls it that. But you build this product and you go to a dark room and you bring all the smart people, hire the PhDs, not just me, but hire more PhDs.
And you gravitate towards building this thing and writing the, I'm going to use one mathematical term, writing the Bayesian reinforcement learning models on the whiteboard. You feel really good about yourself. And I've been there in my career, by the way. My last venture raised about $6 million, hired all the smart people, built the product in a vacuum.
And then we did, of course, we're smart people, so we knew that the market wanted exactly what we had built, right? No, nobody wanted what we built. So I've really reworked the way that I'm thinking about tackling these big problems, how to infuse AI into product. So what we did is we looked very carefully. Where is product usage? Which are the product elements today that are used?
And how can we basically upgrade these properties, these primitives one by one with elements towards that vision such that we basically battle test all the components? Think about it as a car. You want to build this luxury car.
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Chapter 7: What lessons has Tobi learned from past mistakes in startups?
Instead of building it in a vacuum, you build the components. You infuse the existing primitives that have a guaranteed and known usage with these primitives or with these elements towards the vision that you build. You get customer feedback. And by the time all these components are validated, then maybe...
You put it all into the big, beautiful Ferrari that is waiting there, the hull, the shell, and then you go. So it's a very different way to think about infusing AI into product and building new AI product. And now, of course, the big question is, can you go top-down? in terms of your thinking and prioritization?
And can you take such a vision as this compounding marketing engine, this autonomous outcomes optimized decisioning system and break it down into components that are small enough that make sense to infuse into existing products and to also ensure that you get usage out of these so you can iterate over them, you can make them better.
And I think that's probably the biggest challenge in this underpinning.
I'm curious, you know, where you started, right? What would you call your quote-unquote MVP? Maybe not the company MVP, but your MVP when you joined. Like, where did you start with this new way of thinking? And, you know, what sort of tools were you using to bring it to life?
So here, I probably spent a month to try to turn this vision into a sequential order of elements that can be built and infused with the existing products, most notably audiences and journeys. And I can walk you through this process.
This process is still higher level, but essentially taking the vision, which we haven't quite talked about yet, but taking the vision and then saying, okay, what is naturally the first step?
And it turned out that the first step was building experimentation, infusing capabilities into particularly audiences, which is our most used product, that pulls experimentation back from the downstream systems Right.
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Chapter 8: What does Tobi envision for the future of AI in marketing?
Positions growth loop as a central hub of experimentation intelligence. So I can lock that data and I can understand causal relationships and the effect, the causal effect of different interventions. So that to me was the foundation. I cannot build an autonomous decision system or decisioning system if I don't give the system a prior understanding.
as to here's a track record of encoded causal relationships or causal statements and the results. So for that to happen, I needed experimentation. I actually needed experimentation to sit in the product primitive that our customers use the most.
Now you have a different problem, which is you've got to go to the market and you've got to convince the market that you better use my experimentation service as opposed to the experimentation service that lives downstream. So in the customer engagement platforms, in the marketing clouds.
And so we had to build features into the experimentation platform to make sure that this pull happens, that we can be the gravity, the gravity of intelligence for experimentation. And that was two features. One of them was... Yeah, we sit on top of all your data so we can forecast exactly how can you run the most efficient experiment.
So our argument all of a sudden was, okay, you run experimentation in your marketing cloud and you usually say, you know what, I'm going to dedicate 50% of the traffic to the control group. and 50% to the treatment group. That's all you can do. But imagine if you move experimentation closer to the data where we sit, where you have much more intelligence.
Now we can use machine learning or AI to forecast how to run the experiment most efficiently. So imagine if there is a 10% gain in the experiment. You're paying real money with this 50-50 split. Because you're withholding the good treatment that you know is going to lift the damn thing by 10%. You're withholding that from 50% of your people in perpetuity.
So how can you get away with a split that still gets you to stat sick while also maximizing the ROI from the experiment? Maybe it's a 1090 split. Maybe it's a 991 split. But in order to do that forecasting, you need to see all the data. Guess what? We're the only player that sees all the data. That was one pull. The other pull is what we call always-on measurement, which is...
The marketing conundrum. It's related, but different. So you run an experiment, you observe the winner, right? One condition is clearly winning on the metric that you care about. Scale it up, right? Bam, 100% of the traffic to this thing. Okay, but now you can't measure ROI anymore. So now your new boss comes in two years later and says, you know what, Noah, you've done this thing two years ago.
You told everybody, I found your PowerPoint slides, right? Said there was a 20% causal lift, 10% increase in LTV. Very impressive. How much revenue did we make because of that in the last quarter? What are you going to say? You don't know. Said, oh, we scaled the experiment. There's nothing else to be done.
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