Is Chalk.ai the 'Next Databricks'? + Tollbit's Bot Paywall for AI Agents | E2167

Today's show: In this TWiST 500 double feature, Alex sits down with two breakout founders: Chalk's Marc Freed-Finnegan & Tollbit's Toshit Panigrahi! First, Chalk's CEO Marc Freed-Finnegan is tackling one of AI's biggest bottlenecks—data freshness. Instead of relying on stale batch jobs, Chalk delivers real-time pipelines for inference compute, automatically transpiling Python into C++/Rust so it can run blazing fast in production. Investors are calling it the 'next Databricks'—and after hearing this convo, you'll see why. Then, Tollbit's CEO Toshit Panigrahi returns after raising a $24M Series A and signing up 1,400+ publishers. With RAG traffic exploding and robots.txt losing its teeth, their 'bot paywall' could reshape how AI agents pay for the content they consume. Are we heading for a Spotify moment for data licensing? A must-watch if you care about the economics of AI, from data pipelines to publisher monetization. Timestamps: (0:00) Intro + sponsors (1:03) Alex tees up two Twist 500 interviews: Chalk & Tolbert (2:47) Why AI is shifting from training compute to inference compute (4:41) Data freshness & real-time inference explained (10:43) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www. Squarespace.com/TWIST (12:00) How Chalk transpiles Python into C++/Rust for speed (18:52) Chalk's business model, margins & $500M valuation (20:11) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist (21:24) Show Continues… (28:02) Tollbit returns: $24M Series A & the rise of RAG bot traffic (30:37) . TECH: Say it without saying it. Head to get.tech/twist or your favorite registrar to get a clean, sharp .tech domain today. (31:41) Show Continues… (36:53) Robots.txt losing relevance & the case for bot paywalls (39:19)Publishers, AI agents & the future economics of the web Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp Follow Lon: X: https://x.com/lons Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis Thank you to our partners: (10:43) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www. Squarespace.com/TWIST (20:11) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist (30:37) . TECH: Say it without saying it. Head to get.tech/twist or your favorite registrar to get a clean, sharp .tech domain today. Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland Check out Jason's suite of newsletters: https://substack.com/@calacanis Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com Subscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Training jobs, these training data pipelines, Chalk is able to get fresh data to machine learning and AI models in context at inference time, which means when someone clicks a button, when they load a page, we can help a model fetch the data from the source. So it's really fresh and really relevant to what you're doing in in that application.

• 0:23Speaker 01

  This week in startups is brought to you by .tech. Say it without saying it. Head to get dot tech slash twist or your favorite registrar to get a clean, sharp .tech domain today. Lemon.io. Hire pre-vetted remote developers and get 15% off your first four weeks of developer time at lemon.io slash twist. And Squarespace. Turn your idea into a beautiful website. Go to squarespace.com slash twist for a free trial. When you're ready to launch, use offer code twist to save 10% off your first purchase of a website or domain. Hey, everybody. Welcome back to Twist. This is Alex. And today we have

• 1:06Speaker 01

  two Twist 500 interviews for you. I did both of these, and I'm incredibly excited about both these companies. All right. First of all, Chalk. Chalk works with ML to bring the right data at the right time into production. So essentially, if you want to run an ML workload and have the most up to date data, well, Chalk has built the data pipelines for that. As we all bring more data into our ML and AI context, this really matters. And what the company is building could become absolutely enormous. Then we're talking to Talbot, the publisher AI data marketplace company, had them on the show last August. They are back. And since then, they have raised a $24 million Series A, led by Lightspeed, and also signed up way more than 1,000 publishers to their

• 1:48Speaker 01

  platform. I want to know what's going on with rag queries? How much money can you make for licensing your data today? Are AI companies playing fair? And is Robots T X T over? Well, all that and more is coming your way. First Chalk, then Talbot. Let's have some fun.

• 2:07Speaker 01

  When a venture capitalist leads around and says that the company in question is the next Databricks and is one of the fastest growing data companies that they have ever seen, I have no choice but to sit up and take notice. Why? Well, Databricks is one of the most valuable startups in the world, period. And there are some really quickly growing data startups out there that we've talked to. So the startup that we're talking about today, Chalk, must be doing something incredibly interesting. And to help us understand why we need new data pipelines for the AI inference era, please welcome to the show, it's Mark Fried Fenigan, co-founder and CEO of Chalk. Mark, hey, welcome to the show.

• 2:43Speaker 00

  Hi, Alex. Thank you so much for having me. I'm psyched.

• 2:45Speaker 01

  Yeah, me too, man. So I want to start by laying some foundations for folks out there who are not familiar with the acronym ETL and don't know about data pipelines. So from a high level, how is AI adoption today driving a shift from training compute to what you guys call inference compute?

• 3:03Speaker 00

  Absolutely. Well, thank you so much for having me. Everyone's talking a lot about training and for good reason. People are training incredible models, they're spending a lot of time and money developing that perfect model. But once you've got it, you want to run the model. And maybe you want to run it forever. And that's the inference part. And so even if you look at how compute is being used, and obviously people are using a lot of computers, a lot of chips, training compute is growing, but compute allocated to inference is exploding. Because that's when you run the model, that's when you incorporate new data, and that's when you get the really exciting answers for users who are interacting with AI. And that's the piece of the puzzle that we're focused on.

• 3:43Speaker 01

  Okay, so back in the day when OpenAI, in the pre-ChatGPT era, they were getting a lot of GPUs together, crunching a lot of numbers, making new models, that was intensive. But now, today, we have a lot of great models, everyone's using them. So the amount of computation we're pushing through, just the use of the models versus the training, is now, I presume, the majority case.

• 4:04Speaker 00

  It's shifting, absolutely. And part of what's happening is that people used to accept like, hey, why don't we train and process and compute a lot of stuff in advance? But I think everyone's realizing that the answers get better, the experiences get better. If you compute more stuff on the fly, if you do multi-step reasoning, if you actually incorporate new data, fresh data into the answer, into the experience, the thing that consumers experience is totally transformed.

• 4:31Speaker 01

  And to put this into kind of groggy terms for folks out there, we all recall an era before LLMs were connected to the internet and they couldn't do search. And so their data just went up to like last September, and if it happened after that, they didn't know. So data freshness really does help the end user get the most value out of a trained AI model. Is that fair?

• 4:52Speaker 00

  Absolutely, totally agree. And maybe let's go even further back in time with some really concrete examples. If you think about stuff that's processed in batch, which is really what training is doing, that's what Databricks is doing, everyone used to process everything in batch. Think about moving money. So I deposit a check, there's this nightly batch process for ACH, they move the money and the next day or two days, you see the money. Cool. But contrast that with debit, right? With a debit card, you need to make this instant decision about approve or decline. You move the money instantly. And so you can't do an overnight process. You need to have a machine learning or an AI model that's evaluating what literally just happened. You might have retried that debit card five times in the past second. And so by shifting where you evaluate the

• 5:36Speaker 00

  risk, by doing it more quickly, by doing it with fresher data, you can produce a much better, faster decision and better experience. The same thing applies to consumer experiences like ranking and recommendations and search and logistics and operations and really anything you can think of gets better when it's faster and fresher.

• 5:55Speaker 01

  Okay. And this brings us to kind of what Chalk does. And I think the best place to start is really, why does a shift from training to inference compute require different data pipelines? So perhaps you could explain to us how the batch system works and then also maybe how Chalk approaches this to make it effectively real time, as far as I can tell.

• 6:14Speaker 00

  Absolutely. So maybe I'll share another example. One of our customers, whatnot, they're the largest live streaming marketplace in the United States. It's kind of like eBay and QVC mashed up a little bit.

• 6:22Speaker 01

  Oh, yeah, yeah. I've seen that. They're growing like mad right now. Yeah, yeah, yeah.

• 6:25Speaker 00

  They are growing like mad. They're a totally incredible company. And before us, when you open the home screen, home screen of the app, you would see a set of recommendations. They actually generated those recommendations on a nightly basis. So they would look at everything you browsed for, shopped for in the past. And let's say it was a lot of sneakers. You come in the app the next day, you're going to see a lot of sneakers. But what if that morning you're browsing for Pokemon cards? It's not going to update. They weren't able to update. Or I'll take a different user scenario. Imagine it's a brand new user where there is no data. And all of a sudden, the home screen of the app is populated there for you feed

• 7:03Speaker 00

  with almost a random set of products because they don't have data, because they haven't done that job in advance.

• 7:11Speaker 01

  Right.

• 7:11Speaker 00

  What we helped them to do was to instantly update the set of recommendations based on what you're browsing for in the moment. And so instead of these batch training jobs, these training data pipelines, Chalk is able to get fresh data to machine learning and AI models in context at inference time, which means when someone clicks a button, when they load a page, we could help a model fetch the data from the source. So it's really fresh and really relevant to what you're doing in in that application.

• 7:41Speaker 01

  So fundamentally, what Chalk does is it responds very quickly to real time requests and makes data available at the time of inference or the time of a request. So that way, whatever is generated can be as fresh as possible. Now, that sounds relatively easy. I presume in practice, it's not. So what's tricky, and therefore, why does Chalk exist, about making data appear inside of an AI context or a machine learning context as you need it? That seems very obvious from my layperson's perspective. I presume technically it's tricky.

• 8:14Speaker 00

  Yeah, well, here's the state of the world before Chalk. So you have things like Databricks and Snowflake, incredible companies that do huge batch trading jobs, a billion rows at a time, billions of users, great, wonderful. But then, in sort of the context of an application, you want to serve those answers really quickly. And so what people do today is they take the train data and they'll cache it in something called a feature store, which is really just a cache. And then they can serve really low latency answers inside of an application.

• 8:43Speaker 01

  But Mark, in that context, there still won't be fresh data. It's still the data from the last batch job.

• 8:49Speaker 00

  That's the trick. So there's this tension because they can serve it in a low latency manner. There's effectively no math, no computation happening. So it's fast to serve, but it's old because it was pre-processed. And so companies found, well, hold on, maybe the pre-processed data isn't good enough, but I still care about latency. And that's kind of the tension. And they wound up building their own custom solutions. So sometimes people ask, what do you compete against? The answer is often companies wind up trying to build something that's a little bit in between a feature store and these batch trading jobs. That's what we're building. And it was inspired by real life experiences. So I'm lucky, my wonderful co-founder Elliot, I've got two, Elliot and Andy. Elliot was one of the first 10 engineers at Affirm.

• 9:33Speaker 00

  So you can imagine buy now, pay later. It's not an overnight job. You click, you have to decide, are we going to loan you $100? And they said, yo Elliot, we have to do this on the fly. He couldn't buy something, so he built something. He built the same thing again at his startup with Andy at NeoBank. They sold it to Credit Karma and built it again. It's because there's a gap. If you want, no, seriously, it's a very tried and true way of starting a company.

• 9:56Speaker 01

  No, no, I'm just laughing that he had to do it twice.

• 9:59Speaker 00

  Yeah. And so now we're doing it as a company because it's a real, it's a real gap in the market that we're able to fill.

• 10:06Speaker 01

  So going back in time to the early days of Affirm, I actually interviewed Max Levchin for TechRush a billion, like probably like 2013 or something. So way back when Affirm was a little baby thing. And at the time, he told me something along the lines of when we do a credit decision or make a credit decision, we bring in tons of individual data points to help us be informed. And that wouldn't have been possible if one of your co-founders hadn't built the backend engine to allow for the quick ingestion and then computation against those things to make that decision. So essentially, without home brewing this originally about Affirm, the firm that we know today probably wouldn't exist.

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• 11:28Jason Calacanis?

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• 12:00Speaker 00

  Max is incredible, and he's built a company with a ton of people. But certainly, Elliot built a really essential piece that gave us the insight for chalk.

• 12:08Jason Calacanis?

  I didn't mean to say that Max didn't.

• 12:10Speaker 00

  Well, credit where credit's due, but yeah, Elliot was a really amazing part of that early team. I think the other sort of key thing here is really about latency and speed. The language of data scientists is really Python. And so, Python is easy to write. It's very flexible, but it's also slow. And that's part of why there's a little bit of tension here, because data scientists come up with a model. You mentioned the firm. Great, you have thousands of pieces of data you want to incorporate in this decision. And if you write it in Python, you think, well, there's no way we can run that in a fast way at inference time, at an e-commerce checkout, when you load an app. So what do you do? What teams do today is they wind up having a separate data engineering team rewrite the Python. Python in a language that's more efficient. Maybe it's C++.

• 12:55Speaker 00

  Maybe it's something else. Maybe it's Rust. But you can imagine you rewrite the same definition a second time. There is huge opportunity for error. It takes a lot of time to get something into production. And part of the magic of Chalk, I'd say, we bend the curve of what's possible. We change the way of thinking because you can write Python and we will automatically transpile it into C++ instantly. So we meet data science teams where they are. They can write Python, but it still executes so fast that they can use it in a production environment.

• 13:28Speaker 01

  You guys use the phrase idiomatic Python, and I wasn't going to bring this up because it reflects my own ignorance. But break that down for me as someone who hasn't written code since high school.

• 13:38Speaker 00

  Yeah, what we mean, so Python and the modifier is just saying, simple, natural Python. I think sometimes tools come out and they have like crazy DSLs and they want you to learn a whole new language. And the point we're really making there is you can write the language you already know. We have some ways that we ask you to structure the Python to talk to Chalk, but you just write Python and we transpile it into C++ and Rust. So it runs crazy fast in a way you never thought it was possible that Python could be that fast or that you could really run Python in production.

• 14:10Speaker 01

  And also it means that a company could use Chalk without having a data science team that was prepped to do the transcription and compilation of Python into Rust or C++. So it makes this more available to more firms, is my read.

• 14:26Speaker 00

  Absolutely. We're making things possible that honestly, people didn't think were possible unless they built huge custom solutions or had data engineering teams translating a lot of code. We're making it possible to do fresher, faster things, which opens up entirely new product categories for our customers.

• 14:45Speaker 01

  So the, the change of Python into a different language that's more machine learning appropriate perhaps, how, how do you guys do that? Is that done via an AI model? Is that done via something else? I'm just curious about how you go from idiomatic Python into what you actually want.

• 15:03Speaker 00

  Yeah, honestly, Alex, a lot of hard work.

• 15:08Speaker 01

  I do it myself. I type all the code by hand.

• 15:11Speaker 00

  I love it. Yes, it actually comes to me first. No, I'm not writing our code. But really, the core of Chalk is a compute engine. And if you really think about what Databricks is, Databricks says, hey, give us all of your data and we'll make your data accessible in a wide variety of ways. And we do something actually very similar. We're tuned to a different set of use cases than Databricks. We are tuned to low latency applications, so Chalk can sit in production. You can never kick off a Databricks job in response to a user click. You're never going to run one row at a time with Databricks. Their newest engine is called Photon. It says on the docs page, don't use us. We don't work for anything that takes under two seconds.

• 15:54Speaker 00

  Chalk is optimized for the exact opposite set of things.

• 15:58Speaker 01

  Yeah, yeah.

• 15:58Speaker 00

  We're optimized exactly for doing things really low latency right in the context of a production application.

• 16:07Speaker 01

  So two seconds is infinite time in regards to an actual application that a user touches. And if you don't believe me, go look at all the Google research listeners and friends about how important it is to load results very quickly, for example. So

• 16:21Speaker 00

  A hundred percent.

• 16:22Speaker 01

  But Databricks, we all know Ali Godzey, we know Databricks, lovely company, lots of smart people over there.

• 16:28Speaker 00

  A hundred percent.

• 16:28Speaker 01

  How hard would it be for them to go from two seconds to something that would be competitive with you guys? Because on one hand, I don't think this is what they want to do. But on the other hand, I'm curious, because you guys are doing so well, it's going to attract more competitive pressure as you scale. So I'm just curious if Databricks could, if they wanted to, stop by the barbecue.

• 16:48Speaker 00

  Absolutely. Well, you know, the age old question, Alex, where an early stage company could Google, Amazon, Databricks Go and build the exact same thing? Maybe, but we know them well and they are focused on a different set of problems. And I mentioned Photon, but they just spent years and billions of dollars coming up with the newest version of their engine. It's optimized for jobs that take over two seconds.

• 17:10Speaker 01

  So essentially

• 17:11Speaker 00

  They're being batch training jobs.

• 17:13Speaker 01

  So they're not coming in. But one thing, just to stick on this one for one more question.

• 17:17Speaker 00

  Yeah.

• 17:17Speaker 01

  The major hyperscalers are building out an increasingly impressive feature set for supporting AI broadly at the enterprise level. Do you think they're ever going to make a version of what you guys are working on that would be competitive, but perhaps less feature rich? Or am I, again, kind of asking you a question about a company that doesn't want to dig into what you're doing?

• 17:39Speaker 00

  Yeah, I think that most of our customers also use Databricks and/or Snowflake and/or Google and/or Amazon. They might be on Vertex or SageMaker. And we complement those tools because we're doing something that's optimized for a different set of use cases. Could they focus on our use cases one day? Of course, that could happen. But explicitly, Amazon is referring customers to us because SageMaker just doesn't do the things that we're optimized for.

• 18:06Speaker 01

  I know that because I was trying to figure out your guys' pricing information and I was bopping around the AWS website and it informed me that it wouldn't tell me unless I have a more serious AWS account. So, speaking of that, I'm curious about what this actually costs. Because on one hand, SaaS is traditional. On the other hand, people have been moving a lot more towards a pay for what you've used kind of approach. So, how do you guys charge for Chalk today?

• 18:28Speaker 00

  Yeah, so we have some SaaS fees, but our core model is based on usage. So, something that's really differentiated about Chalk, although other companies do similar things, we deploy our software into the cloud environments of our customers. We work with big customers. We work with venture-backed companies that have raised hundreds of millions of dollars. We work with big public companies. We work with Fortune 500 companies. They don't want their data leaving their environment, and neither do we. So, Chalk deploys into their cloud environments where they can retain complete control, privacy, security over their data. With that model, we then just look at how many computers are running Chalk. And we charge per computer, per hour, a lot like Databricks, so that it's really based on how much Chalk you're using.

• 19:10Speaker 01

  So, this is a nerdy financial question, but if you guys are running Chalk on, let's say, Alex Incorporated's cloud instance, and you're keeping track of how many of machines that I'm spinning up and down that are running on Chalk, and I'm paying you, does that mean that your gross margins are effectively like 99% because I'm paying for all the compute on my side?

• 19:31Speaker 00

  Yeah, thank you. So they're not 99%, but we do have great margins and we're really proud of that. We also don't want to-

• 19:37Speaker 01

  Do they start with an eight?

• 19:38Speaker 00

  We also don't want to get in the middle of our customers and their negotiated rates with AWS or GCP. They pay directly for their hosting costs based on whatever they've negotiated, and they pay us separately for software. In a model that's per computer, per hour, we have credits and it sort of works in a way that a lot of people are familiar with. But yeah, you know, we'll maybe host the metadata plane or our dashboard or some alerting or other things like that. But really, the data sits with them and it does provide for a really great business for us.

• 20:07Speaker 01

  This is the best business ever.

• 20:11Jason Calacanis?

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• 20:53Jason Calacanis?

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• 21:23Speaker 01

  You guys just get to hang out in someone else's cloud instance and you get you get paid to it's like going to a party and not bringing a present and then being sent home with $5.00. That's fantastic.

• 21:32Speaker 00

  Don't you worry, Alex. We do a lot of work and we are

• 21:34Speaker 01

  Oh, no, no, no. Trust me.

• 21:35Speaker 00

  happy customers.

• 21:36Speaker 01

  I know this is very hard. I'm just saying on the monetization side, that's genius and I freaking love it. Okay, so Databricks, the comparison that your investor, Idinson Cut from Felices, mentioned at the top of the show, you guys recently raised 50 million at a $500 billion valuation. Databricks has a really long history with Apache Spark and open source in general. I'm kind of curious, does the comparison with Databricks extend to a future open source approach? Or is it more of a, we both work in data and so forth?

• 22:06Speaker 00

  Yeah, absolutely. So we have a lot of different components of chalk that are already open source and lots of plans to extend there as well. So, you know, starting with, you're just writing Python. So you can take your Python and run it somewhere else. Granted, the core of chalk is a compute engine that helps you run it, but you want to replace chalk with a different engine? You certainly could. A big component of chalk, obviously, we rely on a ton of open source projects, including Facebook. Now Meta has a product called V-locks that they've open sourced. We're big contributors to V-locks and we're big users of V-locks and we have lots more plans to open source different pieces of chalk as we go.

• 22:41Speaker 01

  All right. Well, that's that's totally encouraging because in my experience, watching companies that have a friendly approach to open source in whatever capacity tend to be worth about five times as much down the road. I don't know why that's the case, but it seems to hold pretty, pretty well. Now, on the customer front, you mentioned Fortune 500 companies, startups that have raised lots of money. That's pretty diverse, diverse plane. I'm kind of curious, are we seeing adoption of chalk products by companies that we would not think of when we would consider industries that are going AI first? How wide is adoption of chalk as opposed to just, you know, the companies that we know are going to be pretty technologically forward?

• 23:18Speaker 00

  Yeah, so where we got our start was in very mature venture-backed tech companies. So I'd say they are kind of progressive companies that are already worth maybe billions of dollars or more and have really strong technical teams, but something was missing. And we don't come in and try to boil the ocean and rip out everything. We come in to help launch a specific product. Hey, we used to apply for an account and get back to you two days later. Now we do instant account onboarding. Great. And we've enabled that. So we're helping their businesses launch new products and new features. But we've moved upmarket from there. And so, as you mentioned, we're lucky to have a lot of great public companies, including Apartment List, Turo, Mission Lane, many more. We can go through the list.

• 24:03Speaker 00

  But also, we do have some early-stage companies as well. Sometimes we have some really amazing Series A and B companies that get on Chalk early and grow with us, and that's exciting, too. But I'd say the core for us tends to be pretty mature, complex companies that already have a lot of data and really want to supercharge what they're doing.

• 24:22Speaker 01

  So this kind of tracks what I was thinking, because I pulled a quote from your website. So I thought it was illustrative of what I was thinking. You guys wrote that quote, "Chalk executes models end-to-end with an optimized engine, eliminating stale streams and ETL jobs by resolving features directly from the source." Which I know what that means, but I don't think most people out in the broader economy do. And I'm curious if there's a way to make the power of Chalk and the power of modern AI more accessible to companies that don't have the ability to even understand that sentence? And so I guess, Mark, as you look towards the future and you want to have every company using Chalk, how do you help people that are less technical but still could benefit from what you guys are working on get on board and get going?

• 25:08Speaker 00

  Yeah, so really what we're describing in that sentence you just quoted is there's an opportunity to go directly to the source of the data at the time you need it, instead of pre-processing. And there are a lot of different ways to talk about this stuff, but I think the idea of preprocessing and batch versus on-demand processing in real time is a great way to just summarize one of the things we're most excited about. With preprocessing, you go to all of your sources of data, you pull them in, you run your math, you run your computation, and you wind up with a data set that you're able to then cache in a feature store and use in an application context. We think, we've seen, you mentioned Google.

• 25:52Speaker 00

  I was lucky to work at Google for a bunch of years, and Google talks about speed as a feature. When things are faster, people search more, they click on more ads, and it's more magical, everything gets better. And we think that applies to every possible application, both for businesses and consumers. So what we're helping people do, a lot of people say, "Okay, so what I need is a feature store." Chalk has a feature store. We think of that as just a data cache, it's useful.

• 26:17Speaker 01

  Right.

• 26:17Speaker 00

  But what if you didn't have to cache the data? What if you could go to the source and pull it on-demand so it's super fresh, and you still can do some math, some computation in real time? And people say, "That's not possible. My data science team is writing Python, I only have 10 or 100 milliseconds. I can't possibly go to the data source in context at inference time." And we say, "Want to try it?"

• 26:43Speaker 01

  Yeah.

• 26:43Speaker 00

  And honestly, Alex, basically, everyone who does a pilot with us winds up buying the software. So we know, not everybody knows about us, although now that we're talking, I guess everyone's gonna know. Not everyone thinks of us. When they have problems, we'd be really good at. But we're really proud that when people do find out, when they try the software, they really think about a different architecture for their applications and their business.

• 27:06Speaker 01

  Mark, an absolute pleasure. I never thought I'd have this much fun talking about data pipelines, but you made it an absolute treat, so thank you much. And I always ask these two questions. One, where can people find the company if they want to? And two, what's a role you're looking to hire for where you're struggling to find the right candidate?

• 27:22Speaker 00

  Thank you so much. Well, we're based in San Francisco. We also just moved to our second office in New York. We're growing. So we work together in person every day and would love to have you here to join us or just to visit us. And of course, at chalk.ai, just if you want to learn a bit more. We're hiring for everything. Obviously, we always want incredible engineers. It is the core of our company, and we're always expanding our sales and go-to-market team. And so even if you're just starting out and want to be an SDR, we'd love to have you spread the word. Or if you want to help write some code on some really technical problems, we'd love to help you build out our engine.

• 28:01Speaker 01

  All right. Next up, we are talking with Tollbit, one of my favorite Twist500 companies. I love all my children the same, but I do love some of them a little bit more than others. Founder Toseh Panagrahi was last on the show in August 5, 2024. That's episode 1989, if you want to go back and look it up. Now, since then, the market for AI data has changed. While we previously spent a lot of our time considering IP rights for data used to train AI models, in 2025, we are seeing user-led AI search queries rise in volume. In other words, the rise of AI bot traffic is straining publishers and other website owners, and may demand a new method of monetization for the entire web. Now,

• 28:41Speaker 01

  if Tollbit wins its market, it could become a truly enormous and very valuable company. But it does face stiff competition from other startups and a raft of wealthy companies that would rather pay nothing for access to information that their AI bots and tools require to operate. Now, to help us untangle the current state of AI data licensing, please welcome Toseh back to the show. Hey, how you doing?

• 29:04Speaker 03

  Hey, Alex. How's it going? Thanks for having me.

• 29:06Speaker 01

  I'm doing okay. I only made like three mistakes in that intro. I'm proud of myself. That's higher than my usual standard. All right. So it's been about a year. You and I have emailed back and forth about a couple of things here and there, but I think the most important thing, Tosted, to start is to explain to people the difference between an AI training scrape and what's going on in the world of RAG. I think that'll give us a good foundation. So could you start there, please?

• 29:29Speaker 03

  Yeah, certainly. So, uh, training is the process by which we teach these models how to make sense of the world and then how to output an answer and sound human, right? And oftentimes, training is, is, there's a few large companies that are, uh, training these, uh, what are called foundational models. Um, and the data access oftentimes we're training might be one time. However, the reason RAG is different is because that represents RAG, stands for Retrieval Augmented Generation. It's just a fancy way of saying that when you ask SearchGPT a question, when you ask Perplexity a question, um, when you ask u.com a question, it has to go out, it has to read the web, uh, load those articles into context

• 30:13Speaker 03

  so that it can intelligently answer a question. So, training is this sort of like one-time access to sort of train the underlying model, whereas, uh, RAG, Retrieval Output, uh, is fetching that article every single time so that, uh, it can be loaded into context so that it can actually make sure that it's answering your question accurately and not hallucinating and making mistakes.

• 30:38Jason Calacanis?

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• 31:20Jason Calacanis?

  instant branding for you. That's why over 500,000 founders have collectively raised over $5 billion in investment, building their companies on .tech. So skip the hassle, head to www.get.tech/twist, or go to your favorite registrar and grab your .tech domain today.

• 31:41Speaker 01

  Now, this often comes up RAG in a search context because if you're searching for something, you want the most recent information. AI models are trained at a certain period of time. And if you want to have more recent information, you have to go out there and get it. Is that the only time in which we see RAG scrapes come into play? Or are there other instances of use for RAG that we should also keep in mind as we talk?

• 32:01Speaker 03

  Yeah, I think there sort of, if you look at the development of AI over the last two years, right, there's turned out to be a few foundational model companies. Then on top of those, you have degrees of magnitude more applications that are made, right? So you take a model, you take a few models, you package them together and you build a new application out of it, right? So think of cursor, right? Think of perplexity, right? Those are good examples of applications. There's not much training happening there. That's more about the retrieval, right? And a lot of them do focus around the either AI coding or say, uh, search use cases. Now, this year, what you've seen is the sort of explosion in agents, right? So especially enterprise agents.

• 32:46Speaker 03

  And when you hear the word agent, that is all RAG because the agent needs to, when you, you're automating something, it needs to go out, it needs to go read your email, it needs to automate a text message, it needs to go read your internal files. That's all retrieval.

• 33:03Speaker 01

  But when we think about RAG in the context of an AI data marketplace, like what Tollbit is building, we're thinking a lot about search and then when AI agents interact with the open web versus an internal database, for example.

• 33:16Speaker 03

  Yes, and it doesn't need to be an internal database, right? There's quite a few of these, of these sort of agentic workflows, right? That still need access to research reports, right? Premium data sources, things that are behind a paywall, right? I'm not sure if you have used, I think, a few weeks ago, OpenAI introduced that agent mode into ChatGPT. And if you try to read Bloomberg articles, Bloomberg will actually block you and, and make the ChatGPT agent, uh, sort of, it'll, it'll put it, uh, put a challenge in front of it where the bot actually needs to click. And then prove that it's human before it can proceed.

• 33:53Speaker 01

  And shockingly enough, the AI agent is not a human, so it probably fails that test. But that's a pretty good segue into just how much of this stuff we're seeing. You guys put out a really excellent Q1 State of the Bots report. I'm hoping, by the way, that there's a Q2, Q3, and Q4 edition coming out. But at the time that launched, you guys said that from the fourth quarter of 2024 to the first quarter of 2025, rag bot scripts per site were up 49%. That's two and a half times the then rate at which training bot scripts were going up. And among websites with Tolba Analytics set up before the start of the year, AI bot traffic nearly doubled in the first quarter. So that shows, I think, just the scale at which we're seeing these these rag queries go up. What's happened since then?

• 34:38Speaker 01

  Are the trends still trending in the same direction?

• 34:41Speaker 03

  Absolutely. I think it's sort of shocking, right? Like, when you think about the implication of what the arrival of both AI applications and AI agents mean, right? And the reason it's not surprising to us that these numbers continue to go up right through the quarter is when you and I search, right? We were limited by our attention span. We're limited by our ability to read and type, right? We will click the first link, we'll click the second link, and we'll move on, right? AI doesn't do that. AI does not get tired. It doesn't have a short attention span. It reads phenomenally fast, right? Because it's a computer. So when we search on any one of these tools, or when we ask an agent to automate something,

• 35:25Speaker 03

  that agent doesn't get tired. It will go, you ask it to do research, find the perfect Lego set for a gift for someone, right? It's going to keep going to those sites and it's not going to get tired, right? You try to use any one of these AI search applications. I'm not sure if you use AI Mode on Google recently, but that will read 200 sites.

• 35:46Speaker 01

  I have, yes.

• 35:46Speaker 03

  That will read 200 sites, to answer your question.

• 35:49Speaker 01

  But, Tose, pause there. That's 200 times in which the Google AI agent is going out there and pinging the website and requesting information from them, bringing it back, and then And showing it to the user per query. So that means that instead of me going to two sites and maybe clicking on an ad, Google is going out 200 times and clicking on nothing.

• 36:08Speaker 03

  Exactly, and there is certainly, as with everything in computer science, there is some level of caching that does happen. But the research that we do continues to show that that's actually the primary driver of this explosion in bot traffic that's happening. Because all of these users, all of us that are asking these questions are forcing these bots to go out and keep, you know, you can't download the entirety of the internet. You can't cache a local copy of the entirety of the internet. That's what's causing this explosion in bot traffic. It's that retrieval use case.

• 36:38Speaker 01

  I think actually when we consider the discussion in 2003, 2024 about AI training data as the focus and this year being a focus much more on RAG, it's really us just saying that a lot of time has been building better AI models. And now people are using them. And so, of course, we're seeing a shift in the overall patterns. What I'm a little surprised by is how willing it seems, according to reports, that some AI companies are willing to go around robots.txt. That's kind of a standard file people put into the website to say, hey, here are the rules for our site. And it was apparently more of a norm and a custom than a hard law or rule. But it was how the internet worked for a good period of time. It seems to have lost some of its bite. So what do you think is driving that, Tessa?

• 37:22Speaker 03

  So I think, you know, if you look at the history of the robots text file, right? In many ways, it helped keep the AI search engines above board and not get in trouble. Small example, when Facebook has its robots text file, they will purposely make sure that, for example, profiles, messages, right, are not crawled, right? So that in case a search crawler accidentally finds that page, it knows not to index it because you and I don't want our private messages showing up on Google for anyone to search. This happened sort of recently, right? Whether it was intentional or not, when you saw that that sort of, you know,

• 38:02Speaker 03

  anytime you share a conversation on OpenAI, that link, if it gets picked up by a search crawler, can actually get indexed, right?

• 38:09Speaker 01

  Literally today. I think XAI's Grok was ding for the same thing. So yeah, that's.

• 38:16Speaker 03

  So now, right, so that was what the purpose of the robots text file was. It was never, you know, it was definitely an honor policy, right? You trusted that the bots wouldn't see those pages, but there was no hard enforcement really of that. Right. And I think now what the bigger conversation is going to be about is, and we see this happen in, you know, when you saw the perplexity cloudflare sort of debacle two weeks ago, right?

• 38:42Speaker 01

  Yep.

• 38:43Speaker 03

  I think that was a fun one. I think we're going to start coming to an ethical question, right? And you might certainly be able to figure out where we sit on this. But the question is going to be, if I ask my agent to do something and it goes out, right? Is that human? Should it be able to pass off as human?

• 39:02Speaker 01

  And this matters quite a lot because some of the companies are saying, we're not disregarding robots text. Instead, what we're doing is we're just following through what the user requests. And so all these rags, great rag queries, rag pings should be treated as if they were done by a human. That's a very friendly perspective for the Googles and perplexities of the world. But if you're, you know, bob.com and people are just scraping the hell out of your content, it doesn't feel much better, Tocit.

• 39:29Speaker 03

  No, and I think where this actually starts breaking down, and we're already starting to see this now, right, with amongst our publishers, is that visit from that agent is not equivalent to human, right? You are not monetizing that visit, for example, right? And actually, the other place where this starts breaking down, even the unit economics, is these bots are coming in droves, right? You can sit in any one of these, you know, whether it's OpenAI's agent platform, it's perplexity's agent platform, or, you know, you use a platform like, say, Mind Studio, right? These agents are going out in droves. They are hammering your site for information, right?

• 40:11Speaker 03

  And that actually costs the website in CDN bills, right?

• 40:15Speaker 01

  Quite a lot.

• 40:16Speaker 03

  And so you're not able to monetize, but at the same time, these AI companies are saying, "Well, it's equivalent to a user, right? The user might have clicked your link anyways, but the unit economics are not the same."

• 40:26Speaker 01

  Not the same at all. So they can search more websites faster, generating more total traffic, but not actual human traffic. So it's all costs, no benefit to the website.

• 40:35Speaker 03

  Exactly.

• 40:36Speaker 01

  Okay. This brings us to your guys' solution to the problem, which is the bot paywall. So my question is, this piece of technology that you guys have built to essentially, as far as I can understand it, erect a wall in front of bots to say, "You may or may not pass," is it as hard a wall as I'm imagining, or is it more of like a hedge that you could maybe wiggle through, but it's a little bit harder?

• 40:59Speaker 03

  Yes, it's more of the latter, right? I think one of the things that we, when, you know, through the course of the company, right, made a decision very early on about is that we are not a cybersecurity company, right? There are a dozen or more cybersecurity company. All of them are really good at what they do, right? And the problem of figuring out whether a bot was, whether a visitor was human or not, is going to become very expensive, right? And the reason sort of even practically, we made this decision, right? If you play out what's going to happen, right? And again, that, you know, cloud perplexity situation two weeks ago is a great example of this. If we just keep maligning bots, for example, Tollbit just went down and said, "You know, we're going to be a cybersecurity

• 41:42Speaker 03

  company. We're just going to focus on blocking bots," right? What you're actually doing is you're creating an incentive structure where you're forcing the bots to just become better, right?

• 41:52Speaker 01

  Yeah, it's an arms race, essentially.

• 41:53Speaker 03

  Right. It becomes an arms race, and the bots are just going to become better. The scrapers are just going to become better. They're going to start passing off as more human. And what's going to end up happening... Sorry, you were about to say it.

• 42:05Speaker 01

  No, no, no, no, no. I'm just excited. You keep going.

• 42:07Speaker 03

  Yeah. What's going to happen is cybersecurity itself will become prohibitively expensive in terms of cost and compute, right?

• 42:15Speaker 01

  Yep.

• 42:16Speaker 03

  Because it will be very, very, it won't be impossible, but it will be prohibitively expensive for anyone, any website, to figure out whether, when a person lands, when a visitor lands, is this a bot or not?

• 42:27Speaker 01

  So I'm starting to get vibes of the old piracy music debate. Back in the day, it was hard to buy stuff. People stole it. Then we made it easier to pay for. And then people switched to Spotify, which now does billions of dollars a year in revenue. I presume you guys are going to offer not the world's hardest paywall nor the most permissive, but instead a, hey, look, here's some annoyances, but you can also pay a reasonable fee and get through without friction.

• 42:51Speaker 03

  There you go. And so that's where we focus on the happy path, right? So let us assume that there are cybersecurity companies out there. There are C D Ns out there who can try to solve that problem of determining who's a bot or not. However, our focus is always, let's assume you are a good actor, right? Suppose you want to go down the happy path. How can we make sure you can have that programmatic exchange of value with that underlying website? Right? How do we make it so that we can actually solve problems for you? Because actually, when you, when you look at it through this lens, there are a lot of problems that AI companies face and agents face on the open internet, which can actually be solved if you get the cooperation of websites, right?

• 43:34Speaker 01

  Ah, so you can actually make it easier for them to interact with the internet by providing them the, the equivalent of an API hook, I presume. Okay, so there, there's a carrot and a stick here.

• 43:44Speaker 03

  Yes.

• 43:45Speaker 01

  Okay, so I love this idea because I'm incredibly biased. I am a writer online. I, I, I do this for part of my day job. And so I care a lot about the value of content because it's, it's what's, you know, filled my 401K over the years. How much can websites charge? Because on one hand, this could be a de minimis amount of money that adds up to a molehill or it could be quite a large pile, but I have no idea how to value an A, like a rag scrape on a marginal basis. So can you talk to me a little bit about how pricing is looking so far?

• 44:20Speaker 03

  Yeah, so I'll give you one, one, one real example, right? So we, whenever we onboard a publisher on Tollbit, we usually set up a 45 day check in after they go live so that we can come back and we can look at their analytics, right? And this is important because we try to.

• 47:02Speaker 03

  Most of that is driven by human consumption. Right? Now, as these sort of AI applications, agents, as more of us use it, more and more of that traffic is going to become autonomous. We call them autonomous visitors. It just means there's not a human on the other side, whether it's a bot, agent, application, whatever it is, it's autonomous. But the, uh, so it's not just a shift that, you know, the sort of page views go from human to autonomous. The amount of pages, the pie itself gets bigger.

• 47:32Speaker 01

  Much larger.

• 47:33Speaker 03

  Exactly.

• 47:34Speaker 01

  Yeah. Now, this all works on the publisher's side for very obvious reasons. They have enormous incentives to, to sign up with Tollbit or one of the other awesome companies that's working in your space. I, I really think this is a cool market. I'm not going to lie. But you have to have the other side of the marketplace show up. And so, reading public pronouncements from AI companies, even the federal government recently, it hasn't shown to me a, a dramatic willingness to start to pay for what they have previously gotten often for free. So, apart from signing up publishers, how are you doing on the getting deals with the perplexities and OpenAI's of the world that are driving an enormous number of rag scrapes?

• 48:15Speaker 03

  Yeah. So we've begun in our integration with a few agent builder platforms, actually. Right? So there are enterprise agent builder platforms where their customers, right, are Fortune 1000 companies. They're public companies. They want to do the right thing. They want to make sure they have the proper licenses in place. It makes scraping, they have, they have money to do it. Right? And that's one of the, I think, surprising places where we've found that actually that integration actually helps and works. The, and it solves a problem for the other side. That process of partnerships is actually very difficult, very expensive and very time consuming. And remember when you gave that analogy about, about Spotify and Netflix, right? And piracy?

• 48:59Speaker 03

  I think one of the things, surprising things that we've found is that it's not that there was an unwillingness to pay. There was an unwillingness to do partnerships. They thought that the partnerships process would take too long. It's too. Too human centric, it involves too many lawyers, for example, right? In terms no one can agree on. And when we can do that upfront, get the cooperation of those sites, actually makes that offering, you know, today, our catalog of sites is actually public on on Tollbit as well, right?

• 49:29Speaker 03

  And as people go live, we add more to that. So that network becomes even more valuable. That's one of the, that's one of the places I think is driving a good number of the transactions. We have some of the longer tail, smaller AI companies that are getting started. These, the application layer companies that are also onboarding. We actually had two in the last week that on boarded and started transacting with publishers. So we're bringing the demand for sure.

• 49:54Speaker 01

  So I just pulled up that that site, which by the way, I should have clicked on before our chat, my bad. I read the rest of your website.

• 50:02Speaker 03

  It's a new page, it's a relatively new page.

• 50:05Speaker 01

  Well, so just laughing. 1400 publishers, including names like USA Today, AP, HuffPo, Forbes, Time, TED, Mumsnet, Futurism, Fast Company, G2, Stocktwits, Inc. I mean, this is a lot. It does seem that you've done a very good job branding of the publishers.

• 50:22Speaker 03

  Well, we're trying. I think, I think as sort of, you know, the last two quarters, three quarters have unfolded. And even people come in for the analytics, I think they then start realizing, wait a second, the future is rat, right? There is real volume on the other side.

• 50:36Speaker 01

  Yeah. And there's two things you have to do. I mean, when I was just going back to my thinking about Tollbit, I was thinking, okay, there's two things that have to happen. You have to understand how often AI is pinging or scraping your site, generally speaking, and then you need to set and enforce terms for those scrapes and pings. And that's why you guys have the analytics service and then also the monetization marketplace. I didn't touch on the first one as much, but I do want to point out to folks that you guys do actually both things.

• 50:59Speaker 03

  Yes, absolutely.

• 51:02Speaker 01

  So, since we last spoke, $24 million Series A led by Lightspeed, actually came out not that long after we chatted. I know that Butcher Capitalists are an impatient bunch at times and you guys are building in a relatively nascent space and that can be difficult. Has the growth rate of the company in monetary terms matched your expectations and projections that you made at the time of that investment?

• 51:29Speaker 03

  Certainly, I think so. I think Life is pretty happy with that investment as well. The publisher onboarding has just, it's been insane. I think our customer, if you look at our go-to-market team, our customer success sort of headcount has also grown. I know that the catalog says 1,400 publishers. That's simply who's live. We have, I think, another 1,000 that are in the process of getting activated, right? Or they're switching from analytics to actually then setting up that bot paywall. So only after they set that up do they go live and get listed there.

• 52:03Speaker 01

  How close are you to a critical mass of people that have signed up to use the bot paywall? Because at some point, it'll become just easier to say, okay, fine, okay, fine. But that's easier to reach if you have more people. So have you hit that tipping point yet? Or are you not quite there?

• 52:20Speaker 03

  I think it's hard to tell. You can only tell that in hindsight. I think we certainly did, it seems, knock on what hit a tipping point, say, with publishers and the messaging. And I think it is always, I think there's something to be said about, there's a growing network effect every time we add one of these publishers, right? And then every time the AI companies even, right? So obviously, we integrate with a wide variety of partners who do some of that detection and they have that stick, obviously, to forward traffic to that bot paywall. And it is interesting on the AI company's side even, the sort of perception that, you know, every time you're going to try to access one of these

• 53:01Speaker 03

  sites, you keep getting redirected to a 12-bit subdomain on these sites, right? Which is actually sort of insane, the sort of

• 53:10Speaker 03

  Mindshare that that that Tollbit gets as a result of that, that sort of enforcement, right? And it is even just cool for us to see on the other side, right? When you see subdomains existing on so many of these publisher sites like tulbit.ted.com, right? tulbit.npr.org, right? And bots keep getting forwarded there and being told, hey, you have to pay. I think it shows how ubiquitous that paywall can become.

• 53:33Speaker 01

  Yeah, and that they're gonna have to start coughing up. So let's look forward to the future and let's play a fun little game called, let's say, when? When do you think that Tollbit will have facilitated, let's say, a big number, like $100 million in in value from rag scrapers writ large to publishers on the Tollbit platform?

• 53:59Speaker 03

  Yeah, I think it is hard to project the future, right? But if you look at the sort of growth in in AI, if you look at the segments in which AI, these, the rag demand is emerging, right? In 2024, it was a lot of these application layer companies. In 2025, it's been enterprise AI agents, right? We do think 2026 is going to be the year of consumer AI agents, where I'm still excited to see what is that first truly agentic consumer app. Maybe it comes in from the browser angle. It's still unclear. But if you look at the sort of growth of the sort of autonomous traffic on the internet,

• 54:41Speaker 03

  the economic forces out there of cybersecurity is becoming more expensive, CDN bills are becoming, are going through the roof, referrals from these AI platforms are not coming through. I think the world will look very different in three years. The unit economics of the internet will look very different in three years. That much I am 95% confident in, and Tollbit's going to be a big part of that.

• 55:05Speaker 01

  Are you guys going to announce milestones that are financial? Like, we have facilitated X million, XX million, X... Well, I'm not gonna say that, but like, you know what I mean? Like, it goes up. Like, will that be a thing that you guys talk about publicly?

• 55:19Speaker 03

  Yes, I'm sure we'll include it in the State of the Bots reports. I know every so often on LinkedIn, I talk about how many tens of millions of transactions we facilitated that quarter. So, we try to be as transparent as possible. And I think the powerful thing will always be, you know, it's not just about the steak, it's about solving problems for the other side, right? If you bring something that is truly valuable, then the AI companies actually are receptive.

• 55:44Speaker 01

  Yeah, I mean, people need data for their RAG agents, or their products stop working, and no one wants that. There's a lot of people betting on them to do well. All right, just to wrap us up, I want to touch on a pet topic of mine. One thing that I adore, Tosh, is when companies, especially startups, take some of their internal data and find a way to break it out to share with the world what they're seeing. Because startups are very private, they don't share that much usually. And we've mentioned the Tollbit State of the Bots report, which I have read a number of times because it's been so useful to my own reporting work. How much of a driver, in terms of publishers reaching out to you and so forth, has that State of the Bots report been?

• 56:21Speaker 03

  Yeah, I think the State of the Bots report, I think, serves an important purpose for us. Obviously, we put it out every quarter, because it helps us sort of lift the veil. I think we realize how little good data there is out there.

• 56:35Speaker 01

  Yes.

• 56:35Speaker 03

  Right? So, as we onboard publishers, right, it allows us to sort of lift the veil and say, hey, on an anonymized level, this is sort of what we're seeing, right, across the board, right? This is what you should care about. These are the things that, you know, this is what we see in terms of referrals. Let's put things in perspective, right? And I think as sort of publishers, you know, over the last, say, quarter, right? In Q2 of this year, there was a big focus on, say, answer engine optimization, right? Caring about how you show up in some of these AI platforms.

• 57:06Speaker 01

  Yeah, GEO.

• 57:06Speaker 03

  GEO, right?

• 57:09Speaker 03

  I think it was sort of useful for publishers to navigate their thoughts around that as they, again, practical example, right? Imagine that publisher in the UK, they had actually wanted to get crawled and indexed, right? And show up in these AI search platforms. And then we showed them how many referrals they got from them. It was 650. Then they started doing the math of, well, what did that 650 cost us in terms of 13 million AI autonomous visitors every month and our CDN bills that we have to pay as a result of that, right? That's when they did the math and said, no, that's not fair anymore. Right? So the BOD report is a way so that, you know, I think the entire industry can become a little wiser, right? So that instead of just looking at your own data, you can look at what your peers, what

• 57:53Speaker 03

  the industry is seeing, what the entire internet, the entire internet, but a good portion of the publishers on Tollbit are seeing, right? So it helps us just make our publishers a little smarter, like wiser about the decisions that they're making.

• 58:06Speaker 01

  Okay, I appreciate that. Does it, when you do have the next milestone in terms of either a publisher count, a AI model partnership count, or really just a revenue milestone, we'd love to have you back. But in the meantime, where can people find you online and what is a role you're having a hard time hiring for?

• 58:22Speaker 03

  I think where people can find us online is definitely at tulbit.com or, you know, follow us on LinkedIn. We're posting snippets, excerpts, data points, thoughts there randomly throughout the quarter all the time. And then a role that I think we're actively interviewing for and, and talking to a lot of people for is a product partnerships manager, right? So this is our person, a rock star who can sort of manage our relationships with our CDN partners, right? So the fast leads, the data domes, the human securities, right? With our cybersecurity partners, right? And that manage those integrations and make sure that both sides are successful.

• 59:07Speaker 01

  All right. Well, this is an absolute pleasure. Best to you and the company and we'll talk to you soon.

• 59:11Speaker 03

  Thanks.