Fiverr International Ltd.

Good day and welcome to the Fiverr second quarter 2026 earnings conference call. All participants will be in listen only mode. Should you need assistance, please signal a conference specialist by pressing the star key followed by zero. After today's presentation, there will be an opportunity to ask questions. To ask a question, you may press star then one on a touch tone phone. To withdraw your question, please press star then two. Please note this event is being recorded. I would now like to turn the conference over to Emily Greenstein, Senior Investor Relations Manager. Please go ahead.

Thank you, Operator, and good morning, everyone. Thank you for joining us on Fiverr's earnings conference call for the second quarter that ended June 30th, 2026. Joining me on the call today are Micha Kaufman, Founder and CEO, and Esti Levy Dadon, CFO. Before we start, I would like to remind you that during this call we may make forward-looking statements and that these statements are based on our current expectations and assumptions as of today and Fiverr assumes no obligation to update or revise them. A discussion of some of the important risk factors that could cause actual results to differ materially from any forward-looking statements can be found under the risk factors section in Fiverr's most recent form 20F and other filings with the SEC. During this call, we'll be referring to some key performance metrics and non-GAAP financial measures, including adjusted EBITDA, adjusted EBITDA margin, and free cash flow. Further explanation and a reconciliation of each of the non-GAAP financial measures to the most directly comparable GAAP measures is provided in the earnings release we issued today and our shareholder letter, each of which is available on our website at investors.fiverr.com. And now, I will turn the call over to Micha.

Thank you, Emily. Good morning, everyone, and thank you for joining us. Q2 was a transitional quarter reflecting an ongoing compression in high volume, low value transactional work driven by accelerating AI adoption. As LLMs continue to evolve rapidly, we're seeing our customers accelerate their adoption of AI and workflow automation. Today, High value work represents 15% of our completed project's gross order value and is expected to continue growing while the transactional base that makes up the remainder continues to compress. That mix is why the strength of the high value business is not yet visible in our headline numbers and why every investment we are making is aimed at shifting it. So based on this, There are two reasons we are excited about the upmarket shift. The ability to attract untapped demand and the opportunity associated with projects between our current spend per buyer and high-end projects. Over the last few weeks, we've seen clear deceleration in overall marketplace traffic and demand, a trend that has carried over into Q3. We believe that recent model updates across various LLMs are a contributing factor behind this softness and are confronting these headwinds head-on. We are not competing for that work. Instead, we are aggressively executing our push-up market toward the higher value work where demand is growing. Navigating this structural shift requires intense financial discipline and thoughtful capital allocation, which Esti will unpack shortly. We have both the team and balance sheet to execute through this transition, and our focus is to deploy our resources where they generate the highest long-term return. Fiverr's multi-year transformation is fundamentally about moving from a transaction-oriented marketplace to a trusted end-to-end work platform for high-end, high-value projects. Put simply, AI is automating simple tasks and Fiverr is moving toward larger, longer-duration projects where AI deployment meets human judgment, strategic partnership and accountability. This is a profound evolution in how the work on our platform is matched, delivered, and managed. Transformations of this magnitude require patience. We expect the financial impact to build over several quarters, but our North Star remains unchanged, positioning Fiverr as the ultimate destination for high-end, high-trust work. And while the financials will take time to catch up, I believe that the underlying operational metrics are proving our strategy. Let me illustrate that across the four strategic pillars we outlined in our shareholder letter. First, our push-up market continues to validate our strategy. Clients completing projects valued at over $1,000 continued to grow this quarter at 13% year-over-year on a trailing 12-month basis. While the macro environment is volatile for smaller buyers, we see areas of strength in our high-value buyers, particularly around programming and tech and graphics and design. Clients completing projects over $1,000 in programming and tech grew 34% year-over-year, while graphics and design grew 25% year-over-year on a gross order amount and TTM basis. From an AI services vertical perspective, we're seeing a clear evolution in how clients deploy AI with us. In commercial video and content generation, clients are using AI to scale their performance marketing. Demand for AI user-generated content video ads surged 265%, while AI video ads rose 63%. Businesses aren't just experimenting, they are actively using freelancers to produce commercial-grade media at a fraction of traditional agency costs. In technical integration and agentic workflows, buyers are moving past basic chatbots toward complex production setups. Search volume for AI voice agents jumped 49%, AI mobile app development rose 92%, and AI website development grew 39%. The biggest opportunity here is the shift from generic AI to what I call personal AI, AI built into the specific workflows of a specific business. Businesses, both tech and traditional, understand that automating their businesses is not a nice-to-have decision but a do-or-die decision to stay competitive. These are demand signals from our own marketplace, and as foundational AI models get more powerful, the execution gap widens. Businesses need skilled talent to orchestrate these tools into functional, revenue-generating outcomes. This quarter, we also continued to see businesses come to us for those multi-phase, mission-critical projects. For example, One, a growing distributor that recently used a multidisciplinary freelance team on our platform to architect their entire B2B wholesale inventory and logistics ecosystem. This isn't just custom code, it's deep operational software running their day-to-day supply chain, ultimately setting up for a phase two rollout of a retail POS. Two, A manufacturer replacing a decade-old legacy system with a ground-up rebuild of their custom 3D design software. By tapping into top-tier engineering talent on Fiverr, they're integrating their new ERP system and driving a complete digital transformation. Lastly, an entrepreneur building a stadium access platform complete with secure ticketing and automated post-event photo monetization. By coordinating multiple APIs and webhooks across payments, storage, and analytics, our freelance talent is delivering complex, integration-heavy consumer platforms at scale. This is where the market is headed, and our target remains this segment of long-duration, high-value engagements. Our second pillar focuses on systematically upgrading our matching infrastructure to make trust and quality native to the user experience. We are leveraging our proprietary knowledge graph to capture incredibly nuanced client intent across four vectors. The client, the talent, the scope, and the order itself. We recently began development of live talent skill extraction capabilities and based on early testing results of over 450 mismatch events, We were able to structurally resolve as high as 58% of conversation skill mismatches, directly tackling a major pain point for high-value order cancellations. Additionally, our newly developed graph neural network model, which helps improve matching capabilities, showed a 7% decrease in cancellation rates for dynamic matching compared to our current model in initial tests. Our third pillar is about moving Fiverr beyond passive matching into an active, comprehensive work platform. We are building a standardized fulfillment layer engineered to dynamically safeguard project success. Phase one of this framework is now live in production. It is currently evaluating transactional quality, reaching as high as a verified 91% precision rate across over 84% of completed projects in our initial testing. This architecture is designed to give us real-time telemetry and sentiment signals. It allows us to track project health, mitigate friction points before order completion, and create the exact infrastructure needed to seamlessly integrate our marketplace with advanced agentic business workflows. Our fourth pillar centers on expanding our growth engines to attract the right kind of demand. We have moved our advanced semantic onboarding models and corporate KYC initiatives into production. These systems capture deep corporate profile data the moment a business joins a route that demands directly to our highest performing talent. Our development test results drove We are also opening up new highly targeted acquisition channels. In June, we launched a dedicated discovery campaign targeting e-commerce merchants looking to scale their TikTok shops. Our goal is to capture higher-value merchants, including Amazon FBA and Shopify merchants, and funnel them into repeatable, long-term service journeys on Fiverr. To close, we are repositioning Fiverr deliberately to where the value is, higher-end, high-trust work, where human expertise, project orchestration, and accountability are irreplaceable. The operational signals across our strategic pillars tell us we are on the right track, and our job now is to translate those signals into durable financial performance. Backed by our balance sheet and focused execution, we are building the foundation for our next chapter while remaining disciplined in how we allocate capital. With that, I'll turn it over to Esti for the financial details.

Thank you, Micha. Today I'll review our second quarter financial performance and provide additional visibility into the ongoing marketplace transition and update on capital allocation as well as third quarter and full year 2026 financial guidance. Starting with the results. Second quarter revenue was $97.8 million, down 10% year over year due to a decline in low-value transactional work. Adjusted EBITDA was $17.5 million, down 18.3% year-over-year, and representing an adjusted EBITDA margin of 17.9% as margins declined 180 basis points from a year earlier. Margin pressure was limited by the proactive steps the team has taken Including our continued efforts in discretionary spent management, internal AI efficiencies, and balanced marketing. Turning to our revenue segments. Q2 Marketplace revenue was $63.1 million, declining 15.5% year-over-year, driven by 2.7 million active buyers, The 22% year-over-year decline in active buyer was mainly due to the ongoing compression in low value transactional work. Low value transactional work currently represent the majority of our market base. and is decelerating at a faster and stronger rate than the growth associated with our high-value work. Additionally, as Micha noted, in recent weeks we observed a noticeable deceleration in marketplace traffic and demand, which has carried into Q3 and is expected to continue for the foreseeable future. These traffic and demand trends impact the entire marketplace. This pressure was broad-based across simple categories in all vertical. Transaction volume was down 10% or more year-over-year across the majority of projects under $1,000 on a TTM basis. Writing and translation saw the steepest decline in more than 24%. Spend-per-buyer increased 15.6% year-over-year, driven by the ongoing mixed shift across the marketplace from low-value transactional projects to 1K-plus projects. At the end of Q2, projects at or above 1K represented 15% of completed projects' gross order amount on a TTM basis. Programming and tech and graphic and design represented the fastest growing verticals of completed 1K plus projects, with both increasing over 25% year over year on a TTM basis. On the other end, services revenue in Q2 was $34.6 million, up 2% year over year, and accounted for 35% of total revenue. Services revenue continues to grow, but at a lower rate compared to last quarter. The deceleration is expected to continue into H2 and exit the year with a double-digit decline. The drivers for the deceleration include softening demand from influencers' campaigns and dropshipping, as well as weakness in Fiverr ads and Seller Plus. Services revenue is also negatively impacted by the lower expectations around marketplace growth given the traffic and demand issues we've discussed. Now on capital allocation. Our capital allocation priorities are guided by a disciplined and balanced framework. First, funding the organic transformation investments required to reposition towards high-value work. Maintaining strategic and financial flexibility in the dynamic AI automation environment. And third, evaluating opportunities to generate value for shareholders including further repurchases. In the current environment, maintaining a strong balance represents the most responsible path forward. Our healthy financial position provides valuable flexibility. as we continue to run a lean organization focused on cost discipline and maintaining profitability. At the same time, we recognize that capital allocation is a critical component of value creation. We finished the quarter with a total cash and investment balance of $308.5 million and generated $13.6 million in free cash flow. Given the uncertainty, full year free cash flow is expected to be lower than the previous two years. We are operating in a dynamic environment and we will constantly evaluate where we deploy our capital for the highest return. That also applies to how we invest in our transformation and related growth opportunities. A primary focus will be on and continue to generate profit and fresh cash flow. Now moving to financial guidance. Our revised guidance for the third quarter and full year 2026 reflects the AI-related demand and traffic headwinds observed in recent weeks, which have continued into Q3, impacting our entire marketplace. Ongoing weakness in categories most exposed to AI automation and declining services revenue. We now expect that meaningful financial impacts from our transformation will require at least six quarters to materialize. For the third quarter of 2026, revenue is expected to be between $80 to $88 million, representing year-over-year growth of negative 26% to negative 18%. and adjusted EBITDA between $8 to $12 million, representing an adjusted EBITDA margin of 11.9% at the midpoint. For the full year 2026, we expect revenue to be in the range of $356 to $372 million, representing an year-over-year growth of negative 17% to negative 14%. and adjusted EBITDA in the range of $52 to $62 million representing an adjusted EBITDA margin of 15.7% at the midpoint. While our updated outlook reflects our view of the current operating reality and the extended timeline for our transformation, we are leaning into this moment with discipline. Stabilizing the core marketplace, shifting towards high-value work at a faster pace, preserving flexibility, and allocating capital with a focus on long-term value creation. With that, we'll now turn the call over to the operator for questions.

We will now begin the question and answer session. To ask a question, you may press star then 1 on your touchtone phone. If you're using a speakerphone, please pick up your handset before pressing the keys. If at any time your question has been addressed and you would like to withdraw your question, please press star then 2. At this time, we will pause momentarily to assemble our roster. The first question today comes from Eric Sheridan with Goldman Sachs. Please go ahead.

Thanks so much for taking the questions, maybe two if I could. Just drilling down on the six-quarter transition period, can you give us a little bit of more granularity about what is it about either business mix or the headwinds and tailwinds you see in the business today that underpin some of the framing of the duration of the transition across that type of time period. And then the second would be, you know, you highlighted the recent updates of various LLMs as a contributing factor to the weakness. What exactly are you seeing out of those LLMs and how is that sort of manifesting in these headwinds if we get a little bit more detail there as well? Thank you so much.

Good morning, Eric.

Thanks for the questions. As for the first one, the answer is yes. It's mostly headwinds, which really impact our calibration or estimation of how long this transformation is going to take. That's mainly what's influencing this. And as Esti mentioned in the opening comments, we run a very lean organization. at the minimal size that is required to move very, very fast. So we're now a much smaller organization that we used to do as we've done the restructuring, but the pace in which we move is much, much faster. Given the fact that we assume the trends that we started seeing in the last couple of weeks, All Q2 going into Q3, the assumption is they'll continue. And this will be the time that we feel the transformation is going to take. Now, I did mention the impact of a few of the LLM models. And again, between those LLMs, obviously, there's different types of usage. And there are some models that are more More impactful or step function versus those who are just adding some improvements. But the really interesting thing is that it's not just the LLMs themselves or the specific models, but it is how those LLMs are being integrated into other client experiences. I think most noticeable is how Gemini is being integrated into the experience of search on Google. And so it exposes more people to LLM experiences, which by turn obviously impacts the traffic that we're getting. So traffic used to come only from search, then it became LLMs as well. And now it's becoming hybrids of LLMs and traditional search, which essentially lowers the volume of traffic, which we called out as one of the major headwinds.

Great. Thank you. Thank you.

The next question comes from Ron Josie with Citi. Please go ahead.

Great, thanks for taking the question. Micha, just a quick follow-up on Eric's question on the timeline and the six quarters. Can you give us some idea or steps that perhaps we can look into or watch in terms of this transition to see just the progression as we go to higher-value projects upmarket? And then to that, as we do get to higher-value projects and given the foundation model to get more powerful, just talk to us about the talent on Fiverr's Marketplace and the ability to basically answer or deliver these higher value projects. Thank you.

Thanks for the questions, Ron. Good morning. So I'll start with the first one, the transformation itself. So we've been calling out in the opening remarks what we're actually building there and what are the early signals that we've seen from tests And those signals or those tests are being deployed more widely, which is why we expect to get more impact out of them. So this transformation is really about the matching parts of our proprietary knowledge graph. And as we said, the initial deployment of seller scale extraction, which addresses about 58% of mismatches in a model That decreases by 7% the high-value cancellations. These are really impactful numbers. And since they've been running on tests, they're being deployed widely on production. The product has now an end-to-end fulfillment layer, and the phase one of it is live. and it's evaluating the quality of transactions with 91% precision across 84% of the completed projects. The go-to-market transformation, again, we've developed and tested new go-to-market, and I've mentioned TikTok as an example, which drove 8% increase in new customer conversion. Again, deploying this at the larger scale. And obviously, we do all of this through keeping this operational excellence and making critical investments to strengthen the high-end talent flywheel and focus on improving market-based quality, prioritizing high-value work, and executing with very strong financial discipline as we do this. Now, when we look at, and this is the second part of your question, when we look at a type of skills, a part of what we're doing in going much faster upmarket means that we're doing, we're both cleaning our talent pool from unnecessary, those who don't have demand for low skills, which is really important, because they occupy space on the marketplace. And on the other end, defining the necessary skills, ensuring that if we're missing in some place the right talent to tackle these needs, these client needs, we have that talent, and if not, we onboard them very, very fast. And we started calling some of these project types and categories But I think the overarching message here is we're not talking about very specific, small AI deployments for our clients. Our clients are interested in AI integration. They want to improve the competitiveness and the efficiency of their entire business, not just put a customer support agent They want to integrate their entire business workflows. That requires highly skilled professionals that can assess and understand the actual business needs and understand how to create multiple agents that are tailored for that specific business so they can extract the most out of this AI transformation The next question comes from Jason Helfstein with Oppenheimer. Please go ahead. Jason Helfstein Thanks for taking the question.

Just kind of thinking about the kind of financial outlook, I mean, you made the point you already run a very lean organization, as you said. I guess what's the right way to think about, you know, EBITDA and pre-cash for next year? I mean, obviously you're not giving us guidance, but if revenue is down next year, are there other cost actions you can still take? And then I guess How far along are you in just deploying AI internally for your business and automation in the organization? If you want to put those two together as one answer, but that's the question. Thanks.

Thank you, Jason. Good morning. Our revised guidance for EBITDA reflects first the issues which we're seeing on traffic and that affects the bottom line. However, as you know, we took some reduction cost initiatives in previous quarters that help us to support EBITDA. In addition, as we see traffic issues, we also adjust our marketing spend as we did before, and we will continue to assess that if needed. We are protecting our R&D spend because that supports the transformation, and this is our number one priority, to be able to go through the transformation and also continue to be profitable. So we should think about that during the transformation, this would be a firm to continue to generate cash. We'll continue to run the organization and continue to be with cost discipline, especially about discretionary costs, marketing, as I said. But again, the focus is going through the transformation.

Yeah, and to answer your second question, essentially the first half of the year was putting all of this infrastructure together and running multiple tests, and I've called out some of them, to prove that doing so is actually significantly improving the 1K transactions and the right client. What we're going to do in the second half of the year is deploy some of these solutions at scale and continue to work on new things that we feel are going to impact and accelerate the ability to drive a quality experience, better conversion, and optimize for a higher spend of those customers.

I would just add on the free cash flow. So free cash flow follows EBITDA, so you should expect the guidance of the EBITDA should be the range for the guidance for the free cash flow. And as I said, we are aiming to continue to be profitable, and that means also to continue to generate cash.

The next question comes from Nat Schindler with Scotiabank. Please go ahead.

Mika, this is kind of a big picture question. For years, you guys have said that really the move to AI is not such a threat because you are basically just a marketplace for freelancers and freelancers are the most adaptable people in the entire economy. They very rapidly change to what is necessary. and the jobs that need to be done. And as a marketplace, you really don't care what those jobs are. You can just provide the connection point. And that's a really compelling argument. But then in February, it seems like the release of Claude was kind of a watershed moment. And the world is now really doing more and more things without touching These freelancers, in a sense, and that's why the revenue seems to be declining as much as it is. I get the transformation. I get the need that companies will have for people to help them do these transitions. But how can you be confident that in six quarters, there aren't going to be more watershed moments where this gets worse? and this gets more and more that you can do more and more with less than you need. And the freelancers will face their first time where even their renowned adaptability just isn't enough.

Good morning. Thanks for the question.

First of all, I should say we stand behind everything we said. And it is true and it still is true What I think we are seeing, and this is also why this requires additional time for this transformation to be highly successful, is the fact that very similar to previous transformations, technology transformations or industrial transformations before, the rate in which some skills get compressed versus the rate of the creation of new skills is not the same. The creation of new skills, the orientation of our clients to understand how do they navigate this new world, putting names to skills, ensuring that talent is qualified to tackle those needs is a cycle. and they don't happen at the same rate. But we are seeing this because we have deep expertise in this economy. We constantly have discussions with clients. We're seeing developments in the market. And it takes time, but they actually realize that this is, as I've said, nothing nice to have. It's not a choice. It's like 30 years ago when people thought that having a website was optional. It took time for businesses to understand that if they don't have an online presence, they are going to start losing some of their businesses to their competitors. The same applies for AI. But it takes time for businesses to understand exactly what they need. It's still true freelancers are in the forefront of this curve. They do understand that they obtain these skills fast, but as I said, it's not the same rate of the compression that we're seeing. And also, because the high end is still a small portion of our business, The growth that we're seeing there, in some cases very high growth, is being masked by the compression in the low end. But this increase gives us the confidence that this transformation is the right thing, and we should be very committed to it, and the early signals that we've seen from our testing is showing this, and that's why we move full force Fire on all cylinders to make that transformation as fast as possible.

And just to follow up on that, as you make a transition to the high end, and it sounds like the high end is evolving to even higher end than previous discussions of high end, is that moving you more into just being a staffing placement company where you're doing shifts? As opposed to what you have been so long and been so capable at is being a marketplace for completed business services. Because it's very hard to define and limit a scope of project in a single listing. This is a, I need someone who has this ability. But are you just totally changing what you are in that respect?

Yeah, so I've addressed this also in previous calls, and the way we think about Fiverr and our role in this ecosystem is to be very focused on the project and the outcome-based work. So for us, identifying the need, and in some cases helping the client better Then understand through the knowledge graph who is the exact most qualified talent and in some cases multiple talents to address and be able to achieve and fulfill that goal, making that matching but also as I said taking a larger portion in the fulfillment of this actual outcome-based result is the function. And that is what defines us and where we focus.

And this allows us to be very agile in this. Okay, thank you.

Thank you.

The next question comes from Bernie McTannin with Needham. Please go ahead.

Hi, this is Stephanos Christ from Burney. Thanks for taking our questions. So the projects above $1,000 are 15% of GOV. What mix do you expect that to be to offset the decline in the legacy low value work? And then just on capital allocation, are there any assets out there that can help accelerate your plans or are you only focused on internal investment? Thank you.

Okay, thank you, Stephanos. So, while the 15% seems small, but you need to remember that currently our center buyer is 368. So, we have a lot of room to grow with the 1,000. Now, it would take time, as we said. We are changing fundamentally our platform. It comes with, as Micha mentioned, a lot of The talent, the matching, the orders, of course, they go to market. Now, it would take time. However, we see the opportunity both within the marketplace, within our current buyers, and also as we go with new go-to-market. So one example is the TikTok example, but we are planning for additional partnerships and additional go-to channels. As for capital allocation, so our primary focus is definitely going through the transformation, and we're investing on that. On the M&A side, nothing to call out. We're always opportunistic, but again, top priority is to do the transformation within the platform. We said we're going to invest in R&D. In order to do that, we have the sufficient staff and we will run lean organization while doing that, as Micha said, so we're going to do it fast but with a lean organization and not planning currently doing inorganic.

The next question comes from Brad Erickson with RBC Capital Markets. You may go ahead.

Hi, thanks. I just want to go back a little on the paper trail of the weakness, if I could. And I realize it's hard to know 100% for certain, but you mentioned Google's distribution with kind of integrating LLMs into search and being a driver of the weakness, which makes sense. But I'm just curious if there's a component from some of these more recent Powerful Model releases. So I guess the question is, is this kind of a marketing issue where Google's making people more aware of these tools being available and they're using them? Or is it more just that newer models are just making it easier to do kind of a wider range of things? Or if it's both, I guess just curious which of those two do you feel like you're seeing more of lately?

Thanks. Thank you for the question, Brad.

The weakness of demand and traffic comes from Google, but it has also, as I've mentioned, the impact of integrating LLM or AI summaries in the Google search. So by definition, for some customers, they would go directly into the LLM instead of clicking on either paid or organic links. And I think it's very visible, the fact that the majority of links anyway are paid. So the organic is actually getting squeezed down, which means that the impact of SEO is going down and this is why we and probably every other company is making also an investment in GEO or Generative Engine Optimization to make sure that we are inside those LLMs and we are. But the click-through in LLMs in general is obviously smaller than it is on search. All of these factors actually create headwind on traffic The same goes for other models. Sometimes it's the introduction of newer tools. That could be in the case of cloud code or design or cloud co-work. And right now we have Kini coming in and, you know, a Chinese alternative that is coming in to compete. And sometimes it's upgrade of a newer model on ChatGPT. Our assumption is that these models are going to continue developing and competing with each other and depending on their usage and their integration and other things might influence traffic. That said, and I'm not going to repeat that, the actual needs of the clients that we're aiming for is not being addressed by just using one of these tools. It requires Very, very deep integration of agentic solutions that are well beyond reach for the vast, vast majority of businesses. Even high-tech businesses, and I know this from firsthand experience, are not having an easy time creating more agentic organizations. So if it's hard for such a sophisticated company like Fiverr, and I've been talking to many other companies, it is a thousand times more complex for traditional businesses. And therefore, they will need help, and they will need this idea of human in the loop.

Got it. And then just bigger picture, you have such a Great lens into the world of models and harnesses and so forth. I'm just curious, like, could you just maybe rest a second and give us a little bit of your thinking around just the open source topic and in particular, you know, we won't talk Frontier specifically, of course, but just, you know, software looking to find any way to build a moat, lock their customers in. versus working with open models. How are you kind of thinking holistically about that? And certainly for your business, but also just how your customers compete at that level. How do you kind of think about that?

Right now, if you think about it, most companies in the world, and I'm excluding maybe the forefront AI companies, are still... If you look at highly sophisticated companies like ours, the type of challenges that we obsess over is inference, for example, because there's now almost endless amount of different models, each one slightly better in different tasks, but there's also a question of how How fast do you need it? So by introducing latency, you can actually pay less for the same model, assuming you don't need the answer or the output right away. So for us, as an example, it's not just how do you extract the most out of a certain model, but how do you use multiple models from multiple foundations to actually maximize this task or the skill that you're trying to solve and also bake rice into it. Because you might use very advanced models that cost a lot. Fable costs twice than Sonnet. But for many, many different tasks, it's simply not needed. And so throttling between those. Dealing with inference is one of the big topics right now. But as I've said, I think that this is way, way too advanced for businesses that are tackling much more basic needs. But even those businesses understand that the deployment of AI is not free. Meaning when you deploy AI, it's not one and done. Running a GenTech environment has a cost and they want to be aware of it. And some of the things that our experts are doing is helping customers ensure that they don't overspend for the wrong things. This is why it's so complex. This is why I keep telling This is beyond the capabilities of most businesses and why they need help with it. I think the question of open source or not open source is not a big difference. Open source just allows for the foundational companies to harvest data faster so they can make it slightly cheaper. But we're all working for foundational companies anyway. So if it's good enough, you should probably pay less for it.

Okay, I riffed enough. Thanks a lot. Thank you.

The next question comes from Matt Condon with Citizens Bank. Please go ahead.

Thank you for taking the questions. My first one, I just want to go back to this traffic question. Are you seeing a significant deterioration in your payback periods in other channels? I'm specifically thinking about is this isolated to SEO or Google traffic? Are you seeing your other channels also deteriorate in performance? And then my second question is, would you ever consider dynamic pricing or any changes in pricing to try and spur demand across the marketplace? Or is that just not a focus for you guys? Thank you so much.

Thanks to the questions.

So, essentially, the situation, if we look at Google as an example. So, first of all, the fact that there's less customers coming to Google is impacting the amount of traffic that Google has in its hands to advertise for, meaning that they increase advertising density, which means that there is more competition for each placement, which means that the cost of acquisition is higher. All of this influences the efficiency of marketing, which is Why we take a multi-channel approach, which is why we're investing in GEO to increase the organic traffic from LLM channels. And it is showing great results, but on a click-through, it's still small. We were also one of the first advertising partners on Chajik Tea, but it is still small. Very early days. It reminds us when we started working over the early days of working with Google. It takes time to build up. They're figuring out, we're figuring out the strategy, but we are taking proactive measures. And so what we want to make sure is we want to make sure that the investment that we're doing, this transformation, in the mashing infrastructure and the fulfillment workflow is exactly enabling Fiverr to seamlessly integrate into a genetic workflow down the road so that we can give more of our core experiences and core solutions inside these LLM or whatever they become experiences.

to entertain the customers where they are.

The next question comes from Marvin Fong with BTIG. Please go ahead.

Great. Good morning. Thanks for taking my questions. Two, if I may. So first one, maybe bigger picture. With these increasing sophistication of the projects that you're targeting, do you feel like your current suite of services, Fiverr Business, Fiverr Pro, is enough to address this coming landscape? Or are you looking at producing additional service channels for your clients? to create something a little bit more comprehensive perhaps. And then second question, just a little more focus on the services revenue, called out that Seller Plus would be down. And I'm just curious, is that because You know, freelancers are canceling their subscriptions or is it that the actual pool of freelancers is kind of shrinking on the side? If you could just kind of provide some insight on what's going on on the freelancer side with respect to services, that'd be great. Thank you.

Thanks for the questions.

So, as we're doing this transformation, we're basically rebuilding a lot of the core functionality and the core product from the ground up. I've called what we're doing with the knowledge graph, what we're doing with the end-to-end fulfillment layer, the matching engine, and the type of experiences that we expect to provide our customers that are better than the best AI out there in the market. I don't want to go into those details because this is being built and is going to be tested and deployed throughout the year. We're working very hard to make sure that those clients that we focus on receive the best experience that they can get to ensure that they achieve their outcome. And we're building new tools. Again, we're not getting into talking about these tools and they're being built, but we are refining the platform to ensure that this is exactly the case.

And as for your second question about services revenue, So services revenue and seller monetization is affected of the traffic of the marketplace. And the traffic headwinds that we saw in recent weeks affect the ads and the seller plus. So we expect that also to be continued in H2. In addition, in services revenue, we saw some headwinds also on AutoDS, and that's also being included into our H2 guidance for services revenue.

Okay, great. Thank you, Micha. Thanks, Esti.

Thank you.

The next question comes from Josh Shan with UBS. Please go ahead.

Hi, good afternoon, Micha, Esti. I guess two questions. First, I guess as you do this transition, you know, standing here, I guess how much of your talent or customer base do you feel like has to switch out or cycle through to complete the transition and how to acquire the right talent and right customer for the next phase. And then the second part is when you give the six quarters, I guess, estimation, what's the starting point of that six quarters? And then as you deploy the solutions that you're testing, should you see some improvement in the trends even during that period? Thank you.

Josh, thanks for the question.

Good morning. I think I made that comment earlier, which is we're both cleaning up the lower end part of our talent that we feel has lower demand to make sure that we optimize the display layer of our marketplace. and a lot of our clients to find what they're looking for easier. The rest goes for the high-end talent. As I've said, identifying the skills on demand and in some cases defining them, helping give those skills names is really important. Then it's about qualifying them. We said in many, many and many more. One of our biggest moats is the fact that we've been the largest transactional market base in the world and we've collected so many data points that using them in the right way, which is how we build the knowledge graph that contains Very deep understanding of the client and their needs and understanding the actual description in the desired outcome and pairing them with the right talent that can address exactly that. And since we have such a rich data of actual transactions, Where we have high level of trust of the talent that we have, the matching becomes much more efficient, resulting in much happier outcomes, which then also drive higher retention and higher spent.

And Josh, as for your second question, so the six quarters, it's starting now. and, yes, of course, as Micha described, although we're still at the beginning of the period, we have some initial things that we're seeing that are encouraging and definitely we'll share more as we go to show all of the progress.

Great. Thank you both for the comment.

This concludes our question and answer session. I would like to turn the conference back over to Micha Kaufman for any closing remarks.

Thanks so much. Thank you Chloe for moderating this call and thank you everyone for calling in and I wish you a great day and we'll talk soon.

Thank you. The conference is now concluded. Thanks for attending today's presentation. You may now disconnect.