WEBVTT 00:00:00.017 --> 00:00:04.317 That's a really great question and a very critical one for any founder, 00:00:04.517 --> 00:00:06.537 especially as they start off in scale. 00:00:06.777 --> 00:00:10.917 So it really depends on your business, the market, and the problem you're solving. 00:00:11.217 --> 00:00:16.277 So let's start with Gen.AI as a feature. That is often the safest and most effective strategy. 00:00:16.497 --> 00:00:21.017 So you should use it when you have a strong existing user base and a clear use 00:00:21.017 --> 00:00:23.357 case for AI to improve a current workflow. 00:00:24.017 --> 00:00:29.177 So instead of building a new product from scratch, you embed AI directly into 00:00:29.177 --> 00:00:31.477 your existing offering to enhance its value. 00:00:31.657 --> 00:00:35.197 So you typically can't charge a premium for a single feature, 00:00:35.257 --> 00:00:37.877 but instead you can use tier pricing, 00:00:38.217 --> 00:00:43.317 offering AI features in higher price plans, or a freemium model where basic 00:00:43.317 --> 00:00:48.837 AI functions are free, but advanced capabilities, imagine more queries or larger 00:00:48.837 --> 00:00:50.957 outputs, are meted with a credit system. 00:00:51.440 --> 00:01:11.280 00:00:56.197 --> 00:01:03.837 Welcome to StartupRad.io, your podcast and YouTube blog covering the German 00:01:03.837 --> 00:01:08.737 startup scene with news, interviews and live events. 00:01:10.817 --> 00:01:15.497 AWS is proud to sponsor this week's episode of StartupRad.io. 00:01:15.497 --> 00:01:22.037 The AWS team compromises former founders, CTOs, venture capitalists, 00:01:22.457 --> 00:01:27.237 angel investors and mentors, ready to help you prove what's possible. 00:01:27.697 --> 00:01:36.037 Since 2013, AWS has supported over 280,000 startups across the globe and provided 00:01:36.037 --> 00:01:42.477 $7 billion in credits through the AWS Active program. 00:01:42.477 --> 00:01:44.977 Big ideas feel at home at AWS. 00:01:45.457 --> 00:01:49.997 And with access to cutting-edge technologies like generative AI, 00:01:50.337 --> 00:01:54.277 you can quickly turn those ideas into marketable products. 00:01:55.157 --> 00:01:59.117 Want your own AI-powered assistant? Try Amazon Q. 00:01:59.897 --> 00:02:05.317 Want to build your own AI products? Privately customize leading foundation models 00:02:05.317 --> 00:02:09.457 on Amazon Badrock. Want to reduce the cost of AI workloads? 00:02:09.777 --> 00:02:13.157 AWS Tranium is the silicon you're looking for. 00:02:13.920 --> 00:02:17.000 Whatever your ambitions you've already had 00:02:17.000 --> 00:02:20.280 the idea now prove it's possible on aws 00:02:20.280 --> 00:02:23.820 visit aws.amazon.com forward 00:02:23.820 --> 00:02:26.520 slash startups to get started if you're a 00:02:26.520 --> 00:02:32.140 founder here's the challenge generative ai is everywhere but turning it into 00:02:32.140 --> 00:02:38.020 actual revenue is far harder than the hype suggests jennifer gruen senior specialist 00:02:38.020 --> 00:02:43.680 for generative ai at aws has worked with startups software vendors and enterprises 00:02:43.680 --> 00:02:45.840 to unlock billions in business value. 00:02:46.040 --> 00:02:51.580 Today we'll break down AI monetization frameworks, return on investment metrics 00:02:51.580 --> 00:02:56.640 that investors demand and real startup case studies so you can turn AI from 00:02:56.640 --> 00:02:58.120 a buzzword into business. 00:02:58.640 --> 00:03:02.440 Jennifer Grun is a senior specialist for generative AI and machine learning 00:03:02.440 --> 00:03:07.700 at AWS helping startups, software vendors and enterprises across Germany and 00:03:07.700 --> 00:03:12.880 in Central Europe to define, scale and monetize their AI use cases. 00:03:13.240 --> 00:03:17.740 She led go-to-market strategies for generative AI beyond AWS. 00:03:18.160 --> 00:03:23.700 She's passionate about democratizing machine learning and runs initiatives to 00:03:23.700 --> 00:03:27.120 bring knowledge to underrepresented groups and universities. 00:03:27.480 --> 00:03:32.920 Today, she'll share what founders need to know about monetization opportunities with AI. 00:03:32.920 --> 00:03:37.520 From metrics like customer acquisition costs and customer lifetime value, 00:03:37.720 --> 00:03:44.440 to Canva's upsell playbook, to why ROI storytelling can make or break an investor 00:03:44.440 --> 00:03:46.120 pitch. Jennifer, welcome. 00:03:46.880 --> 00:03:50.140 Thank you, Jörn. Really excited to be here today. 00:03:50.780 --> 00:03:54.920 Totally. My pleasure. We had some issues with the setup, but now we're totally 00:03:54.920 --> 00:04:00.220 good and everything should work out. Welcome to our episode. 00:04:00.500 --> 00:04:09.660 You often describe the trillion dollar question as not what to build with AI, but how to monetize it. 00:04:09.880 --> 00:04:12.580 Why is this gap so persistent? 00:04:13.686 --> 00:04:18.366 Thanks, Joe. So that's a really great question. So for me, the main reason for 00:04:18.366 --> 00:04:20.466 this gap comes down to three things. 00:04:20.746 --> 00:04:23.566 So first, you have the economics of Gen AI. 00:04:24.046 --> 00:04:28.606 So unlike traditional software, where adding a new user has a very low marginal 00:04:28.606 --> 00:04:30.906 cost, generative AI is different. 00:04:31.206 --> 00:04:35.486 So every time a user interacts with a model, so every query, 00:04:35.766 --> 00:04:39.946 every word that is generated, it has a real tangible operational cost. 00:04:39.946 --> 00:04:42.066 And this also creates a paradox. 00:04:42.366 --> 00:04:47.346 So if you have a user who gets a ton of value from your service and uses it 00:04:47.346 --> 00:04:50.926 frequently, that might actually become less profitable for you. 00:04:51.086 --> 00:04:55.926 And this makes it really difficult to achieve the high gross profit margins 00:04:55.926 --> 00:05:00.746 that investors have come to expect, especially, for instance, for SaaS startups. 00:05:01.246 --> 00:05:06.466 And the second issue that I see founders are facing is often defaulting to familiar 00:05:06.466 --> 00:05:09.766 pricing models that just don't fit the technology. 00:05:10.366 --> 00:05:15.486 So, we are seeing a lot of startups try to use a per-seed or per-user model, 00:05:15.706 --> 00:05:17.166 but that can also backfire. 00:05:17.426 --> 00:05:21.186 So, imagine if your AI product makes the team more efficient, 00:05:21.486 --> 00:05:25.786 they might actually need fewer people, and that means also fewer seats. 00:05:26.758 --> 00:05:30.138 And this means that it would actually shrink your addressable market. 00:05:30.738 --> 00:05:34.418 And also, there's a market perception issue that I can observe. 00:05:34.638 --> 00:05:39.398 So when a lot of DEI features are given away for free to drive adoption, 00:05:39.818 --> 00:05:42.958 Customers can actually start to see them as stable stakes. 00:05:43.138 --> 00:05:47.018 So this makes it really tough to convince them to pay for what they believe 00:05:47.018 --> 00:05:51.918 should be a standard feature, where startups can't afford to raise to the bottom 00:05:51.918 --> 00:05:54.138 because every dollar counts in a startup. 00:05:54.138 --> 00:05:59.298 And then thirdly, many founders are so focused on the technical what, 00:05:59.538 --> 00:06:04.518 building the next great model or creating a really interesting feature that 00:06:04.518 --> 00:06:07.258 they fail to properly consider the business how. 00:06:07.478 --> 00:06:11.958 So in many of the conversations that I have with startups, I mentioned that 00:06:11.958 --> 00:06:15.038 you can't just build technology and hope for the best. 00:06:15.058 --> 00:06:19.518 So you really have to start with an outcome oriented mindset from day one. 00:06:19.518 --> 00:06:23.878 So this means understanding the specific customer pain point you're solving 00:06:23.878 --> 00:06:25.518 and how you will measure success. 00:06:25.778 --> 00:06:32.258 And a great example of this is a company that has navigated this beautifully is Lovable. 00:06:32.418 --> 00:06:35.478 So we are also going to have them soon in a hackathon here in Munich. 00:06:35.798 --> 00:06:40.398 And they're building a platform that allows anyone to create full stack web 00:06:40.398 --> 00:06:43.298 applications just by typing in a simple prompt. 00:06:43.298 --> 00:06:48.138 So they're solving a huge problem for non-technical founders who want to build 00:06:48.138 --> 00:06:51.758 an MVP quickly without the high cost of a development team. 00:06:52.158 --> 00:06:56.758 And what they did here is that Lovable has aligned their monetization with the 00:06:56.758 --> 00:06:57.938 value that they provide. 00:06:58.038 --> 00:07:02.158 So they use a credit-based pricing model where users pay for what they use, 00:07:02.298 --> 00:07:03.918 which directly aligns with their 00:07:03.918 --> 00:07:07.018 operational cost and the value of the application they are generating. 00:07:07.018 --> 00:07:11.738 And this really shows, again, an example of a company that has successfully 00:07:11.738 --> 00:07:15.658 moved beyond the what of the technology, but also figured out the how. 00:07:16.387 --> 00:07:19.707 Again Madhu, many startups get stuck in proof of concept. 00:07:20.067 --> 00:07:27.687 Why do POC technology choices fail to scale and how does this kill monetization potential? 00:07:28.007 --> 00:07:32.467 And that's an excellent question and it's something that we see actually all the time. 00:07:33.107 --> 00:07:37.427 So a significant reason for failure is really the fundamental difference in 00:07:37.427 --> 00:07:40.767 purpose between a POC and a production ready system. 00:07:41.427 --> 00:07:45.947 So many AI pilots that I have observed are driven by a technology-first mindset, 00:07:46.207 --> 00:07:49.587 where a solution is developed in search of a problem to solve. 00:07:49.767 --> 00:07:52.607 So think about a cool technology that people want to build. 00:07:52.867 --> 00:07:59.167 But to really transform the organization, the product requires buy-in from the entire organization. 00:07:59.507 --> 00:08:04.247 So we've seen startups where a lack of internal alignment on business priorities, 00:08:04.567 --> 00:08:09.247 funding, or success metrics completely stores a project. And this is why it's 00:08:09.247 --> 00:08:11.247 so important to use a structured approach. 00:08:11.587 --> 00:08:17.287 So I've worked here on an AI canvas that is forcing a shift in thinking beyond technology. 00:08:17.427 --> 00:08:21.627 So this canvas helps you to clearly articulate the customer needs, 00:08:21.827 --> 00:08:26.307 the value proposition and the metrics for success up front to make sure that 00:08:26.307 --> 00:08:30.147 the solution is tied to a real business problem with a measurable outcome. 00:08:30.147 --> 00:08:35.587 And the shift from a proof of concept to a proof of value, how we call it, 00:08:35.647 --> 00:08:38.487 is really crucial in addressing this issue that I just mentioned. 00:08:38.867 --> 00:08:43.347 So the proof of value really focuses on delivering and measuring a tangible 00:08:43.347 --> 00:08:47.787 business impact rather than really simply demonstrating technical feasibility. 00:08:47.787 --> 00:08:52.847 And what we've seen here is without a clear quantifiable metric for success, 00:08:53.067 --> 00:08:55.687 imagine cost savings or revenue growth, 00:08:55.987 --> 00:09:01.527 even a technically sound POC that wows your team will struggle to secure the 00:09:01.527 --> 00:09:04.727 continued investment required for full scale development. 00:09:05.127 --> 00:09:10.467 So this requires strong cross-functional collaboration among business, 00:09:10.667 --> 00:09:15.207 engineering and product teams to ensure alignment on a unified strategy. 00:09:16.370 --> 00:09:21.770 And also, what I like to outline as well is that the technology choices made 00:09:21.770 --> 00:09:25.650 for POC are often not suitable for a production application. 00:09:26.050 --> 00:09:30.190 So, for instance, POC might use a popular general purpose model, 00:09:30.470 --> 00:09:34.890 but a production application requires choosing the right model that balances 00:09:34.890 --> 00:09:37.810 capabilities, performance and cost. 00:09:38.050 --> 00:09:43.190 And also, it might need to be fine-tuned on proprietary data to create a truly 00:09:43.190 --> 00:09:44.310 differentiated product. 00:09:45.190 --> 00:09:50.750 Also, what I observe a lot is that POCs are often designed for a few users only, 00:09:50.910 --> 00:09:54.810 and they are really not built to handle thousands or millions of queries. 00:09:55.170 --> 00:09:59.530 So this means that they lack the necessary architecture for resilience, 00:09:59.790 --> 00:10:01.650 security, and global scalabilities. 00:10:02.590 --> 00:10:08.490 And that brings me a little bit to the challenges and the startup's monetization potential. 00:10:08.650 --> 00:10:14.210 That is quite direct and critical. So, if we think about the generative AI space, 00:10:14.410 --> 00:10:21.050 a unique characteristic is that the cost of goods sold is often tied directly to user consumption. 00:10:21.510 --> 00:10:26.010 So, if the underlying technology is not designed for efficiency and cost optimization, 00:10:26.530 --> 00:10:31.270 every user interaction can become an expense that quickly erodes your profitability. 00:10:31.870 --> 00:10:36.870 So imagine an inefficient, unoptimized POC may function really on the small 00:10:36.870 --> 00:10:38.730 scale that we discussed, but its 00:10:38.730 --> 00:10:42.490 per unit cost can make it financially unsustainable at a larger volume. 00:10:42.690 --> 00:10:47.070 So when a startup fails to address these issues, they end up with a product 00:10:47.070 --> 00:10:49.830 that is slow, expensive and unreliable. 00:10:50.110 --> 00:10:54.470 So their monetization potential is killed because you simply cannot charge for 00:10:54.470 --> 00:10:58.750 a service that doesn't work consistently and that can't handle a growing user base. 00:10:58.750 --> 00:11:03.710 So as a result, the startup is unable to leverage its technology for monetization 00:11:03.710 --> 00:11:09.330 through methods like premium features, tiered subscription or revenue sharing models. 00:11:10.239 --> 00:11:13.479 I see. So what do you see? 00:11:13.599 --> 00:11:20.419 What's a common mistake that startups make when defining monetization models for AI features? 00:11:20.739 --> 00:11:25.639 Really, the most common mistake that I see startups make is creating a pricing 00:11:25.639 --> 00:11:29.179 model that doesn't align with the underlying cost and the value of AI. 00:11:29.659 --> 00:11:34.659 So many startups that I've spoken with try to use a traditional perceived model 00:11:34.659 --> 00:11:37.879 to start with, which works well for conventional software. 00:11:37.879 --> 00:11:42.839 But if we take, for instance, agentic AI models, especially an autonomous one 00:11:42.839 --> 00:11:45.999 where we're developing towards, this may be completely ineffective. 00:11:46.539 --> 00:11:51.699 So an AI agent doesn't really need a user to be active, so you can't charge per seat. 00:11:52.039 --> 00:11:57.479 So unlike traditional software with its low marginal cost, every interaction 00:11:57.479 --> 00:12:00.439 with a generative AI model has a real tangible cost. 00:12:00.699 --> 00:12:03.439 So as a result, startups that charge 00:12:03.439 --> 00:12:07.459 a flat fee for unlimited usage find themselves in a dangerous paradox. 00:12:07.879 --> 00:12:12.199 So their most engaged and arguably most valuable customers are actually making 00:12:12.199 --> 00:12:16.939 them less profitable because their high usage drives operational expenses and 00:12:16.939 --> 00:12:18.139 eats into the bottom line. 00:12:18.399 --> 00:12:22.439 So this is, of course, the exact opposite of what you want in a business model. 00:12:23.279 --> 00:12:28.739 Beyond the cost paradox, another significant mistake is a failure to properly 00:12:28.739 --> 00:12:31.179 package offerings for different customer segments. 00:12:31.499 --> 00:12:35.099 So a good pricing strategy isn't just about the price itself. 00:12:35.099 --> 00:12:38.199 It's really about how you differentiate your product. 00:12:38.639 --> 00:12:43.119 For instance, you can create a basic package that offers a generic AI agent 00:12:43.119 --> 00:12:47.719 with broad expertise, and then a premium package that gives customers access 00:12:47.719 --> 00:12:51.439 to specialized agents trained on specific high-value topics, 00:12:51.799 --> 00:12:53.959 let's imagine legal or finance. 00:12:54.479 --> 00:12:59.859 And you can also package in enterprise-grade features that are crucial for larger 00:12:59.859 --> 00:13:05.179 clients, but not necessarily for every user. So this could mean single sign-on, 00:13:05.319 --> 00:13:09.119 audit logs, and data privacy assurances in your top-tier packages. 00:13:09.459 --> 00:13:13.439 So by doing this, you ensure that the customers with the highest willingness 00:13:13.439 --> 00:13:19.239 to pay are getting the most expensive product, which is a key principle of effective monetization. 00:13:20.556 --> 00:13:25.536 Another big mistake is choosing the wrong price metric, so the unit of measurement 00:13:25.536 --> 00:13:27.236 you use to charge for your product. 00:13:27.656 --> 00:13:30.956 Charging per token can be confusing and meaningless to a customer. 00:13:31.416 --> 00:13:35.496 Instead, you can use a more value-based metric, such as charging per completed 00:13:35.496 --> 00:13:38.356 task, per generated image, or per video minute. 00:13:38.756 --> 00:13:43.816 And these metrics are more tangible and directly relate to the value the customer is receiving. 00:13:44.156 --> 00:13:49.596 So ultimately, the goal is to tie your price to the actual business outcome you deliver. 00:13:50.436 --> 00:13:54.776 Imagine the percentage of cost savings or fixed fee per support ticket resolved. 00:13:55.236 --> 00:14:00.676 However, in the practical space, it is often too complex to attribute a specific 00:14:00.676 --> 00:14:02.576 outcome to a single AI agent. 00:14:02.756 --> 00:14:06.516 So especially if you think about multi-agent environments, so you might need 00:14:06.516 --> 00:14:08.656 to find a simpler proxy for value. 00:14:09.096 --> 00:14:14.396 And also, lastly, many startups make the mistake of not building a go-to-market 00:14:14.396 --> 00:14:16.516 strategy that can sell a new business model. 00:14:16.516 --> 00:14:20.916 So without a clear and compelling story that justifies a new pricing structure 00:14:20.916 --> 00:14:25.856 and quantifies its value, startups will struggle to get adoption and generate revenue. 00:14:26.076 --> 00:14:30.656 I have to tell my audience, you shared a lot of data, a lot of information with 00:14:30.656 --> 00:14:32.576 me for the preparation of this interview. 00:14:32.756 --> 00:14:38.076 In one of the monetization decks, you've outlined metrics like customer acquisition 00:14:38.076 --> 00:14:44.396 costs, monthly returning revenue, churn, customer lifetime value, and so on and so forth. 00:14:44.396 --> 00:14:48.616 Which are the most convincing for investors and customers? 00:14:48.936 --> 00:14:53.776 I think what is key about this question is to remember that investors and customers 00:14:53.776 --> 00:14:56.376 are looking for fundamentally different things. 00:14:56.676 --> 00:15:01.556 So if we take an investor, the most compelling metrics are those that prove 00:15:01.556 --> 00:15:05.456 your business is a sustainable, scalable and efficient growth machine, really. 00:15:05.856 --> 00:15:11.116 So they are looking for signals that the investment will lead to a predictable and high return. 00:15:11.986 --> 00:15:16.986 So if we take Monthly Recurring Revenue, or MRR for short, that shows investors 00:15:16.986 --> 00:15:20.326 that your business has a predictable and repeatable revenue stream. 00:15:20.406 --> 00:15:22.546 So not just one-off projects. 00:15:22.906 --> 00:15:25.786 And it's really the cornerstone for me of a healthy business. 00:15:25.806 --> 00:15:31.786 It demonstrates that your customers are continuously willing to pay for the value you provide. 00:15:32.326 --> 00:15:38.166 Then, Customer Lifetime Value, or CLV, demonstrates the long-term value of a 00:15:38.166 --> 00:15:39.266 customer to your business. 00:15:39.266 --> 00:15:44.106 So when an investor sees a high customer lifetime value, they see a company 00:15:44.106 --> 00:15:48.986 with a durable competitive advantage and a clear path to long-term profitability. 00:15:49.906 --> 00:15:54.686 Also, even better if you have a strong ratio between the lifetime value of a 00:15:54.686 --> 00:15:56.526 customer and the cost to acquire them. 00:15:56.666 --> 00:16:00.866 So this shows that your business model is fundamentally sound and that your 00:16:00.866 --> 00:16:05.166 go-to-market strategy is working efficiently. It tells an investor that you 00:16:05.166 --> 00:16:11.106 can pour money into a customer acquisition and get more back over the lifetime of these customers. 00:16:11.726 --> 00:16:15.046 Lastly, and I find this also a very important metric to consider, 00:16:15.306 --> 00:16:19.906 a low churn rate tells investors that customers are finding so much value in 00:16:19.906 --> 00:16:24.206 your solution that they are staying with you, which really reinforces a high 00:16:24.206 --> 00:16:27.966 customer lifetime value and proves that your business model is working. 00:16:28.686 --> 00:16:35.186 If we take customers, however, so they don't really care about your MRR or CLV. 00:16:35.446 --> 00:16:39.626 They really care about their own business. So the number one thing that will 00:16:39.626 --> 00:16:44.266 convince a customer is a clear, compelling demonstration of value and a positive 00:16:44.266 --> 00:16:45.526 return on their investment. 00:16:45.866 --> 00:16:50.986 So your pitch and your decks must really focus on the tangible business benefits you deliver. 00:16:51.406 --> 00:16:54.906 So customers want to know if you're reducing their manual processes, 00:16:55.406 --> 00:16:57.626 saving them money or increasing their revenue. 00:16:57.966 --> 00:17:03.086 And these factors directly impact their bottom line and are what they truly care about. 00:17:03.806 --> 00:17:08.326 Secondly, customers are convinced by the results that they can see with their own eyes. 00:17:08.346 --> 00:17:12.546 So they need to know that your product can deliver on its promises and that 00:17:12.546 --> 00:17:14.806 it is accurate, scalable and cost effective. 00:17:15.246 --> 00:17:19.506 And these factors then ultimately build their confidence in your solution and 00:17:19.506 --> 00:17:22.286 give them the evidence they need to justify the investment. 00:17:23.206 --> 00:17:28.106 Lastly, also the price metric has to be tied to the value you provide. 00:17:28.386 --> 00:17:33.446 So charging per completed task or a direct outcome rather than per user makes 00:17:33.446 --> 00:17:36.306 the value proposition very clear to the customer. 00:17:36.426 --> 00:17:40.066 So it shows them that they are paying for a solution that delivers a measurable 00:17:40.066 --> 00:17:42.086 result, but not just a tool. 00:17:42.286 --> 00:17:46.846 Not sure where I've seen this. Maybe even in your DAX, Canvas, 00:17:47.086 --> 00:17:51.666 AI Feature, Upsells are well-known case studies. 00:17:51.666 --> 00:17:58.466 What can SaaS founders learn from, for example, how Canva monetizes Gen AI? 00:17:59.717 --> 00:18:04.457 So the core lesson from Canva is really to avoid building a separate AI product. 00:18:04.797 --> 00:18:10.657 So instead, they embedded Gen AI capabilities directly into the core offering as a value booster. 00:18:11.077 --> 00:18:16.857 So to be more specific here, Canva doesn't put all its AI features behind a paywall from the start. 00:18:16.857 --> 00:18:22.497 So they give users a limited number of credits to use features like Magic Eraser 00:18:22.497 --> 00:18:24.857 or Magic Edit for free for their presentations. 00:18:24.857 --> 00:18:30.037 And this freemium approach allows users to experience what one source calls 00:18:30.037 --> 00:18:33.657 the AI-managed JIC without any upfront commitment. 00:18:33.917 --> 00:18:39.377 So this strategy is highly effective because it demonstrates the value proposition firsthand. 00:18:39.537 --> 00:18:44.857 So if you take a designer or a small business owner, if they are able to remove 00:18:44.857 --> 00:18:49.917 a background or generate an image, that can save 15 to 20 minutes on a single task. 00:18:50.097 --> 00:18:54.597 And that's also what I call often the 10 times value. So it means that for a 00:18:54.597 --> 00:18:59.417 customer to justify the cost, the risk, and the effort of adopting a new solution, 00:18:59.657 --> 00:19:03.877 the value it provides has to be at least 10 times greater than the price they pay. 00:19:04.097 --> 00:19:09.077 So in this case, once the user has experienced that level of value and hits 00:19:09.077 --> 00:19:12.137 their credit limit, the decision to upgrade is easy. 00:19:12.517 --> 00:19:17.417 The cost of a paid subscription, perhaps take a few dollars a month, 00:19:17.617 --> 00:19:21.697 becomes insignificant compared to the hours of work that they have saved. 00:19:21.697 --> 00:19:26.257 And so they're not just paying for a tool, they're paying to unlock the value 00:19:26.257 --> 00:19:27.977 they've already experienced. 00:19:28.417 --> 00:19:33.557 And another critical lesson from Canva is the need to align your pricing model 00:19:33.557 --> 00:19:35.537 with the underlying cost of GenEI. 00:19:36.017 --> 00:19:41.017 So unlike traditional model, traditional software, every interaction with a 00:19:41.017 --> 00:19:45.357 GenEI model, as we said earlier, has a real tangible compute cost. 00:19:45.637 --> 00:19:51.397 So Canva's credit-based system is a perfect example of a usage-based pricing model. 00:19:51.697 --> 00:19:57.337 Which is then again crucial for monetizing AI and SaaS. It really directly matches 00:19:57.337 --> 00:19:59.517 the model's cost with the value delivered. 00:20:00.057 --> 00:20:04.117 And it really ensures that the compute-heavy features remain a profitable part 00:20:04.117 --> 00:20:05.857 of the business as it scales on. 00:20:06.297 --> 00:20:11.237 So by charging for usage after a certain free limit, companies can manage their 00:20:11.237 --> 00:20:16.337 unit economics and ensure that the most engaged high-usage customers are also 00:20:16.337 --> 00:20:17.777 their most profitable ones. 00:20:17.777 --> 00:20:22.457 So this strategy prevents the cost paradox that I discussed earlier, 00:20:22.737 --> 00:20:27.537 where a flat fee, unlimited usage model can become financially unsustainable 00:20:27.537 --> 00:20:29.177 as customer engagement increases. 00:20:29.537 --> 00:20:34.237 So this approach really offers flexibility and a lower barrier to entry for 00:20:34.237 --> 00:20:38.017 customers who want to test the product without a large upfront commitment, 00:20:38.237 --> 00:20:42.457 while also still at the same time providing a clear path to monetization for 00:20:42.457 --> 00:20:44.817 high volume users. So really a great use case. 00:20:44.817 --> 00:20:52.157 When listening to this, I was wondering, how do you help startups connect operational savings? 00:20:53.109 --> 00:20:58.689 For example, like faster development to hard revenue metrics that resonate with, 00:20:58.849 --> 00:21:01.249 that resonates, for example, with boards or VCs. 00:21:01.629 --> 00:21:05.909 And that's a challenge really that I see a lot of the times when talking to startups. 00:21:06.049 --> 00:21:09.629 And it's a major hurdle, generally speaking, on the path to production. 00:21:09.909 --> 00:21:13.909 So you can't just go into a board meeting and say, we are building things faster. 00:21:14.149 --> 00:21:17.809 So you really have to speak the language of the board, which is revenue, 00:21:18.009 --> 00:21:22.309 valuation and return on investment. So the key here is to build a bridge between 00:21:22.309 --> 00:21:26.049 your operational metrics and your business financial health. 00:21:26.489 --> 00:21:31.149 So let's explore a three-step framework for how you can do just that. 00:21:31.629 --> 00:21:36.629 So first, you have to get granular and define the current reality and the future 00:21:36.629 --> 00:21:39.829 reality, as you can't improve what you can't measure, obviously. 00:21:40.309 --> 00:21:45.029 So the first step here is to define the exact time or resource you're saving. 00:21:45.029 --> 00:21:49.809 For example, a startup might be able to shorten its delivery timeline from 12 00:21:49.809 --> 00:21:54.869 months to just two months by using AI, or reduce the time for a machine learning 00:21:54.869 --> 00:21:57.729 model to go live from six months to under two weeks. 00:21:57.949 --> 00:22:02.429 So you have to define these metrics first, whether it's lines of code written 00:22:02.429 --> 00:22:06.569 per day, bugs found per sprint, or the number of support tickets that are handled, 00:22:06.689 --> 00:22:07.969 for instance, by an AI agent. 00:22:08.189 --> 00:22:12.429 So this establishes your baseline and provides a clear point of comparison. 00:22:12.429 --> 00:22:17.929 And once you have gathered all this operational data, you also need to connect it to your finances. 00:22:18.329 --> 00:22:22.789 So this is really where you translate the what into the so what for investors. 00:22:23.089 --> 00:22:27.729 So, for instance, faster development means fewer engineering hours on a project, 00:22:27.889 --> 00:22:30.829 which really translates to reduced personal cost. 00:22:30.969 --> 00:22:34.249 And this improves your burn rate and extends your cash runway, 00:22:34.509 --> 00:22:39.209 both of which are critical metrics for VCs. And when you can develop and launch 00:22:39.209 --> 00:22:42.929 new features faster, you are also accelerating your time to market. 00:22:43.149 --> 00:22:47.049 And this means you can start generating revenue from those features sooner. 00:22:47.209 --> 00:22:51.869 And it also means you can respond to competitive threats and customer demands 00:22:51.869 --> 00:22:55.169 more quickly, which can lead to a higher customer satisfaction, 00:22:55.589 --> 00:23:00.489 reduced churn and ultimately what we discussed earlier, a higher customer lifetime value. 00:23:01.356 --> 00:23:07.536 And finally, you have to package the story in a way that resonates with VCs and board members. 00:23:07.756 --> 00:23:12.936 So this is where you bring the operational and financial metrics together in 00:23:12.936 --> 00:23:14.396 a single powerful statement. 00:23:14.736 --> 00:23:19.256 For example, you can say, by reducing our development cycle by 50%, 00:23:19.256 --> 00:23:24.076 we were able to launch Feature X, for instance, a quarter ahead of schedule, 00:23:24.316 --> 00:23:26.796 which is already contributing, let's say, 00:23:27.156 --> 00:23:29.936 200K in new monthly recurring revenue. 00:23:29.936 --> 00:23:34.596 And this is a repeatable process that will allow us to continuously improve 00:23:34.596 --> 00:23:39.256 our product and our go-to-market efficiency, which then in turn will reduce 00:23:39.256 --> 00:23:42.996 our customer acquisition cost and increase our customer lifetime value. 00:23:43.236 --> 00:23:47.636 And if you follow this framework, you're not just selling a product or technology, 00:23:47.636 --> 00:23:52.836 you're really selling a quantifiable financial improvement that directly impacts 00:23:52.836 --> 00:23:55.436 your company's valuation and growth potential. 00:23:55.436 --> 00:23:58.916 And this is really the language that boards and investors understand. 00:23:59.056 --> 00:24:04.356 And it's the key also in terms of fundraising based on what I've seen so far. 00:24:07.256 --> 00:24:13.536 Guys, we'll be back after a short ad break talking about monetization models here. 00:24:18.376 --> 00:24:23.116 Guys, welcome back to our interview with Jennifer from AWS. 00:24:24.396 --> 00:24:28.436 AWS works with a lot of very complex customers. 00:24:29.056 --> 00:24:32.716 That's why we decided to pick a very big example. Jennifer, 00:24:32.876 --> 00:24:37.256 can you, for example, share how Pfizer used Gen.AI for scientific applications 00:24:37.256 --> 00:24:45.596 to unlock something around $1 billion annual savings and what that teaches early-stage 00:24:45.596 --> 00:24:48.976 founders about return on investment storytelling? Erling? 00:24:50.196 --> 00:24:54.576 That's really a fantastic example because it perfectly illustrates the power 00:24:54.576 --> 00:24:59.076 of moving beyond a simple proof of concept and into a production mindset with 00:24:59.076 --> 00:25:01.296 a clear focus on tangible business value. 00:25:01.536 --> 00:25:06.516 So the core of the Pfizer story is that they apply Gen.E.I. to their most complex 00:25:06.516 --> 00:25:08.736 and time-consuming internal processes. 00:25:09.036 --> 00:25:12.236 So let's think about scientific research and development. 00:25:12.576 --> 00:25:17.676 So if you imagine developing a single drug can generate over 20,000 documents 00:25:17.676 --> 00:25:23.196 and working through this information manually is an enormous drain on a scientist's time. 00:25:23.476 --> 00:25:28.416 So in the Pfizer-Amazon collaboration team, or PACT for short, 00:25:28.696 --> 00:25:33.876 we implemented rapid prototyping with a fail-fast cycle. So this typically takes 00:25:33.876 --> 00:25:38.456 no more than six weeks for a project that would have taken mums to do internally. 00:25:39.408 --> 00:25:43.428 So their first high ROI use case was data discovery. 00:25:43.688 --> 00:25:47.848 They built an internal platform called Box using generative AI, 00:25:48.108 --> 00:25:49.528 which allowed scientists to 00:25:49.528 --> 00:25:53.588 search a massive repository of documents using natural language queries. 00:25:53.948 --> 00:25:59.348 So Pfizer estimates this has the potential to save its scientists up to 16,000 00:25:59.348 --> 00:26:04.568 hours of search time annually and reduce infrastructure costs by 55%. 00:26:04.568 --> 00:26:09.748 However, they didn't stop there with us. So they also applied machine learning 00:26:09.748 --> 00:26:13.628 and Gen AI to other critical processes like manufacturing. 00:26:13.928 --> 00:26:18.968 So by using services like Amazon SageMaker, they developed a prototype to detect 00:26:18.968 --> 00:26:23.768 anomalies in their continuous manufacturing processes, which also helps predict 00:26:23.768 --> 00:26:26.728 maintenance needs and reduce equipment downtime. 00:26:27.048 --> 00:26:32.668 So this is really a perfect example of applying AI to a valuable and unique business process. 00:26:32.748 --> 00:26:37.368 So in this case, ensuring the quality and consistency of drug production. 00:26:38.408 --> 00:26:42.488 And especially if we tie it back to the learnings for early stage founders, 00:26:42.848 --> 00:26:46.528 it isn't the specific technology or the scale of the savings. 00:26:46.528 --> 00:26:48.328 It's really the story that they told. 00:26:48.628 --> 00:26:54.768 So the $70, $50 million to $1 billion in annual savings, that wasn't just a number for them. 00:26:54.908 --> 00:27:00.508 So it was the result of a clear, cohesive narrative that allowed them to redirect 00:27:00.508 --> 00:27:04.908 resources, shorten development cycles, and ultimately get their products to 00:27:04.908 --> 00:27:06.568 patients and the market sooner. 00:27:07.508 --> 00:27:12.308 And to tie it back to what we said earlier, Pfizer didn't just tell its stakeholders, 00:27:12.608 --> 00:27:14.548 our research and development is faster. 00:27:14.768 --> 00:27:19.648 They said, because our research and development is faster, we can bring new 00:27:19.648 --> 00:27:26.128 medicine to market sooner, which will unlock this much annual revenue and cost savings. 00:27:26.488 --> 00:27:32.028 So they connected really an internal operational gain to a clear external business outcome. 00:27:32.328 --> 00:27:35.608 So for Founder, this is the blueprint for a compelling pitch. 00:27:35.608 --> 00:27:39.788 So you need to show your board investors how an operational improvement, 00:27:40.088 --> 00:27:44.808 imagine what we said earlier, the past development cycles, translates into a 00:27:44.808 --> 00:27:48.708 higher customer lifetime value or lower customer acquisition cost. 00:27:49.108 --> 00:27:53.308 So by focusing your generative AI application and your most valuable and unique 00:27:53.308 --> 00:27:58.048 business processes, you create actually a snowball effect of value that becomes 00:27:58.048 --> 00:27:59.368 central to how you operate. 00:28:00.068 --> 00:28:05.148 Also, you should apply Gen AI to your most valuable and unique business processes. 00:28:05.608 --> 00:28:08.248 For Pfizer, in that case, that was research and development. 00:28:08.268 --> 00:28:11.808 For a SaaS company, it might be your product development, your go-to-market 00:28:11.808 --> 00:28:13.368 strategy or your customer support. 00:28:13.708 --> 00:28:18.368 So really, the production mindset is about focusing on these areas to create 00:28:18.368 --> 00:28:22.828 a snowball effect of value that becomes really the fabric of how you operate. 00:28:23.228 --> 00:28:28.608 For our audience, you've seen startups crash because they scale Gen.AI features 00:28:28.608 --> 00:28:30.288 without a monetization plan. 00:28:30.748 --> 00:28:35.928 Could you share a raw story of what went wrong? The full story is in the founder's fault. 00:28:36.879 --> 00:28:40.919 Unfortunately, due to kind of the confidential nature of this question, 00:28:41.039 --> 00:28:42.519 I can't really answer to that. 00:28:42.619 --> 00:28:46.619 But I've definitely observed a number of startups that initially started without 00:28:46.619 --> 00:28:51.859 a clear monetization plan, but where I actually helped them to develop that. 00:28:52.059 --> 00:28:53.299 So, for instance, we've been doing. 00:28:54.259 --> 00:28:58.819 Jennifer, we don't need that. It's just for our audience. Okay. Okay. 00:28:59.819 --> 00:29:03.359 Jennifer, let's talk a little bit about monetization models here. 00:29:04.199 --> 00:29:11.219 Do you see startups better off charging for AI as a feature added to an existing 00:29:11.219 --> 00:29:16.199 SaaS or a standalone enabler or like a new product line? 00:29:16.459 --> 00:29:20.979 That's a really great question and a very critical one for any founder, 00:29:21.219 --> 00:29:23.199 especially as they start off in scale. 00:29:23.439 --> 00:29:27.579