Speaker1: That's a really great question and a very critical one for any founder,
Speaker1: especially as they start off in scale.
Speaker1: So it really depends on your business, the market, and the problem you're solving.
Speaker1: So let's start with Gen.AI as a feature. That is often the safest and most effective strategy.
Speaker1: So you should use it when you have a strong existing user base and a clear use
Speaker1: case for AI to improve a current workflow.
Speaker1: So instead of building a new product from scratch, you embed AI directly into
Speaker1: your existing offering to enhance its value.
Speaker1: So you typically can't charge a premium for a single feature,
Speaker1: but instead you can use tier pricing,
Speaker1: offering AI features in higher price plans, or a freemium model where basic
Speaker1: AI functions are free, but advanced capabilities, imagine more queries or larger
Speaker1: outputs, are meted with a credit system.
Music:
Speaker0: Welcome to StartupRad.io, your podcast and YouTube blog covering the German
Speaker0: startup scene with news, interviews and live events.
Speaker0: AWS is proud to sponsor this week's episode of StartupRad.io.
Speaker0: The AWS team compromises former founders, CTOs, venture capitalists,
Speaker0: angel investors and mentors, ready to help you prove what's possible.
Speaker0: Since 2013, AWS has supported over 280,000 startups across the globe and provided
Speaker0: $7 billion in credits through the AWS Active program.
Speaker0: Big ideas feel at home at AWS.
Speaker0: And with access to cutting-edge technologies like generative AI,
Speaker0: you can quickly turn those ideas into marketable products.
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Speaker0: visit aws.amazon.com forward
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Speaker0: founder here's the challenge generative ai is everywhere but turning it into
Speaker0: actual revenue is far harder than the hype suggests jennifer gruen senior specialist
Speaker0: for generative ai at aws has worked with startups software vendors and enterprises
Speaker0: to unlock billions in business value.
Speaker0: Today we'll break down AI monetization frameworks, return on investment metrics
Speaker0: that investors demand and real startup case studies so you can turn AI from
Speaker0: a buzzword into business.
Speaker0: Jennifer Grun is a senior specialist for generative AI and machine learning
Speaker0: at AWS helping startups, software vendors and enterprises across Germany and
Speaker0: in Central Europe to define, scale and monetize their AI use cases.
Speaker0: She led go-to-market strategies for generative AI beyond AWS.
Speaker0: She's passionate about democratizing machine learning and runs initiatives to
Speaker0: bring knowledge to underrepresented groups and universities.
Speaker0: Today, she'll share what founders need to know about monetization opportunities with AI.
Speaker0: From metrics like customer acquisition costs and customer lifetime value,
Speaker0: to Canva's upsell playbook, to why ROI storytelling can make or break an investor
Speaker0: pitch. Jennifer, welcome.
Speaker1: Thank you, Jörn. Really excited to be here today.
Speaker0: Totally. My pleasure. We had some issues with the setup, but now we're totally
Speaker0: good and everything should work out. Welcome to our episode.
Speaker0: You often describe the trillion dollar question as not what to build with AI, but how to monetize it.
Speaker0: Why is this gap so persistent?
Speaker1: Thanks, Joe. So that's a really great question. So for me, the main reason for
Speaker1: this gap comes down to three things.
Speaker1: So first, you have the economics of Gen AI.
Speaker1: So unlike traditional software, where adding a new user has a very low marginal
Speaker1: cost, generative AI is different.
Speaker1: So every time a user interacts with a model, so every query,
Speaker1: every word that is generated, it has a real tangible operational cost.
Speaker1: And this also creates a paradox.
Speaker1: So if you have a user who gets a ton of value from your service and uses it
Speaker1: frequently, that might actually become less profitable for you.
Speaker1: And this makes it really difficult to achieve the high gross profit margins
Speaker1: that investors have come to expect, especially, for instance, for SaaS startups.
Speaker1: And the second issue that I see founders are facing is often defaulting to familiar
Speaker1: pricing models that just don't fit the technology.
Speaker1: So, we are seeing a lot of startups try to use a per-seed or per-user model,
Speaker1: but that can also backfire.
Speaker1: So, imagine if your AI product makes the team more efficient,
Speaker1: they might actually need fewer people, and that means also fewer seats.
Speaker1: And this means that it would actually shrink your addressable market.
Speaker1: And also, there's a market perception issue that I can observe.
Speaker1: So when a lot of DEI features are given away for free to drive adoption,
Speaker1: Customers can actually start to see them as stable stakes.
Speaker1: So this makes it really tough to convince them to pay for what they believe
Speaker1: should be a standard feature, where startups can't afford to raise to the bottom
Speaker1: because every dollar counts in a startup.
Speaker1: And then thirdly, many founders are so focused on the technical what,
Speaker1: building the next great model or creating a really interesting feature that
Speaker1: they fail to properly consider the business how.
Speaker1: So in many of the conversations that I have with startups, I mentioned that
Speaker1: you can't just build technology and hope for the best.
Speaker1: So you really have to start with an outcome oriented mindset from day one.
Speaker1: So this means understanding the specific customer pain point you're solving
Speaker1: and how you will measure success.
Speaker1: And a great example of this is a company that has navigated this beautifully is Lovable.
Speaker1: So we are also going to have them soon in a hackathon here in Munich.
Speaker1: And they're building a platform that allows anyone to create full stack web
Speaker1: applications just by typing in a simple prompt.
Speaker1: So they're solving a huge problem for non-technical founders who want to build
Speaker1: an MVP quickly without the high cost of a development team.
Speaker1: And what they did here is that Lovable has aligned their monetization with the
Speaker1: value that they provide.
Speaker1: So they use a credit-based pricing model where users pay for what they use,
Speaker1: which directly aligns with their
Speaker1: operational cost and the value of the application they are generating.
Speaker1: And this really shows, again, an example of a company that has successfully
Speaker1: moved beyond the what of the technology, but also figured out the how.
Speaker0: Again Madhu, many startups get stuck in proof of concept.
Speaker0: Why do POC technology choices fail to scale and how does this kill monetization potential?
Speaker1: And that's an excellent question and it's something that we see actually all the time.
Speaker1: So a significant reason for failure is really the fundamental difference in
Speaker1: purpose between a POC and a production ready system.
Speaker1: So many AI pilots that I have observed are driven by a technology-first mindset,
Speaker1: where a solution is developed in search of a problem to solve.
Speaker1: So think about a cool technology that people want to build.
Speaker1: But to really transform the organization, the product requires buy-in from the entire organization.
Speaker1: So we've seen startups where a lack of internal alignment on business priorities,
Speaker1: funding, or success metrics completely stores a project. And this is why it's
Speaker1: so important to use a structured approach.
Speaker1: So I've worked here on an AI canvas that is forcing a shift in thinking beyond technology.
Speaker1: So this canvas helps you to clearly articulate the customer needs,
Speaker1: the value proposition and the metrics for success up front to make sure that
Speaker1: the solution is tied to a real business problem with a measurable outcome.
Speaker1: And the shift from a proof of concept to a proof of value, how we call it,
Speaker1: is really crucial in addressing this issue that I just mentioned.
Speaker1: So the proof of value really focuses on delivering and measuring a tangible
Speaker1: business impact rather than really simply demonstrating technical feasibility.
Speaker1: And what we've seen here is without a clear quantifiable metric for success,
Speaker1: imagine cost savings or revenue growth,
Speaker1: even a technically sound POC that wows your team will struggle to secure the
Speaker1: continued investment required for full scale development.
Speaker1: So this requires strong cross-functional collaboration among business,
Speaker1: engineering and product teams to ensure alignment on a unified strategy.
Speaker1: And also, what I like to outline as well is that the technology choices made
Speaker1: for POC are often not suitable for a production application.
Speaker1: So, for instance, POC might use a popular general purpose model,
Speaker1: but a production application requires choosing the right model that balances
Speaker1: capabilities, performance and cost.
Speaker1: And also, it might need to be fine-tuned on proprietary data to create a truly
Speaker1: differentiated product.
Speaker1: Also, what I observe a lot is that POCs are often designed for a few users only,
Speaker1: and they are really not built to handle thousands or millions of queries.
Speaker1: So this means that they lack the necessary architecture for resilience,
Speaker1: security, and global scalabilities.
Speaker1: And that brings me a little bit to the challenges and the startup's monetization potential.
Speaker1: That is quite direct and critical. So, if we think about the generative AI space,
Speaker1: a unique characteristic is that the cost of goods sold is often tied directly to user consumption.
Speaker1: So, if the underlying technology is not designed for efficiency and cost optimization,
Speaker1: every user interaction can become an expense that quickly erodes your profitability.
Speaker1: So imagine an inefficient, unoptimized POC may function really on the small
Speaker1: scale that we discussed, but its
Speaker1: per unit cost can make it financially unsustainable at a larger volume.
Speaker1: So when a startup fails to address these issues, they end up with a product
Speaker1: that is slow, expensive and unreliable.
Speaker1: So their monetization potential is killed because you simply cannot charge for
Speaker1: a service that doesn't work consistently and that can't handle a growing user base.
Speaker1: So as a result, the startup is unable to leverage its technology for monetization
Speaker1: through methods like premium features, tiered subscription or revenue sharing models.
Speaker0: I see. So what do you see?
Speaker0: What's a common mistake that startups make when defining monetization models for AI features?
Speaker1: Really, the most common mistake that I see startups make is creating a pricing
Speaker1: model that doesn't align with the underlying cost and the value of AI.
Speaker1: So many startups that I've spoken with try to use a traditional perceived model
Speaker1: to start with, which works well for conventional software.
Speaker1: But if we take, for instance, agentic AI models, especially an autonomous one
Speaker1: where we're developing towards, this may be completely ineffective.
Speaker1: So an AI agent doesn't really need a user to be active, so you can't charge per seat.
Speaker1: So unlike traditional software with its low marginal cost, every interaction
Speaker1: with a generative AI model has a real tangible cost.
Speaker1: So as a result, startups that charge
Speaker1: a flat fee for unlimited usage find themselves in a dangerous paradox.
Speaker1: So their most engaged and arguably most valuable customers are actually making
Speaker1: them less profitable because their high usage drives operational expenses and
Speaker1: eats into the bottom line.
Speaker1: So this is, of course, the exact opposite of what you want in a business model.
Speaker1: Beyond the cost paradox, another significant mistake is a failure to properly
Speaker1: package offerings for different customer segments.
Speaker1: So a good pricing strategy isn't just about the price itself.
Speaker1: It's really about how you differentiate your product.
Speaker1: For instance, you can create a basic package that offers a generic AI agent
Speaker1: with broad expertise, and then a premium package that gives customers access
Speaker1: to specialized agents trained on specific high-value topics,
Speaker1: let's imagine legal or finance.
Speaker1: And you can also package in enterprise-grade features that are crucial for larger
Speaker1: clients, but not necessarily for every user. So this could mean single sign-on,
Speaker1: audit logs, and data privacy assurances in your top-tier packages.
Speaker1: So by doing this, you ensure that the customers with the highest willingness
Speaker1: to pay are getting the most expensive product, which is a key principle of effective monetization.
Speaker1: Another big mistake is choosing the wrong price metric, so the unit of measurement
Speaker1: you use to charge for your product.
Speaker1: Charging per token can be confusing and meaningless to a customer.
Speaker1: Instead, you can use a more value-based metric, such as charging per completed
Speaker1: task, per generated image, or per video minute.
Speaker1: And these metrics are more tangible and directly relate to the value the customer is receiving.
Speaker1: So ultimately, the goal is to tie your price to the actual business outcome you deliver.
Speaker1: Imagine the percentage of cost savings or fixed fee per support ticket resolved.
Speaker1: However, in the practical space, it is often too complex to attribute a specific
Speaker1: outcome to a single AI agent.
Speaker1: So especially if you think about multi-agent environments, so you might need
Speaker1: to find a simpler proxy for value.
Speaker1: And also, lastly, many startups make the mistake of not building a go-to-market
Speaker1: strategy that can sell a new business model.
Speaker1: So without a clear and compelling story that justifies a new pricing structure
Speaker1: and quantifies its value, startups will struggle to get adoption and generate revenue.
Speaker0: I have to tell my audience, you shared a lot of data, a lot of information with
Speaker0: me for the preparation of this interview.
Speaker0: In one of the monetization decks, you've outlined metrics like customer acquisition
Speaker0: costs, monthly returning revenue, churn, customer lifetime value, and so on and so forth.
Speaker0: Which are the most convincing for investors and customers?
Speaker1: I think what is key about this question is to remember that investors and customers
Speaker1: are looking for fundamentally different things.
Speaker1: So if we take an investor, the most compelling metrics are those that prove
Speaker1: your business is a sustainable, scalable and efficient growth machine, really.
Speaker1: So they are looking for signals that the investment will lead to a predictable and high return.
Speaker1: So if we take Monthly Recurring Revenue, or MRR for short, that shows investors
Speaker1: that your business has a predictable and repeatable revenue stream.
Speaker1: So not just one-off projects.
Speaker1: And it's really the cornerstone for me of a healthy business.
Speaker1: It demonstrates that your customers are continuously willing to pay for the value you provide.
Speaker1: Then, Customer Lifetime Value, or CLV, demonstrates the long-term value of a
Speaker1: customer to your business.
Speaker1: So when an investor sees a high customer lifetime value, they see a company
Speaker1: with a durable competitive advantage and a clear path to long-term profitability.
Speaker1: Also, even better if you have a strong ratio between the lifetime value of a
Speaker1: customer and the cost to acquire them.
Speaker1: So this shows that your business model is fundamentally sound and that your
Speaker1: go-to-market strategy is working efficiently. It tells an investor that you
Speaker1: can pour money into a customer acquisition and get more back over the lifetime of these customers.
Speaker1: Lastly, and I find this also a very important metric to consider,
Speaker1: a low churn rate tells investors that customers are finding so much value in
Speaker1: your solution that they are staying with you, which really reinforces a high
Speaker1: customer lifetime value and proves that your business model is working.
Speaker1: If we take customers, however, so they don't really care about your MRR or CLV.
Speaker1: They really care about their own business. So the number one thing that will
Speaker1: convince a customer is a clear, compelling demonstration of value and a positive
Speaker1: return on their investment.
Speaker1: So your pitch and your decks must really focus on the tangible business benefits you deliver.
Speaker1: So customers want to know if you're reducing their manual processes,
Speaker1: saving them money or increasing their revenue.
Speaker1: And these factors directly impact their bottom line and are what they truly care about.
Speaker1: Secondly, customers are convinced by the results that they can see with their own eyes.
Speaker1: So they need to know that your product can deliver on its promises and that
Speaker1: it is accurate, scalable and cost effective.
Speaker1: And these factors then ultimately build their confidence in your solution and
Speaker1: give them the evidence they need to justify the investment.
Speaker1: Lastly, also the price metric has to be tied to the value you provide.
Speaker1: So charging per completed task or a direct outcome rather than per user makes
Speaker1: the value proposition very clear to the customer.
Speaker1: So it shows them that they are paying for a solution that delivers a measurable
Speaker1: result, but not just a tool.
Speaker0: Not sure where I've seen this. Maybe even in your DAX, Canvas,
Speaker0: AI Feature, Upsells are well-known case studies.
Speaker0: What can SaaS founders learn from, for example, how Canva monetizes Gen AI?
Speaker1: So the core lesson from Canva is really to avoid building a separate AI product.
Speaker1: So instead, they embedded Gen AI capabilities directly into the core offering as a value booster.
Speaker1: So to be more specific here, Canva doesn't put all its AI features behind a paywall from the start.
Speaker1: So they give users a limited number of credits to use features like Magic Eraser
Speaker1: or Magic Edit for free for their presentations.
Speaker1: And this freemium approach allows users to experience what one source calls
Speaker1: the AI-managed JIC without any upfront commitment.
Speaker1: So this strategy is highly effective because it demonstrates the value proposition firsthand.
Speaker1: So if you take a designer or a small business owner, if they are able to remove
Speaker1: a background or generate an image, that can save 15 to 20 minutes on a single task.
Speaker1: And that's also what I call often the 10 times value. So it means that for a
Speaker1: customer to justify the cost, the risk, and the effort of adopting a new solution,
Speaker1: the value it provides has to be at least 10 times greater than the price they pay.
Speaker1: So in this case, once the user has experienced that level of value and hits
Speaker1: their credit limit, the decision to upgrade is easy.
Speaker1: The cost of a paid subscription, perhaps take a few dollars a month,
Speaker1: becomes insignificant compared to the hours of work that they have saved.
Speaker1: And so they're not just paying for a tool, they're paying to unlock the value
Speaker1: they've already experienced.
Speaker1: And another critical lesson from Canva is the need to align your pricing model
Speaker1: with the underlying cost of GenEI.
Speaker1: So unlike traditional model, traditional software, every interaction with a
Speaker1: GenEI model, as we said earlier, has a real tangible compute cost.
Speaker1: So Canva's credit-based system is a perfect example of a usage-based pricing model.
Speaker1: Which is then again crucial for monetizing AI and SaaS. It really directly matches
Speaker1: the model's cost with the value delivered.
Speaker1: And it really ensures that the compute-heavy features remain a profitable part
Speaker1: of the business as it scales on.
Speaker1: So by charging for usage after a certain free limit, companies can manage their
Speaker1: unit economics and ensure that the most engaged high-usage customers are also
Speaker1: their most profitable ones.
Speaker1: So this strategy prevents the cost paradox that I discussed earlier,
Speaker1: where a flat fee, unlimited usage model can become financially unsustainable
Speaker1: as customer engagement increases.
Speaker1: So this approach really offers flexibility and a lower barrier to entry for
Speaker1: customers who want to test the product without a large upfront commitment,
Speaker1: while also still at the same time providing a clear path to monetization for
Speaker1: high volume users. So really a great use case.
Speaker0: When listening to this, I was wondering, how do you help startups connect operational savings?
Speaker0: For example, like faster development to hard revenue metrics that resonate with,
Speaker0: that resonates, for example, with boards or VCs.
Speaker1: And that's a challenge really that I see a lot of the times when talking to startups.
Speaker1: And it's a major hurdle, generally speaking, on the path to production.
Speaker1: So you can't just go into a board meeting and say, we are building things faster.
Speaker1: So you really have to speak the language of the board, which is revenue,
Speaker1: valuation and return on investment. So the key here is to build a bridge between
Speaker1: your operational metrics and your business financial health.
Speaker1: So let's explore a three-step framework for how you can do just that.
Speaker1: So first, you have to get granular and define the current reality and the future
Speaker1: reality, as you can't improve what you can't measure, obviously.
Speaker1: So the first step here is to define the exact time or resource you're saving.
Speaker1: For example, a startup might be able to shorten its delivery timeline from 12
Speaker1: months to just two months by using AI, or reduce the time for a machine learning
Speaker1: model to go live from six months to under two weeks.
Speaker1: So you have to define these metrics first, whether it's lines of code written
Speaker1: per day, bugs found per sprint, or the number of support tickets that are handled,
Speaker1: for instance, by an AI agent.
Speaker1: So this establishes your baseline and provides a clear point of comparison.
Speaker1: And once you have gathered all this operational data, you also need to connect it to your finances.
Speaker1: So this is really where you translate the what into the so what for investors.
Speaker1: So, for instance, faster development means fewer engineering hours on a project,
Speaker1: which really translates to reduced personal cost.
Speaker1: And this improves your burn rate and extends your cash runway,
Speaker1: both of which are critical metrics for VCs. And when you can develop and launch
Speaker1: new features faster, you are also accelerating your time to market.
Speaker1: And this means you can start generating revenue from those features sooner.
Speaker1: And it also means you can respond to competitive threats and customer demands
Speaker1: more quickly, which can lead to a higher customer satisfaction,
Speaker1: reduced churn and ultimately what we discussed earlier, a higher customer lifetime value.
Speaker1: And finally, you have to package the story in a way that resonates with VCs and board members.
Speaker1: So this is where you bring the operational and financial metrics together in
Speaker1: a single powerful statement.
Speaker1: For example, you can say, by reducing our development cycle by 50%,
Speaker1: we were able to launch Feature X, for instance, a quarter ahead of schedule,
Speaker1: which is already contributing, let's say,
Speaker1: 200K in new monthly recurring revenue.
Speaker1: And this is a repeatable process that will allow us to continuously improve
Speaker1: our product and our go-to-market efficiency, which then in turn will reduce
Speaker1: our customer acquisition cost and increase our customer lifetime value.
Speaker1: And if you follow this framework, you're not just selling a product or technology,
Speaker1: you're really selling a quantifiable financial improvement that directly impacts
Speaker1: your company's valuation and growth potential.
Speaker1: And this is really the language that boards and investors understand.
Speaker1: And it's the key also in terms of fundraising based on what I've seen so far.
Speaker0: Guys, we'll be back after a short ad break talking about monetization models here.
Speaker0: Guys, welcome back to our interview with Jennifer from AWS.
Speaker0: AWS works with a lot of very complex customers.
Speaker0: That's why we decided to pick a very big example. Jennifer,
Speaker0: can you, for example, share how Pfizer used Gen.AI for scientific applications
Speaker0: to unlock something around $1 billion annual savings and what that teaches early-stage
Speaker0: founders about return on investment storytelling? Erling?
Speaker1: That's really a fantastic example because it perfectly illustrates the power
Speaker1: of moving beyond a simple proof of concept and into a production mindset with
Speaker1: a clear focus on tangible business value.
Speaker1: So the core of the Pfizer story is that they apply Gen.E.I. to their most complex
Speaker1: and time-consuming internal processes.
Speaker1: So let's think about scientific research and development.
Speaker1: So if you imagine developing a single drug can generate over 20,000 documents
Speaker1: and working through this information manually is an enormous drain on a scientist's time.
Speaker1: So in the Pfizer-Amazon collaboration team, or PACT for short,
Speaker1: we implemented rapid prototyping with a fail-fast cycle. So this typically takes
Speaker1: no more than six weeks for a project that would have taken mums to do internally.
Speaker1: So their first high ROI use case was data discovery.
Speaker1: They built an internal platform called Box using generative AI,
Speaker1: which allowed scientists to
Speaker1: search a massive repository of documents using natural language queries.
Speaker1: So Pfizer estimates this has the potential to save its scientists up to 16,000
Speaker1: hours of search time annually and reduce infrastructure costs by 55%.
Speaker1: However, they didn't stop there with us. So they also applied machine learning
Speaker1: and Gen AI to other critical processes like manufacturing.
Speaker1: So by using services like Amazon SageMaker, they developed a prototype to detect
Speaker1: anomalies in their continuous manufacturing processes, which also helps predict
Speaker1: maintenance needs and reduce equipment downtime.
Speaker1: So this is really a perfect example of applying AI to a valuable and unique business process.
Speaker1: So in this case, ensuring the quality and consistency of drug production.
Speaker1: And especially if we tie it back to the learnings for early stage founders,
Speaker1: it isn't the specific technology or the scale of the savings.
Speaker1: It's really the story that they told.
Speaker1: So the $70, $50 million to $1 billion in annual savings, that wasn't just a number for them.
Speaker1: So it was the result of a clear, cohesive narrative that allowed them to redirect
Speaker1: resources, shorten development cycles, and ultimately get their products to
Speaker1: patients and the market sooner.
Speaker1: And to tie it back to what we said earlier, Pfizer didn't just tell its stakeholders,
Speaker1: our research and development is faster.
Speaker1: They said, because our research and development is faster, we can bring new
Speaker1: medicine to market sooner, which will unlock this much annual revenue and cost savings.
Speaker1: So they connected really an internal operational gain to a clear external business outcome.
Speaker1: So for Founder, this is the blueprint for a compelling pitch.
Speaker1: So you need to show your board investors how an operational improvement,
Speaker1: imagine what we said earlier, the past development cycles, translates into a
Speaker1: higher customer lifetime value or lower customer acquisition cost.
Speaker1: So by focusing your generative AI application and your most valuable and unique
Speaker1: business processes, you create actually a snowball effect of value that becomes
Speaker1: central to how you operate.
Speaker1: Also, you should apply Gen AI to your most valuable and unique business processes.
Speaker1: For Pfizer, in that case, that was research and development.
Speaker1: For a SaaS company, it might be your product development, your go-to-market
Speaker1: strategy or your customer support.
Speaker1: So really, the production mindset is about focusing on these areas to create
Speaker1: a snowball effect of value that becomes really the fabric of how you operate.
Speaker0: For our audience, you've seen startups crash because they scale Gen.AI features
Speaker0: without a monetization plan.
Speaker0: Could you share a raw story of what went wrong? The full story is in the founder's fault.
Speaker1: Unfortunately, due to kind of the confidential nature of this question,
Speaker1: I can't really answer to that.
Speaker1: But I've definitely observed a number of startups that initially started without
Speaker1: a clear monetization plan, but where I actually helped them to develop that.
Speaker1: So, for instance, we've been doing.
Speaker0: Jennifer, we don't need that. It's just for our audience. Okay. Okay.
Speaker0: Jennifer, let's talk a little bit about monetization models here.
Speaker0: Do you see startups better off charging for AI as a feature added to an existing
Speaker0: SaaS or a standalone enabler or like a new product line?
Speaker1: That's a really great question and a very critical one for any founder,
Speaker1: especially as they start off in scale.
Speaker1: So it really depends on your business, the market and the problem you're solving.
Speaker1: So let's start with Gen AI as a feature. That is often the safest and most effective strategy.
Speaker1: So you should use it when you have a strong existing user base and a clear use
Speaker1: case for AI to improve a current workflow.
Speaker1: So instead of building a new product from scratch, you embed AI directly into
Speaker1: your existing offering to enhance its value.
Speaker1: So you typically can't charge a premium for a single feature,
Speaker1: but instead you can use tiered pricing, offering AI features in higher price
Speaker1: plans, or a freemium model where basic AI functions are free.
Speaker1: But advanced capabilities, imagine more queries or larger outputs,
Speaker1: are meted with a credit system. So, the good example is Canvas Magic Studio
Speaker1: that we discussed before.
Speaker1: So, it's not a new separate product. It's a suite of tools that makes the existing
Speaker1: design process faster and better.
Speaker1: It's also a great way to get customer feedback for iterative experimentation
Speaker1: and continuous improvement.
Speaker1: So, the main advantage here are that it reduces the friction of adoption because
Speaker1: customers are already in your product.
Speaker1: And the value is immediately clear and it can be a powerful upsell driver.
Speaker1: On the other hand, you can't charge a premium for a single feature,
Speaker1: so your monetization has to be tied to a different value metric.
Speaker1: Imagine a premium model with a credits or a tiered pricing structure.
Speaker1: If you move to the second part of your question, so generative AI is a standalone
Speaker1: enabler, it is the path often I see with startups that are building foundational
Speaker1: technology for other developers.
Speaker1: So you're not selling a finished product. You're selling, let's call it the
Speaker1: picks and shovels for others to build their own AI applications.
Speaker1: So this is when your core intellectual property is in the AI model or a specific repeatable AI process.
Speaker1: So you're giving other companies the ability to add product innovation to their
Speaker1: own platforms by integrating your API.
Speaker1: The main advantage I see here is that this model is highly scalable.
Speaker1: So your market could be potentially every developer or company that needs your
Speaker1: specific AI capabilities.
Speaker1: And it can lead to rapid adoption and significant revenue without the overhead
Speaker1: of building a complete end-user product.
Speaker1: The most common approach here is usage-based pricing. So you can charge here
Speaker1: per API call, per token, or per unit of data processed.
Speaker1: It's also known as the bait-as-you-go model. So it aligns the cost directly
Speaker1: with the value a developer gets from your technology.
Speaker1: And this is also where you might see a hybrid model with a fixed subscription
Speaker1: fee for a certain level of usage and an additional usage fee once a customer surpasses that usage.
Speaker1: So while this model is very scalable, it faces also direct competition from
Speaker1: big companies like OpenAI, and they can have less predictable revenue streams
Speaker1: than a flat subscription.
Speaker1: And for me, this is a model for companies with a very unique or highly specialized AI capability.
Speaker1: To give you an example of a startup that I worked with over here is DeepSet.
Speaker1: So they don't create the end user facing product, but they build the tools,
Speaker1: infrastructure and platforms that other companies use to create their own Gen AI applications.
Speaker1: So DeepSat's business model is based on an open source or freemium model combining
Speaker1: their open source framework haystack with a commercial SaaS platform called DeepSat AI Platform.
Speaker1: So this platform provides enterprise-grade features and support that goes beyond
Speaker1: the free open-source version, including managed infrastructure,
Speaker1: advanced tools, and enterprise-grade security, as well as dedicated support.
Speaker1: And then, closing the loop to the new product line. For me, this is really the
Speaker1: most ambitious strategy.
Speaker1: So you're building brand-new end-to-end products with Gen.AI at its core.
Speaker1: So when the AI provides a fundamentally new experience or solves a problem that
Speaker1: couldn't be solved before, the AI really isn't an add-on. It's the product itself.
Speaker1: So this approach offers the highest potential for revenue and market disruption.
Speaker1: So if you succeed, you can create a completely new market category and build
Speaker1: a defensible model around your business.
Speaker1: Nevertheless, it carries the highest risk as well, because you have to solve
Speaker1: a real problem, build a product from scratch, and also deal with the high cost
Speaker1: per inference and unit economics of running complex AI models.
Speaker1: You also need to get to market before your end of runway, which is a key metric for startups.
Speaker1: An example of a new product line in this context would be our partner Entropic.
Speaker1: So as you might have used their cloud model as well.
Speaker1: So rather than being an enabler for others to build applications,
Speaker1: Antropic's business model is really centered on creating and selling the core,
Speaker1: let's call it going back to the analogy, picks and shovels themselves.
Speaker1: So they create and continuously improve their co-product, in this case,
Speaker1: the Claude family of large language models.
Speaker1: So Entropic monetizes its models through various tiers, offering different levels
Speaker1: of intelligence, speed, and cost to meet a range of user needs.
Speaker1: So these models are offered on a tiered pricing model, often based on token
Speaker1: usage with different plans for individuals, teams, and enterprises.
Speaker1: As you might know, Entropic also created specialized products like CloudCode,
Speaker1: which is specifically designed for developers and their workflows.
Speaker1: So this is a form of new product features that are aimed at a specific customer segment.
Speaker1: So Entropic's strategy involves making their models available through multiple
Speaker1: channels, including their own web interface, direct API access,
Speaker1: and partnerships with cloud providers like us.
Speaker1: So this ensures that their product can reach a wide range of customers from
Speaker1: individual developers to large enterprises.
Speaker1: So, this approach has the highest potential for market disruption revenue,
Speaker1: but it also carries the highest risk and requires significant investment in
Speaker1: building a new product from the ground up, as well as, as I mentioned,
Speaker1: managing that high cost per inference of running complex model.
Speaker1: So, really, to answer the question, the best approach depends on your specific circumstances.
Speaker1: So, for most startups, especially those that already have an existing customer
Speaker1: base, adding AI as a feature is a great way to start because it offers a clear
Speaker1: path to value realization. But it really depends where you're standing.
Speaker0: What role do usage-based versus subscription monetization models play in AI?
Speaker0: And how do you get founders in choosing between them?
Speaker1: That's really, for me, a fundamental question that I like to take very early
Speaker1: because it's one of the most critical ones that a founder can make.
Speaker1: So it's not really just about a pricing model. It's a statement about your company
Speaker1: values and how you see your relationship with your customers.
Speaker1: So in the world of AI, this decision is even more critical because it directly
Speaker1: ties to the unique unit economics of your product.
Speaker1: So what we're seeing in the market is that successful markets are moving beyond
Speaker1: the traditional flat fee subscription and even the pure usage-based model to
Speaker1: embrace a hybrid approach.
Speaker1: So this gives you the best of both worlds.
Speaker1: So you can start with a predictable stable subscription that includes a certain
Speaker1: amount of AI credits or usage, as we've seen in the Canva example.
Speaker1: And then once a customer becomes a powerful user and exceeds that included amount,
Speaker1: you automatically move them to a usage-based model.
Speaker1: And this really ensures that you're compensated for the value you provide.
Speaker1: And it also helps manage the high variable cost of your AI workload that we've
Speaker1: just seen in what I shared earlier.
Speaker1: And this hybrid approach also opens up a number of specific monetization avenues
Speaker1: for founders that have Gen.E.I.
Speaker1: As the end product, where you can, for instance, charge per image generated or per query answered.
Speaker1: And on the other hand side, Gen.E.I. is a super tier means that for an existing
Speaker1: product, you can create a higher price subscription tier that offers exclusive
Speaker1: access to your advanced AI capabilities.
Speaker1: And this really is helpful for your most engaged users to give them a clear path to get more value.
Speaker1: And you also get to charge a premium for it, just like we discussed in the Canva example.
Speaker1: Another possibility is Gen AI as a value booster.
Speaker1: So in this model, the AI functionality enhances an existing product,
Speaker1: but it's not the central feature.
Speaker1: So the AI adds value to the core service, which makes it more efficient or powerful,
Speaker1: which can justify a higher over price point. A good example which many startups
Speaker1: are using is Slack, for instance.
Speaker1: And lastly, Gen AI as an add-on means that the AI feature is an optional component
Speaker1: that customers can pay for separately.
Speaker1: So this lowers the barrier to entry for your core product while giving you still
Speaker1: an opportunity to upsell and monetize those who want to use your AI capabilities.
Speaker1: But for me, it's also important, apart from the monetization strategy,
Speaker1: to look at the infrastructure supporting it.
Speaker1: So really, it's about managing the variable cost of AI workloads.
Speaker1: So when a founder is building their platform, they have to choose a lot of different things.
Speaker1: So, for instance, the right inference consumption strategy, which directly impacts
Speaker1: their ability to scale profitability.
Speaker1: And there are many options here. So, for instance, there's the pay-as-you-go
Speaker1: model with no commitment.
Speaker1: It's great for prototyping proof of concepts and small workloads because it's
Speaker1: so flexible, but it can mean that you have higher latency because you're using shared resources.
Speaker1: If you want to go for consistent production workloads with a predictable need
Speaker1: for large scale processing, go for provision throughput.
Speaker1: It's redesigned to process big workloads at scale, but it can also be expensive
Speaker1: if your usage has low utilization during off peak hours.
Speaker1: And then another one that I see a lot of startups use is batch inference.
Speaker1: So it's really great for asynchronous workloads, like imagine processing large
Speaker1: documents or conducting offline experiments.
Speaker1: It's a lot more cost effective, up to 50 percent cheaper than on demand.
Speaker1: But it's not, of course, suitable for every application that need real time responses.
Speaker1: So really, it depends, as always, for me, the successful founders are really
Speaker1: the ones who think about this from both a business and a technical perspective.
Speaker1: So they don't just pick a pricing model. So they align it with a smart,
Speaker1: cost-effective infrastructure strategy, which allows their business to scale
Speaker1: profitably as their customers find more value in their product.
Speaker0: We know startups work a lot with examples, with tests, with their own set-up,
Speaker0: so how important is pricing experimentation?
Speaker0: Can Gen.ai itself help optimize pricing points for AI products based on promotion data?
Speaker1: Yeah, you hit it here on a really great concept that I quite often discuss now with founders.
Speaker1: So really, as a founder, your job is never truly done when it comes to pricing.
Speaker1: Though there's always a need for iterative experimentation, how we call it.
Speaker1: So it's about being outcome oriented and not being afraid to act and test potential
Speaker1: solutions, even if they are not perfect.
Speaker1: So this is particularly true for Generative AI because the value proposition
Speaker1: and the customer willingness to pay can be unclear at the beginning.
Speaker1: So you have to constantly learn and adapt based on how people actually use your
Speaker1: product, what your customers truly value and how they're willing to pay for it.
Speaker1: And for me, this is where Gen.EI can be a good sparing partner,
Speaker1: the game changer for pricing itself.
Speaker1: So we just discussed how to price an AI product. But Gen AI can also be used
Speaker1: as a powerful tool to determine the optimal pricing.
Speaker1: So imagine if you have Gen AI models that can process and analyze vast amount
Speaker1: of data from your promotions, user interactions, and even market trends.
Speaker1: So it can define and identify patterns that a human analyst might miss.
Speaker1: So let's think about a specific type of user response to a discount or how a
Speaker1: certain feature usage correlates with a particular pricing tier.
Speaker1: So if you learn from past promotion data and user behavior, DEI can build predictive models.
Speaker1: So you can ask it, for instance, to forecast if we want a 20 percent off promotion
Speaker1: for our super tier, what will be the impact on our monthly recurring revenue and customer churn?
Speaker1: It can then provide data-driven insights to help you make a more informed decision.
Speaker1: Lastly, Gen AI can enable really a dynamic pricing model that automatically
Speaker1: adjusts in real time based on demand, usage, and even individual user profiles.
Speaker1: So, for example, if a user is a heavy power user, which is who is perhaps on
Speaker1: the bridge of exceeding the included credits, the AI could serve them a personalized
Speaker1: offer to upgrade to the next tier.
Speaker1: And this means that you can capture more value while providing the user with a predictable bill.
Speaker1: So really, to sum this up, instead of just using spreadsheets and intuition,
Speaker1: founders can leverage Gen.E.I.
Speaker1: To become more scientific and automated in their pricing.
Speaker1: So it's really a shift from a static pricing page to a living,
Speaker1: breathing pricing engine that's constantly learning, adapting and optimizing
Speaker1: for both customer value and business profitability.
Speaker0: Now that we talked so much about monetizing, let's go a little bit into risk and barriers.
Speaker0: In the past, we had a lot of startups and subject matter experts talk about
Speaker0: the data quality that needs to go into models.
Speaker0: What hurdles around data quality and use case alignment most often block the monetization?
Speaker1: This is a problem I observe a lot of the times.
Speaker1: And it also ties back to what we discussed earlier, why promising proof of concepts fail to scale.
Speaker1: So a POC is often built for curated success, how we call it.
Speaker1: So it really operating on a small, clean data set in an isolated environment.
Speaker1: But when you really try to bring it to a production-ready state,
Speaker1: you, of course, also encounter the messy, fragmented data of the real world.
Speaker1: And the challenge here is that Gen AI models require high-quality,
Speaker1: up-to-date data to perform effectively.
Speaker1: So if your data is incomplete, outdated, or unstructured, the garbage in,
Speaker1: garbage out principle applies.
Speaker1: So an AI solution built on a weak data foundation will produce inaccurate,
Speaker1: unreliable, or nonsensical outputs.
Speaker1: So this really directly kills monetization.
Speaker1: So as we discussed, a customer will only pay for a product that delivers a tangible return.
Speaker1: So if your AI isn't producing consistent, high-quality results,
Speaker1: it can't deliver on its promises. So no matter how elegant your pricing model
Speaker1: is, a product that doesn't work is impossible to monetize.
Speaker1: And secondly, and perhaps the most interesting hurdle, is the lack of clear use case.
Speaker1: So many startups fall into the trap of being a solution in search of a problem.
Speaker1: So they develop a technically impressive AI model, but fail to connect it to
Speaker1: real quantifiable business need.
Speaker1: This is why, together with a group of colleagues, we launched an EMEA-wide Gen
Speaker1: AI launchpad for about 200 customers in five cities in Europe,
Speaker1: where we worked with each startup prior to the event to refine their use case,
Speaker1: align architecture, and ensure data readiness.
Speaker1: So as part of the event series, we also worked on the AI Canvas I created,
Speaker1: which gets customers to define measurable KPIs to track Gen AI and data-driven impact.
Speaker1: So the program's success came from pushing customers to dive deeper into the
Speaker1: business metrics, which helped them select the right use case from the start.
Speaker1: So really, the AI canvas to elaborate starts by asking questions around the
Speaker1: customer's pain point and the negative consequence on the productivity or revenue.
Speaker1: So the value added by your project and what's the impact of not doing it?
Speaker1: And lastly, very important, how do you measure success?
Speaker1: And when a founder fails to clearly define these elements, their project becomes
Speaker1: a costly experiment with no clear path to revenue.
Speaker1: So really, this is a job number zero without a tangible business problem to solve.
Speaker1: The solution can't be tied to a measurable outcome and therefore it can't be monetized.
Speaker1: And in the end, these hurdles that I just mentioned are very linked.
Speaker1: So a great use case on its own is just an idea. So it really needs a foundation
Speaker1: of good data to become a reality.
Speaker1: And good data on its own is also just a resource. So it needs a clear use case
Speaker1: to unlock its value. So for an AI solution to be monetized, it must consistently
Speaker1: deliver on a clear, compelling value proposition.
Speaker1: And it really can only do so if it's built on the right data and solves a real business problem.
Speaker0: You've warned about risks like cross-tenant attacks in SaaS AI products.
Speaker0: How should founders communicate risks transparently without scaring away their investors?
Speaker1: That's a really interesting one because, first of all, when I talk to investors
Speaker1: as well, they are not really looking for a risk-free company.
Speaker1: They know also that those don't really exist.
Speaker1: They're really looking for founders who are aware of the risks and have a clear,
Speaker1: well-thought-out plan to manage them.
Speaker1: So by presenting your security strategy with confidence and specific details,
Speaker1: you're not scaring them off.
Speaker1: Instead, you're really building trust and showing them that you have thought
Speaker1: about this And also you are thinking about building a successful, resilient business.
Speaker1: So the first step is to communicate that security isn't an afterthought.
Speaker1: It's really a foundational principle to your business.
Speaker1: So this means that you've baked in security measures from the very beginning,
Speaker1: rather than patching them on at a later stage.
Speaker1: And this is really key for gaining investor trust.
Speaker1: So instead of talking about the risk of a cross-tenant attack as a scary possibility,
Speaker1: frame it as a known challenge that you are actively solving.
Speaker1: So to also recap for people that don't know about cross-tenant attacks,
Speaker1: that means that there's a malicious actor gaining unauthorized access to a single
Speaker1: instance that it uses to breach other customers' data.
Speaker1: And it is a well-known vulnerability in a multi-tenant SaaS system.
Speaker1: And this is just my kind of recommendation, how you can communicate your solution
Speaker1: in a confident, transparent way.
Speaker1: So this could be explaining how you've implemented strong guardrails to ensure
Speaker1: that customer data is secure and private, or how each customer's data is stored
Speaker1: in its own dedicated encrypted database or schema.
Speaker1: So this is a crucial technical defense against cross-tenant attacks.
Speaker1: So mention that you've implemented robust identity and access management policies
Speaker1: to ensure that one customer's credentials can't be used to access another's.
Speaker1: Talk, for instance, also about your robust monitoring and alerting systems that
Speaker1: don't just tell you when something has failed, but also provide early warnings of potential issues.
Speaker1: So this really demonstrates to your investor a productive and proactive approach
Speaker1: rather than a reactive way and thinking towards security.
Speaker1: So you can show also that your applications are designed to gracefully degrade
Speaker1: rather than completely fail.
Speaker1: So if one service becomes unavailable, your system will continuously operate
Speaker1: with reduced functionality instead of shutting down entirely.
Speaker1: So by presenting your security strategy in this way, you're really not scaring investors.
Speaker1: Instead, you're building trust and showing that you have the foresight and technical
Speaker1: capabilities to build a successful, resilient business.
Speaker1: So this moves the conversation from what if this happens to what makes your
Speaker1: security plan better than the competition.
Speaker1: So this is a lot more powerful when you are in the fundraising discussions, for instance.
Speaker0: Let's talk after so much input and we may add for our audience that we are already
Speaker0: recording here for more than 45 minutes.
Speaker0: Let's go a little bit into the outlook. which industry verticals do you expect
Speaker0: to see the fastest return on investment from Gen.AI, SaaS, FinTech Healthcare, or actually others?
Speaker1: So based on what I've observed, so the quickest ROI tends to come from operational
Speaker1: improvements rather than really high risk product innovations.
Speaker1: So this is where companies can automate internal processes, reduce manual labor
Speaker1: and drive immediate efficiency gains.
Speaker1: So industries that have a high density of these types of tasks are more likely to win early.
Speaker1: So SaaS and fintech are at the top of the list for rapid GNI return on investment.
Speaker1: So the primary reason for this is that these industries have a massive volume
Speaker1: of customer interactions and data, much of which that can't be automated.
Speaker1: So, for instance, startups can deploy AI agents to handle common customer inquiries.
Speaker1: So this reduces the need for human agents on the front line.
Speaker1: And this directly translates into lower operational cost and faster response
Speaker1: time, which provides ultimately a clear and quantifiable ROI.
Speaker1: And FinTech in particular has many high-volume repetitive tasks in thinking
Speaker1: about verification, data mapping for B2B clients.
Speaker1: And Gen AI can automate these workflows, really improving the time to value
Speaker1: for customers and freeing up human employees for more strategic work.
Speaker1: SaaS companies can also use Gen AI to create personalized brand-compliant content at scale.
Speaker1: Think about marketing emails to sales collateral. So this really speeds up go-to-market
Speaker1: efforts and reduce the cost of content creation.
Speaker1: I also am a strong believer of healthcare and life science industry because
Speaker1: they have a compelling case for rapid ROI, as we just discussed in the Pfizer example,
Speaker1: because they have the potential to automate the administrative and research-related tasks.
Speaker1: So, GNI can automate the generation of medical reports, summarize patient records
Speaker1: for doctors, and even streamline billing and coding processes.
Speaker1: So this frees up doctors and nurses to focus on patient care,
Speaker1: which is a significant value driver.
Speaker1: And also, as we've seen before, in drug discovery and clinical trials,
Speaker1: Gen AI can analyze vast amounts of data to identify new compounds or predict
Speaker1: the yield of new medicine.
Speaker1: So as we've seen in the Pfizer example, they have focused their Gen AI efforts
Speaker1: on these scientific applications and predicted annual cost savings of hundreds of millions of dollars.
Speaker1: So the overall takeaway here is that while the product innovation and customer
Speaker1: phasing features offer the promise of high returns, they often come with higher risk.
Speaker1: So if we contrast this with internal operational improvements,
Speaker1: they offer a lower risk path to a medium risk path, but they often also have a faster return.
Speaker1: So for founders, ultimately, the key is to prioritize use cases with a clear
Speaker1: link between a Gen AI solution and a measurable business outcome, as we said earlier.
Speaker1: So this could be cost reduction or efficiency gains. So you can really demonstrate
Speaker1: a clear ROI to stakeholders and build momentum for even more ambitious projects down the road.
Speaker0: Do you believe in the next five years, we'll see AI-first companies valued differently
Speaker0: by investors from traditional SaaS companies?
Speaker0: We've seen this, for example, with remote-first, with cloud-first, and so on and so forth.
Speaker0: Do you see that also playing out in the future?
Speaker1: So absolutely, really, when looking at the current state of the market.
Speaker1: So I believe that over the next five years, we are very certain to see AI-first
Speaker1: companies that are valued differently by investors than traditional SaaS firms,
Speaker1: for instance. So let's break that down a bit more.
Speaker1: So for me, an AI-first company is a business whose core product,
Speaker1: internal operations and competitive advantage really depend on artificial intelligence.
Speaker1: So this is often agentic AI, which means software not just performing really
Speaker1: narrow tasks, but autonomously making decisions, for instance,
Speaker1: setting goals and refining actions across complex multi-step processes.
Speaker1: So agentic AI is a step beyond classic automation. We're talking about systems
Speaker1: that can analyze problems, coordinate with other AIs and adapt over time.
Speaker1: So essentially managing entire workflows without constant human input.
Speaker1: So this is also what the research shows around this.
Speaker1: So if you think about investors, major VCs, and enterprise-focused funds,
Speaker1: they are already allocating significant capital to agentic AI startups.
Speaker1: So according to DROOM, there are more than 755 agentic AI startups as of mid
Speaker1: of this year, with combined funding that is topping $10 billion for categories
Speaker1: like coding agents, business process automation,
Speaker1: health agents, and agent building frameworks.
Speaker1: Gartner predicts that while the current environment is experimental with limited production examples,
Speaker1: agentic AI projects will both attract acquisitions from legacy automation manufacturers
Speaker1: and also face challenges, including also high cancellation rates for projects
Speaker1: that don't meet real autonomy needs.
Speaker1: And we're really seeing a pivot here. So if we think about these deterministic
Speaker1: domains like finance or logistics,
Speaker1: enterprises chase a lot of these proven automation solutions and may also acquire
Speaker1: Agentec AI startups as an addition.
Speaker1: And we see this also or what I see here happening is to have this for creative
Speaker1: or knowledge based workflows.
Speaker1: So Agentec AI is opening doors for brand new players. So these are the companies
Speaker1: really investors are keeping a close look on because they're not just placing
Speaker1: old tech, they're really redefining what's possible.
Speaker1: And I'm working actually with different startups that are using Gentic AI for
Speaker1: workflow automation, data science and cybersecurity, where nearly half of them are AI first.
Speaker1: So they're building solutions like autonomous AI, employees,
Speaker1: decision making, executives and multi-agent platforms that handle everything
Speaker1: from debt collection to healthcare automation.
Speaker1: And if we think about SaaS businesses, they still deliver value by digitizing
Speaker1: and streamlining manual processes.
Speaker1: So they're offering software plus a service.
Speaker1: But agentic AI firms really promise scalable autonomy.
Speaker1: So the ability to grow and adapt without requiring a customer to upsize their human team.
Speaker1: So investors see a lot of growth potential here. So imagine a startup that is
Speaker1: serving thousands of customers with
Speaker1: only a handful of staff and a suite of autonomous AIs running the show.
Speaker1: So it really changes a lot how value is created.
Speaker1: But also I want to outline a little bit the challenges. So reliability,
Speaker1: hallucinations, context limitations, as we also mentioned earlier,
Speaker1: These all pose a big hurdle.
Speaker1: So it is really important to have evaluation and oversized frameworks in place,
Speaker1: which are really, for me, timeless principles that are still high in demand
Speaker1: and will remain that way.
Speaker1: So let's also acknowledge this. So I see a lot of agent washing also happening.
Speaker1: So labeling really simple automations as agentic AI for hype,
Speaker1: really savvy investors are probing deeper here to really find true autonomy and scalability.
Speaker1: So if we think also about numbers that Gardner is publishing here,
Speaker1: more than 40% of agentic projects will be canceled because many use cases don't
Speaker1: actually need genuine creative autonomy.
Speaker1: So often partial autonomy, not really full independence, is actually the way to go.
Speaker1: So on the flip side, those who get the tech at this stage, and that's mostly
Speaker1: startups with really true agentic capabilities, will actually receive good valuations
Speaker1: and get a lot of interest.
Speaker1: So there's a high probability that EI first companies, especially those that
Speaker1: are generally agentic, will be valued differently by investors than traditional SaaS firms.
Speaker1: So the premium really will come from this autonomous scaling, new margin structures.
Speaker1: And yeah, investors will really look for those where AI is not just a feature,
Speaker1: but the engine powering growth and innovation.
Speaker1: But also there will be experimentation insurance. So as mentioned,
Speaker1: some projects and even startups will not survive this hype cycle.
Speaker1: So for me, the winners will be those who combine deep agentic autonomy with
Speaker1: robust evaluation security and operational infrastructure.
Speaker0: I have to admit, I have to smile when you talk about mislabeling of agentic AI.
Speaker0: We've seen this with AI in general. We've seen this with Greentech,
Speaker0: so it's kind of a repeating pattern.
Speaker0: Jennifer, thank you very much for being a guest. We will have you back for a
Speaker0: second episode together with AWS. Thank you very much.
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