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While some generative AI applications have indeed changed the game, far more have struggled to deliver sustained value, all while incurring highly variable costs. This creates a pronounced “boom-or-bust” dynamic in GenAI products. In many ways, building a GenAI product today feels less like rolling out a steady SaaS service and more like producing a Hollywood blockbuster: you pour in massive upfront investment, pray for a smash hit, and risk a flop that yields little return. This isn’t just figurative speech; the market data bears it out.
OpenAI’s ChatGPT, for example, reached an unprecedented 100 million users in just two months, a growth trajectory more akin to a viral entertainment release than a typical B2B SaaS rollout. By contrast, successful SaaS companies like Zoom or Squarespace traditionally took years to cultivate their user bases and revenues through gradual, repeatable growth.
Let’s break down why GenAI products follow a hit-driven model, and what that means for product strategy and monetization.
The Hit-Driven Hype Cycle of GenAI vs. SaaS Steady Growth
GenAI products often experience explosive growth, akin to the rapid success of a blockbuster movie's opening weekend. A prime example is ChatGPT, which became the fastest-growing consumer app in history, reaching 100 million monthly users just two months after launch in January 2023. For context:
- TikTok: Took 9 months to hit 100M users
- Instagram: Took 2.5 years to hit 100M users
This instant scalability is rare in traditional SaaS, where growth tends to be more gradual. Even Zoom’s pandemic-fueled spike, from 10 million to 300 million daily users, was driven by unique circumstances. Typically, SaaS products grow incrementally over quarters or years, not through viral surges.
GenAI’s boom times feel more like cultural moments, marked by viral adoption followed by a decline. For example, Lensa, an AI image generator, peaked in December 2022 with:
- 4.3 million daily downloads
- $1.8M in daily revenue
But, like many GenAI products, the excitement quickly tapered off. GenAI’s pattern generally includes:
- Initial spike in interest (due to novelty, media coverage, and word-of-mouth)
- A rapid rise in users or usage
- A drop-off in engagement once the novelty fades or competition increases
In contrast, SaaS success is driven by long-term retention and incremental adoption. For SaaS, growth looks like:
- A product starting with one team and expanding over years
- Steady, sustained engagement rather than a one-time burst
Not all GenAI companies follow this pattern. Some, like Midjourney, an AI image generation platform, have successfully turned initial hype into sustained growth. Key highlights:
- Midjourney surpassed 1 million users in 6 months and reached 19 million by early 2024
- It achieved this without big-budget marketing or VC funding
- By 2024, Midjourney earned $200 million in annual revenue, relying solely on a paid subscription model and a lean team of just 11 employees
This rapid, lean growth resembles the phenomenon of a low-budget film becoming a blockbuster hit. Investors have taken note, with Bessemer Venture Partners observing that top “Vertical AI” startups often experience growth patterns more like consumer apps than B2B software, with some hitting $2M ARR on <$2M invested in under 2 years.
The Retention Challenge for GenAI
While GenAI products can generate explosive growth, they face significant challenges in retention. Once the initial excitement fades, user engagement often drops. For instance:
- ChatGPT saw a 10% dip in website traffic for three months in mid-2023 as early users moved on, before picking up again with feature upgrades and the new school year.
Many GenAI users drop off once the novelty wears off unless there’s a compelling reason to stay engaged. In contrast, SaaS businesses focus heavily on retention and steady engagement, measured by metrics like Net Revenue Retention (NRR), which is typically above 100% in successful SaaS companies.
- SaaS NRR benchmarks:
- Median: ~102%
- Best-in-class: 120-150% (e.g., Snowflake, Twilio)
This consistent, compounding growth from existing customers is a hallmark of SaaS success and is rarely seen in GenAI, where usage can be more unpredictable.
Monetization models like freemium, subscription, or enterprise bundles are often directly tied to user engagement patterns. For instance, a freemium model works best when users experience a quick, initial spike in interest but need ongoing features to stay engaged. For subscription models, such as ChatGPT Plus, retaining users after the initial surge is key to ensuring predictable revenue. In contrast, enterprise pricing often ties to long-term engagement and larger-scale adoption, where businesses are looking for consistent value over time. In all cases, the monetization model must align with the growth pattern, whether it's capturing users rapidly during the initial boom or maintaining steady engagement for sustainable revenue.
As we’ve seen, while GenAI products can generate explosive growth, they also face significant unpredictability, with early excitement often giving way to a drop in user engagement. This volatility, combined with high production costs, creates a unique financial challenge for GenAI companies, which we'll explore in terms of unit economics and profitability.
Unpredictable Outcomes: 10x Differentiators vs. Duds
One of the key characteristics that sets GenAI apart from traditional SaaS is its unpredictability. While SaaS products typically deliver deterministic, reliable outputs, GenAI products offer probabilistic results that can vary.
- GenAI's All-or-Nothing Nature: When GenAI works, it can deliver a 10x game-changer. When it fails, it can be a total dud. This variance is much like the movie business, where a film is either a massive hit or a box-office flop, with little room for middle ground.
- The Gamble of GenAI: For users, this unpredictability can feel like a gamble. One moment, an AI tool might produce brilliant results; the next, it could fail miserably. A notable example is Google’s rushed Bard chatbot demo in February 2023, which flubbed a factual answer, erasing $100 billion off Alphabet’s market value in a day. Unlike traditional SaaS, where bugs or subpar features might cause frustration, a single GenAI mistake can lead to massive consequences.
- The Challenge for Companies: Companies deploying GenAI solutions face the challenge of delivering "wow" moments while minimizing the risk of failure. A single bad release can tarnish the brand’s reputation, just as a disastrous movie premiere can sink a studio’s profits.
- Inconsistent ROI from GenAI: The variability extends to business outcomes as well. Some enterprises experience dramatic improvements, like Klarna, which integrated AI agents to handle two-thirds of customer service inquiries, saving an estimated $40M in 2024. But other businesses see minimal or inconsistent returns. Much like Hollywood studios, where a few blockbuster hits subsidize the flops, AI investments can be hit-or-miss.
- The AI Financial Paradox: There’s a clear “AI financial results paradox.” While companies believe in the disruptive potential of GenAI, directly linking current AI pilots to tangible revenue growth remains challenging. This uncertainty has made early AI adoption a bit of a leap of faith, companies invest in AI to avoid missing out, even when the metrics are unclear.
In contrast, SaaS products are generally more predictable:
- Reliability of SaaS: SaaS tools consistently deliver value with fewer surprises. If a SaaS product fits the use case, it performs reliably over time, building trust and leading to long-term commitments and renewals.
- GenAI’s Path to Predictability: GenAI, on the other hand, must earn that trust by accepting some misfires. Product teams are addressing this by improving model accuracy, adding safeguards, and communicating the limitations of AI transparently. As these models improve, GenAI will gradually behave more like SaaS, delivering consistent, reliable results.
While the cost and performance of AI continue to improve, e.g., the cost of certain AI model inference has dropped 100-fold in two years, there will always be an inherent creative risk with launching GenAI products. Until then, it’s akin to releasing a new film: you hope it will resonate, but the true measure of success only comes after the release.
Big Upfront Costs and Uncertain Unit Economics
GenAI products share many of the high-budget risks of blockbuster movies, including hefty production costs. Training state-of-the-art AI models can cost tens or even hundreds of millions of dollars. For instance, GPT-4 reportedly cost over $100 million to train, a price tag similar to other large AI models. This is like a Hollywood studio spending large sums before any revenue is generated.
In contrast, SaaS products require less upfront investment, usually a few engineers developing an MVP. Once built, the incremental cost of serving new customers is low, allowing SaaS companies to achieve high gross margins (80-90%). In other words, while the first copy is expensive, every additional one is almost free, enabling scalable profitability as the business grows.
However, GenAI flips this equation:
- High Variable Costs: Each customer request requires significant computing power (e.g., GPUs, cloud servers), leading to ongoing costs.
- Lower Gross Margins: Many GenAI startups operate with gross margins in the range of 50-60%, which is 10-30 points lower than the margins seen in traditional software firms. For example, many AI companies find their margins stuck at 50-55%, while pure software companies with the same revenue would typically have margins of 80% or higher.
This means each dollar of revenue in the GenAI sector has less enterprise value, making profitability harder to achieve. For example, if every ticket sale had to cover both production and distribution costs, breaking even would require far more sales. GenAI products face a similar challenge: they need high scale or premium pricing (or both) to make the economics work, with constant cloud compute costs in the background.
Many GenAI products initially used free or freemium models to drive adoption but quickly shifted to paid plans as usage grew and costs mounted:
- OpenAI: Introduced ChatGPT Plus at $20/month to support heavy usage, along with usage caps to manage cloud costs.
- Midjourney: Avoided a freemium model entirely, offering only paid subscriptions (ranging from $10 to $120/month), contributing to its $200M/year revenue.
- Jasper AI: Jasper rode the GPT-3 wave to acquire tens of thousands of customers on a subscription model and hit an $80M ARR within its first 18 months, an impressive pace by any standard. But even Jasper had to adjust its pricing and strategy when OpenAI’s free ChatGPT appeared as a near substitute. Jasper found that catering to enterprise needs (e.g., brand voice, collaboration features) and charging accordingly was essential to differentiate its service and justify its value over the free alternative.
The key takeaway is that GenAI companies must be highly aware of their unit economics. Unlike SaaS businesses, which often "grow first and monetize later," GenAI startups cannot afford prolonged free usage periods due to high cloud costs. Many companies are adjusting their models to incorporate:
- Usage-Based Pricing: OpenAI charges API customers per 1,000 tokens, while offering premium subscription plans for ChatGPT.
- SaaS-like Bundling: Enterprise-focused GenAI firms bundle AI features into higher tiers or as paid add-ons, ensuring that heavy users pay more.
This hybrid model mirrors a film studio selling not just tickets, but subscriptions for unlimited viewing or charging extra for special experiences.
The Path to Sustainability
The good news is that GenAI costs are decreasing. For instance, the cost of training models has dropped by up to 100x in some cases. As costs improve, the financial dynamics should soften, allowing AI products to enjoy more SaaS-like margins and predictability. According to VC Tomasz Tunguz, a sophisticated AI assistant could cost around $7/month to run at scale, bringing it closer to a sustainable SaaS structure.
Until these efficiencies fully materialize, GenAI startups must remain disciplined in both monetization and cost management to avoid failure. The sector has already attracted massive venture funding, with over over $26B in 2023 alone, as investors bet on the potential for big hits to justify the capital-intensive, high-risk nature of the industry.
Product-Market Fit: From Novelty to Sustainable Franchise
After the initial excitement and high costs, the challenge for GenAI products is turning from a fleeting novelty to a lasting franchise. While SaaS product-market fit is often measured by renewal rates and customer expansion, GenAI can initially achieve quick traction through demos, but sustaining engagement and embedding the product into daily workflows is harder. Several strategies are emerging for GenAI companies to maintain relevance over time:
- Integrating into the Customer’s “Daily Life” or Workflows
The more a GenAI tool becomes part of a user's regular tasks, the more it shifts from novelty to necessity. GitHub Copilot, for example, quickly gained traction with developers, becoming integral to coding in IDEs. Within a year, it was generating 46% of developers' code and had over 1 million users, showcasing strong user retention. For GenAI products, the goal is to solve ongoing problems consistently.
- Enterprise-Grade Solutions and Customizability
Moving upmarket to offer tailored solutions for businesses can solidify a product’s place. Jasper’s pivot from a general content tool to an AI co-pilot for marketing teams is a case in point. By offering features for brand consistency and collaboration, Jasper integrated deeper into business processes. OpenAI followed suit with ChatGPT Enterprise, offering data privacy and admin controls, aiming to make its viral app an essential business tool. Similarly, Synthesia started as a demo for AI video avatars but now serves over 60,000 customers, including 60% of Fortune 100 companies, by offering a scalable platform for corporate training and communications.
- Building Ecosystems and Communities
GenAI products can benefit from network effects and community-driven engagement. Midjourney's Discord community and OpenAI’s plugin ecosystem extend the product’s utility. Just as SaaS platforms like Salesforce and Slack have grown through app marketplaces and integrations, GenAI products leverage community contributions. OpenAI’s API, for instance, allowed developers to embed GPT capabilities into apps, increasing its staying power and turning it into a platform. Early community-led growth, like Jasper’s focus on educating users through shared use cases, has helped drive rapid adoption.
- Iterating Quickly Based on Feedback
Unlike movies, where re-cuts are rare, GenAI products, like SaaS, are built to evolve continuously. User feedback is essential for refining models and adding features, enabling companies to adjust quickly. GenAI companies that treat their products as dynamic, ever-evolving services rather than static releases are gaining better traction. For example, when users pointed out biases or errors, companies like OpenAI responded with model updates and enhanced prompt tools. This rapid iteration, a hallmark of SaaS (think continuous deployment and A/B testing), has become vital for GenAI. Runway ML, for instance, iterated from Gen-1 to Gen-2 models in under a year, improving quality and unlocking new capabilities for video generation. In a fast-moving field, GenAI products that don’t keep evolving risk being surpassed by more agile competitors.
Achieving product-market fit in GenAI means transitioning from a "cool demo" to a solution that delivers consistent value. GenAI companies must bridge the gap between novelty and necessity to retain long-term users. Those who succeed will turn their AI "blockbuster" into a franchise, capturing loyal users and advocates. Applying SaaS principles such as effective onboarding, customer success, and reliability will be crucial for sustainable growth.
What This Means: Balancing Blockbuster Ambitions with SaaS Discipline
The comparison of GenAI products to blockbuster movies highlights both the tremendous potential and inherent risks. For founders, product leaders, and investors, the challenge lies in leveraging the upside while applying solid SaaS principles. Here's how to find that balance:
- Plan for Uneven Outcomes
Experimental AI features can be unpredictable. Instead of focusing on one use case, adopt a portfolio approach like a film studio developing multiple movies. If one AI application isn't gaining traction, pivot and explore adjacent opportunities. For instance, Jasper shifted from generic copywriting to focused marketing content and remained relevant.
- Monetize Early, but Smartly
Start testing pricing models once user engagement picks up. Consider usage-based pricing, tiered subscriptions, or add-ons. Monitor your gross margins and unit costs closely, as GenAI often has 10-30% lower margins than SaaS. To improve profitability, adjust the offering (e.g., limit free usage or bundle with other high-margin services).
- Focus on Retention and Real Adoption
The goal isn’t just sign-ups, but retaining users and encouraging repeat usage. If trial users aren’t converting, investigate the causes: is the AI output inconsistent? Are there workflow issues? High churn can quickly derail a potential blockbuster. If you see strong retention in certain segments, double down on those areas and use the feedback to refine the product.
- Manage Expectations and Messaging
While AI has a lot of hype, over-promising can backfire. Set realistic expectations about what your AI can do, such as “this AI improves X process by ~20%,” which builds trust and credibility. The Google Bard incident, where overhyped claims led to a stock drop, is a cautionary tale. Transparency ensures your audience trusts you when significant breakthroughs occur.
- Stay Agile and Focus on Cost/Value
The GenAI space evolves quickly, and today's hit can be outdated tomorrow. Continue innovating, but also stay aware of the cost-to-value ratio. If cheaper alternatives can provide 90% of the value at a fraction of the cost, your product faces more competition. Building a strong moat, like proprietary data or user experience, can protect your position.
In Conclusion:
Generative AI products have the potential for massive success but come with higher risks, including cost overruns and competitive disruption. By applying SaaS principles, recurring value delivery, customer-centric design, and scalable economics, GenAI companies can increase the likelihood of turning an initial hit into long-term success. Think like Spielberg, but bill like SaaS: be bold with creativity, but back it up with a solid business model that continuously improves to keep customers coming back.
