Consumer software companies no longer need to struggle with user churn; instead, they can achieve growth through the continuous expansion of user value.

Have you ever wondered why AI consumer products that have emerged in the past two years have grown from zero to millions of users and surpassed $100 million in annual revenue in less than two years? This growth rate was almost unimaginable before AI. On the surface, it seems to be due to faster distribution and higher average revenue per user. However, I have noticed a deeper change that most people overlook: AI has fundamentally altered the revenue retention model of consumer software.

I recently read an analysis by a16z partner Olivia Moore titled "The Great Expansion: A New Era of Consumer Software," where she refers to this phenomenon as the "Great Expansion." I believe she has captured a very critical trend. After reflecting on this perspective, I realize that this is not just an adjustment in business models, but a fundamental transformation of the entire consumer software industry's rules of the game. We are witnessing a historic turning point: consumer software companies no longer need to fight against user churn; instead, they can rely on the continuous expansion of user value to achieve growth. The boundaries between the consumer market and the enterprise market are gradually becoming blurred in a certain sense.

The impact of this change is enormous. Traditional consumer software companies spend a significant amount of effort and resources each year to replace lost users just to maintain the status quo. Now, those companies that have seized the AI opportunity find that each batch of users not only retains value but also contributes more revenue over time. It’s like transforming from a leaky bucket into an ever-expanding balloon; the growth model is completely different.

From this perspective, I personally believe this is a huge opportunity for companies going overseas, because consumer products can achieve growth and revenue through PLG, perfectly avoiding the shortcoming of Chinese teams in the overseas SLG space. Although it is in the enterprise market, the entire growth model is similar to that of consumer products. On this point, I can relate personally; my own project has been online for a month, fully targeting the enterprise B-end Vibe coding product, but has achieved customer growth through PLG, receiving good data feedback.

Fundamental Flaws of the Traditional Model

Let’s first review how consumer software made money before AI. Moore mentioned two main models in her analysis, and I think her summary is very accurate. The first is the ad-driven model, primarily used in social applications, directly linked to usage, so the value per user is typically flat over time. Instagram, TikTok, and Snapchat are representatives of this model. The second is the single-layer subscription model, where all paying users pay the same fixed fee monthly or annually for product access. Duolingo, Calm, and YouTube Premium adopt this approach.

In both models, revenue retention is almost always below 100%. A certain percentage of users churn each year, while those who remain continue to pay the same amount. For consumer subscription products, maintaining a user and revenue retention rate of 30-40% at the end of the first year is considered "best practice." Such numbers sound despairing.

I have always felt that this model has a fundamental structural flaw: it creates a basic constraint where companies must constantly replace lost revenue to maintain growth, let alone expand. Imagine if you have a leaky bucket; you not only have to keep adding water to maintain the water level, but you also have to add more than what leaks out to raise the water level. This is the dilemma faced by traditional consumer software companies: they are trapped in an endless cycle of customer acquisition, churn, and reacquisition.

The problem with this model is not just numerical; it also affects the overall strategy and resource allocation of the company. Most efforts are spent on acquiring new users to compensate for churn rather than deepening relationships with existing users or enhancing product value. This is why we see many consumer applications frantically pushing notifications and employing various means to increase user stickiness, as they know that once users stop using the product, revenue will immediately disappear.

I believe this model fundamentally underestimates the potential value of users. It assumes that user value is fixed; once they subscribe to the product, their contribution to revenue is capped. But in reality, as users become more familiar with the product, their needs often grow, and the amount they are willing to pay increases. The traditional model fails to capture this opportunity for value growth.

Rewriting the Rules of the Game in the AI Era

The emergence of AI has completely changed the game. Moore refers to this change as the "Great Expansion," and I think this name is very apt. The fastest-growing consumer AI companies are now seeing revenue retention rates exceeding 100%, which is almost unimaginable in traditional consumer software. This phenomenon occurs in two ways: first, consumer spending increases as usage-based revenue replaces fixed "access" fees; second, consumers are bringing tools into the workplace at an unprecedented speed, where these tools can be reimbursed and supported by larger budgets.

One key change I have observed is a fundamental shift in user behavior patterns. In traditional software, users either use the product or they don’t; they either subscribe or cancel their subscription. But in AI products, user engagement and value contribution grow incrementally. They may start by occasionally using basic features, but as they discover the value of AI, they become increasingly reliant on these tools, and their demand continues to expand.

The trajectory of this difference is dramatic. Moore notes that at a 50% revenue retention rate, a company must replace half of its user base each year to remain stable. In contrast, at over 100%, each user cohort expands, with growth compounding on growth. This is not just a numerical improvement; it represents a new growth engine.

I believe there are several deep reasons behind this change. AI products have a learning effect; they become more useful with use. The more time and data users invest, the greater the product's value to them. This creates a positive feedback loop: more usage leads to greater value, and greater value leads to more usage and higher willingness to pay.

Another key factor is the practical nature of AI products. Unlike many traditional consumer applications, AI tools often directly address specific user problems or enhance their productivity. This means users can easily see the direct benefits of using these tools, and they are more willing to pay for this value. When an AI tool can save you several hours of work, it becomes very reasonable to pay for additional usage.

Sophisticated Pricing Structure Design

Let me delve into how the most successful consumer AI companies construct their pricing strategies. Moore points out that these companies no longer rely on a single subscription fee but instead use a hybrid model that includes multiple subscription tiers along with usage-based components. If users exhaust their included credits, they can purchase more or upgrade to a higher plan.

I think there is an important insight from the gaming industry here. Game companies have long derived most of their revenue from high-spending "whale" users. Limiting pricing to one or two tiers is likely wasting revenue opportunities. Smart companies build tiers around variables such as the number of generations or tasks, speed and priority, or access to specific models, while also offering credits and upgrade options.

Let me look at some specific examples. Google AI offers a $20 monthly Pro subscription and a $249 monthly Ultra subscription, charging extra for Veo3 credits when users (inevitably) exceed their included amounts. Additional credit packages start at $25 and extend up to $200. From what I understand, many users may spend as much on additional Veo credits as on the base subscription. This is a perfect example of how to allow revenue to grow alongside user engagement.

Krea's model is also interesting; they offer plans ranging from $10 to $60 per month based on expected usage and training jobs, and if you exceed the included computing units, you can purchase additional credit packages for $5 to $40 (valid for 90 days). The brilliance of this model lies in its reasonable entry price for light users while providing expansion space for heavy users.

Grok's pricing takes this strategy to the extreme: the SuperGrok plan is $30 per month, while the SuperGrok Heavy plan is $300 per month, unlocking new models (Grok 4 Heavy), expanded access to models, longer memory, and new feature testing. This tenfold price difference is almost unimaginable in traditional consumer software, but it becomes reasonable in the AI era due to the vast differences in needs and value perception among different users.

I believe the success of these models lies in their recognition of the diversity and dynamism of user value. Not all users have the same needs or payment capabilities, and the same user’s needs can change over time. By offering flexible pricing options, these companies can capture the full spectrum of user value.

Moore mentions that some consumer companies have achieved over 100% revenue retention solely through this pricing model, even without considering any expansion into enterprises. This illustrates the power of this strategy. It not only addresses the churn problem of traditional consumer software but also creates an inherent growth mechanism.

The Golden Bridge from Consumer to Enterprise

Another important trend I have observed is the unprecedented speed at which consumers are bringing AI tools into the workplace. Moore emphasizes this in her analysis: consumers are actively rewarded for introducing AI tools into the workplace. In some companies, failing to become "AI-native" is now considered unacceptable. Any product with potential workplace applications—essentially any product that is not NSFW—should assume that users will want to bring it into their teams, and when they can get reimbursed, they will pay significantly more.

The speed of this transformation impresses me. In the past, the shift from consumer to enterprise typically took years, requiring significant market education and sales efforts. However, the practicality of AI tools is so evident that users spontaneously introduce them into their work environments. I have seen many instances where employees first purchase AI tools personally and then persuade their companies to buy enterprise versions for the entire team.

The transition from price-sensitive consumers to price-insensitive enterprise buyers creates enormous expansion opportunities. However, this requires fundamental sharing and collaboration features, such as team folders, shared libraries, collaborative canvases, authentication, and security. I believe these features have now become essential for any consumer-grade AI product with enterprise potential.

Equipped with these features, pricing differences can be substantial. ChatGPT is a great example; although it is not widely regarded as a team product, its pricing highlights the disparity: individual subscriptions are $20 per month, while enterprise plans range from $25 to $60 per user. This 2-3 times price difference is rare in traditional consumer software but has become common in the AI era.

I think some companies even price their individual plans at breakeven or slight loss to accelerate team adoption. Notion effectively used this approach in 2020, offering unlimited free pages for individual users while charging aggressive fees for collaboration features, which drove its most explosive growth period. The logic behind this strategy is to build a user base by subsidizing individual use and then monetize through enterprise features.

Let me look at some specific examples. Gamma's Plus plan is $8 per month, which removes watermarks— a requirement for most enterprises— along with other features. Users then pay for each collaborator added to their workspace. This model cleverly leverages the enterprise demand for a professional appearance.

Replit offers a $20 per month plan for Core users. Team plans start at $35 per month, including additional credits, viewer seats, centralized billing, role-based access control, private deployment, and more. Cursor provides a $20 per month Pro plan and a $200 per month Ultra plan (with 20 times the usage). Team users pay $40 per month for the Pro product, which includes organization-wide privacy modes, usage and management dashboards, centralized billing, and SAML/SSO.

These features are important because they unlock enterprise-level ARPU (average revenue per user) expansion. I believe that any consumer-grade AI company that does not consider an enterprise expansion path is missing a huge opportunity. Enterprise users not only pay higher fees, but they are also generally more stable with lower churn rates.

Investing in Enterprise Capabilities from Day One

Moore offers a seemingly counterintuitive but actually very wise suggestion: consumer companies should now consider hiring a sales executive within one to two years of their founding. I completely agree with this viewpoint, even though it indeed goes against traditional consumer product strategies.

Individual adoption can only take a product so far; ensuring widespread organizational use requires navigating enterprise procurement and closing high-value contracts. This necessitates specialized sales capabilities rather than simply relying on the natural spread of the product. I have seen too many excellent consumer-grade AI products miss significant opportunities due to a lack of enterprise sales capabilities.

Canva was founded in 2013 and waited nearly seven years to launch its Teams product. Moore points out that such delays will no longer be feasible by 2025. The pace of enterprise AI adoption means that if you delay enterprise features, competitors will capture the opportunity instead. This competitive pressure has been greatly accelerated in the AI era, as the speed of market changes is faster than ever.

I believe there are several key features that often determine outcomes. In terms of security and privacy, SOC-2 compliance and SSO/SAML support are required. In operations and billing, role-based access control and centralized billing are necessary. In product features, team templates, shared themes, and collaborative workflows are essential. These may sound basic, but they are often critical factors in enterprise procurement decisions.

ElevenLabs is a great example: the company initially relied heavily on consumer usage but quickly built enterprise-level capabilities, adding HIPAA compliance for its voice and conversational agents and positioning itself to serve healthcare and other regulated markets. This rapid enterprise transformation allowed them to capture high-value enterprise clients rather than relying solely on consumer revenue.

I have observed an interesting phenomenon: consumer-grade AI companies that invest in enterprise capabilities early on often establish stronger moats. Once enterprise clients adopt a tool and integrate it into their workflows, the switching costs become high. This creates stronger customer stickiness and more predictable revenue streams.

Additionally, enterprise clients provide valuable product feedback. Their needs are often more complex, driving the product toward more advanced directions. I have seen many consumer-grade AI products discover new product directions and feature needs by serving enterprise clients.

Deep Reflections on This Transformation

After carefully analyzing Moore's points and my own observations, I believe we are witnessing not just an adjustment in business models but a reconstruction of the entire software industry's infrastructure. AI has not only changed the capabilities of products but also the ways in which value is created and captured.

What I find most interesting is that this change challenges our traditional assumptions about consumer software. For a long time, it was believed that consumer software was inherently low-priced, had high churn, and was difficult to monetize. However, the reality of the AI era shows that consumer software can achieve enterprise-level revenue scales and growth rates. The implications of this shift are profound.

From a capital allocation perspective, this means that investors can now invest more in consumer-grade AI companies earlier, as these companies can achieve meaningful revenue scales more quickly. Traditionally, consumer software companies needed to wait until they reached a massive user scale to effectively monetize, but now they can achieve strong revenue growth on a relatively small user base.

I have also contemplated the impact of this change on entrepreneurial strategies. Moore mentioned that many of the most important enterprise companies in the AI era may have started as consumer products. I think this is a very profound insight. The traditional B2B software entrepreneurial path often involves extensive market research, customer interviews, and sales cycles. In contrast, starting from a consumer perspective allows for faster product iteration and market validation.

Another advantage of this approach is that it creates a more natural product-market fit. When consumers voluntarily use and pay for a product, it signals a strong product-market fit. Then, when these users bring the product into the workplace, enterprise adoption becomes more organic and sustainable.

I have also noticed an interesting shift in competitive dynamics. In the traditional software era, consumer and enterprise markets were often separate, with different players and strategies. However, in the AI era, these boundaries are becoming blurred. A product can compete in both markets simultaneously, creating new competitive advantages and challenges.

From a technical perspective, I believe this dual nature of AI products (consumer-grade usability + enterprise-grade functionality) is driving new standards in product design and development. Products need to be simple enough for individual users to easily adopt while also being powerful and secure enough to meet enterprise needs. Achieving this balance is not easy, but companies that do it well will gain a significant competitive advantage.

I have also considered how this trend affects existing enterprise software companies. Traditional enterprise software companies now face competition from AI companies that started in the consumer space, and these newcomers often offer better user experiences and faster iteration speeds. This may force the entire enterprise software industry to raise its product standards and user experiences.

Finally, I believe this change also reflects a fundamental shift in the way we work. Remote work, increased personal tool choice, and higher expectations for productivity tools are all driving the blurring of boundaries between consumer and enterprise tools. AI has only accelerated this ongoing trend.

Future Opportunities and Challenges

While I am excited about the "Great Expansion" phenomenon described by Moore, I also see some challenges and opportunities that need attention.

On the challenge side, I believe competition will become more intense. As successful paths become clearer, more companies will attempt to follow the same strategies. Companies that can establish strong differentiation and network effects will prevail in long-term competition.

From a regulatory perspective, the rapid adoption of AI products in enterprise environments may raise new compliance and security challenges. Companies need to ensure that their AI tools comply with various industry standards and regulatory requirements. This may increase development costs and complexity but will also create new competitive barriers.

On the opportunity side, I see enormous space for innovation. Companies that can creatively combine consumer-grade usability with enterprise-grade functionality will open up new market categories. I also believe that vertical AI tools have significant opportunities, as deep optimization for specific industries or use cases may be more valuable than generic tools.

I also see opportunities for network effects in data and AI models. As users increase and usage deepens, AI products can become smarter and more personalized. This data-driven improvement can create strong competitive advantages, as new entrants find it difficult to replicate this accumulated intelligence.

From an investment perspective, I believe this trend will continue to attract significant capital. However, investors need to be more discerning in identifying companies that truly have sustainable competitive advantages, rather than just those that are experiencing rapid short-term growth. The key will be to understand which companies can build real moats rather than just capitalizing on early market opportunities.

Ultimately, I believe that the "Great Expansion" described by Moore is just the beginning of the AI revolution. We are redefining the essence of software—from tools to intelligent partners, from features to outcomes. Companies that can seize this transformation and execute successfully will establish the next generation of tech giants. This is not just an innovation in business models but a reimagining of the relationship between humans and technology. We are in an exciting era where software is becoming smarter, more useful, and more indispensable.

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