Original Title: The Great Expansion: A New Era of Consumer Software

Original Source: Olivia Moore, a16z Partner
Original Compilation: Leo, Deep Thinking Circle

Have you ever wondered why AI consumer products that have emerged in the past two years have been able to grow from zero to millions of users in less than two years, with annual revenue surpassing $100 million? This growth rate was almost unimaginable before AI. Superficially, this is because distribution is faster and average user income is higher. But I have found a deeper change that most people have overlooked: AI has completely transformed the revenue retention model of consumer software.

Recently, I read an analysis article by a16z partner Olivia Moore titled "The Great Expansion: A New Era of Consumer Software," in which she referred to this phenomenon as the "Great Expansion." I think she has captured a very crucial trend. Upon further contemplation of this point, I realized that this is not just a business model adjustment, but a fundamental transformation of the rules of the entire consumer software industry game. We are witnessing a historic turning point: consumer software companies no longer need to struggle with user churn but 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.

The impact of this change is enormous. Traditional consumer software companies spend a significant amount of time and money each year replacing lost users just to maintain the status quo. And now, those companies that have seized the AI opportunity have found that each batch of users not only does not lose value but instead contributes more revenue over time. It's like going from a leaking bucket to a continuously expanding balloon, completely changing the growth pattern.

From this perspective, I personally believe that this is a huge opportunity for overseas companies, as consumer products can leverage PLG for growth and revenue, perfectly avoiding the weakness of Chinese teams in overseas SLG. While focusing on the enterprise market, the entire growth pattern is similar to that of consumer products. In this regard, I can relate personally, as my own project has been online for a month, offering a completely enterprise-oriented B2B Vibe coding product, but achieving good data feedback through PLG-driven customer acquisition growth.

The Fundamental Flaw of the Traditional Model

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

Under these two models, revenue retention is almost always below 100%. There is a certain percentage of user churn every year, while those who remain continue to pay the same amount. For consumer subscription products, being able to maintain a 30-40% user and revenue retention rate at the end of the first year is considered a "best practice." This number sounds quite despairing.

I have always felt that this model has a fundamental structural flaw: it creates a basic constraint where companies must consistently replace lost revenue to sustain growth, let alone expansion. Imagine if you have a leaking bucket, you not only need to continuously add water to maintain the water level but also add more than what leaks out to make the water level rise. This is the dilemma traditional consumer software companies face: they are trapped in an endless cycle of acquisition, churn, and reacquisition.

The issue with this model is not just numerical; it also affects the company's overall strategy and resource allocation. Most of the energy is 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 apps aggressively push notifications, employ various means to increase user stickiness because they know once a user stops using, the revenue immediately disappears.

I believe this model fundamentally underestimates the potential value of users. It assumes that the user's value is fixed, and once they subscribe to the product, the revenue they can contribute 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 also increases. The traditional model has not captured the opportunity for this value growth.

The Rewrite of the Rules in the AI Era

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

One key change I've 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 they unsubscribe. However, in AI products, user engagement and value contribution are progressively growing. They may start by only occasionally using basic features, but as they discover the value of AI, they become increasingly reliant on these tools, with their needs continuously expanding.

This trajectory of 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 constant. However, in scenarios where retention exceeds 100%, each user cohort is expanding, with growth stacking on top of growth. This isn't just a numerical improvement; it represents an entirely new growth engine.

I believe there are several deep-seated 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, greater value leads to more usage, and a higher willingness to pay.

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

Thoughtful Pricing Architecture Design

Let me delve into how the most successful consumer-facing AI companies have structured their pricing strategies. Moore points out that these companies no longer rely on a single subscription fee but instead use a mix of multiple subscription tiers plus usage-based components. If a user exhausts their included credits, they can purchase more or upgrade to a higher plan.

I believe there is a significant insight here from the gaming industry. Gaming companies have long derived much of their revenue from high-spending "whale" users. Restricting pricing to one or two tiers is likely missing out on revenue opportunities. Smart companies build tiers around variables like generated quantity or number of tasks, speed and priority, or access to specific models, while also offering credit and upgrade options.

Let's look at some specific examples. Google AI offers a $20 per month Pro subscription and a $249 per month Ultra subscription, with additional charges for Veo3 credits when users inevitably exceed their included amount. Additional credit packs start at $25 and extend up to $200. From what I understand, many users may spend as much on additional Veo credits as they do on the base subscription. This is a perfect example of how to allow revenue to grow alongside user engagement.

Krea's pricing model is also very interesting. They offer a monthly plan ranging from $10 to $60, based on expected usage and training jobs. If you exceed the included computing units, you can purchase additional credit packs for $5 to $40 (valid for 90 days). The beauty of this model lies in providing a reasonable entry price for light users while offering room for expansion for heavy users.

Grok takes this strategy to the extreme with its pricing: the SuperGrok plan is priced at $30 per month, while the SuperGrok Heavy plan costs $300 per month. The latter unlocks new models (Grok 4 Heavy), extended model access, longer memory, and new feature testing. This tenfold price difference is almost unimaginable in traditional consumer software but becomes reasonable in the AI era due to significant differences in user needs and value perception.

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

Moore mentioned that some consumer companies have achieved over 100% revenue retention solely through this pricing model, even without considering any enterprise expansion. This demonstrates the strength of this strategy. It not only addresses the churn issue in traditional consumer software but also creates an intrinsic growth mechanism.

The Golden Bridge from Consumer to Enterprise

Another significant 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 bringing AI tools into the workplace. In some companies, not being "AI-native" is now considered unacceptable. Any product with potential work applications—essentially anything that is not NSFW—should assume users will want to bring it to their teams, and they will pay significantly more when they can expense it.

The speed of this transition is remarkable to me. In the past, the shift from consumer to enterprise usually took years, requiring a lot of market education and sales efforts. However, the practicality of AI tools is so evident that users are spontaneously introducing them into the work environment. I have seen many cases where employees first purchase AI tools personally and then convince the company to buy the enterprise version for the whole team.

The shift from price-sensitive consumers to price-insensitive enterprise buyers has created a significant expansion opportunity. However, this requires fundamental sharing and collaboration features such as team folders, shared libraries, collaboration 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 differentials can be substantial. ChatGPT is a good example, although not widely considered a team product, its pricing highlights the difference: a personal subscription at $20 per month, while enterprise plans range from $25 to $60 per user. This 2-3x price differential is rare in traditional consumer software but has become common in the AI era.

I feel some companies even price their personal plans at breakeven or a slight loss to accelerate team adoption. Notion effectively employed this approach in 2020, offering unlimited free pages for individual users while charging aggressively for collaboration features, driving its most explosive growth phase. The logic behind this strategy is: subsidize individual use to build a user base, then monetize through enterprise features.

Let's look at some specific examples. Gamma's Plus plan at $8 per month is for watermark removal—a requirement for most enterprise use—and other functionalities. Then users 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 extra credits, viewer seats, consolidated billing, role-based access control, private deployments, and more. Cursor provides a $20 per month Pro plan and a $200 per month Ultra plan (with usage increased by 20x). Team users pay $40 per month for the Pro product, with organization-wide privacy mode, usage and management dashboards, consolidated billing, and SAML/SSO.

These features are crucial because they unlock enterprise-level ARPU (Average Revenue Per User) expansion. I believe that any consumer-grade AI company now missing out on an enterprise expansion path is missing a massive opportunity. Enterprise users not only pay higher fees but are also typically more stable with lower churn rates.

Investing in Enterprise Capability from Day One

Moore has put forth what seems like a counterintuitive but actually very wise suggestion: consumer-facing companies should now consider hiring a sales lead within the first one to two years of existence. I wholeheartedly agree with this perspective, even though it does go against the grain of traditional consumer product strategy.

An individual adoption approach can only take a product so far; ensuring broad organizational usage necessitates navigating enterprise procurement and closing high-value contracts. This requires specialized sales acumen rather than simply relying on the organic spread of the product. I have seen too many excellent consumer-facing AI products miss out on significant opportunities due to a lack of enterprise sales capability.

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

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

ElevenLabs is a prime example: the company started heavily consumer-focused but quickly built enterprise capabilities, adding HIPAA compliance to its voice and chat agents and positioning itself to serve healthcare and other regulated markets. This rapid enterprise transformation enabled them to capture high-value enterprise customers, rather than relying solely on consumer revenue.

I observed an interesting phenomenon: consumer-facing AI companies that invested in enterprise capabilities early on often were able to build stronger moats. Once an enterprise customer adopts a tool and integrates it into their workflow, the switching costs are high. This creates stronger customer stickiness and a more predictable revenue stream.

Additionally, enterprise customers provide valuable product feedback. Their needs are often more complex, driving the product towards more sophisticated directions. I have seen many consumer-facing AI products discover new product directions and feature demands through serving enterprise customers.

My Deep Dive into this Transformation

After carefully analyzing Moore's insights and my own observations, I believe that what we are witnessing is not just a realignment of business models but a restructuring of the entire software industry infrastructure. AI is not only altering the capabilities of products but also changing how value is created and captured.

What I find most interesting is that this transformation challenges our traditional assumptions about consumer-grade software. For a long time, people believed that consumer-grade software was naturally low-priced, high-churn, and hard to monetize. But the reality of the AI era shows that consumer-grade software can achieve enterprise-level revenue scale and growth rate. The implications of this shift are profound.

From a capital allocation perspective, this means that investors can now allocate more capital to consumer-grade AI companies earlier, as these companies can achieve meaningful revenue scale more quickly. Traditionally, consumer-grade software companies had 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 been thinking about the impact of this change on entrepreneurial strategy. Moore mentioned that many of the most important enterprise companies in the AI era may well have started with consumer-grade products. I think this is a very profound insight. The traditional B2B software startup path often involves a lot of market research, customer interviews, and sales cycles. However, starting with a consumer-grade path 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 is a strong signal of 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-grade and enterprise-grade markets were often separate, with different participants and strategies. However, in the AI era, these boundaries have become blurred. A product can compete in both markets simultaneously, creating new competitive advantages and challenges.

From a technological standpoint, I believe this dual nature of AI products (consumer-grade usability + enterprise-grade functionality) is driving a new standard 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. This balance is not easy to achieve, but companies that do it well will gain a significant competitive edge.

I have also been reflecting on the impact of this trend on existing enterprise software companies. Traditional enterprise software companies are now facing competition from AI companies that started on the consumer-grade end, and these newcomers often have better user experiences and faster iteration speeds. This may force the entire enterprise software industry to raise its product standards and user experience.

Finally, I believe this transformation also reflects a fundamental shift in how we work. Remote work, increased individual tool choice, and higher expectations for productivity tools are all blurring the lines between consumer-grade and enterprise-grade tools. AI is simply accelerating a trend that was already underway.

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 to be considered.

On the challenge front, I believe that competition will become more intense. As the path to success becomes clearer, more companies will try to follow the same strategy. Those able to establish strong differentiation and network effects will emerge as winners in long-term competition.

From a regulatory perspective, the rapid adoption of AI products in the enterprise environment may pose 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 it will also create new competitive barriers.

On the opportunity side, I see a vast space for innovation. Companies that can creatively combine consumer-grade usability with enterprise-level functionality will pave the way for 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 general-purpose tools.

I also see opportunities for network effects with data and AI models. As user numbers grow and usage deepens, AI products can become more intelligent and personalized. This data-driven improvement can create a strong competitive advantage, as new entrants will find it challenging to replicate this accumulated intelligence.

From an investment perspective, I believe this trend will continue to attract significant capital. However, investors need to more astutely identify companies with genuinely sustainable competitive advantages, rather than just those with rapid short-term growth. The key will be understanding which companies can build true moats, not just leverage early market opportunities.

Ultimately, I believe the "Great Expansion" described by Moore is just the beginning of the AI revolution. We are redefining the essence of software—from a tool to an intelligent partner, from a feature to an outcome. Companies that can seize this transformation and successfully execute on it will establish the next generation of tech giants. This is not just 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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