a16z's Latest Insight: Consumer AI Companies Will Redefine the Enterprise Software Market

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

Source: Olivia Moore, a16z Partner
Original collation and translation: Leo, Deep Thinking Circle

Have you ever wondered why AI consumer products that have emerged in the past two years can grow from zero to millions of users and achieve annual revenues exceeding 100 million USD in less than two years? Such growth speed was almost unimaginable before AI. On the surface, this is because distribution is faster and the average revenue per user is higher. But I have found a deeper change that most people overlook: AI has completely transformed the revenue retention model of consumer software.

Recently, I read an analytical article by a16z partner Olivia Moore, "The Great Expansion: A New Era of Consumer Software," in which she refers to this phenomenon as the "Great Expansion." I think she has captured a very critical trend. After thinking deeply about this viewpoint, I realized that this is not just an adjustment of business models, but a fundamental change in the rules of the game for the entire consumer software industry. We are witnessing a historic turning point: consumer software companies no longer need to fight user churn, but can achieve growth by relying on the continuous expansion of user value. The boundaries between the consumer and enterprise markets are gradually blurring in a certain sense.

The impact of this change is enormous. Traditional consumer software companies have to spend a lot of energy and money every year to replace lost users, just to maintain the status quo. Now, companies that have seized the AI opportunity find that each batch of users not only does not lose value, but actually contributes more revenue over time. It's like going from a leaky bucket to an ever-expanding balloon—the growth model is completely different.

From this perspective, I personally believe this is a huge opportunity for companies going global, because consumer products can leverage PLG (Product-Led Growth) to achieve growth and revenue, perfectly avoiding the shortcoming that Chinese teams find it difficult to break into the overseas SLG sector. Although it is aimed at the enterprise market, the entire growth model is similar to that of C-end products. I personally resonate with this; my own project, a B-end Vibe coding product fully oriented towards enterprises, has been online for a month and has achieved good data feedback through PLG-driven customer acquisition and growth.

The Fundamental Flaw of Traditional Models

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, mainly used for social apps, directly tied to usage, so the value per user is usually flat over time. Instagram, TikTok, and Snapchat are representatives of this model. The second is the single-tier subscription model, where all paying users pay the same fixed fee monthly or annually for product access. Duolingo, Calm, and YouTube Premium use this approach.

In both models, revenue retention is almost always below 100%. A certain proportion of users churn every year, and those who stay continue to pay the same amount. For consumer subscription products, retaining 30-40% of users and revenue by the end of the first year is considered "best practice." These numbers sound discouraging.

I have always felt that this model has a fundamental structural flaw: it creates a basic constraint where companies must constantly replace lost revenue just to maintain growth, let alone expand. Imagine if you have a leaky bucket—not only do you have to keep adding water to maintain the water level, but you have to add more than what leaks out to make the water level rise. This is the dilemma faced by traditional consumer software companies: they are trapped in a never-ending cycle of acquisition-churn-reacquisition.

The problem with this model is not just numerical; it also affects the company’s overall strategy and resource allocation. Most energy is spent acquiring new users to make up for churn, rather than deepening relationships with existing users or increasing product value. That’s why we see many consumer apps frantically push notifications and use various tactics to increase user stickiness—because they know that once users stop using the product, revenue disappears immediately.

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

AI Era: Rewriting the Rules of the Game

The advent of AI has completely changed this game. Moore calls this change the "Great Expansion," and I think the name is very apt. The fastest-growing consumer AI companies are now seeing revenue retention rates exceeding 100%, which was 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 unprecedented speed, 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 not; either subscribe or cancel. But in AI products, user engagement and value contribution grow progressively. 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 needs continue to expand.

The trajectory of this difference is dramatic. Moore mentions that with a 50% revenue retention rate, a company must replace half its user base each year just to stay flat. But with over 100% retention, each user cohort is expanding, and growth compounds on growth. This is not just a numerical improvement—it represents an entirely 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 more valuable the product becomes to them. This creates a positive feedback loop: more use leads to more value, more value leads to more use and higher willingness to pay.

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

Ingenious Pricing Architecture Design

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

I think there’s an important lesson here from the gaming industry. Game companies have long derived most of their revenue from high-spending "whales." 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’s look at some specific examples. Google AI offers a $20/month Pro subscription and a $249/month Ultra subscription, and when users (inevitably) exceed their included quota, they are charged extra for Veo3 credits. Extra credit packs start at $25 and go up to $200. From what I understand, many users spend as much on extra Veo credits as on the base subscription. This is a perfect example of how revenue can grow with user engagement.

Krea’s model is also interesting—they offer plans from $10 to $60 per month based on expected usage and training jobs, and if you exceed the included compute units, you can buy extra credit packs for $5 to $40 (valid for 90 days). The beauty of this model is that it provides a reasonable entry price for light users and room for expansion for heavy users.

Grok’s pricing takes this strategy to the extreme: the SuperGrok plan is $30/month, while SuperGrok Heavy is $300/month, unlocking new models (Grok 4 Heavy), extended model access, longer memory, and new feature testing. Such a 10x price difference is almost unimaginable in traditional consumer software, but in the AI era it makes sense, as user needs and value perceptions vary greatly.

I think the success of these models lies in recognizing the diversity and dynamism of user value. Not all users have the same needs or ability to pay, and even the same user’s needs 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 with this pricing model alone, even before considering any enterprise expansion. This shows the power of this strategy. It not only solves the churn problem of traditional consumer software but also creates an intrinsic growth mechanism.

The Golden Bridge from Consumer to Enterprise

Another important trend I’ve 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 be "AI-native" is now considered unacceptable. Any product with potential work applications—basically anything not NSFW—should assume users will want to bring it to their teams, and when they can get reimbursed, they will pay significantly more.

The speed of this shift impresses me. In the past, the transition from consumer to enterprise typically took years and required extensive market education and sales efforts. But the practicality of AI tools is so obvious that users spontaneously introduce them into work environments. I’ve seen many cases where employees first purchase AI tools personally, then persuade the company to buy the enterprise version for the whole team.

The shift from price-sensitive consumers to price-insensitive enterprise buyers creates huge expansion opportunities. But this requires basic sharing and collaboration features, such as team folders, shared libraries, collaborative canvases, authentication, and security. I believe these features are now essential for any consumer AI product with enterprise potential.

With these features, pricing differences can be huge. ChatGPT is a good example—even though it’s not widely regarded as a team product, its pricing highlights the difference: personal subscriptions are $20/month, while enterprise plans range from $25 to $60 per user per month. Such a 2-3x price difference is rare in traditional consumer software, but common in the AI era.

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

Let’s look at some specific examples. Gamma’s Plus plan is $8/month to remove watermarks—a requirement for most enterprise use—plus other features. Then users pay for each collaborator added to their workspace. This model cleverly leverages enterprises’ need for a professional appearance.

Replit offers a $20/month plan for Core users. Team plans start at $35/month and include extra credits, viewer seats, centralized billing, role-based access control, private deployments, and more. Cursor offers a $20/month Pro plan and a $200/month Ultra plan (20x more usage). Team users pay $40/month for the Pro product, with organization-wide privacy mode, 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 any consumer AI company not considering an enterprise expansion path now is missing a huge opportunity. Enterprise users not only pay higher fees, but are usually more stable and have 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 head of sales within one to two years of founding. I completely agree with this view, even though it does run counter to traditional consumer product strategies.

Personal adoption can only take a product so far; ensuring widespread organizational use requires navigating enterprise procurement and closing high-value contracts. This requires professional sales capabilities, not simply relying on the product’s natural spread. I’ve seen too many excellent consumer AI products miss major 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 in 2025, such delays are no longer feasible. The pace of enterprise AI adoption means that if you delay enterprise features, competitors will capture the opportunity instead. This competitive pressure is greatly accelerated in the AI era, as the market changes faster than ever before.

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

ElevenLabs is a good example: the company started with heavy consumer usage but quickly built enterprise capabilities, adding HIPAA compliance to its voice and conversational 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’ve observed an interesting phenomenon: consumer AI companies that invest in enterprise capabilities early tend to build stronger moats. Once enterprise customers adopt a tool and integrate it into their workflows, the switching cost is high. This creates stronger customer stickiness and more predictable revenue streams.

Additionally, enterprise customers provide valuable product feedback. Their needs are often more complex, driving the product in more advanced directions. I’ve seen many consumer AI products discover new product directions and feature needs by serving enterprise customers.

My Deep Thoughts on This Transformation

After carefully analyzing Moore’s views and my own observations, I believe what we are witnessing is not just a business model adjustment, but a reconstruction of the entire software industry’s infrastructure. AI not only changes product capabilities, but also the way 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, people believed consumer software was inherently low-priced, high-churn, and difficult to monetize. But the reality of the AI era shows that consumer software can achieve enterprise-level revenue scale and growth rates. The implications of this shift are profound.

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

I’ve also thought about the impact of this change on entrepreneurial strategy. Moore mentions that many of the most important enterprise companies of the AI era may start as consumer products. I think this is a very profound insight. The traditional B2B software startup path typically involves extensive market research, customer interviews, and long sales cycles. Starting from the consumer side 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’s 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’ve also noticed an interesting change in competitive dynamics. In the traditional software era, consumer and enterprise markets were usually separate, with different players and strategies. But in the AI era, these boundaries are blurring. 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-level ease of use + enterprise-level features) is driving new standards in product design and development. Products need to be simple enough for individual users to get started easily, yet powerful and secure enough to meet enterprise needs. This balance is not easy to achieve, but companies that do it well will gain a huge competitive advantage.

I’ve also thought about the impact of this trend on existing enterprise software companies. Traditional enterprise software companies now face competition from AI companies that started on the consumer side, which 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 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 blurring the lines between consumer and enterprise tools. AI has only accelerated this already 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 to watch for.

On the challenge side, I think competition will become more intense. As the path to success becomes clearer, more companies will try to follow the same strategy. Those able to build strong differentiation and network effects will win in the long run.

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

On the opportunity side, I see huge room for innovation. Companies that can creatively combine consumer-level ease of use with enterprise-level features will open up new market categories. I also believe there are great opportunities for vertical AI tools—deep optimization for specific industries or use cases may be more valuable than general-purpose 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 it is difficult for new entrants to replicate this accumulated intelligence.

From an investment perspective, I believe this trend will continue to attract large amounts of capital. But investors need to be more discerning in identifying companies with truly sustainable competitive advantages, not just those with rapid short-term growth. The key will be understanding which companies can build real moats, not just exploit early market opportunities.

Ultimately, I believe the "Great Expansion" described by Moore is only 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 build the next generation of tech giants. This is not just business model innovation, but a reimagining of the relationship between people and technology. We are in an exciting era where software is becoming smarter, more useful, and more indispensable.

Original Link