Why has this advertising placement platform become a "100-billion-yuan" catfish monopolizing the market?

A company that has been listed for four years and seen its stock price soar nearly 40 - fold, outshining even NVIDIA, whose growth exceeded 200%. Many people might instinctively categorize it as a "speculative stock".

However, when you look at its financial report data, you'll realize that this isn't just a bubble: with an annual profit exceeding $4 billion and a net income year - on - year growth of over 300%. Given its profitability, the company's stock price performance seems quite reasonable.

This company, AppLovin, isn't the kind of well - known star company we're familiar with. There are no sensational product launches, no "explosive" or "game - changing" AI miracles on the hot search, and no overnight success stories of its founders. Yet, in the global advertising market, it has become a powerful and low - key profit - making machine.

AppLovin doesn't develop large models, but it has built a leading global mobile advertising placement platform with its self - developed advertising engine system, Axon. It isn't a content platform, but it understands user preferences and behaviors better than social networks. Its customers aren't end - users but millions of app developers and advertisers worldwide. And what it sells isn't traffic but the ability to make every penny yield real returns.

Compared with internet giants deeply entrenched in the advertising market, AppLovin is more like a business infrastructure deeply embedded in the system and operating silently: with a calm belief in technology, extreme engineering execution, and continuous updates to every aspect of the advertising system, it has ultimately reshaped the collaboration mode among advertisers, developers, and the platform.

Its core philosophy can be summed up in one sentence: "Challenge the status quo."

Tearing Open a Crack: Building the Foundation of Trust for Advertisers

In an industry where platform rules are already set in stone, the ability to "rewrite" is the rarest and most capable of changing the industry's landscape.

In the highly mature mobile advertising industry, AppLovin, a mobile advertising aggregation platform connecting small and medium - sized app developers with advertisers, has torn open an unassuming crack: besides creating content and building platforms, it also respects advertising effectiveness.

Since its establishment in 2012, the monetization of its own apps has always been the major source of AppLovin's revenue, thanks to the well - developed developer ecosystem it built in the past. Another part of the revenue comes from advertising matchmaking services, recorded as a difference or commission.

The change in the revenue structure occurred in 2023. With the launch of the AXON 2.0 advertising engine, the efficiency of the advertising alliance business increased significantly. More and more customers were willing to let the system handle traffic purchases automatically, so the revenue from this part climbed rapidly and now accounts for 70% of the total revenue.

This transformation is somewhat similar to the path change when Apple launched In - App Purchase (IAP, i.e., purchasing apps within the app store) on the App Store in 2011. IAP isn't just a simple payment interface; it changed the business model of apps as products, turning one - time purchases into sustainable operations and transforming "content" into "services".

In the highly mature and monopolized market of performance advertising, what AppLovin does isn't just improving conversion efficiency. It has transformed the mobile app advertising placement platform from "manual strategy adjustment" to "system automation". Instead of challenging the traffic scale or model complexity, it chose another breakthrough point: by providing more accurate predictions, more stable conversions, and lower cold - start costs, it makes customers' money "go further".

Customers' evaluations are straightforward: "Meta can help you find people who might click on ads, while AppLovin can help you find people who are willing to spend money, and a lot of it." Behind this is the difference in the thinking of the two models.

On AppLovin, the decision - making path is very simple and direct. Its ROAS (Return On Advertising Spend) model is extremely accurate, almost approaching the real value (≈1). For advertisers managing million - dollar budgets daily, this slight difference means they can confidently hand over decision - making power to the system. Instead of relying on manual strategy adjustment, they let the model automatically "target the right people".

What AppLovin pursues isn't "more clicks" or "faster conversions" but "better value for money". Industry insiders explain this difference vividly: "On other platforms, spending $10 might result in 5 conversions but only generate a total purchase of $20. On AppLovin, the same amount of money might only lead to 2 conversions, but the total value of these two users is $100." The key isn't how many people you target but who you target.

Its accurate value judgment is reflected not only in user quality but also in the understanding of the time dimension. The industry usually uses a 7 - day payback period, but AppLovin extends the window to 28 days to capture the behavior of those "late - arriving but high - value" users. In reality, many users make decisions after 1 - 2 weeks. If only short - term conversions are considered, the model might misjudge them as "ineffective" and miss out on potential users.

Even in the most difficult cold - start phase, AppLovin can quickly complete modeling and placement. Different from other platforms that require advertisers to manually configure a large number of parameters, it only needs basic information, and the system can quickly learn and achieve precise targeting. Behind all this is its global traffic pool with over 1.4 billion daily active users and a deeper understanding of user behavior.

The stability of the model and the predictability of the system are quietly reshaping the distribution structure of the advertising ecosystem.

Financial report data shows that AppLovin doesn't only serve large - scale customers but also a large number of small and medium - sized developers who may not have the ability to build their own models or understand attribution optimization. However, they can also achieve advertising monetization on AppLovin.

Behind this is the change in the traffic structure. Beyond the mainstream traffic entrances dominated by Meta (Facebook, Instagram) and Google (Search, YouTube), there are tens of thousands of long - tail traffic sources in the mobile advertising market - apps from mini - games, utility tools, and niche communities. Each of them is small, but together they form a "dark web" of traffic.

AppLovin's greatest breakthrough in the past two years has been to gain a foothold in this fragmented market. Instead of challenging the total traffic of the giants, it has transformed these previously "unworthy of investment" long - tail apps into usable traffic assets through its system capabilities.

When model capabilities change from being a "barrier" to a "service", advertising monetization is no longer a technical privilege exclusive to large companies but has become a common ability for developers. More and more developers are flocking to the AppLovin platform, forming a self - driving cycle: more advertisers place ads, bringing more abundant traffic channels, better conversion results, and users are willing to pay, which further attracts more advertisers to join - the platform efficiency feeds back into the ecosystem, and the ecosystem in turn amplifies the platform's value.

AppLovin didn't try to replicate the traffic moats of the giants. Instead, it started from redefining advertising value and challenged the industry's default perception of "measurability of advertising effectiveness". They didn't compete for attention but reconstructed the "trust path".

In the highly competitive performance advertising market that has entered the deep - water zone, AppLovin didn't follow in the footsteps of its competitors. Instead, it opened up a neglected exit for advertisers and developers.

Technology That Doesn't Chase Trends Can Go Further

The fluctuation of the stock price is just a reflection of the company's performance, and the performance itself is merely a projection of technological accumulation. At AppLovin, this accumulation has a concrete name: AXON. In the Q1 2025 earnings conference call, CEO Adam Foroughi called it "the world's best advertising AI model".

Against the backdrop of the large - model wave from 2022 to 2023, almost all companies were trying to reshape their businesses with AI. There were many loud slogans, but few successful implementations. After the AI wave, most companies can be divided into two types: those with an AI gene from the start and those that were reshaped under the impact.

AppLovin is a rare company that lies between the two. AI is more of an enhancement rather than a complete overhaul.

Behind this is AppLovin's systematic accumulation in advertising technology over the years.

After the acquisition of the attribution platform Adjust, there were discussions in the outside world about "data sources". In fact, as one of the mainstream platforms in the industry, Adjust has always maintained a neutral stance and independent operation towards all advertisers. The use of its data still requires customer authorization. Currently, there are three major attribution platforms globally, and advertisers can fully verify and choose among different platforms.

The core of AppLovin's model capabilities doesn't come from a specific data entry point but from the algorithm's continuous optimization ability for ROAS.

Although it doesn't wave the "large - model" flag or claim to be an AI company, AppLovin is actually using AI to deconstruct and reconstruct the question of "how to build an advertising system" from the ground up.

In terms of the technical path, they don't chase the latest algorithms. Instead, they focus on business goals, use multiple models, and complete system integration through a structured combination. It isn't a "one - size - fits - all" large model, nor is it a simple stacking of small modules. It's an engineering system where "every layer works correctly".

All the easily overlooked details - label definition, cold - start sample processing, and delayed feedback modeling strategies - are carefully polished. Even if they use the same - generation model architecture as companies like Google, the control of details can vary greatly. AppLovin still insists on building its own training process, attribution logic, and loss - function optimization methods, never copying existing engineering routines.

One of their principles is not to over - optimize short - term gains and not to incorporate too many rule engines, keeping the model "clean". This means that the system can better restore the real causal relationship: when you make a change, the result is more likely to be correct. This design that "preserves the causal rate" is the foundation for the long - term effectiveness of the AppLovin model.

For example, when setting model goals, they focus more on the accurate return of ROI (Return on Investment) rather than the increase in the click - through rate on the surface. In data sampling, AppLovin tries to restore the real distribution of users during advertising placement to avoid model bias. In label design, it also considers the time span and activity level of user behavior to determine which users truly have long - term value and which are just short - term fluctuations.

AppLovin doesn't tell the trendy "large - model" story. It doesn't conduct basic research on large - language models but focuses on the application of LLM (Large - Language Model) and generative AI. It doesn't focus on boasting about "how powerful AI is". Instead, it cares more about whether the problem can be solved, whether the system can operate stably, and whether customers can ultimately make money.

This is the fundamental difference between AppLovin and most "AI Storytelling" companies. It doesn't impress the market with the vision of a large model but builds certainty layer by layer through engineering. The polishing of each link isn't for show but to build a system that advertisers can trust in the long run.

Here, technology isn't emphasized as "innovation" but is reflected as a low - key yet extreme engineering philosophy in every aspect of architecture selection, training process, data governance, and system feedback.

How to Make "Challenging the Status Quo" a Team Consensus?

We always try to explain a company's success with one or two secrets, but the truly long - lasting capabilities often lie in those underlying judgments that are difficult to replicate. It's more of a natural result of long - term value - based precipitation rather than a single popular product or technological breakthrough.

Since its establishment in 2012, AppLovin has always been challenging the status quo.

At AppLovin, the company culture isn't often talked about. There are no obvious value slogans or written behavior guidelines in the handbook. However, some behavior patterns have remained the same for years.

For example, once, before boarding a cruise ship for a vacation with his family, the VP of Engineering had a sudden inspiration during the short 20 - minute waiting in the queue and came up with a new idea for the system's underlying architecture. At that moment, he immediately entered a state of flow. After boarding the ship, while others went to the deck or for sightseeing, he stayed in the room alone writing code. He hardly left the cabin during the vacation and took advantage of the isolated time on the ship to write the core prototype of the system. This dynamic configuration system is still one of the widely used infrastructures at AppLovin today.

Customers have mentioned that the response speed of the technology and support teams has always been very fast. Once a problem is raised, there's almost no need to urge. In contrast, it takes several days for the other two large companies to respond. This sense of presence doesn't come from KPIs but from a commitment to the product and its effectiveness.

There are few meetings at AppLovin. The foundation of the team's operation isn't processes but the independent judgment of each person within their respective responsibilities. If you want to lead a team, you need to be the most technically proficient person in the team - this standard applies to everyone, including the CTO and Vice Presicent of Engineering, who still write code to this day.

AppLovin employees summarize that most people here are smart, hard - working, and don't care much about conforming to the industry mainstream. Many decisions aren't made through discussions but through actions that form a consensus.

These details are the daily responses driven by engineering intuition: identify problems, find solutions, and launch products.

This culture didn't exist from the start but was gradually formed as the company evolved.

When AppLovin was founded in 2012, it was just a technical intermediary that helped mobile - game developers buy traffic and distribute it. However, they soon realized that the real barrier wasn't in traffic but in optimization capabilities