The AI Economics Paradox: When Growth Increases Costs

?? Hello hello! Welcome. Thanks to 3,507 and counting subscribers to my FREE LinkedIn newsletter, UNLOCK.

If we haven't connected yet, please, hit the subscribe button or hit the bell on my LinkedIn profile Mahdi Barkhordari, share this with anyone you think my next article will be valuable for.

Now let's get started.

I've been playing with AI and building products and apps during the last two years. Recently, while developing an iOS app with a subscription model in a competitive space, I bumped into this notion of the economy of scale paradox in using AI as an integral part of a product. I was mind blown! Why hadn't I thought of this before?

The Growing Role of AI APIs in Product Development

As artificial intelligence (AI) takes a more prominent role in mobile applications, the majority of developers resort to third-party APIs to implement advanced features such as natural language processing, image recognition and classification, Optical Character Recognition (OCR), deep research, and recommendations etc. Third-party APIs are easier to implement and take less internal expertise, bringing AI within everyone's reach more than ever. AI has been here for a long time but this is a new era; The age of "Democratizing AI". But as AI penetrates to the core of an app's value, one concern becomes unavoidable: the price of running AI. This increase in cost occurs whether or not AI has a positive effect on classic metrics of success such as engagement or user retention.

AI APIs have become a go-to solution for developers looking to integrate cutting-edge features without building models from scratch. Their appeal lies in several key advantages:

• Ease of Integration: APIs provide pre-built solutions that can be quickly integrated into apps, reducing development time and complexity.
• Scalability: API providers manage the backend infrastructure, allowing apps to scale without requiring significant investment in hardware or engineering resources.
• Continuous Improvement: Many API providers regularly update their services with the latest advancements in AI, ensuring developers always have access to state-of-the-art capabilities.

These benefits have made AI APIs indispensable for developers seeking to enhance their apps with minimal overhead.

Like always, I introduce a podcast or a newsletter in every article I share. This week, I'm sharing an article from Lenny's newsletter. The author believes that AI will have the most profound impact on the high-level (and historically most valued) skills of product management: developing a strategy, crafting a vision, identifying new opportunities, and setting goals. Furthermore, soft skills like product sense, communication, creativity, and being the glue that enables a team to operate at their very best will become even more important (and a differentiator among companies). Read more by clicking below.

The Economic Reality: Rising Costs as AI becomes Integral

As AI becomes central to the functionality of an app—whether it's driving a recommendation engine, content moderation system, or voice assistant—the costs necessarily escalate that are associated with it. Unlike standard app features that may have one-time or periodic costs, API-based AI services are available in usage-based cost structures. This builds a direct correlation between frequency of usage of AI features and operational cost.

Key cost drivers include:

1. Usage-Based Pricing Models: Most AI APIs charge per request or per transaction. As users interact more frequently with AI-driven features, the number of API calls increases, driving up costs.
2. Scaling User Bases: Even if user engagement or retention metrics remain constant, a growing user base means more interactions with AI features—and thus higher API bills.
3. Complexity of Features: Advanced features such as real-time processing or multi-modal AI (e.g., combining text and image analysis) often require more expensive API endpoints.

This economic reality creates a paradox for developers: even if AI doesn’t directly improve key success metrics like retention or engagement, its operational costs will still grow as it becomes an integral part of the app’s offering.

The SaaS vs. AI Paradox: The Inversion of Economies of Scale

Unlike traditional SaaS platforms where economies of scale decrease the cost as more users are added, AI-based applications might experience the opposite situation. In SaaS, the more users come on the platform, the more fixed costs like infrastructure, engineering, and support are distributed over a larger base, decreasing the cost per user.

However, in AI-powered apps, and especially API-dependent models, the cost per user does not decrease as the business scales. In fact, if left uncontrolled, it increases linearly or even exponentially, propelled by increased API usage. What this means is that a popular AI-powered app must actively strive to control its API costs—or profitability will be eroded even as user usage increases.

App developers must rethink traditional metrics like retention and engagement in light of AI cost structures. If an AI-powered feature is driving high engagement but also incurring unsustainable costs, it may not be a net benefit. Instead, developers need to weigh engagement against cost-efficiency and consider strategies such as:

• Encouraging user behavior that aligns with lower-cost AI operations.
• Incentivizing premium users to cover AI expenses.
• Optimizing AI interactions to minimize unnecessary API calls.

However, hopefully as we go forward, we might see a significant decrease in the costs of AI.

The Trend of Decreasing AI Costs

While API usage involves costs, the broader trend is toward decreasing AI costs, making this approach even more attractive for developers. Several factors are driving this trend:

Advancements in Hardware

• More efficient processors and specialized AI chips (e.g., TPUs) reduce the computational cost of running AI models.
• This efficiency trickles down to API providers, who can offer lower prices.

Increased Competition

• As more providers enter the AI API market (e.g., AWS, Google, Microsoft, OpenAI), competition drives down prices.
• Providers often reduce rates or offer more generous free tiers to attract customers.

Economies of Scale in Cloud Computing

• The cost of cloud computing resources has been steadily declining due to technological improvements and increased adoption.
• According to a 2023 report by Canalys, cloud service prices have dropped by an average of 20% over the past five years, benefiting both API providers and developers.

Open-Source Models

• The availability of open-source AI models (e.g., Hugging Face’s Transformers) allows developers to explore low-cost alternatives, though APIs remain more convenient for plug-and-play integration.

These trends suggest that while API costs are a concern today, they are likely to become more affordable over time. Developers can expect pricing to continue trending downward, making AI integration in mobile apps increasingly accessible.

Unlock your startup potential & scale

Trust us to help you build, grow, or scale your startup. Need help with?

? Pitch Deck

? Business Model

? iOS App Development

? Scaling Up

? MVP for Your Startup

?? Book a FREE Discovery Session! Let’s talk about your startup and how we can help. Click here to schedule.

Strategies to Manage API Costs

To address this challenge, entrepreneurs and businesses must adopt proactive strategies to manage the rising costs of relying on AI APIs:

Optimize API Usage

• Implement caching mechanisms to store frequently requested data and reduce redundant API calls.
• Batch requests where possible to minimize the number of individual transactions

Monitor and Analyze Costs

• Use analytics tools to track API usage patterns and identify areas where costs can be reduced.
• Regularly review billing data from API providers to ensure alignment with budget expectations.

Negotiate Pricing Models

• As your app scales, negotiate volume-based discounts or custom pricing tiers with your API provider.
• Consider multi-provider strategies to avoid over-reliance on a single vendor’s pricing structure.

Focus on High-Impact Features

• Prioritize AI functionalities that deliver the most value relative to their cost.
• Evaluate whether all interactions need real-time processing or if some can be handled asynchronously at a lower cost.

Explore Alternatives

• For high-cost operations, consider hybrid approaches where critical features use APIs while less frequent tasks are handled by lightweight in-house solutions.
• Investigate open-source alternatives for certain functionalities to reduce dependency on commercial APIs.

Conclusion

The application of AI through third-party APIs has revolutionized product, software and mobile app development by enabling the speedy deployment of sophisticated features. And this is just the start. However, as these features become the center of an app's usefulness, rising cost of operations is inevitable even when traditional metrics of success like retention or engagement are not impacted. Unlike SaaS business models in which cost per user will decrease over time naturally, AI-based apps are exposed to the opposite effect where costs increase with engagement unless addressed actively.

By adopting cost-conscious strategies such as usage optimization, model price negotiation, and high-impact feature prioritization, developers will be in a position to effectively navigate this economic paradox. While broader market trends indicate diminishing AI prices in the future, the challenge is in finding an innovation-versus-cost balance. Eventually, the developers who will grapple with AI integration strategy will be better placed to leverage its power while holding cost disciplines.