Generative AI is taking the world by storm, but companies’ success may depend on one key factor: the ability to keep prices low. By looking at the cloud infrastructure wars of the past, we can uncover relevant lessons that businesses should consider when navigating their pricing strategies.

With this post, we aim to provide a comparison of the market pressures faced by generative AI companies, drawing parallels with cloud providers and how those pressures will influence their future decisions:

• Part I delves into the impact of pricing wars on cost-cutting measures in cloud infrastructure and how it’s relevant today.
• Part II focuses on the current challenges facing generative AI companies and the strategies they should employ to maintain their competitiveness.

No matter where you stand on this issue, there’s no denying that generative AI companies must make smart decisions in order to succeed in this market — the kind of decisions that will have lasting implications for the industry.

Let’s dive in.

Generative AI companies are competing fiercely in a business landscape where the stakes have not been this high for a long time. Just like the cloud computing industry in the early 2000s, there is a “prisoner’s dilemma” currently in play: each company vies for the largest piece of the market share, market price and margins are pushed down as competition increases, which can prevent any one competitor from maximizing profits.

Let’s take a closer look at how cloud infrastructure wars of the past can provide insight on what we can expect from Generative AI companies going forward.

The Cloud War: What Happened and What We Learned

The cloud computing war of the last decade pitted tech giants like Microsoft, Amazon, Google and IBM against each other and gave companies countless options to choose from in terms of infrastructure, cost, features and more. The result of this competition was the ever-decreasing cost of cloud services as the giants pushed each other to offer better deals to attract customers. This created a prisoner’s dilemma-like situation in which no company wanted to be the first to raise prices due to fear of losing market share.

In addition, clients benefited from increased transparency as it became easy for companies to compare cloud providers and identify the most appropriate service for their needs. This also led to open source cloud-native projects such as Kubernetes that enabled customers to easily move services between clouds with minimal effort.

A. The Background of Cloud Wars

The cloud computing industry began to take shape in the early 2000s as companies realized the benefits of shifting their IT infrastructure to the cloud. AWS, launched by Amazon in 2006, was one of the first major players in the market, offering cloud services such as storage, computing power, and databases. Other players in the market at that time included Microsoft with its Azure platform and Google with its Google Cloud Platform (GCP).

As the market for cloud services grew, competition among the key players intensified, with each provider looking to differentiate itself and capture market share. Pricing was one of the areas where competition was particularly fierce. AWS, Azure, and GCP were engaged in a price war, each provider trying to undercut the other in terms of pricing.

This resulted in significant price cuts over the years, with AWS reducing the price of its EC2 instances by more than 80% since its launch in 2006 and Azure reducing its prices by up to 90% since its launch in 2010. This led to a massive increase in the adoption of cloud services, with businesses of all sizes migrating their IT infrastructure to the cloud to take advantage of its benefits.

The cloud wars were a pivotal moment in the development of the cloud computing industry, resulting in aggressive pricing strategies and the development of new and innovative services, ultimately making cloud services more affordable and accessible for businesses of all sizes.

B. What We Learned From the Outcome

A few lessons can be drawn from what happened in the cloud, especially from big players. For example:

• AWS had a head start in the market and was able to offer a wide range of cloud services to customers. In early 2013, they announced the launch of several new services, including a data warehousing service and a big data analytics platform. This allowed them to stay ahead of the curve and continue to attract customers who were looking for the latest and greatest cloud technologies.
• Azure had strong ties to the Microsoft ecosystem and was able to offer seamless integration with other Microsoft products.
• GCP, on the other hand, had a strong reputation for innovation and was able to offer new technologies. They also had a reputation for providing excellent technical support to its customers, which helped to build strong relationships and foster loyalty.

To stay relevant in the industry, it was imperative for each of these companies to establish a distinctive brand identity that set them appart from their competitors. They had to constantly be thinking about how they could differentiate themselves from their competitors and provide unique value to their customers.

So, whether they’re using the latest tech, coming up with creative ways to use AI, or just providing exceptional customer service, GenAI companies need to find special ways to stand out if they want to stay ahead of the competition. It’s all about thinking strategically and finding that special something that sets you apart from the rest.

GenAI: Moving Towards an Unprecedented Price War

A lot of Generative AI companies will face the same dilemma that cloud infrastructure companies faced years ago. Prices for AI algorithms and products are being pushed down aggressively, as larger companies invest heavily in natural language processing, computer vision, and various other related technologies.

With this increased competition, it has become harder for smaller firms to compete in terms of cost.

To make matters worse, many larger firms are willing to offer generous discounts and bundled services to larger customers, creating an unlevel playing field. This leaves startups and other small firms competing from a position of weakness, as they lack the resources necessary to offer similar deals. As a result, many GenAI companies will be forced to lower their prices or risk losing out on potential business opportunities.

Moreover, the cost of developing new AI algorithms is increasing exponentially, making it difficult for smaller firms to remain competitive. This is especially true given the increased complexity of NLP and CV algorithms, which require significant investment in research and development before they can be used commercially. For example, ImageNet-trained models typically require tens of thousands of images for training — an expensive endeavor for smaller firms with limited resources.

A. How Venture Capital Is Driving the Price War

The venture capitalists are driving the price war in GenAI, and it’s not necessarily a bad thing (for customers). Thanks to funding, GenAI companies have additional resources and manpower to scale quickly and invest heavily in research and development. This drives top players to be more competitive with their pricing, which can lead to lower prices for customers.

But Venture Capital comes with its own risks. Many startups rely heavily on raising capital to continue their operations, which will create instability as investors may become wary of investing in a highly competitive market.

Investors may fear that their investments will not yield any returns as GenAI companies continually lower their prices, or that their investments will be wasted as these companies struggle to create a sustainable income stream.

VCs may find difficult to predict which company will emerge as a leader in the industry due to its nascent stage of development. More than two-thirds of generative AI companies have not yet raised a Series A round, indicating a nascent market and a lack of clarity on where the true value of the technology lies.

B. What Startups Could Do to Compete

As with the Cloud Infrastructure wars, Generative AI startups need to be aware of the prisoner’s dilemma faced by competitors. The ability to make swift decisions and maintain agility will be critical to stay ahead of the competition.

There are several steps startups can take to gain a competitive edge in this industry:

1. Invest in scalable technologies that can handle large data sets: Generative AI start-ups should invest in scalable technologies that can handle large data sets. This will allow them to process and analyze vast amounts of data quickly and efficiently, giving them an advantage over competitors who are unable to handle large data sets.
2. Utilize open source technology whenever possible: Open source technology are a valuable resource for generative AI start-ups, enabling fast iteration in the product. But the core techno/data sets should be proprietary. Arbitrage between what’s proprietary/what’s not will be crucial.
3. Take a customer-centric approach, providing support for current customers as well as creating new markets: This means understanding customer needs, providing excellent customer service to build strong relationships and brand loyalty and finding use cases that go beyond the typical “xxx empowered by genAI” thing.
4. Employ a lean system for data science development and deployment: Developing a flexible and adaptable approach to data science, using agile methodologies and continuous integration and deployment to quickly iterate and improve products and services.
5. Establish clear pricing models and marketing plans to ensure maximum profit potential: Have a deep understanding the market and competition, and develop pricing and marketing strategies that effectively position products and services to attract and retain your customers.
6. Unique data sets for better outcomes: Consider the uniqueness and diversity of data sets generated by their algorithms as a crucial factor. The capability of producing high-quality and varied data sets can distinguish startups in the genAI market and provide more dependable outcomes for their customers.
7. Tackle a very niche market and develop it: Carve out a niche market by targeting specific industries or use cases, like vertical software did. Develop a deeper understanding of customer needs, provide tailored solutions and build strong brand recognition within that specific market.

Few companies that are leveraging these strategies: Adept, PhotoRoom, Lalaland.ai, Raidium, Mini Studio, Dust.tt, Replicate.ai, Anthropic, Eleuther.ai

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At Ovni, we invest in pre-seed stages and partner with founders who have global ambitions from day one. If you are a founder in this space or know someone who is, feel free to contact me at thomas@ovni.vc.