The two tribes of AI development

AI development is progressing along two distinct paths. One path involves companies raising significant capital at extremely high valuations. The other involves more focused entities taking a different approach. Both strategies have their advantages in different situations.

The funding landscape continues to evolve rapidly. SSI quadrupled its valuation to $20B by February 2025 and later secured $2B at a $32B valuation in April 2025, led by Greenoaks with Alphabet and Nvidia participation. Meanwhile, Mira Murati aims for a $2B seed round at a $10B valuation.

Companies such as OpenAI and Grok are pursuing scale-driven strategies, while others, like Mistral and Hugging Face, are exploring a different approach. They are focusing on open-source models, developer communities, and specific niches instead of chasing the AGI moonshot, which requires billions in capital.

Meta occupies an interesting middle ground with its Llama family of models. Despite being a tech giant with vast resources, Meta has embraced an open-source approach by releasing increasingly powerful models, including Llama 3 and, most recently, Llama 4. Llama 4 features innovative multimodal capabilities and a mixture-of-experts architecture.

Mistral is targeting underexplored areas like local language models such as Arabic, and value-added niches like OCR and small models that can run without extensive infrastructure. Similarly, Hugging Face has built the "GitHub for machine learning" - a collaborative platform hosting over 500,000 AI models, most of which are freely available and open-source.

What's remarkable is that while Hugging Face maintains this open approach, they've still built a rapidly growing business. They hit an estimated $70 million in annual recurring revenue by the end of 2023 - growing 367% year-over-year according to industry reports. Their business model intelligently balances community contribution with enterprise services, charging primarily for hosted inference, enterprise features, and expert consulting while keeping the core platform open.

This open-source-first approach creates a powerful dynamic. Open source establishes a flywheel of adoption, contribution, and improvement, which accelerates development cycles. There are historical precedents to consider for this approach.

In the history of technology, both proprietary and open systems have found success in various contexts. Android, with over 70% market share, and iOS both effectively serve their respective markets. Linux powers the majority of internet infrastructure, while Windows dominates personal computing. Open web standards and proprietary solutions coexist, each finding its own niche.

Different funding approaches also create different incentives. OpenAI recently raised a total of $40 billion with a $300 billion valuation. Anthropic has secured a total of $13,7 billion. Meanwhile, Mistral raised $644 million with a $6,2 billion valuation, and Hugging Face has raised under $400 million to date. These different capital structures enable different strategies and create different expectations for returns.

The economic tradeoffs are fascinating. High-valuation companies gain resources for ambitious long-term research. However, they also face intense pressure to monetize aggressively to justify their valuations. This often leads to locking capabilities behind APIs, implementing tiered pricing, and optimizing for investor returns. In contrast, the leaner approach of Mistral and Hugging Face could potentially reach profitability with a fraction of the revenue that OpenAI needs. This creates the flexibility to focus on serving customers and communities rather than satisfying massive growth expectations, while still maintaining resources for innovation.

By concentrating on particular markets and applications instead of trying to compete in every area, these community-focused AI companies are using a targeted strategy. This approach may enable them to build expertise in specific niches. Furthermore, their smaller models, which operate with less infrastructure, could broaden AI accessibility for more organizations. For example, Hugging Face's customer base includes companies like Amazon, Nvidia, and Microsoft, even though these companies have their own AI projects. This fact demonstrates the value of Hugging Face's community-centered approach.

Both approaches are valuable in the ecosystem. Some organizations will benefit from the scale and resources of larger players. Others may find that the flexibility and specificity of focused solutions better suit their needs. I am particularly optimistic about these community-centered models; they are building sustainable businesses while democratizing access to AI. The AI landscape can accommodate multiple successful models, but those that empower developers, rather than restrict them, may have the most lasting impact.