Digital Product Studio

Why Giving Away AI Models for Free Actually Makes Business Sense

Recently, major tech companies like Meta, Microsoft, Google and NVIDIA have been openly releasing powerful AI models and tools. On the surface, it may seem like an act of generosity, but there are strategic business reasons behind such moves. Let’s get to know:

Reason Why Big Tech Giving Free AI

Commoditizing AI Products

A classic strategy in Silicon Valley involves commoditizing products that complement a company’s core offering. Joel Spolsky explained this well in his Strategy Letter V. By lowering the costs of complementary products like hardware, usage of software rises since the overall costs are reduced.

For example, when Microsoft brought down PC costs by facilitating standardization, it drove up demand for Windows. Similarly, Google releasing the Android OS cheapened smartphones and expanded its search and ad business beyond PCs. Today, big tech is applying this to AI by freely releasing powerful models.

Meta Releasing AI Models For Free

Meta New Llama AI Model

In July 2024, Meta open-sourced its Llama 3.1 model weights for free use by organizations with under 700 million users. At 405B parameters, Llama 3.1 is competitive with giants like GPT-3 and Claude. By releasing such an advanced model, Meta aims to commoditize AI.

Reasons behind Meta’s Move

While open-sourcing confers goodwill, Meta’s primary motivation is expanding user engagement on its platforms. Freely available AI tools allow users to generate more content, boosting time spent and ad potential on Facebook and Instagram.

Additionally, training larger models requires immense computation. With 600,000 H100 GPUs by the end of 2024, Meta can potentially release 100+ GPT-4 scale models yearly, dwarfing competitors unable to match such scale. Freely releasing earlier models maintains Meta’s AI leadership and sets standards without direct monetization.

AI is Becoming a Commodity

Meta isn’t alone in open-sourcing powerful AI model. NVIDIA released Nemotron-3, Microsoft their Phi, and Google released Gemini and many more. Even smaller startups like Cohere and Mistral openly shared research models, showing AI is rapidly becoming commoditized at scale.

As AI tools freely proliferate, the opportunity for direct monetization diminishes. Instead, tech giants use AI to amplify engagement with their platforms and cloud or hardware products. This poses an existential threat to startups focused on standalone AI applications and APIs.

Effect on AI Startups

Top AI startups like OpenAI, Anthropic, and Character made their general purpose, scaling AI models their core product. However, direct monetization remains challenging with the largest companies possessing scale to commoditize such products.

When tech titans can generate dozens of models at GPT-4 scales, startups cannot plausibly outpace that capability or compete on price. While some maintain R&D edges, commoditization pressures may force a strategic pivot away from standalone AI products towards other applications. The AI gold rush is transforming before our eyes.

Augmenting Platforms and Hardware

For big tech, AI augments existing lines of business rather than being an end itself. Google, Amazon and Microsoft sell cloud computing infrastructure, so freely available models fuel servers and TPU demand. NVIDIA thrives on data center GPU sales boosted by commoditized AI research use cases.

Meta aims to leverage AI for richer user experiences, greater engagement, and ads on Facebook and Instagram. User-generated content and independently fine-tuned models may see higher volumes through open tools. Ultimately, platforms remain the true end goal as AI capabilities proliferate for ‘free’.

The Infrastructure Build-Out

Scaling state-of-the-art AI requires vast computing resources that were inconceivable just a few years ago. Building this infrastructure enables constant model advancement, as each iteration only takes weeks and months at hypescale. NVIDIA’s Jensen Huang asserts that 1.8 trillion parameter GPT-4 models can train in 90 days on 8000 GPUs. Meta’s 600,000 H100 GPU deployment by the end of 2024 imbues exponentially greater capability than any startup. 

Bottom Line

Commoditizing AI through freely available AI tools makes business sense for big tech giants that want to amplify platform engagement and hardware sales through open AI standards.

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Picture of Faizan Ali Naqvi
Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

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