News Analysis
A group of big tech companies are perhaps getting closer to demonstrating the profitability of AI.
Microsoft, Google, Amazon and Meta each reported in their recent quarterly earnings that their AI investments are growing AI revenue in their businesses.
Here, we look at what these publicly traded AI companies have in common as well as what other companies can do to emulate their success — and tap into the AI demand and spending that's clearly happening in the AI marketplace:
AI Models Drive AI Revenue
Each of the companies has invested in, developed and released a foundation AI model, which can power a growing portfolio of customer-facing AI products.
Google has Gemini. Amazon has Amazon Titan and a major investment in Anthropic, which has Claude. Meta has Llama. And Microsoft's major investment in OpenAI gives it access to GPT.
The AI foundation model is the underlying innovation and technology that is powering the biggest moves, products and features in the AI market. Without the foundation model, their AI offerings would be based on legacy AI and non-starters.
For non-big tech companies, they should use a foundation model to build a custom AI model that's differentiated from other AI models in their domains or plan to be uncompetitive in the AI era.
See more: How to Evaluate AI Foundation Models? Don't!
AI Infrastructure Drives AI Revenue
Each of the companies is heavily invested in the essential hardware infrastructure that powers AI.
Amazon AWS, Microsoft Azure and Google Cloud are leading cloud computing providers, and Meta can lean on its global network of data centers.
Significantly, each of the companies is now in the AI microchip market to be less dependent on AI chipmakers, such as NVIDIA, gain more control of their AI futures and cut into the under-supplied AI chip market. Their list of AI chips is getting longer: with AWS Trainium, AWS Inferentia, Microsoft Azure Maia AI Accelerator, Microsoft Azure Cobalt CPU, Google Axion Processor and Meta MTIA.
Other companies should establish their AI infrastructure strategy before they pursue AI product development and rollouts, ensuring the infrastructure is financially sustainable, technically scalable and interoperable to prevent vendor lock-in in a fast-evolving AI market.
See more: 10 Top AI Chip Companies
Software Portfolios Drive AI Revenue
Each of the companies has a large portfolio of both stand-alone and complementary software products that are tied to their AI offerings.
Their software portfolios are being integrated with their growing AI technologies, and monetization is part of the integration. Without their software portfolios, the value of their generative AI offerings would be much harder to monetize, if at all at this juncture.
Other companies should look at their software portfolio and see where AI can be integrated to add revenue-generating end user value.
If there isn't a viable AI-ready software product, product development should create AI-based software to, in fact, enter and compete in the AI market.
User Bases Drive AI Revenue
Each of the companies has a massive user base that its global marketing and sales apparatus can distribute to and pitch.
Their user bases are built on monopolistic-type technologies and diversified business units. Without the user base, the business case for their AI investments isn't there.
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Other companies should be realistic about the size of their user base and immediate revenue opportunities when developing and pricing AI products. Their software user base dictates their AI potential.
If the user base doesn't justify the AI investment, the AI strategy should be adapted to specialize in a lower-volume, under-served niche where they can seize open market share and expect AI returns.
See more: What NVIDIA's No. 1 Market Cap Tells the AI Industry
