AI Wars
High-stakes, futuristic race between all the platforms unfolding
BY: Taj Tsonga, George Gil, Behzad Ranjar, Alex Gamhewa, Lotis Soriano, and Varun Raman
What is your assessment of Google's gen AI strategy to date?
Google's generative AI game plan is ambitious, widespread, and geared towards practical implementation and safe integration across its huge network. Yet, despite being a trailblazer in AI, Google has faced challenges and a perception of playing catch-up with OpenAI (ChatGPT) in the consumer-facing generative AI hype. Currently, Google's generative AI strategy takes a two-pronged approach, diving into cutting-edge models like Gemini and deeply embedding AI tools throughout its ecosystem. This tactic lets Google tap into its products, such as Search, by incorporating AI features directly into user experiences. It's all about making AI a core part of the infrastructure, much like electricity or the internet, signaling a future where AI seamlessly integrates across all its services. By investing in foundation models like large language models (LLMs), Google shows it's serious about staying at the forefront of AI innovation.
Yet, its public AI deployment is slower than competitors. Google's strategy leans towards vertical integration to manage its AI stack, but it must find a balance between expansion and protection, and between openness and control in a rapidly changing market. Generating bank in the future will depend on cutting inference costs and enhancing user loyalty through integrated tools not allowing for its other products to take a financial hit like less website clicks because AI is fully integrated into search or businesses spending less on Google Advertisements pay per clicks.
Let's talk about the strengths and weaknesses of Google's major gen AI competitors - Microsoft, Meta, and Amazon.
Microsoft nails it with its smart move partnering up with OpenAI, which hands them an advantage in foundational model research and development without skipping a beat. This collaboration has allowed Microsoft to swiftly sprinkle AI like Copilot and Azure AI into its popular business software, giving a major boost to work efficiency. But hey, let's not forget - they're kinda putting a lot of eggs in the OpenAI basket, so any change in that relationship could shake things up tremendously.
Now, let's dive into Meta. Their ace in the hole is their love for open-source AI models like LLaMA, spreading the AI magic far and wide by getting the community involved in the innovation.This open-armed approach could mean more people jumping on the bandwagon and boosting Meta's influence. Yet, here's the catch - the downside of this strategy is the loss of control over how the models are used, leading to tricky situations like concerns over deepfakes, jailbreaks, or spreading fake news. Additionally, hallucinations are at an all time high with the usage of more open source models.
Last but not least, Amazon is flexing its muscle in the generative AI arena with its rock-solid cloud hosting and AWS infrastructure chops. Their play revolves around Bedrock, a nifty platform hosting various AI models and offering tools for fine-tuning - a dream come true for companies diving into AI. Amazon is also backing up Anthropic, a safety-focused AI player. However, there's a little chink in their armor as Amazon seems more about providing a stage for others' models rather than leading the pack with their frontier models, which might put a cap on their direct influence over cutting-edge AI innovations.
Should Google go open source with Gen AI?
The question of whether Google should dive headfirst into an open-source approach for generative AI is no walk in the park. It's a strategic puzzle with its fair share of pros and cons. On one side of the ring, we have arguments in favor of open-sourcing, a la Meta's playbook. The perks? Think turbocharged innovation and ecosystem growth thanks to the community pitch-ins. This could spark more experimentation, speed up app development, and get more folks on board with Google's tech. Plus, open models might just break the chains of vendor lock-in for users and still pack a punch performance-wise with fewer computing resources.
Yet hold your horses; there's another contender in the ring – the hybrid approach. Picture Google like a DJ spinning tracks, open-sourcing some smaller, specialized models while keeping the big guns under lock and key. This way, Google gets to ride the wave of community innovations and ecosystem expansion for select scenarios, all while holding the reins on core AI tech safety and cash flow. It's a delicate dance of balancing diffusion with defensibility, and openness with control – a strategic gem unearthed in the playbook.
The path forward should be hybrid with no hesitation. This strategy will allow Google to balance the economic where its data leads in dominance to the technical where they are investing at a rate keen with competitors. Out of all of the AI leaders mentioned previously they all lack the brand recognition and dominance of a space where Google trumps. To win the AI Wars, Google's hybrid strategy should strategically balance the diffusion benefits of open-source with the control and monetization advantages of proprietary models:
● Continue developing its frontier proprietary models like Gemini
○ Open-source less sensitive or slightly older versions of its models
● Reinforce its Google Cloud offerings (Vertex AI)
○ Continue prioritize responsible AI development across both proprietary and open models
