Why Knowledge Is Power in the Clash of Big Tech’s AI Titans

The global artificial intelligence arms race is accelerating in real time. In May, Google and Microsoft-backed OpenAI both unveiled new multimodal AI models with stunning voice and visual analysis capabilities that offered a glimpse into the next era of advanced technology. The launches came within 24 hours of each other, underscoring just how hyper-competitive the arms race has become over the first half of 2024. Meta also recently announced plans for aggressive investments in AI, and in March Amazon completed its $4 billion investment in generative AI (GenAI) startup Anthropic, marking the largest outside investment in the company’s three-decade history.  

A true clash of the technology titans is unfolding before our eyes with big tech’s power players in a race against the clock to create the world’s most advanced AI systems that redefine the boundaries of possibility across enterprise environments.  

That starts with building large-scale data centers capable of facilitating the development of advanced AI systems. Microsoft and OpenAI for example are working on plans for a $100 billion data center project -- 100 times more costly than the biggest existing data centers today. However, AI system development is only one part of the arms race equation. With global AI spending expected to exceed $1.8 trillion by 2030, effectively monetizing these systems through integrations with intelligent business applications is what will separate the real winners from the pack. This will require organizations to build deep knowledge bases of “expert” data that transcend the massive amounts of raw, unfiltered information scraped from the internet for the first wave of GenAI engines. Considering those systems learned from an ocean of unvalidated data, they are incapable of filtering garbage in from garbage out to measure and confirm accuracy. It makes them ill-suited for supporting autonomous applications that amplify human performance and transform how modern enterprises operate.   

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The ocean is also starting to run dry. A recent Wall Street Journal report projected that demand for high-quality text data could outstrip supply within two years, potentially hindering AI system development. The advanced AI models currently under development across big tech -- models designed to drive the next class of intelligent applications -- must learn from more extensive datasets than the internet can provide. In response, some AI developers have turned to experimenting with AI-generated synthetic data, a risky proposition that could potentially put an entire engine at risk if even a small semblance of the learning model is inaccurate. Others have pivoted to content licensing deals for access to useful, albeit limited, proprietary training data. OpenAI for example reached a landmark agreement with News Corp on May 22 to leverage current and archived journalism content from major News Corp mastheads like the Wall Street Journal, Barron’s, and MarketWatch to enhance its products.  

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Neither approach is a viable long-term option for winning the AI arms race. The real differentiating edge lies in who can develop a systemic means of achieving GenAI data validation, integrity, and reliability with a certificated or “trusted” designation, in addition to acquiring expert knowledge from trusted external data and content sources. These two twin pillars of AI trust, coupled with the raw computing and computational power of new and emerging data centers, will likely be the markers of which big tech brands gain the immediate upper hand. 

Acquiring Domain Expertise 

It’s important to remember that GenAI is only one piece of the arms race puzzle. The new class of generational systems currently in development will also include other additive forms of AI that round out platforms with diverse features and functions tailored to granular business use cases. Then, large scale business applications can be layered on top of the platform to produce outcomes that generate value for individual users and collective enterprises, in turn opening wider pathways to monetization. This ensemble of technology is imperative for designing AI-powered autonomous systems that can attack robust classes of complex operational challenges.   

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As such, we could start to see major AI developers increasingly target smaller technology firms via corporate acquisitions that secure deep foundations of niche industry domain data to create a competitive advantage when training future AI engines. For example, envision a naval warfare scenario where future models of Gemini and ChatGPT resemble the U.S.S. Gerald R. Ford, the world’s largest and most advanced warship that weighs 100,000 tons and took 12 years to build with $13 billion in funding. Both AI models require a great deal of resources to build, advance, stop, or change directions. However, through acquired firms, Google and OpenAI could amplify their battle posture with more nimble destroyers, cruisers and speedboats -- the industry-leading intelligent applications that drive commercial value by utilizing the size and breadth of the foundational GenAI platform. Leveraging external sources of agility will be critical to emerging from the AI arms race victorious.  
 
Outside the Box Partnerships 

The academia world could be another foundational knowledge source for big tech titans to pursue. There are more than seven million books in the Princeton University library alone. Just imagine if Meta or Amazon struck a deal with all eight Ivy League universities to create digital copies of every physical book in their libraries. Then, with exclusive rights to that proprietary information, they could create expert datasets to leverage for training AI-powered autonomous systems that address fundamental challenges across complex industries like industrial manufacturing and aerospace. Compounded at scale across the entire higher education landscape, the academia approach could generate enormous new knowledge bases that enables them to close the gap with Google and OpenAI, the arms race’s two frontrunners.  

This would also provide new monetization opportunities to higher-ed institutions, potentially helping offset America’s affordability problem across major colleges and universities. Enrollment at 4-year private for-profit colleges has decreased by more than 55% since 2010. Total US student loan debt currently exceeds $1.7 trillion, and federal student loan interest rates (6.53%) just reached their highest peak since 2012. By opening a massive untapped revenue stream, schools could lower tuition rates to increase enrollment, reduce student loan usage, and expand equal access to high quality education. It’s a win-win on both sides on the dividing line.     

Avoiding an Orwellian State 

The big tech titans must proceed with caution as the arms race accelerates. The potential for technology to transform America into an Orwellian state is not a fictional doomsday scenario. It’s a reality of our rapidly advancing digitalized world. US congressional representatives have raised concerns about China viewing AI as “a weapon with which to perfect its Orwellian techno-totalitarian surveillance state” -- emphasizing the need for stronger collaboration between Silicon Valley and the Pentagon. AI system development speed, scale, and monetization cannot come at the expense of societal health and safety.  

After all, if this genie gets let out of the bottle, it’s never going back in.