Big Tech Chases Big AI for Big Reasons

The capital-intensive battle to lead the generative artificial intelligence market is important enough for Alphabet, OpenAI, Meta and a few others, as it arguably creates a platform for the next era of computing and computing-based applications and business models. 

In fact, AI might be a somewhat-rare general-purpose technology that ultimately affects virtually the entire economy. 

But big hopes are accompanied by huge capex spending. 

source: Sherwood News 

And that big spending is chasing big potential. 

Generative AI is  "a really unusually large, maybe once-in-a-lifetime type of opportunity," according to Andy Jassy, Amazon CEO. 

"When we go through a curve like this, the risk of underinvesting is dramatically greater than the risk of overinvesting for us here,” said Sundar Pichai, Alphabet CEO. 

"This next generation of AI will reshape every software category and every business, including our own,” said Satya Nadella, Microsoft CEO. 

That won’t calm unease about the huge upsurge in capital spending to create and support large language models, but the huge potential is the driver. 

But the strategic implications also might be profound, as GenAI might be key to defending or extending digital real estate. In other words, GenAI possibly is about protecting platforms and ecosystems: keeping users engaged within the ecosystem. 

As with computing platform battles of the past (operating systems, hardware and application ecosystems),  generative AI is viewed as a key tool for protecting business moats around existing ecosystems. 

Beyond that, the heavy investments in generative and other forms of AI--though unsettling--are made not only to secure leadership of a platform that might drive the next era of computing and technology, but also might do so in large part because AI might reset expectations about the “cost of doing things,” as has tended to be the case for earlier general-purpose technologies. 

GPT	Key Innovation	Cost Reductions Produced
Steam Engine (18th century)	Mechanized power generation	- Reduced transportation costs (rail, shipping) - Lowered manufacturing costs through mechanization
Electricity (19th century)	Reliable, distributed power	- Reduced costs of lighting, heating, and powering factories - Enabled more efficient production processes
Computers (mid-20th century)	Automation of data processing	- Lowered costs of calculation, record-keeping, and data management - Improved productivity in administrative and technical tasks
Internet (late 20th century)	Communication and information access costs	- Drastically reduced communication costs - Enabled e-commerce, lowering retail and transaction costs
AI (21st century)	Intelligent data processing and automation	- Potential for reduced labor costs in repetitive or cognitive tasks - Lower costs in decision-making through better predictions and insights