AI Unpacked: Chips, Cuts, and Caution

Week 1: Nov. 24 - 28

AI Unpacked

Welcome to the first-ever edition of AI Unpacked, a weekly blog that provides updates, insights, and easy-to-understand explanations on how artificial intelligence is shaping today’s financial landscape. This week’s report will cover the increasing competition between Alphabet and Nvidia, AI’s continued effects on employment, and AI spending habits. 

Google Challenges Nvidia in the AI Chip Race:

As AI development accelerates, the semiconductor race is tightening, and Google is quickly emerging as one of Nvidia’s most serious competitors.

Nvidia still leads, but Google’s new Gemini 3, powered by its own AI chips, is outperforming rival models in reasoning, knowledge tasks, and visual problem-solving. This marks a major step forward in Google’s push into AI hardware.

Google also received a boost after reports that Meta Platforms plans to spend billions on Google’s AI chips, signaling broader industry confidence. The news pushed Alphabet’s stock up roughly 3%, while Nvidia’s shares slipped as investors weighed the growing competitive pressure.

Meanwhile, Nvidia bore much of the pressure from renewed AI bubble* concerns as tech stocks fell throughout the month, while Google continued to gain momentum.

Nvidia Pushes Back as Google Gains Ground in the AI Chip Race

Despite Google’s recent momentum, Nvidia insists it remains a full generation ahead of its competitors. The company reiterated that its GPU* platform is “the only platform that runs every AI model and does it everywhere computing is done,” emphasizing its flexibility and broad compatibility compared to Google’s more specialized ASIC-based TPU chips*.

Nvidia’s confidence is backed by market reality: the company still controls about 90% of the AI chip market, selling GPUs to virtually every major tech firm. Google, on the other hand, primarily uses its TPUs internally, though that may be changing.

Nvidia’s dominance, and the versatility of its GPUs, remains unmatched for now, even as Google continues to chip away at the edges of the market.

HP Cuts Up to 10% of Workforce as It Ramps Up AI Strategy

HP, a major computer hardware company, announced plans to lay off 4,000–6,000 employees, about 7% to 10% of its global workforce, by the end of fiscal 2028 as part of a broad restructuring effort aimed at accelerating its shift toward AI-powered products and operations. The company expects the move to streamline costs and refocus investment on AI-enabled PCs, services, and internal automation.

The restructuring will cost roughly $650 million, with about $250 million recognized this fiscal year, but HP projects at least $1 billion in annual savings once the transition is complete. The cuts come despite a relatively strong quarter, boosted in part by growing demand for AI-capable PCs, which now account for more than 30% of HP’s shipments.

HP joins a wave of major tech companies trimming staff as they reorient around AI, reflecting industry-wide pressure to adopt automation, strengthen product offerings, and maintain competitiveness in a rapidly evolving market.

Is it Possible to Spend Too Much on AI?

With recent discussions around a potential AI bubble and concerns that companies are leaning too heavily into artificial intelligence too quickly, many are questioning whether businesses are spending at an unsustainable pace. At AI Unpacked, we believe AI investment, like any other strategic allocation, needs balance and discipline. Just as investors diversify their portfolios, organizations should avoid placing all their bets on a single technology, no matter how transformative it appears.

While Nvidia’s latest earnings eased some of the immediate bubble fears due to higher-than-expected revenues, caution remains warranted. For many, skepticism toward rapid AI spending isn’t a rejection of the technology, it’s a reminder that long-term success requires measured, intentional investment rather than unchecked enthusiasm.

Glossary (*):

AI Bubble - An AI bubble is when people and businesses get so excited about AI that they overspend and overestimate what it can do right now. If the hype gets too far ahead of reality, the market could “pop,” leading to cuts, slower growth, or re-evaluation of AI investments.

GPU - Graphics Processing Unit, is a specialized electronic circuit in a computer designed to rapidly create and manipulate graphics and video. 

ASIC-based TPU chips -  a special type of computer chip designed specifically for running AI tasks—especially Google’s machine-learning models