The AI supply chain reports: AMD, Alphabet, Amazon, Palantir take the stage

• AI demand matters, but guidance matters more because it sets the next 12 months of expectations.

• Watch margins and cash flow, not only revenue growth. AI can boost both, or pressure both.

• The clearest winners show “proof of use”, not only “proof of interest”.

Artificial intelligence (AI) is starting to feel less like a product launch and more like a monthly utility bill. Everyone wants it, few enjoy paying for it, and the market now wants to see who can pass the cost on.

This week, four different parts of the AI chain report results: Advanced Micro Devices (chips), Alphabet (ads and cloud), Amazon.com (cloud and retail), and Palantir Technologies (software used to deploy AI at work).

Think of it as an AI relay. Palantir shows how fast AI moves from demo to real use. Advanced Micro Devices shows whether the hardware supply keeps up. Alphabet and Amazon.com then reveal whether the cloud can sell AI at scale without the profit line flinching.

The common thread is simple: the market is no longer pricing “AI excitement”. It is pricing “AI outcomes”. That is why this week is useful for long-term investors. You do not need to pick a winner in a single quarter. You do want to understand where the bottleneck is: supply of computing, demand for computing, or the ability to turn computing into value.

What “good AI” looks like, company by company

For Advanced Micro Devices, the headline is not the word “AI”. It is the phrase “volume and mix”. Investors want evidence that its data-centre graphics processing units (GPUs, specialised chips for heavy computing) keep ramping, and that supply constraints ease rather than tighten. The second watch item is profitability. AI chips can be high value, but early ramps often come with costs, discounts, and messy product mixes. A clean margin story tells you the ramp is maturing.

For Alphabet, the big question is balance. AI features can improve search and advertising, but they also raise computing costs. Investors will watch whether AI helps keep ad growth healthy without quietly squeezing margins. The other focal point is Google Cloud. Cloud is where AI demand often shows up first, because companies need computing to train and run models. A simple tell is whether cloud growth holds up and whether profitability keeps improving while spending stays disciplined.

For Amazon.com, everything funnels through Amazon Web Services (AWS, the company’s cloud division). Investors will watch whether AWS growth re-accelerates as AI workloads expand, and whether AWS margins stay resilient despite heavy investment in data centres and chips. Outside the cloud, Amazon’s retail operations still matter because they fund the buildout. If retail margins stay firm, the market tends to view AI spending as a choice, not a strain.

For Palantir Technologies, the watch list is “proof of deployment”. Palantir sells software that helps organisations use data and AI in real-world operations, often in government and large companies. Investors will focus on commercial growth, contract momentum, and whether customers expand usage after initial projects. A strong quarter is not just more pilots. It is larger deals, repeat customers, and clear commentary that AI moves from experiment to embedded workflow.

The shared scoreboard: guidance, cash, and the cost of confidence

Across all four, the most important numbers are often not in the income statement. They are in the forward-looking comments.

First, look for management language that ties AI demand to customer behaviour, not buzzwords. Are customers committing to longer contracts, bigger deployments, or broader rollouts? That is a stronger signal than “interest”.

Second, watch capital spending. AI requires infrastructure, and infrastructure requires cash. The market is increasingly fine with high spending if companies show a credible path to returns, either through higher revenue, higher margins, or both. When investors get nervous, it is usually because spending rises while growth slows.

Third, watch operating leverage in plain terms: do profits grow faster than revenue? In early AI cycles, costs can run ahead. Over time, the winners tend to show that each extra unit of revenue becomes cheaper to serve.