Prices of Multiple PC Components Surge Recently, with HDDs Also Rising Sharply

Recently, prices of multiple components in the PC industry have surged significantly. In addition to memory modules and solid-state drives (SSDs), the prices of hard disk drives (HDDs) have also skyrocketed. This round of price increases is mainly driven by AI demand. AI data centers have extremely high requirements for large-capacity storage, and HDDs remain indispensable. As a result, Seagate and Western Digital —the two major leading suppliers of HDDs—have seen explosive growth in their performance, which has in turn led to a sharp surge in their stock prices. Since the start of the year, their stock prices have risen by over 200%.

Dave Mosley, CEO of Seagate, stated that the company’s production capacity for ultra-large-capacity hard drives targeting the cloud market has been completely sold out for 2026, and there is even clear visibility regarding the production capacity for 2027. Despite the current shortage of supply and rising prices, manufacturers have no plans to expand production capacity; instead, they are enjoying the current period of soaring profits.

When asked whether the company would increase production capacity due to the significant surge in demand, Dave Mosley responded that Seagate is focusing on increasing hard drive density to meet demand. The company is launching new hard drives equipped with HAMR (Heat-Assisted Magnetic Recording) technology, which can significantly increase storage capacity.

Measure the full contribution margin

Gross revenue can hide payment fees, AI serving cost, creator share and support burden. Contribution margin is more useful for deciding whether growth is creating economic value. Segment the calculation by product or customer type because one blended percentage can conceal weak economics in a fast-growing area.

Use scenario analysis for uncertain inputs. AI businesses face volatile usage and infrastructure cost. Base, upside and downside cases help management see which assumptions matter most and where protective actions are available.

Reconcile forecast with actual drivers monthly

Instead of only comparing total spend or revenue, explain how much variance came from volume, price, mix and unit cost. This turns the forecast into a learning system for the business. Start with the business driver behind the metric.

A finance ratio is useful only when teams understand what operational behavior moves it. Break revenue, margin or payback into user volume, conversion, pricing, usage and variable cost so a variance can be traced to a real product or sales change. This makes forecasts easier to update because assumptions can change independently instead of rebuilding one large top-down model.

Use cohort evidence before extrapolating growth

AI products often change quickly, so recent cohorts may behave differently from early adopters. Compare retention, spending and support cost by signup period or customer segment before assuming historical averages will continue. Connect financial metrics to operational decisions.

The purpose of the model is not only reporting. It should help decide pricing, hiring, infrastructure commitments and channel spend while there is still time to change course.

Separate leading indicators from financial outcomes

Usage, retention and pipeline often move before recognized revenue or cash. A useful dashboard combines these leading signals with financial results so management can distinguish a temporary accounting lag from a real deterioration in demand. Definitions should remain stable enough that teams do not change the metric when performance becomes inconvenient.

Document assumptions beside the forecast. Model outputs are only as reliable as their assumptions about pricing, conversion, cost and timing. Keeping those assumptions visible makes scenario analysis faster and reduces false precision.

Review the model when product behavior changes

New modalities, pricing plans or sales motions can invalidate old averages. Finance models should evolve with the product rather than forcing new behavior into outdated categories. Practical finance check.

Before acting on the metric described above, define the decision it is supposed to support and the operating assumptions behind it. Compare the forecast with actual user behavior, payment timing and serving cost so changes can be traced to real drivers. Scenario ranges are usually more useful than one precise projection when usage, collections or infrastructure cost remain uncertain.