GoPro just officially warned investors in their 8-K with the SEC, that the AI memory crunch may push the action-camera company past its survival line.

That AI data-center driven memory-demand has tightened supply, pushed memory prices up, and left smaller consumer-hardware companies like GoPro fighting for parts against richer buyers.

GoPro’s Q1 revenue fell 26%, its lender rules have already been breached, and the company now says there is substantial doubt about staying alive as a business.

The brutal part is that GoPro sells hardware in a price-sensitive market, so higher component costs can hurt margins even when customers refuse to pay much more.

Morgan Stanley just published the most important data package of the AI cycle (Save this).

Four companies, Google, Amazon, Microsoft, Meta are on track to spend $1 trillion in a single year in 2027.

To understand how we got here, look at the progression.

$250 billion in 2024, $413 billion in 2025, $737 billion in 2026 and $1.018 trillion in 2027.

Combined, these four companies will have spent over $2 trillion on AI infrastructure between 2024 and 2027.

Now look at the capacity chart because the dollars alone miss the most important story.

In 2025, hyperscalers added roughly 6.7 GW of incremental compute capacity globally, in 2027, they will add 19.5 GW.

That is 3x more physical AI infrastructure coming online in a single year than came online just two years prior.

Google leads the entire buildout adding an estimated 6.8 GW in 2027 alone.

Morgan Stanley's note puts that number in staggering context, AWS's total installed base at the end of 2024 was roughly 4 to 6 GW and that capacity supported a $108 billion annual revenue business.

Google is adding more new capacity in a single year than Amazon built in its entire history and the cost per GW data is where the investment thesis sharpens into something actionable.

The cost to build one gigawatt of AI compute capacity is falling from $62 billion per GW in 2024 to $52 billion per GW in 2027 even as the compute density per GW is rising dramatically.

Google builds at $44 billion per GW, Microsoft builds at $59 billion per GW.

That $15 billion gap per gigawatt is almost entirely explained by one decision, Google uses custom ASICs, Microsoft uses NVIDIA GPUs.

NVIDIA's current GB300 racks cost roughly $19 billion per GW of compute capacity. Vera Rubin, the next generation pushes that to around $25 billion per GW as rack power density climbs to 600 kilowatts.

Custom ASIC racks built by Broadcom for Google, Marvell for Amazon cost between $6 and $11 billion per GW.

At the scales being discussed here, the companies that shift to custom silicon do not just save money on chips, they structurally outcompete every hyperscaler still running on merchant silicon because they get exponentially more compute for the same dollar.

Here is what that means as an investor.

The orders are placed, power contracts are signed, land is acquired and the hyperscalers have already committed the capital, the only question is whether the supply chain can keep up.

That supply chain bottleneck is the exact thesis we have been building.

Broadcom designs the ASICs, Marvell designs the custom silicon and the optical DSPs, AAOI makes the InP lasers that move the data and every single one of those companies is directly in the path of $1 trillion per year in committed spending by the most cash-rich companies on Earth.