For the last three years, the AI story has been told like this: chips are the bottleneck, Nvidia is the toll booth, and the world will keep paying. But the next phase of AI isn’t about how fast you can compute… it’s about how fast you can move the data between all those expensive GPUs without melting the power budget.

That’s why Nvidia just did something that should make investors sit up straight: it agreed to invest $2 billion into Coherent and $2 billion into Lumentum, alongside multibillion-dollar purchase commitments and future capacity access rights for advanced laser/optics components. And the agreements are nonexclusive—which is a key tell. Nvidia isn’t “buying a supplier.” It’s doing something far more important: securing scarce photonics capacity for the next generation of AI infrastructure.

AI is building “factories,” not “data centers.” When you’re wiring up tens of thousands (or hundreds of thousands) of GPUs, the plumbing becomes destiny. The more compute you add, the more bandwidth you need to keep it fed. Copper works within short distances. But as speed requirements climb and distances stretch across racks and rows, copper runs into physics: signal loss, heat, power draw, and latency.

Optics solves that by moving data as light—photons instead of electrons. And the really big step-change is co-packaged optics (CPO): instead of plugging optical modules into a switch like Lego bricks, you push the optical conversion into (or right next to) the switch silicon, cutting power and improving bandwidth density. In plain English: less conversion, less waste, more speed.

Here’s the part most people miss: at AI scale, networking optics isn’t a rounding error. Industry commentary around these architectures has highlighted that in a very large “AI factory,” optical networking power can become a meaningful slice of total compute power, and CPO is pursued specifically because it can materially reduce the networking power burden versus traditional pluggable optics.

Nvidia didn’t just “make an investment.” It effectively announced:
“We’re not letting the next bottleneck be someone else’s production schedule.”

From Nvidia’s own language, these investments are about accelerating optical interconnects and advanced packaging / integration as foundational tech for the next phase of AI infrastructure—because that’s what unlocks “ultrahigh-bandwidth, energy-efficient connectivity” across AI factories.

And note what else is embedded in the announcements:

This is the “picks-and-shovels” phase of AI infrastructure—except the shovel is a laser, and the pickaxe is a wafer-scale optical supply chain.

These are the “capacity + complexity” names—high engineering difficulty, hard to replicate, and now explicitly supported by Nvidia commitments.

Why these matter: If the market is shifting toward inference, agents, and distributed AI workloads, the interconnect load doesn’t shrink—it often grows. More “always-on” query traffic means more network pressure. Photonics is how you keep the factory from choking.

These names are the “arms dealers” around the photonics buildout. Not all are pure photonics, but they’re directly levered to the bandwidth/power race.

Optical modules / transceivers & manufacturing

Switch + interconnect silicon (the brains that drive the light)

Network systems that “drink” optics

Photonics isn’t just “faster.” It has to be serviceable and reliable in monster clusters. One of the big debates is whether CPO adoption happens fast or stays gated by operational concerns (swapability, failure modes, yield). The industry is still early, and timelines matter.

So the winners aren’t just “who has optics.” It’s “who has optics that hyperscalers trust at 2 a.m. when something breaks.”

Copper isn’t disappearing. But if you’re positioned for a world where copper scales cleanly into the next bandwidth regime, you’re fighting physics. As lane speeds and overall fabric throughput push higher, optics moves closer to the compute, not farther away.

Translation: if a company’s story depends on “we’ll just do more of what we did last cycle,” the market will eventually reprice it.

The market spent years treating the AI buildout like a one-variable equation. But the bigger AI gets, the more the bottlenecks multiply: power, cooling, packaging, memory, networking—and now optics explicitly.

Nvidia’s move is a signal that interconnect is now strategic. When the platform owner starts locking up lasers, you don’t want to be the investor still modeling AI like it’s 2023.

Optics can be both:

If hyperscaler spend wobbles, or if photonics timelines slide, the highest-multiple, most crowded optics names can get hit hardest—because they’ve become the market’s favorite “pure play” expression of the AI network thesis.

Nvidia’s $2B + $2B photonics commitments aren’t a side quest. They’re a flashing sign that the AI race is moving from “who has the best GPU” to “who can build the fastest, most power-efficient AI factory.”

We’re watching a new kind of arms race:
Compute is expensive. Power is scarce. Bandwidth is the new oxygen.
And the companies that control the light—lasers, optical engines, and the manufacturing capacity behind them—just got promoted from “supplier” to “strategic asset.”

Yesterday highlighted some of the difficulties of trading and investing during a war. Monday we saw the traditional playbook—-fade the dip. Yesterday we saw the real move based on fear of a prolonged war and higher oil prices (see below). But, we closed way off the lows on a statement from Trump about teaming up with the Kurds.

So far this morning the biggest takeaway is crypto and precious metals up at the same time for a change. Wish there was an ETF for that :)……

The tape they’re describing is classic risk-off duration pressure: futures down hard, global equities weak, front-end yields ripping higher, and the market repricing to <50 bps of Fed easing. In that regime:

If this persists I’d stay away from anything related to the consumer.

Bottom line is we are going to have underlying volatility until this is over, and it’s going to be very hard to make moves based on what you are seeing.

UBER: I view Uber as one of the cleaner “AI-in-the-real-economy” compounders because AI isn’t a bolt-on feature here—it’s the operating system that prices, matches, routes, fights fraud, and continuously improves utilization across a two-sided network (Mobility + Delivery). In 2025, the core platform kept scaling while profitability tightened: Gross Bookings grew 19% to $179.9B, revenue grew 18% to $52.0B, Uber exited Q4 with 202M MAPCs (+18% y/y), and Adjusted EBITDA rose to $8.73B with Free Cash Flow up 42% to $9.76B. The “asymmetry” angle is that this is already a sizable cash-flow engine and it has credible upside optionality from autonomy (Uber doesn’t need to own AV hardware to monetize demand aggregation, dispatch, payments, and network density)—but you need to watch whether take-rates stay resilient as the ecosystem evolves. Capital return is also a real support: Uber’s board authorized $27B of repurchases (with $19.2B remaining as of 12/31/25), which gives management meaningful flexibility if the stock is weak while fundamentals hold.

The H.E.A.T. (Hedge, Edge, Asymmetry and Theme) Formula is designed to empower investors to spot opportunities, think independently, make smarter (often contrarian) moves, and build real wealth.

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