For the last three years, AI investing has been sold like a moonshot: train bigger models, buy more GPUs, repeat. And Nvidia became the “picks and shovels” king of the gold rush.

But the market is changing the question. It’s no longer “Who can train the smartest model?” It’s “Who can run it cheaply, instantly, and at insane scale… without blowing up the power bill?” That’s inference — the moment a model actually answers a user, generates code, drafts a report, or runs an agent. And inference is where AI becomes a business instead of a science project.

That’s why this Nvidia move matters: reports indicate Nvidia is preparing a new inference-focused system that even pulls in Groq’s inference architecture — and OpenAI is positioned as a major customer. Translation: even the GPU emperor is acknowledging that the next bottleneck isn’t just training FLOPS. It’s inference efficiency, latency, and watts. And when the market leader starts redesigning the stack, it usually means the old stack is getting attacked.

Nvidia already signaled this direction in other ways: it has been pushing CPUs harder (Grace/Vera), including standalone CPU deployments for certain “AI agent” workloads — i.e., cases where GPUs are expensive overkill. And it has reportedly inked a deal that licenses Groq’s inference technology and brings Groq’s founder/CEO (and other execs) into Nvidia, underscoring how seriously Nvidia is taking the inference arms race.

Training is a giant, lumpy capex event. You buy the machines, train the model, brag on stage.

Inference is a 24/7 utility bill.
It’s the cost of every answer your AI gives.

If AI agents really spread through the enterprise, inference becomes the “new internet traffic.” Not one big build — a permanent throughput problem: latency, memory bandwidth, networking, storage, and power. The winner isn’t just the company with the biggest chip — it’s the company that can deliver the most useful tokens per watt per dollar, reliably, at scale.

And here’s the uncomfortable twist: the biggest buyers of AI compute (hyperscalers) are also the companies Wall Street loved for being asset-light, cash-flow machines. Now they’re becoming capital-intensive infrastructure operators. That puts a spotlight on a question nobody could answer in 2023:

“Where does the ROI show up?”

If ROI is slow, CFOs do the only rational thing: they squeeze costs. And inference is the biggest “squeezable” line item in the AI stack. That’s why customers are diversifying away from one-chip-to-rule-them-all dependency. OpenAI, for example, has been expanding compute options and capacity — including major commitments for AWS capacity powered by Trainium chips. OpenAI has also pursued capacity with alternative compute providers like Cerebras.

So yes — this Nvidia inference pivot can be read as bullish (new TAM!), but it’s also a tell:
the compute customer is getting serious about price, performance, and power.

If you want to frame the “inference era” like an investor, forget who has the coolest demo. Focus on who gets paid when inference scales.

Core inference silicon / custom compute

The stuff inference devours: memory, networking, storage

Power & cooling: the unsexy constraint that decides who wins

If inference is the future, the market doesn’t just need chips — it needs electrons.

This is where you can build a short watchlist that isn’t just “short anything with AI in the deck.”

AI capex stories where the ROI is fuzzy

Seat-based software models that don’t control the outcome

Infrastructure middlemen with weak moats

And the “sneaky” loser category:
anything priced like it will capture infinite AI growth while ignoring power, latency, and customer bargaining power. Inference puts the customer back in charge.

The easiest way to understand this moment:

Training is building the brain. Inference is running the business.
And Nvidia’s move into inference-specific systems is the market leader telling you the next war is about speed, cost, and power efficiency — not just raw compute.

The AI race isn’t ending. It’s maturing.
And when a tech theme matures, the biggest money moves from hype… to unit economics.

Yesterday played out as planned. Stocks dip, oil rips, and then the market ended green. Today is starting off ugly, and I would expect that it stays that way unless we get something new. As I said yesterday, keep an eye on oil prices, that’s the best tell for where stocks should go.

There is still a lot going on in the market, but for now it will all be obscured by the back and forth in the war. As always, make sure you have hedges in place.

Two of the most interesting areas in the market lately have been photonics and memory. Photonics stocks ripped yesterday on news that NVDA will invest $4bn into LITE and COHR. These have both been featured here as a Stock I’m Watching, and are in UFOD. This area is extended, and these stocks are selling off so far this morning.

As far as memory names go, what I talk about above could have an impact. Groq relies on Static Random Access Memory (SRAM) not DRAM and HBM. That could cause a reaction in these stocks. These are also selling off hard this morning.

OUST is one of the cleaner “physical AI” ways to play autonomy: it sells digital LiDAR sensors that act as the eyes for robots, industrial automation, mapping, and increasingly defense/UAS workflows. The bull case isn’t “AI hype” so much as forced spend: once customers decide to automate warehouses, perimeter security, ports, rail yards, or drones, they have to buy reliable perception hardware and the software layer around it—LiDAR is a gating item. In its latest update, Ouster highlighted product revenue around $41M and total revenue around $62M (including royalties), with ~8.1k sensors shipped and ~$177M in product bookings, then guided the next quarter to ~$45–48M revenue with ~28–30% gross margin as it integrates StereoLabs (which it says closed in early February 2026). The “why now” catalyst that matters for a UFOD-style sleeve is defense credibility: Reuters reported Ouster’s OS1 being added to the DoD’s Blue UAS Framework, which is the kind of compliance/approval milestone that can unlock program-level demand vs. one-off pilots. The key risk is still classic small-cap hardware execution: competition and pricing pressure can be brutal, and even with demand, margin/FCF can lag if capacity, go-to-market, and software investment ramp faster than revenue.

The stock is currently up over 16% in the premarket and would open above the 50 day. Bulls would like to see it break back above the 200 day as well.

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