1) NVIDIA: $68.1B quarter, datacenter = $62.3B (\~91%), \~75% gross margin

If these numbers hold, the most important part isn’t the revenue growth headline—it’s the *structure*:

* Datacenter has basically swallowed the company. Gaming is still alive, but NVIDIA is now priced and operated like “AI infrastructure, the firm.”
* \~75% gross margin is closer to a software business than a semiconductor business. That only happens when you’re capturing value across the stack: GPU (Blackwell) + interconnect (NVLink) + networking (Spectrum-X) + software (CUDA / AI Enterprise) + systems (DGX / GB200 pods).

The “Agentic AI inflection” comment is also strategically timed. Agentic systems aren’t “better chat.” They’re: task decomposition → tool use → actions → feedback loops. That pushes inference into longer chains, higher call frequency, tighter latency constraints—i.e., more inference compute per unit of useful work, and heavier systems integration. If training was the first wave, “agentic inference at scale” is a plausible second wave.

The obvious risk: concentration. When >50% of datacenter demand is a handful of hyperscalers, capex cyclicality turns into near-term volatility. Also, geopolitics (esp. China exposure) is still a wildcard.

**2) AMD + Nutanix: “open full-stack AI infrastructure” (plus AMD money behind it)**

This is AMD acknowledging the real fight: not just silicon performance, but enterprise delivery.

Nutanix’s superpower is the control plane: HCI, operations, “it runs on Tuesday” reliability, and a channel full of non-internet enterprises. If you’re AMD, that’s exactly where you want to embed ROCm + Instinct: into a workflow where customers buy a *system* (deploy/operate/upgrade/compliance), not a GPU SKU.

But “open” is doing a lot of work in that sentence. Enterprises like the idea of avoiding lock-in, but they hate integration entropy. The difference between “open” and “painful” is: certification matrices, version governance, observability, reproducible performance, and someone taking responsibility when it breaks.

If they can deliver “open, but not chaotic,” it’s a credible wedge against CUDA lock-in for a big chunk of the market that just wants a dependable private/hybrid AI stack.

**3) IonQ: $61.9M revenue + EPS $1.93 vs expected -$0.47**

This one sets off my “check the footnotes” reflex. A sudden flip from expected loss to large profit in an early-stage deep-tech company often means non-operating items (fair value changes, one-time gains, accounting effects) are dominating EPS.

Not saying it can’t be real—contract timing can make revenue lumpy—but if you’re trying to understand whether quantum is hitting a commercial inflection, EPS is not the right first metric. The real questions are still: roadmap execution, error rates, scalability, repeatable enterprise contracts, and whether usage expands beyond bespoke projects.

# Most important AI events from the past 72 hours

* [Meta’s $100B AMD bet and the NAND cycle fight over AI storage valuations (Feb 25)](https://iaiseek.com/en/news-detail/feb-25-2026-24-hour-ai-briefing-metas-100b-amd-bet-and-the-nand-cycle-fight-over-ai-storage-valuations)
* [Gemini turns music into a chat feature and training-data allegations heat up again (Feb 24)](https://iaiseek.com/en/news-detail/feb-24-2026-24-hour-ai-briefing-gemini-turns-music-into-a-chat-feature-and-training-data-allegations-heat-up-again)

NVIDIA says “Agentic AI inflection is here” (75% gross margin). AMD + Nutanix want an open full-stack. IonQ posts a shock-profit quarter. Are we entering the “infrastructure + delivery” era?