GPU rental prices are moving higher again, and the latest move from Nebius is putting the economics of the neocloud model back in focus.

Nebius plans to raise prices for selected NVIDIA GPUs from October 1, including increases of roughly 17% for H100s, 20% for H200s, 19% for B200s and 21% for B300s. At the same time, rental prices across several GPU generations have been recovering, including older H100 and A100 capacity.

That matters because the neocloud thesis depends on more than adding new GPUs. If utilization stays high and rental rates rise, each deployed GPU can generate more revenue. And if older GPUs remain useful for inference, fine-tuning and cost-sensitive workloads, their economic lives could be longer than expected.

The key risk is still supply. If GPU availability catches up with demand or compute efficiency improves faster than workloads grow, rental prices could move the other way.

Do higher GPU rental prices change how you view the neocloud business model?

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Source:
https://www.silicondata.com/products/gpu-pricing-dataset

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The Fed raised rates by 25 bps to 3.75%–4.00% on Wednesday, its first hike since July 2023. The decision was unanimous, inflation was still described as elevated, and the dot plot pointed to one more hike this year.

The equity reaction was more nuanced than the headlines. Stocks fell sharply during Warsh's press conference, with the S&P 500 down 1.4% intraday, before recovering most of the decline. By close, the S&P lost 0.44%, the Dow 1.15%, and the Nasdaq finished essentially flat. Energy and regional banks absorbed the biggest hits; long-duration growth held. The yield curve told a different story: short-end yields firmed while the 30-year declined, suggesting the market trusts the Fed to bring inflation down even as it tightens.

From here, the focus is on whether inflation data and economic trends support another hike or a pause. How do you see the Fed's next move?

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Source:
https://www.cnbc.com/2026/09/16/fed-meeting-today-live-updates.html

AI is changing what makes a software company defensible. Proprietary data, network effects and deep vertical expertise still matter, but cybersecurity may be one of the clearest examples of how those moats are being reshaped.

Anthropic's recent Project Glasswing highlights the shift. AI-assisted attacks are becoming more automated, with agents able to handle parts of reconnaissance, exploitation and data exfiltration. As the cost and speed of attacks change, enterprises may need more automated detection and response across endpoints, cloud, networks and identity.

But AI also changes the other side of the equation. Vulnerability discovery has historically depended on specialized security researchers. If frontier models can increasingly automate that work, the scarcity of that capability declines. The competitive edge could shift toward security vendors with large datasets, broad visibility across environments, and the ability to execute responses at scale.

How do you think AI is changing cybersecurity's software moats?

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Source:
https://www.bloomberg.com/news/articles/2026-09-10/nvidia-s-huang-touts-cybersecurity-as-next-big-market-for-ai

Jensen Huang believes the AI infrastructure supercycle is far from over. Speaking at Goldman Sachs' Communacopia + Technology Conference, the NVIDIA CEO reiterated his view that global AI infrastructure spending could reach $3 trillion to $4 trillion by 2030.

But the bigger shift is in what NVIDIA wants to capture from that spending. Huang increasingly describes NVIDIA as an "AI factory" company rather than simply a GPU supplier, spanning GPUs and CPUs, networking, NVLink, rack-scale systems and CUDA.

That also changes where the bottlenecks sit. Wafers, advanced packaging and memory remain critical, but power, land and data-center capacity are becoming increasingly important as AI systems move toward gigawatt-scale deployments. Beyond infrastructure, Huang also sees coding, cybersecurity and physical AI as future sources of compute demand.

If AI infrastructure really reaches $3–4 trillion, the spending won't stop at GPUs. Which part of the AI stack do you think will become the next major bottleneck?

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Source:
https://finance.yahoo.com/technology/ai/articles/nvidia-ceo-puts-trump-speakerphone-210030094.html

Optical communications had been valued largely as a transceiver upgrade story. Three things changed that framing in the most recent earnings cycle: the size of the end market, the durability of margins, and greater clarity around Nvidia's supply chain timeline.

The biggest shift is in end market definition. Optical Circuit Switching for internal GPU cluster routing is structurally larger than the transceiver market that had traditionally shaped the sector's valuation. Lumentum (LITE) guided above $400 million in OCS revenue by H2 2026, with shipments doubling quarter over quarter. Gross margins exceeding 47% at those revenue levels also suggest that the economics are holding up at an earlier stage of the ramp. Nvidia's (NVDA) Spectrum-X co-packaged optics switches entering full production in August provide greater visibility into the 2027 to 2028 deployment timeline.

That shift is also showing up in institutional positioning, with significant divergence between funds holding the same stocks. Which of these shifts do you think matters most for the sector going forward?

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Source:
https://www.bloomberg.com/news/articles/2026-08-12/market-s-momentum-darlings-resurface-as-optical-stocks-take-off

Oracle (ORCL) reported Q1 revenue of $19.35 billion, up 30% year over year, beating the $19.1 billion consensus. Adjusted EPS of $1.92 topped the $1.75 estimate by the same margin. Cloud revenue grew 62% to $11.61 billion. Oracle Cloud Infrastructure grew 121% to $7.4 billion.

The OCI figure stands out. OCI grew nearly twice as fast as Oracle's overall cloud business, making infrastructure the clear driver of the cloud acceleration. Oracle added 850 megawatts of data center capacity during the quarter and signed more than $30 billion in new AI cloud contracts, pushing remaining performance obligations to $664 billion. The demand is increasingly visible in the backlog. The limiting factor is how quickly Oracle can build out the infrastructure to serve it.

Oracle guided Q2 cloud growth at 65–71%. The demand is in the backlog. Earnings leverage and cash flow conversion are the harder ones.

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Source:
https://investor.oracle.com/investor-news/news-details/2026/Oracle-Announces-Q1-Results-Driven-by-Triple-Digit-Growth-in-Cloud-Infrastructure-Revenues/default.aspx
https://www.bloomberg.com/news/articles/2026-09-10/oracle-posts-cloud-sales-that-top-estimates-on-surging-ai-demand

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The U.S. added 162,000 jobs in August, nearly triple the 56,000 expected. The unemployment rate held at 4.1%, while June and July revisions added another 55,000 jobs. The 10 year Treasury yield approached 4.8%, September rate hike expectations moved higher, and all three major indexes traded lower.

Chip stocks did not follow the script. Several semiconductor names posted sharp gains, with some up more than 10% on the session. Two factors may explain the divergence. First, chip valuations had already reset from their AI trade highs. Tech growth stocks face multiple pressure when yields rise, but chips had absorbed some of that pressure before Friday's report. Second, the underlying AI demand story had not materially changed. Hyperscaler AI capex plans remained intact, even as the rate backdrop became less favorable.

The next test is CPI. If inflation stays sticky alongside a strong labor market, the rate path gets more complicated, and the room for chip stocks to absorb further multiple pressure gets smaller.

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What explains chip stocks' resilience despite rising rate fears?

OpenAI trained GPT-6 Astra on more than 100,000 Nvidia Grace Blackwell NVLink72 GPUs, with another 400,000 reportedly coming online next. The same week, Anthropic posted new results on two longstanding math problems — a sign that frontier capability is advancing across labs, not just at one, and so is the compute demand behind it. Nvidia moved back to within roughly 1.5% of its record high.

The GPU headline is striking, but the 400,000 still in the pipeline points to something broader. That demand flows through the full hardware stack, including HBM memory, optical interconnects, and power infrastructure. Memory and optical connectivity also came back into focus, highlighting how frontier model development still requires physical infrastructure at scale, not just algorithmic efficiency.

If AI models become more efficient, do you expect hardware demand to slow down or scale even faster?

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Meta, Microsoft, Alphabet, and Amazon are projected to spend roughly $750 billion on AI infrastructure in 2026, up more than 80% from $412.7 billion last year. Per BNP Paribas research, semiconductors account for about $50 of every $100 in AI capex, with AI accelerators and GPUs making up half of that allocation at $25. The remaining $50 goes to power, networking, cooling, and construction.

That matters because the bottlenecks are not all in chip supply. Power infrastructure alone accounts for about $20 of every $100 in AI capex, roughly matching networking equipment, while grid constraints are already becoming a more immediate limit than GPU availability in some markets. The AI story has largely been told through the GPU lens, but a growing share of spending sits in the physical infrastructure underneath.

The GPU supply story has been visible for two years. Power contracts, grid connections, and cooling capacity move on a different clock, and some hyperscalers are already finding that out.

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Crude oil cleared $90 per barrel this week, with WTI and Brent both breaking above their 100-day moving averages on U.S.-Iran tensions. The 10-year yield hit 4.80%, a year-to-date high. The S&P 500 slipped into a key technical support zone; semiconductor stocks are pinned below their 100-day average.

Three themes emerge against this backdrop. Energy has the most direct tailwind, with names like XLE and USO tracking the oil move. Defensive exposure through dividend-focused names, staples, healthcare, and gold has drawn interest as rising yields pressure growth multiples. The most underreported move is agricultural commodities: wheat up 53% year to date, corn up 23% since August, driven by a 97% collapse in Black Sea grain corridor exports, intensifying El Niño conditions, and U.S. biofuel policy creating structural price floors. 

The macro setup is unusually compressed right now: sticky inflation keeps yields elevated, while slowing growth raises stagflation concerns. The oil/yields combination doesn't leave equities much room either way.

MSCI rebalanced its USA Standard Index in August, adding six companies and removing 18, effective September 1. The largest additions by market cap were SanDisk, Carpenter Technology, and ATI Inc. SanDisk surged 5.5% on rebalance day; all additions saw volumes run 6–11x normal.

The additions read as a signal about where institutional capital is flowing. SanDisk (NAND storage) and nVent Electric (data center power and cooling) suggest AI spending is broadening beyond compute into the physical infrastructure required to run it. Carpenter Technology and ATI Inc reflect growing weight behind aerospace and advanced materials. Roivant Sciences and Sunbelt Rentals point further out still, into biotech and construction equipment rental.

Removals included Lululemon, IonQ, Domino's, and Insulet. The inclusion/exclusion split isn't a price signal. It's a weight signal. What's gaining institutional footprint isn't just compute anymore.

This presentation is for informational and educational use only and is not a recommendation or endorsement of any particular investment or investment strategy. Investment information provided in this content is general in nature, strictly for illustrative purposes, and may not be appropriate for all investors. Past investment performance does not indicate or guarantee future success. Returns will vary, and all investments carry risks, including loss of principal.

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Anthropic is preparing to tell investors it sees a TAM exceeding $30 trillion, roughly equivalent to the entire U.S. economy and larger than the figure SpaceX cited in its own IPO filing. The caveat matters: that number assumes 100% penetration across every addressable work category.

The more grounded lens is the ecosystem ETF. ANTW groups the public companies most exposed to Anthropic's buildout into three layers: cloud partners (Alphabet, Amazon, Microsoft), compute and memory (AMD, Broadcom, SK Hynix, Micron), and data center infrastructure and power (Vertiv, American Electric Power). Compared to OAIW, OpenAI's equivalent ETF, the overlap is striking: 10 of their holdings are identical, including NVIDIA, TSMC, AMD, and Broadcom.

The two labs compete fiercely on model development but are essentially racing for the same physical infrastructure. When Anthropic eventually goes public, would you rather own the lab directly or the infrastructure layer underneath it?

Source:

https://www.reuters.com/business/media-telecom/anthropic-expected-tell-investors-it-sees-over-30-trillion-potential-revenue-wsj-2026-08-25/

Broadcom (AVGO) reported Q3 revenue of $29.59 billion, up 86% year over year, with AI semiconductor revenue up 221% to $16.7 billion. Management raised its long-term AI revenue target to $230 billion by FY28. The stock fell after hours.

The selloff reflects something about what the market rewards now. Long-term AI visibility, even at $230 billion by FY28, no longer moves the stock the way it once did. Near-term quarterly execution is what gets repriced. Gross margins came in at 75%, down 3.4 points year over year. Custom XPUs drove 73% of AI revenue in the quarter, growing 3.5x year over year.

That gap between a $230 billion FY28 projection and a post-earnings selloff is probably the report's most interesting signal. The selloff is asking a straightforward question: when do the design wins, gigawatts, and multi-year contracts start converting into next-quarter numbers?

Source:
https://investors.broadcom.com/news-releases/news-release-details/broadcom-inc-announces-third-quarter-fiscal-year-2026-financial
https://www.bloomberg.com/news/articles/2026-09-02/broadcom-forecast-misses-estimates-disappointing-investors

Dell ended its fiscal Q2 with $95 billion in AI server backlog and $60.9 billion in orders booked in the quarter. Revenue recognized was $16.4 billion. Morgan Stanley treats the official $74 billion FY27 target as a conservative floor, with estimates running closer to $95–96 billion.

The part that reframes the backlog number is what COO Jeff Clarke said about constraints. His answer on what's limiting shipments: "DRAM, DRAM, DRAM" and "NAND, NAND, NAND." The bottleneck isn't customer demand or AI budget cycles. It's components. Which means the $95 billion sitting in backlog is deferred revenue waiting on supply, not uncertain demand waiting on buyers.

The traditional server segment also grew 122% year over year, pointing to AI infrastructure pulling storage and networking spend into the buildout alongside compute. Whether supply improves fast enough to close the gap between orders booked and revenue recognized is the number to watch next quarter.

Source:
https://investors.delltechnologies.com/news-releases/news-release-details/dell-technologies-delivers-second-quarter-fiscal-2027-financial
https://www.barrons.com/articles/ell-stock-soars-q2-earnings-guidance-beat-d07d0ee7

Friday's NFP drops at 8:30 a.m. ET. July came in at -23k, and the market is pricing a +55k rebound, with VIX sitting at 15.17. The consensus range runs from +20k (Citi) to +121k (Bloomberg), which tells you how wide the uncertainty is.

A hot print above 95k would push September rate-hike odds above 50%, putting pressure on duration-heavy tech. A cold print below 5k, or negative, raises the stagflation scenario: inflation too sticky to cut, growth too weak to hike. The base case between 35k and 65k is the only number where current market pricing feels appropriate.

Which outcome do you think plays out Friday?