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Sunday, October 25, 2026

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@convequity

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Why I think Nvidia buying Reflection AI helps Palantir more than it hurts $NVDA wants its models inside enterprises, not just hyperscalers, neo-clouds and AI labs. Buying Reflection AI would speed that up. The FT reported on 10 Oct that Nvidia is in early talks to buy Reflection, raise its stake, supply it more chips, or hire its team and license its technology. Bloomberg's source says no terms have been reached. Nvidia has already invested $800m, so a buyout or a bigger stake means pricing a company it already partly owns. The enterprise push Nvidia agreed in September to buy Hugging Face for $12.9bn. Jensen Huang says more than 200,000 companies use it to deploy AI. Reflection is validated on Dell's AI Factory with Nvidia, and $PLTR's sovereign AI stack runs on Nvidia's AI factory design. Reflection is built for enterprise code. Its Asimov agent reads a company's code, Slack messages, emails and documents. Its first model, Beam (501bn parameters, 23bn active), is trained for coding. Reflection says the weights ship this month under Apache 2.0, for on-prem or air-gapped use. The bear case: Nemotron is moving too slowly at Palantir One reading is that Nvidia wants Reflection because its Nemotron models are moving too slowly with Palantir. I think that is unlikely, given how Palantir has executed. At AIPCon on 10 Sep, Nvidia's supply-chain team said Nemotron 3.5 Lightning, post-trained in Palantir, beat Nemotron 3 Ultra by a large gap on Nvidia's own benchmark. It runs on-prem and retrains on one GPU in a couple of hours. Cisco will offer Nemotron post-trained with Palantir in its AI Factory with Nvidia. L3Harris said open models fine-tuned on its own data beat the frontier models it had used within 48 hours, at 95% lower cost. Palantir has kept shipping open-model deployments. In June it launched an engine to run Nemotron in sovereign environments, and customers own the retrained models. On 1 Oct Armada became its first certified modular data-centre partner. On 12 Oct $ONDS formed Regna AI on AIP and open-weight models. The likelier case: Nvidia is backing more than one route Palantir fits open models to a company's data, permissions and workflows. Reflection is built to read the company's code. Buying a team that has already built that is quicker than building it in-house. The limit is vendor API rules Reflection reaches a company's systems through the vendors' APIs, and vendors decide what passes through them. $SAP's April API policy bars AI agents that plan or execute API calls, and bulk data extraction, outside SAP-endorsed pathways. Since May 2025, Slack's API terms bar third-party apps from using Slack data to train a large language model or bulk-exporting messages. Those rules apply to Palantir too. In May, SAP made Palantir AIP for data migration an SAP Endorsed App. An independent Reflection would likely need similar approval from each vendor whose systems it reads. Who pays the compute rent Reflection trained Beam on Nvidia GB300s rented from $SPCX at Colossus 2 in Memphis, under a June deal worth up to $6.3bn: about $150m a month through 2029. $NBIS agreed in July to sell it $1bn more capacity. If Nvidia buys Reflection, it takes on those contracts and pays SpaceX rent on Nvidia chips that SpaceX bought. In an acqui-hire, the contracts stay with what remains of Reflection unless the deal says otherwise. The FT says an acqui-hire would likely avoid a long regulatory review. Nebius holders should watch which company ends up owing that $1bn contract. What it means for Palantir The risk is an independent Reflection selling its own forward-deployed team and turnkey stack, which is how Palantir sells. If Nvidia owns Reflection, I expect Beam to reach AIP customers the same way Nemotron does, with each customer fine-tuning it on its own data inside Palantir.
@StableBread

Fajasy on X

In November 2025, BWX Technologies $BWXT sold $1.25B of 0% convertible notes due November 2030 and used $830.8M of the proceeds to repay its old credit facility. The conversion price is $262.51/share, 80.2% above $145.70, and $131.9M of capped calls offset dilution up to $396.24/share. Dilution has been minimal so far. Diluted shares have held at ~92M since 2023, helped by small buybacks ($30M in 2025). But stock comp rose to $20.1M in H1 2026 from $13.7M a year earlier, although it’s still only 1.1% of revenue, up from 0.9%. Management also said on the Q2 call it doesn’t plan buybacks in 2026, despite $347.6M left on the authorization, so nothing will offset the new shares. Even after paying ~$200M for Precision Components Group (PCG) on July 1, $BWXT’s leverage is within its 2-3x target: → Debt: $2.05B ($400M of 4.125% notes due June 2028, $400M of 4.125% notes due April 2029, and the $1.25B convert). → Cash and liquidity: $400M of cash and $1.7B of total liquidity, including an undrawn $1.25B revolver running to November 2030. → Leverage: 2.7x net debt to TTM adjusted EBITDA, or $1.65B of net debt on $602.1M of EBITDA. The $750M from selling its medical isotope business would cut leverage to 1.5x. → Ratings: Fitch BBB (investment grade) since September 2026; S&P BB+ and Moody’s Ba1, one notch below. → Other obligations: $152.0M of underfunded pension and retiree benefit plans. Net debt has hovered between $1.0B and $1.5B since 2023. $BWXT pays a dividend of $0.27/quarter ($1.08/year, a 0.7% yield), raised from $0.25 in February 2026. The company expects $5.5B of capital to deploy through 2030 at 2-3x leverage, with $3.5B left after capex and dividends. That’s what CFO Mike Fitzgerald said at $BWXT’s Investor Day on September 29, 2026, and he also put organic growth first for the $3.5B. But he said $BWXT is “not anchored to the same size of transactions” it has done before, and the deck lists “Disciplined Acquisitions” among the revenue drivers behind the 2030 targets. In other words, $BWXT has the track record and, once the medical sale closes, the balance sheet to go after its 2030 targets.
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Convequity on X

3D chips could create more room for specialist EDA startups. We are not convinced that more difficult chip design automatically translates into a large revenue windfall for $SNPS, $CDNS and Siemens. Fine-pitch stacking changes decisions that used to be made within one die. Put a circuit upstairs and its wires may get shorter, but its heat affects the circuit below. Placement, signal timing and power delivery have to be optimized together across the stack. That creates a reason for $AVGO and $NVDA to build custom tools around their architectures. Broadcom already describes a proprietary design flow built on industry-standard tools. Nvidia publishes its own research on assigning connections across tiers in 3D chips. Our inference is that the chip designers will keep some of the most differentiating optimization work in-house. A startup that solves a costly gap in cross-die planning or optimization could also win business without replacing an entire EDA suite. The incumbents still supply implementation, verification and manufacturing sign-off tools, and they are developing 3D capabilities themselves. Internal tooling is evidence that customers need more than the standard flow; it does not establish that incumbent spending falls. We see a more interesting startup opportunity where a tool can shorten repeated design iterations or improve the resulting chip enough to earn a separate budget. That is the part of the 3D transition we would investigate before assuming the largest EDA vendors capture most of the upside.zz

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