SLC

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[Gavin Baker @GavinSBaker]

Market is overreacting to hyperscale credit spreads widening from my perspective. TL;DR Spot pricing for renting GPU compute materially above contracted rates implies hyperscalers are underearning while operating cash flow acceleration is an underestimated source of funds for AI capex.

The fact that spot prices for GPU rentals are at least 2x higher than contracted rates is the missing piece from the discussion about hyperscaler credit, which is the only fundamental factor behind this selloff. Multiple private companies are planning on spending at least 2x more per GPU for compute as contracts roll-off and some have spoken about this publicly.

As contracts roll-off, hyperscale growth rates are going to continue to accelerate as their installed bases of compute reprice higher. Hyperscale operating cash flow growth using a mix of estimates and actuals is modeled to accelerate from 31% in the first quarter of 2026 to 50% in the second quarter. This acceleration should continue for the rest of the year and this is not in estimates which incorrectly model a deceleration in the third quarter from my perspective.

Some math. Consensus estimates are probably for 25-35 gigawatts added by hyperscale and neoclouds in CY28 (using a range as standing up datacenters is hard and a lot of the neos plus labs are still private).  At 60b per gigawatt, that is 1.5 to 2.2 trillion in capex. Consensus estimates for hyperscale/neo operating cash flow is 1.3 to 1.4 trillion. I think this gets revised up materially as contracts reprice and growth accelerates so the 100b to 700b that would hypothetically need to be plugged by debt goes away. And their credit profiles materially improve. Not to mention the said 100b to 700b would be less than 1 turn of incremental leverage on consensus EBITDA estimates. And obviously the Nvidia and Broadcom “credit wrappers” help improve creditworthiness as well given their FCF profiles.

OpenAI, Cursor/Grok and the various Open Source inference clouds have accelerated materially over the last two months per public data and Anthropic continues to grow insanely fast while likely generating FCF. This - along with the fact that spot prices for GPU rentals are so far ahead of contract - are the missing pieces from the BofA chart on hyperscale FCF vs. semiconductor FCF.

Hyperscalers are underearning and anyone who signed a contract for GPU compute in 2024 and 2025 is overearning. Operating cash flow will be enough to fund capex but as contracts reprice and cloud growth continues to accelerate then spreads likely come in as well. 

Would also note that CDS markets are easy to manipulate - was a huge feature of the GFC - short the stock and then buy the CDS. So I would not put attach much signal to CDS.

Net, net I’m not that concerned about the widening spreads in hyperscale credit. The real risk is that bringing power online and energizing all these GPUs is really hard but we are getting better at this every day.

[SLC @SLC_Fund]

Stifel on the robot assisted surgery competitive landscape

$ISRG $MDT $JNJ

[Michael J. Duarte @michaeljduarte]

It happened again! Trump lingers on the stage as Infantino tries to direct him off of it so Spain can lift the World Cup trophy. 🤣

[Gavin Baker @GavinSBaker]

Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis.

Rationale:

A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers. 

Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software.

This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time.

Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3.

The reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead.

Time will tell on both points. And likely fairly quickly.

Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.

[SLC @SLC_Fund]

If you think the US stock market is concentrated...

[SLC @SLC_Fund]

OpenAI & Anthropic compute commitments with hyperscaler backlog

$MSFT $ORCL $GOOG $AMZN

[SLC @SLC_Fund]

Biggest healthcare specialist managers and holding overlap

$ARGX $RVND $ABVX $INSM $NBIX $PRAX $UTHR

[SLC @SLC_Fund]

$CLPT $QURE 🥂

[SLC @SLC_Fund]

@DurableCreators Semi component costs hurt. Plus healthcare sucks right now. Hard to believe they won’t continue winning long term.

[SLC @SLC_Fund]

$MSFT

[Dylan Patel @dylan522p]

Usage share of OpenAI grew vs Anthropic yesterday despite Mythos 5 / Fable 5 launch
Multiple power users at SemiAnalysis tried Mythos / Fable
Got refusals for nonsensical reasons
Got pissed off at Anthropic
Gave Codex a legitimate try
Now they actually prefer it to 4.8 Opus

[SLC @SLC_Fund]

@peonyKingOF half cash, half stock

[SLC @SLC_Fund]

$CBRE data center role is program management & facilities maintenance & cabling (not brokerage)

DC = 15% of profits last year growing 25%

Acquired Direct Line 2yrs ago which does technical cabling and fiber optic work inside DC

$META partnership training ~1k technicians/6wks

[SLC @SLC_Fund]

@Crussian17 🐎

[SLC @SLC_Fund]

Wouldn't $UBER spending $10B on autonomous cars be a headwind for ROIC? Capital-light to depreciating asset-heavy cars

At this point is it not a price taking Waymo (with a food delivery business and network?)

Does the higher take rate not get competed away by an aggregator?

[SLC @SLC_Fund]

@CapexAndChill Does it even matter?

[SLC @SLC_Fund]

$NOW

"AI reasoning constitutes less than 10% of cost-to-serve with subscription gross margins remaining above 80%, framing AI overall as a "revenue tailwind for ServiceNow" with margin expansion that is "structural, not cyclical."

[SLC @SLC_Fund]

EPS a-chaingin'

[SLC @SLC_Fund]

Capital allocations a-changin'

[SLC @SLC_Fund]

(2)F2Q was the fastest organic rev growth (+15%) posted in three years, (3) non-VAS revenue was up low-teens, fastest in almost four years according to our model, (4) Visa bought back $8B in shares in the quarter, the highest amount ever

[SLC @SLC_Fund]

"Visa did a nice job making the case that some of the perceived threats to the business (i.e. stablecoins and Agentic commerce) will be tailwinds

[SLC @SLC_Fund]

"$V posted its largest beat vs Street rev estimates in four years, while better than expected FX vol drag contributed, momentum is clearly broad-based: (1) U.S. volume growth (overall +8%; credit +10%) was faster than it has been in three years quelling high penetration concerns