Rayno in the Clouds💨🤓

R. Scott Raynovich, Principal Analyst @futuriom following #cloud #security #networking #techstocks. Ex. Light Reading, Red Herring. Views my own

[Rayno in the Clouds💨🤓 @rayno]

Just put in an order to buy one share each of $META and $AMZN at $99 each just for chuckles. Will hold forever. Will post updates here. #markets #techstocks @aschmitt @LaMonicaBuzz

[Futuriom @futuriom]

Hedge fund investor Michael Burry (@michaeljburry), who correctly called the 2027-2008 housing finance crisis and rose to fame after being featured in the book and movie The Big Short, is once again critiquing the circular financing deals backing the AI boom, specifically those featuring @nvidia and @OpenAI . $NVDA

https://t.co/jQ8fmkUfmZ

[Futuriom @futuriom]

Data management firm @qumulo Streamlines Hybrid Cloud Data Management for AI. #data #AIdata
(Cloud Tracker Pro - subscription required)

https://t.co/FR2Wh9luXs

[StockMarket.News @_Investinq]

Nicolai Tangen, CEO of Norges Bank Investment Management pressed IBM CEO Arvind Krishna directly on whether AI is a bubble (Save this).

And Krishna responded with what has become known inside financial circles as the $8 trillion math problem.

A single gigawatt of AI data center capacity filled with accelerators, liquid cooling, and power infrastructure costs roughly $60 to $80 billion to build and populate.

The industry has committed to more than 100 gigawatts of buildout globally.

That is $6 to $8 trillion in capital expenditure and because AI grade hardware depreciates on a five-year cycle, that entire sum must be effectively replaced and refreshed every five years.

To service the interest on $8 trillion in capital at a conservative 10% borrowing rate, the AI ecosystem would need to generate approximately $800 billion in annual profit, a number that currently exceeds the combined net income of every large technology company in the world.

Goldman Sachs estimates $7.6 trillion in aggregate AI CapEx between 2026 and 2031 alone, and Reuters Breakingviews has flagged that even if the capital is available, physical bottlenecks power permits, land, cooling infrastructure, and electrical grid connections mean that half of the planned data center projects are being cancelled or delayed before they ever go live.

Krishna also raised a second, structurally distinct concern that markets have largely ignored.

He argued that the largest foundation models, GPT, Gemini, Claude, Llama are converging toward commodity status.

When a product is a commodity, switching costs collapse.

When switching costs collapse, pricing power evaporates and margins compress regardless of how much capital was spent building the capability.

Morningstar's equity research team conducted a review of 132 technology companies in 2026 and found that AI had caused moat rating downgrades across roughly 40 major stocks concentrated in enterprise software, IT services, and SaaS with Adobe, Salesforce, Workday, and ADP among the companies whose competitive moats have materially weakened.

The implication is that the companies spending the most on AI model development may be building an asset that is simultaneously the most expensive to produce and the most difficult to monetize with durable margins.

This bear case is serious but it is also incomplete and that is what makes Krishna's framing so important to understand precisely.

When pressed further, Krishna explicitly said he does not believe there is an AI bubble in the technology itself only in a subset of the infrastructure capital that is being deployed against speculative assumptions rather than proven demand.

He draws the same analogy, the fiber optic overbuild of the late 1990s. Dozens of companies went bankrupt laying cable that nobody was using.

And yet that exact "wasted" infrastructure became the physical backbone of every cloud company, every streaming service, every mobile network, and every modern AI training cluster that followed.

The builders lost, the infrastructure won.

And the companies that were built on top of it, Amazon, Google, Netflix, Salesforce compounded for two decades.

The question, as Krishna framed it, is not whether AI is real.

It is which capital deployment earns a return versus which gets stranded and crucially, whether you own the stranded assets or the companies built on top of them.

On winners, Krishna was direct that distribution is the moat on the consumer side, and enterprise is wide open.

The data supports this, Meta with 3.3 billion daily active users across Facebook, Instagram, and WhatsApp is building AI into a distribution network that no startup can replicate at any cost.

Meanwhile, the productivity evidence arriving in real time is beginning to challenge the bear case's revenue projections.

Jensen Huang just showed on stage at Computex that GitHub commits, the universal measure of global software output nearly tripled in the first months of 2026, effectively converting $3 trillion in developer salaries into $9 trillion in productive output.

That is measurable, real time economic value already flowing through the system and it feeds directly back into token demand in a compounding loop that Krishna's static CapEx math does not fully capture.

[Rayno in the Clouds💨🤓 @rayno]

@amitisinvesting Private equity and shadow debt. It’s hard to know how much debt there is and its rating. More debt has moved into dark pools where there is less data. Less transparent now than in 2008. A lot of this debt is financing datacenter projects which have unknown ROI.

[Rayno in the Clouds💨🤓 @rayno]

Nvidia Is Expanding Infra Partnerships. Will A Big Deal Happen? via @forbes $NVDA @nvidia $DELL $CSCO

https://t.co/yiqUTLpvxH

[Andrew Feldman @andrewdfeldman]

For years, every major AI launch told the same story.
A new model, smarter than the last.
Google I/O was different.

The lead model wasn't the smartest one.
It was their fastest inference model - Gemini 3.5 Flash.

Sundar spent the keynote describing how they tuned it for speed.
When the largest AI company on earth makes speed the headline, you know it matters.

We agree.
Inference speed drives adoption.

If it's fast, you use it more often, you stay on it longer, and you use it for more interesting problems.

That's been true in every technology wave.
It was true of search, it was true of the internet, and it's true of AI.

And in the agentic era, it compounds.
Speed moves from a nice-to-have to a necessity.

Yesterday we brought Kimi K2.6, a trillion-parameter open-weight model, into enterprise trials.

Artificial Analysis measured us at 981 tokens per second - 6.7x faster than the next-fastest GPU cloud, 23x faster than the median provider.

Front-end iteration feels instant.
Hard refactors finish in a fraction of the time.

Google made fast inference the headline.
And @cerebras delivers it. The fastest inference in the world.

[Futuriom @futuriom]

Why @Cisco's record growth reflects new AI infra mojo. $CSCO

Full analysis of Cisco's earnings announcement below:

https://t.co/JhY3vFhNhZ

[Futuriom @futuriom]

How @cerebras became the largest chip IPO in history. $CBRS #AI #Chips

https://t.co/Yl2no3VoDi

[Rayno in the Clouds💨🤓 @rayno]

@DougKass @GuyAdami @TheJudgeCNBC @CNBC What about the wizards of Ozs behind the machines?

[Rayno in the Clouds💨🤓 @rayno]

@danielnewmanUV It has nothing to do with the multiples it has to do where the money is coming from.

[Rayno in the Clouds💨🤓 @rayno]

@andrewdfeldman @cerebras @Nasdaq Congrats my man! Follow our coverage on @futuriom

[Rayno in the Clouds💨🤓 @rayno]

Holy sketchy transactions

[Evan @StockMKTNewz]

DOES 🇺🇸 PRESIDENT TRUMP OWN $1+ MILLION OF NVIDIA $NVDA STOCK

Apparently Trump just filed this showing some of the moves he has personally made so far in 2026

The document is 113 pages long and has 3,642 transactions including

More than $1 Million of:

ServiceNow $NOW
Nvidia $NVDA
Adobe $ADBE
Workday $WDAY
Oracle $ORCL
Microsoft $MSFT
Broadcom $AVGO
Amazon $AMZN
$UBER
Apple $AAPL
Boeing $BA
$DELL

And much more ⬇️

[Rayno in the Clouds💨🤓 @rayno]

Wild AH action in $CSCO. Stock up 20%. Record revenue of $15.8 billion, up 12% year over year. Three new hyperscaler design wins.

I guess @Cisco got the #AI infra strategy dialed in. Full blog post tomorrow.

[Rayno in the Clouds💨🤓 @rayno]

How is #AI being applied to the #edge?

The need for efficient AI inference is growing, driving demand for edge infrastructure. #techprimer sponsored by @ZededaEdge

https://t.co/MhK9OKLxUM

[Context Football @futbolscreen]

BREAKING NEWS 🔴🔥This goal has been voted as the best team goal ever

[Rayno in the Clouds💨🤓 @rayno]

@EricJhonsa totally agree. When it looks like the World's Fair, run the other way.

[Eric Jhonsa @EricJhonsa]

Could be wrong, but much of the AI infra trade looks like it's at or near a short-term top. Massive call-buying, shorts capitulating, FOMO among PMs, random SMIDs blasting off after getting pumped online, all while a big macro risk is ignored...I've seen this movie before.

[Howard Lindzon @howardlindzon]

keep an eye on semiconductors here ...

Hard for good things to happen this far from the 200 day moving average

but it could of course be even better than 1999 so manage accordingly

ht @Barchart @Marlin_Capital

[ℏεsam @Hesamation]

Cloudflare lays off +1,100 employees. meanwhile, they have 640 job openings right now.

tech job market is a scam.

[Ryan Cohen @ryancohen]

I’m selling stuff on eBay to pay for eBay

https://t.co/REaITX9iXr