Bartex

Just another schmuck running some money.

[*Walter Bloomberg @DeItaone]

GOLDMAN SEES SPACEX AI REVENUE EXPLODING TO $322B BY 2030

Goldman Sachs projects SpaceX AI revenue rising from $3.2B in 2025 to $322B by 2030, a ~100x increase, forming the core justification for its $1.78T IPO valuation. Total revenue is forecast to reach $474B, with Starlink at $144B and rockets at $8.3B.

AI segment growth is tied to aggressive market assumptions despite current losses and execution concerns around xAI. Overall EBITDA is seen surging to $352B by 2030.

[Bartex @bartexcapital]

@SweeneySteve Do you ever report on Russian missile or drone attacks on Ukraine?

[based16z @based16z]

Markets are so inefficient these days… Jensen Huang said MRVL should be 1 trillion and instead of being up 5x after hours to 1 trillion it’s only up 20%

[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.

[Bartex @bartexcapital]

Bullshit kabuki theater

[Faytuks Network @FaytuksNetwork]

Trump told Netanyahu that bombing Beirut would further isolate Israel globally. Trump also reminded Netanyahu he’d helped keep him out of prison during his corruption trial, telling him: “You’re fucking crazy. You’d be in prison if it weren’t for me. I’m saving your ass. Everybody hates you now. Everybody hates Israel because of this.” A second source said Trump was “pissed” and at one point yelled: “What the fuck are you doing?” - Axios

[Boring_Business @BoringBiz_]

If you are having a bad day, remember that Mohnish Pabrai had 79% of his portfolio in Micron but sold the entire stake in the middle of 2023

The stock is +1400% since then

[Brandon Carl @brandonjcarl]

Earnings Before Tokens

[Yoshik @AskYoshik]

The AI numbers are starting to look very ugly.

Even under "best case" assumptions, FT's own data shows Microsoft AI ROI at -9%, Google at -15%, Meta at -28%, Oracle at -35%. Only Amazon barely comes out positive.

This is exactly why I keep comparing this to the dot-com era. Incredible technology does not automatically mean sustainable economics. The internet survived. Most internet companies didn't.

Right now hyperscalers are spending trillions hoping future demand catches up to present capex. That's not certainty. That's a leveraged bet.

[Gregg Carlstrom @glcarlstrom]

We are so far into "mad king" territory that the White House may not even bother to clarify whether Trump just confused Oman with Iran or is indeed threatening to bomb Oman

[Democrats @TheDemocrats]

@StephenM shut up you ugly fuck

[Vivek Garipalli @VivekGaripalli]

overheard from a fortune 20 company - ceo asked for $1 billion in AI generated opex savings at the beginning of this year.

the team as a result has spent $200 million on tokens trying to achieve those savings year-to-date, with minimal results other than some modest Cx savings and a bit of savings on engineering due to less hiring driven by coding assistants. now as back-half budgets are being reviewed, it appears that the ceo has ordered token costs to be dramatically slashed as he/she doesn't feel the ROI is there yet (for their company).

gonna be interesting to see if this is a trend amongst the rest of the fortune 500.

[Ben Meiselas @meiselasb]

You are seeing in real time why Trump companies went bankrupt so many times. Set aside your politics. This guy is incapable of making a deal or meaningful decision and it follows the same cycle. Big flashy announcement (Epic Fury). Adversity hits (Hormuz closed). Defraud stakeholders (promise two week solution). Freeze up (endless two week cycle loop). Compound the problem (resource depletion). Final chance to save face rejected (what you’re seeing now). Bankruptcy strikes and blame others during chaos. Rinse repeat.

[NYTPitchbot @DougJBalloon]

Donald Trump promised the war in Iran would end with unconditional surrender. But he never said which country would do the surrendering.

[Norveçli @norveclifinance]

DeepSeek just popped the American AI bubble.

Not by killing AI.

By killing the fantasy of unlimited AI pricing power.

DeepSeek V4 Pro:
Input: $0.435 per 1M tokens
Output: $0.87 per 1M tokens

OpenAI GPT-5.5:
Input: $5.00
Output: $30.00

Claude Opus 4.7:
Input: $5.00
Output: $25.00

Claude Sonnet 4.6:
Input: $3.00
Output: $15.00

DeepSeek is roughly:

11.5x cheaper than GPT-5.5 on input
34.5x cheaper than GPT-5.5 on output

28.7x cheaper than Claude Opus on output
17.2x cheaper than Claude Sonnet on output

If a model is “good enough” at 1/20th or 1/30th the cost, margins will compress faster than Wall Street expects.

AI is not dead.

But the AI bubble just lost its pricing power.

$NVDA $MU $AMD $SMCI $AVGO $ARM $MSFT $GOOGL $META $AMZN $ORCL $PLTR $CRWV $NBIS

#AI #DeepSeek #AIBubble #ArtificialIntelligence #Semiconductors #HBM #DRAM #Nvidia #Micron #AMD #TechStocks #Nasdaq #StockMarket #Investing #WallStreet #YapayZeka #Borsa

[Roger Seheult, MD @RogerSeheult]

My wife discovered this years ago.

[Andrew Kuhn @FocusedCompound]

"Mom, how did we get so rich?"

"Your father invested early in SpaceX, OpenAI, and Anthropic, then dumped his shares on retail at IPO"

[Boring_Business @BoringBiz_]

SpaceX IPO bankers coming up with a $28 trillion TAM to convince you to buy the stock

[jbulltard @jbulltard1]

If you think this is worth $2 trillion you should find a new hobby bc finance isn’t for you

[rosey🌹 @thechosenberg]

This is my quant

[Joe Kunkle @OptionsHawk]

BLDR flying, 1700 June 75 calls w/ buyers, big hammer engulfing today