DWin

Posting about financial markets, quality stocks and other topics

[DWin @DWin10]

@dnystedt Would be funny if ASML can charge an Elon-Premium on their tools ;)

[Charlie Bilello @charliebilello]

Crazy stat: just 3 companies are expected to generate over half of total earnings growth for the entire Emerging Markets universe.

1) Taiwan Semiconductor
2) Samsung Electronics
3) SK Hynix

All driven by the AI CapEx boom.

Video: https://t.co/HdoFAnN6sC

[Daily Chartbook @dailychartbook]

"The beta of the Value & Growth indices have completely switched places"

@MichaelKantro

[GURGAVIN @gurgavin]

JP MORGAN HAS RAISED IT’S TESLA PRICE TRAGET TO $475 FROM $145

THIS IS AFTER THE BANK WAS CHOSEN BY ELON MUSK TO BE PART OF THE SPACX AND EARN 100’S OF MILLIONS IN FEES

I HOPE PEOPLE CAN SEE THRU THIS BS

$TSLA

[Rob Anderson @_rob_anderson]

NDR's pattern matching tool shows that the NASDAQ has closely tracked the dotcom analog and is closer to 1998 than 2000. It still suggests near-term volatility ahead.

[DWin @DWin10]

#Nvidia $NVDA grew revenues by 85% yoy. Their Q2 outlook is not assuming any Data Center compute revenue from China. Overall strong numbers.

[ Q-Cap  @qcapital2020]

The Greatest Trader of All Time

[Helene Meisler @Chartfest1]

If you weren't trading in 1999-2000, then you're in luck. This is what it was like. Now you can experience it.

[dylan ツ @demian_ai]

Inference got a hundred times cheaper this year. The compute bill went up anyway.

If you understand why those two sentences are both true at the same time, you understand the most important thing happening in AI right now.

I work on inference for a living, at @nebiustf, where we run open-source managed inference at scale. Most of what follows is what I'm seeing from inside the bill.

12 months ago, the cost of 1M tokens of frontier-class reasoning was somewhere on the order of $60.
Today, an equivalent quality of output costs roughly $0.50.

Price /token of o1-level intelligence has dropped about a 128x in a year.
Price of GPT-4-level output has dropped roughly 100x since the original GPT-4 shipped.

By any normal reading of a technology cost curve, this should be deflationary. It should be saving customers money.

The opposite has happened. The total compute bill at every hyperscaler is going up, not down. Anthropic just signed multi-year capacity deals with both XAI and Amazon. Microsoft's Azure capex guide for 2026 starts with an eight. OpenAI is reportedly spending more on compute every quarter than it did in all of 2023. Nvidia paid roughly twenty billion dollars to acquire Groq, an inference-specialist company that did not exist as a serious commercial entity three years ago.
The cost curve and the demand curve crossed, and then the demand curve lapped the cost curve.

Here is what happened underneath.
A reasoning model burns roughly 10x the output tokens of a non-reasoning model on the same task, because it spends most of its tokens thinking out loud before answering. An agentic workflow chains roughly twenty times the requests of a single-shot completion, because it loops, calls tools, plans, retries, and synthesizes. A modern deep-research query (the kind a research analyst can fire off in fifteen seconds and then walk away from for ten minutes) costs more compute than 10 original GPT-4 queries combined. We made every individual token a hundred times cheaper, and then we built a generation of products that consume ten thousand times more tokens.

This is the Jevons paradox playing out at trillion-dollar scale, in compressed time, in front of everyone. Jevons noticed in 1865 that making coal-burning more efficient did not reduce coal consumption. It increased it, because efficiency unlocked uses that were previously uneconomic. Steam engines became more practical at smaller scales. Whole industries that could not afford coal at the old price suddenly could. Britain's coal consumption rose sharply, not despite the efficiency gains, but because of them.

The same thing is happening to AI compute right now and it is happening faster than any analogous historical cycle. Falling token prices did not contract demand. They unlocked agents, deep research, code-writing systems, multi-step reasoning, persistent memory, the entire next layer of AI products. Every product in that next layer consumes orders of magnitude more compute than the chat interfaces it is replacing.

The math at the aggregate level is brutal: 100x cheaper tokens times 10 000 more tokens equals a 100x larger total bill.

The implications stack quickly.
If you are running a hyperscaler, your 2026 capex guide is not a peak. It is a step on a curve. Inference is structurally always-on, twenty-four hours a day, in a way that training never was. Training is bursty. You spin up a cluster, run for weeks or months, and stop. Inference runs continuously, scales with usage, and the usage curve is exponential. Your power bill, your cooling bill, your transceiver count, your storage footprint, all of these were sized for a workload mix that no longer exists.

If you are running an AI software company built on top of someone else's closed API, you have a problem that did not exist a year ago. Your gross margins get worse as your customers get more value out of your product, because the more they use it, the more compute you pay for. The companies that win this are the ones that figured out vertical integration before the math caught them.

If you are watching this from a distance and trying to understand where the next bottlenecks form, the answer is everywhere downstream of "more inference compute, always-on, with massive memory state per session." The KV cache, the running memory state of a long conversation or an agent loop, is the silent monster of the inference era. It does not scale linearly with parameters. It scales linearly with context length and number of agent steps. A long agent session can hold tens of gigabytes of state per user, per session.

Multiply that by every concurrent user of every product, and you understand why $MU, $SNDK, $TOWCF, and the entire memory and packaging layer have re-rated the way they have.

The CPU-to-GPU ratio is evolving. Training is 1:8. Basic chat inference is 1:4. Agentic inference is 1:1, sometimes CPU-heavy. Google has split its TPU line in two, with a dedicated inference chip carrying tripled SRAM for KV cache. $INTC and $AMD just spent two earnings calls explaining that this shift is structural, not cyclical. The hardware map is redrawing in real time and the financial press is mostly still writing about training clusters.

The right framing of where we are right now is not that AI is hitting a wall. The framing a year ago that scaling was hitting a wall was the most expensive bad take of the cycle. The right framing is that AI got dramatically cheaper, dramatically more capable, and dramatically more useful, and the cost of running it at the new equilibrium of demand is much higher than the cost at the old equilibrium of demand, because the new equilibrium is enormous.

A meaningful share of what we actually do at Token Factory, day to day, is help customers stop their bills from running away from them. KV-cache management. Speculative decoding. Quantization. Routing. The kind of vertical integration that, eighteen months ago, every product team was happy to leave abstracted away behind a closed API. The reason this stack matters now is the same reason this whole essay matters: at the new equilibrium of inference demand, the cost of treating compute as a commodity is no longer survivable. The companies that figure out the layer beneath the API are the ones who keep their margins.

Cheaper tokens. More tokens.
Same coal as 1865.

[Alex Albert @alexalbert__]

With the help of Claude Mythos Preview, the Firefox team fixed more security bugs in April than in the past 15 months combined.

[DWin @DWin10]

@long_equity For sure but the last months have not been fun!

[DWin @DWin10]

#Fortinet $FTNT is collaborating with multiple leading AI companies, including #Anthropic, #OpenAI and others. Billings grew 31% yoy in Q1 2026. Looks like a sentiment shift is happening ✌️

[Eric Jhonsa @EricJhonsa]

$AMD: "Based on the demand signals we are seeing today and the structural increase in CPU compute requirements driven by Agentic AI, we now expect the server CPU TAM to grow at greater than 35% annually, reaching over $120 billion by 2030." (transcript via Quartr)

[Collin Martin @CollinMartinCS]

Markets are now pricing in rate hike rather than a rate cut by the April 2027 meeting. This chart highlights the number of 25 basis point moves expected by next April - at the end of February the market was pricing in nearly three cuts, and now there is nearly a 50% implied probability of one hike.

[Jamin Ball @jaminball]

Cloud Giants Update:

AWS (Amazon): $150B run rate growing 28% YoY (last Q grew 24%)

Azure (Microsoft): ~$108B run rate (estimate) growing 39% YoY (last Q grew 38%)

Google Cloud (includes GSuite): $80B run rate growing 63% YoY (last Q grew 48%, neither are cc)

[DWin @DWin10]

#NXP #Semiconductor reported +12% yoy revenue growth with broad based improvement across all end markets. The momentum is expected to accelerate through the remainder of 2026.

[DWin @DWin10]

#CadenceDesignSystem is raising their 2026 revenue outlook to 17% year-over-year growth

[DWin @DWin10]

#Pizza 🍕is having a tough time. #Domino‘s Pizza is lowering their full-year US same-store sales forecast to low-single digit growth. Watch out for other consumer brands reporting in the coming days.

[DWin @DWin10]

@pip_net Including child porn!

[Sam Ro 📈 @SamRo]

Morgan Stanley's sum-of-the-parts valuation of Tesla