r/AIFU_stock

• Revenue $81.6B vs Est. $79.2B

• EPS $1.85 vs Est. $1.78

• Data Center $75.2B vs Est. $73.5B

Q2 guidance

• Revenue $90.6B vs Est. $87.2B

Top 10 positions:

1| $SMH - 15% (put)

2| $NVDA - 11% (put)

3| $ORCL - 8% (put)

4| $AVGO - 7% (put)

5| $AMD - 7% (put)

6| $BE - 6% (stock)

7| $SNDK - 5% (stock)

8| $MU - 4% (put)

9| $CRWV - 4% (stock)

10| $TSM - 4% (put)

• 2025: $0.5B

• 2030: $33B (Est.)

That's 66x in 5 years at 129% annual growth rate.

→ Jan 2025: $1B ARR

→ Dec 2025: $9B ARR

→ Apr 2026: $30B ARR

That’s a 30x in 15 months.

One analyst is now projecting $100B by end of 2026, $340B in 2027, and $2T+ by 2030.

Compare that to Google’s current revenue run-rate. The forecast says Anthropic could surpass it by mid-2028.

Is it too aggressive? Probably. But the direction of travel is real.

The bigger signal here isn’t Anthropic specifically — it’s what this means for the compute stack.

If AI model companies are monetizing this fast, demand for chips, memory, networking, power, and cooling is going to be far larger than the market priced in.

The infrastructure thesis just got stronger.

Before a potential Anthropic IPO, here’s where you can get exposure today:

→ $AMZN — lead cloud partner + investor

→ $GOOG — major backer + TPU development partner

→ $NVDA / $AMD / $AVGO — AI chip layer

→ $TSM — foundry capacity

→ $MU — HBM + DRAM demand surge

→ $MRVL / $FN / $LITE / $COHR — optical networking

→ $VRT / $MPWR — power & cooling

Pre-IPO fund exposure:

→ $VCX — Anthropic ~20.7% of portfolio

→ $DXYZ — meaningful Anthropic position

→ $AGIX — one of the few ETFs with direct private AI exposure

→ $BSTZ — private market tech exposure including Anthropic

The AI model race winner is still unknown.

The infrastructure winners are less uncertain.

Layer 1: Materials & wafers

• $GLW

• $AXTI

• $IQE

• $AIXA

• $AMS

Layer 2: Core photonic devices

• $IPGP

• $COHR

• $LITE

• $LASR

• $SIVE

• Layer 3: Components & modules

• $AAOI

• $MTSI

• $FN

• $VIAV

• $LPTH

Layer 4: Systems & equipment

• $ASML

• $BESI

• $ASM

• $LPKF

• $MKS

Layer 5: Test, metrology & yield

• $CAMT

• $FORM

• $AEHR

• $ONTO

• $VIAV

1. $AXTI +612%

2. $SNDK +558%

3. $AEHR +382%

4. $AAOI +327%

5. $DOCN +241%

6. $INTC +238%

7. $BE +200%

8. $VIAV +187%

9. $WDC +179%

10. $MU +162%

11. $NVTS +155%

12. $LITE +145%

13. $OSS +118%

14. $AMD +113%

15. $NBIS +111%

$RKLB

— Only scaled launch alternative. SpaceX IPO validates the market, RKLB gets the multiple expansion.

$ASTS

— Highest-beta space name. Sector ETF inflows hit this first.

$PL

— Space data pure-play. IPO headlines bring retail back to Earth imaging.

$FLY

— Speculative flows lift all space-adjacent mobility names in a sector re-rating.

$LUNR

— Only pure-play Moon stock. Artemis ecosystem gets a spotlight when SpaceX lists.

$DRAM — +80.3% YTD

→ The pure-play memory ETF

→ SK Hynix 26% | $MU 25% | Samsung 21% | Others 28%

→ Maximum memory concentration in one ticker

→ Best if you want direct exposure to the DRAM/HBM supercycle

$EWY — +87.1% YTD

→ Plays the Korea memory duopoly — SK Hynix + Samsung in one fund

→ SK Hynix: 19%–24% | Samsung: 22% | Others: 54%–59%

→ Added macro beta on Korean won + broader Korean economy

→ Solid middle ground between pure memory and diversification

$KORU — +362.1% YTD

→ 3x leveraged version of $EWY essentially

→ Not a hold — a trade

→ Massive upside in a supercycle, brutal drawdowns on reversals

→ Only for those who understand leveraged ETF decay

$SMH — +52.6% YTD

→ Broader semi exposure with memory embedded→ $NVDA 16% | $TSMC 10% | $AVGO 7%| $INTC 5% | $AMD 5% | $MU included

→ Lowest memory concentration but highest diversification

→ Best entry point for those new to the theme

Risk Ladder (lowest → highest):$SMH → $DRAM → $EWY → $KORU

Jensen is claiming 1000x compute demand in this interview. Agentic inference chains reasoning tokens across many steps, calls external tools repeatedly, and runs orders of magnitude longer than single-pass generation. Even a fraction of 1000x is an enormous demand multiplier. Jensen compounds this with user growth as AI becomes genuinely useful, arriving at a framing where legacy GPUs from four or five years ago are now appreciating in value rather than depreciating.

His claim is rooted into profitable customers racing for more of NVDA.

Both OpenAI and Anthropic, plus most AI native companies, have turned to strongly positive gross margins in the 3-6 months prior to this conversation. When token economics are profitable, the rational move is to maximize token output, which means buying more compute. He names Claude Code specifically as the first agentic system to demonstrate genuinely productive work at scale, and uses this to anchor his entire narrative about when AI "became useful."

Infrastructure is another big moat that is incoming and can give NVIDIA a chance to capture larger piece of the pie. A single Vera Rubin rack runs $4-5 million, weighs three tons, contains 1.5 million parts, and spans seven distinct chip types including silicon photonics, advanced 3D memory packaging, and specialized cooling electronics. A full data center fills a football field with these racks. At $4-5M per rack, a football field of them implies data center costs well into the hundreds of millions per site, with NVIDIA capturing value at multiple points in each rack. 

These details makes me believe that $10 Trillion target can be achieved sooner than later.

SPACE

$RKLB

$ASTS

MEMORY

$DRAM

$MU

$SNDK

COMPUTE

$AMD

$IREN

$NBIS

$INTC

+179%

Designs and manufactures CPUs for PCs and servers while rebuilding its foundry business to compete with TSMC.

$MU

+103%

Manufactures DRAM and NAND flash memory, the core storage components inside every server, PC, and smartphone, with demand accelerating sharply from AI workloads.

$ARM

+85%

Licenses the processor architecture that powers virtually every smartphone on Earth and a fast-growing share of data center and edge computing chips.

$MRVL

+84%

Develops custom AI networking and storage chips for cloud hyperscalers, becoming one of the most important picks-and-shovels names in the AI data center.

$AMD

+82%

Designs high-performance CPUs and GPUs for data centers, gaming, and PCs, and has taken meaningful share from Intel in servers over the last five years.

$AMAT

+52%

Supplies the deposition, etching, and inspection equipment that chip factories depend on to build every layer of a modern semiconductor.

$ASML

+31%

Makes the extreme ultraviolet lithography machines that are the only tools capable of printing the world's most advanced chips, giving it an unmatched monopoly position.

$TSM

+29%

The world's largest contract chip manufacturer, fabricating advanced processors for virtually every major chip designer including Apple, Nvidia, and AMD.

$AVGO

+18%

Builds custom AI accelerators, networking chips, and broadband semiconductors, supplying hyperscalers like Google and Meta with critical data center silicon.

$NVDA

+12%

Designs the world's leading GPUs for AI training, data centers, and gaming, making it the backbone of the modern AI infrastructure build-out.