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Pazzzi

@Pazzzi

1Mo·

Amodei's call for a "pause" targets the wrong part of the AI value chain

Slows down Dario Amodeis Is the call for a slower pace in AI now hampering the industry’s growth? The Anthropic CEO called for this over the weekend in his essay “We Must Pace the Frontier”—to deliberately slow the advancement of AI model capabilities— OpenAI CEO Sam Altman and xAI CEO Elon Musk publicly agreed within hours.

Amodei’s core argument: Recursive self-improvement and security incidents such as the OpenAI Hugging Face incident showed that the models’ capabilities are growing faster than the ability to control them. His three-step plan begins with independent auditors working directly within the companies. By this, he means not
halt to training, but rather more time for safety work to proceed in parallel with progress.

Why many see this as a brake on growth

Within the community, the call is being interpreted as a warning sign regarding the AI industry’s high growth rates. A slower pace in the release of new models could dampen demand for fresh computing power and thus weigh on the valuations of AI companies and Neoclouds. Coinciding with the announcement, market prices for Anthropic and OpenAI shares fell.

Why this misses the actual driver of growth

Amodei’s appeal targets the training of new models—and it is precisely this part of the AI value chain that is now the smaller and shrinking segment. According to Deloitte, by 2026, around two-thirds of global AI computing power will be directed toward inference (the ongoing operation of already trained models).

And it is precisely this demand for inference that is growing faster than ever before:

$GOOGL (+0,06 %)-Cloud order backlog: from $240 billion to $460 billion within one quarter
Tokens processed by Google: 16 billion per minute, an increase of 60% compared to the previous quarter
$MSFT (+0,6 %): AI order backlog increased more than twelvefold
GPU rental prices have grown from ~10 million/MW in early 2026 to as much as ~40–50 million/MW today

So the real bottleneck remains computing power itself, not the political will to slow things down. Microsoft and Google each have investment budgets of around $180–190 billion just to meet demand at all. Training pacing is unlikely to make much of a difference to this scarcity as long as current models are already so powerful for users that demand for their use continues to grow.

Overall , the appeal seems more like a shift in priorities—away from a pure race for capabilities and toward greater safety and control. In my opinion, it is wrong to believe that this will act as a brake on the entire AI value chain.

The largest and fastest-growing part of it—inference—remains virtually untouched by the debate.

This announcement could also come at a strategically opportune time: Training new frontier models costs a great deal of money (tens of billions per model). A deliberately slowed pace would alleviate precisely this cost burden and improve the profitability of Anthropic and OpenAI, just before both are set to make potential IPOs . This safety call—which, incidentally, protects their own balance sheets—comes at just the right time for the model providers.

What are your thoughts on this?

$IREN (-4,02 %)$NBIS (-3,49 %)$NVDA (-2,31 %)$AMZN (-0,41 %)$AMD (-3,47 %)$WULF (+2,16 %)$ORCL (+1,99 %)$CSNDX (-0,99 %)$AVGO (-2,69 %)$SPCX (-3,88 %)$PLTR (-0,66 %)$META (-0,33 %)$CRWV (+2,6 %)$DELL (-8,86 %)$BE (-3,4 %)$MU (-0,29 %)

Sources:

https://darioamodei.com/post/we-must-pace-the-frontier

https://www.deloitte.com/global/en/about/press-room/2026-tmt-predictions.html

darioamodei.comDario Amodei — We Must Pace the Frontier

1 Commentaire

Ruben Schoff@Sand

3Sem.

It's really starting to pick up steam! If a bit of euphoria and FOMO kicks in, we might even see four-digit prices. It's also noticeable that it's being traded pretty heavily—up and down—which means there's a lot of speculation involved.

••

Pazzzi

@Pazzzi

2Mo·

Texas Puts New Grid Connections for Data Centers on Hold—What This Means for Neoclouds

On August 3, 2026, Texas Governor Greg Abbott halted all data center projects in the Texas connection queue ..

In a letter to the PUCChairman Thomas Gleeson (Public Utility CCommission—the Texas regulatory agency for electric and water utilities) and ERCOTCEO Pablo Vegas (Electric Reliability Council of Texas—the operator of the Texas power grid—has instructed Abbott to conduct a comprehensive review of every data center as part of the connection process. No new projects will be approved until this audit is complete.

The reason for this is the staggering size of the connection waiting list:

It now includes over 1,800 projects with a total capacity of more than 474 gigawatts—more than five times Texas’s previous peak electricity consumption.

About 90% of the applications are for data centers.

In the future, anyone wishing to pass the review must disclose subsidies and tax benefits, electricity and water consumption, the planned power source, the ownership structure, and the impact on the community. Pure “behind-the-meter” projects—which generate their own electricity on-site and do not connect to ERCOT in the first place—are not affected.

What could this mean for Neoclouds?

The key point is that existing, already approved capacity remains unaffected—only future growth.

Bernstein sees $CIFR (-1,55 %) , $CLSK (-1,51 %) and $CORZ (+1,74 %) as being most at risk, since their short-term expansion depends on new grid connections in Texas.

$RIOT (-1,63 %) , $IREN (-4,02 %) and $WULF (+2,16 %) are considered relative beneficiaries, as they already have approved grid connections.

Even $CRWV (+2,6 %)connections could be affected by delays, according to Bernstein.

In my view, this is the opportunity: As permits become scarcer, the value of what has already been approved increases.

Overall, it can be seen that approvals for future grid connections will become more difficult and the processes will take longer. Consequently, there will also be some rejections, which could pose a major risk for some Neocloud providers. Company valuations based on a 2027/28 pipeline thus become less predictable—and delays cost money in debt-financed construction projects, regardless of whether approval is ultimately granted.

Concrete progress is expected on August 20: On that date, ERCOT plans to apply to the PUC for an extension of the deadlines for the proceedings—specifically for the first round of approvals, the results of which were originally supposed to be available by August 7. There is currently no deadline by which the review must be completed—it will therefore be crucial whether the agency specifies a timeline or leaves the process open-ended.

Sources:

www.foley.com/insights/publications/2026/08/governor-abbott-pauses-texas-data-center-interconnections-and-calls-for-verification-and-audit-what-data-center-developers-need-to-know-now/

www.theblock.co/post/410545/bernstein-texas-data-center-audit

www.foley.comGovernor Abbott Pauses Texas Data Center Interconnections And Calls For Verification and Audit; What Data Center Developers Need to Know Now

8 Commentaires

Klein-Anleger@Klein-Anleger

2Mo

💯%—and that's exactly what I wanted to achieve with my thesis at IREN ✌️

•

Voir toutes les 5 autres réponses

Code & Capital

@Codeandcapital

2Mo·

Quarterly Results August 3–7, 2026

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Pazzzi

@Pazzzi

2Mo·

The AI Sell-Off Is Out of Touch with Reality

Moonshot AI posted the following update on Sunday:

"Kimi K3 has been much more popular than we expected, and our GPUs are feeling the strain.

Over the past 48 hours, demand has nearly reached the limits of our current capacity. To protect the experience of existing subscribers, we’re temporarily pausing new subscriptions and prioritizing computing power for current members. Existing subscribers are not affected by this.

We’re scaling up additional capacity as quickly as possible and will gradually reopen new subscription slots in batches.

Going forward, we’ll also be splitting the membership into two more targeted plans: Kimi Membership for Kimi Web, App, and Work, and Kimi Code Membership for coding workflows. This will help us allocate computing power more precisely and keep the experience stable.

Thank you very much for your patience and understanding!"

$IREN (-4,02 %)
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$ (-2,16 %)HIVE (-2,16 %)

Pazzzi

@Pazzzi

2Mo·

China's new AI model, Kimi K3, challenges the U.S. AI industry's billion-dollar bill

China's AI company Moonshot unveiled its new flagship model "Kimi K3" —and in independent tests, it’s nearly on par with the best models from Anthropic and OpenAIin independent tests, which may have contributed to the sharp decline in AI stock prices.

What happened?

Moonshot AI, a Chinese AI company with a valuation of approximately $31.5 billion, has unveiled its new model, Kimi K3. By comparison, Anthropic and OpenAI are each projected to be valued at over $1 trillion —more than 30 times that amount.

Key figures:

Size: 2.8 trillion parameters (= the “adjustment screws” that store the model’s knowledge—roughly speaking: the more there are, the more powerful the model, but also the more expensive it is to run). This makes K3 the largest model ever to come out of China.

Open-Weight: Moonshot plans to make the model available for free download by July 27—companies could then run it on their own hardware without paying licensing fees to Moonshot. As of now, however, it’s only available through Moonshot’s paid interface.

Context: Capable of processing 1 million tokens at a time (tokens = text units; 1 million tokens correspond roughly to 750,000 words).

Unknown: The actual training costs. Anthropic also published evidence in February 2026 that Chinese labs had copied U.S. models via “distillation” (= a model learns by querying and imitating vast amounts of responses from another model). Whether and to what extent this plays a role in K3 remains unclear.

The comparison: How much do the models cost—and how smart are they?

Pricing is per 1 million tokens, broken down by input (what you send to the model) and output (what it responds with):

Kimi K3 (Moonshot): $3 input / $15 output – Intelligence Index: 57

Claude Fable 5 (Anthropic): $10 / $50 – Intelligence Index: 60 (1st place)

GPT-5.6 Sol (OpenAI): $5 / $30 – Intelligence Index: 59

Claude Opus 4.8 (Anthropic): $5 / $25 – Intelligence Index: 56

Grok 4.5 (xAI): $2 / $6 – the most affordable model among the top performers

DeepSeek V4 Pro (China): approx. $0.44 / $0.87 – low-cost segment, but index only 44

The Intelligence Index comes from Artificial Analysis, an independent testing provider that compares models across nine task areas (programming, logic, knowledge work) on a scale of up to 100.

Strengths – K3 trails the leader, Fable 5, by only 3 points, but costs only one-third as much per output token ($15 instead of $50) and half as much as GPT-5.6 Sol. In Artificial Analysis’s practical test, completing a task with K3 cost an average of $0.94—compared to $1.04 for GPT-5.6 Sol and $1.80 for Opus 4.8. When it comes to building web interfaces, K3 even took first place in blind tests with real developers.

Weakness – K3 isn’t quite as cheap as the headline suggests: It costs three to four times as much as its predecessor, is the most expensive model among its Chinese competitors, and, according to tests, “burns” up to 1.9 times as many tokens as GPT-5.6 Sol for the same task—so the cost advantage per token effectively disappears in practice. And the promised free model weights have not yet been released.

What could this mean for the market?

The AI stock boom hangs by a thread: U.S. labs like Anthropic, OpenAI, and xAI have committed to spending hundreds of billions of dollars—on chips, data centers, and electricity. Chip manufacturers, data center operators, energy providers, and construction companies have seen their stock valuations rise because this money is expected to flow to them. The plan is to refinance this through high prices for cutting-edge AI.

If a model now delivers nearly the same performance for a fraction of the price—and is soon expected to be available for free download—it could create the impression that the U.S. labs’ pricing power is crumbling (“commoditization of intelligence”: cutting-edge AI is becoming an interchangeable mass-market commodity). This wouldn’t immediately cost revenue, but it sows doubt about the business case behind the massive investments—and in overheated market phases, even a single doubt can trigger a chain reaction.

Opportunities – Cheaper AI could accelerate AI adoption overall: More adoption means greater demand for computing power for ongoing operations (inference)—and K3 is just as hardware-intensive in operation as U.S. models. Chip and infrastructure providers would benefit from this, regardless of which model is running.

The latest quarterly figures support this interpretation: On Tuesday, ASML raised its annual forecast for the second time (to €43–45 billion in revenue) and reported that its capacity is nearly fully booked through 2027. On Wednesday, TSMC increased its capital expenditure budget to a record-high $60–64 billion and stated that investments over the next three years would once again be significantly higher than those of the past three years—and that it is monitoring the construction progress of data centers to ensure its own chips do not end up in inventory. This can be interpreted as a sign that the demand side remains strong.

Risks – If price compression were to actually hit U.S. labs, training investments could be stretched out or cut back—which would eventually affect the recipients of this spending (chips, data centers, energy). Furthermore, those who train using distillation save precisely the billions in GPU costs that make the U.S. approach so expensive. The cost bases of the providers are therefore not directly comparable, but the price risk for U.S. labs still exists. A countervailing factor is that Chinese models are unlikely to be considered for security-critical U.S. applications (government agencies, robotics) due to potential backdoors.

Context

The bottom line, in my view, is that Kimi K3 is less a “DeepSeek moment 2.0” than a reality check for the valuation logic:

In 2025, the question was which chips China would use for training—now the question is whether cutting-edge intelligence permanently justifies premium prices.

The supply chain’s response (ASML, TSMC) has been clear so far: demand exists—the response at the model level is less certain.

Limitations

Initial benchmarks are partly based on manufacturer specifications = independent long-term tests are still pending.

Training costs and the potential distillation ratio for K3 cannot be verified.

Moonshot’s valuation ($31.5 billion) is from a private funding round; the trillion-dollar valuations of Anthropic and OpenAI are prospective—neither is directly comparable to market capitalizations.

Sources:

Artificial Analysis – Kimi K3 Benchmarks: https://x.com/ArtificialAnlys/status/2077832874183860404

OpenRouter – Kimi K3 Prices & Specs: https://openrouter.ai/moonshotai/kimi-k3

Anthropic – Official API Pricing Documentation: https://platform.claude.com/docs/en/about-claude/pricing

OpenRouter – Grok 4.5 Pricing: https://openrouter.ai/x-ai/grok-4.5

Price Comparison: Kimi K3 / Fable 5 / GPT-5.6 Sol: https://drawpie.com/blog/kimi-k3-vs-fable-5-vs-gpt-5-6-sol-price-benchmarks/

Decrypt – Cost per Task in Benchmark Comparison: https://decrypt.co/373716/china-kimi-k3-largest-open-source-ai-model-ever-beats-claude-fable-gpt-5-6-sol

The Decoder – K3 Classification & Price Level: https://the-decoder.com/kimis-open-model-k3-nears-gpt-5-6-sol-and-fable-5-while-signaling-the-end-of-super-cheap-chinese-ai/

ASML – Q2 2026 Earnings Call Transcript: https://www.investing.com/news/transcripts/earnings-call-transcript-asml-q2-2026-beats-guidance-as-ai-demand-lifts-outlook-93CH-4792156

TSMC – Q2 2026 Earnings Call Transcript: https://www.investing.com/news/transcripts/earnings-call-transcript-tsmc-lifts-2026-outlook-as-ai-demand-stays-hot-in-q2-2026-93CH-4794777

Anthropic – Report on Distillation Attacks (February 2026): https://anthropic.com/news/detecting-and-preventing-distillation-attacks

$NVDA (-2,31 %)
$2330
$ASML (-1,03 %)
$MSFT (+0,6 %)
$AMZN (-0,41 %)
$GOOGL (+0,06 %)

Pazzzi

@Pazzzi

3Mo·

NVIDIA's New Financing Model for AI Clouds

$NVDA (-2,31 %)

Nvidia announced a new partnership model for Neoclouds (specialized GPU cloud providers): Nvidia acts as a financial backstop and commits to leasing back unused GPU capacity at a fixed price. In return, Nvidia receives a share of its partners’ cloud revenue in addition to hardware revenue.

First partners: Firmus (170,000 GPUs in Indonesia) and Sharon AI (40,000 GB300 GPUs)
Predecessors: similar deals with $CRWV (+2,6 %) (6.3 billion $, 2025) and Lambda (1.5 billion $)

$NBIS (-3,49 %)$USCTF (-1,35 %)$CIFR (-1,55 %)$WULF (+2,16 %)$APLD (-4,1 %)$KEEL (-2,42 %)$CORZ (+1,74 %)$HIVE (-2,16 %)$BTDR (-0,51 %)$CLSK (-1,51 %)$MARA (-2,2 %)$HUT (-1,01 %)$RIOT (-1,63 %)

$IREN (-4,02 %) has so far not named as a participant—but has had a strategic partnership with Nvidia since May covering up to 5 GW of AI infrastructure, a $3.4 billion cloud contract , and Nvidia holds the right to purchase up to 2.1 billion IREN shares .

Adopting this model seems like a very logical step, since IREN will require extremely high investment costs (>$100 billion) to expand its entire pipeline.

In my view, this would be very positive in the short to medium term.

The biggest risk with $IREN (-4,02 %) and other Neocloud providers is not demand, but rather the financing of their expansion plans, which run into the billions. A backstop from Nvidia would mitigate precisely this risk: Banks are much more willing to grant loans when the world’s largest chipmaker guarantees capacity utilization—this would provide significant security and more favorable, virtually lower-risk financing. However, the revenue share would directly impact the profit margin . In the long term, companies would thereby cede part of their overall potential to Nvidia—essentially trading long-term profitability for medium-term security.

Source: https://www.datacenterdynamics.com/en/news/nvidia-acts-as-backstop-for-customer-gpus-in-return-for-cut-of-cloud-revenue/

www.datacenterdynamics.comJust a moment...

Jake Quinten

@jaketradingcalls

4Mo·

The AI supercycle is in year 3 of 15. You didn't miss it.

You'd make millions by knowing whats coming and buying dips until 2030+

Pay attention, we just finished Phase 1 2023-2025

chips · memory · connectivity

$NVDA (-2,31 %) → Designs the GPUs every AI model trains and runs on.

$MU (-0,29 %) → Makes high-bandwidth memory inside every AI server.

$COHR (-7,79 %) → Moves data at light speed between GPUs optically.

$MRVL (-3,68 %) → Custom silicon connecting every chip in a hyperscaler's cluster.

$AVGO (-2,69 %) → Builds Google's, Meta's, and Apple's custom AI chips quietly.

$AMD (-3,47 %) → Only credible GPU rival to NVDA for AI training.

PHASE 2 — The grid gets built (2026–2027)

power · cooling · networking

$IREN (-4,02 %) → AI-native data centers built to scale compute and power.

$WULF (+2,16 %) → Energy-efficient infrastructure hosting the world's most power-hungry AI workloads.

$VRT → Cooling and power systems keeping AI data centers running.

$ETN → Electrical gear powering every hyperscale AI facility being built.

$CEG → Nuclear energy feeding AI's insatiable around-the-clock power demands.

$ANET → High-speed switches moving massive AI workloads across GPU networks.

$GEV → Gas turbines physically delivering power to data centers.

$SMCI → Liquid-cooled GPU server racks — pick-and-shovel for AI density.

PHASE 3 — The massive bottleneck (2027–2029)

materials · space · autonomy

$MP → Mines rare earth materials used in AI hardware and defense.

$USAR → Domestic minerals securing U.S. AI manufacturing independence.

$ASTS (+3,79 %) → Satellites delivering AI connectivity to every corner of Earth.

$RKLB (-3,35 %) → Low-cost rockets launching satellites powering AI communication networks.

$KTOS → AI-driven autonomous weapons systems entering mass military deployment now.

$TSLA (-2,16 %) → Leads real-world AI through robotics, autonomy, and manufacturing.

$SYM → AI-powered warehouse robots automating global logistics at scale.

$ALAB (-3,36 %) → Chip packaging bottleneck — critical past 100K GPU nodes.

$PLTR (-0,66 %) → Software turning AI compute into defense and enterprise decisions.

PHASE 4 — Full automation (2030+)

platforms · agents · quantum

$MSFT (+0,6 %) → Deploys AI agents across every enterprise software product it sells.

$GOOGL (+0,06 %) → Controls AI search, cloud, and consumer distribution globally.

$META (-0,33 %) → AI assistants across 3 billion users in social and commerce.

$CRM (+3,22 %) → AI agents inside enterprise sales — 150K customer moat.

$NOW (+5,08 %) → AI workflow OS for Fortune 500 enterprises.

Quantum

$IONQ $RGTI $QUBT — next-gen compute unlocking exponential AI breakthroughs.

4 Commentaires

MsFinance@MsFinance

4Mo

Thanks, Jake

•

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Jake Quinten

@jaketradingcalls

4Mo·

NBIS

In 1 year, $NBIS (-3,49 %) blew up 10x from $20 → $270.

Recently, Leopold owns 6% stake in it.

These 6 stocks under $30 can easily 10x:

1. $CIFR (-1,55 %) ~$23

$AMZN (-0,41 %) + $GOOG 15-year leases. $9.3B contracted. Analyst target: $70

2. $WULF (+2,16 %) ~$27

Google-backed Fluidstack deal. 1GW+ capacity. Analyst target: $80

3. $CORZ ~$28

CoreWea's landlord. $3.3B raised. 12-year $10B deal. Analyst target: $60

4. $BTDR ~$13

170% revenue growth. Owns its own AI chips. Analyst target: $50

5. $KEEL ~$6

Leopold accumulating. First colo deal Q3 2026 expected. Analyst target: $18

6. $HIVE ~$4

$30M AI cloud contracts signed. Liquid-cooled GPU clusters. Analyst target: $16

$NBIS (-3,49 %)
$CRWV (+2,6 %)
$IREN (-4,02 %) are Neocloud companies with deals with $MSFT $NVDA $GOOG $AMZN. They are providing compute to everyone.

Code & Capital

@Codeandcapital

5Mo·

Quarterly figures 04.05-08.05.26

$NCLH (+2,67 %)
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$DUOL
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$UCG (-1,83 %)
$BOSS (+0,33 %)
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$CCO (+0,63 %)
$PYPL (+1,31 %)
$BNTX (+1,48 %)
$DOCN (-6,07 %)
$SHOP (+0,63 %)
$ROK (-1,29 %)
$RACE (+2,22 %)
$PFE (+1,38 %)
$ANET (-6,56 %)
$TEM (+2,24 %)
$AMD (-3,47 %)
$EA
$LITE (-5,21 %)
$MSTR (+3,01 %)
$SMCI (-5,08 %)
$CPNG (+2,13 %)
$IFX (-3,78 %)
$DTG (+0,65 %)
$KOG (+1,08 %)
$HFG (+5,43 %)
$BMW (+1,21 %)
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$CVS (+1,46 %)
$UBER (+1,76 %)
$OSCR (-2,88 %)
$DIS (+1,9 %)
$ADYEN (+2,72 %)
$DASH (+3,2 %)
$FTNT (-2,84 %)
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$IONQ (+4,97 %)
$SNAP (+1,74 %)
$APP (+4,95 %)
$ARM (+0,94 %)
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$DDOG (-2,71 %)
$RHM (+5,65 %)
$PTON (+4,02 %)
$MCD (+1,45 %)
$SHEL (-0,18 %)
$WULF (+2,16 %)

Pazzzi

@Pazzzi

5Mo·

IREN catalysts for a price explosion

$IREN (-4,02 %) Move away from being a pure Bitcoin miner and become a vertically integrated AI infrastructure player with its own power, data centers and GPUs.

Why I hold ~30% of my portfolio in $IREN (-4,02 %) shares and which catalysts speak for a massive price increase in the near future:

1.Sweetwater 1 Energization

The fourth and final bulk substation transformer for Sweetwater 1 has arrived and the energization should be completed by April 2026 (now).

2.Sweetwater deal announcements

With the connection to the power grid complete, it is only a matter of time before $IREN (-4,02 %) deals will be announced. According to quarterly reports, the company has been in talks with global AI and cloud companies (hyperscalers) for many months to implement, finance and lease the capacity. An official announcement - with a hyperscaler, for example - would validate the business model and significantly boost the share price.

3.Childress construction progress

Childress remains on schedule: DC 1 has been running since April 2023, DC 2-5 are being systematically ramped up. Solid operational implementation and construction without delay is the key driver here. CEO Daniel Roberts recently gave updates on the delivery and connection of the GPUs from the $9,700,000,000 Microsoft deal on X.

https://x.com/i/status/2045007521497415701

https://x.com/i/status/2049220187082350773

4.Childress complete conversion to GPUs

Iris Energy is actively transforming Childress from Bitcoin miner to GPU AI infrastructure. This change is steadily reducing the company's dependence on Bitcoin volatility and positioning it as a broad-based AI cloud provider. However, only around half of Childress capacity is currently allocated to Microsoft. The rest can still be converted at limited cost.

5.GPU price/hour price explosion

The structural demand for AI compute is accelerating GPU hourly rates. As an infrastructure provider, Iris Energy benefits directly from this trend and can achieve higher margins. For example, GPU rental costs have risen from 2.31$/hour at the beginning of March 2026 to currently 4.95$/hour. +114%

6.Australia Expansion

There are numerous images of a massive advertising campaign in Australia. Various announcements show that Microsoft and Antrophic want to build new data centers in Australia. $IREN (-4,02 %) has already reported in a quarterly publication in 2024 that they are looking for suitable land. There are also new open job offers in Australia, which are publicly available on LinkedIn. www.linkedin.com/company/iren/jobs

A planned announcement to expand in Australia would increase the global network of locations and open up additional growth options for IREN.

https://www.sydneyswans.com.au/news/1963949/sydney-swans-join-forces-with-iren

The major hyperscalers ($GOOGL (+0,06 %)
$META (-0,33 %)
$MSFT (+0,6 %)
$AMZN (-0,41 %) Antrophic and OpenAI) are currently reporting critical capacity utilization and are currently significantly limiting AI output.

Energy, land and short construction times are the moat to solve this problem.

$IREN (-4,02 %) Antrophic has everything and many announcements ahead of it that could give the share price a massive boost.

Attention: This article does not contain any information on potential risks, such as capital requirements and execution.

No investment advice

I would be very grateful for your support, as these contributions entail a great deal of effort 🫶 🚀

$CRWV (+2,6 %)
$NBIS (-3,49 %)
$CIFR (-1,55 %)
$WULF (+2,16 %)
$APLD (-4,1 %)
$GLXY (-0,52 %)
$HUT (-1,01 %)

1 Commentaire

Basti92@BavarianLion

5Mo

I'm definitely invested! Let's see what the future holds 😉

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Code & Capital

@Codeandcapital

7Mo·

Quartalszahlen 23.02-27.02.2026

$DPZ (+3,04 %)
$HIMS (+0,68 %)
$KTOS (+0,77 %)
$DOCN (-6,07 %)
$FME (+2,51 %)
$KDP (+1,59 %)
$AMT (+0,51 %)
$HD (+2,95 %)
$WDAY (+3,97 %)
$FSLR (-1,3 %)
$TEM (+2,24 %)
$O (+0,46 %)
$MELI (+0,25 %)
$HPQ (+0,88 %)
$LCID (+5,25 %)
$DRO (-5,72 %)
$HSBA (-0,77 %)
$FRE (+0,12 %)
$AG1 (+4,51 %)
$CRCL (+7,99 %)
$UTHR (+1,32 %)
$LDO (+2,72 %)
$IDR (+0,25 %)
$NTNX (-1,57 %)
$PARA (+3,16 %)
$NVDA (-2,31 %)
$TTD (+7,63 %)
$AI (+0,7 %)
$CRM (+3,22 %)
$SNPS (-4,18 %)
$SNOW (-1,5 %)
$PSTG (-7,83 %)
$ZIP (+8,61 %)
$ZM (+2,21 %)
$NU (-0,34 %)
$RR. (+0,83 %)
$MUV2 (+2,03 %)
$BIDU (-0,27 %)
$CELH
$DTE (+1,13 %)
$STLAM (+1,5 %)
$WBD
$HAG (+2,92 %)
$QBTS (+3,45 %)
$LKNCY (-1,5 %)
$BABA (-1,31 %)
$G24 (+7,26 %)
$HTZ (+5,58 %)
$PUM (+3,3 %)
$AIXA (-4,17 %)
$RUN (+2,67 %)
$INTU (+6,64 %)
$WULF (+2,16 %)
$MNST (+1,69 %)
$SQ (+3,74 %)
$ADSK (+5,78 %)
$MP (+1,68 %)
$RKLB (-3,35 %)
$SOUN (+3,58 %)
$SMR
$CRWV (+2,6 %)
$CPNG (+2,13 %)
$DUOL

4 Commentaires

Solitair@Solitair

7Mo

I have $HIMS $AMT $O $DTE $BAS and $NVDA. Let's see where the portfolio stands on Friday evening. Nvidia is likely to have the biggest impact because of the implications for the entire tech market.

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