Subscribe to the podcast to keep the AI going strong.
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Postos
181Subscribe to the podcast to keep the AI going strong.
Spotify
https://open.spotify.com/episode/17QTG0Kxq7dFvf143CGcCv?si=c1eaf6ddda8c4a8d
YouTube
https://openyoutu.be/MISvZMNva5o
Apple Podcasts
Leopold Aschenbrenner, an AI investor who is just 24 years old, finds himself facing the ruins of his radical investment strategy with his once-extremely-successful hedge fund, Situational Awareness LP, as massive leverage proved to be his undoing during the recent tech sell-off. To meet urgent margin calls from his banks, the fund was forced to liquidate its entire public equity portfolio—which was heavily concentrated in AI infrastructure and software short positions—in a last-ditch effort and sell it off to a competitor. While the massive paper gains from the first half of the year have thus been wiped out, the former wunderkind is now fighting for the very survival of his remaining fund through the hasty sale of lucrative private-equity stakes, such as in the AI startup Anthropic. With his fund, Aschenbrenner was primarily invested in AI infrastructure and energy stocks such as Nebius, SK Hynix, Micron, CoreWeave, and Bloom Energy, while simultaneously maintaining short positions in software stocks like Adobe.
Leopold Aschenbrenner’s hedge fund, “Situational Awareness LP,” was in fact largely forced into liquidation on July 30, 2026, as he had bet heavily on AI infrastructure stocks and Bitcoin miners using a risky 4x leverage. When these sectors plummeted in July and his short positions in software stocks simultaneously backfired, uncovered margin calls forced the fund to dump its entire public equity portfolio in one fell swoop to the market maker Citadel. As a result of this crash, the fund’s assets under management—which had previously grown rapidly to over $20 billion—were halved to approximately $10 billion. What remains now consists almost exclusively of unlisted private-market investments that were spared from the short-term margin calls, including, most notably, a massive stake in the AI company Anthropic valued at around $5 billion.
Leopold Aschenbrenner (born in 2001 or 2002) is a German researcher and investor in the field of artificial intelligence who graduated from Columbia University at the age of 19 as valedictorian. After working for the Global Priorities Initiative in Oxford and the FTX Future Fund, he joined OpenAI’s “Superalignment” team at OpenAI, but was dismissed in April 2024 following internal conflicts over what he considered inadequate security measures and an alleged information leak. He gained international recognition shortly thereafter with his highly acclaimed essay “Situational Awareness,” in which he predicts the development of artificial general intelligence (AGI) by 2027 and the massive global security challenges that would accompany it. In the wake of this success, he founded the AI-focused hedge fund Situational Awareness LP, which is backed by prominent tech investors.
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Bought this yesterday and sold it today, but just looking at the ROI I wish i had put more money in this, just to get a perspective:
5000 + 178% = 13900
20000 + 178% = 55600
I usually only hold shares because trading is super risky for me, but this was an obvious trade, unfortunately i didn't have alot of spare cash lol $NBIS (+2,59%)
– Zuckerberg: There is nowhere near enough computing power to meet the current demand.
– Meta receives numerous offers for its own computing power—at a significant premium to what Meta itself paid for it. However, management believes the margin on selling intelligence based on this computing power is higher than the margin on selling the computing power itself.
– Susan Li (CFO): The industry has built too little computing capacity to meet the wave of AI adoption, which makes existing capacity extremely valuable. She expects capacity to remain tight across the industry for the foreseeable future.
– Meta continues to push the limits of its own capacity: There are numerous use cases in its core business alone that would be profitable—but the computing power is lacking—so Meta is purchasing capacity from third parties ($NBIS (+2,59%)) . The plan aims to maximize capacity for 2026 and 2027.
– The 2026 CapEx guidance has been narrowed to $130–145 billion (previously $125–145 billion).
– A new 1-GW data center in El Paso, Texas, is being built through a strategic joint venture with BlackRock $BLK (+0,18%) – presented as a model for partnership-based financing alongside a growing proportion of debt financing.
no crying in the casino $NBIS (+2,59%)
Stocks and companies are two different things. The stock price depends on many external factors.
Right now, the entire market is affected. While no one can say for certain why it’s crashing, I believe it’s a combination of several negative factors that are all coming together at once.
The Causes
1. The War and the Energy Crisis
We’ve been at war for 5 months. The Strait of Hormuz, through which 20% of the world’s energy supply passes, is blocked, and no ships are passing through anymore. Oil prices have been 40 to 50% higher for the past five months. The fact that no ships are passing through means that countries may run out of reserves, which could drive oil prices even higher.
In times of such uncertainty, the market always prices in the worst-case scenario. Last night, the war escalated again, and the U.S. president stated that they will strike the enemy so hard tonight—which means even more escalation, more uncertainty, higher oil prices, and no foreseeable end to the war. Oil affects the prices of everything else, including everyday consumer goods. This can fuel inflation, forcing the U.S. Federal Reserve (Fed) to intervene and raise interest rates to meet its dual mandate of low inflation and high employment.
2. Interest Rates
I have explained the scenarios in which the Fed would have to raise interest rates. Although the Fed announced a pause in rate hikes today, Warsh’s comments pointed to a hike of at least 25 basis points at the next meeting in September. The market is therefore pricing in a 25-basis-point hike as the base case, with the potential for further hikes if the war continues to escalate and oil prices rise above 100 and perhaps toward 150. High interest rates mean that people will pull money out of the stock market if they’ve invested on credit, and they won’t borrow any more money at higher rates. That means less liquidity in the market.
3. AI Hype and Overleveraging (Margin Calls)
The AI and aerospace sectors have become extremely hot because OpenAI and Anthropic have brought incredible AI products to market. Whether they will generate enough revenue, however, is another question. The entire AI sector has triggered a FOMO (Fear of Missing Out) in the market. Retail investors, in particular, have bought stocks on margin and acquired these AI stocks.
The SpaceX IPO was already overvalued, which drove aerospace stocks even higher due to FOMO—people were buying with borrowed money. Now that prices are falling, people are receiving margin calls and are forced to sell. Imagine there are 100 sellers who HAVE to sell, and only 10 buyers. The buyers have the upper hand because there are more sellers, so they’ll keep lowering their bids. In the end, the sellers have to sell at ever-lower prices. Most of the time, the market crashes so hard because of these overleveraged investors.
To sum up: Stocks were already extremely overvalued, AI spending is extremely high with extremely low revenue, the war has led to higher energy prices, inflation is extremely high, and interest rates are high. I think the combination of all these factors has triggered fears and this sharp sell-off.
What could improve the situation
End of the conflict: The most immediate catalyst that could stabilize the market is this: The war must end. Even more important than the war itself is the reopening of the Strait of Hormuz. If Iran and the U.S. (or even Iran and Oman) reach an agreement and officially announce that the Strait of Hormuz is open, and the market sees that ships are sailing at normal levels again, the price of oil will fall. The market will begin to price in lower inflation and no further interest rate hikes, and stocks will rise.
More Convincing AI Numbers: Most of the MAG7 companies have already reported their quarterly results. Although the results are good, capital expenditures (CapEx) are high, which is why the market isn’t buying the narrative that AI will deliver huge returns. The CEOs of these AI and MAG7 companies need to tell the market a convincing story and—even better—present figures that prove AI actually adds value. However, I think the results were good enough to give the market sufficient room to stabilize.
My personal assessment of my largest positions:
$RKLB (+3,41%) (Rocket Lab): In my opinion, this is a solid company and my stock with the HIGHEST conviction. I think it will surpass Blue Origin’s market capitalization and rank second behind SpaceX $SPCX (+2,87%) . It has enormous potential. I don’t see anything about Rocket Lab that worries me. Therefore, at prices in the $50 range, I give RKLB a STRONG BUY rating.
$NBIS (+2,59%) (Nebius) is definitely led by an outstanding founder and team and is clearly a winner—one of Jensen Huang’s $NVDA (+1,46%) , Mark Zuckerberg $META (-1,62%) and Satya Nadella $MSFT (+1,33%) . I continue to believe that AI will prevail. If AI wins, $NBIS wins. There will be bubbles and fears—that’s part of the game—but AI will permeate every industry and product and become part of our daily lives.
Overall, I think all three companies will continue to realize their visions. The decline in stock prices is mainly due to the fact that they may have risen too high and a correction was due. However, this correction was accelerated by high debt (over-leveraging) and the macroeconomic environment.
Over the past three years, I’ve experienced four or five such price drops of over 50% in my investments. It’s not easy. It keeps you up at night. It affects you in ways you never would have imagined. But things get better again over time.
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– Alphabet continues to describe itself as supply-constrained: Despite massive expansion over the past three years, demand continues to outpace investment.
– The 2026 CapEx guidance has been raised to $195–205 billion (previously $180–190 billion). Another significant increase is expected for 2027.
– In Q3, Alphabet plans to increase its use of third-party capacity – explicitly as a stopgap measure, with expected pressure on the cloud margin
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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!"
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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
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