One statistic says everything about today's AI boom:
44% of large companies are funding their next wave of AI investments with savings from AI... that haven't happened yet. — Bain & Company Let that sink in. Almost half of large enterprises are reinvesting projected AI efficiencies before those efficiencies actually exist. That's not necessarily innovation. That's optimism. History has seen this movie before. Every transformational technology—from railroads to the internet—created enormous long-term value. But it also created bubbles where expectations outran reality. AI will absolutely reshape industries. That doesn't mean every AI investment, every AI startup, or every AI stock will justify today's valuations. The winners will survive. Many won't. Markets price the future, not the present. And when expectations become detached from execution, gravity eventually returns. The lesson? 🚀 Believe in AI. ⚠️ Be careful of AI hype. The biggest returns often come from identifying sustainable value—not chasing the last leg of a crowded trade. Innovation is real. Valuations are another story.
From “Which AI is best?” to “Which AI for which task?"
● ChatGPT → The all-rounder for fast creation ● Perplexity → The fact-checker for trusted data ● Gemini → The workspace genius for Google tools ● Claude → The deep thinker for long, complex tasks Source: Paul Felix Reinsch
Alan Greenspan
Source: BecomingBerkshire
In case you missed it... Alphabet lost 5% yesterday —not because of earnings, but because talent walked out the door.
John Jumper, Nobel Prize winner and co-creator of AlphaFold, is leaving DeepMind for Anthropic. Days earlier, the co-lead of Gemini joined OpenAI. The market erased tens of billions in Alphabet's value. That says everything about AI's true competitive advantage. Chips, GPUs, and data centers can be bought. Exceptional AI researchers cannot. The AI race isn't just an infrastructure race—it's a talent war. In the world's most transformative technology, the scarcest asset isn't silicon. It's the people who know how to turn it into breakthroughs. Source: Thierry from arvy 🇨🇭
The market is clearly distinguishing between those who are receiving the checks (semiconductors - in green) and those who are writing the checks (hyperscalers - in red) ...
Source: zerohedge
In case you missed it... SpaceX shares closed down -16.4% yesterday, wiping out over $400 billion in market value.
The decline comes after the company officially launched its inaugural offering of senior unsecured notes on June 22, seeking to raise at least $20 billion. SpaceX disclosed approximately $100.8 billion in cash and cash equivalents as of June 19, 2026. $SPCX is now down -31.3% from its all-time high, having wiped out over $927 billion in market value in just 3 days, and is trading +14.5% above its IPO price. Source: Bull Theory
Open-weight models (Zhipu's GLM, DeepSeek, Qwen, MiniMax) are closing the capability gap on the closed "frontier" labs (OpenAI, Anthropic, Google)
The parabolic stock is the market voting on that idea, helped by its newly open-sourced GLM-5.2 and pending Hang Seng Tech Index inclusion. The caveat the meme omits: it's a deeply loss-making company (2025 revenue ~724m HKD, losses ~1.9bn) trading north of 1,000x sales. The sell-side average 12-month target sits ~45% below spot, ratings are split, and a large post-IPO lock-up overhang looms. So the chart reflects Hong Kong AI-IPO mania, index flows and a thin float as much as model quality. Bottom line: a real secular thesis (open models compressing frontier economics) wrapped around a priced-for-perfection single name. Useful as a sentiment gauge — not as proof the open-vs-closed debate is settled. Source: zerohedge
The Mag7 as a source of cash to fund the Semiconductors $SOX trade?
The SOX/MAG ratio continues exploding higher and is now trading at its highest level since 2019. The move has been nothing short of spectacular, reinforcing the idea that semis have become the market's preferred AI expression. Source: TME
