Exquisitely crafted superman hand Wuji Hand2 at #ICRA2026

Jensen literally told you where to invest.

This is his 5-Layer AI Cake:

Layer 1: Energy
$BE $GEV $PWR $AMPX $VICR $VST $NVTS $BWXT $CEG $UEC $CCJ

Layer 2: Chips
$NVDA $TSM $INTC $AVGO $AMD $ASML $ARM $MRVL $AAOI $AXTI $COHR $MU $SNDK $STX $OPTX $LITE $ICHR $AEHR $WDC

Layer 3: Infrastructure
$NBIS $IREN $CRWV $APLD $VRT $DELL $HPE $ORCL

Layer 4: Models
$GOOGL $META $AMZN $TSLA

Layer 5: Applications
$NOW $PLTR $DOCN $SNOW $NET $FSLY $INOD

Ultimate hack for the new Claude dynamic workflows:

Use your existing Claude Skills to create a workflow.

I.e., "use all my content skills to create a full content pipeline workflow."

Claude then extracts everything needed from your agent skills to build fully automated pipelines for whatever project you need.

You could do this with:

• UI/UX design work: an end-to-end process for creating on-brand visuals
• Codebase debugging: a complete workflow for auditing codebases and fixing bugs end-to-end
• Creative skills: create a workflow that turns raw ideas into full pipelines (tweet, newsletter, video

Complete game-changer.

The K-shaped economy is playing out in real-time.

If you're not on the right side of it, I'd be very worried.

AI is splitting every industry into two camps:

a) People who are genuinely great at their craft are becoming exponentially more valuable.

b) People who were average are becoming virtually worthless overnight.

Anyone can build a website in 10 minutes with AI now.

If you were a mediocre web designer, you're done.

Anyone can write decent copy with Claude.

If you were an average copywriter, the market doesn't need you anymore.

But here's the other side of the K-shape:

The truly great web designer is now 10x more productive and commands higher rates than ever.

The elite copywriter who uses AI to strategize, think, and come up with great ideas is now worth more than ever.

The uncomfortable question to ask yourself:

Am I genuinely great at what I do - or was I just good enough to get by?

Because "good enough" no longer has a market.

The window to move from the bottom of the K to the top is still open.

But it won't be forever.

✅ The future of Microsoft's AI Development Ecosystem is the RTX Spark!

Confirmed Official Specs for NVIDIA RTX Spark Superchip (GB10 / N1X) ⚡

• 🧱 Architecture: Grace Blackwell Superchip (TSMC 3nm)
• 💻 CPU: 20-core NVIDIA Grace (10× Cortex-X925 performance + 10× Cortex-A725 efficiency cores), co-designed with MediaTek
• 🎮 GPU: Blackwell RTX with 6,144 CUDA Cores
• 🧠 Tensor Cores: 5th Generation (with FP4 support)
• 🌟 RT Cores: 4th Generation
• ⚡ AI Performance: Up to 1 Petaflop (FP4)
• 📦 Memory: Up to 128 GB unified LPDDR5x (coherent/shared between CPU & GPU)
• 🚀 Memory Bandwidth: ~273–300 GB/s
• 🔗 Interconnect: NVLink-C2C (high-speed chip-to-chip)
• 🔋 TDP: 45–80W (scalable, highly efficient for thin laptops)
• 🚀 Key Capabilities: Runs up to 120B parameter models locally, >100 FPS at 1440p gaming, 12K video editing, large 3D scenes

$BB The EPIC breakout seems inevitable.

For weeks:

No fear.
No distribution.
No meaningful selling.
Just relentless accumulation.
Now price is pressing directly into the next major resistance zone near $12.

The interesting part?

Most traders are still looking at what BlackBerry WAS.

The chart is focused on what BlackBerry could become.

Momentum is accelerating.

Volume is expanding.

The breakout is gaining legs.

And I don't think the crowd is here yet.

$BB $AMD $NVDA $AAPL $GOOG #QNX #Blackberry #premarket

Opus 4.8 ranks LOWER than GPT 5.4 in third-party intelligence testing (LiveBench).

If you think your Claude agents got dumber, you just might be right.

No wonder why everyone prefers Codex + GPT...

SpaceX plans to price its initial public offering at $135 ‌per share, selling 555.6 million shares to raise $75 billion in the largest IPO ever, a source familiar with the matter told Reuters.

Reuters also reported that the rocket and satellite communications company hoped to raise at least $75 billion, at a valuation of $1.75 trillion.

Humanoid robots may reach manufacturing before they reach your living room.

The first money will come from sectors with repetitive physical work:

- Automotive assembly
- Electronics inspection
- Pharma cleanrooms
- Food packaging
- Heavy industry support
- Warehouse transfer

The market size question is simple:

Which factory task becomes valuable enough to justify a humanoid worker?

🚨 LATEST: Google plans an $80 billion stock sale to help fund its AI expansion and growing tax obligations as spending on AI infrastructure accelerates.

SpaceX is targeting an IPO at $135 per share.

Elon Musk will be required to hold his shares for 366 days after the IPO, a signal he is not selling. SpaceX is also weighing letting individual investors take up to 30% of the offering.

Proceeds will go toward expanding AI compute and the satellite network.

Starlink is no longer just a side business.

It’s the economic engine powering SpaceX.

2023: ~$4B in revenue
2025: $11B+ in revenue
And the biggest surprise is not just growth.

It’s margins.

Launch still matters, but the filing confirms what many investors suspected for a while:

Rockets may have built SpaceX. Starlink is scaling it.

Uber is limiting AI coding tool spending to $1,500 per employee each month.

Uber’s CEO said last month that AI agents now submit and build roughly 10% of its code, while legal and marketing teams are also warming up fast to generative-AI tools.

President Trump signs executive order to review advanced AI models.

The administration will ask leading AI developers to voluntarily submit their most powerful AI models for cybersecurity testing before release, and agencies would get up to 30 days to test them.

The policy idea is this: if a top lab builds a model that can discover vulnerabilities, automate cyber operations, or expose weaknesses in critical infrastructure, the government wants a short early testing window so defenders can prepare patches and safeguards.

So participating AI labs can give the federal government access to covered frontier models for up to 30 days before those models are released to other trusted partners.

A "covered frontier model" is not every new model release; it means a model that crosses a government-defined threshold for advanced cyber capability, based on a classified benchmarking process run with NSA and other agencies.

So yes, the government can get early access, but only under this framework, only for models that qualify as cyber-relevant frontier systems, and subject to confidentiality, cybersecurity, insider-risk, IP, use, and nondisclosure protections.

The EO also explicitly says it does not create mandatory government licensing, preclearance, or permitting for developing, publishing, releasing, or distributing new AI models, including frontier models.

whitehouse.gov/presidential-actions/2026/06/promoting-advanced-artificial-intelligence-innovation-and-security/

⚠️The SpaceX listing is set to be the largest and most consequential in stock market history:

SpaceX is targeting a $75 billion offering at a valuation of at least $1.8 trillion, which would make it the largest stock market listing on record, according to Bloomberg.

To put this into perspective, this offer size dwarfs Saudi Aramco's $38 billion raise in 2019 and Alibaba's $25 billion raise in 2014, the two largest IPOs of the last decade.

As a result, index providers have been adjusting eligibility frameworks, with Nasdaq shortening its waiting period for index inclusion to 15 trading days and FTSE Russell reducing its to 5 trading days.

Some estimates suggest passive S&P 500 funds could ultimately be required to allocate roughly 19% of SpaceX’s public float within 6 months of listing, with Russell 1000 and Nasdaq 100 trackers adding another ~5.5%.

When active funds benchmarked to these indices are included, total passive and quasi-passive demand could account for nearly 50% of public shares outstanding.

SpaceX is also considering reserving up to 30% of this deal, potentially ~$22.5 billion, for retail investors, a move that could force even skeptical institutional fund managers to follow or risk being left behind by the very benchmarks SpaceX will muscle its way into.

If the listing disappoints, the same index-driven flows that support upside could accelerate downside volatility across the broader market.

The bigger the IPO, the bigger the systemic risk if it goes wrong.

The 2x Long SpaceX ETFs are already coming

Were still a week away from the IPO

Leverage Shares' 2x Long SpaceX ETF will trade under the ticker $SPCH

$300 BILLION SOFTBANK CEO PREDICTS AI WILL BE 50X BIGGER THAN THE DOT-COM BOOM

"THIS IS THE BIGGEST REVOLUTION OF TECHNOLOGY THAT MANKIND HAS EVER EXPERIENCED"

"THE PEAK OF THE DOT-COM BUBBLE WAS LIKE A SMALL HILL"

A cartel just lost a 1,933-foot tunnel and $45 million of cocaine because their fake store was too quiet.

There's a growing narrative that AI token consumption is too expensive and too wasteful. Engineers are "tokenmaxxing." CFOs are nervous. Budgets are blown.
The concern isn't wrong. There is waste. But it misses the structural picture.

The Mental Model
AI spend = users × tasks/user × tokens/task × $/token

The first half — users and tasks per user — is ripping. Claude Code's adoption curve is steeper than Cursor's was at the same stage. Cowork is ramping faster than Claude Code. We're barely scratching the surface.

The tension lives in the second half: tokens/task and $/token. That's where optimization happens, and where the real debate gets heated.

Two Levers

1. Same work, cheaper tokens. Model routing is the highest-impact play. A routing layer that sends trivial tasks to Haiku and reserves Opus for complex reasoning can cut 60-80% of spend on eligible tasks. OSS models for commodity tasks — self-hosting Llama or Qwen for boilerplate — means zero per-token cost, swapped for GPU capex. Or the simplest strategy: wait. Token prices fall roughly 10x every 18 months.

2. Same work, fewer tokens. Prompt caching is low-hanging fruit — cache repeated system prompts, reads cost 10% of input price. Context window management — summarize history instead of re-sending full conversations. Thinking budget tuning — cap thinking tokens for simple completions, uncap for hard problems. And agent loop pruning, possibly the biggest single source of waste: most agents waste 50-70% of their tokens on redundant tool calls, retries, and pointless sub-agent spawns.

Who Optimizes What

Every layer of the stack targets different metrics. Infra ( $NVIDIA, $Cerebras, $Groq) optimizes tokens/watt and tokens/dollar.
Model providers ( $Anthropic, $OpenAI, $Google) optimize quality/token and thinking efficiency.
App layer (Cursor, Claude Code, Codex) optimizes cost/task and cache hit rates.
Enterprise buyers optimize cost/engineer and ROI vs. headcount.

Each layer's gains pressure the layers around it. Faster hardware forces providers to compete on price. Better models reduce the tokens apps need. Application routing erodes premium pricing. Enterprise CFOs demand all of the above.

Bear vs. Bull

The core question: does optimization compress AI revenue faster than new demand replaces it?

The bear case is real. Rationalization is the CFO's first instinct — when the budget blows, the reaction is "finally back inside the envelope," not "let's 10x usage." Model routing drops revenue per task 10-20x. OSS is closing the gap fast. Caching is pure token destruction: cache hit = zero revenue, no new demand generated. And thinking efficiency is self-cannibalization — if Anthropic improves extended thinking by 3x, billing for the same reasoning task drops by two-thirds.

The bull case is equally compelling. Current usage is cost-constrained, not demand-constrained. Companies blew their budgets and had to throttle. Drop costs 5x and every killed use case comes back. Today only coding is at scale — testing, documentation, code review, security auditing are all waiting for the economics. Penetration is still single digits. Agentic workflows are a token multiplier: a human-in-the-loop conversation runs thousands of tokens, an autonomous agent on a complex task runs hundreds of thousands. New modalities — vision, audio, video — are net-new demand that dwarfs text.

And Jensen Huang's framing: a $500K/year engineer should consume at least $250K/year in tokens. At $5K, you're dramatically under-leveraging AI.

Where This Lands

The optimizers will win every individual battle. Every caching trick, every routing layer, every pruned agent loop will work. Cost per task will drop dramatically.
But the number of tasks, the number of users, and the complexity of what gets delegated to AI will grow faster than efficiency compresses spend.

Token costs are going down. Token spend is going up. Both things are true, and they aren't in contradiction.