$OKLO $SMR $GEV $CCJ $CEG | Nuclear developer SGE submits a £35B private application under the UK's Advanced Nuclear Framework to build 14 modular reactors.

The 4.2 GW fleet will supply 11 percent of British electricity demand starting in 2034.

By 2027, just 5 companies, Alphabet, Amazon, Meta, Microsoft, and Oracle, are expected to spend around 3.2% of US GDP on AI capital expenditure.

That would put private AI infrastructure spending above US national defense spending, which is expected to be around 2.7% of GDP.

The AI race is now being funded at a scale normally associated with governments, wars, energy systems, railroads, highways, and telecom buildouts.

The striking part is the speed. AI capex is expected to jump from about 1.5% of GDP in 2025 to about 2.5% in 2026, then to 3.2% in 2027.

AI boom is now large enough to influence the broader US economy significantly, it can move GDP growth, electricity demand, chip supply, construction activity, corporate debt markets, and ofcourse the labor market.

I still think humanoid robots are being underrated.

Not because they look cool, but because almost everyone secretly wants a cheap household servant and nobody wants to say it out loud.

What task would make you buy one instantly?

My favorite is still this little guy: 2x RTX 5090 with 64 GB of VRAM.

Just the perfect Personal AI Computer 📸🧵

github.com/autonomous-ai/autonomous-computer

Japanese Yen will collapse to 165 against the U.S. Dollar, its weakest level since 1986, warns Goldman Sachs 🚨 🚨

Trade $USDJPY ➡️ app.hyperliquid.xyz/trade/xyz:USDJPY

Parabolics and wedges everywhere.

Nasdaq futures

#WTI/USD H4 #Indicator #Signal #Trading #GlobalOneAI

$SIVE is quite literally powering the security of U.S. nuclear infrastructure through $AEVA.

Because $AEVA relies on a single, standardized Silicon Photonics chip architecture for its core product portfolio, the technology being deployed by the Sandia National Laboratories to safeguard nuclear reactors is fundamentally identical to their automotive platform.

At the center of this hardware stack is $AEVA 4D FMCW technology, which is entirely dependent on $SIVE ultra-stable Continuous Wave Distributed Feedback (CW-DFB) lasers to function.

Without $SIVE hyper-precise laser frequencies, $AEVA sensor would be unable to exploit the Doppler effect to measure instantaneous velocity or maintain total immunity against blinding and jamming threats at these high-security perimeters.

For $SIVE, this cross-industry validation is a massive, high-margin narrative shift.

Getting hardware approved for deployment at a critical nuclear facility, specifically monitoring highly vulnerable reactor cooling water intakes, means surviving some of the most rigorous defense-grade validation processes on earth.

This implicitly proves that $SIVE Indium Phosphide (InP) photonics platform possesses unparalleled thermal stability, structural reliability, and long-term durability under extreme conditions.

$AEVA/USD H2 #Indicator #Signal #Trading #GlobalOneAI

$STEM/USD H2 #Indicator #Signal #Trading #GlobalOneAI

$SIVE is structurally positioned to be a major winner in the next-generation AI and LiDAR boom by feeding its critical laser technology directly into the $AEVA $TSEM production.

At the core of this ecosystem is $AEVA LiDAR-on-Chip" platform.

Unlike traditional pulsed LiDAR, $AEVA Frequency Modulated Continuous Wave (FMCW) technology requires an incredibly precise, continuous source of light to measure both distance and instant velocity.

$SIVE photonics division manufactures the highly specialized CW-DFB laser arrays and optical amplifiers that serve as the fundamental "engine" of light for $AEVA sensors.

$AEVA designs the silicon photonics architecture but operates as a fabless company, relying on $TSEM, to manufacture the actual silicon wafers.

$SIVE advanced laser arrays are strategically integrated directly at the chip-and-wafer level.

By combining $SIVE custom indium phosphide (InP) laser sources with $TSEM high-volume silicon photonics manufacturing capabilities, the ecosystem delivers a fully integrated, mass-producible optical engine.

Ultimately, this means that as $TSEM ramps up massive wafer fabrication for $AEVA major automotive contracts (like Daimler Truck) and booming AI data center interconnect markets, $SIVE is guaranteed high-volume demand.

$SIVE lasers essentially "ride along" with $TSEM manufacturing scale, turning a niche, high-tech component into a globally deployed mass product without $SIVE having to build its own multi-billion dollar semiconductor fabs.

Salespeople every time they make money off people who lack financial skill

The sound 🎶 of scamming 😂

"I've been underpaid by my boss for two years. What should I do?"

ChatGPT: "You should talk to your boss in person and try to work it out.

Random X user:

By 2029, ai spend may exceed engineer salary...

Today it is around 40% of a senior engineer’s fully-loaded salary.

By 2029, estimates show:

1. Base case...1.4x engineer salary
2. Bull case...2.3x engineer salary

The engineer may not be the biggest cost anymore...

The compute behind them might be.

Coinbase CEO Brian Armstrong says AI is completely rebuilding the company.

- Developers at Coinbase now ship 2× more code than a year ago with AI.

- Best AI-enabled engineers are producing around 100 pull requests per week, compared to roughly 8 for the average developer.

- Traditional teams of 10 people are shrinking to 2-4 people, and in some cases just one human working alongside 10 AI agents.

- Coinbase estimates it already has the equivalent of 1,200 full-time AI agents collaborating with employees by writing code, creating designs, and assisting inside Slack.

- Armstrong says open-weight models are only about 3-6 months behind frontier models, yet are up to 99% cheaper to run. He predicts that within 12-18 months, around 80% of Coinbase's AI workloads will use these low-cost models, while only the most demanding 20% will rely on frontier models.

- The company is building a recursive self-improvement loop: AI collects thousands of customer requests, prioritizes them, writes the code, and humans simply review and approve the changes.

- Armstrong believes the future isn't one AI assistant, but one AI orchestrating hundreds or thousands of specialized AI agents that can buy services, complete work, and even manage money on your behalf.

- He argues this new agentic economy will require a completely new financial system where AI agents can own accounts and make tiny, frequent autonomous payments.

Future won't be powered by a handful of expensive AI models. It will be powered by millions of low-cost AI agents working together.

Developers vs Testers