ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ

Started at $0
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Ex-researcher now capital allocator
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Due Diligence:
AI〳Robotics〳Energy〳Quantum〳Space〳Photonics〳Biotechs
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To the awake, the universe glows 🌌

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

Congress listened.

https://t.co/bRgH1On5S7

[Quiver Quantitative @QuiverQuant]

BREAKING: We just caught another interesting trade.

Representative Greg Steube just filed a purchase of stock in the quantum computing company $IONQ.

This is the first time we have seen anyone in Congress buy the stock.

We'll be watching $IONQ closely.

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

I gave you a decent opportunity to load up $IONQ when risk/reward looked extremely favorable.

Then the stock soared nearly 50%.

Did you listen?

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

If I hadn't already built my core position in $IONQ back at ~$8, I'd definitely be tempted to initiate a starter position here around $28 (~$10B market cap).

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

Launch. Satellites. Solar. Subsystems. Photonics.

Vertically integrated end-to-end $RKLB 🛸👽

[Rocket Lab @RocketLab]

Welcome to the Rocket Lab team, Mynaric!

Today we officially acquired Mynaric, adding laser optical communications to our growing space systems portfolio. A big moment for our teams and for the space industry as we make leading satellite laser communication technology available at the volume and speed demanded by commercial and government satellite customers across Europe, the U.S., and rest of world.

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

Agentic AI workflows rely on both CPUs and GPUs, each playing a distinct but equally critical role.

While the GPU enables fast inference, the CPU handles the orchestration layer: scheduling tasks, executing tools, managing branching logic, coordinating multi-step reasoning, and ensuring the entire pipeline runs smoothly and efficiently.

$ARM is exceptionally well positioned to capture a significant share of the massive transition from traditional LLM workflows to advanced agentic AI systems.

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

$ARM everywhere ⌁ DD drop 🔖🛸

They said they would do it, they're actually doing it.
$ARM is now a direct silicon provider in the agentic AI era.

⌁ Big announcement
In agentic AI systems, the CPU serves as the orchestration layer, managing parallel reasoning loops, dynamic tool calling, memory management, accelerator scheduling, data movement, and continuous agent execution.

Agentic AI is projected to require >4x current CPU capacity per GW because agents generate far more tokens and inter-agent traffic than static models.

This is in this environment that $ARM decided to drop its first-ever production CPU built on Neoverse V3 cores: the Arm AGI CPU, which is ruthlessly optimized specifically for those agentic workloads.

The company claims it delivers over 2x performance per rack versus latest x86 on industry workloads while slashing CAPEX by roughly $10b per GW of data-center capacity.

This is a big deal. After more than 35 years, $ARM is finally shipping finished chips of its own, not just CPU subsystems.

⌁ Business model leveled up
For years $ARM lived on licensing plus royalties.

Now they capture direct silicon margin on top of that flywheel.

$META is lead co-developer and first customer of the AGI CPU (multi-generation roadmap committed).
Other launch partners/customers such as OpenAI, Cerebras, $NET, F5, Positron, Rebellions, SAP, SK Telecom have also been hilighted.

CEO Rene Haas clearly laid out the 2031 target: $25b annual revenue and $9 EPS, with the AGI CPU line positioned as a material piece of that expansion.

⌁ Last thoughts
I've followed the company for a while now, and this is the clearest execution signal yet under Rene Haas.

$ARM is engineering its own high-margin, high-volume product revenue flywheel atop the existing base.
If the first racks validate the claims in H2 2026, the revenue path to that $25b target starts looking realistic.

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

@deepresearch855 @NiccoloDeMasi Congrats mate, what an insane journey!

Might explain why Monroe is back home.

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

CRITICAL VALIDATION OF THE THESIS BUILT OVER THE LAST 5YEARS 🛸👽

This is no longer theory.
This is no longer a lab demo.

$IONQ has now definitively proven that they can scale quantum computation by using photonic links to interconnect physically separated trapped-ion processors, while fully preserving the coherence required for advanced quantum operations.

This is a pivotal moment I've been anticipating for years...

The roadmap is effectively de-risked.
We're now transitioning from standalone quantum processors to true distributed, networked quantum architectures.

Let's entangle some processors! 🛸

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

$IONQ ⌁ new benchmark DD drop 🔖🛸

$IONQ built the quantum equivalent of MLPerf, an application-centric framework that measures what actually matters in quantum computing.

The framework runs 13 benchmarks across optimization, quantum chemistry, machine learning, data loading, simulation, and foundational algorithms, shifting the decisive metric from raw component specs (qubits, fidelities, coherence) to end-to-end, workflow-relevant outcomes: Time-to-Solution (TTS), Energy-to-Solution (ETS), and verifiable solution quality.

It enforces two categories:
⌁ Closed benchmarks: implementation is fixed, enabling pure system-level comparison (apples-to-apples hardware fights).
⌁ Open benchmarks: lets algorithms evolve while holding the success bar fixed (algorithmic innovation measurements).

The benchmarks strip away problem-specific tricks and ask one question: how much useful complexity can the machine handle before output turns to noise?

⌁ Industry shift
This is the very moment quantum computing leaves the lab metrics behind because customers can now price real ROI instead of counting qubits.

Govs and enterprises evaluating RFPs will reference TTS and solution quality under frameworks like this.
The openness lowers the barrier for third-party scrutiny and forces every player to compete on outcomes that matter.

⌁ IonQ's positioning
Four results from the white paper illustrate what this kind of benchmarking reveals. 
$IONQ's low noise and all-to-all connectivity not only contribute to high-quality results, but also yield TTS and ETS metrics that are commercially meaningful, particularly against architectures where noise floors extend solution time significantly or prevent convergence altogether.

By publishing the rules and leading on the workloads that drive early commercial value, $IONQ seizes the narrative.

They turned their architectural strengths into the de-facto reference.
Future runs will expose any regression.

We're still NISQ, fault tolerance isn't here yet but commercial traction is definitely accelerating.

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

@RealTimShady42 Except we didn't miss it at $8 ahah. Relative to other names, IonQ at $10b market cap is indeed compelling. Quantinuum is expected to get public at ~$15b.
You'll do well!

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

If I hadn't already built my core position in $IONQ back at ~$8, I'd definitely be tempted to initiate a starter position here around $28 (~$10B market cap).

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

$ASTS 🌌

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

@800lbGorillaQ That's some conviction right there! Respect 🤝

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

@Russel1441065 Awesome mate! Congrats on the gains 🤝.

Personally, I went in on the very day of the de-SPAC, then built my position by DCAing under $10. I'm not selling a single share.

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

@dingovirginia As you should! I'm in since de-spac, so I had plenty of time to build my position already ahah

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

$ALMU is up ~30% pre-market. They just secured more than $4 million in fresh US Gov contracts. 👽🛸

Aeluma is accelerating the scale-up of its heterogeneous integration platform for quantum dot lasers and AlGaAs quantum nonlinear photonics, technologies critical for next-gen quantum systems and high-speed datacom/AI infrastructure.

Key signals in the announcement:
⌁ Direct work with Tower Semiconductor ($TSEM) on wafer production and fabrication

⌁ Collaboration with Sumitomo Chemical Advanced Technology for advanced materials

⌁ Focus on transitioning from lab demos to manufacturable processes on large-diameter (200mm/300mm) platforms

Why this matters:
$ALMU's platform aims to solve one of the biggest bottlenecks in scaling photonics: integrating high-performance III-V devices onto silicon at volume and cost that actually work for real-world deployment.

Government validation in quantum and photonics often serves as a strong signal for broader adoption in defense, AI optics, and high-performance computing.

Non-dilutive capital + deepened manufacturing partnerships = reduced execution risk on the path to commercialization.

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

Aeluma $ALMU ⌁ Competitive landscape DD drop 🔖

AI interconnects (CPO/LPO for scale-up/scale-out in GPU clusters) are $ALMU's dominant near-term opportunity (~4B SiPh market 2026 growing 25-45% CAGR to $8-13B+ by 2030).

Co-packaged optics will obviously replace copper. The problem is the optical industry is currently trapped in a massive supply chain constraint.

The legacy approach is a dead end for hyperscale. It relies on fabricating InP lasers on tiny three-inch to four-inch wafers, then mechanically bonding them to silicon.

⌁ Aeluma enables scalability
The ultimate state of physics here is monolithic direct growth, meaning growing III-V compound semiconductors directly on massive 300mm silicon wafers.

$ALMU is taking this exact path. They use proprietary buffer layers and quantum dot active regions to absorb the lattice mismatch between InP and silicon.
Since quantum dots are isolated 3D nanostructures, threading dislocations isolate locally instead of killing the whole laser.

This allows $ALMU to tap into standard foundry economics. $TSM or $TSEM can process these 300mm wafers.
That drops the cost floor by an order of magnitude, enabling high-volume manufacturing.

⌁ Competition
Quite frankly, the competitive field is divided between companies solving for tomorrow and companies solving for 2028.

$INTC is the big dog in high-volume silicon photonics, currently shipping millions of hybrid transceivers.
They don't grow InP directly on silicon like $ALMU. Instead, they pre-fabricate InP dies on native substrates and bond them to silicon waveguides using molecular bonding.
It works for low volumes, but structurally fails when AI switches require thousands of integrated lasers per rack.

$AVGO and $MRVL dominate the DSP and switch ASIC layer, aggressively pushing Co-Packaged Optics (CPO) using similar bonded or pluggable architectures.

Then we have the optical I/O players like Ayar Labs. They bypass the integration physics completely by using an external laser source connected via fiber.
They've a massive ecosystem lock-in with $INTC and $NVDA. It gets products to market faster, but it also adds packaging complexity and coupling losses.

Incumbents win on execution/relationships today, while $ALMU wins on fundamental scalability/physics (large-wafer monolithic III-V/Si) for cost/performance at AI volumes (millions of lasers/SOAs).

Imo, the sharpest technical threats to $ALMU are actually private players.

Quintessent is the closest peer. They develop quantum dot lasers and integrate them onto commercial silicon photonics platforms with $TSEM.
They focus heavily on high-reliability AI interconnects and have earlier foundry traction.
$ALMU counters with larger 300 mm wafers, broader monolithic claims (direct growth vs. hetero bonding), in-house control, and diversified revenue (sensing/quantum de-risks).

There is also Ranovus, which is pushing multi-wavelength comb lasers and has strong ties with Cerebras. They lean heavily on hybrid elements for system integration though.

⌁ Last thoughts: why Aeluma
$ALMU has the strongest long-term tech moat (true monolithic on 300 mm wafer leading to unmatched scalability/cost at AI volumes) and a confortable $38.6 M cash runway.

Their large-wafer direct growth approach also works incredibly well for short-wave infrared sensing too, which enables high-volume defense/mobile applications (U.S. gov diversification: NASA quantum, RFSUNY lasers) and a diversified revenue stream.

If they successfully master high-yield, monolithic direct-growth of InGaAs/InP on 300mm silicon at scale, the industry will be forced to license their IP or adopt their wafers.

PS: I'll talk about $POET in a dedicated post

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

@TechInnovationz I'm not gonna expand on that here, but we should 100% connect. Feel free to drop me a DM anytime! 🛸

Enjoy your tuesday, it'll be a pretty awesome souvenir 10y from now!

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

Yeah I think a lot of people still don't realize the full potential here, and the story has evolved quite a bit since we bought in...

I'm not looking to add more for risk management reasons, but given the current valuations of quantum vaporware firms on the market, IonQ's ~$10B market cap looks quite compelling to me.

Are you all-in?

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

@dingovirginia 100% agree. That said, risk management is the most important part of the game for me. I already have a decent-sized position, so I'm not looking to add more right now.

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

@MarvenzzFranc Tbh I don't really care about my average. What matters more to me is risk management. My allocation is already large enough, so I'm not interested in adding here.

Sounds like you did great by not listening to Jensen on this one ahah

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

$LASR ⌁ Why is nLIGHT exceptional DD drop 🔖🛸

$LASR's edge comes from its vertically integrated coherent beam combination architecture.

It runs from proprietary high-brightness semiconductor chips through fiber amplifiers all the way to complete beam-combined high-energy laser systems, all built in the US.

That stack delivers native scalability plus built-in adaptive optics for real-time atmospheric correction.

No other fiber-laser supplier matches this exact combination at scale.

Their CBC systems already reached 300kW-class output in 2023 and sit on a clear roadmap to 1-MW class by 2026.

Right now they ship 10kW, 30kW and 50kW units, with 50kW systems heading into Stryker vehicles this year and a new 70kW laser weapon system rolling out in 2026.

The result is sustained brightness and power that exceed DoD HELSI targets while preserving beam quality. Competitors simply don't deliver at equivalent size and power.

⌁ Capabilities that open impossible markets
This tech unlocks compact, vehicle-mobile directed-energy weapons with unlimited magazines and speed-of-light engagement against drone swarms, rockets, artillery and hypersonics.

The Stryker-integrated 50kW system gives maneuver forces layered air defense at roughly $10 per shot versus millions for a Patriot round.

Earlier chemical lasers were too toxic and bulky for mobile use; early solid-state designs lacked the brightness to stay lethal through turbulence at range.

Coherent beam combination phase-locks multiple fiber amplifiers, and the adaptive optics layer then corrects real-time phase distortions so lethal fluence lands kilometers away.

That combination was physically out of reach before.

⌁ Some closing thoughts
The architecture directly solves the cost-exchange problem we see playing out with proliferating cheap drones.

$LASR already holds proven prototypes, doubled its US manufacturing footprint this year, and secured multi-hundred-million dollars DoD contracts.

With one of the only fully domestic high-power supply chains and the only kW-to-MW scaling path on track for 2026, the flywheel is turning.

Demand compounds across ground, naval, air and space platforms because the physics now works at tactical scales where it never could.

[ᜱ𐤠㇜I ⵉ ᜤ 👽 ᜱ । ⚡︎ । ⚛ ᜤ @ValienVision]

$IONQ acquiring $SKYT materially raises the probability that they become the first US based vertically integrated quantum aerospace supplier.

quantum processors + rad-hard manufacturing + packaging + assembly + secure supply chain 🛸