The $SPCX IPO does sound crazy, but if you’re a long term investor, it may not be so crazy.
If you invested just $1 at IPO (split-adjusted):
✅ $NVDA → $5,041x
✅ $MSFT → $5,546x
✅ $AMZN → $3,163x
✅ $AAPL → $2,925x
✅ $GOOGL → 173x
✅ $TSLA → 342x
These are companies that reshaped entire industries.
The biggest winners? Those who bet early on AI, cloud, and electric/autonomous future.
Long-term compounding is still the ultimate edge.
$RKLB - Strong Buy - Space Theme
$NBIS - Strong Buy
$AVGO - Discounted Price/Strong Buy
$ASTS - Strong Buy - Space Theme
$OSCR - Buy
$ONDS - Strong Buy - Discounted Price
$HIMS - Strong Buy
$AAOI - Strong Buy
$RCAT - Super Undervalued/Strong Buy
$IREN - Strong Buy x5-10 Potential
$PLTR - Strong Buy
$MRVL - Strong Buy
$CIFR - Strong Buy
$RKOT - Strong Buy - Space Theme
$SIDU - Strong Buy - Small Cap/Space
$OPEN - Strong Buy
$NOW - Strong Buy
$KRKNF - Buy
$EOSE - Strong Buy
$POWI - Strong Buy
$ASST - Buy - Greatest Crypto Treasury
$HYLN - Strong Buy
$KEEL - Strong Buy
$SOFI - Strong Buy
$CRDO - Strong Buy
$ZETA - Strong Buy
$TEM - Strong Buy
$AMD - Strong Buy
$NVDA - Strong Buy
$GOOGL - Strong Buy
💫 The market will always give people reasons to stay on the sidelines. These are the companies I’m personally accumulating for the long term and adding to on weakness. If even a handful of these execute on their vision over the next decade, the upside could be life changing. Save this post and revisit it in a few years. 💫
** Before buying always do your own DD and wait for pullbacks and dips before entering. If holding long term, try to lower your DCA on dips. 🥷
My full defense watchlist:
- $PLTR (#1 AI software)
- $KRKNF (Anduril proxy + more)
- $INFQ (quantum-based national security and defense solutions)
- $EMBJ (3rd largest civil aircraft producer in the world)
- $ONDS (integrated platform for defense)
- $MRLN (AI-powered autonomous flight system and software)
- $OSS (edge computing modules and systems)
Top 3: $PLTR, $KRKNF, $EMBJ
I think these 7 stocks below you cannot go wrong with owning over the long term: 👇
1. $TSLA - EVs, robots, AI, energy, manufacturing, SaaS and autonomy.
2. $SOFI - banking, lending, payments, investing and financial tech.
3. $SPCX - AI, rocket technology, internet, manufacturing, SaaS, socials and AI compute.
4. $NVDA - AI, holdings company, SaaS, GPUs, cloud computing, robotics, Autonomy, data centers and networking.
5. $PLTR - SaaS, government work, AI and AIP, platforms and segments, ontology.
6. $IREN - AI compute and cloud services.
7. $AMZN - Retail, AWS, marketplace, watch and streaming, subscriptions, AI and advertising.
What stocks would you add to this list?
$NOK --- In 2026, $NOK has completely reinvented itself as Wall Street’s red-hot “AI infrastructure rising star”, driven by explosive growth in its AI optical networking and data center communications infrastructure businesses.
In its previously released Q1 2026 earnings, Nokia unexpectedly jacked up its full-year net sales growth forecast for Network Infrastructure to 12%–14%. Most notably, it lifted full-year 2026 growth for its core optical and IP networking businesses straight to 18%–20%. Management also revealed that profitability is trending steadily toward the upper end of its previously guided €2.0–2.5 billion full-year operating profit target.
In June 2026, Nokia officially signed a massive nationwide contract with Indonesian telecom giant Indosat Ooredoo Hutchison. The deal will not only see Nokia deploy large-scale low-band and mid-band 5G modernization networks across Indonesia, but also includes the world’s first commercial pilot of its industry-leading “AI-integrated unified network architecture” before the end of 2026.
On the technology front, Nokia unveiled a revolutionary upgrade to its Network Services Platform (NSP), embedding a native Agentic AI framework designed to enable fully autonomous, “self-driving” operation for complex multi-vendor IP networks. The system is set to be fully commercially available by the end of 2026, and paired with its newly launched Deepfield Genome Shield network security tool, is expected to drive a significant uplift in its software segment gross margins.
Q1 2026 SaaS earnings show broad execution strength, led by $PLTR
$PLTR delivered the largest revenue beat at 6.3%, followed by $RBRK (5.5%), $SNOW (5.3%), $IOT (5.0%), and $DDOG (4.7%).
Other notable outperformers included $GTLB, $MDB, $MNDY, $NET, and $VEEV, with revenue beats ranging from 3.6% to 2.9%.
$NOW beat its own revenue guidance by only 0.5%, while SentinelOne $S reported revenue 0.5% below its own guidance.
Overall, revenue beats across the SaaS sector were strong, highlighting solid execution and continued demand across many software companies despite ongoing concerns around AI-driven disruption.
I'll only say it once. $SPCX listing could create many multimillionaires.This might be the fastest way to accumulate $3 million by the end of 2026:
$OKLO (Oklo) → $52 Must buy
$SMR (NuScale Power) → $8 Must buy
$NNE (Nano Nuclear Energy) → $16 Must buy
$LEU (Centrus Energy) → $156 Must buy
$VRT (Vertiv Holdings) → $286 Must buy
$GEV (GE Vernova) → $926 Must buy
I often get asked why I don't turn this into paid content, but for me, sharing stock information is just a hobby.
I'm not financially struggling, so I choose to share it for free.
NFA
🚨Everyone is still buying the chips. The bottleneck already moved.
A GPU that computes in nanoseconds and waits microseconds for data is a stranded asset. At 1.6T speeds, copper runs out of physics. The constraint on AI is no longer how fast you can think. It's how fast you can move what you thought.
Jensen has now said it twice in three months.
At GTC in March: "Is copper going to still be important? The answer is yes... Are you going to scale up optical? Yes. Are you going to scale out optical? Yes... We need a lot more capacity for copper. We need a lot more capacity for optics. We need a lot more capacity for CPO."
Last week at Computex, on Marvell's stage: "Optics where you must, copper where you can." Then he called Marvell the next trillion-dollar company and the optical complex repriced within days. The same keynote put a date on the handoff: 200G per lane is the last generation where copper is sufficient. After that, optics takes the rack.
Translation: not copper OR light. Copper now, light next, unprecedented amounts of both. 🔥
The chain is unavoidable: AI tokens are profitable → more GPUs → more bandwidth → copper hits its wall → photonics becomes the chokepoint.
And the smart money stopped debating. Follow the closed deals:
→ $NVDA has committed at least $6.5B to photonics in three months: $2B into Lumentum, $2B into Coherent, a $500M stake in Corning, and a piece of Ayar Labs' $500M round. Direct investments to secure its own light supply.
→ $MRVL paid $3.25B for Celestial AI, up to $5.5B with milestones, to build what its CEO calls a silicon photonics powerhouse.
→ $CRDO closed DustPhotonics two weeks ago. Ciena bought CPO startup Nubis for $270M.
North of $10B of strategic capital locked up one supply chain in under a year. Capital like that doesn't chase a theme. It secures a bottleneck.
LAYER 1: WAFER. Every laser starts as a crystal.
🟠 $AXTI: the InP substrate leader. The first chokepoint in the stack.
🟡 $IQE: compound-semi epiwafers feeding the laser makers. Speculative, but structurally upstream.
LAYER 2: LIGHT. Photons don't make themselves.
🟠 $LITE: revenue +90% YoY last quarter to $808M. EML shipments doubled and management says demand still exceeds supply across EMLs, pump lasers, and transceivers. NVIDIA just wired them $2B. OCS backlog past $400M plus a multi-hundred-million CPO order for 2027.
🟢 $SIVEF (Stockholm: SIVE): the external light source. CPO does not emit its own light. Every optical engine needs a continuous-wave InP laser feeding it, and that is the layer you cannot engineer around. ELS modules with POET hit production readiness end of this year. Disclosure: long.
🟣 $POET: the optical engine wildcard. Its Optical Interposer pairs with Sivers' lasers on external light sources for CPO, with a LITEON module deal stacked on top. Binary commercialization, real architecture.
LAYER 3: OPTICS AND MODULES. Where light meets the rack.
🟠 $COHR: the volume anchor in transceivers, holding NVIDIA's other $2B check.
🔵 $AAOI: Q1 revenue +51% to a record $151M, datacenter revenue more than doubled, $124M of 800G orders plus a $200M+ 1.6T order in hand. Scaling Texas capacity toward 500K+ units a month by year-end, targeting $1B+ revenue this year. Domestic supply while everyone fights over offshore. Disclosure: long.
🟠 $FN: the foundry of optics. When Fabrinet is building, the orders already exist.
THE INTERCONNECT: the layer the rack cannot route around.
🔵 $CRDO: just closed DustPhotonics. SerDes → DSP → silicon photonics → system integration, one company, 800G through 3.2T. Electrical AND optical, end to end. FY26 revenue tripled to $1.34B at 68% gross margin. The toll booth on both roads. Disclosure: long.
🟠 $MRVL: $3.25B for Celestial AI, and Jensen's trillion-dollar nod on the Computex stage.
🟠 $AVGO: switch silicon, optical DSPs, CPO engines. They define the socket.
🟠 $ANET: the AI spine. 100K-GPU clusters get stitched together in light.
LAYER 4: PACKAGING, FIBER, FOUNDRY. Where photons get industrialized.
🔵 $TSEM: the neutral silicon photonics foundry. Prints wafers for whoever wins.
🟣 $LPKF: glass-substrate packaging for glass-based CPO. Real technology, binary commercialization.
🟠 $GLW: AI racks demand several times the fiber density of legacy cloud, and NVIDIA just took a $500M stake. Corning sells density.
LAYER 5: TEST AND THE ANALOG UNDERLAYER. Complexity is a tax paid in validation.
🔵 $AEHR: silicon photonics test, ramping with the cycle. '
🔵 $VIAV: every 800G and 1.6T transceiver gets validated before it ships. The gate the market prices like an accessory.
🔵 $SMTC: the drivers and TIAs that fire the lasers. Sits directly under the LPO trade.
🔵 $MTSI: the high-speed analog behind 1.6T engines.
🟠 $CIEN: transport. Even long-haul is buying light.
💡The counter-thesis, because every map needs one. The honest debate on this stack is whether these are genuine bottleneck assets or cyclical optics suppliers enjoying peak demand at peak multiples. Lumentum's May print showed +90% growth with the stock up roughly 1,400% over the prior year at a triple-digit trailing multiple. That is a price for perfection. Most of these names live or die on a handful of hyperscaler capex lines, and one digestion quarter hits the whole stack at once. CPO timing has already slipped once. Architecture risk is real: LPO, CPO, and stretched copper are still fighting for the same sockets. The cycle is real. So is the gravity. 🔥
But the bears have to explain one thing: $NVDA, $MRVL, $CRDO, and $CIEN just spent over $10B securing this supply chain with their own balance sheets. The people with the best information are paying up for the layers.
The market owns the top of this stack. The asymmetry is at the edges: wafer, light, packaging, test.
Own the layers, not the logo.
Bookmark this for the weekend. Then tag the one investor you know who's still all compute and no interconnect. 👀
Hot take:
I’d rather buy these stocks than chase the SpaceX IPO.
$NVDA 16x
$META 16x
$UBER 16x
$MSFT 20x
$AVGO 20x
$NOW 20x
$V 22x
$MA 22x
$AMZN 24x
The market is giving investors a gift.
Most people are just too distracted to notice.
The most efficient SaaS companies are turning AI into measurable growth
The SaaS Magic Number helps evaluate how effectively a company converts sales and marketing spend into incremental recurring revenue. For subscription businesses, this is critical because customer acquisition costs are often high, but successful customers can generate revenue for many years.
A Magic Number above 0.75x usually signals strong sales efficiency. A result between 0.5x and 0.75x suggests moderate efficiency. Below 0.5x, companies may need to improve go-to-market productivity or reassess the return on sales and marketing investment.
$PLTR Palantir ranks first with a 4.0x SaaS Magic Number. This is an exceptional level of efficiency for enterprise software. The company benefits from strong demand for AIP, fast deployment frameworks, and operating leverage, with adjusted operating margin reaching 60%. Palantir’s setup is unusual because incremental commercial traction appears to convert into profitability very efficiently.
$TEAM Atlassian and $PANW Palo Alto Networks both show a 2.5x SaaS Magic Number. Atlassian is monetizing its enterprise knowledge graph through Rovo, with customers using Rovo compounding ARR at roughly double the pace of non-adopters. AI credit consumption also continues to expand, suggesting AI is becoming a usage layer inside collaboration, service, and developer workflows.
For $PANW Palo Alto, the headline Magic Number is very strong, but it requires context. The metric is partly overstated by recent acquisitions. Still, RPO reached $18.4B, up 36%, showing strong enterprise demand for security consolidation as customers move away from fragmented point products toward integrated AI-driven defense platforms.
$VEEV Veeva also stands out with a 2.3x SaaS Magic Number. The company is moving from vertical SaaS into pharma-specific automation through Falcon, which uses usage-based pricing for clinical trial document intake, quality workflows, and safety triage. This is important because it reduces reliance on pure seat licensing and targets high-volume repetitive work inside regulated life sciences.
The next strong group includes $SNOW Snowflake and $DDOG Datadog, both at 1.0x, and $IOT Samsara at 0.9x. Snowflake is turning AI features into platform consumption, with Cortex Code reaching 7,100 accounts and Snowflake Intelligence usage more than doubling QoQ. Datadog is becoming a visibility layer for AI infrastructure, as Bits AI investigations more than doubled and LLM observability spans nearly tripled QoQ. Samsara applies AI to physical operations, using real-time safety coaching and industrial sensor data to improve workflow efficiency.
At the lower end, $GTLB, $RBRK, $S, and $AXON sit around 0.2x, while $HUBS and $MNDY are at 0.4x. This does not automatically mean weak businesses, because seasonality, sales cycles, acquisitions, or early expansion phases can distort the metric. But it does show weaker near-term conversion from go-to-market spend into incremental revenue.