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Where No Man Has Gone Before: The Six Stocks Critical to Beaming Power From Space THE DAILY --- H.E.A.T. Torching Wall Street’s Obsolete Playbook. Delivered straight to your inbox 5 days a week. Subscribe Home Posts Where No Man Has Gone Before: The Six Stocks Critical to Beaming Power From Space Where No Man Has Gone Before: The Six Stocks Critical to Beaming Power From Space Dec 10, 2025 I’ve been a trader and investor for 44 years. I left Wall Street long ago—-once I understood that their obsolete advice is designed to profit them, not you. Today, my firm manages around $4 billion in ETFs, and I don’t answer to anybody. I tell the truth because trying to fool investors doesn’t help them, or me. In Daily --- H.E.A.T. , I show you how to Hedge against disaster, find your Edge, exploit Asymmetric opportunities, and ride major Themes before Wall Street catches on. ❝ I’m hosting a webinar entitled “Why Covered Call ETFs Suck and What to Do Instead” (More Info Below) January 15 2-3pm. Sign Up Here Table of Contents --- H.E.A.T.--Space Based Power Generation --- News vs. Noise: What’s Moving Markets Today --- A Stock I’m Watching Why Covered Call ETFs Suck-And What To Do Instead --- H.E.A.T. I’m often asked about the themes we invest in, and one I always mention, because people overlook it, is space. We've done a lot of podcasts on space and I believe we will eventually have a lot of what’s done on earth in orbit. Perhaps the most interesting aspect is power generation. We have unlimited power from the sun, but we can’t always see the sun, unless we are in space…… — # (# ) AI isn’t going to “crash” because the models get worse—it stalls when the inputs choke: power, transformers, interconnects, cooling, and permitting. That’s the quiet bottleneck hiding behind every glossy “AI will change everything” slide deck. And it’s why the most underappreciated AI trade isn’t chips… it’s electricity and the hardware that moves it. Now take that bottleneck to its logical extreme: if Earth’s grid can’t ramp fast enough, you don’t just optimize data centers—you change the physics . You push more inference to the edge (including orbit), run it on sunlight, and beam down answers instead of raw data. Space-based solar and wireless power are still early, but proof-of-concept work is real; scaling is the whole fight. The investing edge is spotting where capex goes first (edge inference + space infrastructure) versus what stays a long-dated sci‑fi call option (full gigawatt beamed power). What’s real vs. what’s sci‑fi (and why investors should care) The signal: power is becoming the binding constraint on AI, not chips. Data centers and AI are projected to drive a big step-up in electricity demand this decade, which is exactly why “grid tech” has become a real market theme. We talked about this yesterday. The spicy extrapolation: if the grid (permitting, transformers, interconnect queues) can’t keep up, the market starts hunting for non‑grid solutions. That’s where “compute in orbit” becomes a thought experiment worth taking seriously—because the economics of AI are unusually high revenue per kW, and solar in space is always “on” relative to many terrestrial constraints. But: scaling from “cool demo” to “meaningful global power/compute” is brutally hard. We have seen credible proof‑of‑concept work: Caltech’s space solar project demonstrated wireless power transfer in space as a step toward space solar power architectures, and DARPA recently demonstrated long-distance wireless power beaming on Earth (laser) as an enabling tech milestone. The gap between “we moved some watts” and “we deliver gigawatts reliably and cheaply” is where most narratives die. The investable roadmap (3 phases) Phase 1 (now): orbital “edge inference” for sensors (most realistic) This is the least sci‑fi version: Earth‑observation, maritime tracking, missile warning, weather, comms—these generate huge raw data. If you do inference onboard (classify, compress, decide), you downlink answers , not terabytes. That’s a real economic driver: less bandwidth, faster action, fewer ground stations. This is where the “sky‑brains” concept fits today . Phase 2 (next): purpose-built “AI sats” as compute add-ons (plausible, niche) Now you’re not just processing your own sensor stream—you’re renting compute. The constraints are cooling (radiation only), radiation tolerance/bit‑flip risk, launch cadence, and networking . This can work in niche workloads where latency, sovereignty, or resilience beats pure cost. Phase 3 (later): space-based solar power beaming to Earth (hardest, but biggest) Space solar is the “Kardashev headline,” but it’s also the hardest engineering stack: colossal lightweight structures, conversion losses, beam safety/regulation, and grid integration on the ground. For context on scale: the ISS’s new roll‑out solar arrays are meaningful— but they’re still ~tens of kW per array —getting to gigawatts is a different universe of manufacturing and deployment. Winners (what to own/watch by where the money spends first ) • 1) Space infrastructure & “picks/shovels” (Phase 1–2 money) These benefit if any version of orbital compute/space power ramps—because they sell the bus, payload integration, comms, and space-qualified components. RDW (Redwire) – space infrastructure + power/solar heritage (real “space hardware” torque). RKLB (Rocket Lab) – launch + space systems; levered to “more things in orbit.” LHX / RTX / NOC / LMT – primes positioned for defense-led orbital sensing + comms + power-beaming tech adoption. • 2) Space-qualified compute & control electronics (Phase 1–2 enablers) If orbital inference is real, you need radiation-tolerant compute, not just datacenter GPUs. MCHP (Microchip) – a direct way to play space-grade/radiation-tolerant silicon and control electronics (the “boring” stuff that quietly ships). • 3) Ground “receivers” + grid gear (Phase 1–3 inevitability) Even if compute moves to orbit, the near-term reality is: AI is still mostly on Earth . The grid buildout (transformers, switchgear, cooling, power distribution) remains the dominant spend path. Think ETN / HUBB / PWR / VRT as representative “keep the lights on for AI” exposures (the trade you already know—this narrative just reinforces why it persists). Losers (or at least: where the risk shows up) Over-levered, single-tenant terrestrial AI infra that underwrote “permanent scarcity pricing” for power and compute. If any alternative supply path emerges (more efficient inference, ASIC substitution, or eventually orbital capacity), the weak hands get repriced first. Bandwidth-only satellite models (no onboard intelligence) over time: if customers can buy “answer streams” instead of raw data, pure transit commoditizes faster. Narrative stocks that treat “space AI” as imminent megascale: the tech stack can progress while the equity story stays early and extremely volatile. Takeaways This is not “Earth is finished.” It’s a signal that power is now the binding constraint on AI , which is why grid and power chains stay structurally bid. The first real monetization is orbital edge inference , not ChatGPT in the sky: defense + earth observation want “answers, not data.” Space solar power is a real research path but a brutal scaling problem —we have proof-of-concepts (Caltech / DARPA), but “watts → gigawatts” is the entire game. If this theme turns from meme to capex, the winners are the boring enablers: launch cadence, satellite manufacturing, optical links, space-qualified power/compute electronics, and ground integration. --- News vs. Noise: What’s Moving Markets Today Noise: everyone is still anchored on “does the Fed cut in December?” News: bond traders are telling you the easing cycle is likely shallower and shorter than the equity market wants — and, more importantly, that the Fed can cut the front end while the long end refuses to cooperate. That’s the message in the price action: long yields aren’t behaving like they’re being “pulled down” by policy cuts, and the debate is getting louder precisely because this kind of disconnect hasn’t shown up in a sustained way since earlier regimes. The investor implication is simple: if long rates stay sticky, you don’t get the classic “multiple expansion” tailwind. You get a market that has to earn its upside through cash flows and winners that can self-fund — and you get a harsher penalty box for anything dependent on cheap refinancing, long-duration narratives, or leverage disguised as “capex investment.” The second piece of real “news” is Oracle earnings tonight as a live stress test of the AI credit cycle. Oracle has become the market’s favorite “canary” because it’s trying to play hyperscaler games without hyperscaler balance-sheet flexibility: Reuters notes Oracle has a roughly $300B OpenAI deal , has tapped the bond market aggressively (including an $18B raise), and is simultaneously staring at negative free cash flow while planning a massive capex ramp (reported as $105B in FY26 ). That cocktail is why its credit is being watched like an index: if Oracle’s print suggests the AI buildout is real, diversified, and financeable , it helps stabilize the “AI infra on margin” narrative. If it suggests timing risk, customer concentration risk, or a funding treadmill , it doesn’t just hit ORCL — it tightens financial conditions for the whole levered AI supply chain. Investor takeaways Bond market “tell” to watch: not the cut — the long end’s reaction after the cut . If the Fed eases but 10s/30s stay elevated, the market is effectively saying: “policy is near-neutral and term premium/fiscal/supply are the driver now.” That’s a different regime than 2010–2021, and it rewards cash-flow certainty over duration . Translation for equities: when the long end won’t rally, “AI wins” get split into two buckets: Self-funders / toll collectors (less rate-sensitive, durable backlog, pricing power) Finance-dependent stories (need perpetual access to cheap capital and perfect utilization) Oracle earnings: focus on 5 numbers, not EPS theater. OCI growth (is demand actually broadening?) RPO / backlog quality and convertibility (how much is real, how much is “optionality”?) Capex + free cash flow trajectory (is the burn rate rising or stabilizing?) Customer concentration & contract structure (how dependent is the ramp on OpenAI behaving perfectly?) Funding plan (more bonds? more leases? more “financial engineering”?) The market is explicitly worried about Oracle’s financing load and OpenAI counterparty dependence. Scenario map (how to trade the reaction , not the headline): Bullish ORCL reaction: OCI/backlog strong and management signals balance-sheet guardrails → risk premium compresses; the “AI buildout is real” camp regains control. Bearish ORCL reaction: solid demand but worse funding optics / bigger capex burn / more reliance on one mega-counterparty → expect pressure across levered AI infra proxies (anyone whose equity story depends on constant refinancing and always-full utilization). Macro link between the two stories: sticky long yields + tighter AI credit scrutiny = the market starts charging a higher hurdle rate for AI capex. That doesn’t kill AI — it re-prices who gets to fund it cheaply and who doesn’t. --- A Stock I’m Watching Today stock is Symbotic (SYM)….. Symbotic is still a real “physical AI” business (warehouse automation at scale), but last week’s -15% move is mostly a capital markets / supply event, not a thesis-breaker: they priced a 10M-share deal at $61.50 (6.5M new shares + 3.5M sold by a SoftBank affiliate, plus an option for 1.5M more) with proceeds for general corporate purposes. That said, whether it’s a “must own” comes down to one question: can they diversify away from Walmart fast enough to earn a platform multiple? Walmart was ~ 85% of FY25 revenue and a “significant majority” of the $22.5B backlog —that’s an enormous concentration risk (and the market will keep punishing the stock for it when sentiment turns). The flip side is the bull case is genuinely asymmetric: they’ve deepened the Walmart relationship by buying Walmart’s robotics unit and signing a separate development agreement for pickup/delivery automation that Walmart funds (reported $200M acquisition + $520M funding structure). Add first meaningful non-WMT wins (e.g., Medline) and the narrative can shift from “one-customer project company” to “repeatable automation platform.” How Else I Can Help You Beat Wall Street at Its Own Game Inside --- H.E.A.T. is our monthly webinar series, sign up for this month’s webinar below…. Why Covered Call ETFs Suck-And What To Do Instead Thursday January 15, 2-3PM EST Covered call ETFs are everywhere — and everyone thinks they’ve found a “safe” way to collect yield in a sideways market. The truth? Most of them suck. They cap your upside, mislead investors with “yield” that’s really your own money coming back , and often trail just owning the stock by a mile. Join me for a brutally honest breakdown of how these funds actually work — and what you should be doing instead. What You’ll Learn: 🔥 Why “high yield” covered call ETFs are often just returning your own capital 📉 How most call-writing strategies quietly destroy compounding 🚫 Why owning covered calls in bull markets is like running a marathon in a weighted vest 💡 The simple structure that can fix these problems — and where the real daily income opportunities are hiding Sign Up Here All rights reserved. Keep Reading No posts found View more caret-right 0