GoodAlexander

GoodAlexander expects SpaceX’s IPO to trade well while draining liquidity from almost every adjacent speculative asset. The next year may bring roughly $200 billion of issuance across three coveted companies, versus $130–140 billion raised by crypto VC over five years. SpaceX could represent a roughly $25 billion slug, with about 30% allocated to retail versus the usual 5–10%. His base case: “SpaceX itself trades well, every other speculative asset trades down.”

The SpaceX valuation rests less on current growth than on Elon Musk’s ability to make investors “dream the dream” of AI data centers in orbit. Musk has filed to launch one million satellites and, in GoodAlexander’s account, proposes putting compute equivalent to 20% of US power supply in space, where cooling and local resistance are less constraining. The thesis may take years to falsify, which is precisely why “the sun is really big” works as an equity narrative.

His preferred SpaceX trade is short legacy telecom, not short SpaceX or Tesla. Satellite-heavy telecom baskets had risen roughly 120%, while some slow growers were up around 180%; if Musk’s architecture works, incumbents lose leverage across connectivity and compute. He refuses to short Tesla absent Chinese EV access to the US: “I try not to short things that could invent the cure for cancer.”

OpenAI is GoodAlexander’s favored AI IPO because he sees the same monetization blind spot that investors once had with Amazon. Amazon’s retail operation was once marked at zero, yet 300 million active shoppers supported a $69 billion-a-year advertising run rate by Q1 2026; OpenAI, he says, has 900 million ChatGPT DAUs. A six-week ad pilot reportedly generated $100 million against a $2.4 billion revenue target, making ads the overlooked option embedded in its consumer distribution.

Anthropic’s Fable model may be technically exceptional while its reported economics remain less attractive than they look. GoodAlexander says Anthropic highlights a non-GAAP operating-profit measure despite remaining deeply free-cash-flow negative, and treats model-training capex as though it were temporary even though stopping investment would destroy future competitiveness. Thread Guy says Fable finds many bugs in GPT-5.5 code that Opus 4.8 missed; GoodAlexander agrees its diagnoses are generally right.

The deeper enterprise-AI risk is that model costs rise while generated software cannibalizes the market it is meant to serve. Two Fable agents may cost around $100 an hour—effectively another full-time engineer—while customers such as Uber spend heavily without producing an obviously better app. Consumer commerce looks greener because models understand intent, while frontier cryptography, biology and mathematics remain the strongest cases where AI can enlarge rather than replace an end market.

The political-market overlay creates a liquidity warning and a possible threat to Palantir. GoodAlexander speculates that official intervention may have supported S&P futures, while stablecoin supply remains stalled near $180 billion. He treats the stablecoin stagnation as a warning for crypto and non-AI risk assets, and sees Palantir as exposed Trump beta trading poorly. Anthropic’s “forward-deployed” work with Goldman Sachs also tests Palantir’s core claim that LLMs require its ontology: “What if Karp’s wrong?”

GoodAlexander argues Chinese open-source models have broken the economics underpinning Claude and other closed-model leaders. After initially dismissing them as benchmark-gamed Opus distills, he tested models delivering Opus- or Sonnet-like performance on a roughly $10,000 GPU, even 3090s. Against protected Opus API usage at about $80 per terminal-hour versus roughly $2 for Qwen 3.6-class compute, the “40 to 80x delta” makes local inference and crypto-financed compute newly credible.

David Choi sees an enormous financing gap beneath the AI-capex boom: Nvidia has an estimated $500 billion of sales over the next five quarters, “99% of that is financed,” yet loans below roughly $200 million barely exist. He says 20-30% of those sales come from smaller emerging clients that cannot efficiently access securitized debt. USD.AI’s next-12-month target is $1 billion of GPU loans—enough, he hopes, to establish a global reference rate for the cost of growing AI.

The core USD.AI pitch is that GPUs should be financed like houses, cars, and aircraft, but their product cycles move too quickly for traditional securitization. Packaging 10,000 loans, tranching them, conducting a roadshow, and selling the instrument can take two years—long enough to move from Hopper through B200, Vera Rubin, and toward an unannounced Feynman generation. DeFi instead adds each originated loan immediately to the sUSDai NAV, creating what David calls “a debt perp.”

USD.AI’s proposed flywheel uses stablecoin economics to lower AI’s financing cost, not merely to put existing debt on-chain. If AI companies settle in USD.AI, the protocol can use the 3-4% earned on each minted dollar to subsidize borrowers while retaining origination fees and net interest margin. CHIP is framed as the protocol’s GP-like governance layer, directing fees, helping set rates across deal types, and potentially prioritizing redemptions—not as a fully disclosed utility package yet.

Compute scarcity is presented as both the reason Claude feels worse and the foundation of a new sovereignty contest. David says providers are rate-limiting users because demand is outgrowing installations; OpenAI’s aggressive compute investment was therefore directionally right. His “AI dollar” thesis is that countries will become net importers or exporters of generated tokens, and a nation without sufficient AI capacity will fall behind much as an oil-poor country did during the petrodollar era.

GoodAlexander’s crypto bull case spans three different clocks: Saylor-driven inflows in one year, capital flight in three, and crypto-native financial rails in five. STRC can bring retirees’ capital into Bitcoin, while failed attempts to suppress privacy coins demonstrate that privacy technology may survive a hostile policy regime. Longer term, taxation, money printing, CBDCs, and capital controls could make portable crypto assets attractive to wealthy people who cannot “get on a plane with a bag of gold.”

His equity view is almost the mirror image: stay bullish into the AI-IPO spectacle, then prepare for a historic reversal. He expects roughly $4 trillion of SpaceX, OpenAI, and Anthropic equity to come onto the market, with a $1.5-1.7 trillion SpaceX IPO as “the main show.” If banks support favorable AI comps and passive indexes rapidly absorb SpaceX, he sees that event as the “Solana $300 moment”—the beginning of a generational top rather than a permanent new plateau.