// capital markets

Source: Bloomberg

Half of Asia’s ten most volatile stocks are now recent AI company IPOs, with Chinese firms like Moore Threads and MiniMax dominating the list—a direct result of sparse institutional ownership that leaves these newly public companies vulnerable to retail trading swings and sentiment whiplash. Retail-driven price discovery without the stabilizing anchor of serious institutional conviction or long-term capital creates conditions for violent corrections that can wipe out retail investors while deterring institutional money. If AI IPO volatility becomes reputationally toxic, it could impair future fundraising for legitimate AI infrastructure plays across the region.

Source: The Next Web

Venture capital has flooded into AI at unprecedented scale, but the investment community is increasingly scrutinizing actual returns rather than accepting hype as justification—a shift from earlier tech booms where scale-first narratives dominated funding decisions. The gap between deployed capital and measurable business outcomes is forcing a reckoning: companies can no longer rely on AI-as-differentiation claims alone; they need concrete metrics showing how these systems reduce costs, increase revenue, or unlock new products. This shift from “build AI at any cost” to “prove AI’s value” is changing which startups get funded and which enterprises actually deploy these tools beyond pilots.

Source: Newcomer

A leaked investor presentation reveals the brutal unit economics required to justify Anthropic’s trajectory—the firm projected $14B in annual EBITDA losses on $18B revenue in 2026, suggesting the AI safety company will need to sustain massive cash burn to train increasingly capable models before achieving profitability. The $1.995T 2030 valuation target (a suspiciously precise miss of $2T) exposes how dependent frontier AI valuations are on faith in future breakthroughs rather than near-term business fundamentals, creating pressure on Anthropic to either dramatically outperform these assumptions or face a reckoning on unit economics that most software companies would never tolerate. Capital intensity and long development timelines have become the competitive advantage in AI—execution risk is massive, but so is the winner-take-most upside if scale effects eventually reverse the losses.

Source: TechCrunch

SoftBank’s ability to secure $40B in unsecured credit signals that Wall Street has fundamentally shifted from valuing AI safety/moats to betting on scale-at-any-cost, with the 2026 IPO timeline revealing an implicit consensus that the “AI winner” will be determined within the next 18 months—making this less about OpenAI’s technical superiority and more about which player can burn capital fastest to lock in market dominance before regulatory frameworks calcify.

Source:
Morning Brew

The market’s AI euphoria finally hitting a reality check signals that the “machines will solve everything” narrative—which has conveniently justified stratospheric valuations without proportional earnings growth—was always more theology than technology, and we’re entering a painful recalibration where actual ROI on billions in AI infrastructure spending will finally matter more than the promise.

Source: TechCrunch

This signals that mega-cap AI infrastructure players are now bankable collateral in themselves—the $40B loan hinges on SoftBank’s Vision Fund stakes in AI companies (particularly OpenAI), not traditional assets, revealing how quickly “AI ownership” has become the new currency of corporate leverage and a de facto bet on a 2026 IPO that will unlock trillions in paper wealth for early backers. The pattern here isn’t just about SoftBank’s financing needs; it’s institutional validation that AI company valuations have decoupled entirely from revenue/profitability and now operate as speculative assets that banks will literally lend against, accelerating the timeline for realized returns before this bubble requires actual business fundamentals.