来自 Maksym Misichenko · CNBC ·
AI智能体对这条新闻的看法
The panel expresses bearish sentiments regarding the current AI market, particularly around the high valuations and lack of profitability of companies like Anthropic, OpenAI, and SpaceX. They also caution about the potential impact of mega-IPOs on the broader market and the data-layer companies like Snowflake and Datadog.
风险: A single disappointing IPO from Anthropic or SpaceX could trigger hyperscaler capex reviews, cutting growth for data-layer companies before their unit economics stabilize (Grok, Gemini).
机会: Investing in the 'data layer' companies like Snowflake and Datadog could be a safer play, but even there, forward P/E ratios remain stretched (Gemini).
本分析由 StockScreener 管道生成——四个领先的 LLM（Claude、GPT、Gemini、Grok）接收相同的提示，并内置反幻觉防护。 阅读方法论 →
Anthropic 正在关注 1 万亿美元的估值，此前又一轮成功的融资，但 Wedbush Securities 分析师丹·艾夫斯表示，对人工智能公司的投资者需求才刚刚开始。
艾夫斯周五在 CNBC 的“欧洲广场”节目中表示：“这是 30 年来美国首次在技术上领先于中国。”
他说，Anthropic 在周四获得 650 亿美元融资后，最新估值为 9650 亿美元，这“只是冰山一角”，投资者应该将注意力转向数据层公司，如 Snowflake、Datadog 和 InnoData。
“我们的观点是，第二次、第三次、第四次衍生，就像我们本周在 Snowflake 和 Dell 看到的，正在显示资金的流向，”他补充道。
艾夫斯对 Nasdaq 到 2027 年突破 30,000 点的评论是更广泛预测的一部分，重申了他之前与 CNBC 的采访呼吁。
艾夫斯预测华尔街历史上将出现“历史性”时期，此前有大量大型 IPO 正在等待中，包括 SpaceX、Anthropic 和 Open AI 的潜在浮动。
“它们真的是第四次工业革命的三大支柱，”他说。“目前，从 Anthropic 来看，它是世界上最好的模型，而且我认为这一点没有争议。
“这将给 Open AI 带来更大的压力，Open AI 是人工智能革命的基础。”
其他分析师警告说，这组巨大的公开募股可能会标志着市场的顶部，并将其与 20 世纪 90 年代末的互联网泡沫联系起来。
SpaceX 备受期待的 IPO，已在周四的监管文件中确认，预计将于 6 月 12 日进行，可能标志着历史上规模最大的浮动。埃隆·马斯克的公司预计在纳斯达克上市的估值为 1.75 万亿美元。OpenAI 和 Anthropic 也已宣布计划在今年晚些时候上市。
这三家公司尚未产生年度利润，但 Anthropic 预计将在其即将发布的财报中实现其有史以来第一个盈利季度。
“我认为这标志着市场顶部，”Zacks 的首席股票策略师 John Blank 告诉 CNBC 的“欧洲广场”节目说。
“每个人都知道市场顶部非常接近，通常这些巨大的 IPO 会被宣传出来。在 1999 年，我们看到了同样的事情，人们只是急于让这些 IPO 出售。”
尽管如此，艾夫斯坚持认为市场更像 1997 年，而不是 1999 年，在泡沫风险方面。
四大领先AI模型讨论这篇文章
"Mega-IPOs from still-unprofitable AI leaders are more consistent with market-top signals than the 1997 continuation Ives claims."
Dan Ives positions Anthropic's $965B valuation and $65B raise as early innings for AI, with data-layer names like Snowflake and Datadog set to capture follow-on spending and Nasdaq hitting 30,000 by 2027. Yet the article underplays that none of Anthropic, OpenAI or SpaceX ($1.75T target) produce annual profits, while three mega-IPOs in one year mirror the 1999-2000 pattern that preceded sharp reversals. Historical leadership shifts and unprofitable growth at scale have repeatedly produced 70-90% drawdowns once incremental capital slows.
The 1995-1997 period also featured large unprofitable tech listings that continued higher for another two years before the real bubble formed.
"The infrastructure/data-layer thesis (SNOW, DDOG) is defensible; the mega-IPO euphoria (SpaceX, OpenAI, Anthropic) is a separate, higher-risk bet that could crater if any flagship float disappoints."
Ives is conflating three separate narratives: (1) Anthropic's valuation sprint, (2) a structural AI capex cycle favoring data/infrastructure plays, and (3) a 2027 Nasdaq forecast. The second claim has merit—Snowflake (SNOW) and Datadog (DDOG) have real revenue and improving unit economics. But the article conflates this with mega-IPO euphoria. Three unprofitable companies (SpaceX, OpenAI, Anthropic) going public in 2025-26 isn't evidence of a healthy market; it's evidence of FOMO-driven capital allocation. Ives' "1997 not 1999" framing is unfalsifiable—everyone says that at peaks. The real risk: if even one of these mega-floats disappoints post-IPO, it could crater sentiment for the entire AI cohort, including profitable infrastructure plays.
Ives may be right that data-layer companies are the real beneficiaries, but lumping them with unprofitable AI model companies muddies the thesis—SNOW and DDOG have sustainable moats and positive unit economics, while Anthropic's path to durable profitability (beyond one quarter) remains unproven.
"The concentration of capital in 'mega-IPO' candidates creates an artificial valuation ceiling that will likely trigger a sharp correction once these firms report their first public quarterly earnings under GAAP standards."
Dan Ives is conflating technological supremacy with equity market sustainability. While Anthropic and SpaceX are undeniably transformative, the valuation multiples being floated—specifically a $1.75 trillion target for SpaceX—defy standard discounted cash flow logic. We are seeing a 'liquidity trap' where massive private capital inflows are inflating valuations before these firms face the harsh reality of public market scrutiny and unit-economic profitability. Pivoting to the 'data layer' (Snowflake, Datadog) is a safer play, but even there, forward P/E ratios remain stretched. Investors are essentially pricing in a decade of perfect execution while ignoring the historical precedent that mega-IPOs often act as liquidity events for early insiders, not retail investors.
If we are truly in the early innings of a Fourth Industrial Revolution, comparing today's AI infrastructure build-out to the 1999 dot-com bubble ignores the massive, tangible enterprise revenue growth currently being reported by the hyperscalers.
"Ultra-high, private-round valuations for AI leaders are unlikely to be sustained without clear, durable earnings and cash-flow upside."
The piece pushes Anthropic toward a trillion-dollar runway and flags data-layer names like Snowflake and Datadog as the next wave, while projecting Nasdaq 30,000 by 2027. The strongest contrarian read: the implied $965B Anthropic valuation after a $65B funding round is highly implausible and likely hyperbole or a misquote; comparable public AI names aren’t trading anywhere near that multiple. The article glosses over core risks: AI spend is capex-intensive, highly cyclical, and susceptible to cloud pricing shifts, compute-cost pressure, and regulatory clampdown. It also omits profitability trajectories, debt implications, and liquidity dynamics around mega-IPOs. Without durable earnings and cash flow, lofty multiple re-rating seems precarious amid macro volatility.
If AI capex stays resilient and liquidity remains abundant, a few of these stories could re-rate despite today’s doubts; the market might price in optionality rather than fundamentals for a while longer.
"Mega-IPO scrutiny risks slowing the very capex that funds data-layer revenue growth."
Gemini's liquidity trap insight links directly to Grok's unprofitable IPO cluster: a single disappointing float from Anthropic or SpaceX could trigger hyperscaler capex reviews, cutting SNOW and DDOG growth before their unit economics stabilize. This transmission channel from model-layer cash burn to data-layer revenue remains unmodeled. Claude's moat argument assumes spending continues regardless of sentiment shocks.
"IPO timing and insider liquidation windows matter more than the valuation itself for predicting contagion speed."
Grok's transmission-channel insight is sharp, but it assumes capex reviews happen *before* these IPOs price. More likely: SpaceX or Anthropic float at peak euphoria, insiders liquidate, *then* disappointing earnings trigger the review. The timing matters—if mega-IPOs close Q2 2025, capex cuts won't hit SNOW/DDOG revenue until Q4 2025 or Q1 2026. By then, sentiment may have already repriced both layers downward. The lag creates false safety.
"The S-1 filing process for mega-AI IPOs will reveal unsustainable burn rates, forcing a market repricing of the entire AI infrastructure stack before the companies even go public."
Claude, you’re missing the regulatory 'dead-man's switch.' If Anthropic or OpenAI IPOs, they enter the public disclosure regime. The SEC will force transparency on their compute-to-revenue ratios, which are likely disastrous. You assume market euphoria lasts until the first earnings miss, but the S-1 filing process alone could trigger a valuation collapse. Once the public sees the true burn rate required to sustain these 'magical' models, the capex-cycle narrative for Snowflake and Datadog will evaporate instantly.
"The real risk is not SEC disclosures but whether post-IPO monetization and macro liquidity can sustain a durable re-rating for data-layer incumbents."
Gemini's 'dead-man's switch' idea risks overstating near-term impact of SEC disclosures. The bigger danger is post-IPO revenue visibility: even with burn-rate clarity, investors will pay for multi-year AI adoption optionality, not just cash burn. Snowflake and Datadog could still re-rate if real enterprise contracts prove durable; the test is monetization steps, not S-1 scrutiny alone. That shift hinges on macro liquidity.
The panel expresses bearish sentiments regarding the current AI market, particularly around the high valuations and lack of profitability of companies like Anthropic, OpenAI, and SpaceX. They also caution about the potential impact of mega-IPOs on the broader market and the data-layer companies like Snowflake and Datadog.
Investing in the 'data layer' companies like Snowflake and Datadog could be a safer play, but even there, forward P/E ratios remain stretched (Gemini).
A single disappointing IPO from Anthropic or SpaceX could trigger hyperscaler capex reviews, cutting growth for data-layer companies before their unit economics stabilize (Grok, Gemini).