The Unsettling Truth: Unpacking Inconsistencies in the AI Market

The current landscape of the artificial intelligence market presents numerous inconsistencies, leading to widespread confusion regarding company performance, technological advancements, and investment strategies.

NVIDIA's reported shipment of six million Blackwell GPUs over the past four quarters, translating to an estimated power consumption of 10GW to 12GW (based on B100, B200 GPUs, and GB200, GB300 racks), appears inconsistent with the actual data center capacity brought online during the same period.

Concurrently, Anthropic asserts it is nearing $10 billion in annualized revenue, positioning it competitively with OpenAI's projected $13 billion. However, analysis extrapolating OpenAI's revenue from Microsoft's share suggests OpenAI may fall short of this projection by billions. This potential shortfall is exacerbated by the launch of Google's Gemini 3, which an internal memo indicated could generate “temporary economic headwinds” for OpenAI, leading to an internal “Code Red” at the company.

This raises critical questions: What truly makes Gemini 3 superior, given that nearly every new AI model claims some level of improvement (e.g., Nano Banana Pro hailed as "the best available image generation model" by Simon Willison)? Furthermore, why has Gemini 3 caused such significant alarm and projected “economic headwinds” for OpenAI, compelling the company to reportedly fast-track the release of a new model, "Garlic," as per The Information?

OpenAI's chief research officer, Mark Chen, recently informed colleagues about "Garlic," a new model demonstrating strong performance in internal evaluations, particularly in coding and reasoning tasks against Gemini 3 and Anthropic’s Opus 4.5. Chen indicated a rapid release strategy for "Garlic," suggesting potential rollouts like GPT-5.2 or GPT-5.5 by early next year. "Garlic" is distinct from "Shallotpeat," another large language model in development, but integrates bug fixes derived from "Shallotpeat's" pretraining phase, where the LLM learns connections from diverse web data.

The persistent question remains: what specific advantages does Gemini 3 offer that warrant such concern from OpenAI regarding "economic headwinds"? It is plausible this narrative serves as a convenient explanation for a pre-existing slowdown in ChatGPT's download and usage growth, as previously reported by Alex Heath.

Discussions with experts reveal two prevailing theories:

1. Gemini 3 outperforms OpenAI's models in established benchmarks.
2. OpenAI's growth and user engagement were already decelerating, making Gemini 3 a convenient scapegoat for underlying issues.

Amidst the rapid model development, questions arise regarding OpenAI's strategic focus. According to an internal Slack memo from CEO Altman, key priorities under the "Code Red" initiative include:

• Enhancing ChatGPT personalization for its over 800 million weekly users, allowing custom interaction.
• Improving Imagegen, their image-generating AI, especially following the positive reception of Google's Nano Banana Pro.
• Optimizing model behavior to improve preference over competitors in public rankings like LMArena.
• Increasing ChatGPT's speed and reliability.
• Reducing "overrefusals," where the chatbot declines to answer benign queries.

Essentially, OpenAI's outlined strategy revolves around fundamental improvements to ChatGPT's core functionalities, image generation, model preference, public rankings, speed, reliability, and response rates. This raises a pertinent question: what has been the primary focus of OpenAI's efforts if not these foundational improvements? The recent launch of Sora 2, which briefly topped app charts but has since fallen significantly (off the top 30 free Android apps in the US, and currently 17th on US free iPhone apps), further underscores concerns about the tangible long-term impact of highly publicized releases.

Evidence increasingly suggests a reduced demand for AI services. The Information reported that several Microsoft divisions have decreased sales growth targets for specific AI products after failing to meet goals in the fiscal year ending June, as confirmed by two salespeople within Microsoft’s Azure cloud unit.

Microsoft, however, challenged these claims. A company spokesperson stated that “aggregate sales quotas for AI products have not been lowered” but refrained from addressing specific lowered growth targets. The spokesperson emphasized the growth in Microsoft’s overall cloud business, attributing it to AI server rentals by OpenAI and other AI developers.

While Microsoft likely faces no issue selling compute resources to OpenAI (which reportedly paid $8.67 billion for inference between January and September), it's crucial to note Microsoft's use of the word "aggregate" when disputing lowered sales quotas—a term not used in the original reporting. This distinction is significant, especially given Microsoft's broader challenges in selling AI products. For instance, in August 2025, only 8 million active paying licenses existed for Microsoft 365 Copilot, despite over 440 million Microsoft 365 subscribers.

Microsoft's AI initiatives face several headwinds:

• Chip Development: The "Maya" AI chip is delayed until 2026, and The Information reports it is projected to significantly underperform Nvidia's Blackwell chip upon mass production.
• Copilot Monetization: As of late October 2025, many customers using Microsoft's AI Copilot suite are not paying for the service.
• Regulatory Scrutiny: In October, the Australian Competition and Consumer Commission (ACCC) sued Microsoft, alleging the company misled 2.7 million Australians by implying that Copilot integration and an increased subscription fee were mandatory, rather than offering an “undisclosed third option” (Microsoft 365 Personal or Family Classic plans) that allowed users to retain existing features without Copilot at a lower price. This pattern of bundling and perceived non-transparent pricing echoes similar actions by Google with its Workspace accounts, raising questions about ethical sales practices.

In September 2025, The Information also reported that Microsoft partially substituted OpenAI's models with Anthropic's for certain Copilot applications. This development is notable given Microsoft's multi-billion dollar investment in OpenAI, a key benefit of which was expected to be exclusive access to OpenAI's models.

Further, in September 2025, The Information stated that Microsoft offered discounts for Office 365 Copilot due to “slow adoption by customers attributable to high cost and unproven ROI.” Earlier, in late 2024, customers had already halted additional Copilot purchases over performance and cost concerns.

The investment landscape for AI companies exhibits increasing ambiguity. Reports of a $100 billion NVIDIA investment in OpenAI, with $10 billion tranches linked to gigawatt compute milestones, were widely publicized as a “sealed” deal, despite lacking clear foundational evidence. However, NVIDIA's most recent 10-Q filing provides a different picture:

"Investment commitments are $6.5 billion as of October 26, 2025, including $5 billion in Intel Corporation which is subject to regulatory approval. In the third quarter of fiscal year 2026, we entered into a letter of intent with an opportunity to invest in OpenAI."

The term "letter of intent with an opportunity" implies a non-binding preliminary agreement, casting doubt on the previously reported scale of commitment. A similar pattern emerged with NVIDIA's stated investment in Anthropic:

"In November 2025, we entered into an agreement, subject to certain closing conditions, to invest up to $10 billion in Anthropic."

Despite media reports confidently asserting a closed deal with NVIDIA contributing $10 billion and Microsoft $5 billion, the inclusion of “closing conditions” and “up to” suggests the final investment amount remains unconfirmed and contingent.

Subsequently, the Financial Times reported that Anthropic is exploring an initial public offering (IPO) as early as 2026, with the Microsoft and NVIDIA investments intended to be part of a funding round valuing the company between $300 billion and $350 billion.

Despite being lauded by The Information and Wall Street Journal as an “efficient” competitor to OpenAI, Anthropic appears to match its rival in fundraising and expenditure. It raises questions why a company allegedly focused on “reducing costs” required substantial funding rounds: $13 billion in September 2025, following $3.5 billion in March 2025, and $4 billion in November 2024. Projections of Anthropic reaching break-even by 2028, or becoming cash flow positive as early as 2027, seem contradictory given its aggressive fundraising.

If Anthropic is indeed as efficient and financially prudent as suggested, the need for an additional $15 billion, potentially just months after a $13 billion raise, is puzzling. Should this $15 billion round close in 2025 (with an assumed $22.5 billion from SoftBank), Anthropic's total fundraising for the year would hit $31.5 billion, closely approaching OpenAI's $40.8 billion.

Even if SoftBank's contribution does not materialize in 2025, Anthropic's fundraising for the year would be approximately $16.5 billion, only $2 billion less than OpenAI's $18.3 billion (comprising a $10 billion June round, split between $7.5 billion from SoftBank and $2.5 billion from other investors, and an $8.3 billion August round).

This analysis suggests that Anthropic's business model may be as financially challenging as OpenAI's. The apparent lack of detailed financial scrutiny in current discourse implies an era where critical examination is bypassed, given the overwhelming nature of the market. This article aims to counteract this trend by delving deeper into these financial discrepancies.

The persistent question surrounding the "AI bubble"—when and how it might burst—lacks a clear answer, primarily because much of the market's valuation is detached from concrete revenues or sustainable growth. While NVIDIA's perceived perpetual growth fuels the market, this growth appears sustained by sentiment rather than tangible fundamentals. Consequently, identifying an exact catalyst for a market correction or predicting its precise manifestation for both private and public companies is challenging. Building upon the themes of "AI Bubble 2027," this analysis will explore various systemic shock scenarios, ranging from likely to possible, that could trigger an unraveling of the current market dynamics, offering insights into potential outcomes in 2026.