Amazon’s contract with Anthropic will move to per-token billing next year, threatening a steep spike in costs for services like Kiro, Quick and Alexa Shopping that use Claude. To curb expenses, Amazon is exploring OpenAI’s models. Meanwhile, Anthropic is deepening ties with Google Cloud and a recent security dispute has driven a wedge between the two.

• Anthropic's shift to per-token billing next year threatens to spike Amazon's costs for Claude-dependent tools like Kiro, Quick, and Alexa Shopping, pushing Amazon to explore OpenAI as an alternative.
• Amazon's OpenAI commitment ($50 billion) now dwarfs its Anthropic investment (grown from $4 billion to a possible $33 billion), signaling a strategic pivot.
• Anthropic is hedging its own bets by committing $200 billion to Google Cloud over five years, making Google a second major infrastructure partner.
• Amazon triggered a government shutdown order against Anthropic's Fable 5 and Mythos 5 models over alleged cyberattack-enabling data, a move that coincided suspiciously with Amazon's own security-AI launch.

This article breaks down the massive debt and revenue milestones that AI leaders (NVIDIA, OpenAI, Anthropic) must hit to justify the $9–15 trillion in planned data-center build-out. It shows how banks, hyperscalers, and chipmakers need AI services to generate over $2 trillion annually by 2030 or risk a market collapse.

• Building the planned 190 GW of AI data-center capacity could cost $9.5–15 trillion (far above Bloomberg's $3 trillion estimate), requiring banks to roughly double annual debt issuance to $500B–$1T just to sustain it
• NVIDIA's projected $1 trillion 2027 revenue depends heavily on three clients (likely ODMs for Microsoft, Google, Meta), tying its fate to those firms' ability to keep raising debt
• OpenAI and Anthropic will drive 70–90% of AI compute demand but are on track for under $360 billion combined revenue by 2029—less than half the ~$875 billion needed even under a scenario where only half the planned capacity gets built
• Outside the major AI labs there are essentially no other large-scale compute buyers, meaning enterprise IT spending on AI would need to grow by orders of magnitude to justify current valuations and debt levels

OpenAI bought Ona to power persistent, secure agents in its Codex platform, while Anthropic lifted its hidden safeguards after researchers flagged degraded outputs. The issue also covers Xiaomi’s MiMo Code AI assistant beating Claude on long tasks and dives into tokenizers, vintage LLM builds, compute markets, data debugging, and PyTorch optimizations.

• OpenAI acquired Ona to bring secure cloud execution and persistent, cross-session agent orchestration to its Codex platform.
• Anthropic secretly rerouted certain Claude requests (training rival models, debugging AI code, tweaking neural nets) to a weaker model, then reversed the policy after researchers and users complained.
• Xiaomi open-sourced MiMo Code V0.1.0, a terminal-native coding assistant that beats Claude Code on tasks over 200 steps using a memory subagent to track context.
• A developer built a full transformer from scratch for about $80 on a home PC.

The article compares OpenAI’s Codex “Oracle” approach—using server-side compaction to maintain a single coherent thread—with Anthropic’s Claude “Firm” method of delegating tasks to multiple sub-agents. It breaks down trade-offs in cost, speed, coherence, and memory loss, and predicts a future hybrid of both strategies.

• OpenAI's Codex keeps one continuous thread alive via server-side compaction (auto-summarizing/filtering tool calls) to preserve coherence across huge token counts, but this serializes work through a single channel.
• Anthropic's Claude delegates subtasks to parallel sub-agents that report back to a parent thread, yielding faster visible output but risking duplicated searches and dropped facts when sub-agents fail to forward key details.
• Claude's approach costs more and risks inconsistency from repeated operations, while Codex's compaction reduces "forgetting" at the cost of speed since work happens serially.
• Both companies are expected to converge toward hybrids—OpenAI adding agent-style delegation, Anthropic tightening compression—to balance coherence, speed, and cost.

Secondary-market trades on Forge Global pushed Anthropic’s valuation to about $1 trillion, surpassing OpenAI’s roughly $880 billion price. The surge reflects scarce share supply, rapid revenue growth (from a $9 billion to $39 billion annual run rate), and partnerships with Amazon and Palantir.

• Anthropic's secondary-market valuation hit ~$1 trillion on Forge Global, surpassing OpenAI's ~$880 billion, up from just $380 billion three months earlier
• Anthropic's annualized revenue run rate jumped from $9 billion (late 2025) to $39 billion (March 2026), fueling investor demand
• Share scarcity is driving frenzied bidding, with offers ranging from $960 billion to $1.05 trillion and some even involving property trades
• Growth is tied to Claude Code's popularity and major partnerships with Amazon and Palantir

OpenAI CEO Sam Altman accused Anthropic of using scare tactics to hype its new Mythos cybersecurity model, likening it to selling a bomb shelter after building a bomb. He argued that fear-based marketing keeps AI tools in the hands of a select elite and noted that such hype is common across the industry.

• Altman accused Anthropic of "fear-based marketing" for restricting its Mythos cybersecurity model to select enterprise clients, comparing it to selling a bomb shelter after building the bomb.
• He argued this hype tactic keeps advanced AI tools in the hands of a privileged few and isn't unique to Anthropic—most AI vendors, including OpenAI, use similar risk hyperbole to drive demand.
• Critics say Mythos's threat is overstated, noting real-world hacking still relies mainly on human actors and simpler tools, and testers haven't seen results beyond existing hacking software.

New CRO Denise Dresser tells staff the AWS Bedrock partnership is driving massive enterprise demand while the long-term Microsoft tie-up has boxed OpenAI in. She also challenges Anthropic’s revenue reporting and compute capacity, urging the team to unite around the Amazon alliance and sharpen customer focus.

• OpenAI's new CRO says Microsoft's exclusivity has limited enterprise reach, while the Amazon Bedrock deal (up to $50B investment) is driving surging demand
• Dresser alleges Anthropic inflates its claimed $30B run rate by ~$8B through gross vs. net revenue accounting, while OpenAI reports Microsoft revenue net
• Dresser claims Anthropic lacks sufficient compute capacity, which Anthropic disputes by pointing to its multi-gigawatt Google/Broadcom deal
• OpenAI is diversifying beyond Microsoft to CoreWeave, Google, and Oracle for cloud capacity

In a memo to investors, OpenAI says it plans to deploy 30 gigawatts of compute power by 2030, versus Anthropic’s expected 7–8 gigawatts by end of 2027, labeling its rival “compute constrained.” The note underscores OpenAI’s infrastructure edge, compounding efficiency gains, and race for dominance ahead of both companies’ potential IPOs.

• OpenAI's investor memo claims it will hit 30 gigawatts of compute by 2030, versus Anthropic's projected 7-8 gigawatts by end of 2027, calling Anthropic "compute constrained"
• OpenAI frames its infrastructure scale as creating a "compounding advantage" — lower cost per token driving more users, revenue, and further compute investment
• Anthropic pushed back by pointing to a new compute deal with Google and Broadcom, with its CFO calling it the company's "most significant compute commitment to date"
• Both companies are valued in the trillions and are considering IPOs this year, with Anthropic also just launching a cybersecurity-focused model ("Project Glasswing")

OpenAI and Anthropic are approaching record IPOs but face enormous costs for AI model training. OpenAI expects a staggering $121 billion in computing expenses by 2028, leading to significant projected losses, while Anthropic anticipates similar challenges but on a smaller scale. Both companies are rapidly releasing new AI models, intensifying the competition and cost pressures.

• OpenAI projects $121 billion in cumulative computing expenses by 2028, driving major projected losses despite revenue growth.
• OpenAI's revenue is set to hit $1 billion in 2024 (up from $540 million in 2023), with a potential valuation around $100 billion.
• Anthropic trails with projected 2024 revenue of $300 million (up from $100 million in 2023), growing more slowly but leaning on its safety-focused reputation to attract investors.
• Both companies are racing to release new models, intensifying competitive and cost pressures ahead of their IPOs.

Anthropic's AI tool, Claude, has gained significant traction among consumers, with paid subscriptions more than doubling this year. The growth coincides with a public feud with the Department of Defense and effective Super Bowl ads that positioned Claude as a safer alternative to competitors. Despite this success, Claude still trails behind ChatGPT in overall user numbers.

• Claude's paid subscriptions have more than doubled in 2024, based on analysis of ~28 million anonymized US credit card transactions, with a sharp jump between January and February.
• Most new subscribers are choosing the cheapest $20/month "Pro" tier rather than the $100 or $200 plans, with growth also driven by new features like Claude Code, Claude Cowork, and Computer Use.
• Anthropic's public refusal to let its AI be used for lethal military operations (unlike OpenAI, which struck its own DoD deal) plus Super Bowl ads mocking ChatGPT have boosted Claude's profile as a "safer" alternative.
• Despite this growth, Claude still trails ChatGPT in total users, and OpenAI continues adding paid subscribers quickly despite backlash over its DoD deal.

The article analyzes the unit economics of large language models (LLMs), focusing on the compute costs associated with training and inference. It discusses how companies like OpenAI and Anthropic manage their financial projections and cash flow, emphasizing the need for revenue growth or reduced training costs to achieve profitability.

• Inference costs are falling faster than training costs, so gross margins on deployed models improve over time even as frontier training runs get more expensive.
• OpenAI and Anthropic's path to profitability depends on either scaling revenue much faster than compute spend or finding ways to cut training costs, since current cash burn is dominated by training rather than serving models.
• Reported "profitability" claims from these labs often exclude massive R&D/training expenditures, making headline numbers misleading about true unit economics.