Exploring Mythos, Muse, and Compute Opportunity Costs
Doug O’Laughlin at Filmogaz.com declared in January 2025 that o1 and new reasoning models could mark the end of Aggregation Theory. He argued model improvements are now limited mainly by economics. That shift has major consequences for cloud providers and AI labs.
Marginal costs and opportunity trade-offs
Marginal cost is the expense to produce one more unit. In tech, digital outputs made marginal cost effectively zero for years.
O’Laughlin says that is changing. AI demand and expensive chips are reintroducing meaningful marginal costs.
Hyperscalers now face hard allocation choices. Microsoft recently missed Azure growth expectations after prioritizing internal workloads. The company allocated GPUs to M365 Copilot and GitHub Copilot instead of third‑party customers.
CFO Amy Hood framed the decision as a capacity allocation. CEO Satya Nadella and other executives prioritized higher margin, long‑term products.
That dynamic is an example of Compute Opportunity Costs. Compute used for one business cannot serve another. Amazon must balance e‑commerce, AWS, and investments. Google juggles GCP, consumer apps, and strategic bets. Meta has no enterprise cloud, which changes its calculus.
Anthropic’s Mythos and cybersecurity
Anthropic unveiled Mythos, its most advanced frontier model. The company launched Project Glasswing to apply Mythos for defensive security tasks.
Anthropic reported the model discovered thousands of high‑severity vulnerabilities across major operating systems and browsers. The firm warned those capabilities could spread and pose risks.
The lab is limiting access to Mythos. Supply constraints and allocation choices make broad releases costly. Anthropic may need to buy more capacity from hyperscalers or neocloud providers to scale.
The company also accused three rival labs—DeepSeek, Moonshot, and MiniMax—of extracting model capabilities. Anthropic said those groups produced over 16 million exchanges using about 24,000 fraudulent accounts. The tactic, known as distillation, can accelerate competitors’ progress.
Exploring Mythos shows why frontier labs guard access. Restricting distribution can preserve pricing power and protect scarce compute. It also risks pushing users toward open source alternatives.
Meta’s Muse Spark
Meta released Muse Spark from Meta Superintelligence Labs. Muse Spark is a multimodal reasoning model with tool use, visual chain of thought, and multi‑agent orchestration.
Meta cited investments across its stack, including a new Hyperion data center. The company called Muse Spark the first step on a larger scaling plan.
Unlike hyperscalers, Meta lacks a cloud business. That reduces internal competition for compute. Meta can focus on consumer use cases without the same opportunity costs faced by others.
Market implications and the road ahead
Demand for tokens and agentic workflows is increasing compute needs rapidly. Agentic systems can run LLMs continuously without human input. That drives consumption and raises costs.
Anthropic’s revenue growth has accelerated. OpenAI is shifting toward enterprise customers while keeping a large consumer base in ChatGPT. Bloomberg reported OpenAI told investors it has been rapidly adding compute to outpace Anthropic.
Some firms are securing hardware supply lines. Anthropic’s TPU agreements show the competition for silicon. That competition is influenced by TSMC production limits and chip availability.
Despite compute scarcity, owning user demand still matters. Companies with compelling products can generate the revenue to buy more capacity. In that sense, the core insight of Aggregation Theory persists.
But the economics have changed. Firms must decide how to allocate scarce compute across customers, products, and internal efforts. Those trade‑offs will shape winners and losers in 2025 and beyond.
Reporting and analysis by Filmogaz.com.
