Artículos Relacionados con Commoditization

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Selling Tokens or Selling Outcomes: Several Paradoxes of the AI Business Model

"The Token vs. Outcome Sale: Key Paradoxes in the AI Business Model By mid-2026, the AI industry shows rapid growth in revenue and token usage, yet the underlying business models differ significantly. This article analyzes four structural paradoxes defining the current landscape, all pointing to the commoditization of intelligence and the concentration of profits in few segments. **The Cost Paradox: Cheaper Tokens, Heavier Bills** Despite a >95% price drop for equivalent AI capability since 2023, total spending has skyrocketed due to the Jevons Paradox: lower prices expand usage into previously uneconomical tasks. Furthermore, the shift to autonomous agents operating 24/7 multiplies consumption. However, efficiency gains often remain unrealized due to unchanged organizational workflows (the Solow Paradox). The focus is shifting from optimizing token price to optimizing the task itself. **The Hierarchy Paradox: The App is King vs. The App is Dead** While conventional wisdom holds that value accrues at the application layer, the AI stack is inverted. Infrastructure (chips) captures ~70% of industry revenue and ~80% of gross profit, while application-layer margins are thin (0-30%). Fast-evolving base models threaten "thin" apps. Sustainable applications are those that embed intelligence into specific contexts, possessing private data, workflows, or delivery capabilities that become more valuable as the base model improves. **The Responsibility Paradox: Profit Follows Accountability** Growth rates alone don't guarantee profit. A key differentiator is a company's willingness and ability to take responsibility for specific outcomes. Selling by the token competes for IT budgets; selling by the outcome (e.g., a resolved support ticket) taps into larger human labor budgets. Low-responsibility, high-volume tasks (e.g., generic客服) face commoditization. High-stakes, regulated domains (e.g., law, healthcare) where vendors assume heavier liability for results command higher margins, as seen with companies like Harvey in legal tech. **The Open-Source Paradox: Open Wins Traffic, Closed Wins Revenue** Open-source models dominate in usage share and developer adoption, often being 5-20x cheaper. However, closed-source models still capture the majority of enterprise spending (~89%). Enterprises pay a premium for closed-source reliability, support, compliance, and accountability. The total cost of ownership (TCO) is converging as closed-source prices fall faster than open-source builds trust, leading to hybrid deployments. Profit is migrating from the model layer itself to upstream (compute) and downstream (orchestration, data, services)."

marsbit07/28 10:41

Selling Tokens or Selling Outcomes: Several Paradoxes of the AI Business Model

marsbit07/28 10:41

Wang Chuan: When the Neighbor Old Wang Made 30x on Memory Stocks, How to Avoid Anxiety (Part Six) - The Trap of Commoditized Goods

Wang Chuan: When the Neighbor Lao Wang Made 30x on Storage Stocks, How to Stay Anxiety-Free (Part 6) - The Trap of Commoditized Goods. This essay uses historical and current examples to analyze the cyclical and high-risk nature of the data storage industry. It begins with the 1990s rise and dramatic fall of Iomega, whose stock soared over 160x in 18 months before collapsing 97% from its peak, illustrating the fleeting success of storage "meme stocks." The core problem is that storage products, like DRAM and flash memory, are highly commoditized. This leads to extreme volatility: prices have plummeted over 80% multiple times, and company stocks often crash 95% or go bankrupt. The industry's dynamic is defined by "elastic demand facing heavy-asset, long-cycle, rigid supply." When demand spikes and supply is fixed, prices skyrocket, as seen recently with AI-driven demand for High Bandwidth Memory (HBM). Companies like Sandisk and Micron have reported massive revenue and gross margin jumps (e.g., Sandisk's gross margin rising from 22.5% to 78.3%) despite minimal increases in production volume. However, these high margins are self-defeating. They incentivize massive new capacity investments (hundreds of billions planned from 2026), with supply expected to surge by late 2027. Once new supply meets demand, prices and profits will crash, potentially leading to a scenario where "selling more results in earning less." The article debunks the safety of long-term supply agreements, comparing them to fragile non-aggression pacts easily broken when market conditions shift. It warns that when an industry is highly profitable but trades at low P/E ratios, the risk is greatest, as plummeting prices quickly erase those earnings. Multiple asymmetric risks loom, including economic recession, reduced AI spending, faster-than-expected capacity expansion (especially from Chinese firms), and technological innovations that reduce memory requirements. In conclusion, the storage sector is a cyclical trap where periods of euphoric profits are often precursors to devastating downturns, luring unprepared investors into a "wealth incinerator."

marsbit06/01 07:13

Wang Chuan: When the Neighbor Old Wang Made 30x on Memory Stocks, How to Avoid Anxiety (Part Six) - The Trap of Commoditized Goods

marsbit06/01 07:13

The Night Before the AI Model Shakeout

China's large language model (LLM) industry is entering a critical consolidation phase. In a concentrated wave of funding in May 2026, leading players Kimi, StepFun, and DeepSeek reportedly secured over $70 billion combined, signaling a dramatic capital rush towards the few remaining independent contenders. This frenzy masks an impending shakeout. The core dynamic has shifted from a pure technology race to a battle for survival and strategic positioning. LLM capabilities are rapidly commoditized; gaps between top models are narrowing. Consequently, investment logic has pivoted from betting on future potential to prioritizing cash flow, user access, and ecosystem integration. The economic model poses a fundamental challenge: while user growth previously meant profits, in the AI era, it drives soaring inference costs. Startups, lacking the cross-subsidy ability of tech giants like ByteDance or Tencent, face immense pressure to achieve financial sustainability. DeepSeek's open-source, high-performance, low-cost strategy has further compressed industry profit margins. Facing this reality, the top players are scrambling to lock in their status before the window closes. StepFun is accelerating its港股 IPO, embedding itself in hardware supply chains. Kimi is aggressively showcasing revenue growth (ARR doubling to $2 billion in a month) to prove viability. DeepSeek, with new state-backed investment, is solidifying its role as a strategic national asset. The parallel to China's previous AI "Four Dragons" is stark. The industry is witnessing extreme capital concentration at the top, while mid-tier companies face a funding winter. The narrative has evolved from "who can build the best model" to "who can survive." For independent LLM companies, securing a public listing or a definitive strategic identity is no longer about expansion—it's about securing the very right to exist in the impending era of industry clearance.

marsbit05/10 02:05

marsbit05/10 02:05

Stablecoin Issuance Seems Crowded, But the Real Moat Lies in the Buyers' Hands

Stablecoin issuance is becoming increasingly commoditized at the technical level, but true differentiation lies in the specific needs of different buyer segments. While token deployment and basic operations are now standardized, the market fragments into distinct clusters: enterprises prioritizing compliance and trust, fintechs and wallets focused on speed and distribution, and DeFi platforms valuing composability and yield optimization. For each segment, only a few issuers can deliver the required outcomes, making them non-interchangeable. Pricing power shifts toward value-added services like liquidity support, regulatory positioning, and integrated infrastructure (on-ramps, cards, accounts). Sustainable advantages may emerge from network effects around interoperability and shared liquidity, though it remains unclear whether value will accrue to issuer-controlled networks or neutral standards. Ultimately, the token itself is just the entry ticket—the real business is built around it.

比推01/26 14:33

Stablecoin Issuance Seems Crowded, But the Real Moat Lies in the Buyers' Hands