Core Viewpoint - The belief that AI model costs will continue to decrease is challenged, as the most advanced models maintain stable costs despite older models becoming cheaper [5][10][19]. Group 1: Cost Dynamics - AI entrepreneurs assume that as model costs decrease, their revenue situation will improve, allowing their businesses to continue [2][3]. - a16z claims that the cost of large language models (LLMs) is decreasing at a rate of 10 times per year, but this is primarily true for outdated models [4][5]. - The actual costs of the best models remain relatively unchanged, leading to a potential misalignment in business strategies for AI startups [19][40]. Group 2: Market Demand and Model Performance - Market demand consistently favors the best-performing models, which keeps their costs stable [19][21]. - When new models are released, consumer interest shifts almost entirely to these advanced models, regardless of the cost of older versions [12][16]. - The expectation for high-quality outputs drives users to prefer the latest models, further complicating the cost-reduction narrative [21]. Group 3: Token Consumption and Business Models - The consumption of tokens has increased dramatically, with tasks requiring significantly more tokens than before, leading to higher operational costs [23][29]. - The shift from simple interactions to complex tasks has resulted in a substantial rise in token usage, which is not accounted for in traditional subscription models [24][37]. - Companies adopting fixed-rate subscription models face challenges as token consumption outpaces revenue generation, leading to financial strain [33][40]. Group 4: Pricing Strategies and Market Competition - Many AI companies recognize the need for usage-based pricing but hesitate to implement it due to competitive pressures from fixed-rate models [41][42]. - The industry is caught in a "prisoner's dilemma," where companies opt for growth over sustainable pricing, risking long-term viability [44][45]. - Successful consumer subscription services typically rely on fixed-rate models, making it difficult for usage-based pricing to gain traction [47]. Group 5: Future Directions and Strategies - Companies are exploring various strategies to avoid the pitfalls of high token consumption, including vertical integration and creating high switching costs for customers [52][51]. - The emergence of "neocloud" providers may offer a path forward, focusing on sustainable business models that can adapt to changing cost structures [59]. - The industry must rethink its approach to pricing and service delivery to remain competitive and financially viable in the evolving landscape [56][58].

Core Viewpoint - The belief among many AI entrepreneurs that model costs will decrease significantly is challenged by the reality that only older models see such reductions, while the best models maintain stable costs, impacting business models in the AI sector [6][20]. Group 1: Cost Dynamics - The cost of models like GPT-3.5 has decreased to one-tenth of its previous price, yet profit margins have worsened, indicating a disconnect between cost reduction and market demand for the best models [14][20]. - Market demand consistently shifts to the latest state-of-the-art models, leading to a scenario where older, cheaper models are largely ignored [15][16]. - The expectation that costs will drop significantly while maintaining high-quality service is flawed, as the best models' costs remain relatively unchanged [20][21]. Group 2: Token Consumption - The token consumption for tasks has increased dramatically, with AI models now requiring significantly more tokens for operations than before, leading to higher operational costs [24][26]. - Predictions suggest that as AI capabilities improve, the cost of running complex tasks will escalate, potentially reaching $72 per session by 2027, which is unsustainable under current subscription models [26][34]. - The increase in token consumption is likened to a situation where improved efficiency leads to higher overall resource usage, creating a liquidity squeeze for companies relying on fixed-rate subscriptions [27][34]. Group 3: Business Model Challenges - Companies are aware that usage-based pricing could alleviate financial pressures but hesitate to implement it due to competitive dynamics where fixed-rate models dominate [35][36]. - The industry faces a dilemma: adopting usage-based pricing could lead to stagnation in growth, as consumers prefer flat-rate subscriptions despite the potential for unexpected costs [39]. - Successful companies in the AI space are exploring alternative business models, such as vertical integration and using AI as a lead-in for other services, to capture value beyond just model usage [40][42]. Group 4: Future Outlook - The article emphasizes the need for AI startups to rethink their strategies in light of the evolving landscape, suggesting that merely relying on the expectation of future cost reductions is insufficient for sustainable growth [44][45]. - The concept of becoming a "new cloud vendor" is proposed as a potential path forward, focusing on integrating AI capabilities with broader service offerings [45].

Core Viewpoint - AI is fundamentally changing the pricing logic of software, shifting from traditional seat-based pricing to usage-based or outcome-based pricing models [2][11][20]. Group 1: AI Pricing Transformation - The traditional seat pricing model is becoming less viable as AI increases efficiency, leading to fewer users and a need for new pricing strategies [11][12]. - Implementing usage-based pricing faces challenges such as the need for real-time billing systems, dynamic pricing models, and maintaining large volumes of accurate data [3][15]. - Pricing models for AI products can be analyzed based on attribution capability and autonomy, with stronger attribution and autonomy leading to greater pricing power [32][36]. Group 2: CEO Considerations for Pricing Transition - CEOs must focus on sales compensation structures and the division of sales responsibilities when transitioning to usage-based pricing [3][22]. - A hybrid business model, combining seat pricing and usage-based pricing, is expected to dominate in the coming years, especially for application-level products [3][13]. - The sales team's role must evolve to ensure that actual usage aligns with revenue recognition, avoiding the pitfalls of recording false revenue [22][23]. Group 3: Challenges in Implementing Usage-Based Pricing - Real-time monitoring is essential to manage the risk of unlimited spending in usage-based pricing models, as seen in cases like Segment [15][16]. - The dynamic nature of pricing models complicates the creation of a universal billing engine, as contracts often vary significantly [15][16]. - Maintaining a reliable data chain is crucial for accurate historical data storage, which is necessary for future pricing adjustments [15][16]. Group 4: Strategic Importance of Usage-Based Pricing - Usage-based pricing directly ties revenue to the value created for customers, allowing for a more flexible and responsive business model [17][20]. - Sales commissions in usage-based models must be adjusted to align with actual product usage, preventing cash flow mismatches [18][22]. - The integration of value creation across departments is essential for the success of usage-based pricing, requiring a shift in company culture and operations [19][21]. Group 5: Future of Pricing Models - The trend is moving towards a mixed pricing strategy, with a significant portion of companies expected to adopt outcome-based pricing in the next few years [37][49]. - Companies must enhance their products' autonomy and attribution capabilities to unlock greater commercial value [37]. - The evolution of pricing models reflects a broader shift in the industry, where agility and adaptability are key to maintaining competitive advantage [43][49].