#ai-economics

#ai-economics

DeepSeek, the Hangzhou-based lab backed by quantitative hedge fund High-Flyer, has forced a reckoning across the AI supply chain by releasing highly capable models at a fraction of the cost charged by OpenAI, Google, and Anthropic. The result: a cascading price war that is reshaping how companies budget for intelligence, how developers choose their tools, and how the entire economics of foundation models may evolve over the next 12 to 18 months.

The research paper written by Jaime Sevilla, Hannah Petrovic and Anson Ho, suggests that while running an AI model may generate enough revenue to cover its own R&D costs, any profit is outweighed by the cost of developing the next big model. So, it said, "despite making money on each model, companies can lose money each year." The paper seeks to answer three questions: How profitable is running AI models? Are models profitable over their lifecycle? Will AI models become profitable?

Microsoft CEO and head AI peddler Satya Nadella wants you to know that it's time for the next phase of AI acceptance, where we focus on how humans are empowered by tools and agents and how we deploy resources to support this growth. Amid doubts that revenue from Microsoft Copilot subscriptions and cloud AI services will compensate for data center capital expenditures any time soon, Satya has some incentive to convince customers and investors that AI is a financially intelligent long-term bet.

You will lead a new area of research, exploring post-AGI economics, the future of scarcity, and the distribution of power and resources in a world fundamentally reshaped by advanced AI,

Artificial intelligence continues to make startling advances while becoming ever more integrated into millions of people's lives. But as America's most valuable companies race to out-compete each other in a high-stakes, extremely costly race to dominate the field, many fundamental questions remain unanswered. Foremost among them: Will AI actually be as disruptive an economic force as its adherents say? To get some perspective on that question, I spoke with Professor Ethan Mollick, a Wharton professor who frequently writes and comments about AI and its applications.

As AI takes off, the whole cycle promises to repeat itself again, and while AI might seem relatively cheap now, it might not always be so. Foundational AI model-as-a-service companies charge for insights by the token, and they're doing it at a loss. The profits will have to come eventually, whether that's direct from your pocket, or from your data, you might be interested in other ways to get the benefits of AI without being beholden to a corporation.