Will DeepSeek R1 Upend the US AI Capex Cycle or Signal a Boom ...
If DeepSeek R1 is ushering in a revolution in cheaper reasoning, here are some ways to monitor developments in realtime.
People jump to conclusions if given the opportunity and this week, they were sure given one. If Deepseek R1’s results are valid and SoTA reasoning models can be developed and served at 1/30th the price, expect to see US labs radically undercutting themselves in an attempt to gain share, while simultaneously reducing inference costs and scaling up the development of these so-called “reasoning models”.
A Closer Look at the AI Capex
If we go back to basics, it’s not clear why major tech companies spending their annual free cash flow on Nvidia chips and data center buildout is bullish for the market. The AI industry has yet to see significant revenue from the commercialization of AI, raising questions about the sustainability of current investment levels.
Recalling Sequoia Capital’s view from Summer ‘24, it's evident that the industry still has a long way to go in terms of generating the necessary revenue for a reasonable rate of return.
No Moat, No Reward
Despite the growing adoption of large language models (LLMs) and productivity gains, the path to profitability for frontier model providers remains uncertain. Companies like Meta are investing in product-centric AI strategies, but the impact on their core business and profitability remains to be seen.
Meta’s Llama series and the potential for AI-driven engagement show promise, but public readiness for such technologies is questionable. The aggressive cost reduction in the AI industry, exemplified by models like Deepseek offering O1 reasoning capabilities at a fraction of the cost, raises concerns about large-scale AI investments.
Impact of DeepSeek R1
With Deepseek reportedly matching OpenAI’s O1 reasoning model at a significantly lower cost, there are growing concerns about the effectiveness of investment programs and GPU export restrictions on Chinese AI development.
This trend highlights the feedback loops and unintended consequences that technological innovation can bring, similar to the concept of Jevons paradox where price reductions may lead to increased consumption without existing profitability.
Monitoring DeepSeek's Efficiency Claims
Evolution of LLM Progress
Transitioning from scaling laws focused on pre-training to inference-time compute, models like DeepSeek R1 are set to drive the next wave of generalization advances in the AI industry.
Agentic workflows and reasoning models consume significant token volumes, paving the way for more efficient and browser-based agent systems. These advancements are expected to revolutionize productivity and reduce the reliance on human labor.
The Path to General Reasoning Capabilities
As AI models like DeepSeek R1 advance in simulating deliberate reasoning, questions arise about their generalization capabilities across different domains. The development of "judge models" and verification mechanisms will be crucial in achieving truly open-ended reasoning capabilities.
Conclusion
Technological innovations like DeepSeek R1 have the potential to reshape the AI landscape, driving efficiency and cost-effectiveness in reasoning models. As the industry continues to evolve, monitoring these advancements in real-time will be essential to understanding the impact on the US AI Capex cycle and the broader AI market.
