April 2, 2025 News
OpenAI's o3 Reasoning Model May Cost Ten Times More Than Initially Estimated
The Arc Prize Foundation has revised its estimate of computing costs for OpenAI's o3 reasoning model, suggesting it may cost around $30,000 per task rather than the initially estimated $3,000. This significant cost reflects the massive computational resources required by o3, with its highest-performing configuration using 172 times more computing than its lowest configuration and requiring 1,024 attempts per task to achieve optimal results.
Skynet Chance (+0.04%): The extreme computational requirements and brute-force approach (1,024 attempts per task) suggest OpenAI is achieving reasoning capabilities through massive scaling rather than fundamental breakthroughs in efficiency or alignment. This indicates a higher risk of developing systems whose internal reasoning processes remain opaque and difficult to align.
Skynet Date (+1 days): The unexpectedly high computational costs and inefficiency of o3 suggest that true reasoning capabilities remain more challenging to achieve than anticipated. This computational barrier may slightly delay the development of truly autonomous systems capable of independent goal-seeking behavior.
AGI Progress (+0.05%): Despite inefficiencies, o3's ability to solve complex reasoning tasks through massive computation represents meaningful progress toward AGI capabilities. The willingness to deploy such extraordinary resources to achieve reasoning advances indicates the industry is pushing aggressively toward more capable systems regardless of cost.
AGI Date (+2 days): The 10x higher than expected computational cost of o3 suggests that scaling reasoning capabilities remains more resource-intensive than anticipated. This computational inefficiency represents a bottleneck that may slightly delay progress toward AGI by making frontier model training and operation prohibitively expensive.
DeepMind Releases Comprehensive AGI Safety Roadmap Predicting Development by 2030
Google DeepMind published a 145-page paper on AGI safety, predicting that Artificial General Intelligence could arrive by 2030 and potentially cause severe harm including existential risks. The paper contrasts DeepMind's approach to AGI risk mitigation with those of Anthropic and OpenAI, while proposing techniques to block bad actors' access to AGI and improve understanding of AI systems' actions.
Skynet Chance (+0.08%): DeepMind's acknowledgment of potential "existential risks" from AGI and their explicit safety planning increases awareness of control challenges, but their comprehensive preparation suggests they're taking the risks seriously. The paper indicates major AI labs now recognize severe harm potential, increasing probability that advanced systems will be developed with insufficient safeguards.
Skynet Date (-4 days): DeepMind's specific prediction of "Exceptional AGI before the end of the current decade" (by 2030) from a leading AI lab accelerates the perceived timeline for potentially dangerous AI capabilities. The paper's concern about recursive AI improvement creating a positive feedback loop suggests dangerous capabilities could emerge faster than previously anticipated.
AGI Progress (+0.05%): The paper implies significant progress toward AGI is occurring at DeepMind, evidenced by their confidence in predicting capability timelines and detailed safety planning. Their assessment that current paradigms could enable "recursive AI improvement" suggests they see viable technical pathways to AGI, though the skepticism from other experts moderates the impact.
AGI Date (-5 days): DeepMind's explicit prediction of AGI arriving "before the end of the current decade" significantly accelerates the expected timeline from a credible AI research leader. Their assessment comes from direct knowledge of internal research progress, giving their timeline prediction particular weight despite other experts' skepticism.