The Download: The startup that says it can stop lightning, and inside OpenAI’s Pentagon deal

· Source: MIT Technology Review · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Cybersecurity & Data Privacy · Depth: Novice, short

Summary

Skyward Wildfire, a startup, claims it can prevent catastrophic wildfires by stopping lightning strikes, reportedly using a 1960s US government approach of seeding clouds with metallic chaff. The company recently secured millions in funding for product development and expansion, despite ongoing uncertainties among researchers regarding the method's effectiveness under various conditions, material release requirements, frequency, and potential environmental impacts. Concurrently, OpenAI has finalized a deal allowing the US military to use its technologies in classified settings, a decision CEO Sam Altman described as "rushed" following Anthropic's public refusal of similar terms. OpenAI asserts its agreement includes safeguards against autonomous weapons and mass domestic surveillance, though the feasibility of implementing these precautions amidst a politicized AI strategy and employee concerns remains unclear.

Key takeaway

For Directors of AI/ML evaluating defense contracts or VPs of Engineering assessing climate tech, carefully scrutinize vendor claims and contractual terms. Your teams should prioritize transparent safety protocols and environmental impact assessments, especially for technologies with unproven efficacy or significant ethical implications. Ensure that any AI deployment in sensitive sectors includes robust safeguards against misuse, aligning with internal ethical guidelines and public commitments.

Key insights

Cloud seeding for lightning suppression and AI military contracts present complex technological and ethical challenges.

Principles

Method

Cloud seeding involves releasing metallic chaff into clouds to potentially disrupt lightning formation, a method evaluated by the US government in the early 1960s.

In practice

Topics

Best for: VP of Engineering/Data, Director of AI/ML, Executive, Tech Journalist, CTO, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by MIT Technology Review.