I am asking you for a report of no more than 3,000 words with deep analysis of which global sectors are likely to be most and least disrupted by Artificial Intelligence.
The following report and images are the Gemini output from the prompt I entered…
Sectoral Disruption and Economic Resilience 2026 I read the Deutsche Bank report, then ran the prompt against the latest version of Google Gemini 3 Pro. I didn’t have all their criteria, so I entered the basic prompt they had utilized.
Predicts significant advancements in AI capabilities within the next decade, which will have a profound impact on society, economy, and individuals, and emphasizes the need for careful governance, equitable distribution of benefits, and responsible development to mitigate risks and maximize benefits ## ## Questions to inspire discussion.
AI Scaling and Progress.
Q: What are the key factors driving AI progress according to the scaling hypothesis?
A: Compute, data quantity and quality, training duration, and objective functions that can scale massively drive AI progress, per Dario Amodei’s “Big Blob of Compute Hypothesis” from 2017.
Q: Why do AI models trained on broad data distributions perform better?
A: Models like GPT-2 generalize better when trained on wide variety of internet text rather than narrow datasets like fanfiction, leading to superior performance on diverse tasks.
Artificial intelligence is rapidly advancing to the point where it may be able to write its own code, potentially leading to significant job displacement, societal problems, and concerns about unregulated use in areas like warfare.
## Questions to inspire discussion.
Career Adaptation.
🎯 Q: How should workers prepare for AI’s impact on employment? A: 20% of jobs including coders, medical, consulting, finance, and accounting roles will be affected in the next 5 years, requiring workers to actively learn and use large language models to enhance productivity or risk being left behind in the competitive landscape.
Economic Policy.
📊 Q: What systemic response is needed for AI-driven job displacement? A: Government planning is essential to manage massive economic transitions and job losses as AI’s exponential growth reaches a tipping point, extending beyond manufacturing into white-collar professions across multiple sectors.
SpaceX, in collaboration with xAI, plans to build a lunar base called Moonbase Alpha using advanced technologies such as mass drivers, solar power, and Starship, aiming to make human activity on the moon visible, affordable, and sustainable ##
## Questions to inspire discussion.
Launch Infrastructure Economics.
🚀 Q: What launch costs could SpaceX’s moon infrastructure achieve? A: Mature SpaceX moon operations could reduce costs to $10/kg to orbit and $50/kg to moon surface, enabling $5,000 moon trips for people under 100kg (comparable to expensive cruise pricing), as mentioned by Elon Musk.
⚡ Q: How could lunar mass drivers scale satellite deployment? A: Lunar mass drivers using magnetic rails at 5,600 mph could launch 10 billion tons of satellites annually with 2 terawatts of power, based on 2023 San Jose State study updating 1960s-70s mass driver literature.
Starship Capabilities.
Optimus robots, with their rapidly advancing capabilities in AI and dexterity, are poised to revolutionize the field of surgery, potentially surpassing human surgeons in precision and accessibility within a few years and making traditional surgical expertise and even medical school obsolete.
## Questions to inspire discussion.
Healthcare Access & Economics.
🏥 Q: How will Optimus robots change healthcare costs and accessibility?
A: Optimus surgeon robots will operate at costs limited to capital expenditure and electricity, enabling deployment in rural villages and developing countries like Zimbabwe and throughout Africa, demonetizing and decentralizing access to medical care that will exceed what presidents currently receive.
Technology Timeline & Capabilities.
By Chuck Brooks
#technology #government #security
By Chuck Brooks, president of Brooks Consulting International
The future of innovation in both government and industry will not be distinguished by singular breakthroughs, but rather by the convergence and meshing of a number of different new technologies. Going forward, industries, national security, economic competitiveness, privacy and almost every aspect of everyday life will all be reshaped as a result of this integrated ecosystem, which encompasses artificial intelligence, quantum computing, improved connectivity, space systems and other areas.
Twelve crucial technical domains will help propel the federal government toward this convergent transformation.
Questions to inspire discussion AI Model Performance & Capabilities.
🤖 Q: How does Anthropic’s Opus 4.6 compare to GPT-5.2 in performance?
A: Opus 4.6 outperforms GPT-5.2 by 144 ELO points while handling 1M tokens, and is now in production with recursive self-improvement capabilities that allow it to rewrite its entire tech stack.
🔧 Q: What real-world task demonstrates Opus 4.6’s agent swarm capabilities?
A: An agent swarm created a C compiler in Rust for multiple architectures in weeks for **$20K, a task that would take humans decades, demonstrating AI’s ability to collapse timelines and costs.
🐛 Q: How effective is Opus 4.6 at finding security vulnerabilities?
He’s not alone. xAI’s head of compute has reportedly bet his counterpart at Anthropic that 1% of global compute will be in orbit by 2028. Google (which has a significant ownership stake in SpaceX) has announced a space AI effort called Project Suncatcher, which will launch prototype vehicles in 2027. Starcloud, a startup that has raised $34 million backed by Google and Andreessen Horowitz, filed its own plans for an 80,000 satellite constellation last week. Even Jeff Bezos has said this is the future.
But behind the hype, what will it actually take to get data centers into space?
In a first analysis, today’s terrestrial data centers remain cheaper than those in orbit. Andrew McCalip, a space engineer, has built a helpful calculator comparing the two models. His baseline results show that a 1 GW orbital data center might cost $42.4 billion — almost 3x its ground-bound equivalent, thanks to the up-front costs of building the satellites and launching them to orbit.
The future of intelligence is rapidly evolving with AI advancements, poised to transform numerous aspects of life, work, and existence, with exponential growth and sweeping changes expected in the near future.
## Questions to inspire discussion.
Strategic Investment & Career Focus.
🎯 Q: Which companies should I prioritize for investment or career opportunities in the AI era?
A: Focus on companies with the strongest AI models and those advancing energy abundance, as these will have the largest marginal impact on enabling the innermost loop of robots building fabs, chips, and AI data centers to accelerate exponentially.
Understanding Market Dynamics.